Search Results (142)

To address the challenges of high thermal loads and limited energy efficiency in an electric tractor operating under complex agricultural conditions, this paper proposes a hierarchical battery thermal management strategy based on liquid cooling. The method integrates an upper-level piecewise linear model predictive control to regulate battery temperature and a lower-level convex optimization scheme for dynamic actuator power allocation among the compressor, cooling fan, and expansion valve. By decomposing the nonlinear thermal dynamics into multiple local subregions, the predictive accuracy is enhanced while maintaining real-time computational feasibility. Comparative simulations reveal that under severe 45 °C ambient conditions, the proposed strategy limits the maximum temperature difference among battery cells to 1.34 °C and average temperature fluctuations to 0.231 °C, significantly outperforming conventional linear baseline methods which resulted in 1.66 °C and 0.349 °C, respectively. Furthermore, the optimized actuator coordination reduces total cooling energy expenditure by 11.4%, effectively minimizing transient peak loads on the high-voltage bus and preserving energy for primary traction tasks. These quantitative results confirm that the proposed control framework substantially improves battery thermal stability and powertrain energy efficiency, demonstrating robust potential for practical implementation in heavy-duty agricultural machinery. Full article

Dongping Lake is a regulating lake where hydrodynamic alteration and heterogeneous inputs may reshape phytoplankton communities; this study aimed to characterize eukaryotic phytoplankton, assess water quality and identify key environmental drivers. In September 2025, eukaryotic phytoplankton were profiled using 18S rDNA V9 eDNA metabarcoding across 18 sites, and community–environment relationships were evaluated using diversity indices, principal coordinates analysis (PCoA), Spearman correlations and redundancy analysis (RDA). This study detected 101 eukaryotic phytoplankton species. Bacillariophyta dominated read abundance at 55.08%, followed by Cryptophyta at 22.20%, whereas species richness was highest in Chlorophyta with 40 species. Site richness ranged from 26 to 63, peaking at sampling sites D17 and D18 and reaching a minimum at sampling site D15; Cryptophyta dominated reads only at sampling site D6. Nine dominant species were identified. Mean diversity values were Shannon-Wiener diversity index (H) 3.45, Pielou evenness index (J) 0.92, Margalef richness index (D) 4.40 and Chao1 richness estimator 44.72, and overall water quality was assessed as slightly polluted, with sampling site D12 or D15 reaching moderate pollution under specific indices. Dominant-species responses were differentiated; for example, Stephanodiscus hantzschii was negatively correlated with NH₄⁺ and TN, and Ceratium hirundinella was positively correlated with salinity but negatively correlated with NH₄⁺. RDA ranked key drivers as salinity > NO₂⁻ > TN > NH₄⁺ > TP > DO > temperature. Salinity and nitrogen-form gradients were closely associated with spatial community differentiation and dominant-species shifts, supporting targeted monitoring and management. Full article

China’s carbon neutrality plan centers on a carbon trading policy system integrating market economy, guidance, regulation, command-and-control, and fiscal measures into an inter-domain synergy. Based on the system science methodology, by leveraging the gray system evaluation method and the multiple regression model, a carbon neutrality policy analysis system has been formed. This paper constructs a policy synergy model to examine its role in achieving net-zero goals. This article measures the policy synergy and effectiveness of China’s carbon neutrality goals in different years through policy evaluation theory and coupling models. Results show China’s net carbon emissions have passed through three cycles—rapid rise, gradual growth, and slow decline—while policy synergy peaked in 2011, 2014, and 2016, aligning with changes in emission growth rates. The significance of this discovery indicates the pulse effect of China’s green policies. In key starting years such as the 12th and 13th Five-Year Plans, China has invested a significant amount of green policy resources. Synergy levels vary by measure: When applying market economy tools, the deterrent effect of command-and-control should be reduced; command-and-control should be paired only with regulation; fiscal measures should be balanced against guidance to avoid counteracting effects. Internal equilibrium between measures is crucial, with mandatory and flexible tools configured separately to maximize policy effectiveness for net-zero emissions. This study expands the quantitative research on policy coordination and effectiveness analysis. At the same time, it provides policy-level guidance and optimization for the realization of the carbon neutrality goal, avoiding the waste and redundancy of policy resources Full article

The transition toward new power systems requires enhanced operational flexibility, within which circulating fluidized bed (CFB) units exhibit considerable peak regulation potential. This study develops an economic optimization framework to evaluate the benefits of flexibility retrofits for CFB units operating in coordination with wind power. A representative integrated system consisting of two 300 MW CFB units and a 300 MW wind farm is analyzed to compare ramping capability enhancement, minimum load reduction, and their combined implementation. The results indicate that the combined retrofit delivers the highest overall economic benefit at the system level. Even under a highly fluctuating wind power scenario, the combined retrofit achieves complete wind power accommodation, demonstrating the effectiveness of coordinated flexibility expansion. The minimum load reduction retrofit and the ramping capability retrofit reduce wind curtailment by 81% and 13%, respectively. Moreover, the economic benefit achieved by the combined retrofit exceeds the aggregate benefit of the two independent measures by about 6%, indicating a synergistic interaction between ramping flexibility and minimum load reduction retrofit. For the studied system, minimum load reduction retrofit contributes substantially greater economic gains than ramping capability enhancement when applied individually. Sensitivity analysis further highlights the influence of coal prices and feed-in tariff structures on retrofit profitability. Compared with existing studies focusing primarily on conventional pulverized coal units, this work establishes a quantitative framework tailored to CFB-specific flexibility retrofits, providing practical support for power systems with high renewable penetration. Full article

ZmSIG2A is a nuclear-encoded plastid sigma factor 2A in maize (Zea mays L.) that is essential for plastid gene transcription and chloroplast biogenesis. As a key regulator of chloroplast development and function, ZmSIG2A may also contribute to the coordination of plant growth and environmental adaptation; however, its roles in root development and stress responses remain largely unclear. We compared two ZmSIG2A mutants, eal1-1 (hypomorphic) and ems110 (nonsense). eal1-1 had increased root number and longer roots, while ems110 had normal root number but shorter roots and failed to mature. The zmsig2a^Val480del transcript was upregulated in eal1-1, and the root-promoting effect of OsSIG2A in rice suggests a conserved role in monocot root growth. DAP-seq indicated that zmsig2a^Val480del targets are involved in metabolism, transport, signaling, and antioxidants, with Chr4 peak clustering near multiple LRR-RLKs, suggesting a ZmSIG2A–LRR-RLK module in root development and stress integration. Physiologically, eal1-1 showed increased antioxidant enzyme activities and reduced MDA, indicating enhanced ROS scavenging, while ems110 exhibited decreased enzyme activities and elevated MDA, indicating compromised ROS detoxification. Upstream, Y1H and dual-luciferase assays demonstrated that the Mediator subunit ZmMed31 positively regulates transcription from the ZmSIG2A promoter. Given Mediator’s role in bridging transcription factors and the core transcriptional machinery, ZmMed31 likely links hormone-responsive transcription factors to the ZmSIG2A regulatory network. Collectively, we propose a stress-responsive ZmMed31–ZmSIG2A–LRR-RLK module that underpins maize root development and drought adaptation, offering mechanistic insight and potential targets for stress-resilient breeding. Full article

Severe Fever with Thrombocytopenia Syndrome (SFTS) is caused by the SFTS virus (SFTSV) and is associated with a high mortality rate. Although previous studies have reported RNA modifications such as m6A on SFTSV RNA, an integrated analysis of native viral transcript architecture and multiple RNA modification types within infected cells remains lacking. Here, we used Oxford Nanopore direct RNA sequencing (DRS) to analyze native SFTSV RNA in infected cells, combining strand-specific alignment, isoform reconstruction through read endpoint clustering, isoform-level quantification, and signal-level modification identification using unmodified in vitro transcripts as a baseline. This approach allowed us to construct detailed maps of the L, M, and bidirectionally encoded S segments at single-molecule, isoform-level resolution. The results reveal a “length-layering” pattern in SFTSV transcription, anchored by recurrent 3′ termination hotspots: only a few full-length transcripts dominate expression, whereas multiple reproducible truncated isoforms were associated with discrete termination windows, a pattern less consistent with random degradation alone and suggestive of regulated transcript termination. At the single-nucleotide level, the modification landscape is predominantly Ψ (pseudouridine), followed by m⁵C (5-methylcytosine), with sparse m⁶A (N6-methyladenosine). Modification hotspots are co-located across isoforms at the same genomic coordinates, exhibiting segmental/strand asymmetry, with sharper peaks on (−) RNA. These patterns provide a testable framework and raise the possibility that transcript-boundary organization and site-constrained Ψ/m5C signals may be associated with variation in viral RNA output. More broadly, isoform proportions around termination hotspots and Ψ/m5C-enriched regions at conserved sites may serve as quantitative features for characterizing viral RNA organization and prioritizing targets for future functional investigation. Our single-molecule integrated map establishes a reproducible methodological framework for studying SFTSV RNA regulation and provides a resource for future work aimed at assessing how transcript boundaries and RNA modification patterns may relate to polymerase activity and virus–host interaction. Full article

This study proposes TNAP-DDQN, a deep reinforcement learning method for urban low-altitude UAV path planning under residential noise threshold constraints. With time cost and safety risk as the optimization objectives, operational constraints such as collision risk and maximum AGL altitude are incorporated to achieve coordinated optimization of noise compliance, operational safety, and efficiency. To mitigate action space contraction and training instability induced by multiple constraints, a Noise-Degradation-Mask-based Action Bias Network (NDM-ABN) is introduced at the action selection layer. A three-tier degradation scheme prevents empty candidate sets, while bias-based decision making is applied to approximately tied actions to stabilize the policy. Moreover, multi-step prioritized experience replay (PER) improves sample efficiency and long-horizon return modeling, and potential-based reward shaping (PBRS) transforms sparse constraint signals into auxiliary rewards. Simulation results indicate that: (1) NDM-ABN is the key module for stabilizing the noise-exposure process by suppressing high-noise actions; (2) the required AGL is related to the UAV source noise level and local noise limits, implying the need for differentiated AGL altitude classes; and (3) the maximum admissible UAV source noise level increases as the threshold is relaxed. The proposed method provides quantitative guidance for noise-entry and AGL altitude regulation, while future work will incorporate additional metrics (e.g., A-weighted equivalent sound level) to better capture noise fluctuations and short-term peaks. Full article

The role of platelet-derived growth factor D (PDGFD) in mesenchymal cells is well-established, but its specific function in skeletal muscle generation remains unknown. This study reveals for the first time PDGFD’s dual regulatory role in myogenesis: it acts both as a “guardian” maintaining the myoblast pool and as an “initiator” driving myogenic differentiation. Through single-cell RNA sequencing analysis of skeletal muscle from Jiangquan Black pigs, we identified PDGFD as a common candidate gene for both muscle and fat development. In the C2C12 cell model, PDGFD knockdown significantly inhibited cell proliferation and promoted apoptosis, while overexpression enhanced viability and inhibited apoptosis, indicating its critical role in maintaining myoprogenic precursor cell homeostasis. Further studies revealed that PDGFD interference downregulated key myogenic differentiation markers MyoD and MyoG, inhibiting differentiation. Its expression peaked during mid-differentiation (D5), suggesting temporal regulation of differentiation. Interestingly, although PDGFD primarily acts through the PI3K/Akt pathway downstream of PDGFR-β, PDGFD knockdown did not show significant synergistic effects with PI3K/Akt pathway activation in inhibiting differentiation. This suggests PDGFD may specifically regulate myogenic differentiation via an independent or parallel signaling axis. This study not only expands the known functions of PDGFD in muscle biology but also provides new insights into the mechanisms by which growth factors coordinate cell fate decisions. Full article

Pollen development is a complex process that is highly sensitive to environmental stresses. Abscisic acid (ABA), a key hormone mediating plant growth and stress responses, has been implicated in the regulation of sexual reproduction, especially pollen development, yet its precise regulatory role remains unclear. This study investigated the effects of exogenous ABA on Arabidopsis thaliana pollen development and function through integrated phenotypic, cytological, and transcriptomic approaches. ABA treatment specifically impaired pollen function by reducing germination rates and inhibiting pollen tube elongation, which resulted in shortened siliques and decreased seed set, without affecting pollen morphology or viability. Transcriptome analysis of mature anthers revealed a transient and time-dependent transcriptional response, with the number of differentially expressed genes (DEGs) peaking at 8 h post-ABA treatment and markedly declining by 22 h. These DEGs were enriched in stress-response pathways (e.g., salt, cold, and dehydration), hormone signaling, and carbohydrate metabolism. Moreover, we identified 25 differentially expressed transcription factors and 16 pollen development and function-related genes, highlighting their key roles in ABA-mediated regulation. In parallel, 146 differentially expressed lncRNAs (DELs) were identified, which formed 144 cis-regulatory pairs with genes involved in ABA response and pollen tube growth, with their predicted targets enriched in pathways such as hormone and MAPK signaling, carbohydrate metabolism and stress response. Trans-regulatory analysis further revealed that these DELs co-expressed with DEGs in modules enriched for stress response, pollen development, and tube growth pathways. Notably, key pollen function genes showed strong co-expression with DELs, indicating that lncRNAs participate in ABA-induced transcriptional reprogramming that shifts metabolic resources from growth to defense, thereby suppressing pollen germination and tube elongation. Together, these findings elucidate a coordinated regulatory network involving mRNAs, lncRNAs and transcription factors roles in modulating ABA responses during pollen/anther development. Full article

Multi-joint hydraulic robotic arms are core equipment in intelligent mining, yet their performance is often limited by strong dynamic coupling and nonlinear hydraulic effects. Traditional control methods struggle to achieve high-precision trajectory tracking and coordinated motion under high loads and flow-coupling constraints. To address these challenges, this paper establishes a coupled hydraulic–mechanical dynamic model for a multi-joint robotic arm. The mechanical dynamics are derived using the Lagrangian formulation, while the hydraulic dynamics account for flow coupling among cylinders. An improved deviation coupling control (IDCC) strategy is proposed, integrating feedforward–feedback compensation, coupling error regulation, and a flow-limiting correction term. Co-simulation in Simulink (2024b) and Amesim (2020) shows that under flow-saturation conditions, the improved strategy reduces the peak trajectory errors by approximately 47.88%, 28.08%, and 49.89% for Joints 1–3, respectively, and shortens the settling time by 27.93%. Experimental results from a three-joint hydraulic test platform confirm error reductions of 10.20–15.58% and a 31.50% decrease in overall adjustment time. The study demonstrates that the proposed control strategy effectively suppresses multi-joint coupling interferences, enhances trajectory tracking accuracy, and improves the adaptability of hydraulic robotic arms under flow-limited conditions, providing a viable solution for high-precision control in intelligent mining applications. Full article

Urban traffic networks are highly vulnerable to external disturbances such as heavy rainfall, which can induce capacity degradation, non-periodic congestion, and delayed system recovery. To address the limitations of existing perimeter control strategies that primarily focus on demand-side fluctuations and assume fixed network capacity, this study proposes a distributed coordinated perimeter control framework that explicitly incorporates rainfall-induced capacity degradation into system feedback. The proposed framework adopts a two-layer control structure, in which a main controller regulates global network accumulation near the critical macroscopic fundamental diagram (MFD) state, while sub-controllers dynamically adjust perimeter control rates in response to localized traffic conditions and water accumulation. A case study based on real taxi trajectory data from Wuhan City, combined with SUMO-based microscopic traffic simulation, is conducted to evaluate the proposed approach under heavy rainfall conditions. The results show that the distributed coordinated control framework reduces peak network accumulation by 39.6%, increases average vehicle speed by 35.28%, and significantly accelerates post-disturbance recovery. These findings indicate that integrating environmental disturbances into distributed perimeter control can effectively enhance the stability and resilience of urban traffic systems under adverse weather conditions. Full article

To enhance the market-oriented operation capability of integrated energy retailers and improve the synergy and economic efficiency of complex microgrids, this paper constructs a bi-level optimization model of “upper-level price optimization, lower-level multi-energy flow scheduling” under the background of multi-energy coupling of electricity, heat, gas, and hydrogen. The upper level optimizes electricity and heat price signals using the APSO and IGWO algorithms, while the lower level realizes coordinated multi-energy flow scheduling based on these signals. The operational performance of the two algorithms is compared across four scenarios. The results show that the scenario with multi-energy storage (Scenario 3) is the optimal adaptive scenario: the charge–discharge regulation of energy storage interacts with price guidance, and the peak-shaving and valley-filling characteristics significantly improve the system’s energy utilization efficiency. This scenario can fully unlock the value of bi-level optimization and meet the operational requirements of complex multi-energy coupling. In the algorithm comparison, the APSO algorithm presents distinct advantages, outperforming the IGWO algorithm in the precise regulation of upper-level electricity and heat prices, lower-level multi-energy flow balance, total operation cost control, and convergence stability. It provides an effective technical solution for the economic and stable operation of integrated energy systems. Full article

Plant leaf drought tolerance is regulated by the coordinated effects of water transport efficiency, transpirational water loss, and hydraulic safety. Although cotton is considered drought-tolerant, the mechanisms that coordinate water transport and gas exchange to confer drought tolerance remain incompletely understood. In this study, four soil moisture gradients were established under field conditions and maintained consistently throughout the growing season. The relationships among leaf turgor loss point (Ψ_tlp), gas exchange, and hydraulic traits were examined in two cotton cultivars at the peak flowering stage. With increasing drought treatments, Ψ_tlp, stomatal aperture ratio (g_ratio), leaf hydraulic conductance (K_leaf), leaf hydraulic conductance inside the xylem (K_x) and leaf hydraulic conductance outside the xylem (K_ox) declined significantly, with K_x showing the greatest reduction. Both K_x and g_ratio were strongly positively correlated with Ψ_tlp. Anatomically, vein density (D_v) and vessel number (Np) increased, whereas xylem vessel area (Ap) decreased. The reduction in Ap was the primary structural factor driving the decline in K_x and contributing to lower Ψ_tlp. We conclude that cotton enhances drought tolerance through a coordinated hydraulic and osmotic strategy, by modifying xylem anatomy (reducing Ap) to downregulate K_x and by adjusting osmotically to depress Ψ_tlp. The synergistic reduction in K_x and g_ratio slows the decline in leaf water potential, thereby delaying Ψ_tlp and enhancing leaf hydraulic safety during drought. This integration optimizes stomatal regulation and water transport while ensuring hydraulic safety. The findings provide a key theoretical basis and potential breeding targets for the targeted improvement of drought tolerance and water use efficiency in cotton. Full article

In order to cope with the challenge of large-scale integration of renewable energy to the balance of power supply and demand, and give full play to the potential of flexible regulation of iron and steel enterprises, a source load coordination optimization scheduling model considering the flexible coordination of iron and steel production and energy storage is proposed. Firstly, the multi-unit coupling adjustable capacity model of electric arc furnace (EAF), air separation unit (ASU), rolling mill and captive power plant is established, and the regulation characteristics and coupling relationship between different production units are clarified. Secondly, a day-ahead and intra-day two-stage scheduling framework is proposed. In the intra-day stage, the energy storage system is introduced to mitigate the fluctuation in wind power, and the mixed integer linear programming method is adopted to minimize the total operating cost of the system. Finally, an example is given to verify the effectiveness of the model. Case studies demonstrate that the proposed approach effectively reduces load variability and enhances operational stability. After the introduction of energy storage, the power standard deviation of EAFs and ASUs decreases by 29.6% and 28%, respectively, and the operational continuity of the rolling process is improved. Although the initial wind curtailment level in the test system is relatively low, the proposed strategy further mitigates peak curtailment and improves renewable accommodation capability. In addition, moderate operational cost savings are achieved. Full article

Under high renewable energy penetration, nuclear power units face significant challenges in peak regulation and market clearing due to constraints on minimum technical output and ramping capability. To address this issue, a long-term power system of Guangdong Province in 2035 is taken as the study case, and an energy–reserve co-clearing simulation framework based on Security-Constrained Unit Commitment (SCUC) and Security-Constrained Economic Dispatch (SCED) is established to systematically evaluate the clearing performance of nuclear power and the formation mechanism of residual electricity under multiple market scenarios. On this basis, a nuclear power-coupled Alkaline Electrolysis (AEL) hydrogen production pathway is proposed as a peak-shaving utilization option, and an economic assessment model for nuclear-based hydrogen production is developed to quantify the investment performance under different hydrogen production capacities and operating modes. The results indicate that the integration of an AEL hydrogen production system can effectively alleviate the rigidity of nuclear power output. Under the “12-3-48-3” flexible peak-shaving mode, the residual electricity available for hydrogen production increases by approximately 30% compared with a typical peak-shaving strategy. Under scenarios with low electricity prices and green hydrogen prices, when the hydrogen production capacity is configured at 50–100 MW, the investment payback period is approximately six years, and the project exhibits strong economic robustness against variations in the discount rate. These findings demonstrate that nuclear-based hydrogen production is economically feasible in future power systems with high renewable penetration, providing quantitative support for nuclear flexibility enhancement and the coordinated development of low-carbon energy systems. Full article