Universiti Sultan Zainal Abidin

Influenza is one of the key persistent viral diseases globally, with the potential to cause seasonal outbreaks and occasional pandemics. Currently, it poses a significant health challenge in various regions, and there is concern that it may evolve into a more severe global threat. In this paper, we introduce a stochastic approach to modeling an influenza outbreak. The study begins by formulating a mathematical representation that incorporates random fluctuations influencing the disease's progression. We demonstrate that the proposed model guarantees a positive global solution. By utilizing Lyapunov functionals, we investigate specific parametric conditions under which the disease may be eliminated from the population. Additionally, an ergodic stationary distribution is used to demonstrate conditions under which the infection may persist. A threshold quantity is identified, offering insight into key parameters that influence other dynamic aspects of the model. The stochastic model of influenza is further examined through graphical representation for better understanding.

Thyroid hormones control crucial physiological activities, such as metabolism, oxidative stress, erythropoiesis, thermoregulation, and organ development. Hormonal imbalances may cause serious conditions like cognitive impairment, depression, and nervous system damage. Traditional diagnostic techniques, based on hormone level measurements (TSH, T3, FT4, T4, and FTI), are usually lengthy and laborious. This study uses machine learning (ML) algorithms and feature selection based on GA to improve the accuracy and efficiency of diagnosing thyroid disorders using the UCI thyroid dataset. Five ML algorithms-LR, RF, SVM, AB, and DT- were tested using two paradigms: (1) default classifiers and (2) hybrid GA-ML models- GA-RF, GA-LR, GA-SVM, GA-DT, and GA-AB. The data pre-processed included handling missing values, feature scaling, and correlation analysis. In this case, the performance metrics used for model evaluation are accuracy, F1 Score, sensitivity, specificity, precision, and Cohen’s Kappa with 80% of the dataset to train the model and the rest 20% used to test it. Among the non-hybrid models, RF achieved the highest accuracy, which was 93.93%. The hybrid GA-RF model outperformed all others, achieving a remarkable accuracy of 97.21%, along with superior metrics across all the evaluated parameters. These findings highlight the diagnostic potential of the GA-RF model in providing faster, more accurate, and reliable thyroid disorder detection. The research illustrated the potential of the hybrid GA-ML approaches to improving the clinical diagnostic process while proposing a strong and scalable approach towards thyroid disorder identification.

Data-driven medical applications, powered by big data and artificial intelligence, generate scalable models from extensive datasets. This innovation attracts both academia and industry, significantly enhancing healthcare quality while posing integration challenges. Federated learning emerges as a transformative approach in healthcare, facilitating collaborative machine learning while preserving data privacy. This article reviews research on federated learning in healthcare, utilizing the "Web of Science" database to examine development trends and application progress. Initially, the federated learning system is dissected, and its application methods are analyzed, summarizing 18 federated learning frameworks and 9 privacy-preserving methods suitable for federated learning, and exploring its limitations in healthcare applications. Subsequently, recent literature on federated learning in four major areas—disease diagnosis and risk assessment, medical image analysis, drug discovery and development, and disease management—is reviewed, summarizing the general process of applying Federated Learning in healthcare. Finally, the challenges faced by federated learning in the medical field and the solutions currently being explored by scholars are summarized. This article aims to comprehensively summarize the research progress and application trends of federated learning technology in the medical field, analyze its limitations and challenges, and anticipate its future development to further promote sustainable advancement in the healthcare industry.

Neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, pose a significant global health burden, progressively impairing cognitive and motor functions. The complex interplay between neuronal health, immune response, and pathological protein accumulation necessitates advanced mathematical modeling for better understanding and intervention strategies. In order to analyze brain diseases, we have developed a five-compartment nonlinear mathematical model to observe the dynamics and focuses on extracellular $\alpha$ -synuclein function, functioning neurons, infected neurons, activated microglia density, and T -cells, which may contribute to neuroinflammation in neurodegenerative contexts. Since our model hasn’t been put forth in the literature before, it is novel. In addition, we modified the recently created model by adding fractional order derivatives to better comprehend the relationship between immune response dynamics and neuronal health in brain disorders. To better comprehend these intricate processes and advance medical therapies, our study combines novel mathematical methods with computer simulations. Stability analysis confirms the existence of a feasible disease-free equilibrium. In contrast, sensitivity analysis highlights the critical influence of parameters such as neuron production $(\Pi_N)$ , infection rate $(\gamma)$ , and microglial activation $(\Theta)$ . Numerical simulations reveal that lower fractional orders $(\nu < 1)$ slow disease progression, indicating the long-term impact of neuroinflammatory feedback mechanisms. To assess the computational efficiency and accuracy of the proposed fractional-order model, we compare numerical solutions obtained using the Lagrange interpolation method and the ODE45 solver. The results demonstrate that the Lagrange interpolation method exhibits superior accuracy and stability in capturing the long-term behavior of neurodegenerative progression, whereas ODE45, a classical numerical approach, struggles with fractional dynamics due to its dependence on integer-order derivatives. The findings of this study provide valuable insights into the progression of neurodegenerative diseases and offer a framework for exploring targeted therapeutic strategies. By refining fractional-order parameters and integrating real-world clinical data, future research can enhance the predictive power of these models, aiding in early diagnosis and optimized treatment strategies for neurodegenerative disorders.

Effective strategic management requires good corporate governance to improve performance and drive shareholder engagement. In recent years, there has been an increasing number of studies focusing on corporate governance. This study aims to explore various fields of study with bibliometric analysis and visualization of VOSviewer networks, as well as to provide the current state of corporate governance research and to understand the development trends of research fields through existing literature. In addition, the study aims to find hotspot keyword trends, impactful corporate governance study trends, and trends for future research using scientific methodology. By focusing on annual production, author network collaborations, journals, co-emerging keywords, and impactful corporate governance articles, the study adds to the existing literature. Bibliometric analysis was used to assess the scope of corporate governance research conducted between 1973 and 2023, which was screened from the Scopus database. The United States has significant influence in the field of corporate governance. The United Kingdom has the most prolific number of writers, but the United States holds the record for the highest number of citations. Key focal points in corporate governance include accountability, agency issues, board diversity, and sustainability. This study emphasizes the need for future research to focus on audit committees, innovation, political connections, corporate reputation, family firms, dividend payout policy, and gender diversity issues.

Continuous oil reserve depletion, coupled with environmental issues faced by oil-dependent countries, has generated an urgent necessity for critical economic diversification. The blue economy sector, which stands out among emerging sectors, has the potential to significantly boost sustainable growth. However, this area is underexplored. Therefore, this study seeks to investigate how Blue Economy [agriculture, forestry, and fishing, value added (AF); aquaculture production (AP); fishery production (FP)] affect long-term economic sustainability (GS) in African oil-dependent nations while examining the nature and direction of these effects through different economic conditions based on annual data spanning from 1980 to 2023. This research employs the Panel Nonlinear Autoregressive Distributed Lag model and the dynamic common correlated effect estimation for the analysis. Key findings show that the Blue Economy factors produce uneven results. Specifically, both FP and AP show positive and statistically significant results. An increase in AF typically reduces GS because natural capital sectors usually underperform in oil-rich countries, as oil revenues mask out investment in these countries. Yet the negative shocks of AF hinder GS through the restricted availability of capital. Whereas the positive shocks of AP create favorable conditions for food security and rural economic expansion; however, its corresponding negative shocks threaten GS. It further reveals that an increase in FP enhances GS, yet a decrease in FP hinders GS, as unsustainable practices in this sector are detrimental to economic performance. These results underscore the essentials of a Blue Economy to achieve long-term economic growth as they provide valuable insights for policymakers.

The integration of rotary wire electrical discharge machining (WEDM) technology broadens its applicability from two-dimensional to cylindrical part machining, enabling the production of complex geometries with high precision. In this study, an ultrasonic vibro-rotary spindle was designed, assembled and evaluated to enhance the machining capabilities of WEDM for cylindrical components. The ultrasonic spindle facilitates both rotational motion and high-frequency vibration, which improves debris removal and enhances machining stability. Experimental results demonstrate that incorporating ultrasonic vibration into the spindle design increases the material removal rate (MRR) and reduces surface roughness across various machining paths, such as straight and tapered turning operations. Specifically, the MRR was improved by 2.9%, and surface roughness was improved, ranging from 2.5% to 30.3%, depending on the machining path that was proposed. These findings highlight the potential of ultrasonic-assisted rotary WEDM as a viable technique for high-precision machining of cylindrical components. This aligns with advancements in manufacturing technologies and the growing demand for intricate geometries.

Excitotoxic damage caused by high extracellular levels of glutamate in the spinal cord results in neuronal loss and severe locomotor impairment. This study investigates the efficacy of NeuroAiD II (MLC901), an herbal formulation, in promoting nerve regeneration following spinal cord injury (SCI) induced by kainic acid (KA). KA, a potent glutamate receptor agonist, causes excitotoxic damage in the spinal cord, leading to neuronal loss and locomotor impairment. To explore the potential of MLC901, KA-injured rats were treated with MLC901, and nerve regeneration was evaluated using various techniques. In this study, KA was administered intrathecally between the T12 and T13 vertebrae in rats, resulting in incomplete paraplegia. MLC901 was then tested for its neuro-regenerative potential. Various assessments were conducted to evaluate the effects of MLC901 treatment, including behavioral, electrophysiological, and histopathological analyses. Behavioral tests, such as the Basso, Beattie, and Bresnahan (BBB) open field test, running wheel, grid walk, inverted grid, and sensory tests, showed significant improvements in locomotor activity in treated rats. Electrophysiological recordings indicated that, while KA injection caused reduced amplitude and delayed latency, MLC901 treatment helped restore lost connections on days 14 and 28. Histopathological and immunohistochemical analyses also revealed improved tissue integrity and neuron survival. The study concludes that MLC901 significantly enhances locomotor recovery, somatosensory evoked potentials, and tissue preservation following SCI. These findings suggest that MLC901 holds promise as a neuro-regenerative therapy for spinal cord injuries.

Aging involves a series of complex physiological changes that progressively impair cellular function. While chronological aging is inevitable, biological aging is influenced by modifiable factors such as oxidative stress, telomere shortening, chronic inflammation, and mitochondrial dysfunction. Recent research highlights the potential of medicinal plants in managing age-related conditions due to their rich phytochemical content. These bioactive compounds can promote cellular repair, scavenge reactive oxygen species (ROS), enhance telomerase activity, and support tissue regeneration. Polyalthia longifolia var. angustifolia (Thw.), a member of the Annonaceae family traditionally used for rejuvenation, has demonstrated significant anti-aging properties in both yeast and animal models. In vitro and in vivo studies, in particular, provide valuable insights into the anti-aging activity of this plant and its potential applications. This review explores the aging process, outlines the pharmacological profile of P. longifolia, and highlights its key anti-aging constituents, particularly flavonoids, tannins, phenolics, and carbohydrates, which are recognized for their well-documented antioxidant, anti-inflammatory, and cellular protective properties. Moreover, P. longifolia has shown promising effects against various age-associated disorders, including diabetes, hypertension, liver and kidney dysfunctions, inflammation, and oxidative damage. These benefits are largely attributed to its ability to modulate inflammatory pathways, minimize oxidative stress, and regulate abnormal cell proliferation, thereby supporting healthy aging. With its diverse pharmacological properties and abundant bioactive compounds, P. longifolia emerges as a promising natural agent in the field of anti-aging research. Its ethnobotanical significance, phytochemical richness, and therapeutic applications suggest strong potential for development into a sustainable, plant-based strategy to mitigate aging and related health issues.

This study examines how vertical and horizontal pay disparities influence corporate innovation in publicly listed Indonesian firms from 2018 to 2022. Using a dataset of 1,505 firm-year observations, we apply Social Comparison Theory to analyze how perceived compensation inequalities impact innovation performance, measured by patent filings and citations. To ensure robustness, we employ Ordinary Least Squares (OLS), Two-Stage Least Squares (2SLS), Propensity Score Matching (PSM), Difference-in-Differences (DID), Entropy Balancing, and Tobit Regression. The results indicate that vertical pay disparity (CV_MT-RDP) positively affects patent quantity but negatively impacts patent quality, implying that larger managerial pay gaps encourage more patents but may not enhance their impact. Conversely, horizontal pay disparity (CV_RDP-OE) consistently reduces both patent output and citation impact, demonstrating that excessive pay differences across departments undermine cross-functional collaboration and innovation efficiency. Further, state-owned enterprises (SOEs) experience stronger negative effects of pay disparities on innovation than private firms, reinforcing the role of fairness concerns in shaping employee motivation. These findings suggest that firms should strategically design compensation policies to balance tournament incentives and pay equity to sustain long-term innovation performance.

This study evaluates the fusion of 6061 Aluminum alloy with Mild Steel — materials known for their superior technical attributes yet distinct mechanical and physical properties — through Metal Inert Gas Arc Welding (MIG). The objective is to create a joint of dissimilar materials that boasts a robust strength-to-weight ratio, suitable for sectors like automotive, aviation, aerospace, and marine. A significant hurdle in this welding technique is preventing the creation of fragile intermetallic compounds (IMCs) that could compromise the joint's integrity and depth of penetration. The research outlines a method for adjusting welding parameters and setups to curtail the IMC thickness at the interface of the mild steel, which was observed to be between 2-6 μm in the conducted tests. The findings suggest that the MIG welding-brazing method can successfully form joints of dissimilar materials with mechanical strengths on par with other welding techniques. Additionally, variations in the maximum IMC layer thickness were noted with changes in welding parameters such as voltage, wire feed rate, gas shielding, and the configuration of the mild steel, as evidenced by the experiments. Notably, an increase in wire feed rate led to a more substantial IMC layer due to the higher heat input and prolonged arc time, facilitating more intense diffusion and interaction between the aluminum and steel. An exponential increase in the IMC layer thickness was recorded on the mild steel side with rising voltage, whereas the aluminum side's IMC layer thickness remained consistent.

This study presents an updated checklist of fish species of the Setiu Wetlands and its adjacent waters based on surveys conducted from November 2021 to August 2023. It is a part of a larger conservation program for this biodiversity hotspot. The checklist comprises a total of 138 species belonging to two classes, 24 orders, 60 families, and 109 genera. The richest order is Perciformes, with 26 species, representing 18.8% of the total recorded species, followed by Gobiiformes with 21 (15.2%), Siluriformes, Cypriniformes and Anabantiformes with 12 (8.7%) each, Acanthuriformes 9 (6.5%), and Clupeiformes 7 (5.1%) of the total recorded species. The other orders are represented by less than eight species each. Two species could only be identified at the genus level. Two species from the family Dasyatidae (Order Myliobatiformes), Himantura uarnak and Brevitrygon walga are listed by the IUCN as Vulnerable (VU) and Near Threatened (NT), respectively. The remaining species are categorized as either Least Concern (LC), Data Deficient (DD) or Not Evaluated (NE). The DD and NE categories call for more taxonomic studies to bridge the knowledge gaps. The presence of two introduced species in the natural waters is of some concern. Thus, this updated checklist lays a sound foundation for the conservation of fish diversity in Setiu Wetlands, with its important biological and fisheries functions.

In the current scenario, the application of new and innovative technologies tends to change the means by which the companies operate. The application of these technologies also causes many issues and hinders the growth prospects of the organization by damaging its image permanently. One such technology is Deepfakes, which affects the organization's image and impacts its survival. Hence, there needs to be a policy and procedure to handle the organization-related information, such as the logo, brand, and other aspects, with more honesty. However, the alternative is for customers to become less invested and have less faith in the company. Companies also need to keep their reputations in good standing and be compliant to avoid fines when authorities review their rules pertaining to technological advances. Refrain from complying in places where maintaining a good reputation depends on compliance can lead to long-lasting trust deficits. Therefore, the leadership team must take crucial measures to protect itself from reputational harm and other adverse outcomes caused by Deepfake and similar technologies. Company executives must understand these technologies’ nuances, weigh their pros and cons, and use them wisely to achieve organizational goals.

Background: Abnormalities in the retina have a profound impact on vision, and accurate diagnosis and monitoring are essential for effective clinical management. Retinal hyperreflective foci (HRF), lesions, or dots, identified using optical coherence tomography (OCT), are observed in both animals and humans and have been associated with several ocular conditions, including diabetic retinopathy (DR), age‐related macular degeneration (AMD), and retinal vascular diseases. Methods: To evaluate the relevance of retinal HRF, we conducted a comprehensive scoping review of the literature published up to July 2024 including in the discussion key papers that emerged in 2025. Our search spanned electronic databases utilizing carefully identified search terms related to HRF and OCT within the last six years. We excluded publications on HRF outside the retina, treatments, non‐peer‐reviewed content, duplicates, studies older than 6 years, and those not focused on AMD, DR, or glaucoma. Results: A total of 141,085 records were initially identified from various databases and further refined based on keywords and content relevance. Finally, 42 reports meeting the criteria were retained for in‐depth analysis. HRF were observed mainly in OCT scans of the AMD retina, as well as in DR and, to a lesser extent, in other retinopathies and interestingly in glaucoma. In AMD, HRF are described as a marker for disease progression, often associated with a compromised photoreceptor structure. In DR, HRF indicated issues such as abnormal blood vessels and cellular changes linked to microglia activation. In glaucoma, HRF may reflect microglia and macrophage activation. Most publications concur that the presence of HRF correlates with inflammatory processes and aging in the retina, with early appearance of small HRF serving as a biomarker for ocular disease. The size of HRF and their location were consistent with disease presentation. Conclusion: There is an agreement that HRF of less than 30 μm are biomarkers of inflammation in the retina despite having variable intraretinal locations. HRF resulting from the effect of aging can be discerned from AMD based on their quantity and appearance. The results show the importance of HRF as a biomarker of ocular disease and confirm that HRF are indicative of an inflammatory eye disorder.

This study examines how wives in an online support group discursively construct power imbalances within their marriages. By conducting a discursive analysis of 192 posts, we identify how they use specific discursive devices to articulate their experiences of power within four primary domains: Resource Power, Social Power, Coercive Power, and Influence Power. The findings highlight how the posts construct perceptions of financial, social, legal, and emotional control, often emphasising vulnerability and a lack of agency. The study demonstrates how the posts within this online space become a powerful tool for participants to reinforce and contest normative power structures, providing a critical avenue for emotional expression and support. The research highlights the significance of online support groups as discursive arenas where women can share their experiences, construct their realities, and potentially reshape their sense of agency.

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.

Background: Critical thinking is fundamental for registered nurses (RNs) when making clinical decisions, which impact patient outcomes. This review aimed to identify studies on critical thinking and clinical decision making among nurses in clinical practice and synthesize their findings based on the regional area, observed findings, and predictive factors, and to assess the measurement tools used. Methods: A comprehensive search of the PubMed, Web of Science, CINAHL, and SCOPUS databases up to December 2024 was conducted in accordance with the PRISMA guidelines. The Newcastle–Ottawa Scale was used to assess the quality of included studies. Studies with similarly themed components were grouped for narrative synthesis. A meta-analysis of random-effects model calculations was performed. Results: This review included forty studies (twenty-four on CT, twelve on CDM, four on both) from various WHO regions, revealing diverse findings on observed skills. Ten CT and four CDM measurement tools were identified. Many studies also explored individual and group-level predictive factors for these skills. Meta-analyses of four common tools (CCTDI, NCT4P, CDMNS, and NDMI) showed significant heterogeneity, with statistically significant pooled mean scores. Conclusions: The synthesis highlights the global research on nurses’ critical thinking and clinical decision making, including the exploration of various predictive factors. However, the significant heterogeneity in the findings from meta-analyses of commonly used measurement tools underscores a need for more standardized measurement and analytical approaches, such as multilevel modeling, to better account for the hierarchical nature of potential predictive factors (individual and group levels), which would allow for more reliable comparisons and stronger conclusions in this field.

Background: Data on the effects of mindfulness-based stress reduction (MBSR) positive psychological traits and experiential avoidance (EA) among cancer patients are lacking. Objective: This randomized controlled trial (RCT) aimed to: (1) compare the efficacy between MBSR and treatment-as-usual (TAU) control groups in increasing posttraumatic growth (PTG), hope, and optimism and reducing EA across time measurements (T0, T1, and T2) among head and neck cancer (HNC) patients and (2) evaluate the mediation effects of hope, optimism, and EA on the relationship between MBSR and PTG. Methods: A total of 80 HNC participants were randomized to MBSR (n = 40) and TAU (n = 40) groups with the researchers and data analyst blinded, and the group allocation of the participants was concealed. A one-hour MBSR session was conducted once a week, with 45 minutes of home assignments, for six weeks in the MBSR group. The outcomes across time measurements were compared using a mixed linear model following intention-to-treat (ITT) analysis. Mediation effects of hope, optimism, and EA on the relationship between MBSR and PTG were assessed with PROCESS. Results: MBSR significantly increased the degree of optimism from T0 to T1 (mean difference = 1.825, 95% CI = 0.907–2.743, SE = 0.381, p < .001) with a medium effect size (d = 0.563) and from T1 to T2 (mean difference = 1.650, 95% CI = 0.829–2.470, SE = 0.328, p < .001) with a medium effect size (d = 0.630). Initially, MBSR did not increase the degree of hope from T0 to T1 (p = .677), but it significantly increased hope from T1 to T2 (mean difference = 2.524, 95% CI = 1.676–3.373, SE = 0.340, p < .001) with a medium effect size (d = 0.735). Conversely, MBSR did not sustain the changes in the degree of PTG and EA beyond T1. EA partially mediated the relationship between MBSR and PTG, but not hope and optimism. Conclusion: MBSR can be recommended as part of the treatment regimen for HNC patients. Trial registration: ClinicalTrials.gov identifier: NCT04800419.