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A Modified PRoPHET Protocol for Energy and Buffer Optimization in Delay Tolerant Networks: Performance Evaluation for an IoT Smart City Scenario
Get PDFIn smart cities, Delay Tolerant Networks (DTNs) support Internet of Things (IoT)-based services, where the density of interconnected devices is very high, making energy management even more critical. Considering energy-constrained scenarios, optimization of energy consumption will ensure the longevity of the individual nodes and sustainability of the whole infrastructure of the smart city. In such scenarios, energy-aware routing is a very important solution for efficient management of limited energy resources. In this work we present a modification of the Probabilistic Routing Protocol using History of Encounters and Transitivity (PRoPHET) protocol that integrates both energy-aware message forwarding and Acknowledgment (ACK) based buffer management to enhance the efficiency of message delivery in DTNs, especially in smart city IoT scenarios with limited energy and buffer resources. To validate the effectiveness of this modification, simulations are conducted to compare the performance of the modified PRoPHET protocol with the original version. The key metrics for evaluation include delivery probability, routing overhead and average buffer time. The modifications improve performance under energy constraints while managing buffer utilization more effectively
Measuring the Knowledge and Attitudes of Healthcare Professionals Towards Telemedicine: A Step Towards Improving Medical Training
Get PDFTelemedicine leverages information and communication technologies for remote healthcare delivery, enhancing access to medical services, improving consultation efficiency, and coordinating care. In Morocco, its integration aims to optimise access to healthcare, especially in remote areas. To maximise the benefits, it is essential to train health professionals in telemedicine, which requires an assessment of their current knowledge and attitudes. This study aims to assess these aspects. This cross-sectional study engaged medical staff from the Faculty of Medicine, Pharmacy, and Dentistry and Hassan II University Hospital in Fez. Data were collected online using a standardized questionnaire covering socio-demographic and professional information, knowledge, experience, and satisfaction with telemedicine. Data analysis was conducted using SPSS V25, adhering to ethical standards of participant anonymity and data protection. Among 145 participants (mean age 24.99 years, 69.7% female), 95.8% were Moroccan, predominantly medical students (76.6%). While 81.7% were familiar with telemedicine concepts, 96.5% were unaware of public telemedicine programs in Morocco. Notably, 94.6% had never experienced a telemedicine consultation, though 46.8% supported its use for non-urgent cases. Most participants relied on the internet for information, with 90.9% unaware of existing regulations. Furthermore, 61% advocated for incorporating telemedicine into medical curricula, emphasizing its significance for future practice. Enhancing telemedicine knowledge and integration in Morocco\u27s healthcare system is vital. Systematic training in medical education will prepare future professionals, improve healthcare access, and underscore the strategic importance of telemedicine in evolving healthcare practices
Hybrid Grey Wolf and Genetic Algorithm for the Flow Shop Scheduling Problem
Get PDFThe Flow Shop Scheduling Problem (FSSP), a pivotal NP-hard combinatorial optimization challenge, is central to enhancing manufacturing efficiency by minimizing makespan across n jobs and m machines. This study introduces a novel hybrid metaheuristic that integrates Grey Wolf Optimization (GWO) for robust global exploration with Genetic Algorithm (GA) for precise local exploitation, augmented by adaptive crossover, mutation, and 2-opt local search, addressing a significant gap in synthesizing swarm intelligence and evolutionary techniques for permutation-based scheduling. Evaluated on 13 Taillard benchmark instances (20-200 jobs, 5-20 machines) over 50 runs, the GWO-GA algorithm demonstrates superior performance compared to established metaheuristics, including SGA, HMSA, NEH, DDE-PFS, DSADE-PFS, and DSADEPFS, with statistical validation via ANOVA and Tukey HSD tests. The study highlights the algorithm\u27s robust convergence and scalability, marking a key contribution to scheduling optimization. Its ability to outperform existing methods underscores its practical significance, while computational overhead for large instances suggests future exploration of parallelization and multi-objective enhancements
Trust and Traceability: Unpacking Technology Readiness in Blockchain Adoption for Supply Chain Management
No full textThe development of blockchain-based innovations marks a paradigm shift within the realm of supply chain optimization, which offers a unitary solution to modern challenges linked with trust and traceability. The purpose of this research work is to assess how Technology Readiness constructs such as technological optimism, creativity, discomfort, and doubt can affect the integration of blockchain-based innovations within supply chains. A conceptual model based on Technology Readiness Index was designed and validated using a large-scale survey study among 300 members of the supply chain industry in India. The optimum level of response was achieved with 231 members providing necessary input for the study. The model assumptions were verified using structural equation modelling techniques. The results emphasize significance attributed to trust and traceability, which were found to be important elements of blockchain functionality. Indeed, these findings reinforce the need to overcome the psychological barriers associated with the implementation of new technologies like blockchain technology. These findings suggest that the relationships implied by the model hold true and establish that the propositions made at the conceptual level are robust. The study offers evidence to the existing body of knowledge pertaining to supply chain governance studies with regard to establishing an association between technological readiness and sustainable blockchain system adoption intention. It has been clear with these findings that cognitive ability and emotions were found to create equal impact on achieving operational advantages. These findings can greatly benefit policymakers and managers with insights on how to increase blockchain technologies\u27 application processes with regard to supply chains. This study represents the first investigation of the Comprehensive Sequential Mediation model incorporating Technology Readiness, Trust, Traceability, and Sustainable Blockchain Adoption Intentions, accounting for 57% of variances a record-high level reported for blockchain studies related to supply chains
HealthBeam: Design and Implementation of a Healthcare Automation System
No full textMany healthcare facilities still rely on outdated communication technology or lack such technology entirely, reducing efficiency in patient interaction and service delivery. The primary objective of this paper is to present the conceptual design, system architecture, and platform implementation of HealthBeam to significantly curtail the dependence on paper-based processes including patient examination records and medical charts. By promoting digitalization, HealthBeam offers an environmentally friendly solution tailored for medical-related environments. Beyond its operational advantages, it has the potential to transform doctor-patient interaction by enabling real-time communication and streamlined workflows. HealthBeam is designed to optimize response times in emergency situations and ensure that medical data is securely stored in centralized databases, where it can be accessed quickly by authorized personnel. By combining accessibility, security, and efficiency, HealthBeam addresses key limitations of traditional healthcare communication methods. Apart from the technical implementation of the platform, another objective is also the public deployment by making HealthBeam accessible through its website. This work demonstrates how targeted digital interventions can advance operational productivity, sustainability, and patient-centered care in modern healthcare systems
Students\u27 Failure in Mathematics: A Case Study of Calculus-Related Modules at a University in Johannesburg
No full textMathematics is widely recognized as a challenging subject for many students, often leading to high failure rates among university learners. This study investigates the factors contributing to student failure in two calculus-related mathematics modules at a university in Johannesburg, South Africa. Five key factors are examined: students’ attitudes toward mathematics, self-doubt, teaching methods employed by lecturers, access to textbooks and learning materials, and short attention spans. Data were collected through a Google Form questionnaire distributed to students, and the findings were analysed using statistical methods. The results indicate that neither age nor gender significantly affects students\u27 performance in mathematics. However, the five identified factors play a substantial role in determining success or failure. These findings are supported by a Chi-Square test, yielding a statistically significant p-value of 0.000128. We also provide some valuable insights from the polarity and subjectivity analyses of the students’ responses. While the insights provided are valuable, this study acknowledges that these factors represent only part of a broader set of influences on student outcomes in mathematics
Machine Learning for Legal Compliance in the Energy Sector: A Predictive Regulatory Framework
Get PDFIncreased complexity in energy regulations and sustainability standards has created a pressing need for automated regulatory compliance monitoring systems. A predictive regulatory system integrating legal compliance analysis with machine learning techniques in the energy sector is proposed in this work. On the Energy Efficiency dataset, Linear Regression, Support Vector Machines (SVM), and Random Forest were used to predict building energy loads and determine compliance with regulatory standards. The research demonstrates that machine learning enhances not just the precision of forecasts but also proactive identification of non-compliant cases, reducing legal vulnerabilities and helping policymakers implement standards of efficiency. Statistical measures and correlation determine the most impactful features, and relative performance metrics (accuracy, precision, F1, and R²) determine the robustness of the models. The system bridges the gap between energy engineering and regulation law and provides an energy sector compliance management solution that is scalable and data-driven
Hybrid Machine‐Learning Framework for Predicting Student Placement
Get PDFAccurate prediction of the results of college student placement can help institutions detect potential risk students and tailor career-readiness interventions. In this study, using a publicly available dataset of 10,000 students with eight predictors intelligence quotient (IQ), previous semester\u27s performance, cumulative grade point average (CGPA), academic rating, internship or not, extra-curricular score, communication skills, and projects completed, we develop and validate a hybrid stacking ensemble classifier. After numerical feature standardization and binary category encoding, we trained three base learners (support vector machine, random forest, and logistic regression) and combined them with a logistic regression meta-learner. Comparative experiments on an 80/20 train–test split show that the stacking ensemble outperforms individual models, with 100 % accuracy, precision, recall, and F1-score on the test set, whereas logistic regression alone attained 90.4 % accuracy. A correlation analysis declares CGPA and performance in the previous semester as the single best predictors for placement. Receiver operating characteristic (ROC) curves and confusion matrices also confirm the greater discrimination power and stability of the ensemble. All these results confirm that stacking heterogeneous classifiers provides a stable and interpretable approach to student placement prediction, with potential use in academic advising and early warning systems
Predictors of Chemotherapy-Induced Cardiotoxicity in Breast Cancer: A Real-World Cohort Study
No full textChemotherapy-induced cardiotoxicity is a major complication in breast cancer patients treated with anthracycline- and trastuzumab-based regimens. Most prediction models come from North American or Western European cohorts. We aimed to develop and internally validate a 12-month cardiotoxicity model in Albanian breast cancer patients and to assess the added value of global longitudinal strain (GLS). It has been performed a retrospective cohort study of 314 consecutive female breast cancer patients treated with anthracycline- and/or trastuzumab-based regimens at the University Hospital Center “Mother Teresa” in Tirana between 2015 and 2022. All underwent baseline and follow-up echocardiography with left ventricular ejection fraction (LVEF) and GLS. Cardiotoxicity at 12 months, defined by contemporary cardio-oncology criteria, was the primary outcome. Multivariable logistic and Cox models were built and internally validated with 1,000-sample bootstrapping. Cardiotoxicity occurred in 63 of 314 patients (20.1%). Age and BMI were similar between groups, whereas patients with cardiotoxicity more often received combined anthracycline–trastuzumab therapy and had worse baseline GLS. A clinical model including age, BMI, hypertension, diabetes, dyslipidemia, previous cardiovascular disease, anthracycline and trastuzumab exposure, and left-sided radiotherapy achieved an AUC of about 0.72. Adding GLS increased the AUC to about 0.79 and improved overall performance. In Cox models, combined anthracycline–trastuzumab therapy and baseline GLS remained independently associated with time-to-cardiotoxicity. In this Albanian cohort, one in five breast cancer patients developed cardiotoxicity within 12 months. A GLS-augmented clinical model showed good performance and may support individualized risk stratification and surveillance in South-Eastern European cardio-oncology practice
A Market Competition Index for the Western Balkans
No full textThis paper provides a data-driven evaluation of market competition across the six Western Balkan countries by comparing retail prices for identical consumer products sold by the same European operator in its European Union (EU) home market and the local markets in the region. After normalizing for Value Added Tax (VAT) differences, a Market Competition Index (MCI) is constructed to capture the average relative price deviation from EU benchmarks in Albania, Kosovo, Serbia, North Macedonia, Montenegro, and Bosnia and Herzegovina, as a reflection of the degree of market competition in the consumer goods retailing sector of these countries. The 2025 results reveal pronounced variations in competitive intensity across the Western Balkans: Albania tops the ranking with the largest average price deviation from EU benchmark (89 points), followed by Montenegro (60 points) and North Macedonia (46 points); Kosovo occupies a middle position (37 points), while Bosnia and Herzegovina (25 points) and Serbia (23 points) record the smallest deviations, indicating the strongest alignment with EU pricing and, by extension, the most competitive local markets. A complementary Burden Index, which adjusts each country’s Market Competition Index by its Purchasing Power Index (PPI) using 2023 Eurostat data, confirms that low-income markets face the greatest consumer strain. This dual-metric framework offers a precise monitoring tool for policymakers. As the Western Balkans progress toward EU accession, the MCI provides a clear benchmark for measuring reform progress, safeguarding consumer welfare, and supporting interventions to enhance market competition