Search Results (375)

Municipal solid waste management (MSWM) systems in Latin America are constrained by limited access to high-resolution operational data, compelling local authorities to depend on aggregated national statistics that are inadequate for behaviorally informed intervention design. This limitation is particularly evident in the State of Mexico, which generates about 16,187 tons of waste every day but only recycles only 11%. In this context, this study introduces a diagnostic data science framework to identify behaviorally grounded citizen segments and their defining attributes, supporting evidence-based decision-making in MSWM. Primary survey data from 560 households across three municipalities were used, and a three-stage analytical pipeline was implemented to account for contextual heterogeneity. First, k-means clustering was applied to identify behavioral segments. Second, random forest classifiers were used to validate cluster coherence and quantify feature importance. Third, the Apriori algorithm was used to extract association rules that capture recurrent material-mixing behaviors. The results revealed municipality-specific segmentation structures (Tequixquiac: K = 6; Tlalpujahua: K = 3; Xalatlaco: K = 2), with material-specific disposal behaviors emerging as stronger segmentation drivers. Random forest classifiers validated cluster coherence with 100% accuracy, confirming that segments represent behaviorally distinct archetypes. The proposed framework converts raw behavioral data into actionable municipal visions. This approach focuses on finding diagnostic patterns instead of making predictions by utilizing machine-learning-driven MSWM research. Full article

Frequent accidents in the construction sector arise from the dynamic coupling of multiple risk factors, while conventional single-factor approaches fail to capture the underlying complexity. Drawing on 702 accident investigation reports, this study develops an intelligent, data-driven framework that integrates large language model–based risk identification with association rule mining to systematically uncover risk factors and their coupling patterns. A DeepSeek-based model is employed to extract risk factors from unstructured text, followed by cosine similarity–based optimization to refine factor representations. The FP-Growth algorithm is then applied to identify strong association rules among risk factors. The results reveal that deficiencies in the management dimension account for 68.30% of all identified risks, with inadequate safety education and training emerging as the central hub in the risk coupling network, which is further corroborated by complex network analysis. Moreover, a cascading transmission pathway is identified, whereby environmental deficiencies induce weakened safety awareness, which in turn leads to unsafe behaviors. These findings further demonstrate the nonlinear amplification effects arising from concurrent management failures. By establishing a transformation pathway from unstructured textual data to structured risk knowledge, this study provides a robust, data-driven foundation for precise risk identification and systematic prevention in construction safety management. Full article

To identify the key causative factors and their characteristics across different types of chemical accidents, text mining techniques were first applied to extract causative factors from accident investigation reports. The extracted factors were then classified according to an improved Human–Machine–Environment–Management (HMEM) framework, which incorporates an additional government influence layer. To address data imbalance, a random undersampling method was employed. Specifically, sampling was repeated 30 times using different random seeds, and association rule mining was conducted for each sampled dataset. On this basis, a hybrid analytical framework integrating the Apriori algorithm and complex network theory was developed to examine the topological characteristics of the causation network. The results indicate that the network exhibits both small-world and scale-free properties, with strong interconnections among causative factors and a limited number of key nodes playing important bridging roles. PageRank centrality analysis further reveals that nodes associated with all accident types are located in the core region of the network, although differences exist in the associated causative factors across different accident types. In addition, the comprehensive importance analysis indicates that D6 (illegal production organization), B5 (pipeline rupture or blockage), and D12 (unsafe work practices) are the top three most important causative factors. These findings provide a theoretical foundation and practical insights for chemical accident prevention and the improvement of safety management. Full article

To address the lack of traversable region awareness in conventional path planning algorithms for obstacle-crossing robots, an adaptive path planning method is proposed. First, a traversal-aware environment model is constructed by introducing graded traversable regions with associated physical traversal costs. To effectively navigate this complex model, a hybrid Ant Colony Optimization (ACO) framework integrating Jump Point Search (JPS) and the Genetic Algorithm (GA) is developed. Specifically, a JPS-inspired pruning strategy is incorporated into the state transition process to significantly reduce redundant node expansion. Crucially, genetic operators—namely crossover and mutation—are embedded within the main ACO iterative loop to dynamically sustain population diversity and effectively mitigate stagnation in local optima. Correspondingly, the pheromone initialization, state transition mechanisms, and update rules are redesigned to incorporate the robot’s obstacle traversal capabilities. The framework is further complemented by path optimization operations that reduce unnecessary turning points. Extensive simulation experiments demonstrate that the proposed method outperforms conventional ACO-based and classical path planning algorithms. In particular, it achieves an average reduction of 11.1% in path length and 65.5% in the number of waypoints, while ensuring effective coordination with the robot’s physical traversal capabilities. These results validate the superior search efficiency, robustness, and practical applicability of the proposed approach. Full article

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

Hospitals in earthquake-prone regions must evacuate heterogeneous occupants rapidly while preserving operational continuity under disrupted conditions. However, many hospital-evacuation studies still rely on static routing assumptions or narrowly defined behavioral rules, which limits their value for building-level resilience planning. This paper develops a comparative hospital-campus evacuation framework that combines GIS-based geodesic routing, heterogeneous agent-based modeling, and reinforcement-learning-based decision policies. Puge County People’s Hospital in Sichuan, China, is used as the case study. Six algorithms are evaluated: three rule-based baselines—Shortest Path (SP), Random Walk (RW), and the Social Force Model (SFM)—together with a training-free density-aware heuristic, Density-Aware Gradient Routing (DAGR), and two reinforcement-learning approaches, Density-Aware Q-Learning (DAQL) and SARSA. Experiments cover three population scales ($N\in \{50,100,200\}$), normal daytime conditions, staffing-variation scenarios, and a blocked-exit disruption scenario, with 30 independent runs for each main condition. The results show that the rule-based and training-free methods remain the most reliable under full multi-agent evaluation: the SFM and RW achieve the highest completion ratios (approximately 100% and 93.5%, respectively), while DAGR provides the strongest balance between completion and evacuation efficiency among the non-trained methods. In contrast, the trained RL agents perform substantially worse in direct multi-agent deployment with DAQL reaching approximately 37% completion and SARSA approximately 17%, highlighting a train–evaluation distribution shift associated with independent Q-learning. The ablation analysis further shows that collision avoidance is the most critical reward component, whereas density-avoidance shaping can unintentionally induce collective deadlock when all agents execute the learned policy simultaneously. Among the enhanced variants, DAQL_RoleAware yields the best overall improvement, increasing the completion ratio to approximately 52% and reducing the 90th-percentile evacuation time to approximately 363 s. Overall, this paper clarifies both the promise and the present limitations of density-aware reinforcement learning for hospital evacuation while providing a more building-centred and reproducible basis for future coordination-aware evacuation design and emergency-planning research. Full article

We propose a Dual-Channel assisted Music Emotion Perception and Visualization (DC-MEPV) framework designed for ambient lighting in intelligent vehicle cockpits, addressing the increasing demand for advanced human–machine interaction in the automotive industry. This framework consists of three main components: the Multi-Scale Feature Extraction Block (MSFEB), the Global Sequence Modeling Block (GSMB), and the Emotional Color Visualization Algorithm (ECV-Algo). The MSFEB extracts valence and arousal (V-A) features from dual channels at multiple temporal scales, with each channel employing a hybrid neural network architecture to capture multi-scale emotional representations. The GSMB integrates positional encoding, bidirectional long short-term memory (BiLSTM) networks, and multi-head self-attention mechanisms to dynamically model global emotional sequences. The ECV algorithm utilizes personalized emotion–color association rules to achieve expressive emotion-driven lighting visualization based on a continuous mapping from emotion space to color space. We conducted comprehensive comparison and ablation experiments to evaluate the model’s emotion perception performance, and designed three metrics to evaluate the quality of the generated visualizations. The model outperformed other networks in both comparative and ablation experiments. Additionally, the generated lights demonstrated strong performance in terms of CIEDE2000 variation rates, unique color ratios, and joint histogram entropy. DC-MEPV achieved excellent performance in emotion perception and visualizations on the DEAM and PMEmo datasets. Full article

This manuscript is a continuation of the research published in Fire 2025, 8(8), 302, i.e., it deals with the examination of the cause-and-effect relationships of fires in the Republic of Serbia from the aspect of human safety. Among others, variables related to gender, age, and severity of injuries caused by fires are introduced, on which various methods of statistical analysis and stochastic modeling are first applied. Continuous age variables are modelled using the flexible Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework, where the Generalized Normal Distribution (GND) is identified as the optimal generative model for injuries, while a Reflected Log-Normal Distribution with positive support (RefLOGND+) provides the best fit for fatalities. The quality of such modeling is formally verified, and the probabilities of injury and death of individuals in certain age categories are predicted, revealing a pronounced concentration of injuries in the working-age population and a markedly higher relative risk of fatal outcomes among elderly individuals. Thereafter, by applying certain Data Mining (DM) techniques, primarily the Apriori algorithm, the most frequently occurring association rules are found, which indicate typical patterns and demographic structure of injuries and deaths in fires in Serbia. Finally, using the CART (Classification and Regression Trees) algorithm, several decision trees are formed that describe the impact and relationship of different causes of fires on injury and death in fires. In this way, some important and frequent patterns are observed that indicate key fire risk factors that significantly affect the demographic structure of human casualties. The results thus obtained provide a basis for developing targeted strategies for fire prevention and improving emergency response planning. Full article

Web application firewalls (WAFs) are critical defenses against persistent threats to web applications, yet their security evaluation remains challenging. Traditional manual testing methods are often inefficient and resource-intensive, while existing reinforcement learning (RL)-based automated approaches face two key limitations: (1) attackers cannot perceive opaque WAF rule logic; (2) boolean feedback from WAFs results in sparse/delayed rewards—sparse rewards trap agents in blind exploration, and delayed rewards hinder the association between early actions and final outcomes, adversely affecting learning efficiency. To address those challenges, we propose Ouroboros—a framework integrating genetic algorithm-based symbolic rule reconstruction (translating WAF rules into interpretable RNNs for fine-grained confidence scoring), timing side-channel analysis (evaluating rule-matching depth), and a multi-tiered reward mechanism to enable self-evolving RL testing. Experiments show that the framework reaches 89.2% bypass success rate on signature-based WAFs. This paper presents an efficient solution for automated WAF testing and delivers insights for optimizing rule logic and anomaly detection mechanisms. Full article

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

Sport tourism has experienced significant growth within the tourism industry, driven by the increasing demand of special interest tourists to watch or participate in sports events with local sightseeing. However, the massive volume of available information related to sport events may cause challenges to existing recommendation systems, which struggle to provide tailored suggestions for these niche tourists. Therefore, this paper proposes a novel, context-aware recommender framework that utilises the ontology-driven approach with unsupervised machine learning techniques to deliver personalised event matches for running tourists. Using an ontology-driven approach, the framework establishes a knowledge base of user profiles and running events. Furthermore, K-modes clustering was also applied to categorise participants based on their event participation characteristics, while the Apriori algorithm was used to uncover hidden relationships influencing event selection. To ensure the statistical integrity of the discovered association rule, permutation testing was implemented to mitigate bias inherent in small sample sizes. By integrating refined association rules with Jena rules, the resulting prototype offers adaptive, personalised, and contextually relevant running event recommendations that evolve with shifting user preferences and trends. The effectiveness of the prototype is confirmed through rigorous validation and evaluation across various sport tourism scenarios. Full article

As a key metering device in the smart grid, the clock battery status of smart meters directly affects the operational efficiency and economy of the grid. In response to the limitations of current evaluation methods in feature correlation analysis and model interpretability, this study proposes a knowledge-and-data-driven low battery voltage status prediction method. We systematically dissected the physical mechanisms underlying battery undervoltage faults and constructed a status features knowledge graph comprising 17 state features across four dimensions. By employing Pearson correlation analysis and association rule mining techniques, we achieved a quantitative correlation analysis between multi-source heterogeneous features and battery status. Building on this foundation, we developed an interpretable model framework based on XGBoost-SHAP. Empirical studies utilized a dataset of 939,000 faulty meters recalled by a provincial power company in 2023, with 9.87% of outlier samples eliminated using the Isolation Forest algorithm during preprocessing. Results demonstrate that the proposed model achieved an R² of 0.851 and a Mean Squared Error (MSE) of 0.0088 on the test set. The prediction performance significantly surpassed that of Random Forest (R² = 0.692) and MLP+BP neural networks (R² = 0.583), thereby validating the effectiveness of the approach in combining predictive accuracy with decision transparency. Full article

Knowledge discovery constitutes a vital component in building intelligent CAPP systems, and the effective discovery of process knowledge has become a prominent research focus within intelligent manufacturing. Process decision knowledge is a type of knowledge that reflects the relations between process data items, represented in the form of production rules. However, PDK discovery faces low accuracy challenges from complex high-dimensional manufacturing data and implicit experience-dependent process decisions. This paper proposed a PDK mining framework that combines knowledge constraint and the water wave optimization algorithm. This approach formulated prior knowledge mathematically using an association discriminant matrix and embedded this representation into the knowledge mining model, thus equipping the algorithmic framework with the ability to discover PDK accurately. The WWO is utilized to search within the sample space for combinations of process data items that constitute valid knowledge. In contrast to traditional association rule mining algorithms that lack accuracy and template-based methods that are inherently rigid, the proposed approach provides a robust solution by achieving over 90% correctness in PDK mining. It also serves as a demonstration and offers insights for mining similar rule-based knowledge in other fields. Full article

Approximately one quarter of the European Union’s (EU’s) CO₂ emissions originate from the transport sector, of which road transport, such as cars and heavy-duty vehicles, contributes roughly 72%. Moreover, according to the European Automobile Manufacturers’ Association, 92% of cars in the EU are internal combustion engine vehicles powered by fossil fuels. Therefore, boosting the adoption of Electric Vehicles (EVs) is considered one of the most prominent solutions for reducing GHG emissions and achieving the EU’s climate targets. To increase EV adoption and fulfil the demand of EV users, adequate EV Charging Stations (EVCSs) are required. Nevertheless, since most EVCSs are supplied by electricity grids that remain predominantly fossil fuel-based, their operation entails substantial indirect GHG emissions. A prominent approach to reducing grid-related emissions is integrating renewable energy sources (RESs) with EVCSs, thereby lowering emissions and alleviating grid stress. Although promising, the energy, economic, and environmental (3E) benefits of this integration remain insufficiently explored. Therefore, this study develops and applies a 3E optimisation framework to assess the feasibility and performance of RES-powered EVCS at NOVA University Lisbon (UNL). Data was collected from the UNL parking area, such as time of arrival, and time of departure. Also, a rule-based algorithm was developed to curate data and estimate the EVCS load profile. Furthermore, HOMER optimisation software was employed to evaluate four scenarios, including (i) an EVCS based on PV, Wind Turbine (WT), and the grid, (ii) an EVCS based on PV and the grid, (iii) an EVCS based on WT and the grid, and (iv) an EVCS based only on energy withdrawal from the grid (base scenario). Under the adopted techno-economic assumptions, in the most optimised scenario, economic and environmental analyses illustrate significant improvements over the base scenario: CO₂ emissions are five times lower, and cost of energy is significantly lower, resulting in significantly lower EV charging costs for users. The results demonstrate that, through developed feasibility studies, researchers, decision-makers, and stakeholders can reach better conclusions about EVCS planning and management. Full article

This study aimed to identify structural relationships between sports participation patterns and injury risk factors among older adults using data mining techniques, addressing limitations of prior descriptive research. Data from the 2024 Sports Safety Accident Survey were analyzed, including 352 adults aged 65 years and older. Eight key variables related to participation and injury were examined using association rule analysis with the Apriori algorithm. High-risk injury patterns were associated with irregular participation in non-sport-specific locations during late-night hours and with excessive movement during prolonged, daily participation, particularly in the morning. Injuries most frequently affected major joints of the upper and lower limbs. Sports injuries in older adults arise from complex interactions among temporal, environmental, and behavioral factors. These findings support the development of targeted safety guidelines and injury prevention strategies tailored to participation patterns in the aging population. Full article