Search Results (9,125)

Food safety culture (FSC) is a structured, measurable and enforceable element of the food industry, which is essential for building consumer trust and safeguarding consumer wellness. Regardless of the size of the organisation, FSC plays a pivotal role in safety and quality assurance and should be embedded in the company’s values and beliefs, ultimately manifesting in employee behaviours. FSC’s principal denominators encompass the fundamental principles of leadership, knowledge, engagement, environment, performance, and outcome. These principles collectively form a holistic framework that is pivotal for enhancing the efficacy and efficiency of safety initiatives in the food supply chain. FSC should be integrated into existing food safety standards, aligning with management systems and enjoying support and ownership at all levels and portfolios. Attaining a strong FSC requires commitment and active participation, not only from departments and sections directly involved with food handling, but also from administrative departments and branches. A non-conducive or weak culture, on the other hand, creates barriers to the achievement of safety goals and creates environments that may lead to product safety failures and non-conformances, with potential detrimental impacts on both consumer and business well-being. The absence of a specific culture constitutes a culture in itself, and, therefore, regular, valid, and reliable assessment is crucial for understanding the current state of FSC, without drawing generalised, superficial, or biased conclusions. In this study, the history and context of FSC are discussed, as well as narratives on assessment protocols and improvement initiatives. A trustworthy and ethical assessment is the first step in a three-phase process, involving assessment, alignment, and intervention to improve FSC, constituting various subcategories. The ultimate intent of this study is to provide turn-key solutions through presenting ideas, debating concepts and proposing interventions to guide and inform FSC improvement, culminating in safe and wholesome products. Full article

Regression testing is a critical activity for maintaining software quality by identifying faults and ensuring system reliability. However, in large-scale software systems, executing all available test cases within limited time and computational resources is often impractical. Therefore, test case prioritization aims to arrange test cases in an effective execution order to maximize testing effectiveness, particularly during the early stages of regression testing. Existing test case prioritization approaches consider various optimization objectives, including fault detection capability, code coverage, risk reduction, and execution cost. In this study, we focus on the requirement-based test case prioritization problem, where the main goal is to maximize the rate of requirement coverage as early as possible. Since the possible orderings of test cases form a factorial-sized search space, this problem exhibits NP-hard characteristics, leading to the widespread use of heuristic and metaheuristic optimization techniques. However, sparse requirement traceability matrices (RTMs) introduce additional challenges, particularly due to isolated requirements and delayed coverage of critical requirement elements. To address these challenges, this study proposes a bottleneck-aware heuristic and metaheuristic framework for requirement-based test case prioritization under sparse RTMs. The main empirical contribution of the study is the deterministic AG+BH strategy, which combines Additional Greedy with the proposed Bottleneck Hunter mechanism. This strategy uses the structure of the RTM to identify test cases associated with delayed coverage, especially singleton requirements. The MH-DBO-GA component is included as a secondary metaheuristic extension based on Dragon Boat Optimization, Genetic Algorithm operators, and memetic local search. Its role is to provide additional search diversity rather than to replace the deterministic AG+BH strategy in the current APRC setting. The proposed framework is evaluated using two sparse requirement traceability matrix datasets. The results show that AG+BH provides the strongest practical deterministic trade-off on the evaluated datasets. It obtains the highest APRC on Dataset 2 and a near-best APRC on Dataset 1, where 2-Optimal gives a slightly higher APRC but requires 23 h 23 min of execution time. MH-DBO-GA does not outperform AG+BH in this setting, but it performs better than the standard stochastic metaheuristic baselines and can be considered as an exploratory extension when additional search diversity is needed. Furthermore, an ablation analysis is performed to examine the individual contributions of informed initialization, Bottleneck Hunter, and hybrid optimization components. Overall, the findings indicate that sparse RTM-based test case prioritization benefits primarily from problem-specific bottleneck-aware heuristic reasoning, while metaheuristic refinement should be interpreted as a complementary layer for more complex or future multi-objective settings. Full article

Background: Age-related macular degeneration (AMD) is one of the most common causes of irreversible impairment of central vision in the elderly population. Although anti-VEGF therapy remains the standard treatment for neovascular AMD, nutritional factors may influence disease progression and retinal health. The aim of this prospective controlled study was to evaluate the effects of an individualized dietary intervention with nutraceutical characteristics on serum concentrations of lutein, zeaxanthin, lycopene, vitamin A, and vitamin E in patients with neovascular AMD receiving anti-VEGF therapy. Methods: This prospective controlled study included 43 patients with neovascular AMD who completed a six-month follow-up period. Participants were allocated to either a control group receiving anti-VEGF therapy alone or an intervention group receiving anti-VEGF therapy combined with an individualized dietary plan. The dietary intervention emphasized foods naturally rich in carotenoids, antioxidant vitamins, trace elements, dietary fiber, and omega-3 fatty acids. Serum concentrations of lutein + zeaxanthin, lycopene, vitamin A, and vitamin E were determined using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) at baseline and after six months. Results: Serum lutein + zeaxanthin concentrations increased significantly within both groups (control 0.31 ± 0.61 to 0.49 ± 0.69 mg/L; intervention 0.51 ± 0.54 to 0.87 ± 0.89 mg/L). The increase was numerically larger in the intervention group, but the between-group difference was not statistically significant after adjustment for baseline concentrations (ANCOVA, p = 0.30). Lycopene increased significantly within the intervention group only (0.20 ± 0.15 to 0.43 ± 0.30 μmol/L); the between-group difference was not significant after baseline adjustment (p = 0.24). Vitamin A increased significantly within both groups and vitamin E within the intervention group only; however, no between-group difference remained significant after baseline adjustment (vitamin A p ≈ 1.00, vitamin E p = 0.96). The greatest increase in lutein + zeaxanthin concentrations was observed among participants with improved retinal status. These descriptive findings should be interpreted cautiously because subgroup analyses were not powered. Correlation analyses performed after six months demonstrated positive associations between serum lutein + zeaxanthin, lycopene, and vitamin A concentrations. Conclusions: An individualized dietary intervention rich in naturally occurring bioactive compounds was associated with within-group improvements in serum carotenoid and antioxidant vitamin status. After adjustment for baseline values, between-group differences did not reach statistical significance, consistent with the exploratory, non-powered pilot design. These preliminary findings, including the observed effect sizes, are intended to inform the design of an adequately powered future study rather than to establish a between-group benefit of dietary management. Particularly pronounced changes were observed for lutein + zeaxanthin and lycopene concentrations. These findings support the potential role of dietary management as an adjunct to anti-VEGF therapy. However, given the nature of the study and the relatively small sample size, larger multicenter studies are needed to confirm these observations. Full article

This review summarizes the development of Ti-based high-entropy and multi-principal element shape memory alloys (SMAs), with a particular focus on TiZrHfCoNiCu, TiHf(Zr)Ni(Pt)Pt, and TiPd-based systems. Alloy composition and heat treatment significantly influence martensitic transformation temperatures (MTTs), thermal hysteresis, superelasticity (SE), shape memory effect (SME), and elastocaloric effect (eCE) through precipitation reactions, compositional partitioning, and lattice strain effects. These parameters are summarized in the tables. Furthermore, recent advances in machine learning have provided powerful tools for predicting MTTs and thermal hysteresis. Important features governing phase transformation behavior, as well as suitable regression models for predicting MTT and thermal hysteresis, are introduced. These developments demonstrate a transition from empirical alloy development toward data-driven and physics-informed design of next-generation HE-SMAs. Full article

Since 2011, Caribbean and West African countries have experienced the deposit of millions of tonnes of the pelagic brown seaweed sargassum. While these algal blooms are well documented in the Caribbean, less information is available on the occurrence, monitoring and composition of sargassum biomass in West Africa. Cabo Verde (CV), one of the volcanic archipelagos of the Macaronesia region, has experienced sargassum beaching events since 2022, with 2025 being the worst year so far. In this study, high quantities of biomass that were beached in December 2025 (>100 tonnes) were determined in two locations in Santiago—CV’s largest island. Additionally, samples taken in 2024 were analysed to explore the potential valorisation of pelagic sargassum in CV. Elemental composition, amino acid profiling and calculation of NPK ratio suggested that this biomass could be processed for applications towards sustainable agriculture, providing benefits for local development. However, processes would have to include low cost and efficient pre-treatment(s) to manage the content of toxic elements (arsenic, cadmium, and lead). The development of sargassum-derived products for soil amelioration and improvement of crop production could contribute to strengthening communities and empowering local women’s groups, who play key roles in Cabo Verdean agriculture and rural economy. Full article

This study, based on the WELL Health Building Standard as the theoretical framework, combined with case analyses of 38 geriatric care hospital wards in South Korea and multi-group questionnaires (60 responses each from the medical service, design field, and elderly population groups), systematically explored the design elements and their importance cognition of healing environments in hospital rooms. Through cross-comparison analysis, quantitative (SPSS 25.0 multiple regressions, correlation analysis) and qualitative (ATLAS.ti 9 coding) data fusion methods, the study proposed four core design principles centered on “Beauty and Design,” “Nature Connectedness,” “Privacy,” and “Community and Information” and selected 36 key assessment indicators. The data analysis indicated that the perceived importance of the elderly population (AX) had the greatest impact on the compliance rate of rooms (AR) (β = 0.506, p = 0.002) and the medical staff group (AT) paid more attention to functional design (β = 0.506, p = 0.005), while the designer group (AV) emphasized aesthetic innovation and natural light planning (β = 0.367, p = 0.28). The study further proposed a hierarchical optimization strategy, emphasizing the balanced design of functional requirements, aesthetic innovation, and patient experience through cross-group collaborative design mechanisms. The research results provide theoretical and practical guidance for the healing environmental optimization of hospital rooms in elderly care hospitals. Full article

Diversion is defined as the practice of providing an alternative outcome for children who have engaged in offending behaviour, which keeps them away from the formal criminal justice system. Diversion is a significant element of the Child First approach, and is widely implemented across England and Wales. The increase in use of diversion in recent years has contributed to reductions in First Time Entrant rates; however, evidence regarding whether these measures reduce reoffending is limited. Diversion practices vary significantly both nationally and internationally. Hence, much could be learnt from reviewing the international literature to establish what could be implemented in England and Wales. Therefore, this systematic review aims to explore the effect of different types of diversion measures on reoffending in First Time Entrants. To do this, a comprehensive search of 10 electronic databases was undertaken to systematically identify literature relating to youth justice diversion. A systematic screening procedure was followed to identify studies that met inclusion criteria, data on key information was extracted and studies were assessed using a quality assessment tool. Results were synthesised using the EMMIE framework, to increase applicability to practice. A total of 12 distinct studies were included in the review, with both experimental and quasi-experimental design. One study was conducted in Norway, one in the Netherlands and the rest in the United States of America. Findings were varied but generally suggested diversion leads to lower rates of recorded reoffending than formal processing. When comparing between types of diversion, often there was no difference in reoffending but generally, the least intensive intervention showed the best results. However, findings from one study that considered self-reported offending prompts consideration of whether diversion genuinely affects behaviour or simply influences the detection of offences. This review indicates promising results for diversion interventions but highlights the need for further research to provide conclusive evidence regarding which measures are most effective. Full article

This paper presents a recurrent wavelet cerebellar brain controller (RWCBC) for synchronizing uncertain 4-D memristive hyper-chaotic master-slave systems affected by nonlinear uncertainty, memory-dependent dynamics, and bounded external disturbances. The proposed controller combines three elements: a linear stabilizing feedback term, a single-branch recurrent wavelet cerebellar approximator, and a smooth tanh-type robust compensation term. The recurrent association memory is used to exploit temporal information in the synchronization error, whereas the wavelet receptive fields improve local approximation of sharp nonlinear variations in the hyper-chaotic trajectory. The tanh compensation attenuates residual approximation errors and disturbances while avoiding the discontinuity of sign-based switching control. A Lyapunov-based analysis is developed to derive the adaptive learning law and to establish uniform ultimate boundedness (UUB) of the synchronization errors with an explicit ultimate bound. Numerical simulations on a 4-D memristive hyper-chaotic system indicate that the proposed RWCBC provides competitive tracking accuracy and a favorable accuracy-complexity trade-off compared with the considered WCMAC and FBELC baselines under the tested conditions. Full article

Growing evidence positions the built environment as an active component of the developmental experience of children with Autism Spectrum Disorder (ASD). In this population, architectural elements such as lighting, acoustics, spatial configuration, and sensory stimuli are consistently associated with learning processes; however, empirical evidence quantifying these relationships through proxy informant instruments remains scarce, particularly in Latin American urban contexts with infrastructure gaps. This study addresses that gap by examining the association between neuroarchitecture, understood as evidence-based sensory design, and learning in children with ASD attending therapeutic centers in the district of Comas, Metropolitan Lima. A quantitative, non-experimental, cross-sectional, and correlational design was adopted. The final sample comprised 98 proxy informants, family members with daily and sustained contact with children with ASD, recruited from four therapeutic centers in Comas (zones 3, 5, 6, and 8), following an instrument validation process that included a pilot test with 15 additional participants. Data were collected through a structured questionnaire of 24 Likert-scale items with five response options, organized around two variables and six dimensions. Content validity was established through expert judgment by five architecture specialists. Construct validity was assessed using Exploratory Factor Analysis (KMO = 0.744; Bartlett’s sphericity test: ${\chi }^2$ = 665.96, df = 276, p < 0.001). Instrument reliability was confirmed through Cronbach’s alpha coefficient ($\alpha$ = 0.844). Given the non-normal distribution of the data across all constructs (Shapiro–Wilk, p < 0.05), Spearman’s rho coefficient was used for inferential analysis. A positive and statistically significant association was identified between neuroarchitecture and learning ($\rho$ = 0.599, p < 0.001). Dimensional analysis revealed a hierarchical pattern: the strongest association corresponded to cognitive processing ($\rho$ = 0.492, p < 0.001), followed by social interaction and communication ($\rho$ = 0.460, p < 0.001) and sensory regulation and adaptive behavior ($\rho$ = 0.460, p < 0.001). A total of 99.0% of proxy informants perceived adequate neuroarchitectural conditions and associated adequate learning outcomes. The findings confirm that neuroarchitectural design is significantly associated with the learning of children with ASD, with cognitive processing emerging as the dimension most sensitive to spatial conditions. The evidence supports the formulation of preliminary design orientations that prioritize sensory stimuli management, spatial legibility, and programmatic differentiation of therapeutic environments, in alignment with Sustainable Development Goals (SDGs) 4 and 11. Full article

The refurbishment and energy-efficient renovation of existing buildings, specifically those before 1945, with complex architectural building elements, require a level of building information that is often unavailable, incomplete or imprecise. As they represent a large percentage of current building stock (up to 25% of all buildings), it is crucial to address such an issue, as such buildings are ideal targets for renovation and energy retrofitting projects. This paper presents a conceptual pipeline, developed through the NERF2BIM research project, focusing on an Artificial Intelligence (AI)-supported holistic pipeline for the creation of as-is Building Information Modeling (BIM) models of existing buildings, expanding upon existing methodologies with the embedding of knowledge-driven semi-automatic detailing-on-demand task. The proposed pipeline integrates uncertainty-aware spatial capture, semantic interpretation and reconstruction, and knowledge-based reasoning within a BIM-oriented workflow. The paper provides an overview of current advancements in the respective aspects of the pipeline, highlighting current gaps. The proposed conceptual pipeline aims at addressing these issues through novel applications of AI and knowledge-driven solutions. Key contributions include: (1) a conceptual approach in addressing the imprecision of more sustainable data gathering approaches; (2) a context-aware BIM reconstruction process, providing multiple data output types; and (3) a formalization of architectural and construction knowledge and its utilization in a detailing-on-demand approach of the reconstructed BIM models. Through the integration of uncertainty-aware data gathering, context-aware reconstructions and domain expertise into existing reconstruction pipelines, the proposed pipeline bridges the data gap for existing buildings, enabling more efficient and knowledge-driven renovation processes. Full article

The Internet of Things (IoT) plays a significant role in digital health systems, supporting applications such as implantable devices, wearables, activity trackers and ingestibles. However, the rapid expansion of IoT networks has outpaced security measures, introducing significant security challenges. This scoping review examines the extent to which existing IoT security frameworks provide comprehensive coverage and identify key security elements that contribute to proactive defence in digital health environments. The specific research questions addressed are as follows: (1) Are there any IoT network security frameworks to provide comprehensive protection for IoT networks and digital health systems specifically? (2) What key security elements are currently used to develop a robust, proactive IoT security framework? Following the PRISMA-ScR guidelines, a systematic search of four databases was conducted to identify peer-reviewed studies from 2015 to 2024. This review included 255 studies, comprising 50 framework papers and 205 non-framework papers. The results reveal that some frameworks address discrete security areas but generally provide limited integrated security coverage. Key security elements identified include vulnerability assessments, threat modelling, feedback loops and the integration of artificial intelligence (AI) for proactive security measures in IoT networks. This review provides a structured synthesis, highlights research gaps and may inform the future development of more integrated and proactive security frameworks for IoT-enabled digital health systems. Full article

Background: Metallic orthopedic implants face persistent clinical challenges that have proved resistant to incremental conventional development. Machine learning and artificial intelligence offer a complementary paradigm for navigating the high-dimensional design spaces governing implant performance, yet the literature remains fragmented across disciplinary silos with no comprehensive synthesis spanning the full development pipeline. Methods: A structured database search of PubMed/MEDLINE, Embase, and Cochrane (executed May 2026), supplemented by hand-searching of reference lists, identified 33 primary studies organized across five sequential domains: alloy composition discovery, additive manufacturing process–property optimization, lattice and porous structure design, surface engineering and coatings, and corrosion and wear prediction. Results: Across all five domains, machine learning approaches, including random forests, convolutional neural networks, Bayesian optimization, generative adversarial networks, physics-informed neural networks, and autonomous multi-agent platforms, have accelerated property prediction and design space exploration beyond experimental or simulation-based methods. Shared barriers to translation include small, heterogeneous datasets, reliance on internal rather than external validation, limited interpretability, and the absence of regulatory frameworks for AI-assisted device design. Representative performance included modulus predictions within ~4 GPa of first-principles values, ML-designed alloys reaching ~42.7 GPa (versus 103–120 GPa for Ti-6Al-4V), property prediction R² often above 0.90 (up to 0.96–0.9991), 98.3% corrosion severity classification accuracy, and acceleration from a roughly fivefold reduction in finite element simulations to surrogates compressing days into minutes. Conclusions: Addressing these limitations will require open standardized databases linking materials parameters to registry-level clinical outcomes, prospective clinical validation studies, and coordinated engagement between researchers, industry, and regulatory agencies. Full article

This study develops the Socio-Technical Impact (ST-Impact) model, a sustainability-oriented curriculum design framework for management information systems (MIS) education, aimed at preparing future professionals to design and govern digital systems in socially and environmentally responsible ways. The rapid diffusion of emerging technologies, particularly generative AI, has intensified the need for MIS professionals who can integrate technical competence with social responsibility, ethical reasoning, responsible digital design, and public-value-oriented problem solving—competencies that remain unevenly addressed in existing MIS curricula. To address this gap, the study adopts a design science research approach, conducting a systematic review, reported in accordance with PRISMA 2020, and a design-oriented narrative synthesis of 120 international studies published between 2010 and 2025. Learning activities, student-produced artifacts, and assessment mechanisms were extracted as curriculum design units, while governance-related indicators were coded according to stakeholder requirements, accountability, equity, accessibility, privacy, safety, explainability, and sustainability. The resulting ST-Impact model comprises six iterative modules and a three-layer evaluation system. Its design coherence and practical plausibility are examined through an evidence-to-model traceability mapping, an illustrative comparative analysis of publicly visible curriculum structures, and an adaptable 15-week syllabus architecture. By translating abstract concepts of societal impact, responsible digital design, and digital sustainability into actionable curriculum design elements, this study contributes a literature-grounded foundation for future empirical validation in MIS education. Full article

Traditional “hórreos”, vernacular granaries widely distributed across northern Spain, constitute a highly valuable form of cultural heritage due to their historical, architectural, and ethnographic significance. However, their progressive deterioration, dispersion in rural contexts, and limited maintenance resources pose significant challenges for their long-term conservation. This research article explores the potential of the Historic/Heritage Building Information Modelling (HBIM) methodology as an innovative and effective tool for the documentation, analysis, conservation, and management of “hórreos” as cultural heritage assets. The study proposes an HBIM-based workflow adapted to the specific characteristics of “hórreos”, integrating data acquisition techniques such as laser scanning, photogrammetry, and historical archival research with parametric modelling of traditional construction elements. The resulting HBIM models are conceived not only as geometric representations, but as comprehensive digital repositories that store historical data, construction techniques, materials, conservation states, and recorded pathologies. The research analyses how HBIM supports decision-making in restoration planning and enables preventive maintenance strategies over time. Furthermore, the article discusses the role of HBIM in improving heritage management at a territorial scale, enabling standardised inventories and supporting institutional protection policies. The potential of HBIM for heritage dissemination, education, and digital preservation is also examined. The results highlight HBIM as a powerful and adaptable methodology that contributes to a more sustainable, informed, and holistic approach to the conservation of “hórreos”, enhancing both their physical preservation and their transmission as living cultural heritage. Full article

In 3D integrated circuits, a placement decision that looks acceptable from a 2D wirelength view can still create a local thermal or stress problem after stacking. This issue becomes more visible as the number of tiers and the density of vertical interconnects increase. We propose ThermIC, a placement framework that brings thermal and mechanical risk estimates into the placement loop rather than treating them only as post-layout checks. The novelty of ThermIC does not lie in treating graph neural networks, reinforcement learning, uncertainty-aware learning, or physics-informed regularization as individually new techniques. Instead, ThermIC contributes a placement-time coupling mechanism in which physically typed graph propagation, dense multi-constraint risk prediction, and action-level reinforcement learning feedback are jointly organized for stacked 3D-IC placement. ThermIC uses a heterogeneous graph encoder to carry thermal, stress, timing, and congestion information through the netlist; a constraint head to estimate local hotspot, stress-risk, timing-violation, and congestion probabilities; and a sequential placement policy trained with physics-informed penalties. We evaluate the method on ThermIC-Bench, a simulated corpus with more than 30,000 finite-element samples from 18 heterogeneous 3D-IC designs with 4–8 tiers. Because the present study does not include proprietary industrial circuits, silicon measurements, or a tape-out case, the experimental results are interpreted as simulation-based benchmark evidence rather than final industrial qualification. ThermIC connects the heat-kernel branch to the discretized heat-conduction equation and the stress-filter branch to linear thermo-elastic equilibrium, providing a mechanism-level basis for physical interpretability. The analysis distinguishes offline simulation/training cost from online deployment cost and reports complexity, runtime, and memory scaling for practical large-scale use. Under joint DRC, thermo-mechanical stress, and thermally coupled timing checks, ThermIC obtains an $82.1\%$ physical verification pass rate. The peak-temperature error is $3.1$ °C, the hotspot localization IoU is 0.89, and the number of placement-closure iterations is reduced by $3.7\times$ relative to the heuristic baseline. Together, these benchmark results indicate that early, differentiable multi-physics feedback can make 3D placement less dependent on late correction cycles. Full article