Search Results (326)

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies. Full article

The icebreaking process of water-exiting vehicles involves complex nonlinear interactions as well as multi-physical field coupling effects among ice, solids, and fluids, which poses enormous challenges for numerical calculations. Addressing the low solution accuracy of traditional grid methods in simulating large deformation and destruction of ice layers, a numerical model was established based on the FEM-SPH-SALE coupling algorithm to study the dynamic characteristics of the water-exiting vehicle on the icebreaking process. The FEM-SPH adaptive algorithm was used to simulate the damage performance of ice, and its feasibility was verified through the four-point bending test and vehicle breaking ice experiment. The S-ALE algorithm was used to simulate the process of fluid/structure interaction, and its accuracy was verified through the wedge-body water-entry test and simulation. On this basis, numerical simulations were performed for different ice thicknesses and initial velocities of vehicles. The results show that the motion characteristics of the vehicle undergoes a sudden change during the ice-breaking. The head and middle section of the vehicle are subject to greater stress, which is related to the transmission of stress waves and inertial effect. The velocity loss rate of the vehicle and the maximum stress increase with the thickness of ice. The higher the initial velocity of the vehicle, the larger the acceleration and maximum stress in the process of the vehicle breaking ice. The acceleration peak is sensitive to the variation in the vehicle’s initial velocity but insensitive to the thickness of the ice. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), and a survey of full- and part-time drivers (n = 436), to examine electric vehicle (EV) adoption attitudes and policy preferences. Access to home charging and prior EV experience emerged as the most statistically significant predictors of EV acquisition. Socio-demographic variables, particularly income and age, could also influence the EV choice and sensitivity to policy design. Full-time drivers, though confident in the EV range, were concerned about income loss from the charging downtime and access to urban fast chargers. They showed a greater interest in EVs than part-time drivers and favored an income-based instant rebate at the point of sale. In contrast, part-time drivers showed greater hesitancy and were more responsive to vehicle purchase discounts (price reductions or instant rebates at the point of sale available to all customers) and charging credits (monetary incentive or prepaid allowance to offset the cost of EV charging equipment). Policymakers might target low-income full-time drivers with greater price reductions and offer charging credits (USD 500 to USD 1500) to part-time drivers needing operational and infrastructure support. Full article

Background/Objectives: The urine sodium/potassium (Na/K) ratio can potentially be used to detect dietary habits that contribute to hypertension. In this prospective cohort interventional trial, we aimed to verify whether private insurance sales staff can help clients change their lifestyle habits based on their urinalysis results. Methods: Clients of the life insurance company (20–65 years old) who were considered to have “high risk” lifestyle factors, which was defined as having high values for two or more of the following indicators: body mass index, blood pressure, triglycerides, liver enzymes, and glucose metabolism, were included. The clients were randomly assigned to three groups: a face-to-face (FF) intervention by sales staff (n = 83), non-FF (Non-FF) intervention via a social networking service (n = 87), and no intervention (Control) (n = 58). Urinalysis and surveys about diet and exercise habits were conducted before and after a 3-month interventional period in all groups. Three interventions were performed for the FF and Non-FF groups, including dietary advice based on urinalysis results, education encouraging reduced salt intake and increased locomotor activity, and viewing an educational video. The Control group only received their urinalysis results by mail. Results: The participants’ mean age was 44.0 years old. Significant improvements in estimated potassium intake were observed in the Non-FF group, and significant reductions in urine Na/K ratios were noted in both the FF and Non-FF groups. Multiple logistic regression analysis indicated that watching the video was the most effective factor for decreasing the urine Na/K ratio (odds ratio = 1.869). The total points for dietary behavior, based on the questionnaire, significantly improved among the individuals who watched the video. Conclusions: This study demonstrates the potential for private health insurance companies to contribute to health promotion and introduces a novel strategy for improving lifestyle habits among individuals at high risk of lifestyle-related diseases. Full article

As an emerging vegetable cultivation technology, plug seedling cultivation significantly improves seedling production efficiency and reduces costs through standardization. Grading and transplanting, as the final step before the sale of plug seedlings, categorizes seedlings into different grades to ensure consistent quality. However, most current grading methods can only detect seedling emergence but cannot classify the emerged seedlings. Therefore, this study proposes an intelligent grading method for pepper plug seedlings based on RGB and point cloud images, enabling precise grading using both RGB and 3D point cloud data. The proposed method involves the following steps: First, RGB and point cloud images of the seedlings are acquired using 2D and 3D cameras. The point cloud data is then converted into a 2D representation and aligned with the RGB images. Next, a deep learning-based object detection algorithm identifies the positions of individual seedlings in the RGB images. Using these positions, the seedlings are segmented from both the RGB and 2D point cloud images. Subsequently, a deep learning-based leaf recognition algorithm processes the segmented RGB images to determine leaf count, while another deep learning-based algorithm segments the leaves in the 2D point cloud images to extract their spatial information. Their surface area is measured using 3D reconstruction method to calculate leaf area. Additionally, plant height is derived from the point cloud’s height data. Finally, a classification model is trained using these extracted features to establish a grading system. Experimental results demonstrate that this automated grading method achieves a success rate of 97%, and compared with manual methods, this method has higher production efficiency. Meanwhile, it can grade different tray seedlings by training different models and provide reliable technical support for the quality evaluation of seedlings in industrialized transplanting production. Full article

Vegetables are commodities frequently sold in local markets and have been associated with foodborne outbreaks in short and local supply outlets worldwide. These outbreaks could potentially be mitigated through the implementation of good handling practices (GHPs) at points of sale. Numerous studies have assessed microbiological contamination in small-scale vegetable outlets in developing countries. In contrast, research on these risks in developed countries is comparatively scarce. However, with the increasing demand for vegetables, along with the increasing popularity of local markets, there is potential for an increase in foodborne outbreaks in developed countries. This study aimed to perform a microbiological assessment in local and short supply chain outlets of farmers in Portugal, as a case study, and to observe behaviors regarding GHPs in these outlets. The study was performed before and after the implementation of improved GHPs. This research employed quantitative analysis to measure the microbial load on vegetables, bench surfaces, and vendors’ hands. Additionally, a qualitative analysis was conducted to understand farmers’ behavior regarding GHPs using observational methods. Microbial hazards were detected in vegetables, on surfaces, and on hands both before and after the implementation of these practices, although the implementation of GHPs reduced the number of contaminations potentially associated with the practices used at the outlets. The results of this study highlight the importance of implementing GHPs in local and short supply chain markets for vegetables and fruits in developed countries, not only to protect consumers’ health, but also the farmers’ businesses. Full article

During farm-to-consumer transport, a live supply chain can aid in maintaining the quality of whiteleg shrimp (Penaeus vannamei). However, the changes in muscle quality and gut microbiota of shrimp in the live supply chain and their interactions are poorly understood. Here, we investigated the dynamics of cumulative survival, muscle quality, and gut microbiota in the key phases of the live shrimp supply chain: post-harvest, post-transport, post-respite, and simulated sales [ambient temperature (AT; 29 °C ± 0.3 °C); low temperature (LT; 23 °C ± 0.3 °C)]. The results suggest that among the various stages, the highest mortality (12%) occurred after transport, while the respite process was associated with enhanced gut-mediated stress resilience. Notably, the transport, 24 h sales, and 40 h sales stages were identified as three potential critical control points. Furthermore, the LT group exhibited an 8% higher survival rate, better quality parameters (34.9% higher hardness), increased abundance of Bacteroidetes (from 3.63% to 7.39%), and a reduced F: B ratio. Correlation analysis identified Xanthomonadales and Oscillospirales as potential biomarkers for maintaining quality, positively linked to survival, muscle hardness, and brightness. Our findings provide valuable insights into optimizing control strategies and microbial biomarkers for enhancing muscle quality in live supply chains and aquaculture. Full article

Energy communities led by local citizens are vital for achieving the European energy transition goals. This study examines the design of a regional energy community in a rural area of Spain, aiming to address the pressing issue of rural depopulation. Seven villages were selected based on criteria such as size, energy demand, population, and proximity to infrastructure. Three energy valorization scenarios, generating eight subscenarios, were analyzed: (1) self-consumption, including direct sale (1A), net billing (1B), and selling to other consumers (1C); (2) battery storage, including storing for self-consumption (2A), battery-to-grid (2B), and electric vehicle recharging points (2C); and (3) advanced options such as hydrogen refueling stations (3A) and hydrogen-based fertilizer production (3B). The findings underscore that designing rural energy communities with a focus on social impact—especially in relation to depopulation—requires an innovative approach to both their design and operation. Although none of the scenarios alone can fully reverse depopulation trends or drive systemic change, they can significantly mitigate the issue if social impact is embedded as a core principle. For rural energy communities to effectively tackle depopulation, strategies such as acting as an energy retailer or aggregating individual villages into a single, unified energy community structure are crucial. These approaches align with the primary objective of revitalizing rural communities through the energy transition. Full article

Everything as a Service (XaaS) is commonly understood as the general tendency to replace sales contracts with service contracts. Most research in this area agrees that XaaS is a trend, but it points to many drivers. It could be strategies improving customers’ expense model, servitization strategies, customer feedback, mass customisation, and machine learning. However, we do not find contributions considering the relationship between XaaS and economies of scale. When sales contracts are replaced by service contracts, ownership is elevated from the customer to the provider. Thus, possible benefits from economies of scale linked to the ownership of products are then also elevated from the customer to the provider. In this article, we consider the claim that economies of scale may be an underlying driver of the XaaS trend. A review of 140 firms shows that the products with the greatest potential for economies of scale are the ones most frequently provided as a service. This suggests that economies of scale linked to ownership are an underlying driver of XaaS. Thus, ownership-related economies of scale may be a predictor of XaaS. Full article

(1) Background: The survival and growth of startups in their early stages are negatively impacted by the lack of financial sustainability and scarce resources that entrepreneurs face during the first 5 years. This is known as the Valley of Death (VoD). This study seeks to identify key factors that influence the financial sustainability of startups during the VoD, which demands a significant amount of funding and government support. (2) Methods: A quantitative methodology was employed, based on a worldwide literature review that pointed out the variables of the object of study; the information collection process was conducted through a questionnaire applied to 352 entrepreneurs in an innovative ecosystem. This study empirically applies a structural equation model to determine the relationship between constructs. (3) Results: A comprehensive analysis of the results indicates that indicators such as positive sales performance, sufficient financial solvency to meet short- and long-term commitments, accurate pricing policies, and access to government and banking support are the primary factors affecting the sustainability of startups in the early stages. (4) Originality: This study provides original and relevant insights into the indicators that affect the financial sustainability of startups during the VoD and offers interesting insights for governments, institutions, and entrepreneurs to foster innovative ecosystems; it also contributes to the extant literature on early-stage entrepreneurial failures. Full article

The low efficiency of carbonization techniques reduces the income of charcoal producers and exacerbates deforestation in the Miombo woodlands. This study examines marketing strategies and the profitability of charcoal production in the rural area of Lubumbashi. Activity monitoring, from production to sale, was conducted with 20 professional charcoal producers from the villages of Maksem, Sela, Luisha, and Mwawa. Economic and statistical analyses show that charcoal is mainly sold in the village (55%), in Lubumbashi (35%), and in the forest (10%). Overall, the activity is profitable: sales generate an average profit of CDF 462,218.78 (approximately USD 225.47), with a profit margin of 0.46 and a benefit–cost ratio of 0.86. The 57 kg packaging format is the most profitable, with an average profit of CDF 661,062.18 (USD 322.47), a profit margin of 0.66, and a benefit–cost ratio of 1.96. In contrast, the 29 kg bag results in losses: –CDF 24,009.60 (–USD 11.71), a profit margin of −0.20, and a benefit–cost ratio of −0.19. These findings indicate that profitability is influenced by the point of sale, packaging type, and season. Sales price, along with production and marketing costs, are the main economic determinants. Despite apparent profitability, the sustainability of this activity remains a concern. This study recommends improving production practices, structuring of charcoal producers through legally recognized associations, standardizing packaging, and implementing per-kilogram pricing in order to enhance profitability while reducing the pressure on forest resources. Full article

This research proposes using a neural network to detect and identify the landmark points of the carapace of the Chinese mitten crab, with the aim of improving efficiency in observation, measurement, and statistics in breeding and sales. A 37-point localization framework was developed for the carapace, with the dataset augmented through random distortions, rotations, and occlusions to enhance generalization capability. Three types of convolutional neural network models were used to compare detection accuracy, generalization ability, and model power consumption, with different loss functions compared. The results showed that the Convolutional Neural Network (CNN) model based on the Differentiable Spatial to Numerical Transform (DSNT) module had the highest R² value of 0.9906 on the test set, followed by the CNN model based on the Gaussian heatmap at 0.9846. The DSNT-based CNN model exhibited optimal computational efficiency, particularly in power consumption metrics. This research demonstrates that the CNN model based on the DSNT module has great potential in detecting landmark points for the Chinese mitten crab, reducing manual workload in breeding observation and quality inspection, and improving efficiency. Full article

A survey was conducted to assess the impact of on-board gutting in the pouting fishery, Trisopterus luscus (L.), from the Bay of Biscay (area FAO 27.VIII) on the parasite burden of macroscopic ascaridoid nematodes, including anisakids (causing anisakidosis) and raphidascaridids (causing consumer rejection) in these fish. The fish were caught in the Bay of Biscay and collected from the fish market in Granada (southern Spain). Fish larger than 25 cm were gutted on board after capture. A detailed examination of the fish revealed the presence of nematode larvae, which were identified morphologically and molecularly (PCR-RFLP: polymerase chain reaction with restriction fragment polymorphism). Results revealed that ungutted fish harbored only third-stage larvae of ascaridoids (Anisakis and Hysterothylacium) while prevalence reached up to 91%. In contrast, gutted fish exhibited a significant reduction in both the prevalence (36%) and mean abundance (MA, 4.44 vs. 0.91) of these larvae. The prevalence of Anisakis spp. larvae was reduced by over 20%, with a more pronounced reduction in abundance of more than 40% (MA, 1.56 vs. 0.91). Hysterothylacium larvae were completely absent (MA 2.88 vs. 0.00). These findings indicate that gutting, while not highly efficient, lowers Anisakis larvae presence, thereby reducing the risk of anisakiasis to consumers. Additionally, the complete removal of Hysterothylacium larvae enhances the fish’s appearance, making it more appealing and increasing its commercial value, as well as reducing the risk of seizure by health authorities. Further research on these on-board evisceration practices is needed to enhance effectiveness and reduce zoonotic nematodes in commercial fishes. Full article

Hydrokinetic turbines (HKTs) are a promising renewable energy source due to the consistency and high energy density in river and tidal resources. One of the primary barriers to the widespread adoption of HKT technologies is a high levelized cost of energy (LCOE). Considering the marine operating environment, the operation and maintenance costs are substantial. The power electronic converter, a key element in the electrical energy conversion system, is a common point of failure in direct-drive turbine applications—leading to increased maintenance efforts. This work presents a reinforcement learning (RL) method built within a quadratic feedback torque control framework to balance energy generation with power electronic device lifetime. The effectiveness of the RL-based control scheme is compared against a static baseline controller through two year-long tidal case studies. The results showed that the proposed method reduced cumulative damage on the device by upwards of 75% but reduced energy generation by up to 25.2%. Using a custom real-time cost estimation function that considers the sale of energy and an estimate of the costs associated with operating a device at a given temperature, it was found that the RL method can increase net income by up to 45.4% depending on the energy market conditions. Full article