University of Business and Technology

With the increasing incorporation of Renewable Energy Sources (RESs) like solar Photovoltaic (PV) systems, maintaining frequency stability has turned out to be a significant challenge owing to decreased system inertia. Despite numerous developments in Load Frequency Control (LFC), existing solutions largely overlooked the issue of Under Frequency Load Shedding (UFLS) relay failure during rapid frequency decline, which led to widespread blackouts. To address this critical gap, a novel intelligent control framework integrating the Fuzzy Doubleton Parabolic Inference System (FDPIS) and the Proportional Quad-Alpine Integral Derivative (PQAID) controller for UFLS relay failure mitigation and enhanced LFC in Solar-PV systems is proposed. Primarily, the Direct Current (DC) power from the solar module is fed into the DC-DC boost converter and Maximum Power Smoothstep Point Tracking (MPSPT) algorithm. A capacitor bank failure is detected using FDPIS, and voltage stabilization is ensured through a Savitzky-Golay Dynamic Polynomial-Z Voltage Restorer (SGDP-ZVR). To predict electrical load demand accurately, a hybrid Deep Learning (DL) model, Deep Dualplus Softshrink Pan–Long Short Term Inverse Parzen Memory (2DSP-LSTIPM), is employed, delivering a high accuracy of 98.98 % with a Root Mean Squared Error (RMSE) of 0.002. When demand exceeds thresholds, transmission overload is mitigated using an Inductive Snubber Cubic Circuits–STATCOM (IS2C-STATCOM). The frequency deviation is identified via FDPIS, followed by the Rate Of Change Of Frequency (ROCOF) analysis. If a UFLS relay failure is detected, then the PQAID controller is activated to ensure stable operation. The proposed PQAID achieves a peak time of 1.91 ms, significantly outperforming traditional PID, PI, and PD controllers in transient and overshoot metrics. Simulation results on the HEDGW dataset assess the proposed approach’s robustness and low time complexity. The system demonstrates superior relay fault detection (fuzzification/defuzzification times of 452ms/463ms) and faster rule generation (597 ms) compared to conventional fuzzy systems. Overall, the proposed methodology provides a comprehensive, real-time, and scalable solution for enhancing frequency stability, relay fault mitigation, and load management in solar PV-based smart grids. • Integrates FDPIS and PQAID for real-time UFLS relay failure detection and mitigation in solar PV systems. • Proposes novel 2DSP-LSTIPM deep learning model achieving 98.98 % demand prediction accuracy with RMSE of 0.002. • Introduces SGDP-ZVR for voltage stabilization during capacitor bank faults using Savitzky-Golay filtering. • Deploys IS2C-STATCOM for efficient transmission overload control with fast reactive power regulation. • Enables seamless SCADA/EMS integration via OPC-UA protocol for smart grid compatibility and deployment.

Abstract Background A transient ischemic attack (TIA) or ischemic stroke often signals a high risk of another event, especially in the days following. While aspirin and clopidogrel are standard treatments, their limitations, such as genetic resistance to clopidogrel, have led to interest in ticagrelor as an alternative. We aimed to address this by comparing the efficacy and safety of ticagrelor (either as monotherapy or in combination with aspirin) with those of aspirin or clopidogrel. Methods This systematic review and meta-analysis adhered to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The PubMed, Web of Science, and Cochrane databases were comprehensively searched to identify randomized controlled trials comparing ticagrelor alone or with aspirin versus aspirin, clopidogrel, or clopidogrel plus aspirin. Odds ratios (OR) with 95% confidence intervals (CI) were calculated using a random-effects model. Results This study included 32,782 patients with ischemic stroke or TIA. Ticagrelor-based regimens lowered the risk of ischemic stroke (OR = 0.81; 95% CI: 0.75–0.88), with the strongest benefit observed with clopidogrel alone. Composite vascular events were also reduced (OR = 0.78; 95% CI: 0.68–0.89), particularly with ticagrelor plus aspirin. However, ticagrelor increased the risk of bleeding (OR = 2.04; 95% CI: 1.63–2.55), fatal bleeding (OR = 2.22; 95% CI: 1.02–4.79), and hemorrhagic stroke (OR = 1.53; 95% CI: 1.02–2.30). Conclusions Early initiation of ticagrelor after TIA or ischemic stroke can significantly reduce recurrent vascular events, particularly in patients with clopidogrel resistance. However, this benefit comes with a higher risk of serious bleeding, underscoring the importance of careful patient selection and individualized treatment planning.

This study examines the intercorrelations that exist between brand equity, expectation confirmation, and sustainable repurchase intentions within Saudi Arabia’s burgeoning e-commerce sector, emphasizing its cultural and digital transformation context aligned with Vision 2030. The main objectives are to identify how brand perceptions influence customer satisfaction, and to explore the applicability of integrated theoretical frameworks, namely Brand Equity Theory and Expectation-Confirmation Theory in explaining sustainable consumer behavior in an emerging market. Utilizing a quantitative research approach, data was collected through an online self-reported questionnaire distributed via social media platforms targeted at active e-commerce consumers in the Hail region. Convenience sampling combined with snowballing yielded a sample size of 361 respondents, ensuring broader demographic representation. Data analysis was conducted using structural equation modeling with partial least squares (SEM-PLS), a technique suited for theory exploration and handling complex variable relationships. The findings demonstrate that brand awareness and brand image significantly positively influence customer satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. Similarly, expectations and perceived performance also have significant positive effects on satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. All hypotheses were supported, with significant relationships observed between the variables, with the model demonstrating robust validity and fit, evidenced by acceptable SRMR, d_ULS, and d_G values. The study’s originality lies in its culturally contextualized application of these theories to a less studied yet vital emerging market, providing novel insights into how cultural nuances influence digital consumer loyalty. These outcomes contribute to both academic theory and practical strategies for e-commerce firms aiming to build sustainable, trust-based relationships within culturally diverse digital environments, offering a valuable blueprint for similar markets undergoing digital transformation.

The rapid accumulation of marine debris poses a substantial threat to ocean ecosystems, specifically the degradation of plastics, composite materials, and metals. Effective detection and classification of debris by material type are essential during the waste management and material recycling process. However, the underwater debris detection is often hampered by low contrast, color distortion, and noise in underwater imagery. Different deep learning models are proposed in the literature for debris detection, however most of them suffered with limitations in real time implementation complexity and inaccurate instance segmentation. This work proposes an adaptive hybrid lightweight Mask R-CNN system including image augmentation, object identification, and real-time instance segmentation to handle these issues. The preprocessed images are feed to the lightweight mask RCNN model for the object detection and segmentation. The proposed model uses an upgraded Region Proposal Network (RPN) for exact localization of underwater trash and MobileNetV3 as a lightweight backbone for effective feature representation. The model uses data augmentation methods including contrast correction, flipping, and blurring to improve robustness; the model also trained on the proprietary underwater debris datasets. Compared to the conventional approaches, performance evaluation metrics employing Mean Average Precision (mAP), Structural Similarity Index Measure (SSIM), Intersection over Union (IoU), and Peak Signal to Noise Ratio (PSNR) shows better accuracy. Furthermore, the model performs real-time computing at 30 FPS, which makes it highly suitable for usage in real-time operations.

This study examined the influence of Environmental, Social, and Governance factors on the financial performance of companies listed on the Saudi Stock Exchange (Tadawul). Employing a panel data approach, the analysis covers 450 firm observations collected annually during the period 2018–2023. Financial performance is measured using Return on Assets (ROA) and Return on Equity (ROE), while ESG disclosure scores are disaggregated into their three constituent pillars. Firm size, revenue per share, and leverage are incorporated as control variables. The fixed effects regression results reveal that social factors demonstrate statistically significant positive relationships with both ROA and ROE, supporting the stakeholder theory-based perspective that strong social practices enhance operational efficiency and investor confidence. Conversely, environmental and governance factors exhibit no significant association with either financial performance metric within the study period. Leverage shows a significant negative relationship with ROA but not with ROE, while revenue per share consistently demonstrates strong positive associations with both performance measures. These findings contribute to the limited literature on ESG–performance linkages in Gulf Cooperation Council markets and offer important implications for corporate managers, investors, and policymakers seeking to advance sustainability objectives within the framework of Saudi Vision 2030.

E-learning platforms and online modules are powerful tools that enable students to learn flexibly and have personalized experiences. Saudi Digital Library (SDL) is a national e-learning resource in the Kingdom of Saudi Arabia (KSA). Although it has been reported that SDL is accessible to all pharmacy students in Saudi Arabia, its use remains questionable. Therefore, the current study was undertaken to examine SDL utilization rates, barriers, self-motivation, and interest among pharmacy students at a public university in southern Saudi Arabia. A cross-sectional, questionnaire-based online study was conducted for six months during the academic year of 2022-2023. Irrespective of gender, all the pharmacy students undertaking undergraduate pharmacy programs, such as BSc. Pharm or Pharm.D at King Khalid University (KKU), Abha, Kingdom of Saudi Arabia, were invited to participate in the current study. The relationships between participants' characteristics and their responses were analysed using chi-square tests and regression analyses. Around 61.7% of the study participants were aware of SDL. The study revealed that the limited use of SDL e-learning resources, with 59.9% of participants indicating they had not used these services in the past year. A significant difference in the usage was identified between the male and female students (p = 0.042). Google was the primary search tool used by students (65.4%), while specialized sources such as Saudi digital libraries accounted for only 4.9% of total searches. Instructors lacked training and motivation, and 65% of participants reported insufficient guidance on e-learning use. Furthermore, the language barrier was identified as a significant obstacle (p = 0.024). Despite these barriers, more than 50% of participants showed keen interest in learning about e-learning resources and recognized their advantages over traditional resources. The study highlights the importance of enhancing student awareness and training, which the teaching faculty can effectively facilitate. Gender-specific workshops can be conducted to improve the use of SDL. In addition, stronger institutional support will optimize the effective use of e-learning resources, such as SDL, among pharmacy students.

Forecasting returns in Real Estate Investment Trust (REIT) markets remains challenging because REIT performance is shaped by nonlinear and time-varying interactions with macro-financial conditions. This study evaluates the forecasting performance of Long Short-Term Memory (LSTM) neural networks relative to a TVP–VAR proxy implemented as an expanding window VAR for weekly U.S. U.S. REIT returns. All models are assessed within a harmonized experimental framework that applies consistent data preprocessing, feature construction, and strictly time-ordered out-of-sample evaluation. The results indicate that the baseline LSTM model delivers modest but more stable error-based performance than the TVP–VAR proxy, with improvements concentrated in RMSE and MAE, while evidence for directional predictability is weak and not consistently distinguishable from benchmark performance. To enhance transparency, SHapley Additive exPlanations (SHAPs) are used to interpret the LSTM forecasts. The attribution analysis highlights recent REIT returns, global equity indicators—particularly the Hang Seng Index—and crude oil prices as influential predictors, and shows that their contributions vary across volatility regimes, consistent with time-varying spillovers and changing risk transmission. Overall, the study positions LSTM forecasting combined with SHAP-based interpretation as a transparent and reproducible framework for comparative evaluation and driver analysis in weekly REIT returns, rather than as a strong directional timing tool.

Enhancing the heat and mass transfer performance of working fluids remains a critical challenge to pursue sustainable and energy-efficient technologies. Although regular working fluids have superior thermo-physical properties to pure base fluids, they often face limitations that hinder their adoption in multifunctional applications. To overcome these challenges, this study develops a novel, comprehensive and physically consistent mathematical model for an unsteady, electrically conducting ternary hybrid nanofluid composed of graphene oxide (GO), cerium oxide (CeO[Formula: see text], and hexagonal boron nitride ([Formula: see text]-BN) suspended in an environmentally friendly ionic liquid (Ethyl-3-methylimidazolium tetrafluoroborate) [EMIM][BF 4 ]. The model integrates magnetic effects, radiation heat transfer, viscous dissipation, Joule heating, and coupled thermo-diffusion effects. A fractal–fractional derivative operator is employed to generalize the governing equations, while the local radial basis functions (RBF) scheme is used to solve them numerically. Computational and graphical results reveal that CeO 2 suppresses the fluid velocity due to increased inertial resistance, while the dispersion of [Formula: see text]-BN significantly enhanced the thermal profile, resulting in a higher Nusselt number. Furthermore, higher values of Dufour and Soret numbers enhance the coupled heat and mass transfer rates, indicating the model’s potential to design advanced heat exchangers and smart cooling devices. These findings provide valuable guidelines for designing compact heat exchangers and thermal energy storage systems for applications in renewable energy and microelectronics cooling.

Abstract We explore the spillover among ASEAN-5 stock markets (Malaysia, Singapore, Thailand, the Philippines, and Indonesia) and the categorical economic policy uncertainty (EPU) of three major economies (China, Japan, and the United States), employing an R 2 decomposed connectivity approach. Our empirical findings depict significant connectedness among ASEAN-5 stock markets and the categorical EPUs of three major economies, where instantaneous temporal spillovers are greater than lagged spillovers. We find that most categorical EPUs are shock receivers, whereas the ASEAN-5 stock markets are shock transmitters. Chinese monetary and exchange rate policy uncertainties, along with Japanese fiscal policy uncertainty, act as net shock transmitters. However, the roles of categorical EPUs and ASEAN-5 stock markets vary across total, contemporaneous, and lagged transmissions. We also find that the GFC, the Euro debt crisis, the U.S.‒China trade war, COVID-19, and the Russia–Ukraine conflict caused dynamic fluctuations in shock transmission. We also show that oil price changes, global financial stress, climate change, and gold market ups and downs are key factors that link economic policy uncertainties and the ASEAN-5 markets in different time frames. Hence, our empirical results provide valuable insights for managing risks and formulating strategies for addressing economic policy uncertainty and world crisis events.