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Network reciprocity has been widely advertised in theoretical studies as one of the basic cooperation-promoting mechanisms, but experimental evidence favoring this type of reciprocity was published only recently. When organized in an unchanging network of social contacts, human subjects cooperate provided the following strict condition is satisfied: The benefit of cooperation must outweigh the total cost of cooperating with all neighbors. In an attempt to relax this condition, we perform social dilemma experiments wherein network reciprocity is aided with another theoretically hypothesized cooperation-promoting mechanism—costly punishment. The results reveal how networks promote and stabilize cooperation. This stabilizing effect is stronger in a smaller-size neighborhood, as expected from theory and experiments. Contrary to expectations, punishment diminishes the benefits of network reciprocity by lowering assortment, payoff per round, and award for cooperative behavior. This diminishing effect is stronger in a larger-size neighborhood. An immediate implication is that the psychological effects of enduring punishment override the rational response anticipated in quantitative models of cooperation in networks.

The notion of ecological networks (EN) and their identification can support approaches to nature conservation strategies aiming at biodiversity, landscape connectivity, and people’s well-being. Integrating ecosystem services (ESs), morphological spatial pattern analysis (MSPA), circuit theory, and landscape pattern index analysis, we proposed a new framework for mapping EN that was expected to promote economic development and ecological protection. Specifically, source areas were extracted through a combination of ESs and MSPA that integrated functional and morphological spatial attributes. Resistance surfaces were determined based on habitat quality. A network linking ecological source areas was then identified using circuit theory, and landscape pattern index analysis was used to identify ecological strategy nodes in view of the heterogeneity within ecological corridors. The results showed that the Sichuan Basin involved 553 ecological sources, 641 ecological corridors, and 33 ecological nodes that altogether included 20 ecological strategy nodes. Constructing regional EN can promote the transformation of multiple, chaotic, and scattered ecological elements to systematic and networked ecological elements and ultimately promote harmonious coexistence between humans and nature. This study provided a methodology for the extraction of ecological source areas and strategy nodes and can provide a significant reference for the management and optimization of EN.

Background Neoadjuvant chemoradiotherapy (nCRT) and surgery have been recommended as the standard treatments for locally advanced esophageal squamous cell carcinoma (ESCC). In addition, nodal metastases decreased in frequency and changed in distribution after neoadjuvant therapy. This study aimed to examine the optimal strategy for lymph node dissection (LND) in patients with ESCC who underwent nCRT. Methods The hazard ratios (HRs) for overall survival (OS) and disease-free survival (DFS) were calculated using the Cox proportional hazard model. To determine the minimal number of LNDs (n-LNS) or least station of LNDs (e-LNS), the Chow test was used. Results In total, 333 patients were included. The estimated cut-off values for e-LNS and n-LNS were 9 and 15, respectively. A higher number of e-LNS was significantly associated with improved OS (HR: 0.90; 95% CI 0.84–0.97, P  = 0.0075) and DFS (HR: 0.012; 95% CI: 0.84–0.98, P  = 0.0074). The e-LNS was a significant prognostic factor in multivariate analyses. The local recurrence rate of 23.1% in high e-LNS is much lower than the results of low e-LNS (13.3%). Comparable morbidity was found in both the e-LNS and n-LND subgroups. Conclusion This cohort study revealed an association between the extent of LND and overall survival, suggesting the therapeutic value of extended lymphadenectomy during esophagectomy. Therefore, more lymph node stations being sampled leads to higher survival rates among patients who receive nCRT, and standard lymphadenectomy of at least 9 stations is strongly recommended.

Passenger behavior analysis is a key issue in passenger assignment research, in which the path choice is a fundamental component. A highly complex transit network offers multiple paths for each origin–destination (OD) pair and thus resulting in more flexible choices for each passenger. To reflect a passenger’s flexible choice for the transit network, the optimal strategy was proposed by other researchers to determine passenger choice behavior. However, only strategy links have been searched in the optimal strategy algorithm and these links cannot complete the whole path. To determine the paths for each OD pair, this study proposes the depth-first path generation algorithm, in which a strategy node concept is newly defined. The proposed algorithm was applied to the Beijing metro network. The results show that, in comparison to the shortest path and the K-shortest path analysis, the proposed depth-first optimal strategy path generation algorithm better represents the passenger behavior more reliably and flexibly.

This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling space to enhance the sampling efficiency. During the node expansion process, an adaptive step-size method is introduced to dynamically adjust the step size based on the obstacle information, while a node rejection strategy is used to accelerate the search process so as to generate a near-optimal collision-free path. A pruning optimization strategy is also proposed to eliminate the redundant nodes from the path. Furthermore, a cubic non-uniform B-spline interpolation algorithm is applied to smooth the generated path. Finally, simulation experiments of the IRRT*-Connect algorithm are conducted in Python and ROS, and physical experiments are performed on a UR5 robotic arm. By comparing with the existing algorithms, it is demonstrated that the proposed method can achieve shorter planning times and lower path costs of the manipulator operation.

The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches encounter drawbacks, primarily, the requirement to transmit substantial volumes of raw data to central servers, which raises concerns about user privacy breaches during transmission. Federated learning (FL) has emerged as a viable solution to these challenges, addressing both data volume and privacy issues effectively. Nonetheless, the deployment of FL faces its own set of obstacles, notably the excessive delay and energy consumption caused by the vast number of devices and fluctuating channel conditions. In this paper, by considering the heterogeneity of devices and the instability of the network state, the delay and energy consumption models of each round of federated training are established. Subsequently, we introduce a strategic node selection approach aimed at minimizing training costs. Building upon this, we propose an innovative, empirically driven Double Deep Q Network (DDQN)-based algorithm called low-cost node selection in federated learning (LCNSFL). The LCNSFL algorithm can assist edge servers in selecting the optimal set of devices to participate in federated training before the start of each round, based on the collected system state information. This paper culminates with a simulation-based comparison, showcasing the superior performance of LCNSFL against existing algorithms, thus underscoring its efficacy in practical applications.

Conventional blockchain consensus protocols tailored for the Internet of Vehicles (IoV) usually face low transaction throughput, high latency, and elevated communication overhead issues. To address these issues, in this paper, we propose ESBCP, an efficient and secure blockchain consensus protocol for the IoV environment. Firstly, considering the significant performance differences among nodes in the IoV, we designed a blockchain consensus model for the IoV. Roadside units execute a trust evaluation mechanism to select high-quality vehicle nodes for the consensus process, thereby reducing the likelihood of malicious nodes in the consensus cluster. Secondly, we designed a node partition strategy to adapt to the dynamic feature of the IoV. Finally, addressing the mobility of nodes in the IoV, we introduced a dynamic unique node list. Vehicle nodes can promptly select nodes with high reliability from the list of communicable nodes to join their unique node list, while also promptly removing nodes with low reliability from their unique node list. Combining these strategies, we propose DK-PBFT, an improved Practical Byzantine Fault Tolerance consensus algorithm. The algorithm meets the efficiency and mobility requirements of vehicular networks. Through theoretical analysis, ESBCP could prevent external and internal security risks while reducing communication overhead. Experimental verification demonstrated that ESBCP effectively reduces consensus latency and improves transaction throughput. Our proposed ESBCP can be used in other application scenarios that require high consensus efficiency.

Due to the complex environment in the field, the number of nodes and the energy consumption of nodes should be considered in the deployment of aquaculture water quality monitoring system. Therefore, according to the actual network framework of aquaculture water quality monitoring system, based on the energy balance mechanism of clustering routing protocol, clustering mode and path energy consumption model, a new node layout and energy consumption optimization strategy is proposed in this paper, by improving artificial bee colony algorithm and genetic algorithm, the number of relay nodes and energy consumption of network are reduced. Through simulation and comparison, it is verified that the network coverage can be increased by 36.92% when the proposed optimization strategy and PSO perform the node placement task in the same scenario. The improved artificial bee colony algorithm has a significant improvement in the network coverage of the monitored area with the same number of nodes. On the basis of this, the final node layout scheme obtained by GA extends the life cycle of the network to a certain extent, and proves the guidance and application value of the strategy in the process of system building.

Nodes in performance heterogeneous wireless sensor networks (HWSNs) often have varying levels of available energy, storage space, and processing power due to the network’s limited resources. Additionally, coverage redundancy and channel conflicts may adversely influence the quality of service in a network when many nodes have been deployed at once. Energy as a major constrained resource requires an effective energy-efficient scheduling mechanism to balance node energy consumption to extend the network lifespan. Therefore, this research proposes an energy-efficient scheduling technique, IMA–NCS-3D for three-dimensional HWSNs on the basis of an improved memetic algorithm and node cooperation strategy. A multi-objective fitness function is created to encode the active and inactive states of nodes as genes, and the optimal scheduling set of the network is built via selection, crossover, variation, and local search. This phase of the process is known as node scheduling. Node-to-node cooperation solutions are offered during data transmission to deal with unforeseen traffic abnormalities and reduce congestion and channel conflicts when traffic volumes are high. Simulation results show that IMA–NCS-3D has superior scheduling capability, cross-network load balancing capability, and a longer network lifespan than other current coverage optimization approaches.

Conventional path planning methods often struggle to efficiently find optimal solutions in complex and dynamic environments, where the terrain and obstacle distributions vary significantly. This paper presents a novel path planning approach based on the Adaptive Weighted Jump Theta* (AWJ-Theta*) algorithm, which synergistically combines the any-angle path-shortening capability of Theta* with the search efficiency of Jump Point Search (JPS). Key innovations include an adaptive weighting mechanism for the heuristic function that dynamically adjusts based on the distance to the target and local environmental complexity, and a novel node screening strategy that evaluates visibility between nodes and their parent nodes, coupled with cost-based prioritization to reduce computational overhead. The algorithm is systematically evaluated through experiments on standard grid maps, randomly generated maps with varying obstacle densities, and maze environments. Results demonstrate that AWJ-Theta* achieves superior performance in terms of execution time, runtime stability, number of expanded nodes, and path cost compared to traditional Theta*, JPS, and J-Theta* algorithms, particularly in complex scenarios. These findings highlight the algorithm’s potential for efficient and robust real-time path planning in obstacle-rich environments.