Search Results (559)

Hand hygiene (HH) is universally recognized as a pillar of infection prevention and control (IPC), both in hospitals and long-term care facilities such as nursing homes (NHs). An annual national survey based on the voluntary notification of purchased alcohol-based hand rubs (ABHRs) has been set up in France since 2019 to appraise the compliance to HH in NHs. The results pertaining to the Hauts-de-France (HdF) region were analyzed over the 2018–2023 period. The impact of factors such as the year, structure status, availability of an infection control link (ICL) nurse and/or an IPC team as well as constant participation in the survey was evaluated. A clear rise in the consumption of ABHRs was witnessed in 2020 followed by a progressive return to the 2018 baseline by 2023 for most NHs. This decrease was significantly less marked in NHs constantly participating in the survey. The ABHR indicator’s annual values were significantly higher in public NHs than in associative ones and in NHs with available ICL nurses/IPC teams. Finally, even though less than 50% of regional NHs reached the target of four daily ABHR uses per resident, they more frequently met it than French ones as a whole. This result underscores the need for ceaselessly reiterating the importance of HH and for pursuing training efforts in NHs with the help of ICL nurses and IPC teams. Full article

The development of information technology allows firms to access consumer purchase records, enabling them to distinguish between new and old consumers. Firms can then provide these groups with respective product pricing and promised delivery time. This paper develops a two-period dynamic model based on game theory to examine the effects of behavior-based pricing and behavior-based promised delivery time strategies on product price, promised delivery time, firm profits, consumer surplus, and social welfare under conditions where consumers exhibit time-sensitive preferences. We find, first, when firms opt to implement behavior-based pricing and promised delivery time strategies, they should offer lower (higher) prices and longer (shorter) delivery times to new (old) consumers. Second, the implementation of behavior-based pricing and promised delivery time strategies may decrease firm profits while enhancing consumer surplus. Third, when consumers exhibit stronger time-sensitive preferences, behavior-based pricing and promised delivery time strategies can enhance social welfare; conversely, they may have a detrimental impact on social welfare. Finally, we extend the model into three aspects—asymmetric strategy selection, firm logistics service costs, and myopic consumer behavior—to enrich our research and test the robustness of the model. The results of this paper supply managerial implications and theoretical references for firms’ strategic implementation and policy-making by relevant government departments. Full article

This work aimed to design a strategy for providing a collection service of urban solid waste in communities without it, using a case study in Sombrerete, Zacatecas, Mexico. The service is provided to the municipal seat and 17 of the 173 communities, resulting in a collection coverage of 10%. Information provided by the Cleaning Department of Sombrerete was collected and analyzed on the number of collection vehicles, communities served, and final waste disposal sites. Communities without urban solid waste collection and disposal services were identified. The strategy was designed to increase the collection coverage using geographic information systems, vehicle routing problem tools, and territory sectorization. Waste collection routes were developed for 11 sectors without service, and final waste disposal sites were evaluated based on environmental protection criteria of the Mexican Official Standard. The technical and economic feasibility of the strategy were analyzed. The results obtained were the design of the collection routes strategy to increase the coverage to 100% in Sombrerete. The designed strategy was feasible since it did not require the purchase of waste collection vehicles and hiring more staff. Approximately MXN 1000 (≈USD 54, EUR 47) in economic benefits were achieved weekly. Full article

Food security among college students is an increasing concern, with potential implications for their health, academic performance, and future well-being. This study investigated food security within a college community in Mexico, focusing on food availability, access (both economic and physical), and consumption patterns. A mixed-methods approach was employed at Ciudad Universitaria, BUAP, Mexico, between 2023 and 2024. Stratified random sampling was used, resulting in a final sample of 606 students. Data were collected through structured questionnaires covering sociodemographic characteristics and eating habits, the ELCSA, structured cafeteria observations, semi-structured interviews with key informants, and three focus groups. Statistical analysis was performed using chi-square tests (p < 0.05). Post hoc analysis with Bonferroni adjustment confirmed that origin (p = 0.0017), mode of transportation (p = 2.31 × 10⁻⁵) and private vehicles (p = 1.77 × 10⁻⁵) were the key determinants. Although the environment offered a variety of options, processed and ultra-processed products dominated the food choices. A total of 95.9% of students purchased food on campus, yet only 21.8% reported engaging in healthy eating habits. Focus groups revealed that students’ food choices were influenced by availability, access, and perceptions of affordability and convenience. These findings highlight the urgent need for targeted interventions to improve food security and promote healthier dietary practices within the college setting. Full article

The quantification of emotional value and accurate prediction of purchase intention has emerged as a critical interdisciplinary challenge in the evolving emotional economy. Focusing on Generation Z (born 1995–2009), this study proposes a hybrid algorithmic framework integrating text-based sentiment computation, feature selection, and random forest modeling to forecast purchase intention for therapeutic toys and interpret its underlying drivers. First, 856 customer reviews were scraped from Jellycat’s official website and subjected to polarity classification using a fine-tuned RoBERTa-wwm-ext model (F1 = 0.92), with generated sentiment scores and high-frequency keywords mapped as interpretable features. Next, Boruta–SHAP feature selection was applied to 35 structured variables from 336 survey records, retaining 17 significant predictors. The core module employed a RF (random forest) model to estimate continuous “purchase intention” scores, achieving R² = 0.83 and MSE = 0.14 under 10-fold cross-validation. To enhance interpretability, RF model was also utilized to evaluate feature importance, quantifying each feature’s contribution to the model outputs, revealing Social Ostracism (β = 0.307) and Task Overload (β = 0.207) as dominant predictors. Finally, k-means clustering with gap statistics segmented consumers based on emotional relevance, value rationality, and interest level, with model performance compared across clusters. Experimental results demonstrate that our integrated predictive model achieves a balance between forecasting accuracy and decision interpretability in emotional value computation, offering actionable insights for targeted product development and precision marketing in the therapeutic goods sector. Full article

This article examines the evolution of the most extensively researched subjects in e-mobility during the previous two decades. The objective of this analysis is to identify the lessons that the State of Kuwait, which is falling behind other nations in terms of e-mobility, can learn from in its efforts to adopt electric vehicles (EVs). To strengthen the body of knowledge and determine the most effective and efficient route to an “EV-ready” nation, the authors compiled data on the latest developments in the EV industry. A bibliometric analysis was performed on 3962 articles using VOSviewer software, which identified six noteworthy clusters that warranted further discussion. Additionally, we examined the sequential progression of these clusters as follows: (1) the environmental ramifications of electric mobility; (2) advancements in EV technology, including range extension and soundless engines, as well as the capital expenditure (CAPEX) and operating expenditure (OPEX) of purchasing and operating EVs; (3) concerns regarding the effectiveness and durability of EV batteries; (4) the availability of EV charging stations and grid integration; (5) charging time; and, finally, (6) the origin and source of the energy used in the development of e-mobility. Delineating critical aspects in the development of e-mobility can help to equip policymakers and decision makers in Kuwait in formulating timely and economical choices pertaining to sustainable transportation. This study contributes by cross-walking six global bibliometric clusters to Kuwait’s ten EV adoption barriers and mapping each to actionable policy levers, linking evidence to deployment guidance for an emerging market grid. Unlike prior bibliometric overviews, our analysis is Kuwait-specific and heat-contextual, and it reports each cluster’s size and recency to show where the field is moving. Using Kuwait driving logs, we found that summer (avg 43.2 °C) reduced the effective full-charge range by 24% versus pre-winter (approximately 244 km vs. 321 km), underscoring the need for shaded PV-coupled hyper-hubs and active thermal management. Full article

To address the shortcomings of poor convergence and the ease of falling into local optima when using the traditional gold rush optimization (GRO) algorithm to solve the complex scheduling problem of a combined cooling, heating, and power (CCHP) microgrid system, an optimal scheduling model for a microgrid based on the improved gold rush optimization (IGRO) algorithm is proposed. First, the Halton sequence is introduced to initialize the population, ensuring a uniform and diverse distribution of prospectors, which enhances the algorithm’s global exploration capability. Then, a dynamically adaptive weighting factor is applied during the gold mining phase, enabling the algorithm to adjust its strategy across different search stages by balancing global exploration and local exploitation, thereby improving the convergence efficiency of the algorithm. In addition, a weighted global optimal solution update strategy is employed during the cooperation phase, enhancing the algorithm’s global search capability while reducing the risk of falling into local optima by adjusting the balance of influence between the global best solution and local agents. Finally, a t-distribution mutation strategy is introduced to improve the algorithm’s local search capability and convergence speed. The IGRO algorithm is then applied to solve the microgrid scheduling problem, with the objective function incorporating power purchase and sale cost, fuel cost, maintenance cost, and environmental cost. The example results show that, compared with the GRO algorithm, the IGRO algorithm reduces the average total operating cost of the microgrid by 3.29%, and it achieves varying degrees of cost reduction compared to four other algorithms, thereby enhancing the system’s economic benefits. Full article

Considering the supply chain of new energy vehicles composed of a local manufacturer, an authorized distributor in the domestic market, and a competitive manufacturer in the export market, this paper studies three different cases of parallel export as well as their decisions about prices, sales scale, and the degree of production differentiation. Three game models are constructed and solved under the cases of no parallel exports (CN), authorized distributors’ parallel exports (CR), and third-party parallel exports (CT), respectively, and the equilibrium analysis is carried out, and finally, the influence of relevant parameters is explored through numerical simulation. It is found that (1) the manufacturer’s decisions on production and sales are influenced by the characteristics of consumer preferences in local and export markets, the cost of differentiated production, and the consumer recognition of parallel exports; (2) the manufacturers’ profits will always be damaged by parallel exports; (3) differentiated production can reduce the negative impact of parallel exports under certain conditions, and then improve the profits of manufacturers; (4) manufacturers can increase their profits by improving the purchase intention of consumers in the local market, improve the level of production differentiation in the export market, or reducing the cost of differentiation. Full article

Driven by carbon neutrality and peak carbon policies, hydrogen energy, due to its zero-emission and renewable properties, is increasingly being used in hydrogen fuel cell vehicles (H-FCVs). However, the high cost and limited durability of H-FCVs hinder large-scale deployment. Hydrogen internal combustion engine hybrid electric vehicles (H-HEVs) are emerging as a viable alternative. Research on the techno-economics of H-HEVs remains limited, particularly in systematic comparisons with H-FCVs. This paper provides a comprehensive comparison of H-FCVs and H-HEVs in terms of total cost of ownership (TCO) and hydrogen consumption while proposing a multi-objective powertrain parameter optimization model. First, a quantitative model evaluates TCO from vehicle purchase to disposal. Second, a global dynamic programming method optimizes hydrogen consumption by incorporating cumulative energy costs into the TCO model. Finally, a genetic algorithm co-optimizes key design parameters to minimize TCO. Results show that with a battery capacity of 20.5 Ah and an H-FC peak power of 55 kW, H-FCV can achieve optimal fuel economy and hydrogen consumption. However, even with advanced technology, their TCO remains higher than that of H-HEVs. H-FCVs can only become cost-competitive if the unit power price of the fuel cell system is less than 4.6 times that of the hydrogen engine system, assuming negligible fuel cell degradation. In the short term, H-HEVs should be prioritized. Their adoption can also support the long-term development of H-FCVs through a complementary relationship. Full article

In response to the increasing demand for environmentally friendly products and the parallel rise of deceptive green marketing practices, this study examines the impact of greenwashing awareness and green perceived benefits on consumers’ propensity to purchase green products, with a focus on the mediating role of green consumer confusion. Drawing upon data collected from 300 consumers in Greece through an online questionnaire, this study employed validated measurement scales and used multiple regression analyses to test its hypotheses. The findings reveal that both greenwashing awareness and green perceived benefits positively influence green purchase propensity. Additionally, green consumer confusion mediates the relationship between greenwashing awareness and green purchase propensity, indicating that the awareness of greenwashing reduces confusion and enhances consumers’ likelihood to choose genuinely green products. This study contributes to the literature by offering an integrated model that connects greenwashing awareness, green consumer confusion, and green perceived benefits in shaping green purchase propensity. Finally, the findings offer valuable insights for organizations to design clearer, more trustworthy green marketing strategies that minimize consumer confusion and foster informed green purchasing decisions. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. Full article

With the acceleration of global climate warming and agricultural modernization, the energy and carbon emission issues of agricultural parks (APs) have drawn increasing attention. An AP equipped with biogas-based combined heat and power (CHP) generation and photovoltaic systems serves as a prosumer terminal in a distribution network (DN). This paper introduces carbon emission flow (CEF) theory into the coordinated dispatch of APs and DNs. First, a CEF model for APs is established. Then, based on this model, a carbon–energy coordinated dispatch is carried out under bidirectional CEF interaction between the park and DN. A bidirectional carbon tax mechanism is adopted to explore the low-carbon synergy potential between them. Finally, the Stackelberg game approach is employed to address the pricing of electricity purchase/sale and carbon taxes in a DN, and the particle swarm optimization algorithm is used for rapid generating solutions. The case study shows that the proposed CEF model can effectively determine CEF distribution in the park. Moreover, the proposed bidirectional carbon tax mechanism significantly enhances the low-carbon economic benefits of both the AP and the DN. Full article

The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging. Full article

Preventing food waste is a pressing global policy concern, with households being the main producers of food waste along the food supply chain. This study aims to analyze consumers’ food waste behavior and identify how different consumer profiles and sociodemographic characteristics influence food waste. A survey was carried out in Spain with a representative sample of 717 participants, and the Theory of Planned Behavior (TPB) was applied to understand the influence of consumers’ attitudes, subjective norms, and perceived behavior control on their intention to reduce food waste and to find out the main drivers of their food waste behaviors. Results demonstrated that food waste reduction is mainly predicted by attitudes, followed by perceived behavior control, and lastly subjective norms. Finally, characteristics such as responsibility in food purchasing and cooking at home as well as sociodemographic factors played a relevant role in how much the intention to reduce food waste affects the final behavior. Our results suggest the potential of communication to reshape individual preferences towards valuing food conservation. Tailored strategies are recommended for specific groups, emphasizing the importance of targeted approaches in addressing food waste at various levels of society. Full article

In recent years, population growth, the enhancement of carbon emissions generation, and higher energy consumption have caused the movement to nearly zero-energy buildings. Additionally, the various strategies, phase change materials (PCMs) are suitable for reducing the energy consumption of a building. The focus of this study is to investigate the results of three scenarios that explore all the effective parameters for selecting a suitable Phase Change Material (PCM) for hot climate conditions in Saudi Arabia. The first scenario worked on choosing the best phase change material based on the climatic conditions and the selected area. To complete the optimization, the best thickness and placement of the two-layer phase change material were investigated in the second and third scenarios. The results indicated that optimized building using PCM 29 with 50 mm thickness reduced the energy consumption and carbon dioxide production by 20.72% and 21.05%, respectively. Furthermore, the outcomes of the study on two-layer phase change materials with different arrangements illustrated that the most proper location of PCMs caused 255.38 MWh of electricity consumption and 155.71 × 10³ kg of carbon dioxide production. Finally, as a novel integration, the results of using one-layer and two-layer PCM were added to the HOMER software to find the optimal hybrid energy systems. The findings showed that by integrating photovoltaic panels, diesel generation, batteries, and the grid, the cost of energy reached USD 0.162. Additionally, the grid purchase by using one-layer and two-layer phase change material was decreased by 21.2% and 24.3% compared to the base case. Full article