Search Results (166)

As tumor research has deepened, the deregulation of cellular metabolism has emerged as yet another recognized hallmark of cancer. Tumor cells adapt different biochemical pathways to support their rapid growth, proliferation, and invasion, resulting in distinct anabolic and catabolic activities compared with healthy tissues. Certain metabolic shifts, such as altered glucose and glutamine utilization and increased de novo fatty acid synthesis, are critical early on, while others may become essential only during metastasis. These metabolic adaptations are closely shaped by, and in turn remodel, the tumor microenvironment, creating favorable conditions for their spread. Anticancer metabolic strategies should integrate pharmacological approaches aimed at inhibiting specific biochemical pathways with well-defined dietary interventions as adjunctive therapies, considering also the role of gut microbiota in modulating diet and treatment responses. Given the established link between the consumption of foods rich in saturated fatty acids and sugars and an increased cancer risk, the effects of diet cannot be ignored. However, current evidence from controlled and multicenter clinical trials remains insufficient to provide definitive clinical recommendations. Further research using modern omics methods, such as metabolomics, proteomics, and lipidomics, is necessary to understand the changes in the metabolic profiles of various cancers at different stages of their development and to determine the potential for modifying these profiles through pharmacological agents and dietary modifications. Therefore, clinical trials should combine standard treatments with novel approaches targeting metabolic reprogramming, such as inhibition of specific enzymes and transporters or binding proteins, alongside the implementation of dietary restrictions that limit nutrient availability for tumor growth. However, to optimize therapeutic efficacy, a precision medicine approach should be adopted that balances the destruction of cancer cells with the protection of healthy ones. This approach, among others, should be based on cell type-specific metabolic profiling, which is crucial for personalizing oncology treatment. Full article

The need for integrated governance of water–energy–land–food (WELF) systems has become paramount in achieving sustainable low-carbon transitions, yet policy consistency across these interdependent sectors remains critically underexplored. This study presents the first systematic assessment of policy consistency and synergy within China’s WELF framework, employing an innovative mixed-methods approach that combines a modified Policy Modeling Consistency (PMC) Index with Content Analysis Methodology (CAM). Policy consistency follows a clear hierarchy: energy (PMC = 9.06, ‘Perfect’), water (8.26, ‘Good’), land (7.03, ‘Acceptable’), and food systems (6.91, ‘Acceptable’), with land–food policies exhibiting critical gaps in multifunctional design. Policy synergy metrics further reveal pronounced sectoral disparities: energy (PS = 0.89) and water (0.81) policies demonstrate strong alignment with central government objectives, whereas land (0.68) and food (0.64) systems exhibit constrained integration capacities due to uncoordinated policy architectures and competing sectoral priorities. Building on these findings, we propose three key interventions: (1) institutional restructuring through the establishment of an inter-ministerial coordination body with binding authority to align WELF sector priorities and enforce consistent and synergy targets, (2) the strategic rebalancing of policy instruments by reallocating fiscal incentives toward nexus-optimizing projects while developing innovative market-based mechanisms for cross-sectoral resource exchange, and (3) adaptive governance implementation through regional policy pilots, dynamic feedback systems, and capacity-building networks to enable context-sensitive WELF transitions while maintaining strategic consistency and synergy. These recommendations directly address the structural deficiencies in WELF governance fragmentation and incentive misalignment identified through our rigorous analysis, while simultaneously advancing theoretical discourse and offering implementable policy solutions for achieving integrated low-carbon transition. Full article

Eliminating all forms of poverty is a core component of the United Nations’ Sustainable Development Goals. At the household level, poverty and income inequality significantly threaten farmers’ sustainable development and food security. Based on a sample of 1234 kiwi farmers from the Shaanxi and Sichuan provinces in China, this paper empirically examines the impact of participation in agricultural industry organizations (AIOs) on household income and income inequality, as well as the underlying mechanisms. The results indicate the following: (1) Participation in AIOs increased farmers’ average household income by approximately 19,570 yuan while simultaneously reducing the income inequality index by an average of 4.1%. (2) Participation increases household income and mitigates income inequality through three mechanisms: promoting agricultural production, enhancing sales premiums, and improving human capital. (3) After addressing endogeneity concerns, farmers participating in leading agribusiness enterprises experienced an additional average income increase of 21,700 yuan compared to those participating in agricultural cooperatives. Therefore, it is recommended to optimize the farmer–enterprise linkage mechanisms within agricultural industry organizations, enhance technical training programs, and strengthen production–marketing integration and market connection systems, aiming to achieve both increased farmer income and improved income distribution. Full article

Optimizing the efficiency of fertilizer use is critical for sustainable maize production and food security, particularly in smallholder systems. Sub-optimal application rates pose a significant risk of soil nutrient depletion and low productivity. Split plot experiments were conducted across four locations in Ghana’s Guinea Savannah using seven maize varieties from three different maturity classes. The study assessed the response to nitrogen fertilizer applications (0, 60, 90, and 120 kg N ha⁻¹) regarding yield, Agronomic Efficiency (AE_N), Water Use Efficiency (WUE), and economic feasibility. Grain yields across locations and varieties demonstrated a strong linear response to nitrogen fertilization. The 90 kg N ha⁻¹ application generally produced the highest AE_N for all sites and varieties. Gross Revenue (GR) and WUE increased with higher N rates, with Value-to-Cost Ratios (VCR) consistently exceeding 2. Applying 90 kg N ha⁻¹ resulted in statistically similar Gross Revenues (GRs) to the 120 kg N ha⁻¹ fertilization. Different maturity classes significantly impacted fertilizer efficiency in semi-arid Ghana, with intermediate varieties outperforming extra-early ones. Though a 90 kg N ha⁻¹ rate was generally identified as the economically optimal rate of N fertilization for the locations, targeted fertilizer recommendations based on maize maturity groups and location are strongly advised. Full article

In a smart microgrid (SMG) system that deals with unpredictable loads and incorporates fluctuating solar and wind energy, it is crucial to have an efficient method for controlling frequency in order to balance the power between generation and load. In the last decade, cyberattacks have become a growing menace, and SMG systems are commonly targeted by such attacks. This study proposes a framework for the frequency management of an SMG system using an innovative combination of a smart controller (i.e., the Fuzzy Logic Controller (FLC)) with three conventional cascaded controllers, including Fractional-Order PI (FOPI), Tilt Integral Fractional Derivative (TID^μ), and Proportional Integral Derivative Acceleration (PIDA). The recently released Eel and Grouper Optimization (EGO) algorithm is used to fine-tune the parameters of the proposed controller. This algorithm was inspired by how eels and groupers work together and find food in marine ecosystems. The Integral Time Squared Error (ITSE) of the frequency fluctuation (ΔF) around the nominal value is used as an objective function for the optimization process. A diesel engine generator (DEG), renewable sources such as wind turbine generators (WTGs), solar photovoltaics (PVs), and storage components such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) are all included in the SMG system. Additionally, electric vehicles (EVs) are also installed. In the beginning, the supremacy of the adopted EGO over the Gradient-Based Optimizer (GBO) and the Smell Agent Optimizer (SAO) can be witnessed by taking into consideration the optimization process of the recommended regulator’s parameters, in addition to the optimum design of the membership functions of the fuzzy logic controller by each of these distinct algorithms. The subsequent phase showcases the superiority of the proposed EGO-based FFOPI-TID^μ-PIDA structure compared to EGO-based conventional structures like PID and EGO-based intelligent structures such as Fuzzy PID (FPID) and Fuzzy PD-(1 + PI) (FPD-(1 + PI)); this is across diverse symmetry operating conditions and in the presence of various cyberattacks that result in a denial of service (DoS) and signal transmission delays. Based on the simulation results from the MATLAB/Simulink R2024b environment, the presented control methodology improves the dynamics of the SMG system by about 99.6% when compared to the other three control methodologies. The fitness function dropped to 0.00069 for the FFOPI-TID^μ-PIDA controller, which is about 200 times lower than the other controllers that were compared. Full article

Introduction: People with disabilities are characterized by suboptimal health and lower self-rating health. Their need for health care is greater, they often have a higher prevalence of health problems and they have more difficulty accessing health care. The aim of this study was to assess the health behaviors and health indicators of 12–18-year-old young people with intellectual disabilities and autism spectrum disorder, and to explore their school-related perceptions in the Northern Great Plain region of Hungary. Materials and Methods: A cross-sectional questionnaire survey was conducted with the participation of 185 young people. A custom questionnaire was used, based on the Health Behavior in School-aged Children (HBSC) survey, assessing eating habits, oral care, physical activity, mental well-being, and self-reported health status. The sample was categorized into three groups: the ID1 (Intellectual Disability level 1) group, encompassing young individuals with mild intellectual disability; the ID2 group, encompassing young people with moderate intellectual disability; and the ID+ASD group, encompassing young individuals affected by both intellectual disability and autism spectrum disorder. Results: Consumption of various food types was below optimal levels. Low intake of fruits and vegetables was common, with only 21.6% of the respondents consuming fruit daily and 23.8% consuming vegetables daily. ID1 group reported significantly higher rates of nervousness several times a week (17.8% vs. 5.6% and 6.9%, p < 0.001), sleep difficulties (28.8% vs. 7.4% and 15.5%, p = 0.032), and dizziness (9.6% vs. 1.9% and 3.4%, p = 0.022) compared to the other two groups. A third school-related factor, related to negative emotions, showed a near-significant difference (p = 0.064), suggesting that students with both autism spectrum disorder and intellectual disability perceive lower levels of acceptance from teachers. On school-free days, computer usage was significantly highest in the ID+ASD group; 50% of them used a computer for at least 4 h per day. Conclusions: To improve mental well-being among affected children, psychological support and the implementation of mental health programs are recommended. In addition to teaching stress management techniques and coping mechanisms, integrating relaxation techniques into comprehensive developmental programs—both individually and in groups—is advised. For teachers, it is recommended to acquire disability-specific communication strategies. Full article

Background/Objectives: The majority of Canadian children bring a home-packed lunch to school, and previous research suggests lunches are of poor nutritional quality. This pilot study aimed to test the feasibility, acceptability, and preliminary impact of an eHealth family-based intervention designed to improve the nutritional quality of home-packed lunches. Methods: In this 12-week intervention, families (n = 20 parents with children aged 4–8 years) received a toolkit which included a cookbook on tips for preparing healthy lunches and 15 tested lunch box-friendly recipes, a lunch box, text messages, and an online cooking class. Feasibility was assessed via documentation of intervention delivery and participant retention rates. Acceptability was assessed via post-intervention surveys and semi-structured interviews in a sub-sample of parents (n = 9). Preliminary impact was assessed using 3-day lunch food records. Descriptive statistics were used to assess feasibility and acceptability, and Wilcoxon signed-rank tests were used to evaluate changes in the nutritional content of packed lunches. Results: Findings indicated a high retention rate (85%), and the majority (94%) of participants reported that the intervention was helpful and that they would recommend it to another parent. Qualitative interviews suggest parents found the recipes practical and diverse, the lunch box and the cooking class helpful, and some reported increased confidence and greater awareness of the foods being packed. No changes in the nutritional content of packed lunches were observed (n = 10 children). Conclusions: In summary, a home-packed lunchbox intervention is feasible and well accepted by families, but further refinements are needed to optimize its impact before a full-scale trial. Full article

The growing need for healthy and sustainable food alternatives has led to a rapid increase in vegan burgers on the market. Specifically, plant-based burgers using legumes as a protein substitute are amongst the most widespread choices for consumers. While these products can offer environmental benefits over traditional meat-based options, further optimization in both ecological and economic aspects can be achieved. This study conducted a life cycle assessment (LCA) and life cycle costing (LCC) analysis to evaluate and optimize the environmental and economic life cycle of a legume-based vegan burger. LCA was performed in accordance with the recommendations of the ISO 14040 and 14044 series, and ReCiPe 2016 Hierarchist served as the impact assessment methodology. For this purpose, a base case scenario, relying on imported raw materials and conventional packaging for a legume-based vegan burger, was established to serve as the comparison benchmark, and various alternative scenarios were examined, focusing on minimizing the distance between cultivation and processing areas for key legume ingredients and improving packaging materials. The results indicate that reducing transportation distances for raw ingredients and using bio-polyethylene packaging significantly enhance sustainability. Specifically, the legume-based vegan burger of the base case scenario had a carbon footprint of 1.30 kg CO₂ eq. and a total life cycle cost of EUR 2.43 per two pieces. In contrast, the optimized scenario, which incorporated shorter transportation distances and bio-polyethylene packaging, achieved a carbon footprint of 0.51 kg CO₂ eq. and a reduced cost of EUR 2.37. The findings of the present work highlight the potential for further environmental and economic improvements in vegan burger production through logistics optimization and selection of climate-friendly packaging solutions, thus contributing to sustainable development. Full article

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article

Background/Objectives: Type 2 diabetes (T2D) is a leading cause of morbidity and mortality worldwide and in Spain, particularly in the elderly population, affecting healthy ageing. Nutritional strategies are key to its prevention. The gut microbiota is also implicated in T2D and can be modulated by nutrition. We hypothesize that precision nutrition through microbiota modulation may help prevent T2D. This article aims to (1) describe a gut microbiota bacterial profile associated with T2D prevention, (2) provide precision nutrition tools to optimize this profile, (3) analyze how overweight influences the microbiota composition and precision nutrition response, and (4) address the technical challenges of microbiome-based precision nutrition clinical implementation to prevent T2D. Methods: A review of gut microbiota associated with T2D prevention was conducted. 13 healthy Spanish participants over 62 with optimal blood glucose levels (7 normal weight and 6 overweight) underwent a 3-month precision nutrition intervention to optimize T2D-preventive gut microbiota using a bioinformatics food recommendation system, Phymofood (EP22382095). Fecal microbiota was analyzed pre- and post-intervention using full-length 16S rRNA gene amplification, MinION sequencing, and NCBI taxonomic classification. Results: 31 potentially preventive bacteria against T2D were selected. The intervention increased the relative abundance of beneficial genera (Butyrivibrio and Faecalibacterium) and species (Eshraghiella crossota, and Faecalibacterium prausnitzii). The overweight influenced microbiota composition and intervention response. Conclusions: A gut microbiota profile associated with T2D prevention was identified, and precision nutrition could increase the relative abundance of beneficial bacteria. Confounding factors such as overweight should be considered when designing microbiome-based precision nutrition interventions. These results contribute to a better understanding of the microbiota associated with T2D prevention and address technical challenges for clinical implementation in future healthy ageing strategies. Full article

Background/Objectives: Canada’s 2019 Food Guide (CFG) encourages the increased consumption of plant-based foods as dietary protein sources. However, the nutritional implications of replacing animal-based proteins with plant-based alternatives in children’s diets remain unclear. This study aimed to examine the association between protein food sources and nutrient intake in Canadian children aged 9–18 years. Methods: We analyzed data from 2324 children from the 2015 Canadian Community Health Survey (CCHS), using the Public-Use Microdata File (PUMF) containing 24 h dietary recalls. Participants were categorized into four groups based on the proportion of protein from plant sources: Group 1 (0–24.9%), Group 2 (25–49.9%), Group 3 (50–74.9%), and Group 4 (75–100%). Nutrient intakes were compared and assessed against the Recommended Dietary Allowances (RDAs) and Adequate Intake (AI). Results: Groups 1 and 3 had less favorable macronutrient profiles than Group 2. A 3:1 animal-to-plant protein ratio (Group 2) aligned most closely with dietary recommendations. Groups 1 and 2 exceeded RDAs for protein, iron, vitamin B12, thiamine, riboflavin, niacin, vitamin B6, and zinc by over 146% (about four SDs above the mean requirement), suggesting a low risk of inadequacy, although saturated fat intake was high. The intakes of vitamin D and folate were below 66% of the RDA, while calcium and magnesium were below 100% in some subgroups, with probabilities of inadequacy of 0.93 and 0.31, respectively. Group 4 (2.71%) was too small for reliable analysis. Conclusions: An approximate 3:1 ratio of animal-to-plant protein sources may represent an optimal balance for supporting nutrient intake and improving macronutrient profiles in the diets of Canadian children. Full article

Shanxi Province, a key energy base and water source in China, has long borne the responsibility of supplying external resources. Ensuring the security of its water–energy–food (WEF) resilience has remained a persistent challenge for local authorities. Conventional WEF nexus optimization policies often overlook the heterogeneity of influencing factors arising from geographic variability, leading to generalized approaches that lack precision and efficiency in resource governance. To address these limitations, this study employed the Moran’s I index, exploratory regression analysis, and the geographically weighted regression (GWR) model to investigate the spatial patterns of factors influencing WEF resilience across 11 cities in Shanxi Province from 2014 to 2023. Based on these analyses, the study proposes targeted policy recommendations that account for regional heterogeneity and prioritize differentiated strategies, thereby avoiding the pitfalls of a one-size-fits-all framework. This tailored approach aims to support Shanxi in managing the enduring pressures of external resource supply. The main findings are as follows: (1) WEF resilience in Shanxi exhibited significant spatial autocorrelation, with Moran’s I values ranging from 0.013 to 0.043, confirming the influence of spatial geographic factors on the studied variables and supporting the applicability of the GWR model; (2) key factors influencing WEF resilience included population density, technological innovation, industrial structure, and resource mismatch, with effect sizes ranging from −0.90 to −0.48, 0.68 to 1.01, 0.43 to 0.79, and −0.45 to −0.22, respectively; (3) drawing on the spatially variable impact of these factors, the study offers optimization strategies that emphasize regional specificity and multi-policy prioritization to enhance WEF resilience across Shanxi Province. Full article

This study investigated the dynamic factors influencing food security in Saudi Arabia, a critical concern for the nation’s stability and development. The purpose of this research was to analyze the impact of several key determinants on the Food Security Index and to distinguish between their short-term and long-term effects, thereby providing evidence-based policy recommendations. Using annual time-series data spanning 1990 to 2023, the research employs the Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) methods. We specifically examined the roles of agricultural GDP contribution, agricultural insurance coverage, food price stability, government policies related to agriculture, climate change impacts, agricultural productivity, and technology adoption. Short-run estimates reveal that agricultural GDP contribution, government policies, and agricultural productivity express a significant positive influence on food security. Importantly, climate change showed a counterintuitive positive association in the short term, potentially indicating immediate adaptive responses. Conversely, food price stability exhibited an unexpected negative association, which may indicate that the index captures high price levels rather than just volatility. The long-run analysis highlights the crucial importance of sustained factors for food security. Agricultural GDP contribution, agricultural insurance coverage, and agricultural productivity are identified as having significant positive impacts over the long term. In contrast, climate change demonstrates a significant negative long-run impact, underscoring its detrimental effect over time. Government policies, while impactful in the short term, become statistically insignificant in the long run, suggesting that sustained structural factors become dominant. Granger causality tests indicate short-term causal relationships flowing from climate change (positively), agricultural GDP contribution, government policies, and agricultural productivity towards food security. The significant error correction term confirms the existence of a stable long-run equilibrium relationship among the variables. On the basis of these findings, the study concludes that strengthening food security in Saudi Arabia requires a multifaceted approach. Short-term efforts should focus on enhancing agricultural productivity and implementing targeted measures to mitigate immediate climate impacts and refine food price stabilization strategies. For long-term resilience, priorities must include expanding agricultural insurance coverage, investing in sustainable agricultural practices, and continuing to boost agricultural productivity. The study contributes to the literature by providing a comprehensive dynamic analysis of food security determinants in Saudi Arabia using robust time-series methods, offering specific insights into the varying influences of economic, policy, environmental, and agricultural factors across different time horizons. Further research is recommended to explore the specific mechanisms behind the observed short-term relationship with climate change and optimize food price policies. Full article

Quality control and predictive maintenance are two essential pillars of Industry 4.0, aiming to optimize production, reduce operational costs, and enhance system reliability. Real-time visual inspection ensures early detection of manufacturing defects, assembly errors, or texture inconsistencies, preventing defective products from reaching customers. Predictive maintenance leverages sensor data by analyzing vibrations, temperature, and pressure signals to anticipate failures and avoid production downtime. Image-based quality control has become critical in industries such as automotive, electronics, aerospace, and food processing, where visual appearance is a key quality indicator. Although advances in deep learning and computer vision have significantly improved anomaly detection, industrial deployments remain challenged by the scarcity of labeled anomalies and the variability of defects. These issues increasingly lead to the adoption of unsupervised methods and generative approaches, which, despite their effectiveness, introduce substantial computational complexity. We conduct a unified comparison of ten anomaly detection methods, categorizing them according to their reliance on synthetic anomaly generation and their detection strategy, either reconstruction-based or feature-based. All models are trained exclusively on normal data to mirror realistic industrial conditions. Our evaluation framework combines performance metrics such as recall, precision, and their harmonic mean, emphasizing the need to minimize false negatives that could lead to critical production failures. In addition, we assess environmental impact and hardware complexity to better guide method selection. Practical recommendations are provided to balance robustness, operational feasibility, and sustainability in industrial applications. Full article

Understanding the coupling mechanisms and coordinated development dynamics of the water–energy–food (WEF) system is crucial for sustainable river basin development. This study focuses on the Yellow River Basin, conducting a comprehensive analysis of the system’s coupling mechanisms and influencing factors. A structured evaluation framework is established, integrating the entropy weight–TOPSIS method, the coupling coordination degree model, and spatial correlation analysis. Empirical analysis is conducted using data from nine provinces (regions) along the Yellow River from 2003 to 2022 to assess the spatiotemporal evolution of the coupling coordination level. The Tobit regression model is employed to quantify the impact of various factors on the system’s coupling coordination degree. Results indicate that the comprehensive evaluation index of the WEF system in the Yellow River Basin exhibits an overall upward trend, with the system coupling degree remaining at a high level for an extended period, up from 0.231 to 0.375. The interdependence among the three major systems is strong (0.881–0.939), and while the coupling coordination degree has increased over time despite fluctuations, a qualitative leap has not yet been achieved. The evaluation index follows a spatial distribution pattern of midstream > downstream > upstream, characterized by a predominantly high coupling degree. However, the coordination degree frequently remains at a forced coordination level or below, with a general trend of midstream > downstream > upstream. From 2003 to 2008, a positive spatial autocorrelation was observed in the coupling and coordinated development of the WEF system across provinces, indicating a strong spatial agglomeration effect. By 2022, most provinces were clustered in “high-high” and “low-low” areas, reflecting a positive spatial correlation with minimal regional differences. Key factors positively influencing coordination include economic development levels, industrial structure upgrading, urbanization, and transportation networks, while technological innovation negatively affects the system’s coordination. Based on these findings, it is recommended to strengthen balanced economic development, optimize the layout of industrial structures, improve the inter-regional resource circulation mechanism, and promote the deep integration of technological innovation and production practices to address the bottlenecks hindering the coordinated development of the water–energy–food system. Policy recommendations are proposed to provide strategic references for the sustainable socioeconomic development of the Yellow River Basin, thereby achieving the high-quality coordinated growth of the water–energy–food system in the region. Full article