Search Results (3,977)

Government policies and market forces have created new possibilities for wage growth in the logistics industry, which can reshape the development direction and labor reward of enterprises. The inclusive financial policy implemented by the Chinese government is effective, and the inputs of inclusive finance can affect the intelligent and low-carbon operations, the technical economic benefits and labor productivity in the logistics industry, thereby promoting wage growth. Meanwhile, the government-led industrial structure transformation and transportation infrastructure have brought a large number of new workers, transport individuals and enterprises into the logistics industry, which intensify the homogeneous service competition of enterprises, thereby hampering wage growth. In the market force, with the scale expansion of Internet access and logistics delivery vehicles and freight volume, the scale effects may enhance the wage level in the logistics industry. In addition, the moderating effect between policy and market forces can also confirm the existence of a positive spillover effect. The heterogeneity of wage growth varies across the eastern, central and western regions, as well as between the northern and southern regions. These findings highlight the importance of promoting the growth of labor wage income by policy implementation in inclusive finance, preferential measures on agricultural product logistics, integrated operation in the manufacturing and logistics field and the Belt and Road Initiative. Full article

Intolerance of uncertainty (IU) and behavioral inhibition (BI) are personality traits associated with avoidance of the unfamiliar. Both are linked to anxiety and other disorders. However, most research on personality has relied on self-report, which may not correspond to actual behavior. An alternative is to observe behavior in computer-based tasks designed to assess personality. The current study sought to develop such a task, based on the conditioned place preference paradigm, which is sensitive to IU but not BI. Participants foraged for reward in a virtual environment consisting of multiple interconnected rooms. In the training phase, the rich room was paired with a higher number of wins than losses. The poor room was the opposite. In the test phase, participants could freely search any of the rooms, including a completely new room. Although most showed a strong initial preference for the new room, those with higher self-reported IU left this room faster, foraging there significantly less than those with lower IU. This preference also depended on information provided about the new room. There was a strong positive correlation between IU and BI; however, the latter was unrelated to behavior. Thus, the task captures a unique component of IU. Full article

The maritime industry, while indispensable to global trade, is a significant contributor to greenhouse gas (GHG) emissions, accounting for approximately 3% of global emissions. As international regulatory bodies, particularly the International Maritime Organization (IMO), push for ambitious decarbonization targets, hydrogen-based technologies have emerged as promising alternatives to conventional fossil fuels. This review critically examines the potential of hydrogen fuels—including hydrogen fuel cells (HFCs) and hydrogen internal combustion engines (H2ICEs)—for maritime applications. It provides a comprehensive analysis of hydrogen production methods, storage technologies, onboard propulsion systems, and the associated techno-economic and regulatory challenges. A detailed life cycle assessment (LCA) compares the environmental impacts of hydrogen-powered vessels with conventional diesel engines, revealing significant benefits particularly when green or blue hydrogen sources are utilized. Despite notable hurdles—such as high production and retrofitting costs, storage limitations, and infrastructure gaps—hydrogen holds considerable promise in aligning maritime operations with global sustainability goals. The study underscores the importance of coordinated government policies, technological innovation, and international collaboration to realize hydrogen’s potential in decarbonizing the marine sector. Full article

Federated Learning (FL) enables privacy-preserving model training across distributed clients. However, its reliance on voluntary client participation makes it vulnerable to strategic behaviors—actions that are not overtly malicious but significantly impair model convergence and fairness. Existing defense methods primarily focus on explicit attacks, overlooking the challenges posed by economically motivated “pseudo-honest” clients. To address this gap, we propose a Reputation-Aware Defense Framework to mitigate strategic behaviors in FL. This framework introduces a multi-dimensional dynamic reputation model that evaluates client behaviors based on gradient alignment, participation consistency, and update stability. The resulting reputation scores are incorporated into both aggregation and incentive mechanisms, forming a behavior-feedback loop that rewards honest participation and penalizes opportunistic strategies. We theoretically prove the convergence of reputation scores, the suppression of low-quality updates in aggregation, and the emergence of honest participation as a Nash equilibrium under the incentive mechanism. Experiments on datasets such as CIFAR-10, FEMNIST, MIMIC-III demonstrate that our approach significantly outperforms baseline methods in accuracy, fairness, and robustness, even when up to 60% of clients act strategically. This study bridges trust modeling and robust optimization in FL, offering a secure foundation for federated systems operating in open and incentive-driven environments. Full article

To meet the escalating demands of massive data transmission, the next generation of wireless networks will leverage the multi-access edge computing (MEC) architecture coupled with multi-access transmission technologies to enhance communication resource utilization. This paper presents queue stability-constrained reinforcement learning algorithms designed to optimize the transmission control mechanism in MEC systems to improve both throughput and reliability. We propose an analytical framework to model the queue stability. To increase transmission performance while maintaining queue stability, queueing delay model is designed to analyze the packet scheduling process by using the M/M/c queueing model and estimate the expected packet queueing delay. To handle the time-varying network environment, we introduce a queue stability constraint into the reinforcement learning reward function to jointly optimize latency and queue stability. The reinforcement learning algorithm is deployed at the MEC server to reduce the workload of central cloud servers. Simulation results validate that the proposed algorithm effectively controls queueing delay and average queue length while improving packet transmission success rates in dynamic MEC environments. Full article

This paper presents a comprehensive deep reinforcement learning (DRL) framework for developing strategic trading models tailored to the Indian NIFTY 50 index, leveraging the temporal and nonlinear nature of financial markets. Three advanced DRL architectures deep Q-network (DQN), double deep Q-network (DDQN), and dueling double deep Q-network (Dueling DDQN) were implemented and empirically evaluated. Using a decade-long dataset of 15-min interval OHLC data enriched with technical indicators such as the exponential moving average (EMA), pivot points, and multiple supertrend configurations, the models were trained using prioritized experience replay, epsilon-greedy exploration strategies, and softmax sampling mechanisms. A test set comprising one year of unseen data (May 2024–April 2025) was used to assess generalization performance across key financial metrics, including Sharpe ratio, profit factor, win rate, and trade frequency. Each architecture was analyzed in three progressively sophisticated variants incorporating enhancements in reward shaping, exploration–exploitation balancing, and penalty-based trade constraints. DDQN V3 achieved a Sharpe ratio of 0.7394, a 73.33% win rate, and a 16.58 profit factor across 15 trades, indicating strong volatility-adjusted suitability for real-world deployment. In contrast, the Dueling DDQN V3 achieved a high Sharpe ratio of 1.2278 and a 100% win rate but with only three trades, indicating an excessive conservatism. The DQN V1 model served as a strong baseline, outperforming passive strategies but exhibiting limitations due to Q-value overestimation. The novelty of this work lies in its systematic exploration of DRL variants integrated with enhanced exploration mechanisms and reward–penalty structures, rigorously applied to high-frequency trading on the NIFTY 50 index within an emerging market context. Our findings underscore the critical importance of architectural refinements, dynamic exploration strategies, and trade regularization in stabilizing learning and enhancing profitability in DRL-based intelligent trading systems. Full article

Background/Objectives: Although nutrition is widely recognized as a key factor in post-event recovery in sports, little attention has been given to how its cultural and social dimensions—embodied in local cuisine—intersect with the needs of traveling athletes, for whom food often also serves as a medium of cultural immersion and sensory exploration. Poland, with its rich regional culinary traditions and numerous international running events, offers a compelling context in which to explore these interactions. This study aims to understand the role of local cuisine in the experiences of foreign runners participating in the Poznan Half Marathon 2025, with particular attention on cultural engagement, tourist motivations, and post-exercise recovery processes. Methods: This study was based on a qualitative approach, utilizing semi-structured in-depth interviews conducted with 12 international runners from the United Kingdom, Germany, and Ukraine. The participants possessed a minimum of two years’ experience in traveling for sports. Results: The findings identified three main areas of the significance of food: (1) food as an element of cultural exploration, (2) local cuisine as a motivator or barrier when choosing a race, (3) food as a symbolic reward and structured recovery practice supporting nutritional and psychological processes. Approaches varied by nationality—British participants preferred spontaneous taste discovery, Ukrainians valued culinary comfort similar to home, and Germans planned their culinary experiences with greater awareness. Conclusions: Local cuisine plays a multifaceted role in international running events, serving not only nutritional needs but also emotional and cultural functions that shape the overall participant experience. Both event organizers and local restaurants should consider offering diverse and culturally sensitive food options to enhance recovery, satisfaction, and the appeal of sports tourism destinations. Full article

With the large-scale power batteries approaching their retirement phase, efforts are being made to advance the recycling and cascade utilization of power batteries for electric vehicles (EVs). This paper constructs a closed-loop supply chain (CLSC) of power batteries led by the battery manufacturer (BM) and composed of the electric vehicle manufacturer (EVM) and third-party recycler (TPR). The study investigates the optimal pricing strategies of this CLSC with the consideration of recycling competition under the government’s reward–penalty mechanism. This paper establishes five recycling modes, namely independent recycling and cooperative recycling, under dual-channel recycling, and further discusses the effects of the government reward–penalty mechanism and recycling competition on the recycling rate, profits, and recycling pricing of the CLSC in each recycling mode. The following conclusions are found: (1) An increase in the reward–penalty intensity will increase the recycling rate, sales price of EVs, wholesale price, transfer price, recycling price, and the profit of each recycler in the CLSC. (2) An increase in the recycling competition will result in the reduction of the profit of each enterprise, and will also lead to the reduction of the recycling rate. (3) Cooperation between enterprises can inhibit the recycling volume of other enterprises to a certain extent. The cooperation between the EVM and BM can increase the recycling volume and the sales volume of EVs. (4) The leadership of the BM in the supply chain is embodied in the recycling and profit. For other members of the supply chain, it is very important to strive for cooperation with the leaders in the supply chain. These research conclusions can provide theoretical support for optimizing the power battery recycling system, formulating relevant policies, and improving the efficiency of resource recycling, thereby promoting the sustainable development of the new energy industry. Full article

Background and aim of this review: The ongoing opioid epidemic underscores the urgent need for innovative pharmacological and behavioral interventions to mitigate the impact of opioid use disorder (OUD). This review aims to explore theoretical overlaps between the neurobiological mechanisms underlying OUD development and the pharmacodynamic profile of methylphenidate (MPH). Particular attention is given to the potential shared molecular targets, safety considerations, and therapeutic implications of MPH use in this clinical context. Main finding: In the development of opioid dependence, the negative reinforcement of the dopaminergic transmission of the mesocorticolimbic pathway induced by the supraspinal action of opioid receptor agonists plays a major role. The induced state of hypodopaminergic and hyperadrenergic modulates the underlying disease process by affecting cognitive control, affective regulation, and motivational drive. MPH, acting as a dopamine reuptake inhibitor and modulator of vesicular monoamine transporter 2 (VMAT-2), increases extracellular dopamine availability and enhances dopaminergic signaling, suggesting potential utility in restoring dopaminergic tone in OUD. Additionally, MPH has shown efficacy in hypoactive delirium in patients with terminal cancer, improving both cognitive function and psychomotor drive. Conclusions and future perspectives: There appear to be converging neurobiological mechanisms between the action of MPH and the pathophysiology of OUD, particularly within the dopaminergic system. However, well-designed clinical trials are essential to identify the patient subgroups that may benefit from adjunctive MPH treatment, to evaluate its efficacy in this setting, and to assess the long-term safety and risk profile of stimulant use in individuals with OUD. Full article

Borehole trajectory planning under complex geological conditions poses significant challenges for intelligent drilling systems. To tackle this issue, a novel optimization framework is developed, leveraging the Soft Actor-Critic (SAC) algorithm enhanced by a self-attention mechanism. A three-dimensional heterogeneous geological model is constructed via generative adversarial networks (GANs), incorporating key formation features such as lithology, pressure, and fault zones. A tailored multi-objective reward function is introduced, balancing directional convergence, trajectory smoothness, obstacle avoidance, and formation adaptability. The self-attention mechanism is embedded into both the actor and critic networks to strengthen the agent’s capacity for spatial perception and decision stability. The proposed approach enables the agent to adaptively generate control sequences for efficient trajectory planning in highly variable formations. Experimental results demonstrate that the model exhibits superior convergence stability, improved curvature control, and enhanced obstacle avoidance, highlighting its potential for intelligent trajectory planning in challenging drilling environments. Full article

The extensive deployment of quadrotors in complex environmental missions has revealed a critical challenge: degradation of trajectory tracking accuracy due to time-varying wind disturbances. Conventional model-based controllers struggle to adapt to nonlinear wind field dynamics, while data-driven approaches often suffer from catastrophic forgetting that compromises environmental adaptability. This paper proposes a reinforcement learning framework with continual adaptation capabilities to enhance robust tracking performance for quadrotors operating in dynamic wind fields. We develop a continual reinforcement learning framework integrating continual backpropagation algorithms with reinforcement learning. Initially, a foundation model is trained in wind-free conditions. When wind disturbance intensity undergoes gradual variations, a neuron utility assessment mechanism dynamically resets inefficient neurons to maintain network plasticity. Concurrently, a multi-objective reward function is designed to improve both training precision and efficiency. The Gazebo/PX4 simulation platform was utilized to validate the wind disturbance stepwise growth and stochastic variations. This approach demonstrated a reduction in the root mean square error of trajectory tracking when compared to the standard PPO algorithm. The proposed framework resolves the plasticity loss problem in deep reinforcement learning through structured neuron resetting, significantly enhancing the continual adaptation capabilities of quadrotors in dynamic wind fields. Full article

This article argues that Thomas Aquinas’s exegesis of the parables in his commentary on the Gospel of Matthew contains—if only in skeletal form, with certain aspects more fully developed than others—the outline of a comprehensive treatise on Christian eschatology. Aquinas approaches parables with a nuanced perspective, acknowledging their inherent obscurity while also emphasizing their capacity to guide minds toward the truth. He understands their dual purpose as both concealing divine mysteries from the ill-intentioned and revealing them to the receptive. Distinguishing his approach from Albert the Great’s, Aquinas’s commentary features substantial eschatological components. Drawing on primary sources, this article examines these elements, starting with the unknowability of the end of time, which serves to promote vigilance. This article then treats death and particular judgment, the damned’s twofold punishment (the poena damni and the poena sensus), and the righteous’s varied, eternal reward, concluding with the Parousia, inseparably linked to the general resurrection, the final judgment, and the renewal of the world. Finally, this article shows how Aquinas’s engagement with these parables provides a robust, biblically-rooted exploration of the Last Things. Full article

Efficient task offloading for delay-sensitive applications, such as autonomous driving, presents a significant challenge in multi-hop Vehicular Edge Computing (VEC) networks, primarily due to high vehicle mobility, dynamic network topologies, and complex end-to-end congestion problems. To address these issues, this paper proposes a graph attention-based reinforcement learning algorithm, named GAPO. The algorithm models the dynamic VEC network as an attributed graph and utilizes a graph neural network (GNN) to learn a network state representation that captures the global topological structure and node contextual information. Building on this foundation, an attention-based Actor–Critic framework makes joint offloading decisions by intelligently selecting the optimal destination and collaboratively determining the ratios for offloading and resource allocation. A multi-objective reward function, designed to minimize task latency and to alleviate link congestion, guides the entire learning process. Comprehensive simulation experiments and ablation studies show that, compared to traditional heuristic algorithms and standard deep reinforcement learning methods, GAPO significantly reduces average task completion latency and substantially decreases backbone link congestion. In conclusion, by deeply integrating the state-aware capabilities of GNNs with the decision-making abilities of DRL, GAPO provides an efficient, adaptive, and congestion-aware solution to the resource management problems in dynamic VEC environments. Full article

Contextual multi-armed bandits (CMABs) are vital for sequential decision-making in areas such as recommendation systems, clinical trials, and finance. We propose a simulation framework integrating Gaussian Process (GP)-based CMABs with vine copulas to model dependent contexts and GARCH processes to capture reward volatility. Rewards are generated via copula-transformed Beta distributions to reflect complex joint dependencies and skewness. We evaluate four policies—ensemble, Epsilon-greedy, Thompson, and Upper Confidence Bound (UCB)—over 10,000 replications, assessing cumulative regret, observed reward, and cumulative reward. While Thompson sampling and LLM-guided policies consistently minimize regret and maximize rewards under varied reward distributions, Epsilon-greedy shows instability, and UCB exhibits moderate performance. Enhancing the ensemble with copula features, GP models, and dynamic policy selection driven by a large language model (LLM) yields superior adaptability and performance. Our results highlight the effectiveness of combining structured probabilistic models with LLM-based guidance for robust, adaptive decision-making in skewed, high-variance environments. Full article

Loneliness is a significant challenge for millions worldwide, with chronic loneliness having harmful effects on physical health, mental well-being, and relationships. Cognitive distortions play an important role in perpetuating loneliness. Psychological interventions targeting such distortions have been effective at alleviating feelings of loneliness. However, less is known about which cognitive distortions are most prevalent among lonely individuals and how these distortions relate to loneliness and mental well-being. This exploratory study prescreened a Census-matched sample of 1000 U.S. adults for loneliness, then asked those in the top quartile (N = 237) to rate multiple patterns of cognitive distortion related to loneliness. Factor analyses identified six common and influential patterns of cognitive distortion (mindreading, future reward, catastrophizing, essentializing, deservedness, and externalizing). Essentializing was the most strongly endorsed factor, followed by mindreading and catastrophizing. Essentializing also evidenced the strongest correlation with loneliness. Additionally, the relationship between loneliness and participants’ stress was completely mediated by mindreading, catastrophizing, and essentializing. These findings highlight the importance of targeting specific cognitive distortions in loneliness interventions to effectively improve the mental well-being of lonely individuals. Full article