Search Results (195)

Rising carbon emissions from international road freight transport in the EU—increasing from 29.4% in 2023 to 31.4% in 2025 under the With Existing Measures (WEM) Road Transport scenario—necessitate the implementation of additional measures within the framework of the EU Carbon Border Adjustment Mechanism (CBAM). For Ukraine, operating under martial law and pursuing a post-war green recovery of its transport and trade sectors, the adoption of EU experience in distributed generation (DG) from renewable energy sources (RESs) is particularly critical. This study evaluates the synergy between energy-efficient and low-carbon management in logistics chains for road freight transportation in Ukraine, drawing on EU evidence of DG based on RESs. To this end, a decoupling analysis was conducted to identify the factors influencing low-carbon and energy-efficient management of logistics chains in Ukraine’s freight transport sector. Under wartime conditions, the EU practice of utilising electric vehicles (EVs) as an auxiliary source of renewable energy for distributed electricity generation within microgrids—through Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies—was modelled. The results confirm the relevance of RES-based DG and the integration of EVs as a means of enhancing energy resilience in resource-constrained and conflict-affected regions. The scientific novelty of this research lies in identifying the conditions for achieving energy-efficient and low-carbon effects in the design of logistics chains through RES-based distributed generation, grounded in circular and inclusive economic development. The practical significance of the findings lies in formulating a replicable model for diversifying low-carbon fuel sources via the development of distributed generation of electricity based on renewable resources, providing a scalable paradigm for energy-limited and conflict-affected areas. Future research should focus on developing innovative logistics chain models that integrate DG and renewable energy use into Ukraine’s transport system. Full article

Potatoes are the most extensively cultivated vegetable crop in Canada and rank as the fifth largest primary agricultural commodity. Given their diverse end uses and significant market value, particularly in processed forms, ensuring consistent quality from harvest to consumption is of critical importance. Total glycoalkaloids (TGA) are nitrogen-containing secondary metabolites that are known to accumulate in the tuber as an effect of greening in-field or elsewhere in the supply chain. In this study, 210 Yukon Gold (YG) potatoes were exposed to a constant light source to green over a period of 14 days and sampled in 7-day intervals. The samples were scanned using a short-wave infrared (SWIR) hyperspectral imaging camera in the 900–2500 nm wavelength range. Once individually scanned, pixel-wise spectral data was extracted and averaged for each tuber and matched with its respective ground truth TGA values which were obtained using a High-Performance Liquid Chromatography (HPLC) system. Prediction models using the partial least squares regression technique were developed from the extracted hyperspectral data and reference TGA values. Wavelength selection techniques such as competitive adaptive re-weighted sampling (CARS) and backward elimination (BE) were deployed to reduce the number of contributing wavelengths for practical applications. The best model resulted in a correlation coefficient of cross-validation (R²_cv) of 0.72 with a root mean square error of cross-validation (RMSE_cv) of 51.50 ppm. Full article

In the face of economic, social, and environmental pressures, the issue of sustainable development has garnered widespread attention in the Logistics Service Supply Chain (LSSC) with risk attitudes under Technical Output Uncertainty. In this regard, this paper first constructs an optimal emission reduction investment game model for an LSSC composed of Logistics Service Integrators (LSIs) and Logistics Service Providers (LSPs) against the backdrop of Technical Output Uncertainty. To this end, it quantifies the participants’ risk attitudes using a mean-variance model to analyze optimal emission reduction investment decisions for centralized and decentralized LSSC under different levels of risk tolerance. Subsequently, it designs a joint contract with altruistic preferences for sharing emission reduction costs in the LSSC. This contract analyzes the parameter constraints for achieving Pareto optimization within the supply chain. Finally, the study employs a case simulation to analyze the changes in expected revenues for centralized LSSC and joint contracts under different risk tolerance levels. The study reveals that (1) in a centralized LSSC, under risk-neutral attitudes, there exists a unique optimal emission reduction investment, which yields the highest expected return from emission reduction. However, under risk-averse attitudes, the expected return is always lower than the optimal expected return under risk neutrality. (2) In a decentralized LSSC, the emission reduction investment decisions of the Logistics Service Providers are similar to those in a centralized LSSC. (3) Under risk-neutral attitudes, the cost-sharing and altruistic preference-based joint contract can also coordinate the risk-averse LSSC under certain constraints, and by adjusting the cost-sharing and altruistic preference parameters, the expected returns can be reasonably allocated. Full article

Carbon emissions from human activities such as production and consumption have exacerbated climate deterioration. A common worldwide objective is to create a low-carbon economy by implementing carbon reduction measures in production, consumption, and other processes. To this end, this paper explores the production, price, carbon reduction rate, and profit or utility for a dual-channel low-carbon supply chain network (DLSCN) that includes numerous competing suppliers, manufacturers, risk-averse retailers, and demand markets under carbon trading. In order to create an equilibrium model for the DLSCN, risk-averse retailers are characterized using the mean-CVaR method, and each member’s optimal decision-making behavior is described using variational inequalities. A projection contraction algorithm is used to solve the model, and numerical analysis is presented to investigate how risk aversion, carbon abatement investment cost coefficients, and carbon trading prices affect network equilibrium. The results indicate that increasing retailers’ risk aversion can enhance supply chain members’ profits and carbon reduction rates. Retailers prioritize expected profits, while other members prefer them to focus more on CVaR profits. When retailers are more risk-averse and value CVaR, traditional retail channels become more popular. Increasing the carbon reduction investment cost coefficients for suppliers and manufacturers can boost their profits, and retailers also support this move to charge more for low-carbon products and enhance utility. When carbon trading prices rise, suppliers and manufacturers opt to increase carbon reduction rates to generate more profits from selling carbon allowances. This study provides decision-making references for achieving both economic and environmental benefits for members of DLSCN. Full article

Accurate demand forecasting is crucial for modern supply chain management, forming the foundation for inventory optimization, cost control, and service level improvement. However, demand time series data often exhibit high volatility and diverse patterns, further complicated by the rapid expansion and heterogeneity of data sources. These challenges can result in significant degradation in predictive accuracy when traditional models are applied to complex demand datasets. To address these challenges, this study proposes an end-to-end demand forecasting framework leveraging Variational Mode Decomposition (VMD) and attention mechanisms. The framework first employs VMD to decompose raw demand time series into multiple modes to extract hierarchical features, including trends, seasonal patterns, and short-term variations. Subsequently, an attention mechanism is introduced to dynamically capture and integrate demand sequences alongside contextual information, enhancing the focus on critical features and improving predictive performance. Experimental results demonstrate that the proposed method achieves superior predictive accuracy compared to conventional approaches, with a 37% reduction in Mean Absolute Error (MAE) relative to baseline models. This substantial improvement in demand forecasting accuracy provides actionable insights for decision-makers, enabling more efficient inventory control, production planning, and overall supply chain optimization. Full article

Lithium-ion batteries are a key technology in supply chains for modern electric vehicles. Their production is complex and can be prone to defects. As such, the detection of defective batteries is critical to ensure performance and consumer safety. Existing end-of-line testing relies heavily on electrical measurements for identifying defective cells. However, it is possible that not all pertinent information is encoded within the electrical measurements alone. Reversible expansion in lithium-ion cells is an indicator of lithiation within the cell, while irreversible expansion is a consequence of the ageing process; unexpected expansion may indicate the presence of undesirable defects. By measuring expansion in addition to electrical measurements, we aim to make better and faster quality predictions during end-of-line testing, thereby facilitating the early detection of potential defects. To make these predictions, we implement artificial intelligence algorithms to extract information from the measurements. Training these networks requires large training datasets, which are expensive to produce. In this paper, we demonstrate a first-order physical modelling approach for generating synthetic data to pre-train artificial intelligence algorithms that perform anomaly detection on lithium-ion battery cells at the end-of-line. The equivalent circuit model used to generate voltage curves could be fit to real data with a mean absolute error of less than 1%, and the expansion model could be fit to a mean absolute error of less than 2% of the measured values. By pretraining the artificial intelligence network using synthetic data, we can leverage existing physical models to reduce the amount of training data required. Full article

The problems created by conventional polymers after their end use have driven research into new biodegradable polymeric materials. PLA is a compostable polymer obtained from renewable sources, but its main drawbacks are its fragility and slow crystallization kinetics. These drawbacks limit its use in different applications. In order to overcome fragility, in the current study, different compositions of PLA/PCL blends, rich in PLA content and without and with DGEBA, were prepared and characterized by means of different techniques, such as FTIR, DSC, DMA, and the mechanical properties. Some compositions show a certain improvement in the deformation capacity compared to the neat PLA at a low test speed. However, when the test speed increases, no improvement is observed in terms of deformation capacity. By SEM, the morphology of injection-molded specimens was observed. All blends showed a biphasic morphology where the PCL droplets are dispersed within the continuous PLA matrix. In the current study, an attempt has been made to improve the compatibility and adhesion between the phases by incorporating a diglycidyl bisphenol A compound. The results obtained indicate that the epoxy groups seem to react with the end groups of the PLA chain; however, the interactions that it creates with the PCL phase are weak, which is in agreement with the FTIR and DSC results obtained. Full article

This study explores the relationship between forest culture (FC) and sustainable consumption by applying the means-end chain (MEC) theory. Compared with general products, FC products are consumed from a value consumption perspective, and their benefits have varying impacts on individuals and society. This study uses MEC theory to link the preferred attributes of FC with the expected benefits and pursued values (PVs) of FC. We (1) identified the indicators of the expected benefits of PVs and preferred activities (PAs) of FC through factor analysis, (2) examined the relationships between these factors using MEC theory, and (3) validated the factors through structural equation modeling (SEM). We surveyed 1700 Koreans to explore how FC benefits, values, and activities relate to consumer behavior. Factor analysis divided PVs into symbolic, social, and consumption values and PAs into tourism–exploration, cultural–artistic, and living–leisure activities of FC. According to SEM analysis, the contributing characteristics of FC affect the PVs and, in turn, the PAs of FC, yielding an acceptable model fit (GFI > 0.9). Thus, the concrete attitudes of consumers toward FC were categorized via abstract concepts, which influenced their practical and behavioral attitudes. In conclusion, FC products should be developed with a focus on value consumption. Full article

The waste generated during metal mining activities contains mixtures of heavy metals (HM) that are not biodegradable and can accumulate in the surrounding biota, increasing risk to human and environmental health. Plant species with the capacity to grow and develop on mine tailings can be used as a model system in phytoremediation studies. Dodonaea viscosa (L.) Jacq. is a shrub with wide geographical distribution and the ability to establish itself in mine tailings. The Sierra de Huautla Biosphere Reserve in Mexico contains a metallurgic district where mining activities have generated 780 million kg of waste with large concentrations of toxic heavy metals, mainly cadmium and lead. The present study evaluated the phytoremediation potential of D. viscosa in in situ conditions on soils contaminated with HMs (exposed) and reference sites (non-exposed) for one year. Also, the effects of cadmium (Cd) and lead (Pb) exposure in D. viscosa were analyzed via DNA damage (comet assay) morphological and physiological characters in exposed vs non-exposed individuals. The concentration of Cd and Pb was measured through atomic absorption spectrophotometry in the roots and leaves of plants. In total, 120 D. viscosa individuals were established, 60 growing in exposed and 60 in non-exposed soils. Exposed individuals of D. viscosa hyperaccumulated Cd and Pb in roots and leaves. At the end of the experiment, eight out of twelve characters under evaluation decreased significantly in HM-exposed plants in relation to individuals growing in non-exposed soils, except for stomatal index, stomatal coverage, and fresh leaf biomass. The micro-morphological and physiological traits of D. viscosa were not influenced by Cd and Pb bioaccumulation. In contrast, the bioaccumulation of Cd and Pb significantly influenced the macro-morphological characters and genetic damage; this last biomarker was 3.2 times higher in plants growing in exposed sites. The bioconcentration factor (BCF) of Cd and Pb in root and leaf tissue increased significantly over time. The mean BCF in root and leaf tissue was higher for Pb (877.58 and 798.77) than for Cd (50.86 and 23.02). After 12 months of exposure, D. viscosa individuals growing on mine tailing substrate showed that the total HM phytoextraction capacity was 7.56 kg∙ha⁻¹ for Pb and 0.307 kg∙ha⁻¹ for Cd. D. viscosa shows potential for phytoremediation of soils contaminated with Cd and Pb, given its capacity for establishing and developing naturally in contaminated soils with HM. Along with its bioaccumulation, biomass production, abundance, and high levels of bioconcentration factors, but without affecting plant development and not registering associated herbivores, it may incorporate HM into the trophic chain. Full article

Background/Objectives: The ketogenic diet (KD) is a dietary model that can impact metabolic health and microbiota and has been widely discussed in recent years. This study aimed to evaluate the effects of a 6-week KD on biochemical parameters, gut microbiota, and fecal short-chain fatty acids (SCFAs) in women with overweight/obesity. Methods: Overall, 15 women aged 26–46 years were included in this study. Blood samples, fecal samples, and anthropometric measurements were evaluated at the beginning and end of this study. Results: After KD, the mean body mass index decreased from 29.81 ± 4.74 to 27.12 ± 4.23 kg/m², and all decreases in anthropometric measurements were significant (p < 0.05). Fasting glucose, insulin, homeostasis model assessment of insulin resistance, hemoglobin A1C, urea, and creatinine levels decreased, whereas uric acid levels increased (p < 0.05). Furthermore, increased serum zonulin levels were noted (p = 0.001), whereas fecal butyrate, propionate, acetate, and total SCFA levels decreased (p < 0.05). When the changes in microbiota composition were examined, a decrease in beta diversity (p = 0.001) was observed. After the intervention, a statistically significant increase was noted in the Firmicutes/Bacteroidetes ratio (p = 0.001). Although Oscilibacter, Blautia, and Akkermensia relative abundances increased, Prevotella relative abundance and Bifidobacter abundance, which were the dominant genera before the KD, decreased. Moreover, the abundance of some pathogenic genera, including Escherichia, Klebsilella, and Listeria, increased. Conclusions: In healthy individuals, KD may cause significant changes in microbial composition, leading to dysbiosis and long-term adverse outcomes with changes in serum zonulin and fecal SCFA levels. Full article

Background: Chronic kidney disease (CKD) geriatric patients experience a premature aging process, compared with the general population of the same age and sex. The uremic milieu is capable of enhancing oxidative stress (OS) and microinflammation, leading to a pro-aging mechanism and an increased protein catabolism. Moreover, cognitive disorders are observed. Objectives: The aim of this pilot study was to evaluate the possible beneficial effects on the body composition, cognitive functions, inflammatory state and OS of CKD–geriatric patients induced by the consumption of two different plant-based bars (PBBs). Methods: A total of 20 male (mean age 73 ± 7 years) and 9 female patients (mean age 71 ± 4 years) were enrolled, divided as follows: 19 in the PBBs group (that consumed both bars) and 10 in the control group. They were monitored for 12 weeks. The PBBs presented a moderate caloric value and were enriched with waste and by-products of wine and olive oil supply chains and with organic saffron. Results and Conclusions: At the end of this study, the PBBs group, compared to the control group, showed an improvement in their body composition, detected by bioimpedance analysis and ultrasound examination, and in their cognitive function, revealed by mini-mental state examination. In the PBBs group, we also observed an OS reduction, through the free oxygen radical test. Full article

This article examines audiovisual ethnography as an anthropological method to understanding community cinema and youth participation through the audiovisual chain of a film piece, which encompasses the processes of watching, making, and exhibiting as an integrated sequence. This chain allows us to understand the dynamics of youth participation in all stages of audiovisual production, from creating an idea through production to the reception by the community. To this end, the relationship between participatory audiovisual practices and the promotion of youth mental health is explored through the case study “Aquí Nos Vemos Program”, which involves the creation of audiovisual narratives as a means of emotional and community expression. Fieldwork in Pudahuel, Santiago, Chile, provides ethnographic data on the creative audiovisual processes of young people around cinema and how these activities foster recognition and improvement of youth well-being. Full article

Background: Older people are more susceptible to deterioration of the gut microbiota. Prebiotics help improve the gut microbiota. Inulin, a major prebiotic, stimulates the growth of Bifidobacterium; however, it produces a large amount of gas, which leads to abdominal symptoms. Methods: In this study, in vitro fecal fermentation was performed using fecal samples from seven older people (mean subject age, 73.4 years; five men and two women) to examine whether combining inulin with another prebiotic material, resistant dextrin, could lead to decreased gas production and show prebiotic effects. Results: The Bifidobacterium counts and short-chain fatty acid production did not differ significantly between the inulin 0.5% group and the inulin 0.25% plus resistant dextrin 0.25% combination group. However, the inulin 0.25% plus resistant dextrin 0.25% combination group had lower gas production than the inulin 0.5% group (p < 0.10). Furthermore, compared with the inulin 0.5% group, the 0.25% combination group showed significantly greater gut microbiota diversity and tended toward a lower pH in the fermentation medium at the end of fermentation (p = 0.09). These effects are believed to be due to the combination of inulin, which is highly selective for Bifidobacterium and rapidly utilized by the gut microbiota, and resistant dextrin, which is slowly utilized by various bacterial genera. Conclusions: These findings suggest that the inulin plus resistant dextrin combination has superior prebiotic effects in older people and causes less gas production than inulin alone. Full article

Background: Disease-related malnutrition, with or without inflammation, in older adults is currently emerging as a public health priority. The use of Foods for Special Medical Purposes, including Oral Nutritional Supplements, and supplements is crucial to support patients in achieving their nutritional needs. Therefore, this article aims to comprehensively provide an analysis of the adequacy of FSMPs in meeting the nutritional requirements of different age-related diseases and takes into account the emerging role of inflammation. Moreover, it provides an identikit of the ideal products, following the pathology-specific guidelines. Methods: Data on 132 products were gathered through face-to-face meetings with companies’ consultants. Specifically, information on energy, macronutrient, and micronutrient contents were collected, as well as on texture and flavors, osmolarity, cost, and packaging. Results: Most FSMPs met the daily recommendations for energy and protein intake. Nonetheless, few products contained β-hydroxy-β-methylbutyrate, optimal Branched-Chain Amino Acids ratios, arginine, glutamine, and omega-3 fatty acids. Furthermore, a marked predominance of FSMPs with a high osmolarity (85.7%), sweet taste (72%), and only animal protein content (79.5%) was observed. Cost analysis of FSMPs revealed a mean cost of EUR 5.35/portion. Products were mostly adequate for cancer, neurodegenerative diseases, diabetes, inflammatory bowel disease, end-stage kidney disease, dysphagiam and chronic obstructive pulmonary disease. However, gaps have been found for sarcopenia and abdominal surgery. Conclusion: In light of the current market landscape, there is a need for a comprehensive regulation that indicates the optimal composition of FSMPs and the production of such products to tackle disease-related malnutrition. Full article

Background/Objectives: Despite efforts within the framework of the Sustainable Development Goal to end malnutrition by 2030, malnutrition and soil-transmitted helminth infections persist in sub-Saharan Africa. A significant barrier to success is the inadequate understanding of effective intervention methods. Most research on the gut microbiota’s role in health has been conducted in developed countries, leaving a critical gap in knowledge regarding low-income populations. This study addresses this gap by expanding research on the gut microbiota of underprivileged populations to help tackle these public health challenges. Methods: We employed 16S rDNA sequencing to assess the bacterial gut microbiota composition of 60 children (mean age: 26.63 ± 6.36 months) and their 58 mothers (mean age: 30.03 ± 6.31 years) in Pemba, with a focus on helminth infection and nutritional status. Results: Our differential abundance analysis identified bacterial taxa that were significantly negatively associated with both helminth infections and malnutrition, highlighting the potential for microbiota-directed interventions to address these health issues simultaneously. Notably, we identified Akkermansia, Blautia, Dorea, and Odoribacter as promising probiotic candidates for such interventions. In stunted children, positive co-occurrences were observed between Lactobacillus, Prevotella, and Bacteroides, while Escherichia/Shigella displayed negative co-abundance relationships with short-chain fatty acid (SCFA) producers in the gut microbiota. These findings suggest that administering Lactobacillus and SCFA-producing probiotics to children may foster the growth of beneficial bacteria like Prevotella and Bacteroides while reducing the relative abundance of Escherichia/Shigella, potentially enhancing overall health. Conclusions: This study underscores the importance of microbiota-directed interventions in children and women of reproductive age as promising strategies, alongside established approaches, for combating helminth infections and malnutrition in vulnerable populations. Full article