Search Results (4,391)

Chile holds ~50% of temperate forests in the Southern Hemisphere, thus constituting a genetic–ecological heritage. However, intense anthropogenic pressures have been inducing distinct forest structural-regeneration patterns. Accordingly, we evaluated 22 soil properties at 0–5 and 5–20 cm depths in two protected sites, with similar perturbation records but contrasting post-disturbance regeneration stages: long-term secondary forest (~50 y) (SEC_FORST) (dominated by Chusquea sp.-understory) and a short-term forest after disturbance (~5 y) (FAD_IST) within a Nothofagus spp. forest to determine the potential of these soils to promote nutrient availability, water cycling, soil organic carbon (SOC) sequestration (CO₂→SOC), and microbiome. Results detected 93 correlations (r ≥ 0.80); however, no significant differences (p < 0.05) in physical or chemical properties, except for infiltration velocity (+27.97%), penetration resistance (−23%), SOC (+5.64%), and % Al saturation (+5.64%) relative to SEC_FORST, and a consistent trend of suitable values 0–5 > 5–20 cm were estimated. The SOC→CO₂ capacity reached 4.2 ± 0.5 (FAD_IST) and 2.7 ± 0.2 Mg C y⁻¹ (SEC_FORST) and only microbial abundance shifts were observed. These findings provide relevant insights on belowground resilience, evidenced by similar ecosystem services provision capacities over time, which may be influenced progressively by opportunistic Chusquea sp. Full article

In the current global change scenario, valuable tools for improving soils and increasing both agricultural productivity and food security, together with effective actions to mitigate the impacts of ongoing climate change trends, are priority issues. Soil Organic Carbon (SOC) acts on these two topics, as C is a core element of soil organic matter, an essential driver of soil fertility, and becomes problematic when disposed of in the atmosphere in its gaseous form. Laboratory methods to measure SOC are expensive and time-consuming. This Systematic Literature Review (SLR) aims to identify techniques and alternative ways to estimate SOC using Remote-Sensing (RS) spectral data and computer tools to process this database. This SLR was conducted using Systematic Review and Meta-Analysis (PRISMA) methodology, highlighting the use of Deep Learning (DL), traditional neural networks, and other machine-learning models, and the input data were used to estimate SOC. The SLR concludes that Sentinel satellites, particularly Sentinel-2, were frequently used. Despite limited datasets, DL models demonstrated robust performance as assessed by R2 and RMSE. Key input data, such as vegetation indices (e.g., NDVI, SAVI, EVI) and digital elevation models, were consistently correlated with SOC predictions. These findings underscore the potential of combining RS and advanced artificial-intelligence techniques for efficient and scalable SOC monitoring. Full article

The feasibility of electric vertical take-off and landing aircraft (eVTOL) relies on high-performance batteries with elevated energy and power densities for long-distance flight. However, systemic evaluation of battery chemistries for eVTOLs remains limited. This paper fills this research gap through a comprehensive investigation of current and emerging battery technologies. First, the properties of current battery chemistries are benchmarked against eVTOL requirements, identifying nickel-rich lithium-ion batteries (LIB), such as NMC and NCA, as the best suited for this application. Through comparison of 300 commercial battery cells, the Molicel INR21700-P45B cell is identified as the best candidate. Among next-generation batteries, SiSu solid-state batteries (SSBs) emerge as the most promising alternative. The performance of these cells is evaluated using a custom eVTOL battery simulation model for two eVTOL aircraft: the Volocopter VoloCity and the Archer Midnight. Results indicate that the Molicel INR21700-P45B underperforms in high-load scenarios, with a state of charge (SoC) at the end of the flight below the 30% safety margin. Simulated SoC values for the SiSu cell remain above this threshold, reaching 64.9% for the VoloCity and 64.8% for the Midnight. These results highlight next-generation battery technologies for eVTOLs and demonstrate the potential of SSBs to enhance flight performance. Full article

This study focuses on the internal temperature field of lithium-ion batteries, aiming to address the temperature variation issues arising from complex operating conditions in new energy batteries. To cope with unpredictable temperature fluctuations and long delay times, we propose an enhanced Convolutional Bidirectional Long Short-Term Memory Neural Network (CNN-Bi-LSTM-AM) model for temperature field prediction. The model integrates CNN for spatial feature extraction, Bi-LSTM for capturing temporal characteristics, and an attention mechanism to enhance the identification of key time-series features. By simulating temperature variations through a lumped model and thermal runaway model, we generate temperature field data, which are then utilized by the deep learning model to effectively capture the complex nonlinear relationships between temperature, voltage, state of charge (SOC), insulation resistance, current, and the internal temperature field. Performance evaluation using accuracy metrics and validation under various environmental conditions demonstrates that the model improves prediction accuracy by 1.2–2.3% compared to traditional methods (e.g., ARIMA, LSTM) with only a slight increase in testing time. Comprehensive evaluations, including ablation studies, thermal runaway tests, and computational efficiency analysis, further validate the robustness and applicability of the model. Furthermore, this study contributes to the optimization of battery life and safety by enhancing the prediction accuracy of the internal temperature field, thereby reducing resource waste caused by battery performance degradation. The findings provide an innovative approach to advancing new energy battery technology, promoting its development toward greater safety, stability, and environmental sustainability, which aligns with global sustainable development goals. Full article

Floodplains with fluvisols in Poland are crucial areas for both agriculture and environmental relevance. The largest areas of fluvisols are located in the floodplains of the Vistula River and have been identified as significant reservoirs of organic carbon. Humic substances were determined using the following procedure: C_dec—carbon after decalcification, C_HA+C_FA—carbon of humic and fulvic acids (extracted with 0.5 M NaOH solution), C_FA—carbon of fulvic acids (extracted with 2 M HCl solution), C_Humin—proportion of carbon in humins. The extraction of soluble organic matter (DOC and DON) was also determined. In the surface layer of grasslands, significantly higher mean contents of total organic carbon (TOC) and total nitrogen (Nt) were found compared with arable soils. In fluvisols used as grasslands, compared to the arable soils, significantly higher contents of C_dec, C_HA, C_FA, C_humin, DOC, DON, and C-stock were observed. The study results indicate that the agricultural use of environmentally valuable lands, such as floodplains, affected the stock of organic carbon and the properties of the humic substances. Grasslands stored significantly more SOC (10.9 kg m⁻²) than arable soils (6.7 kg m⁻²), emphasizing their role as organic carbon resevoirs. Agricultural practices such as limiting plowing and introducing grasslands can support carbon sequestration. Therefore, the role of fluvisols in floodplains in carbon sequestration should be emphasized in climate change mitigation strategies. Full article

Lithium-ion batteries are the core energy storage technology for electric vehicles and energy storage systems. Accurate state-of-charge (SOC) estimation is critical for optimizing battery performance, ensuring safety, and predicting battery lifetime. However, SOC estimation faces significant challenges under extreme temperatures and complex operating conditions. This review systematically examines the research progress on SOC estimation techniques over a wide temperature range, focusing on two mainstream approaches: model improvement and data-driven methods. The model improvement method enhances temperature adaptability through temperature compensation and dynamic parameter adjustment. Still, it has limitations in dealing with the nonlinear behavior of batteries and accuracy and real-time performance at extreme temperatures. In contrast, the data-driven method effectively copes with temperature fluctuations and complex operating conditions by extracting nonlinear relationships from historical data. However, it requires high-quality data and substantial computational resources. Future research should focus on developing high-precision, temperature-adaptive models and lightweight real-time algorithms. Additionally, exploring the deep coupling of physical models and data-driven methods with multi-source heterogeneous data fusion technology can further improve the accuracy and robustness of SOC estimation. These advancements will promote the safe and efficient application of lithium batteries in electric vehicles and energy storage systems. Full article

This paper presents an application of the Ant Colony Optimization (ACO) algorithm combined with the Logistic Regression (LR) method in the lead acid battery charging process. The ACO algorithm is used to obtain the best current pattern in the battery charging system to produce a smart charging system with a fast and safe charging current for the battery. The best current pattern is conducted gradually and repeatedly to obtain termination in the form of the best current pattern according to the ACO algorithm. The results of the algorithm design produce a current pattern consisting of 10 A, 5 A, 3 A, 2 A, and 0 A. The charging system with this algorithm can charge all types of lead acid batteries. In this research, the capacity of battery 1’s State of Charge (SOC) is 56%, battery 2’s SOC is 62%, and battery 3’s SOC is 80%. When recharging the battery’s full condition to a SOC of 100%, the length of time for charging battery 1 for 12.73 min, battery 2 takes 15.73 min, and battery 3 takes 29.11 min. Smart charging with the ACO can charge the battery safely without current fluctuations compared to charging without an algorithm such that the amount of charging current used is not dangerous for the battery. In addition, data analysis is carried out to determine the value of accuracy in estimating SOC charging using supervised learning linear regression. The results of the data analysis with linear regression show that the battery’s SOC estimation has good accuracy, with an RMSE value of 0.32 and an MAE of 0.27. Full article

The importance of urban forest management and carbon cycle research has increased amidst ongoing urbanization. Understanding the potential impact of fine woody debris (FWD) retention as a management strategy on the soil organic carbon (SOC) levels and stability in urban forests is crucial. In this study, four FWD retention treatments (no retention, CK; low retention, LR; medium retention, MR; and high retention, HR) were implemented in Harbin urban Picea koraiensis Nakai plantations to investigate the stability of the SOC pool in response to these treatments. The FWD retention treatment had no significant effect on the soil’s physical and chemical properties and SOC concentration, but significantly reduced the total potassium and NO₃⁻ concentrations. The FWD retention treatment increased active SOC fractions and carbon-degrading enzyme activities, while reducing leucine aminopeptidase, polyphenol oxidase enzyme activities, and the stability of the SOC pool. The random forest model showed that FWD retention, particulate organic carbon, cellobiohydrolases, and β-xylosidase enzyme activities were factors that significantly affected the stability of the SOC pool. These findings suggest that retaining a large amount of FWD in northeast China can benefit the soil carbon cycle in urban plantations by accelerating the turnover of active SOC fractions. Full article

The equivalent factor (EF) of adaptive equivalent consumption minimization strategy (A-ECMS) has a direct impact on the performance of hybrid electric trucks (HETs). Although EF on the state of charge (SoC) can effectively achieve fuel economy and SoC maintenance, battery life issues still need to be considered. Battery replacement costs are extremely high, directly affecting the operational costs of HETs. Thus, A-ECMS with enhanced battery life (A-ECMS-EBL) is proposed. Firstly, the near-optimal boundary of EF is determined to ensure the fuel economy of A-ECMS-EBL by analyzing the working mechanism of the HET powertrain. Secondly, a new EF calculation method is developed to enhance battery life. This method utilizes accelerator pedal opening (APO) feedback to optimize the power distribution between the engine and battery under high load conditions, thereby reducing the ratio of battery output power and number of battery cycle (NBC). Finally, the simulation results show that under typical cycle conditions, the equivalent fuel consumption (EFC) of A-ECMS-EBL increased by only 2.3% compared to the dynamic programming (DP), decreased by 1.1% compared to the A-ECMS, and the NBC significantly decreased by 6.12%. The results indicate that A-ECMS-EBL has significant advantages in improving fuel economy and enhancing battery life. Full article

This study investigates the post-agricultural transformation of Plaggic Podzols in a Subarctic environment, focusing on the Yamal region, Western Siberia. Agricultural practices historically altered the natural Histic Entic Podzols, leading to their conversion into anthropogenic soils with enhanced organic matter and nutrient profiles. Using a chronosequence approach, soil profiles were analyzed across active and abandoned agricultural fields to assess changes in soil properties over 25 years of abandonment. Results revealed a significant decline in SOC (2.73 → 2.21%, r² = 0.28) and clay (5.26 → 12.45%, r² = 0.84), which is reflected in the values of SOC/clay and SOC/(silt + clay) ratios. Nevertheless, the values of the ratios are still above the thresholds, indicating that the “health” of the soils is satisfactory. We detected a decrease in N_t (0.17 → 0.12%, r² = 0.79) and consequently an increase in the C:N ratio (18.6 → 22.1), indirectly indicating a decrease in SOM quality. Nutrient losses (NPK) with increasing abandonment periods were pronounced, with their concentrations indicative of soil quality degradation. Trace metal concentrations remained below pollution thresholds, reflecting minimal ecological risk according to Igeo, RI, and PLI indexes. The results highlight the necessity for further research on organo-mineral interactions and SOM quality assessment. The findings provide insights into the challenges of soil restoration in Polar regions, emphasizing the role of climate, land-use history, and management practices in shaping soil health and fertility. Full article

Soil health is essential for sustainable agriculture, influencing ecosystem health and orchard productivity of plum orchards. Global challenges such as climate change and soil contamination threaten to affect fertility and food security, requiring sustainable practices. The study assessed the effect of different orchard management practices on soil quality and nutrient distribution in Prunus domestica L. orchard located on the Moldavian Plateau in northeastern Romania under temperate humid subtropical climate conditions. Two systems were analyzed: conventional (herbicide-based) and conservative (cover crop-based). Soil samples (0–20 cm and 20–40 cm) were analyzed for soil organic carbon (SOC), total nitrogen (N_t), available phosphorus (P), and potassium (K). Results showed that conservative management improved soil health by increasing SOC nutrient cycling, mainly through organic matter inputs. Compared to 2022, the effectiveness of phosphorus in the conservative management system significantly increased (by 6%) in 2023, while potassium content decreased (by 30%), suggesting potential nutrient competition or insufficient replenishment under organic practices. SOC levels remained stable, supporting long-term carbon inputs. Conventional management maintained phosphorus and potassium but showed lower SOC levels and higher risks of soil fertility depletion. Strong correlations between SOC and nutrient indicators emphasize the critical role of organic inputs in nutrient mobilization. The findings indicate that cover crops are essential for sustainable soil management by enhancing carbon sequestration and nutrient cycling, thereby supporting the long-term sustainability of agricultural systems. Full article

Objectives: This study aimed to compare the pancreatic volume between beta-thalassemia major (β-TM) and beta-thalassemia intermedia (β-TI) patients and between thalassemia patients and healthy subjects and to determine the predictors of pancreatic volume and its association with glucose metabolism in β-TM and β-TI patients. Methods: We considered 145 β-TM patients and 19 β-TI patients enrolled in the E-MIOT project and 20 healthy subjects. The pancreatic volume and pancreatic and hepatic iron levels were quantified by magnetic resonance imaging. Results: The pancreatic volume indexed by body surface area (PVI) was significantly lower in both β-TI and β-TM patients compared to healthy subjects and in β-TM patients compared to β-TI patients. The only independent determinants of PVI were pancreatic iron in β-TM and hepatic iron in β-TI. In β-TM, there was an association between alterations of glucose metabolism and PVI, and PVI was a comparable predictor of altered glucose metabolism compared to pancreatic iron. Only one β-TI patient had an altered glucose metabolism and showed a reduced PVI and pancreatic iron overload. Conclusions: Thalassemia syndromes are characterized by a reduced pancreatic volume, associated with iron levels. In β-TM, the pancreatic volume and iron deposition are associated with the development and progression of alterations of glucose metabolism. Full article

Background: Subcutaneous fat deposition is associated with ducks’ meat quality and the methods used to cook them. However, the reasons underlying the differences in the lipid deposition of small-sized Wuqin10 meat ducks remain unclear. Method: In the present study, to elucidate the metabolic mechanisms of lipid deposition, we comprehensively analyzed the transcriptomics and lipidomics of subcutaneous fat in Wuqin10 meat ducks with different subcutaneous thicknesses with six replicates. Results: A total of 1120 lipids were detected in the lipidomic analysis, and 39 lipids were inexorably regulated in the ducks with the thick subcutaneous layer compared to those with the thin layer; further, the up-regulated lipids were primarily triglycerides (TGs), which may have resulted in adipocyte enlargement. Furthermore, the transcriptomic analysis identified 265 differentially expressed genes (DEGs), including 119 down-regulated and 146 up-regulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that the DEGs were significantly enriched in the histidine, arginine, proline metabolism signaling and adipocytokine signaling pathways. The protein–protein interaction (PPI) network in Cytoscape 3.8.2 identified hub genes HSP90AA1, RUNX2, ACTN2, ACTA1, IL10, CXCR4, EGF, SOCS3 and PTK2, which were associated with the JAK-STAT signaling pathway and regulation of adipocyte hypertrophy. Conclusion: Taken together, our findings reveal the patterns of lipids and the gene expression of subcutaneous fat, providing a basis for future studies of subcutaneous fat deposition in small-sized meat ducks. Full article

With the rapid development of lightweight network models and efficient hardware deployment techniques, the demand for real-time semantic segmentation in areas such as autonomous driving and medical image processing has increased significantly. However, realizing efficient semantic segmentation on resource-constrained embedded platforms still faces many challenges. As a classical lightweight semantic segmentation network, ENet has attracted much attention due to its low computational complexity. In this study, we optimize the ENet semantic segmentation network to significantly reduce its computational complexity through structural simplification and 8-bit quantization and improve its hardware compatibility through the optimization of on-chip data storage and data transfer while maintaining 51.18% mIoU. The optimized network is successfully deployed on hardware accelerator and SoC systems based on Xilinx ZYNQ ZCU104 FPGA. In addition, we optimize the computational units of transposed convolution and dilated convolution and improve the on-chip data storage and data transfer design. The optimized system achieves a frame rate of 130.75 FPS, which meets the real-time processing requirements in areas such as autonomous driving and medical imaging. Meanwhile, the power consumption of the accelerator is 3.479 W, the throughput reaches 460.8 GOPS, and the energy efficiency reaches 132.2 GOPS/W. These results fully demonstrate the effectiveness of the optimization and deployment strategies in achieving a balance between computational efficiency and accuracy, which makes the system well suited for resource-constrained embedded platform applications. Full article

In the field of sustainable mobility, this study highlights the importance of using machine learning for predictive modeling based on real traffic data collected from instrumented bicycles. The advent of advanced technologies like sustainable mobility apps, sensors, and advanced data analysis methods led to the ability to collect data from various sources, which enabled researchers to estimate battery state of charge (SOC) accurately. Most current research uses them in the lab experiments for data collection. In this work, we use real-time sensors data to construct data-driven models for lithium-ion battery SOC estimation. This research integrates both electric bicycle battery, environmental and route variables to achieve the following goals: (1) Collect a multimodal data set including operational, topography, vehicle, and external variables, (2) Preprocess data obtained from sensors installed on the electric bicycle battery, (3) Create models of lithium-ion battery SOC based on electric bicycle battery and environmental variables, and (4) Assess data-driven models and compare their performance for lithium-ion battery SOC with high accuracy. To achieve that, we conducted a real study to predict the Remaining Useful Life (RUL), as a measure of state of charge, of electric bicycle battery. The study was carried out on a 15 km cycle route in Medellín, Colombia, for 28 days. To estimate the RUL, we used four different machine learning algorithms: Long Short-Term Memory (LSTM), Support Vector Regression (SVR), AdaBoost, and Gradient Boost. Notably, data preprocessing techniques played a pivotal role, with a particular focus on smoothing sensor data using Convolutional Neural Networks (CNN). The results showed a significant improvement in prediction accuracy when using data preprocessing, confirming its importance in improving model performance. Furthermore, the comparison of network performance facilitated the selection of the most effective model for the test data. This study underscores the value of using real-world data to develop and validate predictive models in the pursuit of sustainable mobility solutions, and highlights the critical role of data-driven methodologies in addressing today’s urban transportation challenges. Full article