The world is currently experiencing the pandemic of coronavirus (CoV). In late 2019, the CoV infection began in Wuhan, Hubei, China. It had been originally called 2019 nCoV and it has been renamed CoVID-19 by the World Health Organization on February 2020. This epidemic began with animal-to-human infection, and the direct cause of death is generally due to ensuing severe atypical pneumonia. CoVID-19 has now been declared a pandemic by the World Health Organization, and people in all countries are under quarantine in order to reduce the spread of the virus, which then also lessens the impact on medical resources. Since quarantine is associated to the interruption of the work routine, this could be result in boredom. Boredom has been associated with a greater energy intake, as well as the consumption of higher quantities of fats, carbohydrates, and proteins [1]. Further, during quarantine continuously hearing or reading about the pandemic without a break can be stressful. Consequently, the stress pushes people toward overeating, mostly looking for sugary “comfort foods” [2]. This desire to consume a specific kind of food is defined as “food craving”, which is a multidimensional concept including emotional (intense desire to eat), behavioral (seeking food), cognitive (thoughts about food), and physiological (salivation) processes [3]. Of interest, a gender difference has been reported in food craving, with a higher prevalence in women than in men. Carbohydrate craving encourages serotonin production that in turn has a positive effect on mood. In a sense, carbohydrate-rich foods can be a way of self-medicating anti stress. The effect of carbohydrate craving on low mood is proportional to the glycemic index of foods. This unhealthy nutritional habit could increase the risk of developing obesity that beyond being a chronic state of inflammation, it is often complicated by heart disease, diabetes, and lung disease that have been demonstrated to increase the risk for more serious complications of CoVID-19 [4]. Quarantinerelated stress also results in sleep disturbances that in turn further worsen the stress and increase food intake thus giving rise to a dangerous vicious cycle. Therefore, it is important to consume food containing or promoting the synthesis of serotonin and melatonin at dinner. A considerable variety of plant species including roots, leaves, fruits, and seeds such as almonds, bananas, cherries, and oats contain melatonin and/or serotonin. These foods may also contain tryptophan, which is a precursor of serotonin and melatonin. Protein foods such as milk and milk products are the main sources of the sleep-inducing amino acid tryptophan. Moreover, tryptophan is involved in the regulation of satiety and caloric intake via serotonin that mainly lowers carbohydrate and fat intake, and inhibits neuropeptide Y, the most powerful hypothalamic orexigen peptides [5]. Further, beyond sleep-inducing properties, milk products such as yogurt could also augmented natural killer cell activity and reduce the risk of respiratory infections [6] During quarantine the increased intake of macronutrients could also be accompanied by micronutrients deficiency as occurs in obesity [7], which is commonly associated with impaired immune responses, particularly cell-mediated immunity, phagocyte function, cytokine production, secretory antibody response, antibody affinity, and the complement system, thus making more susceptible to viral infections [8]. Thus, during this time it is important to take care of nutritional habits, following a healthy and balanced nutritional pattern containing a high amount of minerals, antioxidants, and vitamins. Several studies reported that fruits and vegetables supplying micronutrients can boost immune function. This happens because some of * Giovanna Muscogiuri giovanna.muscogiuri@gmail.com
Hyunseo Koh, Chang-Yong Song, Youngjae Choi
et al.
Food cutting is a highly practical yet underexplored application at the intersection of vision and robotic manipulation. The task remains challenging because interactions between the knife and deformable materials are highly nonlinear and often entail large deformations, frequent contact, and topological change, which in turn hinder stable and safe large-scale data collection. To address these challenges, we propose a unified framework that couples a vision-language-action (VLA) dataset with a physically realistic cutting simulator built on the material point method (MPM). Our simulator adopts MLS-MPM as its computational core, reducing numerical dissipation and energy drift while preserving rotational and shear responses even under topology-changing cuts. During cutting, forces and stress distributions are estimated from impulse exchanges between particles and the grid, enabling stable tracking of transient contact forces and energy transfer. We also provide a benchmark dataset that integrates diverse cutting trajectories, multi-view visual observations, and fine-grained language instructions, together with force--torque and tool--pose labels to provide physically consistent training signals. These components realize a learning--evaluation loop that respects the core physics of cutting and establishes a safe, reproducible, and scalable foundation for advancing VLA models in deformable object manipulation.
David Hernandez-Cuellar, Krystel K. Castillo-Villar, Fernando Rey Castillo-Villar
Produce supply chains play a critical role in ensuring fruits and vegetables reach consumers efficiently, affordably, and at optimal freshness. In recent decades, hub-and-spoke network models have emerged as valuable tools for optimizing sustainable cold food supply chains. Traditional optimization efforts typically focus on removing inefficiencies, minimizing lead times, refining inventory management, strengthening supplier relationships, and leveraging technological advancements for better visibility and control. However, the majority of models rely on deterministic approaches that overlook the inherent uncertainties of crop yields, which are further intensified by climate variability. Rising atmospheric CO2 concentrations, along with shifting temperature patterns and extreme weather events, have a substantial effect on crop productivity and availability. Such uncertainties can prompt distributors to seek alternative sources, increasing costs due to supply chain reconfiguration. This research introduces a stochastic hub-and-spoke network optimization model specifically designed to minimize transportation expenses by determining optimal distribution routes that explicitly account for climate variability effects on crop yields. A use case involving a cold food supply chain (CFSC) was carried out using several weather scenarios based on climate models and real soil data for California. Strawberries were selected as a representative crop, given California’s leading role in strawberry production. Simulation results show that scenarios characterized by increased rainfall during growing seasons result in increased yields, allowing distributors to reduce transportation costs by sourcing from nearby farms. Conversely, scenarios with reduced rainfall and lower yields require sourcing from more distant locations, thereby increasing transportation costs. Nonetheless, supply chain configurations may vary depending on the choice of climate models or weather prediction sources, highlighting the importance of regularly updating scenario inputs to ensure robust planning. This tool aids decision-making by planning climate-resilient supply chains, enhancing preparedness and responsiveness to future climate-related disruptions.
Sotiroula Ioannidou, Sotiroula Ioannidou, Vassilis D. Litskas
et al.
Mixed orchards, planted with different species of tree crops, are a form of a traditional cropping system that has been practiced for millennia in the Mediterranean and provides the important ecosystem service of carbon sequestration. We used six allometric equations (M1-M6) based on existing literature and data from 49 orchards for estimating tree total biomass (TB) and carbon sequestration, based on C content of dry biomass. A species/geographically-specific equation (M1), a genus-specific (M2), a genus/geographically-specific forest equation (M3), two generalized forest allometric equations (M4 and M5) and a generalized agricultural landscape equation (M6) were compared and yielded an average of 15.42, 10.80, 11.39, 6.12, 6.66, and 9.88 Mg C ha−1, respectively. Organic and conventional orchards at the same productive stage did not differ significantly from each other in CO2 sequestration (CO2seq) per tree per year (10.42 and 10 kg CO2eq, respectively). Equation M1, was considered as the most representative (species and environment) for use in perennial Mediterranean orchards. The use of allometric equations is proposed as a simple, effective, and efficient method to estimate CO2 sequestration from mixed orchards using easily measurable biometric characteristics of the trees. The findings are important for the future estimation of CO2 stocks of agricultural landscapes.
Nutrition. Foods and food supply, Food processing and manufacture
This report presents the first documented application of fecal microbiota transplantation (FMT) for the management of extensive multi-food intolerance involving 52 specific foods in a pediatric patient with autism spectrum disorder (ASD). A 7 years-old autistic child was diagnosed with food intolerance to 52 items, presenting with generalized rashes, diarrhea, and malnutrition (BMI of 12.9) upon exposure or ingestion of the implicated foods. The child received oral fecal microbiota capsule treatment, with a daily dose of nine capsules (a total of 120 capsules per course) for two consecutive treatment courses. The rashes resolved, the child regained tolerance to previously intolerable foods, nutritional status improved, and stool consistency normalized. This case suggests that FMT may hold therapeutic potential for managing food intolerance in autistic patients.
IntroductionIn Ethiopia, acute malnutrition is one of the potential challenges to achieving the United Nations’ Sustainable Development Goals in reducing child mortality. Thus, this study aimed to determine factors associated with acute malnutrition among children aged 6–59 months attending public health facilities in Jimma town, South West Ethiopia, from March to December 2017.MethodsAn institution-based age-matched case–control study design was used. Two hundred and thirty-four children aged 6 to 59 months were randomly selected. Data were analyzed using SPSS version 20. Variables with a p-value of ≤0.25 in the bivariate analyses were entered into a multivariable regression analysis to determine the independent predictors of acute malnutrition.ResultsThis study showed that lack of maternal education (AOR = 4.08, 95% CI, 1.46, 11.40), poor child feeding (AOR = 5.97, 95% CI, 1.83, 19.44), low wealth index (AOR = 3.76, 95% CI, 1.24, 11.38), less hand washing (AOR = 5.57, 95% CI, 1.82, 16.97), exposure to diarrhea (AOR = 3.58, 95% CI, 1.15, 11.07), and bottle-feeding (AOR = 3.98, 95% CI, 1.29, 12.36) were significantly associated with acute malnutrition among children attending public health facilities in Jimma town.ConclusionThe findings of this study indicated that the sex of the child, family size, household wealth index, bottle-feeding, and maternal knowledge of child feeding were found to be independent predictors of acute malnutrition. Therefore, emphasis should be given to strengthening caregivers’ socioeconomic status and improving the knowledge of mothers regarding childfeeding practices.
Laura Rossi, Vincenza Gianfredi, Antonella Lezo
et al.
The Mediterranean Diet Guidelines (MDGs) represent a structured, evidence-informed effort to redefine and promote the Mediterranean Diet (MD) as a tool for health promotion and disease prevention. This work originated from a rigorous methodological process that integrates systematic reviews, expert consensus, and NUTRIGRADE-based evaluations to generate actionable recommendations. The updated definition of the MD extends beyond its nutritional aspects to embrace key sociocultural dimensions, such as culinary traditions, conviviality, and sustainability. This reconceptualization positions the MD as a holistic lifestyle model rather than a restrictive dietary pattern. It also reflects current scientific and public health priorities by explicitly excluding alcohol consumption, including red wine, from its core recommendations. This decision acknowledges growing evidence that even moderate alcohol intake increases the risk of cancer and cardiovascular disease. The MDGs addressed a broad spectrum of health outcomes. They highlighted varying degrees of positive association between adherence to MD and reduced incidence or mortality across different conditions. Adherence to the MD showed strong protective effects against cardiovascular and metabolic diseases. On the other hand, its association with oncological, neurocognitive, musculoskeletal, and autoimmune conditions appears to be positive, though supported by weaker evidence. Specific recommendations are tailored for different life stages and target groups, including children, pregnant women, older adults, and individuals with chronic conditions. Dissemination and implementation strategies emphasize integration into clinical care, schools, public policies, and digital health platforms. The MDGs provide a scalable and adaptable framework for national and regional adoption, promoting guideline harmonization. MD is thereby recognized as a pivotal public health tool in the face of global nutritional transitions and rising non-communicable diseases.
This paper investigates the optimal infrastructure planning and order assignment problem of an on-demand food-delivery platform with a mixed fleet of drones and human couriers. The platform has two delivery modes: (a) ground delivery and (b) drone-assisted delivery (i.e., air delivery). In ground delivery, couriers directly collect and transport orders from restaurants to destinations. For air delivery, the delivery process involves three legs: initially, a human courier picks up the order from the restaurant and transports it to a nearby launchpad, where personnel load the orders onto drones and replace batteries as needed. The loaded drone then transports the order from the launchpad to a kiosk, where another courier retrieves the order from the kiosk for final delivery. The platform must determine the optimal locations for launchpads and kiosks within a transportation network, and devise an order assignment strategy that allocates food-delivery orders between ground and air delivery considering the bundling probabilities of ground deliveries and the waiting times at launchpads and kiosks. We formulate the platform's problem as a mixed-integer nonlinear program and develop a novel neural network-assisted optimization method to obtain high-quality solutions. A case study in Hong Kong validates our model and algorithm, revealing that drone delivery reduces operational costs, minimizes courier fleet size, and increases order bundling opportunities. We also find that the expansion of air delivery services may entail larger delivery times due to the trade-off between the travel time savings induced by the faster air delivery and the associated detours incurred by intermodal transfer and extra waiting times at launchpads and kiosks, which crucially depends on the distance of the orders and the sequence of activating long-distance air delivery routes versus short-distance ones.
Karen Jia-Hui Li, Simone Balloccu, Ondrej Dusek
et al.
The increasing trust in large language models (LLMs), especially in the form of chatbots, is often undermined by the lack of their extrinsic evaluation. This holds particularly true in nutrition, where randomised controlled trials (RCTs) are the gold standard, and experts demand them for evidence-based deployment. LLMs have shown promising results in this field, but these are limited to intrinsic setups. We address this gap by running the first RCT involving LLMs for nutrition. We augment a rule-based chatbot with two LLM-based features: (1) message rephrasing for conversational variety and engagement, and (2) nutritional counselling through a fine-tuned model. In our seven-week RCT (n=81), we compare chatbot variants with and without LLM integration. We measure effects on dietary outcome, emotional well-being, and engagement. Despite our LLM-based features performing well in intrinsic evaluation, we find that they did not yield consistent benefits in real-world deployment. These results highlight critical gaps between intrinsic evaluations and real-world impact, emphasising the need for interdisciplinary, human-centred approaches.\footnote{We provide all of our code and results at: \\ \href{https://github.com/saeshyra/diet-chatbot-trial}{https://github.com/saeshyra/diet-chatbot-trial}}
This study introduces and evaluates Healthy Choice, an innovative theory-driven and AI-enhanced simulation platform designed to cultivate nutrition literacy through interactive scenario-based learning experiences. We collected feedback from 114 university students with diverse backgrounds who completed simulated product selection scenarios. Quantitative ratings of usefulness and ease of use demonstrated high user satisfaction.
M. C. Schoppema, B. H. M. van der Velden, A. Hürriyetoğlu
et al.
Since 2012, tetrodotoxin (TTX) has been found in seafoods such as bivalve mollusks in temperate European waters. TTX contamination leads to food safety risks and economic losses, making early prediction of TTX contamination vital to the food industry and competent authorities. Recent studies have pointed to shallow habitats and water temperature as main drivers to TTX contamination in bivalve mollusks. However, the temporal relationships between abiotic factors, biotic factors, and TTX contamination remain unexplored. We have developed an explainable, deep learning-based model to predict TTX contamination in the Dutch Zeeland estuary. Inputs for the model were meteorological and hydrological features; output was the presence or absence of TTX contamination. Results showed that the time of sunrise, time of sunset, global radiation, water temperature, and chloride concentration contributed most to TTX contamination. Thus, the effective number of sun hours, represented by day length and global radiation, was an important driver for tetrodotoxin contamination in bivalve mollusks. To conclude, our explainable deep learning model identified the aforementioned environmental factors (number of sun hours, global radiation, water temperature, and water chloride concentration) to be associated with tetrodotoxin contamination in bivalve mollusks; making our approach a valuable tool to mitigate marine toxin risks for food industry and competent authorities.
The urban habitat provides a landscape that increases the chances of human-animal interactions, which can lead to increased human-animal conflict, but also coexistence. Some species show high levels of socio-cognitive abilities that enable them to perceive communicational gestures of humans and use them for their own benefit. This study investigated the ability of Indian free-ranging dogs (Canis lupus familiaris) to utilise human social-referential cues (pointing and gazing) to locate hidden food, focusing on the relative effectiveness of unimodal versus multimodal cues. A total of 352 adult free-ranging dogs were tested in an object-choice task involving six different cue conditions: control (no cue), negative control (one baited bowl, no cue), combined pointing and gazing, pointing-only, gazing-only, and conflicting cues (pointing and gazing at opposite bowls). The dogs successfully chose the correct target only in the combined pointing and gazing condition, while performance under unimodal and conflicting cue conditions did not differ significantly from chance. This highlights the importance of signal redundancy and clarity in interspecific communication for this population. A dog's demeanor was a significant predictor of its willingness to engage: affiliative dogs were significantly more likely to succeed in the overall experiment and displayed a significantly shorter approach latency compared to anxious and neutral dogs. While demeanor affected the approach latency, it did not affect the accuracy of the choice, decoupling the dogs' personality from its cognitive ability to comprehend the clear cue. Neither the dogs' sex nor the experimental condition significantly predicted approach latency.
Daniel Jason Tan, Jiayang Chen, Dilruk Perera
et al.
ICU enteral feeding remains sub-optimal due to limited personalization and uncertainty about appropriate calorie, protein, and fluid targets, particularly under rapidly changing metabolic demands and heterogeneous patient responses. This study introduces DeepEN, a reinforcement learning (RL)-based framework that personalizes enteral nutrition (EN) dosing for critically ill patients using electronic health record data. DeepEN was trained on over 11,000 ICU patients from the MIMIC-IV database to generate 4-hourly, patient-specific targets for caloric, protein, and fluid intake. The model's state space integrates demographics, comorbidities, vital signs, laboratory results, and prior interventions relevant to nutritional management, while its reward function balances short-term physiological and nutrition-related goals with long-term survival. A dueling double deep Q-network with Conservative Q-Learning regularization is used to ensure safe and reliable policy learning from retrospective data. DeepEN achieved a 3.7 $\pm$ 0.17 percentage-point absolute reduction in estimated mortality compared with the clinician policy (18.8% vs 22.5%) and higher expected returns compared with guideline-based dosing (11.89 vs 8.11), with improvements in key nutritional biomarkers. U-shaped associations between deviations from clinician dosing and mortality suggest that the learned policy aligns with high-value clinician actions while diverging from suboptimal ones. These findings demonstrate the feasibility of conservative offline RL for individualized EN therapy and suggest that data-driven personalization may improve outcomes beyond guideline- or heuristic-based approaches.
The velocity jump found in the crack propagation for rubbers has been a powerful tool for developing tough rubber materials. Although it is suggested by a theory that the jump could be observed widely for viscoelastic materials, the report on a clear jump is very limited and, even in such a case, reproducibility is low, except for elastomers. Here, we use a mundane food-wrapping film as a sample and observe the crack propagation velocity with pulling the sample at a constant speed in the direction perpendicular to the crack. As a result, we find the jump occurs at a critical strain with high reproducibility. Remarkably, the plot of the crack-propagation velocity as a function of strain can be collapsed onto a master curve by an appropriate rescaling, where the master curve is found to be universal for change in the pulling speed and in the sample height. The result reveals a key parameter for the jump is the strain, suggesting the existence of a small length that governs the deformation along the crack. The present study sets limitations on future theories and opens an avenue for the velocity jump to become a tool for developing a wide variety of tough polymer-based materials.
Valeria Calcaterra, Hellas Cena, Vittoria Garella
et al.
Epicardial adipose tissue (EAT) stands out as a distinctive repository of visceral fat, positioned in close anatomical and functional proximity to the heart. EAT has emerged as a distinctive reservoir of visceral fat, intricately interlinked with cardiovascular health, particularly within the domain of cardiovascular diseases (CVDs). The aim of our overview is to highlight the role of EAT as a marker for cardiovascular risk in children. We also explore the influence of unhealthy lifestyle habits as predisposing factors for the deposition of EAT. The literature data accentuate the consequential impact of lifestyle choices on EAT dynamics, with sedentary behavior and unwholesome dietary practices being contributory to a heightened cardiovascular risk. Lifestyle interventions with a multidisciplinary approach are therefore pivotal, involving a nutritionally balanced diet rich in polyunsaturated and monounsaturated fatty acids, regular engagement in aerobic exercise, and psychosocial support to effectively mitigate cardiovascular risks in children. Specific interventions, such as high-intensity intermittent training and circuit training, reveal favorable outcomes in diminishing the EAT volume and enhancing cardiometabolic health. Future clinical studies focusing on EAT in children are crucial for advancing our understanding and developing targeted strategies for cardiovascular risk management in this population.
Hongquan Zhang, Hongquan Zhang, Lizhuo Zhao
et al.
Nutritional support is crucial for the prognosis of children supported by extracorporeal membrane oxygenation (ECMO). This article discusses the latest research progress and guideline recommendations for nutritional support during ECMO. We summarize the nutritional status and evaluation of ECMO patients, nutritional support methods and timing, trace elements, the impact of continuous renal replacement therapy (CRRT), and energy requirements and algorithms. The article shows that malnutrition is high in ECMO patients compared to other critically ill patients, with nearly one-third of patients experiencing a decrease in nutritional indicators. The timing of the initiation of nutrition is very important for the nutritional status of the child. Early enteral nutrition can improve patient prognosis, which is the most commonly used, with parenteral nutrition as a supplement. However, the proportion of enteral nutrition is relatively low, and a stepwise nutrition algorithm can determine when to initiate early enteral nutrition and parenteral nutrition. Malnourishment during critical illness have been associated with increased morbidity as well as increased mortality. Nutritional status should be evaluated at admission by screening tools. In addition, changes in the levels of several metabolites in vivo, such as blood lipids, carnitine, and thiamine, can also reflect the degree of nutritional deficiency in critically ill children. This article provides a reference for the implementation of nutrition of pediatric ECMO patients and further research on nutritional support.
Based on varieties, area and distribution of wheat in China, 1 377 newly harvested wheat samples from 12 provinces were collected in 2022. The physio-chemical quality as well as end-use quality evolutions were carried out, and the current quality status of wheat were analyzed. The results showed that the average crude protein content and gluten content of wheat were 13.1% and 29.6%, respectively, and the mean gluten index was 69. Among the rheological parameters of dough, the water absorption of flour was 63.2 mL, and the average stability time was 6.7 min. The mean tensile curve area, elongation and maximum tensile resistance were 75 cm2, 140 mm and 398 EU, respectively. All samples were evaluated for the processing quality of noodles and steamed bread, and samples with strong gluten wheat varieties and wet gluten content ≤25% were evaluated for the processing quality of bread and cake, respectively. The average scores of noodles, steamed bread, bread and cake were 79, 75, 73 and 69 points, respectively. Only 1.7% of the samples met the requirements of “High quality wheat-Strong gluten wheat” (GB/T 17892—1999), while 1.3% of the samples met the requirements of “High quality wheat-Weak gluten wheat” (GB/T 17893—1999). In addition, 44.9%, 31.4% and 2.8% of the samples met the requirements of quality classification, high-quality wheat and high-quality strong gluten wheat in “Quality classification of wheat varieties” (GB/T 17320—2013), respectively, and 56.0% and 3.0% of the samples met the requirements of quality classification and strong gluten hard wheat in “The Grain & Oil Products of China-Wheat” (LS/T 3109—2017), respectively. The results showed that the processing quality of wheat in China was general, and the food quality of steamed food was higher than that of baked food. A few varieties with excellent food processing quality bred were also planted.
Food processing and manufacture, Nutrition. Foods and food supply
This paper presents an extension of Keller's classical model to address the dynamics of long-distance trail running, a sport characterized by varying terrains, changing elevations, and the critical influence of in-race nutrition uptake. The optimization of the generalized Keller's model is achieved through rigorous application of optimal control theory, specifically the Pontryagin Maximum Principle. This theoretical framework allows us to derive optimal control strategies that enhance the runner's performance, taking into account the constraints imposed by the changing terrain, nutritional dynamics, and the evolving fatigue factor. To validate the practical applicability of the model, simulations are performed using real-world data obtained from various mountain races. The scenarios cover various trail conditions and elevation profiles. The performance of the model is systematically evaluated against these scenarios, demonstrating its ability to capture the complexities inherent in long-distance trail running and providing valuable insight into optimal race strategies. The error in the total race-time prediction is of the order of several percent, which may give the runner a reliable tool for choosing an optimal strategy before the actual race.
Hyung-il Ahn, Young Chol Song, Santiago Olivar
et al.
Successful supply chain optimization must mitigate imbalances between supply and demand over time. While accurate demand prediction is essential for supply planning, it alone does not suffice. The key to successful supply planning for optimal and viable execution lies in maximizing predictability for both demand and supply throughout an execution horizon. Therefore, enhancing the accuracy of supply predictions is imperative to create an attainable supply plan that matches demand without overstocking or understocking. However, in complex supply chain networks with numerous nodes and edges, accurate supply predictions are challenging due to dynamic node interactions, cascading supply delays, resource availability, production and logistic capabilities. Consequently, supply executions often deviate from their initial plans. To address this, we present the Graph-based Supply Prediction (GSP) probabilistic model. Our attention-based graph neural network (GNN) model predicts supplies, inventory, and imbalances using graph-structured historical data, demand forecasting, and original supply plan inputs. The experiments, conducted using historical data from a global consumer goods company's large-scale supply chain, demonstrate that GSP significantly improves supply and inventory prediction accuracy, potentially offering supply plan corrections to optimize executions.