Search Results (3,758)

For the first time, this paper introduces Nabla fractional calculus into the distributed Nash equilibrium (NE) seeking problem of aggregative games (AGs) with partial decision information in undirected communication networks, and proposes two novel fractional-order distributed algorithms. In the considered setting, each agent can access to only local information and collaboratively estimates the global aggregate through communication with its neighbors. Both algorithms adopt a backward-difference scheme followed by an implicit fractional-order gradient descent step. One updates local aggregate estimates via fractional-order dynamic tracking and the other uses fractional-order average dynamic consensus protocols. Under standard assumptions, convergence of both algorithms to the NE is rigorously proved using nabla fractional-order Lyapunov stability theory, achieving a Mittag-Leffler convergence rate. The feasibility of the developed schemes is verified via numerical experiments applied to a Nash-Cournot game and the coordination control of flexible robotic arms. Full article

To address the challenge of imprecise detection and control of fertilizer application rates caused by high granular flow during fertilization operations, a parallel diversion detection method with real-time application rate regulation is proposed. The mechanism of uniform distribution of discrete particles formed by high-throughput aggregated granular fertilizer was elucidated. Key components including the uniform fertilizer tube, sensor detection structure, six-channel diversion cone disc, and fertilizer convergence tube underwent parametric design, culminating in the innovative development of a six-channel parallel diversion detection device. A multi-channel parallel signal detection method was studied, and a synchronous multi-channel signal acquisition system was designed. Through calibration tests, relationship models were established between the measured flow rate of granular fertilizer and voltage, as well as between the actual flow rate and the rotational speed of the fertilizer discharge shaft. A fuzzy PID control model was constructed in MATLAB2023/Simulink. Using overshoot, response time, and stability as evaluation metrics, the control performance of traditional PID and fuzzy PID was compared and analyzed. To validate the control system’s precision, device performance tests were conducted. Results demonstrated that fuzzy PID control reduced the time required to reach steady state by 66.87% compared to traditional PID, while overshoot decreased from 7.38 g·s⁻¹ to 1.49 g·s⁻¹. Divergence uniformity tests revealed that at particle generation rates of 10, 20, 30, and 40 g·s⁻¹, the coefficient of variation for channel divergence consistency gradually increased with rising tilt angles. During field operations at 0–5.0° tilt, the coefficient of variation for channel divergence consistency remained below 7.72%. Bench tests revealed that the fuzzy PID control system achieved an average accuracy improvement of 3.64% compared to traditional PID control, with a maximum response time of 0.9 s. Field trials demonstrated detection accuracy no less than 92.64% at normal field operation speeds of 3.0–6.0 km·h⁻¹. This system enables real-time, precise detection of fertilizer application rates and closed-loop regulation. Full article

Cooperative task allocation is one of the critical enablers for multi-Autonomous Underwater Vehicle (AUV) missions, but existing approaches often assume reliable communication that rarely holds in real underwater acoustic environments. We study here the performance and robustness of the Consensus-Based Bundle Algorithm (CBBA) for multi-AUV task allocation under realistically degraded underwater communication conditions with dynamically appearing tasks. An integrated simulation framework that incorporates a Dubins-based kinematic model with minimum turning radius constraints, a configurable underwater acoustic communication model (range, delay, packet loss, and bandwidth), and a full implementation of improved CBBA with new features, complemented by 3D trajectory and network-topology visualization. We define five communication regimes, from ideal fully connected networks to severe conditions with short range and high packet loss. Within these regimes, we assess CBBA based on task allocation quality (total bundle value and task completion rate), convergence behavior (iterations and convergence rate), and communication efficiency (message delivery rate, average delay, and network connectivity), with additional metrics on the number of conflicts during dynamic task reallocation. Our simulation results indicate that CBBA maintains performance close to the optimum when the conditions are good and moderate but degrades significantly when connectivity becomes intermittent. We then introduce a local-communication-based conflict resolution strategy in the face of frequent task conflicts under very poor conditions: neighborhood-limited information exchange, negotiation within task areas, and decentralized local decisions. The proposed conflict resolution strategy significantly reduces the occurrence of conflicts and improves task completion under stringent communication constraints. This provides practical design insights for deploying multi-AUV systems under weak underwater acoustic networks. Full article

The increasing urgency to mitigate plastic pollution has accelerated the shift from linear manufacturing toward circular systems. This review synthesizes current advances in mechanical, chemical, biological, and upcycling pathways, emphasizing how artificial intelligence (AI) is reshaping decision-making, performance prediction, and system-level optimization. Intelligent sensing technologies—such as FTIR, Raman spectroscopy, hyperspectral imaging, and LIBS—combined with Machine Learning (ML) classifiers have improved material identification, reduced reject rates, and enhanced sorting precision. AI-assisted kinetic modeling, catalyst performance prediction, and enzyme design tools have improved process intensification for pyrolysis, solvolysis, depolymerization, and biocatalysis. Life Cycle Assessment (LCA)-integrated datasets reveal that environmental benefits depend strongly on functional-unit selection, energy decarbonization, and substitution factors rather than mass-based comparisons alone. Case studies across Europe, Latin America, and Asia show that digital traceability, Extended Producer Responsibility (EPR), and full-system costing are pivotal to robust circular outcomes. Upcycling strategies increasingly generate high-value materials and composites, supported by digital twins and surrogate models. Collectively, evidence indicates that AI moves from supportive instrumentation to a structural enabler of transparency, performance assurance, and predictive environmental planning. The convergence of AI-based design, standardized LCA frameworks, and inclusive governance emerges as a necessary foundation for scaling circular plastic systems sustainably. Full article

Balancing path-following accuracy and error convergence with graceful motion in steering control is challenging due to the competing nature of these requirements, especially across a range of operating speeds and conditions. This paper demonstrates that an integrated, multi-tiered steering controller considering slip in kinematic control, dynamic control, and steering actuator rate commands achieves accurate and graceful path following. Kinematic and dynamic models are adapted to include slip. A path-following kinematic controller is then derived using a continuous, time-varying, and speed-based variable-structure controller (VSC) to balance safe and graceful motion with robust error convergence. Yaw rate commands from the kinematic controller are nested in a backstepping slip–yaw dynamic tracking controller to generate steering rate commands. A high-gain observer (HGO) estimates the sideslip and yaw rate, which are used in sensor-based output feedback control. Stability analysis of the output feedback controller is provided, and peaking is resolved. The work focuses on lateral control alone so that the steering controller can be combined with other speed controllers. Field results demonstrate gracefulness and accuracy along complex paths in variable terrain, in different weather conditions, and with perturbations. Full article

The highly squinted mode, as an operational configuration of synthetic aperture radar (SAR), fulfills specific remote sensing demands. Under equivalent conditions, it necessitates a higher pulse repetition frequency (PRF) than the side-looking mode but produces inferior imaging quality, thereby constraining its widespread application. By applying the sparse SAR imaging method to highly squinted SAR systems, imaging quality can be enhanced while simultaneously reducing PRF requirements and expanding swath. Hyperparameters in sparse SAR imaging critically influence reconstruction quality and computational efficiency, making hyperparameter optimization (HPO) a persistent research focus. Inspired by HPO techniques in the deep unfolding network (DUN), we modified the iterative soft-thresholding algorithm (ISTA) employed in fast sparse SAR reconstruction based on approximate observation operators. Our adaptation enables adaptive regularization parameter tuning during iterations while accelerating convergence. To improve the robustness of this enhanced algorithm under realistic SAR echoes with noise, we integrated hypergradient descent (HD) to automatically adjust the ISTA step size after regularization parameter convergence, thereby mitigating overfitting. The proposed method, named Hyper-ISTA-GHD, adaptively selects regularization parameters and step sizes. It achieves high-precision, rapid imaging for highly squinted SAR. Owing to its training-free iterative minimization framework, this approach exhibits superior generalization capabilities compared to existing DUN methods and demonstrates broad applicability across diverse SAR imaging modes and scene characteristics. Simulations show that the hyperparameter selection and reconstruction results of the proposed method are almost consistent with the optimal values of traditional methods under different signal-to-noise ratios and sampling rates, but the time consumption is only one-tenth of that of traditional methods. Comparative experiments on the generalization performance with DUN show that the generalization performance of the proposed method is significantly better than DUN in extremely sparse scenarios. Full article

Unmanned aerial vehicle (UAV)-assisted satellite downlink transmission is a promising solution for improving coverage and throughput under challenging propagation conditions. However, the achievable performance gains are fundamentally constrained by the coupling between access transmission and the satellite–UAV backhaul, especially when decode-and-forward (DF) relaying and hybrid multiple access are employed. In this paper, we investigate the problem of end-to-end downlink sum-rate maximization in a UAV-assisted satellite network with hybrid non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission. We propose a performance-driven end-to-end optimization framework, in which UAV placement is optimized as an outer-layer control variable through an iterative procedure. For each candidate UAV position, a greedy transmission mode selection mechanism and a KKT-based satellite-to-UAV backhaul bandwidth allocation scheme are jointly executed in the inner layer to evaluate the resulting end-to-end downlink performance, whose feedback is then used to update the UAV position until convergence. Simulation results show that the proposed framework consistently outperforms benchmark schemes without requiring additional spectrum or transmit power. Under low satellite elevation angles, the proposed design improves system sum rate and spectral efficiency by approximately 25–35% compared with satellite-only NOMA transmission. In addition, the average user rate is increased by up to 37% under moderate network sizes, while maintaining stable relative gains as the number of users increases, confirming the effectiveness and scalability of the proposed approach. Full article

Value factorization methods have become a standard tool for cooperative multi-agent reinforcement learning (MARL) in the centralized-training, decentralized-execution (CTDE) setting. QMIX (a monotonic mixing network for value factorization), in particular, constrains the joint action–value function to be a monotonic mixing of per-agent utilities, which guarantees consistency with individual greedy policies but can severely limit expressiveness on tasks with non-monotonic agent interactions. This work revisits this design choice and proposes Relaxed Monotonic QMIX (R-QMIX), a simple regularized variant of QMIX that encourages but does not strictly enforce the monotonicity constraint. R-QMIX removes the sign constraints on the mixing network weights and introduces a differentiable penalty on negative partial derivatives of the joint value with respect to each agent’s utility. This preserves the computational benefits of value factorization while allowing the joint value to deviate from strict monotonicity when beneficial. R-QMIX is implemented in a standard PyMARL (an open-source MARL codebase) and evaluated on the StarCraft Multi-Agent Challenge (SMAC). On a simple map (3m), R-QMIX matches the asymptotic performance of QMIX while learning substantially faster. On more challenging maps (MMM2, 6h vs. 8z, and 27m vs. 30m), R-QMIX significantly improves both sample efficiency and final win rate (WR), for example increasing the final-quarter mean win rate from $42.3\%$ to $97.1\%$ on MMM2, from $0.0\%$ to $57.5\%$ on 6h vs. 8z, and from $58.0\%$ to $96.6\%$ on 27m vs. 30m. These results suggest that soft monotonicity regularization is a practical way to bridge the gap between strictly monotonic value factorization and fully unconstrained joint value functions. A further comparison against QTRAN (Q-value transformation), a more expressive value factorization method, shows that R-QMIX achieves higher and more reliably convergent win rates on the challenging SMAC maps considered. Full article

Automatically generated chains-of-thought (gCoTs) have become common as large language models adopt deliberative behaviors. Prior work emphasizes fidelity to internal processes, leaving explanatory properties underexplored. Our central hypothesis is that these traces, produced by highly capable reasoning models, are not arbitrary by-products of decoding but exhibit stable and practically valuable textual properties beyond answer fidelity. We apply a multidimensional text-evaluation framework that quantifies four axes—structural coherence, logical–factual consistency, linguistic clarity, and coverage/informativeness—that are standard dimensions for assessing textual quality, and use it to evaluate five reasoning models on the GSM8K arithmetic word-problem benchmark (~1.3 k–1.4 k items) with reproducible, normalized metrics. Logical verification shows near-ceiling self-consistency, measured by the Aggregate Consistency Score (ACS ≈ 0.95–1.00), and high final-answer entailment, measured by Final Answer Soundness (FAS0 ≈ 0.85–1.00); when sound, justifications are compact, with Justification Set Size (JSS ≈ 0.51–0.57) and moderate redundancy, measured by the Redundant Constraint Ratio (RCR ≈ 0.62–0.70). Results also show consistent coherence and clarity; from gCoT to answer implication is stricter than from question to gCoT support, indicating chains anchored to the prompt. We find no systematic trade-off between clarity and informativeness (within-model slopes ≈ 0). In addition to these automatic and logic-based metrics, we include an exploratory expert rating of a subset (four raters; 50 items × five models) to contextualize model differences; these human judgments are not intended to support dataset-wide generalization. Overall, gCoTs display explanatory value beyond fidelity, primarily supported by the automated and logic-based analyses, motivating hybrid evaluation (automatic + exploratory human) to map convergence/divergence zones for user-facing applications. Full article

Deep reinforcement learning (DRL) has shown great potential in path planning tasks. However, in sparse reward environments, DRL still faces significant challenges such as low training efficiency and a tendency to converge to suboptimal policies. Traditional reward shaping methods can partially alleviate these issues, but they typically rely on hand-crafted designs, which often introduce complex reward coupling, make hyperparameter tuning difficult, and limit generalization capability. To address these challenges, this paper proposes Curriculum-guided Learning with Adaptive Reward Shaping for Deep Q-Network (CLARS-DQN), a path planning algorithm that integrates Adaptive Reward Shaping (ARS) and Curriculum Learning (CL). The algorithm consists of two key components: (1) ARS-DQN, which augments the DQN framework with a learnable intrinsic reward function to reduce reward sparsity and dependence on expert knowledge; and (2) a curriculum strategy that guides policy optimization through a staged training process, progressing from simple to complex tasks to enhance generalization. Training also incorporates Prioritized Experience Replay (PER) to improve sample efficiency and training stability. CLARS-DQN outperforms baseline methods in task success rate, path quality, training efficiency, and hyperparameter robustness. In unseen environments, the method improves task success rate and average path length by 12% and 26%, respectively, demonstrating strong generalization. Ablation studies confirm the critical contribution of each module. Full article

New-generation communications aim for ubiquitous and pervasive communications with high data rates. Electromagnetic spectrum saturation and increasing data volumes can employ the use of free-space optical communication to ease capacity loads in modern networks. In this writing, we review the impact of the atmospheric channel on the optical signal dynamics for long-range data links between high-speed and maneuverability suborbital platforms in full atmosphere. This work presents the main propagation constraints, such as path loss, turbulence, and aero-optics, which are environment-dependent and geometry-dependent for this worst-case scenario. To carry out our study, we recall experimental results collected in the literature since the early times, showing system constraints and performance limits. This provides a historical timeline perspective. Theoretical models and channel management techniques that appeared through time are briefly summarized, and their impact on link budget and stability on reference link geometries is addressed through analytical simulation. In conclusion, this paper shows that an integrated approach to this kind of link is successful mainly with a convergence of mitigation techniques and tailored engineering, which cannot neglect the knowledge of the operating environment and strongly relies on accurate physics modeling, which remains an area of active open research. Full article

Container-based cloud platforms enable flexible and lightweight application deployment, yet container scheduling remains challenged by resource fragmentation, load imbalance, excessive energy consumption, and service-level agreement (SLA) violations. To address these issues, this paper proposes a hybrid multi-objective optimization approach, termed HHO-GWO, which combines Harris Hawks Optimization (HHO) with the Grey Wolf Optimizer (GWO) for container initial placement in cloud environments. A unified fitness function is designed to jointly consider resource utilization, load balancing, resource fragmentation, energy consumption, and SLA violation rate. In addition, a dynamic weight adjustment mechanism and Lévy flight perturbation are incorporated to improve search adaptability and prevent premature convergence. The proposed method is evaluated through extensive simulations under different workload scales and compared with several representative metaheuristic algorithms. The results show that HHO-GWO achieves improved convergence behavior, solution quality, and stability, particularly in large-scale container deployment scenarios. These findings suggest that the proposed approach provides a practical and energy-aware solution for multi-objective container scheduling in cloud data centers. Full article

Lymph node evaluation is a central determinant of oncologic quality in the surgical management of non-small-cell lung cancer (NSCLC). Accurate assessment of hilar and mediastinal lymph nodes underpins pathologic staging, informs postoperative treatment decisions, and remains essential for prognostic stratification and assessment of resection completeness. Although international guidelines provide clear recommendations, real-world data consistently demonstrate substantial variability in lymph node staging practices, with inadequate evaluation frequently observed across institutions and surgical settings. Insufficient nodal assessment, manifested as the omission of mediastinal staging, limited station sampling, or low lymph node yield, is associated with reduced nodal upstaging, inappropriate omission of adjuvant therapy, higher recurrence rates, and inferior long-term survival. Contemporary guidance from major societies, including the National Comprehensive Cancer Network, European Society of Thoracic Surgeons, International Association for the Study of Lung Cancer, and the Commission on Cancer, has increasingly converged on a station-based definition of adequacy, emphasizing systematic evaluation of both N1 and N2 nodal stations rather than reliance on absolute node counts alone. In parallel, preoperative mediastinal staging algorithms have evolved toward routine use of endobronchial and esophageal ultrasound as first-line invasive modalities, reserving surgical mediastinoscopy for selected high-risk or inconclusive cases. Evidence from randomized trials, population-level databases, and meta-analyses indicates that thorough nodal assessment improves staging accuracy and survival, while recent data support the selective use of lobe-specific or tailored lymphadenectomy in carefully staged, low-risk early disease. Finally, emerging quality improvement interventions, including standardized specimen handling, operative checklists, and multidisciplinary feedback mechanisms, have demonstrated measurable improvements in guideline adherence and patient outcomes. This narrative review integrates contemporary evidence and guideline recommendations to outline a practical framework for implementing reliable, high-quality lymph node staging in modern lung cancer surgery. Full article

This study proposes a reinforcement learning (RL)-based optimization framework for the environmental control system of battery rooms in Energy Storage Systems (ESS). Conventional rule-based air-conditioning strategies are unable to adapt to real-time temperature and humidity fluctuations, often leading to excessive energy consumption or insufficient thermal protection. To overcome these limitations, both value-based (DQN, Double DQN, Dueling DQN) and policy-based (Policy Gradient, PPO, TRPO) RL algorithms are implemented and systematically compared. The algorithms are trained and evaluated using one year of real ESS operational data and corresponding meteorological data sampled at 15-min intervals. Performance is assessed in terms of convergence speed, learning stability, and cooling-energy consumption. The experimental results show that the DQN algorithm reduces time-averaged cooling power consumption by 46.5% compared to conventional rule-based control, while maintaining temperature, humidity, and dew-point constraint violation rates below 1% throughout the testing period. Among the policy-based methods, the Policy Gradient algorithm demonstrates competitive energy-saving performance but requires longer training time and exhibits higher reward variance. These findings confirm that RL-based control can effectively adapt to dynamic environmental conditions, thereby improving both energy efficiency and operational safety in ESS battery rooms. The proposed framework offers a practical and scalable solution for intelligent thermal management in ESS facilities. Full article

Green cities can contribute to greater mental and physical well-being. In addition, many people enjoy being active outdoors (green exercise). As yet, no questionnaire jointly emphasises mental health, willingness to pay for sustainability, and the incentive of a green environment for physical exercise in cities. Therefore, I developed the new Green Cities Questionnaire (GCQ), comprising 18 items, and used it to survey the perceptions of 249 participants (130 female, 119 male, 0 diverse; aged 18 to 84). Then, I applied exploratory factor analyses where the three factors of mental health (MH; nine items), willingness-to-pay (WTP; five items), and green exercise (GE; four items) were extracted. Additional statistical analyses revealed that women reported higher values on the MH and GE factors than men. In particular, women and men reported a beneficial effect of green cities on mental health (higher ratings on MH than on GE and on WTP). However, there was no gender effect on WTP. From an urban-planning perspective, the two strongest implications are as follows: First, the GCQ facilitates measurement of the three key latent factors: MH, WTP, and GE. However, future validation studies with larger sample sizes and applications of the GCQ alongside additional similar and different recognised scales are necessary to establish convergent and discriminant validity. Second, mental health is reported to be much more important than WTP and GE. Hence, green initiatives, educational programs, and green city workshops should not only focus on expanding urban green spaces but also on providing appropriate relaxation areas to promote and foster psychological well-being and quality of life in green cities. Full article