Search Results (281)

Flight delays pose a significant challenge to the modern aviation industry, with prediction difficulties arising from the need to accurately model spatio-temporal dependencies and uncertainties within complex air traffic networks. To address this challenge, this study proposes a novel hybrid predictive framework named DenseNet-LSTM-FBLS. The framework first employs a DenseNet-LSTM module for deep spatio-temporal feature extraction, where DenseNet captures the intricate spatial correlations between airports, and LSTM models the temporal evolution of delays and meteorological conditions. In a key innovation, the extracted features are fed into a Fuzzy Broad Learning System (FBLS)—marking the first application of this method in the field of flight delay prediction. The FBLS component effectively handles data uncertainty through its fuzzy logic, while its “broad” architecture offers greater computational efficiency compared to traditional deep networks. Validated on a large-scale dataset of 198,970 real-world European flights, the proposed model achieves a prediction accuracy of 92.71%, significantly outperforming various baseline models. The results demonstrate that the DenseNet-LSTM-FBLS framework provides a highly accurate and efficient solution for flight delay forecasting, highlighting the considerable potential of Fuzzy Broad Learning Systems for tackling complex real-world prediction tasks. Full article

Traffic congestion has increased significantly in today’s rapidly urbanizing world, influencing people’s daily lives. Traffic signal control systems (TSCSs) play an important role in alleviating congestion by optimizing traffic light timings and improving road efficiency. Yet traditional TSCSs neglected pedestrians, cyclists, and other non-monitored road users, degrading traffic signal optimization (TSO). Therefore, this framework proposes a multi-object-based traffic flow analysis and intensity estimation model for efficient TSO using Upper Confidence Bound Multi-agent Reinforcement Learning Cubic Spline Fuzzy Logic (UCB-MRL-CSFL). Initially, the real-time traffic videos undergo frame conversion and redundant frame removal, followed by preprocessing. Then, the lanes are detected; further, the objects are detected using Temporal Context You Only Look Once (TC-YOLO). Now, the object counting in each lane is carried out using the Cumulative Vehicle Motion Kalman Filter (CVMKF), followed by queue detection using Vehicle Density Mapping (VDM). Next, the traffic flow is analyzed by Feature Variant Optical Flow (FVOF), followed by traffic intensity estimation. Now, based on the siren flashlight colors, emergency vehicles are separated. Lastly, UCB-MRL-CSFL optimizes the Traffic Signals (TSs) based on the separated emergency vehicle, pedestrian information, and traffic intensity. Therefore, the proposed framework outperforms the other conventional methodologies for TSO by considering pedestrians, cyclists, and so on, with higher computational efficiency (94.45%). Full article

Construction projects still face persistent barriers to adopting whole life costing (WLC), such as fragmented data, a lack of standardization, and inadequate tools. This study addresses these limitations by proposing a core ontology for WLC, developed using an ontology design science research methodology. The ontology formalizes WLC knowledge based on ISO 15686-5 and incorporates professional insights from surveys and expert focus groups. Implemented in web ontology language (OWL), it models cost categories, temporal aspects, and discounting logic in a machine-interpretable format. The ontology’s interoperability and extensibility are validated through its integration with the building topology ontology (BOT). Results show that the ontology effectively supports cost breakdown, time-based projections, and calculation of discounted values, offering a reusable structure for different project contexts. Practical validation was conducted using SQWRL queries and Python scripts for cost computation. The solution enables structured data integration and can support decision-making throughout the building life cycle. This work lays the foundation for future semantic web applications such as knowledge graphs, bridging the current technological gap and facilitating more informed and collaborative use of WLC in construction. Full article

Escalating environmental challenges severely impede global sustainable development, prompting countries worldwide to innovate environmental governance approaches. As the world’s largest developing country, China’s paradigm shifts in environmental governance from “pollution control” to “ecological conservation” embody many inherent complexities. To investigate the evolution and underlying logic of such paradigm shifts, this study introduces a nested asynchrony framework. Employing a mixed-methods approach that integrates qualitative content analysis, Social Network Analysis, and machine learning, this study analyzes China’s environmental planning documents since the 11th Five-Year Plan to clarify the process of the paradigm shifts and their driving mechanisms. The principal conclusions derived from this study are as follows: (1) Environmental planning is uniquely valued as an analytical lens for identifying paradigm shifts in environmental governance. (2) The paradigm shifts in environmental governance are temporally distinct, wherein transformations in value norms precede structural reforms, while shifts in action logic and disciplinary foundations exhibit path-dependent inertia. (3) Inconsistencies within the planning authority framework spanning central and local governments impede the effective allocation and implementation of resources. This study reconstructs the transformation pathway of environmental governance paradigms, validates computational methods in policy analysis, and presents a longitudinal framework for tracking governance evolution. Applicable to other countries or sectors undergoing similar sustainable development transitions, the framework can provide broader utility. Full article

The study of dynamical processes on complex networks constitutes a foundational domain bridging applied mathematics, statistical physics, systems theory, and data science. Temporal evolution, not static topology, determines the controllability, stability, and inference limits of real-world systems, from epidemics and neural circuits to power grids and social media. However, the methodological landscape remains fragmented, with distinct communities advancing separate formalisms for spreading, control, inference, and design. This review presents a unifying six-pillar framework for the analysis of network dynamics: (i) spectral and structural foundations; (ii) deterministic mean-field reductions; (iii) control and observability theory; (iv) adaptive and temporal networks; (v) probabilistic inference and belief propagation; (vi) multilayer and interdependent systems. Within each pillar, we delineate conceptual motivations, canonical models, analytical methodologies, and open challenges. Our corpus, selected via a PRISMA-guided screening of 134 mathematically substantive works (1997–2024), is organized to emphasize internal logic and cross-pillar connectivity. By mapping the field onto a coherent methodological spine, this survey aims to equip theorists and practitioners with a transferable toolkit for interpreting, designing, and controlling dynamic behavior on networks. Full article

The challenges and increasing number of elderly individuals requiring remote monitoring at home highlight the need for technological innovations. This study devised an eHealth mobile application designed to detect abnormal movement behavior and alert caregivers when a lack of movement is detected for an abnormal period. By utilizing the built-in accelerometer of a conventional mobile phone, an application was developed to accurately record movement patterns and identify active and idle states. Fuzzy logic, an artificial intelligence (AI)-inspired paradigm particularly effective for real-time reasoning under uncertainty, was integrated to analyze activity data and generate timely alerts, ensuring rapid response in emergencies. The approach reduced development costs while leveraging the widespread familiarity with mobile phones, facilitating easy adoption. The approach involved collecting real-time accelerometry data, analyzing movement patterns using fuzzy logic-based inferencing, and implementing a rule-based decision system to classify user activity and detect inactivity. This pilot study primarily validated the devised fuzzy logic method and the functional prototype of the mobile application, demonstrating its potential to leverage universal smartphone accelerometers for accessible remote monitoring. Using fuzzy logic, temporal and behavioral symmetry in movement patterns were adapted to detect asymmetric anomalies, e.g., abnormal inactivity or falls. The study is particularly relevant considering lonely individuals found deceased in their homes long after dying. By providing real-time monitoring and proactive alerts, this eHealth solution offers a scalable, cost-effective approach to improving elderly care, enhancing safety, and reducing the risk of unnoticed deaths through fuzzy logic. Full article

Mobile scaffolding is essential in construction but presents significant safety risks, particularly falls from height (FFH) due to improper use and insufficient monitoring. While prior research has identified hazards, it often lacks robust, actionable solutions, especially regarding the comprehensive analysis of worker behaviors and the spatial context. This study proposed a computer vision-based safety monitoring system that leverages depth cameras for accurate spatial assessments and incorporates temporal conditions to reduce false alarms. The proposed system extends object detection algorithms with mathematical logic derived from safety rules to classify four key unsafe conditions related to safety helmet use, guardrail and outrigger presence, and worker overcrowding on mobile scaffolds. A diverse dataset from multiple sources enhances the model’s applicability to real-world scenarios, while a status trigger module verifies worker behavior over a 3 s window, minimizing detection errors. The experimental results demonstrate high precision (0.95), recall (0.97), F1-score (0.96), and accuracy (0.95) for safe behaviors, with similarly strong metrics for unsafe behaviors. The qualitative analysis further confirms substantial improvements in worker position detection and safety compliance using 3D data over 2D approaches. These findings highlight the effectiveness of the proposed system in improving mobile scaffolding safety, addressing critical research gaps, and advancing construction industry safety standards. Full article

This paper describes the modelling of Key Health Indicators (KHI) of frail individuals through non-invasive sensors located in their environment and wearable devices. Primary care professionals defined four indicators for daily health monitoring: sleep patterns, excretion control, physical mobility, and caregiver social interaction. A minimally invasive and low-cost sensing architecture was implemented, combining indoor localisation and physical activity tracking through environmental sensors and wrist-worn wearables. The health outcomes are modelled using a knowledge-based framework that integrates knowledge graphs to represent control variables and their relationships with data streams, and fuzzy logic to linguistically define temporal patterns based on expert criteria. The proposed approach was validated in a real-world case study with an older adult living independently in Granada, Spain. Over several days of deployment, the system successfully generated interpretable daily summaries reflecting relevant behavioural patterns, including rest periods, bathroom usage, activity levels, and caregiver proximity. In addition, supervised machine learning models were trained on the indicators derived from the fuzzy logic system, achieving average accuracy and F1 scores of 93% and 92%, respectively. These results confirm the potential of combining expert-informed semantics with data-driven inference to support continuous, explainable health monitoring in ambient assisted living environments. Full article

Intuitionistic fuzzy sets (IFSs), proposed in 1983, are one of the most viable and widely explored extensions of Zadeh’s fuzzy sets. In the decade following their introduction, they were extended to interval-valued IFSs (IVIFSs), temporal IFSs, IFSs of the second type (incorrectly called “Pythagorean fuzzy sets” by some authors) IFSs of n-th type, and IFSs over different universes. For each of these extensions, at least one geometrical interpretation has been defined, and for IVIFSs, at least seven different interpretations are known. In the present paper, revisiting some existing results on IVIFSs, some necessary modifications, additions, and corrections to the planar and spatial geometrical interpretations are introduced here for the first time. A new, eighth, geometrical interpretation of IVIFSs is proposed. A basic logic operation and two modal operators are illustrated and a comparison is made between the planar and the new “two-rods” geometrical interpretations of identical IVIFS elements. Finally, a new operator over IVIFSs is proposed for the first time, some of its properties are proven, and its geometrical interpretations are described. Full article

Accurate traffic light status detection and the appropriate response to changes in that status are crucial for autonomous driving systems (ADSs) starting from SAE Level 3 automation. The dilemma zone problem occurs during the amber phase of traffic lights, when the ADS must decide whether to stop or proceed through the intersection. This paper proposes a methodology for developing a runtime monitor that addresses the dilemma zone problem and monitors the autonomous vehicle’s behavior at traffic lights, ensuring that the ADS’s decisions align with the system’s safety requirements. This methodology yields a set of safety requirements formulated in controlled natural language, their formal specification in linear temporal logic (LTL), and the implementation of a corresponding runtime monitor. The monitor is integrated within a safety-oriented software architecture through a modular autonomous driving system pipeline, enabling real-time supervision of the ADS’s decision-making at intersections. The results show that the monitor maintained stable and fast reaction times between 40 ms and 65 ms across varying speeds (up to 13 m/s), remaining well below the 100 ms threshold required for safe autonomous operation. At speeds of 30, 50, and 70 km/h, the system ensured correct behavior with no violations of traffic light regulations. Furthermore, the monitor achieved 100% detection accuracy of the relevant traffic lights within 76 m, with high spatial precision (±0.4 m deviation). While the system performed reliably under typical conditions, it showed limitations in disambiguating adjacent, irrelevant signals at distances below 25 m, indicating opportunities for improvement in dense urban environments. Full article

A remarkable feature of manifestly covariant quantum gravity theory (CQG-theory) is represented by its unconstrained Hamiltonian structure expressed in evolution form. This permits the identification of the corresponding dynamical evolution parameter advancing the quantum-wave equation for the $4-$scalar quantum wave function defined on an appropriate Hilbert space. In the framework of CQG-theory, such a temporal parameter is represented by a $4-$scalar proper time s identifying a canonical variable with conjugate quantum operator. The observable character of the evolution parameter is also established through its correspondence with the quantum representation of the cosmological constant originating from non-linear Bohm quantum–vacuum interaction, which is shown to admit an intrinsic functional dependence on s. These conclusions overcome the conceptual limitations about the so-called “problem of time” mentioned in alternative approaches to quantum gravity available in the literature. Hence, the outcome permits one to promote CQG theory as a viable mathematical setting for the establishment of a theory of quantum gravity consistent with the logical and physical principles of both general relativity and canonical quantum mechanics. Full article

An event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong to the “normal” or “usual” spectrum and result in a change in a given situation; thus, their prediction would be very beneficial, both in terms of timely response to them and for their prevention, for example, the prevention of traffic accidents. However, this is currently challenging for researchers, who are called upon to manage and analyze a huge volume of data in order to design applications for predicting events using artificial intelligence and high computing power. Although significant progress has been made in this area, the heterogeneity in the input data that a forecasting application needs to process—in terms of their nature (spatial, temporal, and semantic)—and the corresponding complex dependencies between them constitute the greatest challenge for researchers. For this reason, the initial forecasting applications process data for specific situations, in terms of number and characteristics, while, at the same time, having the possibility to respond to different situations, e.g., an application that predicts a pandemic can also predict a central phenomenon, simply by using different data types. In this work, we present the forecasting applications that have been designed to date. We also present a model for predicting traffic accidents using categorical logic, creating a Knowledge Base using the Resolution algorithm as a proof of concept. We study and analyze all possible scenarios that arise under different conditions. Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL. Full article

We present Stratified Metric Temporal Logic (SMTL), a novel formalism for specifying and verifying the properties of complex cyber–physical systems that exhibit behaviors across multiple temporal and abstraction scales. SMTL extends existing temporal logics by incorporating a stratification operator, enabling the association of temporal properties with specific abstraction levels. This allows for the natural expression of multi-scale requirements while maintaining formal reasoning about inter-level relationships. We formalize the syntax and semantics of SMTL, proving that it strictly subsumes metric temporal logic (MTL) and offers enhanced expressiveness by capturing properties unattainable in existing logics. Numerical simulations comparing agents operating under MTL and SMTL specifications show that SMTL enhances agent coordination and safety, reducing collision rates without substantial computational overhead or compromising path efficiency. These findings highlight SMTL’s potential as a valuable tool for designing and verifying complex multi-agent systems operating across diverse temporal and abstraction scales. Full article

The Yangtze River Delta (YRD) region, located inside the Yangtze River Basin, functions as a vital ecological and economic area in China, with its natural environment directly impacting human existence. This study seeks to elucidate the spatial and temporal evolution of rural resilience in the Yangtze River Delta region and its underlying mechanisms by establishing a comprehensive assessment framework for rural resilience, thereby offering a scientific foundation and policy guidance for the region’s sustainable development. The research first established the DPSIR (driving force–pressure–state–impact–response) assessment index system. Subsequently, the entropy weighting method and TOPSIS were utilized to assess and rank the rural resilience levels in the Yangtze River Delta region (Shanghai, Jiangsu, Zhejiang, and Anhui) from 2012 to 2022. Ultimately, partial least squares structural equation modeling (PLS-SEM) was employed to examine the intrinsic logical relationships among the five dimensions of the DPSIR framework and to extract conclusions. The study effectively met the goals of SDG 7 (clean water and sanitation), SDG 8 (decent work and economic growth), and SDG 11 (sustainable cities and communities). The research indicated that (1) the resilience level in the Yangtze River Delta region initially declined, then increased, and eventually attained a condition of stabilization. Changes in the “driving force”, influenced by the “response level” and environmental “pressure”, have affected the resilience level of rural areas. There is heterogeneity in the assessment values and ranges of change among provinces, with the “impact” component exhibiting the most substantial evaluation value. The findings yield policy recommendations for the implementation of diverse regional governance, the establishment of connectivity mechanisms, the customization of strategies to address the specific deficiencies of each province, and the systematic enhancement of rural resilience. Full article

The Logical Range is a mission-oriented, reconfigurable environment that integrates testing, training, and simulation by virtually connecting distributed systems. In such environments, task-processing devices often experience highly dynamic workloads due to varying task demands, leading to scheduling inefficiencies and increased latency. To address this, we propose GCSG, a hybrid load forecasting model tailored for Logical Range operations. GCSG transforms time-series device load data into image representations using Gramian Angular Field (GAF) encoding, extracts spatial features via a Convolutional Neural Network (CNN) enhanced with a Squeeze-and-Excitation network (SENet), and captures temporal dependencies using a Gated Recurrent Unit (GRU). Through the integration of spatial–temporal features, GCSG enables accurate load forecasting, supporting more efficient resource scheduling. Experiments show that GCSG achieves an $R^2$ of 0.86, MAE of 4.5, and MSE of 34, outperforming baseline models in terms of both accuracy and generalization. Full article