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An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

This study aims to examine the research productivity of Pakistan Agricultural Research Council’s (PARC’s) researchers published during 2001–2020 by using scientometric indicators. The study explored the growth and collaborative trends along with authorship and institutional collaborative patterns at the national and international levels.

The study was conducted in four phases. Firstly, a search strategy was designed to retrieve reliable data sets. During the second phase, data from PARC research was retrieved from Scopus and Web of Science (WoS). In the third phase, the data were combined, and duplications were removed. Finally, the data were analysed using RStudio and VOSviewer.

The study identified 2,868 research publications from 16 communication channels spanning over the period of 2001–2020. The growth rate varied during the study period and the year 2020 was the most productive year of the organization. Most of the research was produced in multi-authorship and five authors were dominant. Pakistan Journal of Botany was the most preferred and cited source. Moreover, PARC research collaboration with Pakistani researchers was more than their international counterparts.

Like other research, this research has some limitations. For example, this research is based on secondary data extracted from WoS and Scopus databases, world-renowned online academic. However, researchers should keep in mind while interpreting the results of this study. Secondly, the research publications published by PARC researchers during 2001–2020 were considered. Finally, this research considered English language literature only.

The study’s key theoretical contribution is its strategy for merging WoS and Scopus in RStudio, while its findings could assist agriculture research stakeholders in identifying new areas of research, awards, promotions and identification of research gaps.

To the best of the author’s knowledge, this study is the first to use scientometric indicators to evaluate PARC’s research productivity. This detailed analysis provides a deeper understanding of PARC’s contribution to agriculture research and its potential implications.

This study aims to investigate the effect of organizational ambidexterity (OA) and organizational green culture (OGC) on corporate sustainability (CS) while incorporating the mediating role of green innovation (GI) to provide a detailed insight into CS. The study also presents a research framework based on the Organizational Ambidexterity theory and Natural Resource-based view to explain the factors contributing to CS.

Using stratified sampling, the study collected data through survey-based empirical research from 307 textile companies registered with the Securities and Exchange Commission of Pakistan (SECP) or the All-Pakistan Textile Mills Association (APTMA). The collected data were analysed using path analysis, mediation analysis and moderation analysis through smart PLS-SEM version 4.0 to assess the composition and causal association of factors.

The study found a significant relationship between OA and OGC with CS. Furthermore, the study revealed that green innovation partially mediates the relationship between OGC and CS. The proposed research framework can be valuable for promoting and recommending actions to enhance CS.

The study on CS in the textile sector of Pakistan has limitations such as a narrow focus, cross-sectional design and reliance on self-reported data. Future research should explore additional factors, conduct longitudinal research, investigate contextual factors, scrutinize specific green innovation practices and broaden the scope of the study to include SMEs and other textile organizations.

The research framework can help senior executives to foster CS by promoting OGC, OA and GI. Practitioners and academicians can also utilize or further investigate the proposed framework for validation and to foster CS.

This study fills gaps in the existing literature by investigating the mediating effect of GI between OGC and CS. The proposed research framework provides a comprehensive understanding of the factors contributing to CS based on the Organizational Ambidexterity theory and Natural Resource-based view.

The purpose of this study is to provide a comprehensive analysis of scientific knowledge in educational research over the past decade. The analysis aims to identify contributions to the field of education and trends in the literature.

Bibliometric analysis was conducted on 117,870 publications from 335 education journals in the Scopus database between 2013 and 2022.

This study shows educational research has increased consistently over the past decade. The USA showed high productivity, while the Netherlands produced the most impactful publications. The USA, UK and Australia have the highest research collaboration. Country collaboration network is divided into two blocks, comprising Western and Eastern countries, with the USA and the UK acting as bridges between these country groups. The bibliographic coupling analysis revealed that educational research is categorized into 11 clusters. Recent educational research aims to address the challenges in education, adapt to the changing technological, economic and social landscape and capitalize on emerging opportunities.

This study analysed over 100 thousand publications to identify the latest trends in educational research and highlight current developments in the field.

Try to find a way to treat wastewater and achieve its purification from suspended waste, which was removed by examining the magneto-Williamson fluid on a horizontal cylindrical tube while taking advantage of solar radiation and nanotechnology.

The effect of Cattaneo–Christoph law of heat transfer, solar radiation, oblique magnetic field, porosity and internal heat generation on the flow was studied. The control system was solved by the numerical technique of Chebyshev pseudospectrum (CPS) with the help of the program MATHEMATICA 12. The tables comparing the published data results with the existing numerical calculation match exactly.

The tables comparing the published data results with the existing numerical calculation match exactly. The current simulation results indicate that when using variable viscosity, the Nusselt number and surface friction decrease significantly compared to their value in the case of constant viscosity, and variable viscosity has a significant effect on flow, which reduces speed. Curves and increasing temperature profiles.

Developing a theoretical framework for the problem of sewage turbidity in a healthier and less costly way, by studying the flow of Williamson fluid with variable viscosity (to describe the intensity of sewage turbidity) on a horizontal cylindrical tube, and taking advantage of nanotechnology, solar radiation, Christoph’s thermal law and internal heat generation to reach water free of impurities. Inclined magnetic force and porous force were used, both of which played an effective role in the purification process.

Natural extracts produced with Annona squamosa and Moringa oleifera leaves through the methanol-based solvent were coated on 100% cotton and 80%:20% polyester/cotton blends to improve the functional properties such as antimicrobial activity, wicking, stiffness and crease recovery of the fabric using an eco-friendly 1,2,3,4-butane tetracarboxylic acid (BTCA) crosslinking agent.

In this study, 100% cotton and 80:20% Polyester/Cotton fabrics with surface densities of 113.5 g/m2 and 101 g/m2 were treated BTCA. Eight different samples were produced by padding through the natural extracts. The FTIR investigation was performed on all the fabric samples. These coated fabrics were studied for their antimicrobial activity, wicking, stiffness and crease recovery properties.

It was found that the BTCA cross-linked fabrics showed higher antimicrobial activity against gram-positive and gram-negative bacteria. Similarly, the percentage crease recovery angle was higher for the Annona squamosa coated sample than for Moringa Oleifera leaf extract in both cotton and polyester cotton blend samples. Furthermore, no significant variation in stiffness values was discovered between the control samples of cotton and polyester cotton blend and its treatment one. It was interesting to note that treating the fabrics with cross-linker showed improved vertical wicking properties, which were closer to control fabric values. The study confirms that crosslinking the fabrics with BTCA has improved the functional properties of the fabrics. The zone of inhibition values of BTCA cross-linked moringa methanolic leaves extract coated cotton and polyester cotton blend were 6 to 6.5 cm, which was more than 50% higher than non-BTCA cross-linked fabric, and BTCA cross-linker has improved the vertical wicking properties.

The outcome of this study will help to gain a better understanding of BTCA cross-linkers for improving the functional coating on textile substrates.

This study was conducted to improve the natural extract coating on textile material with eco-friendly aspects, enhancing the commercial utility of these finished fabrics

Quality 4.0 is essential to the Industry 4.0 framework, notably in the electronics sector. It evaluates product quality in real-time using automatic process controls, quality tools and procedures. The implementation of Quality 4.0 criteria in the electronics industry is the subject of this study’s investigation and analysis. In this study, nine Customer Requirements (CRs) and 18 Design Requirements (DRs) have been defined to adopt Quality 4.0, aiming to increase yield while reducing defects. This study has developed a Quality 4.0 framework for effective implementation, incorporating the People, Process and Technology categories.

Many CRs and DRs of Quality 4.0 exhibit interdependencies. The Analytic Network Process (ANP) considers interdependencies among the criteria at various levels. Quality Function Deployment (QFD) can capture the customer’s voice, which is particularly important in Quality 4.0. Therefore, in this research, we use an integrated ANP-QFD methodology for prioritizing DRs based on the customers' needs and preferences, ultimately leading to better product and service development.

According to the research findings, the most critical consumer criteria for Quality 4.0 in the electronics sector are automatic systems, connectivity, compliance and leadership. The Intelligent Internet of Things (IIOTs) has emerged as the most significant design requirement that enables effective control in production. It is observed that robotics process automation and a workforce aligned with Quality 4.0 also play crucial roles.

Existing literature does not include studies on identifying CRs and DRs for implementing Quality 4.0 in the electronics industry. To address this gap, we propose a framework to integrate real-time quality measures into the Industry 4.0 context, thereby facilitating the implementation of Quality 4.0 in the electronics industry. This study can provide valuable insights for industry practitioners to implement Quality 4.0 effectively in their organizations.

Reactive power in radial distribution networks (RDN) leads to detrimental effects like power factor degradation, voltage profile alterations and increased power losses, ultimately impacting network stability. This paper aims to present a novel two-phase optimization approach to address the challenging task of locating, sizing and determining the optimal number of capacitors in RDNs.

The first step of the proposed methodology is using a hybrid technique that combines the loss sensitivity factors (LSF) with voltage sensitivity factors (VSF) to identify network nodes requiring capacitor installation efficiently. The second step uses an external approximation technique to optimize the size and number of capacitors for each identified node, achieving significant power loss reductions.

The effectiveness of this new approach is evaluated on two RDNs: 33- and 69-bus. Simulations on these test systems demonstrate the effectiveness of the proposed approach, reducing total power loss by 34.7% in the first case and 35.3% in the second. The method’s robustness compared to other approaches further highlights its potential for practical implementation in RDNs, contributing to improved network stability and efficient power distribution.

This paper presents a novel, efficient and robust approach to determining the optimal number, location and size of an RDN capacitor. The problem is addressed through a new formulation with modified constraints. The method consists of two stages: initially, a hybrid LSF–VSF method identifies potential capacitor locations, followed by an external approximation-based mixed-integer nonlinear programming (MINLP) solver to optimize capacitor numbers and sizes. The proposed methodology is applied to the widely used 33-bus and 69-bus RDN test systems. Comparative analysis with existing methods highlights the proposed approach’s effectiveness. Key contributions of this study include the following: Proposes a new problem formulation with modified constraints. Proposes a novel two-stage framework for optimally locating and sizing capacitors in RDNs. Introduces a hybrid LSF–VSF algorithm to identify promising capacitor locations efficiently. Using an external approximation-based MINLP for optimal sizing. Demonstrates the effectiveness of the proposed approach through rigorous testing on standard benchmark systems. Provides a comprehensive comparative analysis against state-of-the-art methods, highlighting the proposed approach’s superior performance.

This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.

This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.

The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.

The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.

Plant-based meat analogues (PBMAs) hold significant promise as a sustainable solution to meet future protein demands, replicating the taste and nutritional value of meat. However, the present reliance on extrusion technology in PBMA production limits the exploration of more accessible and affordable methods. The current investigation aims to meet the market demand for a scalable and cost-effective processing approach by exploring saturated steam-assisted technology that could broaden the production volume of PBMAs, thereby supplementing protein security and planet sustainability.

A one-factor-at-a-time (OFAT) approach is employed to evaluate the effect of ingredients and process conditions on the governing quality attributes (texture, colour and sensory).

Among the ingredients, monosodium glutamate (MSG) and nutritional yeast (NY) significantly enhanced the hardness and chewiness of saturated steam-assisted plant-based meat analogues (ssPBMAs) followed by potato protein isolate (PPI), defatted soy flour (DSF) and salt. The addition of PPI and DSF led to a decrease in lightness (L* value) and an increase in the browning index (BI). Sensory evaluations revealed that higher concentrations of DSF imparted a noticeable beany flavour (>20%), whereas PPI (30%) improved the overall sensory appeal. Increased levels of NY (10%) and MSG (5%) enhanced the umami flavour, enhancing consumer preference. Higher thermal exposure time (TTi) (45 min) and temperature (TTe) (120 °C) during processing resulted in softer products with reduced L* values. These findings establish a foundation for selecting and optimizing the ingredients and processing parameters in ssPBMA production.

The novelty of the current study includes process behaviour of selected ingredients such as PPI, NY, MSG, DSF, salt and adopted process conditions, namely, dough processing time (DPT), protein network development time (PNDT), TTi and TTe on the quality of ssPBMAs.