Search Results (73)

Sustainable reservoir-system management requires coordinated operation to balance economic benefits, sediment regulation, and ecological requirements while maintaining equitable and durable cooperation among participating reservoirs. Focusing on the Wanjiazhai, Sanmenxia, and Xiaolangdi reservoirs in the middle and lower Yellow River Basin, this study develops an integrated framework that links multi-objective reservoir operation, coordination-oriented scheme selection, and compensation allocation under representative dry, normal, and wet hydrological conditions. NSGA-II is used to identify Pareto trade-offs among sediment transport, electricity production, and ecological water-deficit control, while the coupling-coordination degree model is applied to select schemes with balanced overall performance. CRITIC-TOPSIS is then used to allocate compensation by integrating static engineering attributes with operation-induced dynamic responses. The results show that the maximum coupling-coordination degrees reach 0.81, 0.88, and 0.90 under dry, normal, and wet conditions, respectively. Compared with actual operation, the recommended schemes increase total electricity production while reflecting hydrologically dependent trade-offs in sediment transport and ecological water-deficit control. Xiaolangdi Reservoir consistently receives the largest compensation share, followed by Wanjiazhai Reservoir and Sanmenxia Reservoir, and this ranking remains consistent across alternative allocation methods. By linking operational trade-offs with reservoir-specific contributions and compensation priorities, the framework supports more adaptive and equitable joint operation and provides a quantitative decision-support approach for improving the long-term environmental, economic, and institutional sustainability of reservoir-system management in the Yellow River Basin. Full article

Incremental automatic modulation recognition is essential for the awareness of complex electromagnetic environments but is prone to catastrophic forgetting. This is fundamentally precipitated by shortcut learning, a phenomenon where deep models prioritize stable but non-essential channel artifacts (e.g., noise, fading) over intrinsic modulation characteristics. Consequently, models rely on spurious correlations that collapse during incremental task updates or environmental shifts, leading to representation drift. To bridge this gap, we propose the ParalIMR framework, which integrates a parallel reconstruction architecture with the segment substitution (SS) strategy to decouple modulation signatures from environmental fingerprints. Specifically, the parallel branch utilizes a Denoising AutoEncoder (DAE) as a task-agnostic structural anchor, purifying feature representations and maintaining geometric consistency across varying signal-to-noise ratios without propagating noise-overfitting to the classifier. In the meantime, the SS strategy actively disrupts the temporal coupling between class labels and hardware fingerprints through random reorganization, forcing the model to extract modulation-invariant structural cues. Experimental results on the RML2016a datasets demonstrate that in a three-stage incremental setup, our method achieves an overall accuracy of 84.32% at 0 dB SNR, representing a 2.69% improvement over the iCaRL baseline. Notably, this advantage expanded to 4.46% on RML2018, demonstrating that ParalIMR effectively arrests catastrophic forgetting. Ultimately, this research provides a robust learning paradigm tailored for cognitive radio and electronic warfare in dynamic electromagnetic landscapes. Full article

The Popeye domain-containing protein 1 (Popdc1), also known as Bves, plays a crucial role in maintaining skeletal muscle homeostasis, with its variants leading to limb–girdle muscular dystrophy type R25. Skeletal muscles of patients with the homozygous missense variant of Bves exhibit impaired membrane trafficking, while skeletal muscle fibers in bves^S191F homozygous mutant zebrafish are significantly reduced and disorganized. However, the mechanism by which the absence of bves induces skeletal muscle atrophy remains unclear. In this study, we discovered a novel mechanism whereby bves deficiency drives skeletal muscle atrophy by disrupting mitochondrial structure and function. Our findings indicate that bves knockout leads to a significant decrease in zebrafish’s ability to swim, atrophy of skeletal muscle tissue, loss of cell membrane localization signals, and abnormalities in mitochondrial structure and function. After an 8-week intervention of regular aerobic exercise, the symptoms of skeletal muscle atrophy in bves knockout zebrafish were significantly alleviated, and the expression levels of genes and proteins related to mitochondrial were effectively rescued. These findings establish a connection between bves deficiency-induced disruption of mitochondrial structure and function and the onset and progression of skeletal muscle tissue atrophy symptoms, thereby laying a molecular foundation for exercise rehabilitation strategies in atrophic myopathy. Full article

Real-time perception of coal–biomass blending during conveyor-belt transport remains challenging because of local aggregation, particle overlap, and illumination variation. In this study, a laboratory-scale conveyor-belt image dataset covering different coal mass fractions, illumination conditions, and particle sizes was constructed. Whole-image classification, cropped-ROI classification, direct regression, horizontal bounding box (HBB)-based detection, oriented bounding box (OBB)-based detection, and RT-DETR-L detection baselines were compared using YOLO-series and auxiliary models. Coal mass fraction was estimated using a frequency-weighted statistical strategy that converts frame-level predictions into continuous estimates. YOLOv8-cls achieved an average RMSE of 13.98 percentage points (pp), indicating the influence of background interference in whole-image classification. Among HBB models, YOLOv8m achieved the lowest mean RMSE of 6.10 pp but required higher computational cost. Compared with YOLOv8n, YOLOv8n-OBB reduced the average RMSE from 9.02 to 6.90 pp by providing a more compact material-region representation and reducing background redundancy. These results show that OBB representation improves the stability of lightweight models. The proposed method provides a feasible vision-based soft-sensing approach for online trend monitoring of coal–biomass blending under lightweight deployment. Full article

Red mud (RM) and fly ash (FA) were used as a 30% replacement of cement in a sodium silicate-activated system. Composite mortar specimens with RM/FA ratios of 0:30, 1:5, 1:2, 1:1, and 2:1 were prepared with polypropylene fibers (PPF) for toughness enhancement. The mechanical properties and microstructure of the fiber-reinforced mortar were systematically investigated. The results showed that RM20F10 (RM/FA = 2:1) exhibited the best overall mechanical performance among all tested proportions. At this ratio, the 28-day compressive, flexural, and splitting tensile strengths reached 32.4 MPa, 7.3 MPa, and 4.2 MPa, exceeding the control mortar by 12.5%, 15.9%, and 23.5%, respectively. The RM/FA ratio of 1:1 achieved the highest 7-day flexural-to-compressive strength ratio. At 28 days, autogenous shrinkage increased from 910 με to 1100 με as the RM/FA ratio rose from 0:30 to 2:1, and all RM-containing specimens exhibited higher water absorption than the control mortar. Microstructural analysis by SEM, XRD, and FTIR revealed a denser matrix with reduced porosity, attributed to the synergistic formation of C–S–H, C–A–S–H, and N–A–S–H gels. RM reduced early-age porosity by promoting C–A–S–H gel formation, while FA facilitated late-age densification through delayed activation. PPF effectively bridged microcracks via fiber pull-out, leading to a ductile failure mode. Full article

Thyroid cancer (TC) is an endocrine malignant tumor with the fastest-growing incidence worldwide. It has complex pathological types and significant heterogeneity, with great differences in clinical prognosis among different subtypes. Among them, aggressive subtypes, such as radioiodine-refractory (RAI-R) TC and anaplastic thyroid cancer (ATC), have become a major challenge in current clinical diagnosis and treatment, due to limited treatment options and high risks of recurrence and metastasis. Tumor metabolic reprogramming is one of the characteristics of cancer, among which the Warburg effect plays a driving role. As a rate-limiting enzyme in the glycolytic pathway, pyruvate kinase M2 (PKM2), with its unique functional plasticity, has become a linchpin of glycolytic metabolism and malignant phenotypes of tumor cells. This article will systematically review the functional regulatory mechanisms of PKM2, its specific role in TC, and explore the targeted therapeutic strategies and research prospects of TC with PKM2, providing a new theoretical basis and potential plans for the clinical diagnosis and treatment. Full article

Identifying critical nodes in a multimodal transport network is fundamental to enhancing the resilience of transport systems against natural or man-made disasters. Although research on identifying critical nodes in complex networks has been deeply accumulated, its practical application in transport systems is still limited and requires further exploration. To bridge this research gap, an improved integrated method for identifying critical nodes in multimodal transport networks is proposed based on resilience theory. This method comprehensively incorporates disaster consequences and recovery time into resilience assessment, and an Improved Binary Particle Swarm Optimization (IBPSO) algorithm is developed to solve this problem efficiently. Then, a real case in China is conducted to verify the effectiveness of the proposed method. The results show that the proposed method can rapidly identify critical nodes in multimodal transport networks from the resilience perspective. These critical nodes, which are mainly concentrated at choke points with long repair durations, are often overlooked in traditional methods. Compared with the five baseline methods, the proposed method improves identification efficiency by more than 20%, verifying its better performance. The research findings can provide a scientific foundation for decisions on the effective protection and rapid recovery of critical nodes in the network. Full article

Quantifying vegetation dynamics has become a critical scientific imperative in the context of global ecosystem restoration initiatives targeting degraded forests. Previous studies have explored vegetation-cover change trends at different spatial scales worldwide using the Theil–Sen (TS) estimator and Mann–Kendall (MK) test, yet few have accounted for the uncertainty in resulting trends across time-series datasets of varying lengths. Taking the coastal zone of Fujian Province in Southeast China as a case study, we investigated the uncertainty of vegetation-cover change trends using normalized difference vegetation index (NDVI) datasets of different lengths (e.g., 20-year, 15-year, and 10-year) via the TS estimator and MK test. Additionally, piece-wise regression was employed to detect turning points and shifts in vegetation trends between 2001 and 2020. The results indicate significant discrepancies in trend estimation across datasets of different lengths, with consistency ratios ranging from 46.1% to 64.7% among the 20-year, 15-year, and 10-year series. The MK test is more sensitive to time-series length than the TS estimator, with areas of significant change decreasing by over 50% when transitioning from a 20-year to a 10-year dataset. The spatial distribution of trend shifts exhibits a distinct “coastal–inland” polarization pattern, with 2010 as the turning point. Eight modes of vegetation trend shifts were identified based on pre- and post-turning point dynamics. Furthermore, piece-wise regression improved trend accuracy by approximately 15%. This research advances the mechanistic understanding of spatiotemporal vegetation dynamics and supports adaptive ecosystem management strategies. Full article

This paper conducts an experimental study to develop a response strategy for lithium-ion battery fires. Guided by the principle of “first control, then extinguish”, the strategy integrates a lithium-ion battery-specific fire-controlling blanket with castable fire-extinguishing agents. Both fire tests of e-bikes and lithium-ion batteries are conducted. From e-bike fire tests, the feasibility of rescuers conducting close-range disposal of LIB (lithium-ion battery) fires is analyzed from three perspectives, i.e., fire evolution stage, battery splashing and high temperature. The results indicate a high risk of fire spread, as well as a strong likelihood of human injury caused by flying LIB debris and extremely hot gases. Subsequently, the fire-controlling capability of the fire blanket is validated. It not only blocks splashing batteries and jet flames, reducing combustion intensity, but also offers a safe way for personnel to operate the portable fire extinguishers. Through two castable extinguishing agents tested, the perfluorohexanone-based agent outperforms the water-based alternative. The reasons are as follows. First, perfluorohexanone evaporates easily in the low-temperature, confined environment created by the fire blanket. Second, it possesses both physical and chemical fire-extinguishing capabilities, ultimately delivering a more potent combustion suppression effect. Full article

Background: Root-knot nematodes (RKNs) are destructive parasites affecting both agricultural and natural plants. Fagopyrum tataricum, a phenolic-rich edible and medicinal plant, has antidiabetic, anti-inflammatory, and anticancer properties, yet the impact of RKN infection on its rhizosphere microbiome remains unclear. Methods: We employed full-length 16S rRNA gene sequencing (FL16S) to profile bacterial communities in the rhizosphere of healthy and RKN-infected F. tataricum plants. Results: FL16S classified 78.41% of operational taxonomic units (OTUs) at the genus level and 69.18% at the species level. Healthy plants showed higher richness, diversity, and evenness, while principal co-ordinate analysis (PCoA) and PERMANOVA indicated significant RKN-associated shifts in community composition. Dominant phyla included Bacteroidota, Proteobacteria, Patescibacteria, Verrucomicrobiota, Actinobacteriota, Acidobacteriota, and Chloroflexi, with Abditibacteriota enriched in healthy and Acidobacteriota in diseased rhizospheres. At the OTU level, 66 differentially abundant taxa were identified, including nine hub OTUs in healthy plants, suggesting keystone roles in network stability. Network analyses revealed reduced diversity, interactions, and altered intra- and inter-phylum dynamics under RKN infection. Conclusions: These findings provide insight into rhizosphere microbial responses to RKN parasitism in F. tataricum and identify potential microbial biomarkers and biocontrol targets, supporting microbiome-based management strategies. Full article

DC-bias may accumulate in bidirectional full-bridge LLC converters during reverse power transfer because the magnetizing branch lacks an inherent DC-blocking mechanism. This bias may cause asymmetric flux excitation in the transformer core, thereby increasing magnetic stress and even leading to core saturation. To address this issue, a confidence-scheduled hybrid DC-bias estimation and suppression method is proposed. An integration-based indicator is constructed for sensitive weak-bias detection, while a reduced-order extended Kalman filter (EKF) is introduced to improve noise immunity and dynamic tracking under strong-bias conditions. Moreover, a confidence-scheduling mechanism is developed to adaptively fuse the two estimates according to bias severity. Based on the fused estimate, a two-level suppression strategy is implemented for severe- and weak-bias conditions. Simulations and experiments on a 2 kW prototype verify that the proposed strategy achieves fast detection, highly accurate robust estimation with a steady-state error of less than 2%, and effective suppression over a wide operating range without additional bulky DC-blocking hardware. Full article

Salt stress is a major abiotic factor limiting grapevine growth and yield. To elucidate the physiological and molecular regulatory mechanisms underlying salt tolerance in grapevine, this study used ‘Carménère’ (Vitis vinifera) and ‘Pinot Noir’ (Vitis vinifera) as experimental materials. Under 200 mmol/L NaCl stress, the physiological response characteristics of the two cultivars were systematically compared, and transcriptome sequencing combined with qRT-PCR analysis was conducted to explore the molecular basis of their differences in salt tolerance. The results showed that salt stress significantly impaired photosynthetic performance and disrupted cellular homeostasis in grapevine; however, the reductions in relative chlorophyll content (SPAD value), maximum photochemical efficiency of photosystem II (Fv/Fm), and photosynthetic performance were significantly smaller in ‘Carménère’ than in ‘Pinot Noir’, indicating greater stability of the photosynthetic apparatus in ‘Carménère’. Meanwhile, ‘Carménère’ maintained higher activities of antioxidant enzymes and higher levels of non-enzymatic antioxidants, effectively reducing reactive oxygen species accumulation and membrane lipid peroxidation. In addition, under salt stress, ‘Carménère’ accumulated greater amounts of osmotic adjustment substances and maintained lower Na⁺ content and higher K⁺ content, demonstrating a more efficient capacity for osmotic regulation and ion homeostasis. Transcriptomic analysis revealed that the plant hormone signal transduction, MAPK signaling, and glutathione metabolism pathways were significantly enriched in ‘Carménère’, with multiple key genes being coordinately upregulated under salt stress. Taken together, these findings indicate that ‘Carménère’ achieves enhanced salt tolerance through a multilayered signaling regulatory network that coordinates physiological defense responses. This study provides a theoretical basis for elucidating the mechanisms of salt tolerance in grapevine and for the molecular breeding of salt-tolerant cultivars. Full article

Stone powder is an inevitable by-product generated during the processing of manufactured sand and gravel. Waste stone powder has been proven to affect concrete properties and has been applied in the transportation and hydropower fields. This study aims to convert waste granite stone powder (GP) to nuclear power concrete by replacing manufactured sand, investigating its effect on the workability, compressive strength, splitting tensile strength, impermeability, and freezing resistance of nuclear power concrete. The mechanism was further elucidated through thermogravimetric (TG), scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP) techniques. The results show that with the increase in GP content, the slump, compressive strength, and splitting tensile strength of concrete increase first and then decrease, and the seepage height under pressure water decreases first and then increases. The workability, strength, and impermeability of concrete are optimal when GP content is 11.0%. Reasonable GP content improves the compactness of concrete by filling pores and optimizing aggregate gradation, resulting in decreases in porosity, with the size being the most probable and average pore size. Full article

Mitochondria play a central role in energy metabolism, redox homeostasis, and signal transduction in the thyroid cells. Increasing evidence indicates that mitochondrial DNA (mtDNA) mutations, copy number variations, and haplogroup-specific polymorphisms are closely associated with metabolic reprogramming and malignant progression of thyroid cancer. This review summarizes recent advances in the understanding of the impact of mitochondrial genome instability and metabolic plasticity on thyroid tumorigenesis. We discuss how mtDNA alterations disrupt oxidative phosphorylation (OXPHOS), trigger adaptive metabolic rewiring, and interact with key oncogenic pathways, such as HIF-1α, BRAFV600E mutations, and TSHR signaling in thyroid cancer. We also highlight the emerging diagnostic and therapeutic potential of mtDNA in thyroid cancer and outline current challenges and future research directions. Gaining deeper insights into the mitochondria–metabolism axis may provide novel biomarkers and metabolic intervention strategies for precision medicine in thyroid oncology. Full article

Thyroid cancer, as a highly hormone-dependent malignancy, is significantly regulated by thyroid hormones (T3/T4) and thyroid-stimulating hormone (TSH) signaling in its initiation and progression. This article comprehensively reviews the roles of thyroid hormones and their regulatory factor TSH in thyroid carcinogenesis and development, addressing related research from molecular mechanisms and clinical correlations to therapeutic strategies. It focuses on elucidating the impact of key mechanisms—such as elevated integrin αvβ3 expression and TRβ receptor mutations under hyperthyroid or hypothyroid conditions—on tumor progression. Furthermore, it evaluates the clinical utility and potential risks of TSH suppression therapy in patients stratified by risk, aiming to provide a theoretical basis for optimizing individualized treatment strategies. Full article