Search Results (595)

Global value chains (GVCs) generate rising volumes of electronic waste (e-waste), of which only 22.3% is formally collected and recycled, and operationalizing circular economy principles within GVCs requires reverse logistics networks that existing optimization models only partially capture. This paper develops a multi-echelon mixed-integer linear programming (MILP) model for integrated forward–reverse e-waste network design that jointly optimizes facility locations, material flows, hybrid distribution–collection co-location, and the collection price offered to consumers. Returns follow uniformly distributed consumer reservation prices, and the resulting price-dependent return mechanism is linearized exactly through a discrete price menu, yielding a fully linear formulation without big-M constants; recyclable fractions re-enter manufacturing as secondary inputs, closing the material loop. The model is evaluated on thirty randomly generated instances of three sizes, with parameter ranges anchored to the literature, solved with the open-source HiGHS solver; the largest instances solve to within 0.1% of optimality in under two minutes. Endogenizing the collection incentive raises total profit by 4.5 to 20.1% over an exogenous-return baseline and lifts material recovery from roughly 25% to 36 to 49%, while co-location adds modest, scale-dependent value and the two mechanisms show a directionally consistent but not statistically significant tendency toward substitutability (Wilcoxon signed-rank test, p > 0.05 across all size classes). These figures characterize the calibrated synthetic instances studied here and should not be read as generalizable empirical estimates. Sensitivity analyses identify consumer responsiveness to incentives, rather than waste stream quality, as the binding determinant of achievable recovery. Full article

Background/Objectives: This study examines the effectiveness of calorie labeling on restaurant menus by investigating how visual attention is allocated between calorie information and food cues. Although calorie labeling policies are widely implemented, prior research suggests limited impact on reducing energy intake. Methods: Using eye-tracking technology, this study explored how menu design factors (specifically visual food cues) influence attention. Results: Results from a within-subject experiment (N = 82) indicated that calorie labels received significantly fewer visual fixations (in terms of frequency and duration) when food images were present. Conversely, calorie labels did not reduce attention to food cues, highlighting an asymmetry in attentional allocations. Interestingly, restricted eaters were associated with more frequent attention to calorie labels but not with longer sustained attention to those labels. Conclusions: Overall, findings suggest that the motivational responses elicited by food cues undermine the effectiveness of calorie labeling by diverting attention away from nutritional information, limiting its utility as a public health intervention. Full article

Background/Aim: Traditional Portuguese cuisine is a key component of the country’s cultural identity. Understanding how traditional Portuguese dishes align with, or diverge from, the principles of a balanced and sustainable diet is essential for promoting healthy eating habits while preserving cultural heritage. The aims of this study were: (a) to evaluate and compare the nutritional composition of traditional Portuguese recipes from different regions; and (b) to assess the adequacy of complete regional menus in relation to recommendations for a balanced and healthy meal. Methodology: A pilot study was conducted analysing 60 traditional Portuguese recipes from three regions of mainland Portugal (North, Centre, and South). Twenty recipes per region were evaluated, including six first courses, eight main courses, and six desserts. Nutritional composition was determined per 100 g, and regional differences were analysed using one-way ANOVA with Tukey’s post hoc test (p < 0.05). Six complete menus were also constructed and assessed against reference values for energy and nutrient intake. Results: Significant differences were observed in the energy (p = 0.02) and sodium (p = 0.03) content of first-course dishes, particularly between the Northern region and the other regions. The Central region showed higher mean dietary fibre values. No statistically significant regional differences were identified for main courses or desserts. Menu analysis revealed that most traditional menu combinations exceeded recommended energy values and showed imbalances in macronutrient distribution, notably excessive saturated fat and insufficient dietary fiber. Conclusions: Traditional Portuguese first-course dishes display relevant regional nutritional differences. Overall, complete traditional menus frequently fall short of current nutritional recommendations, emphasizing the need for culturally sensitive adaptations that maintain culinary heritage while supporting public health goals, namely by lowering saturated fat and sodium content and enhancing dietary fibre levels. Full article

In Brazil, Community Restaurants (CRs) are regarded as an essential public initiative to expand food access for groups experiencing severe social vulnerability. This cross-sectional study analyzed the strategic effect of CRs as an indirect mechanism to ensure food availability in municipalities with more than 100,000 inhabitants, located in the North and Northeast regions of the country, using the CR evaluation matrix system of structural and process indicators as proxies to classify units as “not very effective,” “effective,” or “very effective,” rather than direct outcome impacts. Of the 94 CRs assessed (North: n = 23; Northeast: n = 71), most were rated as effective or very effective (95.6% in the North, 95.8% in the Northeast), with only one CR in Pará and three in the northeast (two in Bahia and one in Sergipe) classified as not very effective. The North region showed strong performance in financial resources, monitoring, location, physical structure, menu planning, incorporation of regional foods, volume of meals served to the attending public, and intersectoral actions. The Northeast excelled in financial resources, food security, physical structure, menu planning, and integration of regional foods, though it scored lower in education and volume of meals served to the attending public. This study represents the first structured application of the Oliveira matrix in the North and Northeast, providing new evidence on regional disparities and management challenges. These findings underscore CR’s strategic effectiveness in supporting regional efforts to improve food access and mitigate food and nutritional insecurity in historically vulnerable regions. However, outcome indicators such as dietary diversity, nutritional adequacy, and user health outcomes were not assessed. Future studies should incorporate these measures, alongside longitudinal designs and onsite evaluations, to better understand the broader impact of CRs on food and nutrition security. Full article

Proximity-based urban models, including the widely discussed 15 min city, aim to bring everyday destinations within a short walk, cycle, or shared-mobility trip, with promising benefits for well-being, public health, local economies, and low-carbon mobility. While the appeal of proximity is widely shared, far less is known about how to deliver and govern such neighbourhoods so that these benefits are realised, durable, and embraced by residents across the full range of their mobility needs. Implementation, public acceptance, and governance therefore remain comparatively under-examined. This article contributes an enabling governance approach for proximity-based, accessible neighbourhoods, centred on co-creation and on a rich, attractive menu of mobility options. Through a cross-disciplinary synthesis organised by the CalmMobility paradigm, a governance-posture lens that emphasises co-creation, sequencing, and readiness, it integrates evidence on proximity, accessibility, active travel, shared mobility, well-being, and low-carbon outcomes. The article argues that proximity and shared mobility widen the range of convenient and attractive options for everyday trips, and that this supports health, quality of life, and lower emissions. It also argues that these benefits are more likely to last under calm, co-created governance, which builds good options first and respects the freedom of residents to choose among all modes, including the car. To put the paradigm to work, the study introduces the Governance Compass and applies it to Paris, Barcelona, and Melbourne. The Compass was applied through a qualitative document analysis carried out by one analyst. It is offered as an exploratory tool that aims to make the reasoning open to inspection, not as a validated measuring instrument. Results are reported as ordinal bands, from Absent to Exemplary, and a 0 to 100 index is kept only as an illustration. The three cases show different patterns. In Paris and in the early Barcelona work, the documents set ambitious proximity goals but say much less about how change is paced, how residents shape it, and how choice is preserved, and these dimensions fall in the lower bands. In Melbourne, whose programme is still a pilot, the pattern is the reverse. The process dimensions are stronger there, but proximity itself is less developed. These bands describe the documents examined, not a fixed ranking of the three cities. Positioned as a constructive complement to the proximity-city agenda rather than a critique of it, the article closes with a cross-disciplinary research agenda spanning planning, transport, public health, and policy. Full article

Publicly available datasets containing Thai and English scene text captured under unconstrained real-world conditions remain limited, particularly for multilingual and mixed-script text recognition. To address this gap, the Thai–English Multiscript Text Image (TEMS) Dataset was developed as a publicly available resource for multilingual scene text recognition research. Natural scene photographs containing Thai and English textual content were collected using smartphone cameras from diverse environments, including billboards, commercial storefronts, road signs, menus, packaging, and publication covers. Text regions were manually identified, verified, and extracted to generate cropped text images with corresponding transcription labels. The resulting dataset comprises 5000 text images derived from 1625 natural scene photographs and contains Thai text, English text, mixed Thai–English text, numerals, punctuation marks, and special symbols spanning 161 unique character classes. The dataset exhibits substantial variation in text length, image dimensions, font style, text size, illumination conditions, spatial arrangement, and background complexity. Statistical analysis indicates an average text length of 15.8 characters per image. In addition, image widths range from 45 to 1512 pixels and image heights range from 18 to 275 pixels. The TEMS Dataset provides a publicly available multilingual scene text resource for optical character recognition, scene text recognition, document understanding, computer vision, pattern recognition, and multilingual artificial intelligence research. Full article

Higher Education Institutions (HEIs) are vital leverage points for climate action, yet the role of campus food environments in supporting these commitments is rarely assessed. This mixed-methods diagnostic study examined the food environment, sustainability literacy, behavioural barriers, and reported preferences regarding plant-based eating at a major European university. Across 29 campus canteens, plant-based meals accounted for only 17.2% of menu options and less than 1% of actual meal volume, with substantial inconsistency in availability and labelling across weekdays and canteens. Student survey findings (n = 198) revealed low food sustainability literacy: 64.7% of participants failed the assessment, particularly males (78.1%) and omnivores (75.7%). Among omnivore respondents, the most frequent barriers to adopting plant-based diets were ideological resistance, nutrition concerns, palatability, convenience, and cost. Students most strongly endorsed improved taste, more options (70.2%), and lower prices (62.1%) as strategies to encourage plant-based choices. Taken together, these findings highlight a structural implementation gap between institutional sustainability commitments and the campus food environment. They underscore the need for targeted interventions to improve food sustainability literacy and to increase the availability and attractiveness of plant-based options required for climate mitigation. To address this systemic challenge, this paper proposes a scalable HEI Food Transition Framework, informed by the results, that integrates choice architecture, curricular reform, inclusive provisioning, and data-driven accountability, offering a strategic pathway to align campus food environments with the United Nations Sustainable Development Goals. Full article

Background/Objectives: Commercial meal delivery diets are increasingly used as a convenient alternative to home-prepared meals. However, limited data are available regarding their mineral composition and potential exposure to toxic elements. This study aimed to evaluate the content of selected essential minerals (Ca, Cu, Fe, Mg, Se and Zn) and toxic elements (Cd and Pb) in daily food rations (DFRs) offered by selected commercial catering services in Poland. Methods: DFRs representing three dietary models (Hashimoto, DASH, and low-carb diets) were collected from commercial catering providers. Concentrations of essential minerals were determined using atomic absorption spectrometry (AAS), whereas toxic elements were determined using inductively coupled plasma mass spectrometry (ICP-MS). Mineral adequacy was assessed using Estimated Average Requirement (EAR) and Tolerable Upper Intake Level (UL) reference values. Exposure to toxic elements was evaluated using Estimated Daily Intake (EDI), estimated weekly Intake (EWI), Target Hazard Quotient (THQ), and Carcinogenic Risk (CR) indices. Results: Ca was the nutrient most frequently supplied in insufficient amounts, with 80–98% of analyzed meal plans failing to meet the EAR. In contrast, the remaining minerals were generally supplied in adequate amounts. Nevertheless, excessive intake of selected minerals was observed in some dietary models, with up to 37% of DASH diets exceeding the UL for Zn and approximately 32% of Hashimoto diets exceeding the UL for Cu. Although Cd and Pb were detected in all analyzed DFRs, THQ and CR values indicated negligible health risk. Conclusions: The analyzed meal delivery diets generally provided adequate amounts of most investigated minerals and did not pose a significant health risk related to Cd or Pb exposure. However, these findings apply only to the analyzed meal plans and should not be generalized to all commercial catering services or seasonal menu cycles in Poland. The widespread inadequacy of Ca intake and the occurrence of excessive Zn and Cu intake in selected dietary models highlight the need for improved nutritional quality control of commercially prepared diets. Full article

Background/Objectives: School feeding is a fundamental component of public policies aimed at promoting health, improving educational outcomes, reducing inequalities, and guaranteeing the human right to adequate food. This study aimed to evaluate the nutritional quality of school meals offered to public school students in a federal unit of Brazil, quantify the environmental impacts using carbon and water footprints, and simulate potential reductions through a strict vegetarian menu. Methods: This cross-sectional, descriptive study analyzed 130 daily menus (390 meals) from full-time public schools in the Federal District of Brazil in 2024. Nutritional quality was assessed based on energy, nutrients, food groups, degree of processing, and food origin. Carbon and water footprints were estimated using literature-based indicators. A nutritionally adequate strict vegetarian menu was then developed and compared with the observed menus. Results: The current menus presented good overall nutritional quality, with high food diversity and predominance of fresh or minimally processed foods. Most nutritional parameters met the recommended levels; however, protein and saturated fat exceeded the recommended limits. Animal-based foods accounted for most of the carbon and water footprints. The simulated strict vegetarian menu demonstrated significantly lower environmental impacts while maintaining nutritional adequacy. Conclusions: These findings highlight the importance of integrating nutritional and environmental strategies, such as a weekly “Meatless Monday” initiative alongside food and nutrition education, to improve student health outcomes and reduce the environmental burden of public school meals. Incorporating environmental sustainability criteria into school meal planning and public food procurement may advance nutritional quality, resource efficiency, and climate goals, positioning school feeding programs as strategic instruments for sustainable development. Full article

Commercial age-friendly modes are increasingly embedded in smartphone applications, yet their task-performance implications for older adults remain uncertain. This two-stage study used a cognitively informed ecological-to-controlled strategy linking evaluation of commercial age-friendly versions with controlled testing of specified interface components while modeling older adults’ task-relevant cognitive ability. In Experiment 1 (n = 22), older adults completed ten tasks in standard and age-friendly versions of WeChat and Pinduoduo. The age-friendly versions showed no overall advantage in clicks, completion time, or erroneous clicks (all ps ≥ 0.548). Activity Theory-informed video coding identified recurrent task-to-interface mismatches, including insufficiently salient feedback. The exploratory Task 8 product-search observations, together with these coded interaction problems, indicated that product search was a relevant context in which menu configuration, action confirmation, and cognitive demands could be examined together. Because the commercial app versions differed across content and interface features, Experiment 2 (n = 30) used a custom Android product-search task to manipulate menu configuration and vibration feedback while modeling delayed recall continuously as an indicator of task-relevant cognitive ability. Relative to the flat configuration, the two-level menu was associated with 56.7% fewer clicks and 30.7% shorter completion time, whereas vibration feedback was associated with 20.2% fewer ineffective clicks. Delayed recall was associated with completion time in the primary model, but this association was attenuated after adjustment for age and sex. Together, the findings show that a commercial age-friendly label should not be treated as evidence of performance benefit. By separating ecological diagnosis from controlled component testing, the study provides an evidence pathway for translating real-world human–computer interaction problems into testable, task-specific interface components and supports a cognitively informed, information-structure-prioritized, and multisensory approach to smartphone-based product-search design for older adults. Full article

Background: Equitable allocation of public health budgets across multiple intervention domains remains a major challenge in regional health governance. In Thailand’s Health Region 10, annual healthcare budgets must address diverse health burdens across several provinces, while current planning approaches rely on expert deliberation and historical precedent without systematic exploration of alternative allocation strategies. Public health resource allocation decisions are inherently multi-criteria, integrating health impact, cost-effectiveness, equity, disease severity, clinical and ethical priorities, feasibility, and alignment with national health policy agendas—dimensions that cannot be reduced to a single metric. This study introduces H-RL-MUSYA (Hierarchical Reinforcement Learning for Multi-Domain Unified System of Yielding Adaptive allocations), a decision-support framework designed to assist—not replace—public health practitioners by systematically generating and evaluating a menu of Pareto-efficient allocation strategies across four priority domains: nutrition, mental health, behavioral risk, and accident prevention. The framework explicitly acknowledges that DALYs averted and cost-effectiveness ratios are valuable but partial indicators, and that final resource allocation must integrate additional considerations—including underpinning health policies, priority population needs, feasibility, and contextual judgment—that lie beyond the model’s scope. Results: Applied to Thailand’s Health Region 10 (4.6 million inhabitants), H-RL-MUSYA identified 127 Pareto-efficient policies yielding a representative compromise allocation that averted 847,293 DALYs (34.1% improvement over historical allocations), improved cost-effectiveness by 31.3%, and reduced the health equity Gini coefficient from 0.243 to 0.187. A 12-month prospective pilot confirmed +23.1% composite health improvement with 91% stakeholder acceptance. Conclusions: H-RL-MUSYA demonstrates that AI-assisted policy exploration can meaningfully enrich public health decision-making by surfacing non-intuitive allocation strategies and quantifying equity–efficiency trade-offs, while human expertise, policy context, and democratic deliberation remain essential for final allocation decisions. Full article

Background/Objectives: Suboptimal dietary patterns are among the leading modifiable contributors to global morbidity and mortality, particularly in cardiovascular disease, type 2 diabetes mellitus (T2DM), obesity, metabolic syndrome, and hypertension. Digital nutrition platforms have emerged to improve adherence to evidence-based dietary strategies; however, many systems lack structured optimization, processing-aware nutrient profiling, and explainable artificial intelligence (AI) mechanisms. The integration of large language models (LLMs) into digital health introduces conversational personalization but also risks hallucination and unsafe outputs without constraint enforcement. This study aimed to describe the system development, architecture, database infrastructure, optimization algorithms, explainability enforcement, and digital health implications of NutriSteppe-AI, a chatbot-first LLM-driven system for personalized health menu generation constrained by deterministic nutrient logic and processing-aware scoring. Methods: NutriSteppe-AI integrates: (1) a multi-source structured nutrient database of 20,000 food products with up to 130 tracked nutrients; (2) energy requirement estimation using the revised Harris-Benedict equation; (3) linear programming-based multi-objective optimization; (4) a Healthy Food Index (HFI; 0.5–5.0 scale) incorporating NOVA processing classification penalties; (5) traffic-light nutrient gating; and (6) a constrained LLM orchestration layer governed by structured API contracts. Algorithmic validation was performed using 10,000 simulated user profiles spanning diverse age, anthropometric, activity, dietary exclusion, and budget parameters. Results: The system achieved 96.8% full constraint satisfaction with macronutrient mean absolute errors of 11.60% (energy), 18.86% (protein), 16.26% (fat), and 20.91% (carbohydrates). Incorporating NOVA processing penalties reduced ultra-processed food HFI scores by 0.73 points (p < 0.001). Median optimized menu HFI improved from 3.6 to 4.3. Median system latency was 1.8 s. Explainability validation confirmed 100% deterministic alignment with zero hallucinated numeric claims. Conclusions: NutriSteppe-AI demonstrates that LLM-driven nutrition chatbots can achieve deterministic, explainable, and clinically aligned performance when governed by structured optimization, processing-aware scoring, and explainability enforcement. This architecture provides scalable digital health infrastructure for cardiometabolic disease prevention in diverse populations. Full article

When a large language model (LLM) answers a question using retrieved documents, retrieval-augmented generation (RAG) is the standard approach. Retrieving more documents improves answer accuracy but increases cost and response time; retrieving fewer documents saves resources but may miss critical information. Most existing RAG systems sidestep this dilemma by applying the same retrieval setting to every query, regardless of how simple or complex the question is. This wastes budget allocation on easy questions and under-serves hard ones. This paper introduces Cost-Aware RAG (CA-RAG), a routing framework that solves this problem by treating each query individually. For every incoming question, CA-RAG selects the most suitable retrieval strategy from a fixed menu of four options, ranging from no retrieval to fetching the top $k=10$ most-relevant documents. The selection is driven by a scoring formula that balances expected answer quality against predicted cost and response time. The weights in this formula act as dials: adjusting them shifts the system toward speed, savings, or quality without any retraining. CA-RAG is built on Facebook AI Similarity Search (FAISS) for document retrieval, OpenAI gpt-4o-mini for generation, and text-embedding-3-small for dense retrieval embeddings. We evaluate CA-RAG on a benchmark of 28 queries. The router assigns different strategies to different queries, achieving 26% fewer billed tokens compared to always using heavy retrieval and 34% lower response time compared to always answering without retrieval, while maintaining answer-quality parity in both cases. Further analysis shows that most savings come from simpler queries, where heavy retrieval was unnecessary. All results are reproducible from logged comma-separated value (CSV) files. CA-RAG demonstrates that a small but well-designed set of retrieval strategies combined with lightweight per-query routing can meaningfully reduce the cost and latency of LLM deployments without compromising answer quality. Full article

Sustainable supply chains are increasingly vulnerable to systemic risks, such as geopolitical conflicts at critical trade routes like the Strait of Hormuz or climate disasters, which reveal deep Environmental, Social, and Governance (ESG) weaknesses. Conventional optimization often fails in these “deep uncertainty” contexts, where reliable historical data are often scarce and qualitative factors are paramount. This study introduces a simulation-optimization framework that reframes risk management as a decision process rather than a purely computational one. Portfolios are parameterized across five key characteristics—prevention, vulnerability, resilience, recovery, and detection—to enable a genetic algorithm (GA) to generate a diverse ensemble of high-performing strategies. Instead of providing one “best” answer, the GA allows managers to evaluate multiple options against quantitative tail-risk measures and qualitative institutional factors. The framework produces a “trade-off map,” or Pareto frontier, visualizing the cost of protecting against downside risks. By adjusting the GA’s settings, decision makers can toggle between improving current plans and exploring new, structurally different strategies. The numerical results demonstrate that the GA consistently identifies high-performing portfolios, achieving at least 99.55% of the true optimal performance across all metrics while requiring only 25% of the computational evaluation budget of an exhaustive search space. Furthermore, the framework successfully generates a structurally diverse menu of near-optimal alternatives across all performance metrics, consistently outperforming Monte Carlo sampling in the quality of near-optimal solutions identified, particularly for tail-risk measures such as conditional value-at-risk. Ultimately, this approach integrates the manager’s professional judgment regarding non-quantifiable factors, such as political stability and social responsibility, with simulation data to support the selection of a robust, sustainable portfolio. Full article

Background: The Nutrition Improvement Program has been implemented in China for over a decade; however, its assessment in Hubei Province has not yet been systematically reported. This study evaluated the implementation outcomes of the Rural Compulsory Education Students’ Nutrition Improvement Program in Hubei Province. Methods: A multi-stage cluster sampling design with stratification by school type was used. Standardized questionnaires were administered to schools, districts, and students to assess the food environment, nutrition policies, and dietary behaviors. Anthropometric and biochemical measurements were collected from all participants in 2014 and 2023. Results: A total of 24 schools across six counties in Hubei Province were surveyed at two time points, comprising 8619 students (4388 in 2014 and 4231 in 2023). Between 2014 and 2023, the proportion of students in the Nutrition Improvement Program reporting satisfaction with school meals increased significantly, and average nutritional knowledge scores improved. Nevertheless, several deficiencies persisted: menus remained poorly aligned with dietary guidelines, food variety was limited, full-time nutrition or health teachers were scarce, and absolute levels of nutritional knowledge remained low. In 2023, the prevalence of undernutrition, overweight/obesity, and anemia among program participants was 9.5%, 15.9%, and 12.2%, respectively. Compared with girls, boys had higher rates of undernutrition and overweight/obesity and a lower anemia rate; the same pattern was observed in primary school students relative to junior high school students. Compared with 2014, the nutritional profile shifted markedly (p < 0.001). Undernutrition and anemia declined by 7.4 and 4.9 percentage points, respectively, whereas overweight/obesity increased by 6.9 percentage points. Conclusions: From 2014 to 2023, students in the Nutrition Improvement Program of Hubei Province experienced observable changes in nutritional status: the primary nutritional concern had shifted from undernutrition to overnutrition, and the prevalence of anemia has generally decreased, while it has increased in some areas. However, given the observational nature of the repeated cross-sectional design, causal inference regarding the program’s impact was not supported. Full article