Pub Date : 2023-01-31DOI: 10.1177/24723444221147967
Xiujin Shi, Yuan Gong, Yiwei Zhang, Yanxia Qin
Existing click-through rate prediction models employ both a shallow model and a deep neural model for better feature interaction. The former shallow model aims to extract explainable explicit features and the latter deep neural model aims to learn efficient implicit features. Deep neural network is a commonly used deep neural model, which can yield better performance with more neural layers. However, increasing the number of neural layers would lead to problems such as gradient vanishing, gradient explosion, and excessive parameters. In addition, the performance of a deep neural network will also decrease rapidly when it becomes too deep. In this article, we propose a novel click-through rate prediction model by improving the deep neural model part to alleviate the above problems of deep neural network-based models. This article proposes to utilize a dense deep neural network model to strengthen feature propagation, which takes the outputs of all previous layers as the input of the current layer, instead of only one previous layer being used in the deep neural network. In addition, we also utilize an advanced shallow model FmFM for better explicit features in this article, and explicit and implicit features are interacted in our model. Experiments on two data sets (Criteo and Avazu) show that the proposed click-through rate prediction model significantly outperforms existing classical models such as DeepFM, xDeepFM, and DeepLight models.
{"title":"A Novel Click-Through Rate Prediction Model Based on Deep Feature Fusion Network","authors":"Xiujin Shi, Yuan Gong, Yiwei Zhang, Yanxia Qin","doi":"10.1177/24723444221147967","DOIUrl":"https://doi.org/10.1177/24723444221147967","url":null,"abstract":"Existing click-through rate prediction models employ both a shallow model and a deep neural model for better feature interaction. The former shallow model aims to extract explainable explicit features and the latter deep neural model aims to learn efficient implicit features. Deep neural network is a commonly used deep neural model, which can yield better performance with more neural layers. However, increasing the number of neural layers would lead to problems such as gradient vanishing, gradient explosion, and excessive parameters. In addition, the performance of a deep neural network will also decrease rapidly when it becomes too deep. In this article, we propose a novel click-through rate prediction model by improving the deep neural model part to alleviate the above problems of deep neural network-based models. This article proposes to utilize a dense deep neural network model to strengthen feature propagation, which takes the outputs of all previous layers as the input of the current layer, instead of only one previous layer being used in the deep neural network. In addition, we also utilize an advanced shallow model FmFM for better explicit features in this article, and explicit and implicit features are interacted in our model. Experiments on two data sets (Criteo and Avazu) show that the proposed click-through rate prediction model significantly outperforms existing classical models such as DeepFM, xDeepFM, and DeepLight models.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44269811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-26DOI: 10.1177/24723444221147966
Jian Li, Yunyi Wang, Jun Li, Rongfan Jiang
The sudden outbreak of COVID-19 has created dramatic challenges for public health and textile export trade worldwide. Such abrupt changes are difficult to predict due to the inherently high complexity and nonlinearity, especially with limited data. This article proposes a novel modified discrete grey model with weakening buffer operators, called BODGM (1,1), for forecasting the impact of pandemic-induced uncertainty on the volatility of cotton exports in China under limited samples. First, the Mann–Kendall test examines how pandemic-induced uncertainty affects cotton exports, based on China’s monthly cotton export data from June 2014 to August 2022. Second, buffer operators are employed to weaken the nonlinear trends and correct the tentative predictions of the discrete grey model. Then, the BODGM (1,1) model was validated by comparison with four alternative models. The results indicate that the BODGM (1,1) model was particularly promising for identifying mutational fluctuations in cotton exports and outperformed the GM (1,1), DGM (1,1), ARIMA and linear regression models in fitting and prediction accuracy under volatility and limited data. The BODGM (1,1) model forecast results for China showed that cotton export volume was expected to show signs of recovery over the next 12 months. The findings of this study may provide a basis for formulating trade policies to mitigate the impact of the COVID-19 outbreak on export resources and build their resilience to future pandemics.
{"title":"Forecasting the Impact of the COVID-19 Outbreak on China’s Cotton Exports by Modified Discrete Grey Model with Limited Data","authors":"Jian Li, Yunyi Wang, Jun Li, Rongfan Jiang","doi":"10.1177/24723444221147966","DOIUrl":"https://doi.org/10.1177/24723444221147966","url":null,"abstract":"The sudden outbreak of COVID-19 has created dramatic challenges for public health and textile export trade worldwide. Such abrupt changes are difficult to predict due to the inherently high complexity and nonlinearity, especially with limited data. This article proposes a novel modified discrete grey model with weakening buffer operators, called BODGM (1,1), for forecasting the impact of pandemic-induced uncertainty on the volatility of cotton exports in China under limited samples. First, the Mann–Kendall test examines how pandemic-induced uncertainty affects cotton exports, based on China’s monthly cotton export data from June 2014 to August 2022. Second, buffer operators are employed to weaken the nonlinear trends and correct the tentative predictions of the discrete grey model. Then, the BODGM (1,1) model was validated by comparison with four alternative models. The results indicate that the BODGM (1,1) model was particularly promising for identifying mutational fluctuations in cotton exports and outperformed the GM (1,1), DGM (1,1), ARIMA and linear regression models in fitting and prediction accuracy under volatility and limited data. The BODGM (1,1) model forecast results for China showed that cotton export volume was expected to show signs of recovery over the next 12 months. The findings of this study may provide a basis for formulating trade policies to mitigate the impact of the COVID-19 outbreak on export resources and build their resilience to future pandemics.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44239562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-26DOI: 10.1177/24723444221147977
Pui-ling Li, K. Yick, Li-Ying Zhang, Yin-Ching Keung
Foot morphological changes induced by degenerative processes are commonly found in old people. Such changes in foot anthropometry may adversely affect foot health and footwear comfort, and prolonged use of ill-fitting footwear may even cause foot deformation. This study compares foot anthropometric measurements between young and old women to determine key foot measurements, which can also act as indicators for developing footwear appropriate for the elderly. Using a three-dimensional handheld scanner, 11 foot anthropometric measurements are captured and used to characterize the dimensions and foot shape between young and old women. Eighty-two women between the ages of 20 and 95 years—that is, 41 young women (mean = 24.0; standard deviation = 3.5) and 41 old women (mean = 82.1; standard deviation = 7.2)—were recruited for this study. The results indicate that old women have a significantly longer and wider heel than young women as well as significantly larger ball and instep circumferences after normalization for foot length. Old women also exhibit larger deformity in the degree of hallux valgus and more pronated feet than young women do. A discriminant analysis linear equation has also been established to classify their foot type based on heel length and heel width with reference to their age group.
{"title":"Evaluation of Age-related Differences in Foot Anthropometry among Women","authors":"Pui-ling Li, K. Yick, Li-Ying Zhang, Yin-Ching Keung","doi":"10.1177/24723444221147977","DOIUrl":"https://doi.org/10.1177/24723444221147977","url":null,"abstract":"Foot morphological changes induced by degenerative processes are commonly found in old people. Such changes in foot anthropometry may adversely affect foot health and footwear comfort, and prolonged use of ill-fitting footwear may even cause foot deformation. This study compares foot anthropometric measurements between young and old women to determine key foot measurements, which can also act as indicators for developing footwear appropriate for the elderly. Using a three-dimensional handheld scanner, 11 foot anthropometric measurements are captured and used to characterize the dimensions and foot shape between young and old women. Eighty-two women between the ages of 20 and 95 years—that is, 41 young women (mean = 24.0; standard deviation = 3.5) and 41 old women (mean = 82.1; standard deviation = 7.2)—were recruited for this study. The results indicate that old women have a significantly longer and wider heel than young women as well as significantly larger ball and instep circumferences after normalization for foot length. Old women also exhibit larger deformity in the degree of hallux valgus and more pronated feet than young women do. A discriminant analysis linear equation has also been established to classify their foot type based on heel length and heel width with reference to their age group.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48544219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-26DOI: 10.1177/24723444221136635
Fangjian Liao, Xingxing Zou, W. Wong
This article proposes a generative adversarial networks (MiniGAN) to tackle both informative and uninformative image transferring. The generator of MiniGAN is based on the structure of StyleGANv2, in which the encoder and style transform block are proposed to extract the high-level feature maps of the source image and capture the latent representation of the target image, respectively. This information guides the generator for the final image generation. The proposed MiniGAN outperforms other models in style transferring while preserving the color information on the informative images. To test the performance of MiniGAN on the uninformative images, a new data set consisting of 10,000 fashion hand drawings is proposed. Extensive experiments and detailed analysis are presented to demonstrate the performance of MiniGAN.
{"title":"MiniGAN: Toward Informative and Uninformative Image Transferring","authors":"Fangjian Liao, Xingxing Zou, W. Wong","doi":"10.1177/24723444221136635","DOIUrl":"https://doi.org/10.1177/24723444221136635","url":null,"abstract":"This article proposes a generative adversarial networks (MiniGAN) to tackle both informative and uninformative image transferring. The generator of MiniGAN is based on the structure of StyleGANv2, in which the encoder and style transform block are proposed to extract the high-level feature maps of the source image and capture the latent representation of the target image, respectively. This information guides the generator for the final image generation. The proposed MiniGAN outperforms other models in style transferring while preserving the color information on the informative images. To test the performance of MiniGAN on the uninformative images, a new data set consisting of 10,000 fashion hand drawings is proposed. Extensive experiments and detailed analysis are presented to demonstrate the performance of MiniGAN.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49352189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1177/24723444221147978
Xianqin Shang, Qiang Wang, Zhe Jiang, Haitao Ma
In order to enhance the shrink-resistant properties of wool to achieve a machine washable effect, the effect of water-soluble polyurethane polymer on a worsted wool fabric which was modified by liquid ammonia and protease was investigated. The worsted wool fabric was pretreated by a continuous liquid ammonia finishing machine and then treated by protease, followed by polyurethane nano-emulsion coating. The results showed that, after liquid ammonia and protease treatment, the surface scales of wool were seriously damaged, even partly peeling off, and the disulfide bond content of the wool decreased while the active group content of the wool increased. Furthermore, after polyurethane finishing, the surface scale and the gap between the scales were covered with a thin film, and the area shrinkage reached 3.1% when the concentration of polyurethane was 20 g/L, showing an effective improvement in the shrink resistance of the worsted fabric. As far as our knowledge goes, this is a systematic report on the synergistic effect of liquid ammonia, protease, and polyurethane on the shrink resistance of wool fiber, and provides a new method for the commercial application of shrink-resistant finishing of wool fabric.
{"title":"Synergetic Effects of Liquid Ammonia, Protease, and Polyurethane Nano-Emulsion on Improving Shrink Resistance of Wool","authors":"Xianqin Shang, Qiang Wang, Zhe Jiang, Haitao Ma","doi":"10.1177/24723444221147978","DOIUrl":"https://doi.org/10.1177/24723444221147978","url":null,"abstract":"In order to enhance the shrink-resistant properties of wool to achieve a machine washable effect, the effect of water-soluble polyurethane polymer on a worsted wool fabric which was modified by liquid ammonia and protease was investigated. The worsted wool fabric was pretreated by a continuous liquid ammonia finishing machine and then treated by protease, followed by polyurethane nano-emulsion coating. The results showed that, after liquid ammonia and protease treatment, the surface scales of wool were seriously damaged, even partly peeling off, and the disulfide bond content of the wool decreased while the active group content of the wool increased. Furthermore, after polyurethane finishing, the surface scale and the gap between the scales were covered with a thin film, and the area shrinkage reached 3.1% when the concentration of polyurethane was 20 g/L, showing an effective improvement in the shrink resistance of the worsted fabric. As far as our knowledge goes, this is a systematic report on the synergistic effect of liquid ammonia, protease, and polyurethane on the shrink resistance of wool fiber, and provides a new method for the commercial application of shrink-resistant finishing of wool fabric.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":"10 1","pages":"194 - 201"},"PeriodicalIF":0.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46231626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1177/24723444221147983
Emire Ülkü Parmakoğlu, A. Çay, J. Yanık
In this study, recycling of solid textile wastes into activated carbon and the potential use of these activated carbons in color removal were investigated. Cotton and cotton/polyester-blended fabric wastes and textile wastewater treatment sludge were selected as solid textile wastes. A two-stage method, low temperature carbonization, and chemical activation with ZnCl2 were applied to prepare large surface area and rich-pore structure activated carbon from fabric wastes and sludge in textile industry. The activated carbons were characterized by elemental analysis, Fourier-transform infrared spectroscopy, X-ray fluorescent, and Brunauer–Emmett–Teller analysis. The reactive dye (Reactive Red 141) adsorption capacity of the activated carbons was investigated by the batch adsorption method. Activated carbon yields were found in the range of 28–34%. Cotton textile waste-based activated carbons were found to have the highest surface area (~1380 m2/g), followed by cotton/polyester and wastewater treatment sludge-based activated carbons. Wastewater treatment sludge-based activated carbons showed the highest dye adsorption capacity (161.29 mg/g), possibly due to its higher mesoporosity. The obtained results showed that the adsorption of the reactive dye onto textile waste-based activated carbons produced by two-step process is favorable. The developed activated carbons fit the circular economy approach well, offering a promising potential for solid textile waste management, as well as for water remediation.
{"title":"Valorization of Solid Wastes from Textile Industry as an Adsorbent Through Activated Carbon Production","authors":"Emire Ülkü Parmakoğlu, A. Çay, J. Yanık","doi":"10.1177/24723444221147983","DOIUrl":"https://doi.org/10.1177/24723444221147983","url":null,"abstract":"In this study, recycling of solid textile wastes into activated carbon and the potential use of these activated carbons in color removal were investigated. Cotton and cotton/polyester-blended fabric wastes and textile wastewater treatment sludge were selected as solid textile wastes. A two-stage method, low temperature carbonization, and chemical activation with ZnCl2 were applied to prepare large surface area and rich-pore structure activated carbon from fabric wastes and sludge in textile industry. The activated carbons were characterized by elemental analysis, Fourier-transform infrared spectroscopy, X-ray fluorescent, and Brunauer–Emmett–Teller analysis. The reactive dye (Reactive Red 141) adsorption capacity of the activated carbons was investigated by the batch adsorption method. Activated carbon yields were found in the range of 28–34%. Cotton textile waste-based activated carbons were found to have the highest surface area (~1380 m2/g), followed by cotton/polyester and wastewater treatment sludge-based activated carbons. Wastewater treatment sludge-based activated carbons showed the highest dye adsorption capacity (161.29 mg/g), possibly due to its higher mesoporosity. The obtained results showed that the adsorption of the reactive dye onto textile waste-based activated carbons produced by two-step process is favorable. The developed activated carbons fit the circular economy approach well, offering a promising potential for solid textile waste management, as well as for water remediation.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":"10 1","pages":"133 - 143"},"PeriodicalIF":0.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43527675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1177/24723444221147982
Muhammad Fahad Arain, H. Memon, Mingxue Wang, Arsalan Ahmed, Jianyong Chen, Huapeng Zhang
The main concern for the limited practical applications of strain-hardening cementitious composite, especially in China, is the high cost of imported materials, mainly polyvinyl alcohol fibers. This study uses local ingredients to develop strain-hardening cementitious composite reinforced with non-oil-coated Chinese polyvinyl alcohol fiber. The cementitious matrix consisting of cement, fly ash, viscosity-modifying agent, and silica fume was prepared, and the matrix tailoring was performed to achieve improved mechanical performance. The prepared composites were evaluated by rheology, three-point bending, and tensile characterizations. It is found that with the given local ingredients and matrix modification, the toughness index value of 100 (I40) according to the American Society for Testing and Materials can be achieved. Besides, the tensile results showed the improvement of 45% and 60% for first-cracking strength and peak tensile strength values, respectively. The effects of matrix modification are also analyzed statistically using the analysis of variance for the mechanical properties of the cementitious composite. The post hoc statistical analysis using the Tukey–Kramer honestly significant difference illustrated the optimum cementitious mix from the experimental data. The presented results of cost-effective strain-hardening cementitious composite are expected to promote the practical applications of strain-hardening cementitious composite in China.
{"title":"Matrix Tailoring for Polyvinyl Alcohol (PVA) Fiber-Reinforced Ductile Cementitious Composites","authors":"Muhammad Fahad Arain, H. Memon, Mingxue Wang, Arsalan Ahmed, Jianyong Chen, Huapeng Zhang","doi":"10.1177/24723444221147982","DOIUrl":"https://doi.org/10.1177/24723444221147982","url":null,"abstract":"The main concern for the limited practical applications of strain-hardening cementitious composite, especially in China, is the high cost of imported materials, mainly polyvinyl alcohol fibers. This study uses local ingredients to develop strain-hardening cementitious composite reinforced with non-oil-coated Chinese polyvinyl alcohol fiber. The cementitious matrix consisting of cement, fly ash, viscosity-modifying agent, and silica fume was prepared, and the matrix tailoring was performed to achieve improved mechanical performance. The prepared composites were evaluated by rheology, three-point bending, and tensile characterizations. It is found that with the given local ingredients and matrix modification, the toughness index value of 100 (I40) according to the American Society for Testing and Materials can be achieved. Besides, the tensile results showed the improvement of 45% and 60% for first-cracking strength and peak tensile strength values, respectively. The effects of matrix modification are also analyzed statistically using the analysis of variance for the mechanical properties of the cementitious composite. The post hoc statistical analysis using the Tukey–Kramer honestly significant difference illustrated the optimum cementitious mix from the experimental data. The presented results of cost-effective strain-hardening cementitious composite are expected to promote the practical applications of strain-hardening cementitious composite in China.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":"10 1","pages":"63 - 72"},"PeriodicalIF":0.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47926437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1177/24723444221147975
Esha Sharma, S. Ralebhat, Dhirendra B. Singh, Gurudatt Krishnamurthy, S. Bhagwat, R. Adivarekar
A mixture of minerals consisting of calcite, dolomite, talc and quartz, known to have infrared (IR) reflecting properties, was mechanically treated and dispersed in aqueous medium and incorporated in viscose solution to be physically entrapped into the fibre structure. The process of incorporation of the mineral mixture in viscose dope involved steps of slurry making by suspending and grinding the particles in aqueous medium followed by mixing the slurry with the viscose dope, ready for spinning. The slurry preparation and grinding were carried out under different mechanical conditions, such as ball milling and ultrasonication. The evaluation of particle size of slurry was carried out under two pH conditions, first at the inherent pH (neutral pH 7) and at high pH (alkaline pH 13) equivalent to the pH of viscose dope. Furthermore, for stable slurry making, different surfactants were used. The phosphate ether-based anionic surfactant was found to effectively stabilize the dispersion better under the pH conditions used. The effect of stable slurry on good spinning was validated through an inline pressure gauge during viscose fibre spinning. To assess the IR reflecting property of viscose fibre, a direct visual evaluation was done through an IR camera, which indicated a significant increase of ~2°C surface temperature of the IR-Viscose Staple Fibre (VSF) in comparison with control VSF.
{"title":"Studies on Incorporating Infrared Reflecting Minerals into Viscose Fibres","authors":"Esha Sharma, S. Ralebhat, Dhirendra B. Singh, Gurudatt Krishnamurthy, S. Bhagwat, R. Adivarekar","doi":"10.1177/24723444221147975","DOIUrl":"https://doi.org/10.1177/24723444221147975","url":null,"abstract":"A mixture of minerals consisting of calcite, dolomite, talc and quartz, known to have infrared (IR) reflecting properties, was mechanically treated and dispersed in aqueous medium and incorporated in viscose solution to be physically entrapped into the fibre structure. The process of incorporation of the mineral mixture in viscose dope involved steps of slurry making by suspending and grinding the particles in aqueous medium followed by mixing the slurry with the viscose dope, ready for spinning. The slurry preparation and grinding were carried out under different mechanical conditions, such as ball milling and ultrasonication. The evaluation of particle size of slurry was carried out under two pH conditions, first at the inherent pH (neutral pH 7) and at high pH (alkaline pH 13) equivalent to the pH of viscose dope. Furthermore, for stable slurry making, different surfactants were used. The phosphate ether-based anionic surfactant was found to effectively stabilize the dispersion better under the pH conditions used. The effect of stable slurry on good spinning was validated through an inline pressure gauge during viscose fibre spinning. To assess the IR reflecting property of viscose fibre, a direct visual evaluation was done through an IR camera, which indicated a significant increase of ~2°C surface temperature of the IR-Viscose Staple Fibre (VSF) in comparison with control VSF.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":"10 1","pages":"144 - 152"},"PeriodicalIF":0.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43518881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-13DOI: 10.1177/24723444221147976
N. Özdil, E. S. Dalbaşı, A. Özgüney, Leman Atiker
Environmental pollution threatens the life of the world nowadays. Increasing the world population, rapidly changing fashion trends, and marketing activities have caused serious increases in textile production and consumption. As the size of the production volume increases, the effects of the textile industry on the ecosystem are increased as well. Recycling is critical in textiles to use less energy, water, and chemicals and to pollute our environment less. The aim of this study to develop functional and high value-added upholstery fabrics by recycling textile wastes. In the scope of this study, the effects of wastes obtained from different sources and different recycling fiber ratios on fabric properties were investigated. Furthermore, fabrics containing recycled cotton wastes blended with polyester fiber were also included. First, the abrasion resistance test was carried out on the fabrics produced from these waste fibers, and the fabrics with the best performance for upholstery fabrics were determined, and then flame retardant, water repellency, and soil release finishing processes were applied to these fabrics. It was concluded that the flame retardant, water, and stain repellent upholstery fabrics from recycled fibers were successfully developed. Also, fabrics containing 15% recycled cotton fabrics obtained better results among the fabrics.
{"title":"Multifunctional Modification with TiO2, SiO2, and Flame Retardant Agent on Upholstery Fabrics Produced From Recycled Cotton Fibers","authors":"N. Özdil, E. S. Dalbaşı, A. Özgüney, Leman Atiker","doi":"10.1177/24723444221147976","DOIUrl":"https://doi.org/10.1177/24723444221147976","url":null,"abstract":"Environmental pollution threatens the life of the world nowadays. Increasing the world population, rapidly changing fashion trends, and marketing activities have caused serious increases in textile production and consumption. As the size of the production volume increases, the effects of the textile industry on the ecosystem are increased as well. Recycling is critical in textiles to use less energy, water, and chemicals and to pollute our environment less. The aim of this study to develop functional and high value-added upholstery fabrics by recycling textile wastes. In the scope of this study, the effects of wastes obtained from different sources and different recycling fiber ratios on fabric properties were investigated. Furthermore, fabrics containing recycled cotton wastes blended with polyester fiber were also included. First, the abrasion resistance test was carried out on the fabrics produced from these waste fibers, and the fabrics with the best performance for upholstery fabrics were determined, and then flame retardant, water repellency, and soil release finishing processes were applied to these fabrics. It was concluded that the flame retardant, water, and stain repellent upholstery fabrics from recycled fibers were successfully developed. Also, fabrics containing 15% recycled cotton fabrics obtained better results among the fabrics.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":"10 1","pages":"223 - 231"},"PeriodicalIF":0.7,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42732169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1177/24723444221136626
Rong Huang, Runchao Lin, Aihua Dong, Zhijie Wang
Recently, a DCNet consisting of a dense relation distillation module and a context-aware aggregation module has achieved remarkable performance for the few-shot object detection task. In this article, we aim to improve the DCNet from the following two aspects. First, we design an adaptive attention module, which is equipped in the front of the dense relation distillation module, and can be trained together with the remainder parts of the DCNet. After training, the adaptive attention module helps to enhance foreground features and to suppress the background features. Second, we introduce a large-margin Softmax into the dense relation distillation module. The large-margin Softmax with a hyperparameter can normalize features without reducing the discriminability between different classes. We conduct extensive experiments on the PASCAL visual object classes and the Microsoft common objects in context data sets. The experimental results show that the proposed method can work under the few-shot scenario and achieves the mean average precision of 50.8% on the PASCAL visual object classes data set and 13.1% on the Microsoft common objects in context data set, which both outperform the existing baselines. Moreover, ablation studies and visualizations validate the usefulness of the adaptive attention module and the large-margin Softmax. The proposed method can be applied to recognize rare patterns in fabric images or detect clothes with new styles in natural scene images.
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