Search Results (8,174)

As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R² Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices. Full article

This study examines hydrogen production across 27 European countries, highlighting disparities due to varying energy policies and industrial capacities. Germany leads with 109 plants, followed by Poland, France, Italy, and the UK. Mid-range contributors like the Netherlands, Spain, Sweden, and Belgium also show substantial investments. Countries like Finland, Norway, Austria, and Denmark, known for their renewable energy policies, have fewer plants, while Estonia, Iceland, Ireland, Lithuania, and Slovenia are just beginning to develop hydrogen capacities. The analysis also reveals that a significant portion of the overall hydrogen production capacity in these countries remains underutilized, with an estimated 40% of existing infrastructure not operating at full potential. Many countries underutilize their production capacities due to infrastructural and operational challenges. Addressing these issues could enhance output, supporting Europe’s energy transition goals. The study underscores the potential of hydrogen as a sustainable energy source in Europe and the need for continued investment, technological advancements, supportive policies, and international collaboration to realize this potential. Full article

Detecting cracks during structural health monitoring is crucial for ensuring infrastructure safety and longevity. Using drones to obtain crack images and automate processing can improve the efficiency of crack detection. To address the challenges posed by the limited computing resources of edge devices in practical applications, we propose CrackScopeNet, a lightweight segmentation network model that simultaneously considers local and global crack features while being suitable for deployment on drone platforms with limited computational power and memory. This novel network features a multi-scale branch to improve sensitivity to cracks of varying sizes without substantial computational overhead along with a stripe-wise context attention mechanism to enhance the capture of long-range contextual information while mitigating the interference from complex backgrounds. Experimental results on the CrackSeg9k dataset demonstrate that our method leads to a significant improvement in prediction performance, with the highest mean intersection over union (mIoU) scores reaching 82.12%, and maintains a lightweight architecture with only 1.05 M parameters and 1.58 G floating point operations (FLOPs). In addition, the proposed model excels in inference speed on edge devices without a GPU thanks to its low FLOPs. CrackScopeNet contributes to the development of efficient and effective crack segmentation networks suitable for practical structural health monitoring applications using drone platforms. Full article

Momordica charantia is a climbing plant often used in traditional medicine to treat a large number of diseases, including diabetes. Salinity is one of the main stressors faced by plants, affecting almost half of irrigated agricultural land and constantly increasing. The aim of this study was to determine the resistance of some bitter cucumber genotypes to salt stress by means of dry matter analysis, chlorophyll a, chlorophyll b, malondialdehyde content, chlorophyll fluorescence, and potassium (K)/silicon (Si) and calcium (Ca)/silicon (Si) atomic ratios. Two varieties of bitter cucumber and three experimental lines were used for the experiment. Treatments with different saline solutions (100 mM of NaCl and 200 mM of NaCl) were applied and compared with an untreated control (0 mM of NaCl). The analyses revealed an increase in the dry matter content of the varieties subjected to salt stress. The Line 4 genotype showed an increase of up to 37.2% compared to the control when treated with 200 mM of NaCl. Following the analysis of the chlorophyll a content, a 38% decrease in its amount compared to the control was observed when treated with 100 mM of saline and 58.6% when treated with 200 mM of NaCl in genotype Line 4. Line 3 showed an increase in the chlorophyll a content compared to the control by 53% in the case of saline treatment with 200 mM. After the analysis of the chlorophyll b content, a 44% decrease was revealed in the case of Line 4 in the variant treated with 100 mM compared to the control and a 61% decrease in the 200 mM NaCl treatment. The highest increase in the concentration of malondialdehyde was recorded in the case of Line 4 in the variant treated with 200 mM of NaCl by 41% compared to the control. The maximum quantum yield of PS II decreased in the treated variants compared to the control plants. The most pronounced difference compared to the control was registered in the case of Line 4, where the treatment with 100 mM of NaCl caused a decrease of 16%, and the treatment with 200 mM caused a decrease of 25%. In the case of the atomic ratio, significant decreases in K and Ca were observed in the NaCl-treated variants. The observed differences between the values obtained for each studied genotype highlight the different degrees of their resistance to salinity. Full article

Introducing pumped storage to retrofit existing cascade hydropower plants into hybrid pumped storage hydropower plants (HPSPs) could increase the regulating capacity of hydropower. From this perspective, a capacity configuration optimization method for a multi-energy complementary power generation system comprising hydro, wind, and photovoltaic power is developed. Firstly, to address the uncertainty of wind and photovoltaic power outputs, the K-means clustering algorithm is applied to deal with historical data on load and photovoltaic, wind, and water inflow within a specific region over the past year. This process helps reduce the number of scenarios, resulting in 12 representative scenarios and their corresponding probabilities. Secondly, with the aim of enhancing outbound transmission channel utilization and decreasing the peak–valley difference for the receiving-end power grid’s load curve, a multi-objective optimization model based on the normal boundary intersection (NBI) algorithm is developed for the capacity optimization of the multi-energy complementary power generation system. The result shows that retrofitting cascade hydropower plants with pumped storage units to construct HPSPs enhances their ability to accommodate wind and photovoltaic power. The optimal capacity of wind and photovoltaic power is increased, the utilization rate of the system’s transmission channel is improved, and the peak-to-valley difference for the residual load of the receiving-end power grid is reduced. Full article

Much attention has been devoted to the effectiveness of forgiveness interventions in children and adolescents featuring two premier programs by Enright and his colleagues. Little attention has been given to psychoeducational forgiveness interventions with emerging adults. This is a narrative review of REACH Forgiveness studies with emerging adults (ages 18–25). The life tasks of emerging adults justify offering psychoeducational interventions to emerging adults. Research studies on REACH Forgiveness (k = 17), non-REACH Forgiveness studies (k = 4), and community campaigns at universities (k = 4) with emerging adults are summarized. Effect sizes per hour (d/h) for REACH Forgiveness studies (k = 13 for psychoeducational groups; k = 4 for self-administered workbooks) are reported. The proto-REACH groups (k = 5) had mean d/h = 0.104; REACH groups (k = 9) had d/h = 0.101; self-administered workbooks (k = 3) had mean d/h = 0.15; non-REACH Forgiveness studies (k = 4) had d/h = 0.09. All studies were from the USA, and most were from universities. However, a recent article reported randomized controlled trials in five non-USA samples of adults (N = 4598). A 3.34-h workbook had d/h = 0.16, suggesting that the workbook might be effective with emerging adults around the world. Finally, three USA Christian universities had public health immersion campaigns to promote forgiveness, and a community psychoeducational campaign in 2878 secular university students in Colombia (of ~9000 total) allowed choices among 16 psychoeducational activities. The number of activities used was proportional to forgiveness experienced. For forgiveness, d = 0.36 plus substantial reductions in depression and anxiety, indicating strong public health potential of forgiveness psychoeducation in emerging adults worldwide. Full article

The design of transportation networks is generally performed on the basis of the division of a metropolitan region into communities. With the combination of the scale, population density, and travel characteristics of each community, the transportation routes and stations can be more precisely determined to meet the travel demand of residents within each of the communities as well as the transportation links among communities. To accurately divide urban communities, the original word vector sampling method is improved on the classic Deepwalk model, proposing a Random Walk (RW) algorithm in which the sampling is modified with the generalized travel cost and improved logit model. Urban spatial community detection is realized with the K-means algorithm, building the F-Deepwalk model. Using the basic road network as an example, the experimental results show that the Deepwalk model, which considers the generalized travel cost of residents, has a higher profile coefficient, and the performance of the model improves with the reduction of random walk length. At the same time, taking the Shijiazhuang urban rail transit network as an example, the accuracy of the model is further verified. Full article

3D object-detection based on LiDAR point clouds can help driverless vehicles detect obstacles. However, the existing point-cloud-based object-detection methods are generally ineffective in detecting small objects such as pedestrians and cyclists. Therefore, a small-object-detection algorithm based on clustering is proposed. Firstly, a new segmented ground-point clouds segmentation algorithm is proposed, which filters out the object point clouds according to the heuristic rules and realizes the ground segmentation by multi-region plane-fitting. Then, the small-object point cloud is clustered using an improved DBSCAN clustering algorithm. The K-means++ algorithm for pre-clustering is used, the neighborhood radius is adaptively adjusted according to the distance, and the core point search method of the original algorithm is improved. Finally, the detection of small objects is completed using the directional wraparound box model. After extensive experiments, it was shown that the precision and recall of our proposed ground-segmentation algorithm reached 91.86% and 92.70%, respectively, and the improved DBSCAN clustering algorithm improved the recall of pedestrians and cyclists by 15.89% and 9.50%, respectively. In addition, visualization experiments confirmed that our proposed small-object-detection algorithm based on the point-cloud clustering method can realize the accurate detection of small objects. Full article

The aim of this study was to determine the most appropriate advanced methods for distinguishing the gait of healthy children (CO) from the gait of children with cerebral palsy (CP) based on electromyography (EMG) parameters and coactivations. An EMG database of 22 children (aged 4–11 years) was used in this study, which included 17 subjects in the CO group and 5 subjects in the CP group. EMG time parameters were calculated for the biceps femoris (BF) and semitendinosus (SE) muscles and coactivations for the rectus femoris (RF)/BF and RF/SE muscle pairs. To obtain a more accurate classification result, data augmentation was performed, and three classification algorithms were used: support vector machine (SVM), k-nearest neighbors (KNNs), and decision tree (DT). The accuracy of the root-mean-square (RMS) parameter and KNN algorithm was 95%, the precision was 94%, the sensitivity was 90%, the F1 score was 92%, and the area under the curve (AUC) score was 98%. The highest classification accuracy based on coactivations was achieved using the KNN algorithm (91–95%). It was determined that the KNN algorithm is the most effective, and muscle coactivation can be used as a reliable parameter in gait classification tasks. Full article

This paper identifies patterns in total and per capita CO₂ emissions among 208 countries considering different emission sources, such as cement, flaring, gas, oil, and coal. This research uses linear and non-linear dimensional reduction techniques, combining K-means clustering with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), which allows the identification of distinct emission profiles among nations. This approach allows effective clustering of heterogeneous countries despite the highly dimensional nature of emissions data. The optimal number of clusters is determined using Calinski–Harabasz and Davies–Bouldin scores, of five and six clusters for total and per capita CO₂ emissions, respectively. The findings reveal that for total emissions, t-SNE brings together the world’s largest economies and emitters, i.e., China, USA, India, and Russia, into a single cluster, while PCA provides clusters with a single country for China, USA, and Russia. Regarding per capita emissions, PCA generates a cluster with only one country, Qatar, due to its significant flaring emissions, as byproduct of the oil industry, and its low population. This study concludes that international collaboration and coherent global policies are crucial for effectively addressing CO₂ emissions and developing targeted climate change mitigation strategies. Full article

Climate change and rising sea levels pose significant threats to coastal regions, necessitating accurate and timely forecasts. Current methods face limitations due to their inability to fully capture nonlinear complexities, high computational costs, gaps in historical data, and bridging the gap between short-term and long-term forecasting intervals. Our study addresses these challenges by combining advanced machine learning techniques to provide region-specific sea level predictions in the Mediterranean Sea. By integrating high-resolution sea surface temperature data spanning 40 years, we employed a tailored k-means clustering technique to identify regions of high variance. Using these clusters, we developed RNN-GRU models that integrate historical tide gauge data and sea surface height data, offering regional sea level predictions on timescales ranging from one month to three years. Our approach achieved the highest predictive accuracy, with correlation values ranging from 0.65 to 0.84 in regions with comprehensive datasets, demonstrating the model’s robustness. In areas with fewer tide gauge stations or shorter time series, our models still performed moderately well, with correlations between 0.51 and 0.70. However, prediction accuracy decreases in regions with complex geomorphology. Yet, all regional models effectively captured sea level variability and trends. This highlights the model’s versatility and capacity to adapt to different regional characteristics, making it invaluable for regional planning and adaptation strategies. Our methodology offers a powerful tool for identifying regions with similar variability and providing sub-regional scale predictions up to three years in advance, ensuring more reliable and actionable sea level forecasts for Mediterranean coastal communities. Full article

This report presents the low-frequency (LF), static, and dynamic dielectric properties of neopentyl glycol (NPG), an orientationally disordered crystal (ODIC)-forming material important for the barocaloric effect applications. High-resolution tests were carried out for $173\mathrm{K}<T<440K$, in liquid, ODIC, and solid crystal phases. The support of the innovative distortion-sensitive analysis revealed a set of novel characterizations important for NPG and any ODIC-forming material. First, the dielectric constant in the liquid and ODIC phase follows the Mossotti Catastrophe-like pattern, linked to the Clausius–Mossotti local field. It challenges the heuristic paradigm forbidding such behavior for dipolar liquid dielectrics. For DC electric conductivity, the prevalence of the ‘critical and activated’ scaling relation is evidenced. It indicates that commonly applied VFT scaling might have only an effective parameterization meaning. The discussion of dielectric behavior in the low-frequency (LF) domain is worth stressing. It is significant for applications but hardly discussed due to the cognitive gap, making an analysis puzzling. For the contribution to the real part of dielectric permittivity in the LF domain, associated with translational processes, exponential changes in the liquid phase and hyperbolic changes in the ODIC phase are evidenced. The novelty also constitutes $tg\mathit{\delta }$ temperature dependence, related to energy dissipation. The results presented also reveal the strong postfreezing/pre-melting-type effects on the solid crystal side of the strongly discontinuous ODIC–solid crystal transition. So far, such a phenomenon has been observed only for the liquid–solid crystal melting transition. The discussion of a possible universal picture of the behavior in the liquid phase of liquid crystalline materials and in the liquid and ODIC phases of NPG is particularly worth stressing. Full article

(1) Background: Fibromyalgia syndrome (FMS) is a chronic pain condition with widespread pain and multiple comorbidities, for which conventional therapies offer limited benefits. The reserpine-induced myalgia (RIM) model is an efficient animal model of FMS in rodents. This study aimed to develop a pharmacokinetic–pharmacodynamic (PK–PD) model of reserpine in rats, linking to its impact on monoamines (MAs). (2) Methods: Reserpine was administered daily for three consecutive days at dose levels of 0.1, 0.5, and 1 mg/kg. A total of 120 rats were included, and 120 PK and 828 PD observations were collected from 48 to 96 h after the first dose of reserpine. Non-linear mixed-effect data analysis was applied for structural PK–PD model definition, variability characterization, and covariate analysis. (3) Results: A one-compartment model best described reserpine in rats (V = 1.3 mL/kg and CL = 4.5 × 10⁻¹ mL/h/kg). A precursor-pool PK–PD model (k_in = 6.1 × 10⁻³ mg/h, k_p = 8.6 × 10⁻⁴ h⁻¹ and k_out = 2.7 × 10⁻² h⁻¹) with a parallel transit chain (k₀ = 1.9 × 10⁻¹ h⁻¹) characterized the longitudinal levels of MA in the prefrontal cortex, spinal cord, and amygdala in rats. Reserpine stimulates the degradation of MA from the pool compartment (Slope₁ = 1.1 × 10⁻¹ h) and the elimination of MA (Slope₂ = 1.25 h) through the transit chain. Regarding the reference dose (1 mg/kg) of the RIM model, the administration of 4 mg/kg would lead to a mean reduction of 65% (C_max), 80% (C_min), and 70% (AUC) of MA across the brain regions tested. (4) Conclusions: Regional brain variations in neurotransmitter depletion were identified, particularly in the amygdala, offering insights for therapeutic strategies and biomarker identification in FMS research. Full article

There is a need to find novel sources of fertilizers to meet the increasing food demands of a growing human population and alternatives to mined and synthetic fertilizers for the certified organic sector. Composting is a common method for processing and stabilizing organic residues for use in horticulture. To that end, a small-scale composting experiment with six combinations of dried and ground rockweed (Ascophyllum nodosum), algae fiber from chemically processed rockweed, ground bones and fishmeal from cod (Gadus morhua), and ground blue mussels (Mytilus edulis) was conducted in Dewar flasks to assess whether these residues are suitable for composting and have potential for use as fertilizers. Expanded clay aggregates were used as a bulking material. Physicochemical analyses were performed on the residues and their mixtures before and after composting, and the temperature in the flasks was monitored for 92 days. Suitability was determined by evaluating the temperature dynamics, changes in physiochemical parameters, and nutrient profiles. All treatments generated heat, with reductions in C/N ratio, weight, and volume, demonstrating suitability for composting. The treatments with algae fiber had a higher mean temperature (34.5 vs. 29.0 °C) and more degree days above the thermophilic range (mean = 176- vs. 19-degree days), the greatest reduction in volume (mean = 35% vs. 27%), and the lowest C/N ratios at the end of active composting (18 vs. 24) compared to the treatments with dried and ground seaweed. In terms of fertilizer value, none of the finished composts were balanced for use as fertilizers alone and, in some cases, contained too much Na, but contained sufficient concentrations of K, S, Mg, and Ca and could be a valuable source of these nutrients and organic matter in combination with other N- and P-rich sources. Full article

Detecting oil spills in marine environments is crucial for avoiding environmental damage and facilitating rapid response efforts. In this study, we propose a robust method for oil spill detection leveraging state-of-the-art (SOTA) deep learning techniques. We constructed an extensive dataset comprising images and frames extracted from video sourced from Google, significantly augmenting the dataset through frame extraction techniques. Each image is meticulously labeled to ensure high-quality training data. Utilizing the Yolov8 segmentation model, we trained our oil spill detection model to accurately identify and segment oil spills in ocean environments. K-means and Truncated Linear Stretching algorithms are combined with trained model weight to increase model detection accuracy. The model demonstrated exceptional performance, yielding high detection accuracy and precise segmentation capabilities. Our results indicate that this approach is highly effective for real-time oil spill detection, offering a promising tool for environmental monitoring and disaster management. In training metrics, the model reached over 97% accuracy in 100 epochs. In evaluation, model achieved its best detection rates by 94% accuracy in F1, 93.9% accuracy in Precision, and 95.5% [email protected] accuracy in Recall curves. Full article