Search Results (4,849)

The energy management strategy of Hybrid Electric Vehicles (HEVs) plays a key role in improving fuel economy and reducing battery energy consumption. This paper proposes a Deep Reinforcement Learning-based energy management strategy optimized by the experience pool (P-HER-DDPG), aimed at improving the fuel efficiency of HEVs while accelerating the training speed. The method integrates the mechanisms of Prioritized Experience Replay (PER) and Hindsight Experience Replay (HER) to address the reward sparsity and slow convergence issues faced by the traditional Deep Deterministic Policy Gradient (DDPG) algorithm when handling continuous action spaces. Under various standard driving cycles, the P-HER-DDPG strategy outperforms the traditional DDPG strategy, achieving an average fuel economy improvement of 5.85%, with a maximum increase of 8.69%. Compared to the DQN strategy, it achieves an average improvement of 12.84%. In terms of training convergence, the P-HER-DDPG strategy converges in 140 episodes, 17.65% faster than DDPG and 24.32% faster than DQN. Additionally, the strategy demonstrates more stable State of Charge (SOC) control, effectively mitigating the risks of battery overcharging and deep discharging. Simulation results show that P-HER-DDPG can enhance fuel economy and training efficiency, offering an extended solution in the field of energy management strategies. Full article

In the electrification of low-altitude aircraft, aviation hybrid power systems have become one of the core research areas in this field due to their significant advantages of low emissions and long endurance. The energy management strategy is an important part of the design of aviation hybrid power systems and has a significant impact on the performance and safety.This paper first develops a 200 kW dual DC-bus series hybrid power system prototype for low-altitude aircraft and its Simulink simulation model; then, it proposes a rule-based energy management strategy that uses the smoothness of the state of charge (SOC) of energy storage batteries as a coordination criterion. The strategy is validated via ground tests, where the battery SOC remains above 30%, the system response time is within 5 s, and the DC-bus voltage fluctuation is within 1%. These results demonstrate the strategy’s feasibility, providing a reference for designing and implementing series hybrid power systems. Full article

While Forgetting Factor Recursive Least Square (FFRLS) algorithms with evaluation mechanisms have been developed to address SOC-dependent parameter mapping shifts and their efficacy has been proven in Li-ion batteries, their applicability to lithium–sulfur (Li-S) batteries remains uncertain due to different electrochemical characteristics. This study critically evaluates the applicability of a Fisher information matrix-constrained FFRLS framework for online parameter identification in Li-S battery equivalent circuit network (ECN) models. Experimental validation using distinct drive cycles showed that the identification results of polarization-related parameters are significantly biased between different current excitations, and root mean square error (RMSE) variations diverge by 100%, with terminal voltage estimation errors more than 0.05 V. The parametric uncertainty under variable excitation profiles and voltage plateau estimation deficiencies confirms the inadequacy of such approaches, constraining model-based online identification viability for Li-S automotive applications. Future research should therefore prioritize hybrid estimation architectures integrating electrochemical knowledge with data-driven observers, alongside excitation capturing specifically optimized for Li-S online parameter observability requirements and cell nonuniformity and aging condition consideration. Full article

Soil desertification control is a global challenge, and the barrenness of sandy soil limits the growth of plants. To enhance the vegetation growth capacity of sandy soils, the preparation of soil amendments and the experiment of improving desertified soil were conducted. The soil amendment is prepared by mixing polyacrylamide (2.7%), biochar (16.2%), sodium bentonite (16.2%), straw fibers (5.4%), corn straw (2.7%), sheep manure organic fertilizer (54.1%), and composite microbial agents (2.7%). The laboratory experiment was conducted to investigate the effects of varying rates (0, 1.5%, 3%, 4.5%, 6%) of composite soil amendments on the properties of sandy soil and the Lolium perenne L. with a growth period of 30–60 days. The results indicated that the application of composite amendments at different rates maintained the soil pH between 7.0 and 7.5, increased the electrical conductivity, and significantly improved the soil moisture content, soil organic carbon (SOC), total nitrogen (TN), and total phosphorus contents. Under the condition of 3% amendment, the soil TN content increased from 0.74 to 1.83 g·kg⁻¹. The composite amendments remarkably promoted L. perenne growth, as evidenced by increased plant height, dry weight, and nitrogen and phosphorus nutrient content, while the SOC content increased by 1–4 times. The application of composite amendments, prepared by mixing materials such as biochar, organic fertilizer, crop straw, microbial agents, bentonite, and water-retaining agents, enhanced the physicochemical properties of sandy soil and promoted L. perenne growth, and 3% was the most suitable application rate. These findings are expected to advance desertification-controlling technologies and enhance soil carbon sequestration capacity. Full article

Background/objectives: Multiple systemic treatments are available for metastatic castration-resistant prostate cancer (mCRPC), with unclear safety profiles. This study seeks to describe the safety determined in randomized clinical trials of a systemic treatment for mCRPC and whether safety differs by age. Methods: We utilized individual patient data from industry-funded phase 2/3 trials in mCRPC on abiraterone acetate (AA). Vivli, a clinical trial repository site, was used. One investigator independently performed screening. Relative effects of treatment were assessed with frequencies and odds of serious adverse events (SAEs). The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was used. Subgroup analysis measured odds of SAEs as modified by age. Results: We identified 14 trials with 4296 patients. The median age of participants was 69 years. Nearly all participants experienced at least one adverse event (98.4% abiraterone, 97.3% standard of care [SOC]). More serious adverse events (grade 3 or 4) and deaths (grade 5) occurred in those receiving SOC (71.8%) compared to abiraterone (64.1%). The most frequent adverse event category was “Musculoskeletal and Connective Tissue Disorders”. The most frequent event types included anemia, back pain, hypertension, fatigue, hypokalemia, and bone pain. The odds of all events were lower in those receiving abiraterone compared to SOC. Odds of a serious musculoskeletal event were lower in older subjects by 22% (OR 0.78; 95% CI 0.63, 0.96). Conclusions: In this IPD meta-analysis, abiraterone acetate provides no greater risk of SAE in those receiving abiraterone than those receiving SOCs. Patients in the RCTs are younger and healthier than those in the general population; consequently, the results of RCTS might not be applied to the general population, especially those under-represented in the RCTs. There is a need to further evaluate abiraterone-related fractures and neuromuscular toxicities (NMTs) as key outcomes to gain insight into risk factors related to these adverse events. A real-world prospective study is warranted to examine the overall risks and benefits associated with treatment. Full article

Black Canadians are one of the fastest-growing groups in Canada, with 59% of this population comprising immigrants. Ongoing systemic racism and discrimination have serious consequences for the mental health of Black Canadians. While research and policy efforts to address the mental health needs of this population are ongoing, a greater understanding of the healing practices relevant to this diverse population is needed. This scoping review synthesized and discussed key features of culturally inclusive, affirming, and contextually relevant approaches and practices for mental health care and healing with Black Canadians, as well as identified limitations and gaps in the current research. This study followed the PRISMA guidelines for scoping reviews and conducted a search in PsycINFO, MEDLINE, Embase, SocINDEX, CINAHL, Sociological Abstracts, and Global Health in October 2023. A total of 34 articles met the inclusion criteria. The review identified that most studies were conducted in one Canadian province (i.e., Ontario) and involved diverse perspectives, including service users and providers. The thematic review of articles revealed limited research regarding specific interventions, but identified many commonly reported features of culturally and contextually relevant approaches to mental health care and healing for Black Canadians that broaden the scope of mental health care beyond Euro-Western clinical models, including taking a holistic and empowerment-based approach, engaging in culturally affirming care, a social justice approach, community-centred and collaborative healing, and the necessity of practitioner education. Recommendations for practice, policy, education, and research are provided to support more inclusive and responsive mental health care systems for Black Canadians. Full article

As human–robot shared service environments become increasingly common, autonomous navigation in narrow space environments (NSEs), such as indoor corridors and crosswalks, becomes challenging. Mobile robots must go beyond reactive collision avoidance and interpret surrounding risks to proactively select safer routes in dynamic and spatially constrained environments. This study proposes a deep reinforcement learning (DRL)-based navigation framework that enables mobile robots to interact with pedestrians while identifying and traversing open and safe spaces. The framework fuses 3D LiDAR and RGB camera data to recognize individual pedestrians and estimate their position and velocity in real time. Based on this, a human-aware occupancy map (HAOM) is constructed, combining both static obstacles and dynamic risk zones, and used as the input state for DRL. To promote proactive and safe navigation behaviors, we design a state representation and reward structure that guide the robot toward less risky areas, overcoming the limitations of traditional approaches. The proposed method is validated through a series of simulation experiments, including straight, L-shaped, and cross-shaped layouts, designed to reflect typical narrow space environments. Various dynamic obstacle scenarios were incorporated during both training and evaluation. The results demonstrate that the proposed approach significantly improves navigation success rates and reduces collision incidents compared to conventional navigation planners across diverse NSE conditions. Full article

At present, environmental pollution is becoming more and more serious, and the energy problem is becoming more prominent. Energy-braking recovery can collect the mechanical energy lost in the traditional braking process and convert it into electricity or other forms of energy for vehicle reuse, thus reducing carbon emissions, achieving energy saving and emission reduction, and promoting green development. Based on this, this paper studies the energy-braking recovery method. The study focuses specifically on the recovery of energy during vehicle braking triggered by brake-signal activation, without addressing alternative deceleration strategies under braking conditions. The proposed energy-braking recovery scheme is evaluated primarily through simulation, with the analysis grounded in practical application scenarios and leveraging existing technologies. Firstly, the principle of energy-braking recovery is introduced, and the method of estimating the State on Charge (SOC) of the battery and controlling the motor speed is determined. Then, the simulation model of the energy brake recovery system is built with MATLAB R2023b (MathWorks, Natick, MA, USA), and the design ideas and specific structures of the three modules of the simulation model are introduced in detail. Finally, the results of the simulated motor speed and SOC value of the battery are analysed, and it is confirmed that they meet the requirements of the system and achieve close to the ideal effect. Full article

The glomalin-related soil protein (GRSP), a class of stable glycoproteins produced by arbuscular mycorrhizal fungi, constitute an important microbial-derived carbon pool in terrestrial ecosystems. However, the response of GRSP accumulation to land-use change and quantitative contribution to soil organic carbon (SOC) pools, as well as the environmental and edaphic factors controlling GRSP dynamics in different land-use systems, require further elucidation. To address these knowledge gaps, we systematically collected surface soil samples (0–20 cm depth) from 72 plots across three land-use types—tea plantations (TP; n = 24), artificial forests (AF; n = 24), and natural forests (NF; n = 24) in China’s Huangshan Mountain region between July and August 2024. GRSP was extracted via autoclaving (121 °C, 20 min) in 20 mM citrate buffer (pH 8.0), fractionated into total GRSP (T-GRSP), and quantified using the Bradford assay. Results revealed distinct patterns in soil carbon storage, with NF exhibiting the highest concentrations of both SOC (33.2 ± 8.69 g kg⁻¹) and total GRSP (T-GRSP: 2.64 ± 0.34 g kg⁻¹), followed by AF (SOC: 14.9 ± 2.55 g kg⁻¹; T-GRSP: 1.42 ± 0.25 g kg⁻¹) and TP (SOC: 7.07 ± 1.72 g kg⁻¹; T-GRSP: 0.58 ± 0.11 g kg⁻¹). Although absolute GRSP concentrations were lowest in TP, its proportional contribution to SOC remained consistent across land uses (TP: 8.72 ± 2.84%; AF: 9.69 ± 1.81%; NF: 8.40 ± 2.79%). Statistical analyses identified dissolved organic carbon and microbial biomass carbon as primary drivers of GRSP accumulation. Structural equation modeling further demonstrated that land-use type influenced SOC through its effects on MBC and fine-root biomass, which subsequently enhanced GRSP production. These findings demonstrate that undisturbed forest ecosystems enhance GRSP-mediated soil carbon sequestration, emphasizing the critical role of natural forest conservation in ecological sustainability. Full article

Islanded DC microgrids face challenges in voltage stability and communication overhead due to renewable energy variability. A novel enhanced distributed coordinated control framework, based on adaptive event-triggered mechanisms, is developed for the efficient management of multiple hybrid energy storage systems (HESSs) in islanded DC microgrids (MGs). We propose a hierarchical distributed control framework integrating ANN-based controllers and adaptive event-triggered mechanisms to dynamically regulate power flow and minimise communication. This system utilises a hierarchical coordinated control method (HCCM) with primary virtual resistance droop control integrated with state-of-charge (SoC) management and secondary control for voltage regulation and proportional current distribution through optimised communication networks. The integration of artificial neural network (ANN)-based controllers alongside traditional PI control leads to an improvement in system responsiveness. The control approach dynamically adjusts the trigger parameters to minimise communication overhead with tight voltage regulation. An extensive simulation using MATLAB/Simulink shows how the system can effectively manage variability in renewable energy sources and maintain stable voltage profiles with precise power distribution and minimal bus voltage fluctuations. Simulations confirm enhanced voltage regulation (±0.5% deviation), proportional current sharing (98% accuracy), and 60% communication reduction under load transients (outcomes). Full article

Background/Objectives: Bolting in lettuce (Lactuca sativa L.) is highly sensitive to elevated temperatures, leading to premature flowering and reduced crop quality and yield. Although SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) is a well-known floral integrator in Arabidopsis, its role in heat-induced bolting in lettuce remains unclear. Methods: In this study, we generated CRISPR/Cas9-mediated LsSOC1 knockout (KO) lines and evaluated their phenotypes under high-temperature conditions. Results: LsSOC1-KO lines exhibited delayed bolting up to 18.6 days, and stem elongation was reduced by approximately 3.8 cm, which is equivalent to a 36.1% decrease compared to wild-type (WT) plants. Transcriptome analysis of leaf and bud tissues identified 32 up-regulated and 10 down-regulated genes common to leaf tissue (|log₂FC| ≥ 1, adjusted p < 0.05). Among them, GA20-oxidase1 was significantly down-regulated in both tissues, which may have contributed to delayed floral transition and possibly to reduced stem elongation, although tissue-specific regulation of gibberellin metabolism warrants further investigation. In contrast, genes encoding heat shock proteins, ROS-detoxification enzymes, and flavonoid biosynthetic enzymes were up-regulated, suggesting a dual role of LsSOC1 in modulating thermotolerance and floral transition. qRT-PCR validated the sustained suppression of flowering-related genes in LsSOC1 KO plants under 37 °C heat stress. Conclusions: These findings demonstrate that LsSOC1 is a key integrator of developmental and thermal cues, orchestrating both bolting and stress-responsive transcriptional programs. Importantly, delayed bolting may extend the harvest window and improve postharvest quality in lettuce, highlighting LsSOC1 as a promising genetic target for breeding heat-resilient leafy vegetables. Full article

Atmospheric aerosols are recognized as a major air pollutant with significant impacts on human health, air quality, and climate. Yet, the chemical composition and seasonal variability of aerosols remain underexplored in several Western Mediterranean regions. This study presents a year-long investigation of PM_2.5 and PM₁₀ in Tetouan, Northern Morocco, where both local emissions and regional transport influence air quality. PM_2.5 and PM₁₀ samples were collected and analysed for total mass and comprehensive chemical characterization, including organic carbon (OC), elemental carbon (EC), water-soluble ions (WSIs), and sugar tracers (levoglucosan, arabitol, and glucose). Concentration-weighted trajectory (CWT) modelling and air mass back-trajectory analyses were used to assess potential source regions and transport pathways. PM_2.5 concentrations ranged from 4.2 to 41.8 µg m⁻³ (annual mean: 18.0 ± 6.4 µg m⁻³), while PM₁₀ ranged from 11.9 to 66.3 µg m⁻³ (annual mean: 30.8 ± 9.7 µg m⁻³), with peaks in winter and minima in spring. The PM_2.5-to-PM₁₀ ratio averaged 0.59, indicating a substantial accumulation of particle mass within the fine fraction, especially during the cold season. Carbonaceous aerosols dominated the fine fraction, with total carbonaceous aerosol (TCA) contributing ~52% to PM_2.5 and ~34% to PM₁₀. Secondary organic carbon (SOC) accounted for up to 90% of OC in PM_2.5, reaching 7.3 ± 3.4 µg m⁻³ in winter. WSIs comprised ~39% of PM_2.5 mass, with sulfate, nitrate, and ammonium as major components, peaking in summer. Sugar tracers exhibited coarse-mode dominance, reflecting biomass burning and biogenic activity. Concentration-weighted trajectory and back-trajectory analyses identified the Mediterranean Basin and Iberian Peninsula as dominant source regions, in addition to local urban emissions. Overall, this study attempts to fill a critical knowledge gap in Southwestern Mediterranean aerosol research by providing a comprehensive characterization of PM_2.5 and PM₁₀ chemical composition and their seasonal dynamics in Tetouan. It further offers new insights into how a combination of local emissions and regional transport shapes the aerosol composition in this North African urban environment. Full article

Mapping Soil Organic Carbon (SOC) at a regional scale is essential for assessing soil health and supporting sustainable land management. This study evaluates the potential of using Sentinel-2 imagery and regional calibration to predict SOC in salt-affected agricultural lands in Portugal while also assessing the influence of soil properties, such as texture and salinity, on SOC prediction. A per-pixel mosaicking approach was set to analyze the relationship of spectral reflectance indices linked to bare soil conditions with SOC. SOC prediction models were developed using linear regression (LR) and Partial Least Squares Regression (PLSR). Among the tested approaches, the combination of the maximum Bare Soil Index (maxBSI) with LR produced the most accurate SOC predictions, achieving moderate prediction performance (R² = 0.52; RMSE = 0.16%; LCCC = 70%). This approach slightly outperformed the application of the 90th percentile of bare soil pixels (R90 reflectance) and the median approaches with PLSR. Notably, our findings indicate that soil salinity did not significantly affect SOC predictions within the observed salinity range of EC_e between 1.2 and 10.4 dS m⁻¹ in topsoil. However, further case studies are needed to validate this observation across diverse agricultural conditions. In contrast, soil texture and moisture content emerged as the dominant factors influencing soil reflectance. The combination of per-pixel mosaicking and regional calibration provides a practical, scalable, and cost-effective method for generating SOC maps using open access satellite imagery. To support wider adoption and improve model generalizability, future studies should incorporate a larger number of fields with a wider range of soil properties, crop types, and management practices. Full article

Accurately assessing the spatiotemporal dynamics and influencing factors of ecosystem stability in arid desert regions (ADR) is crucial for ecological conservation and the achievement of high-quality regional development. However, existing assessment frameworks generally fail to adapt to the extremely fragile ecological conditions of ADR. Therefore, the Alxa Desert, a typical region, was selected as the research region, and an ecosystem stability assessment framework tailored to regional characteristics (perturbation–resilience–function) was constructed. Perturbation represents external pressure, resilience reflects the capacity for recovery and adaptation, and function serves as the supporting foundation. The three dimensions are dynamically coupled and jointly determine the stability status of the ecosystem in the Alxa Desert. Methodologically, this study innovatively introduces the Cloud Model–Analytic Hierarchy Process (CM-AHP) to calculate indicator weights, which more effectively addressed the widespread fuzziness and uncertainty inherent in ecosystem assessments compared to traditional methods. In addition, spatial autocorrelation methods was applied to reveal the spatial and temporal evolution characteristics of ecosystem stability from 2000 to 2020. Furthermore, the optimal parameters geographical detector model (OPGDM) was applied to analyze the effects of natural and human factors on the spatial differentiation of ecosystem stability in Alxa Desert. In addition, the Markov–FLUS model was employed to simulate the future trends of ecosystem stability over the next two decades. The results indicate that ecosystem stability in Alxa Desert from 2000 to 2020 was primarily characterized by vulnerable and moderate levels, with the area classified as extremely vulnerable decreasing significantly by 10% relative to its extent in 2000. Spatially, higher stability was observed in oasis regions and southeastern mountainous regions, while lower stability was concentrated in the desert hinterlands. Overall, ecosystem stability shifted from vulnerable toward moderate levels, reflecting a trend of gradual improvement. From 2000 to 2020, the Moran’s I varied between 0.78 and 0.81, showing strong spatial clustering. Surfce Soil moisture content (SSMC), Soil organic carbon (SOC), and enhanced vegetation index (EVI) were the primary factors influencing the spatial differentiation of ecosystem stability in Alxa Desert. The interaction between these factors further enhanced their explanatory power. Future forecasting results indicate that ecosystem stability will further improve by 2030 and 2040, particularly in the northern and southern areas of Alxa Left Banner and Alxa Right Banner. The findings can offer a theoretical foundation for future ecological conservation and environmental management in ADR. Full article