Salem University

The modern rate of the artificial intelligence (AI) technology penetration into organizational processes is transforming the manner in which companies develop, examine and undertake corporate strategies. In this paper, we discuss the way AI-based decision making will change the nature of strategic planning, competitive positioning, and value creation in the digital age. According to the latest tendencies in the predictive analytics, machine learning and intelligent automation, the current research analyses the impact of data-driven insights on transforming organizations to build on reactive decision models to proactive evidence-based strategic models. With the synthesis of scholarly articles by various fields and a framework of the most recent examples in the field, the paper describes the ways AI may be used to achieve strategic agility, modelling of complex scenarios, and risk-management. The given results presuppose that the company relying on AI technologies becomes more accurate in its predictions, notices the changes in the market place more quickly, and elaborates the strategies that grow more and more adaptable to the fluctuating business environment. The other field that was explored by the research is the managerial and ethical consequences of AI-targeted decision-making, namely the issue of algorithmic bias, transparency, and redistribution of decision-making authority within the corporate hierarchy. The expanded strategic capacity can be achieved with the help of AI, but the reliance on the automated decision-making can destroy the human intuition and strategic decision-making unless it is well-balanced upon. The paper suggests an intermediate solution of making decisions, which combines human intelligence and algorithmic intelligence, ensuring criticality and local sensitivity. Generally, the paper has demonstrated that AI is not a technological change but a structural change that is transforming the conceptualization and use of strategy by organizations. According to the research, it may be concluded that organizations, which are able to combine AI potential with organizational culture, governance principles, and business strategies, will be better placed to survive in the digital age.

Cytokines are central regulators of immune responses and have emerged as key biomarkers in diverse pathological conditions, including infections, autoimmune disorders, and cancer. Conventional laboratory methods for cytokine detection, while accurate, often lack the speed, portability, and multiplexing capacity required for timely clinical decision-making. Recent advances in biosensor technology-particularly at the point-of-care (POC)-are reshaping this landscape by enabling rapid, decentralized, and sensitive detection of cytokine panels in complex biological samples. AI-enabled multiplex POC platforms now achieve limits of detection as low as 0.01-100pg/mL, with dynamic ranges spanning 3-4 orders of magnitude, using 1-50 µL of sample and delivering results within 5-30min. Compared with centralized single-plex workflows, these systems provide faster, lower-volume, and more clinically actionable testing. Artificial intelligence further strengthens performance by providing calibrated predictive outputs, uncertainty estimates, and drift monitoring. This review highlights the convergence of multiplexed biosensing strategies with artificial intelligence (AI) to enhance the analytical performance, interpretability, and clinical utility of cytokine diagnostics. We first discuss the evolution from traditional platforms to portable and miniaturized systems, and then summarize emerging review literature addressing cytokine biosensing in contexts such as sepsis, metabolic disorders, and systemic inflammation. Next, we examine experimental studies demonstrating POC-compatible platforms for multiplexed cytokine detection, and finally focus on next-generation biosensors that integrate machine learning (ML) algorithms-including convolutional neural networks (CNNs) and decision-tree models-for autonomous signal processing and decision support. Despite challenges in validation, hardware integration, and explainability, these technologies hold transformative potential for real-time immune monitoring, precision medicine, and global health applications.

This study presents a detailed electromagnetic investigation of hot-carrier generation, local optical chirality, and Raman enhancement in bimetallic core-shell-satellite nanostructures. Four representative configurations: Au-Pd, Ag-Pd, Au@Ag-Pd, and Ag@Au-Pd, were modeled to explore how core-shell arrangements of Au and Ag, in combination with Pd nanoparticle, influence their optical responses. Real-space distributions of hot-carrier generation rates reveal that Au-based systems support broader and more intense plasmonic excitations, while Ag-based counterparts exhibit sharper, more confined hot-carrier localization. Calculations of the local density of optical chirality (LDOC) and the polarization-resolved optical-activity LDOC (OA-LDOC) show that the core-shell structure induces spatially asymmetric chiral fields, with the Ag@Au-Pd configuration demonstrating the highest sensitivity to polarization. Raman enhancement factors, derived from near-field intensity profiles, highlight the critical role of interfacial composition in modulating surface-enhanced Raman scattering (SERS) performance. Overall, these findings elucidate field-driven phenomena in complex plasmonic heterostructures and point to promising strategies for tailored applications in photocatalysis, chiral molecular sensing, and nonlinear optical systems.

West Virginia University (WVU) is a large R1 land grant institution with a multitude of STEM majors that includes students from underrepresented demographics (e.g., rural, low-income/Pell grant eligible, first-generation, etc.). Though STEM students are supported by dedicated disciplinary advisors, these advisors meet with large numbers of students and may need to limit their discussions to academic scheduling and financial aid. To broaden students’ connections and belonging to STEM and to provide underrepresented STEM students with ongoing career mentoring, we initiated an institutional change project involving the distribution of biweekly opportunity emails (fall 2022 - spring 2025). Opportunity emails included listings, links, and deadlines for paid summer undergraduate research at national labs and institutions, and abstract submissions for institutional, statewide and national symposia. At several points, a survey was sent to collect information from recipients on their self-reported impressions and perceived usefulness of the biweekly emails. Survey respondents indicated that on average they read/skimmed 79% of the emails and acted on information 1.7 times during an academic semester. In addition, respondents indicated that the emails assisted them in career planning and made them more positive toward majoring in STEM by influencing their sense of acceptance and belonging at college. The nature of this change work was informed by and documented as a Plan-Do-Study-Act cycle, a quasi-experimental method for effecting quality improvements. The progression of opportunity emails from basic to professional formatting is discussed along with aggregate responses from the surveys.

Academic stress can endanger physical and mental health. This study aims to assess the impact of academic stress on the nutritional status of undergraduates in Kogi State, Nigeria. The longitudinal study was conducted among 134 Undergraduates selected from two Universities in the State. A four-sectioned questionnaire was used to collect data on the dietary pattern, socio-economic, demographic, household characteristics variables and academic stress score of all respondents. The anthropometric indices of the participants were determined in the first week of resumption and after 100 days of academic activities on campus. Data analysis was done using the 26.0 version of the SPSS software. The results indicated that 8.2% of the study population are highly stressed, while 44.77% and another 44.77% of the respondents are slightly and moderately stressed, respectively. More than half of the respondents (68.7%) had a normal weight, 22.4% were underweight, 6% were overweight, while 2.9% had class I and class II obesity. After 100 days on campus, the hip, waist circumference, and BMIs of the respondents were significantly (p < 0.001) reduced. The decrease in BMI correlates significantly (p<0.05) with the prevalence of academic stress among the respondents. No significant correlation was found between perceived stress, gender (p = 0.813), age group (p = 0.113), field of study (p = 0.750), and year of education (p = 0.626). This study revealed that the incidence of academic stress among undergraduates in Kogi State is significantly high. We recommend the development of special intervention programs tailored towards identifying and alleviating individual sources of academic stress among these undergraduates.

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such as Wormhole and Sinkhole attacks. The aim of this research was to develop a methodology for detecting security incidents in WSNs by conducting real-time analysis of Wormhole and Sinkhole attacks. Furthermore, the paper proposes a novel detection methodology combined with architectural enhancements to improve network robustness, measured by hop counts, delays, false data ratios, and route integrity. A real-time WSN infrastructure was developed using ZigBee and Global System for Mobile Communications/General Packet Radio Service (GSM/GPRS) technologies. To realistically simulate Wormhole and Sinkhole attack scenarios and conduct evaluations, we developed a modular cyber–physical architecture that supports real-time monitoring, repeatability, and integration of ZigBee- and GSM/GPRS-based attacker nodes. During the experimentation, Wormhole attacks caused the hop count to decrease from 4 to 3, while the average delay increased by 40%, and false sensor readings were introduced in over 30% of cases. Additionally, Sinkhole attacks led to a 27% increase in traffic concentration at the malicious node, disrupting load balancing and route integrity. The proposed multi-stage methodology includes data collection, preprocessing, anomaly detection using the 3-sigma rule, and risk-based decision making. Simulation results demonstrated that the methodology successfully detected route shortening, packet loss, and data manipulation in real time. Thus, the integration of anomaly-based detection with ZigBee and GSM/GPRS enables a timely response to security threats in critical WSN deployments.

This paper introduces an enhanced Iterative Finite Difference (IFD) method for efficiently solving strongly nonlinear, time-dependent problems. Extending the original IFD framework for nonlinear ordinary differential equations, we generalize the approach to address nonlinear partial differential equations with time dependence. An improved strategy is developed to achieve high-order accuracy in space and time. A finite difference discretization is applied at each iteration, yielding a flexible and robust iterative scheme suitable for complex nonlinear equations, including the Sine-Gordon, Klein–Gordon, and generalized Sinh-Gordon equations. Numerical experiments confirm the method’s rapid convergence, high accuracy, and low computational cost.

This report details a face-to-face educational event hosted by the Urgo Foundation, which brought clinicians together to learn from wound care experts in addressing the challenges faced when discussing, recommending, and applying therapeutic graduated compression. Education and networking opportunities sought to increase both confidence and capability.

This study presents a comprehensive numerical investigation of radiative heat transfer and flow dynamics involving Maxwell and Casson nanofluids flowing past a vertically aligned plate, incorporating the effects of internal heat generation. These non-Newtonian fluids exhibiting memory and yield stress behaviors are examined to understand their influence on velocity, temperature, and concentration profiles under coupled thermal and solutal gradients. The governing partial differential equations representing momentum, energy, and mass conservation are transformed into coupled nonlinear ordinary differential equations using similarity variables. These are solved using the fourth-order Runge-Kutta method with a MATLAB-based shooting technique. Key dimensionless parameters such as the Prandtl number, Grashof number, Biot number, heat source parameter, Nusselt number, Sherwood number, and skin friction coefficient are analyzed. The results show that internal heat generation significantly increases fluid temperature and velocity while reducing nanoparticle concentration and thermal transfer efficiency. Furthermore, elevated Biot numbers and thermal radiation broaden the thermal boundary layer, adversely affecting heat transfer. These findings have practical implications for engineering applications such as electronic cooling systems, chemical reactors, biomedical devices, and thermal insulation design. The insights gained enable improved modeling and thermal regulation in systems requiring efficient heat and mass transfer under non-Newtonian fluid behavior and internal heat sources.

This study examined the influence of social media influencers, particularly edu-influencers, on improving E-learning experiences. Drawing from social constructivist theory and prior research on E-learning, this study investigates how factors such as the quality of interaction with influencers, their perceived authenticity, and the alignment of their content with educational objectives impact learners’ motivation and satisfaction with digital learning platforms. Using structural equation modeling, the study analyzed data from 357 participants within the Asian social media industry. The analysis revealed significant positive associations between key influencer attributes and learner motivation, with motivation as a critical mediator in the relationship between influencer characteristics and overall learner satisfaction. These findings enhance the theoretical understanding of social media’s impact on digital education and provide actionable insights for educators, platform developers, and influencers aiming to enhance E-learning environments.