Search Results (288)

As a critical technological foundation for electric vehicles, power battery state estimation primarily involves estimating the State of Charge (SOC), the State of Health (SOH) and the Remaining Useful Life (RUL). This paper systematically categorizes battery state estimation methods into three distinct generations, tracing the evolutionary progression from single-state to multi-state cooperative estimation approaches. First-generation methods based on equivalent circuit models offer straightforward implementation but accumulate SOC-SOH estimation errors during battery aging, as they fail to account for the evolution of microscopic parameters such as solid electrolyte interphase film growth, lithium inventory loss, and electrode degradation. Second-generation data-driven approaches, which leverage big data and deep learning, can effectively model highly nonlinear relationships between measurements and battery states. However, they often suffer from poor physical interpretability and generalizability due to the “black-box” nature of deep learning. The emerging third-generation technology establishes transmission mechanisms from microscopic electrode interface parameters via electrochemical impedance spectroscopy to macroscopic SOC, SOH, and RUL states, forming a bidirectional closed-loop system integrating estimation, prediction, and optimization that demonstrates potential to enhance both full-operating-condition adaptability and estimation accuracy. This progress supports the development of high-reliability, long-lifetime electric vehicles. Full article

Our understanding of the molecular mechanisms by which DNA meiotic recombination occurs has significantly increased in the past decades. A more representative molecular model has also undergone repeated revisions and upgrades with the continuous expansion of experimental data. Considering several apparent issues in the field, we intend to make necessary upgrades to previous models and reanalyze those data, exploring structural details and molecular mechanisms of DNA meiotic recombination. Eligible studies were identified from PubMed/Medline (up to June 2024). Key related publications and experimental data were retrieved from eligible studies, displaying five major issues. Meanwhile, the biophysical modeling method was used to establish an enlacement model. Then, the model was used to wholly reanalyze the collected data. An updated molecular model was supplemented. In the current model, a copy choice mechanism can initiate DNA meiotic recombination. The copy choice is based on a branched structure of DNA, which results from relative motion between homologous single strands. The reanalysis of previous experimental data based on this model can lead to new interpretations that can better address the discrepancies between previous experimental observations and theoretical models, including (1) the intertwinement model having embodied the particular characteristics of the SDSA model; (2) hDNA arising from JM resolution rather than being followed by a JM; (3) strand specificity of hDNA mismatch repair seeming to be an illusion and copy choice more likely to be the actual state; (4) parity in resolution patterns of a dHJ leading to parity of gene conversion; (5) the cooperation of multiple HJs readily generating a high correlation between gene conversion and crossover; and (6) transpositional recombination and site-specific recombination seeming to have a common pathway to meiotic recombination. The results indicate that both revisions and reanalysis are necessary. The novel interpretations would be critical to the understanding of the mechanisms of DNA recombination as well as its role in DNA repair. Additionally, the work could have implications for how the field views the importance of factors such as Spo11 or the mechanisms that drive meiotic pairing. Full article

Integrating artificial intelligence (AI) with the Quantitative Structure-Activity Relationship (QSAR) has transformed modern drug discovery by empowering faster, more accurate, and scalable identification of therapeutic compounds. This review outlines the evolution from classical QSAR methods, such as multiple linear regression and partial least squares, to advanced machine learning and deep learning approaches, including graph neural networks and SMILES-based transformers. Molecular docking and molecular dynamics simulations are presented as cooperative tools that boost the mechanistic consideration and structural insight into the ligand-target interactions. Discussions on using PROTACs and targeted protein degradation, ADMET prediction, and public databases and cloud-based platforms to democratize access to computational modeling are well presented with priority. Challenges related to authentication, interpretability, regulatory standards, and ethical concerns are examined, along with emerging patterns in AI-driven drug development. This review is a guideline for using computational models and databases in explainable, data-rich and profound drug discovery pipelines. Full article

Multi-threshold image segmentation plays an irreplaceable role in extracting discriminative structural information from complex images. It is one of the core technologies for achieving accurate target detection and regional analysis, and its segmentation accuracy directly affects the analysis quality and decision reliability in key fields such as medical imaging, remote sensing interpretation, and industrial inspection. However, most existing image segmentation algorithms suffer from slow convergence speeds and low solution accuracy. Therefore, this paper proposes an Adaptive Cooperative Pelican Optimization Algorithm (ACPOA), an improved version of the Pelican Optimization Algorithm (POA), and applies it to global optimization and multilevel threshold image segmentation tasks. ACPOA integrates three innovative strategies: the elite pool mutation strategy guides the population toward high-quality regions by constructing an elite pool composed of the three individuals with the best fitness, effectively preventing the premature loss of population diversity; the adaptive cooperative mechanism enhances search efficiency in high-dimensional spaces by dynamically allocating subgroups and dimensions and performing specialized updates to achieve division of labor and global information sharing; and the hybrid boundary handling technique adopts a probabilistic hybrid approach to deal with boundary violations, balancing exploitation, exploration, and diversity while retaining more useful search information. Comparative experiments with eight advanced algorithms on the CEC2017 and CEC2022 benchmark test suites validate the superior optimization performance of ACPOA. Moreover, when applied to multilevel threshold image segmentation tasks, ACPOA demonstrates better accuracy, stability, and efficiency in solving practical problems, providing an effective solution for complex optimization challenges. Full article

The holobiont paradigm, conceptualizing host–microbiome assemblages as functionally integrated entities, has fundamentally altered interpretations of adaptive responses to environmental pressures spanning multiple organizational levels. This review synthesizes the current knowledge on microbiome-host coevolution, focusing on three key aspects. First, it examines the evolutionary origins of holobionts from primordial microbial consortia. Second, it considers the mechanistic basis of microbiome-mediated stress resilience in plants and animals. Finally, it explores the ecological implications of inter-holobiont interactions. We highlight how early microbial alliances (protomicrobiomes) laid the groundwork for eukaryotic complexity through metabolic cooperation, with modern holobionts retaining this plasticity to confront abiotic and biotic stressors. In plants, compartment-specific microbiomes (e.g., rhizosphere, phyllosphere) enhance drought tolerance or nutrient acquisition, while in animals, the gut microbiome modulates neuroendocrine and immune functions via multi-organ axes (gut–brain, gut–liver, etc.). Critically, we emphasize the role of microbial metabolites (e.g., short-chain fatty acids, VOCs) as universal signaling molecules that coordinate holobiont responses to environmental change. Emerging strategies, like microbiome engineering and probiotics, are discussed as tools to augment stress resilience in agriculture and medicine. By framing adaptation as a collective trait of the holobiont, this work bridges evolutionary biology, microbiology, and ecology to offer a unified perspective on stress biology. Full article

The aromaticity of small boron clusters remains under scrutiny due to persistent inconsistencies between magnetic and electronic descriptors. Here, we reexamine B₃⁻, B₃⁺, B₄, B₄²⁺, and B₄²⁻ using a multidimensional approach that integrates Adaptive Natural Density Partitioning, Electron Density of Delocalized Bonds, magnetically induced current density, and the z-component of the induced magnetic field. We introduce a model in which σ-aromaticity arises from two distinct delocalization topologies: a radial 2e⁻ σ-pathway and a tangential multicenter circuit formed by alternating filled and vacant sp² orbitals. This framework accounts for the evolution of aromaticity upon oxidation or reduction, preserving coherence between electronic structure and magnetic response. B₃⁻ features cooperative radial and tangential σ-delocalization, together with a delocalized 2e⁻ π-bond, yielding robust double aromaticity. B₃⁺ retains σ- and π-aromaticity, but only via a tangential 6e⁻ σ-framework, leading to a more compact delocalization and slightly attenuated ring currents. In B₄, the presence of a radial 2e⁻ σ-bond and a 4c–2e π-bond confers partial aromatic character, while the tangential 8e⁻ σ-framework satisfies the 4n rule and induces a paratropic current. In contrast, B₄²⁺ lacks the radial σ-component but retains a tangential 8e⁻ σ-circuit and a 2e⁻ 4c–2e π-bond, leading to a σ-antiaromatic and π-aromatic configuration. Finally, B₄²⁻, exhibits delocalized π- and σ-circuits, yielding consistent diatropic ring currents, which confirms its fully doubly aromatic nature. Altogether, this analysis underscores the importance of resolving σ-framework topology and demonstrates that, when radial and tangential contributions are correctly distinguished, Hückel’s rule remains a powerful tool for interpreting aromaticity in small boron rings. Full article

Land disputes pose a severe challenge for many developing countries worldwide. Understanding the driving factors of land disputes is crucial for social stability and sustainable development. China is one of the countries with the most severe situations of land disputes. This paper evaluates the land dispute intensity (LDI) across 30 provinces in China from 2011 to 2022. Using the GBDT model and interpretability methods, this study reexamines the importance of multidimensional variables in LDI, while also uncovering their nonlinear and interaction effects. The results show that LDI across 30 provinces generally and continuously increased after 2014, with this trend being notably curbed after 2019. In terms of the driving factors of LDI, the number of specialized farmers’ cooperatives plays the most critical role (mean |SHAP value| = 0.4). Variables such as share of primary industry, coverage of land transfer service centers, and agricultural product price index also exert a stronger influence on LDI. Clear nonlinear effects on LDI are observed for the agricultural product price index, the number of specialized farmers’ cooperatives, and the mediation rate of non-litigation disputes. In terms of interaction effects, when the mediation rate of non-litigation disputes is lower than 0.9, increases in the number of specialized farmers’ cooperatives and coverage of land transfer service centers tend to enhance their influence on raising LDI. When the ratio of cultivated land transfer is below 0.3, an increase in coverage of land transfer service centers is associated with a stronger effect in reducing LDI. Overall, this study uses the GBDT model, Shapley additive explanation (SHAP), and partial dependency plots (PDPs) to identify the main driving factors of land disputes. This paper can provide valuable references for developing countries and regions worldwide in addressing land disputes and conflicts. Full article

This paper proposes a collaborative framework to support task offloading in connected vehicular environments. The approach relies on the dynamic formation of temporary teams of connected vehicles in a mobile edge computing scenario. A novel trust model is introduced, which integrates both quality of service and quality of results into a unified reliability score, and combines this score with distributed reputation to build a comprehensive trust metric. This trust metric is then exploited to guide a decentralized team formation algorithm, ensuring lightweight, interpretable, and scalable decision-making processes. Simulation results demonstrate that the proposed framework improves task execution quality and fairness, especially for low-performing vehicles. These contributions highlight the novelty and strengths of our collaborative model, positioning it as a promising solution for enhancing cooperation in vehicular edge systems. Full article

Designing fair and sustainable pricing mechanisms for health insurance requires accurate risk assessment and the formulation of incentive-compatible strategies among stakeholders. This study proposes a hybrid framework that integrates machine learning with game theory to determine optimal, risk-based premium rates. Using a comprehensive dataset of insured individuals, the XGBoost algorithm is employed to predict medical claim costs and calculate corresponding premiums. To enhance transparency and explainability, SHAP analysis is conducted across four risk-based groups, revealing key drivers, including healthcare utilization and demographic features. The strategic interactions among the insurer, insured, and employer are modeled as a repeated game. Using the Folk Theorem, the conditions under which long-term cooperation becomes a sustainable Nash equilibrium are explored. The results demonstrate that XGBoost achieves high predictive accuracy (R² ≈ 0.787) along with strong performance in error measures (RMSE ≈ 1.64 × 10⁷ IRR, MAE ≈ 1.08 × 10⁶ IRR), while SHAP analysis offers interpretable insights into the most influential predictors. Game-theoretic analysis further reveals that under appropriate discount rates, stable cooperation between stakeholders is achievable. These findings support the development of equitable, transparent, and data-driven health insurance systems that effectively align the incentives of all stakeholders. Full article

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments. Full article

This study examines the evolution of the European Union’s regulatory framework addressing energy market manipulation, focusing on recent amendments introduced by Regulation (EU) 2024/1106 (REMIT II) to the original REMIT—Regulation on Market Integrity and Transparency (EU) No. 1227/2011. Employing logical interpretation and comparative legal analysis, the paper explores the rationale and challenges of developing a proportionate yet dissuasive sanctioning regime for acts of market manipulation. The study commences with a comprehensive overview of manipulative practices within energy markets and the legal thresholds they must meet to fall under REMIT. A critical evaluation of the role of the European Union Agency for the Cooperation of Energy Regulators (ACER) is conducted, with particular attention to its updated guidelines following the revision of the REMIT regulation. A particular emphasis is placed on the evidentiary standard that is required to establish manipulation, a matter of particular significance in the context of enforcement. The comparative section analyses REMIT and REMIT II, identifying significant legal innovations and the regulatory intent behind them. The study highlights the need for enhanced legislative harmonization among Member States and strengthened coordination among national regulators under ACER. It is noteworthy that Romania has proactively aligned its policies with those of REMIT II, a development that is presented as a case study and a call for more widespread implementation. This analysis contributes to the existing body of knowledge in academic discourse since this topic has not been widely covered in the literature, despite the heightened relevance of energy market regulation in the current European context. Full article

Connected Autonomous Vehicles (CAVs) technology holds immense potential for enhancing traffic safety and efficiency; however, its inherent complexity presents significant challenges for conventional autonomous driving. World Models (WMs), an advanced deep learning paradigm, offer an innovative approach to address these CAV challenges by learning environmental dynamics and precisely predicting future states. This survey systematically reviews the advancements of WMs in connected automated driving, delving into the key methodologies and technological breakthroughs across six core application domains: cooperative perception, prediction, decision-making, control, human–machine collaboration, and scene generation. Furthermore, this paper critically analyzes the current limitations of WMs in CAV scenarios, particularly concerning multi-source heterogeneous data fusion, physical law mapping, long-term temporal memory, and cross-scenario generalization capabilities. Building upon this analysis, we prospectively outline future research directions aimed at fostering the development of more robust, efficient, and interpretable WMs. Ultimately, this work aims to provide a crucial reference for constructing safe, efficient, and sustainable connected automated driving systems. Full article

The complex interdependencies among numerous safety risk factors influencing oil tanker loading/unloading operations constitute a focal point in academic research. To enhance safety management in oil port operations, this study conducts a risk analysis of oil tanker berthing and cargo transfer safety. Initially, safety risk factors are identified based on the Wu-li Shi-li Ren-li (WSR) systems methodology. Subsequently, a hybrid weighting approach integrating the Fuzzy Analytic Hierarchy Process (FAHP), G2 method, and modified CRITIC technique is employed to calculate indicator weights. These weights are then synthesised into a combined weight (GVW) using cooperative game theory and variable weight theory. Further, by integrating grey theory with the cloud model (GCM), a risk assessment is performed using Tianjin Port as a case study. Results indicate that the higher-risk indicators for Tianjin Port include vessel traffic density, safety of berthing/unberthing operations, safety of cargo transfer operations, safety of pipeline transfer operations, psychological resilience, proficiency of pilots and captains, and emergency management capability. The overall comprehensive risk evaluation value for Tianjin Port is 0.403, corresponding to a “Moderate Risk” level. Comparative experiments demonstrate that the results generated by this model align with those obtained through Fuzzy Comprehensive Evaluation Methods. However, the proposed GVW-GCM framework provides a more objective and accurate reflection of safety risks during tanker operations. Based on the computational outcomes, targeted recommendations for risk mitigation are presented. The integrated weighting model—incorporating game theory and variable weight concepts—coupled with the grey cloud methodology, establishes an interpretable and reusable analytical framework for the safety assessment of oil port operations under diverse port conditions. This approach provides critical decision support for constructing comprehensive management systems governing oil tanker loading/unloading operations. Full article

This conceptual position is based on the diagnosis that artificial intelligence (AI) accentuates existing economic and geopolitical divides in communities in the Global South, which provide data without receiving rewards. Based on bioethical precedents of fair distribution of genetic resources, it is proposed to transfer the principle of benefit-sharing to the emerging algorithmic governance in the context of AI. From this discussion, the study reveals an algorithmic concentration in the Global North. This dynamic generates political, cultural, and labor asymmetries. Regarding the methodological design, the research was qualitative, with an interpretive paradigm and an inductive method, applying documentary review and content analysis techniques. In addition, two theoretical and two analytical categories were used. As a result, six emerging categories were identified that serve as pillars of the studied principle and are capable of reversing the gaps: equity, accessibility, transparency, sustainability, participation, and cooperation. At the end of the research, it was confirmed that AI, without a solid ethical framework, concentrates benefits in dominant economies. Therefore, if this trend does not change, the Global South will become dependent, and its data will lack equitable returns. Therefore, benefit-sharing is proposed as a normative basis for fair, transparent, and participatory international governance. Full article

There is a long tradition of studying river sharing problems. A central question frequently examined and addressed is how common benefits or costs can be distributed fairly. In this context, axiomatic approaches of cooperative game theory often use contradictory principles of international water law, which are strictly rejected in practice. That leads to the question: Are these methods suitable for a real-world application? First, we conduct a systematic literature review based on the PRISMA approach to categorise the river sharing problems. We identified several articles describing a variety of methods and real-world applications, highlighting interdisciplinary interest. Second, we evaluate the identified axiomatic literature related to TU games with regard to their suitability for real-world applications. We exclude those “standalone” methods that exclusively follow extreme principles and/or do not describe cooperative behaviour. This is essential for a fair distribution. Third, we propose to use the traditional game-theoretical approach of airport games in the context of river protection measures to ensure a better economic interpretation and to enforce future cooperation in the joint implementation of protective measures. Full article