Search Results (52)

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

Background/Objectives: Malnutrition is a common problem in children with cerebral palsy (CP). The aim of this study was to investigate the suitability and diagnostic performance of mid-upper arm circumference (MUAC) z-score in diagnosing wasting in children with CP, and its impact on diagnostic accuracy when evaluated concomitantly with additional clinical factors (birth weight, history of phototherapy). Methods: This single-center, cross-sectional study included 83 children with CP, aged 6 months–17 years, followed-up in our clinic. Anthropometric measurements (MUAC, Body Mass Index (BMI)) and clinical data (birth weight, history of phototherapy, Gross Motor Function Classification System (GMFCS)) were prospectively collected. Wasting was defined according to the BMI z-score ≤ −2 criteria. The diagnostic performance of MUAC z-score was evaluated by Receiver Operating Characteristic (ROC) analysis. The contribution of additional covariates was examined using logistic regression analysis and the backward elimination method. Results: MUAC z-score alone demonstrated good discrimination in diagnosing wasting with an Area Under the Curve (AUC) value between 0.805 and 0.821, but its sensitivity was limited (67.0%). No statistically significant difference was found in diagnostic performance between MUAC measurements of the right arm, left arm, and the unaffected arm (p > 0.050). In logistic regression analysis, MUAC z-score (p = 0.001), birth weight (p = 0.014), and a history of phototherapy (p = 0.046) were found to be significantly associated with wasting malnutrition. The simplified model including these variables yielded an AUC value of 0.876. Conclusions: MUAC z-score is a usable tool for wasting malnutrition screening in children with CP. Although its sensitivity is limited when used alone, its diagnostic accuracy increases when evaluated concomitantly with additional clinical factors such as birth weight and a history of phototherapy. This combined approach may offer clinicians a more robust tool for the early diagnosis and management of wasting malnutrition in children with CP. Full article

Accurate crop row detection is crucial for determining weed pressure (weeds item per square meter). However, this task is complicated by the similarity between crops and weeds, the presence of missing plants within rows, and the varying growth stages of both. Our hypothesis was that in drone imagery captured at altitudes of 20–30 m—where individual plant details are not discernible—weed presence among crops can be statistically detected, allowing for the generation of a weed distribution map. This study proposes a computer vision detection method using images captured by unmanned aerial vehicles (UAVs) consisting of six main phases. The method was tested on 208 images. The algorithm performs well under normal conditions; however, when the weed density is too high, it fails to detect the row direction properly and begins processing misleading data. To investigate these cases, 120 artificial datasets were created with varying parameters, and the scenarios were analyzed. It was found that a rate variable—in-row concentration ratio (IRCR)—can be used to determine whether the result is valid (usable) or invalid (to be discarded). The F1 score is a metric combining precision and recall using a harmonic mean, where “1” indicates that precision and recall are equally weighted, i.e., β = 1 in the general Fβ formula. In the case of moderate weed infestation, where 678 crop plants and 600 weeds were present, the algorithm achieved an F1 score of 86.32% in plant classification, even with a 4% row disturbance level. Furthermore, IRCR also indicates the level of weed pressure in the area. The correlation between the ground truth weed-to-crop ratio and the weed/crop classification rate produced by the algorithm is 98–99%. As a result, the algorithm is capable of filtering out heavily infested areas that require full weed control and capable of generating weed density maps on other cases to support precision weed management. Full article

Evaluating the walkability of embedded community service facilities is crucial for fostering age-friendly neighborhoods. Existing studies predominantly focus on qualitative analyses of single age groups, lacking a quantitative framework that integrates multigenerational demands and dynamically combines accessibility with facility usability. This study proposes a novel age-differentiated walkability evaluation framework through a case study of the Maple Community in Chengdu, China. The framework innovatively integrates facility classification weighting systems, population-specific distance decay functions, and multisource spatial data analysis to reveal intergenerational disparities in facility demand and spatial suitability. Key contributions include the following: (1) developing mobility-attenuated accessibility models tailored to distinct age groups, overcoming the limitations of traditional single-function approaches; (2) establishing a multidimensional evaluation system that bridges accessibility theory and usability metrics; (3) empirically demonstrating the spatial equity improvements through embedded facility optimization. The results highlight that elderly residents prioritize healthcare facilities (weight = 0.22), while adolescents rely heavily on cultural–recreational amenities (weight = 0.32). Post-renewal walkability scores in core areas increased by 52.3%, yet persistent peripheral disparities underscore intergenerational accessibility gaps. The framework provides theoretical and methodological advancements for age-friendly community planning, offering actionable insights for data-driven urban renewal policies. Full article

In this research, different hydrological and hydraulic methods were employed to estimate the environmental flow demands of the Sofi Chay River, Iran. In total, 50 years (1969–2018) of flow data exhibited high variability with a mean annual flow of 9.37 m³/s and standard deviation of 42.15 m³/s. Hydrological techniques included Tennant, Flow Duration Curve, and Range of Variability Approach; recommended minimum flows ranged from 0.53 to 2.66 m³/s, respectively, or in other words, 10–50% of mean annual flow. In contrast, hydraulic techniques such as Wetted Perimeter, R2CROSS, and Hydraulic Habitat Simulation suggested higher flows of 1.60–5.38 m³/s, or 30–101% of mean annual flow. The Hydraulic Habitat Simulation Method provided a maximum Weighted Usable Area for target species at the flow of 5.38 m³/s. Sediment analysis showed that there was a power relationship between discharge and SSC, where SSC = 14.23 × Q1^.68 and R² = 0.99. Integration of methods yielded a proposed environmental flow regime of base flows of 1.5–2.5 m³/s during the dry season and 3.0–5.0 m³/s during the wet season, with small floods contributing 15.0–20.0 m³/s and large floods > 35.0 m³/s to maintain channel morphology and ecosystem functions. After realizing the need to incorporate all the approaches in the environmental flow assessment, the hydraulic methods consistently recommended higher flows than the hydrologic methods. An adaptive management framework has been put forward for implementing and refining these recommendations to ensure long-term ecosystem health, coupled with meeting human water needs within the Sofi Chay River basin. Full article

Coreius guichenoti, once widely distributed in the upper reaches of the Jinsha River, has become a nationally protected species in China due to the profound impacts of cascade reservoirs. To assess the influence of substrate on the suitability of spawning habitat for C. guichenoti, this study develops a substrate-inclusive habitat model using fuzzy logic based on expert knowledge. Taking the Pingdi Town section of the lower Jinsha River—a historical spawning site for C. guichenoti—as a case study from March to July 2020, we simulated changes in the spawning habitat suitability index (HSI) and compared the results with those from traditional models that exclude substrate factors. The results showed that in the first and second halves of May, Weighted Usable Area (WUA) and Overall Suitability Index (OSI) increased by 42.31% and 38.73%, respectively, while MSP exhibited dramatic increases of 236.04% and 614.56%. These improvements were primarily observed along the riverbanks, where HSI increased by approximately 0.25. From a management perspective, the HSI results provide a scientific basis for optimizing ecological flow regulation. Incorporating substrate factors into spawning habitat models offers a more objective and comprehensive assessment of habitat quality. Habitat restoration measures, such as targeted substrate improvement in key riverbank areas, may further increase habitat suitability, providing additional opportunities for conservation planning in regulated rivers. Full article

An alternative method of plant nutrition involves the utilization of different by-products. In this study, a combination of two by-products was applied to investigate this method: fermented deproteinized alfalfa juice (FDAJ), a by-product of alfalfa leaf protein production, and fly ash filtrate from a wood-fired power plant. A pot experiment was conducted with winter wheat in an open-sided greenhouse in sandy soil. The aim was to evaluate the efficacy and usability of the combination of these by-products (SFDAJ) for enhanced plant nutrition via spraying or irrigation. Prior to overwintering, photosynthetic pigments, relative chlorophyll content, specific leaf area, and shoot dry weight were measured. At full maturity, we determined morphological parameters, yield, and the element content of the grain. Significant differences were observed between treatments at full maturity. The application of SFDAJ resulted in 73.2% of the yield having the optimal nutrient supply. Compared to the treatment with no nutrients added, the application of SFDAJ increased yields by 260%. Our results show that SFDAJ alone is suitable for providing enhanced nutrient supply in soils with good nutrient supply or in extensive cultivation technology. When supplemented with fertilizer, it can be used on soils with low nutrient supply or in intensive cultivation technology. Based on our results of field applications of SFDAJ, the addition of 10 v v^−1% FDAJ via irrigation is recommended. Full article

The selection of an appropriate operating system (OS) in educational environments is a critical decision that impacts the functionality, user experience, and overall efficiency. Financial factors, along with the availability and functionality of tools used on these systems, play a crucial role in this selection. Furthermore, the OS affects the user experience, security, and adaptability to learning. Previous research in this area is scarce, and this paper contributes to a better understanding of the OS selection process in education. This paper proposes a novel multi-criteria decision-making approach to evaluate and select the most suitable OS for educational institutions. The methodology integrates the PIPRECIA method for assessing weighted criteria, alongside utility analysis (NWA), enabling a balanced decision-making process between institutional management and IT experts. The evaluation considered the following criteria: performance, cost, security, usability, implementation, support, and documentation. The study compared three popular operating systems: Microsoft Windows 11, GNU Linux Ubuntu 22.04 LTS, and Apple macOS 12 using the proposed approach. The results, based on the integrated evaluation of all criteria, indicate that GNU Linux Ubuntu 22.04 LTS (0.562) ranked highest, followed by Microsoft Windows 11 (0.553) and Apple macOS 12 (0.543). This paper emphasizes the importance of objective, group-based decision-making in selecting an OS for education, providing practical insights and guidelines for effective technology integration in academic settings. Full article

This research evaluates 20 advanced convolutional neural network (CNN) architectures for classifying mushroom diseases in Agaricus bisporus, utilizing a custom dataset of 3195 images (2464 infected and 731 healthy mushrooms) captured under uniform white-light conditions. The consistent illumination in the dataset enhances the robustness and practical usability of the assessed models. Using a weighted scoring system that incorporates precision, recall, F1-score, area under the ROC curve (AUC), and average precision (AP), ResNet-50 achieved the highest overall score of 99.70%, demonstrating outstanding performance across all disease categories. DenseNet-201 and DarkNet-53 followed closely, confirming their reliability in classification tasks with high recall and precision values. Confusion matrices and ROC curves further validated the classification capabilities of the models. These findings underscore the potential of CNN-based approaches for accurate and efficient early detection of mushroom diseases, contributing to more sustainable and data-driven agricultural practices. Full article

In this paper, we study a novel static bike-sharing repositioning problem. There is a set of stations spread over a given area, each containing a number of operative bikes, damaged bikes, and free slots. The customers may pick up an operative bike from a station, use it, and return it to another station. Each station should have a target number of operative bikes to make it likely to meet customer demands. Furthermore, the damaged bikes should be removed from the stations. Given a fleet of available vehicles, the repositioning problem consists of designing the vehicles’ routes and calculating the number of operative (usable) and damaged (unusable) bikes that will be moved (loading instructions/loading policy) between stations and/or the depot. The objective is to minimize the weighted sum of the deviation from the target number of bikes for each station, the number of damaged bikes not removed, and the total time used by vehicles. To solve this problem, we propose a matheuristic based on a variable neighborhood search combined with several improving algorithms, including an integer linear programming model to optimize loading instructions. The algorithm was tested in instances based on real-world data and could find good solutions in reasonable computing times. Full article

In light of Switzerland’s 2050 energy goals, the nation aims to boost its domestic hydroelectric output, notably focusing on small-scale hydroelectric power plants. Concurrently, there is an effort to renovate hydroelectric plants to make them more environmentally friendly, emphasizing ecological flow regulation to improve river conditions. This study explores the application of a non-proportional flow allocation method to better assess both ecological and economic outcomes. Unlike traditional fixed or proportional flow methods, this approach allows for a more dynamic balance between hydropower generation and riverine ecosystem health. This study focuses on two key species, brown trout and grayling. In particular, this work highlighted that trout are better suited for low-flow conditions (Weighted Usable Area, WUA, peaks below 1 m³/s), while grayling require significantly higher flows (WUA peaks over 4.5 m³/s). This disparity in habitat preferences raises concerns about the current reliance on single-species models, emphasizing the need for multi-species ecological assessment in future studies. When applied to a small hydropower plant in the Swiss Jura, the non-proportional flow method resulted in an improvement of ecological conditions of at least 37.7%, which consequently led to a reduction of the hydroelectric production of at least 10%. Through strategic upgrades to the facility (e.g., by minimizing hydraulic losses, implementing more efficient turbines, or incorporating photovoltaic panels over water channels), it is possible to simultaneously enhance both energy output and environmental sustainability. These findings suggest that non-proportional flow allocation holds significant potential for broader use in sustainable hydropower management, providing a pathway toward meeting both energy production and ecological conservation goals. Full article

As an integral part of the urban public health infrastructure, public toilets play a significant role in shaping a city’s image. With the advancement of the economy, people have increasingly higher expectations for public toilets. While existing standards primarily focus on physical indicators, there is still room to incorporate measures that prioritize humanistic care. Therefore, based on the Human Rights to Water and Sanitation in Public Spaces (HRWS-PS) standards, this study develops a comprehensive index system for public toilets with a specific emphasis on humanistic care, utilizing expert questionnaires and employing methods such as the analytic hierarchy process (AHP) and entropy weight, and highlights safety and quality as paramount concerns for users, followed by usability and accessibility as secondary needs. Acceptability, which encompasses additional amenities, depends on meeting these secondary needs through solid basic design and construction practices. Affordability, related to consumer costs, is comparatively less demanding. By applying this framework, an analysis of the current state of upgrading urban public toilets in Shijiazhuang was conducted, leading to recommendations for improvements across four key areas: environmental sanitation, architectural design, infrastructure development, and urban planning. The final results provide practical insights for evaluating and suggesting enhancements to ongoing toilet revolution initiatives and urban basic service facilities while also informing urban renewal efforts and designs. Full article

The Tibetan Plateau is the “Asia Water Tower” and is pivotal for Asia and the whole world. Groundwater is essential for sustainable development in its alpine regions, yet its chemical quality increasingly limits its usability. The present research examines the hydrochemical characteristics and origins of phreatic groundwater in alpine irrigation areas. The study probes the chemical signatures, quality, and regulatory mechanisms of phreatic groundwater in a representative alpine irrigation area of the Tibetan Plateau. The findings indicate that the phreatic groundwater maintains a slightly alkaline and fresh status, with pH values ranging from 7.07 to 8.06 and Total Dissolved Solids (TDS) between 300.25 and 638.38 mg/L. The hydrochemical composition of phreatic groundwater is mainly HCO₃-Ca type, with a minority of HCO₃-Na·Ca types, closely mirroring the profile of river water. Nitrogen contaminants, including NO₃⁻_, NO₂⁻, and NH₄⁺, exhibit considerable concentration fluctuations within the phreatic aquifer. Approximately 9.09% of the sampled groundwaters exceed the NO₂⁻ threshold of 0.02 mg/L, and 28.57% surpass the NH₄⁺ limit of 0.2 mg/L for potable water standards. All sampled groundwaters are below the permissible limit of NO₃⁻ (50 mg/L). Phreatic groundwater exhibits relatively good potability, as assessed by the entropy-weighted water quality index (EWQI), with 95.24% of groundwaters having an EWQI value below 100. However, the potential health risks associated with elevated NO₃⁻ levels, rather than NO₂⁻ and NH₄⁺, merit attention when such water is consumed by minors at certain sporadic sampling locations. Phreatic groundwater does not present sodium hazards or soil permeability damage, yet salinity hazards require attention. The hydrochemical makeup of phreatic groundwater is primarily dictated by rock–water interactions, such as silicate weathering and cation exchange reactions, with occasional influences from the dissolution of evaporites and carbonates, as well as reverse cation-exchange processes. While agricultural activities have not caused a notable rise in salinity, they are the main contributors to nitrogen pollution in the study area’s phreatic groundwater. Agricultural-derived nitrogen pollutants require vigilant monitoring to avert extensive deterioration of groundwater quality and to ensure the sustainable management of groundwater resources in alpine areas. Full article

As rivers face growing environmental challenges due to climate change and the construction of artificial structures, it is essential that we improve river ecosystems to maintain their ecological functions and preserve the health of aquatic habitats. The aim of this study was to assess the aquatic ecosystem health of the lower reaches of the Tan River. We employed the Physical Habitat Simulation System and Hydrologic Engineering Center’s River Analysis System to calculate the ecological flow rate based on the weighted usable area (WUA) of Zacco platypus, which is a representative fish species in the Tan River, and the flow rate relationship curves. By analyzing the flow rates in the Tan River across different seasons from 2012 to 2021, we determined that the seasonal optimal ecological flow rate was 10.21–10.27 m³/s. Meanwhile, the WUAs for spring, summer, and autumn and winter were 90–100%, 95–100%, and 75–100%, respectively. Despite meeting the ecological flow criteria for summer, fall, and winter over 50% of the time, spring fell short at 41%; hence, the Tan River flow rates should be secured particularly in spring. This study highlights the urgency of addressing seasonal variations to ensure the overall health of the Tan River ecosystem. Full article

Multi-criteria decision-making methods based on reference points and distances from them are essential for evaluating alternatives across multiple criteria. These methods provide structured approaches to comparing and ranking alternatives relative to specified reference points. The main objective of this paper is to present the Multi-Criteria Method Integrating Distances to Ideal and Anti-ideal Points (MIDIA), which, through a weighted system, allows for the consideration of balance and asymmetry in assessing alternatives based on their distances from the ideal and anti-ideal points. As a multi-criteria algorithm, MIDIA is user-friendly and reflects the human mind’s natural tendency to assess objects based on fundamental concepts—comparison with the ideal solution and the anti-ideal solution—that are familiar from everyday experiences and provide valuable insights from a behavioral perspective. Moreover, the proposed method can be seen as an extension of Hellwig’s approach, designed to facilitate the ranking of alternatives based on two reference points: the ideal point and the anti-ideal point, measuring the distance between the alternative and the ideal point and the distance between the ideal and anti-ideal points. The MIDIA method integrates elements from both TOPSIS and VIKOR, by incorporating the structure of TOPSIS and the compromise perspective of VIKOR, offering a balanced approach to multi-criteria decision-making by focusing on the distances from ideal and anti-ideal points. Illustrative examples are given to demonstrate the usability of the proposed tool in situations where the decision-maker has asymmetrical preferences concerning the importance of ideal and anti-ideal points in ranking alternatives. Moreover, the MIDIA method is applied to one of the Sustainable Development Goals, in the area of education (SDG4), to obtain the rankings of EU member countries in 2022. The results obtained using the MIDIA method were compared with those obtained using the TOPSIS and VIKOR approaches. The study concludes that the ranking of alternatives depends on the coefficients of the importance of the distances to reference points and the data setup. Full article