With the development of the times and the progress of science and technology, smart wearable devices have received extensive attention from researchers. Among various wearable devices, smart socks are expected to play an important role in the fields of health monitoring, athletic training and therapy, disease prevention and interactive learning because of their comfort, softness, high precision, small size and convenience.
In recent years, the application of smart wearable devices is getting increasingly important in people’s daily life. Devices with characteristics of softness, comfort, compactness, convenience, and skin-friendliness have become a research hotspot. And in order to meet people’s demand of comfort and convenience, smart wearable devices are gradually developing towards flexibility and miniaturization, which gradually makes textiles with small volume, good softness, breathableness and friendliness an ideal carrier for smart wearable devices. The flexible fabric sensor, with the advantages of lightness, thinness, breathableness, softness, deformability, and high integration with other materials, sees great development potential and can be highly adaptable to smart socks. Smart socks for pressure monitoring can identify the wearer’s gait information, thus providing gait assistance. They can also be used for disease prevention or treatment; smart socks for temperature monitoring can prevent venous congestion and foot ulcers. Smart socks with multi-functional monitoring are widely used in sports health, disease prevention, human-computer interaction and other fields. As smart socks continue to expand their application range, future research should focus on the development of comfortable and intelligent materials and better integration methods. In such a way, the daily use of smart socks can be realized.  
The flexibility and skin-friendliness of flexible fabric sensors are crucial for smart socks. Smart socks can be highly integrated with flexible fabric sensors while meeting the condition of being ideal carriers. At present, smart socks have broad application prospects in gait recognition, disease prevention, motion monitoring, human-computer interaction and other fields. With further optimization and development, smart socks are expected to realize the daily usage and bring us more intelligent life experiences.
As a foot wearable device, smart socks have great potential in the smart wearable field. In the future, these exquisite and multi-functional smart socks are expected to be integrated into people’s daily life. They are not only wearable, but also play an important role in sports monitoring, health monitoring, disease prevention, human-computer interaction and other fields. Although smart socks have been developed in a variety of styles and functions, their durability, wearability and scale remain as problems that researchers need to face and solve. Therefore, it is necessary to improve the materials, integrated processes, and energy supply methods of flexible fabric sensors to speed up the daily usage of smart socks.

With the development of the artificial intelligence, the demand for flexible electronic devices is constantly increasing. Flexible resistive strain sensors, with a simple structure and fabrication process, can convert external mechanical stimuli into electrical signals, and exhibit high stretchability and adaptability, leading to wide utilization in smart wearables, medical diagnostics, soft robotics, and other fields. Traditional resistive strain sensors usually employ flexible polymer membranes as their substrate, but their poor breathability and water permeability reduce comfort of wearing, resulting in redness and even allergic reactions. Electrospun ultrafine fibers, characterized by their rich porosity, lightness, thinness, softness, and good conformability, are an ideal flexible electronic platform. However, ultrafine fibers obtained through conventional spinning processes are usually randomly or unidirectionally arranged. Under low strain conditions, fiber sliding buffers most of the strain, resulting in minimal changes in the morphology of the conductive layer and limited sensitivity. This problem greatly compromises the monitoring accuracy of the sensor and reduces its ability to capture valuable but small deformations and provide feedback.
To enhance the sensitivity of ultrafine fiber-based flexible strain sensors, a thermoplastic polyurethane (TPU) ultrafine fiber-based strain sensor with “furrow-ridge” structure was proposed. Firstly, a metal sheet array collector was designed to control the local fiber accumulation density and fiber alignment orientation under the driving force of the electric field, resulting in TPU ultrafine fiber substrate with a “furrow-ridge” structure. Subsequently, a highly sensitive flexible strain sensor was prepared by deposition of a brittle conductive silver (Ag) layer by using a fast and efficient spray coating technique. This method is simple and versatile, and can be used to prepare various electrospun fiber-based materials with a “furrow-ridge” structure, significantly enhancing sensitivity while maintaining a wide working range. It is found that the “furrow-ridge” structured ultrafine fiber substrate has orthogonal fiber alignment angles. This structure not only improves the tensile strength (13.26 MPa), elongation at break (355.81%), and elasticity (84.71%) of the fiber membrane, but also enhances local strain and induces significant changes in the morphology of the surface conductive layer material. Additionally, the unique fiber orientation guides the generation of cut-through cracks, leading to a sharp increase in electrical resistance and a significant improvement in sensitivity, with a maximum gauge factor reaching 151.36. The research can provide suggestions for the design and development of highly sensitive strain sensors and have broad prospects for applications in electronic skin, medical diagnostics, human-computer interaction, and other fields.

Artificial muscles can produce reversible expansion, twisting, bending, stretching, and other motions under the action of external stimuli (heat, electricity, light, magnetism, humidity, pressure, etc.), which holds potential application value in robotics, medicine, aerospace and other fields. As a class of intelligent stimulus-responsive materials, two-way shape memory polymer (2W-SMP) is an excellent substrate for preparing artificial muscles, because it can facilitate the reversible transition between its original and temporary shapes by adjusting the ambient temperature. However, 2W-SMP has shortcomings such as poor thermal conductivity, slow response speed, and single response mode. Therefore, increasing the response speed of 2W-SMP, diversifying its stimulus-response modes, and achieving precise control over reversible actuation strain are of great significance for the application and development of 2W-SMP in the field of artificial muscles.
Ethylene-vinyl acetate copolymer (EVA) is a semi-crystalline polymer. Research indicates that cross-linked EVA fibers possess excellent two-way shape memory properties, large reversible actuation strains, good flexibility, and designability. In this paper, nano hexagonal boron nitride (h-BN) was used as thermal conductive fillers, and carbon nanotubes (CNTs) as conductive fillers. BN/EVA composite fibers were prepared by melt spinning process, and CNTs/BN/EVA composite fibers with skin core structure were prepared by swelling and impregnation method. The experimental results show that the CNTs were closely combined with the fiber matrix, forming a good electrically conductive path on the surface of the fiber, and the electrical conductivity of the CNTs/BN/EVA composite fibers could reach 58.03 S/m. The CNTs/BN/EVA composite fibers have good strain sensing performance, with a detection range of up to 281%, and high sensitivity and linearity (in the range of 175%-281% strain, Gauge Factor (GF) of 35.19, R2=99.03%), with response and recovery times of 196 ms and 189 ms, respectively. The twisted CNTs/BN/EVA composite yarns could be rapidly warmed up to 111.45 ℃ in 12 s at 18 V and exhibit stable temperature rise and fall behavior under periodic electrical stimulation conditions, achieving precise regulation of the electrically induced reversible actuation strain in yarn-based artificial muscles.
In summary, the EVA-based composite fibers prepared in this thesis combine excellent two-way shape memory properties, reversible electric-driven properties and strain sensing properties, integrate actuation and sensing capabilities into one, and have potential applications in the fields of artificial muscles, wearable devices and robotics.

In recent years, the demand for flexible sensors has been increasing. Among them, conductive hydrogel flexible sensors have attracted wide attention due to their excellent flexibility, biocompatibility and conductivity. Conductive hydrogels can be prepared by physical crosslinking, chemical crosslinking and radiation crosslinking. The crosslinking degree of conductive hydrogels directly affects their electrical conductivity and mechanical strength. However, the control of crosslnking degree of conductive hydrogels prepared by physical cross-linking is usually difficult. In order to control it, we chose to add hydroxypropyl cellulose (HPC) to reduce the crosslinking degree of polyvinyl alcohol (PVA) hydrogels. In addition, the traditional conductive hydrogel is hard to meet the high conductivity and high mechanical strength at the same time. Therefore, in order to address the issue of poor mechanical properties of traditional conductive hydrogels, we coated conductive hydrogels directly onto fabrics to produce conductive hydrogel fabrics. With good elasticity, high mechanical strength, good air permeability, and comfortable softness, knitted fabrics, particularly, can fit the human body well and are excellent substrates for preparing conductive hydrogel fabrics. Meanwhile, the introduced HPC can be used as a nano-filler to toughen and improve the mechanical strength of the conductive hydrogel. In this paper, HPC was introduced into the PVA hydrogel, and a hydrogel with controllable crosslinking degree was prepared by cyclic freezing-thawing method, and it was coated on polyester/spandex fabric (90% polyester & 10% spandex). Sodium chloride (NaCl) was used as a conductive material to endow the hydrogel with electrical conductivity. Water (H2O) and ethylene glycol (EG) were used as solvents to make the prepared hydrogel have frost resistance. The introduction of polyester ammonia fabrics and HPC together enhanced the strength of the hydrogel. Analysis of scanning electron microscope images, Fourier transform infrared spectroscopy, mechanical properties, frost resistance and sensing properties was conducted on the conductive hydrogel fabrics we prepared.. It was found that when the PVA content was 10% and the HPC content was 2%, the HPC-PVA conductive hydrogel fabric we prepared had the best performance. The mechanical strength was as high as 16.77 MPa, which was about 25 times that of pure hydrogels. At the same time, it had good conductivity, and the conductivity reached 1.48 S/m. It could recognize a variety of human movements including facial expressions. In addition, due to the introduction of EG, the conductive hydrogel fabric prepared by us had good frost resistance and could still work normally at -18℃. It also proved that the flexible sensor we prepared has a broader application range.

Cotton is an important economic crop. However, the impurity content of cotton will have an impact on the cotton ginning and spinning effects, ultimately affecting the quality of cotton products such as yarn and fabrics. The impurity content of cotton is one of the main indicators for cotton grading and pricing, so the detection of cotton impurities is meaningful and valuable in practical application. The research on cotton impurity detection is rich in content and diverse in methods, mainly including hand selection, image methods, and spectral methods. Hyperspectral imaging technology was first applied in the field of remote sensing, and in recent years, it has been sprouting up in cotton impurity detection. In order to detect and identify cotton impurities, the unginned cotton was firstly picked by machine; the cotton fibers were then rolled down from the unginned cotton and impurities were removed to get the raw cotton. Then, the raw cotton was purified again and separated to obtain four types of substances: pure cotton, cotton branches and leaves, broken seeds, and mudstone. Each sample weighed 10g, with 30 samples for each substance, and their hyperspectral images were collected in the hyperspectral imaging system. Black-and-white plate correction and smoothing processing were applied to the collected hyperspectral images, then the region of interest was selected and spectral data were extracted. The spectra of the same substance were averaged, and the standard spectral curve of the corresponding substance was obtained and analyzed. By using three methods of multiplicative scatter correction (MSC), standard normal variate transformation (SNV), and first derivative (FD) to preprocess the extracted spectra of pure cotton and three types of impurities, the paper analyzed the effects of different preprocessing methods. Principal component analysis was used to reduce the dimensionality of all original spectra and preprocessed spectra of the samples. The top eight principal components were selected as factors based on cumulative contribution rate to establish a discriminant analysis model and determine the optimal impurity identification and classification model. The results showed that the trends of spectral curves of pure cotton, cotton branches and leaves, and broken seeds were similar, while the spectral curves of mudstone were flat and significantly different from the other three types of spectra. The above three methods could effectively eliminate baseline drift in the spectral curves, making the spectra smoother. Spectral trends preprocessed by MSC and SNV were similar. The FD preprocessing amplified the original small characteristic peaks, making them more prominent. Dimensionality reduction by principal component analysis (PCA) could effectively solve the problem of huge information content and wide spectral bands in hyperspectral data. When the number of principal components reached eight, the cumulative contribution rates of the original spectrum and the three preprocessed spectra could all reach 85%; the training sets' accuracy of the discriminant analysis model corresponding to the original spectrum and three spectra preprocessed by MSC, SNV, and FD was 100%, 96.67%, 98.89%, and 100%, respectively. The accuracy of the test sets was 100%, 90%, 93.33%, and 100%, respectively. In conclusion, hyperspectral imaging technology can effectively detect and identify impurities in raw cotton.

Cocoon silk contains approximately 2 wt% of polyphenols, and the residue of these polyphenols is closely related to some factors, such as producing area, variety, season, processing methods and so on. Therefore, we can know the identity of raw silk products by detecting the polyphenols residue in the silk. Currently, there is no reliable and accurate method for determining the polyphenol content in cocoon silk. In this study, a quantitative method for detecting the content of polyphenols in cocoon and silk using the Folin phenol colorimetric method through Response Surface Methodology (RSM) was established. The effects of the dosage of Folin phenol reagent, the dosage of Na2CO3 solution, reaction time, and detection wavelength on the determination of polyphenol content in cocoon and silk were investigated. On the basis of single factor experiments, the detection conditions of cocoon and silk polyphenols were further optimized through Box Behnken design. It is shown that the detection wavelength, reaction time and amount of Na2CO3 solution have significant influence on the quantification results, while the amount of Folin phenol solution within the range of 1.0 mL to 1.4 mL has insignificant influence on the result. According to the result of Box Behnken design trial, the optimal detection conditions for cocoon and silk polyphenols are: 1.1 mL of Folin phenol solution, 0.65 mL of Na2CO3 solution, 32 minutes of reaction time, and detection wavelength of 760 nm. This method has good repeatability and reproducibility, and high detection stability within 40 minutes. The proposed method is accurate, reliable and low-cost, and can be applied for the detection of the content of polyphenols in silk cocoons, raw silks and related silk products.

Silk fabrics have a long history in China. They are also an important part of Chinese civilization. Silk fabrics are natural polymer compounds, which are easily damaged by light, temperature, humidity, microorganisms and so on. It is different to preserve cultural relics permanently. Therefore, studies on the protection of ancient silk are particularly crucial. Among them, the origin of ancient silk has always been hotly discussed. As a new technology in modern ecological research, stable isotope technique has become a powerful tool for the traceability of silk fabrics with its advantages of tracing, indication, fast detection and accurate results. During the preservation process of ancient silk, the original pollutants may cause significant changes in the structure and isotopic information of silk fabrics, thereby affecting the accuracy of provenance tracing. Therefore, in order to use isotope technology to trace the origin of ancient silk, it is necessary to figure out how the isotopic information of silk has changed during the cleaning process,.
To explore the influence of cleaning treatment on the morphology, structure, and stable isotope of the ancient silk from the Ming Dynasty, silk fabric samples were simply cleaned by four gentle cleaning methods (deionized water cleaning, ethyl alcohol cleaning, deionized water/ethyl alcohol cleaning, and steam cleaning). The surface morphology, structure and stable isotope ratios of silk samples before and after cleaning were examined by using scanning electron microscope (SEM), fourier transform infrared spectrometer (FTIR), thermal gravimetric analyzer (TGA) and isotope mass spectrometer. The results indicate that deionized water and ethyl alcohol had certain cleaning effects, while steam cleaning caused secondary pollution on the surface of the silk, requiring further removal of contaminants. The relative content of β-sheet in the fibroin decreases after steam cleaning. After cleaning treatment, the hydrogen stable isotope ratio of silk samples decreased, the oxygen stable isotope ratio increased, the carbon stable isotope ratio had little change within 0.30‰, while the variation of nitrogen stable isotope ratio showed no clear appearance. The research results provide certain reference for eliminating external interference and conducting data correction in tracing the origin of ancient silk. 

 With the continuous development of modern technology, emerging forces have been injected into the textile industry. The combination of advanced control theory and computer technology has created countless intelligent workshops. The intelligentization of workshop industrial lines has greatly increased the production of fabrics and saved the loss of human and material resources. However, the core technology mastered by China's textile industry still lags behind that of early developed countries, which is not conducive to the development of the domestic textile industry. As a highly automated equipment, air-jet looms require advanced control algorithms to further improve the quality of fabric and weaving efficiency. The airflow weft insertion system, as its core component, plays a crucial role in increasing the speed of the loom spindle. Improving the structure or algorithm of the airflow weft insertion system can improve the overall performance of the loom and respond to national policies.
To improve the weft insertion rate of jet looms, modern control theory was combined to improve the control algorithm of airflow weft insertion, so as to enhance the system's control accuracy and resistance to interference. This study aimed to develop a neural integral sliding mode fractional backstepping controller (RBFISMFOBC-ESO) based on an extended state observer to improve the efficiency of weft insertion. Firstly, a time-varying mathematical model of the weft insertion system was obtained through force analysis of the yarn, and it was transformed into a state equation. In order to improve chattering and enhance the control accuracy of the system, a new integral sliding mode fractional order backstepping control method was derived and proved through Lyapunov theory. At the same time, RBF neural network was used to compensate and correct the sliding surface coefficients, further improving the control accuracy of the system. In order to reduce the impact of time-varying factors such as pressure fluctuations and unmodeled data, finite time extended state observations were used to estimate the total disturbance of the system. In a simulation experiment environment, it was compared with PID control and traditional state observer based ISMFOBC control (ISMFOBC-ESO). The results showed that RBFISMFOBC-ESO control improved the robustness of the system, as well as the response speed and control accuracy.
The designed weft insertion control algorithm has been successfully verified through simulation and experiments, which effectively improves the robustness, stability, and control accuracy of the system. However, the process of yarn passing through the shed is completed by multiple units in collaboration, and the weft insertion control system is relatively complex, requiring more detailed research in the future from two main points. On the one hand, further exploration is needed for the duration of weft insertion and the magnitude of yarn tension under varying process parameters, so as to  screen out the optimal conditions for the RBFISMFOBC-ESO controller. On the other hand, further optimization is required for the controller's hardware. A controller with faster processing speed would further minimize yarn flight time, thereby enhancing the loom's weaving efficiency.

 In recent years, with the popularity of the concept of green environmental protection and low-carbon development, biodegradable natural fiber reinforced composites have become a hot research point in the field of polymer material research. In this paper, one-way silk sheet was used as reinforcement, polycaprolactone was used as resin matrix, and one-way silk sheet was degummed with different mass fractions of Na2CO3 solution. The effects of different mass fractions of Na2CO3 solution on the surface morphology and wettability of one-way silk sheet were investigated. One-way silk/PCL composites were prepared by hot pressing molding, and the effects of degumming treatment of one-way silk sheet on the interfacial bonding, mechanical properties and thermal stability of composites were studied.
 In this study, 0.0%, 0.5%, 1.0%, 1.5% and 2.0% Na2CO3 solutions were used to degum the one-way silk sheet after drying. The degumming rate was calculated and the contact angle was tested. The surface micro-topography of the one-way silk sheet before and after degumming was photographed by SEM. The degummed one-way silk sheet was immersed in PCL solution, and then the one-way silk/PCL composites were taken out and tiled in a ventilated environment. After the volatilization of dichloromethane, the one-way silk/PCL composites were prepared by tiled hot pressing. The bending resistance and impact resistance of one-way silk/PCL composites were tested and characterized by SEM. The effect of one-way silk sheet degumming on the thermal stability of one-way silk/PCL composites was studied by thermogravimetric analysis.
The results showed that with the increase of Na2CO3 mass fraction, the sericin, grease and impurities on the surface of silk were gradually removed, the one-way silk sheet performed better in wettability and became looser, the infiltration effect of resin matrix on one-way silk sheet became better, the combination of silk and PCL matrix was closer, and the mechanical properties of one-way silk/PCL composites were significantly improved. When the mass fraction of Na2CO3 was 1.5%, the sericin on the surface of silk fiber was basically removed, and the mechanical properties of one-way silk/PCL composites reached the highest level, which were 126.19 MPa and 133.33 kJ/m2, respectively. Compared with the un-degummed silk reinforced composite material, the bending strength increased by 87.06%, and the impact strength increased by 56.25%. The results of thermogravimetric analysis showed that the thermal stability of one-way silk/PCL composites was improved by degumming. It can be seen that the most suitable amount of Na2CO3 for one-way silk sheet degumming treatment is 1.5%. One-way silk sheet degumming treatment improves the infiltration effect of PCL resin matrix and silk fiber, improves the mechanical properties and thermal stability of one-way silk/PCL composites. The research results can provide reference for expanding the application of one-way silk sheet in the field of natural fiber reinforced composites.

As one of the main synthetic fibers, polyester has the characteristics of excellent abrasion resistance, wrinkle recovery, and elasticity, and is irreplaceable in the garment industry. Currently, the alkali reduction process has become an important processing step in improving the quality of polyester, but this step has several drawbacks, such as high alkali consumption, difficulties in processing, high energy consumption, severe environmental pollution, and considerable fabric damage.
This paper focused on the study of alkali deweighting of polyester fibers, aiming to address the high alkali and energy consumption, and significant environmental pollution issues present in the traditional alkali deweighting process. Combined with previous research findings, this study used octadecyl dimethyl benzyl ammonium chloride (accelerator 1827) and benzyl alcohol as plasticizers, intending to increase the alkali deweighting rate of polyester fabrics through the synergistic effect of the accelerator-plasticizer combination. This, in turn, would reduce the required NaOH dosage, thereby reducing environmental pollution and minimizing production costs while ensuring the alkali deweighting effect on polyester fabrics. Experimental results showed that the addition of accelerator 1827 and benzyl alcohol significantly increased the alkali deweighting rate of polyester fabrics. Compared to a 10.0 g/L NaOH dosage, when the benzyl alcohol volume ratio in the treatment solution was 10%, and the concentration of accelerator 1827 was 2.0 g/L, the alkali deweighting rate of polyester fabrics increased from 6.3% to 34.9%, nearly an increase of 4.5 times, surpassing the deweighting rate (22%) at 35.0 g/L NaOH concentration. Thus, a high deweighting effect was achieved at a lower NaOH mass concentration, significantly reducing alkali usage and environmental pollution. Comprehensive tests of fabric properties showed that polyester fabrics treated with the accelerator-plasticizer synergy exhibited notably improved hydrophilicity, and the whiteness showed few changes, favoring subsequent dyeing and finishing processes.
In conclusion, this paper successfully demonstrates that by regulating the concentration and usage ratio of the plasticizer and accelerator, the alkali deweighting rate of polyester can be significantly improved while production costs and environmental damage are lowered. This discovery not only has important implications for enhancing the production efficiency and environmental friendliness of polyester fabrics, but also offers a new technical path for the green and sustainable development of polyester pre-treatment processes.

In the printing and dyeing industry, maintaining stable tension in fabrics during the printing and dyeing process is the key to ensuring their dyeing quality. Excessive tension will lead to weft contraction, warp stretching, and in extreme cases, the fabric can break; on the contrary, it will cause the fabric to wrinkle and slip, which will seriously damage the dyeing effect and reduce the production efficiency. In this series of processes, the pad dyeing process is particularly critical, and the tension control is directly related to fabric quality. So it is extremely important to keep the fabric's tension during the operation of the whole equipment. This article, with focus on the back-end process of continuous pad dyeing equipment, conducted in-depth research on the composite force and various interferences that the tension of fabrics is susceptible to in the printing and dyeing process, mainly from the aspects of establishing the tension system model, designing the controller and conducting experimental verification.
Firstly, the back-end of the continuous pad dyeing equipment was described, and the dynamic model of the pad dyeing system was constructed according to the discussion of the variation pattern of the rotational inertia of the fabric roll, combined with principles such as the conservation of mass and Hooke's law.
Secondly, through the analysis of the established mathematical model, it can be found that there is a coupling relationship about the tension between the adjacent two rollers, so this paper proposed a method to decouple the tension. Moreover, the tension control system exhibits not only strong coupling but also characteristics such as nonlinearity, time-varying nature, and susceptibility to interference, and traditional PID controllers are frequently hard to achieve the desired control effect on this system. The active disturbance rejection control (ADRC) was chosen as a solution. Considering the complexity of parameter tuning in ADRC, this study addressed the issue of premature optimization stagnation in the gray wolf algorithm, combined the optimized gray wolf algorithm (IGWO) with the ADRC, and utilized the IGWO to optimize and fine-tune the critical parameters of the ADRC in real-time, designing improved gray wolf active disturbance rejection control (IGO-ADRC).
Finally, MATLAB/Simulink was used to build a mathematical model, and the simulation experiments of three controllers were carried out. The results show that the designed IGO-ADRC performs well in suppressing tension fluctuations caused by internal and external disturbances, with the response speed and control accuracy better than those of traditional PID controllers. It also demonstrates excellent performance in reducing tension fluctuations caused by the coupling between rollers, ensuring constant tension and stable operation of the rolling mill, improving the quality of fabric production, and significantly reducing the production of defective products..

In the field of intelligent patchwork, despite existing research on the organization of quilting fabric databases and rapid fabric selection, research on the rapid digital design of quilting patterns is still lacking. This study aims to develop a tool for rapidly designing patchwork patterns with similar geometric shapes, incorporating intelligence into traditional patchwork design to fulfill the demand for personalized patchwork designs, and improving design efficiency and creativity.
Utilizing parametric modeling and intelligent design techniques, this study rapidly created homomorphic patchwork patterns. A feature-based parametric design model was established to store geometric features, coordinates, and filling styles. Specific design parameters were abstracted into design variables, with value range constraints and initial values specified for each. The constructed parametric model allows for observation and iterative adjustments of design changes by modifying the values of design variables, providing flexible control over the design process and achieving efficient and controllable parametric design. The innovation of this study lies in applying parametric modeling and intelligent design techniques to traditional patchwork design, providing flexible control over the design process, and realizing efficient and controllable parametric design. The study investigated the parametric design model of patchwork patterns using design variable methods and used matrix mathematical models to represent the patterns, allowing for flexible control over the design variables of patchwork patterns. Within the design platform, this study rapidly achieved the parametric design of patchwork patterns. The platform supports users in selecting pattern types, inputting parameters, and viewing real-time design outputs. The core design component uses Canvas rendered with Pixi.js for interactive, real-time design. Additionally, the platform utilized Pinia as a local data cache-sharing repository, combined with automatic cloud data caching technology, optimizing data processing. A decoupled software architecture was used, leveraging the Vue3 and FastAPI frameworks for front-end and back-end development, enhancing development efficiency and user experience. The design interface includes three modules: pattern selection, attribute configuration, and style design, supporting dynamic parameter adjustment and real-time preview, reducing manual design efforts and enhancing the realism of the displayed outputs. Overall, this study has successfully developed a rapid design tool that modernizes the traditional fabric design process, enhancing design efficiency and creativity.
The outcomes of this study provide innovative tools and platform support for the rapid design of homomorphic patchwork patterns, meeting the demand for personalized fabric patterns. Future work can explore further applications and improvements to this framework to further enhance design efficiency and creativity.

The aim of this study is to develop a new method to calculate the characteristic section of the waist and hip without clothing pressure by using 3D scanning and finite element simulation technology. This goal contributes to a deeper understanding of morphological characteristics in the human body’s natural state and its interactions with tight-fitting clothing, advancement of the development of personalized clothing design and manufacturing, and enhancement of the wearing comfort and overall well-being.
To achieve this goal, based on the central limit theorem in statistics, a sample of 30 young women aged between 20 and 25 years old was selected for 3D scanning. Then, through reverse engineering technology, the characteristic sections of the waist and hip under clothing pressure and without clothing pressure were extracted. On this basis, a pressure iteration algorithm based on finite element method was developed, which can simulate the clothing pressure effect of tights on the waist and hip sections, and then calculate the non-uniform clothing pressures and body surface displacement vector caused by tights. In addition, the high-and low-level output response regression method was used to comprehensively consider three groups of main factors including human parameters, tight size and section shape characteristics, and to establish a regression model to predict the pressure distribution and displacement. Finally, goodness of fit (R²) and the global comparison hashing algorithm were used to evaluate the validity of the model.
The pressure iteration algorithm based on finite element simulation can calculate the pressure and displacement vectors for 120 key points in the characteristic sections of waist and hip. Through the five-level and two-factor experimental design, it is found that the factors such as waist and hip circumference, body fat percentage, waist and hip circumference of tights, curvature and soft tissue thickness have important effects on the pressure and displacement of tights. Accordingly, a regression model was constructed to predict the clothing pressure and body surface displacement of tights. The empirical test shows that the pressure goodness of fit of the model reaches 89.73% on average, and the displacement vector goodness of fit is also as high as 85.21%. Furthermore, the characteristic sections of the waist and hip without clothing pressure were compared with the section without garment compression obtained by scanning. The average shape similarity is 85.28%, and the standard deviation is 4.99%, which confirms the accuracy of the method in restoring the human shape without garment compression.

The PBO fiber is currently one of the high-performance organic fibers with the best comprehensive performance, featuring high strength, modulus, flame retardancy, and heat resistance. It is dubbed the "super fiber of the twenty-first century." The strength of PBO fibers reaches 5.8 GPa, and their modulus can reach 280 GPa. Their limiting oxygen index (LOI) is 68, and the thermal decomposition temperature is 650 °C. Due to these excellent properties, PBO fibers can be applied in aerospace, weaponary and naval vessels, building reinforcement, high-temperature filtration, special protection and other fields.
However, PBO fibers have performance shortcomings such as poor UV-aging resistance and weak interfacial adhesion, which limit its further application and development. They can not meet the stringent criteria of aerospace, weaponary, and other fields. Therefore, it is urgent to modify PBO fibers to improve their performance. This paper summarized the research progress of copolymerization modification technology of PBO fibers at home and abroad in recent years. It can be divided into four aspects based on the modification objectives: improving tensile mechanical properties, enhancing compressive strength, improving interfacial adhesion performance, and improving UV-aging resistance. The mechanical characteristics of PBO fibers can be further enhanced by copolymerizing PBO molecular chains with functionalized carbon nanotubes or graphene; although PBO fibers have a high tensile strength and modulus, they have a weak transverse compressive strength. By introducing chemical cross-links into the PBO macromolecular chain, their compressive strength can be significantly improved. However, PBO fibers have a smooth surface and do not contain polar groups, resulting in a weaker ability to bond with resin matrices. Adding carboxyl or hydroxyl groups to PBO molecular chains can effectively enhance the composite ability of PBO fibers with resins; the anti-UV aging performance of PBO fibers is relatively weak. Introducing intermolecular hydrogen bonds or fused ring structures into PBO molecular chains can help improve the anti-UV aging performance of PBO fibers.
In summary, PBO fibers possess excellent physicochemical properties but also have certain performance defects. The in-situ copolymerization modification technique, designed from the perspective of chemical structural, can fundamentally address the performance defects of PBO fibers and has high practical value. Nonetheless, the preparation process of PBO fibers involves liquid crystals spinning. If the addition of a third monomer disrupts the liquid crystal behavior of the spinning solution, the spinnability may be diminished. Co-polymerization modification may also break the structural symmetry and sequence consistency of PBO macromolecules, which could change the regularity of the PBO fibers' ultimate condensed state structure and lessen their mechanical qualities. Therefore, copolymerization modified PBO fibers should completely evaluate the impact of the third monomer on spinning performance and fiber condensed structure. However, it can not be denied that PBO fibers remain one of the organic fibers with the best comprehensive properties at the moment. Introducing new monomer structures into PBO fibers and developing new benzoxazole-based high-performance fibers would allow them to broaden their application value and play a larger role in military and civilian industries.

Excessive reliance on traditional fossil resources not only leads to environmental pollution and high energy consumption but also poses significant threats to human survival and development. Accelerating the advancement and utilization of renewable energy is imperative to achieve green and sustainable development. Photovoltaic cells have emerged as a prime candidate for environmentally-friendly energy generation, playing a pivotal role in the field of green energy generation. In recent years, with the rapid development of flexible electronics, wearable devices, and the Internet of Things (IoT), portable textile-based solar cell devices have made significant progress. Due to the lightweight, flexible, foldable, portable, and twistable characteristics of textiles, textile-based solar cell devices have attracted more attention and are considered a promising energy solution for the IoT. Flexible and wearable photovoltaic devices can seamlessly adapt to various platforms and power other wearable electronic and mobile devices by harnessing energy from sunlight. 
Textile-based solar cells primarily encompass dye-sensitized solar cells (DSSCs), perovskite solar cells (PSCs), and organic solar cells (OSCs). By integrating textiles with solar cell technology, textile-based solar cells can not only serve as power sources for wearable and portable electronic devices to create self-sustaining systems, but also retain the comfort and superior flexibility inherent to textiles. Crucially, textile-based solar cells can undergo mass production through continuous roll-to-roll technology, significantly more efficient than the batch production process required for rigid cells. Rigid solar cell production tends to have slower manufacturing speeds, more complex equipment requirements, and higher costs. Furthermore, due to their heavier weight and increased thickness, rigid solar cells incur higher storage and transportation costs. In recent years, textile-based solar cells have made progress but still face significant challenges. These include limited cell bending radius, low electrical conductivity, high surface roughness, low light transmittance, low power conversion efficiency (PCE), poor stability, etc., which are the key factors restricting the further development of textile-based solar cells. 
Although the research on textile-based solar cells has received extensive attention, to date, a comprehensive review summarizing their preparation and performance is rare. With the rapid development of smart textiles, and new photovoltaic materials, discussing the future development directions of textile-based solar cells has become  vitally important. Herein, we present an up-to-date review of recent representative advancements in textile-based solar cells, including DSSCs, PSCs, and OSCs. Furthermore, we delve into the structural design and working mechanisms of textile-based solar cells. Additionally, we highlight the research on fabrication methods and performance enhancement of these cells. Finally, we summarize the challenges and opportunities faced in the development of textile-based solar cells.