Hanshen Li

Automatically extracted chemical records can appear complete even when names, depictions, formulas, masses, and spectra disagree. Because databases consume records rather than evidence, such errors propagate silently. TRACE-Chem (Typed Relational Attestation with Counterfactual Editing for Chemistry) is an inference-time framework for verifying and repairing them. It organizes source-linked observations, candidate fields, and verifier outcomes in a Symmetric–Asymmetric Evidence Graph (SAEG) separating symmetric identity checks from directional scientific derivations. Counterfactual Localization and Dependency-Constrained Re-decoding (CLDR) masks candidate fault nodes to identify the view whose removal most restores coherence, then revises only dependency-affected fields. Executable checks and spectral compatibility feed a calibrated accept, repair, or abstain decision. On 124 open-access synthesis papers containing 1852 compound and 638 reaction records, TRACE-Chem achieved 84.3% canonical-record hard-match F1, 9.7 percentage points above the same extractor without verification. Against a single-pass multimodal baseline, invalid structures fell from 10.8% to 0.8%, unsupported fields from 14.3% to 2.4%, and expected calibration error from 0.281 to 0.052. Removing derivation direction lowered fault-localization accuracy from 91.2% to 82.8%; removing dependency discounting raised the unsafe-edit rate from 2.4% to 4.1%. Explicitly modeling relational symmetry and asymmetry therefore improves verifiability, calibration, and repair safety in multimodal chemical extraction.

This work presents a new Battery Thermal Management System (BTMS) concept utilizing a metal foam manifold cold plate (MFMCP), developed specifically to meet the rising thermal dissipation needs of lithium-ion batteries. By coupling a manifold flow design and high-conductivity metal foams (characterized via SEM), the system significantly enhanced heat transfer and temperature uniformity. A Local Thermal Equilibrium (LTE) model evaluated thermal–hydraulic performance across varying Reynolds numbers (40–200), foam structures (PPI and porosity), and Al₂O₃ nanofluid concentrations. Results indicated that an optimal foam structure (95 PPI, porosity of 0.905) yielded a significant surface temperature reduction of 15 K. Although the Al₂O₃ nanofluid provided an additional cooling effect of 0.3 K, its pressure penalty lowered the performance evaluation criterion (PEC); the highest initial PEC of 2.57 was established when employing the pure base fluid without any functional additives. A Multi-Layer Perceptron (MLP) model was developed to expedite design validation, achieving high predictive accuracy as evidenced by an R² of 0.983 and an MRE of 1.8%. Subsequent optimization via a genetic algorithm (GA) further increased the maximum PEC by 12% to 2.88. Equal pumping power system simulations indicated that the MFMCP design achieved a peak cell temperature of 304.35 K, outperforming traditional designs which peaked at 308.33 K. Furthermore, transient tests at 1C to 2C discharge rates confirmed consistent cooling improvements, demonstrating the MFMCP’s promising potential for dynamic operational conditions.

Small plateau lake basins are sensitive social–ecological systems in which ecological protection, rural development, and institutional governance are closely intertwined. Therefore, fine-scale resilience assessment is needed to support adaptive basin governance. However, integrated township-scale assessments of social–ecological system resilience in such basins remain limited. Taking 13 township-level units in the Dali City portion of the Erhai Lake Basin as a case study, this paper integrates social–ecological system theory with the pressure–state–response framework to construct a 30-indicator resilience evaluation system. It measures social resilience, ecological resilience, and comprehensive social–ecological resilience at four time points from 2010 to 2025, and examines their temporal trends, spatial differentiation, and governance implications. The results show that average social resilience rose from 0.509 to 0.682, ecological resilience from 0.503 to 0.658, and comprehensive resilience from 0.506 to 0.668. Linear mixed-effects modelling confirmed a significant upward trend in comprehensive resilience, with an average increase of 0.053 per five-year interval, while Global Moran’s I indicated weak but consistently positive spatial clustering. However, township-level heterogeneity persisted in improvement magnitude and resilience sources. Neither ecological advantages nor social development alone can sustainably enhance comprehensive resilience; stronger coordination between the two is required. Based on an “ecological protection × social development” matrix, the townships are classified into four types: dual vulnerability, economic priority, ecological priority, and coordinated development. This typology provides a basis for differentiated adaptive governance in ecologically fragile basins.

Understanding how carbon emission responsibilities evolve within interconnected electricity systems is essential for effective environmental governance and sustainable energy transitions. This study develops a carbon-extended multi-dynamic interregional input-output shift-share framework to examine how structural dynamics reshape carbon emission responsibilities in China’s power sector. Using provincial multi-regional input-output data for 31 provinces in 2012, 2015, and 2017, the framework integrates production-based and consumption-based accounting into a unified multi-level analytical structure. The results reveal four key findings. First, production-based emissions are primarily concentrated in central and western power-generation provinces, whereas consumption-based emissions cluster in eastern and central demand centers, reflecting a persistent spatial mismatch between electricity production and consumption. Second, under production-based accounting, the power sector shifts from having a lower emission growth rate than the provincial average to exceeding it, while consumption-based emissions consistently grow more slowly than the provincial average. Third, the national level increasingly dominates emission growth transmission in both accounting perspectives, with stronger influence on the production side and greater provincial heterogeneity on the consumption side. Fourth, structural upgrading becomes increasingly concentrated at the provincial level under both perspectives. These findings highlight the importance of multi-level structural dynamics in shaping carbon responsibility allocation and provide policy-relevant insights for coordinated decarbonization, sustainable electricity transition, and cross-regional carbon governance. This study contributes to the understanding of sustainable development pathways in carbon-intensive energy systems and offers practical implications for achieving low-carbon and sustainable power sector transformation.

Proximal Policy Optimization (PPO) is widely adopted for robotic continuous control, yet it can suffer from insufficient exploration and unstable policy updates in high-dimensional action spaces. This paper proposes Adaptive Exploration Proximal Policy Optimization (AE-PPO), an enhanced PPO framework that integrates (i) adaptive clipping, which adjusts the clipping range according to the observed magnitude of policy updates to better balance stability and learning progress, (ii) adaptive entropy regularization, which schedules the entropy weight across training to maintain effective exploration while avoiding excessive randomness. AE-PPO is evaluated on standard MuJoCo continuous control benchmarks (e.g., Walker2d, HalfCheetah, and Humanoid) and compared with PPO and representative baselines such as Trust Region Policy Optimization (TRPO) and Soft Actor Critic (SAC). The results show that AE-PPO achieves faster convergence and an improved final performance with reduced training variance, demonstrating more stable and efficient learning in challenging high-dimensional tasks.

Long-term multivariate time series forecasting serves as a fundamental analytical tool across diverse domains, such as energy management, transportation analysis, and meteorology. However, conventional modeling paradigms often yield suboptimal results as they fail to adequately capture non-stationarity and multi-scale temporal correlations. While frequency-domain methods offer theoretical clarity, representative efficient spectral-domain architectures often rely on magnitude-based spectral pruning to ensure efficiency, inadvertently discarding high-frequency transient signals essential for non-stationary forecasting. To address these limitations, we propose the Structural Component-based Temporal Wavelet-Refine Network (SC-TWRNet), a framework that orchestrates adaptive wavelet filtering with explicit structural temporal decomposition. The architecture is anchored by the Adaptive Multi-Resolution Wavelet (AMRW) filter, designed to generate time-frequency representations while maintaining linear computational complexity. Concurrently, a structural temporal decomposition module decouples the input stream into distinct trend, seasonal, and residual components for targeted modeling. Extensive experiments on eight standard datasets demonstrate that SC-TWRNet achieves superior predictive accuracy compared to state-of-the-art baselines while maintaining linear computational complexity for efficient high-dimensional modeling.

Amid the exponential growth of streaming data and rising demands for real-time decision-making, Online Convex Optimization (OCO) has emerged as a foundational framework for sequential data processing in dynamic environments. This survey presents a systematic review of recent evolutionary and adaptive OCO strategies, offering a detailed taxonomy that classifies algorithms according to their constraint-handling mechanisms and environmental feedback. The analysis first examines Constrained OCO, elucidating the trade-offs between computational efficiency and theoretical guarantees across projection-based methods, projection-free Frank–Wolfe variants, and general convex optimization approaches. It then explores the Unconstrained OCO landscape, emphasizing the shift from parameter-dependent methods to fully adaptive, parameter-free algorithms capable of handling unknown comparator norms and gradient scales. Furthermore, the study synthesizes state-of-the-art applications in power systems, network communication, and quantitative finance, bridging theoretical OCO models with robust engineering solutions. The paper concludes by outlining critical open challenges and future research directions, such as the integration of OCO with deep learning, non-convex optimization, and robustness against adversarial corruptions in data-intensive scenarios.

With the continuous growth in the scale of engineering simulation and intelligent manufacturing workflows, more and more problem-solving tasks are migrating to cloud computing platforms to obtain elastic computing power. However, a core operational challenge for cloud platforms lies in the difficulty of stably obtaining high-quality scheduling solutions that are both efficient and free of symmetric redundancy, due to the coupling of multiple constraints, partial resource interchangeability, inconsistent multi-objective evaluation scales, and heterogeneous resource fluctuations. To address this, this paper proposes a Dual-Improved Whale Optimization Algorithm (DI-WOA) accompanied by a modeling framework featuring discrete–continuous divide-and-conquer modeling, a unified monetization mechanism of the objective function, and separation of soft/hard constraints; its iterative trajectory follows an augmented Lagrangian dual-rollback mechanism, while being rooted in a three-layer “discrete gene–real-valued encoding–decoder” structure. Scalability experiments show that as the number of tasks J increases, the DI-WOA ranks optimal or sub-optimal at most scale points, indicating its effectiveness in reducing unified billing costs even under intensified task coupling and resource contention. Ablation experiment results demonstrate that the complete DI-WOA achieves final objective values (OBJ) 8.33%, 5.45%, and 13.31% lower than the baseline, the variant without dual update (w/o dual), and the variant without perturbation (w/o perturb), respectively, significantly enhancing convergence performance and final solution quality on this scheduling model. In robustness experiments, the DI-WOA exhibits the lowest or second-lowest OBJ and soft constraint violation, indicating higher controllability under perturbations. In multi-workload generalization experiments, the DI-WOA achieves the optimal or sub-optimal mean OBJ across all scenarios with H = 3/4, leading the sub-optimal algorithm by up to 13.85%, demonstrating good adaptability to workload variations. A comprehensive analysis of the experimental results reveals that the DI-WOA holds practical significance for stably solving high-quality scheduling problems that are efficient and free of symmetric redundancy in complex and diverse environments.

Intelligent fruit-picking robots have emerged as a promising solution to labor shortages and the increasing costs of manual harvesting. This review provides a systematic and critical overview of recent advances in three core domains: (i) vision-based fruit and peduncle detection, (ii) motion planning and obstacle-aware navigation, and (iii) robotic manipulation technologies for diverse fruit types. We summarize the evolution of deep learning-based perception models, highlighting improvements in occlusion robustness, 3D localization accuracy, and real-time performance. Various planning frameworks—from classical search algorithms to optimization-driven and swarm-intelligent methods—are compared in terms of efficiency and adaptability in unstructured orchard environments. Developments in multi-DOF manipulators, soft and adaptive grippers, and end-effector control strategies are also examined. Despite these advances, critical challenges remain, including heavy dependence on large annotated datasets; sensitivity to illumination and foliage occlusion; limited generalization across fruit varieties; and the difficulty of integrating perception, planning, and manipulation into reliable field-ready systems. Finally, this review outlines emerging research trends such as lightweight multimodal networks, deformable-object manipulation, embodied intelligence, and system-level optimization, offering a forward-looking perspective for autonomous harvesting technologies.

Driven by carbon neutrality policies, the cumulative issuance volume of the global green bond market has surpassed $2.5 trillion over the past five years, with China, as the second largest issuer, accounting for 15%. However, there exists a yield difference of up to 0.8% for bonds with the same credit rating across different policy regions, and the premium level fluctuates dramatically with market cycles, severely restricting the efficiency of green resource allocation. This study innovatively constructs a Bayesian hierarchical spatiotemporal model framework to systematically analyze pricing deviations through a three-level data structure: the base level quantifies the impact of bond micro-characteristics (third-party certification reduces financing costs by 0.15%), the temporal level captures market dynamics using autoregressive processes (premium volatility increases by 50% during economic recessions), and the spatial level reveals policy regional dependencies using conditional autoregressive models (carbon trading pilot provinces and cities form premium sinkholes). The core breakthroughs are: 1. Designing spatiotemporal interaction terms to explicitly model the policy diffusion process, with empirical evidence showing that the green finance reform pilot zone policy has a radiation radius of 200 km within three years, leading to a 0.10% increase in premiums in neighboring provinces; 2. Quantifying the posterior distribution of parameters using the Markov Chain Monte Carlo algorithm, demonstrating that the posterior mean of the policy effect in pilot provinces is −0.211%, with a half-life of 0.75 years, and the residual effect in non-pilot provinces is only −0.042%; 3. Establishing a hierarchical shrinkage prior mechanism, which reduces prediction error by 41% compared to traditional models in out-of-sample testing. Key findings include: the contribution of policy pilots is −0.192%, surpassing the effect of issuer credit ratings, and a 10 yuan/ton increase in carbon price can sustainably reduce premiums by 0.117%. In 2021, the “dual carbon” policy contributed 32% to premium changes through spatiotemporal interaction channels. The research results provide quantitative tools for issuers to optimize financing timing, investors to identify cross-regional arbitrage, and regulators to assess policy coordination, promoting the transformation of the green bond market from an efficiency priority to equitable allocation paradigm.