Crowdsourced delivery platforms (e.g., Meituan, Uber Eats, DoorDash) have become vital infrastructure in urban logistics, yet their competitive order-grabbing mechanisms often lead to strategy homogenization, inefficiency, and income inequality. This paper presents ARDE (Adaptive Regulation via Dual-layer Evolution), an evolutionary governance framework that integrates individual reinforcement learning with adaptive platform-level regulation. The outer agent dynamically generates governance signals based on system diagnostics (strategy entropy, Gini coefficient, completion rate), while inner agents employ Diffusion Q-Learning guided by a language-model-driven reward shaping module to promote fairness and strategy diversity. Experiments on real-world datasets show that ARDE achieves stable diversity (0.997 ± 0.184), reduces inequality (Gini change 1.3%), and maintains high efficiency. Further comparison (ARDE-PPO vs. MAPPO) confirms that its advantages stem from explicit hierarchical governance rather than algorithmic coincidence. Overall, ARDE offers a scalable and interpretable paradigm for reconciling individual rationality with collective welfare in gig economies and other multi-agent socio-technical systems.

Metaverse service is a product of the convergence between Metaverse and service systems, designed to address service-related challenges within Metaverse. With the rise of large language models (LLMs), agents are employed to represent service entities, facilitating various service events and interactions in Metaverse service ecosystem. However, existing LLM-based agents exhibit critical limitations in emotional state integration, failing to emulate the bounded rationality required for bridging virtual-world services with real-world services, such as emotion measurement, emotional state evolution and emotional decision. This paper proposes an explainable emotion alignment framework for LLM-based agents in Metaverse Service Ecosystem. It aims to integrate factual factors into the decision-making loop of LLM-based agents, systematically demonstrating how to achieve more relational emotion alignment for these agents. Finally, a simulation experiment in the Offline-to-Offline food delivery scenario is conducted to evaluate the effectiveness of this framework, obtaining more realistic social emergence.

With the rise of service computing, cloud computing, and IoT, service ecosystems are becoming increasingly complex. The intricate interactions among intelligent agents make abnormal emergence analysis challenging, as traditional causal methods focus on individual trajectories. Large language models offer new possibilities for Agent-Based Modeling (ABM) through Chain-of-Thought (CoT) reasoning to reveal agent intentions. However, existing approaches remain limited to microscopic and static analysis. This paper introduces a framework: Emergence Analysis based on Multi-Agent Intention (EAMI), which enables dynamic and interpretable emergence analysis. EAMI first employs a dual-perspective thought track mechanism, where an Inspector Agent and an Analysis Agent extract agent intentions under bounded and perfect rationality. Then, k-means clustering identifies phase transition points in group intentions, followed by a Intention Temporal Emergence diagram for dynamic analysis. The experiments validate EAMI in complex online-to-offline (O2O) service system and the Stanford AI Town experiment, with ablation studies confirming its effectiveness, generalizability, and efficiency. This framework provides a novel paradigm for abnormal emergence and causal analysis in service ecosystems. The code is available at https://anonymous.4open.science/r/EAMI-B085.

Linear frequency modulation (LFM) radar which can measure distance of target is the key sensor in autonomous driving system in the context of Internet of Things (IoT). However, huge amount of raindrops in rainfall environment would reflect signal and result in negative influence. In order to improve the detection ability of LFM radar, echo signal model of LFM radar target in rainfall environment is established. Working principle of LFM radar, characteristics of raindrops, influence of rain clutter, and comparison with measured data in time-domain and frequency-domain distribution are given in sequence. Second, three features are defined and extracted from frequency-domain spectrum and singular-domain spectrum to recognize target echo signal, rain clutter, and mixed signal with high precision. When the rainfall rate is 3.33 mm/h, the overall recognition accuracy can still be up to 95.15%. Third, an adaptive ensemble empirical mode decomposition (EEMD) algorithm is proposed to suppress rain clutter in mixed signal. Simulation results prove the effectiveness of EEMD in improving ranging accuracy in rainfall environment. At last, outfield experiments in normal environment and rainfall environment are conducted, respectively. Measured distance obtained by proposed method in rainfall environment is still more accurate than that obtained by traditional fast Fourier transform method in normal environment, illustrating that the proposed method can ensure the high performance of LFM radar in rainfall environment. Research in this article has positive influence on improving reliability of LFM radar in inclement environment and can contribute to more stable target information for the whole in-vehicle IoT.

In this article, we explore the Mechanism Design aspects of the Maximum Vertex-Weighted \(b\) -matching (MVbM) problem on bipartite graphs \((A\cup T,E)\) . The set \(A\) comprises agents, while \(T\) represents tasks. The set \(E\) , which connects \(A\) and \(T\) , is the private information of either agents or tasks. In this framework, we investigate three mechanisms— \(\mathbb{M}_{BFS}\) , \(\mathbb{M}_{DFS}\) , and \(\mathbb{M}_{G}\) . We examine scenarios in which either agents or tasks are strategic and report their adjacent edges to one of the three mechanisms. In both cases, we assume that the strategic entities are bounded by their statements: They can hide edges, but they cannot report edges that do not exist. First, we consider the case in which agents can manipulate. In this framework, \(\mathbb{M}_{BFS}\) and \(\mathbb{M}_{DFS}\) are optimal but not truthful. By characterizing the Nash Equilibria induced by \(\mathbb{M}_{BFS}\) and \(\mathbb{M}_{DFS}\) , we reveal that both mechanisms have a Price of Anarchy ( \(PoA\) ) and Price of Stability ( \(PoS\) ) of \(2\) . These efficiency guarantees are tight; no deterministic mechanism can achieve a lower \(PoA\) or \(PoS\) . In contrast, the third mechanism, \(\mathbb{M}_{G}\) , is not optimal, but truthful and its approximation ratio is \(2\) . We demonstrate that this ratio is optimal; no deterministic and truthful mechanism can outperform it. We then shift our focus to scenarios where tasks can exhibit strategic behavior. In this case, \(\mathbb{M}_{BFS}\) , \(\mathbb{M}_{DFS}\) , and \(\mathbb{M}_{G}\) all maintain truthfulness, making \(\mathbb{M}_{BFS}\) and \(\mathbb{M}_{DFS}\) truthful and optimal mechanisms. In conclusion, we investigate the manipulability of \(\mathbb{M}_{BFS}\) and \(\mathbb{M}_{DFS}\) through experiments on randomly generated graphs. We observe that (i) \(\mathbb{M}_{BFS}\) is less prone to be manipulated by the first agent than \(\mathbb{M}_{DFS}\) , and (ii) \(\mathbb{M}_{BFS}\) is more manipulable on instances in which the total capacity of the agents is equal to the number of tasks. 1

Water resources management over long term has faced a great challenge due to the increasing demands on water from a growing number of population and a huge variance of water usage in different time and place. Therefore, a new time series model based on Recurrent Neural Network (RNN), has been proposed and developed in this study for intelligent prediction of future water level in different reservoirs. We have carried out experiments on reservoirs in Ningbo, China, and the results have shown that our proposed model is more efficient on intelligent prediction of water level in reservoirs.

DAG-based blockchain systems have been deployed to enable trustworthy peer-to-peer transactions for IoT devices. Unique address checking, as a key part of transaction generation for privacy and security protection in DAG-based blockchain systems, incurs big latency overhead and degrades system throughput.

ProtoMol is a high-performance framework in C++ for rapid prototyping of novel algorithms for molecular dynamics and related applications. Its flexibility is achieved primarily through the use of inheritance and design patterns (object-oriented programming). Performance is obtained by using templates that enable generation of efficient code for sections critical to performance (generic programming). The framework encapsulates important optimizations that can be used by developers, such as parallelism in the force computation. Its design is based on domain analysis of numerical integrators for molecular dynamics (MD) and of fast solvers for the force computation, particularly due to electrostatic interactions. Several new and efficient algorithms are implemented in ProtoMol. Finally, it is shown that ProtoMol's sequential performance is excellent when compared to a leading MD program, and that it scales well for moderate number of processors. Binaries and source codes for Windows, Linux, Solaris, IRIX, HP-UX, and AIX platforms are available under open source license at http://protomol.sourceforge.net.