These findings inspire a facile andgeneral strategy to develop highly emissive imine-bonded COFs fordetecting various molecules.The development ofhighly luminescent two-dimensional covalentorganic frameworks (COFs) for sensing applications remains challenging.To suppress commonly observed photoluminescence quenching of COFs,we propose a strategy involving interrupting the intralayer conjugationand interlayer interactions using cyclohexane as the linker unit.By variation of the building block structures, imine-bonded COFs withvarious topologies and porosities are obtained. Experimental and theoreticalanalyses of these COFs disclose high crystallinity and large interlayerdistances, demonstrating enhanced emission with record-high photoluminescencequantum yields of up to 57% in the solid state. The resulting cyclohexane-linkedCOF also exhibits excellent sensing performance for the trace recognitionof Fe Therefore, an efficient strategy for synthesizingluminescent imine-bonded 2D COFs remains elusive.To prevent ACQin 2D COFs, aggregation-induced emission (AIE) isa promising approach.C2-symmetric linkers for establishinghighly crystalline imine-bonded 2D COFs. In sharp contrast, the useof aliphatic linkers has rarely been explored.34 Herein, we conceived a strategyto enhance the PL of 2D COFs through the introduction of cyclohexaneas a linker, instead of the commonly used aromatic linkers, to suppressboth intralayer π-conjugation and interlayer π–πinteractions.To date, aromaticdialdehydes or diamines have predominantly beenused as trans-1,4-diaminocyclohexane(CHDA)with different aromatic tri- or tetra-aldehydes, all the well-knownlattice topologies of imine-bonded 2D COFs could be achieved. Bothexperimentally recorded powder X-ray diffraction (PXRD) patterns andtheoretical calculations revealed that the interlayer distances ofthese cyclohexane-linked 2D COFs were unexpectedly large. Notably,these 2D COFs were highly luminescent, showing tunable emission colors,depending on the building blocks employed. In particular, incorporationof the TPE units into the cyclohexane-linked 2D COF led to a recordphotoluminescence quantum yield (PLQY) of 57% in the solid state.Unlike most imine-bonded COFs, the cyclohexane-linked COFs also allowedthe retention of emission under acidic conditions. As a result, weachieved the trace detection of (i) dangerous explosives, (ii) essentialmetal elements in the metabolic process, and (iii) a biomarker, phenylglyoxylic acid, demonstrating the promise of such COFs for sensingapplications.By polymerizing 35 thus hindering the formationof an ordered framework (Table S1). Thepolycondensation of different aromatic tri- and tetra-aldehydes withCHDA allowed the formation of cyclohexane-linked 2D COFs with threekey lattice topologies: honeycomb (hcb), Kagome (kgm), and squarelattice (sql) (see below for the characterization details). The resultingCOFs were categorized into three classes depending on the latticetopology (Schemes S1–S5):The synthesis of varyingcyclohexane-linked COFs was designed through a general reaction strategybased on the copolymerization of CHDA and various aromatic tri- ortetra-aldehydes. The solvothermal conditions were carefully optimizedto obtain these cyclohexane-linked COFs with high crystallinity andyields. The most decisive parameter for obtaining highly crystallineCOFs with CHDA was the amount of acetic acid (HAc) used as the catalyst.This is presumably because excess HAc can protonate the amino groupsof CHDA,Class 1: TFB-CHDA COF and TFPB-CHDA COF with hcbtopology2a, prepaClass 2: ETTA-CHDA COF with kgm topology3a, prepap-phenylenediamine (PDA) (Scheme S6).Class 3: TFPPy-CHDA and TFPPery-CHDA COF with sql topology 4a, prepa13C cross-polarization magic-angle spinning nuclear magnetic resonance(CP-MS NMR) spectroscopy, and high-resolution transmission electronmicroscopy (HR-TEM). The FT-IR spectra of all the obtained COF samplesrevealed the characteristic imine (C=N) stretching bands inthe range of 1639–1641 cm–1, corroboratingthe formation of imine linkages, and the greatly attenuated peaksin the range of 2720 cm–1 attributed to the aldehydegroups . Moreover,the 13C CP-MAS NMR spectra showed a signal at 158 ppm , providing further evidence of theformation of imine bonds.36The obtained COFs werethoroughly characterized by utilizing elemental analysis (EA), Fouriertransform infrared (FT-IR) spectroscopy, powder X-ray diffraction(PXRD), nitrogen sorption isotherms, field emission scanning electronmicroscopy (FE-SEM), thermogravimetric analysis (TGA), Table S2). This is in contrast to a previousreport by Zhou et al., who attempted to synthesize the TFB-CHDA COFbut only obtained powders with undesired crystallinity,37 demonstrating the importance of optimizing solvothermalconditions. Pawley refinement and lattice modeling  resulted in optimized parameters. The refined PXRD patternsmatched well with the experimental profiles (Tables S3–S8). A comparison of the observed and simulated PXRDpatterns suggested slipped AA stacking for the TFB-CHDA COF .The PXRD analyses of TFB-CHDA COF 2b, TFPB-CHDA COF 2c and ec38 TFB-CHDA COFrevealed an increased layer distance of approximately 4.7 Å.The skewing and separation of the COF layers can be ascribed to thesteric repulsions at the axial hydrogens of the cyclohexane units.Similarly, TFPB-CHDA COF showed a large layer distance of 5.1 Åcompared to the 3.5 Å reported for the corresponding phenylene-linkedTFPB-PDA COF (NUS-15).39 ETTA-CHDA COF,TFPPy-CHDA COF, and TFPPery-CHDA COF also demonstrated large layerdistances of 4.9–5.0 Å.Notably, while the reported benzene-based2D COF-LZU1 (TFB-PDA),constructed from TFB and PDA, exhibited eclipsed stacking with a layerdistance of 3.7 Å,Figures S13–S18). The Brunauer–Emmett–Teller (BET)surface areas of TFB-CHDA and TFPB-CHDA COFs were analyzed to be1177 and 478 m2 g–1, respectively . Quenched solid densityfunctional theory (QSDFT) calculations based on the nitrogen adsorptioncurve furnished pore sizes centered at 1.8 nm for the TFB-CHDA COFand 3.8 nm for the TFPB-CHDA COF, which agreed with the pore sizeof every single layer predicted from their crystal structures . On the other hand, measurementsof the activated ETTA-CHDA COF indicated a larger BET surface areaof 1637 m2 g–1 . The QSDFT simulation indicated that the pore sizes ofETTA-CHDA COF were 1.5 and 2.8 nm, consistent with the predicted poresizes of 1.3 nm for micropores and 2.9 nm for mesopores from the theoreticalmodel constructed with Materials Studio .40 The activated TFPPy-CHDA COF, TFPPery-CHDACOF, and TFPPery-PDA COF displayed BET surface areas of 620, 559,and 176 m2 g–1, respectively . Their pore sizedistributions were derived from nitrogen absorption curves and QSDFTcalculations, and the pore sizes of TFPPy-CHDA and TFPPery-CHDA COFwere shown to be only approximately 2.2 nm  and 2.7 nm ,respectively. This is in contrast to the TFPPery-PDA COF, for whichtwo types of pore sizes centered at 1.9 and 3.7 nm were revealed , in agreement with a previous reportby Bein et al.41Nitrogenadsorption–desorption experiments at 77 K werecarried out to measure the porosity of the COF samples, and all ofthem revealed a reversible type-I isotherm with a slight hysteresis,characteristic of a mesoporous structure /acetone and drying undervacuum.The BET surfacearea of the TFPPery-CHDA COF was much larger thanthat of the TFPPery-PDA COF, which was attributed to the presenceof more structural defects in the latter. The limited solubility oflarge π-conjugated organic chromophores as building blocks canoften account for the failure to obtain highly ordered COF structures.Figures S19 and S20), while ETTA-CHDA COF, TFPPy-CHDA COF, TFPPery-PDACOF, and TFPPery-CHDA COF all showed a rodlike morphology  consisting of microcrystals.In addition, TGA revealed the exceptional thermal stability of theobtained COF samples, namely, TFB-CHDA COF and TFPB-CHDA COF up to400 °C, ETTA-CHDA COF up to 460 °C, and TFPPy-CHDA COF andTFPPery-CHDA COF up to 550 °C under a nitrogen atmosphere . Moreover,all of these COFs also showed stability up to 300 °C under airbased on the TGA analysis .As seen by FE-SEM, TFB-CHDA COF and TFPB-CHDA COF appearedas aggregatedsubmicrometer-sized granular crystallites . While the d-spacings of 1.18, 0.87, 0.64, and0.49 nm matched well with the peaks in the experimental PXRD, theother five values did not correspond to any of the experimentallyobserved PXRD peaks. However, the simulation from the molecular modelafter Pawley refinement showed the diffraction peaks correspondingto these five d-spacings , providing additional structural support for ETTA-CHDACOF. We assumed that these five peaks were broadened and nondetectablein the experimental PXRD, presumably due to the small crystal domainsizes of the obtained COFs. On the other hand, HR-TEM images of highlyordered TFPPery-CHDA COF fully supported its successful formationwith the tetragonal topology . Additionally, a honeycomb pattern was observed for the TFB-CHDACOF, in agreement with the formation of a hexagonal lattice with slippedAA stacking .Notably, HR-TEM images of the exfoliated film of ETTA-CHDA COFshowed a stripe pattern with ∼3.0 nm intervals, which correspondedto the distance between two parallel edges around the mesopore, validatingits crystalline Kagome-type lattice structure with AA-type stacking3e and 3f41 revealing a kgm topology with dual pores, and we reproduced thisresult with the TFPPery-PDA COF in this work. In contrast, the replacementof the phenylene with cyclohexane linkers led to the sql topologyas in TFPPery-CHDA COF. To further elucidate the mechanism of topologycontrol by cyclohexane linkers, their packing energies were calculatedby a reported density functional theory (DFT)-based method.23 The total crystal stacking energy of the tetragonalTFPPery-CHDA COF was calculated to be 4.8 kcal mol–1 higher than that of the Kagome topology. The sql topology is thusenergetically favored. In sharp contrast, the total stabilizationenergy of the Kagome-type TFPPery-PDA COF is 2.1 kcal mol–1 higher than that of the tetragonal lattice, supporting the optimizedpacking structure of the Kagome-type (Table S9). This observation provides guidance for the facile modulation ofthe topology and pore size of the target 2D COFs for various applications,in particular, sensing of different analytes.The introductionof the cyclohexane units appeared to exert a majorinfluence on the resulting topology. For instance, Bein et al. reporteda series of perylene-linked 2D COFs in 2019,S34). The spectra of TFB-CHDA COF, TFPB-CHDACOF, and ETTA-CHDA COF present absorption edges at 358, 442, and 490nm, respectively, which are close to those of the corresponding TFB,TFPB, and ETTA monomers , signifyingthe interrupted π-conjugation within the COF layers. The absorptionedges of TFPPy-CHDA COF and TFPPery-CHDA COF are red-shifted to 515and 541 nm, respectively, reflecting the planar π-conjugatedstructures of the TFPPy and TFPPery units. By comparison, TFPPy-PDA27 and TFPPery-PDA COF with phenylene linkers insteadof cyclohexane linkers exhibit much broader and red-shifted absorption,especially in the case of the former, presumably reflecting theirless ordered structures and the further extension of the π-conjugationas well as interlayer interactions.UV–vis diffuse reflectancespectroscopy (DRS) was performed on the powder samples of the obtainedCOFs as well as their monomer precursors to study their optical properties. We also measuredthe solid-state emission spectra of the building blocks of TFB, TFPB,ETTA, TFPPy, and TFPPery .A comparison of the emission spectra of TFPPy-CHDA COF, TFPPy-PDACOF, and tri/tetra-aldehyde monomers demonstrates that insertion ofthe saturated cyclohexane units can suppress the red-shift of theemission wavelength of the organic chromophore upon integration intothe COF structure. This feature is advantageous for avoiding colorimpurities for applications as light-emitting materials, especiallyfor blue-color emission.The emission spectra of TFB-CHDA COF, TFPB-CHDACOF, ETTA-CHDACOF, TFPPy-CHDA COF, and TFPPery-CHDA COF display maxima at 465, 494,544, 510, and 633 nm, respectively 5b, correSI for details). The fluorescence micrographsof ETTA-CHDA COF as a representative case with the highest PLQY revealeda beltlike morphology, exhibiting bright green emission across thewhole belt . The PL decay timesof these COFs were measured, revealing PL lifetimes in the range of5.1–5.9 ns , 7% (TFPB-CHDA COF), 23% (TFPPy-CHDA COF), 33%(TFPPery-CHDA COF), and 57% (ETTA-CHDA COF) in the solid state  but onlyshowed red-shifts of the absorption and emission bands both in thesolid state  and in THF solution  (S38). The responseto acid was found to be reversible in the cycling test, demonstratingfull recovery upon the addition of triethylamine (TEA) . Swift protonation might occur sincethe nitrogen atoms of the imine bonds in cyclohexane-incorporatingCOFs are more basic than those in COFs with aromatic linkers and improvethe stability under acidic conditions through the formation of hydrogenbonds.45 Moreover, the PL from the TPEunits as independent emitters is not significantly affected, in sharpcontrast to most of the reported imine-bonded COFs,46 highlighting the potential of ETTA-CHDA COF for practicalapplications.The luminescence of imine-bondedCOFs can be readily quenched by acids, which greatly limits theirapplicability for sensing in acidic environments.3+ ions, which are known to be essentialin many biological processes. ETTA-CHDA COF gave a brilliant luminescencewith a PLQY of 23% in THF dispersion. The luminescence intensity ofthe ETTA-CHDA COF solution drastically decreased with increasing concentrationsof Fe3+ ions  was calculated to be 2.00 × 104 M–1 , with a low limit of detection (LOD) of 680 ppb, indicating thehigh sensing performance of ETTA-CHDA COF. We further investigatedthe scope of sensing other ion species (S41). Comparedto Fe3+ ions, ETTA-CHDA COF exhibited relatively low sensitivityto other transition metal ions and alkali metal ions. For instance,the PL intensity was quenched by only 33% and 14%, respectively, uponthe addition of Ni2+ and Cu2+. Moreover, thesensitivity of ETTA-CHDA COF to Co2+, Fe2+,Na+, and Mg2+ was negligible, showing only lessthan 5% change in the PL intensity.To assess the sensing performanceof ETTA-CHDA COF, we examinedthe detection of Fee3+ ions 6b. When 3+ detection. Notably, ETTA-CHDA COF shows a negligibleresponse to Fe2+, allowing one to precisely distinguishFe3+ from Fe2+ ions. The pronounced luminescencequenching of ETTA-CHDA COF for Fe3+ might be attributableto the energy transfer from ETTA-CHDA COF to Fe3+.47 In water, ETTA-CHDA COF could be only suspendedwithout showing any color of dispersion but showed the PL quenchingof 17% upon addition of 120 μL of aqueous Fe3+ solution. ETTA-CHDA COF and possiblyother cyclohexane-based COFs with appropriate energy levels are thuspromising for real-time biosensing of specific metal ion species.These results highlightthe high selectivity of ETTA-CHDA COF forFeKsv was estimated to be 3.04 × 105 M–1  with a detectionlimit of 188 ppb. The reversibility of the luminescence response towardPA was also established. To the best of our knowledge, this Ksv value is among the highest ones reportedfor porous materials.48 The high PA sensitivityof ETTA-CHDA COF is probably attributable to its brilliant luminescenceand large BET surface area. In addition, the large 1D open nanochannelsmay also provide pathways for PA molecules to efficiently interactwith the TPE units inside the framework, causing PL quenching throughphotoinduced energy transfer.49Remarkably, ETTA-CHDA COF also exhibited prominent PL turn-offresponses to explosive and toxic picric acid (PA). For the recognitionof PA, the PL intensity of ETTA-CHDA COF in a THF solution was quenchedby up to 98% with increasing concentrations of PA 6d. The o50 making it important to develop sensitive sensorsfor PGA. Remarkably, ETTA-CHDA COF was also capable of detecting tracesof PGA, with PL quenching of up to 99% upon addition of PGA to theTHF solution . Since the LUMO energy level of ETTA-CHDA COF  and SEM images  of ETTA-CHDACOF before and after sensing did not show any noticeable difference,indicating the high stability of the COF under practical conditions.ETTA-CHDA COF can thus provide an effective platform for sensing traceamounts of PGA for analyzing the EB/S content in the human body.Asa human urine metabolite of ethylbenzene and styrene (EB/S),phenyl glyoxylic acid (PGA) can indicate the amount of EB/S absorbedby the human body through daily contact with plastic pollutants,solution 6e. The E3+ ions, explosive and toxic PA, and PGA as urine metabolites (Table S10). Distinct from most of the other reportedCOF systems that require complicated syntheses of building blocks,this approach can readily provide brilliant imine-bonded 2D COFs withcontrollable topologies and pore sizes via polycondensation of organicchromophores having formyl groups and commercially available CHDA.Many more cyclohexane-linked COFs are now within reach, paving theway toward improved sensing, imaging, and light-emitting devices.Efficient design concepts for luminescentimine-bonded2D COFshave remained challenging despite great research efforts. In thiswork, we introduced a series of cyclohexane-linked 2D COFs with remarkablyhigh solid-state photoluminescence. The cyclohexane linkers restrictthe π-conjugation within the COF layers and enlarge the interlayerdistances to approximately 5 Å, thus suppressing PL quenchingthrough intra- and interlayer interactions. In particular, ETTA-CHDACOF with AIE-active TPE units demonstrates a record-high solid-statePLQY of 57%. This cyclohexane-linked COF also exhibits outstandingsensitivity and selectivity for the recognition of Fe


Most unsupervised domain adaptation (UDA) methods align feature distributions across different domains through adversarial learning. However, many of them require introducing an auxiliary domain alignment model, which incurs additional computational costs. In addition, they generally focus on the global distribution alignment and ignore the fine-grained domain discrepancy, so target samples with significant domain shifts cannot be detected or processed for specific tasks. To solve these problems, a bi-discrepancy network is proposed for the cross-domain prediction task. Firstly, target samples with significant domain shifts are detected by maximizing the discrepancy between the outputs of the dual regressor. Secondly, the adversarial training mechanism is adopted between the feature generator and the dual regressor for global domain adaptation. Finally, the local maximum mean discrepancy is used to locally align the fine-grained features of different degradation stages. In 12 cross@-domain prediction tasks generated on the C-MAPSS dataset, the root-mean-square error (RMSE) was reduced by 77.24%, 61.72%, 38.97%, and 3.35% on average, compared with the four mainstream UDA methods, which proved the effectiveness of the proposed method. Aero engine prognostics and health management (PHM) is an indispensable technology to enhance production dependability, operational safety, equipment maintenance effectiveness, and cost efficiency ,2,3. TheThe existing RUL prediction approaches are roughly divided into physical-model-based and data-driven approaches. The standard physical-model-based prediction method struggles to provide an accurate deterioration model because of aero engines’ complicated structure, the mathematical model’s severe nonlinearity, and the close coupling between sensor data . In contNevertheless, data-driven methods have some limits for the RUL prediction in industrial applications due to the following reasons: (1) the modeling process requires a large number of labeled datasets, and collecting enough labeled data is often complicated in many practical applications ,11; (2) In order to improve the generalization ability of the model under different operating conditions and failure modes, domain adaptation (DA)-based transfer learning (TL) models were widely developed and applied to cross-domain RUL prediction tasks , which aDespite some success in extracting domain-invariant features using the migration learning techniques mentioned above, it is essential to note that these issues still exist: (1) The sensor data of complex systems collected under different operating conditions and failure modes have significant distributional differences, and sensor data at multiple degradation stages also have different distributions. Therefore, solely adopting the global domain adaptation (GDA) technique could confuse the fine-grained features between the sub-domains denoted by various degradation stages , lowerinThis paper proposes an aero-engine RUL prediction model based on a bi-discrepancy network (BDnet) to solve the above issues. First, target samples with significant domain shifts are identified by maximizing the regressor discrepancy without introducing additional models. Then, domain-invariant features are searched for based on the adversarial relationship between the feature generator and the dual regressor to achieve the GDA. Second, the fine-grained features of multiple degradation stages are extracted by the local maximum mean discrepancy (LMMD) to achieve the SDA. In particular, the SDA is performed on top of the GDA, which helps resolve the issue that direct sub-domain adaptation is ineffective due to the significant disparity in distribution between the source and target domains. Finally, 12 cross-domain prediction tasks from the aviation turbofan engines’ C-MAPSS dataset are used for evaluation. The outcomes demonstrate that the RUL prediction model based on the BDnet outperforms various popular feature-based transfer learning algorithms and adversarial-based transfer learning techniques.The main contributions of this article are summarized as follows.First, the maximum regressor discrepancy (MRD) is proposed to detect and handle target samples with significant domain shifts without introducing additional models to achieve the GDA.Second, the local maximum mean discrepancy (LMMD) is designed to capture fine-grained information from multiple degradation stages, from which the domain-invariant features can be better learned to promote RUL prediction performance.Finally, the proposed BDnet-based RUL prediction approach is evaluated by an aircraft turbofan engine dataset under cross-domain conditions. The research results denote that the BDnet-based approach is better than some deep learning approaches without transfer learning and conventional transfer learning approaches.The remainder of this paper is organized as follows. In this paper, we draw on the concept of the maximum classifier discrepancy (MCD)  and propThe distributional discrepancies between the source and target domain, in MCD’s opinion, may result in either excellent or unsatisfactory classification results if the network trained in the source domain is applied straight to the target domain. The target domain samples with subpar classification outcomes more accurately denote the domain variations. Two separate classifiers, The specific training process Is as follows:1 and F2, to correctly classify the source samples, which can be expressed asFirst, the cross-entropy loss function is used to train the feature generator, G, and classifiers, F1 and F2, to detect the samples with large domain shifts, which can be expressed asSecond, on the basis of guaranteeing the classification ability of the model, the feature generator, G, is fixed, and the difference loss function is used to train two different classifiers, FFinally, the feature generator, G, is trained to minimize the classifier discrepancies, as shown in Equation (3).Unlike MCD, MRD consists of a feature extractor, Considering the data imbalance problem, this paper utilizes the Tversky coefficient to quantify the difference between the dual regressors; however, the non-differentiable nature of the Tversky coefficient leads to the problem of updating the parameters of the network, so a differentiable form of the Tversky coefficient is proposed and is given byTo facilitate the understanding of the process of implementing the GDA using a dual regression network, Step A: In order to ensure the prediction ability of the model for the source domain samples, the feature extractor and the dual regressors are pre-trained using the source domain data to minimize the mean squared error (MSE), as shown in Equation (5):Step B: In this step, we train the dual regressors as a discriminator for a fixed feature extractor. Since two regressors can easily produce inconsistent predictions for target instances with different sources, the prediction discrepancy means that two regressors involve different predictive abilities for the target samples. Thus, we utilize the prediction inconsistency to detect those target samples. Specifically, by training the dual regressors to increase the discrepancy, if the source and target regressors provide different predictions for the same target samples, we consider them as target domain samples with significant domain shifts. This step corresponds to Step B in Step C: We train the feature extractor to minimize the discrepancy for fixed dual regressors to move these target samples into the source support set. This step corresponds to Step C in Traditional DA methods usually use the maximum mean discrepancy (MMD)  to measug et al.  to discr degrade . RULti iAlgorithm 1 RUL prediction based on bi-discrepancy networkInput: Train data Train data 1:Selection variable and swipe the time window to generate samples.2:Initialize randomly the model parameters 3:Initialize while do4:5:  Input 6:  Update 7:  Input 8:  Update 9:  Calculate source domain data’s degenerative level 10:  Input 11:  Calculate target domain data’s degenerative level 12:  Calculate source weights and target weights 13:  Calculate LMMD loss 14:  Update 15:  end while16:17:where λ is a hyperparameter used to control the ratio between GDA and SDA. Bi-LSTM  is used  Bi-LSTM  with MonFirst, this paper uses the C-MAPSS dataset provided by the NASA-Ames Research Center , dividedc and c = 1 represents the complete correlation, and m = 1 represents the monotonic increasing or monotonic decreasing. Thus, sensors with a larger The monitoring data for each flight cycle consist of 26 dimensions of feature data, where the first two dimensions denote the engine (unit) number and the cycle number. The following three dimensions are the flight conditions , and the remaining 21 dimensions are the monitoring data. In addition, to prevent the redundancy and interaction of the multidimensional sensor data from adversely affecting the RUL prediction, this paper analyzes the degree of correlation among the sensors and the trend of degradation of each sensor over time to select the optimal sensor signal for the RUL prediction. The correlation and monotonicity measures can be expressed as follows .(13)c=|Moreover, the same sensor may have different measurements under different operating conditions. In order to reduce the effect of the operating conditions, the sensor data under each operating condition are min–max normalized so that the data size is limited to the range of . Finally, a time window with a window size of 30 and a step size of 1 is utilized to divide the raw data to generate the training and testing datasets.The hardware and software environment of the simulation is an NVIDIA GeForce RTX 3060 Laptop GPU, an Intel Core i7-11800 H CPU, 32 G RAM, Windows 11, and Python 3.7, based on the PyTorch framework. Manufacturer is Lenovo .The BDnet consists of three main parts: a feature extractor, a dual regressor, and a subdomain adaptor. The main parameters are defined as follows: the time window length is set to 30, the number of loops is 200, the early stop mechanism is set, the degeneracy inflection point is 125, the hidden layer dimension of the dual regressor is 32, and the optimizer adopts the Adam optimizer. In addition, the performance of the prediction model may be affected by the different batch sizes in the neural network, the learning rate of the feature extractor, the learning rate of the dual regressor, the dimension of the hidden layer of the Bi-LSTM, the number of layers of the Bi-LSTM, and the scaling factor. Therefore, it is essential to investigate the adaptability and robustness of the model for different tasks by adjusting the hyperparameters. The hyperparameter configuration is shown in Take FD001–FD002 as an example to discuss the influence of the hyperparameters on prediction performance, and the results are shown in In order to measure the predictive performance of the proposed model, the scoring function (SF) and root-mean-square error (RMSE) are used for quantitative assessment.The SF is an asymmetric function often used to evaluate the performance of the remaining life prediction. For the remaining life prediction of an aero engine, the ahead prediction is better than the lagged prediction, so the lagged prediction is more severely penalized than the ahead prediction for the same error. The SF is defined asCompared to the SF, the RMSE equally penalizes the over-advanced and lagged prediction errors. The RMSE is defined asIn order to validate the performance of the BDnet on the cross-domain prediction tasks, ablation experiments are first performed. The model that only utilizes the feature extractor and dual regressor directly trained with the source domain data on the target domain data is defined as model 1, which can be considered as the benchmark model for the RUL prediction problem. The model that uses the MCD is defined as model 2, and the model that uses both the MCD and the LMMD is defined as model 3. The three models’ predictions, as mentioned above, are compared with the real RUL. The models are trained using the source domain data and evaluated on the target domain. Since the C-MAPSS dataset has four subsets, 12 cross-domain prediction tasks are generated, and the results are shown in First, model 3 gives the highest accuracy on all 12 cross-domain prediction tasks. Second, model 2 and model 3 are more stable and less fluctuating than model 1.Taking FD001 as the source domain data, the cross-domain prediction results for the target domains of FD002, FD003, and FD004 are shown in Like the last set of experiments, the source domain, FD004, also has two fault modes. However, the prediction results are better because FD004 has more training data and adequate feature extraction.As shown in To assess the quality of the model in transferring the degradation patterns from a source to a target domain, the BDnet-based predictive model is compared with four mainstream UDA methods targeting the C-MAPSS dataset. TCA-DNN and CORAL-DNN are UDA methods that combine traditional machine learning algorithms with deep neural architectures. TCA  uses MMDTwelve engines were randomly selected for comparison experiments on 12 cross-domain prediction tasks, and The comparative experimental results of the RMSE are shown in The comparative experimental results of the SF are shown in In addition, the BDnet significantly improves the prediction performance of the larger domain offset prediction task while maintaining the performance of the smaller domain offset prediction task, proving the superiority of the BDnet without introducing other auxiliary models.In this paper, an unsupervised domain adaptive regression method based on the BDnet was proposed to address the realistic problem of unlabeled samples in the aero engine RUL prediction task. Unlike previous adversarial DA methods that introduce discriminators for domain feature alignment, the BDnet achieves the GDA by maximizing the regressor difference to gradually detect and adjust the target domain samples with significant domain shifts, without introducing an additional model. In addition, to extract the fine-grained features of the aero engine at different degradation stages, the LMMD is utilized to achieve the SDA based on the GDA. Finally, experiments are conducted on 12 cross-domain prediction tasks to verify the effectiveness of the BDnet-based RUL prediction model in solving the unsupervised domain adaptive regression problem.


Objectives: to analyse the clinical–pathological characteristics, treatment, and evolution of uterine smooth muscle tumours with uncertain malignant potential (STUMP) diagnosed in the Salamanca University Hospital with the implementation of the 2014 WHO criteria. Materials and methods: a retrospective descriptive study of patients diagnosed with STUMP from January 2015 to March 2023 at the Salamanca University Hospital. Demographic data, preoperative clinical data, treatment, complications, therapeutic results, anatomopathological findings and recurrence time were obtained. Results: a total of four patients were identified and included in the study. The mean age at diagnosis was 48 years (range 36–67). The surgical indications were abnormal uterine bleeding, compressive symptoms, and the growth of a pelvic mass suspected to be a degenerated myoma from the residual cervix after a subtotal hysterectomy 6 years earlier. In all cases, a laparotomic procedure was performed. A total hysterectomy, sub-total hysterectomy, and the excision of the cervix with STUMP localization were accomplished in two, one, and one patient, respectively. The mean diameter of the tumour pieces was 13 cm (range 8–17 cm), with a mean volume of 816 cc (range 234–1467 cc). The mean follow-up was 47 months, with no recurrence to date. Conclusions: STUMPs are a heterogeneous group of tumours with a difficult-to-predict clinical evolution. In most cases, their diagnosis is histological after performing surgery for suspected leiomyoma. Due to their low incidence, there are no specific guidelines for their treatment and control. However, considering their potential risk of recurrence and metastasis, it is advisable to maintain six-monthly controls for 5 years and then annual controls for 5 years more. Uterine smooth muscle tumours have, historically, been classified into benign leiomyomas and malignant leiomyosarcomas according to the degree of cytological atypia, mitotic activity, and other molecular tissue markers . HoweverIt is thought that STUMP may be the transition from leiomyoma to leiomyosarcoma or a sometimes underestimated low-grade leiomyosarcoma ,8. Most A careful histopathological study is needed to confirm the right diagnosis of STUMP, due to the unreliability of preoperative imaging techniques in differentiating between leiomyoma, STUMP and leiomyosarcoma . There aThis study aims to analyse the clinical–pathological characteristics, treatment, and follow-up of STUMPs diagnosed in the Salamanca University Hospital from 2015 to 2023.A retrospective descriptive study of patients diagnosed with STUMP from January 2015 to March 2023 at the Salamanca University Hospital was performed. Demographic data, preoperative symptoms, treatment, complications, therapeutic results, anatomopathological findings, and recurrence time were obtained from the patients’ records.The tumour volume was calculated according to the size of the surgical pieces, applying the volume of an ellipse formula (length × width × depth × 0.5233).Recurrence was defined as a diagnosis of STUMP or leiomyosarcoma at least 6 months after surgery. A diagnosis of leiomyoma was not considered a recurrence. This study was approved by the Salamanca University Hospital Ethics Commission.In total, 4 patients with a pathological diagnosis of STUMP out of 915 women undergoing surgery for uterine leiomyomas during the study period were included.Their characteristics are presented in Two patients had severe surgical complications. Case 3 suffered a distal injury of the left ureter, requiring ureteral neo-cystostomy 7 months later. Case 4 was complicated by a perforation of the sigmoid colon, requiring colon resection and termino-terminal (end to end) anastomosis. During the first surgery day, a left ureteral lesion was diagnosed and followed by ureteral neo-cystostomy, according to the Lich-Gregoir technique. A double J catheter was left and retired 2 months later.The mean follow-up was 47 months, without diagnosis of any recurrence. The patients had a check-up one month after surgery and every six months for up to five years.Mesenchymal smooth muscle cell tumours are the most common type of uterine neoplasia. They include leiomyomas and their subtypes, mesenchymal smooth muscle tumours of uncertain malignant potential (STUMP), and leiomyosarcomas . The maiAt present, the imaging studies available to us do not show a clear difference between STUMPs or leiomyomas and leiomyosarcomas. Ultrasonography is the diagnostic tool most commonly used by gynaecologists. However, in the studies found in the literature, ultrasound is not effective for the differentiation of these tumours . A recenAccording to the 2014 WHO criteria , the patThe Stanford criteria for the diagnosis of leiomyosarcoma reported by Bell et al. include at least two of the following parameters: moderate–severe diffuse cytological atypia, tumour necrosis, and at least 10 mitoses per high-magnification field (≥10 MF/10 HPF) . If a tuTumours with focal or moderate-to-severe multifocal atypia, without cell tumour necrosis and a mitotic count less than or equal to 10 mitotic figures per field.Tumours with moderate-to-severe diffuse atypia and a mitotic count of less than 10 mitotic figures per field.Tumours with cell necrosis, mild or absent atypia, and a mitotic count less than 10 mitotic figures per field.Tumours without cell necrosis, mild or absent atypia, and a mitotic count greater than or equal to 15 mitotic figures per field.Currently, the diagnosis of STUMP is based on criteria approved by the WHO in 2014  Table 2Table 2:TAmong the three major criteria for establishing the biological potential of uterine tumours , the one most strongly associated with malicious behaviour is the latter . In the The expression of immunohistochemical markers could be helpful in differentiating these tumours. Ki 67, p53, and p16 have been studied either individually or in combination. Steroid hormone receptors, such as the estrogen receptor and progesterone receptor, could be present in STUMP and leiomyomas. The results of studies have concluded that the expression of immunohistochemical markers in STUMP is similar to that in leiomyomas. However, the immunohistochemical markers in leiomyosarcoma are different from the others. This fact allows a research line for the study of new specific markers that can improve the accuracy in differentiating STUMP from other smooth muscle tumours [The incidence of STUMP is difficult to estimate. Among women undergoing a hysterectomy or myomectomy for a presumed diagnosis of leiomyoma, 0.01% receive a diagnosis of STUMP. An average age at diagnosis of 43 years has been suggested (similar to our study) .In our case series, as in the published literature ,6,8, susGiven the rarity of this tumour and the scarcity of the published series, there are no clinical guidelines for the management of STUMP, so the clinical approach in terms of the diagnosis, treatment, and control of its recurrence is based on observational data . For patThe high rate of severe surgical complications in our case series could have been due to the large size of the pelvic tumours. The cases that suffered surgical complications (no 3 and 4) had a larger volume than a 26-week pregnant uterus.The estimated recurrence rate of this kind of tumour is between 3.7 and 27%, with an average time of 51 months . This vaIn our series, there were no recurrences, although the short follow-up period (mean of 46.5 months with a range of 7–64 months) have interfered with this result.Given the difficulty of establishing reliable recurrence rates and their potential for metastasis, it is important to maintain patient adherence and adequate controls. Although there is no consensus regarding the necessary controls after surgery, the most accepted standard follow-up consists of six-monthly clinical reviews for 5 years, followed by annual reviews for 5 more years and an annual MRI ,13. FurtSTUMPs are a rare and heterogeneous group of tumours with difficult-to-predict clinical behaviour. The pathologic criteria for STUMP diagnoses are not defined and it is necessary to create a consensus to resolve this problem. In most cases, its histological diagnosis is achieved after surgery for a suspected leiomyoma. The challenge is to find diagnostic tests that help us to suspect these tumours before surgery. Although immunohistochemical studies may help in differentiating these tumours, more research is required to improve their diagnosis. A total hysterectomy is the standard and most accepted surgical treatment, although a myomectomy may be considered for women who wish to preserve their fertility. To date, neither immunohistochemical characteristics nor serological markers seem to be useful in identifying worse prognoses and a higher risk of recurring tumours. Due to STUMP’s potential for recurrence and metastasis, long-term follow-ups seem necessary.


T0, lower values of Peakη and Finalη, as well as higher values of the breakdown and setback region than the slurries with refined flours. The slurry with black flour exhibited high values of viscosity η0 and breakdown, together with low values of Finalη, setback region and total setback. Bread characteristics were strongly correlated with the pasting properties. The presence of bran particles did not negatively impact loaf volume, crumb hardness, springiness, and chewiness. Some defects were observed in glutinous and red rice bread. Fine flour exhibited better baking performance than semi-coarse flour. Glutinous flour has the potential to become an ingredient in gluten-free baking. The applicability of various black and red rice flours may be limited by the flavor and the taste.The flours from pigmented and glutinous rice have a great potential to increase the nutritional quality of gluten-free breads. The characteristics of whole-meal fine flours, slurries, doughs, and breads prepared from black, red, and white glutinous rice were, therefore, compared with commercially used refined fine and semi-coarse white rice flours. The pasting properties of different flours were strongly influenced by the type of rice they were made from. Slurries with red and glutinous flours exhibited a shift to a lower pasting temperature  Research into gluten-free bread was previously focused mainly on increasing dough’s ability to trap leavening gas, increasing bread volume, and preparing bread with a softer crumb. Commercially baked breads have low nutritional value as they are mainly prepared from starch blends. This is a significant problem that requires solving since a strict gluten-free diet is often necessary throughout a person’s entire life. Our research is, therefore, focused on the applicability of nutritionally valuable rice grains in gluten-free baking.Oryza sativa L.) flour is used for cakes, puddings, and noodles. It may also replace wheat flour in bread making. Flour that is prepared from white rice exhibits several significant properties, such as bland taste, colorlessness, and hypoallergenic characteristics, which make it suitable for gluten-free bread making. Low levels of protein, sodium, fat, fiber and a high amount of easily digestible carbohydrates were also reported [Rice  . PigmentOryza sativa var. glutinosa) is a type of rice that is mainly cultivated in Asia [White glutinous rice . The ground material was sieved using the AS200 Control  machine to obtain fine flours. The objective of the white rice milling was to remove hull, bran, and embryo. The remaining endosperm was ground using a pin mill , and the ground material was sieved to obtain refined white fine and semi-coarse flours. The flours were vacuum-packed in polyethylene bags and stored at 4 °C before further analysis was conducted.All fine flours were characterized by being made up of at least 96% of particulate that passed through a 257 μm sieve opening and a maximum of 75% of particulate that passed through a 162 μm sieve opening. Semi-coarse flour requires at least 96% of particulate to pass through a 366 µm sieve opening and a maximum of 75% of particulate to pass through a 162 μm sieve opening.The contents of starch and proteins in flour were determined according to ISO 10520  and ISO The microstructure of the flour samples was evaluated using a scanning electron microscope . Gold-plated flour samples were observed at the following condition: 15 kV, 0.055 Pa, and magnification of 900×.HAAKE RheoStress 1  was used for assessing the pasting properties of flours. The suspension was prepared by mixing (6.0 ± 0.1) g of the flour with (30.0 ± 0.1) g of water. The rotation temperature ramp was performed using coaxial cylinders Z34 DIN Ti with a gap of 7.2 mm. The viscosity of a sample was measured over a given period of time while it was stirred. The test was divided into five stages: the addition of water to the flour sample; the heating phase; holding at maximum temperature; the cooling phase, and a final holding stage. The profile was: the addition of water to the flour sample, holding at the temperature of 30 °C for 120 s, heating to 90 °C for 220 s, holding at 90 °C for 300 s, cooling to 40 °C for 220 s and holding at 40 °C for 120 s. The suspension was stirred at 160 rpm during the test . Each teThe basic dough formula consisted of flour (100 g), water (90 g), sucrose (1.86 g), salt (1.50 g) and dry yeast (1.80 g). The amounts of all ingredients were related to flour dry matter.R (mN), i.e., resistance to extension, and the distance at which this peak force occurs, which is expressed as dough elongation E (mm). Extension area A (mN/mm) is the area under the curve, which is proportional to the energy required to stretch the test piece until it ruptured. At least six measurements were performed. The results are represented as mean values.Samples used for the uniaxial deformation test were prepared according to a dough formula without yeast. The dough was shaped into thin rolls, put onto the lubricated surface of a Teflon mold, and compressed with a lubricated top plate. Test pieces of dough were formed into 5 cm long pieces with a trapezoidal cross-section . The doughs were kept for (40 ± 2) min at (30 ± 2) °C. Uniaxial elongation tests were performed using a texture analyzer TA.XT plus  equipped with an SMS/Kieffer Dough and Gluten Extensibility Rig. The measuring conditions were as follows: measure force in tension, pre-test speed 2.00 mm/s, test speed 3.00 mm/s, post-test speed 10.00 mm/s, and trigger force 5 g. The force required to stretch the dough sample and the displacement of the hook were recorded as functions of time. The values of importance were the peak force Dry yeast was reactivated for (10 ± 1) min in a sugar solution (35 ± 1) °C prepared from sucrose and 60 mL of water. The flour, sugar solution, salt, and the rest of the water were placed into an Eta Gratus mixer bowl  and mixed for (6 ± 1) min. The prepared dough (600 g) was scaled into bread pans of 9.4 × 18.3 × 7.0 cm and placed into a proofer for (20 ± 2) min at (30 ± 1) °C and 85% relative air humidity. The loaves were baked for (40 ± 2) min at (180 ± 5) °C . The baked bread loaves were removed from the pans and stored at room temperature (21 ± 3) °C for 2 h. Loaf volume was determined using plastic granulates the size of rape seed. Loaf-specific volume (mL/g) was obtained by dividing the bread volume by bread weight. Three batches of three varieties of bread were baked for each flour. The results are represented as mean values.Texture profile analysis (TPA) was performed on TA.XT plus texture analyzer  was used to test the textural properties of the breadcrumb. The test was carried out on bread samples 35 mm in diameter and 10 mm in height obtained from the center of each loaf. Each sample was placed on the analyzer base and squeezed twice with a 75.0 mm diameter cylinder probe P/75. Parameters of the test were: pre-test speed 1.00 mm/s; test speed 5.00 mm/s, strain 40%, and trigger force 5 g. Crumb parameters  were then determined . At leasThe results were statistically analyzed using analysis of variance (ANOVA). The differences between samples were tested on a 0.05 significance level using the Fisher LSD test. Principal component analysis (PCA) was used to illustrate the relationship between variables and samples. All statistical analyses were performed using Statistica 13.0 .The tested flours significantly varied in their contents of starch, proteins, fibers, sugars, and the Hagberg falling number . These rThe content of damaged starch was observed in the range of (2.0–10.7)%. The flours from polished japonica rice with 4% and 6% content of damaged starch were previously found to have good characteristics and be suitable for gluten-free bread making . Even ifR (92–250) mN, extensibility E (2.8–7.6) mm and area under curve A (74–1020) mN·mm were recorded in the tested doughs . However, further research is necessary to confirm this hypothesis. The relation between dough behavior during the uniaxial elongation test and bread quality is well described in the wheat dough. However, it is not clear whether uniaxial elongation is a suitable tool for the prediction of gluten-free bread quality.η30°C than the slurries with refined flours. The viscosity of the slurries with all types of fine flours was, moreover, lower than the slurry with semi-coarse flour. The slurry with black rice was the only exception since its viscosity was the highest among the tested flours. The slurries prepared with glutinous and red rice flours exhibited a shift to a lower pasting temperature T0, lower values of Peakη and Finalη as well as higher values of breakdown and setback region compared to the slurries with refined flours. The decrease in the effective concentration of starch, together with competition for water between bran particles and starch, may explain the lower viscosity in the slurries with red and glutinous flours [The viscosity of the slurries at 30 °C varied at the range of (2.7–8.9) mPa . The slus flours ,33. Highη0 and breakdown, together with the lowest values of Finalη, setback region and total setback. The slurry viscosity during the initial and heating stages was probably increased by the high-water binding and holding capacity of the substances present in this flour. A rapid decrease of viscosity during the holding and cooling stages may be explained by the extensive release of water from previously hydrated substances. It may be hypothesized that this behavior was closely related to proteins. The proteins probably bound an extensive amount of water, which was subsequently released from the denatured proteins. Starch and fibers were not able to absorb such an extensive amount of water, leading to a decrease in viscosity due to the presence of an extensive amount of free water. Low values of setback region and total setback indicated low tendency to retrograde [The slurry prepared from black rice flour exhibited the highest values of viscosity trograde . The preThe various loaves of bread prepared from different refined white flours had relatively large pores d,e, but The negative impact of bran particles on bread volume and bread hardness ,9 was noThe crumb of the bread prepared from black flour did not exhibit any defect a. The crEven if the study was mainly focused on testing flour, dough, slurry, and bread characteristics, together with relations among them, the informal sensory evaluation was also performed. Bread prepared from glutinous flour had sticky crumbs, with a neutral taste and flavor. Similar neutral tastes and flavors were recorded in loaves of bread from refined white flours. The evaluation of breads from pigmented flours varied among the evaluators. Some of them found the color of these loaves of bread attractive, while others found them unacceptable. The evaluators recognized intensive flavor and taste in these loaves of bread. The flavor of bread from black rice flour was described as fruity and floral, while the flavor of bread from red rice flour was leafy, floral, nutty or amaranth-like. The recorded intensive flavor and taste may impact the applicability of these flours in bread making. The blends of black and red rice flours with white rice or glutinous flours may decrease the intensity of flavor and taste to an acceptable level. A formal sensory evaluation analysis has to be performed to confirm these assumptions.Two principal components (PC) were located to describe 86.0% of the total data variance. The principal component loadings a showed Peakη, as well as final viscosity Finalη. It is evident that starch content significantly influenced bread characteristics since starch is a major component of the flours used. Crumb hardness was related to slurry viscosity and its increase during gelatinization. Peak viscosity Peakη had a stronger impact on the crumb hardness than the final viscosity Finalη. This observation was evident mainly from the results of black rice flour. The peak viscosity of slurry with this flour was higher than in slurries with glutinous and red rice flours , final viscosity was lower  and hardness was higher . As can be seen in Bread characteristics  were closely related to each other and strongly positively correlated with the content of starch in flour, slurry peak viscosity Crumb hardness and resilience were positively correlated with dough extensibility E during the uniaxial deformation test. The correlation of bread characteristics with the other characteristics recorded during the uniaxial elongation test  was very weak. Even if dough behavior during this test is often used to predict the bread-baking performance of wheat flour, this test seems to be of minor importance in gluten-free bread baking.Breadcrumb cohesiveness, chewiness, hardness, and resilience were negatively correlated with protein content. Even if proteins present in gluten-free flour are known to exhibit quite different behavior compared to wheat gluten, they are probably able to decrease crumb hardness. This effect is desirable, since hard bread crumb is typical in gluten-free bread.Bread springiness and baking loss were negatively correlated. The baking loss was positively correlated with the content of damaged starch and setback region and negatively with breakdown, the content of maltose (sugars), and viscosity at the initial stage of the test. The processes taking place in the slurry during the test may differ from the processes occurring in the dough during baking since the slurry is prepared using a significantly higher amount of water than is present in the dough during baking. However, it is evident that baking loss was strongly influenced by the content of free water in the dough during baking and cooling. If the dough or bread contained free water, this water evaporated during bread baking and cooling, increasing baking loss. Water bound in starch, proteins and brans was kept in the dough and bread, decreasing baking loss. The baking loss also rose with an increasing amount of damaged starch. The damaged starch granules are known to have higher water absorption ability, but this water is released during heating  and evapT0. Black rice flour was characterized mainly by slurry viscosity at the beginning of the test (η0) and breakdown. The impact of flour granulation on slurry and bread characteristics was also recorded in the samples prepared from white rice.As can be seen from the scatterplot b, the chThe tested flours prepared from pigmented (black and red), glutinous, and common rice exhibited different characteristics. The type of rice also influenced flour pasting characteristics and dough behavior during the uniaxial deformation test. The characteristics of loaves of bread were mainly impacted by flour pasting properties. The loaves of bread prepared from black, red, and glutinous rice flours exhibited higher loaf-specific volume and springiness, as well as lower hardness, cohesiveness, resilience, and chewiness than loaves of bread from refined flours. The significance of the differences varied among the parameters. The bread prepared from black rice flour was the only one without any crumb defects. A crack from the center to the upper corners was observed in bread from red rice flour. One large pore was situated under the crust of the bread from glutinous rice. Moreover, the crumb of this bread was sticky. The tested whole-meal flour prepared from glutinous rice exhibited a good baking performance and has the potential to become a nutritionally valuable ingredient in gluten-free baking. The informal sensory evaluation revealed the intensive flavor and taste in loaves of bread prepared from pigmented rice flours, which may limit their applicability in gluten-free bread baking. The intensity of flavor and taste may be expected to be decreased by the blending of these flours with other gluten-free flours of neutral flavor and aroma. The breadmaking potential of these blends should be the goal of the next research.


The Arrhenius constitutive model was built for the corresponding conditions, and the model’s accuracy was verified through error analysis. Additionally, hot processing maps were constructed to analyze the processing zone of the steel under different hot deformation conditions. Finally, the microstructure of the processing zones was observed and verified using the electron backscattered diffraction (EBSD). The results indicate that the interaction of work hardening and dynamic softening influences the hot deformation behavior of 26CrMo7S steel. The Arrhenius constitutive equation with a value of the correlation coefficient (r = 0.99523) accurately predicts the flow behavior of 26CrMo7S steel under different strains. The optimal processing zone obtained with the hot processing maps is within a deformation range of 1010~1190 °C and a strain rate range of 0.01~10−1.5 s−1, and the obtained microstructure is in good agreement with the analysis results.The hot deformation behavior and flow stress characteristics of experimental 26CrMo7S steel were analyzed using a thermal simulator under a range of conditions, including a strain rate range of 0.01~10 s With the decrease in traditional oil resources, unconventional oil fields like shale and tight oil are being explored. Deeper oil wells mean increased temperature and pressure, leading to a more adverse operating environment for oil well pipes, resulting in the need for steel with higher safety and reliability standards ,2,3. The−1. The research found that fully recrystallized structures corresponded to a high-energy dissipation zone. Zhang et al. [−1, using the Gleeble-1500D thermal simulation test machine. they were able to develop stress–strain curves, a complex flow stress constitutive equation, and hot processing maps. Furthermore, they determined the optimal hot deformation parameters within the temperature range of 800~850 °C and strain rate range of 0.01~1 s−1. These studies showed that the establishment of a constitutive equation and the use of hot processing maps can accurately predict deformation mechanisms and prevent processing instability of material during hot deformation. In addition, the constitutive and hot processing maps are beneficial to identify the appropriate hot deformation parameters of material and provide valuable guidance for safe process and gaining the suitable performance of material during the hot deformation. Although numerous studies concern the constitutive models of various alloys merged with hot processing maps, there is a lack of systematic research on 26CrMo7S steel, and the microstructure and final properties of 26CrMo7S steel are affected by hot deformation conditions. Therefore, it is necessary to study the hot deformation parameters of 26CrMo7S steel and derive a corresponding constitutive model and hot processing maps, facilitating the optimization of hot deformation parameters for this particular steel.The study of hot processing maps helps control microstructure and ensure optimal performance by avoiding generating defects caused by inappropriate hot deformation parameters. In recent years, experts have studied different alloys to create models and maps for hot processing. Zhou et al.  tested tg et al.  studied g et al.  performeIn this paper, a high-temperature simulation compression test was conducted on the 26CrMo7S steel sample. The true stress–strain curve was plotted from the experimental data, and then the Arrhenius constitutive equation was modeled. According to the curve and the model, the distribution of stress, strain, and temperature fields, as well as the relevant flow properties, were investigated under different hot deformation situations. The relationship between flow stress and the deformation process was analyzed quantitatively, and hot processing maps were created to examine the cooperative deformation and microstructural characteristics of 26CrMo7S steel. These results provide a mathematical model for predicting flow stress under different hot deformation conditions and a theoretical basis for obtaining suitable properties of experimental steel through the building of hot processing maps.The experimental material is 26CrMo7S steel, and its chemical composition is shown in The experimental procedure and the hot compression test carried out on a Gleeble-3800 thermal simulation testing machine are presented following the schematic map in −1 to 10 s−1. But with the deformation temperature increased from 850 °C to 1250 °C, the flow stress value gradually decreased.The true stress–strain curves of 26CrMo7S steel are depicted in −1 and 0.1 s−1, the flow stress curve exhibited a dynamic recovery type at deformation temperatures of 850 °C and 950 °C. When the deformation temperature was 1050 °C or above, the flow stress curve exhibited dynamic recrystallization, the same result as Chen et al. [When the strain rate was 0.01 sn et al. , and then et al.  suggeste−1, at a deformation temperature of 850~1050 °C, the flow stress curve increased with increasing strain, displaying dynamic recovery [−1, the flow stress curve exhibited a dynamic recovery type at deformation temperatures ranging from 850 °C to 1250 °C. As the strain increased, the flow stress peaked and demonstrated a tendency towards flattening or continuous rise.When the strain was 1 srecovery ,26, whilThe constitutive equation can determine the relationship among the deformation parameters, such as flow stress, temperature, and strain rate. The strain rate was controlled by the hot activation process, which occurred during hot deformation. Flow stress is essential data for high-temperature flow characteristics, while deformation temperature and strain rate greatly influence it.Based on the true stress–strain curve, the relationship between flow stress and deformation parameters was often expressed using the classical Arrhenius constitutive model.The Arrhenius constitutive model proposed by Sellars and Tegart is widely acknowledged for its ability to demonstrate the correlation among flow stress, temperature, and strain rate . The ArrThe Arrhenius constitutive equation can be expressed in three equations following the different stress levels. The constitutive equation of 26CrMo7S steel is based on the following three forms, respectively .When When T is the absolute temperature, and Q is the activation energy of deformation, All Z parameter.Then, using the definition of the hyperbolic sine function to solve Equation (6), the flow stress The natural logarithm of Equations (2)–(4) was taken respectively, and the obtained expression is shown in Equations (8)–(10).Equation (10) is deformed to obtain Equation (11).The data points of the peak stress shown in Based on Equations (8) and (9), the relationships of n = 5.957176.Similarly, Q = 452,648 kJ/mol, and ln A is calculated to be 38.98675.The substitution of the flow stress value under peak conditions into Equation (11) enabled the linear fitting of the scatters of The Q value was substituted to obtain the parameter Equation (13).In Equation (1), the calculated hot deformation activation energy Z parameters under peak conditions, as shown in Equation (14).In Equation (7), substituting the calculated parameters can obtain the stress equation of 26CrMo7S steel expressed in r can express the linear strength between the predicted and experimental values, and r can be computed as shown in Equation (15).Error analysis of the model prediction and experimental values can further describe the accuracy of the above constitutive equations. The correlation coefficient The deviation between the predicted and experimental values can be reflected by the absolute error , and the experimental data were substituted into Equations (15)–(17). The corresponding the correlation coefficient and absolute error were obtained, as illustrated in It can be seen from Based on the Dynamic Material Model (DMM) theory, the material dissipates as a power dissipator during hot deformation. The absorbed energy is mainly dissipated in two forms: one is the deformation viscoplastic heat generated at high temperature and high strain rate, which is dissipated through heat; the other one is the microstructure evolution, which is dissipated through dynamic recovery, dynamic recrystallization, hole formation, and phase transformation. σ), strain (ε), and strain rate .m is the strain rate sensitivity coefficient. Also, they are decisive for the allocation of K is the material constant, and Deriving from m = 1, the material exhibits the ideal linear dissipation process, as shown in According to the dynamic material models, when m ≤ 1, the material does not exhibit energy dissipation behavior. When the m-value is between 0 and 1, it indicates that a nonlinear dissipation process occurs, as shown in η to represent the microscopic deformation mechanism in the hot deformation process. The η value is the ratio between the Jmax, as shown in Equation (21).η is the power dissipation factor, J is the dissipative covariate in the nonlinear dissipation process, and maxJ is the ideal linear dissipative covariate.Prasad et al. ,33,34 inJ could be expressed as Equation (22).(1) Prasad et al.  proposedm = 0, m = 1, η, the dimensionless parameter, can be expressed with Equation (23).Thus, according to Equation (22), when The flow instability criterion is shown in Equation (24).(2) The Murty criterion believed that only tionship . Then, GWhen The corresponding flow instability criterion is shown in Equation (27).Various criteria, such as the Malas and Gegel stabilization zone and Semiatin instability criteria, are useful in predicting the behavior of specific materials ,37. Howeη values. The flow instability values are computed through the flow instability criterion. The hot processing safety zone region is the area with high power dissipation. For example, when the strain was 0.1 , as shown in The high-power dissipation occurred mostly in areas with high temperatures and low strain rates because it is more beneficial for dislocation to migrate at such temperatures. A strain of 0.7 resulted in peak Different regions in the processing maps indicated that some specific microstructural evolutions occurred in the samples treated by corresponding processing parameters. To better understand the microscopic evolution characteristics of the samples, various areas from the hot processing maps of strain 0.7 were selected for EBSD testing. The EBSD results of experimental steel under different thermal conditions are shown in −1, which was dissipation low-value region η = 0.09. It can be obviously seen that there were recrystallization and grain growth phenomena in −1, which belonged to the flow instability region of medium temperature and high strain rate, and η = 0.15. It can be seen that the original grain of the material was significantly elongated, and the dynamic recrystallization (DRX) grew at the original grain boundary. This phenomenon can lead to uneven structure, making mechanical instability during hot deformation more likely. −1, the power dissipation coefficient η hit 0.49; the corresponding EBSD images are shown in When the temperature reached 1150 °C, and the strain rate was 0.01 sBased on the recrystallization distribution maps and IPF maps, it can be observed that new dynamically recrystallized grains occur mainly at triple junctions and original boundaries marked respectively in black boxes and circles, as shown in Ultimately, these findings above emphasized that hot processing maps of the 26CrMo7S steel were crucial in forecasting the microstructure of hot deformation.(1)Experimental steel has negative temperature sensitivity and positive strain rate sensitivity, and the peak stress decreases with increasing temperature or decreasing strain rate, which is the result of the interaction of dynamic softening and work hardening.(2)The developed Arrhenius constitutive model has high accuracy in predicting the peak stress of the 26CrMo7S steel. The hot deformation activation energy is 452.648 kJ/mol. The predicted value is strongly linearly correlated with the experimental value, and the correlation coefficient reaches 0.99523.(3)−1.5 s−1, where the peak efficiency η exceeds 0.4, and more fine grains occurred, leaving only a few initially deformed grains.The suitable domain of hot processing found from the hot processing maps is at a temperature of approximately 1010~1190 °C and a strain rate of 0.01~10The hot deformation behaviors and microstructure of 26CrMo7S steel were investigated in order to understand the hot deformation mechanism and hot processing parameters. The major conclusions are as follows:


The use of zinc (Zn) alloys as a biodegradable metal for medical purposes has been a popular research topic. This study investigated the strengthening mechanism of Zn alloys to enhance their mechanical properties. Three Zn-0.45Li (wt.%) alloys with different deformation amounts were prepared by rotary forging deformation. Their mechanical properties and microstructures were tested. A simultaneous increase in strength and ductility was observed in the Zn-0.45Li alloys. Grain refinement occurred when the rotary forging deformation reached 75.7%. The surface average grain size reached 1.19 ± 0.31 μm, and the grain size was uniformly distributed. Meanwhile, the maximum elongation of the deformed Zn-0.45Li was 139.2 ± 18.6%, and the ultimate tensile strength reached 426.1 ± 4.7 MPa. In situ tensile tests showed that the reinforced alloys still broke from the grain boundary. Continuous and discontinuous dynamic recrystallization during severe plastic deformation produced many recrystallized grains. During deformation, the dislocation density of the alloy first increased and then decreased, and the texture strength of the (0001) direction increased with deformation. Analysis of the mechanism of alloy strengthening showed that the strength and plasticity enhancement of Zn-Li alloys after macro deformation was a combination of dislocation strengthening, weave strengthening, and grain refinement rather than only fine-grain strengthening as observed in conventional macro-deformed Zn alloys. Zinc (Zn) is a nutrient for developing the immune organ thymus. Only adequate Zn intake can effectively ensure thymus development, normal differentiation of T lymphocytes, and promote cellular immune function . Zn meetThe most common method to improve the plasticity of alloys is grain refinement. Huang et al.  fabricatIn this study, high-strength Zn-Li alloy was processed by rotary forging. A grain-refined Zn-Li alloy was then obtained, and the plasticity improvement and suppression of the processing softening of the Zn alloy were realized. This work can guide the application of Zn-Li alloys as medical biodegradable metals.T is the cross-sectional area of the extruded bar, and Sa is the cross-sectional area after rotary forging.The Zn-0.45Li alloy used in this study was melted in an induction furnace using pure Zn (99.99 wt.%) and Li (99.99 wt.%) under Ar gas protection through the following steps. First, 85% pure Zn and Li were placed in the induction furnace and heated to 650–700 °C. After melting, the remaining Zn and Li were added and stirred continuously to mix well and remove the slag. Second, the molten alloy was held for 40 min with constant stirring, and the impurities were removed during the holding period. Third, the molten alloy was poured into a mold with a diameter of 60 mm. The ingots were shaped into blocks with 50 mm diameter and formed into bars with 11 mm diameter via hot extrusion. The hot extrusion temperature was 220 °C, and the extrusion ratio was 20:1. Lastly, the Zn-0.45Li bars were rotary-forged several times at a speed of 1 mm/s at room temperature. The deformation in each step was <18%. Coolant was used to cool the sample and increase surface lubrication during rotary forging. The solid bars obtained by rotary forging using a rotary forging machine exhibited total deformation rates of 6.2% (10.85 mm diameter), 58.1% (7.25 mm diameter), and 75.7% (5.52 mm diameter) under the extruded condition. For convenience, the 6.2%, 58.1%, and 75.7% deformation bars were labeled as R1, R2 and R3 alloys, respectively. Total deformation (Rp):The alloy chemical composition was analyzed by inductively coupled plasma emission spectrometry (PE Optima 2100DV), an important method of qualitatively and quantitatively analyzing trace elements. A powder of 0.2 g ± 0.001 g was removed from the center of a Zn-0.45Li alloy bar and dissolved in 6 mL of hydrochloric acid and 2 mL of nitric acid. After the complete reaction, the powder was fixed in a 100 mL steel volumetric flask and tested to obtain the specific composition of the prepared alloy. The content of Li element in Zn-Li alloy is 0.4388 (wt.%), and the balance is Zn element.The phases of the alloys were determined by scanning electron microscopy  and X-ray diffraction . XRD was performed using CuKα radiation (λ = 1.540562 Å). The scanning speed was 0.025°/s, and the scanning range was 20–90° with a step size of 0.02°. An in-situ stretching experiment was conducted under SEM observation, and a scanning electron microscope was used to observe the change in the microstructure of the sample under stretching conditions. In addition, electron backscatter diffraction  and transmission electron microscopy  were performed to examine the microstructure and morphology of the alloys. The samples used for the EBSD measurements were carefully ground on silicon carbide paper with more than 2000 mesh. EBSD samples were prepared by electrolytic polishing under Fischione 1061 ion plane polishing treatment. In particular, 6 KV was used for 1 h, followed by 4 KV for 30 min. In situ tensile samples of 14 mm length and 1 mm thickness were cut out of the alloy bars. In situ tensile samples of 14 mm length and 1 mm thickness were cut out of the alloy bars. The in situ stretched samples were fixed on the insitu stretching table and observed continuously by SEM. A TEM specimen disk with a diameter of 3 mm and a thickness of 150 μm was cut from the sample surface. Grinding was performed using 2000 mesh silicon carbide sandpaper. The sample was ground to a thickness of 30–50 μm and then ion-milled and polished with Gatan 691 at a low angle to prepare electron transparent areas on the sample. The sample was bombarded at two angles, 15° and 8°, with the voltage set to 4 and 3.5 KV, and then perforated to form the thin zone required for observation. TEM was performed at an accelerating voltage of 200 KV.The sample used to measure tensile properties was processed into a dog bone shape with a standard distance of 28.0 ± 0.1 mm and a diameter of 5 mm. Mechanical tests were performed with a desktop electronic universal testing machine (MTS 100 KN). In accordance with ISO6892-1, three experiments were conducted for each group of samples under room temperature conditions. The crosshead speed of the universal material testing machine was 1 mm/min . Due to Ultimate tensile strength (UTS) is the maximum stress the material can withstand before fracturing under external force. Elongation is the percentage of the total deformation ΔL of the standard distance section after the tensile fracture of the sample to the original standard distance length L.4 . This finding agrees with the Zn-Li phase diagram and other findings [4 [findings ,26. The dings [4 ,28. ThisSince recrystallization consumes the stored energy, recrystallization occurs first, where the stored energy is sufficient. During deformation, deformation energy storage occurs inside the grain, placing the grain system in an unstable state. The greater the degree of deformation, the more defects form inside the metal and the more unstable the overall organization. As a result, recrystallization temperature decrease. Recrystallization occurs when the deformation amount reaches a certain level. During extrusion, the surface of the workpiece is subjected to a large external force, and its surface energy storage is large. Therefore, recrystallization occurs first at the part where the energy storage is large . After rAs shown in s is the alloy yield strength, σ0 is roughly equivalent to the yield strength of a single crystal, d is the grain diameter, and k is a constant associated with the grain boundary. According to the Hall–Petch equation, the finer the grain diameter d during plastic deformation of the alloy, the higher the yield strength σ. In addition, the plastic toughness of the material will not decrease. For most metallic materials, grain refinement can improve strength. For example, magnesium alloys, copper alloys and CoCrFeNi high entropy alloys [The second phase has a key influence on grain refinement. During the grain refinement of Zn alloys, the second phase exerts a pinning effect at grain boundaries, stabilizing them and inhibiting the growth of newly refined grains ,3. As a y alloys ,38,39. Hσs= σ0+kdy alloys . When gry alloys . The gra14 m−2, that of R2 is 4.63 × 1014 m−2, and that of R3 is 3.64 × 1014 m−2.The correlation between the dislocation density and the integrated mechanical properties is consistent with the increase in alloy strength. The dislocation density is proportional to the strength and hardness of the sample ,42. The 4 and precipitation strengthening [The trend of dislocation density is increasing and then decreasing, and the rate of dislocation density of R3 alloy is greater than that of R1. The significant grain refinement, accompanied by a significant increase in KAM values during the early stages of spin-forging deformation, is responsible for the increase in deformed grains and the decrease in grain size. This phenomenon indicates a significant accumulation of dislocations in the material after the spin-forging deformation of R1 to R2 alloy. The low KAM values of the spin-forged deformed samples are mainly concentrated inside the grains, and the high KAM values are mainly located at the boundaries around the grains. Thus, dislocation evolution plays a crucial role in the nucleation and growth of DRX grains by stimulating nucleation, consuming dislocations and mitigating deformation. The sub-boundaries formed by these dislocations stimulate the refinement of the original grains into deformed grains. With increasing deformation, the KAM of R3 alloy decreases compared with that of R2 because the proportion of R3 recrystallization increases. Recrystallization consumes some of the dislocations . In addigthening . HoweverThis study investigated Zn-0.45Li alloys with different amounts of rotary forging deformation. As a result, the following conclusions were obtained. (1)Grain refinement becomes highly pronounced with increasing deformation. However, the slightly larger grain size on the surface of R3 than that on the surface of R2 is due to recrystallization.(2)The strength and ductility of the Zn-0.45Li alloy were improved simultaneously after rotary forging deformation.(3)In all the Zn-0.45Li alloy samples, the (0001) texture is dominant. The texture strength in the (0001) direction increases deformation. Therefore, the increase in texture strength after rotary forging improves the strength of Zn-0.45Li alloy.(4)The strength and plasticity enhancement of Zn-Li alloys after macro deformation was a combination of dislocation strengthening, weave strengthening, and grain refinement rather than only fine grain strengthening as observed in conventional macro deformed Zn alloys.


The present study aimed to assess the impact of substituting wheat flour with three different pretreated green pea flour at different addition levels (10–50%) on fresh bread quality during a 7-day storage period. Dough and bread enriched with conventionally milled (C), pre-cooked (P), and soaked under-pressure-steamed (N) green pea flour were evaluated for their rheological, nutritional, and technological features. Compared to wheat flour, legumes had lower viscosity but higher water absorption, development time, and lower retrogradation. Bread made with C10 and P10 showed similar specific volume, cohesiveness, and firmness to the control, whereas addition levels beyond 10% decreased specific volume and increased firmness. During storage, incorporating legume flour (10%) delayed staling. Composite bread increased proteins and fiber. C30 had the lowest rate of starch digestibility, while pre-heated flour increased starch digestibility. In conclusion, P and N can be considered valuable ingredients for making soft and stable bread. Bread is a staple food worldwide, and it is chiefly prepared from refined wheat flour. There is an increasing interest in fortified bread for health, sustainability, and marketing . ComposiTherefore, several pretreatment strategies were applied to overcome these limitations. Milling and size fractionation are conventional processes affecting the physicochemical properties of legume flour ,7. For iinvestigation of pasting properties of individual untreated and pretreated green pea flour in addition to dough mixing properties of selected composite flour in comparison with wheat flour;effect of pretreated green pea flour on fresh bread technological quality and during storage. In this section, a full factorial design was considered in association with multivariate analysis for a better understanding of the impact of each studied factor ;effect on nutritional quality with a focus on starch digestibility in the product with industrial relevance.As such, the properties of pretreated legume flour differ based on the type and severity of the pretreatment. For this reason, the present study went deeper, investigating the performance of pretreated green pea flour using conventional and emerging physical treatments. Conventional flour obtained using traditional milling, precooked flour using hydrothermal treatment, drying, and milling, and a “new generation” flour obtained by water-soaking under pressure, steaming, drying, and milling were used as wheat flour substitutes. The objective was to evaluate the effect of pretreated green pea flour addition (at different levels from 10 to 50%) on the nutritional and technological properties of fresh composite bread during storage. To achieve this goal, the experimental design was divided into three subjective phases:Wheat flour  and three types of commercial green pea flour  were considered in this study . Green pViscoamylograph analysis of flour was determined using a Micro Visco-Amylo-Graph Brabender Instruments . Different types of flour (9 g) were suspended in distilled water (60 mL) forming flour slurries, heated from 30 °C to 98 °C at a heating rate of 7.5 °C/min, held at 98 °C for 2 min, cooled to 30 °C, and held for 4 min. The viscosity values at the beginning , at the peak , at the end of the holding period at 98 °C , and at the end of the cooling phase at 30 °C  were recorded and expressed in arbitrary Brabender units (BU). The differences between PV and MV and between CMV and MV were defined as a “breakdown” and a “setback”, respectively. The analysis was conducted in triplicate.Farinograph curves were obtained using a Brabender instrument . The analysis was made with 50% blends of pretreated green pea flour and wheat flour to evaluate the legume flour’s effect on wheat flour dough quality. The 50% replacement level was selected to perform the farinograph experiments to understand the potential changes in dough behavior at the maximum level of the addition and compared to wheat flour. The farinograph parameters were measured according to AACC Approved Method 54-21.02 . Three d®, Milan, Italy), sugar , yeast , and olive oil ] were bought from a local supermarket. ® ZERO-GLU, Azzano San Paolo (BG), Italy]. First, water and yeast were mixed for 2 min, followed by flour, sugar, olive oil, and then salt (after 2 min of mixing). The selected baking program  consisted of the dough mixing phase (22 min), levitation , and baking . After baking, the bread was left to cool down at room temperature. Then, each bread loaf was placed into a polyethylene bag sprayed with 2 mL of ethyl alcohol (to prevent mold growth) and stored at 25 °C for a week. Each bread formulation was performed in triplicate.The bread was produced following a standard white bread recipe (AACC method 10-10.03) . For breThe volume of the fresh bread was measured using 3 different bread loaves for each bread type using a standard rapeseed displacement method according to AACC method 10–05.01 . SpecifiMoisture and texture were determined on fresh bread and after 1, 3, and 7 days. Crumb moisture content was measured from weight loss at 105 °C until constant weight, as described in the AACC method 44-15.02 . Bread cThe nutritional facts for fresh bread were computed based on data provided on commercial product labels or technical sheets provided by the manufacturers. The energy value was calculated using the energy factors provided in EU Regulation n° 1169/2011 , in whicIn vitro, the starch digestibility of selected fresh bread was evaluated . In brieResistant starch (RS) and total starch of bread samples were determined using the Resistant Starch Assay kit  . The uniMultivariate analysis of variance (MANOVA) was used to determine the effect of the type of pretreatment (PT), level of addition (LA), and storage (S) on the bread properties. MANOVA was performed based on fixed factors using Pillai’s trace test. The percentages of total variations were computed to determine the contribution of the factors  and their interactions in the variance of each parameter. The percentage of the total variation was computed to explain the variance of each parameter as a function of the sum squares of the main factors and their interaction. Significant differences among the mean values were calculated using Duncan’s test. A principal component analysis (PCA) was performed based on the correlation matrix. All experimental data were statistically analyzed using SPSS version 19.0 .The pasting properties of the flour used for preparing the bread are reported in According to Farinograph results , the wheThe effects of pretreatment (PT) and the level of addition (LA) of green pea flour on the quality of fresh bread are summarized in Fresh bread properties are shown in Proportionally to the level of wheat flour substitution, enriched bread showed significantly higher moisture content than the control. This can be attributed to the composition of pea flour enabling more water-holding capacity compared to wheat flour ,41. OverThe crumb firmness of C10 and P10 was found to be similar to the control. This aligns with a previous study showing that roasted yellow pea flour at 10% did not cause any change in bread texture . Beyond Regardless of the pretreatment, cohesiveness was found statistically unchanged at up to a 10% addition level. Then, it was gradually reduced with the addition of conventional green peas flour, but it remained statistically similar to the control, up to 30% of P and N. The decrease in cohesiveness can be explained by the reduction in gluten content and the increase in non-gluten proteins .Crumb moisture content and textural property changes in bread samples during storage are shown in For the moisture content, the addition level had the most pronounced effect, followed by the pretreatment effect compared to the effect of storage. Moisture content did not follow a trend during the storage period, and it significantly fluctuated for all the formulations. However, previous studies showed that the addition of legumes decreased moistness loss during storage ,43. ThesCrumb firmness was chiefly affected by the level of the addition and, to a lesser extent, by the storage period, while pretreatment had a minimal but significant effect. At 10%, all types of flour showed small fluctuations but no significant increase after 7 days. Probably, this result was due to starch retrogradation. It was observed that low levels of legume addition (5%) did not reveal a significant difference in the bread hardness . This maCohesiveness is mainly governed by the storage time followed by the level of the addition, whereas the effect of the pretreatment was limited. Cohesiveness was reduced significantly during storage. The cohesiveness of W decreased faster than in green pea-enriched bread. This can be related to the high protein and fibers in green peas interfering with staling mechanisms, including moisture loss, starch retrogradation, and/or gluten network changes.The data in The total starch content and in vitro starch digestibility  were evaIn conclusion, the addition of pretreated green pea flour induced relevant changes in composite dough and bread properties. Flour pasting properties suggested that pretreated flour had high stability and lower retrogradation. It also showed higher water absorption, development time, and lower stability than wheat flour. By using 10% pretreated (P and N) flour, bread quality resulted in being similar to the control in terms of specific volume and textural properties, while increasing the inclusion level resulted in harder and more compact bread. During storage, bread made with legume flour at a low level (<10%) preserved its moistness. Nutritionally, increasing legume inclusion increased proteins and fiber. Based on PCA, the best compromise was the use of a 30% addition level to have balanced nutritional and technological quality. The use of 30% of milled legumes reduced the starch digestibility. P30 showed the lowest RS and the highest SDS. Overall, P and N can enable the production of soft and more stable bread.


According to the flow curves obtained from the experiments, the flow behavior of this alloy was analyzed, and the Zener–Hollomon (Z) parameter equation was established. The hot processing maps of this alloy were developed based on the dynamic material model, and the optimal hot working region was determined to be 410–460 °C/0.01–0.001 s−1. The electron backscattered diffraction (EBSD) microstructure analysis of the deformed sample shows that the dynamic softening mechanism and microstructure evolution strongly depend on the Z parameter. Meanwhile, a correlation between the dynamic softening mechanism and the lnZ value was established. Dynamic recovery (DRV) was the only softening mechanism during isothermal compression with lnZ ≥ 20. Discontinuous dynamic recrystallization (DDRX) becomes the dominant dynamic recrystallization (DRX) mechanism under deformation conditions of 15 < lnZ < 20. Meanwhile, the size and percentage of DDRXed grains increased with decreasing lnZ values. The geometric dynamic recrystallization (GDRX) mechanism and continuous dynamic recrystallization (CDRX) mechanism coexist under deformation conditions with lnZ ≤ 15.The hot deformation behavior and dynamic softening mechanism of 7B50 aluminum alloy were studied via isothermal compression experiments in the range of 320–460 °C/0.001–1.0 s The thermal deformation parameters  during high-temperature plastic deformation can significantly affect the flow stress and microstructure evolution of the alloy ,9,10. IsCompared with 7050 aluminum alloy, 7B50 aluminum alloy appropriately increases the content of Zn and Mg elements, and further reduces the content of impurity elements, such as Fe and Si, by improving the melting and casting processes. Related studies ,20,21 ha−1. In this paper, the influence of deformation parameters on flow behavior is discussed using isothermal compression experiments on 7B50 aluminum alloy. Based on the Arrhenius constitutive model, the thermal deformation activation energy of 7B50 aluminum alloy was obtained, and the Z parameter equation of the alloy was established. The optimum hot processing parameter range of 7B50 aluminum alloy was determined by constructing its hot processing maps. Based on the EBSD microstructure analysis, the influence of Z parameters on microstructure was discussed and the relationship between Z parameters and the dynamic softening mechanism was established. The research work in this paper provides a basis for reasonably formulating the hot working process of 7B50 aluminum alloy and controlling the microstructure and properties of the product.In order to investigate the hot deformation behavior and microstructure evolution of 7B50 aluminum alloy, isothermal compression experiments were conducted on the 7B50 aluminum alloy using a Gleeble-3800 isothermal simulation machine in the temperature range of 320–460 °C and the strain rate range of 0.001–1.0 sThe experimental material was commercial 7B50 aluminum alloy produced by Chinalco Southwest Aluminum Co., Ltd.  The specific chemical composition is shown in −1, and the compression deformation of the sample was 60% for the isothermal compression experiments. Graphite sheets were placed between the two end faces of the experimental sample and the moLd for lubrication. Before isothermal compression, the experimental sample was heated to the required temperature at a rate of 5 °C/s and held for 2 min to eliminate the temperature gradient. Water cooling was carried out immediately after compression to retain the deformed microstructure.In order to reduce the composition segregation and improve the uniformity of the ingot microstructure, the as-cast 7B50 aluminum alloy was subjected to a two-step homogenization treatment at 400 °C/10 h + 470 °C/48 h. Hot compression standard specimens with a dimension of ϕ10 × 15 mm were machined via wire cutting. The Gleeble-3800 isothermal simulation machine , and the microstructure at the center of the sample thickness position was characterized. The specimens used for observation were sequentially ground with sandpaper to 2000 grit and then mechanically polished. After mechanical polishing, the samples for EBSD observation were subjected to electropolishing treatment on a double-jet thinning instrument. The electropolishing solution was a mixture of 70% alcohol and 30% nitric acid. The temperature and voltage of electropolishing were −25 °C and 15V, respectively. The scanning electron microscope used for microstructure detection was the TESCAN MIRA3 equipped with an electron backscatter diffraction (EBSD) system. EBSD data was processed via channel5 software.2 phases precipitated inside the grains during the slow cooling process. Based on the inverse pole figure (IPF) diagram obtained by the EBSD system  and T (K) are the strain rate and deformation temperature, R represents the gas constant (8.314 kJ/moL), and Q (kJ/moL) represents the activation energy of deformation, which describes the difficulty of deformation. Furthermore, the Arrhenius constitutive model commonly used to describe the flow curves can be represented as follows:lnlnσ; and lnσ, which can be obtained by linearly fitting the data of Zener and Hollomon jointly proposed using an exponential equation to describe the comprehensive effect of strain rate and deformation temperature and the equation of Z parameter is as follows :(1)Z=ε˙eAccording to Equation (3), the deformation activation energy Q can be expressed as:lnln[sinh(ασ)] and ln[sinh(ασ)] − 1/T are obtained by linear fitting. The calculated average thermal activation energy Q using the same method is only 130.85 kJ/moL. Numerous scholars have studied the heat deformation behavior of 7050 aluminum alloy . Deng et al. [Therefore, based on the obtained α value, the slope values of g et al.  calculatg et al. . In addig et al.  investigg et al.  proposedg et al.  showed t−1.Hot processing maps based on the dynamic material model are widely used to guide the hot plastic working of aluminum alloys ,27. FiguIt is well known that aluminum alloys belong to high stacking fault energy metals, which are prone to dislocation climb and cross-slip due to the narrow expansion width of dislocations. Therefore, DRV, rather than DRX, usually dominates the thermal deformation process of aluminum alloys. However, the significant dynamic recrystallization behavior of 7××× aluminum alloy has also been reported in much of the literature due to the addition of large numbers of various alloying elements ,34,35,36−1 and 420 °C/0.1 s−1 (lnZ ≥ 20). In the IPF maps, the high-angle grain boundaries (HAGBs) with a misorientation angle greater than 10° is represented by a black solid line, while the white solid line is the low-angle grain boundaries (LAGBs) with a misorientation angle of 2–10°. As can be seen, the microstructure under high lnZ values exhibits typical deformation characteristics. The coarse original grains are elongated perpendicular to the compression direction. There are a large number of distributions of discontinuous and staggered LAGBs within the matrix, which have not formed obvious sub-grains. The grain boundary misorientation angle distributions in −1) is presented in The deformation behavior of individual grains under the deformation condition of lnZ = 22.71 (420 °C, 1 s−1 is composed of partial dynamic recrystallization as shown in −1 (To investigate the DRX mechanism under deformation conditions of lnZ ≤ 15, the typical regions in −1 a. The HAFurthermore, some CDRX phenomena also exist under the deformation condition of lnZ ≤ 15 as shown in −1. The main research findings are as follows:In this paper, the hot deformation behavior and dynamic softening mechanism of 7B50 aluminum alloy were systematically studied via isothermal compression experiments at 320–460 °C/0.001–1.0 sBased on the Arrhenius conservative equation, the deformation activation energy of 7B50 aluminum alloy was calculated to be 130.85 kJ/moL. Meanwhile, the Z parameter equation of this alloy was established.−1.Based on the dynamic material model, the hot processing maps of 7B50 aluminum alloy were established. The optimal processing parameter range for 7B50 aluminum alloy was determined to be 410–460 °C/0.01–0.001 sThe DRV softening phenomenon exists in all deformation conditions of 7B50 aluminum alloy. However, DRV is the only softening mechanism at lnZ ≥ 20. DDRX is the dominant dynamic recrystallization mechanism under deformation conditions of 15 < lnZ < 20. The size and proportion of DDRXed grains increased with decreasing lnZ values. Both GDRX and CDRX mechanisms exist under deformation conditions of lnZ ≤ 15.