Search Results (3,737)

Vehicular edge computing (VEC) represents a concrete application of mobile edge computing (MEC) in the field of intelligent transportation, with task offloading serving as one of its core components. The design of efficient task offloading strategies poses significant challenges due to the dynamic network topology, stringent low-latency requirements, and massive data processing demands. This paper proposes a digital twin (DT)-assisted intelligent task offloading approach, which establishes a dynamic interaction and mapping between the virtual and physical worlds to enable real-time monitoring of VEC network states, thereby optimizing offloading decisions. First, to meet diverse user service requirements, an optimization model is formulated with the objective of minimizing task processing latency and energy consumption. Next, a gravity model-based vehicle clustering algorithm is integrated with digital twin technology to find the optimal offloading space and ensure link stability among vehicles within aggregated clusters. Furthermore, to minimize overall system costs, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is utilized to train the offloading policy, enabling automatic optimization of both latency and energy consumption. consumption. Finally, a feedback mechanism is introduced to dynamically adjust parameters and enhance the robustness of the clustering process. Simulation results demonstrate that the proposed approach significantly outperforms baseline methods in terms of task completion cost, energy consumption, delay, and success rate, thereby validating its potential and superior performance in dynamic vehicular network environments. Full article

Habitat and climate changes driven by human activities are altering the distribution of organisms globally. In South Korea, recent temperature increases have exceeded twice the global average, and habitats have markedly changed and shrunk due to urban development driven by population growth and economic expansion. Despite its high biodiversity and over 500 years of preservation, Gwangneung Forest in South Korea has experienced habitat alterations due to the urbanization of surrounding rural areas since the 1990s. In this study, we aimed to evaluate how butterfly communities respond to urbanization and climate change using long-term monitoring data (1998–2015) from the conserved Gwangneung Forest. We considered the thermal adaptation types (cold-, warm-, and moderately adapted species), habitat types (forest edge, forest inside, and grassland), diet breadth (monophagous, oligophagous, and polyphagous), and wingspan of butterflies. Linear regression analysis of the abundance trends for each species revealed that cold-adapted species experienced population declines, while warm-adapted species showed increases. Changes in butterfly abundance were associated with both thermal adaptation type and wingspan, with larger, more mobile species showing greater resistance to habitat loss in surrounding areas. To preserve butterfly diversity in Gwangneung Forest and across South Korea, it is crucial to conserve open green habitats—such as gardens, small arable lands, and grasslands—within urban areas, especially considering the impacts of climate change and habitat loss, which disproportionately affect smaller species with limited mobility. Full article

Mobile genetic elements such as integrons are key drivers of microbial evolution, enabling rapid adaptation to environmental pressures through the acquisition and rearrangement of gene cassettes. In this study, we explored the structural diversity and synteny of class 1 integrons (intI1) across a set of agroecosystem-related environments, including digestate, compost, and rhizosphere soils from wheat crops (Triticum durum and T. aestivum). Our results reveal distinct gene cassette architectures shaped by the origin of the samples: digestate harbored the most diverse and complex arrays, while compost displayed streamlined structures. Rhizosphere soils exhibited intermediate configurations, reflecting a dynamic balance between environmental exposure and host influence. Genes associated with resistance to antibiotics and heavy metals, such as qacEΔ1 and ebrA, were differentially distributed, suggesting site-specific selective pressures. The observed patterns of cassette organization and diversity underscore the role of integron synteny as a molecular fingerprint of microbial adaptation. These findings position class 1 integrons as promising bioindicators of soil health and functional resilience, supporting a One Health approach to sustainable agriculture and microbial risk monitoring. Full article

This paper presents a case study on the integration of embedded IoT hardware with a modern Android application, demonstrated through the development of a compact autoclave system for small-scale food sterilization. The device is controlled by an ESP8266-based module and communicates securely with a Kotlin-based Android app via MQTT using HiveMQ. The app incorporates advanced Android design patterns such as coroutines, LiveData, Navigation UI, and DataStore. Each device is uniquely addressable and fully configurable from the mobile interface. The work highlights Android’s role as a powerful interface for managing embedded IoT systems. Full article

Understanding and monitoring the phenological phases of trees is essential for ecological research and climate change studies. In this work, we present a comprehensive evaluation of state-of-the-art convolutional neural networks (CNNs) and transformer architectures for the automated classification of the flowering phase of Tilia cordata Mill. (small-leaved lime) based on a large set of real-world images acquired under natural field conditions. The study introduces a novel, automated image quality filtering approach using an XGBoost classifier trained on diverse exposure and sharpness features to ensure robust input data for subsequent deep learning models. Seven modern neural network architectures, including VGG16, ResNet50, EfficientNetB3, MobileNetV3 Large, ConvNeXt Tiny, Vision Transformer (ViT-B/16), and Swin Transformer Tiny, were fine-tuned and evaluated under a rigorous cross-validation protocol. All models achieved excellent performance, with cross-validated F1-scores exceeding 0.97 and balanced accuracy up to 0.993. The best results were obtained for ResNet50 and ConvNeXt Tiny (F1-score: 0.9879 ± 0.0077 and 0.9860 ± 0.0073, balanced accuracy: 0.9922 ± 0.0054 and 0.9927 ± 0.0042, respectively), indicating outstanding sensitivity and specificity for both flowering and non-flowering classes. Classical CNNs (VGG16, ResNet50, and ConvNeXt Tiny) demonstrated slightly superior robustness compared to transformer-based models, though all architectures maintained high generalization and minimal variance across folds. The integrated quality assessment and classification pipeline enables scalable, high-throughput monitoring of flowering phases in natural environments. The proposed methodology is adaptable to other plant species and locations, supporting future ecological monitoring and climate studies. Our key contributions are as follows: (i) introducing an automated exposure-quality filtering stage for field imagery; (ii) publishing a curated, season-long dataset of Tilia cordata images; and (iii) providing the first systematic cross-validated benchmark that contrasts classical CNNs with transformer architectures for phenological phase recognition. Full article

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions. Full article

Background: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and acute respiratory failure (ARF) are leading causes of hospitalization and functional decline in Italy, posing a significant burden on the healthcare system. In 2024, new national guidelines mandated the use of Intensive Care Rehabilitation Units (ICRUs) within MDC4 to provide structured post-acute respiratory rehabilitation. Objective: This study aimed to evaluate functional outcomes in patients with AECOPD and ARF treated in a single ICRU, assessing the effectiveness of guideline-based rehabilitation protocols. Methods: A retrospective audit was conducted on patients admitted in 2024 to a dedicated ICRU. Functional outcomes were assessed using the Barthel Index, Six-Minute Walking Test (6MWT), and Rehabilitation Complexity Index (RCI-e13). Correlation analyses were performed to explore relationships between baseline status, rehabilitation progression, and discharge outcomes. Results: Thirty-six patients were included. Significant improvements were observed across all scales from admission to discharge. The Barthel Index showed a strong positive correlation between initial and final scores (r = 0.72), while the 6MWT indicated a similarly robust correlation (r = 0.73). Greater functional gains were noted among patients with lower baseline scores, especially in mobility. The RCI-e13 reflected decreased clinical complexity by discharge, with moderate correlations to baseline severity. Age moderately correlated with length of stay (r = 0.30), but not with outcome scores. Conclusions: The implementation of early, intensive rehabilitation in an ICRU setting—aligned with Italy’s 2024 national guidelines—led to measurable functional improvements in patients with AECOPD and ARF. These findings support the utility of structured outcome monitoring and reinforce the role of ICRUs in optimizing post-acute care pathways within respiratory rehabilitation services. Full article

Background/Objectives: This study aimed to investigate the prevalence of liver damage and its associated non-invasive markers and echocardiographic risk factors in patients who underwent surgery for congenital heart disease. Methods: This retrospective observational study was conducted at a single tertiary-care university hospital in Niigata, Japan. Of 142 patients (ventricular septal defect [VSD] n = 47, tetralogy of Fallot [TOF] n = 67, Fontan n = 28), 52.8% were male [median age: 22.7 years; VSD (24.3 years), TOF (24.0 years), and Fontan (12.5 years)]. Pediatric patients with liver diseases unrelated to congestive liver disease, such as viral hepatitis and alcoholic liver disease, were excluded. We compared non-invasive liver fibrosis age-invariant biomarkers, such as the aspartate aminotransferase-to-platelet ratio index (APRI), and various serum markers and echocardiographic parameters to assess the prevalence and predictors of hepatic fibrosis. Results: The Fontan circulation group had the highest APRI, followed by the TOF group, while the VSD group had a low risk of APRI elevation. Postoperative TOF patients required monitoring for cirrhosis progression. Inferior vena cava mobility was associated with echocardiographic parameters and fibrosis severity, along with a loss of respiratory variability. The limitations of other cardiac assessments were highlighted by poor anatomical measurements. Gamma-glutamyl transpeptidase (γ-GTP) demonstrated strong discriminatory ability. The optimal cutoff value was 53.0 U/L, suggesting its use as a clinical marker. Conclusions: Assessing fibrosis is crucial in CHD patients, especially those with late post-TOF repair findings. Non-invasive markers (APRI, γ-GTP, and B-type natriuretic peptide), along with echocardiographic findings, may help detect fibrosis early, enabling timely intervention and improving long-term outcomes. Clinical trial registration: 2020-0199. Full article

Maintaining muscle health is essential for preserving mobility, independence, and quality of life with age. As muscle mass and function decline, the risk of frailty, chronic disease, and disability increases. Sarcopenia, characterized by the progressive loss of muscle mass, strength, and function, is a major contributor to these adverse outcomes in older adults. Early identification and monitoring of sarcopenia are critical for timely intervention to prevent irreversible decline. Muscle biomarkers offer a promising approach for detecting muscle deterioration and guiding treatment strategies. This review explores key biomarkers—including insulin-like growth factor 1 (IGF-1), myostatin, interleukin-6 (IL-6), irisin, interleukin 15 (IL-15), and procollagen type III N-terminal propeptide (P3NP)—that reflect underlying processes such as muscle anabolism, inflammation, metabolism, and remodeling. Alterations in these markers are associated with muscle health status. Furthermore, hormonal status, biological sex, and nutritional factors all modulate biomarker levels, emphasizing the need for personalized assessments. Integrating biomarker analysis into clinical practice has the potential to enhance early diagnosis, inform personalized interventions, and ultimately promote healthy aging by maintaining muscle function and reducing disability risk. Full article

Visible and infrared thermal imaging are crucial techniques for detecting structural and temperature anomalies in electrical power system equipment. To meet the demand for multimodal infrared/visible light monitoring of target devices, this paper introduces CBAM-YOLOv4, an improved lightweight object detection model, which leverages a novel synergistic integration of the Convolutional Block Attention Module (CBAM) with YOLOv4. The model employs MobileNet-v3 as the backbone to reduce parameter count, applies depthwise separable convolution to decrease computational complexity, and incorporates the CBAM module to enhance the extraction of critical optical features under complex backgrounds. Furthermore, a feature-level fusion strategy is adopted to integrate visible and infrared image information effectively. Validation on public datasets demonstrates that the proposed model achieves an 18.05 frames per second increase in detection speed over the baseline, a 1.61% improvement in mean average precision (mAP), and a 2 MB reduction in model size, substantially improving both detection accuracy and efficiency through this optimized integration in anomaly inspection of electrical equipment. Validation on a representative edge device, the NVIDIA Jetson Nano, confirms the model’s practical applicability. After INT8 quantization, the model achieves a real-time inference speed of 40.8 FPS with a high mAP of 80.91%, while consuming only 5.2 W of power. Compared to the standard YOLOv4, our model demonstrates a significant improvement in both processing efficiency and detection accuracy, offering a uniquely balanced and deployable solution for mobile inspection platforms. Full article

This study presents a methodology to automatically assess visibility distance on secondary roads using mobile LiDAR systems. The method evaluates both braking and overtaking visibility distances based on the 3D geometry of the road, applying a dynamic analysis through a series of parametrised quadrangular pyramids that simulate the driver’s field of view. Road segments are classified into three risk levels, low, medium, and high, according to the feasibility of stopping or overtaking safely. The methodology was validated on three secondary roads in Spain, achieving an average accuracy of 92.7% when compared to existing road signage. These results demonstrate the method’s potential to improve road safety through continuous, data-driven visibility monitoring. Its application supports advanced driver assistance systems and offers road authorities a reliable tool for proactive risk assessment and road infrastructure planning. Full article

Exhaled breath analysis (EBA) is an advanced, non-invasive diagnostic technique that utilizes volatile organic compounds (VOCs) to detect and monitor various diseases. This review examines EBA’s historical development and current status as a promising diagnostic tool. It highlights the significant contributions of modern methods such as gas chromatography–mass spectrometry (GC-MS), ion mobility spectrometry (IMS), and electronic noses in enhancing the sensitivity and specificity of EBA. Furthermore, it emphasizes the transformative role of nanotechnology and machine learning in improving the diagnostic accuracy of EBA. Despite challenges such as standardization and environmental factors, which must be addressed for the widespread adoption of this technique, EBA shows excellent potential for early disease detection and personalized medicine. The review also highlights the potential of photonic crystal fiber (PCF) sensors, known for their superior sensitivity, in the field of EBA. Full article

Penetration depth is a critical factor determining joint strength in butt welding; however, it is difficult to monitor in keyhole-mode laser welding due to the dynamic nature of the keyhole. Recently, optical coherence tomography (OCT) has been introduced for real-time keyhole depth measurement, though accurate results require meticulous calibration. In this study, deep learning-based models were developed to estimate penetration depth in laser welding of 780 dual-phase (DP) steel. The models utilized coaxial weld pool images and spectrometer signals as inputs, with OCT signals serving as the output reference. Both uni-sensor models (based on coaxial pool images) and multi-sensor models (incorporating spectrometer data) were developed using transfer learning techniques based on pre-trained convolutional neural network (CNN) architectures including MobileNetV2, ResNet50V2, EfficientNetB3, and Xception. The coefficients of determination values (R²) of the uni-sensor CNN transfer learning models without fine-tuning ranged from 0.502 to 0.681, and the mean absolute errors (MAEs) ranged from 0.152 mm to 0.196 mm. In the fine-tuning models, R² decreased by more than 17%, and MAE increased by more than 11% compared to the previous models without fine-tuning. In addition, in the multi-sensor model, R² ranged from 0.900 to 0.956, and MAE ranged from 0.058 mm to 0.086 mm, showing better performance than uni-sensor CNN transfer learning models. This study demonstrated the potential of using CNN transfer learning models for predicting penetration depth in laser welding of 780DP steel. Full article

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, ease of use, computer literacy, and self-efficacy. Results showed that usefulness and ease of use significantly influence user attitudes and intentions. The findings suggest actionable recommendations for improving IBON system adoption, including training programs to enhance computer literacy and self-efficacy and strategies to demonstrate the system’s relevance to user needs. Future research should explore additional external factors, such as cultural influences and user experience design, and conduct longitudinal studies to assess sustained use and impact on biodiversity monitoring outcomes. This study underscores the importance of fostering user acceptance to maximize the potential of innovative technologies like IBON Monitoring in advancing biodiversity conservation efforts. Full article

This study evaluates a solar-powered electric mobility pilot implemented at the University of Cuenca (Ecuador), combining two electric vans with daytime charging from a 35 kWp PV microgrid. Real-world monitoring with SCADA covered one year of operation, including efficiency tests across urban, highway, and mountainous routes. Over the monitored period, the fleet completed 5256 km in 1384 trips with an average occupancy of approximately 87%. Energy use averaged 0.17 kWh/km, totaling 893.52 kWh, of which about 98.2% came directly from on-site PV generation; only 2.41% of the annual PV output was required for vehicle charging. This avoided 1310.52 kg of CO₂ emissions compared to conventional vehicles. Operating costs were reduced by institutional electricity tariffs (0.065 USD/kWh) and the absence of additional PV investment, with estimated savings of around USD 2432 per vehicle annually. Practical guidance from the pilot includes aligning fleet schedules with peak solar generation, ensuring access to slow daytime charging points, maintaining high occupancy through route management, and using basic monitoring to verify performance. These results confirm the technical feasibility, economic competitiveness, and replicability of solar-electric transport in institutional settings with suitable solar resources and infrastructure. Full article