Urtica simensis). Consequently, there was a scientific report gap on the effect of incorporating stinging nettle leaf flour into local staple foods like flatbread. Therefore, this study was intended to investigate the nutritional composition and sensory acceptability of unleavened maize (Zea mays L.) flatbread (Kitta) supplemented with stinging nettle (Urtica simensis) flour. The flatbread was developed from composite flour of germinated maize and nettle leaf in a ratio of 90 : 10, 85 : 15, 80 : 20, and 75 : 25, respectively. Hundred percent (100%) nongerminated maize flour flatbread was used as control. Proximate composition, minerals , and vitamin C contents were analyzed. The sensory acceptability test was rated by a nine-point hedonic scale. The result revealed that crude protein and fat decreased from 11.02 g to 7.21 g and 1.12 g to 0.48 g, respectively, when the amount of nettle flour supplementation increased from 0% to 25%. On the contrary, total ash, crude fiber, and total carbohydrate slightly increased from 1.84 to 3.81 g, 2.19 to 3.05, and 75.53 to 80.05 g, respectively. The calcium, zinc, and iron content significantly (p < 0.05) increased from 60.51 to 283.74 mg, 5.09 to 9.24 mg, and 1.72 to 3.59 mg when the amount of nettle flour increased from 0% to 25%, respectively. All sensory acceptability tests showed decrement with increasing the amount of nettle flour, but the control group has the highest acceptability.In Ethiopia, a few studies had been conducted to improve the nutritional values and sensory acceptability of maize-based flatbread. These studies did not address indigenous edible wild green vegetables like stinging nettle ( Kitta in local language (Amharic), is a baked product made from cereal flour [Unleavened flatbread, al flour . It is mal flour .Urtica simensis). Some studies conducted outside Ethiopia, like Maietti et al. [A few studies had been conducted in Ethiopia to improve nutritional values and sensory acceptability of maize-based flatbread. For instance, Serka et al.  investigi et al. , investii et al.  also stuUrtica simensis) is an annual and perennial undervalued wild green herb distinguished by stinging hairs [Urtica of the family Urticaceae [Stinging nettle (ng hairs . It is cticaceae . It mostticaceae .Urtica simensis) leaves are eaten in many parts of the world as cooked leaves at times of famine [Stinging nettle  leaves add not only variety to the menu but also valuable sources of macro- and micronutrients and other bioactive compounds. Hughes et al. [Phaseolus vulgaris) and chicken . Amino acids from dehydrated nettle meal are also nutritionally better compared to alfalfa meal [Stinging nettle  developed from stinging nettle (Urtica simensis) leaf and maize (Zea mays L.) flour.Utilizing such wild edible plants may help to close food gaps during famine and combat food insecurity concomitantly improving malnutrition in low-income countries. However, there is a limited scientific report on the effect of incorporating nettle leaves on the chemical composition and sensory acceptability of unleavened flatbread in Ethiopia. On the other hand, this plant is considered as poor man's food , weeds dZea mays L.) and stinging nettle (Urtica simensis) were procured from Hawassa town open market and Wondogenet district, respectively. Maize was sorted, cleaned, and then soaked for 24 hrs in clean water. The soaked maize was rinsed with clean tap water. Then, it was allowed to germinate for 48 hrs and sun-dried. The young shoot of the nettle (Uritca simensis) leaf was separated from its steam using sanitized protective gloves. The leaves were washed by deionized water and then dried at room temperature in the food processing laboratory of the Department of Food Science and Technology Hawassa University, Ethiopia. Finally, the dried control maize, germinated maize (treatment recipes), and young shoot of nettle leaves were separately milled into flour passing a 1 mm diameter sieve using a laboratory miller . The sieved flour was packed in a polyethylene bag and stored at room temperature until flatbread preparation.Maize (enset leaves (Enset ventricosum). Then, it was baked by a preheated local clay griddle called “Mitad” for 15-20 minutes. The baked flatbread was milled into flour. Finally, the flour was packed in a polyethylene bag and kept in desiccators until compositional analysis.Five treatments  were formulated from nongerminated traditional maize flour (control), germinated maize flour, and nettle leaf flour as depicted in The chemical composition of the flatbread was analyzed in the Ethiopian Public Health Institute (EPHI) food analysis laboratory, Addis Ababa. Proximate compositions , vitamin C, and minerals  were analyzed by the methods of the Association of Official Analytical Chemists (AOAC) . VitaminW1). About 3.00 g of fresh sample were thoroughly mixed and transferred to the dried and weighed dishes. Then, dishes and their contents were weighed (W2). The contents were placed in the drying oven and dried for 3 hr at 105°C. Finally, the dishes and their contents were cooled in a desecrator to room temperature and reweighed (W3). The moisture contents of the sample were determined and expressed as follows:Moisture content was determined by drying the samples in a drying oven . Empty dishes and lids (made of porcelain) were dried at 100°C for 1 hr. The dried dishes were then transferred to the desiccators which contain granular silica gel and cooled for 30 minutes. The dried empty dishes were weighed (W1 is the weight of empty dish (g), W2 is the weight of empty dish + fresh sample before drying (g), W2–W1 is the weight of sample (g), W3 is the weight of container + sample after drying (g), and W2–W3 is the loss of weight (g).where Crude protein was analyzed by the Kjeldahl method using the Kjeldahl analyzer in three steps . Digestion, step 1: initially, the sample was homogenized and then 2.00 g of fresh samples was taken in a Tecator tube. Concentrated orthophosphoric acid and concentrated sulfuric acid in ratio 5 : 100, respectively, were added and mixed thoroughly. A 3.5 ml of 30% hydrogen peroxide was added step by step. Immediately after the violent reaction had ended, the tubes were shaken and placed back to the rack. Three grams of catalyst, a mixture of ground 0.5 g of selenium metal with 100 g of potassium sulfate was added into each tube and allowed to stand for about 10 minutes before digestion. Then, the tubes were lowered into protein digester when the temperature of the digester attained 370°C. The digestion was continued for about 1 hour until a clear solution was obtained. The tubes in the rack were cooled in a fume hood; 15 ml of deionized water was added and shaken to avoid precipitation of sulfate in the solution. Distillation, step 2: the digested and diluted sample solution was distilled using boric acid. Titration, step 3: the distillate was titrated using 0.1 N sulfuric acid to reddish color using the Kjeldehal apparatus .W1). The bottom of the extraction thimble was covered with about 2 cm layer of fat-free cotton. Two grams (Wo) of fresh sample was added into the extraction thimbles and then covered with about 2 cm layer of fat-free cotton. The thimbles with the sample content were placed into a Soxhlet extraction chamber. The cooling water was switched on, and a 50 ml of diethyl ether was added to the extraction flask through the condenser. The extraction was conducted for about 3 hours. The extraction flasks with their content were removed from the extraction chamber and placed in the drying oven at 70°C for about one hour, cooled to room temperature in the desiccators for half an hour. The content was reweighed (W2) immediately after it was taken out of desiccators. The amount of crude fat in the sample was calculated by the difference in weight of the extraction flask before and after extraction shown as follows:The crude fat content was determined by exhaustively extracting a measured and known weight of sample in diethyl ether (boiling point 55°C) in a soxhlet extractor (model: 2055 Soxtec Foss Tecator). The ether was evaporated from the extraction flask. The amount of fat was quantified gravimetrically and calculated from the difference in weight of extraction flask before and after extraction as percentage. The extraction flasks were cleaned, dried in a drying oven  at 70°C for 1 hour, cooled in desiccators (with granular silica gel) for 30 minutes, and then weighed .where W1) of fresh sample was placed into a 600 ml beaker, and 200 ml of 1.25% H2SO4 was added. The content was then gently boiled for 30 minutes placing a watch glass over the mouth of the beaker. After 30 minutes of boiling, 20 ml of 28% KOH was added and boiled gently for further 30 minutes with occasional stirring. Filtration, step 2: the bottom of sintered glass crucible was covered with a 10 mm sand layer, and the layer of the sand was wetted with a little distilled water. The solution was poured from the beaker into sintered glass crucible, and then, the vacuum pump was turned on. The wall of the beaker was rinsed with hot distilled water several times; washings were transferred to a crucible and filtered. Washing, step 3: the residue in the crucible was washed with hot distilled water and filtered. The residue was washed with 1% H2SO4 and filtered and then washed with hot distilled water and filtered. Again, the residue was washed with 1% NaOH and filtered; again, it was washed with hot distilled water and filtered. Again, the residue was washed with 1% H2SO4 and filtered; again, it was washed twice more with hot, distilled water and filtered. Finally, it was washed with water-free acetone. Drying and combustion, step 4: the crucible with its content was dried for 2 hours in an electric drying oven at 130°C and cooled for 30 min in the desiccators (with granular silica gel) and then weighed (W2). The crucible was transferred to a muffle furnace  and incinerated for 30 min at 550°C for ashing. The crucible was cooled in the desiccators and weighed (W3). Then, the crude fiber was calculated as a residue after subtraction of the ash shown in the following formula:The crude fiber was determined in four steps  using a muffle furnace . Digestion, step 1: 2.00 g . About three grams of fresh sample in duplicate was placed in dishes (W2). The dishes were placed on a hot plate under a fume hood, the temperature was slowly increased until smoking ceases, and the samples become thoroughly charred. The dish with sample was placed inside the muffle furnace at 550°C for an hour and cooled in desiccators for 1 hour. The crucible was removed from the furnace and allowed to cool to room temperature. The cooled samples were moistened again with a few drops of deionized water and allowed to evaporate in a hot plate. Again, it was ashed at 550°C for 30 minutes and the crucible was allowed to cool; some drops of deionized water and 5 drops of concentrated HNO3 were added and evaporated. Again, the content was reached at 550°C for 30 minutes. When cooled to room temperature, each dish with ash was reweighed to the nearest milligram (W3). Then, the total ash was determined as follows:Total ash was determined by ashing the sample at 550°C using a muffle furnace. Porcelain dishes were placed in a muffle furnace  for 30 min at 550°C. The dishes were cooled in desiccators (with granular silica gel) for about 30 minutes and weighed to the nearest milligram . Gross energy was determined by summing energy from fat, carbohydrate, and protein contents using Atwater's conversion factors considering protein and carbohydrate each gives 4 kcal and fat yields 9 kcal per 100 gram . (6)Gros3 were added and allowed to evaporate on a hot plate. Finally, the sample was ashed once again for 30 minutes at 550°C, the crucible was cooled in desiccators for 1 hour, and the content was weighed. Dissolution, step 2: the ash was treated with 5 ml of 6 N HCl to wet it completely and dried on a low temperature hot plate. A 7 ml of 3 N HCl was added to the dried ash and heated on the hot plate until the solution just boils. The ash solution was cooled to room temperature and filtered through a filter paper  into graduated flask. A 5 ml of 3 N HCl was added into each crucible dishes and heated until the solution just boiled, cooled, and filtered into the flask. The crucible dishes were again washed three times with deionized water; the washings were filtered into the flask. A 2.5 ml of 10% lanthanum chloride solution was added into each graduated flask. Then, the solution was cooled and diluted to the mark (50 ml) with deionized water. A blank was prepared by using the same procedure as the sample. Minerals were determined using the Shimadzu atomic absorption spectrophotometer (Model: AAS6800). Crucibles were washed with 6 N HCl and glasswares with 10% nitric acid. Then, the crucibles were placed in an oven for 30 minutes at 100°C and cooled in desiccators for 30 minutes. Then, 2.5 g of sample was weighed and charred at a hot plate starting from low temperature under a hood. Dry ashing, step 1: ashes were obtained by ashing samples in a muffle furnace at 550°C for 1 hour. The crucible was cooled and moistened with a few drops of deionized water, and water was evaporated on a hot plate. Again, the sample was ashed for 30 minutes at 550°C and cooled; a few drops of deionized water and 5 drops of concentrated HNOW is the weight (g) of samples; V the volume (V) of extract; a is the concentration (μg/ml) of sample solution; b is the concentration (μg/ml) of blank solution.where 3·6H2O and 0.3% of sulfosalicylic acid in water) was added to 3 ml of the sample solution (supernatant), and the mixture was mixed on a Vortex (Maxi Maxi II) for 5 seconds. The absorbance of the sample solutions was measured at 500 nm using the UV-VIS spectrophotometer . A series of standard solutions were prepared containing 0, 5, 10, 20, and 40 μg/ml of phytic acid  in 0.2 N HCl. A 3 ml of standard was added into 15 ml of centrifuge tubes with 3 ml of water which were used as a blank. A 1 ml of the Wade reagent was added to each test tube, and the solution was mixed on a Vortex mixer for 5 seconds. The mixtures were centrifuged for 10 minutes, and the absorbance of the solutions (both the sample and standard) was measured at 500 nm by using deionized water as a blank. A standard curve was made from absorbance versus concentration, and the slope and intercept were used for calculation. Phytate was determined by the method of Latta and Eskin (1980) and later modified by Vantraub and Lapteva . About 0ρ is density.where The mole of phytate and minerals  was determined by dividing the weight of phytate and minerals with its atomic weight. The molar ratio between phytate and zinc was obtained after dividing the mole of phytate with the mole of minerals. Thus, the phytate zinc molar ratio was calculated using the following formula :(9)PhytFlatbread transferred into identical plates coded with three-digit random numbers. Triplicate samples from the same treatments were served for the acceptability test. Taste, color, flavor, mouth fullness, appearance, and overall acceptance of flatbread samples were rated by 15 untrained panelists using a 9-point hedonic scale (9 = like extremely and 1 = dislike extremely) , 30. Finp value less than 0.05. Finally, the result was described by means and standard deviation.Proximate composition, minerals, and vitamin C data were entered into a Statistical Package for the Social Sciences (SPSS) version 20 software program . A complUrtica simensis) is indicated in p < 0.05) different from the treatment groups. As the proportion of nettle leaf flour increased, the moisture content slightly decreased from 10.68 in control to 8.44% in SN25 (25% nettle leaf flour supplemented) flatbread. The crude protein content of the SN0 was not significantly different from SN10 (10% supplemented bread), but it was different from SN15 (15% nettle supplemented), SN20 (20% nettle supplemented), and SN25. The value of crude protein showed a slight decrement with increasing the proportion of nettle flour supplement. The crude fat and total ash of the control group did not show a significant difference (p > 0.05) from SN10 and SN15, but it was significantly (p < 0.05) different from SN20 and SN25. Crude fat content decreased, and the total ash increased when the proportion of nettle flour supplement increased. Except for SN10, which did not show a difference, the crude fiber content of the control group (SN0) was significantly different from SN15, SN20, and SN25. The total carbohydrate value of SN0 was significantly different from all nettle flour-supplemented flatbread. Both crude fiber and total carbohydrate contents slightly increased with increasing the amount of nettle flour. The gross energy value of SN0 was not significantly (p > 0.05) different from nettle flour-supplemented flatbread.The Proximate composition of the flatbread formulated from maize flour with diffrent levels of stinging nettle (p < 0.05) increment when the amount of nettle flour added increased. The calcium, iron, and zinc content increased by approximately 5 folds, 2 folds, and 2 folds in 25% nettle flour-supplemented flatbread compared to the control group, respectively. Vitamin C was not detected in the control, but 12.61 μg, 23.99 μg, 34.99 μg, and 47.57 μg in 10%, 15%, 20%, and 25% nettle flour supplemented flatbread, respectively. Regarding phytate, the numeric value of phytate slightly decreased (84.64-70.47 mg/100 g) when comparing the control and supplemented groups. However, the content showed increment within the treatment group. There was a significant difference (p < 0.05) in the phytate content of the control and treatment groups. Similarly, the phytate to the mineral molar ratio  was decreased when the proportion of nettle flour increased.p < 0.05) between the color preference of control and the other four treatments. The taste and flavor of SN15 and SN20, the appearance of SN10, and the mouthfeel of SN10 and SN15 were not significantly (p > 0.05) different from the control group. All the nettle leaf flour-supplemented flatbread was rated like very much at 10% supplementation.The result of sensory acceptability of the nettle flour-supplemented unleavened maize flatbread is depicted in The result of proximate composition indicated that the moisture and crude protein contents of flatbread were slightly decreased with an increase in the proportion of nettle leaf flour. A similar finding was reported in pasta made from wheat, oat, and moringa leaf flour . HoweverThe crude protein and fat contents were significantly decreased from 11.2% to 7.21% and 1.12 to 0.48%, respectively, with an increase in the amount of nettle flour from 0% to 25%. The finding is similar to another study in Ethiopia . The finThe total carbohydrate content was increased from 75.33% to 80.05% in the highest supplemented (SN25) flatbread. Contradicting result was recorded in noodles from wheat and nettle leaf flour  in whichThe gross energy content of the control group and nettle-supplemented flatbread was not significantly different. This might be due to a decrease in crude protein and fat content and increased total carbohydrate with increasing nettle flour content. Thus, the reduced energy value due to decreased protein and fat content is likely compensated by increased carbohydrate content since the gross energy was coming from the three macronutrients. The result disagrees with the finding by , in whicAccording to the present study, the total ash content was increased from 1.84% in control to 3.81% in 25% nettle flour-supplemented flatbread. Comparable results were recorded in other studies , 30, 33.The phytic acid content of flatbread was reduced by 16.7-20.2% with the increasing proportion of nettle leaf flour from 10% to 25%. However, the content showed increment within the supplemented group. The decreased in phytate content when increasing nettle flour might be due to the low proportion of maize and activation of phytase enzyme during germination. On the other hand, the increased phytate contents within nettle leaf-supplemented flatbread may be due to increased nettle leaf flour. This might need further processing such as blanching or soaking or searching for another drying method that minimizes antinutritional factor (phytate) without compromising the nutritional composition of nettle leaf. According to the international zinc consultative group suggestion, the phytate to zinc molar ratio is an important index for zinc bioavailability, and a ratio of less than 15 indicates bioavailability. A ratio of less than five has good bioavailability . In the p < 0.05) reduced with increasing the proportion of nettle leaf flour. The result is in agreement with other studies [Sensory qualities such as taste, color, odor flavor, and texture are the most important feature for the formulation of a new product than quantity . Thus, i studies , 30, 38. studies . Drying  studies , 42. Hen studies , 42. The studies , but con studies , 32.This study did not address other antinutritional factors such as polyphenols that can affect the bioavailability of the minerals.The crude protein and fat contents were decreased from 11.02 to 7.21 g and 1.12 to 0.48 mg when the nettle flour supplementation increased from 0% to 25%. The reduced fat content could be beneficial to limit unnecessary excess energy intake from diet and might contribute to the management of chronic diseases like obesity and overweight. On the other hand, the crude ash was increased from 1.84 to 3.81 g; the calcium, zinc, and iron content increased from 60.51 to 283.74, 1.72 to 3.94 mg, and 5.09 to 9.24 mg while the proportion of nettle flour supplementation increased from 0 to 25%. There may also decrease mineral deficiencies and contribute to healthy life. Vitamin C content was very low. On the other hand, increasing nettle flour decreased crude protein and crude fat content but has no effect on gross energy. Incorporating the nettle leaf flour with maize flour compromised and reduced the sensory acceptability of flatbread.Optimum baking temperature and time combination that minimizes nutrient losses should be studied. Since the sensory acceptability is compromised with the increasing amount of nettle flour, the supplementary ratio of nettle flour should be reduced to a maximum of 15%. Treatments like soaking of nettle leaves with ethanol shock are recommended to increase recovery of nutrients as suggested by Maimaiti et al. . On the


The residual stress plays an important role in composite flywheel rotors composed of filament windings. The fiber tension during high-prestressed winding is the main source of the rotor deformation and residual stress of composite layers. In this study, the effect of the winding tension gradient on deformation was monitored in real-time. Two types of in-plane winding tension fluctuation methods were developed to investigate the effect of tension on deformation. Online and offline measurements were performed for the strain acquisition. A wireless strain instrument was used for online deformation monitoring and a laser scanner was used for the offline surface reconstruction. Additionally, different filament winding strategies were carried out to improve the efficiency of the winding tension by finite element analysis. The results indicated that the deviation between numerical and experimental results was within 8%. Based on the proposed numerical method, the influence of the in-plane and out-of-plane winding tension gradient distributions on the rotation process of the H-shaped rotor was analyzed. An in-plane winding strategy with variable tension was developed, which increased the initial failure speed by 160%. With the rapid development of electricity-related industries, the demand for energy storage has become more severe ,2,3,4. FThe hoop strength of FRP composite rotors is sufficiently high, whereas the weak interlaminar strength limits their energy storage potential. Two-dimensional woven and 3D textile structure composite rotors have been proposed to enhance the radial strength ,19,20. HReasonable hub designs are also vital for high-energy storage density flywheels. Kim et al.  proposedFilament wound composites have been proven to be an effective solution for flywheel rotors. The residual stress of flywheel rotors made of filament windings can significantly affect the burst speed during service, while the prestress is mainly determined by the winding tension. However, few researchers have considered the effects of metal hubs on the preloading process of composite rotors. Additionally, few studies paid attention to the mismatch between the design winding tensions and the residual stress caused by the complex relaxation in the manufacturing process. In this paper, the mismatch between the prestress caused by the winding tension and the residual stress measured in real time was studied through experimental and numerical analysis. H-shaped hubs were wrapped with FRP composites using different winding tensions. The process-induced rotor deformations were monitored by online wireless strain gauges and confirmed by offline reconstruction. A finite element-based model was used to study the influence of winding tensions on the prestressing effect of a mandrel and the subsequent failure process. The outcome of this study provides a tension design model considering the manufacturing process, which is helpful for the wide application of continuous fiber-reinforced thermosetting composites on high-quality standard rotary components.High-performance fibers used here included glass fiber (S6 supplied by Nanjing Fiberglass R&D Institute) and carbon fiber . The detailed performance parameters are shown in In the filament winding process, fiber tows were wound to the surface of the mandrel successively through a feeding shaft, a dipping tank, a tension amplifier, a tension detector, and a fiber guide nozzle see . The iniThe flywheel rotor with hybrid materials and the distribution of both inner glass fiber and outer carbon fiber can obtain higher energy storage density . ComposiThe winding process should be stopped in advance when the deformation of rotors enters the plastic zone. The deformation of the mandrel causes the radial displacement of the wound composite layers, which leads to the relaxation of the fiber tension and weakens the prestressing effect of the fibers. Once the plastic deformation occurs, the deformation of the mandrel will be larger at the same level of the winding tension and the relaxation will also be amplified. This is not conducive to improving the energy storage density of the rotor. On the other hand, large mandrel deformation brings the risk of rotor size change. If the baffle stiffness is insufficient, the lateral dimensions of the rotor and subsequent parts’ assembly will be affected. θε is the deformation of the mandrel; F is the winding tension, MPa; E is the modulus of the mandrel, MPa; and a and b is the inner and outer radius of the mandrel, mm. We can find a linear relationship between winding tension F and deformation θε according to the principle of elasticity. In this paper, the influence of the winding tension on the mandrel was quantitatively analyzed by deformation. Although the equation was established based on a cylindrical mandrel without a hub, the effect of the stress fluctuations can be effectively quantified by the deformations. Based on this assumption, the deformation of the inner mandrel was monitored by online and offline methods. Online strain collection adopted the DH3819 wireless strain gauge  with a sampling frequency of 1 Hz. Offline strain acquisition was performed with the help of the ATOS Compact Scan . Additionally, it was calculated by the relative change of the reconstructed inner surface.The relationship between the deformation and the applied stress had been derived in our previous study .(1)εθ=2−6. The measured elastic parameters of the mandrel laid the foundation for deformation monitoring.The typical compressive stress–strain curve of the Al7075 is shown in λ was defined to describe the thickness of the winding layer,ir is the outer radius of the winding layers and br is the outer radius of the metal hubs. The strain of the composite flywheel rotors formed by the two winding tension systems went up with the winding process, indicating that the winding tension of the external fibers can be effectively transferred to the mandrel. However, the effect of outer fiber tension on the mandrel decreased with increasing thickness, which may have been caused by resin matrix relaxation and fiber mesh deformation. The slope of the strain curve of the mandrel increased significantly at the initial stage of winding, indicating that the cross-linking of the polymer matrix froze the deformation of the inner fiber mesh and resin flow, thus effectively pre-tightening the mandrel. The slope the of strain curve of the glass fiber layers was significantly higher than that of the carbon fiber in both winding processes. The prestressing effect of glass fibers on the inner layers was better than that of the carbon fiber, which was consistent with the conclusion drawn in the literature [The detection positions of the strain gauges were symmetric see a. The onterature ,26,32,35The slope of the strain curve of the flywheel rotor with CTW was higher than that with IPWVT. However, the slope of the strain curve at the edge of the rotor of CTW was sharp, leading to the yield of the hub. From the side picture after the removal of the baffle see a, it canIn both CTW and IPWVT processes, it could be found that there was a large stress relaxation phenomenon after every multiple curing processes. The temperature change had a significant influence on the deformation and the relaxation of the glass fiber layers after curing reached approximately 17%. However, the change in winding tension had little influence on stress relaxation. This indicates that the preload difference formed in the winding process is retained in proportion to the cured product. As the number of winding layers increased, the heat input from the infrared lamp was surrounded by glass fibers and was difficult to transfer outward, leading to a gradual increase in the deformation of the mandrel. It can be seen from The offline deformation reconstruction process is shown in The model established in this paper was based on the commercial software ABAQUS, in which the simulation during the winding process used a comparison of the model change and the realization of the life and death unit technology in ABAQUS. According to the results of the winding experiments, the residual stress of CTW and IPWVT were quite different. The influence of winding tension fluctuations on rotor deformations was further discussed by FEA.To simulate the influence of winding tensions on the rotor deformation, a 1/8 model was established see . A solidThree types of winding tension rules were adopted, which were constant tension winding (CTW), in-plane variable tension winding (IPWVT), and out-of-plane variable tension winding (OPWVT). For IPWVT, the winding area was divided into five symmetric sub-units along the axial direction, as shown in The flywheel rotor was mainly composed of a metal hub and composite layers. The elastic parameters of the materials used in the model are shown in The Mises criterion was used to determine the failure of metal materials, expressed as followsContact properties were inserted between composite layers and metal liners so the output contact pressure could be used directly to determine whether the interface was separated.A modified Hashin failure criterion was used to predict the damage initiation of composites. The fiber damage initiation index is expressed by the criterionThe matrix damage initiation index is expressed by the criterionThe shear damage of the fiber and matrix is expressed as follows:K equals the yield strength of AL7075. F1 > 1) in the service process, the structure will fail. Once the pressure between the metal hub and composite layers is equal to zero (F2 = 0), the structure will also fail. However, the failure of composite layers is a gradual process. When an element fails, its bearing capacity decreases, but the surrounding elements still have a certain bearing capacity. At the same time, the ability of the structure to resist deformation decreases correspondingly, which is called stiffness softening [The out-of-plane delamination damage is expressed as follows:oftening . The defλ reached about 1.3, the simulated strain of CTW was 10% higher and the simulated strain of IPWVT was almost 19% higher. This indicates that the model proposed in this paper cannot accurately simulate the deformation of mandrel when the relative thickness λ is greater than 1.3. When λ was less than 1.3, the experimental and simulation results of the residual strain were in good agreement; it may be the relaxation caused by the flow of resin. With the increase of winding thickness, although the influence of the thermal effect on deformation gradually decreased, the loss of the resin matrix influenced the deformation. The uncured resin in the inner layers could not restrain the deformation of the fibers in the accumulated high-stress area, thus the stress in the fibers relaxed gradually after each winding of the outer layers. However, it was difficult to simulate the resin extrusion process in the numerical model, thus deviation gradually increased after λ was greater than 1.3.The comparison between the simulation results and the experimental results is shown in The number of failure elements of the rotors by different winding tensions was obtained by using the failure criteria proposed in this paper. It can be seen from Furthermore, As can be seen from the differences in contact pressures of the rotors with rotational speeds under several winding tension schemes, the contact pressure at the lateral edge position increased with rotational speed while the contact pressure at the middle position with support decreased. A reasonable tension design can control the failure position of the interface in the most favorable position and also can realize the design of an equal strength at a specific speed so as to maximize the utilization rate of raw materials.A comparison experiment was conducted to understand the influence of in-plane winding tension fluctuations on the H-hub deformation and a finite element model was proposed to further investigate both the influence of out-of-plane winding tension variations and their failure processes. The online and offline strain results confirm the viability of the proposed FEA model. The main conclusions of this study are summarized below.(1)−6 and it was effective to use the rotor deformation to characterize the composite deformation in this range.The compression properties of the elastic range of the rotor was within −6480 × 10(2)The constant winding tension caused a large deformation at the edge of the H-hub, leading to the plastic deformation of the hub, while the in-plane winding with variable tension prestressed the mandrel more evenly in the axial direction.(3)λ is greater than 1.3, the influence of the outer winding layer on the deformation of the mandrel gradually decreases. The subsequent curing process results in a relaxation of more than 17%, which is insensitive to the winding tension.When the relative thickness (4)Interface delamination failure comes first, followed by hub failure and composite failure, for a rotor with a relative thickness of 1.3. The rotor failure speed of IP-2 was 160% higher than that of CT-1 and reached 26,000 rpm.(5)The beneficial residual stress in filament-wound flywheel rotors was successfully introduced by different fiber tensions, which could effectively improve the failure speed of flywheels. However, due to the viscoelastic characteristics of resin matrix, the effective storage time of this beneficial manufacturing stress needs to be further studied, which is valuable for the long-term performance of rotors.


Structures from the PDB are divided into domains and classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.08, we have developed search and display tools for analysis of genetic variants we mapped to structures classified in SCOPe. In order to improve the utility of SCOPe to automated methods such as deep learning classifiers that rely on multiple alignment of sequences of homologous proteins, we have introduced new machine-parseable annotations that indicate aberrant structures as well as domains that are distinguished by a smaller repeat unit. We also classified structures from 74 of the largest Pfam families not previously classified in SCOPe, and we improved our algorithm to remove N- and C-terminal cloning, expression and purification sequences from SCOPe domains. SCOPe 2.08-stable classifies 106 976 PDB entries (about 60% of PDB entries).The Structural Classification of Proteins—extended (SCOPe,  Nearly all proteins have structural similarities with other proteins, and in some of these cases, share a common evolutionary origin. First released to the public 27 years ago, the Structural Classification of Proteins (SCOP) database  was a maFamily contains related proteins with similar sequences but typically distinct functions. The Superfamily level brings together protein families with common functional and structural features inferred to share a common ancestor. Near the root, the basis of classification is purely structural: similar superfamilies without compelling evidence of a common evolutionary origin are grouped into Folds, which are arranged into Classes based mainly on secondary structure content and organization  tool  to retrihttps://www.molsoft.com/). If the variant is in a structurally uncharacterized region of the protein, we report the nearest neighboring residues in the structure. Our viewer also displays relevant structural and evolutionary context from the SCOPe hierarchy, including members of the same family or superfamily as the impacted protein domain. Although our tools are currently limited to searching for and visualizing variant impacts in structurally characterized human proteins, future development will allow us to visualize the predicted impact of human variants using these homologous protein structures as well. See Figure In cases in which the variant is in a protein coding region, we map the location of the variant onto the structure, displayed to the user using the MolSoft molecule viewer  that rely on multiple alignments of homologous SCOPe domains will be less likely to produce incorrect results due to variable domain lengths within the same family. Early releases of SCOP were meant to be browsed by humans, and focused on correct identification of relationships rather than utility for algorithms; for example, gene duplication was often annotated in comments rather than in a machine-parseable format. As a result of this legacy, in some SCOPe families that arose from a gene duplication, some of the structures are split into two domains and others left as one. We have identified other types of structural heterogeneity as well, as detailed below. In many cases, we changed the outliers to be consistent with the most common structure in the family. All remaining cases we identified were annotated in our database. These annotations are displayed in our website and provided to the public through a new machine-parseable file .To locate potentially structurally heterogeneous domains, we assumed that if all domains in a family were structurally similar, the length of the domains would also be very similar. Thus, we collected domain length data and flagged families with significant length variation for manual review. Two different domain lengths were calculated: one based on PDB SEQRES records, the genetically encoded protein sequence; and one based on ATOM records, which were the parts of the protein that were experimentally observed . For larAfter identifying potential heterogeneous families, we reviewed these families manually and identified 340 families and 6 folds total that contain structurally heterogeneous domains. We then annotated each entry with one or more of the following labels to describe the type of heterogeneity:Multiple alternative domain divisions: Applies to families that are inconsistently divided into domains. See Figure Additional (sub)domain(s): Applies to domains that contain additional (sub)domain(s) which are not in the common domain. See Figure Additional insertion(s)/extension(s): Applies to domains that contain additional insertion(s) or extension(s) that are not arranged into (sub)domain(s). These can be additional secondary structures, significantly longer family-specific secondary structures, disordered loops, etc. See Figure Fragment: Applies to domains that are missing at least 1/3 of the common fold in the family (based on missing either sequence or structure). See Figure Missing some secondary structure(s): Applies to domains that are missing secondary structures from the family-specific domain . See Figure Additional element(s): Applies to domains that contain additional secondary structure elements or disulfide bonds. See Figure Missing element(s): Applies to domains that are missing some secondary structure elements or disulfide bonds. See Figure Different number of (sub)domain(s) of a multi-domain family: Applies to all members of a family in SCOPe class e (multi-domain proteins) in which individual members contain different numbers of domain(s). See Figure Heterogeneous fold: Applies to domains that adopt a different fold from the most common structure found in the fold, but which have similar sequence and number of secondary structure elements. This category does not include domain swaps . See FigNot a true fold: Applies to all domains of a fold that was described by SCOP(e) curators as ‘not a true fold.’ See Figure Repeat and inconsistent: Applies to all domains of a family if the common domain is composed of a different number of small repeating units. See Figure Users can download the parseable files from the ‘downloads’ section of our website, and find more documentation about the parseable file format in the ‘help’ section.This method is still not comprehensive. For example, it cannot detect a heterogeneous family in which all domains have similar lengths; it also does poorly in detecting domains with small additional or missing structures. However, it is effective in flagging major differences such as large insertions or deletions. We plan to improve our method by including structural comparison to help identify more heterogeneity, especially for heterogeneous folds.Some protein domains in SCOPe consist of a number of smaller tandem repeating units. The number of repeats may or may not be the same between the domains in the same family. To facilitate automated algorithms developed or trained on the SCOPe database, we provide machine-parseable annotations of the extent of a single repeat unit for all families of repeats in classes a to g. Tandem repeats are often also annotated in other databases, such as Pfam .Previously, in SCOPe version 2.06, we moved non-natural sequences that represented cloning, expression, or purification tags to a new class (l: Artifacts) in order to separate them from the homology-based curations and prevent spurious similarity between non-homologous protein sequences. Tags were identified using PDB metadata (SEQADV records) referring to cloning, expression, or purification tags at the N- or C-terminal of each chain, as described in more detail elsewhere  is not 1:1. Among the 74 Pfam families, 22 (30%) had at least one domain classified into a new SCOPe fold, 10 (14%) into a new superfamily in an existing fold, 33 (45%) into a new family within an existing superfamily, and 9 (12%) as new proteins within an existing family. These results are similar to the novelty of newly classified structures in SCOP and SCOPe, as we previously reported .Annotations of structural heterogeneity and repeat units are provided in new machine-parseable files, similar to the dir.* files released with previous versions of SCOP and SCOPe . The dirWe have also made a minor update to the RAF maps, which summarize the SEQRES - ATOM relationship within each protein chain in a form that can be rapidly parsed in most computer languages . Residue


Supervised machine learning is a traditionally remaining useful life (RUL) estimation tool, which requires a lot of prior knowledge. For the situation lacking labeled data, supervised methods are invalid for the issue of domain shift in data distribution. In this paper, a adversarial-based domain adaptation (ADA) architecture with convolution neural networks (CNN) for RUL estimation of bearings under different conditions and platforms, referred to as ADACNN, is proposed. Specifically, ADACNN is trained in source labeled data and fine-tunes to similar target unlabeled data via an adversarial training and parameters shared mechanism. Besides a feature extractor and source domain regressive predictor, ADACNN also includes a domain classifier that tries to guide feature extractor find some domain-invariant features, which differents with traditional methods and belongs to a unsupervised learning in target domain, which has potential application value and far-reaching significance in academia. In addition, according to different first predictive time (FPT) detection mechanisms, we also explores the impact of different FPT detection mechanisms on RUL estimation performance. Finally, according to extensive experiments, the results of RUL estimation of bearing in cross-condition and cross-platform prove that ADACNN architecture has satisfactory generalization performance and great practical value in industry. With the development of sensing technology, deep learning (DL) technology with nonlinear deep representation capabilities has gradually matured. A large amount of studies9 on RUL estimation of bearing in same operating condition have obtained good results.Remaining useful life (RUL) estimation, which is one facet of prognostics and health management (PHM) aiming to provide users with an integrated view about the health state of a machine or an whole systemAlthough every healthy mechanical component will has a rated working time after it is created, varying operating conditions and platforms will affect the service life of mechanical component to varying degrees. In the case where the training data and the test data come from different operating conditions and platforms, the generalization ability on the test data is usually poor.N1 to a target bearing entity with observation samples N2, if N1 is much smaller than N2, the RUL predicted by the target domain usually fluctuates more strongly. We assume that the bearing entity degrades linearly after the failure prediction time point. The source bearing entity 1 has N1 samples, target bearing entity 1 has N2 samples, and their FPT are respectively FPT1 and FPT2. It is a challenge for feature extractors and regressive predictors to accurately predict RUL. Particularly, the feature extractor needs to obtain discriminative features, and the regressive predictor needs to map the discriminative features to actual RUL value as accurately as possible.In the case of varying operating conditions and platforms, there are many factors influencing on performance of RUL estimation, such as feature extractor and regressive predictor, but in the final analysis, it is caused by the number of observation samples, first predictive time (FPT) detection method, etc. For example, when migrating from a source bearing entity with observation samples 10 suggested that the regression space is usually continuous on the contrary, for example, there is no clear decision boundary. In fact, the observation samples have limited, the RUL estimation is still in a large discrete space. Jiang et al.10 firstly proposed regressive DA for keypoint detection, which proved the effectiveness of DA in regressive prediction tasks. For RUL estimation of bearing under varying conditions or platforms, the key factor is how to assign cross-invariant features built by neural network corresponding same health status a equal estimated RUL, which is vital for unsupervised RUL estimation in cross-domain.Though there are many effective domain adaptation (DA) methods for classification, but we practically found that few regressive DA methods. Jiang et al.F(x) is the feature representation of sample x, i-th source bearing entity. j-th target bearing entity.Domain-invariant features. Discriminative features. Y is the function of regressive predictor.If the input of regressive predictor are some similar domain-invariant features respectively come from source domain and target domain, and the predicted results of regressive predictor should be approximately equal with each other. We use the formula to express as: Motivated by domain adaptation neural networks (DANN)11, in this paper, we introduce the original intention proposed by the DANN architecture into machinery RUL estimation. Through combining with the superiority of CNN in vibration signals process, adversarial-based domain adaptation (ADA), refers to ADACNN, consists of three parts: feature extractor, regressive predictor and domain classifier. Feature extractor firstly acquires domain-invariant and discriminative representation from raw vibration signals. The domain classifier, as an important auxiliary tool, forces feature extractor finding common space where samples have corresponding domain-invariant representation. The regressive predictor input a domain-invariant feature outputs a corresponding estimated RUL close to actual RUL value of the domain-invariant feature.Therefore, we found that in fact, the idea of regressive DA is still the same as the classification algorithm, such as SVM trying to find margins of boundaries between different classes. RUL estimation under varying operating conditions or varying platforms aims to find a boundary to distinguish the different operating states of bearing entity. In whole, there are three key points worth paying attention to: For scenarios lacking labels or no label, ADACNN can be simply switched for maximizing known labels’ value in real application scenarios. At the same time, it ensures excellent estimation accuracy in source domain and generalization ability in target domain. To our best knowledge, this framework with ADA is firstly introduced for RUL estimation of bearing in varying conditions and platform. In addition, in this paper, we just pay attention to a harder unsupervised case, that is, there are all unlabeled data in target domain.The ADACNN was verified on two public datasets: cross-condition experimental scenarios and cross-platform experimental scenarios on FEMTO and XJTU-SY dataset introduced afterthis.The proposed methodology compares with two non-adapted models respectively training with only source data and only target data to verify the generalization ability of proposed ADACNN.Overall, in this paper, we proposes a novel neural networks framework with ADA for RUL estimation of bearings in different condition and platform. The main contributions of this paper are as follows: 12. Given a source domain 12, DA belongs to transductive TL, that is, 13: discrepancy-based methods, adversarial-based methods and reconstruction-based methods. Among them, DA methods are the most popular. Discrepancy-based methods use source and target domain data to fine-tune the model to reduce domain shift. Discrepancy-based DA can be divided into the following according to the criteria used: class criterion, statistic criterion, architecture criterion and geometric criterion. Adversarial-based methods use domain classifier to encourage domain confusion through an adversarial objective. Specifically, a domain classifier tries to guide feature extractor to find a common space with domain-variant from source or target domain, until the classifier cannot distinguish whether the feature comes from source or target, the prediction (classification or regression) function of the source domain can be shared with target domain data. Reconstruction-based methods utilize data reconstruction as an auxiliary task to ensure feature invariance between domains (source and target)12. Obviously, DANN11 belongs to adversarial-based methods.Transfer learning (TL) was grouped into three classifications: inductive TL, transductive TL and unsupervised TLAs shown in the Fig. Therefore, from the perspective of theoretical realization, the composition of the loss function of DANN is divided into two parts: the prediction loss of the main task and the domain classification loss. The prediction loss mentclass2pt{minimEquations  and 2)  2) are mIn short, the training processes of DANN are constraint through the min–max formula Eqs.  and 2, ai-th sample of the j-th bearing entity in source domain, where j-th bearing entity in source domain. By analogy, i-th sample of the j-th bearing entity in target domain, where j-th bearing entity in target domain.In this paper, we proposed a framework with ADA for RUL estimation, which constitutes feature extractor, domain classifier, and a regressive predictor. It should be pointed out that we assume the source domain training are run-to-failure vibration data. Let i-th sample of the j-th bearing entity in source domain.Data preparation. Calculate RUL percentage label A regressive predictor parameterized by A feature extractor parameterized by A domain classifier parameterized by Building and training the ADACNN model. In addition to entclass1pt{minimaThe methodology proposed in this paper mainly includes two steps as follows: The source domain data are divided into training data  from original healthy state, which is a relative feature and suitable for some scenarios of vibration signal processing. In this paper, we will explore the impact of different FPT detection methods on RUL estimation.Usually, before FPT point, it is not necessary or difficult to predict RUL because there is no obvious sign of degradation, and after this point, signs of degradation began to appear. Therefore, a FPT detection mechanism is very important to capture real-time changes. KurtosisIn order to speed up the training speed and align the test data with the training data during the test, we have some pre-processing for the vibration data. Data normalization mainly includes four parts:norm represents the normalization function and consists of two steps: firstly to calculate the mean m and variance d of b, and secondly to normalize a by m and d.18), and the remaining layers are initialized according to input parameters. The feature extractor mainly includes one dimensional convolution layer (Conv1D), activation layer, Dropout, and MaxPooling1D. The output of feature extractor are latent features, its dimension depends on the initialization parameters f.Initialize feature extractor: The input parameters of feature extractor include input data, number of CNN layers, number of filters per layer, and dropout rate. The kernel size of first layer equals to 25 by default , and Score are used as performance metrics to evaluate the error between the predicted RUL and the true RUL. RMSE has been used in many publications the Eq. 4\documen19 and has been used in many studies23. The predicted RUL is greater than or less than the actual RUL should be treated differently. In other words, in the case of the same absolute value, the penalty for a positive value is less than the penalty for a negative value K is the total number of test samples. i-th testing data sample.Score, defined as Eq. , used inIn adaptation training processes, the learning rate of source domain regression and domain classification and the parameter of CNN largely determine the experimental performance. Therefore, we use the grid-search method to find the optimal learning rate  on the FEMTO dataset listed from E1 to E6 in Table From the perspective of whether to use domain adaption technology in Table Judging from the results of the five methods (Source-Only, Target-Only, MD-ADACNN, NoFPT-ADACNN and Kur-ADACNN in Table In order to further verify the performance of the proposed ADACNN method in the inter-platform domain for RUL estimation, we choose the three conditions in the FEMTO dataset as the source domain or target domain data, and at the same time, 2 conditions in the XJTU-SY dataset are used as target domain or source domain data, so there are a total of 12 experiments (E9–E20 tabulated in Table It can be seen from Table It can be seen from Fig. In order to demonstrate the effectiveness of the proposed model, we use the t-SNE method to visualize high-level features. Fig. Figure Due to the difficulty in predicting the remaining life of cross-platform migration, such as different operating conditions, different sampling frequencies, and large differences in life span. In short, compared with Fig.


Given the complex business environment worldwide and rapid development of information technologies, employees’ deviant innovation behavior has attracted increasing attention. However, few scholars have explored the internal mechanism of the influence of differential leadership on the deviant innovation behavior of outsider subordinates from a positive perspective. Based on relative deprivation theory and attribution theory, we construct a theoretical model to study the influence of differential leadership in family businesses on the deviant innovation behavior of outsider subordinates, and hypothesis testing is conducted based on 243 questionnaire responses. The result shows that: differential leadership has a positive impact on the deviant innovation behavior of outsider subordinates; relative deprivation plays a mediating role; and internal control personality enhances the moderation effect between relative deprivation and outsider subordinates’ deviant innovation behavior. This study provides a reference point for the promotion of the innovation performance both of employees and organizations. Innovation behavior has always been widely regarded as an inexhaustible driving force in the development of organizations. However, in recent years, enterprises have gradually found that the innovation behavior of employees has become deviant, and that there is a potentially close and interesting relationship between innovation activities and workplace deviance . AlthougPrevious research has focused on the consequences of deviant innovations, but research on its antecedents is lacking . EspeciaMost current research has focused on the positive effects of differential leadership on insider subordinates . In contThis study contributes to the current leadership, human resource management, and enterprise change management literature by formulating original hypotheses to reveal the impact of differential leadership styles on employees’ deviant innovation behaviors. Using social exchange theory, relative deprivation theory, and attribution theory, the scale design was carried out, and exploratory factor analysis, confirmatory factor analysis, descriptive statistics and correlation analysis, Bootstrap test, “moderated mediation test and other methods were used to obtain.” The theoretical model of the positive impact of differential leadership on employees’ deviant and innovative behaviors from the perspective of outsiders The internal relationship between leadership style and employees’ deviant innovative behaviors, discussing employees’ deviant innovative behaviors and their governance countermeasures, and clarifying the importance of flexible adjustment of leadership styles to effectively manage employees’ deviant innovative behaviors.The remainder of this paper is structured as follows: the next section describes the theoretical support of this research and the development of the hypotheses tested in this study. Next in the research design, presents data resource, and statistical model and software that was used for testing the hypotheses. Subsequently, the empirical findings of the proposed hypothesized model are presented. The paper concludes with a summary of the important findings, limitations of the study, and directions of future research to develop this burgeoning area of organizational change management.The relationship between a leader and subordinates is dynamic. Leaders will judge insider and outsider subordinates according to intimacy, loyalty, and ability, and then treat them differently . Jiang aDeviant innovation behavior is a complex concept that consists of two very different factors: “deviance;” and “innovation.” Obedience to instructions is a basic requirement for participating in organizational work, but deviant behaviors ignore formal and informal rules and regulations and violate normative expectations in the workplace . InnovatGiven that the leaders’ subordinate categorization model is dynamic, the relationship between leaders and subordinates resulting from differential leadership is not static . OutsideHypothesis 1: Differential leadership has a positive effect on outsider subordinates’ deviant innovation behavior.Since According to relative deprivation theory, when employees perceive unfair or discriminatory treatment in the workplace, this generates a sense of frustration and relative deprivation . TherefoHypothesis 2: Differential leadership has a positive effect on outsider subordinates’ relative deprivation.status quo . Regarding the valid responses, the following key demographic distributions were found: 51.03% of employees were male and 48.97% were female; 65.43% of employees were aged 31–40 years; 11.93% of employees had a junior college degree and 63.79% had a Bachelor’s degree; 68.31% of employees had been working in their current organization for 3–5 years; 19.75% of employees were from state-owned enterprises, 14.81% from private enterprises, 21.40% from foreign ventures, 22.63% from joint ventures, and 21.40% from other types of enterprises; and 73.66% of respondents were business employees.This study adopted the Vineland Social Maturity Scale, and all items in the scale were scored on a seven-point Likert scale to avoid many neutral answers from respondents. The description of the variables is as follows:•Differential leadership. We adopted a 14-item scale developed by •Relative deprivation. We adopted a five-item scale developed by •Internal control personality. We adopted an eight-item scale developed by •Deviant innovation. We adopted a five-item scale developed by •Control variables. We selected several demographic variables, including gender, age, education, work seniority, organization type, and occupation type, that may affect differential leadership, relative deprivation, internal control personality, and deviant innovation.p-value less than 0.001. In addition, the extracted four common factors were consistent with the number of variables set in this study, and the degree of variance of the first variance was 29.498%, which is lower than the critical value of 50%. Therefore, there is no serious common method bias in the data of this study.Harman’s single factor analysis was adopted in this study to conduct exploratory factor analysis on all items of the four variables. The results revealed a KMO value of 0.860, a Bartlett’s Chi-square test of sphericity value of 5,437.908, and a 2/df = 1.979, CFI = 0.915, TLI = 0.907, RMSEA = 0.064, and IFI = 0.916. In summary, the four variables in this study have good discriminative validity.In this study, confirmatory factor analysis was adopted to test the discriminative validity of differential leadership, relative deprivation, internal control personality, and deviant innovation behavior. As shown in r = 0.532, p < 0.01), and with relative deprivation . There is a significant positive correlation between relative deprivation and deviant innovation .The results of the correlation analysis of the research are shown in The steps followed to explore the moderating effect of internal control personality are as follows. First, we put six control variables into the regression equation. Second, relative deprivation and internal control personality were introduced into the regression equation. Third, the interaction terms of relative deprivation and internal control personality were put into the regression equation to explore their influence.p < 0.01), thus verifying H1. There is a significant positive correlation between differential leadership and relative deprivation , which verifies H2. When the mediating variable of relative deprivation is introduced in the relationship between differential leadership and deviant innovation, there is a significant positive correlation between relative deprivation and deviant innovation , thus verifying H3. In addition, the positive effect of differential leadership on deviant innovation is weakened by introducing the mediating variable of relative deprivation , indicating that relative deprivation plays a mediating role in the relationship between differential leadership and deviant innovation, thus verifying H4.As shown in SE = 0.022).In order to further test the mediating effect of relative deprivation, this study adopted the PROCESS macro program to conduct bootstrap analysis. The results showed that the mediating effect of relative deprivation with a bootstrap 95% confidence interval is , excluding 0, which indicates that the mediating effect of relative deprivation is significant, and the effect value is 0.042 (p < 0.05), indicating that internal control personality has a moderating effect on the relationship between relative deprivation and deviant innovation, thus verifying H5. As shown in SE = 0.028). The mediating effect of relative deprivation under low internal control personality is comparatively weaker and insignificant, and the effect value is 0.002 (SE = 0.024). Therefore, the partial mediating effect of relative deprivation on the relationship between differential leadership and deviant innovation is affected by internal control personality, and there is a moderated mediating effect. The higher the level of internal control personality, the stronger the mediating effect of relative deprivation, which verifies H6.This study adopted the PROCESS macro program developed by H1); differential leadership has a positive effect on outsider subordinates’ relative deprivation (H2); relative deprivation has a positive effect on outsider subordinates’ deviant innovation behavior (H3); relative deprivation is the mediator between differential leadership and outsider subordinates’ deviant innovation behavior (H4); internal control personality has a positive moderating effect on the relationship between relative deprivation and outsider subordinates’ deviant innovation behavior (H5); and internal control personality plays a moderated mediating role in the process of differential leadership indirectly influencing outside subordinates’ deviant innovation behavior through the mediating effect of relative deprivation (H6).Based on relative deprivation theory, this study has explored the mechanism of differential leadership influencing employees’ deviant innovation behavior, as well as the mediating effect of relative deprivation and the moderating effect of internal control personality. The results verify all six hypotheses, confirming that: differential leadership has a positive effect on outsider subordinates’ deviant innovation behavior (First, this study has explored the influence of differential leadership on employees’ deviant innovation behaviors, extending research on the antecedents of outsider subordinates’ deviant innovation behaviors. Most previous studies have focused on the consequences of employees’ deviant innovation behaviors, while the exploration of its logical deconstruction is relatively scarce. Therefore, a systematic study on the influence of leadership style on employees’ deviant innovation behaviors is lacking. Starting with the construction of “China’s own leadership values,” this study has conducted empirical testing on the mechanism of differential leadership influencing employees’ deviant innovation behavior, analyzing the internal relationship between them.Second, most previous studies on differential leadership have focused on its negative influences, while the positive effects have largely been ignored. This study has explored differential leadership and its effectiveness in the context of Chinese culture, which enriches the theoretical study of differential leadership. By incorporating differential leadership into research on employees’ deviant innovation behavior, this study strengthens the theoretical framework of employees’ deviant innovation behavior in the context of Chinese culture, providing new ideas for related research and expanding the theoretical research perspective.Finally, based on relative deprivation theory, this study has introduced the relative deprivation of outsider subordinates as a mediating variable to explore the influence of differential leadership on employees’ deviant innovation, which enriches research on the mediating mechanism of relative deprivation and provides a theoretical reference for further exploration of the causes of deviant innovation. In addition, based on attribution theory, this study has used internal control personality as a moderating variable to systematically explain the moderating mechanism of the influence of differential leadership on employees’ deviant innovation behavior. It thus expands the boundary conditions for the generation of deviant innovation, enriches the related research pertaining to attribution theory, and provides a theoretical reference point for optimizing deviant innovation behavior.First, in view of the important influence of differential patterns in the economy, studying differential leadership and its effectiveness has important practical value in optimizing differential leadership. The findings help understand the influence and effectiveness of differential leadership in the context of management localization in China, and provide a reference point for the appropriate adjustment of leadership style and the cultivation of managers with differential leadership skills, in order to guide management styles and thinking modes to fit sustainable development.Second, this study has explored the influence mechanism of differential leadership on employees’ deviant innovation, providing practical reference for the effective optimization of employees’ deviant innovation behavior. The finding helps leaders to fully understand the path of differential leadership in improving employees’ deviant innovation behavior and provides insights into different strategies to improve employees’ innovation. The paper provides a theoretical foundation for leaders to motivate employees to break their shackles and effectively govern deviant innovative behaviors, providing empirical support for improving enterprises’ innovation values and promoting sustainable development, thus realizing the improvement of the overall innovation performance of organizations.Finally, this study has explored the mediating effect of relative deprivation between differential leadership and deviant innovation. This helps managers to correctly understand employees with feelings of relative deprivation and to provide the necessary psychological counseling and encouragement in order to address feelings of relative deprivation. Although relative deprivation is an unpleasant feeling, the findings indicate that moderate relative deprivation can motivate employees to work harder to change their situation. Therefore, managers need to pay attention to the psychological status of employees and give full play to the positive role of relative deprivation.There are some limitations in this study that should be addressed in future studies. First, the scales adopted in the questionnaire were translated from foreign scales. However, due to cultural differences between China and foreign countries, there may be some limitations in applicability. Future studies should further develop localization scales and improve the applicability in China. Second, all data in this study came from the self-assessment of employees; thus, common method bias may exist. Future studies could adopt the pairing method to collect data to test the hypotheses. Third, this study only explored the influence mechanism of differential leadership on employees’ deviant innovation behavior from the perspective of outsider subordinates. In the future, studies could compare the different influences of differential leadership on employee behavior both from the perspectives of insiders and outsiders. Finally, this study only took China as the research object. It does not consider the differential impact of organizational culture. Subsequent research could consider the cross-cultural applicability of differential leadership theory and promote the localization theory of China to other regions.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.YZ, MW, and QH contributed to the conception of the study. MW and LZ performed the experiment. LZ contributed significantly to analysis and manuscript preparation. JL, YZ, and LZ performed the data analyses and wrote the manuscript. JL and YZ helped to perform the analysis with constructive discussions. All authors contributed to the article and approved the submitted version.


Antisense oligonucleotides (ASOs) are single-stranded nucleic acid strings that can be used to selectively modify protein synthesis by binding complementary (pre-)mRNA sequences. By specific arrangements of DNA and RNA into a chain of nucleic acids and additional modifications of the backbone, sugar, and base, the specificity and functionality of the designed ASOs can be adjusted. Thereby cellular uptake, toxicity, and nuclease resistance, as well as binding affinity and specificity to its target (pre-)mRNA, can be modified. Several neurodegenerative diseases are caused by autosomal dominant toxic gain-of-function mutations, which lead to toxic protein products driving disease progression. ASOs targeting such mutations—or even more comprehensively, associated variants, such as single nucleotide polymorphisms (SNPs)—promise a selective degradation of the mutant (pre-)mRNA while sparing the wild type allele. By this approach, protein expression from the wild type strand is preserved, and side effects from an unselective knockdown of both alleles can be prevented. This makes allele-specific targeting strategies a focus for future personalized therapies. Here, we provide an overview of current strategies to develop personalized, allele-specific ASO therapies for the treatment of neurodegenerative diseases, such Huntington’s disease (HD) and spinocerebellar ataxia type 3 (SCA3/MJD). Antisense oligonucleotides (ASO) are short, modified single-stranded DNA, RNA, or hybrid DNA-RNA sequences that bind complementary cellular RNAs, such as (pre-)mRNAs, or noncoding RNAs, such as microRNAs, thereby influencing their further processing. In the central dogma of protein synthesis, such an ASO–(pre-)mRNA interaction would prevent the translation of a potentially toxic gene variant. Continuous new modifications of ASOs at their sugar, backbone, or base aim to improve pharmacokinetics and dynamics Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), Alzheimer’s disease (AD) and spinocerebellar ataxia (SCA) are neurodegenerative diseases in which a toxic version of a protein can lead to abnormal protein aggregation ,9,10. InIn monogenetic disorders with dominant inheritance, a genetic diagnosis can potentially be made early in life, even before a clinical manifestation of the disease. This offers, in principle, the chance to prevent disease manifestation, if treatment becomes available that “silences” the toxic gain-of-function mutations before the disease-causing effects can no longer be offset.One possibility to develop a causal therapy could be to degrade the disease protein. This could ideally involve strategies to selectively degrade the toxic protein variant while preserving the wild type form of the protein. ASOs could play a key role in such a therapeutic strategy. In autosomal dominant inherited neurodegenerative diseases with toxic gain-of-function mutations, minor differences between wild type and pathogenic alleles  could be used to selectively degrade the mutant (pre-)mRNA and by this, prevent expression of the toxic protein variant while preserving expression of the healthy copy from the wild type allele ,7. Here,Antisense oligonucleotides are composed of a strand of DNA or RNA as an initial structure that binds via Watson–Crick hybridization to its complementary RNA strand. The internalization of ASOs is not necessarily linked to a single pathway. Uptake is mostly receptor-dependent with subsequent intracellular trafficking from early endosomes to late endosomes, Golgi apparatus, and lysosomes. However, to fulfill its function in the cytoplasm and nucleus, the ASO must escape the endosome in a process that is likely associated with endosomal transport proteins ,23. A siThe most common modification of the backbone is a replacement of a phosphodiester (PO) with a phosphorothioate (PS) bond . This inSpSpRp-5′ in the center of the DNA gapmer as the most suitable site, with the cleavage site located two nucleotides upstream from the Rp with respect to the DNA . A. AP525L  studies ,130,133.xtension . Testing mild AD ,135. Furse of AD ,137.LRRK2 and SCNA are ongoing .For Parkinson’s disease, preclinical experiments in rodents achieved promising results in gapmer ASO treatment of different PD-associated genes ,138,139.Steric block ASOs that modulate splicing can be used to generate a semi-functional or non-functional protein. This shortened protein version lacks the toxic gain-of-function mutation of interest. Nevertheless, the total functional protein concentration is also lowered in this approach. Comparable effects can be achieved by non-allele-specific degradation via RNase H. Alternatively, an allele-specific degradation by targeting an SNP could surpass these non-allele-selective approaches. If the wild type allele and the disease-associated allele differ in one base at a specific site, this site might be a potential target for an allele-specific treatment strategy that aims to degrade the toxic allele at the (pre-)mRNA level. The association of a targetable SNP with the disease causing mutation might be ideal, but is not essential for such an approach. Even distant SNPs that are present in coding or non-coding regions of the disease gene far from the mutation can be used as a target to tag the disease-associated allele for degradation. The same principle can be used for steric block ASOs. Distant SNPs involved in splicing that are located in the same gene as the disease mutation could be used to disrupt the splicing machinery at the mutant allele.50 value against mtHTT from over 400 nM to single digit nM values for an ASO targeting the SNP rs7685686_A. To achieve this, the modifications of the flanking wings played an important role, as well as the shortening of the length of the ASO and the length of the DNA gap region. It was shown that a shorter gap, due to the shortened binding region of RNase H, resulted in enhanced allele-selective degradation. Furthermore, this study demonstrated that the SNP does not need to be in the central position of the DNA gap to achieve allele-specific degradation. By targeting the selected SNP, it would be possible to treat 48.7% of the analyzed HD population allele-specifically and another 44.9% in a non-allele-specific manner [HTT and showed positive effects in behavioral tests [HTT from people of Canadian, Swedish, French, Italian, Korean, Japanese, Chinese, and European-Canadian ethnicity, revealing three major gene-spanning haplotype groups. Differences between controls and patients of each ethnic group were used to identify the most suitable SNPs for an allele-specific treatment approach, including the analysis of the association of some SNPs to the expanded CAG repeat [In 2014, Skotte et al. published an approach to use HD-associated SNPs for allele-specific RNase H-mediated degradation of mtHTT. They evaluated 50 previously published HD-associated SNPs for targetability ,140,141.al tests ,143. In G repeat ,145. In G repeat . The ideATXN3 for an allele-specific ASO-based treatment in spinocerebellar ataxia type 3 (SCA3). Different ASOs were designed targeting a variant that is present in ~70% of SCA3 patients [In a recent study, our group made use of an already known disease-associated SNP in patients ,147. By patients .Together, these studies demonstrate that rare neurodegenerative diseases could play an important pioneering role in the development of allele-specific ASOs, especially when different modifications need to be tested in an in vitro screening. For this, an easily detectable and robust readout to quantify the allele-specific targeting efficiency is a crucial prerequisite. In the case of SCA3, iPSC-based patient-derived neuronal cultures are an ideal tool. Due to the repeat expansion, the mutant and wild type protein differ not only functionally, but also in their molecular mass. This difference can be used to display both forms separately at the protein level. Experience gained from an allele-specific design for ASOs targeting in HTT and SCA3 could then be transferred to diseases where allele-specific readout poses significant hurdles.https://www.ensembl.org/index.html (accessed on 23 June 2022) or https://bravo.sph.umich.edu/freeze8/hg38/ (accessed on 23 June 2022) can be used to identify a list of candidate SNPs. Suitable new targets should have a high MAF or a high association with the disease mutation (https://ldlink.nci.nih.gov/?tab=home (accessed on 23 June 2022)) [http://rna.tbi.univie.ac.at (accessed on 23 June 2022)) [https://gggenome.dbcls.jp (accessed on 23 June 2022)). Especially for an allele-specific approach, it is advantageous if the sequence is as short as possible. With each additional nucleotide, the affinity increases, while the contribution of the individual base to the total melting temperature decreases. Thus, the influence of a single match or mismatch decreases the longer an ASO is. In addition, base variation may also play a role. Some allelic SNP constellations might be more suitable for allele-specific targeting than others. Cytosines may be more suitable as mismatch partners, as they show a greater difference in melting temperature compared to the match [One of the biggest challenges is to find a suitable target for an allele-specific ASO approach. If the variant that causes the disease can be directly targeted, it is usually well described. This becomes more difficult if new targets or SNPs need to be identified. As described, these can be SNPs that are located in the disease-causing gene, but are not causally related to the disease. To identify SNPs that are unrelated to the disease per se, but are in close linkage disequilibrium (LD) with a disease-associated mutation (point mutations or repeats), long-coding sequencing of larger cohorts would be of help . Alternamutation . A hint e 2022)) . Once a e 2022)) . Short se 2022)) . The prohe match ,152. In he match . Once anhe match ,154). ThAnother challenge is to find a suitable in vivo model, mainly mouse strains. This model should carry the target SNP on the same allele that bears the disease-associated variant. Lentiviral transduction of a humanized mutant allele with the respective target SNP has the advantage that the inserted allele and its SNPs can easily be adapted and that wild type mice can be used. Such transductions with the target allele can be performed prior to an ASO application ,156. In In addition to the identification of appropriate SNPs, the identification of suitable readouts to prove allele-specificity is another challenge that varies in difficulty between diseases. As previously mentioned, repeat expansion disorders, such as HD  or DRPLAA more elaborated approach would be to artificially create an in vitro model by transfecting cells with either the wild type sequence or a sequence with the corresponding SNP. Subsequently, cells could be treated with ASOs, which are then examined for their allele-specific effects. A fluorescence tag could help not only to control the transfection, but also to distinguish the transfected allele from the endogenous variant. Alternatively, cells could be transfected with both variants simultaneously, if the two variants have distinct tags. This would allow for studying allele-specific effects upon an ASO treatment directly in a cell culture model and later on, in vivo. A benefit of this approach could be that multiple SNP targets could be inserted into the mutant allele variant. Therefore, cooperative ASO effects could also be part of an in vitro investigation. With this approach, multiple ASOs initiate degradation of the same target by binding to different positions.An indirect approach to test allele specificity of an ASO could be an RNase H assay. Both variants of the target RNA need to be incubated as a duplex with the corresponding ASO (with and without mismatch) and RNase H. In a fluorometric RNase H assay, the released DNA of the gapmer binds with a N-methyl mesoporphyrin IX (NMM), forming a G-quadruplex with a fluorescence signal , which cCurrently, no allele-specific ASO has been approved for the treatment of a neurodegenerative disease. However, an allele-selective approach with ASOs via RNase H degradation could have great potential, especially for toxic gain-of-function mutations. If successful, the toxic allele would be eliminated, and the wild type allele could continue to maintain its physiological function. In such an approach, all coding or non-coding SNPs that are on the same allele as the disease-causing variant are potential candidates for a personalized therapeutic approach ,144,145,To standardize the identification of targetable candidate SNPs for a specific disease, long-read sequencing can help (reviewed in ) to direRegarding ASO modifications, much time and effort has been spent on sugar phosphate backbone modifications. Since base modifications, such as G-clamp, can have the greatest impact on melting temperature per nucleotide ,56 and tAnother hurdle is to establish a standardized workflow to identify and characterize ASOs in preclinical trials. We here propose a workflow for the design and preclinical testing of allele-specific ASOs . After s