Search Results (884)

The malicious contamination of food has been recognized by the World Health Organization (WHO) as a real and current threat that must be integrated into food safety systems to ensure preparedness for deliberate attacks. Traditional approaches, such as HACCP, effectively address unintentional hazards but remain insufficient against intentional contamination and sabotage. Food defense frameworks such as HACCP (Hazard Analysis and Critical Control Points), VACCP (Vulnerability Assessment and Critical Control Points), and TACCP (Threat Assessment and Critical Control Points) represent complementary methodologies, addressing unintentional, economically motivated, and deliberate threats, respectively. This review critically examines food defense frameworks across the European Union, the United States, and the United Kingdom, as well as standards benchmarked by the Global Food Safety Initiative (GFSI), drawing on peer-reviewed and grey literature sources. In the United States, the Food Safety Modernization Act (FSMA) mandates the development and periodic reassessment of food defense plans, while the European Union primarily relies on general food law and voluntary certification schemes. The United Kingdom’s PAS 96:2017 standard provides TACCP-based guidance that also acknowledges cybercrime as a deliberate threat. Building on these regulatory and operational gaps, this paper proposes the Cyber-FSMS model, an integrated framework that combines traditional food defense pillars with cyber risk management to address cyber–physical vulnerabilities in increasingly digitalized supply chains. The model introduces six interconnected components (governance, vulnerability assessment, mitigation, monitoring, verification, and recovery) designed to embed cyber-resilience into Food Safety Management Systems (FSMS). Priority actions include regulatory harmonization, practical support for small and medium-sized enterprises (SMEs), and the alignment of cyber-resilience principles with upcoming GFSI benchmarking developments, thereby strengthening the integrity, robustness, and adaptability of global food supply chains. Full article

Background/Objectives: The aging population faces a multitude of health challenges, particularly when it comes to maintaining proper nutrition. Age-related physiological changes, such as decreased metabolism, diminished taste perception, and difficulty in chewing, can lead to insufficient nutrient intake, ultimately resulting in malnutrition. It is crucial to address these issues to promote not only physical health but also overall well-being. In this modern era, artificial intelligence (AI) technologies, including robots and machine learning algorithms, are being increasingly harnessed to encourage healthy eating habits among older adults. This is critical to support healthy aging and mitigate diet-related chronic diseases. However, little or no synthesis has established their effectiveness in delivering personalized, scalable, and adaptive interventions for older adults. This systematic review considers the state-of-the-art application of AI-based interventions aimed at improving dietary behaviors and nutritional outcomes in older adults. Methods: Following the PRISMA 2020 guidelines and a registered PROSPERO protocol (ID: CRD420241045268), we systematically analyzed 30 studies we collected from five databases, published between 2015 and 2025 based on different AI techniques, including machine learning, natural language processing, and recommender systems. We synthesized data collected from these studies to examine the intervention types, outcomes, and methodological approaches. Results: Findings from our review highlight the potential of AI-based interventions to promote engagement among older adults and improve adherence to healthy eating guidelines. Additionally, we found some challenges related to ethical concerns such as privacy and transparency, and limited evidence of their long-term effectiveness. Conclusions: AI-based interventions offer significant promise in promoting healthy eating among older adults through personalized, adaptive, and scalable interventions. Yet, current evidence is constrained by some methodological limitations and ethical concerns, which calls for future research to design inclusive, evidence-based AI interventions that address the unique physiological, psychological, and social needs of older adults. Full article

Due to its unique benefits over conventional subtractive manufacturing, additive manufacturing methods continue to attract interest in both academia and industry. One such method is called Cold Spray Additive Manufacturing (CSAM), a solid-state coating deposition technology to manufacture repair metallic components using a gas jet and powder particles. CSAM offers low heat input, stable phases, suitability for heat-sensitive substrates, and high deposition rates. However, persistent challenges include porosity control, geometric accuracy near edges and concavities, anisotropy, and cost sensitivities linked to gas selection and nozzle wear. Interdisciplinary research across manufacturing science, materials characterisation, robotics, control, artificial intelligence (AI), and machine learning (ML) is deployed to overcome these issues. ML supports quality prediction, inverse parameter design, in situ monitoring, and surrogate models that couple process physics with data. To demonstrate the impact of AI and ML on CSAM, this study presents a systematic literature review to identify, evaluate, and analyse published studies in this domain. The most relevant studies in the literature are analysed using keyword co-occurrence and clustering. Four themes were identified: design for CSAM, material analytics, real-time monitoring and defect analytics, and deposition and AI-enabled optimisation. Based on this synthesis, core challenges are identified as small and varied datasets, transfer and identifiability limits, and fragmented sensing. Main opportunities are outlined as physics-based surrogates, active learning, uncertainty-aware inversion, and cloud-edge control for reliable and adaptable ML use in CSAM. By systematically mapping the current landscape, this work provides a critical roadmap for researchers to target the most significant challenges and opportunities in applying AI/ML to industrialise CSAM. Full article

The analysis of dynamic remote sensing scenes from unmanned aerial vehicles (UAVs) is shifting from reactive processing to proactive, predictive intelligence. Central to this evolution is video prediction—forecasting future imagery from past observations—which enables critical remote sensing applications like persistent environmental monitoring, occlusion-robust object tracking, and infrastructure anomaly detection under challenging aerial conditions. Yet, a systematic review of video prediction models tailored for the unique constraints of aerial remote sensing has been lacking. Existing taxonomies often obscure key design choices, especially for emerging operators like state-space models (SSMs). We address this gap by proposing a unified, multi-dimensional taxonomy with three orthogonal axes: (i) operator architecture; (ii) generative nature; and (iii) training/inference regime. Through this lens, we analyze recent methods, clarifying their trade-offs for deployment on UAV platforms that demand processing of high-resolution, long-horizon video streams under tight resource constraints. Our review assesses the utility of these models for key applications like proactive infrastructure inspection and wildlife tracking. We then identify open problems—from the scarcity of annotated aerial video data to evaluation beyond pixel-level metrics—and chart future directions. We highlight a convergence toward scalable dynamic world models for geospatial intelligence, which leverage physics-informed learning, multimodal fusion, and action-conditioning, powered by efficient operators like SSMs. Full article

Accurate thermal modeling of Terminal Test Masses (TTMs) is crucial for optimizing the sensitivity of gravitational wave interferometers like Virgo. In fact, in such gravitational wave detectors even minimal laser power absorption can induce performance-limiting thermal effects. This paper presents a detailed investigation into the steady-state thermal behavior of TTMs. In particular, future scenarios of increased intracavity laser beam power and optical coating absorption are considered. We develop and compare two numerical models: a comprehensive model incorporating volumetric heat absorption in both the multilayer coating and the bulk substrate, and a simplified reduced model where the coating’s thermal impact is represented as an effective surface boundary condition on the substrate. Our simulations were focused on a ternary coating design, which is a candidate for use in next-generation detectors. Results reveal that higher coating absorption localizes peak temperatures near the coating–vacuum interface. Importantly, the comparative analysis demonstrates that temperature predictions from the reduced model differ from the detailed model by only milli-Kelvins, a discrepancy often within the experimental uncertainties of the system’s thermo-physical parameters. This finding suggests that computationally efficient reduced models can provide sufficiently accurate results for thermal management and first-order distortion analyses. Moreover, the critical role of accurately characterizing the total power absorbed by the coating is emphasized. Full article

This paper provides a perspective on the use of RGB-D cameras and non-invasive brain–computer interfaces (BCIs) for human activity recognition (HAR). Then, it explores the potential of integrating both the technologies for active and assisted living. RGB-D cameras can offer monitoring of users in their living environments, preserving their privacy in human activity recognition through depth images and skeleton tracking. Concurrently, non-invasive BCIs can provide access to intent and control of users by decoding neural signals. The synergy between these technologies may allow holistic understanding of both physical context and cognitive state of users, to enhance personalized assistance inside smart homes. The successful deployment in integrating the two technologies needs addressing critical technical hurdles, including computational demands for real-time multi-modal data processing, and user acceptance challenges related to data privacy, security, and BCI illiteracy. Continued interdisciplinary research is essential to realize the full potential of RGB-D cameras and BCIs as AAL solutions, in order to improve the quality of life for independent or impaired people. Full article

Photovoltaic (PV) fault detection faces significant challenges in distinguishing subtle defects from complex backgrounds while maintaining reliability across diverse environmental conditions. Traditional approaches struggle with scalability and accuracy limitations, particularly when detecting electrical damage, physical defects, and environmental soiling in thermal imagery. This paper presents SolarFaultAttentionNet, a novel dual-attention deep learning framework that integrates channel-wise and spatial attention mechanisms within a multi-path CNN architecture for enhanced PV fault classification. The approach combines comprehensive data augmentation strategies with targeted attention modules to improve feature discrimination across six fault categories: Electrical-Damage, Physical-Damage, Snow-Covered, Dusty, Bird-Drop, and Clean. Experimental validation on a dataset of 885 images demonstrates that SolarFaultAttentionNet achieves 99.14% classification accuracy, outperforming state-of-the-art models by 5.14%. The framework exhibits perfect detection for dust accumulation (100% across all metrics) and robust electrical damage detection (99.12% F1 score) while maintaining an optimal sensitivity (98.24%) and specificity (99.91%) balance. The computational efficiency (0.0160 s inference time) and systematic performance improvements establish SolarFaultAttentionNet as a practical solution for automated PV monitoring systems, enabling reliable fault detection critical for maximizing energy production and minimizing maintenance costs in large-scale solar installations. Full article

Background: Medical resident burnout is a critical threat to healthcare workforce sustainability, with physical activity (PA) posited as a protective factor. This bibliometric analysis maps the global research landscape on this topic from 2005 to 2025. Methods: Systematic search of the Web of Science Core Collection identified 110 relevant English-language articles. Performance analysis and scientific mapping were conducted using R and VOSviewer. Results: The field saw an annual growth rate of 3.35%, with a peak of 16 publications in 2019. The United States was the dominant contributor, accounting for 68% of the total output. Analysis identified several major thematic areas, including stress and behavioral factors, occupational mental health, and institutional support mechanisms. The findings reveal a rapidly growing but geographically concentrated body of research, underscoring a significant gap in globally representative evidence. Conclusions: This analysis provides a foundational map for future research, underscoring the need for institutional wellness programs incorporating PA, international collaborative studies, and policy-level interventions. We conclude that integrating physical activity is not a luxury but a critical strategy for healthcare system sustainability. Full article

The development of phenolic-based functional food ingredients is of growing interest due to their beneficial effects on human health. This study investigated the combined influence of microparticle physical state, phenolic compound type (gallic acid, GA; and ellagic acid, EA), and model food matrix on the release profile, bioaccessibility, and antioxidant activity of GA and EA during in vitro gastrointestinal digestion. GA and EA were encapsulated with inulin (In) by spray-drying. By varying formulation and operational conditions, both semicrystalline (GA-InSc, EA-InSc) and amorphous (GA-InA, EA-InA) microparticles were obtained. Microparticles were characterized for crystallinity, encapsulation efficiency, particle size, morphology, and release profile during in vitro simulated gastrointestinal digestion following the INFOGEST method. The physical state of microparticles and type of phenolic compound critically influenced release profile, bioaccessibility, and antioxidant activity during digestion. GA, being more water-soluble, was rapidly released, reaching nearly 100% in the gastric phase, whereas EA exhibited limited gastric release and higher intestinal release, particularly in EA-InSc. Incorporation into different food matrices further modulated these effects; carbohydrate- and blend-based matrices improved phenolic release and antioxidant activity for both compounds. These findings highlight the importance of microparticle formulation, phenolic characteristics, and matrix interactions in designing functional food ingredients with optimized health benefits. Full article

As a critical technological foundation for electric vehicles, power battery state estimation primarily involves estimating the State of Charge (SOC), the State of Health (SOH) and the Remaining Useful Life (RUL). This paper systematically categorizes battery state estimation methods into three distinct generations, tracing the evolutionary progression from single-state to multi-state cooperative estimation approaches. First-generation methods based on equivalent circuit models offer straightforward implementation but accumulate SOC-SOH estimation errors during battery aging, as they fail to account for the evolution of microscopic parameters such as solid electrolyte interphase film growth, lithium inventory loss, and electrode degradation. Second-generation data-driven approaches, which leverage big data and deep learning, can effectively model highly nonlinear relationships between measurements and battery states. However, they often suffer from poor physical interpretability and generalizability due to the “black-box” nature of deep learning. The emerging third-generation technology establishes transmission mechanisms from microscopic electrode interface parameters via electrochemical impedance spectroscopy to macroscopic SOC, SOH, and RUL states, forming a bidirectional closed-loop system integrating estimation, prediction, and optimization that demonstrates potential to enhance both full-operating-condition adaptability and estimation accuracy. This progress supports the development of high-reliability, long-lifetime electric vehicles. Full article

Health represents a state of complete physical, mental, and social well-being, with lifestyle factors accounting for approximately 60% of health determinants. Suboptimal health describes an intermediate condition between wellness and disease. According to 2023 WHO data, infertility affects approximately 17.5% of global adults, with male factors implicated in 30–50% of cases, establishing infertility as a critical public health challenge. Substantial preclinical and clinical evidence links suboptimal lifestyles to male reproductive dysfunction, positioning these behaviors as modifiable infertility risk factors encompassing environmental contaminants and lifestyle patterns. This systematic review synthesizes evidence on five key lifestyle determinants—tobacco, alcohol, microplastics, sedentariness, and sleep disruption—affecting male genitourinary health. Adopting an evidence-based medicine framework, we integrate epidemiological and experimental research to establish foundational knowledge for developing novel preventive strategies targeting male suboptimal health. Full article

Background/Objectives: Agility and reaction speed are critical components of sports performance and are influenced by both physical conditioning and psychological state. Interventions such as SmartACT, which integrate mindfulness, acceptance, and commitment, guided imagery and hypnosis techniques are still underexplored in high-performance sport, despite their potential to affect both psychological and motor dimensions. Methods: This 7-week controlled trial investigated the effectiveness of SmartACT in reducing psychological and somatic symptoms and enhancing motor performance in adolescent athletes. A total of 193 athletes aged 15–18 years were assigned to three groups: SmartACT (n = 69), MAC (Mindfulness–Acceptance–Commitment, the standardized Gardner & Moore protocol; n = 65), and a control group (n = 59). Agility was measured using the T-Drill Agility Test with Microgate electronic timing, and reaction speed was assessed using BlazePod devices. Psychological and somatic symptoms were evaluated using the Depression, Anxiety, and Stress Scale (DASS-21) and the Ghent Multidimensional Somatic Complaints Scale (GMSCS). Results: The SmartACT group showed significantly improved agility (MD = −1.07 s, p < 0.001, d = 2.50, 95% CI [1.79, 3.35]), faster reaction times (MD = −643.75 ms, p < 0.001, d = 0.85, 95% CI [0.35, 1.41]), and a higher number of BlazePod touches (MD = +2.53, p < 0.001, d = 1.43, 95% CI [0.87, 2.07]). Psychological symptoms (DASS-21) and somatic complaints (GMSCS) decreased significantly more than in the MAC and control groups. Conclusions: SmartACT appears to be an effective hybrid psychological intervention to simultaneously improve physical performance and reduce psychological and psychosomatic distress in adolescent athletes. Full article

Fire spotting (FS), the process by which firebrands are lofted, transported, and ignite new fires ahead of the main flame front, plays a critical role in escalating extreme wildfire events. This systematic literature review (SLR) analyzes peer-reviewed articles and book chapters published in English from 2000 to 2023 to assess the evolution of FS models, identify prevailing methodologies, and highlight existing gaps. Following a PRISMA-guided approach, 102 studies were selected from Scopus, Web of Science, and Google Scholar, with searches conducted up to December 2023. The results indicate a marked increase in scientific interest after 2010. Thematic and bibliometric analyses reveal a dominant research focus on integrating the FS model within existing and new fire spread models, as well as empirical research and individual FS phases, particularly firebrand transport and ignition. However, generation and ignition FS phases, physics-based FS models (encompassing all FS phases), and integrated operational models remain underexplored. Modeling strategies have advanced from empirical and semi-empirical approaches to machine learning and physical-mechanistic simulations. Despite advancements, most models still struggle to replicate the stochastic and nonlinear nature of spotting. Geographically, research is concentrated in the United States, Australia, and parts of Europe, with notable gaps in representation across the Global South. This review underscores the need for interdisciplinary, data-driven, and regionally inclusive approaches to improve the predictive accuracy and operational applicability of FS models under future climate scenarios. Full article

Three-dimensional concrete printing (3DCP), an innovative fabrication technique, has emerged as an environmentally friendly digital manufacturing process for using recycled waste materials in the construction industry. The aim of this review paper is to critically evaluate the current state of research on the use of recycled materials such as aggregates and powders in 3DCP, correlating the environmental, economic, and performance parameter effects. This review comprehensively evaluates the potential benefits of incorporating recycled waste materials in 3D printing by critically reviewing the existing peer-reviewed articles through a scientometric review. The resulting bibliometric analysis identified 73 relevant papers published between 2018 and 2024. Through the critical review, five main research categories were identified: recycled materials in 3DCP arising mainly from construction demolition in powder and aggregate forms, which investigates the types of recycled materials used, their extraction methods, morphology and physical and chemical properties. The morphology properties of the materials used displayed high irregularities in terms of shape and percentage of adhered mortar. In the second category, printability and performance, the buildability, rheological properties and the mechanical performance of 3DCP with recycled materials were investigated. Category 3 assessed the latest developments in terms of 3D-printed techniques, including Neural Networks, in predicting performance. Category 4 analysed the environmental and economic impact of 3DCP. The results indicated anisotropic behaviour for the printed samples influencing mechanical performance, with the parallel printing direction showing improved performance. The environmental performance findings indicated higher global warming potential when comparing 3DCP to cast-in situ methods. This impact was reduced by 2.47% when recycled aggregates and binder replacements other than cement were used (fly ash, ground slag, etc.). The photochemical pollution impact of 3DPC was found to be less than that of cast-in situ, 0.16 to 0.18 C2H4-eq. This environmental impact category was further reduced up to 0.10 C2H4-eq following 100% replacement. Lastly, category 5 explored some of the challenges and barriers for the implementation of 3DCP with recycled materials. The findings highlighted the main issues, namely inconsistency in material properties, which can lead to a lack of regulation in the industry. Full article

Modern societies are increasingly experiencing limited contact with nature, a phenomenon referred to as the “nature deficit.” The article presents a systematic review of the literature on this issue, with particular emphasis on the role of forests in mitigating its effects. The analysis, based on the Scopus and Web of Science databases, synthesizes the current state of knowledge on the consequences of nature deficit for physical, mental, and social health, while also highlighting the potential of forests as spaces supporting human well-being. The review process followed a systematic methodology, using precisely defined keyword combinations and multi-stage screening. From an initial pool of 88 publications, a critical selection process led to 11 articles that met the inclusion criteria and were analyzed in depth. The findings show that regular contact with nature reduces stress, anxiety, and ADHD symptoms, supports cognitive development, and im-proves concentration, creativity, and social skills. At the same time, there is a lack of consistent tools for clearly diagnosing nature deficit, and existing studies face significant methodological limitations (small samples, subjective measurements, lack of laboratory control). The article also identifies research gaps, particularly in the context of sustainable forest management, cultural differences, and the long-term health effects of exposure to nature. Full article