The growing need for sustainable and efficient energy conversion has driven the development of advanced catalytic materials. In this quest, nanozymes-nanomaterials that mimic the catalytic functions of natural enzymes emerge as promising candidates due to their tunable catalytic properties, high operational stability, and cost-effectiveness. This review presents recent advancements in the applications of nanozymes for clean energy technologies, focusing on their mechanistic roles and engineering strategies within the scope of key reactions, including hydrogen evolution reaction (HER), oxygen evolution and reduction reactions (OER, ORR), CO2 reduction, biofuel production, and methane-to-methanol conversion. The fundamental classes of nanozymes, their structure-activity relationships, and how their fine-tuned properties aid energy conversion in systems such as biofuel cells, electrolyzers, and fuel cells are also discussed. To underscore their practical advantages, nanozymes are benchmarked against conventional catalysts using key performance metrics such as turnover frequency, cost, and stability. Additionally, the review addresses challenges associated with limited selectivity, incomplete mechanistic understanding, and scalability while also highlighting emerging technologies such as nanostructuring, doping, hybridization, and 3D printing. By mapping recent advances and identifying critical research gaps, this review underscores the potential of established nanozymes and nanozyme-inspired catalytic systems as next-generation catalysts for clean energy applications and their role in advancing the transition toward a carbon-neutral and circular energy economy.

Purpose This study aims to systematically analyze the intellectual structure of metaverse research in the hospitality and tourism domain. It identifies key theoretical foundations, emerging themes and conceptual relationships by leveraging bibliometric analysis. The research seeks to bridge the gap between conceptual studies and practical applications, offering an integrative framework for adoption by industry. Design/methodology/approach This study integrates bibliometric and text-mining approaches to systematically review the metaverse research in hospitality and tourism. Findings The study identifies six key research clusters, highlighting the metaverse’s role in immersive tourism, digital engagement and sustainability. The findings reveal that technological adoption, virtual presence and brand engagement are central to metaverse research in hospitality. Co-citation analysis highlights the dominant theoretical models, such as the stimulus–organism–response model, while text mining identifies gaps in empirical applications. The research underscores the metaverse’s potential to enhance customer experience, marketing strategies and sustainable tourism development. Originality/value This study is among the first to conduct a systematic bibliometric review of metaverse research in the hospitality and tourism sector. By integrating co-citation analysis and text mining, it offers a comprehensive and objective examination of the existing literature. The research provides a novel conceptual framework for understanding metaverse adoption, digital engagement and sustainability in hospitality. It bridges theoretical and practical gaps, offering valuable insights for scholars, industry practitioners and policymakers. The study also highlights critical research gaps and proposes a future research agenda, ensuring its relevance in guiding strategic decision-making and technological advancements in the hospitality industry.

Exposure to environmental (chemical and biological) hazards and children undernutrition: A scoping review.

Environmental pollution from wastewater and soil contamination remains a critical global concern, with current treatment methods often facing limitations in scalability, cost, or environmental safety. Biochar, a carbon-rich material derived from biomass pyrolysis, has emerged as a sustainable adsorbent for heavy metals and organic pollutants. While its use in environmental remediation is well-established, the fate and reuse of spent biochar have received limited attention. This comprehensive review explores the untapped potential of used biochar, particularly for the removal of hexavalent chromium (Cr(VI)) from wastewater and its role in enhancing soil fertility. We critically analyze current practices, mechanisms of Cr(VI) removal using spent biochar, regeneration techniques, and field applications, while highlighting circular economy frameworks that promote resource efficiency. The study integrates empirical evidence from recent case studies and offers policy recommendations to support large-scale implementation. This work is the first to provide an integrative review of the reuse of spent biochar with a dual focus on wastewater treatment and soil enhancement, underpinned by a circular economy perspective. It addresses critical research gaps by evaluating regeneration techniques, post-use functionality, and practical field applications, thereby positioning spent biochar as a viable, low-cost, and eco-friendly alternative in environmental management systems.

ABSTRACT Immigration is one of the drivers of the demographic, economic, social and physical landscapes of countries like the United States, Canada, Australia, and New Zealand. Understanding how and why immigrants choose their residential locations and how urban infrastructure, especially transportation, influences the decision remain a research area that is critical but under-explored. Residential Location Choice (RLC) is a crucial focus in transportation planning research, as both land use and residential patterns significantly shape travel behaviour and transportation infrastructure. This study has three main goals based on a systematic review of 84 scientific publications. First, it examines the factors influencing immigrant location decisions, including socio-demographic characteristics, economic opportunities, social networks, housing affordability, transportation networks and institutional policies. Second, it assesses the methodologies and models used in the studies on immigrant residential location choice, and thirdly, it identifies critical research gaps and offers recommendations for future research. The findings reveal that social networks and economic factors facilitate immigrant settlement. We emphasise the need to better understand how immigrants choose where to live based on transportation networks through integrated land use and transport models and the need for a more nuanced understanding of diverse immigrant needs, which is crucial for creating inclusive and considerate policies. We also highlight the need for longitudinal studies and better predictive models to further our understanding of immigrant settlement patterns.

This systematic review analyzes the effectiveness of Digital Storytelling (DST) as a teaching tool for learning English. It integrates and critiques prior research regarding the tool's effectiveness, theoretical justification, and challenges in implementation. Indicative studies do point DST's potential to promote certain critical language skills as a result of the motivation, autonomy, and critical thinking an individual DST learner develops. It is true the motivation is a positive factor, yet autonomy is an achievement. It is notable for many studies DST is based on the combination of constructivist and multimodal learning theories. DST accomplishes the development of writing fluency, the speaking skill, and vocabulary acquisition in language learning. Most importantly, the review outlines the major and critical shortcomings in the existing studies. Most importantly the review outlines critical research gaps. The largest of these is probably the absence of longitudinal studies to verify the retention of language skills over time. In addition, most uses of DST lack attention to the specific linguistic subsystems, such as pragmatic competence or complex syntax, that might be addressed through task design. The prospect of studying multimodal constructs opens new avenues for research, given data's efficiency in enhancing learning. The lack of longitudinal, empirical studies that capture learning over time is a critical missed opportunity. There is great potential for multimodal DST methods to change student learning, but the main gap that defines the research gap for DST is the lack of documentation for longitudinal learning.

BackgroundEven with significant strides in technology, medicine, and science, the burden of malnutrition continues to weigh heavily on children, particularly in developing nations like India. This issue of malnutrition is particularly acute in developing and underdeveloped countries, including India. While India has made progress in reducing child malnutrition, severe malnutrition (acute and chronic) remains a critical public health concern, especially in regions like the Aspirational Districts (ADs), which are more resource-deficient regions.MethodThis study used data from the National Family Health Survey-5(NFHS-5) to examine the prevalence and predictors of severe malnutrition among children under five years in both ADs and non-ADs. Thedescriptive statistics and logistic regression models were used andwe examined how residence in ADs influences the likelihood of severe malnutrition, accounting for socio-economic, demographic, household, and parental factors. The novelty of this study lies in addressing a critical research gap by examining all three severe malnutrition indicators: severe wasting(SW), severe stunting (SS), and severe underweight(SU), simultaneously and comparing their prevalence between ADs and non-ADs. This approach provides crucial evidence on the state of child malnutrition in resource-limited regions.ResultsThe study revealed that children in ADs have increased odds of SS, SW, and SU after accounting for different factors. Children who were male, had low birth weight, were of higher birth order, were anemic, experienced diarrheal episodes, resided in rural areas, had mothers with no education, belonged to poor wealth status, belonged to Scheduled Caste/Tribe families and had no exposure to mass media were more likely to be SS, SW, and SU.ConclusionThese findings are crucial to address regional inequalities in severe malnourishment, and targeted interventions in these resource-limited areas are pertinent. Improving nutrition programs, raising awareness among mothers, and providing socio-economic support, especially in rural areas can reduce the odds of severe malnourishment and contribute to achieving global Sustainable Development Goals related to health, hunger, and poverty.

BackgroundInfancy and early childhood represent critical periods for language development, as well as for the diagnosis and intervention of language disorders. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI) and magnetoencephalography (MEG), have revolutionized our understanding of the brain’s role in language development and disorders. These techniques provide detailed insights into the neural mechanisms underlying language acquisition and the deviations associated with language disorders. However, there is a notable lack of comprehensive literature reviews on the application of neuroimaging techniques in studying pediatric language development and disorders, particularly in children under eight years old in the field of speech-language pathology. This gap in the literature hinders the ability to form a cohesive understanding of the current state of research and its clinical implications, i.e., garnering an understanding of the contributions of these technological tools in understanding language development and disorders.MethodsTo address this gap, we will conduct a scoping review to synthesize literature involving neuroimaging techniques that examines language development and disorders in typically developing children and those diagnosed with language disorders under the age of eight. This review will be guided by the methodological framework proposed by Arksey and O’Malley [18]. A comprehensive and systematic search will be performed across multiple databases (MEDLINE, Embase, EBSCO CINAHL, PsycINFO, SCOPUS, and The Cochrane Library) to identify relevant peer-reviewed publications as well as grey literature. Studies will be screened according to predefined inclusion criteria. Key data from eligible studies will be extracted, synthesized and presented using both quantitative (numerical) and qualitative (narrative) approaches to address the research questions.DiscussionThis scoping review aims to provide a comprehensive overview and summary of the methodologies and key findings in neuroimaging studies related to pediatric language development and disorders. The results will identify critical gaps in the current research, highlight the strengths and limitations of various neuroimaging techniques, and suggest future research directions in the field of pediatric neuroimaging for language development.Systematic review registrationOpen Science Framework (osf.io/5jhk6).Supplementary InformationThe online version contains supplementary material available at 10.1186/s13643-025-02969-y.

This review presents an integrated assessment of the current state-of-the-art in sustainable machining, with particular emphasis on advanced lubrication and cooling strategies for titanium alloys. Given the high strength, low thermal conductivity, and chemical reactivity of titanium alloys, selecting environmentally responsible and effective machining approaches is essential. The review critically examines the growing use of nanoparticle-amended vegetable oils in Minimum Quantity Lubrication (MQL) systems, highlighting their tribological advantages, enhanced heat dissipation, improved wettability, and positive influence on tool wear, cutting forces, surface integrity, and overall machining efficiency. Complementary sustainable techniques including cryogenic cooling, hybrid cooling–lubrication methods, waste-reduction practices, and energy-efficient process optimization are also evaluated to position nano-enhanced MQL within the broader framework of green manufacturing. The discussion further incorporates sustainable manufacturing principles focused on reducing machining stages, minimizing material and energy consumption, and improving operational efficiency. By synthesizing current knowledge and identifying critical research gaps related to nanoparticle stability, high-speed lubrication delivery, mist formation, and environmental impact, this review contributes to directorial future studies toward more resource-efficient, eco-friendly, and high-performance machining solutions for titanium alloys.

The concept of technological singularity, a projected future point where artificial intelligence surpasses human cognitive abilities, represents a profound inflexion point for human civilisation. This article examines the conceptual foundations underlying this transformative phenomenon, exploring its definitions, theoretical underpinnings, and associated existential risks. It critiques established paradigms in AI development, such as A. Azimov's robotics laws and the Turing Test, proposing radical updates necessary for ensuring human safety in an era of self-improving intelligent machines. . Furthermore, the article analyses the intricate patterns observed in AI's rapid evolution and its diverse applications, from advanced cybersecurity measures and medical diagnostics to complex data management and human-AI collaboration dynamics. Through a systematic evaluation of current research, this analysis integrates multiple perspectives on the trajectory towards singularity, highlighting key findings, persistent challenges, and critical research gaps that necessitate a concerted, interdisciplinary approach to navigate our shared future responsibly Ultimately, this study calls for a globally coordinated and ethically grounded strategy to navigate the challenges and potentials of a future increasingly defined by superintelligent entities, ensuring that the pursuit of technological advancement remains aligned with human survival and moral responsibility.

Abstract: The sustained achievement of Financial Well-being (FWB) for Indian households, the core objective of the National Strategy for Financial Education (NSFE), remains challenged by a pervasive financial literacy (FL) deficit, leading to low financial resilience. Concurrently, the Indian capital market has seen a rapid influx of retail investors. This study addresses the critical research gap by empirically testing the structural causality: whether Informed Stock Market Participation (SMP) acts as the necessary mediating pathway through which financial knowledge translates into improved household welfare. A quantitative, cross-sectional survey design utilizing Structural Equation Modeling (SEM) is employed on a sample of active Indian retail investors. The methodology specifically tested the hypothesized indirect effect (FL → SMP → FWB) using a bootstrapping technique. The study confirms a positive and statistically significant relationship between FL and FWB. Crucially, the core hypothesis (H3) is supported: Informed SMP significantly mediates the relationship between FL and FWB. High FL positively predicts engagement in high-quality participation, characterized by strategic risk management, portfolio diversification, and reliance on professional analysis over speculation. Furthermore, FL is strongly correlated with the adoption of FinTech platforms, confirming the digital pathway to participation. The study concludes that the efficacy of financial literacy policy is structurally dependent on prudent application in the capital market. For Indian policy to maximize FWB, the focus must shift from simply driving inclusion to enabling and enforcing the quality of participation.

Purpose Artificial intelligence (AI) is transforming diabetes management in India, yet its adoption remains limited beyond metropolitan areas. This study aims to explore AI’s role across different phases of diabetes care, focusing on healthcare access, behavioral change and patient engagement in non-metropolitan regions of Maharashtra and Karnataka. Design/methodology/approach A qualitative study was conducted using semi-structured interviews with healthcare professionals and their patients exclusively from 15 hospitals and super-specialty clinics in Maharashtra and Karnataka. Thematic analysis identified key trends in AI adoption, particularly in diagnosis, treatment and patient engagement. Findings AI-powered platforms enhance early access to healthcare, risk assessment and patient-clinician interactions. AI-driven insights support personalized treatment, real-time monitoring and predictive healthcare interventions. In addition, AI fosters behavioral change through continuous engagement and lifestyle recommendations. However, challenges such as infrastructure limitations, data security concerns and lack of AI literacy among healthcare providers hinder widespread adoption. Research limitations/implications This study’s findings are specific to 15 hospitals in Maharashtra and Karnataka, which may limit broader applicability. The perspectives of frontline healthcare workers and patients require deeper exploration. Expanding research to a larger, more diverse sample and conducting longitudinal studies will strengthen insights into AI’s long-term impact on diabetes care. Addressing AI literacy, data security and infrastructure gaps is essential for widespread adoption. Policymakers must establish robust frameworks ensuring algorithmic transparency and equitable access, reinforcing AI’s effectiveness in healthcare. Practical implications Integrating AI in diabetes care enhances early diagnosis, personalized treatment and continuous monitoring, improving patient outcomes. Hospitals must invest in AI literacy programs to equip healthcare professionals with the necessary skills for effective adoption. Policymakers should establish regulatory frameworks to ensure data security, ethical AI use and interoperability with existing healthcare systems. AI developers must focus on user-friendly interfaces to increase patient trust and engagement. Expanding AI adoption in non-metropolitan areas requires infrastructure improvements and public–private partnerships. Strengthening these areas will accelerate AI-driven healthcare transformation, making diabetes management more efficient, accessible and patient-centric. Social implications AI-driven diabetes care can bridge healthcare accessibility gaps, particularly in underserved regions, by enabling early diagnosis and remote monitoring. Increased AI adoption fosters health equity, reducing disparities between urban and rural populations. However, digital literacy and trust in AI remain challenges, necessitating awareness campaigns and patient education initiatives. Ethical AI implementation must prioritize data privacy and algorithmic transparency to maintain public confidence. In addition, AI-driven healthcare can empower individuals with personalized health insights, promoting proactive disease management and healthier lifestyles. By fostering collaboration between healthcare providers, policymakers and technology developers, AI can contribute to a more inclusive and patient-centric healthcare system. Originality/value This study fills a critical research gap by evaluating AI’s impact on diabetes care beyond metropolitan India. With Maharashtra and Karnataka facing high diabetes prevalence and limited AI adoption, understanding its regional healthcare applications is essential. The study highlights practical strategies to overcome adoption barriers, advocating collaborative efforts between hospitals, policymakers and AI developers to maximize AI’s potential in healthcare.

The escalating environmental concerns associated with petroleum-based lubricants have intensified the search for sustainable alternatives. While vegetable oils offer promising biodegradability and renewability, their tribological performance often falls short of mineral oils. This study introduces a novel approach to enhance the performance of soybean oil by blending it with Carbon Nano Particles (CNPs), addressing a critical research gap in the application of nano-additives to this specific bio-oil. The primary motivation is to develop an environmentally friendly lubricant with performance characteristics that rival or surpass conventional mineral oil. The methodology involved an experimental investigation using a four-ball tribotester according to ASTM D4172 standards. Soybean oil was blended with CNP additives at concentrations of 1.0, 2.0, and 3.0 wt.%, and its performance was compared to both neat soybean oil and a commercial mineral oil. Key findings reveal that the 3.0 wt.% CNP blend (SOC3) demonstrated superior performance: it yielded the smallest wear scar diameter (WSD) (364.01 µm), the lowest coefficient of friction (0.0133), and the highest flash temperature parameter (164.62), significantly outperforming both neat soybean oil and the mineral oil benchmark. The significance of these results is profound, demonstrating that CNP additives can effectively mitigate the inherent limitations of soybean oil. This study conclusively shows that soybean-CNP blends are not merely eco-friendly alternatives but are high-performance lubricants capable of reducing wear and friction, thereby offering a viable and sustainable substitute for petroleum-based products in industrial applications.

Haemorrhoids are one of the most common anorectal disorders worldwide, presenting with symptoms such as discomfort, bleeding, swelling, and reduced quality of life. Although traditional therapies such as dietary changes, pharmacological agents, minimally invasive interventions, and surgery remain the cornerstone of management, they are often associated with high recurrence rates, postoperative discomfort, complications, and a significant financial burden. These limitations have sparked increased interest in complementary and alternative medicine (CAM), with herbal agents such as garlic (Allium sativum) gaining attention for their potential therapeutic significance. Garlic has a wide pharmacological profile, including anti-inflammatory, antioxidant, vasomodulatory, antibacterial, antithrombotic, and wound-healing properties. Its bioactive sulphur compounds, such as allicin, ajoene, and diallyl disulphide, help reduce venous congestion, inhibit inflammatory mediators, promote microcirculation, prevent thrombosis, and enhance tissue healing. Traditional medicinal practices and anecdotal evidence support the use of garlic suppositories for localised haemorrhoidal symptoms, while preclinical investigations confirm their biological plausibility. However, despite promising pharmacological findings, robust clinical evidence remains limited, with few randomised controlled trials (RCTs) and no standardised formulations currently available. This gap highlights the importance of conducting rigorous research to determine safety, efficacy, and appropriate therapeutic strategies. Garlic suppositories, due to their low cost, accessibility, and multi-targeted mechanisms, may serve as a valuable adjunct to conventional therapies rather than a replacement. This review synthesises current evidence, identifies critical research gaps, and underscores the need for future studies to translate garlic’s traditional use into evidence-based therapy for haemorrhoid management.

ABSTRACT Emerging technologies play a pivotal role in enabling cities to mitigate climate change and enhance climate resilience. However, there remains a critical research gap regarding how artificial intelligence (AI), as a novel tool, can contribute to urban climate resilience. This study adds to the research literature on emerging technologies and urban governance by analysing the impact of AI technology development on urban climate resilience through a theoretical framework of technological innovation systems (TIS). This study employs causal inference methodology, and the empirical results reveal that AI significantly enhances climate resilience (CR), particularly in regions characterized by advanced digital economies, high economic density, and robust urban innovation capacity. Moreover, the effect of AI on CR exhibits regional heterogeneity. These findings reveal that the effectiveness of system functions is moderated by regional economic characteristics such as digital economy and economic density, as well as external extreme weather, validating the applicability of TIS theory in the field of climate resilience. Among them, this paper identifies the causes of failures in AI technology development across different regions, providing differentiated policy recommendations for TIS policy intervention targets, which goes beyond the generalized policy suggestions in traditional theories.

The widespread production and application of per- and polyfluoroalkyl substances (PFASs) have created a global environmental and public health crisis. This review aimed to consolidate the foundational knowledge on PFASs by synthesizing research on their environmental fate, human health impact, analytical methods, and regulatory status and by highlighting their critical challenges. A comprehensive literature search focusing on publications from the last five years (2020–2025) was conducted using global scientific databases (e.g., PubMed, Web of Science) and regulatory reports (e.g., EPA, ECHA). The persistent and pervasive nature of PFASs stems from the highly stable carbon–fluorine (C-F) bond, leading to their widespread release from diverse industrial and consumer products into water, soil, and air. Key outcomes reveal significant analytical challenges in their detection, including sample matrix complexity, widespread laboratory contamination, and a lack of standards for the vast number of specific compounds. Critical research gaps were identified, particularly the limited data on PFAS concentrations in air and dust, the need for standardized analytical methods and reporting units, and the urgent necessity for developing scalable, sustainable remediation strategies. The ongoing environmental contamination and associated health risks necessitate continued, focused interdisciplinary research to improve detection, risk assessment, and the effective management of this complex class of pollutants.

Kawasaki disease (KD) is an acute multisystem vasculitis that represents the leading cause of acquired pediatric heart disease in children aged 1-5 years in developed nations. The diagnosis of KD remains clinically challenging due to its diverse clinical manifestations and the absence of definitive laboratory tests. Growing evidence suggests that inflammatory processes play a pivotal role in the pathogenesis of KD, implicating a significant involvement of the immune system in disease development. Given the established associations between KD and various biomarkers, ranging from genetic factors to immune system components, this review systematically examines the current knowledge on the immunological and genetic aspects of KD, with a particular focus on the Iranian population. Meanwhile, artificial intelligence (AI) may be revolutionized disease diagnosis, prognosis, and predictive modeling. Its applications have extended to KD, including early detection and classification, as briefly discussed in this review. By synthesizing existing evidence, we aim to identify critical research gaps and enhance understanding of KD's unique characteristics in this demographic context.

Culturally relevant pedagogy is as much about what teachers do in the classroom as how they think about students and their students’ social environment. There remain critical gaps in research on teacher knowledge of student lives outside of school and how this knowledge informs instructional decisions. This study investigates teacher observations of the lives of English learners with disabilities through remote instruction during the COVID-19 pandemic. We analyzed qualitative interviews with 21 teachers from 20 schools in seven districts, approximately two-thirds of whom taught in rural community schools. Interviews were conducted during the pandemic, when participants had a unique opportunity to peek into the lives of their students onscreen. We reported what they learned through this proverbial window into student lives and how they responded to support their students. These findings contribute to the research on culturally relevant pedagogy and critical place pedagogy for English learners with disabilities in rural communities.

Financial Technology (FinTech) is fundamentally reshaping the accounting profession, accelerating the shift from routine transactional activities to more strategic, data-driven functions. This transformation demands advanced digital competencies, yet the scholarly understanding of these skills remains fragmented. To provide conceptual and analytical clarity, this study defines FinTech as an ecosystem of enabling technologies, including artificial intelligence, data analytics, and blockchain, that collectively drive this professional transition. Addressing the lack of systematic synthesis, the study employs a systematic literature review (SLR) guided by the PRISMA 2020 framework, complemented by bibliometric analysis, to map the intellectual landscape. The review focuses on peer-reviewed journal articles published between January 2020 and June 2025, thereby capturing the accelerated digital transformation of the post-pandemic era. The analysis identifies four dominant thematic clusters: (1) the professional context and digital transformation; (2) the educational response and curriculum development; (3) core competencies and their technological drivers; and (4) ethical judgement and professional responsibilities. Synthesising these themes reveals critical research gaps in faculty readiness, curriculum integration, ethical governance, and the empirical validation of institutional strategies. By offering a structured map of the field, this review contributes actionable insights for educators, professional bodies, and firms, and advances a forward-looking research agenda to align professional readiness with the realities of the FinTech era.

Abstract This study investigates the effects of plasma surface modification parameters—specifically gas type, plasma power, and treatment duration—on the performance of commercial polysulfone (PS) membranes in a membrane bioreactor (MBR) treating synthetic fruit juice wastewater. Two gases (H₂O vapor and N₂) were tested at different power levels (40–100 W) and exposure durations (1–30 minutes). The membrane characteristics were evaluated using contact angle, FTIR, SEM, and AFM analyses. Operational performance was assessed under varying organic loading rates (OLRs: 0.5, 1.0, and 2.0 kg COD m⁻³ d⁻¹), including flux and resistance profiles. Results showed that increasing plasma power and exposure time significantly improved membrane hydrophilicity and roughness. The optimal condition—H₂O plasma at 100 W for 30 minutes—reduced the contact angle by 53% and increased surface roughness by 371%. These surface enhancements directly translated into improved filtration performance: the equilibrium flux increased by up to 44%, and total membrane resistance (Rt) decreased by up to 51%. Among the resistance components, cake resistance (Rc) remained the dominant factor under all conditions. To the best of our knowledge, this is the first study to systematically optimize plasma treatment parameters for PS membranes in an MBR treating high-strength industrial effluent. This work addresses a critical research gap by linking specific plasma parameters to longterm hydrophilicity, fouling resistance, and filtration stability, thereby providing a scalable, environmentally friendly approach for industrial wastewater treatment.