Identification of virgin nylon and recycled nylon based on HPLC and ResNet

WITHDRAWN: Sustainable circular supplier selection for textile manufacturing industries in India: An integrated WINGS-MABAC approach

Identification of maize leaf diseases using red, green, blue-based images with convolutional neural network (CNN) and residual network (ResNet50) models

Textile industry is China's traditional leading industries and pillar industries. Total economic output of Textile industry occupy a pivotal position in all large-scale industries, and is the pillar industry in Jiangsu province's industrial economy, and makes an important contribution for the province's economic development and employment. This article discusses existing problems and countermeasures to address these issues of textile export in the Jiangsu Province. At present, Textile Industry is undergoing a critical period of structural adjustment and industrial upgrading. The textile export industry of Jiangsu Province would continue to grow and develop in the increasingly open a mature socialist economy with Chinese characteristics and a closely intertwined world economy.

Correctly handling the relationship between economic development and pollution emissions in the textile industry is an important basis for achieving green and sustainable economic development in the textile industry. This paper takes the textile industry as the research object, selects the textile industry micro data from 1998 to 2013, firstly uses the centre of gravity model to explore the spatial and temporal trajectory of the centre of gravity of the textile industry's economy and pollution emission; then uses the grey correlation method to measure the correlation between the textile industry and six sub-sectors in two dimensions of economic development and pollution emission; finally uses the centre of gravity coupling posture model to explore the coupling posture of the centre of gravity of the textile industry's economy and the centre of gravity of pollution emission The results show that: (1) from 1998 to 2013, the economic centre of gravity of the textile industry was always located within the Anhui provincial boundary; and the output value of the cotton, chemical fibre textile and dyeing and finishing industries had the greatest impact on the economic growth of the textile industry. (2) The centre of gravity of the textile industry's pollution emissions from 1998-2013 moved along Shanxi-Henan-Hubei-Shaanxi to the western region; the pollution emissions from the manufacture of textile manufactured goods and the cotton, chemical fibre textile and dyeing finishing industries were the main factors causing the pollution problems in the textile industry; (3) From 1998 to 2013, the coupling between the textile economy and the centre of gravity of pollution emissions showed a "W"-shaped trend change, and was influenced by the strategy of shifting the centre of gravity of the textile industry to the west. The purpose of this paper is to provide theoretical suggestions for the green development of the textile industry economy by studying the relationship between the textile industry economy and the focus of pollution emissions.

Rapid detection assay for the molecular imprinting of gossypol using a two-layer PMAA/SiO2 bulk structure with a piezoelectric imprinting sensor


 
 
 As a result of urbanization, constructions of high-rise buildings become a major component of urban development. Constructing high-rise buildings contributes to social, economic and environmental development as well. There are many stages of construction including planning, designing, procurement and implementation. There is a wide range of sustainability practices which can be implemented in each stage of construction. In leading Sri Lanka to Sustainable Development, the green building rating system was introduced by National Green Building Council of Sri Lanka in 2002. This rating system has 100 total points and minimum of 40 points is required for green building certification. The rating system has different components. Among them sustainable sites category includes 25% of total points in green building rating system. This research study highlights the significance of sustainable sites category in order to award the green certification in green building rating system. For this research five major high-rise building construction sites in Colombo were selected and existing site selection practices of each site were investigated. Each site was visited and in-depth discussions were carried out with the planning engineer and other people in the construction site. The structured questionnaire was filled and scores were given accordingly where they have met the sustainable sites criteria. Two sites out of five could not earn single point as they did not fulfill the prerequisites of the site section category. The rest of the sites (three) earned 20, 19, and 18 respectively out of 25 points. In this study, lack of awareness in sustainable site selection practices has dropped the chances of being eligible for the green building certification. Since the minimum points required for green building certification is 40, buildings which are under planning or pre-construction stage have higher chances of obtaining silver, gold and platinum awards for green building certification. Therefore, sustainable sites category of the green building rating system contributes significantly to increase the score and therefore it is highly important in green certification & sustainable construction.
 Keywords: Green concept, High-rise buildings, Sustainable development, Sustainable site selection
 
 

The objective of this article is to study sustainability of the European textile industry. The descriptive-analytical method is used to fulfill the aim - the theoretical aspects of the researched scientific field are investigated on the basis of a current literature survey. The collected information is summarized and analyzed using a systematic approach; an analytical approach; a study of the works of authors in the field; a comparative analysis; an observational method; and the determination of relative shares. The textile industry in the context of the circular economy and the Union's policies should lead to improvements in the environmental performance of textile raw materials and materials, production, consumption, textile waste management, as well as their reduction, recycling, processing and environmental impact calculation. The sustainability of the textile industry can also be realized through the development and implementation of digitalization. Sustainability development of textile industry includes the three dimensions of sustainability: social, ecological and economic, which can be implemented in the context of the circular economy, digitalization and Sustainable Development Goals for the textile industry, in line with Europe 2030 policies. Keywords: sustainability, circular economy, digitalization, textile sector, European textile industry.

Diagnosing diabetic eye diseases by doctors using medical equipment requires significant resources. It is advisable to use automated tools. Using combinations of models improves classification accuracy. The features of the architectures of convolutional neural networks EfficientNetB7 and ResNet50 are presented. The creation of a neural network model by concatenating the EfficientNetB7 and ResNet50 models is justified. Transfer learning is applied. The GlobalAveragePooling2D layer is added to each model. The models are combined using the Concatenate layer. The Flatten layer is added to the resulting model to convert the vector into a one-dimensional array. Two Dropout layers are added to prevent overtraining. Two Dense layers with 512 and 256 neurons and the ReLU activation function are added for nonlinear data transformation and abstract feature extraction. A Dense layer with 4 neurons and the softmax activation function is added to determine the image class. l2-regularization is used in all Dense layers. The developed neural network model was applied to process a dataset of 4 classes: cataract images, diabetic retinopathy images, glaucoma images, and healthy retina images. The model is compiled using the Adam optimizer, the categorical cross-entropy loss function. The callback functions ModelCheckpoint, LearningRateScheduler, EarlyStopping, and ReduceLROnPlateau are used to adjust the learning rate. The validation accuracy of the model is improved by augmentation (horizontal and vertical flipping), using l2-regularization, Dropout, and adjusting the callback functions. The training lasted 30 epochs. The best validation accuracy of 97.39% was achieved at the 29th epoch. The best value of the validation function 0.4323 was achieved at the 30th epoch. The proposed neural network model outperforms the accuracy indicators of models proposed in similar studies. The model can be applied to disease detection and classification tasks.

The recognition accuracy of traditional image recognition methods is heavily dependent on the design of complicated and tedious hand-crafted features. In view of the problems of poor accuracy and complicated feature extraction, this study presents a methodology for the estimation of the severity of wheat Fusarium head blight (FHB) with a small sample dataset based on transfer learning technology and convolutional neural networks (CNNs). Firstly, we utilized the potent feature learning and feature expression capabilities of CNNs to realize the automatic learning of FHB characteristics. Using transfer learning technology, VGG16, ResNet50, and MobileNetV1 models were pre-trained on the ImageNet. The knowledge was transferred to the estimation of FHB severity, and the fully connected (FC) layer of the models was modified. Secondly, acquiring the wheat images at the peak of the outbreak of FHB as the research object, after preprocessing for size filling on the wheat images, the image dataset was expanded with operations such as mirror flip, rotation transformation, and superimposed noise to improve the performance of the model and reduce the overfitting of models. Finally, under the Tensorflow deep learning framework, the VGG16, ResNet50, and MobileNetV1 models were subjected to transfer learning. The results showed that in the case of transfer learning and data augmentation, the ResNet50 model in Accuracy, Precision, Recall, and F1 score was better than the other two models, giving the highest accuracy of 98.42% and F1 score of 97.86%. The ResNet50 model had the highest recognition accuracy, providing technical support and reference for the accurate recognition of FHB.

Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants.

Economic, financial and political development has strongly influenced on the textile industry, which accelerated the pace of change. In order to catch the pace it is necessary to take certain steps now or in the near future. The Far East countries record high economic growth, while other, mostly developed industrial countries growth has considerably declined. Consumer behaviour tends to restrain from purchasing of clothing products, raw material prices are growing and lack of raw materials on the market is noticeable. These trends are causing a certain amount of restlessness in the textile industry. The textile and clothing industry have their distinctive features visible in a manufacturing sector which dominantly depends on brand name firms that spread their business all over the world. Production mainly takes place in developing and fast growing countries, since their production destinations, working conditions and wages are most affordable. For example, about 90 % of clothing items sold in northern countries are produced in Eastern Europe or at the Far East. At the same time, in the northern countries, where most of clothing products are sold, manufacturing facilities of the clothing industry almost doesn’t exist.

High-performance liquid chromatography (HPLC) coupled to atmospheric pressure ionization (API) mass spectrometry (MS) has become a useful technique in the direct analysis of low concentrations of conjugated opiate metabolites. Previous methods using HPLC with traditional detection methods do not have the sensitivity to detect low concentrations of most conjugated drug metabolites. Methods using gas chromatography-mass spectrometry (GC-MS) require hydrolysis and derivatization of the sample followed by an indirect quantitation of conjugated metabolites. Recently, several reports have described direct analysis of opiates and their glucuronide conjugates by HPLC and API-MS. These methods report lower limits of detection than GC-MS methods and quantitation in the low nanogram-per-milliliter range for the glucuronide metabolites of morphine. This report describes an HPLC-electrospray-MS-MS method capable of detecting subnanogram concentrations of morphine (MOR) and its 3- and 6-glucuronide metabolites (M3G and M6G, respectively). The assay has a dynamic range of 250-10,000 pg/mL for M3G and M6G and 500-10,000 pg/mL for MOR. Inter- and intra-assay precision and accuracy varied by less than 8% for all analytes at 750-, 2500-, and 7500-pg/mL concentrations. This assay was used for the determination of MOR, M3G, and M6G in human plasma after intravenous (i.v.) and intrathecal (i.t.) administration of MOR and its effects on the ventilatory response to hypoxia. Peak plasma concentrations of MOR and M6G were measured 1 h after i.v. administration of MOR. Peak concentrations of M3G were measured 2 h after i.v. administration of MOR. After i.t. administration of MOR, peak concentrations of M3G were measured 8 h postdose. MOR was not detected in plasma of patients administered MOR i.t.. Subnanogram concentrations of M6G were measured in the plasma of five of nine patients administered MOR i.t..

Despite being an impressive contributor to the world economy, traditional production and processing methods in the textile manufacturing industry cause significant waste and pollution. Green technology has emerged as a viable approach for mitigating environmental consequences. Numerous scholars have investigated green technologies; nonetheless, a more thorough analysis of the development and characteristics of green technology research in the textile industry is needed. This bibliometric study examined the growth patterns and trends of green technology research in the clothing industry from 2000 to 2023. The data were collected from the Scopus database, and the bibliometric analysis tool VOSviewer was used to visualise the data. The study results demonstrated the exponential growth trends of green textile technology research since 2020. The study also uncovered productive journals, countries, and institutions researching green textile technology. The results additionally showed that the United States (US), the United Kingdom (UK), and China exhibit substantial publications, and the UK is the leading country for collaborative research in green textile technology research. This study identified five primary research areas in the green textile technology literature: environmental impact assessment, life cycle assessment of textiles, sustainable design of textiles, sustainable fashion, and circular fashion supply chains. This analysis can assist academics in identifying novel research methodologies.

The transition towards a Circular Economy is a topic of great concern for the textile and fashion industry, since such industry is one of the main polluting in the world. Although Circular Economy is now on the rise in the academia and on the policy agendas, this model is still underdeveloped in practice. Unlike polyester, wool has long been compatible with recycling processes, thus reducing environmental impact and contributing to closing the loop in a Circular Economy. In this context, the industrial district of Prato (Italy) has successfully established a woollen industry based on recycled wool since centuries. Thus, this paper presents three case studies of companies operating in the Prato district, to explore challenges and opportunities of Circular Economy in the textile industry. Design, legislation and competences emerged as the main challenges preventing the uptake, on a larger scale, of this model.KeywordsCircular EconomySustainabilityFashion industryCircular Supply Chain

The textile and apparel industry is one of the most resource-intensive and environmentally polluting sectors, traditionally operating under a linear “take-make-dispose” model. The Circular Economy (CE) has emerged as a promising framework to reduce waste, enhance resource efficiency, and promote closed-loop production systems. This study conducts a Systematic Literature Review (SLR) of research published between 2020 and 2025 to examine the adoption of CE practices in the textile industry, with a particular focus on India. Using data from Scopus, IEEE Xplore, and Google Scholar, relevant studies were identified and analyzed to understand technological developments, governance frameworks, and socio-economic challenges influencing circular transition. The findings reveal that while advancements in recycling technologies and digital traceability are progressing, large-scale implementation remains constrained by infrastructural gaps, financial limitations of MSMEs, and governance deficiencies. The review also highlights the spatial bias of existing CE models toward Western contexts, which often overlook the realities of manufacturing hubs in the Global South. The study concludes that effective circular transformation requires integrated policy frameworks, improved reverse logistics infrastructure, and the formal inclusion of informal recycling networks.