bluuebunny/crossref_metadata_2025 huggingface dataset:
Atmospheric Water Vapor Variability over Houston: Continuous GNSS Tomography in the Year of Hurricane Harvey (2017)
Authors: Mateus Pedro , Catalão João , Fernandes Rui , Miranda Pedro M. A.
Published: August 2024

This study evaluates the capability of an unconstrained tomographic algorithm to capture 3D water vapor density variability throughout 2017 in Houston, U.S. The algorithm relies solely on Global Navigation Satellite System (GNSS) observations and does not require an initial guess or other specific constraints regarding water vapor density variability within the tomographic domain. The test domain, featuring 9 km horizontal, 500 m vertical, and 30 min temporal resolutions, yielded remarkable results when compared to data retrieved from the ECMWF Reanalysis v5 (ERA5), regional Weather Research and Forecasting Model (WRF) data, and GNSS-Radio Occultation (RO). For the first time, a time series of Precipitable Water Vapor maps derived from the Interferometric Synthetic Aperture Radar (InSAR) technique was used to validate the spatially integrated water vapor computed by GNSS tomography. Tomographic results clearly indicate the passage of Hurricane Harvey, with integrated water vapor peaking at 60 kg/m2 and increased humidity at altitudes up to 7.5 km. Our findings suggest that GNSS tomography holds promise as a reliable source of atmospheric water vapor data for various applications. Future enhancements may arise from denser and multi-constellation networks.
High-Precision Mango Orchard Mapping Using a Deep Learning Pipeline Leveraging Object Detection and Segmentation
Authors: Afsar Muhammad Munir , Bakhshi Asim Dilawar , Iqbal Muhammad Shahid , Hussain Ejaz , Iqbal Javed
Published: August 2024

Precision agriculture-based orchard management relies heavily on the accurate delineation of tree canopies, especially for high-value crops like mangoes. Traditional GIS and remote sensing methods, such as Object-Based Imagery Analysis (OBIA), often face challenges due to overlapping canopies, complex tree structures, and varied light conditions. This study aims to enhance the accuracy of mango orchard mapping by developing a novel deep-learning approach that combines fine-tuned object detection and segmentation techniques. UAV imagery was collected over a 65-acre mango orchard in Multan, Pakistan, and processed into an RGB orthomosaic with a 3 cm ground sampling distance. The You Only Look Once (YOLOv7) framework was trained on an annotated dataset to detect individual mango trees. The resultant bounding boxes were used as prompts for the segment anything model (SAM) for precise delineation of canopy boundaries. Validation against ground truth data of 175 manually digitized trees showed a strong correlation (R2 = 0.97), indicating high accuracy and minimal bias. The proposed method achieved a mean absolute percentage error (MAPE) of 4.94% and root mean square error (RMSE) of 80.23 sq ft against manually digitized tree canopies with an average size of 1290.14 sq ft. The proposed approach effectively addresses common issues such as inaccurate bounding boxes and over- or under-segmentation of tree canopies. The enhanced accuracy can substantially assist in various downstream tasks such as tree location mapping, canopy volume estimation, health monitoring, and crop yield estimation.
Mapping of Clay Montmorillonite Abundance in Agricultural Fields Using Unmixing Methods at Centimeter Scale Hyperspectral Images
Authors: Ducasse Etienne , Adeline Karine , Hohmann Audrey , Achard Véronique , Bourguignon Anne , Grandjean Gilles , Briottet Xavier
Published: August 2024

The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance are a challenge since they are usually intimately mixed with other minerals, soil organic carbon and soil moisture content. Imaging spectroscopy coupled with unmixing methods can address these issues, but the quality of the estimation degrades the coarser the spatial resolution is due to pixel heterogeneity. With the advent of UAV-borne and proximal hyperspectral acquisitions, it is now possible to acquire images at a centimeter scale. Thus, the objective of this paper is to evaluate the accuracy and limitations of unmixing methods to retrieve montmorillonite abundance from very-high-resolution hyperspectral images (1.5 cm) acquired from a camera installed on top of a bucket truck over three different agricultural fields, in Loiret department, France. Two automatic endmember detection methods based on the assumption that materials are linearly mixed, namely the Simplex Identification via Split Augmented Lagrangian (SISAL) and the Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF), were tested prior to unmixing. Then, two linear unmixing methods, the fully constrained least square method (FCLS) and the multiple endmember spectral mixture analysis (MESMA), and two nonlinear unmixing ones, the generalized bilinear method (GBM) and the multi-linear model (MLM), were performed on the images. In addition, several spectral preprocessings coupled with these unmixing methods were applied in order to improve the performances. Results showed that our selected automatic endmember detection methods were not suitable in this context. However, unmixing methods with endmembers taken from available spectral libraries performed successfully. The nonlinear method, MLM, without prior spectral preprocessing or with the application of the first Savitzky–Golay derivative, gave the best accuracies for montmorillonite abundance estimation using the USGS library (RMSE between 2.2–13.3% and 1.4–19.7%). Furthermore, a significant impact on the abundance estimations at this scale was in majority due to (i) the high variability of the soil composition, (ii) the soil roughness inducing large variations of the illumination conditions and multiple surface scatterings and (iii) multiple volume scatterings coming from the intimate mixture. Finally, these results offer a new opportunity for mapping expansive soils from imaging spectroscopy at very high spatial resolution.
A Denoising Method Based on DDPM for Radar Emitter Signal Intra-Pulse Modulation Classification
Authors: Yuan Shibo , Li Peng , Zhou Xu , Chen Yingchao , Wu Bin
Published: August 2024

Accurately classifying the intra-pulse modulations of radar emitter signals is important for radar systems and can provide necessary information for relevant military command strategy and decision making. As strong additional white Gaussian noise (AWGN) leads to a lower signal-to-noise ratio (SNR) of received signals, which results in a poor classification accuracy on the classification models based on deep neural networks (DNNs), in this paper, we propose an effective denoising method based on a denoising diffusion probabilistic model (DDPM) for increasing the quality of signals. Trained with denoised signals, classification models can classify samples denoised by our method with better accuracy. The experiments based on three DNN classification models using different modal input, with undenoised data, data denoised by the convolutional denoising auto-encoder (CDAE), and our method’s denoised data, are conducted with three different conditions. The extensive experimental results indicate that our proposed method could denoise samples with lower values of the SNR, and that it is more effective for increasing the accuracy of DNN classification models for radar emitter signal intra-pulse modulations, where the average accuracy is increased from around 3 to 22 percentage points based on three different conditions.
Improved Winter Wheat Yield Estimation by Combining Remote Sensing Data, Machine Learning, and Phenological Metrics
Authors: Li Shiji , Huang Jianxi , Xiao Guilong , Huang Hai , Sun Zhigang , Li Xuecao
Published: August 2024

Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R2 of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R2 of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R2 of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking.
Improved Scheme for Data Aggregation of Distributed Oracle for Intelligent Internet of Things
Authors: Gao Ruiyang , Xue Yongtao , Wang Wei , Lu Yin , Gui Guan , Xu Shimin
Published: August 2024

Oracle is a data supply mechanism that provides real-world data for blockchain. It serves as a bridge between blockchain and the IoT world, playing a crucial role in solving problems such as data sharing and device management in the IoT field. The main challenge at this stage is determining how to achieve data privacy protection in distributed Oracle machines to safeguard the value hidden in data on the blockchain. In this paper, we propose an improved scheme for distributed Oracle data aggregation based on Paillier encryption algorithm, which achieves end-to-end data privacy protection from devices to users. To address the issue of dishonest distributed Oracle machines running out of funds, we have designed an algorithm called PICA (Paillier-based InChain Aggregation). Based on the aggregation on the Chainlink chain and the Paillier encryption algorithm, random numbers are introduced to avoid the problem of dishonest Oracle machines running out of funds. We use the traffic coverage method to solve the problem of exposed request paths in distributed Oracle machines. Simulation and experimental results show that in small and medium-sized IoT application scenarios with 10,000 data nodes, each additional false request in a single request will result in a delay of about 2 s in data acquisition and can achieve a request response time of 20 s. The proposed method can achieve user data privacy protection.
Comparing Device-Generated and Calculated Bioimpedance Variables in Community-Dwelling Older Adults
Authors: Lafontant Kworweinski , Sterner Danielle A. , Fukuda David H. , Stout Jeffrey R. , Park Joon-Hyuk , Thiamwong Ladda
Published: August 2024

Despite BIA emerging as a clinical tool for assessing older adults, it remains unclear how to calculate whole-body impedance (Z), reactance (Xc), resistance (R), and phase angle (PhA) from segmental values using modern BIA devices that place electrodes on both sides of the body. This investigation aimed to compare both the whole-body and segmental device-generated phase angle (PhADG) with the phase angle calculated using summed Z, Xc, and R from the left, right, and combined sides of the body (PhACalc) and to compare bioelectric variables between sides of the body. A sample of 103 community-dwelling older adults was assessed using a 50 kHz direct segmental multifrequency BIA device. Whole-body PhACalc values were assessed for agreement with PhADG using 2.5th and 97.5th quantile nonparametric limits of agreement and Spearman’s rho. Bioelectrical values between sides of the body were compared using Wilcoxon rank and Spearman’s rho. A smaller mean difference was observed between PhADG and right PhACalc (−0.004°, p = 0.26) than between PhACalc on the left (0.107°, p = 0.01) and on the combined sides (0.107°, p < 0.001). The sum of Z, R, and PhACalc was significantly different (p < 0.01) between the left (559.66 ± 99.55 Ω, 556.80 ± 99.52 Ω, 5.51 ± 1.5°, respectively) and the right sides (554.60 ± 94.52 Ω, 552.02 ± 94.23 Ω, 5.41 ± 0.8°, respectively). Bilateral BIA values do not appear to be interchangeable when determining whole-body measurements. Present data suggest that using right-sided segmental values would be the most appropriate choice for calculating whole-body bioelectrical variables.
Research on Detection Method of Chaotian Pepper in Complex Field Environments Based on YOLOv8
Authors: Duan Yichu , Li Jianing , Zou Chi
Published: August 2024

The intelligent detection of chili peppers is crucial for achieving automated operations. In complex field environments, challenges such as overlapping plants, branch occlusions, and uneven lighting make detection difficult. This study conducted comparative experiments to select the optimal detection model based on YOLOv8 and further enhanced it. The model was optimized by incorporating BiFPN, LSKNet, and FasterNet modules, followed by the addition of attention and lightweight modules such as EMBC, EMSCP, DAttention, MSBlock, and Faster. Adjustments to CIoU, Inner CIoU, Inner GIoU, and inner_mpdiou loss functions and scaling factors further improved overall performance. After optimization, the YOLOv8 model achieved precision, recall, and mAP scores of 79.0%, 75.3%, and 83.2%, respectively, representing increases of 1.1, 4.3, and 1.6 percentage points over the base model. Additionally, GFLOPs were reduced by 13.6%, the model size decreased to 66.7% of the base model, and the FPS reached 301.4. This resulted in accurate and rapid detection of chili peppers in complex field environments, providing data support and experimental references for the development of intelligent picking equipment.
Comprehensive Analysis of Magnetic Flux Density and RF-EMF Exposure in Electric Buses: A Case Study from Samsun, Turkey
Authors: Albayrak Zafer Emre , Kurnaz Cetin , Karadag Teoman , Cheema Adnan Ahmad
Published: August 2024

This study investigates magnetic flux density (B) and radiofrequency electromagnetic field (RF-EMF) measurements on electric buses operating in Samsun, Turkey, focusing on two bus routes (called E1 and E4) during the morning and evening hours. Measurements were taken under diverse operational conditions, including acceleration, cruising, and braking, at locations of peak passenger density. Along the E1 route, the magnetic field intensity varied significantly based on the bus position, road slope, and passenger load, with notable increases during braking. In contrast, the E4 route showed a lower magnetic field intensity and RF-EMF values due to its straighter trajectory and reduced operational stops. The highest RF-EMF measurement recorded was 6.01 V/m, which is below the maximum levels established by the ICNIRP guidelines. In 11 out of the 12 different band-selective RF-EMF measurements, the highest contribution came from the downlink band of the base stations, while in only one measurement, the highest contribution originated from the uplink bands of the base stations. All data were subject to the Anderson–Darling test, confirming the generalized extreme value distribution as the best fit for both B and RF-EMF measurements. Additionally, the study assessed B levels inside and outside the bus during charging, revealing heightened readings near the pantograph. These findings significantly contribute to our understanding of electromagnetic field exposure in electric bus environments, highlighting potential health implications and informing the development of targeted mitigation strategies.
Internal Thread Defect Generation Algorithm and Detection System Based on Generative Adversarial Networks and You Only Look Once
Authors: Jiang Zhihao , Dou Xiaohan , Liu Xiaolong , Xue Chengqi , Wang Anqi , Zhang Gengpei
Published: August 2024

In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects.
A Study of the Improved A* Algorithm Incorporating Road Factors for Path Planning in Off-Road Emergency Rescue Scenarios
Authors: Zhao Dequan , Ni Li , Zhou Kefa , Lv Zhihong , Qu Guangjun , Gao Yue , Yuan Weiting , Wu Qiulan , Zhang Feng , Zhang Qing
Published: August 2024

To address the problem of ignoring unpaved roads when planning off-road emergency rescue paths, an improved A* algorithm that incorporates road factors is developed to create an off-road emergency rescue path planning model in this study. To reduce the number of search nodes and improve the efficiency of path searches, the current node is classified according to the angle between the line connecting the node and the target point and the due east direction. Additionally, the search direction is determined in real time through an optimization method to improve the path search efficiency. To identify the path with the shortest travel time suitable for emergency rescue in wilderness scenarios, a heuristic function based on the fusion of road factors and a path planning model for off-road emergency rescue is developed, and the characteristics of existing roads are weighted in the process of path searching to bias the selection process toward unpaved roads with high accessibility. The experiments show that the improved A* algorithm significantly reduces the travel time of off-road vehicles and that path selection is enhanced compared to that with the traditional A* algorithm; moreover, the improved A* algorithm reduces the number of nodes by 16.784% and improves the search efficiency by 27.18% compared with the traditional 16-direction search method. The simulation results indicate that the improved algorithm reduces the travel time of off-road vehicles by 21.298% and improves the search efficiency by 93.901% compared to the traditional A* algorithm, thus greatly enhancing off-road path planning.
Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science
Authors: Hofman Jelle , Lazarov Borislav , Stroobants Christophe , Elst Evelyne , Smets Inge , Van Poppel Martine
Published: August 2024

This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO2, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O3). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R2 = 0.68–0.9, Uexp = 16–66%, BSU = 0.1–0.7 µg/m3) and BC (R2 = 0.82–0.83), but maturity of the tested NO2 sensors is still low (R2 = 0.38–0.55, Uexp = 111–614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R2 = 0.75–0.83, Uexp = 37–44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.
Exploring University Students’ Career Resources Profiles to Cope with Career Insecurity and Promote Employability
Authors: Antonio Audrey Ansay , Chiesa Rita
Published: August 2024

The aim of this study was to characterize profiles of career resources among university students and identify differences in career insecurity and employability between these profiles. The conservation of resources theory (Hobfoll 1989) and career resources model (Hirschi 2012) were used as theoretical lenses. Specifically, psychological career resources (i.e., present positive time perspective, future positive time perspective, resilience) and social career resources (i.e., university career support, social career support, studies challenge) were explored as university students’ resources to cope with their career insecurity and promote their employability. Survey data were collected from university students (N = 281) in this cross-sectional research. Cluster analysis results revealed four career resources profiles of university students: (1) low career resources; (2) high career resources; (3) high psychological career resources–low social career resources; (4) low psychological career resources–high social career resources. Additionally, career resources profiles with high levels of psychological career resources were found to have significantly lower scores on career insecurity and higher scores on employability in comparison to other career resources profiles. The study has theoretical and practical implications for a person-centered approach to promoting career preparation among university students.
Reduction of Runoff Pollutants from Major Arterial Roads Using Porous Pavement
Authors: Holzer Katie , Poor Cara
Published: August 2024

Stormwater runoff from large roads is a major source of pollutants to receiving waters, and reduction of these pollutants is important for sustainable water resources and transportation networks. Porous pavements have been shown to substantially reduce many of these pollutants, but studies are lacking on arterial roads. We sampled typical stormwater pollutants in runoff from sections of an arterial road 9–16 years after installation of three pavement types: control with conventional asphalt, porous asphalt overly, and full-depth porous asphalt. Both types of porous pavements substantially reduced most of the stormwater pollutants measured. Total suspended solids, turbidity, total lead, total copper, and 6PPD-quinone were all reduced by >75%. Total nitrogen, ammonia, total phosphorus, biochemical oxygen demand, total and dissolved copper, total mercury, total zinc, total polycyclic aromatic hydrocarbons, and di-2-ethylhexyl phthalate were all reduced by >50%. Reductions were lower or absent for nitrate, orthophosphate, E. coli, dissolved lead, and dissolved zinc. Most reductions were statistically significant. Many pollutants exceeded applicable water quality standards in the control samples but met them with both types of porous pavement. This study demonstrates that porous overlays and full-depth porous asphalt can provide substantial reductions of several priority stormwater pollutants on arterial roads for many years after installation. Porous pavements have the potential to substantially enhance water quality of urban waterways and provide ecological benefits on urban thoroughfares.
The Impact of Digital Literacy on Farmers’ Green Production Behavior: Mediating Effects Based on Ecological Cognition
Authors: Liu Xiao , Wang Zhenyu , Han Xiaoyan
Published: August 2024

Farmers’ green production behavior is one of the main determinants of the sustainability of the agricultural economy. In this study, Ordered Logit, OLS, and 2SLS models were conducted to evaluate the impact of digital literacy on farmers’ green production behavior. On this basis, the Propensity Score Matching (PSM) method was conducted to deal with the endogeneity bias that may result from the sample self-selection problem. We also adopt the mediation effect model to test the mediating mechanism of ecological cognition between digital literacy and farmers’ green production behavior. The results showed that three different types of digital literacy significantly improved farmers’ green production behavior. We also found that farmers’ green production behavior improved by 19.87%, 15.92%, and 24.16% through digital learning, social, and transaction literacy. Meanwhile, the mediating effect showed that digital literacy improves farmers’ green production behavior by increasing ecological cognition. We demonstrate that three different types of digital literacy significantly improved farmers’ green production behavior. Therefore, policies to increase digital literacy among farmers should be further improved to promote farmers’ green production behavior.
Deep Learning for Predicting Hydrogen Solubility in n-Alkanes: Enhancing Sustainable Energy Systems
Authors: Tatar Afshin , Shokrollahi Amin , Zeinijahromi Abbas , Haghighi Manouchehr
Published: August 2024

As global population growth and urbanisation intensify energy demands, the quest for sustainable energy sources gains paramount importance. Hydrogen (H2) emerges as a versatile energy carrier, contributing to diverse processes in energy systems, industrial applications, and scientific research. To harness the H2 potential effectively, a profound grasp of its thermodynamic properties across varied conditions is essential. While field and laboratory measurements offer accuracy, they are resource-intensive. Experimentation involving high-pressure and high-temperature conditions poses risks, rendering precise H2 solubility determination crucial. This study evaluates the application of Deep Neural Networks (DNNs) for predicting H2 solubility in n-alkanes. Three DNNs are developed, focusing on model structure and overfitting mitigation. The investigation utilises a comprehensive dataset, employing distinct model structures. Our study successfully demonstrates that the incorporation of dropout layers and batch normalisation within DNNs significantly mitigates overfitting, resulting in robust and accurate predictions of H2 solubility in n-alkanes. The DNN models developed not only perform comparably to traditional ensemble methods but also offer greater stability across varying training conditions. These advancements are crucial for the safe and efficient design of H2-based systems, contributing directly to cleaner energy technologies. Understanding H2 solubility in hydrocarbons can enhance the efficiency of H2 storage and transportation, facilitating its integration into existing energy systems. This advancement supports the development of cleaner fuels and improves the overall sustainability of energy production, ultimately contributing to a reduction in reliance on fossil fuels and minimising the environmental impact of energy generation.
4IR Applications in the Transport Industry: Systematic Review of the State of the Art with Respect to Data Collection and Processing Mechanisms
Authors: Ajayi Olusola , Kurien Anish , Djouani Karim , Dieng Lamine
Published: August 2024

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the Industrial Revolutions, which have continued expansion and progress into the fourth Industrial Revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. In order to achieve a better understanding of the impact of these technologies, in this study, we methodically reviewed the literature on the subject of the data collection and processing mechanisms of 4IR technologies in the context of transport. Gaps in present practices are identified in the study, especially with regard to the integration and scalability of these technologies in transportation networks. In order to fully reap the rewards of 4IR technologies, it is also necessary to apply standardized methods for data gathering and processing. In this report, we offer insights into current obstacles and make recommendations for future research to solve these concerns through a comprehensive evaluation of the literature, with the goal of promoting the development of intelligent and sustainable transportation systems.
Energy Consumption of Electric Vehicles in Europe
Authors: Weiss Martin , Winbush Trey , Newman Alexandra , Helmers Eckard
Published: August 2024

As the European Union advances its regulatory framework on energy efficiency, the introduction of an energy label for electric cars appears increasingly relevant. Anticipating this policy development, we present a scoping analysis of energy consumption and efficiency trade-offs across 342 fully electric cars available in Europe. Our results suggest that certified and real-world energy consumption average 19 ± 4 kWh/100 km and 21 ± 4 kWh/100 km, translating into drive ranges of 440 ± 120 km and 380 ± 110 km, respectively. Energy consumption is correlated with mass, frontal area, and battery capacity but less so with rated power and vehicle price. Each 100 kg of vehicle mass and 0.1 m2 of frontal area increases energy consumption by 0.2 ± 0.1 kWh/100 km and 0.9 ± 0.1 kWh/100 km, respectively. Raising battery capacity by 10 kWh elevates vehicle mass by 143 ± 4 kg, energy consumption by 0.6 ± 0.1 kWh/100 km, drive range by 44 ± 2 km, and vehicle price by 12,000 ± 600 EUR. Efficient cars are available at any price, but long drive ranges have a cost. These findings point to considerable efficiency trade-offs that could be revealed to consumers through a dedicated energy label. We propose several options for classifying vehicles on an efficiency scale from A to G, with and without drive range and battery capacity as utility parameters. Our analysis provides a rationale for the energy labeling of electric cars in the European Union and could inspire similar analyses for other vehicle categories such as e-scooters, lightweight electric three- and four-wheelers, e-busses, e-trucks, and electric non-road machinery.
The Exploration of Predictors for Peruvian Teachers’ Life Satisfaction through an Ensemble of Feature Selection Methods and Machine Learning
Authors: Holgado-Apaza Luis Alberto , Ulloa-Gallardo Nelly Jacqueline , Aragon-Navarrete Ruth Nataly , Riva-Ruiz Raidith , Odagawa-Aragon Naomi Karina , Castellon-Apaza Danger David , Carpio-Vargas Edgar E. , Villasante-Saravia Fredy Heric , Alvarez-Rozas Teresa P. , Quispe-Layme Marleny
Published: August 2024

Teacher life satisfaction is crucial for their well-being and the educational success of their students, both essential elements for sustainable development. This study identifies the most relevant predictors of life satisfaction among Peruvian teachers using machine learning. We analyzed data from the National Survey of Teachers of Public Basic Education Institutions (ENDO-2020) conducted by the Ministry of Education of Peru, using filtering methods (mutual information, analysis of variance, chi-square, and Spearman’s correlation coefficient) along with embedded methods (Classification and Regression Trees—CART; Random Forest; Gradient Boosting; XGBoost; LightGBM; and CatBoost). Subsequently, we generated machine learning models with Random Forest; XGBoost; Gradient Boosting; Decision Trees—CART; CatBoost; LightGBM; Support Vector Machine; and Multilayer Perceptron. The results reveal that the main predictors of life satisfaction are satisfaction with health, employment in an educational institution, the living conditions that can be provided for their family, and conditions for performing their teaching duties, as well as age, the degree of confidence in the Ministry of Education and the Local Management Unit (UGEL), participation in continuous training programs, reflection on the outcomes of their teaching practice, work–life balance, and the number of hours dedicated to lesson preparation and administrative tasks. Among the algorithms used, LightGBM and Random Forest achieved the best results in terms of accuracy (0.68), precision (0.55), F1-Score (0.55), Cohen’s kappa (0.42), and Jaccard Score (0.41) for LightGBM, and accuracy (0.67), precision (0.54), F1-Score (0.55), Cohen’s kappa (0.41), and Jaccard Score (0.41). These results have important implications for educational management and public policy implementation. By identifying dissatisfied teachers, strategies can be developed to improve their well-being and, consequently, the quality of education, contributing to the sustainability of the educational system. Algorithms such as LightGBM and Random Forest can be valuable tools for educational management, enabling the identification of areas for improvement and optimizing decision-making.
More than Just Buying a Van: Lessons Learned from a Mobile Telehealth HCV Testing and Treatment Study
Authors: Bianchet Elyse , de Gijsel David , Del Toro-Mejias Lizbeth M. , Stopka Thomas J. , Hoskinson Randall A. , Dowd Patrick , Friedmann Peter D.
Published: August 2024

Hepatitis C virus (HCV) disproportionately affects people who inject drugs (PWID). Although HCV has become universally curable since the arrival of direct-acting antivirals, barriers exist to facilitating care and cure in this historically hard-to-reach population, including limited testing and healthcare services and healthcare stigma, issues that are compounded in rural areas. Telehealth is effective in increasing access to HCV care and cure, but innovative approaches of testing and care are required to fully address the need among rural PWID, which led to our study examining a mobile telehealth model for treating HCV. In this commentary, we discuss lessons learned delivering telehealth on a mobile unit, important factors for consideration when designing a mobile intervention, and we suggest an ideal model to increase access to HCV testing and treatment and other services for rural PWID.
Vaccine Confidence and Coverage among Medical Students at a Federal University in Brazil
Authors: Feijó Ricardo B. , Bertotto Jordana V. H. , Pinto Amanda C. , Leal Maria Eduarda T. G. , de Souza Víctor M. , Sakai Vitória F.
Published: August 2024

Background: Declining vaccination coverage (VC) and vaccine hesitancy among medical students are global challenges. These challenges reflect individual and logistical barriers to a sufficient adherence toward essential vaccines for healthcare professionals, as well as presenting a need for educational strategies during undergraduate training. Methods: This is a prospective study for evaluating VC rates, sociodemographic associations, and the vaccine confidence among medical students at a federal university in Brazil. The data collection included questionnaires and individual analyses of the participants’ vaccination records. Results: A total of 237 medical students from all six years of an undergraduate program participated, of whom 124 (52.3%) had a vaccination record. Although the majority considered the vaccines to be “Completely Safe” (86.9%), the VC rates for complete vaccination schedules were relatively low, ranging from 87.9% (hepatitis B vaccine) to 3.2% (meningococcal B vaccine), including the vaccines from the National Immunization Program (NIP) and the private sector. Higher VC rates were found to occur among students in the final years of their undergraduate studies, in those from families with higher monthly incomes, and those from private secondary schools. Conclusions: Given the low VC rates among medical students, other factors in addition to vaccine confidence may be determinants, thus highlighting the importance of reviewing policies for the inclusion of priority groups in the NIP and in implementing educational interventions during undergraduate training.
A Data-Driven Methodology for Assessing Reuse Potential in Existing Wastewater Treatment Plants
Authors: Areosa Inês , Martins Tiago A. E. , Lourinho Rita , Batista Marcos , Brito António G. , Amaral Leonor
Published: August 2024

Wastewater reuse is a proven strategy to mitigate water stress in drought-prone regions. However, this practice is still limited due to high implementation costs, regulatory hurdles, and limited public acceptance. In regions with low reclaim rates, a thorough evaluation of the potential for reuse is needed to support decision-making, focusing on opportunities that address both low-hanging fruit and high-leverage projects. This paper introduces a streamlined, data-centric methodology for assessing wastewater reuse potential, adaptable to various regional contexts. The methodology involves comprehensive data collection and processing to evaluate wastewater treatment plant (WWTP) capabilities and identify potential users, allowing the prioritisation of case studies based on demand alignment. Different treatment and distribution systems are analysed to match WWTP capabilities with user needs, considering volume, quality, and infrastructure requirements. Cost analysis incorporates capital expenditure (CAPEX), operational expenditure (OPEX) and unit costs using novel cost functions for treatment and distribution. Risk analysis adheres to WHO methodology to ensure safety and sustainability. A case study in the Lisbon and Oeste areas in Portugal validates this approach, revealing key insights into the potential and economic viability of water reuse. By comparing tariffs and costs associated with different reuse scenarios, this paper offers benchmarks for the economic feasibility of reuse projects.
Identifikasi Keanekaragaman Mamalia Di Hutan Pendidikan Gunung Walat (HPGW) Jawa barat
Authors: Fariati Chery Kurnia
Published: August 2024

Studi tentang keanekaragaman jenis mamalia sangatlah penting untuk dilakukan, karena dapat menghasilkan data dasar yang bisa digunakan sebagai salah satu pedoman pengelolaan suatu kawasan konservasi. Tujuan penelitian ini adalah untuk mengidentifikasi keanekaragaman mamalia yang ditemukan di Hutan Pendidikan Gunung Walat, Sukabumi. Metode yang digunakan adalah metode transek jalur, metode ini merupakan salah satu metode yang sering digunakan dalam pengumpulan data jenis dan jumlah individu satwa liar. Ditemukan 3 jenis mamalia yang baik secara langsung ataupun melalui jejak, feses dan sisa pakan. Satu jenis mamalia ditemukan secara langsung maupun tidak langsung melalui bekas sisa makanan yaitu Macaca Fascicularis, dan 2 jenis mamalia ditemukan secara tidak langsung yaitu Sus Scrofa melalui jejak jalur, dan Paradoxurus hermaphroditus melalui kotoran (feses).
Kata kunci: Hutan Pendidikan Gunung Walat; Mamalia; Satwa liar
Pengetahuan Lokal Pengolahan Sagu (Metroxylon Sagu Rottb) Sebagai Bahan Pangan Oleh Masyarakat Lokal Kampung Menawi Distrik Angkaisera Kabupaten Kepulauan Yapen
Authors: Silalahi Marina
Published: August 2024

Pohon sagu (Metroxylon sagu Rottb) merupakan pohon palem penghasil pati dengan kadar pati lebih hingga mencapai 3-4 kali dari sumber karbohidrat lainnya. Penelitian ini betujuan menjelaskan pengetahuan lokal Masyarakat lokal Kampung Menawi Distrik Angkaisera, Kepulauan Yapen tentang proses pengolahan sagu dan potensi pengembangannya sebagai bahan pangan tradisional. Penelitian dilakukan dengan pendekatan etnobotani dengan cara wawancara semi terstruktur. Wawancara dilkukan pada 30 orang responden dengan usia 15-40 tahun. Data yang diperoleh dianalisa secara kualitatif. Masyarakat lokal Kampung Menawi, Distrik Angkaisera menyebut sagu dengan nama lokal taung dan mengenali tiga jenis kareakter sagu yaitu Sagu Kurai, Sagu Wewa dan Sagu Barai. Untuk mendapatkan pati sagu dilakukan secara bertahap dengan yaitu pemilihan pohon, pembersihan dan pemotongan batang, pengupasan dan pemarutan bagian dalam batang, pencucian, pembekuan, dan pengeringan. Pati sagu diolah menjadi makanan pokok (papeda) dan kudapan lainnya (sagu porna, sagu bambu dan sagu kacang kelapa).
ANALISIS TOTAL BAKTERI COLIFORM PADA AIR SUMUR DI MUARA BARU PENJARINGAN JAKARTA UTARA
Authors: Annisa Tiara Nur , Pratiwi Rina Hidayati , Alamsyah Mashudi
Published: August 2024

Muara Baru merupakan perairan yang menjadi wilayah sentra perikanan laut, kawasan industri, dan pemukiman warga. Kawasan ini memiliki kepadatan penduduk yang tinggi, sehingga menyebabkan kualitas air terus menurun dan muara mulai tercemar yang disebabkan tekanan dan kebutuhan penduduk. Pencemaran dapat terjadi secara biologis, kimia, maupun fisika. Kondisi perairan tercemar dapat dapat dilihat dari keberadaan mikroba patogen seperti bakteri Coliforrm, dikarenakan bakteri ini sering digunakan sebagai indikator kualitas air. Tujuan dari penelitian ini adalah menganalisis keberadaan dan mengetahui total bakteri Coliform sebagai indikator pcemaran air sumur di Muara Baru. Metode analisis yang digunakan ialah Total Plate Count (TPC) dengan pendekatan deskriptif kuantitatif. Penelitian ini dilakukan menggunakan 10 sampel air sumur bor rumah penduduk di Muara Baru. Hasil penelitian menunjukkan bahwa seluruh sampel air memiliki ambang batas cemaran mikroba yang berlebih dibandingkan standar ambang batasnya. Hal ini ditunjukkan oleh jumlah koloni terendah dan tertinggi pada 2 sampel memiliki jumlah berturut-turut, yaitu 1,6 x 103 CFU/100 mL dan 8,0 x 104 CFU/100 mL. Jumlah koloni bakteri Coliform tersebut dapat mengkontaminasi biota-biota yang berada diperairan Muara Baru sehingga ketika biota tersebut dikonsumsi dapat menyebabkan gangguan kesehatan secara tak langsung kepada manusia.
RIBA IN UMMAH ECONOMIC'S LIFE
Authors: Lubis Zulkarnain
Published: August 2024

Dalam konteks syariah (hukum Islam) memakan riba termasuk salah satu dosa besar. Namun pada praktiknya masih banyak masyarakat yang bingung dengan praktik riba tersebut dalam kehidupan sehari-hari khususnya yang terkait dengan transaksi perbankan. Riba secara bahasa bermakna tambahan atau meminta kelebihan uang dari nilai awal. Secara lebih spesifik lagi riba adalah meminta tambahan uang dari pinjaman awal baik dalam transaksi jual beli maupun pinjam meminjam yang bertentangan dengan prinsip syariah Islam. Dalam hal ini pinjam meminjam atau jual beli tersebut masuk kategori transaksi yang haram. Misalnya si A memberi pinjaman kepada si B, dengan syarat si B harus mengembalikan uang pokok pinjaman beserta sekian persen tambahannya.
Riba sangat berdampak  di tengah-tengah masyarakat tidak saja berpengaruh dalam kehidupan ekonomi, tetapi dalam seluruh aspek kehidupan manusia,adapun dampaknya adalah sebagai berikut:
(1).Riba dapat menimbulkan permusuhan antara pribadi dan mengurangi semangat kerjasama/saling menolong dengan sesama manusia. Dengan mengenakan tambahan kepada peminjam akan menimbulkan perasaan bahwa peminjam tidak tahu kesulitan dan tidak mau tahu kesulitan orang lain
(2).Menimbulkan tumbuhnya mental pemboros dan pemalas. Dengan membungakan uang, kreditur bisa mendapatkan tambahan penghasilan dari waktu kewaktu. Keadaan ini menimbulkan anggapan bahwa dalam jangka waktu yang tidak terbatas ia mendapatkan tambahan pendapatan rutin, sehingga menurunkan dinamisasi, inovasi dan kreativitas dalam bekerja;
(3).Riba merupakan salah satu bentuk penjajahan. Kreditur yang meminjamkan modal dengan menuntut pembayaran lebih kepada peminjam dengan nilai yang telah disepakati bersama;
(4).Menjadikan kreditur mempunyai legitimasi untuk melakukan tindakan-tindakan yang tidak baik untuk menuntut kesepakatan tersebut. Karena dalam kesepakatan, kreditur telah memperhitungkan keuntungan yang diperoleh dari kelebihan bunga yang akan diperoleh, dan itu sebenarnya hanya berupa pengharapan dan belum terwujud.
THE ROLE OF THE MOTHER IN THE DISTRIBUTION OF INHERITANCE
Authors: Uyuni Badrah , Wahidah Nurul , Sam Nadia , Wildan Muhammad , Zaky Muhammad
Published: August 2024

Inheritance law according to Islamic law is a part of family law (al-Ahwalus Syahsiyah). This knowledge is very important to learn so that in the implementation of the distribution of inheritance there are no mistakes and it can be carried out in the fairest way, because by studying Islamic inheritance law for Muslims, they will be able to fulfill rights relating to inheritance after being abandoned by the muwarris (heir). and delivered to the heirs who are entitled to receive it. Thus, a person can avoid the sin of not eating someone's property that is not his right, because Islamic law regarding inheritance is not fulfilled. The legal system of inheritance does not differentiate between sons and daughters, between husband and wife, they are entitled to inheritance, and the son's share is twice that of the daughter's, the share of a wife or husband is the same, depending on whether there are children or not. . The author conducted research on several respondents to find out how the procedure for dividing inheritance in the community and how the role of a mother is in distributing the inheritance. This type of research method uses qualitative methods by interviewing several respondents.
Hukum Kewarisan menurut hukum Islam merupakan salah satu bagian dari hukum keluarga (al-Ahwalus Syahsiyah). Ilmu ini sangat penting dipelajari agar dalam pelaksanaan pembagian harta waris tidak terjadi kesalahan dan dapat dilaksanakan dengan seadil-adilnya, sebab dengan mempelajari hukum kewarisan Islam bagi umat Islam, akan dapat menunaikan hak-hak yang berkenaan dengan harta waris setelah ditinggalkan oleh muwarris (pewaris) dan disampaikan kepada ahli waris yang berhak untuk menerimanya. Dengan demikian,seseorang dapat terhindar dari dosa yakni tidak memakan harta orang yang bukan haknya, karena tidak ditunaikannya hukum Islam mengenai kewarisan. Sistem hukum kewarisan  tidak membedakan antara anak laki-laki dan anak perempuan, antara suami dan isteri, mereka berhak terhadap harta warisan, dan bagian anak laki-laki dua kali dari bagian anak perempuan, bagian seorang isteri atau suami sama yaitu tergantung ada atau tidaknya anak. Penulis melakukan penelitian kepada beberapa responden untuk mengetahui bagaimana tatacara pembagian waris di tengah masyarakat dan bagaimana pula peran seorang ibu dalam pendistribusian waris tersebut. Jenis metode penelitian menggunakan metode kualitatif dengan cara mewanwancarai beberapa responden.