Traektoria Nauki
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Demographic Characteristics and Dietary Patterns Among Adoles-cents In Daura, Katsina State, Nigeria: A Cross-Sectional Assessment Using 24-Hour Dietary Recall
Get PDFAdolescence is a critical period for nutritional interventions; however, there is a paucity of data on dietary habits among Nigerian adolescents. To drive a specific nutritional intervention, it is imperative to understand how population composition affects food patterns.The objectives of the study included a demographic characterisation and dietary patterns of adolescents in Daura town, Katsina State, Nigeria, as well as the establishment of nutritional patterns using 24-hour dietary recall methodology.The study was a cross-sectional study of 100 adolescents (71 males, 29 females) randomly sampled from seven wards in Daura town. Structured questionnaires were used to record sociodemographic characteristics, and trained interviewers used 24-hour dietary recalls. Statistical information was analysed using SPSS version 24.0, including frequency distributions and descriptive statistics.Results indicate that the study population was men (71.0%), with a mean age distribution skewed toward older adolescents (26% aged 16 years). All respondents were Muslims and enrolled in secondary school. Breakfast food consumption remained conventional, with koko/kunu/kosai accounting for 31.0 %. Shinkafa (rice dishes) accounted for the smallest percentages, with 28.0% consumed at lunch and 31.0% consumed at dinner. Men and women differed in their meal timing and food choices.The dietary habits of adolescents in Daura are characterised by low nutritional diversity and are highly traditional in terms of Hausa dietary practices. The high proportion of carbohydrate-rich foods indicates possible micronutrient deficiencies that may require exceptional dietary support
Combatting Veteran PTSD with Deep Learning on Longitudinal UK Health Data: A Comprehensive Review
Get PDFPTSD in UK veterans often emerges long after service, complicating detection. The objective of this review is to assess whether deep learning applied to longitudinal NHS and Ministry of Defence data can facilitate a shift from late recognition to anticipatory intervention, and to define the requirements for its safe deployment. Evidence on sequence models (LSTM, transformers), multimodal integration of structured records and clinical notes, and explanation layers that render outputs legible to clinicians is synthesised. UK constraints, fragmented records, inconsistent veteran identifiers, and uneven digital maturity limit scale; feasible mitigations include secure data environments and federated training. Priority actions include standardising veteran coding, establishing a Veteran Health Analytics Hub, conducting prospective trials with health-economic endpoints, and integrating risk scores with concise, validated explanations into existing workflows. Properly implemented, longitudinal deep learning can reduce missed cases, accelerate access to effective support, and enable services to learn from their data while protecting privacy and trust
A Comparative Study Of Data Visualisation Techniques For Effective Decision-Making In Business Intelligence
Get PDFThis paper discusses the relative success of several methods of data visualisation to improve decision-making in business intelligence (BI) systems. By analytically examining chart-based visuals, interactive dashboards, and premium analytics tools across various business departments, this study identifies the most effective visualisation techniques in marketing, finance, and operations. The paper assesses three case studies from the retail, financial services, and manufacturing industries to determine decision-making efficiency, the accuracy of insights, and the rate of adoption by users. The main results indicate that interactive dashboards can facilitate decision-making 35 times faster, while real-time visualisations can increase operational efficiency by 28 times. The study contributes to the theory of BI by developing a framework for selecting visualisations in accordance with cognitive load theory and organisational circumstances. It offers practical suggestions on choosing tools and their implementation
Surviving Climate Disaster Through Impact Risk Assessment Analysis of Climate Change in Sukadana Village and Segala Anyar Village, Indonesia
Get PDFThis study analyses the risks associated with climate change impacts, including mitigation and adaptation strategies implemented by communities in Sukadana Village and Segala Anyar Village, Pujut District, Central Lombok. The research adopts the Sendai Framework for Disaster Risk Reduction as its analytical framework, focusing on risk identification, capacity building, and strengthening community resilience. Methodologically, the study employs a descriptive qualitative approach, with data collected through Focus Group Discussion (FGD), in-depth interviews, field observations, and document analysis. The findings reveal that both villages are vulnerable to drought due to their reliance on agriculture and limited water resources. The lack of community knowledge about mitigation and adaptation exacerbates the impacts of drought, aligning with Priority 1 of the Sendai Framework, which emphasises the identification and understanding of disaster risk. Nevertheless, these villages have undertaken mitigation efforts through the development of clean-water infrastructure and natural resource conservation, as well as through community capacity-building through the establishment of Climate-Conscious Communities (MSI), which supports Priority 3 of the Sendai Framework on strengthening community resilience
Development of an Anatomical Variants Database for Teaching and Clinical Applications
Get PDFMedical education lacks systematic approaches to instruction on anatomical variants, despite the documented clinical significance of morphological diversity in patient care. This study aimed to develop a comprehensive methodological framework for constructing an anatomical variants database adaptable across diverse institutional contexts. We developed a systematic framework using a literature analysis of 47 eligible sources, expert consultation protocols with anatomical specialists, and technical architecture specifications drawn from established medical education database standards. Framework development employed systematic literature searches across PubMed, EMBASE, and Google Scholar databases, structured expert consultation procedures, and technical specification protocols based on principles of medical education technology development. The resulting framework comprises four sequential development phases, incorporating 47 specific procedural steps and 23 quality assurance checkpoints. Technical architecture specifications establish relational database structures using MySQL, featuring 14 primary entity tables. Performance benchmarks achieved sub-0.5-second query responses, and scalability requirements supported over 500 concurrent users, meeting 99% uptime standards. Content validation protocols employed modified Delphi methodology with 80% expert consensus thresholds, whilst quality assurance procedures specified accuracy verification, consistency checking, and educational appropriateness assessment criteria. The framework addresses multidimensional classification schemes for frequency patterns, clinical significance levels, morphological characteristics, and population-specific anatomical variants. This methodological framework provides systematic, evidence-based approaches for developing anatomical variants databases that institutions can adapt to their specific educational contexts and technological capabilities
AI-Powered Intrusion Detection and Prevention Systems for the Next Generation Network
Get PDFThe rapid evolution of next-generation networks (NGNs), driven by 5G, IoT, edge computing, and software-defined networking, has introduced new opportunities alongside complex security challenges. Traditional intrusion detection and prevention systems (IDS), built on signature-based and anomaly-based methods, struggle to cope with the scale, heterogeneity, and dynamic threat landscape of NGNs. In response, artificial intelligence (AI) has emerged as a powerful enabler of modern IDPS. This review surveys AI-powered approaches, beginning with classical machine learning methods such as decision trees, support vector machines, and random forests, and then examining deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and autoencoders. It further analyses hybrid frameworks that integrate ensemble learning, federated learning, and meta-learning, as well as specialised methods tailored for SDN, IoT, edge, and cloud/5G environments. Benchmark datasets, including NSL-KDD, CICIDS2017, UNSW-NB15, Bot-IoT, IoT-23, and TON_IoT, are reviewed, highlighting their contributions and limitations. The paper identifies key challenges, including dataset scarcity, generalisation gaps, computational overhead, adversarial robustness, explainability, and privacy. Future directions emphasise the need for realistic NGN datasets, lightweight yet accurate architectures, privacy-preserving and federated frameworks, and integrated detection and prevention mechanisms. Overall, AI-powered IDPS demonstrate significant potential to secure NGNs, but realising this vision will require advances that balance accuracy, efficiency, interpretability, and resilience
From Play to Pathology: Mental Health, Risk Factors, Neurobiological Mechanisms, and Drivers of Gambling Addiction in Adolescents and Young Adults
Get PDFGambling addiction in youth and young adults is a modern, complex public health issue rooted in a convergence of factors, including developmental risks, psychosocial challenges, and disordered neurotransmitter systems. This review of the literature highlights current research in the areas of epidemiology and mental health comorbidity, (i.e., depression, anxiety, ADHD, drug use, suicidality), psychosocial risk factors (i.e., family and peer social norms, socioeconomic disadvantage, internet advertising), and neurochemical processes (i.e., dopaminergic sensitisation, serotonergic dysregulation, GABAergic dysregulation, glutamatergic dysregulation, noradrenergic hyperarousal, endogenous opioid reinforcement) involved in the transition from recreational to pathological gambling. Online gambling formats (i.e., online gambling, micro-transactions, loot boxes) create an increased reinforcement density and accessibility that serves to elevate harms and downstream effects, including financial debt, substance co-use, and suicidality, especially among young adults. The primary contribution of this paper is both theoretical and integrative: it presents an interactional developmental model that specifies and extends the concept of neurodevelopmental immaturity, psychosocial forces, and neurotransmitter dysregulation, which together shift behaviours from play to pathology. The review addresses a critical gap in the knowledge base by showing how to connect epidemiological observations to neurobiological mechanisms and knowledge on prevention, while advocating for longitudinal and culturally appropriate research.
Deepfake Detection and Authentication Using Hybrid Artificial Intelligence Models: A Case Study
Get PDFThe progress of artificial intelligence (AI) has enabled the creation of very realistic synthetic media, also known as deepfakes, which poses a serious threat to information integrity and social confidence. The article examined the process of detecting and authenticating deep fakes using hybrid AI models. The researchers employed the case study methodology, based on the Celeb-DF V2 dataset, one of the most challenging datasets for generating high-quality manipulated videos. The suggested system combined convolutional neural networks (CNNs) to extract spatial features, recurrent neural networks (LSTMs/GRUs) to model temporal consistency, and transformer systems to analyse fine-grained context. The researchers bundled these parts together to enhance robustness and generalisation in an ensemble mechanism. They also introduced provenance tracking and semi-fragile watermarking to supplement detection, enabling proactive authentication and watermark verification of media through blockchain-based provenance tracking. The experimental findings showed that the hybrid models were more accurate, achieved higher F1 Scores, and were more robust to adversarial manipulations than the single-model baselines. The hybrid with a transformer achieved the best accuracy (0.95 AUC) and the lowest false-positive rate (6%), but at the expense of slower processing speeds. Authentication tools also helped strengthen trust by verifying the originality of content and flagging potential manipulation before it was classified. The results have revealed that hybrid AI models, when implemented with authentication strategies, represent a more effective and legitimate approach to addressing the threats of misinformation, fraud, and loss of trust among the population in the face of deepfakes
Advances in Microscopy, Biophotonics, Opto Acoustic: Role in Biology and Medicine
Get PDFAdvanced microscopy, biophotonics, and optoacoustic technologies have emerged as transformative scientific platforms that enable unprecedented insights into complex biological systems. This research investigates the integrated performance of these technologies through a comprehensive interdisciplinary methodology. By combining quantum sensing techniques, advanced computational approaches, and sophisticated signal processing strategies, researchers developed innovative technological platforms that generated high-resolution observations of molecular interactions. Key findings revealed significant improvements in imaging resolution and signal detection capabilities, with quantum-enhanced sensing techniques enabling molecular-level observations that transcend traditional limitations. Statistical validation confirmed robust performance across multiple research dimensions, demonstrating extraordinary reliability in biological research methodologies. The study identified critical technological challenges, including computational complexity and signal resolution limitations, while providing recommendations emphasising quantum computational technologies and interdisciplinary collaboration. The investigation demonstrates that integrated technological platforms can generate transformative scientific insights, creating new pathways for understanding complex biological systems. These findings have significant implications for medical diagnostics, personalised healthcare, and complex scientific observation, promising continued technological innovation and scientific discovery
Analysis of the Level of Knowledge and Physical Condition of the PSSI Askab Referee of Southwest Aceh Regency, Indonesia
Get PDFReferees are part of the match apparatus and must understand all the rules related to football as mandatory knowledge to lead matches with measurable quality in making every decision. This research aims to: 1) find out the Level of Knowledge of the Referee Askab PSSI Southwest Aceh Regency in 2024, 2) find out the Level of Physical Condition of the Referee Askab PSSI Southwest Aceh Regency in 2024. This study uses a quantitative approach with a descriptive type of research. The population in the study was 20 people, and the entire population was used as a research sample, so this study is called a population study with details of C-1 license referees as many as three people, C-2 license referees as many as five people and C-3 license referees as many as 12 people. The data collection technique is done by distributing test questions directly to respondents related to referee knowledge data.Meanwhile, the physical condition ability test was carried out by measuring the ability of the cardiopulmonary endurance test (Balke 15 minutes), speed ability (30-meter running test), and agility ability (T-Test). The results of the measurement of referee knowledge showed that the category was very good for two people (10%), the good category was for 13 people (65%), and the category was acceptable for five people (25%). Meanwhile, the level of physical condition related to the element of agility shows that 14 people are in the very good category (70%), four people are in the good category (20%), and two people are in the medium category (10%). The speed test results obtained the speed ability of the Aceh Southwest Askab referees, namely 20 people are in the good category (100%). Furthermore, the results of the cardiopulmonary endurance test of the Southwest Aceh assab referees were 20 people, or all of them were at the medium category level (100%). Based on the study results, it can be concluded that the average level of knowledge of the Askab PSSI Southwest Aceh referees is in the medium category, with a frequency of 13 people (65%). Meanwhile, for the ability of physical condition of each, namely agility is in the very good category of 14 people (70%), speed ability is in the good category (100%), and cardiopulmonary endurance ability is in the medium category as many as 20 people (100%)