Recently, the accuracy of spike neural network (SNN) has been significantly improved by deploying convolutional neural networks (CNN) and their parameters to SNN. The deep convolutional SNNs, however, suffer from large amounts of computations, which is the major bottleneck for energy efficient SNN processor design. In this paper, we present an input-dependent computation reduction approach, where relatively unimportant neurons are identified and pruned without seriously sacrificing the accuracies. Specifically, a neuron pruning in temporal domain is proposed that prunes less important neurons and skips its future operations based on the layer-wise pruning thresholds of membrane voltages. To find the pruning thresholds, two pruning threshold search algorithms are presented that can efficiently trade-off accuracy and computational complexity with a given computation reduction ratio. The proposed neuron pruning scheme has been implemented using 65 nm CMOS process. The SNN processor achieves a 57% energy reduction and a 2.68× speed up, with up to 0.82% accuracy loss and 7.3% area overhead for CIFAR-10 dataset. SNNs use sparse temporal–spatial patterns of spikes to convey information. With event-driven temporal data processing, SNNs are expected to be implemented with energy efficient hardware. IBM’s TrueNorth  and InteTo reduce the amount of computations, efficient conversion methods from deep CNN to SNN have been proposed . By re-sIn this paper, we present an input-dependent computational complexity reduction approach, where temporal domain information is efficiently exploited to remove the computational redundancies that inherently exist in convolutional SNN. In the proposed scheme, the relatively less important neurons are first identified, and those are removed in the temporal domain by monitoring the changes of the neuron’s membrane voltages. Since the approach performs the pruning based on the membrane voltages, the overhead in hardware is minor. For the search of each layer’s pruning threshold, the threshold search process is modeled as a graph search problem, and greedy best-first search is used to find the thresholds for a given target computing reduction ratio. In addition, a layer-wise pre-search procedure is also presented to expedite the overall threshold search to automatically find a good starting point of the pruning threshold. The SNN processor that supports the proposed input-dependent computational reduction technique, has been implemented using 65 nm CMOS process. The implementation results show that the SNN processor shows significant energy reduction with minor hardware overhead.The rest of the paper is organized as follows. In Section 2, the preliminaries for SNN architectures and the previous computation reduction approaches are introduced. The proposed neuron pruning scheme is presented in Section 3, and the experimental and hardware implementation results are presented in Section 4 and 5, respectively. Finally, conclusions are drawn in Section 6.22.12.2Although CNN shows excellent classification accuracies, such performance comes at the cost of an enormous number of computations. To reduce the number of computations, various computation reduction approaches have been studied for CNNs. Zero prediction  is one oSimilarly, SNN has inherent temporal domain information in both algorithm and hardware since the information is encoded and processed in a time series of spikes , which is processed through physical time in an SNN processor . This increase in information in the temporal domain also can cause errors in the network, where inactivated neurons can fire spikes . However3In this section, we present an input-dependent computation reduction approach, where the temporal redundancies in SNN are identified and removed with minor accuracy degradation.3.1Before talking about the pruning techniques, let us describe the training method for SNN used in this work. Among numerous methods to train SNN, ANN-to-SNN conversion method  is selecAlthough the proposed pruning technique is applied to the above mentioned conversion method as a case example, since temporal redundancies exist in almost any of SNNs, the proposed techniques can also be applied to the SNNs obtained through other training methods.3.2Neuron Pruning in Temporal Domain (NPTD) is motivated by the observations that the changes of neuron membrane voltages are predictable. Neuron Pruning in Temporal Domain (NPTD), NPTD with three pruning thresholds  are simulated, and the results are shown in Therefore, searching a good pruning threshold is definitely needed to minimize the accuracy loss of the NPTD with a given target computation reduction ratio.The 3.3NPTD, if the pruning thresholds are individually assigned to all the neurons, search space becomes prohibitively large and the memories to store the pruning thresholds should be large as well. Referred from the previous literatures , as a measure of the number of computations, the synaptic operation (SOP) , determine the pruning threshold of each layer such that accuracy degradation can be minimized.” As an output of the search, a set of pruning thresholds is determined as on (SOP)  is used.where 3.3.2Ln), where L is the number of total layers and n refers the number of possible threshold candidates. Considering the prohibitively large time complexity, we adopt the greedy best-first search  increment over subset of the training dataset NPTD. While the search problem is to find the pruning thresholds with minimum accuracy loss at a given target computation ratio, the output loss is used instead of accuracy during the search. It is because output loss and the classification accuracy of the network are closely related, output loss is widely used instead of accuracy in previous works  using a fast layer-wise search will allow the quick search of pruning thresholds of the whole network.To reduce the total number of iterations in the greedy search and the time spent in this n method . First, se error . In otheThe outline of the pre-search procedure for a layer is as followings and it is also presented in Set search interval of the pruning threshold [output loss that is close to output loss before the search of this layer.Using the bisection method, find a pruning threshold that results in the output loss is smaller than Perform backward steps until the output loss ratio with respect to the layer-wise pruning threshold. The output loss ratio is calculated by dividing the current output loss by the output loss when the pruning threshold of the layer is the initial pruning threshold, which is −32 in this figure).output loss of the right endpoint becomes smaller than output loss to be a very small. Then, the step ② performs bisection for predefined iterations (MI) to find a pruning threshold that has output loss close to output loss can introduce multiple roots, failing to find a pruning threshold. Lastly, in step ③ of NPTD. After all three steps, pre-search of the preceding layer is performed. When the layer-wise pre-search is finished, a set of pruning thresholds found is used as the initial pruning threshold set of the greedy best-first search. The detailed procedure is described as pseudocode in As shown in MI, it should be set with consideration of the search interval and the desired resolution of the threshold pre-search. For instance, when the desired resolution is 1 and 5), MI of 5 can be used to achieve the desired resolution of 1. In case of β, a value between 0.05 ~ 0.1 is found to be a good balance point between the stability of the search and the total time spent for backward steps. For γ, the value around 0.01 works well for various conditions. It is noteworthy that the value of β has little to no effect to the final results of the pre-search, as long as γ is set to a small value . This is because backward steps diminish the error increment caused by β.Regarding the hyperparameters used in the pre-search, 4NPTD with threshold search algorithms. The simulations have been performed using PyTorch . For this case, the greedy best-first search is not performed as the target SOP ratio is set to 0.5.After the layer-wise pre-search, pruning thresholds found by the pre-search are used as the initial thresholds of the greedy best-first search. Since the target SOP ratio 4.2In this section, to demonstrate the effectiveness of the layer-wise pre-search (PS), analyses on the search runtime, classification accuracy, and SOP are performed. While the simulations are conducted with parameters explained at the beginning of this section, ‘GS’ in Please note that for VGG-16, using both the pre-search and the greedy search shows higher accuracy than only using the greedy search, and the reason for this outcome is shown in 5NPTD has been added to the baseline. 65 nm CMOS standard cell library has been used for the implementation, and the energy results are obtained from post-layout simulations using the CIFAR-10 dataset. Considering the required on-chip memory size to implement the network to hardware, a small convolutional SNN is selected for hardware implementation. The network has 3 convolutional layers and 1 fully connected layer, arranged as architecture of 48c5-AP2-96c5-AP2-96c5-AP2-10. In the network architecture, 48c5 means a convolutional layer with 48 5 × 5 convolutional filters, AP2 means 2 × 2 average pooling layer, and 10 means fully connected layer with 10 neurons. In the case of the first convolutional layer, it is processed off-chip due to the direct input encoding used in NPTD, respectively, on CIFAR-10 with 128 timestep.This section presents the SNN processor that implements the proposed neuron pruning techniques. The baseline architecture is designed based on 5.1The timing diagram of the proposed architecture is shown in NPTD because the pruning threshold thP can be larger than the threshold voltage (Vth). In other words, the proposed NPTD needs wide dynamic ranges for the negative membrane voltages to represent values down to the pruning thresholds. Since large dynamic ranges are only needed for negative values, using additional bits to represent both positive and negative membrane voltages can be wasteful. So, dynamic fixed-point arithmetic is employed to allow a wider dynamic range for the negative values to realize NPTD without adding too much hardware overhead. For example, when the magnitude of the thP of a layer is 5 times of Vth, 3 bits NPTD without accuracy loss and additional memory overhead due to increasing bit-widths.In the proposed SNN processor, 11 bits are used to store the membrane voltages with a negligible accuracy loss of 0.05% compared to the floating-point baseline. However, larger bit-widths are needed to implement In the proposed architecture, although power consumption can be improved with the neuron pruning by simply not performing the computations of pruned neurons, it cannot increase the throughput of the processor. So, to increase the throughput of the proposed SNN processor, the pruned neurons are identified ahead by counting stored flags using leading one (zero) counter  and comp5.2NPTD incurs 7.3% area overhead to the baseline architecture. The previous input-dependent pruning scheme  per second by consuming 109.1uJ for each frame. NPTD, the lowest energy per image has been achieved by the proposed SNN processor. In NPTD.The normalized energies show even more difference between this work and previous works, which further highlights the energy reduction effects of the proposed 6NPTD) approach, an input-dependent neuron pruning technique that efficiently removes temporal redundancies in the convolutional SNNs. The proposed NPTD skips less important neuron operations by identifying relatively unimportant neurons, based on the membrane voltage of the neurons and pre-decided pruning thresholds. The pruning thresholds are also searched using the proposed layer-wise pre-search and greedy best-first search algorithms. With the proposed neuron pruning schemes and pruning search algorithms, a target SOP reduction can be reached with good accuracy computation trade-offs. The NPTD has also been implemented to the SNN processor using 65 nm CMOS process and the processor achieves 57% energy reductions and 2.68 × speed ups, with 0.82% accuracy loss for CIFAR-10 dataset. The proposed input-dependent neuron pruning technique can assist the use of SNNs in edge devices, particularly for low energy applications with limited hardware resources.In this work, we present a neuron pruning in temporal domain (The original contributions presented in the study are included in the article/DL: Conceptualization, Methodology, Writing – original draft. HT: Conceptualization, Writing – original draft. JP: Writing – review & editing.


Triticum aestivum from the mountain areas of Lunigiana , where traditional farming communities have contributed to its success and on-farm conservation. G23 flour, traditionally used for typical food products, is characterized by particular nutritional and sensory traits but has technological properties which limit its suitability for breadmaking. The aim of this work was to evaluate how to promote the use of G23 through the optimization of bread formulation by leveraging both flour blending and the leavening system. During the preliminary test, three different mixes of G23 flour and a strong flour (C) were tested in terms of their leavening power as a function of leavening agent (baker’s yeast or sourdough). The selected M2 flour, composed of G23:C (1:1 w/w), was used for further breadmaking trials and 100% C flour was utilized as a control. The sourdough bread obtained with the M2 flour (SB-M2) showed an improved sensory profile compared with the related control (SB-C). Furthermore, SB-M2 exhibited the best aromatic  and phytochemical profile . In contrast, the use of baker’s yeast, although optimal from the point of view of breadmaking, did not result in the same levels of aromatic complexity because it tends to standardize the product without valorizing the sensory and nutritional qualities of the flour. In conclusion, in the experimental conditions adopted, this old wheat variety appears to be suitable for the production of sourdough bakery products.“Avanzi 3-Grano 23” (G23) is an old variety of  Triticum aestivum) [One of the major cereal crops worldwide is common wheat (estivum) . Rich inestivum) ,2,3. Howestivum) ,5. Neverestivum) ,7,8. In estivum) ,10,11,12estivum) ,14.Triticum aestivum cultivated since the early decades of the twentieth century in the mountain areas of Lunigiana  [“Avanzi 3-Grano 23” (G23) is an old variety of , Italy) . The tra, Italy) . The com, Italy) ,17.G23 flour is characterized by particular nutritional and sensory features; nevertheless, it has technological properties which limit its suitability for baking. Indeed, this flour is traditionally used to make local products such as “testaroli” and “panigacci” historically produced in the Lunigiana area .This old variety is now listed as an endangered species as its agricultural production has mostly been interrupted due to the massive focus on the cultivation of modern high-yielding wheat varieties. For these reasons, it is important to create a local food system to promote this variety of wheat through establishing a short supply chain, direct sales, exchange and purchase of specialty agricultural and food products in local markets, which will certainly contribute to a general process of economic revitalization of the territory .A possible approach to increasing G23 economic valorization and thus spreading its use could be the optimization of its flour technological properties by blending with other strong flours and using a suitable leavening agent in order to obtain a bread with nutritional and sensory characteristics particular to this old variety.Several studies ,20,21,22On the other hand, baker’s yeast is widely used, especially by industrial bakeries, due to its technological properties . Baker’sFor these reasons, the aim of this work was to evaluate how to enhance this old wheat variety through the optimization of bread formulation, by leveraging both flour blending and a leavening system.During the preliminary tests, doughs obtained from different mixes between G23 flour, and a strong flour (C) leavened by brewer’s yeast or sourdough were evaluated considering their volume increase and their fermentation metabolites. At a later stage during further breadmaking trials, the blend of flour selected on the basis of leavening performance was compared with the control (100% strong flour) as a function of the leavening system (brewer’s yeast vs. sourdough). In particular, our attention was focused on the bread’s technological properties, bread sensory profile and compositional traits.Triticum aestivum) supplied by the Department of Agriculture, Food, Environment, and Forestry of the University of Florence.Control (C) is a wheat flour consisting of a mix of four varieties  of common wheat (Triticum aestivum) and was provided by a small local farmer in Lunigiana .G23 is a wheat flour of an old variety (“Avanzi 3-Grano 23”) of common wheat  at the Department of Agriculture, Food, and Environment (DAFE) of the University of Pisa. The sourdough used was maintained over one year at the DAFE of the University of Pisa by a daily refreshment procedure as reported by Taglieri et al., 2020 , while tThe chemical and technological parameters of flours  or baker’s yeast (Y).w/w), sterile water (33% w/w), and sourdough (11% w/w). The mixture was then left to ferment for 18 h at 20 °C. Baker’s yeast biga (Y-biga) was obtained by mixing a strong wheat flour type 0 (68% w/w), sterile water (31% w/w), and 1% (w/w) of baker’s yeast and then fermenting for 21 h at 18 °C. The recipes of S-biga and Y-biga were defined in previous studies [Sourdough biga (S-biga) was prepared by mixing a strong wheat flour type 0  and identify the best blend on the basis of dough volume increase and fermentation metabolites . The different doughs  were prepared following the protocol  reported in Preliminary leavening tests were conducted using two different types of leavening agent (sourdough (S) and baker’s yeast (Y)) to test different mixes of G23 and C flours  and two formulation of baker’s yeast bread  were produced with water (32%), biga leavening agent (16%), and flour (52%).The first leavening was allowed to occur for 90 min at 26 ± 1 °C, and then the dough was broken and shaped into 500 g loaves which were left for 2.5 h at 35 ± 1 °C (second leavening). Finally, the loaves were baked at 220 °C for 45 min. The bread was then cooled at room temperature (23 ± 1 °C) and sliced (20 mm) for the analysis.The moisture content of samples  was determined on an approximately 5 g sample dried at 105 °C until constant weight. The pH, total titratable acidity (TTA) and the fermentative metabolites  were measured as previously reported by Bianchi et al., 2022 .w/v) from 0.5 g of fresh sample , and the mixture was then sonicated for 30 min. All the extracts were subsequently centrifuged , filtered on a syringe filter (0.45 μm) and stored at 4 °C for immediate analysis.In addition, the flour, biga and bread samples were characterized from a phytochemical point of view. In particular, for total polyphenols, total flavonoids and anti-radical activity evaluation, 80% methanol solution was used to perform a solid/liquid extraction , according to Macaluso et al., 2020 , with thThe total flavonoids analyses were performed according to the procedure reported by Tavarini et al., 2020 , with th−1 for the DPPH assay, a range of 0.2–1.5 mM for ABTS and 0–2.0 mM for the FRAP assay.The anti-radical activity of the extracts was determined by the DPPH , ABTS 4 and FRAPIn order to better evaluate the technological properties of the baked bread, the crumb was analyzed to assess water activity, softness and color, as reported below.w) of the crumb of bread was assessed by a HygroPalm HP23-AW-A device .The water activity , Italy) using the method described by Taglieri et al., 2021 . Each saab) were calculated as previously reported [The crumb color of bread was measured according to the CIE L*a*b* color System by means of a tristimulus colorimeter . The Chroma value C* and hue value h* were also calculated as previously reported . The colreported  and exprThe whiteness (WI) and yellowness (YI) indices of the samples were calculated, as reported by Alam et al., 2022 , accordiThe bread VOC profile (sliced bread samples) was assessed according to the protocol previously described in Sanmartin et al., 2018 , samplinThe bread sensory profile was evaluated by a panel of 8 long-term members of the “Committee of Experts” of DAFE of the University of Pisa, according to the protocol previously described , includip ≤ 0.05 significance.All the evaluations were performed in quadruplicate, and data are reported as mean values ± standard deviation (SD). A one-way ANOVA  on the physico-chemical data was performed, followed by the Tukey’s HSD test at Statistical analysis of volatile organic compounds and hierarchical cluster analysis (HCA) applying the Ward method and using two-way clustering were performed using the JMP Pro 17.0 software package . The 3D principal component analysis (PCA) was performed by selecting the three principal components (PCs) obtained by the linear regressions operated on mean-centered, unscaled data.Sensory analysis data were processed by Big Sensory Soft 2.0 (ver. 2018), carrying out a two-way ANOVA, with samples and panelist as main factors , followeAs a first step, we performed a leavening test comparing different percentages of G23 flour and leavening systems to assess their breadmaking suitability through the evaluation of the dough volume increase (DVI) and the fermentative metabolites .During the fermentation time, all the doughs underwent structural changes depending on the fermentation methods used (Y and S). The modifications are related mainly to an increase in resistance to the extension and a decrease in extensibility, with a noticeable change in volume and pH. As shown in w/w) exhibited statistically lower values in terms of DVI, as expected considering the low production of leavening metabolites  both for baker’s yeast (Y) and sourdough (S). The other two flour mixes behaved in a similar way when leavened with baker’s yeast (Y-M1 and Y-M2). Conversely, M2 seems to be more favorable for sourdough fermentation than M1 since a higher content of lactic acid was observed in S-M2, with a consequent lower pH, higher TTA, and a significantly higher DVI  seems to be influenced by the leavening agent and the flour used, as shown by the significant differences in the whiteness index (WI).The a* index in bakery products is generally related to the Maillard reaction , but in The yellow color of crumb depends on both the carotenoid content of the flour and on the baking process that promotes the yellow hue .The yellowness index (YI) of the evaluated flour seems to be more represented in the control samples (YB-C and SB-C), regardless the leavening system.Moreover, the C samples, having a higher saturation (C*) and a lower hue value (h*), exhibit a color turning to a warm yellow, regardless the leavening system used .Conversely, when M2 flour is used, a significant difference associated with the leavening system is observed, probably due to a higher sensitivity to the pH reduction linked to the use of sourdough .ab values were calculated and are reported in In order to quantify the color differences among the samples, the ΔE*A noticeable difference  was founFrom a purely aromatic point of view, bread is a complex product characterized by a multitude of volatile substances that influence the final aromatic profile. To date, in fact, many studies have focused on evaluating and quantifying the volatile substances of the final aroma of bread, coming to describe over 540 substances produced depending on the type of formulation, type of yeast and cooking method ,54.Quantitatively, the analysis of the spontaneous emission of the VOCs allowed the identification of about 62 different compounds , where tThe analysis of the main components  highlights the different volatile substances that allowed differentiation of the loaves into three different groups .The gas chromatographic analysis shows that the type of leavening agent used led to the production of diverse VOCs. Breads made with baker’s yeast (YB-C and YB-M2) showed a higher production of alcohols and aldehydes with slight differences related to the type of flour: YB-M2 was characterized by a good production of alcohols, such as isobutyl alcohol, 2-methyl-1-butanol, and phenylethyl alcohol, and aldehydes, such as 3-ethyl-2-methyl-1,3-hexadiene, whereas YB-C showed a greater production of aldehydes and alcohols . On the other hand, sourdough breads (SB-C and SB-M2) had more complex VOC profiles with a greater variety of compounds, including pyrazines, pyrimidines, and carboxylic acids. In particular the flour used strongly influenced the volatile expression, allowing clear grouping of the two sourdough breads into separate clusters. . The difThe sensory profiles are fully in agreement with all the chemical-physical results obtained for all the samples, including color and VOCs, thus confirming that the differences in chemical composition among samples can be clearly perceived by consumers.In general, the main parameter that enabled differentiation of the breads from a sensory point of view seems to be the formulation. Considering the quantitative parameters, the type of flour clearly influenced the color intensity, but also the crumb structure, while the leavening system strongly affected the acidity. As expected, sourdough breads had a greater perceived acidity, with the highest value reported for the SB-M2 sample, closely followed by SB-C. Regarding the hedonic descriptors, the bread produced with M2 flour was evaluated more positively than that produced with C flour, regardless the leavening agent utilized.As shown in The results of this study suggest that the incorporation of flour of an old wheat variety such as “Avanzi 3-Grano 23” along with sourdough fermentation represents an important tool in the development of functional bakery products with improved antioxidant capacity and phenolics bio-accessibility. Moreover, the use of sourdough conferred to the bread a lower pH, due to the higher acidity, which could prolong its shelf-life.The sourdough bread with the M2 mix in particular, achieved the best results, also from a sensory point of view, reaching a high level of acceptability, as expressed by the hedonic index (HI > 8).Even if the combination of baker’s yeast and M2 flour was optimal in terms of leavening, it did not achieve the same levels of aromatic complexity and sensory pleasure. According to our results, the baker’s yeast tends to standardize the product, without valorizing the sensory and nutritional potential of the flour


Processing fluids (PFs) are novel diagnostic specimens consisting of serosanguineous exudate obtained from tissues after piglets’ castration. PFs can be used for some diagnostic tests that would otherwise require a blood sample. This has the advantage of not subjecting piglets to an additional stressful procedure and, at the same time, reducing sampling costs. In the present study, the efficacy and reliability of a monitoring plan for porcine reproductive and respiratory syndrome (PRRS), one of the main pig diseases, based on PF sampling has been assessed in the Italian swine production system. Twenty-five breeding herds were monitored for 4–5 months by RT-PCR performed on both PFs and blood serum (the standard specimen used for monitoring). Based on the results obtained from each method, the herds were classified following the standard PRRS status categories. The two methods fully agreed in discriminating between herds with stable and unstable PRRSV circulation. However, we observed a slight discrepancy in classifying high- and low-prevalence herds within unstable herds. We conclude that PFs are a reliable material for the PRRSV surveillance and classification of breeding herds, but in case of unstable circulation, a strategy combining blood and PF sampling is recommended.The porcine reproductive and respiratory syndrome (PRRS) control strategy within swine breeding farms is based on herd classification relative to PRRSV infection status. This study aims to assess the efficacy of a monitoring plan based on processing fluids (PFs) by comparing it with the classification of herds based on the analysis of blood serum. Twenty-five breeding herds were enrolled in the study, with at least five consecutive batches sampled from each herd. Each batch was tested for PRRSV by RT-PCR performed on (i) pre-weaning blood serum from 30 piglets and (ii) PFs from all the male piglets in the batch. PRRS categories following the Holtkamp classification were assigned based on the results of each testing protocol. The two protocols assigned the same category to 18 out of 25 herds: while they showed perfect agreement in identifying positive unstable and stable herds, we observed some discrepancy in discriminating between low- and high-prevalence classes within unstable herds. PFs are thus a reliable sample to assign PRRS categories in Italian breeding herds characterized by widespread PRRSV circulation. However, in case of an unstable epidemiological scenario, we recommend the adoption of an integrated monitoring strategy that combines blood sampling with PFs. Porcine reproductive and respiratory syndrome (PRRS) is one of the most important diseases affecting the swine industry due to its severe impact on pigs’ health  and releThe first step in the control strategy within breeding farms is herd classification based on infection status. Currently, the most commonly applied classification is based on virus shedding and previous exposure to the virus  defined The reliability of PFs as a testing material has been highlighted by several studies ,12,13,14This study is aimed at assessing the reliability of a PF-based monitoring program by comparing the PRRS classification of breeding herds based on the laboratory outcome obtained from the analysis of blood serum and PFs.Twenty-five commercial breeding farms located in the north of Italy were enrolled in this study. The study was performed between July 2019 and June 2020, according to farmer and farm veterinarian compliance. All of the farms followed a three-week batch management system. The herd size ranges from 250 to 1200 sows (median 950 sows). All of the enrolled herds adopted a PRRS vaccination protocol using a modified live vaccine administered only to sows every four months and no later than 3 weeks before parturition. A monthly PRRSV monitoring program based on RT-PCR analysis performed on blood serum collected from weaning-age pigs was already routinely adopted at all of the farms. No restriction based on the clinical history of recent or late PRRS outbreaks was applied.Since all of the farms adopted a three-week batch management system, an every-three-weeks monitoring plan was set up by farm veterinarians. The monitoring plan was carried out through two simultaneous sampling protocols. The first protocol (hereafter “PFs protocol”) consisted of collecting PFs from the testicles of all male piglets following routine castration, according to the method proposed by Lopez and colleagues . Since rAll samples were submitted by farmers and/or farm veterinarians to Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna (IZSLER) and tested for PRRSV using a Virotype PRRSV RT-PCR kit . The positive cut-off cycle-threshold (Ct) value was set at ≤37. The PFs were pooled by 10–15 litters (each pool including PFs from approximately 70–100 piglets), while blood serum was pooled by five piglets. Each batch was defined as positive when at least one of the pools tested was found to be positive.To assign a herd to a PRRSV category, at least five consecutive batches range: 5–9) were tested with both protocols, meaning that each herd was monitored for at least 100 days. Following [ were tesThe agreement between the two testing protocols in defining the PRRS status was estimated using the caret package  in R Stap = 0.013) with Cohen’s k = 0.52, indicating moderate agreement  and I-B  categories. The PFs protocol assigned four herds to I-B instead of the I-A category by detecting a smaller proportion of positive batches compared to the blood sampled at the pre-weaning stage. Conversely, in three cases, the reverse occurred and the PFs identified a higher proportion of positive batches than blood, assigning the herd to the high-prevalence category. Of course, this discrepancy occurs because the two materials are sampled at two different time points in piglets’ life stages, thus capturing two diverse epidemiological pictures. The PFs protocol will indeed miss PRRSV in herds where piglets become infected during the second or third week of life, while by relying exclusively on blood sampled at three weeks of age, piglets that were positive at birth but have no detectable viremia at the pre-weaning stage might be missed. Since PRRSV viremia is known to change over time and the purpose of monitoring programs is to identify at least one positive sample per sampling event, combining PFs and blood sampling protocols would allow to increase the sensitivity of the monitoring program. This strategy has been suggested by Trevisan and colleagues , especiaDefinitively, while PFs showed good reliability for PRRSV monitoring in Italian breeding farms, the application of both PFs and blood serum protocols is still recommended ,13. HoltThe PRRSV monitoring plans adopted in this study included at least five consecutive batches, which means approximately four to five months of surveillance, a tight timeframe that needs to be considered as a limitation of the study. Further studies monitoring a longer timeframe and including a greater number of herds with a more unstable PRRS epidemiology would be needed. Finally, it must be noted that in the European Union (EU), processing procedures are considered a poor practice for animal welfare  and methOur results showed that PFs are a reliable material for PRRSV surveillance and the related classification of breeding herds in high-circulation epidemiological contexts such as Italian swine production. Our findings are consistent with previous studies ,12,13 an


Tailed bacteriophages and herpesviruses use a transient scaffold to assemble icosahedral capsids with hexameric capsomers on the faces and pentameric capsomers at all but one vertex where a 12-fold portal is thought to nucleate the assembly. How does the scaffold orchestrate this step? We have determined the portal vertex structure of the bacteriophage HK97 procapsid, where the scaffold is a domain of the major capsid protein. The scaffold forms rigid helix-turn-strand structures on the interior surfaces of all capsomers and is further stabilized around the portal, forming trimeric coiled-coil towers, two per surrounding capsomer. These 10 towers bind identically to 10 of 12 portal subunits, adopting a pseudo-12-fold organization that explains how the symmetry mismatch is managed at this early step. The assembly of a viral shell is a ballet tightly regulated by a versatile molecular scaffold. Tailed double-stranded DNA bacteriophages and their eukaryotic counterparts, the herpesviruses, are the largest family of viruses on Earth, sharing capsid protein folds, icosahedral geometry, and assembly pathways  and a 281-residue C-terminal domain (mcpC) that exhibits the HK97-fold including a triangular A-domain; a P-domain, composed of a long spine α helix; an adjacent three-stranded β sheet; the flexible E-loop; and the N-arm. The N-arm is the N-terminal part of mcpC to which the mcpN scaffold domain attaches. Biophysical measurements indicate that mcpN is largely α-helical  to N. This segment of scaffolding domain adopts a helix-turn-strand motif with residues S76 to S88 forming a rigid α helix lining the interior surface of the capsomer subunits, followed by a turn, and residues K92 to G97 adding an additional strand to the three-strand β sheet of the mcpC P-domain. The final model  vertices or the portal vertex, with either 5-fold or 12-fold symmetry imposed or no symmetry at all. The icosahedrally averaged map reveals a well-defined HK97 procapsid shell shown in a surface view , right tal model  reveals C spine helix that also forms a short coiled coil with the mcpN. As a consequence, removal of mcpN might allow relaxation of the spine helix and drive the local threefold reorganization observed in Prohead II (The asymmetric units of our Prohead I model and of Prohead II [Protein Data Bank (PDB) ID: 3E8K] are very similar in overall structure but have notable differences at the interior of the capsomers and at the intracapsomer local threefold interfaces . In ProhN and accounted for all 30 scaffold domains around the portal. Besides the clip domain, the portal density exhibits strict 12-fold symmetry and the 10 towers of mcpN interact at symmetrically equivalent positions on the side of the portal (N residues 76 to 105) visualized under the regular penton vertices is also found under the portal-adjacent hexons along with additional weak density that links the ends of the spoke helices (S76) to their associated coiled-coil tower helices (S66), yielding a complete view of the entire mcpN scaffolding domain adjacent to the portal . Cells were collected, lysed, and digested with deoxyribonuclease I, and cell debris was removed by centrifugation. Proheads were precipitated using 0.5 M salt and ~6% (w/v) polyethylene glycol, molecular weight 8000; collected by centrifugation; suspended in buffer G [20 mM tris-HCl (pH 7.5) and 100 mM NaCl]; clarified by additional centrifugation; and then pelleted in an ultracentrifuge [2 hours in Type 45 Ti rotor ]. Proheads were resuspended in buffer G, loaded on top of 10 to 30% (v/v) glycerol gradients in the same buffer and centrifuged in an SW32 Ti rotor (Beckman) at 32,000 rpm. Prohead dimer bands  and then blotted and plunge-frozen in a Vitrobot Mk 4 . Grids were mounted on a Thermo Fisher Scientific Krios 3Gi cryo–electron microscope operating at 300 kV and equipped with a Thermo Fisher Scientific Falcon 3 direct electron detecting camera. A total of 2909 movies were collected in electron-counting mode under the control of the Thermo Fisher Scientific EPU software using a total dose of 60 e/ÅC, we based our initial mcp model on the crystallographic structure of the mcp in Prohead II (PDB ID: 3e8k)  into the asymmetric portal map using Isolde’s tools. Phenix real-space refinement with only adenosine diphosphate was used to generate B-factors and evaluate iterations of the model. Statistics of the models are listed in table S1. The trimeric coiled-coil models for the HK97 scaffolding domain towers that interact with the portal were built using the program CCBuilder 2.0 (


Finite element analysis of complex bodies is frequently used in design to determine the size of deformations. Successive iterations, with progressive refinement of mesh densities, are most often required to obtain a sufficiently accurate convergent numerical solution. This process is costly, time consuming, and requires superior hardware and software. The paper presents a quick and effortless way to determine a sufficiently accurate value of the numerical solution. The mentioned solution is obtained by amending the numerical solution resulting for a certain value of the mesh density of the studied body with an adequate proportionality coefficient determined following the deformation study of simple bodies differently subject to external forces. It is assumed that the elastic displacement of the various bodies has a similar evolution as the mesh density increases and that the values of the proportionality coefficients considered are approximately equal for identical mesh densities. Examples presented are related to the reference body of the mechanical press PAI 25. Finite element analysis (FEA) has many and various uses in engineering. For numerous applications, it is interesting to determine with sufficient precision, from the design phase, the deformation of bodies or assemblies used in various branches of engineering, mechanical engineering being one of them. In general, the finite element method is frequently used because it provides a general and systematic approach  regardleAbout elastic deformation of complex bodies, it is impossible to obtain the analytical solution of the exact result, but Bathe ,3 emphasEstablishing the mesh characteristics  is a keyLiu  emphasizFor a cantilever beam, Krishnamoorthi et al.  present Hansbo and Larson  show, reFor the study of bodies with complex geometry, networks with particularly good resolution are used. For example, Xu Z. et al.  used fouHutton  comparatFor the study of strain and deformation states of a gearbox housing, also a complex body, Cojocaru et al.  uses netFor bodies in which one or more areas present a special interest, a much higher local density of the mesh is used, a new concept in this sense being bicameral mesh gradation (or anisotropic mesh size control) . The useWith the increase of the computer’s performance, it is possible to use of highly efficient software packages , but comWhen designing complex parts, variously loaded, at various design stages, it is important to anticipate for areas of interest or criticality regions how they behave under a certain level of external load, for example what is their most likely elastic deformation. As an exact analytical solution is unavailable, the finite element method is used for such parts.In the design process, the constructive solution of the part evolves, supporting various changes and additions. Implicitly, the body’s response to external load also changes. Design engineers are interested in repeatedly finding out this information, in a short time and with a high degree of confidence, with common hardware and software resources. It is preferable that the software used for FEA to be related to the software used for the design and 3D modelling of the part in question.A professional FEA analysis conducted after the completion of the geometrical design of the complex part, to confirm that the appropriate constructive solution is chosen, is not excluded.This article took as its starting point the design routine of the authors and does not intend to compare various FEM analyses. At the same time, the authors wanted to identify a fast method for obtaining results with practical application without changing the default FEM analysis parameters and not related to a certain software solution. The authors were interested in identifying whether and how it would be possible, using a single iteration in FEA, to obtain for a large and complex body a reliable result for elastic deformation, obtained quickly and inexpensively using common hardware and software resources.As a preview of the final conclusions, it was found that the degree of confidence of the obtained result is dependent on the number of discretization elements and this rule also applies to geometrically simple bodies for which exact solutions are available.If for the latter a proportionality is identified between the deformation determined analytically (the exact solution) and the one determined by FEA, then the same ratio should also exist between a presumed exact analytical solution and the one obtained by FEA for the complex geometric body, under the conditions that both the simple geometric body and the complex body share the same external load, the same volume, are made from the same material, and have the same level of discretisation. Therefore, it would be possible to obtain a realistic result of the FEA analysis for a common level of discretization by applying an appropriate proportionality coefficient determined for a simple geometric body for which an exact analytical solution exists.3. The frame is made of cast steel St 50-2 according to DIN, for which the following values were considered: Young’s modulus E = 210,000 N/mm2, shear modulus G = 80,000 N/mm2, and ν = 0.3 for the Poisson’s ratio.Several studies ,24 have NF = 250 kN, evenly distributed on the support surfaces of the main shaft bores, and on the work surface of the table, on the other hand, as shown in To determine, on real conditions the deformation state, the frame was loaded with the nominal press force, The finite element analysis was performed in Catia V5 R16, the software into which the 3D model made in ProEngineering was exported. The 3D model was discretized into tetrahedral elements, as shown i of deformation iy from the deviation indicated for the last iteration (y7), can be found in The study was conducted for seven levels of mesh density. The resulting values for each iteration, i.e., number of proposed and resulting elements, number of nodes, elastic deformation indicated by the program, and the deviation ζi being relevant. The variation of the deformation according to the discretization levels suggests an equilateral hyperbole law.The values of the elastic deformations obtained for models with better and better resolution are ordered in a strictly ascending row. The convergence condition, stated by Bathe , is obvi-a law can be established to link the deformations to the levels of the discretization;-for various bodies, the most probable value of the elastic deformation can be determined;-a coefficient of proportionality can be established between the theoretically exact deformation and the deformation that corresponds to a certain level of discretization, regardless of the complexity of the studied body;-it is possible to obtain particularly good results in a short time, using common software and hardware resources, as a result of a single FEA analysis for a reasonable mesh density, related to the elastic deformation of complex geometry and large bodies.The research conducted by the authors aimed to show that:x—the number of elements of the discretization network of the body subject to finite element analysis,y = f(x)—the deformation of the body (under the action of the force F) corresponding to the discretization level to which corresponds the number x of elements of the network, andF.As mentioned, the variation of the deformation according to the level of the discretization suggests an equilateral hyperbole law. A simple enough variation law that corresponds to the cases studied is the Equation (1)a, b, and c, three sets of values are required ) ≡ , in this case ; , and .To determine the values of the constants a, b, and c are obtained according to Equations (3)–(5):Solving the system of Equation (2)a, b, and c, it is possible to determine with Equation (1) the very probable value of the deformation corresponding to any value of the number x of elements of the discretization network of the body undergoing finite element analysis.Knowing the values of the constants k ≥ 3 discretization levels, ix, iy), for the constant a, a value ras-t-  is determined.When performing the study for a number k = 7 different levels of discretization, a, For the example presented, the determination of the elastic deformation of the PAI 25 press frame in the direction of the application of technological force, performed by finite element analysis for amin = 0.1824 mm and amax = 0.23309 mm are identified, and the average value amed = 0.19375 mm is determined. Analysing the values in a = f(x→∞) = 0.191 … 0.192 mm. The following values are noticeable a1-2-7 = 0.19185 mm, a1-3-6 = 0.1915 mm and a1-4-6 = 0.19183 mm, but also a2-3-6 = 0.19209 mm, a2-4-6 = 0.19206 mm, a3-4-6 = 0.19205 mm.The limits ras-t- values can be less than y7 = 0.189 mm, value of the elastic deformation indicated by the study with the greatest level of discretization. As a result, the following values should not be considered a1-2-3 = 0.18124, a1-2-4 = 0.18745, a1-2-5 = 0.18724, a1-3-5 = 0.18803, a1-4-5 = 0.18710, a2-3-5 = 0.18860, a2-4-5 = 0.18708 and a3-4-5 = 0.18666. High values that are significantly above average, such as those over 0.196 mm , can also be excluded.Obviously, none of the determined amed-1 = 0.192688 mm against which the new limits amin-1 = 0.19011 mm and amax-1 = 0.19588 mm deviate by −1.3379% respectively +1.6565%.Under these conditions, the corrected average value is An adequate result is obtained if the results obtained for discretisation of the studied body in 5000, 18,000–20,000, and 70,000 elements is considered.To obtain confirmation, it is necessary to perform studies on bodies with low geometric complexity for which the value of the elastic deformation can be determined analytically.To be able to extrapolate the results, the volumes of the analysed bodies must be identical to that of the PAI 25 press frame, be made of the same material, the discretization levels must be comparable, and the external loads must be of the same value as PAI 25. In this respect, three examples are presented, chosen to differ in the nature of the general load of the bodies: compression, compression and bending only, and compression, bending, and torsion.l = 1000 mm and section A = 380 × 380 = 144,400 mm2, and a simple compression force F = 250 kN evenly distributed, For a pole-type body the following are considered: length The elastic deformation of the pole is determined analytically using the Relation (6)  (p. 296)Finite element analysis was performed for seven levels of levels of discretization. Similar to a were calculated for all 35 combinations of three levels of discretization, a1-5-6 = 30.211 µm and a4-5-7 = 6.9119 µm they are obviously aberrant, and value a2-5-7 = 10.003 µm is over the limit. These are not considered. The values a2-5-6 = 8.3598 µm or a3-5-6 = 8.3222 µm, both lower than any of the y values based on which the values of a2-5-6 are a3-5-6 were determined, cannot be accepted. The average value of the 30 acceptable values of the deformation is amed = 8.9006 µm, and extreme values are amin = 8.4796 µm and amax = 9.6634 µm . It should be noted that the average value amed = 8.9006 µm determined this way is practically identical with the deformation y7 = 8.902 µm resulting for a discretization in 123,673 elements of the studied body , the values for the constant ied body , its devras-t- ϵ , then the corrected average value amed-1 = 9.084 µm is greater by 10.185% compared to theoretical elastic deformation δ = 8.2443 µm.If only reasonable values greater than the highest determined deformation are considered from F distributed linearly on the free end of the arm in the console. The pole is presented schematically through the average geometric fibre of the two elements.A more complex load is considered in this case, compression and bending, and this is applicable to a pole with arm in the console, shown in 3, a square section with side s2 = 300 mm was adopted for the studied body. The relevant geometric features are lengths l1 = 950 mm and l2 = 650 mm, areas A1 = A2 = 300 × 300 = 90,000 mm2, with axial moments of inertia I1 = I2 = (s2)4/12 = 675 × 106 mm4. The external force has the same value F = 250 kN.To have the same volume (as the reference) of 0.144 mThe elastic deformation of the body was determined analytically using the Castigliano theorem  (p. 461)i of deformation iy from the deviation indicated for the last iteration (y7)) are presented in The finite element analysis was performed for seven discretization levels, as in the study performed on the PAI 25 press frame. Consistent with the reasoning used in a were calculated for all Using Relation (3), the values for the constant a2-5-6 = −0.85313 mm is aberrant, and the values a3-5-6 = 0.79783 mm and a3-5-7 = 1.17208 mm are exaggerated. These are eliminated and the average for the remaining values amed = 0.959 mm determined. This is higher than the deformation y7 = 0.95 mm, resulting for the finest discretization of the studied body, with only +0.92%. The minimum and maximum values are amin = a4-5-7 = 0.90398 mm and amax = a1-5-6 = 1.05624 mm, respectively. Compared to the theoretical elastic deformation δ = 0.881808 mm ≈ 0.882 mm, the values amin, amed, and amax deviate by +2.51%, +8.71%, and +19.78% respectively.The value ras-t- ϵ , then the corrected average value is amed-1 = 0.9633 mm, being 9.24% higher than the theoretical elastic deformation δ = 0.882 mm.If only reasonable values, greater than the highest determined value, are taken from s3 = 280 mm, lengths l1 = 940 mm, l2 = 500 mm, and l3 = 400 mm, areas A1 = A2 = A3 = 280 × 280 = 78,400 mm2) ensure that the body volume is 0.144 m3. Axial moments of inertia are I1 = I2 = I3 = (s3)4/12 = 512.2 × 106 mm4 and the moment of polar inertia is (pI)2 = (s3)4/6 = 1024.43 × 106 mm4. The body is loaded  with a force F = 250 kN evenly distributed at the end of the arm.Another case of loading–compression, bending and torsion–is exemplified by a pole with double arm in the console as in The elastic deformation of the body was determined analytically using the Castigliano theorem  (p. 461)ix ≈ 100,000 elements, the value of elastic deformation indicated in the FEA analysis (iy = 1.35 mm) is 3.85% higher than the elastic deformation analytically determined using the Relation (8).Once more, the finite element analysis was performed for seven discretization levels. Similar to a, as shown in By using Relation (3), for all 35 combinations and three levels of discretisation, the values of the constant a3-5-6 = 0.94147 mm, a3-5-7 = 0.47699 mm, a4-5-6 = 1.22024 mm, a4-5-7 = 1.18606 mm and a4-6-7 = 0.65967 mm are small, and the value of a3-6-7 = 2.40260 mm is exaggerated. By removing these values, the minimum and maximum values, amin = a3-4-5 = 1.29288 mm, amax = a5-6-7 = 1.57798 mm are identified, and the average value amed = 1.37979 mm is determined. The latter is higher than the deformation y7 = 1.35 mm, resulting for the finest discretization level of the studied body, by +2.21%. The values a2-5-6 = 1.55263 mm, a2-5-7 = 1.55865 mm, a2-6-7 = 1.56498 mm, and a5-6-7 = 1.57798 mm are also unrealistically high. Excluding these values, the value of amax-1 = a1-6-7 = 1.48448 mm becomes the maximum, and the corrected average value is amed-1 = 1.35038 mm, extremely close (deviation of only +0.03%) to the deformation corresponding to the finest mesh considered in the study, y7 = 1.35 mm. Compared to the theoretical elastic deformation δ = 1.300476 mm, the values amin, amed, amed-1, amax, and amax-1 deviate by −0.58%, +6.10%, +3.84%, +21.34%, and +14.15%, respectively.Compared to the elastic deformation values obtained by FEA for the higher levels of the discretization , the valiy , for each level i of discretization can be highlighted values of the proportionality coefficients (kδ)i = δ/iy, (km)i = amed/iy and (ek)i = amed-1/iy. For the three simple cases presented, the values of the proportionality coefficients mentioned are given in Knowing the deformations yi from  obtainedkδ)i, (km)I, and (ek)i. presented in The following preliminary conclusions emerge from the analysis of the coefficients (kδ)i become subunit, i.e., the analytically determined deformation is smaller than the one resulting from the FEA, regardless of whether the body load is simple or more complex. For low levels of discretization, the differences between the values of the coefficients (kδ)i are relatively large regardless of whether the body is subject to simple  or more complex  loads. However, for high discretization levels , the differences between the values of the coefficients (kδ)i fade, becoming less than 4%.As the level of discretization increases, the values of the coefficients (km)i and (ek)i decrease with the increase of the discretization level of the studied bodies, with an asymptotic variation towards 1 being evident. For low discretization levels (characterized by ix ≈ 1000 elements), the values of the coefficients (km)i and (ek)i are significantly higher than the asymptotic limit, even by more than 40%, the magnitude of the deviation being even as the complexity of the body is rising.Similarly, the values of the coefficients (kδ)i, (km)I, and (ek)i determined according to the level of discretization of some bodies with relatively simple geometry, bodies for which it is possible to analytically determine the elastic deformation corresponding to a certain external load, it is sufficient to determine by FEA the elastic deformation for a certain discretization level to be able to estimate with sufficient precision values of interest of the respective body deformation, such as theoretical elastic deformation δ, average elastic deformation amed or corrected average elastic deformation amed-1. They are obtained simply as a product of the value of the elastic deformation determined through FEA for the level of discretization adopted and the value of the coefficient kδ, km, or ek corresponding to that level of discretization.Knowing values of the coefficients i, (km)I, and (ek)i shown in i, the average elastic deformation (amed)i, and the corrected average elastic deformation (amed-1)i, corresponding to each of the discretization levels are given in Obviously, this approach can also be applied to the PAI 25 press frame, the ng known . The fra7 determined for the finest discretization, to the average elastic deformation amed and to the corrected average elastic deformation amed-1, the deviations of the values δi, (amed)i and respectively (amed-1)i, determined using the proportionality coefficients kδ, km, and ek, are shown in By reference to the elastic deformation δx5 ≈ 50,000 elements and x6 ≈ 75,000 elements respectively. These are accessible levels of discretion for common software and hardware resources and allow particularly good results in a short time in terms of the value of elastic deformation of large and complex bodies.For discretization levels in 20,000 items or more, the deviations of all three values are becoming smaller, within a maximum range of 4%, acceptable for mainstream practical applications. For bodies with the complexity of the PAI 25 press frame, the mentioned deviations are large or much too large for discretization levels of up to 20,000 elements and as a result a study at such a level cannot be recommended. Based on the results presented in There is a great interest in determining the elastic deformation of various bodies with complex structure and varied loaded with external forces. For such bodies, most often, an exact analytical solution is not achievable, but FEA offers the possibility of obtaining a close approximate solution. However, more iterations are needed, with increasingly fine densities, which requires time, supplementary hardware, and software resources.There are several scientific papers which, for bodies with simple geometry, show values of deformations obtained numerically compared to the exact value determined analytically. The numerical solution converges to the analytically determined value for sufficiently fine discretization levels of the studied bodies. Examples addressed in this paper only partially confirm the convergence of the numerical solution to the analytical solution. As the mesh density increases , the numerical solution becomes larger than the analytical solution by 4–8%. Maximum differences between FEA and analytical results were obtained in the case of a simple body subjected to compression. It would have been expected that such (relatively large) differences would be identified in more complex loaded bodies, in which the analytical model took into account the geometric mean fibre and not the undeformed mean fibre.amed of the reasonable values of the numerical solutions and the corrected average elastic deformation amed-1 .The convergence to zero of the deviation of the numerical solution allows the highlighting of a function of variation for the values of the numerical solution depending on the level of the discretization of the investigated body. This law is one with an asymptotic increase towards the most probable value of the numerical solution of the elastic deformation related to the body under study. A value for the numerical solution is determined for each mesh density, and further analysis of such values leads to the determination of relevant values, i.e., the average value amed value of reasonable values and corrected average elastic deformation amed-1), the proportionality coefficients kδ, km, and ek, were determined for each i level of discretization for which the study was performed. As the level of discretization increases, the values of the coefficient kδ become subunit, i.e., the analytically determined deformation is smaller than that the one resulting from the FEA, regardless of complexity of force loading. For high mesh density  the differences between the values of the coefficient kδ are diminishing, becoming less than 4%. The values of coefficients km and ek are decreasing with the increase of the discretization level of the studied bodies, being evident an asymptotic variation towards 1.For the simple geometrically studied bodies, knowing the deformations obtained from the finite element analysis for each mesh density , should be replaced with a similar one in which the denominator is of higher order, e.g., the one presented in Relation (9)For such a variation, the convergence towards the most probable deformation of the numerically determined values is much faster, being evident especially for discretization in a relatively small number of elements.kδ, km, and ek determined according to the discretization level of some bodies with relatively simple geometry for which the analytical solution is easily determined, it is sufficient to determine by FEA the elastic deformation for a certain discretization level to be able to estimate with sufficient precision, by similarity, values of interest of the deformation of a complex body, such as δ, amed, or amed-1. They are obtained simply as a product of the value of the elastic deformation determined through FEA for the discretization level adopted and the value of the coefficient kδ, km, or ek corresponding to that discretization level.Knowing values of the coefficients kδ, km, and ek become small, within a maximum range of 4%, acceptable for many practical applications. Discretion levels of 20,000–100,000 elements are accessible for common software and hardware resources and allow in a reasonable time to obtain particularly reliable results for the elastic deformation of large and geometrically complex bodies.This approach was applied to the PAI 25 press frame, for which elastic deformation values were determined using FEA for different discretization levels. To determine the elastic deformation for bodies with the complexity of the PAI 25 press frame, discretization levels in 20,000 elements or more for FEA studies are recommended, levels for which the deviations of the values of the coefficients


C0) of the in situ TiB fibers improved from 0.125 in the as-cast composite to 0.520, 0.688 and 0.858 by the processes with deformation ratios of 0.39, 0.26 and 0.14, respectively. The results of the tensile tests showed that the measured values of C0 gradually deviated from the modeled ones with an increase in the ratio of deformation applied during processing, and the deviation could be interpreted with the strengthening models.Thermal processing is a useful method for improving the strengthening effects of fibers used to reinforce metal matrix composites (MMCs), but the corresponding models have not been constructed. In this work, a Ti matrix composite (TMC) reinforced by in situ TiB fibers was prepared, then thermal processing was applied to it at different levels of deformation to align the TiB fibers along the loading direction. Changes in the microstructure of the matrix, the orientation and the aspect ratio of the TiB fibers during this process were investigated. It was found that the aspect ratio of TiB fibers decreased sharply after a large amount of deformation. The strengthening effect of TiB fibers in the composite was simulated by strengthening models of the fibers, and the simulated results were verified by the results of tensile tests. The modeled results show that the strengthening factor ( Because of their high elastic modulus and strength, fibers  are widely used in composites such as metal and polymer matrix composites ,2,3,4,5.Fiber reinforcements added into MMCs include continuous long fibers and discontinuous short fibers. Compared with MMCs reinforced with long fibers, MMCs reinforced with short fibers have many advantages, such as their low preparation cost and easy fabrication, and the availability of pressure processes used for metals ,14,15. AIn recent years, traditional pressure processes for metals such as forging, rolling and extrusion have been applied to MMCs in many studies. These studies have shown that the mechanical behavior of MMCs is affected by the fibers’ orientation, which can be controlled by the processing method applied to the MMCs ,20,21,22A number of studies have been carried out on the techniques used to produce the preferred fiber orientation in composites reinforced with short fibers. These techniques include rolling, drawing, extruding and other methods of deformation ,26,27,28In this study, a three-dimensional deformation process was applied to a composite reinforced with short fibers to produce the preferred fiber orientation in the composite. The changes in the fibers’ orientation and the strength of the composite with different fiber orientations achieved by this process were modeled.The composite prepared in this study was 4 vol.% TiB-reinforced Ti–6Al–2.75Sn–4.0Zr–0.4Mo–0.45Si alloy. First, stoichiometric raw alloys and boron powder were mixed mechanically. Second, the mixture of raw materials and the reactant were pressed into rods with a die on a hydraulic press c, and thThermal deformation of the composite was performed with a horizontal hydraulic press equipped with electrically heated upper and lower molds. Before thermomechanical processing, the ingot of the composite was heated to 1323 K in a furnace; meanwhile, the dies used in the open-die forge were heated electrically to 1173 K. During thermal deformation, the temperature of the composite was monitored with an infrared radiation thermometer to prevent cracks forming from lower temperatures at the end of forging. After thermomechanical processing, the composite rods were air-cooled to ambient temperature. The deformation ratio was used to characterize the extent of deformation, and it was calculated as 3 per 100 mL water). Scanning electron microscopy observations were carried out on FEG Quanta 450 equipment. The foils used in the transmission electron microscopy (TEM) analysis were prepared with a twin-jet device working at 30–50 V at a temperature range of 293–303 K, and the electrolyte was 10% HClO4 and 90% CH3COOH. TEM analysis was carried out on a JEM-2010 microscope working at 200 kV.Metallographic samples were cut from the composite for observation with an optical microscope before and after thermomechanical processing. These were polished mechanically and etched with Kroll’s reagent . The gauge size of the tensile cylindrical specimens was 50.0 mm and 10.0 mm in length and diameter, respectively, and the size of the specimen holders was 25.0 mm and 20 mm in length and diameter, respectively. The tensile behavior of the composite was tested on a Zwick/Roell 50 kN tensile testing machine. Before the tensile test, the sample was heated to the test temperature in a furnace on this tensile machine. The strain rate used in these tensile tests was 5.0 × 100.456 nm .The morphology of in situ TiB fibers may result from their crystal structure and growth mechanism. Lundstrom  reportedY = 1 and λX = l/(λZ). In this case, Equations (2) and (3) are reduced toZ. In this work, because the same extent of deformation was applied in the X and Y directions, thenBecause of the constant volume of the composite sample during deformation (Equations (2) and (3) can then be simplified toThese equations are identical to the results of a number of other studies that aimed to resolve purely elongational deformation ,39. In FBecause the same extent of deformation was applied along the X direction and Y direction in this work, Equation (6) was applicable. Therefore, the orientation angle θ after deformation can be predicted by Equation (9)Equation (9) can be rewritten for θ as follows:If we substitute Equation (6) By substituting Equation (11) into Equation (8), we haveBefore deformation was applied during thermal processing, the TiB fibers’ orientation was distributed randomly in the as-cast composite. A spherical coordinate system was used to characterize the probability density function of this random distribution of the TiB fibers’ orientation, as presented in From Equation (13), the following equation can be deducedWe can substitute By combining Equation (15) into Equation (12), we haveEquation (16) was used to calculate the As shown in However, as shown in Huang et al.  investigARc, The strengthening effect of TiB in the composite was modeled with a stress transfer model by the shear-lag method suggested by Cox . In thise fibers :(19)σf=σBecause of the high shear strength of the TiB–matrix interface prepared by the in situ technique, the shear strength is assumed to be larger or equal to the shear strength of the titanium matrix. In other words, the maximum shear stress of the TiB–matrix composite is equal to the shear strength of the titanium matrix. The composite’s strength is given in Equation (20) (20)σc=σC0) varying from 0 to 1. C0 is given by Equation (22)l, lc and h(l) was regarded as a constant, and l for all fibers was set to be larger than lc to simplify this model. Thus, For the case of fibers with variable lengths and a disordered orientation, Fukuda and Chou ,44 suggen IMI834 . In thisC0. As mentioned above, the TiB fibers in the as-cast composite were distributed randomly. In this case, From Equation (23), it can be seen that p. The evolution of the TiB fibers’ strengthening effect can be predicted with Equation (16) and Equation (23).The calculated If we substitute Equation (16) into Equation (23), we haveK and Ged are the strength constant, the strengthening coefficient of grain refinement and the size of the equivalent grains, respectively. Our previous research showed that dislocations can slide through different alpha laths in the same colony because they have the same crystal orientation. However, the dislocations’ slippage is hindered effectively by the interface between the alpha colonies because of the different crystal orientations in them. The equivalent grain size of the composite is used as the average size of the alpha colonies [The strengthening effect resulting from microstructural refinement is evaluated with the Hall–Petch equation (25)σym=colonies . The comC0 of the composite after thermal processing with different deformation ratios. The values of C0 modeled by Equations (24) and (26) are also listed in C0 of TiB fibers with different deformation ratios. As shown in C0 of the TiB fibers agreed well with those measured in the as-cast composite. However, the modeled value deviates gradually from the measured one with an increase in the deformation ratio. The C0 of TiB fibers was overestimated by Equation (24). This overestimation may have resulted from the TiB fibers breaking during thermomechanical processing, as presented in C0 of the TiB fibers.A composite reinforced with TiB was prepared by the arc remelting and casting method by the authors. TiB was formed through the in situ synthesis reaction between titanium and boron, then grown into fibers.C0) of in situ TiB fibers was simplified. In this equation, C0 can be calculated with the distribution density function of the TiB fibers’ orientation (P(θ)), and C0 was calculated to be just 0.125 for the as-cast composite because of the random distribution of the TiB fibers’ orientation.The orientation of the TiB fibers was totally random in the as-cast composite, and the probability density distribution function of the TiB fibers’ orientation (P(θ)) with respect to the direction of elongation can be described as The distribution of the aspect ratio of the TiB fibers was changed by thermal processing because the TiB fibers broke. The aspect ratio of the fibers decreased with the deformation ratio, which could be interpreted by the shear-lag model. According to this model, TiB fibers tend to break if their length/aspect ratio is larger than the critical length/aspect ratio. Furthermore, the alpha colonies in the composite were refined significantly by thermal deformation.C0) was calculated to be 0.520, 0.688 and 0.858 for λZ = 0.39, 0.26 and 0.14, respectively. Compared with the as-cast composite, C0 was improved significantly by thermal processing with different deformation ratios. The modeled C0 of the TiB fibers agreed well with the measured C0 of the as-cast composite. However, the modeled values deviated gradually upward from the measured ones when the deformation ratio increased. This deviation in C0 may be attributed to the TiB fibers breaking during thermomechanical processing, which decreased the stress in the TiB fibers transferred from the matrix described by the shear-lag model.With the pseudo-affine deformation scheme, the evolution of the TiB fibers’ orientation and their probability density distribution function during compression deformation was deduced and modeled. With this model, the strengthening factor (


Apis mellifera) and buff-tailed bumblebee (Bombus terrestris) activity across a variety of weather conditions at seven apple orchards to determine how four weather variables  influenced the flight activity of each species. Each orchard contained three honeybee and three bumblebee colonies, and so we were able to observe a colony of each species concurrently in the same weather conditions. Overall, honeybees were more sensitive to changes in weather than bumblebees and could be more predisposed to future changes in within-day weather conditions. Our results indicate bumblebees could compensate for low honeybee activity in inclement conditions, which supports the theory that pollinator diversity provides resilience. This may be particularly important in management of pollinators in crops that flower in the spring when weather is more variable, and to allow varied responses to global climate change.Insect pollination, and in particular pollination by bees, is a highly valued ecosystem service that ensures plant reproduction and the production of high-quality crops. Bee activity is known to be influenced by the weather, and as the global climate continues to change, the flying frequency and foraging behaviour of bees may also change. To maximise the benefits of pollination in a changing world, we must first understand how current weather conditions influence the activity of different bee species. This is of particular interest in a country such as Ireland where inclement weather conditions are nominally sub-optimal for foraging. We observed honeybee (The online version contains supplementary material available at 10.1007/s00442-023-05332-x. Apis mellifera L.), and bumblebees (Bombus spp.), are important crop pollinators in temperate regions around the world  are primarily managed for their pollination services, where they account for approximately 50% of global crop pollination  and buff-tailed bumblebee (Bombus terrestris) colony entrance activity at seven apple orchards in Ireland across a spectrum of weather conditions, and specifically asked the following questions:i.How does weather influence the number of honeybees exiting, returning to the colony, and returning to the colony with pollen loads?ii.How does weather influence the number of bumblebees exiting, returning to the colony, and returning to the colony with pollen loads?The main objectives of this study were to identify how weather, and specifically which weather variables, influence honeybee and bumblebee flight activity and foraging behaviour. To do this, we made concurrent observations of honeybee  and afternoon (12:00–17:00), as bees have been shown to exhibit bimodal patterns of activity with peaks in the morning and the afternoon  and the low rate of activity  made it feasible to do so, with each observer standing 3–4 m away from the colony so as not to interfere with any flights. While observing bumblebees, we simultaneously recorded honeybee colony returns with a video camera (Canon Legria HFR806), which allowed us to obtain data for both species largely under the same weather conditions.Videos of honeybees were subsequently watched in the lab and data recorded. Following pilot observations, we found that activity in the third minute of each 5-min video was closely correlated with activity in the full observation, so we multiplied the count from this third minute by five to obtain our recordings of honeybee activity. Any videos of swarming honeybees were removed as they were not representative of typical honeybee behaviour as determined by the weather.−2) and relative humidity (%) at intervals of 30 s using a HOBO Micro Station Logger from Onset Computer Corporation. Temperature was measured to a resolution of 0.02 °C, relative humidity to 0.1%, and solar radiation to 1.25 W m−2. We situated the logger equidistantly between the honeybee and bumblebee colonies and levelled the logger sensors between the heights of the honeybee and bumblebee colony returns to capture the microclimate at this height. We also recorded wind on the Beaufort scale prior to each observation.We recorded temperature (°C), solar radiation pk = 1,…,K (colony nested within site)ql = 1,…,L (site)β1,…,β12 are the fixed-effect coefficients, which are the same for all groupsαj ~ N,…,αj ~ N indicate that the variable α is distributed according to the normal distribution N with mean vector μ and standard deviation σ2 for each groupwhereχ2 statistic for each covariate by removing each term from the full model to identify which covariates most influenced bee activity , before declining as wind speed rose.Honeybee exits from the colony were most related to the linear term of temperature, the linear and quadratic terms of solar radiation, and wind Table a. By plo−2 to ~ 40  bees at 1019 W m−2; at all measures of solar radiation more bees returned to the hive when relative humidity was lower. Honeybee returns also increased until wind speed at Beaufort scale 2, with ~ 334 individuals , and decreased after this point.The number of honeybees returning to the colony without pollen was most associated with the linear effect of temperature, the linear term of solar radiation, the interaction of solar radiation and relative humidity, and wind speed Table a. HoneybThe number of honeybees returning with pollen was related to the interactions of temperature and relative humidity, and solar radiation and relative humidity. Bees with pollen were predicted to decrease as relative humidity rose from ~ 145 bees  at 39% RH to ~ 6 bees  at 93% RH. This relationship was expected to change with temperature and solar radiation; at all recorded relative humidity values the number of bees returning to the colony was greater at higher temperatures  at 39χ2 = 8.72, p = 0.003). The number of honeybees collecting pollen decreased immediately as relative humidity rose, while the effect of temperature on both types of honeybee returns was expected to be strong; honeybees were more active at higher temperatures in most recorded humidities  = 7.99, p = 0.005) returns to the colony; the rate of bees returning varied by whether individuals carried pollen or not (Table S3). Bumblebees returning with pollen were predicted to increase until 57% relative humidity before decreasing, and more bumblebees were expected to be recorded at higher temperatures. The number of bumblebees returning without pollen loads did not vary widely with temperature or relative humidity  on pollen collection in both species.There was no detected effect of wind speed on any bumblebee activity, corroborating results from Crall et al. , who obse et al.  found B.In our study, it appears the activity of bumblebees in less sunny, windier, and more humid conditions may compensate for low honeybee activity at such times, a phenomenon known as functional complementarity  has already demonstrated reductions in floral abundance, nectar volumes, and the abundance of flower-visiting insects Below is the link to the electronic supplementary material.


L* and increased a* of the crust and decreased the hardness, adhesiveness, and springiness of the deep-fried meat. A spongy inner structure with a flatter surface was formed in all composite flour-based crusts, and the substitution rate influenced the flatness of the crust. Thus, highland barley flour could be used for batter preparation with partial substitution, enhancing the quality of deep-fried meat and acting as an oil barrier-forming ingredient for fried batter foods. Highland barley flour-based coating batter has rarely been reported, although highland barley flour is promising due to its high β-glucan and amylose content. In this study, highland barley flour was used to substitute 40% to 80% of wheat flour to form a highland barely-wheat composite flour used in the coating batter. The characteristics of the highland barley-wheat composite flour and its effect on the properties of coating batter and deep-fried meat were analyzed. Results showed that the composite flour significantly improved water holding capacity, oil absorbing capacity, and water solubility index. In contrast, no significant change was observed in the water absorption index or swelling power. The incorporation of highland barley flour significantly changed the pasting properties of the composite flour. Compared with the wheat flour, the viscosity and the pickup of the coating batter made with composite flour increased from 4905 Pa·s and 0.53% to more than 12,252 Pa·s and 0.63%, respectively, and its water mobility decreased. These changes were closely related to the substitution rate of highland barley flour. The composite flour significantly increased the moisture content from 27.73% to more than 33.03% and decreased the oil content of the crust from 19.15% to lower than 16.44%, respectively. It decreased  The demand for foods made with non-wheat flour has increased in recent years. One of the motivations is that there is an increased number of individuals who want to follow a healthy diet. These health-conscious consumers relate refined wheat foods to increased mortality from lifestyle-related diseases such as diabetes, cardiovascular disease, and cancer . Non-wheWheat flour is the typical base of coating batter due to its exceptional viscoelastic gluten, which can expand during frying, providing a desirable spongy coating. Flour/starch made from other cereals has recently been promoted to replace part of wheat flour to reduce the oil absorbed by deep-fried products. Moreover, different flours have differences in viscosity and gelatinization patterns, which affect the flavor and textures of fried products . Among tIt is reported that a new β-glucan-rich hydrocolloid containing 32% β-glucan (C-trim 30) increased the batter pickup, decreased the moisture loss, and reduced the oil content of the sample . This fiWheat flour  was bought from Jinshahe Industry Co., Ltd. . Highland barley flour  was provided by Rikaze Tibetan Research Food Co., Ltd. . Corn starch, baking powder, and salt were bought from the Suguo Supermarket Co., Ltd. .The WHC and OAC of composite flour were determined according to the method of Beuchat and Lin, respectively ,10. A toThe water absorption index (WAI), water-soluble index (WSI), and swelling power (SP) of the composite flour were determined with the modified method of Toyokawa, with minor modifications . A totalThe pasting properties of the composite flour were determined with a rapid visco-analyzer (RVA) instrument  by using the RVA Standard Pasting Method . BrieflyThe formulation consisted of 60 g of wheat flour, 40 g of corn starch, 2.0 g of salt, 1.0 g of baking powder, and 100 g of water (25 °C). Highland barley flour was used to substitute part of the wheat flour, and in the composited flour, its substitution ratio was controlled to be 40%, 50%, 60%, 70%, and 80%. All powder was pre-blended and mixed with water for 2 min. The coating batters were used for frying, and the determination of processing properties was performed immediately after preparation.Processing properties of the coating batter, including the viscosity, thermo-gravimetric properties, water mobility, and batter pickup, were determined immediately after the coating batter was prepared.The viscosity of the prepared coating batter was measured using a rotational viscometer . A total of 65 g of coating batter was placed in a 50 mL beaker to ensure that the rotor was submerged by the coating batter as required by the operation guide of the rotational viscometer. Measurements were conducted at 25 °C with a head speed of 12 rpm with a No. 3 rotor. Measured values were recorded and reported as Pa·s .Thermogravimetric analysis (TGA) on the coating batter was carried out immediately after the coating batter was prepared with a thermogravimetric method and simultaneously differential thermal analysis (TGA/SDTA) . Approximately 10 mg of coating batter was used for each determination, and each sample was heated from 25 °C to 200 °C at a heating rate of 10 °C/min in a nitrogen (20 mL/min) atmosphere.Water mobility of the coating batter was determined with low-field nuclear magnetic resonance (LF-NMR) relaxation measurements using an NMI20-060 H-I-25 mm  as described by Cao et al. , with slB and mA are the mass of the meat before and after batter coating, respectively.Batter pickup was calculated with the mass of meat before and after coating with the following formula:Frozen pork chops were cut into sheets (3 cm × 3 cm × 0.5 cm) and thawed at room temperature. Drop loss from the meat was removed from the surface with kitchen paper. Then, they were immersed in the coating batter for 10 s, drained for 10 s, and deep-fat fried at 200 ± 5 °C for 3 min with a commercial fryer  with soya bean oil. Deep-fried samples were cooled at room temperature for 10 min before analysis, during which excessive oil was drained.The crust peeled off the meat was used to analyze the moisture and oil content of the crust using a direct drying method at 105 °C and according to the AOAC official methods 960.39 ,19.L*, a*, and b* were recorded.The color profile of the crust of the deep-fried meat was determined with an automatic colorimeter . The textural properties of the meat in the deep-fried meat were measured using a TA analyzer  with a TA/75 probe at a deformation rate of 75%, a load cell weight of 20 kg, and a test speed of 2 mm/s. The meat was cut into a round shape with a 1.1 cm diameter with a mold before determination. Hardness, adhesiveness, springiness, chewiness, cohesiveness, and resilience were recorded.Deep-fried meat was freeze-dried, sliced, and sputter-coated with a gold–palladium alloy, and the microstructure of the crust and the interface of the crust and the meat were observed with SEM  under vacuum and at an accelerating voltage of 5 kV, respectively. Representative micrographs were taken for each sample at a magnification of ×100 and ×50 for the crust and the interface of the crust and the meat, respectively.p < 0.05 was used. The Origin 2022  was used in the data treating and diagram making.SPSS Statistics 21.0  was used to analyze the significance of the impact factor data using one-way analysis of variance (ANOVA). All statistical analyses were performed with Duncan’s post hoc test, and a significance level of The composited flour’s hydration and gel hydration properties were determined, and the results are shown in Determination of the pasting properties of starch-based paste is helpful in cereal blend formulations and in the feasibility of its application assessments . Thus, RThe effect of highland barley flour substitution on the viscosity of the coating batter is shown in Batter pickup depends mainly on its viscosity. That is, the higher the viscosity, the higher the pickup , as prov22 , and T23 . T21 cannot be clearly seen from the curve because it only takes up 0.034% to 0.150% of the total peak area. The small T21 showed that water hydration in the coating batter was restricted during the mixing of the flour and water. The peak area ratio of T22 decreased from 15.827% to 14.526% when 40% highland barley flour was used as the substitution and then decreased to 13.649% when the substitution rate was further increased to 80%. In contrast, the peak area ratio of T22 decreased from 15.827% to 14.526% when 40% highland barley flour was used for the substitution and decreased to 13.649% when the substitution rate was further increased to 80%. The shift of the curves and the change in the peak area indicated that the incorporation of highland barley decreased the water mobility in the coating batter, and this influence was closely related to the substitution rate of highland barley flour. The water replacement mechanism is one of the three proposed mechanisms of oil absorption during deep-fat frying [Water mobility of the coating batter was determined with LF-NMR. It can be seen from t frying . It is cTGA-SDTA on the coating batter was conducted to monitor water loss by mimicking the heating process during deep frying. Results are shown in Moisture and oil content of the crust after deep frying was determined, and is listed in L* and a* of the crust of fried meat, as shown in L* and increased a* were translated into a more intense golden color, which was perfectly acceptable in all of the batter-coated products. A similar change was also reported when various ingredients were added to a batter coating for fried seafood [L*, whereas no significant influence was observed on L* and a*. However, a significant increase in b* was observed when it was added to white salted noodles. Thus, the influence of highland barley flour on the color parameters of the final products varied according to the type of product. Moreover, changes in L* and a* were not linearly along with the highland barley flour substitution rate. That is because the color of the crust was not only influenced by the ingredients of the coating batter or by the thickness of the coating batter, that is, the pickup of the coating batter.In general, the incorporation of highland barley flour in composite flour significantly changed the  seafood . When hiExcept for the resilience and cohesiveness, all textural properties of the meat were significantly changed with composite flour in the coating batter. The hardness of cooked meat is crucial to its overall acceptance. Thus, it can be used to predict sensory textural acceptance . The lowTransport phenomena in deep-fried foods depend strongly on their structure . SEM is L* and increased a* of the crust, decreased hardness, adhesiveness, and springiness of the deep-fried meat, and helped to form a spongy inner structure with a flatter surface in composite flour-based crusts. This influence was closely related to the substitution rate of highland barley flour. Thus, highland barley flour can be used in coating batter preparation to obtain deep-fried products with a lower oil content and improved textural properties. Consequently, the composite flour’s high WHC and pasting viscosity limits its substitution rate because it leads to excessive viscosity, decreased pickup, and increased processing difficulty. Nevertheless, it is prospective in rice flour-based coating batter to provide it with a suitable viscosity, which commonly requires a greater proportion of solids or the addition of a thickener to achieve this.As one of the non-wheat flours, highland barley flour is a promising base for coating batter due to its high β-glucan and amylose content. Highland barley-wheat composite flour was produced by partially substituting wheat flour with highland barley flour to explore its possibility of being utilized as a coating batter. Characteristics of highland barley-wheat composite flour and its effect on the properties of coating batter and deep-fried meat were analyzed. Results showed that the incorporation of highland barley flour significantly influenced and increased the WHC, OAC, and WSI and significantly changed the pasting properties of the composite flour. Moreover, it increased the viscosity and the pickup, and decreased the water mobility of the coating batter. The composite flour significantly increased the moisture content and decreased the oil content of the crust, decreased