Sohar University

This study aims to optimize the Wire‐Cut Electrical Discharge Machining (WEDM) parameters for Inconel 800, a high‐performance superalloy known for its remarkable mechanical properties and resistance to elevated temperatures. The research leverages Particle Swarm Optimization (PSO) to improve machining outcomes, including material removal rate, surface finish, and cost‐efficiency. A structured experimental approach, following Taguchi's L18 design, was used to evaluate the effects of key machining parameters such as pulse on‐time, pulse off‐time, peak current, and spark gap voltage. The results demonstrate that the PSO model significantly enhances machining performance by reducing surface roughness and increasing material removal rate (MRR), showcasing marked improvements in efficiency. With a mean prediction error of < 1%, the PSO model proves highly accurate and reliable. Additionally, the study examines the economic aspects of WEDM by calculating the total machining costs, which include power, wire, and dielectric fluid consumption. By filling a critical research gap in the machining of Inconel 800, this work offers valuable insights into optimizing WEDM processes for superalloys. The findings highlight the potential of PSO as a powerful tool for multi‐objective optimization in advanced manufacturing applications.

The intensifying issue of global warming is worsened by the fast economic growth of the world’s leading economies, which have emerged as the most polluted countries globally. The present study has tried to make an intensive environmental analysis of the selected five most polluted countries of the world—China, Russia, the United States, India, and Japan to see how adaptation technology (ATEC) and alleviating energy poverty (EPO) help these countries to raise their environmental quality. The study hypothesizes that ATEC raises the environmental quality of these countries while controlling energy poverty is also required. The study also delves into the impact of the financial development (FD) of these selected countries on the environmental quality of these polluted economies. The study covers the period from 2000 to 2020. To generate a comprehensive set of outcomes, the study has utilized the panel quantile regression (PQR) approach, which is better suited to handle data non-normality and the existence of outliers, which is most expected in using a dynamic set of variables. The outcomes of the study confirm the constructive role of ATEC and the need for controlling energy poverty in the most polluted countries to raise their environmental quality. Following the empirical outcomes, the study proposes the policy framework for not only enhancing environmental quality but also securing several SDGs like SDG 01 working for no poverty, SDG 07 aimed at making green energy affordable, SDG 09 concerns industrial development with innovation and infrastructure, SDG 12 assured responsible consumption as well as production, SDG 13 considered climate actions, and SDG 17 forced partnership for goals, particularly in the five most polluted countries.

Background Soaps are vital for preserving our health and personal hygiene since they not only eliminate germs but also rid the body of pollutants. Method The current study aims to determine the physicochemical and antibacterial properties of Eucalyptus camaldulensis leaves using the agar disc diffusion technique and assess the effectiveness of different branded liquid soaps (25 mg/ml, 50 mg/ml, 75 mg/ml, and 100 mg/ml) with the Eucalyptus leaf extract against skin-infecting human pathogenic bacteria. Results The combined antimicrobial susceptibility of E. camaldulensis and five liquid soaps showed an inhibition zone of 17.67±0.58, 13.33±0.58, 12.67±0.58, and 15.67±0.58 against Staphylococcus aureus, Streptococcus pyogenes, Pseudomonas aeruginosa, and Escherichia coli. The antibacterial properties of Av soap by itself did not work against S. pyogenes. Nevertheless, the extract and DI together showed a detrimental effect against S. aureus and P. aeruginosa, with no halo forming. Conclusion Antimicrobial activity was observed to increase with higher concentrations of the soap-extract combinations. Although liquid soap (seve) was effective against bacterial isolates, a combination of eucalyptus and aqua vera was shown to be more effective.

This study investigates the impact of the COVID‐19 pandemic on sustainability disclosure and environmental, social, and governance (ESG) scores in 726 healthcare companies across 10 European countries over the period 2016 to 2022, with a specific focus on the relevance of following Global Reporting Initiative (GRI) guidelines. The findings reveal that COVID‐19 cases and deaths have a significant negative effect on sustainability reporting scores for GRI‐non‐compliant firms. Interestingly, the study uncovers a positive relationship between COVID‐19 cases and sustainability reporting scores for firms that follow GRI guidelines. This implies that GRI‐compliant firms are more proactive in addressing and disclosing the impact of the pandemic, leading to higher sustainability reporting scores. Conversely, non‐compliant firms exhibit a negative relationship between COVID‐19 cases and sustainability reporting scores, suggesting challenges in effectively addressing and communicating their response to the pandemic. Furthermore, the impact of COVID‐19 on ESG scores differs based on GRI compliance. GRI‐compliant firms demonstrate minimal impact on ESG scores, indicating the resilience of their established ESG practices. In contrast, non‐compliant firms experience a negative impact of COVID‐19 cases on ESG scores, highlighting the importance of adopting standardized reporting frameworks to effectively manage and communicate ESG performance during crises. The results underscore the need for firms to proactively address and communicate their response to the pandemic's challenges, particularly in terms of sustainability reporting and ESG performance. The study highlights the role of GRI guidelines as a potential facilitator of effective pandemic response and emphasizes the significance of standardized reporting frameworks for managing and communicating sustainability performance during crises in the health sector and beyond.

Motivated by the rapid advancement of digital technologies and their transformative impact on governance, this study investigates how government digitalization influences national innovation capability. As countries increasingly integrate digital tools to streamline public administration and enhance online service delivery, understanding its implications on innovation becomes crucial. This study employed panel data from 72 countries to empirically explore the relationship between government digitalization and national innovation capability. First, a fixed‐effect model revealed a positive impact of government digitalization on national innovation capability, and this positive effect withstood robustness checks. Subsequently, three fixed‐effect models with interaction terms examined the mechanisms underlying this effect, finding that government digitalization can enhance national innovation capability by improving ease of doing business, curbing corruption, and enhancing the legal environment. Furthermore, heterogeneity analysis examined and confirmed the moderating effects of telecommunications infrastructure, residents' digital skills, natural resource abundance, financial development, industrial upgrading, and ruling party ideology on the relationship between government digitalization and national innovation capability. Finally, a panel threshold regression model revealed a non‐linear impact of government digitalization on national innovation capability, which varied according to economic development levels. The findings of this study provide new empirical evidence for the rationality of governments' digital transformation.

The extensive use of chemical pesticides poses risks to the environment and human health due to the toxicity and poor biodegradability. Alternative natural practices, including the use of natural molecules, are needed to achieve more sustainable production methods to meet consumer and societal expectations. Plants contain a wide range of potential phytochemicals that target a specific target, are rapidly biodegradable, are environmentally friendly, and have a variety of therapeutic effects, making them a treasure trove of biological materials. Toxicity of hot Capsicum annuum extracts was tested against 3rd instar larvae of Culex pipiens and Musca domestica. LC₅₀ values were determined using serial concentrations, and phytochemical profiling was performed to identify active compounds with molecular docking studies. In this study, different exposure periods of various Capsicum annuum extracts showed high insecticidal activity against mosquito and housefly larvae. The petroleum ether (CAPe) extract from C. annuum was the most effective (100 MO%) against Culex pipiens (LC50 = 150.46 ppm) and Musca domestica larvae (LC50 = 0.18 mg/ml) 24 h after treatment. The LC50 dose of the CAPe extract led to a negative effect on the insect metabolism process represented by a significant decrease in the activity level of protease, lipase, α-amylase, and invertase enzymes in both mosquito and fly larvae. Antimicrobial activity tests showed that the CAPe extract killed all of the microbes that were tested, except for Penicillium glabrum. The UPLC/MS comparison of the four Capsicum extracts led to the possible identification of eighty metabolites. The large amounts of flavonoids, phenolic acids, and capsaicinoids were in line with what has been written about the genus Capsicum. Moreover, the multivariate data analysis showed that capsaicinoids, sophorolipids, triterpenoids, and phenolic acids were abundant in the methanol extract compared to flavonoids, triterpenoids, and fatty acids for the petroleum ether extract. Simultaneously, the docking results showed that all of the docked compounds could fit into the digestive lysosome active site of M. domestica (2H5Z). The major compounds in petroleum ether extract were able to interact with essential amino acids at the target sites of both Cx. pipiens and M. domestica, and therefore the insects’ life-supporting functions were negatively affected. Overall, CAPe extract from Capsicum annuum could be a promising ecofriendly bioinsecticide.

Environmental innovation (EI) plays a pivotal role in achieving sustainable development. However, the influence of minority shareholder protection (MSP) on EI remains insufficiently explored. This research investigates the impact of MSP on EI within an emerging economy, considering ESG ratings as an interactive component. By analyzing a decade dataset spanning 2013–2022, comprising 4234 firms with 33,718 observations, this study finds that MSP exerts a positive effect on EI performance. Although prevailing literature emphasizes minority shareholders' inclination toward short‐term gains, our findings suggest that strong ESG ratings help counteract this tendency, encouraging a more active engagement. Moreover, an assessment incorporating international ESG standards highlights the importance of tailoring such frameworks to the specific needs of developing economies. Firm characteristics and regulatory intensity can be the channels for MSP propelling EI performance. Regional variations in China further demonstrate that ESG ratings function most effectively as regulatory instruments in the central region, where they reinforce corporate alignment with sustainability objectives. The robustness of these conclusions is confirmed through two‐stage IV‐GMM and propensity score matching (PSM) estimations. Drawing on stakeholder and signaling theories, this study offers fresh insights into corporate governance and sustainability. It expands stakeholder theory by illustrating how MSP can harmonize with corporate sustainability goals, while signalling theory underscores ESG's role in signaling long‐term commitment. Practically, these findings emphasize the necessity of embedding ESG principles within corporate governance frameworks, recommending that policymakers enhance MSP and ESG disclosure mechanisms. Future studies could examine additional sustainability strategies across varying industrial and regulatory landscapes.

Motorcycles are a popular low-cost personal transport mode. Despite their convenience, motorcycles are significantly more dangerous than other modes of transport, accounting for up to 39% of road fatalities in low-income countries. Speeding is among the most common factors causing road accidents. Thus, this research extends the theory of planned behavior to investigate young motorcyclists’ speeding behavior by incorporating the latent variables of hedonic motivation and transport policy interventions using data collected through a questionnaire survey conducted among young motorcyclists in Lahore, Pakistan. Purpose-based sampling method was deployed to collect 394 responses. The results indicated that speeding attitudes (SA), perceived behavioral control (PBC), hedonic motivation (HM), and policy intervention (PI) variables are strong predictors of speeding intentions (SI), which act as a mediator of speeding behavior (SB). While HM positively affects SB, and the PI variable negatively influences SB. Moreover, unmarried and employed respondents are positively associated with SB. This research has provided important insights on how to improve young motorcyclists’ safe behavior, which can be utilized by policymakers to make informed decisions to enhance road safety in Pakistan and other developing economies with similar socio-economic dynamics, with motorcycles as a popular low-cost personal travel mode.

Based on trade data from 2005 to 2020, this study investigates the driving forces behind China’s grain virtual water (VW) import trade, with a particular focus on the role of the Belt and Road Initiative (BRI). By incorporating economic distance (ED) and institutional distance (ID) into the gravity model framework and applying a high-dimensional fixed-effects Poisson pseudo-maximum likelihood estimation method, the study offers new empirical insights. The results indicate that ED is negatively associated with virtual water trade (VWT) in grains, while ID exhibits an inverted U-shaped relationship with VWT. Furthermore, the BRI significantly moderates the effects of ED and ID, weakening their influence on VWT. Additionally, the initiative demonstrates a clear trade creation effect, promoting increased VW imports. These findings contribute to a deeper understanding of the mechanisms shaping VWT and offer valuable policy guidance for enhancing international cooperation under the BRI framework.

The rapid growth in traffic on road networks demands smart and sustainable mobility patterns to reduce traffic congestion and externalities while fulfilling the travel needs of the people. Carpooling is a sustainable alternative that can shift from single occupancy to high occupancy vehicles and reduce users’ travel costs in the era of rising fuel prices. This study aims to categorize travelers into latent classes considering the carpooling barriers, motives, and benefits, and derive suitable transport policies. A questionnaire was designed and conducted online with travelers (N = 400) in Islamabad and Rawalpindi, Pakistan. The data were analyzed using factor analysis, latent class analysis (LCA), and logistic regression analysis. The LCAs yielded three classes of carpooling barriers, i.e., barriers conscious, apathetic about barriers, and carpooling spectators; three classes of carpooling motives, i.e., non-carpoolers, apathetic carpoolers, and dedicated carpoolers; and also, three classes of carpooling benefits, i.e., non-believers of benefits, casual believers of benefits, and benefits passionate. The comparison based on average scores and ANOVA results showed significant heterogeneity in perceptions about various carpooling attributes and characteristics across three classes of barriers, motives, and benefits. The binary logistic regression showed that gender, profession, travel mode, income level, driving a car, trip distance, and cost reduction expectation are significant attributes in predicting the specific class of travelers. Travelers who are in classes of dedicated carpoolers, carpooling spectators, and ‘benefits passionate’ have a higher likelihood of carpooling. Travelers with a 6–15 km trip distance are likely to fall in the non-carpoolers class and less likely to fall in the ‘benefits passionate’ class. A 50% cost reduction with carpooling positively impacts the propensity to carpool and the belief in carpooling benefits. These findings would provide useful insight to transport planners in designing appropriate carpooling programs that focus on specific classes of travelers.

Accurate reservoir inflow predictions are critical for effective flood control and optimizing hydropower generation, thereby enhancing water resource management. This study introduces an advanced hydrological modeling approach that leverages a basic recurrent neural network (bRNN), convolutional neural network (CNN) with gated recurrent units (GRU) (bRNN-CNN-GRU), GRU with long short-term memory (LSTM) (GRU-LSTM) hybrid models, and deep neural network (DNN) to predict daily reservoir inflow at the Sefid Roud Dam. By utilizing historical data from 2018 to 2024, this study examined the following two multivariate scenarios: one incorporating water parameters such as water level, evaporation, and temperature extremes, and another focused solely on inflow delays. Training and testing sets were created from the dataset, with 80% for training and 20% for testing. For benchmarking purposes, the performance of the bRNN-CNN-GRU was evaluated against a deep neural network (DNN) and a GRU-LSTM hybrid. The evaluation metrics used were root mean square error (RMSE), correlation coefficient (r), and Nash Sutcliffe coefficient (NSE). Results demonstrated that, while all models performed better under the scenario incorporating inflow delays, the bRNN-CNN-GRU model achieved the best performance, with an RMSE of 0.71, r of 0.97, and NSE of 0.95, outperforming both the DNN and GRU-LSTM models. These findings highlight the significant advancements in hydrological modeling and affirm the applicability of the bRNN-CNN-GRU model for improved reservoir management in diverse settings.

This study presents an innovative approach to analyzing finite-time stability (FTS) and synchronization (FTSYN) in integer-order reaction-diffusion systems (RDs), particularly in the context of epidemiological modeling. By integrating Gronwall’s inequality, Lyapunov functionals (LFs), and linear control strategies, a comprehensive framework is developed to address transient dynamics within finite time frames. The proposed methodology advances the theoretical understanding of FTS and FTSYN by addressing the relatively unexplored dynamics of spatially extended systems. MATLAB simulations validate the theoretical findings, demonstrating the effectiveness of the control schemes and their practical applicability in modeling real-world disease transmission. Integrating spatial diffusion and disease dynamics provides critical insights into the influence of parameters such as diffusion rates and mortality on system behavior. This work contributes a robust framework for enhancing the analysis and management of nonlinear systems, with significant implications for epidemiology and other fields requiring rapid convergence and synchronization.

This essay systematically reviews articles on geopolitical risk (GPR), providing a detailed analysis of publication trends and research themes. The results revealed that the rise in the GPR has led to severe economic repercussions, including depressed economic activities, reduced investments, constrained domestic credit, and disrupted global supply chains. While there is broad consensus on the significant influence of GPR on oil prices and market volatility, the findings reveal the nuanced and sometimes conflicting impacts and predictive power of these risks. This underscores the importance of considering the context and timing of geopolitical events in market analysis and policy formulation. The study suggests that scholars should incorporate a variety of GPR indices, both country‐specific and global, to capture the multifaceted impacts of geopolitical events on markets. Context‐specific analyses are essential for understanding the differential impacts of GPR under various economic environments and market conditions. Related Articles Lemire, S., L. R. Peck, and A. Porowski. 2023. “The Evolution of Systematic Evidence Reviews: Past and Future Developments and Their Implications for Policy Analysis.” Politics & Policy 51, no. 3: 373–396. https://doi.org/10.1111/polp.12532. Hernández‐Moreno, J., J.‐B. Harguindéguy, and B. Carrasco‐Ariza. 2025. “Review Essay—Territorial Politics and COVID: A Comprehensive Review.” Politics & Policy 53, no. 1: E70010. https://doi.org/10.1111/polp.70010. Robles, P., and D. J. Mallinson. 2023. “Review Essay—Catching Up With AI: Pushing Toward a Cohesive Governance Framework.” Politics & Policy 51, no. 3: 355–372. https://doi.org/10.1111/polp.12529.

The manipulation of digital images has become a popular trend. Due to the development of image processing tools and visualization techniques integrating deep learning and artificial intelligence (AI) algorithms (in particular generative adversarial networks–GAN), this can pose serious threats to privacy and security. In recent years, Deepfake algorithms have been designed to exchange faces or modify facial features, potentially leading to more severe problems in this context. In this manuscript, we provide a comprehensive review of the two most important facial image processing technologies: (i) deepfake face manipulation; and (ii) face manipulation detection techniques. Furthermore, we explore the state-of-the-art of popular GAN techniques. In particular, three types of Deepfake face detection technologies are reviewed: (i) hand-crafted features (ii) artifacts; and (iii) learning-based features, while highlighting related improvements and challenges. Furthermore, this article discusses potential challenges and promising research directions for future investigation. We believe that this review has been organized to provide a structured analysis of important research papers and to discuss each study’s main findings and conclusions. Our investigation reveals shortcomings in manipulation detection benchmarks due to real-world scenario variations and biased dataset comparisons. Current research priorities revolve around enhancing GAN training stability, resolution, and manipulable facial features. Moreover, GANs have shown superior results in identifying fake images; however, their reliance prompts a systematic approach to detecting fakes. This dependency raises questions about detecting fake images with or without manipulated GAN architecture, urging the need for novel computational techniques to identify manipulations without GAN assistance.

This study presents a novel gradient-based algorithm designed to enhance the performance of optimization models, particularly in computer science applications such as image restoration and robotic motion control. The proposed algorithm introduces a modified conjugate gradient (CG) method, ensuring the CG coefficient, β κ, remains integral to the search direction, thereby maintaining the descent property under appropriate line search conditions. Leveraging the strong Wolfe conditions and assuming Lipschitz continuity, we establish the global convergence of the algorithm. Computational experiments demonstrate the algorithm's superior performance across a range of test problems, including its ability to restore corrupted images with high precision and effectively manage motion control in a 3DOF robotic arm model. These results underscore the algorithm's potential in addressing key challenges in image processing and robotics.

The main aim of this study is to investigate the moderating role of financial development between political stability and environmental sustainability in the context of ASEAN-5 economies (Malaysia, Indonesia, Singapore, Thailand, and the Philippines) from 1996 to 2021. Using FMOLS, DOLS, and the Dumitrescu–Hurlin panel causality test, the research explores how these factors, along with the shadow economy (SE), environmental diplomacy (ED), technology innovation (TP), and population density (PD), influence the ecological footprint (EF). The results indicated that FD positively correlates with EF, signifying that economic growth driven by financial development leads to environmental pressure. On the contrary, SE has a negative relationship with EF, suggesting that informal economic activities are socially detrimental to environmental performance. Moreover, PS and its interaction with FD (PS*FD) are significant for EF; this reveals the significance of stable political climates for forming sound economic policies. Nevertheless, ED has a weak relationship with EF, showing a less direct influence of environmental diplomacy on environmental pollution. The Dumitrescu–Hurlin causality test also strengthens these findings by revealing the bilateral causality between FD and EF and the one-way causality between SE, PS, PS*FD, and TP and EF. Hence, policy implications draw attention to environmental issues' inclusion in the financial systems, legalizing the shadow economy, stabilizing the political environment for sustainability policies, and supporting technological progress focused on environmental objectives. Understanding these patterns is vital for the policymakers to achieve the aim of economic development and conservation of natural resources in the selected ASEAN countries. Graphical abstract

The huge generation of municipal solid waste along with the reliance on natural resources to meet the ever-increasing demand of energy has stimulated the world towards the exploration of novel methods for the recovery of energy and resources by using the generated waste. Despite the numerous advantages of waste-to-energy (WtE) technologies, these techniques are not widely implemented. The review has summarized the various aspects of WtE techniques including advantages and limitations, techno-economic analysis, challenges and prospects, framework and implementation. The review has identified that the WtE techniques are more efficient than conventional waste management practices. The characteristics of municipal solid waste (MSW) vary with geographical conditions, living standards, socio-economic conditions, etc. Therefore, no particular WtE technique is equally feasible for the treatment of MSW. The strict environmental strategies, policies, and guidelines can assist in selecting the best WtE practice. The thermal treatment methods can effectively reduce the volume of generated waste by up to 90%. Techno-economic analysis has revealed that WtE techniques are economically feasible with suitable measures. The life-cycle assessments have found that WtE techniques can recover up to 27.40% of energy. The food and agriculture waste constitutes 50–56% of the generated waste stream in developing countries thereby highlighting the significance of anaerobic digestion. The implementation of WtE techniques can considerably reduce the emission of greenhouse gases and is beneficial to environmental health. The potential of WtE techniques for effective waste management and promotion of sustainability is underscored. The review contributes to the implementation of more effective measures for MSW management and promotes a circular economy.

In industrial effluent water, heavy metal ions are considered as common toxic contaminants. Using thermophilic bacterial strains, the present study offers a pilot study for eliminating excess iron elements from the wastewater and environment. The iron bioremediation potential of metal resistant bacterial isolate (GA6), which originated from the Gauri Kund hot spring in Uttarakhand, India, was examined. With a 98.14% similarity, 16S rRNA sequencing revealed that this isolate was Bacillus thermophilus HS-BTL2. Based on the findings, the Bacillus thermophilus HS-BTL2 bacterium successfully removed iron from its solutions after 48 h incubation period at a temperature of 55 °C and pH 7.0. A considerably greater 91.79% iron was removed from the metal-contaminated solution by the dead biomass of Bacillus thermophilus HS-BTL2. Compared to the dead biomass of the bacterial strain, the live Bacillus thermophilus HS-BTL2 bacterial cell can only biosorb 86.17% iron from the metal-containing solution. Following its inoculation into the industrial effluent water, the Bacillus thermophilus HS-BTL2 strain was able to biosorb 63.94% of the iron metal elements present in the effluent.

Green supply chain integration (GSCI) has emerged as a significant technique for improving sustainable performance by promoting collaboration with supply chain partners and breaking down organizational barriers to utilize complementary resources. This study investigates the relationships among GSCI, supply chain agility (SCA), digital orientation (DO), and sustainable performance, grounded in the Natural Resource-Based View (NRBV) and Contingency Theory (CT), based on survey data from 288 Chinese pharmaceutical manufacturing enterprises. Using mediation, moderation, and moderated mediation analyses, the findings indicate that SCA serves as a mediator between GSCI and sustainable performance. Significantly, DO strengthens both the direct effect of SCA on sustainable performance and the overall mediating pathway; nevertheless, it does not substantially boost the association between GSCI and SCA. This study’s innovation lies in elucidating the significance of GSCI as a resource for sustainable performance within the pharmaceutical enterprises, while further delineating the pathways and contingent elements for achieving sustainable performance in a digital context. This study offers valuable implications for both academic research and managerial practice.

Based on the Hermitian and skew Hermitian splitting of the coefficient matrices, we demonstrate a shift-splitting hierarchical identification (SSHI) iterative algorithm to solve the matrix equation $AX - \overline{X} B=C$. For any initial value, the suggested method converges to the exact solution under certain conditions. Three numerical examples are presented to demonstrate the effectiveness of the shift-splitting hierarchical identification (SSHI) iterative method and to compare it to the Jacobi-gradient iterative algorithm (JGI) (Bayoumi in Appl. Math. Inf. Sci. (2021)) and the gradient iterative algorithm (GI).