Search Results (2,433)

The growing demand for reliable and secure vehicle-to-vehicle (V2V) communication in next-generation intelligent transportation systems has accelerated the adoption of reconfigurable intelligent surfaces (RIS) as a means of enhancing link quality, spectral efficiency, and physical layer security. In this paper, we investigate the problem of secrecy rate maximization in a cooperative dual-RIS-aided V2V communication network, where two cascaded RISs are deployed to collaboratively assist with secure data transmission between mobile vehicular nodes in the presence of eavesdroppers. To address the inherent complexity of time-varying wireless channels, we propose a novel evolutionary transformer-gated recurrent unit (Evo-Transformer-GRU) framework that jointly learns temporal channel patterns and optimizes the RIS reflection coefficients, beam-forming vectors, and cooperative communication strategies. Our model integrates the sequence modeling strength of GRUs with the global attention mechanism of transformer encoders, enabling the efficient representation of time-series channel behavior and long-range dependencies. To further enhance convergence and secrecy performance, we incorporate an improved gray wolf optimizer (IGWO) to adaptively regulate the model’s hyper-parameters and fine-tune the RIS phase shifts, resulting in a more stable and optimized learning process. Extensive simulations demonstrate the superiority of the proposed framework compared to existing baselines, such as transformer, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), long short-term memory (LSTM), and GRU models. Specifically, our method achieves an up to 32.6% improvement in average secrecy rate and a 28.4% lower convergence time under varying channel conditions and eavesdropper locations. In addition to secrecy rate improvements, the proposed model achieved a root mean square error (RMSE) of 0.05, coefficient of determination ($R^2$) score of 0.96, and mean absolute percentage error (MAPE) of just 0.73%, outperforming all baseline methods in prediction accuracy and robustness. Furthermore, Evo-Transformer-GRU demonstrated rapid convergence within 100 epochs, the lowest variance across multiple runs. Full article

The future of marine sustainability depends on public understanding and trust in the policy recommendations that emerge from scientific research. For common pool marine resource decisions made by the people who depend on these resources for their food, employment, and economic future, understanding the current status of these marine systems and change is essential to ensure these resources will persist into the future. As such, the informal learning infrastructure is essential to increasing marine science literacy in a changing world. This mixed-methods research study analyzed the distribution and accessibility of marine science education and research across Alaska’s five geographic regions. Using the PRISMA framework, we synthesized data from 198 institutions and analyzed peer-reviewed literature on marine ecosystems to identify geographic and thematic gaps in access to informal science learning and research focus. In parallel, we undertook geospatial analysis and resource availability to describe the distribution of resources, types of informal learning infrastructure present across the state, regional presence, and resources to support informal marine science learning opportunities. Findings from this multifactor research revealed a concentration of resources in urban hubs and a lack of consistent access to learning resources for rural and Indigenous communities. The configurative literature review of 9549 publications identified topical underrepresentation of the Bering Sea and Aleutian Islands, as well as a lack of research on seabirds across all regions. Considered together, these results recommend targeted investments in rural engagement with marine science programming, culturally grounded partnerships, and research diversification. This review concludes that disparities in learning resource support and government-funded priorities in marine wildlife research have created conditions that undermine the local people’s participation in the sustainability of sensitive resources and are likely exacerbating declines driven by rapid change in Arctic and sub-Arctic waters. Full article

Understanding and mapping forest fire vulnerability is essential for informed landscape management and disaster risk reduction, especially in the context of increasing anthropogenic and climatic pressures. This study aims to model and spatially predict forest fire vulnerability across Romania using two machine learning algorithms: MaxEnt and XGBoost. We integrated forest fire occurrence data from 2006 to 2024 with a suite of climatic, topographic, ecological, and anthropogenic predictors at a 250 m spatial resolution. MaxEnt, based on presence-only data, achieved moderate predictive performance (AUC = 0.758), while XGBoost, trained on presence–absence data, delivered higher classification accuracy (AUC = 0.988). Both models revealed that the impact of environmental variables on forest fire occurrence is complex and heterogeneous, with the most influential predictors being the Fire Weather Index, forest fuel type, elevation, and distance to human proximity features. The resulting vulnerability and uncertainty maps revealed hotspots in Sub-Carpathian and lowland regions, especially in Mehedinți, Gorj, Dolj, and Olt counties. These patterns reflect historical fire data and highlight the role of transitional agro-forested landscapes. This study delivers a replicable, data-driven approach to wildfire risk modelling, supporting proactive management and emphasising the importance of integrating vulnerability assessments into planning and climate adaptation strategies. Full article

Mitotic figures in tumor tissues are an important criterion for diagnosing malignant lesions, and physicians often search for the presence of mitosis in whole slide imaging (WSI). However, prolonged visual inspection by doctors may increase the likelihood of human error. With the advancement of deep learning, AI-based automatic cytopathological diagnosis has been increasingly applied in clinical settings. Nevertheless, existing diagnostic models often suffer from high computational costs and suboptimal detection accuracy. More importantly, when assessing cellular abnormalities, doctors frequently compare target cells with their surrounding cells—an aspect that current models fail to capture due to their lack of intercellular information modeling, leading to the loss of critical medical insights. To address these limitations, we conducted an in-depth analysis of existing models and propose an Inter–Intra Hypergraph Neural Network (II-HGNN). Our model introduces a block-based feature extraction mechanism to efficiently capture deep representations. Additionally, we leverage hypergraph convolutional networks to process both intracellular and intercellular information, leading to more precise diagnostic outcomes. We evaluate our model on publicly available datasets under varying imaging conditions, and experimental results demonstrate that our approach consistently outperforms baseline models in terms of accuracy. Full article

Hydroxyl-terminated-polybutadiene (HTPB)-based composite solid propellants are extensively used in aerospace and defense applications due to their high energy density, thermal stability, and processability. However, the presence of highly sensitive energetic components in their formulations leads to a significant risk of accidental ignition under electrostatic discharge, posing serious safety concerns during storage, transportation, and handling. To address this issue, this study explores the prediction of electrostatic sensitivity in HTPB propellants using machine learning techniques. A dataset comprising 18 experimental formulations was employed to train and evaluate six machine learning models. Among them, the Random Forest (RF) model achieved the highest predictive accuracy (R² = 0.9681), demonstrating a strong generalization capability through leave-one-out cross-validation. Feature importance analysis using SHAP and Gini index methods revealed that aluminum, catalyst, and ammonium perchlorate were the most influential factors. These findings provide a data-driven approach for accurately predicting electrostatic sensitivity and offer valuable guidance for the rational design and safety optimization of HTPB-based propellant formulations. Full article

Research shows mnemonics promote knowledge retention in different contexts; hence, they are increasingly being used in serious games aimed to support long-term learning while providing “edutainment.” However, there is limited research on their effectiveness. As such, we conducted a systematic review of 32 mnemonics mobile apps and evaluated them using two established frameworks from the literature. Our analysis revealed that most of the games teach language or medicine, take the form of puzzles or quizzes, and feature acronyms and/or images, with players rating them at least three out of five stars on average. All 32 apps supported feedback, interactivity, and challenge. A few supported agency, identity and self-presence, while many did not support key characteristics such as social and spatial presences. The overall finding indicates a need to create a mnemonics-based and tailored framework to guide the design of mnemonics games in the future to make them more effective. Full article

Glutamate, the primary excitatory neurotransmitter in the central nervous system, plays a pivotal role in synaptic signaling, learning, and memory. Abnormal glutamate levels are implicated in various neurological disorders, including epilepsy, Alzheimer’s disease, and ischemic stroke. Despite the utility of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in diagnosing such conditions, the development of effective glutamate-sensitive contrast agents remains a challenge. In this study, we present ultrasmall, citric acid-coated superparamagnetic iron oxide nanoparticles (CA-SPIONs) as highly selective and sensitive MRS probes for glutamate detection. These 5 nm magnetite CA-SPIONs exhibit a stable dispersion in physiological buffers and undergo aggregation in the presence of glutamate, significantly enhancing the T₂ MRS contrast power. At physiological glutamate levels, the CA-SPIONs yielded a pronounced signal change ratio of nearly 60%, while showing a negligible response to other neurotransmitters such as GABA and dopamine. Computational simulations confirmed the mechanism of glutamate-mediated aggregation and its impact on transversal relaxation rates and relaxivities. The sensitivity and selectivity of CA-SPIONs underscore their potential as eco-friendly, iron-based alternatives for future neurological sensing applications targeting glutamatergic dysfunction. Full article

In complex environments where satellite signals may be interfered with, it is difficult to achieve precise positioning of high-speed aerial vehicles solely through the inertial navigation system. To overcome this challenge, this paper proposes an NGO-ELM geomagnetic matching-assisted navigation algorithm, in which the Northern Goshawk Optimization (NGO) algorithm is used to optimize the initial weights and biases of the Extreme Learning Machine (ELM). To enhance the matching performance of the NGO-ELM algorithm, three improvements are proposed to the NGO algorithm. The effectiveness of these improvements is validated using the CEC2005 benchmark function suite. Additionally, the IGRF-13 model is utilized to generate a geomagnetic matching dataset, followed by comparative testing of five geomagnetic matching models: INGO-ELM, NGO-ELM, ELM, INGO-XGBoost, and INGO-BP. The simulation results show that after the airborne equipment acquires the geomagnetic data, it only takes 0.27 µs to obtain the latitude, longitude, and altitude of the aerial vehicle through the INGO-ELM model. After unit conversion, the average absolute errors are approximately 6.38 m, 6.43 m, and 0.0137 m, respectively, which significantly outperform the results of four other models. Furthermore, when noise is introduced into the test set inputs, the positioning error of the INGO-ELM model remains within the same order of magnitude as those before the noise was added, indicating that the model exhibits excellent robustness. It has been verified that the geomagnetic matching-assisted navigation algorithm proposed in this paper can achieve real-time, accurate, and stable positioning, even in the presence of observational errors from the magnetic sensor. Full article

Existing log-based anomaly detection methods typically require large volumes of labeled data for training, presenting significant challenges when applied to new systems with limited labeled data. This limitation has spurred the need for cross-system log anomaly detection (CSLAD) methods. However, current CSLAD approaches often face challenges in effectively handling distributional differences in log data across systems. To address this issue, we propose ELFA-Log, a transfer learning-based approach for cross-system log anomaly detection. By enhancing pseudo-label generation with uncertainty estimation and feature alignment, ELFA-Log improves detection performance even in the presence of data distribution shifts. It uses entropy-based metrics to generate high-confidence pseudo-labels, minimizing reliance on labeled data. Additionally, a distance-based loss function optimizes the shared representation of cross-system log features. Experimental results on benchmark datasets demonstrate that ELFA-Log enhances the performance of CSLAD, offering a practical solution to the challenge of high labeling costs in real-world applications. Full article

This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is on single-level assembly systems, where both component delivery lead times and finished product demand are subject to randomness. The problem is formulated as a Markov decision process (MDP), in which an agent determines optimal order quantities for each component by accounting for stochastic lead times and demand variability. The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. To enhance learning performance, we develop a tailored simulation environment that captures multi-component interactions, random lead times, and variable demand, along with a modular and realistic cost structure. The environment enables dynamic state transitions, lead time sampling, and flexible order reception modeling, providing a high-fidelity training ground for the agent. To further improve convergence and policy quality, we incorporate local search mechanisms and multiple action space discretizations per component. Simulation results show that the proposed method converges to stable ordering policies after approximately 100 episodes. The agent achieves an average service level of 96.93%, and stockout events are reduced by over 100% relative to early training phases. The system maintains component inventories within operationally feasible ranges, and cost components—holding, shortage, and ordering—are consistently minimized across 500 training episodes. These findings highlight the potential of deep reinforcement learning as a data-driven and adaptive approach to inventory management in complex and uncertain supply chains. Full article

Human Factors and Ergonomics (HF/E) play an essential role in the development of sustainable manufacturing systems. By prioritizing worker wellbeing through the mitigation of occupational hazards and the enhancement of workplace health, HF/E contributes significantly to improved system performance. In accordance with the principles of Industry 5.0 and Society 5.0, which emphasize human-centered design and wellbeing, organizations that effectively integrate HF/E principles can achieve a competitive advantage on the market. Based on a globally recognized ranking system utilized by investors in making informed decisions, the study focuses on manufacturing companies ranked by their occupational health and safety (OHS) scores, a key criterion for assessing the social dimension of company performance. This research aims to identify and analyze top-ranked companies that explicitly highlight HF/E-related benefits within their public documents and sustainability reports. The paper investigates aspects related to the integration of AI and digital technologies to enhance safety and health in manufacturing systems, with a specific focus on human presence detection in hazardous zones, improvements in machines and equipment design, occupational risk assessments, and initiatives for enhancing worker wellbeing. The findings are expected to provide compelling evidence for companies to prioritize HF/E consideration during the design and redesign phases of sustainable manufacturing systems. The paper provides significant value to non-indexed companies by offering a dual approach for improving OHS performance, based on an empirical evaluation assessment method and practical strategies for effective OHS implementation in different manufacturing industries and countries. Full article

Background/Objectives: This study evaluates whether combining ⁶⁸Ga-PSMA-11-PET/CT derived imaging biomarkers with clinical risk factors improves the prediction of early biochemical recurrence (eBCR) or clinical progress in patients with high-risk prostate cancer (PCa) after primary treatment, using machine learning (ML) models. Methods: We analyzed data from 93 high-risk PCa patients who underwent ⁶⁸Ga-PSMA-11 PET/CT and received primary treatment at a single center. Two predictive models were developed: a logistic regression (LR) model and an ML derived probabilistic graphical model (PGM) based on a naïve Bayes framework. Both models were compared against each other and against the CAPRA risk score. The models’ input variables were selected based on statistical analysis and domain expertise including a literature review and expert input. A decision tree was derived from the PGM to translate its probabilistic reasoning into a transparent classifier. Results: The five key input variables were as follows: binarized CAPRA score, maximal intraprostatic PSMA uptake intensity (SUVmax), presence of bone metastases, nodal involvement at common iliac bifurcation, and seminal vesicle infiltration. The PGM achieved superior predictive performance with a balanced accuracy of 0.73, sensitivity of 0.60, and specificity of 0.86, substantially outperforming both the LR (balanced accuracy: 0.50, sensitivity: 0.00, specificity: 1.00) and CAPRA (balanced accuracy: 0.59, sensitivity: 0.20, specificity: 0.99). The decision tree provided an explainable classifier with CAPRA as a primary branch node, followed by SUVmax and specific PET-detected tumor sites. Conclusions: Integrating ⁶⁸Ga-PSMA-11 imaging biomarkers with clinical parameters, such as CAPRA, significantly improves models to predict progression in patients with high-risk PCa undergoing primary treatment. The PGM offers superior balanced accuracy and enables risk stratification that may guide personalized treatment decisions. Full article

This paper presents a novel system designed to enhance pedestrian safety in urban environments by utilizing real-time video analysis and machine learning techniques. With a focus on the bustling streets of Macao, known for its high pedestrian traffic and complex road conditions, the proposed model alerts drivers to the presence of pedestrians, significantly reducing the risk of accidents. Leveraging the You Only Look Once algorithm, this research demonstrates how timely alerts can be generated based on risk assessments derived from video footage. The model is rigorously tested against diverse driving scenarios, providing robust accuracy in detecting potential hazards. A comparative analysis of various machine learning algorithms, including Gradient Boosting and Logistic Regression, underscores the effectiveness and reliability of the system. The key finding of this research indicates that dataset refinement and enhanced feature differentiation could lead to improved model performance. Ultimately, this work seeks to contribute to the development of smart city initiatives that prioritize safety through advanced technological solutions. This approach exemplifies a vision for more responsive and responsible urban transport systems. Full article

Class-incremental learning (CIL) in deep neural networks is affected by catastrophic forgetting (CF), where acquiring knowledge of new classes leads to the significant degradation of previously learned representations. This challenge is particularly severe in medical image analysis, where costly, expertise-dependent annotations frequently contain pervasive and hard-to-detect noisy labels that substantially compromise model performance. While existing approaches have predominantly addressed CF and noisy labels as separate problems, their combined effects remain largely unexplored. To address this critical gap, this paper presents a dual-stage clean-sample selection method for Incremental Noisy Label Learning (DSCNL). Our approach comprises two key components: (1) a dual-stage clean-sample selection module that identifies and leverages high-confidence samples to guide the learning of reliable representations while mitigating noise propagation during training, and (2) an experience soft-replay strategy for memory rehearsal to improve the model’s robustness and generalization in the presence of historical noisy labels. This integrated framework effectively suppresses the adverse influence of noisy labels while simultaneously alleviating catastrophic forgetting. Extensive evaluations on public medical image datasets demonstrate that DSCNL consistently outperforms state-of-the-art CIL methods across diverse classification tasks. The proposed method boosts the average accuracy by 55% and 31% compared with baseline methods on datasets with different noise levels, and achieves an average noise reduction rate of 73% under original noise conditions, highlighting its effectiveness and applicability in real-world medical imaging scenarios. Full article

The presence of culturally and linguistically diverse children increases in early education classrooms worldwide. In monolingual education settings, multilingual children are at a disadvantage regarding their learning opportunities compared to monolingual children. Knowledge about how monolingual educators support children in a multilingual classroom has not been systematized yet. The present scoping review aims to synthesize the existing empirical evidence on (a) characteristics of the learning environment of monolingual teachers with multilingual children and (b) how teacher characteristics relate to the learning environment characteristics in early education institutions worldwide. The inclusion criteria used in this review included articles that report empirical evidence from 1990 to 2024, with multilingual children aged 0–6 of minoritized languages and monolingual teacher practices within a naturalist environment. Subsequently, through the PRISMA-ScR method on the articles found in the WOS and SCIELO databases, 40 articles were included with qualitative, quantitative, and mixed-methods designs. The results showed that in the Latin American context, culturally responsive practices are scarce for bilingual children with immigrant backgrounds. In the USA and European contexts, educators are more likely to implement activities that include children’s L1, even if they have limited knowledge of that language. The international perspective of this review allows us to identify contributions and challenges in different geographic regions. Full article