Search Results (149)

Climate change and river flow alterations in the Mekong River have significantly exacerbated drought conditions in the Vietnamese Mekong Delta (VMD). Understanding the temporal dynamics and propagation mechanisms of drought, coupled with the compounded impacts of human activities, is crucial. This study analyzed meteorological (1992–2021) and hydrological (2000–2021) drought trends in the Lower Mekong River Basin (LMB) using the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI), respectively, complemented by Mann–Kendall (MK) trend analysis. The results show an increasing trend of meteorological drought in Cambodia and Lao PDR, with mid-Mekong stations exhibiting a strong positive correlation with downstream discharge, particularly Tan Chau (Pearson r ranging from 0.60 to 0.70). A key finding highlights the complexity of flow regulation by the Tonle Sap system, evidenced by a very strong correlation (r = 0.71) between Phnom Penh and the 12-month SDI lagged by one year. Crucially, the comparison revealed a shift in drought severity since 2010: hydrological drought has exhibited greater severity (reaching severe levels in 2020–2021) compared to meteorological drought, which remained moderate. This escalation is substantiated by a statistically significant discharge reduction (95% confidence level) at the Chau Doc station during the wet season, indicating a decline in peak flow due to upstream dam operations. These findings provide a robust database on the altered hydrological regime, underlining the increasing vulnerability of the VMD and motivating the urgent need for comprehensive, adaptive water resource management strategies. Full article

Salt stress represents a significant abiotic factor that constrains maize growth. Epigenetic modifications play a crucial role in enabling plants to respond effectively to such stresses. Among these alterations, m⁶A methylation, which is the most common post-transcriptional modification of eukaryotic mRNA, shows dynamic variations that are closely linked to stress responses. In this study, we conducted a transcriptome-wide m⁶A methylation analysis on maize roots from the inbred line PH4CV, following treatment with 180 mM NaCl. The results identified 1309 differentially m⁶A methylated peaks (DMPs) and 2761 differentially expressed genes (DEGs) under salt stress conditions. Association analysis revealed that 179 DEGs contain DMPs. Key pathways involved in stress responses, including Ca²⁺ signaling transduction and ABA signaling, as well as ion homeostasis regulation (involving AKT, HKT, and other families) and the reactive oxygen species scavenging system (including POD, SOD, and CAT), play crucial roles in coping with salt stress. Furthermore, we identified a total of 26 m⁶A-related genes, comprising 7 eraser genes, 10 reader genes, and 9 writer genes. Notably, several key salt-responsive genes, such as RBOHB, AKT1, HKT1, and POD12, are correlated with m⁶A modification. This study provides a comprehensive map of m⁶A methylation dynamics in maize roots under salt stress, laying a foundational resource for future investigations into the epigenetic regulation of salt tolerance in maize. Full article

Chinese fir (Cunninghamia lanceolata) is a cornerstone timber species in southern China. However, yet its plantation productivity frequently declines under successive rotations, threatening long-term sustainability. While belowground processes are suspected drivers, the mechanisms—particularly plant–soil–microbe interactions—remain poorly resolved. To address this, we examined a chronosequence of C. lanceolata plantations (5, 15, 20, and 30 years) in Jingdezhen, Jiangxi Province, integrating soil physicochemical assays, high-throughput sequencing, and extracellular enzyme activity profiling. We found that near-mature stands (20 years) exhibited a 60.7% decline in mean annual volume increment relative to mid-aged stands (15 years), despite continued increases in individual tree volume—suggesting a strategic shift from resource-acquisitive to nutrient-conservative growth. Peak values of soil organic carbon (32.87 g·kg⁻¹), total nitrogen (2.51 g·kg⁻¹), microbial biomass carbon (487.33 mg·kg⁻¹), and phosphorus (25.65 mg·kg⁻¹) coincided with this stage, reflecting accelerated nutrient turnover and intensified plant–microbe competition. Microbial communities shifted markedly over time: Basidiomycota and Acidobacteria became dominant in mature stands, replacing earlier Ascomycota and Proteobacteria. Random Forest and Partial Least Squares Path Modeling (PLS-SEM) identified total nitrogen, ammonium nitrogen, and total phosphorus as key predictors of productivity. PLS-SEM further revealed that stand age directly enhanced productivity (β = 0.869) via improved soil properties, but also indirectly suppressed it by stimulating microbial biomass (β = 0.845)—a “dual-effect” that intensified nutrient competition. Fungal and bacterial functional profiles were complementary: under phosphorus limitation, fungi upregulated acid phosphatase to enhance P acquisition, while bacteria predominately mediated nitrogen mineralization. Our results demonstrate a coordinated “soil–microbe–enzyme” feedback mechanism regulating productivity dynamics in C. lanceolata plantations. These insights advance a mechanistic understanding of rotation-associated decline and underscore the potential for targeted nutrient and microbial management to sustain long-term plantation yields. Full article

Subsea environments are vital for global biodiversity, climate regulation, and human activities such as fishing, transport, and resource extraction. Accurate mapping and monitoring of these ecosystems are essential for sustainable management. Airborne LiDAR bathymetry (ALB) provides high-resolution underwater data but produces large and complex datasets that make efficient analysis challenging. This study employs deep learning (DL) models for the multi-class classification of ALB waveform data, comparing two recurrent neural networks, i.e., Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). A preprocessing pipeline was developed to extract and label waveform peaks corresponding to five classes: sea surface, water, vegetation, seabed, and noise. Experimental results from two datasets demonstrated high classification accuracy for both models, with LSTM achieving 95.22% and 94.85%, and BiLSTM obtaining 94.37% and 84.18% on Dataset 1 and Dataset 2, respectively. Results show that the LSTM exhibited robustness and generalization, confirming its suitability for modeling causal, time-of-flight ALB signals. Overall, the findings highlight the potential of DL-based ALB data processing to improve underwater classification accuracy, thereby supporting safe navigation, resource management, and marine environmental monitoring. Full article

Forests play a key role in sustaining global water cycles by regulating precipitation partitioning, which in turn influences both water yield and ecosystem stability. Thinning is a silvicultural tool used to improve forest plantation productivity, but it is increasingly recognized as a means for water resource management. This study investigated hydrological changes following 40% thinning of tree density with contour-aligned log placement in paired headwater catchments of a Japanese cypress forest. Annual runoff in the treated catchment was 108.7 mm above the pre-thinning baseline in the thinning year (2020), followed by smaller increases of 99.7 mm, 43.7 mm, and 0.4 mm in 2021 to 2023, after which annual yields effectively returned to pre-thinning levels. Despite these temporary increases, peak discharge and storm quickflow metrics remained within the pre-thinning range. Flow duration curve analysis revealed a sustained enhancement of low-flow discharge and baseflow throughout the post-thinning period, indicating improved low-flow resilience without increased stormflow risk. These findings demonstrate that moderate thinning combined with contour felled logs can enhance water availability in plantation forests while maintaining flood protection. They also highlight the need for long-term, multi-site studies to test the persistence and generality of these low-flow benefits under varying forest and climate conditions. Full article

The penetration of distributed energy resources (DERs), such as photovoltaic (PV) generation and electric vehicles (EVs), in distribution systems has been increasing rapidly. At the same time, load demand is rising due to the proliferation of data centers and the growing use of artificial intelligence. These trends have introduced new operational challenges: reverse power flow from PV generation during the day and low-voltage conditions during periods of peak load or when PV output is unavailable. To address these issues, this paper proposes a two-stage adaptive rolling horizon (ARH)-based model predictive control (MPC) framework for coordinated voltage and power factor (PF) control in distribution systems. The proposed framework, designed from the perspective of a distributed energy resource management system (DERMS), integrates EV charging and discharging scheduling with PV- and EV-connected inverter control. In the first stage, the ARH method optimizes EV charging and discharging schedules to regulate voltage levels. In the second stage, optimal power flow analysis is employed to adjust the voltage of distribution lines and the power factor at the substation through reactive power compensation, using PV- and EV-connected inverters. The proposed algorithm aims to maintain stable operation of the distribution system while minimizing PV curtailment by computing optimal control commands based on predicted PV generation, load forecasts, and EV data provided by vehicle owners. Simulation results on the IEEE 37-bus test feeder demonstrate that, under predicted PV and load profiles, the system voltage can be maintained within the normal range of 0.95–1.05 per unit (p.u.), the power factor is improved, and the state-of-charge (SOC) requirements of EV owners are satisfied. These results confirm that the proposed framework enables stable and cooperative operation of the distribution system without the need for additional infrastructure expansion. Full article

The integration of renewable energy sources (RES) and distributed energy resources (DER) into local energy markets is transforming modern power grids toward a decentralized architecture. To enhance the efficiency of decentralized energy trading, blockchain technology has been widely adopted in constructing peer-to-peer energy trading platforms, providing incentives for renewable energy generation and utilization. However, the rapid growth of small-scale suppliers and intermittent DERs introduces significant challenges to grid stability, including supply–demand imbalances and voltage fluctuations. To address these challenges, we propose a blockchain-based energy trading system architecture designed to enable a self-regulating, sustainable, and resilient grid. The proposed system architecture achieves grid stability through three key components: (i) precise endpoint control via AI Agents with lightweight forecasting models integrated into existing hardware systems, (ii) flexible distributed control through an efficient incentive mechanism, named Proof of Prediction, based on a blockchain-based automated trading process, and (iii) macro-level coordination via global regulation roles. We implemented a prototype of the proposed architecture on the Ethereum Blockchain and applied it to a microgrid-scale distributed automated trading environment. Our evaluation results show that using the architecture we proposed achieves a peak-shaving rate of up to 29.6%, while maintaining the overall supply–demand deviation of around 5% on average, demonstrating its strong potential as a foundation for building stable and modern power grids. Full article

Flood Limited Water Level (FLWL) serves as the core control parameter for the synergistic optimization of flood control operation and beneficial water utilization efficiency in reservoirs during the flood season. Addressing the critical issue of insufficient adaptability in static control schemes, this study innovatively proposes a staged dynamic FLWL regulation model based on pre-release rules. This methodology combines hydrometeorological division theory with frequent flood control mechanisms and establishes a dual-threshold control equation with safe pre-release discharge (q_pre) and effective pre-release duration (t_pre) as sensitive factors. The dynamic FLWL scheme is designed to ensure that no additional risk is imposed on the reservoir and its upstream/downstream regions, and it incorporates a set of hierarchical rules for the strategic pre-release and standard safety modes. Taking the Wuxikou Reservoir in Jiangxi Province as a case study, the safe pre-release discharge value under regular flood conditions and the effective pre-release duration are determined. Additionally, a dynamic FLWL control model is developed according to the reservoir’s characteristics. The verification results demonstrate the significant benefits of the dynamic FLWL model in reducing peak water levels and shortening flood duration. Compared with the original operation plan, the proposed model effectively lowers the maximum water level of the reservoir by 10% and simultaneously shortens the duration of high water levels by nearly 24 h. The research results provide a reference for the efficient utilization of water resources in reservoir basins in monsoon humid areas. Full article

Global soil moisture has undergone significant changes in recent decades due to climate change and vegetation greening. However, the seasonal and climate zonal variations in soil moisture dynamics at different depths, driven by both climate and vegetation, remain insufficiently explored. This study provides a comprehensive analysis of the global patterns in rootzone and surface soil moisture and leaf area index (LAI) across different seasons and climate zones, utilizing satellite observations from 1982 to 2020. We investigate how climatic factors and LAI influence soil moisture variations and quantify their dominant contributions. Furthermore, by employing key vegetation phenological indicators, namely the peak of growing season (POS) and the corresponding maximum LAI (LAI_MAX), we assess the feedback effects of vegetation phenology on soil moisture dynamics. The results indicate that the greening trend (as reflected by LAI increases) from 2000 to 2020 was significantly stronger than that observed during 1982–1999 across all seasons and climate zones. Both rootzone and surface soil moisture shifted from a decreasing (drying) trend (1982–1999) to an increasing (wetting) trend (2000–2020). From 1982 to 2020, the LAI induced moistening trends in both surface and rootzone soil moisture. In arid and temperate zones, precipitation drove rootzone soil moisture increases only during the summer. Among all seasons and climate zones, solar radiation induced the strongest surface soil drying in tropical summers, with a rate of −0.04 × 10⁻³ m³m⁻³/Wm⁻². For rootzone soil moisture, LAI dominated over individual climatic factors in winter and spring globally. In contrast, solar radiation became the primary driver during summer and autumn, followed by precipitation. For surface soil moisture, precipitation exhibited the strongest control in winter, but solar radiation surpassed it as the dominant factor from spring through autumn. In the tropical autumn, the sensitivity of rootzone and surface soil moisture to POS (and LAI_MAX) was highest, at 0.059 m³m⁻³·d⁻¹ (0.256 m³m⁻³/m²m⁻²) and 0.052 m³m⁻³·d⁻¹ (0.232 m³m⁻³/m²m⁻²), respectively. This research deepens the understanding of how climate and vegetation regulate soil moisture across different climate zones and seasons. It also provides a scientific basis for improving global soil moisture prediction models and managing water resource risks in the context of climate change. Full article

The large-scale cultivation of medicinal plants generates substantial agricultural by-products that are often discarded. A notable example is the floral biomass of Bupleurum chinense DC. (B. chinense), which is routinely removed during cultivation to promote root yield. To explore the potential valorization of these discarded tissues, we performed an integrated transcriptomic and metabolomic analysis of flavonoid biosynthesis across three developmental stages: F1 (Initial Flowering Stage), F2 (Full Bloom Stage), and F3 (Late Flowering Stage). Our results revealed distinct stage-specific regulatory dynamics. Flavonoid biosynthesis was initiated at F1 through the activation of upstream structural genes, reached its peak at F2 with strong up-regulation of branch-specific genes and the accumulation of diverse flavonols and anthocyanins, and declined at F3, despite the sustained presence of several antioxidant metabolites. These findings indicate that F2 represents the optimal stage for harvesting B. chinense flowers to obtain a broad spectrum of bioactive flavonoids, while late-stage flowers may serve as a complementary source of stable antioxidant compounds. Collectively, this study highlights the potential for transforming discarded floral biomass into valuable phytochemical resources and provides a framework for exploring underutilized tissues in other medicinal plants. Full article

Background: Dromedary camel in Saudi Arabia thrive across diverse desert ecosystems where trace minerals are vital for key physiological functions, yet data on how regional and seasonal factors affect these minerals in dams and neonates are limited. Aim: This study investigated the effects of regional and seasonal variability on trace mineral status in dam serum (DS), dam milk (DM), and neonatal serum (NS) across major camel-rearing regions of Saudi Arabia. We hypothesized that environmental factors—particularly heat stress and local feed resources—drive regional and seasonal differences in mineral profiles and maternal–neonatal transfer. Methods: Samples of serum, milk, feed, water, and soil were collected from five major regions during three seasons. Concentrations of selenium (Se), zinc (Zn), copper (Cu), iron (Fe), manganese (Mn), and iodine (I) were quantified, and correlations among biological compartments were analyzed. Meteorological data were used to compute the temperature-humidity index (THI). Results: The THI ranged from thermoneutral levels in the Northern winter (17.4) to severe heat stress in Eastern summer (33.8). Milk minerals exhibited strong seasonal and regional effects, with selenium peaking in summer and zinc in spring. Western dams showed elevated iron and iodine, whereas northern dams had higher zinc. Serum minerals in dams varied moderately with season but differed regionally for zinc, selenium, and iron. Neonatal serum reflected maternal and regional influences, showing significant season-by-region interactions for selenium and iodine. Positive correlations indicated coordinated maternal–neonatal mineral transfer, particularly for selenium, iodine, and zinc. Feed represented the main environmental source of Cu and Se. In conclusion, camel trace mineral status is mainly driven by environmental factors but regulated through maternal transfer, with selenium and iodine emerging as key heat-stress markers supporting targeted, region- and season-specific supplementation to improve health and productivity in arid regions. Full article

Seed germination is crucial for seedling establishment and is regulated by precise reactive oxygen species (ROS) signaling. Class III peroxidases (PRXs), which are plant-specific enzymes, play crucial roles in plant growth, development, and responses to abiotic stress by maintaining ROS homeostasis. However, members of the PRX gene family in tomato, particularly their functions in modulating seed germination, remain poorly understood. In this study, 102 tomato PRXs (SlPRXs) were identified, and they were classified into five groups based on phylogenic analysis. Chromosomal localization revealed that these SlPRX genes are unevenly distributed across 12 tomato chromosomes, with chromosome 02 harboring the highest densities. Gene structure analysis revealed that SlPRXs contain 1 to 10 exons, and SlPRX4 possesses the most exons. All SlPRX proteins possess the characteristic peroxidase domain and share conserved structural motifs. Collinearity analysis suggested that segmental duplications might be the main contributor to the expansion of the SlPRX family. Promoter analysis revealed numerous cis-acting elements related to abiotic/biotic stress responses, phytohormones, and growth and development. Notably, seed germination-related elements such as CARE and RY element were identified in some SlPRXs. Enzymatic and electrophoresis assays indicated that PRX activity increased with seed germination. Moreover, SHAM, the inhibitor of PRX, exerted an inhibitory effect on tomato seed germination. Transcriptome data revealed stage-specific induction of SlPRXs during germination, with distinct expression peaks between 0 and 96 h post imbibition. These findings were further validated by qRT-PCR of the selected SlPRX genes. Overall, the findings enhance our understanding of SlPRX family members in tomato and highlight their potential for improving seed germination. This study also provides valuable genetic resources and potential molecular markers for breeding tomato varieties with improved germination vigor and stress resilience. Full article

Active distribution systems (ADS) are increasingly strained by rising energy demand and the widespread deployment of distributed energy resources (DERs) and electric vehicle charging stations (EVCS), which intensify voltage deviations, power losses, and peak demand fluctuations. This study develops a coordinated optimization framework for Mobile Battery Energy Storage Systems (MBESS) and Dynamic Feeder Reconfiguration (DFR) to enhance network performance across technical, economic, and environmental dimensions. A Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to minimize six objectives the active and reactive power losses, voltage deviation index (VDI), voltage stability index (FVSI), operating cost, and CO₂ emissions while explicitly modeling the MBESS transportation constraints such as energy consumption and single-trip mobility within coupled IEEE 33-bus and 33-node transport networks, which provide realistic mobility modeling of energy storage operations. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to select compromise solutions from Pareto fronts. Simulation results across six scenarios show that the coordinated MBESS–DFR operation reduces power losses by 27.8–30.1%, improves the VDI by 40.5–43.2%, and enhances the FVSI by 2.3–2.4%, maintaining all bus voltages within 0.95–1.05 p.u. with minimal cost (0.26–0.27%) and emission variations (0.31–0.71%). The MBESS alone provided limited benefits (5–12%), confirming that coordination is essential for improving efficiency, voltage regulation, and overall system sustainability in renewable-rich distribution networks. Full article

This article takes the perspective of Hydrogen Load Aggregator (HLA) to optimize the declaration strategy of peak shaving market, improve the flexible regulation capability of power system and HLA economy as the research objectives, and proposes an optimization strategy method for HLA to participate in peak shaving market. Firstly, an improved Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM) time series prediction model is developed to address peak shaving demand uncertainty. Secondly, a bidding strategy model incorporating dynamic pricing is constructed by comprehensively considering electrolyzer regulation costs, market supply–demand relationships, and system constraints. Thirdly, a market clearing model for peak shaving markets with HLA participation is designed through analysis of capacity contribution and marginal costs among different regulation resources. Finally, the capacity allocation model is designed with the goal of minimizing the total cost of peak shaving among various stakeholders within HLA, and the capacity won by HLA in the peak shaving market is reasonably allocated. Simulations conducted on a Python3.12-based experimental platform demonstrate the following: the improved CNN-LSTM model exhibits strong adaptability and robustness, the bidding model effectively enhances HLA market competitiveness, and the clearing model reduces system operator costs by 5.64%. Full article

This study proposes an Automatic Identification System (AIS)-based spatiotemporal allocation methodology to estimate catch distribution and fishing effort for large purse seine fisheries in Korean waters. AIS trajectory data from July 2019 to June 2022 were analyzed to identify fishing grounds, while carrier vessel port-entry records were used to estimate daily landings. These were allocated to specific fishing segments to derive spatially explicit catch quantities. Compared with periodic surveys or voluntary reports, the AIS-based approach significantly enhanced the accuracy of fishing ground identification and the reliability of catch estimation. The results showed that fishing activity peaked between November and February, with the highest catch densities observed south of Jeju Island and in adjacent East China Sea waters. Catch declined markedly from April to June due to the mackerel closed season. These findings demonstrate the method’s potential for evaluating the effectiveness of Total Allowable Catch (TAC) regulations, supporting dynamic and adaptive management frameworks, and strengthening IUU fishing monitoring. Although the current analysis is limited to TAC-regulated species, AIS-equipped vessels, and a three-year dataset, future studies could expand the timeframe, integrate environmental data, and apply this methodology to other fisheries to improve sustainable resource management. Full article