Search Results (456)

Multi-unmanned aerial vehicle (UAV)-assisted communication is a critical technology for the low-altitude economy, supporting applications from logistics to emergency response. Semantic communication effectively enhances transmission efficiency and improves the communication performance of multi-UAV-assisted systems. Existing research on multi-UAV semantic communication networks predominantly assumes static ground devices, overlooking computation offloading and resource allocation challenges when ground devices are mobile. This overlooks the critical challenge of dynamically managing computation offloading and resources for mobile users, whose varying channel conditions and semantic compression needs directly impact system performance. To address this gap, this paper proposes a multi-UAV-assisted semantic communication model that novelly integrates user mobility with adaptive semantic compression, formulating a joint optimization problem for computation offloading and resource allocation. The objective is to minimize the maximum task processing latency through the joint optimization of UAV–device association, UAV trajectories, transmission power, task offloading ratios, and semantic compression depth. To solve this problem, we design a MAPPO-APSO algorithm integrating alternating iteration, multi-agent proximal policy optimization (MAPPO), and adaptive particle swarm optimization (APSO). Simulation results demonstrate that the proposed algorithm reduces the maximum task latency and system energy consumption by up to 20.7% and 16.1%, respectively, while maintaining transmission performance and outperforming benchmark approaches. Full article

Persistent regional imbalances and widening urban–rural income gaps hinder progress toward Sustainable Development Goal 10 (Reduced Inequalities). In response, China has implemented a typical regional cooperation program—East–West Cooperation (EWC). Using a balanced panel of 642 western counties from 2013 to 2020 and the staggered difference-in-differences (DIDs) model, we assess the impact of EWC on the urban–rural income gap. We show that EWC narrows the urban–rural income gap, primarily by increasing rural incomes rather than changing urban incomes. Mechanism analyses indicate that expanded rural employment and higher agricultural production efficiency are the principal channels. The greater the economic disparity and the shorter the distance between paired counties, the stronger the effect of EWC. This effect is particularly pronounced in southwestern assisted counties and in agriculture-intensive assisted counties. The above evidence suggests that horizontal regional cooperation can deliver equity-enhancing growth. Policy should prioritize rural-first resource allocation, employment-oriented labor cooperation, and agricultural upgrading, while refining pairing rules to account for the magnitude of economic gaps and geographic proximity. Full article

Improving green total factor productivity (GTFP) is critical for balancing economic benefits and ecological constraints. While most existing studies emphasize the pivotal role of governments in GTFP enhancement, they predominantly treat governments as homogeneous entities, overlooking the fundamental premise of local government attention allocation. Analyzing 2010–2020 data from 285 Chinese cities, this study reveals that increased local government green attention significantly stimulates GTFP through three channels: fostering green technology collaboration among firms, deepening green involvement of public research institutions, and elevating green innovation quality. Heterogeneity analyses demonstrate amplified effects in cities characterized by intense intergovernmental competition, stringent intellectual property protection, robust fiscal capacity, and advanced technological infrastructure, but attenuated impacts in resource-dependent regions with heavy reliance on extractive industries. Full article

In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) satellite communications, but its performance is often limited by inaccurate Channel State Information (CSI) prediction and suboptimal resource allocation, particularly in dynamic channels with coupled parameters like SNR, Doppler, and delay. To address these limitations, this paper proposes an adaptive OTFS frame configuration scheme based on multi-domain channel prediction. We utilize a Long Short-Term Memory (LSTM) network to jointly predict multi-dimensional channel parameters by leveraging their temporal correlations. Based on these predictions, the OTFS transmitter performs two key optimizations: dynamically adjusting the pilot guard bands in the Delay-Doppler domain to reallocate guard resources to data symbols, thereby improving spectral efficiency while maintaining channel estimation accuracy; and performing optimal power allocation based on predicted sub-channel SNRs to minimize the system’s Bit Error Rate (BER). The simulation results show that our proposed scheme reduces the required SNR for a BER of $1\times {10}^{-3}$ by approximately 1.5 dB and improves spectral efficiency by 10.5% compared to baseline methods, demonstrating its robustness and superiority in high-mobility satellite communication scenarios. Full article

The digital economy is significantly transforming the global economic environment and has emerged as the primary driver behind China’s high-quality development. The comprehensive melding of the cultural and tourism sectors (CTI) serves as a strategic approach to boost regional competitiveness and enhance public welfare. This study investigates the mechanisms and boundary conditions through which the growth of the digital economy across China’s 31 provinces from 2011 to 2023 impacts CTI, aiming to address existing research gaps related to micro-level transmission mechanisms and the analysis of contextual variables. Utilizing a two-way fixed-effects moderated mediation model complemented by instrumental variable (IV-2SLS) regression for testing endogeneity, the research uncovers intricate interactions among the digital economy, CTI, and significant influencing factors. The results strongly suggest that advancements in the digital economy substantially facilitate the integration of cultural and tourism sectors. This beneficial effect is partially mediated through two primary channels: the construction of new infrastructure and enhancements in employment quality, underscoring the critical role of both material and human capital in digital empowerment. Significantly, this research uniquely identifies that new quality productive forces (NQP) have a notable negative moderating impact on the link between the digital economy and cultural–tourism integration. This indicates that in provinces exhibiting high levels of NQP, the positive influence of the digital economy on cultural–tourism integration is considerably diminished. This unexpected finding can be interpreted through mechanisms such as resource dilution, varied integration pathways or maturity effects, along with differences in developmental stages and priorities. Furthermore, it resonates well with the resource-based view, innovation ecosystem theory, and dynamic capability theory. Instrumental variable regression further substantiates the notable positive influence of the digital economy on the integration of cultural tourism. This approach effectively tackles potential endogeneity concerns and reveals the upward bias that may exist in fixed-effects models. The findings contribute significantly to theoretical frameworks by enhancing the understanding of the intricate mechanisms facilitating the digital economy and, for the first time, innovatively designating NQP as a surprising key boundary condition. This enriches theories related to industrial advancement and resource allocation in the digital age. On a practical note, the research provides nuanced and differentiated policy guidance aimed at optimizing pathways for integration across various Chinese provinces at different stages of development. Additionally, it underscores significant implications for other developing nations engaged in digital tourism growth, thereby improving its global relevance. Full article

An experiment with a two interaction modalities (traditional: touch; novel: touch–speech) × two task complexities (low: visual single task; high: audio–visual dual task) within-subjects design was conducted to observe alterations in crew performance (including task performance, subjective workload, and eye responses) in a typical planning task-based on a high-fidelity special vehicle simulation platform. The results revealed that (1) compared to the traditional interaction modality, the novel interaction modality significantly improved task performance, reduced subjective workload, increased mean peak saccade velocity, and decreased fixation entropy; (2) under high task complexity, subjective workload, mean pupil diameter, and the nearest neighbor index showed significant increases, while no significant decline in task performance was observed; (3) no interaction effect of crew performance was observed between interaction modality and task complexity. The foregoing results imply that (1) the novel interaction modality incorporating speech input exhibits advantages over the traditional touch-based modality in terms of enhancing task performance (over 45% improvement) and reducing cognitive workload; (2) leveraging dual-channel audio–visual information processing facilitates the maintenance of task performance under high task complexity and multi-tasking demands; (3) eye movement characteristics may serve as informative indicators for evaluating the benefits of speech-based interaction and the effectiveness of cognitive resource allocation under high-complexity task conditions. The results can provide a basis for the design of the display and control interface in special vehicles. Full article

Both water and sediment resource allocation are critical for achieving sustainable development in sediment-laden river basins. However, current understanding lacks a holistic perspective and fails to capture the inseparability of water and sediment. The Yellow River Basin (YRB) is the world’s most sediment-laden river, characterized by pronounced ecological fragility and uneven socio-economic development. This study introduces integrated water-sediment allocation frameworks for the YRB based on the perspective of the water-sediment nexus, aiming to regulate their impacts on socio-economic and ecological systems. The frameworks were established for both artificial units (e.g., irrigation zones and reservoirs) and geological units (e.g., the Jiziwan region, lower channels, and estuarine deltas) within the YRB. The common feature of the joint allocation of water and sediment across the five units lies in shaping a coordinated water–sediment relationship, though their focuses differ, including in-stream water-sediment processes and combinations, the utilization of water and sediment resources, and the constraints imposed by socio-economic and ecological systems on water-sediment distribution. In irrigation zones, the primary challenge lies in engineering-based control of inflow magnitude and spatiotemporal distribution for both water and sediment. In reservoir systems, effective management requires dynamic regulation through density current flushing and coordinated operations to achieve water-sediment balance. In the Jiziwan region, reconciling socio-economic development with ecological integrity requires establishing science-based thresholds for water and sediment use while ensuring a balance between utilization and protection. Along the lower channel, sustainable management depends on delineating zones for human activities and ecological preservation within floodplains. For deltaic systems, key strategies involve adjusting upstream sediment and refining depositional processes. Full article

This study explores how firms’ strategic orientations—operational efficiency, customer intimacy, and product innovation—along with corporate social responsibility (CSR) disclosure, influence investment efficiency in emerging markets. Using 1594 firm-year observations from companies listed on the Tehran Stock Exchange (TSE) between 2015 and 2024, we combine quantitative analysis with textual evidence from Management Discussion and Analysis (MD&A) reports. The findings show that operational efficiency and customer intimacy are generally linked to lower investment efficiency, reflecting possible resource misallocation and short-term priorities. In contrast, product innovation has a more nuanced impact: it improves investment efficiency in R&D-intensive sectors and during stable economic periods. CSR disclosure is also negatively associated with investment efficiency, suggesting that while CSR reporting enhances legitimacy and stakeholder trust, it may shift managerial attention and resources away from core investments. Robustness checks—including firm fixed effects, alternative keyword dictionaries, placebo tests, and endogeneity controls—support these results. Additional sub-sample analyses indicate that strategic orientations and CSR disclosure also function as channels of financial innovation: operational efficiency fosters disciplined resource allocation, product innovation supports sustainable growth, and customer intimacy strengthens transparency and stakeholder engagement. Full article

To meet the dual demands of 6G cellular networks for high spectral efficiency and environmental sensing, this paper proposes a full-duplex (FD) integrated sensing and communication (ISAC) dynamic resource allocation framework. At the heart of the framework lies a dynamic frame structure that can self-adapt the time-domain resource ratio between sensing and communication, designed to flexibly handle complex traffic demands. In FD mode, however, the trade-off between communication and sensing performance, exacerbated by severe self-interference (SI), morphs into a non-convex, NP-hard multi-objective optimization problem (MOP). To tackle this, we propose an Adaptive Hybrid Memetic Multi-Objective Grey Wolf Optimizer (AM-MOGWO). Finally, simulations were conducted on a high-fidelity platform that integrates 3GPP-standardized channels, which was further extended to a challenging multi-cell interference scenario to validate the algorithm’s robustness. AM-MOGWO was systematically benchmarked against standard Grey Wolf Optimizer (GWO), random search (RS), and the genetic algorithm (GA). Simulation results demonstrate that in both the single-cell and the more complex multi-cell environments, the proposed algorithm excels in locating the Pareto-optimal solution set, where its solution set significantly outperforms the baseline methods. Its hypervolume (HV) metric surpasses the second-best approach by more than 93%. This result quantitatively demonstrates the algorithm’s superiority in finding a high-quality set of trade-off solutions, confirming the framework’s high efficiency in complex interference environments. Full article

The demand for low-latency computing from the Internet of Things (IoT) and emerging applications challenges traditional cloud computing. Mobile Edge Computing (MEC) offers a solution by deploying resources at the network edge, yet terrestrial deployments face limitations. Unmanned Aerial Vehicles (UAVs), leveraging their high mobility and flexibility, provide dynamic computation offloading for User Equipments (UEs), especially in areas with poor infrastructure or network congestion. However, UAV-assisted MEC confronts significant challenges, including time-varying wireless channels and the inherent energy constraints of UAVs. We put forward the Lyapunov-based Deep Deterministic Policy Gradient (LyDDPG), a novel computation offloading algorithm. This algorithm innovatively integrates Lyapunov optimization with the Deep Deterministic Policy Gradient (DDPG) method. Lyapunov optimization transforms the long-term, stochastic energy minimization problem into a series of tractable, per-timeslot deterministic subproblems. Subsequently, DDPG is utilized to solve these subproblems by learning a model-free policy through environmental interaction. This policy maps system states to optimal continuous offloading and resource allocation decisions, aiming to minimize the Lyapunov-derived “drift-plus-penalty” term. The simulation outcomes indicate that, compared to several baseline and leading algorithms, the proposed LyDDPG algorithm reduces the total system energy consumption by at least 16% while simultaneously maintaining low task latency and ensuring system stability. Full article

As a pivotal instrument for integrating environmental governance with a low-carbon transition, green finance plays a critical role in achieving China’s dual-carbon goals. This study draws on a panel dataset covering 2008–2023, combining city-level indices of green finance development with firm-level emissions data from Chinese A-share listed companies. It investigates how green finance influences firms’ ability to reduce pollution and carbon emissions in a coordinated way, as well as the mechanisms and boundary conditions of this relationship. The results reveal that green finance significantly enhances firms’ synergistic performance in pollution and carbon abatement. The effect operates mainly through two channels: reallocating resources more efficiently and strengthening ESG performance. The benefits are particularly evident among firms with a stronger green innovation capacity, higher levels of carbon market participation, and more advanced environmental management systems. Green finance also helps deter corporate greenwashing. In addition, financial technology and environmental information disclosure amplify its positive impact. These findings highlight the need to deepen the integration of ESG evaluation with capital allocation and to design green financial instruments suited to firms at different stages of transition. They also point to the importance of harnessing the complementarities of fintech and environmental transparency to further enhance firms’ sustainable performance. Full article

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique acoustic channel characteristics and the inadequacy of traditional delay-based performance metrics that fail to capture the spatio-temporal value of information in dynamic underwater scenarios. This paper presents a comprehensive underwater ISAC framework centered on a novel Spatio-Temporal Information-Theoretic Freshness metric that fundamentally transforms resource allocation from delay minimization to value maximization. Unlike conventional approaches that treat all data equally, our spatio-temporal framework enables intelligent prioritization by recognizing that obstacle detection data directly ahead of an autonomous underwater vehicle (AUV) require immediate processing. Our framework addresses key underwater ISAC challenges through spatio-temporal-guided power allocation, adaptive beamforming, waveform optimization, and cooperative sensing strategies. Multi-agent reinforcement learning algorithms enable coordinated resource allocation and mission-critical information prioritization across heterogeneous networks comprising surface buoys, AUVs, and static sensors. Extensive simulations in realistic Munk profile acoustic environments demonstrate significant performance improvements. The spatio-temporal framework successfully filters spatially irrelevant data, resulting in substantial energy savings for battery-constrained underwater nodes. Full article

The partnership between government and market plays a crucial role in allocating green resources and fostering collaboration across organizations and departments. It integrates diverse knowledge types into the green innovation process and offers multifaceted insights into enterprises’ responses to green governance decisions. However, existing research predominantly examines the interplay among government green governance instruments, with insufficient exploration of the synergistic impacts of government and market in green governance. This study constructs a capacity coupling coefficient model to measure the synergy degree of “government–market” green governance (GMGG). Exploiting a balanced dynamic panel of 28,451 firm-year observations for 3807 Chinese listed companies from 2010 to 2020, we estimate the causal effect of GMGG synergy on corporate green innovation (CGI) and further dissect the underlying transmission mechanisms as well as the moderating channels through which the effect operates. Empirical results reveal that the effect of GMGG synergy on CGI is subject to diminishing marginal returns, with the effect being significantly more pronounced for substantive green innovation. Heterogeneity analysis indicates that non-state-owned firms, eastern-region firms, and those in non-heavy-polluting industries respond with markedly greater sensitivity. Mechanism analysis further demonstrates that the extent of marketization serves as a mediating channel, whereas an elevated level of digital-economy development mitigates the impact of GMGG synergy on CGI. This study delineates the effective boundary of GMCC synergy in stimulating CGI, providing empirical benchmarks for the synergistic implementation of effective government and efficient market actions in green governance. It further corroborates the positive roles of marketization and the digital economy as novel governance instruments, thereby offering critical policy insights for the coordinated advancement of the “dual-carbon” goals and high-quality economic development. Full article

Wireless sensor networks (WSNs) are a key enabling technology for modern e-Health applications. However, their deployment in clinical environments faces critical challenges due to dynamic network topologies, signal interference, and stringent energy constraints. Static resource allocation schemes often prove inadequate in these mission-critical settings, leading to communication failures that can compromise data integrity and patient safety. This paper proposes a novel hybrid framework for intelligent, dynamic resource allocation that addresses these challenges. The framework combines classical graph-coloring heuristics—Greedy and Recursive Largest First (RLF) for efficient initial channel assignment with the adaptive power of metaheuristics, specifically Simulated Annealing and Genetic Algorithms, for localized refinement. Unlike conventional approaches that require costly, network-wide reconfigurations, our method performs targeted adaptations only in interference-affected regions, thereby optimizing the trade-off between network reliability and energy efficiency. Comprehensive simulations modeled on dynamic, hospital-scale WSNs demonstrate the effectiveness of various hybrid strategies. Notably, our results demonstrate that a hybrid strategy using a Genetic Algorithm can most effectively minimize interference and ensure high data reliability, validating the framework as a scalable and resilient solution. These results validate the proposed framework as a scalable, energy-aware solution for resilient, real-time healthcare telecommunication infrastructures. Full article

Many algorithms related to vehicular applications, such as enhanced perception of the environment, benefit from frequent updates and the use of data from multiple vehicles. Federated learning is a promising method to improve the accuracy of algorithms in the context of vehicular networks. However, limited communication bandwidth, varying wireless channel quality, and potential latency requirements may impact the number of vehicles selected for training per communication round and their assigned radio resources. In this work, we characterize the vehicles participating in federated learning based on their importance to the learning process and their use of wireless resources. We then address the joint vehicle selection and resource allocation problem, considering multi-cell networks with multi-user multiple-input multiple-output (MU-MIMO)-capable base stations and vehicles. We propose a “vehicle-beam-iterative” algorithm to approximate the solution to the resulting optimization problem. We then evaluate its performance through extensive simulations, using realistic road and mobility models, for the task of object classification of European traffic signs. Our results indicate that MU-MIMO improves the convergence time of the global model. Moreover, the application-specific accuracy targets are reached faster in scenarios where the vehicles have the same training data set sizes than in scenarios where the data set sizes differ. Full article