Search Results (1,076)

In the context of economic globalization, effective cooperation and information sharing are critical for enhancing the efficiency and competitiveness of supply chains. Fresh agricultural product supply chains face distinctive challenges arising from high perishability, rapid demand fluctuations, and information asymmetry among supply chain members. This study develops an analytical framework comprising the following three conceptually connected but independently calibrated modules: an EOQ cost module, a comparative profit module, and a two-period, two-task dynamic principal–agent module. The modules are connected through the common economic logic of value creation, value distribution, and incentive design, rather than through a one-to-one numerical mapping. Using operational and financial information from the Erli River Crab supply chain in Panshan County, the EOQ and comparative profit models are independently calibrated at different decision scales to evaluate the benchmark economic effects of information-sharing-enabled coordination. The EOQ analysis uses annual aggregate operational quantities, whereas the comparative profit analysis uses a normalized transaction-demand scale; therefore, their numerical quantity magnitudes are not intended for direct one-to-one comparison. Within the EOQ module, information-sharing-enabled coordination reduces total relevant supply chain cost by 8.41% relative to the farmer-led decentralized benchmark and by 60.75% relative to the retailer-led decentralized benchmark. The difference arises because the farmer-led batch quantity is closer to the coordinated optimum, whereas the retailer-led order quantity is substantially smaller, implying a higher modeled frequency of upstream setup activities and a larger setup-cost component under the benchmark parameterization. These results capture the joint effect of full information availability and coordinated batch optimization under two alternative decentralized decision regimes. Within the comparative profit module, over the benchmark wholesale-price interval, the coordinated full-information-sharing benchmark increases total supply chain profit by 1.38–2.30% relative to the decentralized no-information-sharing benchmark. Under the unchanged-wholesale-price comparison, the farmer’s profit increases by 13.21–17.65%, whereas the retailer’s profit decreases by 1.74–3.11%. These results identify positive aggregate value creation and an asymmetric initial allocation under the linear-demand and unchanged-wholesale-price benchmark. This asymmetric allocation is benchmark-specific rather than an unavoidable consequence of information-sharing-enabled coordination, because a negotiated transfer or wholesale-price adjustment can redistribute the additional surplus. The extended analysis derives a participation-compatible transfer interval within which both the farmer and the retailer are weakly better off than under decentralized decision-making. Within the dynamic principal–agent module, a case-motivated illustrative simulation using standardized benchmark parameters shows that more informative intertemporal performance signals strengthen the optimal second-period incentive coefficient, whereas greater risk exposure limits the appropriate intensity of performance-based compensation. Continuation–payoff and ratchet-like effects jointly shape first-period information-sharing effort. Together, the three modules show how information-sharing-enabled coordination affects operational efficiency, surplus allocation, and intertemporal incentives. Full article

The tourism industries of the Chinese Mainland, Hong Kong SAR, Macao SAR, and the Taiwan region are globally competitive yet diverse, but persistent developmental disparities have hindered robust inbound tourism cooperation. This study assesses the synergistic potential and driving factors of the joint inbound tourism market among the Mainland, Hong Kong, Macao and Taiwan. Using panel data from 16 source countries (2000–2021), we construct a coupling framework for the inbound tourism relationship circle. We develop an inbound tourism relationship index and employ text analysis alongside ArcGIS Pro3.4.1-based spatiotemporal mapping to reveal the dynamic evolution of coupling coordination. A panel model is then applied to examine six influencing factors: economic, geographic, population, cultural, visa, and institutional. The results indicate that the coupling coordination degree remains generally high, though with stage-specific variations. Population, visa, and institutional factors exert significant positive effects, while economic and geographic factors show significant negative effects—economic because smaller-economy source countries are more likely to treat the four regions as a combined destination, facilitating inter-regional synergy, and geographic because greater distance raises travel costs and reduces visitor flows, thereby weakening coordination. Cultural factors are insignificant. Future efforts should prioritize visa facilitation and institutional building, providing a reference for market promotion, national integration, and sustainable regional cooperation. Full article

The recently proposed Animated Oat Optimization (AOO) algorithm exhibits competitive search behavior, but its fixed branching rules and limited use of inter-individual information may cause diversity loss and premature stagnation. This study proposes an Improved Animated Oat Optimization algorithm (IAOO) that integrates the original AOO operator, a hybrid DE/rand/1–DE/best/1 operator with a decreasing scale factor, and an elite neighborhood-directed local search within a feedback-driven framework. Strategy probabilities are updated according to normalized successful fitness gains, enabling search effort to adapt to the current optimization state. IAOO was evaluated through 30 independent runs on the CEC2017 (dim = 30/100), CEC2020, and CEC2022 suites and achieved Friedman mean ranks of 1.50, 1.37, 2.70, and 1.83, respectively, achieving competitive Friedman mean ranks among the compared algorithms and demonstrating statistically supported advantages on most benchmark suites. In three-dimensional UAV reference-path planning, IAOO reduced the mean path cost from 406.26 for AOO to 298.94, corresponding to a 26.4% reduction, while the standard deviation decreased from 67.01 to 40.73. Its runtime increased only from 26.99 s to 27.13 s. These results indicate that feedback-based operator cooperation improves solution quality and robustness with limited computational overhead. The current UAV model produces geometrically feasible and kinematically constrained reference paths; full six-degree-of-freedom tracking validation remains future work. Full article

Multi-agent deep reinforcement learning (MARL) extends deep reinforcement learning (DRL) to environments involving multiple interacting agents and has enabled applications in domains such as autonomous vehicles, robotics, unmanned aerial vehicles (UAVs), and multi-player games. Compared with single-agent learning, MARL introduces additional challenges, including non-stationarity, partial observability, multi-agent credit assignment, and scalability. This paper presents a narrative survey of recent developments in MARL and discusses major approaches proposed to address these challenges. In particular, we examine research directions centred on centralised training with decentralised execution (CTDE), value decomposition, learned communication, graph-based methods, and model-based learning. We further discuss commonly used benchmark environments and evaluation practices, highlighting considerations related to reproducibility, robustness, and generalisation. Finally, we outline open research challenges and future directions concerning theoretical understanding, sample efficiency, scalable coordination, and deployment in real-world settings. Rather than providing an exhaustive systematic review, this survey aims to offer an organised and up-to-date synthesis of recent progress in MARL. Full article

In the burgeoning live stream landscape, manufacturers face critical dilemmas regarding strategic channel governance and how intermediaries’ bargaining ability dictates sustainable cooperation under Stackelberg leadership change. Addressing these issues is vital, as misaligned governance risks severe profit losses, yet standard heuristics fail to capture how leadership shifts disrupt channel coordination and model selection. To fill this gap, we model a manufacturer’s self-operated (Model SO) live stream channel and an intermediary-operated (Model IO) live stream channel to evaluate trade-offs among bargaining ability, operational costs, and spillover effects. Key findings show that in Model SO, spillover and price adjustments transform the live stream channel into a traffic-channeling gateway in which rising operational costs paradoxically boost total profits. In Model IO, bargaining ability serves as a key determinant, as high pit fees weaponize this ability for predatory commission-squeezing, while low pit fees redirect it toward volume expansion, transforming the intermediary into a synergistic partner. Furthermore, bargaining ability shifts pricing from intermediary-introduction to profit-recapture strategies while exerting cost-magnification and revenue-magnification effects under varying pit fees. Crucially, we uncover two Pareto-optimal cooperation zones alongside a non-cooperation zone caused by incentive incompatibility under mid-tier bargaining ability. Extensions show that intensified price competition turns dominant intermediaries into welfare killers and high service sensitivity induces an over-service trap, though popularity cost-sharing contracts restore coordination. Overall, this study fills a crucial analytical gap by establishing precise theoretical boundaries for channel governance, bargaining ability dynamics, and cost-driven functional transformations in live stream supply chains. Full article

Tomicus pilifer is an important wood-boring forest pest in China, and its host localization and intraspecific communication rely on the perception of volatile chemical cues. However, the molecular mechanisms underlying odor recognition in this species remain largely unknown. In this study, we systematically investigated the behaviorally active volatiles present in the hindgut and feces of T. pilifer and elucidated the roles of odorant-binding proteins (OBPs) in their recognition. Gas chromatography–mass spectrometry (GC–MS) identified eight volatile compounds common to both hindgut and fecal samples. Among them, five terpenoid compounds, α-pinene, 3-carene, D-limonene, camphene, and β-myrcene, elicited significant electroantennogram (EAG) responses and induced positive behavioral attraction in adults. Based on antennal transcriptome data, phylogenetic relationships with functionally characterized homologous OBPs, preliminary molecular docking analyses, and tissue-specific expression patterns, three candidate OBPs (TpilOBP5, TpilOBP16, and TpilOBP29) were selected from 51 identified TpilOBP genes and subsequently expressed as recombinant proteins. Fluorescence competitive binding assays demonstrated that all three OBPs bound to the five behaviorally active terpenoid volatiles, with TpilOBP29 exhibiting the broadest ligand-binding spectrum and the highest binding affinity. Molecular docking and interaction analyses further revealed that the binding pocket of TpilOBP29 forms a continuous hydrophobic core composed of multiple conserved hydrophobic residues, which cooperatively stabilizes ligand binding through hydrophobic interactions, π–alkyl interactions, and van der Waals forces, thereby conferring broad-spectrum and high-efficiency odorant recognition. These findings provide new insights into the molecular mechanisms underlying the recognition of key behaviorally active terpenoid volatiles in T. pilifer, identify TpilOBP29 as a key mediator of odor recognition, and provide a potential molecular target for the development of environmentally friendly semiochemical-based behavioral management strategies against bark beetle pests. Full article

As an industry policy commonly adopted by governments worldwide, the digital industry cluster policy facilitates data resource accessibility. Whether this policy can also promote enterprise collaborative innovation, especially facilitating strong alliances among enterprises with high breakthrough innovation capabilities, is the key to cultivate internationally competitive advantages. To address this question, this paper takes China’s digital industry cluster policy as a quasi-natural experiment and employs the staggered difference-in-differences model to analyze the impact of digital industry clusters on enterprise collaborative innovation. This study finds that digital industry cluster policy drives an increase in the number of scientific, technological, and legal intermediaries, improves the level of judicial protection, and thus optimizes the external environment for enterprise collaborative innovation. Meanwhile, the policy promotes the deepening of enterprise division of labor, strengthens specialized agglomeration, and improves the allocation efficiency of data and labor factors, thereby stimulating the internal demand of enterprise collaborative innovation. Further analysis reveals that the policy does not facilitate strong–strong enterprise partnerships; instead, it promotes weak–weak and strong–weak enterprise partnerships. The academic contributions of this research are twofold: first, it investigates the impact of improved enterprise conditions on collaborative innovation from the perspective of enterprises’ own factor allocation capabilities; second, it examines the cooperative characteristics of enterprises with distinct breakthrough innovation capabilities, which expands the research scope of cooperative innovation characteristics. Full article

Trajectory planning, which determines a route from a starting position to a target position within a given airspace, is critical to unmanned aerial vehicle (UAV) mission execution. Many existing meta-heuristic approaches to three-dimensional (3D) trajectory planning aggregate competing requirements into a weighted cost and may suffer from limited adaptability when the environment changes. This paper formulates 3D UAV trajectory planning in dynamic multi-threat environments as a dynamic bi-objective optimization problem and proposes a multi-swarm dynamic multi-objective crow search algorithm (MDMCSA). The proposed method organizes objective-oriented swarms within a cooperative search framework and facilitates information exchange through archive sharing, thereby coordinating the search process among different objectives. The memory-time and diverse behavior strategies adjust search behaviors and solution perturbation to balance convergence and diversity. A hybrid change response strategy combines historical information reuse with diversity restoration after dynamic changes. Comparative experiments on dynamic benchmark problems and UAV trajectory planning scenarios demonstrate competitive convergence and adaptation performance, together with a favorable trade-off between solution quality and computational cost. Incremental ablation and parameter-sensitivity analyses further indicate the cumulative benefit of the integrated design and the stable performance of the selected parameter configuration across the tested settings. Full article

Energy detection is widely used in cooperative spectrum sensing because it requires little prior information about the primary signal, but its performance is sensitive to node-dependent noise power and the signal-to-noise ratio (SNR). This paper proposes a dynamic dual-threshold method under bounded noise power uncertainty. For each sensing node, the local energy statistic is modeled under the idle and occupied hypotheses, and a Bayes-optimal one-sided threshold is evaluated for every admissible noise power value in a multiplicative interval. The lower and upper thresholds are defined as the minimum and maximum of these candidate thresholds. Observations outside the interval are transmitted as one-bit hard decisions, whereas uncertain region observations are normalized, uniformly quantized, and combined at the fusion center by equal gain fusion. Controlled simulations at a matched global false alarm probability show that a well-calibrated fixed dual-threshold benchmark can be competitive near its design point, while node-specific dynamic adaptation becomes advantageous as the uncertainty level increases. The uncertain region reporting probability rises with the uncertainty bound, making the robustness–reporting tradeoff explicit. The results support dynamic threshold adaptation under moderate or relatively large noise power uncertainty and clarify its associated communication cost. Full article

Modern Global Navigation Satellite System (GNSS) architectures incorporate authorized signals like GPS M-code; however, conventional extraction methods suffer from performance degradation under low signal-to-noise-ratio (SNR) conditions and exhibit strong dependence on high-gain antennas and precise synchronization. This paper proposes a machine learning-based end-to-end extraction framework leveraging time-frequency domain feature fusion. This method breaks through the constraint of relying solely on either time-domain or frequency-domain features. It jointly feeds the time-domain waveforms and spectral features of baseband signals into models such as Multi-Layer Perceptron (MLP) and Transformer, enabling automatic learning of the nonlinear time-frequency characteristics of M-code. This approach effectively suppresses interference from P(Y) code sidelobes and fully exploits the information contained in both the main and side lobes of the M code spectrum. Experimental results demonstrate that under the extremely low SNR condition of −10 dB, the extraction accuracy of the proposed method is improved by 13.7% compared with conventional methods. Systematic accuracy–efficiency trade-off analysis shows that the lightweight MLP model achieves comparable accuracy to the complex Transformer model, with only 6.8% of the parameter scale and 6.2 times faster inference speed, making it the most competitive solution for real-time engineering deployment. In the real-world measurement scenario using a 7.5-m antenna, an extraction accuracy of 94.7% is achieved with only 10 ms of small-sample training data. This method significantly enhances the extraction performance of authorized signals under low-SNR non-cooperative reception conditions. Full article

Background: MicroRNAs are key post-transcriptional regulators of gene expression and contribute to cancer progression, tumor heterogeneity, and context-dependent regulatory rewiring. However, most computational approaches rely on sequence-based target prediction or bulk expression association and are not designed to jointly model regulatory priors, expression context, and heterogeneous cancer states, particularly when matched single-cell microRNA/mRNA co-profiling data are scarce. Methods: We developed a context-aware graph transformer framework for microRNA–gene regulatory analysis across biological resolutions. The framework represents microRNAs, genes, and biological contexts as a heterogeneous graph, where contexts correspond to individual cells in single-cell data and tumor samples or subtype-defined profiles in bulk cohorts. Heterogeneous graph transformer learning generated regulatory embeddings, Bayesian topology optimization refined candidate microRNA–gene interactions, and a dominance-based competition layer with Dominance Share scoring identified master regulators and cooperative target modules. Results: We applied miR-CellMap to matched single-cell miRNA/mRNA co-sequencing data from K562 leukemia cells and paired bulk cancer datasets spanning pan-cancer and subtype-specific cohorts, including breast, colon, glioblastoma, lower-grade glioma, and ovarian cancer. The framework identified recurrent and dataset-specific miRNA regulatory programs, including regulators such as miR-186-5p, miR-214-3p, miR-27a-3p, and let-7 family members. Embedding-derived context analysis showed that predicted miRNA target programs were consistently closer to observed context-specific gene programs than random matched gene programs across all seven datasets. Dominance Share analysis further identified cooperative target modules and co-repressed target programs, supporting the use of miR-CellMap for interpretable cancer-focused miRNA regulatory discovery. Conclusions: This framework provides an interpretable strategy for mapping conserved, cancer-specific, and context-dependent microRNA–gene regulatory programs across single-cell and bulk cancer datasets. Full article

Background/Objectives: Bullying represents a serious threat to the physical, psychological, and social well-being of students, with particular relevance in Physical Education (PE) contexts, where the physical and competitive nature of the setting may facilitate specific forms of aggression. Despite growing research interest, evidence on the prevalence and distribution of bullying roles and typologies within PE remains limited, particularly in primary education. This study aimed to analyse the prevalence of bullying victimisation, perpetration, and bully-victim profiles among primary school pupils in PE, as well as to examine differences by sex and year group across the different forms of bullying behaviour. Methods: A cross-sectional study was conducted with an analytical sample of 831 pupils from Year 3 to Year 6 of compulsory primary education in Córdoba, Spain. Bullying roles and typologies were assessed using a validated self-report instrument. Differences by sex and year group were examined through appropriate inferential analyses. Results: A total of 21.1% of pupils were identified as victims, 5% as perpetrators, and 8.5% as bully-victims. The most frequent form of perpetration was exclusion/rejection (7.9%), surpassing physical and verbal aggression. Boys showed greater physical victimisation and a higher tendency towards the pure perpetrator role, whilst exclusion showed no significant differences by sex. Physical and verbal behaviours decreased as year group advanced, whereas exclusion remained stable throughout the entire primary stage. The bully-victim profile was more prevalent in lower year groups. Conclusions: The findings highlight the particular relevance of exclusion/rejection as the predominant form of bullying in PE, which persists across year groups regardless of sex. These results underscore the need to design inclusive groupings, promote cooperative tasks, and implement early intervention strategies. Specific training for PE teachers in bullying detection and intervention is identified as a priority to address this phenomenon effectively within the PE context. Full article

Network Intrusion Detection Systems (NIDS) face a trade-off between detection accuracy and computing efficiency, particularly in the edge environment with strict real-time requirements and limited resources. Current approaches rely on complicated models, which are computationally demanding, or simple ones, which sacrifice detection performance. To deal with this issue, we propose a lightweight cloud-edge cooperative two-stage NIDS architecture, which separates the real-time detection and detailed classification. At the edge, a decision tree based on feature selection is used for rapid binary classification by using only the top 10 most informative features, thus efficiently screening out abnormal traffic with minimum processing cost. Meanwhile, the cloud server identifies attack classification accurately by using a hybrid CNN-BiLSTM-Attention model to capture the spatial structures, temporal relationships, and semantic relevance. This hierarchical design effectively balances detection performance and system efficiency. Experiments conducted on UNSW-NB15, NSL-KDD, and CIC-IDS2017 datasets indicate that our suggested scheme can obtain competitive performance both at the edge and in the cloud. The edge model obtains binary classification accuracy of 86.04%, 95.27%, and 99.01%, respectively, with very low processing cost (less than 100 FLOPs per sample). The cloud model achieves multi-class accuracy of 92.23%, 97.18%, and 98.66%, respectively, with AUC values higher than 0.98. The hierarchical cloud–edge collaborative design provides an efficient and accurate solution for intrusion detection under resource-restricted situations. Full article

Opium poppy (Papaver somniferum L.) has a long tradition in North Macedonia and is internationally recognized for its high quality. Despite this, cultivated areas have steadily declined, reaching only 35 hectares in 2022, with capsule yields of 300 kg ha⁻¹ and seed yields of 583 kg ha⁻¹. The SWOT analysis shows that opium poppy production has several important strengths. The country has favorable agroclimatic conditions, domestic varieties, experienced farmers, and support from government subsidies. The presence of a pharmaceutical buyer is also an important advantage, because it gives this production a clearer market direction. However, there are still many limits that slow down further development. Production is mostly based on small farms, while yields and prices are often low. Many farmers also use outdated machinery, which makes production less efficient. Another problem is the limited interest of buyers and the lack of a secure market for poppy seed. In addition, legal restrictions make the export of capsules more difficult. There are also several opportunities for future growth. The TOWS matrix suggests that the sector could become more competitive through value-added products, stronger cooperation between farmers, modernization of machinery, public information campaigns, and public–private investment. Although opium poppy production faces many challenges, it still has good potential to develop from a declining traditional activity into a sustainable and competitive niche sector, but this requires coordinated institutional support, modernization, market diversification, and the development of value-added products. With better policies and a clearer market strategy, it could develop into a more sustainable and competitive agricultural sector. Full article

Polycyclic aromatic hydrocarbons (PAHs), characterized by their high stability, are typical persistent organic pollutants that pose irreversible risks to human health. Conventional chemical oxidation methods exhibit limitations that hinder effective remediation in practice. In contrast, sulfate-radical-based advanced oxidation processes have emerged as promising alternatives, among which the heat-alkaline activation system for persulfate (PS) demonstrates distinct advantages. In this study, a heat-alkaline-activated PS system was established to investigate the degradation of PAHs in both simulated contaminated soils and coal chemical industrial site soils, as well as the modulatory effects of natural organic matter (NOM). Response surface methodology optimized critical experimental parameters to 12.53 mmol PS dosage, 60.31 °C reaction temperature, and a 1.55 CaO/PS molar ratio. Under these conditions, degradation efficiencies of 98.32% and 82.26% were achieved in simulated and field soils, respectively. Radical test experiments revealed a cooperative mechanism dominated by SO₄⁻• > •OH > O₂⁻• radicals, accompanied by auxiliary involvement of non-radical ¹O₂. Low concentrations of NOM plausibly facilitate degradation via a hypothesized electron transfer protective effect and boosted radical generation, whereas excessive NOM inhibits degradation through competitive consumption of reactive radicals. Density functional theory calculations identified preferred radical attack sites on the aromatic rings of PAHs and corroborated the degradation pathway involving aromatic ring oxidation, functional group addition, ring cleavage, and mineralization. QSAR-based theoretical toxicity predictions via T.E.S.T. suggested that the ultimate degradation products exhibit lower potential toxicity than parent PAHs. Experiments fill the knowledge gap regarding NOM-mediated regulation in heat-alkaline activated PS systems, and elucidate degradation mechanisms and toxicity evolution. Full article