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We report a new environmentally-friendly synthetic strategy for large-scale preparation of 16 nm-ultrathin NiCo based layered double hydroxides (LDH). The Ni 50 Co 50 -LDH electrode exhibited excellent specific capacitance of 1537 F g −1 at 0.5 A g −1 and 1181 F g −1 even at current density as high as 10 A g −1 , which 50% cobalt doped enhances the electrical conductivity and porous and ultrathin structure is helpful with electrolyte diffusion to improve the material utilization. An asymmetric ultracapacitor was assembled with the N-doped graphitic ordered mesoporous carbon as negative electrode and the NiCo LDH as positive electrode. The device achieves a high energy density of 33.7 Wh kg −1 (at power density of 551 W kg −1 ) with a 1.5 V operating voltage.

This paper focuses on tight-fitting sportswear as the research subject and investigates the dynamic influence of comfort perception on overall comfort. By developing a comprehensive evaluation system that encompasses both subjective and objective measures of comfort, this study systematically reveals the interactive mechanisms and dynamic characteristics of multi-dimensional comfort perceptions, including heat and humidity comfort, compression comfort, touch comfort, among others. The findings indicate that (1) local comfort levels fluctuate over time, with varying weights influencing overall comfort; (2) during 6 km/h exercise, key local discomfort sensations impacting overall comfort include restraint feelings of shank, stuffy feeling (cool feeling) of shank, restraint feeling of thigh, etc.; (3) there are significant correlations between stuffy feeling with sticky body feeling and humidity feeling respectively; additionally, notable correlations exist between sticky body feeling with humidity feeling and rough feeling. This study offers provides a more scientific basis for optimizing tight-fitting sportswear design.

In order to improve the precision of clothing development of fast fashion brands, consumers’ sense of experience, and brand loyalty, a design method of clothing pattern is proposed by combining Kansei engineering theory and improved particle swarm optimization (IPSO)–back propagation neural network (BPNN) model. First, based on the theory of Kansei engineering, the perceptual image experiment of clothing patterns was designed, and the mean value of perceptual image evaluation of clothing patterns by young consumers was obtained through an online questionnaire survey. Second, based on the IPSO and the BPNN, the nonlinear correlation mapping model between the design elements of clothing pattern and consumers’ perceptual image is established. Finally, based on the calculation of target image weight by analytic hierarchy process (AHP) method and IPSO-BPNN model, the optimal combination of clothing pattern design elements under the requirement of multi-target image is output. Taking the paper-cut pattern of sweater shirt as an example, the feasibility of this research method is verified. The research not only helped the designer to design a costume pattern that meet the individual emotional needs of consumers, but also provided a clear design index and reference, and made the costume design process more targeted, precise, and intelligent.

In order to understand consumers’ cognition of clothing style and design clothing products more in line with people’s emotional needs, a garment style perceptual image prediction model based on PSO-BP neural network was constructed by taking professional dress as an example. Firstly, the professional dress samples were screened and the style design elements were deconstructed and coded. The Kansei engineering theory and factor analysis method were used to determine the representative adjectives, so as to reduce the cognitive dimension of the target users for the style characteristics and perceptual image of the dress. Then, using the sample style design element code as the input layer and the user’s perceptual image evaluation score as the output layer, the PSO-BP neural network’s perceptual image prediction model for professional dress styles is constructed. Finally, the sample data were input into the PSO-BP model, BP neural network and GA-BP model for simulation and calculation, and the error analysis of the results proved that the PSO-BP prediction model is effective and advanced. Designers can use this model to quickly transform customers’ perceptual needs with dress style design elements, so as to improve the scientificity of design decision-making and better meet customer needs.

The vascular system maintains cellular homeostasis by transporting oxygen, nutrients, and metabolic waste products. The vascular system is involved in a variety of fundamental physiological phenomena and is closely associated with human vascular diseases. Additionally, the stability of drugs in the vasculature affects their efficacy. Therefore, researchers have used vascular models to study vascular diseases, assess drug stability, and screen drugs. However, there are many shortcomings in the animal models and in vitro two-dimensional vascular models that have been extensively developed. In this paper, we specifically review the construction methods of in vitro vascular models and classify the specific methods into photolithography, soft lithography, self-assembly, template, 3D bioprinting, and laser degradation/cavitation. The first two are microfluidics-based methods and the last three are non-microfluidics-based methods. The vascular model construction methods reviewed in this paper overcome the shortcomings of traditional models—which cannot accurately reproduce the human vascular microenvironment—and can assist in the construction of in vitro 3D vascular models and tissue engineering vascularization. These models can be reused by perfusion devices, and the cells within the channels reside on biocompatible materials that are used to simulate the microenvironment and 3D cellular organization of the vasculature in vivo. In addition, these models are reproducible in shape and length, allowing experiments to be repeated, which is difficult to do with natural vessels. In vitro vascular models are widely used in research and drug screening for diseases associated with endothelial dysfunction, cancer, and other vascular abnormalities.

PurposeFor comfort evaluation of underwear pressure, this paper proposes an improved GA algorithm to optimize the weight and threshold of BP neural network, namely PSO-GA-BP neural network prediction model.Design/methodology/approachThe objective parameters of underwear, body shape data, skin deformation and other data are selected for simulation experiments to predict the objective pressure and subjective evaluation in dynamic and static state. Compared with the prediction results of BP neural network prediction model, GA-BP neural network prediction model and PSO-BP neural network prediction model, the performance of each prediction model is verified.FindingsThe results show that the BP neural network model optimized by PSO-GA algorithm can accelerate the convergence speed of the neural network and improve the prediction accuracy of underwear pressure.Originality/valuePSO-GA-BP model provides data support for underwear design, production and processing and has guiding significance for consumers to choose underwear.

Memristors are regarded as promising candidates for breaking the problems including high off‐chip memory access delays and the hash rate cost of frequent data moving induced by algorithms for data‐intensive applications of existing computational systems. Recently, organic–inorganic halide perovskites (OIHPs) have been recognized as exceptionally favorable materials for memristors due to ease of preparation, excellent electrical conductivity, and structural flexibility. However, research on OIHP‐based memristors focuses on modulating resistive switching (RS) performance through electric fields, resulting in difficulties in moving away from complex external circuits and wire connections. Here, a multilayer memristor has been constructed with eutectic gallium and indium (EGaIn)/ MAPbI3/poly(3,4‐ethylenedioxythiophene): poly(4‐styrenesulphonate) (PEDOT: PSS)/indium tin oxide (ITO) structure, which exhibits reproducible and reliable bipolar RS with low SET/RESET voltages, stable endurance, ultrahigh average ON/OFF ratio, and excellent retention. Importantly, based on ion migration activated by sound‐driven piezoelectric effects, the device exhibits a stable acoustic response with an average ON/OFF ratio greater than 103, thus realizing non‐contact, multi‐signal, and far‐field control in RS modulation. This study provides a single‐structure multifunctional memristor as an integrated architecture for sensing, data storage, and computing. The memristors with EGaIn/MAPbI3/PEDOT:PSS/ITO structure are successfully fabricated. The devices present reproducible electrical RS behavior. Importantly, the devices exhibit piezo‐acoustic RS behaviors, enabling non‐contact, multi‐signal, and far‐field control. This phenomenon is strongly dependent on different frequencies and SPL. A physical model is proposed to enhance understanding. This work introduces a single‐structure multifunctional memristor for sensing, data storage, and computing.

(R)-1-[3,5-bis(trifluoromethyl)phenyl]ethanamine, a key chiral intermediate of selective tetrodotoxin-sensitive blockers, was efficiently synthesized by a bienzyme cascade system formed by with R-ω-transaminase (ATA117) and an alcohol dehydrogenase (ADH) co-expression system. Herein, we report that the use of ATA117 as the biocatalyst for the amination of 3,5-bistrifluoromethylacetophenone led to the highest efficiency in product performance (enantiomeric excess > 99.9%). Moreover, to further improve the product yield, ADH was introduced into the reaction system to promote an equilibrium shift. Additionally, bienzyme cascade system was constructed by five different expression systems, including two tandem expression recombinant plasmids (pETDuet-ATA117-ADH and pACYCDuet-ATA117-ADH) and three co-expressed dual-plasmids (pETDuet-ATA117/pET28a-ADH, pACYCDuet-ATA117/pET28a-ADH, and pACYCDuet-ATA117/pETDuet-ADH), utilizing recombinant engineered bacteria. Subsequent studies revealed that as compared with ATA117 single enzyme, the substrate handling capacity of BL21(DE3)/pETDuet-ATA117-ADH (0.25 g wet weight) developed for bienzyme cascade system was increased by 1.50 folds under the condition of 40 °C, 180 rpm, 0.1 M pH9 Tris-HCl for 24 h. To the best of our knowledge, ours is the first report demonstrating the production of (R)-1-[3,5-bis(trifluoromethyl)phenyl]ethanamine using a bienzyme cascade system, thus providing valuable insights into the biosynthesis of chiral amines.

PurposeIn order to study the static and dynamic comfort of tight sportswear in winter, the subjective comfort was aimed to be evaluated by collecting sensory data such as humidity feeling, cold feeling and other perceptions. In this paper, the experiment was divided into standing, squatting, jumping, jogging, walking and so on.Design/methodology/approachThrough particle swarm optimization-cuckoo search model, the sensory factors that affect the overall comfort were optimized, and it was found that there were great differences in the overall comfort factors under different motions. Then, analytic hierarchy process was used to sort the optimized sensory indicators in each experimental stage, and the influence degree of sensory indicators was studied. Finally, by the long short-term memory (LSTM) model, taking comfort senses of standing, squatting, jumping and jogging as input parameters, and regarding comfort senses of walking, lifting legs and resting as output parameters, the prediction model was founded.FindingsThe results showed that there were certain differences between the prediction value and the real subjective evaluation value, but most of the predicted values were consistent with the real values on the sensory level, and the overall prediction level was good, which meant that the LSTM model had more accurate prediction ability for subjective evaluation and could be extended to other sports.Originality/valueThe research results could provide scientific methods for the design of tight-fitting sportswear in winter.

Cascade reactions catalyzed by multi-enzyme systems are important in science and industry and can be used to synthesize drugs and nutrients. In this study, two types of macromolecules of bi-enzyme self-assembly clusters (BESCs) consisting of carbonyl reductase (CpCR) and glucose dehydrogenase (GDH) were examined. Stereoselective CpCR and GDH were successfully fused with SpyCatcher and SpyTag, respectively, to obtain four enzyme modules, namely: SpyCatcher-CpCR, SpyCatcher-GDH, SpyTag-CpCR, and SpyTag-GDH, which were covalently coupled in vitro to form two types of hydrogel-like BESCs: CpCR-SpyCatcher-SpyTag-GDH and GDH-SpyCatcher-SpyTag-CpCR. CpCR-SpyCatcher-SpyTag-GDH showed a better activity and efficiently converted ethyl 2-oxo-4-phenylbutyrate (OPBE) to ethyl(R)2-hydroxy-4-phenylbutanoate ((R)-HPBE), while regenerating NADPH. At 30 °C and pH 7, the conversion rate of OPBE with CpCR-SpyCatcher-SpyTag-GDH as a catalyst reached 99.9%, with the ee% of (R)-HPBE reaching above 99.9%. This conversion rate was 2.4 times higher than that obtained with the free bi-enzyme. The pH tolerance and temperature stability of the BESCs were also improved compared with those of the free enzymes. In conclusion, bi-enzyme assemblies were docked using SpyCatcher/SpyTag to produce BESCs with a special structure and excellent catalytic activity, improving the catalytic efficiency of the enzyme.