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In the present study, the polyvinylidene fluoride (PVDF) was incorporated with polyvinylpyrrolidone (PVP) and titanium dioxide (TiO2) nanoparticles to prepare PVDF-TiO2-PVP composite membrane for Bovine Serum Albumin (BSA) rejection from protein contaminated wastewater. The preparation of PVDF-TiO2-PVP composite membrane was conducted by a non-solvent phase inversion (NIPS) process with varying PVDF weight percentages ranging from 16 to 19 wt%. The polymer concentration plays a critical role in the formation of composite membranes that is commonly overlooked. The primary goal of this study is to examine how the concentration of polymers affects the process of membrane creation, the ensuing structure, and the properties of these membranes. Presently, there is a restricted comprehension concerning the impact of PVDF polymer concentration in the fabrication of membranes using the NIPS approach, particularly for PVDF-TiO2-PVP composite membranes. FE-SEM analysis revealed the lowest skin layer thickness and the number of macro-void formations in a PVDF composite membrane were at 16 wt% PVDF, compared to composite membranes over higher PVDF concentrations from 17 to 19 wt%. There were only slight variations in the percentages of individual elements across all PVDF-TiO2-PVP membranes, yet the values were identical for C, O, F, and Ti. The viscosity was lowest at 16 wt percent PVDF (576.5 MPa s) and greatest at 19 wt percent PVDF (2054.3 MPa s). It showed that 19%wt PVDF viscosity increased fourfold higher than 16%wt. As the concentration of PVDF was increased, a related decrease in the surface porosity of the membrane was observed. The highest hydrophobicity with the contact angle of 79.62° of the composite membranes were detected at 19 wt% of PVDF loading. The maximum water flux (265.43 L/m2h) and BSA flux (250.31 L/m2h) were obtained at 19 wt% of PVDF loading. The lowest PVDF concentration of 16 wt% rejected BSA with 91.01 % efficiency. At 19 wt% PVDF, the rejection rate dropped nearly sixfold to 16.34 %.

This study examines the implementation of Green Supply Chain Management (GSCM) practices in managing paper waste within Indonesian higher education institutions, focusing on the influence of recycling infrastructure, subjective norms, and recycling awareness on campus sustainability. Employing a quantitative approach with data from 224 students, the study utilized Structural Equation Modelling (SEM) to evaluate relationships among key variables: Paper Waste Capacity (PWC), Subjective Norms (SN), Recycling Awareness (RA), and Ecosystem Sustainability (ES). Findings indicate that enhanced paper waste capacity significantly affects subjective norms around recycling, while subjective norms positively influence individual recycling awareness. Both subjective norms and recycling awareness significantly contribute to ecosystem sustainability, suggesting that GSCM practices, supported by accessible recycling infrastructure and normative reinforcement, can foster a culture of sustainability within universities. This study also highlights the importance of creating a recycling-supportive environment through social campaigns and peer initiatives. The results underscore the broader potential of GSCM as a model for promoting environmentally responsible behaviours in educational settings and beyond. While limited by its specific context, the study calls for further research across diverse institutions and regions to expand understanding of GSCM's impact on sustainability practices. [ABSTRACT FROM AUTHOR]

Conductive polymer (CP) research has exploded in popularity over the years, with applications ranging from nanoelectronics to material science. CPs are, for all sorts of purposes, Nobel Prize-winning compounds, as their inventors received the Nobel Prize in Chemistry in 2000. Conducting polymers have sparked a lot of interest in academic and industrial sectors because they combine the electrical characteristics of semiconductors and metals with the typical benefits of ordinary polymers, such as ease of preparation and low cost production. Conducting polymers have also received a lot of attention because of their unique characteristics, which include customizable electrical properties, excellent optical and mechanical capabilities, ease of synthesis and manufacturing, and superior environmental durability to traditional inorganic materials. In this study, the molecular structures and behaviors of the most common forms of CPs, namely, polyacetylene, polyaniline, polypyrrole, and polythiophene and derivatives of polythiophene are discussed. The transport phenomenon that allow to understand the conduction process, are also described in this review. An in-depth investigation of conducting polymerbased binary, ternary, and quaternary composites with carbon-based materials, metal oxides, transition metals, and inorganic particles is utilized to analyze their applications as supercapacitors and batteries. There are also explanations of recent advancements in their applications in the areas of energy storage systems including batteries and supercapacitors. The development of their applications in the energy storage devices such as supercapacitors, lithium, and other -ions batteries, as well as their current issues and future prospect to advance energy storage systems are broadly discussed. This review is intended to contribute to a better understanding of this conducting polymer and, as a result, to the development of new research areas. KEYWORDS conductive polymer, conjugated polymer, mechanism of conductive polymers, supercapacitors, 1 | INTRODUCTION In all eras, people have always aspired to pursue a better quality of life and carry it on to their descendants. People are currently confronting an energy [...]

Computation ranking algorithms are widely used in several informatics fields. One of them is the PageRank algorithm, recognized as the most popular search engine globally. Many researchers have improvised the ranking algorithm in order to get better results. Recent research using Rayleigh Quotient to speed up PageRank can guarantee the convergence of the dominant eigenvalues as a key value for stopping computation. Bolzano's method has a convergence character on a linear function by dividing an interval into two intervals for better convergence. This research aims to implant the Bolzano algorithm into Rayleigh for faster computation. This research produces an algorithm that has been tested and validated by mathematicians, which shows an optimization speed of a maximum 7.08% compared to the sole Rayleigh approach. Analysis of computation results using statistics software shows that the degree of the curve of the new algorithm, which is Rayleigh with Bolzano booster (RB), is positive and more significant than the original method. In other words, the linear function will always be faster in the subsequent computation than the previous method.

Magnetic chitosan/cellulose nanofiber-Fe(III) [M-Ch/CNF-Fe(III)] composites were isolated for the elimination of Cr(VI), Cu(II), and Pb(II) from aqueous solution. Various analytical methods, such as field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction analysis (XRD), and thermogravimetric analysis (TGA) were employed to determine the morphological, physicochemical, and thermal properties of the isolated M-Ch/CNF-Fe(III) composites. It was found that the M-Ch/CNF-Fe(III) composites were porous materials, and they have the potential to be implemented as an adsorbent for heavy metals removal. The adsorption efficiency of M-Ch/CNF-Fe(III) composites was determined for Cr(VI), Cu(II), and Pb(II) elimination with changing pH (pH 1.0–8.0), adsorbent doses (0.05–1.0 g), time (15–90 min), and temperature (28–80 °C). In addition, isothermal and kinetics studies were conducted to assess the adsorption behavior and mass transfer phenomena of M-Ch/CNF-Fe(III) composites as an adsorbent for Cr(VI), Cu(II) and Pb(II) elimination from aqueous solution. The outcomes of the present study reveal that the M-Ch/CNF-Fe(III) composites could be utilized as an adsorbent for the Cr(VI), Cu(II), and Pb(II) elimination from industrial effluents.

Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d'Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way. [ABSTRACT FROM AUTHOR]

Dewatered sludge (DS) is a sewage sludge with a unique property due to extracellular polymeric substances (EPSs) and polymer flocculants. These components form a stable 3D polymer network to increase dewatering efficiency, leaving behind valuable materials such as lipids. This article explored the influences of DS particle size on lipid yield and the effects of extraction on the chemical, morphological, and thermal properties of the residual dewatered sludge (RDS). Lipid yields with unimodal distribution were observed across the particle size ranges (<0.5, 0.5–1.0, 1.0–2.0, 2.0–4.0, and 4.0 mm). The highest lipid yield of 1.95% was extracted from 1.0–2.0 mm after 4 h at 70 °C and 0.1 g/mL sludge-to-solvent ratio. Efficiency was influenced by the DS's morphology, facilitating solvent infiltration and pore diffusion. The extraction process reduced water and organic fractions, resulting in higher thermal stability. Bibliometric analysis of "extraction*" and "sewage sludge" shows increasing research interest from 1973 to 2024. Five research clusters were observed: heavy metal speciation and stabilization, sludge and its bioavailability, extraction techniques and resource recovery, contaminants remediation, as well as phosphorus recovery and agricultural applications. These clusters highlight the diverse approaches to researching DS and RDS while promoting sustainable waste management. [ABSTRACT FROM AUTHOR]

The integration of compatibilisers with thermoplastics has revolutionised the field of polymer composites, enhancing their mechanical, thermal, and rheological properties. This study investigates the synergistic effects of incorporating SEBS-g-MAH on the mechanical, thermal, and rheological properties of polycarbonate/acrylonitrile-butadiene-styrene/graphene oxide (PC/ABS/GO) (PAGO) and the properties of polycarbonate/acrylonitrile-butadiene-styrene/graphene oxide (PC/ABS/rGO) (PArGO) composites through the melt blending method. The synergistic effects on thermal stability and processability were analysed by using thermogravimetry (TGA), melt flow index (MFI), and Fourier-transform infrared spectroscopy (FTIR). The addition of SEBS-g-MAH improved the elongation at break (EB) of PAGO and PArGO up to 33% and 73%, respectively, compared to the uncompatibilised composites. The impact strength of PAGO was synergistically enhanced by 75% with the incorporation of 5 phr SEBS-g-MAH. A thermal analysis revealed that SEBS-g-MAH improved the thermal stability of the composites, with an increase in the degradation temperature (T80%) of up to 17% for PAGO at 1 phr SEBS-g-MAH loading. The compatibilising effect of SEBS-g-MAH was confirmed by FTIR analysis, which indicated interactions between the maleic anhydride groups and the PC/ABS matrix and GO/rGO fillers. The rheological measurements showed that the incorporation of SEBS-g-MAH enhanced the melt flowability (MFI) of the composites, with a maximum increase of 38% observed for PC/ABS. These results demonstrate the potential of SEBS-g-MAH as a compatibiliser for improving the unnotched impact strength (mechanical), thermal, and rheological properties of PC/ABS/GO and PC/ABS/rGO composites, achieving a synergistic effect. [ABSTRACT FROM AUTHOR]

Software reliability growth models (SRGMs) are universally admitted and employed for reliability assessment. The process of software reliability analysis is separated into two components. The first component is model construction, and the second is parameter estimation. This study concentrates on the second segment parameter estimation. The past few decades of literature observance say that the parameter estimation was typically done by either maximum likelihood estimation (MLE) or least squares estimation (LSE). Increasing attention has been noted in stochastic optimization methods in the previous couple of decades. There are various limitations in the traditional optimization criteria; to overcome these obstacles metaheuristic optimization algorithms are used. Therefore, it requires a method of search space and local optima avoidance. To analyze the applicability of various developed meta-heuristic algorithms in SRGMs parameter estimation. The proposed approach compares the meta-heuristic methods for parameter estimation by various criteria. For parameter estimation, this study uses four meta-heuristics algorithms: Grey-Wolf Optimizer (GWO), Regenerative Genetic Algorithm (RGA), Sine-Cosine Algorithm (SCA), and Gravitational Search Algorithm (GSA). Four popular SRGMs did the comparative analysis of the parameter estimation power of these four algorithms on three actual-failure datasets. The estimated value of parameters through meta-heuristic algorithms are approximately near the LSE method values. The results show that RGA and GWO are better on a variety of real-world failure data, and they have excellent parameter estimation potential. Based on the convergence and R2 distribution criteria, this study suggests that RGA and GWO are more appropriate for the parameter estimation of SRGMs. RGA could locate the optimal solution more correctly and faster than GWO and other optimization techniques. [ABSTRACT FROM AUTHOR]

This study explores green work engagement in response to the global demand for sustainability in businesses and the shift toward green-oriented agendas. Specifically, this study aims to examine how green work engagement (GWE) is affected by artificial intelligence awareness (AIA) through job stress (JS) as a mediator. It also explores the moderating roles of technological self-efficacy (TSE) in the AIA→JS relationship and trust in leadership (TIL) in the GWE→JS relationship. A PLS-SEM analysis was conducted on 392 valid replies from full-time employees of five-star hotels in Egypt using WarpPLS 7.0. The findings indicated that artificial intelligence awareness (AIA) negatively affects employees' green work engagement (GWE) and positively affects job stress (JS). In addition, GWE is negatively affected by JS. Moreover, TSE negatively moderates the AIA→JS relationship, while TIL negatively moderates the JS→GWE relationship. The study also found a significant mediating effect of JS on the AIA→GWE relationship. The study enhances research on AIA's impact on JS and GWE, addressing a gap in existing empirical studies on the relationship between these elements in hotels. Overall, the study of green work engagement has the potential to be a valuable contribution to the growing field of sustainable business practices. [ABSTRACT FROM AUTHOR]

Gastrointestinal disorders are a class of prevalent disorders in the world. Capsule endoscopy is considered an effective diagnostic modality for diagnosing such gastrointestinal disorders, especially in small intestinal regions. The aim of this work is to leverage the potential of deep convolutional neural networks for automated classification of gastrointestinal abnormalities from capsule endoscopy images. This method developed a deep learning architecture, GastroNetV1, an automated classifier, to detect abnormalities in capsule endoscopy images. The gastrointestinal abnormalities considered are ulcerative colitis, polyps, and esophagitis. The curated dataset consists of 6000 images with "ground truth" labeling. The input image is automatically classified as ulcerative colitis, a polyp, esophagitis, or a normal condition by a web-based application designed with the trained algorithm. The classifier produced 99.2% validation accuracy, 99.3% specificity, 99.3% sensitivity, and 0.991 AUC. These results exceed that of the state-of-the-art systems. Hence, the GastroNetV1 could be used to identify the different gastrointestinal abnormalities in the capsule endoscopy images, which will, in turn, improve healthcare quality. [ABSTRACT FROM AUTHOR]

Polymeric membranes offer straightforward modification methods that make industry scaling affordable and easy; however, these materials are hydrophobic, prone to fouling, and vulnerable to extreme operating conditions. Various attempts were made in this study to fix the challenges in using polymeric membranes and create mixed-matrix membrane (MMMs) with improved properties and hydrophilicity by adding titanium dioxide (TiO2) and pore-forming agents to hydrophobic polyvinylidene fluoride (PVDF). The PVDF mixed-matrix ultrafiltration membranes in this study were made using the non-solvent phase inversion approach which is a simple and effective method for increasing the hydrophilic nature of membranes. Polyvinylpyrrolidone (PVP) and polyethylene glycol (PEG) as pore-forming chemicals were created. Pure water flux, BSA flux, and BSA rejection were calculated to evaluate the mixed-matrix membrane’s efficiency. Bovine serum albumin (BSA) solution was employed in this study to examine the protein rejection ability. Increases in hydrophilicity, viscosity, and flux in pure water and BSA solution were achieved using PVP and PEG additives. The PVDF membrane’s hydrophilicity was raised with the addition of TiO2, showing an increased contact angle to 71°. The results show that the PVDF–PVP–TiO2 membrane achieved its optimum water flux of 97 L/(m2h) while the PVDF–PEG–TiO2 membrane rejected BSA at a rate greater than 97%. The findings demonstrate that use of a support or additive improved filtration performance compared to a pristine polymeric membrane by increasing its hydrophilicity.

Demand for determining renewable lipids feedstock for the production of biodiesel is increasing with the rapid depletion of petroleum diesel. The present study was conducted to assess the feasibility of utilizing municipal sewage sludge (MSS) as a potential lipids feedstock for biodiesel production. The lipids’ extraction and separation from MSS were conducted using supercritical CO2 (scCO2) with varying treatment time (15–120 min), temperature (30–80°C), pressure (10–50 MPa), and addition of cosolvents (1–10 wt.%). The modified Gompertz equation and Arrhenius equation were employed to evaluate lipids’ extraction and kinetics behavior from municipal sewage sludge using scCO2. About 27% of lipids were extracted from MSS with scCO2 at a temperature of 60°C, pressure of 30 MPa, treatment time of 60 min, and 5 wt.% of ethanol (EtOH) as cosolvent. The modified Gompertz equation was adequately fitted with experimental data of the lipids’ extraction from MSS using scCO2. The kinetics properties analyses revealed that the scCO2 extraction technology was highly dependent on pressure than the temperature for the extraction of the lipids from MSS. The physicochemical characteristics and fatty acid contents of the scCO2 extracted lipids from MSS and sewage sludge biodiesel were determined using a variety of analytical techniques. The physicochemical properties of the sewage sludge biodiesel were compared with the international standard specifications of biodiesel, such as the American Society for Testing and Materials specifications for diesel fuel (ASTM D6751) and European Standard (EN 14214) specifications.