The enhancement of fabric quality prediction in the textile manufacturing sector is achieved by utilizing information derived from sensors within the Internet of Things (IoT) and Enterprise Resource Planning (ERP) systems linked to sensors embedded in textile machinery. The integration of Industry 4.0 concepts is instrumental in harnessing IoT sensor data, which, in turn, leads to improvements in productivity and reduced lead times in textile manufacturing processes. This study addresses the issue of imbalanced data pertaining to fabric quality within the textile manufacturing industry. It encompasses an evaluation of seven open-source automated machine learning (AutoML) technologies, namely FLAML (Fast Lightweight AutoML), AutoViML (Automatically Build Variant Interpretable ML models), EvalML (Evaluation Machine Learning), AutoGluon, H2OAutoML, PyCaret, and TPOT (Tree-based Pipeline Optimization Tool). The most suitable solutions are chosen for certain circumstances by employing an innovative approach that finds a compromise among computational efficiency and forecast accuracy. The results reveal that EvalML emerges as the top-performing AutoML model for a predetermined objective function, particularly excelling in terms of mean absolute error (MAE). On the other hand, even with longer inference periods, AutoGluon performs better than other methods in measures like mean absolute percentage error (MAPE), root mean squared error (RMSE), and r-squared. Additionally, the study explores the feature importance rankings provided by each AutoML model, shedding light on the attributes that significantly influence predictive outcomes. Notably, sin/cos encoding is found to be particularly effective in characterizing categorical variables with a large number of unique values. This study includes useful information about the application of AutoML in the textile industry and provides a roadmap for employing Industry 4.0 technologies to enhance fabric quality prediction. The research highlights the importance of striking a balance between predictive accuracy and computational efficiency, emphasizes the significance of feature importance for model interpretability, and lays the groundwork for future investigations in this field.

The key to high-quality development in the textile and apparel industry lies in enhancing technological innovation and optimizing the efficiency of technological innovation. Based on data from 60 A-share listed companies in the textile and apparel sector in China from 2013 to 2022, this study employs a three-stage DEA model and the Malmquist index model to measure changes in technological innovation efficiency from static and dynamic perspectives. Additionally, it uses a Tobit model to analyze the impact and mechanisms of management and financial factors on technological innovation efficiency. The results indicate that: (1) Compared to the manufacturing industry and its sub-sectors, the overall technological innovation efficiency of listed textile and apparel companies was relatively low and showed a declining trend between 2013 and 2022; (2) Over the decade, the average total factor productivity of these listed companies increased by 1.7%, exhibiting a \"W\" shaped fluctuation, with technological progress, pure technical efficiency, and scale efficiency all showing weak improvement; (3) Management and financial factors significantly influence technological innovation efficiency. Specifically, employee quality, profitability, and operational capability are positively correlated with technological innovation efficiency and have long-term effectiveness, while firm age, management costs, equity concentration, development ability, and debt repayment capacity are negatively correlated with technological innovation efficiency; (4) Different types of enterprises show differences in the significance of management factors, while whether the same person holds both managerial positions significantly affects financial factors.

In the industrial sector, vegetable residual materials have received attention in the production of bio-colorant for textile dyeing. The current research endeavor is centered on investigating the possibility of using sugar beet leaves as a natural source of dye for the purpose of dyeing cotton fabrics. Different extraction methods were utilized to isolate the bio-colorant present in sugar beet residual material, and the most favorable colorant yield was obtained using a 5% methanolic KOH solution. For optimal dyeing results, the cotton fabric performed dyeing for a duration of 45 min at a temperature of 60 °C, using a salt solution concentration of 6 g/100 mL and 50 mL of the extracted dye solution. Characterization of dye using Fourier transform infrared spectroscopy analysis confirmed the presence of quercetin in the leaf extract. For the creation of a range of color variations, mordants that were chemical in nature, such as tannic acid, iron sulfate, potassium dichromate, and copper sulfate, as well as mordants that were bio-based, such as onion peel, pomegranate peel, henna, golden shower bark, and turmeric, were employed in harmony. In comparison, the utilization of bio-mordants resulted in darker shades that exhibited enhanced color intensity and superior color fastness properties with the value of 4-5 for wash, 4 for wet rubbing, 4-5 for dry rubbing, and 4-5 for light. The findings of this study hold significant value in terms of ecofriendly waste management and contribute to advancements in the industrial sector by utilizing waste residual materials as a natural source of colorants.

UNASSIGNED: Some synthetic dyes used mainly in textile industries have been associated with endocrine disruption, resulting in infertility, among other disorders. It is unknown if occupational exposure to Vat textile dyes among premenopausal dyers alters hormonal levels.
UNASSIGNED: We aimed at determining the probable effects of occupational exposure to Vat dyes on reproductive hormones of female textile dyers in the follicular and luteal phases while relating this to age categories and duration of exposure.
UNASSIGNED: Thirty-three premenopausal Vat textile dyers at \"Itoku\", Abeokuta, Nigeria, among a population of about 80 female dyers were age and sex-matched with 55 non-exposed (control) female participants. Using semi-structured questionnaires, socio-demographic, occupational details and the LMP of participants were obtained. Serum samples were collected in follicular and luteal phases and assayed for female sex hormones using Enzyme Immunoassay. Mann-Whitney U and Z- statistic were used for comparison of the two groups. P-value < 0.05 was considered to be significant.
UNASSIGNED: In the follicular phase, the result showed a lower mean FSH ranking (in age category ≤20 years) and higher (p<0.05) Estradiol ranking (in age category 31-40 years) in the exposed than the unexposed. Mean ranks of Progesterone and Estradiol in the luteal phase (age category 31-40 years) were higher (p<0.05) in the exposed, while Estradiol (age category ≥41years) ranked lower (p<0.05). Prolactin demonstrated a significant inverse relationship with the duration of exposure.
UNASSIGNED: Occupational exposure to Vat dye among female dyers in Abeokuta is associated with some sex hormone disruption which appears to be age and duration of exposure-related.

The development of the textile industry has negative effects on the natural environment. Cotton cultivation, dyeing fabrics, washing, and finishing require a lot of water and energy and use many chemicals. One of the most dangerous pollutants generated by the textile industry is dyes. Most of them are characterized by a complex chemical structure and an unfavorable impact on the environment. Especially azo dyes, whose decomposition by bacteria may lead to the formation of carcinogenic aromatic amines and raise a lot of concern. Using the metabolic potential of microorganisms that biodegrade dyes seems to be a promising solution for their elimination from contaminated environments. The development of omics sciences such as genomics, transcriptomics, proteomics, and metabolomics has allowed for a comprehensive approach to the processes occurring in cells. Especially multi-omics, which combines data from different biomolecular levels, providing an integrative understanding of the whole biodegradation process. Thanks to this, it is possible to elucidate the molecular basis of the mechanisms of dye biodegradation and to develop effective methods of bioremediation of dye-contaminated environments.

Efficiently mitigating and managing environmental pollution caused by the improper disposal of dyes and effluents from the textile industry is of great importance. This study evaluated the effectiveness of Streptomyces albidoflavus 3MGH in decolorizing and degrading three different azo dyes, namely Reactive Orange 122 (RO 122), Direct Blue 15 (DB 15), and Direct Black 38 (DB 38). Various analytical techniques, such as Fourier Transform Infrared (FTIR) spectroscopy, High-Performance Liquid Chromatography (HPLC), and Gas Chromatography-Mass Spectrometry (GC-MS) were used to analyze the degraded byproducts of the dyes. S. albidoflavus 3MGH demonstrated a strong capability to decolorize RO 122, DB 15, and DB 38, achieving up to 60.74%, 61.38%, and 53.43% decolorization within 5 days at a concentration of 0.3 g/L, respectively. The optimal conditions for the maximum decolorization of these azo dyes were found to be a temperature of 35 °C, a pH of 6, sucrose as a carbon source, and beef extract as a nitrogen source. Additionally, after optimization of the decolorization process, treatment with S. albidoflavus 3MGH resulted in significant reductions of 94.4%, 86.3%, and 68.2% in the total organic carbon of RO 122, DB 15, and DB 38, respectively. After the treatment process, we found the specific activity of the laccase enzyme, one of the mediating enzymes of the degradation mechanism, to be 5.96 U/mg. FT-IR spectroscopy analysis of the degraded metabolites showed specific changes and shifts in peaks compared to the control samples. GC-MS analysis revealed the presence of metabolites such as benzene, biphenyl, and naphthalene derivatives. Overall, this study demonstrated the potential of S. albidoflavus 3MGH for the effective decolorization and degradation of different azo dyes. The findings were validated through various analytical techniques, shedding light on the biodegradation mechanism employed by this strain.

The study explores the strategic pricing and quality improvement decisions under uncertain demand in a three-layer textile and garment supply chain. According to whether the fabric manufacturer (FM) invests in quality or not and whether the garment manufacturer (GM) or garment retailer (GR) is willing to share the costs or not, five game models are constructed to investigate the impact of different members\' cost sharing on the optimal decisions and profits. By conducting a theoretical and numerical analysis, we find that: (1) The GM\'s or GR\'s cost sharing plays a positive effect on the quality improvement, as for whose cost sharing performs better in improving the quality depending on the proportion of cost sharing, and the quality improvement is highest with both members share the costs simultaneously. (2) The FM receives the highest profit when both members share the costs simultaneously, however, whose cost sharing is more profitable for the FM is also related to the proportion of cost sharing; in short, the FM always benefits from the cost sharing, no matter one member does this or two members do this. (3) The GM (GR) gains the highest profit when only the GR (GM) shares the costs, and the results indicate that if one member has shared the costs, whether the other member engaging in cost sharing could benefit the former depending on their proportions. Specifically, when the GM (GR) chooses to share the costs and the proportion is relatively low, the GR(GM) joining in cost sharing is beneficial to the former; otherwise, is harmful.

Alkylphenol ethoxylates comprise of many anthropogenic chemicals such as nonylphenol (NP), octylphenol (OP) and nonylphenol ethoxylates (NPEOs). The objectives of this study were to assess the frequency and magnitude of detections of 4-NP, OP and NPEOs in Canadian sediment downstream of textile associated municipal wastewater treatment plants (MWWTPs) to determine if regulatory actions have had a beneficial impact on the receiving environment. Surficial sediments were obtained in four locations in the province of Québec (Canada) and were analyzed for nonylphenol, nonylphenol monoethoxylates (NP1EO), nonylphenol diethoxylates (NP2EO) and octylphenol from 2015 to 2018. Individual concentrations of the compounds varied from non detect to 419 ng/g. Of the four compounds analyzed, NP was detected the most frequently with a 75% detection rate while OPs were not detected in any of the samples. Since the Canadian regulatory actions have drastically reduced NP/NPEOs usage in textile mill factories and manufactured products, the potential source of these compounds in sediment for this study could stem from the outfall from the MWWTPs but not related to textile mills as well as from the usage of these compounds as formulants in pesticide products. Lastly, there were no exceedances to the Canadian Sediment Quality guideline toxic equivalency approach (TEQ) of 1400 ng/g or the 1310 ng/g guideline for NP in freshwater sediment from the European Scientific Committee on Health, Environmental and Emerging Risks. We hypothesize that the significant concentrations of these compounds in sediment may be a relevant and continuous source of 4NP in surface waters due to resuspension of sediment in the water column.

Laccase is an exothermic enzyme with copper in its structure and has an important role in biodegradation by providing oxidation of phenolic compounds and aromatic amines and decomposing lignin. The aim of this study is to reach maximum laccase enzyme activity with minimum cost and energy through optimization studies of Proteusmirabilis isolated from treatment sludge of a textile factory. In order to increase the laccase enzyme activities of the isolates, medium and culture conditions were optimized with the study of carbon (Glucose, Fructose, Sodium Acetate, Carboxymethylcellulose, Xylose) and nitrogen sources (Potassium nitrate, Yeast Extract, Peptone From Soybean, Bacteriological Peptone), incubation time, pH, temperature and Copper(II) sulfate concentration then according to the results obtained. Response Surface Method (RSM) was performed on six different variables with three level. According to the data obtained from the RSM, the maximum laccase enzyme activity is reached at pH 7.77, temperature 30.03oC, 0.5 g/L CuSO4, 0.5 g/L fructose and 0.082 g/L yeast extract conditions. After all, the laccase activity increased 2.7 times. As a result, laccase activity of P. mirabilis can be increased by optimization studies. The information obtained as a result of the literature studies is that the laccase enzymes produced in laboratory and industrial scale are costly and their amounts are low. This study is important in terms of obtaining more laccase activity from P.mirabilis with less cost and energy.

This paper develops an outsourcing collaboration model from a firm\'s perspective operating in a developing economy. The model considers that producers of the final goods residing in a developed country, and operators of manufacturing plants in a developing country collaborate with each other. The final goods producer supplies headquarter services for the production of intermediate goods in the developing country. The operators of manufacturing plants also supply their services in the domestic economy. This arrangement leads to foreign outsourcing collaborations (FOC) between firms of developed country and developing country. The operators of manufacturing plant maximize revenue subject to the cost constraint. The first order conditions suggests that an increase in wages of skilled labor, price of domestic inputs, and cost of production deter FOC. On the other hand, an increase in demand for and price of foreign headquarter services increases the FOC. Empirical analysis based on data collected from 217 clothing (textile and apparel) firms in the city of Faisalabad (Pakistan) reveals that an increase in wage to labor-productivity ratio reduces FOC. An improvement in skilled of the labor and foreign headquarter services give rise to FOC, whereas an increase in economies-of-scope enhances FOC. Additionally, an inverted U-shaped relationship is found between the cost of production and FOC, which shows that at the initial stage, the firm\'s cost of production increases with an increase in the level of FOC, but soon after the tipping point, the firm\'s cost starts decreasing with a further increase in FOC.