Search Results (114)

Standard laboratory housing often restricts the expression of natural behaviors in rodents, raising concerns regarding animal welfare and the translational value of experimental data. The current study investigated whether increasing cage bedding volume functions as an effective, low-impact environmental enrichment strategy that enhances welfare without confounding physiological parameters. An in-depth analysis was conducted comparing the effect of Deep Bedding (DB; 1600 mL) versus Normal Bedding (NB; 400 mL) on the microenvironment, physiology, and behavior of wild-type (129SV) and desmin-knockout (Des−/−) mice. Results demonstrated that deep bedding acts as a significant environmental buffer, effectively decoupling intra-cage temperature and humidity from ambient room fluctuations. While physiological parameters, including body surface temperature, body weight, and food intake, remained stable across bedding conditions, behavioral analysis revealed a robust upregulation of species-specific fossorial activities, such as digging and burrowing, in the DB group. This suggests a beneficial “bioenergetic reallocation” where energy is directed toward natural behaviors rather than thermal stress responses. Furthermore, deep bedding significantly improved cage hygiene through the mechanical sequestration of waste. These findings indicate that deep bedding serves as a multifactorial refinement strategy that supports animal welfare and hygiene without increasing experimental variability, proving safe even for physiologically sensitive models like Des−/− mice. Full article

Soybean (Glycine max) is a vital food and oil crop in China, yet its yield and quality are severely threatened by piercing–sucking damage caused by Riptortus pedestris (Hemiptera: Alydidae) to soybean pods. Under global climate warming and expanded soybean cultivation, temperature has become a key environmental factor driving the spread of and aggravated damage caused by R. pedestris. We investigated the effects of temperature (32, 36, 40, 42, and 44 °C) and exposure duration (1–4 h) on the energy substances and antioxidant enzyme activities in adult R. pedestris. These two factors also had significant effects on the pest’s energy substances and antioxidant defense. Under short-term high-temperature stress, the water loss rate and fat, total sugar, and glycogen contents increased significantly, while protein content showed a fluctuating upward trend, with distinct sexual differences in these responses; the water loss and energy substance levels within the lethal high-temperature range, around 44 °C, were generally higher than those in the sublethal range (36–42 °C). R. pedestris showed physiological changes consistent with enhanced heat tolerance and adaptability, including water balance regulation, carbohydrate and lipid accumulation, and modulation of protein synthesis and degradation. In the sublethal high-temperature range, antioxidant enzyme activity patterns were altered, and SOD activity was increased; meanwhile, the MDA content also rose, and POD and CAT activities decreased. In the lethal high-temperature range, the overall antioxidant enzyme activities were lower than in the suitable temperature range, with the POD activities and MDA content still rising. These results suggest that the dynamic adjustment of antioxidant enzyme activities may contribute to alleviating oxidative damage and rapid adaptation to temperature-induced oxidative stress in R. pedestris. These findings indicate that R. pedestris possesses physiological plasticity to cope with sublethal heat stress through metabolic reallocation and antioxidant defense activation, but extreme temperatures cause severe physiological disruption. This study provides insights into the thermal biology and heat resistance mechanisms of this pest under climate warming scenarios. Full article

Infestation by boring sponges poses a serious problem for Pacific oyster Magallana gigas (Thunberg, 1793) aquaculture. This study aimed to assess the effect of Pione vastifica sponge infestation on the oysters’ capacity for shell repair, antioxidant defence status, and hemocyte functional state. We analysed the expression of VEGF pathway genes and biomineralisation enzymes, molecular chaperones (Hsp70, Hsp90), growth arrest and DNA damage gene (Gadd45α), antioxidant enzyme activity and lipid peroxidation levels in the hemolymph and various mantle parts (central and outer-edge). Intracellular reactive oxygen species (ROS) levels and mitochondrial membrane potential in hemocytes were evaluated. The results showed that infection significantly increases intracellular ROS levels in hemocytes without changing mitochondrial membrane potential. Oxidative damage was localised primarily in the central mantle contacting the damaged shell. In the outer-edge mantle responsible for shell growth, marked upregulation of SodMn, Cat, and Gadd45α was observed, coupled with suppression of VEGF-R receptor expression and organic matrix genes. Heat shock protein expression decreased in all examined tissues of infected molluscs. Our results demonstrate that shell damage induced by boring sponges triggers a tissue-specific reorganisation of physiological priorities, manifesting as a bioenergetic trade-off where limited energy resources are reallocated from the ATP-demanding process of biomineralisation to sustain antioxidant defence and cell survival. Full article

Fusarium head blight (FHB), caused by Fusarium graminearum, is a devastating wheat disease leading to significant yield loss and mycotoxin contamination. This study elucidated the biocontrol mechanism of Bacillus velezensis 20507 fermentation broth against FHB during wheat infection. The broth exhibited strong, time-dependent antifungal activity in vitro, with optimal growth suppression (inhibition rates up to 75%) achieved using broth fermented for 3–7 days. In planta experiments confirmed its efficacy in alleviating disease symptoms. Employing a dual RNA-seq strategy, we analyzed the tripartite interaction between the biocontrol agent, pathogen, and wheat host. Transcriptomic analysis revealed that the broth directly suppressed the pathogen, causing 1510 differentially expressed genes (DEGs, predominantly down-regulated) and disrupting pathways related to carbohydrate metabolism and cell wall integrity. In wheat, the fermentation broth of B. velezensis 20507 counteracted F. graminearum infection by reprogramming the host transcriptome. KEGG analysis during co-inoculation showed that the broth up-regulated defense-related pathways involved in energy, hormone signaling, and cellular maintenance, while down-regulating primary metabolic pathways, indicating a resource reallocation strategy. Furthermore, transcriptomic analysis revealed that the broth alone primed the wheat defense system, and this primed state significantly enhanced the defense response upon pathogen challenge. Untargeted metabolomics identified key antimicrobial compounds, including lipopeptides and the macrolide Macrolactin A. Bioassay-guided fractionation isolated two active fractions (Fr A and Fr B) with potent antifungal activity. This integrated multi-omics study demonstrates that B. velezensis 20507 combats FHB through a coordinated dual mechanism: direct inhibition of the fungus via specific metabolites like Macrolactin A, and simultaneous reprogramming of the host defense and metabolic landscape. These findings provide a scientific foundation for developing this strain as an effective biocontrol agent. Full article

Geopolitical volatility and conflict are increasingly altering the operating conditions of maritime transport by affecting route feasibility, service reliability, port operations, regulatory compliance, and energy-related decisions. However, the relevant literature remains fragmented across smart port studies, shipping optimization research, cybersecurity analysis, and resilience-oriented discussions. This review addresses that fragmentation by examining smart port and shipping optimization as interdependent components of maritime resilience rather than as separate efficiency-oriented domains. Methodologically, the paper adopts a structured, semi-systematic review design combining bibliometric mapping and thematic synthesis to identify the evolution, thematic structure, and major research gaps of the field. The review shows that smart port research highlights the resilience value of real-time visibility, interoperable data exchange, dynamic terminal control, digital twins, and cyber-secure infrastructure, while shipping-optimization research emphasizes conflict-aware routing, schedule recovery, network redesign, capacity reallocation, and fuel-related decision support. At the same time, the literature provides only limited integration across the port–shipping interface, where resilience is actually produced through coordination between nodes, networks, and governance arrangements. Based on this synthesis, the paper argues that future research should move beyond isolated technical solutions and develop more integrated approaches that jointly address digitalization, operational adaptation, security, and decarbonization under geopolitical stress. The review contributes by clarifying the intellectual structure of this emerging field and by proposing a more system-oriented perspective on maritime resilience. Full article

Background: Anoectochilus roxburghii (Wall.) Lindl. is an endangered medicinal herb, and salt stress has been reported to promote the accumulation of bioactive secondary metabolites. Central carbon metabolism plays a key role in carbon allocation in plants; however, the integrated molecular and metabolic responses of A. roxburghii to salt stress remain largely unclear. Method: In this study, an integrated transcriptomic and metabolomic approach was employed to investigate the reprogramming of central carbon metabolism in A. roxburghii under 50, 100, and 200 mM NaCl treatments. Results: Metabolomic analysis revealed a significant accumulation of soluble sugars, which suggests enhanced osmotic adjustment and alteration in energy metabolism. Transcriptomic profiling identified 7019 upregulated and 5192 downregulated DEGs, with pathways related to the TCA cycle, galactose metabolism, and fructose/mannose metabolism predominantly upregulated, while oxidative phosphorylation was suppressed. Integrative transcriptome–metabolome profiling further identified key genes associated with oxaloacetate and fructose-6-phosphate, suggesting a coordinated regulation between central carbon metabolism and polysaccharide biosynthesis. Conclusions: Collectively, these findings demonstrate that salt stress induces coordinated metabolic and transcriptional reprogramming in A. roxburghii, driving carbon flux reallocation from growth-related processes toward osmoprotective metabolism. This provides a mechanistic basis for the enhancement of bioactive compounds under moderate stress and offers insights for improving both salt tolerance and medicinal quality in saline environments. Full article

To address the issues of unbalanced residual energy caused by heterogeneous initial robot states and dynamic environmental disturbances, this paper proposes a dynamic task allocation and rescheduling strategy considering energy balance. A Multiple Traveling Salesman Problem (MTSP) mathematical model that incorporates energy constraints and load balancing is established. Furthermore, an Improved Genetic Algorithm (IGA) based on K-Means initialization and adaptive mutation strategies is proposed. By introducing an energy-aware operator, the algorithm achieves energy consumption balance within the robot swarm while optimizing the total path length. In addition, an event-triggered dynamic rescheduling mechanism is designed. When sudden robot failures or task updates are detected, a Local Greedy Insertion (LGI) strategy is activated to achieve rapid task takeover and reallocation. Experimental results show that the proposed IGA consistently reduces the system’s state of charge (SoC) range to less than 1%, significantly outperforming baseline algorithms. It strikes an excellent balance between solution accuracy and computational time overhead. Finally, by simulating sudden new tasks and robot failure scenarios, the effectiveness of the dynamic rescheduling mechanism is verified, ensuring the timeliness and high robustness of the system. Full article

Synchronous Alternating Current (AC) power systems are increasingly supported by embedded High-Voltage Direct Current (HVDC) links to enhance operational flexibility and ensure security of supply. However, the loss of High-Voltage Alternating Current (HVAC) interconnections in these synchronous areas may lead to transmission network splitting, posing serious challenges to frequency stability due to the reduction in overall system inertia and stiffness. In this paper, a supplementary control layer is proposed to enable embedded HVDC systems, particularly those based on modern Voltage Source Converters (VSCs), to support frequency stability under post-splitting conditions. The proposed control strategy combines Angle-Difference Control (ADC), Frequency-Difference Control (FDC), and feedforward action, enabling fast and coordinated active-power modulation. A single-bus, dynamic multi-area Load Frequency Control (LFC) model is developed, combining the regulation of thermal units, Renewable Energy Sources’ (RESs’) Fast Frequency Response (FFR) with Synthetic Inertia (SI), and VSC-HVDC modulation. The effectiveness of the proposed control layer is demonstrated by applying it to the East Tyrrhenian Link (ETL), an embedded VSC-HVDC interconnection connecting Sicily with the mainland of Italy, under a post-splitting low-inertia condition in which Sicily operates as an islanded synchronous system, i.e., after losing synchronism with the mainland of Italy, in a 2030 scenario condition. The simulation results demonstrate that the proposed controller enables embedded VSC-HVDC systems to actively participate in post-splitting frequency containment and damping, as well as coordinated active power reallocation, thereby enhancing overall system stability and resilience. Full article

Understanding how Bitcoin mining is distributed across countries is important for evaluating both the sustainability and resilience of the network. In this study, we examine the evolution of total Bitcoin electricity consumption alongside the geographic distribution of Bitcoin mining. Data are provided by the Cambridge Centre for Alternative Finance (Licensed under CC BY–NC–SA 4.0): Annual data from the Cambridge Bitcoin Electricity Consumption Index (2010–2025) and a monthly panel of country-level Bitcoin hashrate shares for 105 countries (September 2019–January 2022). To assess the degree of decentralization in the global mining network, we employ entropy-based measures, inequality indices, and panel convergence tests. The results indicate that total electricity consumption grew exponentially during the early years of Bitcoin, but later transitioned to a more stable and approximately linear path. Country-level permutation entropy reveals highly volatile and dynamic mining trajectories. The Theil index shows that cross-sectional inequality declines over time, while increasing symbolic entropy reflects a progressively more even cross-country distribution of mining activity. Further evidence from $\sigma$-convergence supports a statistically significant reduction in cross-country dispersion of mining shares. Dynamic panel fixed-effects estimates reveal mean-reverting behavior in relative country shares, consistent with stochastic convergence. Finally, Phillips–Sul analysis points to heterogeneous early transition paths but ultimately supports convergence toward a single global club. The gradual geographical decentralization occurs alongside persistent core–periphery asymmetries in long-run mining shares. Overall, our findings suggest that Bitcoin mining behaves as a globally integrated industry in which computational capacity reallocates rapidly across countries in response to economic and regulatory conditions. Full article

The European Union aims to cut greenhouse gas emissions by 55% by 2030 and reach climate neutrality by 2050, targets that depend on expanding renewable generation in the European energy system. While photovoltaic (PV) capacity has grown quickly in several member states, others remain far behind. This paper frames that divergence as a systems planning problem: installed MW expands through diffusion-like dynamics, but the conversion of investment into energizable capacity is filtered by grid-integration constraints and institutional throughput. The study develops a macro-level framework for systems-level assessment and decision support to guide PV capacity planning and budget allocation using official 2012–2022 data for 22 EU countries. We combine (i) unsupervised clustering of standardized national deployment trajectories, (ii) bounded logistic fits interpreted as an operational diffusion-with-saturation representation that yield comparable growth parameters and maturity years (80–90% of the estimated ceiling), and (iii) a proportional reallocation scenario for countries below 5 GW in 2022. Three clusters emerge—steady growth, early plateau, and atypical paths—and an analytically tractable maturity indicator integrates capacity, rate, and timing in a single measure. In a 10 GW reallocation scenario, average progress toward the 5 GW benchmark rises from 9.8% to 23.1%, closing about 14.8% of the aggregate shortfall. The allocation experiment reveals a clear asymmetry: systems with an existing installed base convert additional MW into benchmark progress more efficiently than very low-baseline systems, where binding constraints are more likely to sit in permitting, interconnection queues, and hosting capacity rather than in finance alone. Turning these allocations into usable capacity depends on timely interconnection and power-electronics integration and on grid-enablement constraints such as interconnection readiness, inverter compliance, and local hosting capacity in high-penetration areas. The contribution is a transparent, updateable decision-support pipeline that links observed trajectory regimes to a maturity “clock” and an auditable allocation baseline, making the trade-off between closing capacity gaps and respecting feasibility filters explicit in an EU system with heterogeneous national subsystems. The proposed approach links macro-level maturity clusters to operational feasibility signals in the grid integration layer, showing that modeling-based allocation can improve system progress but cannot substitute grid-enablement measures, highlighting the importance of regional coordination in the EU energy system under heterogeneous national trajectories. Full article

High-dimensional global optimization and microgrid economic scheduling problems are often dominated by nonlinear search landscapes, strong coupling among decision variables, and stringent operational constraints, which severely limit the effectiveness of conventional metaheuristic approaches. In response to these challenges, this study presents a multi-strategy cooperative optimization framework derived from stock exchange trading principles, referred to as MESETO. The proposed method departs from the single-path evolutionary process of the standard SETO algorithm by introducing a diversified strategy collaboration mechanism that enables the dynamic adjustment of search behaviors throughout the optimization process. Multiple complementary update strategies are jointly employed to balance global exploration and local exploitation, while an adaptive probability regulation scheme continuously reallocates computational effort toward strategies that demonstrate superior performance. In addition, a solution validation mechanism is incorporated to prevent population degradation by rejecting ineffective evolutionary moves, thereby enhancing convergence stability. Extensive numerical experiments conducted on the CEC2017 and CEC2022 benchmark suites across different dimensional configurations demonstrate that MESETO consistently achieves improved solution accuracy, faster convergence, and stronger robustness compared with several representative state-of-the-art metaheuristic algorithms. Furthermore, the applicability of the proposed optimizer is verified through a 24 h microgrid economic scheduling case that integrates renewable energy sources, energy storage systems, dispatchable generators, and grid interaction. Simulation results confirm that MESETO effectively reduces operational costs while maintaining stable and efficient scheduling performance. Overall, the results indicate that MESETO constitutes a reliable and efficient optimization framework for solving complex global optimization problems and practical energy management applications. Full article

This study examines how strongly demand-load prediction and adaptive load control in photovoltaic heating systems rely on computationally intensive artificial neural network (ANN) models. To streamline the computational workflow and reduce runtime resource requirements, we propose an ANN load-prediction-and-validation algorithm coupled with a corresponding offline control strategy. By optimizing the algorithmic structure and shifting heavy computations away from online execution, the proposed method substantially lowers the operational computational burden while preserving predictive accuracy, enabling efficient real-time load prediction and adaptive control. Based on a modelling study of a monocrystalline PV string comprising two 330 W modules connected in series, the proposed simplified prediction method produced annual cumulative energy outputs of 139.9, 391.2, 320.2, 251.4, and 154.1 kW·h across the five irradiance intervals [200, 400), [400, 600), [600, 800), [800, 1000), and [1000, ∞), respectively. Compared with a conventional artificial neural network (ANN)-based prediction approach, the corresponding deviations were 1.1%, −0.1%, 0.0%, 0.1%, and −0.4%, the total annual cumulative energy outputs across all intervals was 1256.7 kW·h with a mean deviation of −0.07%. Moreover, the simplified load-control strategy required only 3.57% of the computational resources consumed by the conventional ANN method. In addition, the method rapidly reallocates computational resources in response to changes in real-time input data, thereby minimizing redundant computation. Overall, the results demonstrate that the proposed framework markedly reduces computational complexity without sacrificing accuracy, providing an effective alternative to traditional ANN-based solutions and facilitating the practical deployment of photovoltaic heating systems. Full article

This paper proposes a unified energy management, fault detection, and fault-tolerant control (EMS–FDI–FTC) framework for Hybrid Hydrogen Electric Vehicles (HHEVs) integrating a fuel cell (FC), battery (Bat), and supercapacitor (SC). While such multi-source architectures enable high-efficiency propulsion under dynamic driving conditions, actuator and state faults such as FC voltage sag, Bat internal resistance increase, and SC capacitance degradation can compromise safety, availability, and component lifetime. The proposed framework converts real-world GPS-recorded vehicle speed profiles into route-aware traction power demand and combines interpretable model-based indicators with an AI-based fault detection and classification module. Based on the diagnosis outcome, a fault-tolerant supervisory strategy performs online power reallocation among the FC, Bat, and SC while enforcing operational constraints. Validation is conducted in a MATLAB-based software-in-the-loop (SIL) environment using three urban driving routes collected from on-road measurements in Tunisia with injected ground-truth faults. The results demonstrate reliable fault classification performance and effective service continuity during fault intervals, supplying over 94% of the demanded energy across all routes, with energy-not-served remaining below 0.02 kWh. In addition, processor-in-the-loop (PIL) implementation on an STM32F407VG controller confirms real-time feasibility with a 10 Hz supervisory sampling rate and execution time margins compatible with embedded automotive deployment. Overall, the proposed closed-loop framework provides a practical route-aware diagnosis-to-control solution for robust and fault-resilient HHEV operation under realistic driving variability. All energy and efficiency indicators reported in this study are derived from control-oriented component models and are intended for consistent comparative evaluation across routes and operating scenarios, rather than absolute representation of a specific commercial vehicle. Full article

Soil salinization severely constrains the productivity of Medicago sativa L. Although exogenous melatonin (MT) has been proven to effectively alleviate salt stress injury in plants, the molecular regulatory networks underlying its function during the early stages of stress response remain not fully elucidated. In this study, we systematically investigated the specific regulatory mechanisms of exogenous MT-mediated salt tolerance in alfalfa seedlings during the early phase (12–24 h) of salt stress by integrating physiological, biochemical, and transcriptomic analyses. The results showed that MT treatment significantly inhibited membrane lipid peroxidation (indicated by decreased MDA content) in leaves and upregulated the activities of antioxidant enzymes as well as the levels of osmoprotectants, such as soluble sugars. Transcriptomic (RNA-seq) analysis revealed that MT induced a precise strategy of temporal transcriptional reconfiguration. At the initial stage of stress (12 h), MT preferentially downregulated the expression of genes related to ribosome biogenesis and chromatin remodeling. This transcriptional suppression suggests that plants adopted an “energy saving strategy,” aiming to minimize basal metabolic consumption and potentially reallocate limited energy resources toward the antioxidant defense system. Subsequently, at 24 h, MT orchestrated the comprehensive activation of the ABA signaling cascade and secondary metabolic pathways, such as phenylpropanoid and flavonoid biosynthesis, thereby establishing a long-term chemical defense barrier. Furthermore, weighted gene co-expression network analysis (WGCNA) identified ABF2 and Susy as key hub genes mediating soluble sugar accumulation. This study elucidates the molecular basis by which melatonin enhances early salt tolerance in alfalfa through a temporal transition from an “energy-saving” strategy to “active defense,” providing new theoretical insights for the molecular breeding of stress resistance in leguminous forage crops. Full article

Rolling-horizon energy management systems (EMSs) and model predictive control (MPC) for microgrids in smart cities face a fundamental trade-off: finer temporal discretization improves operational performance but rapidly increases the size of the optimization problem and execution time, jeopardizing real-time feasibility. Furthermore, in short-horizon operation, only the first control actions are implemented, while long-horizon decisions primarily guide feasibility and constraints. This paper proposes a computation-aware temporal mesh design layer that jointly selects a variable granularity of blocks and an optimization horizon, explicitly bounded by market-aligned settlement steps and per-cycle computation budgets. Candidate configurations are represented as pairs ⟨B, H⟩, where B is a constant-step block programme, and H is the optimization horizon, and they are uniquely tracked through an auditable mesh signature. The method first evaluates a predefined, market-consistent set of solutions ⟨B, H⟩ to establish reproducible cost and execution-time benchmarks, then applies a greedy value-of-information (Greedy-VoI) search that generates valid neighbouring meshes through local refinement, thickening, and resolution reallocation without violating the basic requirements that every solution must meet. All candidates are evaluated using the same microgrid use case and the same comparative KPIs, enabling the systematic identification of near-optimal mesh–horizon designs for practical EMS implementation. Full article