Search Results (3,111)

Motivated by the inefficiencies where multi-agent systems fail to reconcile individual agent self-interest with global optimality and accommodate dynamic tasks as its population increases, this paper investigates a clustering allocation problem for large-scale multi-agent systems. A novel coalitional game clustering allocation scheme that can simultaneously reconcile individual agent self-interest and adapt to dynamic tasks is proposed. In this scheme, a coalition switching strategy is newly constructed and incorporated to select optimal switching operation and obtain stable coalition partition. Simulation and comparative results are provided to verify the effectiveness of the developed allocation scheme. It is shown both theoretically and simulation experimentally that in the case of large-scale multi-agent systems, the generated clustering allocation strategy is Nash stable using the proposed scheme. Full article

New-generation geostationary meteorological satellite radiometric imagers widely employ multi-column redundant long-linear-array detectors, for which the Best Detector Selection (BDS) strategy is crucial for enhancing the quality of remote sensing data. Addressing the limitation of current BDS methods that often rely on a single metric and thus fail to fully exploit the detector’s comprehensive performance, this paper proposes a detector evaluation method based on a modified Z-score. This method systematically categorizes detector metrics into three types: positive, negative, and uniformity. It introduces, for the first time, spectral response deviation (SRD) as an effective quantitative measure for the Spectral Response Function (SRF) and employs a robust normalization strategy using the Interquartile Range (IQR) instead of standard deviation, enabling multi-dimensional detector evaluation and selection. Validation using laboratory data from the FY-4C/AGRI long-wave infrared band demonstrates that, compared to traditional single-metric optimization strategies, the best detectors selected by our method show significant improvement across multiple performance indicators, markedly enhancing both data quality and overall system performance. The proposed method features low computational complexity and strong adaptability, supporting on-orbit real-time detector optimization and dynamic updates, thereby providing reliable technical support for high-quality processing of remote sensing data from geostationary meteorological satellites. Full article

Background and Rationale: Tinospora crispa (L.) Hook.f. & Thomson (T. crispa) is a climbing medicinal plant with long-standing ethnopharmacological use, particularly in inflammatory and hepatic disorders and cancer-related conditions. There is a knowledge gap regarding how wild versus cultivated ecotypes differ in chemotype, bioactivity, and safety, and how this might support or refine traditional use. Study Objectives: This study aimed to compare wild and cultivated ecotypes of T. crispa from the Nile Delta (Egypt) in terms of quantitative and qualitative phytochemical profiles; selected in vitro biological activities (especially antioxidant and cytotoxic actions); genetic markers potentially associated with metabolic variation; and short-term oral safety in an animal model. Core Methodology: Standardized extraction of plant material from wild and cultivated ecotypes. Determination of total phenolics, total flavonoids, and major phytochemical classes (alkaloids, tannins, terpenoids). Metabolomic characterization using UHPLC-ESI-QTOF-MS, supported by NMR, to confirm key compounds such as berberine, palmatine, chlorogenic acid, rutin, and borapetoside C. In vitro bioassays including: Antioxidant activity (e.g., radical-scavenging assay with EC₅₀ determination). Cytotoxicity against human cancer cell lines, with emphasis on HepG2 hepatoma cells and calculation of IC₅₀ values. Targeted genetic analysis to detect single-nucleotide polymorphisms (SNPs) in the gen1 locus that differentiate ecotypes. A 14-day oral toxicity study in rats, assessing liver and kidney function markers and performing histopathology of liver and kidney tissues. Principal Results: The wild ecotype showed a 43–65% increase in total flavonoid and polyphenol content compared with the cultivated ecotype, as well as substantially higher levels of key alkaloids, particularly berberine (around 12.5 ± 0.8 mg/g), along with elevated chlorogenic acid and borapetoside C. UHPLC-MS and NMR analyses confirmed the identity of the main bioactive constituents and defined a distinct chemical fingerprint for the wild chemotype. Bioassays demonstrated stronger antioxidant activity of the wild extract than the cultivated one and selective cytotoxicity of the wild extract against HepG2 cells (IC₅₀ ≈ 85 µg/mL), being clearly more potent than extracts from cultivated plants. Genetic profiling detected a C → T SNP within the gen1 region that differentiates the wild ecotype and may be linked to altered biosynthetic regulation. The 14-day oral toxicity study (up to 600 mg/kg) revealed no evidence of hepatic or renal toxicity, with biochemical markers remaining within physiological limits and normal liver and kidney histology. Conclusions and Future Perspectives: The wild Nile-Delta ecotype of T. crispa appears to be a stress-adapted chemotype characterized by enriched levels of multiple bioactive metabolites, superior in vitro bioactivity, and an encouraging preliminary safety margin. These findings support further evaluation of wild T. crispa as a candidate source for standardized botanical preparations targeting oxidative stress-related and hepatic pathologies, while emphasizing the need for: More comprehensive in vivo efficacy studies. Cultivation strategies that deliberately maintain or mimic beneficial stress conditions to preserve phytochemical richness. Broader geographical and genetic sampling to assess how generalizable the present chemotypic and bioactivity patterns are across the species. Full article

Existing video inpainting methods typically combine optical flow propagation with Transformer architectures, achieving promising inpainting results. However, they lack adaptive inpainting strategy optimization in diverse scenarios, and struggle to capture high-level temporal semantics, causing temporal inconsistencies and quality degradation. To address these challenges, we make one of the first attempts to introduce reinforcement learning into the video inpainting domain, establishing a closed-loop framework named CLIP-RL that enables adaptive strategy optimization. Specifically, video inpainting is reformulated as an agent–environment interaction, where the inpainting module functions as the agent’s execution component, and a pre-trained inpainting detection module provides real-time quality feedback. Guided by a policy network and a composite reward function that incorporates a weighted temporal alignment loss, the agent dynamically selects actions to adjust the inpainting strategy and iteratively refines the inpainting results. Compared to ProPainter, CLIP-RL improves PSNR from 34.43 to 34.67 and SSIM from 0.974 to 0.986 on the YouTube-VOS dataset. Qualitative analysis demonstrates that CLIP-RL excels in detail preservation and artifact suppression, validating its superiority in video inpainting tasks. Full article

To address the challenge of effectively extracting early-stage failure features in rolling bearings, this paper proposes a two-stage harmonic optimization-gram method based on spectral amplitude modulation (SAM-TSHOgram). The method first employs amplitude spectra with varying weighting exponents to preprocess the signal, performing nonlinear adjustments to the vibration signal’s spectrum to enhance weak periodic impact characteristics. Subsequently, a two-stage evaluation strategy based on spectral coherence (SCoh) was designed to adaptively identify the optimal frequency band (OFB). The first stage employs the Periodic Harmonic Correlation Strength (PHCS) metric, based on autocorrelation, to coarsely screen candidate bands with strong periodic structures. The second stage utilizes the Sparse Harmonic Significance (SHS) metric, based on spectral negative entropy, to refine the candidate set, selecting bands with the most prominent harmonic features. Finally, SCoh is integrated over the selected OFB to generate an Improved Envelope Spectrum (IES). The proposed method was validated using both simulated and experimental vibration signals from bearings and gearboxes. The results demonstrate that SAM-TSHOgram significantly outperforms conventional approaches such as EES, Fast Kurtogram, and IESFOgram in terms of signal-to-noise ratio (SNR) enhancement, harmonic clarity, and diagnostic robustness. These findings confirm its potential for reliable early fault detection in rolling bearings. Full article

Corporate Social Responsibility (CSR) in China has evolved from reproducing Western-centric frameworks to engaging with the institutional and political particularities that shape how CSR is reconfigured and practiced. Yet few studies have critically reviewed this growing body of literature to capture the core characteristics and mechanisms of state-corporate coordination in China. This paper fills this gap by reviewing 112 peer-reviewed English-language studies published between 2007 and 2025, synthesizing how CSR in China is conceptualized, embedded, and operationalized across cultural, economic, political, and global dimensions. This review identifies three institutional logics structuring Chinese CSR: (1) moral–cultural framing rooted in Confucian ethics and socialist collectivism; (2) economic coordination under state-led capitalism and selective neoliberalism; and (3) political signaling through Party-state governance and legitimacy negotiation. It also outlines six major research themes—CSR as a legitimacy strategy, CSR reporting, CSR in Chinese multinational enterprises, CSR’s link to financial performance, environmental CSR, and civil CSR—highlighting the mechanisms underlying each. Findings show that CSR in China is different from the managerial-stakeholder framework (e.g., explicit/implicit CSR, pyramid model or integrative model). Instead, it operates as an adaptive political technology within state-led capitalism, reinforcing moral legitimacy and political conformity as firms—especially SOEs and politically connected private enterprises—align with state-defined priorities. Through a comparative perspective, this review demonstrates how China’s CSR model fundamentally recalibrates corporate agency toward political negotiation rather than stakeholder responsiveness, offering a distinct configuration that challenges the presumed universality of Western CSR theories. Full article

Plastic pollution in aquatic environments is a major ecological problem requiring scalable autonomous solutions for cleanup. This study addresses the coordination of multiple Autonomous Surface Vehicles by formulating the problem as a Partially Observable Markov Game and decoupling the mission into two tasks: exploration to maximize coverage and cleaning to collect trash. These tasks share navigation requirements but present conflicting goals, motivating a multi-objective learning approach. The proposed multi-agent deep reinforcement learning framework involves the utilisation of the same Multitask Deep Q-network shared by all the agents, with a convolutional backbone and two heads, one dedicated to exploration and the other to cleaning. Parameter sharing and egocentric state design leverages agent homogeneity and enable experience aggregation across tasks. An adaptive mechanism governs task switching, combining task-specific rewards with a weighted aggregation and selecting tasks via a reward-greedy strategy. This enables the construction of Pareto fronts capturing non-dominated solutions. The framework demonstrates improvements over fixed-phase approaches, improving hypervolume and uniformity metrics by 14% and 300%, respectively. It also adapts to diverse initial trash distributions, providing decision-makers with a portfolio of effective and adaptive strategies for autonomous plastic cleanup. Full article

Deep learning holds considerable promise for electroencephalography (EEG) analysis but faces challenges due to scarce and noisy EEG data, and the limited generality of existing data augmentation techniques. To address these issues, we propose an end-to-end EEG augmentation framework with an adaptive mechanism. This approach utilizes contrastive learning to mitigate representational distortions caused by augmentation, thereby strengthening the encoder’s feature learning. A selective augmentation strategy is further incorporated to dynamically determine optimal augmentation combinations based on performance. We also introduce NeuroBrain, a novel neural architecture specifically designed for auditory EEG decoding. It effectively captures both local and global dependencies within EEG signals. Comprehensive evaluations on the SparrKULee and WithMe datasets confirm the superiority of our proposed framework and architecture, demonstrating a 29.42% performance gain over HappyQuokka and a 5.45% accuracy improvement compared to EEGNet. These results validate our method’s efficacy in tackling key challenges in EEG analysis and advancing the state of the art. Full article

The Yan goose (YE, Anser cygnoides) is a valuable indigenous poultry genetic resource, renowned for its superior meat quality and environmental adaptability. Despite its economic importance, the genetic basis underlying these adaptive traits remains unclear. In this study, we employed whole-genome resequencing (WGS) to perform high-throughput sequencing on a conserved population of 15 samples. Bioinformatic analyses were conducted to systematically evaluate the population’s genetic structure, and a genome-wide scan for selection signals related to economically significant traits was performed using the integrated haplotype score (iHS) method. An average of 4.43 million high-quality SNPs were identified, which were predominantly located in intergenic and intronic regions. Population structure analysis revealed a close genetic relationship within the conserved population of YE, with no significant lineage stratification observed. Pairwise sequentially Markovian coalescent (PSMC) analysis indicated that the YE underwent a severe genetic bottleneck during the Last Glacial Maximum (LGM), followed by gradual population recovery in the early Neolithic period. Genome-wide selection signal scanning identified multiple genomic regions under strong selection, annotating key genes associated with growth and development (e.g., GHRL, AKT1, and MAPK3), lipid deposition (e.g., PLPP4, SAMD8, and LPIN1), and disease resistance and stress resilience (e.g., TP53, STAT3). Functional enrichment analysis revealed significant enrichment of these genes in pathways related to glycerophospholipid metabolism (p < 0.01), purine metabolism (p < 0.01), and immune response (p < 0.01). This study not only provides a theoretical foundation for the scientific conservation of the YE germplasm resources but also offers valuable genomic resources for identifying functional genes underlying important economic traits and advancing molecular breeding strategies. Full article

Traditional front-wheel-steering (FWS) unmanned vehicles frequently encounter maneuverability bottlenecks in confined spaces or during rapid formation changes due to inherent kinematic limitations. To mitigate these constraints, this study proposes an adaptive multi-mode (AMM) cooperative formation control framework tailored for four-wheel independent drive and steering (4WIDS) platforms. The methodology constructs a unified planner based on the virtual structure concept, integrated with an autonomous steering-mode selector. By synthesizing real-time mission requirements with longitudinal and lateral tracking errors, the system dynamically switches between crab steering, four-wheel counter-steering (4WCS), and conventional FWS modes to optimize spatial utilization. Validated within a seven-vehicle MATLAB/Simulink environment, simulation results demonstrate that the crab-steering mode significantly reduces relocation time for small lateral adjustments by eliminating redundant heading changes, whereas FWS and 4WCS modes are preferentially selected to ensure stability during high-speed or large-span maneuvers. These findings confirm that the proposed AMM strategy effectively reconciles the trade-off between agility and stability, providing a robust solution for complex cooperative maneuvering tasks. Full article

With the rapid development of applications such as edge computing, the Internet of Things (IoT), and embodied intelligence, massive multimodal data are continuously generated on end devices in a streaming manner. To maintain model adaptability and robustness in dynamic environments, incremental learning has gradually become the core training paradigm on edge devices. However, edge devices are constrained by limited computational, storage, and communication resources, making it infeasible to retain and process all data samples over time. This necessitates efficient data selection strategies to reduce redundancy and improve training efficiency. Existing sample selection methods primarily focus on overall sample difficulty or gradient contribution, but they overlook the heterogeneity of multimodal data in terms of information content and discriminative power. This often leads to modality imbalance, causing the model to over-rely on a single modality and suffer performance degradation. To address this issue, this paper proposes a multimodal sample selection strategy based on the Modality Balance Score (MBS). The method computes confidence scores at the modality level for each sample and further quantifies the contribution differences across modalities. In the selection process, samples with balanced modality contributions are prioritized, thereby improving training efficiency while alleviating modality bias. Experiments conducted on two benchmark datasets, CREMA-D and AVE, demonstrate that compared with existing approaches, the MBS strategy achieves the most stable performance under medium-to-high selection ratios (0.25–0.4), yielding superior results in both accuracy and robustness. These findings validate the effectiveness of the proposed strategy in resource-constrained scenarios, providing both theoretical insights and practical guidance for multimodal sample selection in learning tasks. Full article

Family Allotment Gardens (FAGs) represent key components of urban cooling and air-purification systems. However, research has mainly focused on their social roles and on their contributions to food production. This study quantified the capacity of FAGs in Warsaw (Poland) to provide two key ecosystem services at distances up to 300 m from their boundaries: air-pollution filtration and microclimate regulation. Measurements of particulate matter (PM1, PM2.5, PM10), air temperature and relative humidity were conducted along transects inside and outside three allotment complexes in autumn 2023, a period characterised by increased traffic emissions and elevated particulate levels. The results show a moderate but significant reduction in PM concentrations inside gardens (by about 2 µg/m³; r = 0.22–0.29) and slightly higher humidity (by 2.1%; r = −0.34). The cooling effect was weak (<0.3 °C; r = 0.06), indicating a limited spatial range under autumn conditions, though selected transects exhibited stronger local effects. The results confirm that FAGs can contribute to air purification and local climate regulation, but their effectiveness depends on vegetation structure and urban context. Strengthening their role requires integration with green-infrastructure planning and emission-reduction practices within gardens. FAGs, beyond their recreational and productive value, should be recognised as active components of urban adaptation strategies. Full article

Heterotrophic bacteria and microalgae are key regulators of marine biogeochemical cycles. The phycosphere, a nutrient-rich microenvironment surrounding microalgae, serves as a crucial interface for bacterial–algal interactions. Our previous work identified Opacimonas immobilis LMIT016^T, a phycosphere isolate from the diatom Actinocyclus curvatulus that possesses the smallest genome within the Alteromonadaceae family. However, its adaptation mechanisms to the phycosphere remain unclear, particularly given its extensive genome streamlining, a process involving the selective loss of non-essential and energetically costly genes to enhance fitness in nutrient-specific niches. Here, the co-cultivation experiments demonstrated significant mutual growth promotion between LMIT016^T and its host microalgae, with the bacterium forming dense attachments on diatom surfaces. Genomic analysis revealed that in addition to loss of motility-related genes, the strain exhibits a substantial reduction in c-di-GMP signaling components, including both synthases and receptors. Conversely, LMIT016^T harbors numerous genes essential for extracellular polysaccharide (EPS) biosynthesis and adhesion, supporting long-term attachment and biofilm formation. Other retained genes encode pathways for nutrient acquisition, stress response, and phosphate and nitrogen metabolism, reflecting its adaptations to the nutrient-rich phycosphere. Furthermore, the genome of LMIT016^T encodes two polysaccharide utilization loci (PULs) targeting laminarin and α-1,4-glucans, whose functions were experimentally validated by the transcriptional induction of the corresponding carbohydrate-active enzyme genes. These findings indicate that this strain counterbalances genome reduction by enhancing its attachment capabilities and metabolic specialization on algal polysaccharides, potentially facilitating stable association with diatom cells. Our results suggest that genome streamlining may represent an alternative ecological strategy in the phycosphere, highlighting a potential evolutionary trade-off between metabolic efficiency and niche specialization. Full article

Cholangiocarcinoma (CCA) is a highly aggressive cancer of the biliary tract, distinguished by significant intratumoral heterogeneity (ITH), which contributes to therapy resistance and unfavorable clinical outcomes. Traditional genome profiling has revealed recurring driver changes in CCA; yet, genomic data alone fails to elucidate functional pathway activation, adaptive signaling, and the diverse treatment responses reported among tumor locations and disease subtypes. This review analyses the use of integrated sequencing technologies, proteogenomics, and phosphoproteomics to systematically characterize intratumoral heterogeneity in cholangiocarcinoma and convert molecular diversity into therapeutically applicable discoveries. We present evidence that the combination of genomic sequencing and mass spectrometry–based proteomics facilitates the direct correlation of genetic mutations with protein expression, post-translational modifications, and signaling system activity. Phosphoproteomic profiling specifically offers functional insights into kinase-driven networks that dictate tumor aggressiveness, therapeutic susceptibility, and adaptive resistance mechanisms, which cannot be anticipated only from DNA-level analysis. We propose that integrating proteogenomic and phosphoproteomic analyses into diagnostic and therapeutic assessments can enhance molecular classification, reveal subtype- and region-specific therapeutic dependencies, and guide rational combination treatment strategies, based on recent extensive proteogenomic studies and functional proteomic investigations in CCA. Pathway-level analysis of intratumoral heterogeneity provides a framework for selecting targeted medicines, predicting resistance, and informing personalized treatment strategies in CCA. The combination of sequencing, proteogenomics, and phosphoproteomics is essential for advancing precision oncology in cholangiocarcinoma. The implementation of this multi-layered analytical approach may better patient classification, refine therapy choices, and eventually improve clinical outcomes for individuals with this particular heterogeneous cancer. Full article

Bread wheat variety development suited to organic farming conditions remains a major challenge mainly because of the high breeding costs involved and the few cultivars adapted to low-input systems. The present work explores whether early generation selection needs to take place under organic conditions for subsequent adaptation or whether conventional testing at an early stage could be adequate. A diverse set of crosses involving Greek landraces and commercial cultivars were developed and advanced by honeycomb pedigree selection under both organic and conventional environments. Subsequently, F₄ progenies and an upgraded landrace were evaluated over two years in neighboring organic and conventional trials. Both statistical and GGE biplot analyses revealed significant genotype × environment interactions. The results clearly indicate that early selection under organic conditions did not provide a consistent advantage for subsequent performance under organic management compared with conventional early selection. Genotypes derived from the Africa × Atheras cross consistently showed the highest and most stable yields across the two environments, irrespective of the early selection environment. These results indicate that genetic background and landrace-derived diversity are more important than the early selection environment for the expression of performance. A staged breeding strategy involving initial selection in conventional management followed by multi-environment testing in organic conditions can provide a cost-effective approach to developing resilient, high-yielding wheat cultivars suitable for organic farming systems, which are typically characterized by low-input management practices, and in tune with the EU targets for expanded organic farming. Full article