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Global peace, security, and sustainable energy development depend on effective nuclear energy governance. While the Nuclear Non-Proliferation Treaty (NPT) has served as a cornerstone in this domain, it faces challenges such as trust deficits, inequitable access to nuclear technologies, and regional instability. This paper proposes the Qudrat-Ullah Nuclear Peace and Trust (Q-NPT) framework, a dynamic implementation roadmap designed to address these issues. The framework focuses on fostering trust among stakeholders, ensuring equitable access to nuclear technologies, and promoting inclusivity in governance structures. A key theoretical contribution is the integration of trust-building measures with sustainable energy transitions, highlighting nuclear energy’s role in decarbonization and global energy security. The paper outlines actionable pathways for implementing the Q-NPT framework, including enhanced oversight by the International Atomic Energy Agency (IAEA), capacity-building initiatives, and training programs to enable safe and sustainable nuclear cooperation, particularly in developing nations. By operationalizing nuclear programs through this approach, the Q-NPT framework aligns nuclear energy governance with global sustainable energy objectives. Full article

Adolescence is a critical period for growth and development, yet research on dietary quality and its influencing factors among Senegalese adolescent girls is limited. This study aims to assess dietary quality, with a focus on dietary diversity (DD), and identify its determinants in a nationally representative sample of adolescent girls in Senegal. A cross-sectional study was conducted in 2023 among 600 girls aged 10–19 years. Food intake was assessed over a seven-day period to evaluate DD. Household food security and sociodemographic data were gathered through face-to-face interviews. Half the sample had adequate DD. As for consumption, 80% had breakfast daily, while the same proportion consumed one snack per day. Fruits and vegetables, meat/poultry/fish, and dairy were consumed daily by less than 25% of the sample. Sweet foods, sweet beverages, and salty and fried food were consumed by less than 10% every day. Not having been sick in the past two weeks (Odds ratio (OR): 1.53, Confidence Interval (CI): 1.05–2.22), taking breakfast daily (OR: 1.89, CI: 1.23–2.93) and micronutrients (OR: 2.75, CI: 1.54–4.92), listening to the radio at least once a week (OR: 1.66, CI: 1.05–2.63), and living in a household with access to an improved source of water (OR: 4.13, CI: 2.28–7.49) were positively associated with adequate DD. Overall, the diet of adolescent girls is of poor quality. Potential determinants of their dietary quality should be considered in future nutrition programs and policies to ensure their optimal growth and development. Full article

Small and medium-sized enterprises (SMEs) face substantial barriers in accessing reliable and cost-effective IT infrastructure, primarily due to economic constraints and limited technical resources. Hybrid cloud computing solutions, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS), offer potential to overcome these barriers by providing scalable, flexible IT services. This study systematically reviews the impact of cloud computing on SME performance, focusing on key performance metrics such as cost-efficiency, operational reliability, and competitive advantage. A systematic literature review was conducted using the PRISMA 2020 framework. Inclusion criteria included studies published in English from 2014 to 2024, focusing on cloud computing for SMEs and presenting clear analytical frameworks for evaluating performance. Out of an initial pool of 18,570 studies, 90 met the criteria for detailed analysis. Findings show that cloud computing adoption enhances SME performance, with approximately 82% of studies reporting improvements in operational efficiency and 76% noting cost savings. Competitive advantage was identified as a key benefit in 64% of studies, driven by cloud-enabled scalability and access to advanced technology. Key adoption drivers include management support (cited by 68% of studies), service quality (56%), and perceived risks (54%), while barriers such as initial cost concerns and data security risks were also prevalent, affecting 48% and 45% of SMEs, respectively. This review provides strategic insights for SMEs and policymakers, emphasizing the importance of tailored cloud strategies that align with specific operational needs and budget constraints. By addressing key predictors and challenges, this study offers a roadmap for SMEs to leverage cloud computing to improve performance, sustainability, and competitiveness. Full article

Among the factors threatening food security in Nigeria are poor access to credit facilities, the high cost of inputs, and poor processing and storage. Cereals and grains are among the staple food crops highly consumed by Nigerians. In this study, multi-stage sampling procedure was used to select 1200 registered rice processors from Agricultural Development Programme zones in the Nigerian states of Lagos, Oyo, Ogun, Ondo, Osun, and Ekiti, and a structured questionnaire was used to obtain data on primary, secondary, and tertiary postharvest operations. The data were analyzed using descriptive statistics and Pearson Product Moment Correlation. The results showed that the majority (65.1%) of the respondents were male, 54.5% were 30–60 years old, 86.9% were married, 96.3% had been formally educated, and 99.9% processed, 71.5% packaged, and 79.4% stored more than 5001 kg of rice monthly. The majority (85.9%) of the respondents had no knowledge of rice moisture content and still used local means of rice processing, while 14.1% of the respondents used modern means of rice processing. We concluded that stored local rice was still subject to more wastage, spoilage, and losses due to the poor processing, packaging, and storage methods used in the study area. We recommend the adoption of modern and suitable rice technologies for processing, packaging, and storage. Furthermore, credit facilities should be made available, and inputs should be subsidized for rice farmers and processors. Full article

The Open Radio Access Network (O-RAN) paradigm promises unprecedented flexibility and cost efficiency for 6G networks but introduces critical security risks due to its disaggregated, AI-driven architecture. This paper proposes a secure optimization framework integrating zero-trust principles and privacy-preserving Federated Learning (FL) to address vulnerabilities in O-RAN’s RAN Intelligent Controllers (RICs) and xApps/rApps. We first establish a novel threat model targeting O-RAN’s optimization processes, highlighting risks such as adversarial Machine Learning (ML) attacks on resource allocation models and compromised third-party applications. To mitigate these, we design a Zero-Trust Architecture (ZTA) enforcing continuous authentication and micro-segmentation for RIC components, coupled with an FL framework that enables collaborative ML training across operators without exposing raw network data. A differential privacy mechanism is applied to global model updates to prevent inference attacks. We validate our framework using the DAWN Dataset (5G/6G traffic traces with slicing configurations) and the OpenRAN Gym Dataset (O-RAN-compliant resource utilization metrics) to simulate energy efficiency optimization under adversarial conditions. A dynamic DU sleep scheduling case study demonstrates 32% energy savings with <5% latency degradation, even when data poisoning attacks compromise 15% of the FL participants. Comparative analysis shows that our ZTA reduces unauthorized RIC access attempts by 89% compared to conventional O-RAN security baselines. This work bridges the gap between performance optimization and trustworthiness in next-generation O-RAN, offering actionable insights for 6G standardization. Full article

The landscape of 5G communication introduces heightened risks from malicious attacks, posing significant threats to network security and availability. The unique characteristics of 5G networks, while enabling advanced communication, present challenges in distinguishing between legitimate and malicious traffic, making it more difficult to detect anonymous traffic. Current methodologies for intrusion detection within 5G communication exhibit limitations in accuracy, efficiency, and adaptability to evolving network conditions. In this study, we explore the application of an adaptive optimized machine learning-based framework to improve intrusion detection system (IDS) performance in wireless network access scenarios. The framework used involves developing a lightweight model based on a convolutional neural network with 11 layers, referred to as CSO-2D-CNN, which demonstrates fast learning rates and excellent generalization capabilities. Additionally, an optimized attention-based XGBoost classifier is utilized to improve model performance by combining the benefits of parallel gradient boosting and attention mechanisms. By focusing on the most relevant features, this attention mechanism makes the model suitable for complex and high-dimensional traffic patterns typical of 5G communication. As in previous approaches, it eliminates the need to manually select features such as entropy, payload size, and opcode sequences. Furthermore, the metaheuristic Cat Swarm Optimization (CSO) algorithm is employed to fine-tune the hyperparameters of both the CSO-2D-CNN and the attention-based XGBoost classifier. Extensive experiments conducted on a recent dataset of network traffic demonstrate that the system can adapt to both binary and multiclass classification tasks for high-dimensional and imbalanced data. The results show a low false-positive rate and a high level of accuracy, with a maximum of 99.97% for multilabel attack detection and 99.99% for binary task classification, validating the effectiveness of the proposed framework in the 5G wireless context. Full article

Our society is becoming increasingly dependent on technology, with immersive virtual worlds such as Extended Reality (XR) transforming how we connect and interact. XR technologies enhance communication and operational efficiency. They have been adopted in sectors such as manufacturing, education, and healthcare. However, the immersive and interconnected nature of XR introduces security risks that span from technical and human to psychological vulnerabilities. In this study, we examined security threats in XR environments through a scenario-driven risk assessment, using a hybrid approach combining Common Vulnerability Scoring System (CVSS) metrics and a custom likelihood model to quantify risks. This methodology provides a comprehensive risk evaluation method, identifying critical vulnerabilities such as Remote Code Execution (RCE), social engineering, excessive permission exploitation, unauthorized access, and data exfiltration. The findings reveal that human vulnerabilities, including users’ susceptibility to deception and excessive trust in familiar interfaces and system prompts, significantly increase attack success rates. Additionally, developer mode, once enabled, remains continuously active, and the lack of authentication requirements for installing applications from unknown sources, coupled with poor permission management on the part of the users, creates security gaps that attackers can exploit. Furthermore, permission management in XR devices is often broad and persistent and lacks real-time notifications, allowing malicious applications to exploit microphone, camera, and location access without the users knowing. By leveraging CVSS scores and a structured likelihood-based risk assessment, we quantified the severity of these threats, with RCE, social engineering, and insecure app installation emerging as the greatest risks. This study highlights the necessity of implementing granular permission controls, formalized developer mode restrictions, and structured user education programs to mitigate XR-specific threats. Full article

The Number Theoretic Transform (NTT) is a cornerstone for efficient polynomial multiplication, which is fundamental to lattice-based cryptographic algorithms such as CRYSTALS-Kyber—a leading candidate in post-quantum cryptography (PQC). However, existing NTT accelerators often rely on integer multiplier-based modular reduction techniques, such as Barrett or Montgomery reduction, which introduce significant computational overhead and hardware resource consumption. These accelerators also lack optimization in unified architectures for forward (FNTT) and inverse (INTT) transformations. Addressing these research gaps, this paper introduces a novel, high-speed NTT accelerator tailored specifically for CRYSTALS-Kyber. The proposed design employs an innovative shift-add modular reduction mechanism, eliminating the need for integer multipliers, thereby reducing critical path delay and enhancing circuit frequency. A unified pipelined butterfly unit, capable of performing FNTT and INTT operations through Cooley–Tukey and Gentleman–Sande configurations, is integrated into the architecture. Additionally, a highly efficient data handling mechanism based on Register banks supports seamless memory access, ensuring continuous and parallel processing. The complete architecture, implemented in Verilog HDL, has been evaluated on FPGA platforms (Virtex-5, Virtex-6, and Virtex-7). Post place-and-route results demonstrate a maximum operating frequency of 261 MHz on Virtex-7, achieving a throughput of 290.69 Kbps—1.45× and 1.24× higher than its performance on Virtex-5 and Virtex-6, respectively. Furthermore, the design boasts an impressive throughput-per-slice metric of 111.63, underscoring its resource efficiency. With a 1.27× reduction in computation time compared to state-of-the-art single butterfly unit-based NTT accelerators, this work establishes a new benchmark in advancing secure and scalable cryptographic hardware solutions. Full article

Human gait motions differ depending on the age of the person. Previous studies have estimated age categories of walkers or have used age analysis for security or commercial surveillance purposes using images. However, few studies have estimated age from gait parameters alone. We estimated the age of people using kernel regression analysis based on their height, weight, and representative gait parameters, i.e., walking features that are interpretable with relative ease. Samples were obtained from 75 Japanese women aged 20–70 in a database. Through a variable selection based on sensitivity analysis, the established model estimated the ages of the women with a correlation coefficient of 0.78 with their actual ages, and the mean absolute error was 9.99 years. The sensitive variables included the minimum foot clearance, body weight, walking velocity, step width, and stride length. Estimation errors were significantly greater for elderly adults than for young people. Specifically, the mean absolute error for people in their 20s was $7.4$ years, whereas that for those over 60 was $13.1$ years. The proposed method uses gait parameters that can be measured with wearable devices, such as inertial measurement units; therefore, it offers an accessible approach to estimating a walker’s age with moderate certainty and promoting healthcare awareness in daily life. Full article

Wireless Local Area Networks (WLANs), particularly Wi-Fi, serve as the backbone of modern connectivity, supporting billions of devices globally and forming a critical component in Internet of Things (IoT) ecosystems. However, the increasing ubiquity of WLANs also presents an expanding attack surface for adversaries—especially Advanced Persistent Threats (APTs), which operate with high levels of sophistication, resources, and long-term strategic objectives. This paper provides a holistic security analysis of WLANs under the lens of APT threat models, categorizing APT actors by capability tiers and examining their ability to compromise WLANs through logical attack surfaces. The study identifies and explores three primary attack surfaces: Radio Access Control interfaces, compromised insider nodes, and ISP gateway-level exposures. A series of empirical experiments—ranging from traffic analysis of ISP-controlled routers to offline password attack modeling—evaluate the current resilience of WLANs and highlight specific vulnerabilities such as credential reuse, firmware-based leakage, and protocol downgrade attacks. Furthermore, the paper demonstrates how APT resources significantly accelerate attacks through formal models of computational scaling. It also incorporates threat modeling frameworks, including STRIDE and MITRE ATT&CK, to contextualize risks and map adversary tactics. Based on these insights, this paper offers practical recommendations for enhancing WLAN resilience through improved authentication mechanisms, network segmentation, AI-based anomaly detection, and open firmware adoption. The findings underscore that while current WLAN implementations offer basic protections, they remain highly susceptible to well-resourced adversaries, necessitating a shift toward more robust, context-aware security architectures. Full article

A secured communications system is a structure or infrastructure that is intended to ensure the confidentiality, integrity, and authenticity of data being exchanged between entities. Such systems use various security technologies to guarantee that communications are not tampered with, read, or accessed by unauthorized parties. The intractability of factoring huge composite numbers is a prerequisite for RSA’s security. With big enough key sizes, it is still computationally infeasible for attackers to defeat RSA encryption with today’s technology. Efficient domination is an idea based on graph theory, specifically in the investigation of domination in graphs. Although it has many applications in problems in computation, it is only for cryptography in contexts involving efficient algorithms, combinatorial structures, and optimization for security that it finds uses. This idea provides minimal redundancy in domination, similar to the optimization of resources in a cryptographic scenario. In this work, we concentrate on enhancing the complexity of secure systems using a mathematical model that is based on fuzzy graph networks. The suggested model combines efficient domination, the RSA algorithm, and triangular fuzzy membership functions. By integrating these optimized parameters, we construct a very secure mathematical fuzzy graph network that can efficiently protect secret information. Full article

The EAT-Lancet diet outlines target consumption for specific food components but overlooks accessibility and cost issues, which may hinder adherence among vulnerable populations. This study examines the acquisition profile of EAT-Lancet diet components by food security and poverty status, using data from 57,920 households in the 2017–2018 Brazilian Household Budget Survey. Poverty and food insecurity were defined according to the World Bank per capita income cutoffs and the Brazilian Food Insecurity Scale, respectively. Food acquisition was classified into 15 EAT-Lancet diet components and expressed as per capita daily averages (g, % of total available energy, and % of food expenditure), by food security and poverty strata. Brazilian households were 37.9% food-insecure and 12% poor. Compared to more privileged counterparts, these households prioritized the acquisition of staples like refined cereals and legumes over most EAT-Lancet diet adequacy components, such as fruits, vegetables, whole grains, nuts, and peanuts. While lower energy shares from moderation components were only slightly evident for red meat and dairy among food-insecure households, pronounced reductions in added sugars and vegetable oils were seen among the poor. These findings suggest that public policies should synergically address particularities of different deprivation contexts to promote sustainable diets in Brazil. Full article

Access control systems protect homes and businesses in the continually evolving security industry. This paper designs and implements a Raspberry Pi-based facial recognition door lock system using artificial intelligence and computer vision for reliability, efficiency, and usability. With the Raspberry Pi as its CPU, the system uses facial recognition for authentication. A camera module for real-time image capturing, a relay module for solenoid lock control, and OpenCV for image processing are essential. The system uses the DeepFace library to detect user emotions and adaptive learning to improve recognition accuracy for approved users. The device also adapts to poor lighting and distances, and it sends real-time remote monitoring messages. Some of the most important things that have been achieved include adaptive facial recognition, ensuring that the system changes as it is used, and integrating real-time notifications and emotion detection without any problems. Face recognition worked well in many settings. Modular architecture facilitated hardware–software integration and scalability for various applications. In conclusion, this study created an intelligent facial recognition door lock system using Raspberry Pi hardware and open-source software libraries. The system addresses traditional access control limits and is practical, scalable, and inexpensive, demonstrating biometric technology’s potential in modern security systems. Full article

Background/Objectives: The nutritional habits of older adults are increasingly relevant to public health, particularly given the rising prevalence of obesity and its associated chronic diseases. This study aims to analyze the relationship between the Healthy Eating Index (IASE) and Body Mass Index (BMI) in older adults in Spain, focusing on food frequency, dietary preferences, and socioeconomic factors influencing nutritional security. Methods: The study is part of the Eating Matters project, assessing food (in)security in older adults across Andalusia and Catalonia between April 2022 and January 2024. A cross-sectional survey was conducted among 190 participants (≥65 years), recruited in primary healthcare centers. The questionnaire included three blocks: food insecurity assessment (FIES scale), diet quality with the Healthy Eating Index for the Spanish Population (IASE), and sociodemographic factors. Data analysis involved descriptive statistics, Pearson correlations, and logistic regression models to identify associated factors with overweight and obesity. Results: The average BMI was 28.5 kg/m² (SD = 4.29), with 46.3% of participants classified as overweight and 32.1% as obese. A significant negative correlation (r = −0.79, p < 0.05) was found between healthy food consumption and BMI, while personal income showed a moderate positive correlation with adherence to a healthy diet (r = 0.42, p < 0.05). Logistic regression indicated that frequent consumption of processed meats and confectionery was a significant identify associated factors with overweight/obesity, with a model accuracy of 68% and sensitivity of 95%. Conclusions: Older adults with lower incomes and higher consumption of ultra-processed foods exhibited a higher risk of obesity. These findings highlight the need for public policies promoting food accessibility and targeted nutrition education for older adults, including guidance on balanced diets, adequate protein intake, and the prevention of sarcopenia, to encourage healthier dietary patterns in aging populations. Full article

Social determinants of health (SDoH) are considered significant determinants of mental health. This study examines the association between SDoH and mental health status in the United States. We analyzed 2023 Behavioral Risk Factor Surveillance System (BRFSS) data from 183,318 U.S. adults using multinomial logistic regression. Several SDoH were significantly linked to the frequency of poor mental health days. After adjusting for all covariates, individuals facing difficulty paying utility bills had lower odds of experiencing episodic (vs. chronic) poor mental health (AOR = 0.47, p = 0.031). Transportation challenges were associated with lower odds of episodic distress rather than chronic mental health issues (AOR = 0.35, p = 0.026). Individuals who were unable to afford a doctor or who experienced employment loss had significantly lower odds of reporting no poor mental health days compared to reporting chronic poor mental health, with adjusted odds ratios of 0.37 and 0.84, respectively. Non-Hispanic Whites and males were more likely to report chronic poor mental health. Policies that prioritize economic stability and job security, reliable transportation, and equal access to education and healthcare are crucial for promoting mental health equity across diverse populations. Full article