Search Results (1,245)

The exponential proliferation of the Internet of Things (IoT) and optical IoT (O-IoT) has introduced substantial challenges concerning computational capacity and energy efficiency. IoT devices generate vast volumes of aggregated data and require intensive processing, often resulting in elevated latency and excessive energy consumption. Task offloading has emerged as a viable solution; however, many existing strategies fail to adequately optimize both latency and energy usage. This paper proposes a novel task-offloading approach based on deep Q-network (DQN) learning, designed to intelligently and dynamically balance these critical metrics. The proposed framework continuously refines real-time task offloading decisions by leveraging the adaptive learning capabilities of DQN, thereby substantially reducing latency and energy consumption. To further enhance system performance, the framework incorporates optical networks into the IoT–fog–cloud architecture, capitalizing on their high-bandwidth and low-latency characteristics. This integration facilitates more efficient distribution and processing of tasks, particularly in data-intensive IoT applications. Additionally, we present a comparative analysis between the proposed DQN algorithm and the optimal strategy. Through extensive simulations, we demonstrate the superior effectiveness of the proposed DQN framework across various IoT and O-IoT scenarios compared to the BAT and DJA approaches, achieving improvements in energy consumption and latency of 35%, 50%, 30%, and 40%, respectively. These findings underscore the significance of selecting an appropriate offloading strategy tailored to the specific requirements of IoT and O-IoT applications, particularly with regard to environmental stability and performance demands. Full article

Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications. Full article

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

Camellia oleifera, a woody oilseed species endemic to China, often experiences growth constraints due to seasonal drought. This study investigates the coordinated regulation of photosynthetic traits, stomatal behavior, and hormone responses during drought–rehydration cycles in two cultivars with contrasting drought resistance: ‘CL53’ (tolerant) and ‘CL40’ (sensitive). Photosynthetic inhibition resulted from both stomatal and non-stomatal limitations, with cultivar-specific differences. After 28 days of drought, the net photosynthetic rate (P_n) declined by 26.6% in CL53 and 32.6% in CL40. A stable intercellular CO₂ concentration (C_i) in CL53 indicated superior mesophyll integrity and antioxidant capacity. CL53 showed rapid P_n recovery and photosynthetic compensation post-rehydration, in contrast to CL40. Drought triggered extensive stomatal closure; >98% reopened upon rehydration, though the total stomatal pore area remained reduced. Abscisic acid (ABA) accumulation was greater in CL40, contributing to stomatal closure and P_n suppression. CL53 exhibited faster ABA degradation and gibberellin (GA₃) recovery, promoting photosynthetic restoration. ABA negatively correlated with P_n, transpiration rate (T_r), stomatal conductance (G_s), and C_i, but positively with stomatal limitation (L_s). Water use efficiency (WUE) displayed a parabolic response to ABA, differing by cultivar. This integrative analysis highlights a coordinated photosynthesis–stomata–hormone network underlying drought adaptation and informs selection strategies for drought-resilient cultivars and precision irrigation. Full article

Burn injuries are complex and require effective wound management strategies. Traditional treatments, such as dermal templates, are limited by simplified extracellular matrix (ECM) composition (e.g., collagen-elastin or collagen-glycosaminoglycan), sheet-based formats, and frequent use of animal-derived materials. These limitations can reduce wound conformity, biocompatibility, and integration with host tissue. Functional hydrogels are being explored as alternatives due to properties such as high water content, biodegradability, adhesiveness, antimicrobial activity, and support for angiogenesis. Unlike standard templates, hydrogels can adapt to irregular wound shapes as in burn wounds and reach deeper tissue layers, supporting moisture retention, cell migration, and controlled drug delivery. These features may improve the wound environment and support healing in burns of varying severity. This review outlines recent developments in functional hydrogel technologies and compares them to current clinical treatments for burn care. Emphasis is placed on the structural and biological features that influence performance, including material composition, bioactivity, and integration capacity. Through an exploration of key mechanisms of action and clinical applications, this review highlights the benefits and challenges associated with hydrogel technology, providing insights into its future role in burn care. Full article

High-intensity interval training (HIIT) has emerged as a promising non-pharmacological intervention for improving cardiometabolic health. In populations with diabetes, cardiovascular disease, obesity, or metabolic dysfunction, redox imbalance—characterized by elevated oxidative stress and impaired antioxidant defense—is a key contributor to disease progression. This narrative review synthesizes current evidence on the effects of HIIT on oxidative stress and antioxidant capacity across diverse cardiometabolic disease cohorts. While findings are heterogeneous, the majority of studies demonstrate that HIIT intervention can reduce levels of oxidative stress markers and enhance antioxidant enzyme expression. These redox adaptations may underpin improvements in vascular endothelial function, inflammation, and metabolic regulation. Importantly, variations in intensity, duration, and health status influence these responses, highlighting the need for individualized exercise prescriptions. Safety considerations are emphasized, including the necessity for medical clearance, gradual progression, and individualized training prescriptions in higher-risk individuals. In conclusion, HIIT shows potential as a targeted strategy to restore redox homeostasis and improve cardiometabolic outcomes, although further research is needed to clarify optimal protocols and the underlying mechanisms. Full article

Acute hepatopancreatic necrosis disease (AHPND), caused by the bacterium Vibrio parahaemolyticus, is a major threat to global shrimp aquaculture. In this study, we evaluated the therapeutic effects of phage therapy in Litopenaeus vannamei challenged with AHPND-causing Vibrio parahaemolyticus. Phage application at various concentrations significantly improved shrimp survival, with the 1 ppm group demonstrating the highest survival rate. Enzymatic assays revealed that phage-treated shrimp exhibited enhanced immune enzyme activities, including acid phosphatase (ACP), alkaline phosphatase (AKP), and lysozyme (LZM). In addition, antioxidant defenses such as superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-PX), and total antioxidant capacity (T-AOC) significantly improved, accompanied by reduced malondialdehyde (MDA) levels. Serum biochemical analyses demonstrated marked improvements in lipid metabolism, particularly reductions in triglyceride (TG), total cholesterol (TC), and low-density lipoprotein (LDL), alongside higher levels of beneficial high-density lipoprotein (HDL). Transcriptomic analysis identified 2274 differentially expressed genes (DEGs), notably enriched in pathways involving fatty acid metabolism, peroxisome functions, lysosomes, and Toll-like receptor (TLR) signaling. Specifically, phage treatment upregulated immune and metabolic regulatory genes, including Toll-like receptor 4 (TLR4), myeloid differentiation primary response protein 88 (MYD88), interleukin-1β (IL-1β), nuclear factor erythroid 2-related factor 2 (Nrf2), and peroxisome proliferator-activated receptor (PPAR), indicating activation of innate immunity and antioxidant defense pathways. These findings suggest that phage therapy induces protective immunometabolic adaptations beyond its direct antibacterial effects, thereby providing an ecologically sustainable alternative to antibiotics for managing bacterial diseases in shrimp aquaculture. Full article

The synergistic development of low-permeability reservoirs such as deep coalbed methane (CBM) and tight gas has emerged as a key technology to reduce development costs, enhance single-well productivity, and improve gas recovery. However, due to fundamental differences between coal seams and tight sandstones in their pore structure, permeability, water saturation, and pressure sensitivity, significant variations exist in their flow capacities and fluid production behaviors. To address the challenges of production allocation and main reservoir identification in the co-development of CBM and tight gas within deep gas-bearing basins, this study employs the transient multiphase flow simulation software OLGA to construct a representative dual-gas co-production well model. The regulatory mechanisms of the gas–liquid distribution, deliquification efficiency, and interlayer interference under two typical vertical stacking relationships—“coal over sand” and “sand over coal”—are systematically analyzed with respect to different tubing setting depths. A high-precision dynamic production allocation method is proposed, which couples the wellbore structure with real-time monitoring parameters. The results demonstrate that positioning the tubing near the bottom of both reservoirs significantly enhances the deliquification efficiency and bottomhole pressure differential, reduces the liquid holdup in the wellbore, and improves the synergistic productivity of the dual-reservoirs, achieving optimal drainage and production performance. Building upon this, a physically constrained model integrating real-time monitoring data—such as the gas and liquid production from tubing and casing, wellhead pressures, and other parameters—is established. Specifically, the model is built upon fundamental physical constraints, including mass conservation and the pressure equilibrium, to logically model the flow paths and phase distribution behaviors of the gas–liquid two-phase flow. This enables the accurate derivation of the respective contributions of each reservoir interval and dynamic production allocation without the need for downhole logging. Validation results show that the proposed method reliably reconstructs reservoir contribution rates under various operational conditions and wellbore configurations. Through a comparison of calculated and simulated results, the maximum relative error occurs during abrupt changes in the production capacity, approximately 6.37%, while for most time periods, the error remains within 1%, with an average error of 0.49% throughout the process. These results substantially improve the timeliness and accuracy of the reservoir identification. This study offers a novel approach for the co-optimization of complex multi-reservoir gas fields, enriching the theoretical framework of dual-gas co-production and providing technically adaptive solutions and engineering guidance for multilayer unconventional gas exploitation. Full article

The intersection of sports and natural forests and green spaces represents an emerging interdisciplinary field with implications for public health, environmental science, and sustainable land management and refers to the variety of cultural ecosystem services demanded by people from ecosystems. This manuscript presents a systematic bibliometric and thematic analysis of 148 publications for the period 1993–2024 identified through Web of Science and Scopus, aiming to evaluate the current state of research on sports activities conducted in natural forest environments. Findings indicated a marked increase in scientific interest of this topic over the past two decades, with key contributions from countries such as England, Germany, China, and the United States. Researchers most frequently examined sports such as hiking, trail running, mountain biking, and orienteering for their capacity to provide physiological and psychological benefits, reduce stress, and enhance mental well-being. The literature analysis highlights ecological concerns, particularly those associated with habitat disturbance, biodiversity loss, and conflicts between recreation and conservation. Six principal research themes were identified: sports in urban forests, sports tourism, hunting and fishing, recreational sports, health benefits, and environmental impacts. Keyword and co-authorship analyses revealed a multidisciplinary knowledge base with evolving thematic focuses. In conclusion, the need for integrated approaches that incorporate ecological impact assessment, stakeholder perspectives, and adaptive forest governance to ensure sustainable recreational use of natural forest ecosystems is underlined. Full article

Glucose-6-phosphate dehydrogenase (G6PD) deficiency, the most common enzymatic disorder, affects over 500 million people worldwide and is often linked to exercise intolerance due to oxidative stress, but its true impact on physical performance remains unclear. This study aimed to evaluate the physiological and metabolic effects of G6PD deficiency on endurance capacity. Using humanized mice carrying the African G6PD variant [V68M; N126D] (hG6PD_A−), we show that despite reduced pentose phosphate pathway activity, these mice exhibit a 10.8% increase in treadmill critical speed (CS)—suggesting enhanced endurance capacity. Multi-omics profiling across red blood cells, plasma, skeletal muscle, spleen, kidney, and liver reveals metabolic adaptations, including elevated glycolysis, fatty acid oxidation, and increased mitochondrial activity, alongside heightened oxidative phosphorylation in muscle and accelerated red blood cell turnover in the spleen and liver. These findings indicate that systemic metabolic reprogramming may offset antioxidant deficiencies, potentially conferring a performance advantage. Given that G6PD deficiency affects up to 13% of African Americans and is associated with cardiovascular health disparities, our results challenge conventional exercise restrictions and highlight the need for personalized exercise guidelines for affected individuals. Full article

Objectives: Light intensity is a critical environmental factor regulating plant growth, development, and stress adaptation. However, the physiological and molecular mechanisms underlying light responses in Aconitum kusnezoffii, a valuable alpine medicinal plant, remain poorly understood. This study aimed to elucidate the adaptive strategies of A. kusnezoffii under different light intensities through integrated physiological and transcriptomic analyses. Methods: Two-year-old A. kusnezoffii plants were exposed to three controlled light regimes (790, 620, and 450 lx). Leaf anatomical traits were assessed via histological sectioning and microscopic imaging. Antioxidant enzyme activities (CAT, POD, and SOD), membrane lipid peroxidation (MDA content), osmoregulatory substances, and carbon metabolites were quantified using standard biochemical assays. Transcriptomic profiling was conducted using Illumina RNA-seq, with differentially expressed genes (DEGs) identified through DESeq2 and functionally annotated via GO and KEGG enrichment analyses. Results: Moderate light (620 lx) promoted optimal leaf structure by enhancing palisade tissue development and epidermal thickening, while reducing membrane lipid peroxidation. Antioxidant defense capacity was elevated through higher CAT, POD, and SOD activities, alongside increased accumulation of soluble proteins, sugars, and starch. Transcriptomic analysis revealed DEGs enriched in photosynthesis, monoterpenoid biosynthesis, hormone signaling, and glutathione metabolism pathways. Key positive regulators (PHY and HY5) were upregulated, whereas negative regulators (COP1 and PIFs) were suppressed, collectively facilitating chloroplast development and photomorphogenesis. Trend analysis indicated a “down–up” gene expression pattern, with early suppression of stress-responsive genes followed by activation of photosynthetic and metabolic processes. Conclusions: A. kusnezoffii employs a coordinated, multi-level adaptation strategy under moderate light (620 lx), integrating leaf structural optimization, enhanced antioxidant defense, and dynamic transcriptomic reprogramming to maintain energy balance, redox homeostasis, and photomorphogenic flexibility. These findings provide a theoretical foundation for optimizing artificial cultivation and light management of alpine medicinal plants. Full article

Hydrazine, a highly toxic and reactive compound widely used as rocket fuel, poses significant environmental and health risks, particularly in long-term space missions. This study investigates the cometabolic capacity of Chlorella vulgaris and seven extremophilic Bacillus spp. strains—isolated from the arid Dead Sea region—to tolerate and degrade hydrazine at concentrations up to 25 ppm. The microalga C. vulgaris reduced hydrazine levels by 81% within 24 h at 20 ppm, while the Bacillus isolates achieved an average reduction of 45% over 120 h. Identified strains included B. licheniformis, B. cereus, and B. atrophaeus. Co-culture experiments demonstrated that C. vulgaris and B. cereus (isolate ISO-36) stably coexisted without antagonistic effects, suggesting a synergistic detoxification interaction. Flow cytometry revealed that most bacteria transitioned into spores under stress, highlighting a survival adaptation. Titanium, representing a biocompatible material common in aerospace hardware, did not inhibit microbial growth or hydrazine degradation. These findings underscore the potential of Dead Sea-derived microbial consortia for cometabolic hydrazine detoxification and support the feasibility of converting spacecraft components into functional photobioreactors. This approach offers dual-use benefits for space missions and industrial wastewater treatment. Future studies should investigate degradation pathways, stress resilience, and bioreactor scale-up. Full article

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS. Full article

The functional role of MAPKK genes in potato (Solanum tuberosum L.) under high-temperature stress remains unexplored, despite their critical importance in stress signaling and yield protection. We characterized StMAPKK1, a novel group D MAPKK localized to plasma membrane/cytoplasm. Quantitative real-time polymerase chain reaction (qRT-PCR) revealed cultivar-specific upregulation in potato (‘Atlantic’ and ‘Desiree’) leaves under heat stress (25 °C, 30 °C, and 35 °C). Transgenic lines overexpressing (OE) StMAPKK1 exhibited elevated antioxidant enzyme activity, including ascorbate peroxidase (APX), catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD), mitigating oxidative damage. Increased proline and chlorophyll accumulation and reduced oxidative stress markers, hydrogen peroxide (H₂O₂) and malondialdehyde (MDA), indicate improved cellular redox homeostasis. The upregulation of key antioxidant and heat stress-responsive genes (StAPX, StCAT1/2, StPOD12/47, StFeSOD2/3, StMnSOD, StCuZnSOD1/2, StHSFA3 and StHSP20/70/90) strengthened the enzymatic defense system, enhanced thermotolerance, and improved photosynthetic efficiency, with significant improvements in net photosynthetic rate (Pn), transpiration rate (E), and stomatal conductance (Gs) under heat stress (35 °C) in StMAPKK1-OE plants. Superior growth and biomass (plant height, plant and its root fresh and dry weights, and tuber yield) accumulation, confirming the positive role of StMAPKK1 in thermotolerance. Conversely, RNA interference (RNAi)-mediated suppression of StMAPKK1 led to a reduction in enzymatic activity, proline content, and chlorophyll levels, exacerbating oxidative stress. Downregulation of antioxidant-related genes impaired ROS scavenging capacity and declines in photosynthetic efficiency, growth, and biomass, accompanied by elevated H₂O₂ and MDA accumulation, highlighting the essential role of StMAPKK1 in heat stress adaptation. These findings highlight StMAPKK1’s potential as a key genetic target for breeding heat-tolerant potato varieties, offering a foundation for improving crop resilience in warming climates. Full article

Strawberry planting requires different amounts of soil water-holding capacity and fertilizer at different growth stages. Determining the stages of strawberry growth has important guiding significance for irrigation, fertilization, and picking. Quick and accurate identification of strawberry plants at different stages can provide important information for automated strawberry planting management. We propose an improved multistage identification model for strawberry based on the YOLOv9 algorithm—the ASHM-YOLOv9 model. The original YOLOv9 showed limitations in detecting strawberries at different growth stages, particularly lower precision in identifying occluded fruits and immature stages. We enhanced the YOLOv9 model by introducing the Alterable Kernel Convolution (AKConv) to improve the recognition efficiency while ensuring precision. The squeeze-and-excitation (SE) network was added to increase the network’s capacity for characteristic derivation and its ability to fuse features. Haar wavelet downsampling (HWD) was applied to optimize the Adaptive Downsampling module (Adown) of the initial model, thereby increasing the precision of object detection. Finally, the CIoU function was replaced by the Minimum Point Distance based IoU (MPDIoU) loss function to effectively solve the problem of low precision in identifying bounding boxes. The experimental results demonstrate that, under identical conditions, the improved model achieves a precision of 97.7%, a recall of 97.2%, mAP50 of 99.1%, and mAP50-95 of 90.7%, which are 0.6%, 3.0%, 0.7%, and 7.4% greater than those of the original model, respectively. The parameters, model size, and floating-point calculations were reduced by 3.7%, 5.6% and 3.8%, respectively, which significantly boosted the performance of the original model and outperformed that of the other models. Experiments revealed that the model could provide technical support for the multistage identification of strawberry planting. Full article