Search Results (602)

With the increasing aggravations of urban traffic jams, intelligent route planning to reduce traffic time is becoming increasingly critical for drivers. However, traditional route-planning methods, such as graph search and Recurrent Neural Network (RNN)-based methods, struggle to capture the complex dynamics of road networks. Specifically in A*-type methods, routes should be searched instantly on the whole graph for the sake of dynamic changes in edge time consumption. As for RNN-based methods, their shortcomings in capturing long-distance sequence dependencies makes them unsuitable for route planning in metropolises with long routes. Therefore, to better adapt to the complexity of urban traffic, in this paper, an innovative route-planning model called Route Planning with Transformer (RoPT) is proposed. This model is based on the fusion of Graph Convolutional Networks (GCNs) and a Transformer, which uses GCNs for capturing complex spatial dependencies between the current intersection and the destination in a road network. Depending on the self-attention mechanism of the Transformer, the long-distance temporal dependencies between intersections could also be captured effectively. With comprehensive experiments on two real-world traffic datasets, the Porto dataset and the Chengdu dataset, it is demonstrated that RoPT outperforms the best methods, to the best of our knowledge. Moreover, the latent features learned from RoPT are more interpretable. Full article

Objectives: This study investigated the visual search behaviour of optometrists and fellowship-trained ophthalmologists when screening for diabetic retinopathy in retinal photographs. Methods: Participants assessed and graded retinal photographs on a computer screen while a Gazepoint GP3 HD eye tracker recorded their eye movements. Areas of interest were derived from the raw data using Hidden Markov modelling. Fixation strings were extracted by matching raw fixation data to areas of interest and resolving ambiguities with graph search algorithms. Fixation strings were clustered using Affinity Propagation to determine search behaviours characteristic of the correct and incorrect response groups. Results: A total of 23 participants (15 optometrists and 8 ophthalmologists) completed the grading task, with each assessing 20 images. Visual search behaviour differed between correct and incorrect responses, with data suggesting correct responses followed a visual search strategy incorporating the optic disc, macula, superior arcade, and inferior arcade as areas of interest. Data from incorrect responses suggest search behaviour driven by saliency or a search pattern unrelated to anatomical landmarks. Referable diabetic retinopathy was correctly identified in 86% of cases. Grader accuracy was 64.8% with good inter-grader agreement (α = 0.818). Conclusions: Our study suggests that a structured visual search strategy is correlated with higher accuracy when assessing retinal photographs for diabetic retinopathy. Referable diabetic retinopathy is detected at high rates; however, there is disagreement between clinicians when determining a precise severity grade. Full article

Quantum walks are a powerful framework for simulating complex quantum systems and designing quantum algorithms, particularly for spatial search on graphs, where the goal is to find a marked vertex efficiently. In this work, we present efficient quantum circuits that implement the evolution operator of continuous-time quantum-walk-based search algorithms for three graph families: complete graphs, complete bipartite graphs, and hypercubes. For complete and complete bipartite graphs, our circuits exactly implement the evolution operator. For hypercubes, we propose an approximate implementation that closely matches the exact evolution operator as the number of vertices increases. Our Qiskit simulations demonstrate that even for low-dimensional hypercubes, the algorithm effectively identifies the marked vertex. Furthermore, the approximate implementation developed for hypercubes can be extended to a broad class of graphs, enabling efficient quantum search in scenarios where exact implementations are impractical. Full article

The Unit Commitment Problem (UCP) is a critical component of power market decision-making and is typically formulated as Mixed Integer Programming (MIP). Given the complexity of solving MIPs, efficiently solving large-scale UCPs remains a significant challenge. This paper presents a hybrid Graph Neural Network (GNN)–Linear Programming (LP) framework to accelerate the solution of large-scale Unit Commitment Problems (UCPs) while maintaining the quality of solutions. By analyzing variable stability through historical branch-and-bound (B&B) trajectories, we classify MIP variables into dynamically adjustable stable and unstable groups. We adopt an MIP formulation that includes multiple types of binary variables—such as commitment, startup, and shutdown variables—and extract additional information from these auxiliary binary variables. This enriched representation provides more candidates for stable variable fixation, helping to improve variable refinement, mitigate suboptimality, and enhance computational efficiency. A bipartite GNN is trained offline to predict stable variables based on system topology and historical operational patterns. During online optimization, instance-specific root LP solutions refine these predictions, enabling adaptive variable fixation via a dual-threshold mechanism that integrates GNN confidence and LP relaxations. To mitigate suboptimality risks, we introduce temporally flexible fixation strategies—hard fixation for variables with persistent stability and soft fixation allowing limited temporal adjustments—alongside a GNN-guided branching rule to prioritize unstable variables. Numerical experiments demonstrate that jointly fixing commitment, startup, and shutdown variables yields better performance compared to fixing only commitment variables. Ablation studies further validate the importance of hard fixation and customized branching strategies, especially for large-scale systems. Full article

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study. Full article

This paper presents a new approach to optimizing route selection in urban road networks with sparsely placed traffic counters. By leveraging graph theory and Dijkstra’s algorithm, we propose a new method to determine the shortest path between origins and destinations in city traffic networks with sparsely placed counters. The method is based on the similarities between traffic flows recorded at the counter and the streets that generate traffic for a given counter. The advantage of this method is the use of a secondary counter function to obtain data that are built into the shortest path determination model and the free choice of the time of day for which the path is searched. The proposed method is implemented using the programming language AutoLISP 2022 and program AutoCAD 2022, providing a valuable tool for transportation engineers and urban planners. This paper presents a model of the shortest path that integrates one-way streets, the average speed of the car, as well as the delay time at traffic-lighted and non-traffic intersections. The model was applied to the traffic network of the city of Sarajevo (Bosnia and Herzegovina), but there are no restrictions for application to any network equipped with traffic counters. The obtained results show a high agreement with the Google Maps service as a reference system. Full article

Efficient parking distribution is crucial for urban traffic management; nevertheless, variable demand and spatial disparities raise considerable obstacles. Current research emphasizes local optimization but neglects the fundamental challenges of real-time parking allocation, resulting in inefficiencies within intricate metropolitan settings. This research delineates two key issues: (1) A dynamic imbalance between supply and demand, characterized by considerable fluctuations in parking demand over time and across different locations, rendering static allocation solutions inefficient; (2) spatial resource optimization, aimed at maximizing the efficiency of limited parking spots to improve overall system performance and user satisfaction. We present a Multi-Agent Reinforcement Learning (MARL) framework that incorporates adaptive optimization and intelligent collaboration for dynamic parking allocation to tackle these difficulties. A reinforcement learning-driven temporal decision mechanism modifies parking assignments according to real-time data, whilst a Graph Neural Network (GNN)-based spatial model elucidates inter-parking relationships to enhance allocation efficiency. Experiments utilizing actual parking data from Melbourne illustrate that Multi-Agent Reinforcement Learning (MARL) substantially surpasses conventional methods (FIFO, SIRO) in managing demand variability and optimizing resource distribution. A thorough quantitative investigation confirms the strength and flexibility of the suggested method in various urban contexts. Full article

Enhancing the performance of aged water distribution networks (WDNs) has become a significant global challenge. Many of these networks face issues such as deteriorated pipes, insufficient pumping heads, and increased water demands. Upgrading existing WDNs is often performed using optimization techniques, characterized by numerous decision variables, resulting in computationally intensive and time-consuming simulations. This paper proposes a novel optimal upgrading methodology for WDNs, leveraging clustering principles from graph theory. The proposed methodology involves adding a new storage tank and rehabilitating selected pipes of an existing WDN. The methodology begins with dividing the WDN into smaller subsystems based on its communication properties. The parameter ranges for adding a new storage tank are determined using a sensitivity analysis, assessing their values and impact on network resilience and water quality. Critical pipes that directly impact the WDN performance are identified and replaced for rehabilitation through three proposed scenarios, each with a distinct selection criterion. The problem is formulated as a multi-objective problem, aiming to minimize total annual costs while maximizing network resilience. The proposed methodology has proven effective in reducing the search space size and computational effort, outperforming the traditional full search space optimization approach. Full article

Objectives: This study aimed to evaluate the prognostic impact of coinfections and other clinical factors on mortality in melioidosis patients, providing a comprehensive analysis through systematic review and meta-analysis. Methods: A systematic search was conducted in PubMed, Embase, Scopus, and other sources for studies published from their inception to August 2023. Studies reporting mortality outcomes in melioidosis patients with and without coinfections were included. Mixed-effects logistic regression models were used to estimate the causal association of each prognostic factor on the outcome. Directed acyclic graphs (DAGs) were used to guide confounding adjustment, and missing data were handled using multiple imputations. Results: A total of 346 studies involving 509 patients were analyzed. Coinfections were observed in 10.8% of patients with tuberculosis and Leptospira spp. being the most common. Disseminated disease significantly increased the odds of death (OR 4.93, 95% CI: 2.14–11.37, p < 0.001). Coinfections were associated with a higher mortality rate, but the association was not statistically significant (OR 2.70, 95% CI: 0.53–13.90, p = 0.172). Sensitivity analyses confirmed the robustness of the findings. Other factors, including diabetes mellitus and agricultural occupation, were evaluated for their associations with mortality. Conclusions: Disseminated melioidosis remains a significant factor influencing prognosis. Although less common, coinfections may contribute to worsen patient outcomes, emphasizing the importance of immediate and accurate diagnosis and comprehensive management. Full article

As the energy crisis is leading to energy shortages and constant increases in prices, green energy and renewable energy sources are trending as a viable solution to this problem. One of the most rapidly expanding green energy methods is RF (RadioFrequency) energy harvesting, as RF energy and its corresponding technologies are constantly progressing, due to the introduction of 5G and high-speed telecommunications. The usual system for RF energy harvesting is called a rectenna, and one of its main components is an antenna, responsible for collecting ambient RF energy. In this paper, the optimization process of an ultra-wideband antenna for RF energy harvesting applications was studied, with the main goal of broadening the antenna’s operational bandwidth to include 5G New Radio. For this purpose, the Bezier Search Differential Evolution Algorithm (BeSD) was used along with a novel CST-Matlab API, to manipulate the degrees of freedom of the antenna, while searching for the optimal result, which would satisfy all the necessary dependencies to make it capable of harvesting RF energy in the target frequency band. The BeSD algorithm was first tested with benchmark functions and compared to other widely used algorithms, which it successfully outperformed, and hence, it was selected as the optimizer for this research. All in all, the optimization process was successful by producing an ultra-wideband optimal antenna operating from 1.4 GHz to 3.9 GHz, which includes all vastly used telecommunication technologies, like GSM (1.8 GHz), UMTS (2.1 GHz), Wi-Fi (2.4 GHz), LTE (2.6 GHz), and 5G NR (3.5 GHz). Its ultra-wideband properties and the rest of the characteristics that make this design suitable for RF energy harvesting are proven by its $S_{11}$ response graph, its impedance response graph, its efficiency on the targeted technologies, and its omnidirectionality across its band of operation. Full article

The multimodal knowledge graph link prediction model integrates entity features from multiple modalities, such as text and images, and uses these fused features to infer potential entity links in the knowledge graph. This process is highly dependent on the fitting and generalization capabilities of deep learning models, enabling the models to accurately capture complex semantic and relational patterns. However, it is this deep reliance on the fitting and generalization capabilities of deep learning models that leads to the black-box nature of the decision-making mechanisms and prediction bases within the multimodal knowledge graph link prediction models, which are difficult to understand intuitively. This black-box nature not only restricts the promotion and popularization of multimodal knowledge graph link prediction technology in practical applications but also hinders our understanding and exploration of the internal working mechanism of the model. Therefore, the purpose of this paper is to deeply explore the explainability problem of multimodal knowledge graph link prediction models and propose a multimodal post hoc model-independent multi-granularity explanation method (MMExplainer) for multimodal link prediction tasks. We learn the importance of each modality through modal separation, use textual semantics to guide a heuristic search to filter candidate explanation triples, and use textual masks to obtain explanation phrases that play an important role in prediction. Experimental results show that MMExplainer can provide coarse-grained explanations at the modal level and fine-grained explanations in structural and textual modalities, and the relevance index of the explanations in model decision-making is better than that of the baseline model. Full article

A Knowledge Graph (KG), which structurally represents entities (nodes) and relationships (edges), offers a powerful and flexible approach to knowledge representation in the field of Artificial Intelligence (AI). KGs have been increasingly applied in various domains—such as natural language processing (NLP), recommendation systems, knowledge search, and medical diagnostics—spurring continuous research on effective methods for their construction and maintenance. Recently, efforts to combine large language models (LLMs), particularly those aimed at managing hallucination symptoms, with KGs have gained attention. Consequently, new approaches have emerged in each phase of KG development, including Extraction, Learning Paradigm, and Evaluation Methodology. In this paper, we focus on major publications released after 2022 to systematically examine the process of KG construction along three core dimensions: Extraction, Learning Paradigm, and Evaluation Methodology. Specifically, we investigate (1) large-scale data preprocessing and multimodal extraction techniques in the KG Extraction domain, (2) the refinement of traditional embedding methods and the application of cutting-edge techniques—such as Graph Neural Networks, Transformers, and LLMs—in the KG Learning domain, and (3) both intrinsic and extrinsic metrics in the KG Evaluation domain, as well as various approaches to ensure interpretability and reliability. Full article

Greenwashing, the phenomenon of companies misleading consumers about their sustainability practices, is prevalent in the fashion industry. This study explores consumer opinions on greenwashing through analysis of social media discourse. Cognitive dissonance theory served as the theoretical framework, explaining how consumers reconcile conflicting information about brands’ sustainability claims. In Study 1, 446 comments on 12 Reddit posts were collected using the search term “fashion greenwashing”. Using the Latent Dirichlet Allocation (LDA) algorithm and manual review, we identified three major themes: the phenomenon of fashion greenwashing, consumer empowerment in sustainable fashion, and skepticism towards fast fashion brands’ marketing strategies. In Study 2, using the search term, “#fashiongreenwashing”, two researchers collected and analyzed 76 Instagram posts with 370 comments. A manual review was employed to extract major themes, and network graphs of caption tags within the same theme were constructed. Three major themes emerged: strategies to combat fashion greenwashing, examples of fashion greenwashing, and advocacy and regulation in sustainable fashion. Findings from Studies 1 and 2 revealed that consumers are increasingly aware of brands’ deceptive practices and advocacy for sustainable practices to resolve this dissonance when they see greenwashing information. This study underscored the need for fashion brands to provide transparent and authentic information. Full article

This research aims to investigate the roles of ontology and Semantic Web Technologies (SWT) in modern knowledge representation and data management. By analyzing a dataset of 10,037 academic articles from Web of Science (WoS) published in the last 6 years (2019–2024) across several fields, such as computer science, engineering, and telecommunications, our research identifies important trends in the use of ontologies and semantic frameworks. Through bibliometric and semantic analyses, Natural Language Processing (NLP), and topic modeling using Latent Dirichlet Allocation (LDA) and BERT-clustering approach, we map the evolution of semantic technologies, revealing core research themes such as ontology engineering, knowledge graphs, and linked data. Furthermore, we address existing research gaps, including challenges in the semantic web, dynamic ontology updates, and scalability in Big Data environments. By synthesizing insights from the literature, our research provides an overview of the current state of semantic web research and its prospects. With a 0.75 coherence score and perplexity = 48, the topic modeling analysis identifies three distinct thematic clusters: (1) Ontology-Driven Knowledge Representation and Intelligent Systems, which focuses on the use of ontologies for AI integration, machine interpretability, and structured knowledge representation; (2) Bioinformatics, Gene Expression and Biological Data Analysis, highlighting the role of ontologies and semantic frameworks in biomedical research, particularly in gene expression, protein interactions and biological network modeling; and (3) Advanced Bioinformatics, Systems Biology and Ethical-Legal Implications, addressing the intersection of biological data sciences with ethical, legal and regulatory challenges in emerging technologies. The clusters derived from BERT embeddings and clustering show thematic overlap with the LDA-derived topics but with some notable differences in emphasis and granularity. Our contributions extend beyond theoretical discussions, offering practical implications for enhancing data accessibility, semantic search, and automated knowledge discovery. Full article

In the power industry, secondary operation risk assessment is a critical step in ensuring operational safety. However, traditional assessment methods often rely on expert judgment, making it difficult to efficiently address the challenges posed by unstructured textual data and complex equipment relationships. To address this issue, this paper proposes a hybrid model that integrates graph convolutional networks (GCNs) with semantic embedding techniques. The model consists of two main components: the first constructs a domain-specific knowledge graph for the power industry and uses a GCN to extract structural information, while the second fine-tunes the RoBERTa pre-trained model to generate semantic embeddings for textual data. Finally, the model employs a hybrid similarity measurement mechanism that comprehensively considers both semantic and structural features, combining K-means clustering similarity search with a multi-node weighted evaluation method to achieve efficient and accurate risk assessment. The experimental results demonstrate that the proposed model significantly outperforms the traditional methods in key metrics, such as accuracy, recall, and F1 score, fully validating its practical application value in secondary operation scenarios within the power industry. Full article