Authors - Mariia Nazarkevych, Vasyl Lytvyn, Oleg Stechkevych, Hanna Nazarkevych, Roman Cholkan, Danyil Korotych Abstract - An information technology for adaptive enterprise management using weak signals has been developed, which is based on the collected information about the environment, the assessment of factors affecting the enterprise, the calculation of the indicator of the impact on the enterprise based on integral dependence, the method of detecting weak signals and predicting the state of the enterprise, which provides high sensitivity taking into account changes in the environment and increases the efficiency of enterprise management. A method of recognizing weak signals is shown, which, by comparing the permissible value with the difference between the found and predicted values of the indicator of the impact on the smart enterprise based on integral dependence, provides early detection of threats or opportunities for the smart enterprise. It is proposed to develop a smart enterprise management system using weak signals based on an integrated approach and in accordance with the following principles: systematicity; integration of computer, communication and software components; modularity; openness; compatibility; variable equipment composition.
Authors - Marlon Kulatunga, Kaavya Raigambandarage, Senali Guruge, Themiya Alwis, Amila Nuwan Senarathne, Kavinga Yapa Abeywardena Abstract - Contemporary Kubernetes deployments suffer from two fundamental shortcomings: admission control mechanisms apply static rule sets without accounting for namespace operational context, and content inspection services governed by RFC 3507 remain disconnected from the orchestration layer. This work presents an integrated four-module security framework that jointly addresses both deficiencies. A probabilistic namespace characterisation algorithm employing seven weighted indicators achieves 96.7% accuracy in determining deployment tiers, even when metadata labels are absent or deliberately misleading. A compliance-driven policy orchestrator aligned with CIS Kubernetes Benchmark controls and PCI-DSS v4.0 requirements translates a unified constraint representation into artefacts for both OPA Gatekeeper and Kyverno, attaining 99.2% cross-engine decision parity. An environment-responsive traffic manager generates tier-specific Istio routing configurations, while a custom Kubernetes operator governs content scanning pod lifecycles through a multi-dimensional wellness metric that captures security-relevant signals invisible to conventional autoscalers. Evaluation on a five-node K3s cluster demonstrates full compliance coverage across 93 benchmark controls and 28 regulatory mandates, sub-five-second failover under all disruption scenarios, and correct detection of degraded scanning capability that CPU and memory metrics alone would overlook.
Authors - Ronewa Gilbert NTHATHENI, Tumiso THULARE Abstract - The rapid advancement of digital technologies has transformed the relationship between governments and citizens, creating new opportunities for participatory governance through e-government initiatives. This study evaluates the effectiveness of online engagement platforms in promoting democratic governance in South Africa. Using a scoping review methodology, the research examines the benefits, challenges, and contextual dynamics shaping citizen participation through digital platforms. Findings suggest that while online engagement tools enhance transparency, accountability, and access to information, their effectiveness is constrained by structural barriers such as the digital divide, limited institutional capacity, and low digital literacy. The study concludes that the success of e-participation initiatives depends on inclusive design, infrastructure investment, and meaningful government responsiveness. Recommendations are provided to strengthen digital governance and improve citizen engagement outcomes.
Authors - Supriya Narad Abstract - Agricultural economies are predominantly relevant in developing countries, wherein the farmers have to struggle operating under the impact of several constraints posed by crop diseases. Among food crops, the potato is a major one with vulnerable destructive diseases like Early Blight and Late Blight, capable of destroying the yield if detected late. Old methods of visual inspection are time-consuming and sometimes erroneous because of laxity, human error, and lack of expertise. With this research, an automated intelligent disease detection system is devised, making use of image processing and deep learning, Arduino, specifically Convolutional Neural Networks (CNNs). The model was trained using potato leaf images from the Plant Village dataset, which are improved using various preprocessing techniques, including color space conversion, image augmentation, and image resizing. The proposed CNN architecture achieved a high rate of classification accuracy of 97.2% in distinguishing healthy leaves vs. Early blight and Late blight infected leaves. Lightweight, reliable, and fast, it supports implementation on mobile or handheld devices in low-resource environments, thus giving farmers the ability to use them for timely diagnostics. The system has good prospects for scaling up for other crops and disease types in future versions.
Authors - Khadidje OUSMANE KOSSI, Mandicou BA, Bachar Haggar SALIM, Simon Antoine SARR, Maboury DIAO, Alassane BAH Abstract - Heart disease in athletes remains a significant challenge in sports cardiology and an important public health concern, particularly among young competitive individuals at risk of sudden cardiac events. Although pre-participation screening programs are widely implemented, diagnostic uncertainty persists, especially in distinguishing physiological cardiac remodeling from pathological cardiomyopathy. This complexity results from the interaction of genetic predisposition, structural adaptation, electrophysiological variability, and cumulative training exposure. Using the PRISMA framework, this study presents a systematic review of research published between 2015 and 2025 to evaluate the application of artificial intelligence (AI) in the diagnosis and monitoring of cardiovascular diseases in athletes. The analysis reveals that most studies rely on unimodal, monocentric, and retrospective designs, often based on limited datasets and lacking external validation. Despite high reported performance metrics, performance degradation of 5–10% in external cohorts is frequently observed. Furthermore, explainability techniques are inconsistently applied, and real-world clinical integration remains limited. Only a small number of studies adopt multimodal approaches integrating electrophysiological, imaging, biological, and training-related data. These limitations restrict the clinical translation of AI models. Future research should prioritize multicenter, diverse, and explainable multimodal frameworks to support reliable cardiovascular risk stratification and return-to-play decision making.
Authors - Oluwaranti A. Omowami Abstract - Work-related musculoskeletal disorders (WMSDs) are among the most prevalent occupational injuries in construction, driven by heavy lifting, awkward postures, repetitive motion, and whole-body vibration. Traditional ergonomic risk assessment methods are retrospective and unable to capture the dynamic conditions of construction sites. Wearable sensor technologies offer a real-time, objective alternative. This structured narrative review examines the implementation, accuracy, and occupational health outcomes of wearable sensor systems applied to ergonomic risk monitoring among construction workers. A structured review of peer-reviewed literature from 2017 to 2024 identified six sensor categories: inertial measurement units (IMUs), wearable insole pressure systems, surface electromyography (sEMG), electrodermal activity (EDA) sensors, heart rate monitors, and smartphone embedded sensors. Reported posture classification accuracy reached up to 99.01% under controlled conditions using deep learning classifiers. Key implementation barriers include sensor discomfort, motion artifacts, worker acceptance, data privacy and cybersecurity concerns, and the multi-employer structure of construction. A consistent gap exists between laboratory validation accuracy and real-world field performance. Occupational health outcome studies remain limited. Future priorities include longitudinal field validation and integration with behavior-based safety frameworks.
