Search Results (21,790)

With the increasing frequency of traffic safety issues and the rapid development of autonomous driving technology, traffic sign detection is highly susceptible to adverse weather conditions such as changes in light intensity, fog, rain, snow, and partial occlusion, which places higher demands on the accurate recognition of traffic signs. This paper proposes an improved DAYOLO model based on YOLOv8n, aiming to balance detection accuracy and model complexity. First, the Bottleneck in the C2f module of the YOLOv8n backbone network is replaced with Bottleneck DAttention. Introducing DAttention allows for more effective feature extraction, thereby improving model performance. Second, an ultra-lightweight and efficient upsampler, Dysample, is introduced into the neck network to further improve performance and reduce computational overhead. Finally, a Task-Aligned Dynamic Detection Head (TADDH) is introduced. TADDH enhances task interaction through a dynamic mechanism and utilizes shared convolutional modules to reduce parameters and improve efficiency. Simultaneously, an additional Layer2 detection head is added to the model to strengthen the extraction and fusion of features at different scales, thereby improving the detection accuracy of small traffic signs. Furthermore, replacing SlideLoss with NWDLoss can better handle prediction results with more complex distributions and more accurately measure the distance between predicted and ground truth boxes in the feature space during object detection. Experimental results show that DAYOLO achieves 97.2% mAP on the SDCCVP dataset, which is 5.3 higher than the baseline model YOLOv8n; the frame rate reaches 120, which is 37.8% higher than YOLOv8; and the number of parameters is reduced by 6.2%, outperforming models such as YOLOv3, YOLOv5, YOLOv6, and YOLOv7. In addition, DAYOLO achieves 80.8 mAP on the TT100K dataset, which is 9.2% higher than the baseline model YOLOv8n. The proposed method achieves a balance between model size and detection accuracy, meets the needs of traffic sign detection, and provides new ideas and methods for future research in the field of traffic sign detection. Full article

Shield thrust is a key control parameter for ensuring the safety and efficiency of tunnel construction. Under complex geological conditions and strong data nonlinearity, conventional prediction methods often fail to achieve sufficient accuracy. This study proposes a hybrid prediction model in which a bidirectional long short-term memory (BiLSTM) network is optimized by intelligent algorithms. A multidimensional input dataset comprising tunnel geometry, geomechanical parameters and tunnelling parameters is constructed, and BiLSTM is used to capture bidirectional temporal dependencies in the tunnelling data. To adaptively determine key BiLSTM hyperparameters (number of neurons, dropout rate and learning rate), four intelligent optimization algorithms—genetic algorithm (GA), particle swarm optimization (PSO), sparrow search algorithm (SSA) and Hunger Games Search (HGS)—are employed for hyperparameter tuning, using the root-mean-square error (RMSE) between predicted and measured values as the fitness function. Four hybrid models, GA–BiLSTM, PSO–BiLSTM, SSA–BiLSTM and HGS–BiLSTM, are validated using real engineering data from Beijing Metro Line 22, Guanzhuang–Yongshun shield-driven section. The results show that HGS–BiLSTM outperforms the other models in terms of RMSE, mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R²) and exhibits faster convergence, supporting real-time prediction and decision-making in shield thrust control. Full article

Growing demand for computing resources is increasing electricity use and cooling needs in data centres (DCs). Simultaneously, it creates opportunities for decarbonisation through the integration of waste heat (WH) into district heating (DH) systems. Such integration reduces primary energy (PE) consumption and emissions, particularly in low-temperature DH networks. In this study, the possibility for utilisation of WH from DC hybrid cooling system into third generation (3G), fourth generation (4G), and fifth generation (5G) DH systems is investigated. The work is based on the dynamic simulations in TRNSYS. The model of the hybrid cooling system consists of a direct liquid cooling (DLC) loop (25–30 °C) and a chilled water rack coolers (CRCC) loop (10–15 °C). For 3G DH, a high-temperature water-to-water heat pump (HP) is applied to ensure the required water temperature in the system. Measured meteorological and equipment data are used to reproduce real DC operating conditions. Relative to the reference system, integrating WH into 5G DH reduces PE consumption and CO₂ emissions by 88%. Results indicate that integrating WH into 5G DH and 4G DH minimises global cost and achieves a payback period of less than one year, whereas 3G DH, requiring high-temperature HPs, achieves 14 years. This approach to integrating waste heat from a hybrid DLC+CRCC DC cooling system is technically feasible, economically and environmentally viable for planning future urban integrations of waste heat into DH systems. Full article

Due to the development of the Internet of Things (IoT) and maritime wireless networks, the wireless networking of vessels will be the future trend. Furthermore, long-range (LoRa) technology is widely used in the marine field with the benefits of long range, lower power consumption, security, scalability, and robustness. In this study, LoRa is used as the solution for internal wireless networks of vessels as well as considering external and internal wireless communication, aiming to reduce construction and maintenance costs. The received signal strength (RSS) and signal to interference plus noise ratio (SINR) were measured and analyzed. The findings demonstrated that the mean value of the RSS and the SINR in the cockpit are above −81.70 dBm and 4.45 dB respectively, which indicates that there is a good communication link between the deck and the cockpit. Furthermore, the RSS value acquired by the nodes located on the same side of the gateway is stronger than that of the other nodes. Additionally, the RSS value acquired by the nodes close to the windows is found to be as high as 6–9 dB over that of the node located in the middle of the cockpit. Full article

Cyber–physical systems (CPS) in healthcare represent a deep integration of computational intelligence, physical medical devices, and human-centric data, enabling continuous, adaptive, and personalized care. These systems combine real-time measurements, artificial intelligence (AI)-based analytics, and networked medical devices to monitor, predict, and optimize patient health outcomes. A key development in the field of CPS is the emergence of patient digital twins (DTs), virtual models of individual patients that simulate biological, behavioral, and social parameters. Using AI, DTs analyze complex medical and social data (genetics, lifestyle, environment, etc.) to support precise diagnosis and treatment planning. The implications of the bibliometric findings suggest that the field emerges from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context. Reflecting insights from medical and social research, AI-based DT systems provide a holistic view of the patient, taking into account not only physiological states but also psychological and social well-being. These systems promote personalized therapy by dynamically adapting treatment based on real-time feedback from wearable sensors and electronic medical records. More broadly, CPS and DT systems increase healthcare system efficiency by reducing hospitalizations and supporting remote preventive care. Their implementation poses significant ethical and privacy challenges, particularly regarding data ownership, algorithm transparency, and patient autonomy. Full article

Urbanicity is a recognized determinant of mental health, yet conventional measures such as population density or the rural–urban divide often fail to capture the complex realities of informal settlements in low- and middle-income countries. This paper conceptualizes neighborhood effects through the lived experiences of young women in Kampala, Uganda, drawing on participatory research from the NIH-funded TOPOWA study. Using community mapping and Photovoice, participants identified neighborhood features that shape wellbeing, including sanitation facilities, drainage systems, alcohol outlets, health centers, schools, boda boda stages (motorcycle taxis), lodges, religious institutions, water sources, markets, and recreational spaces. These methods revealed both stressors—poor waste management, flooding, violence, gendered harassment, crime, and alcohol-related harms—and protective resources, including education, places of worship, health centers, social networks, identity, and sports activities. We argue that urbanicity in slum contexts should be understood as a multidimensional construct encompassing deprivation, fragmentation, exclusion, and resilience. This reconceptualization advances conceptual clarity, strengthens the validity of mental health research in low-resource settings, and informs interventions that simultaneously address structural risks and promote community assets. The case of Kampala demonstrates how participatory evidence can reshape the understanding of neighborhood effects with implications, for global mental health research and practice. Full article

Accurate recognition of lower limb activities is essential for wearable rehabilitation systems and assistive robotics like exoskeletons and prosthetics. This study introduces SDALLE, a custom hardware data acquisition system that integrates surface electromyography sensors (EMGs) and inertial measurement sensors (IMUs) into a wireless, portable platform for locomotor monitoring. Using this system, data were collected from nine healthy subjects performing four fundamental locomotor activities: walking, jogging, stair ascent, and stair descent. The recorded signals underwent an offline structured preprocessing pipeline consisting of time-series augmentation (jittering and scaling) to increase data diversity, followed by wavelet-based denoising to suppress high-frequency noise and enhance signal quality. A temporal one-dimensional convolutional neural network (1D-TCNN) with three convolutional blocks and fully connected layers was trained on the prepared dataset to classify the four activities. Classification using IMU sensors achieved the highest performance, with accuracies ranging from 0.81 to 0.95. The gyroscope X-axis of the left Rectus Femoris achieved the best performance (0.95), while accelerometer signals also performed strongly, reaching 0.93 for the Vastus Medialis in the Y direction. In contrast, electromyography channels showed lower discriminative capability. These results demonstrate that the combination of SDALLE hardware, appropriate data preprocessing, and a temporal CNN provides an effective offline sensing and activity classification pipeline for lower limb activity recognition and offers an open-source dataset that supports further research in human activity recognition, rehabilitation, and assistive robotics. Full article

Conventional educational assessments enforce a rigid and symmetrical framework of identical question sequences upon a learner population inherently defined by asymmetry in cognitive capabilities and knowledge profiles. This mismatch results in inefficient measurement, where the uniform distribution of difficulty fails to mirror the heterogeneous nature of student learning. We address these topological and informational asymmetries through HARLA-ED, a hybrid framework combining deep knowledge modeling with intelligent question selection. The system integrates hierarchical cognitive graph networks to map the structural symmetries of concept dependencies while tracking evolving knowledge states across multiple time scales. By capturing both immediate working-memory constraints and long-term retention patterns, the model resolves the temporal asymmetry between learning and forgetting rates. A hierarchical reinforcement learning agent then orchestrates an assessment strategy through three decision levels: high-level planning determines diagnostic objectives, mid-level control sequences question types, and low-level actions select specific items. Crucially, the agent employs information-theoretic reward functions designed to restore distributional symmetry in assessment outcomes, ensuring demographic parity and minimizing algorithmic bias. Empirical results demonstrate a 47.5% average reduction in assessment duration compared to standard computer-adaptive tests while preserving measurement accuracy. The system successfully adapts to varying proficiency levels, effectively bridging the information asymmetry between the testing system and the learner’s true latent state. Full article

Amidst the accelerating trend of population aging, addressing regional disparities in basic elderly care services (BECS for short) and identifying the key obstacles to their development have become crucial prerequisites for development. Taking urgent transformation measures is indispensable for enhancing the quality of fundamental senior care provisions and advancing the attainment of the United Nations Sustainable Development Goals (SDGs for short) by 2030. However, the extant literature does not have a sufficient understanding of the evolution of differences, spatial correlations, and sources of obstacles. Therefore, this paper takes the period from 2021 to 2023 as the investigation period and comprehensively applies the entropy weight method, Dagum Gini coefficient, kernel density estimation, Moran Index, and obstacle degree model to conduct a systematic analysis of BECS in China. Quantitative results obtained from the research demonstrate that the level of BECS in China follows the pattern of eastern > western > central > northeastern regions. The overall difference slightly increases, and the differences within and between regions vary. The kernel density estimation results are highly consistent with the current landscape of the level of BECS in China, and the spatial correlation and aggregation characteristics are obvious. It was also found that the main obstacles in the quasi-measurement layer (including the indicator layer) were concentrated in the dimension of welfare subsidies. Based on this, a policy combination proposal is put forward in terms of strengthening the construction of a multi-subject supply network, promoting the cross-regional coordinated development of human, financial, and material factors, and enhancing the government’s governance capacity, with the aim of increasing Chinese contributions to improving the level of BECS and achieving the United Nations 2030 Sustainability Goals on schedule. Full article

Blast-induced rock fragmentation plays a critical role in mining and civil engineering. One of the primary objectives of blasting operations is to achieve the desired rock fragmentation size, which is a key indicator of the quality of the blasting process. Predicting the mean fragmentation size (MFS) is crucial to avoid increased production costs, material loss, and ore dilution. This study integrates three tree-based regression techniques—gradient boosting regression (GBR), histogram-based gradient boosting machine (HGB), and extra trees (ET)—with two optimization algorithms, namely, grey wolf optimization (GWO) and particle swarm optimization (PSO), to predict the MFS. The performance of the resulting models was evaluated using four statistical measures: coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results indicate that the GWO-HGB model outperformed all other models, achieving R², RMSE, MAE, and MAPE values of 0.9402, 0.0251, 0.0185, and 0.0560, respectively, in the testing phase. Additionally, the Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME), and neural network-based sensitivity analyses were applied to examine how input parameters influence model predictions. The analysis revealed that unconfined compressive strength (UCS) emerged as the most influential parameter affecting MFS prediction in the developed model. This study provides a novel hybrid intelligent model to predict MFS for optimized blasting operations in open-pit mines. Full article

This work demonstrates the synthesis of ferromagnetic metallic cobalt nanoparticles embedded in a graphene framework through a graphene-assisted carbothermal reduction process. Cobalt oxide (Co₃O₄) was employed as the starting material, with graphene nanopowder functioning simultaneously as the reducing medium and structural scaffold. Thermal treatment at 850 °C under an argon atmosphere triggered the phase transformation. X-ray diffraction (XRD) confirmed the successful conversion of cobalt oxide into face-centered cubic (FCC) metallic cobalt. The graphene network not only accelerated the reduction reaction but also ensured the homogeneous distribution of cobalt nanoparticles within the matrix. Magnetic measurements using vibrating sample magnetometry (VSM) revealed a substantial improvement in ferromagnetic behavior: the graphene-mediated samples reached a saturation magnetization (M_s) of approximately 130 emu/g, compared to the nearly non-magnetic response of cobalt oxide annealed under the same conditions without graphene. Collectively, the structural, compositional, and magnetic results highlight graphene’s critical role in driving the formation of metallic cobalt nanoparticles with enhanced ferromagnetism, emphasizing their promise for use in magnetic storage, sensing, and spintronic applications. We anticipate that this study will inspire further research into the dual functionality of graphene, serving as both a reductive agent for metal oxides and a supportive matrix for nanoparticles, toward enhancing the structural integrity and functional properties of graphene-based metal nanocomposite materials. Full article

This paper proposes a privacy-preserving framework that couples probabilistic wind power forecasting with decentralized anomaly detection in complex power networks. We first design an adaptive federated learning (FL) scheme to produce probabilistic forecasts for multiple geographically distributed wind farms while keeping their raw data local. In this scheme, an artificial neural network with quantile regression is trained collaboratively across sites to provide calibrated prediction intervals for wind power outputs. These forecasts are then embedded into an alternating direction method of multipliers (ADMM)-based load-side dispatch and anomaly detection model for decentralized power systems with plug-and-play industrial users. Each monitoring node uses local measurements and neighbor communication to solve a distributed economic dispatch problem, detect abnormal load behaviors, and maintain network consistency without a central coordinator. Experiments on the GEFCom 2014 wind power dataset show that the proposed FL-based probabilistic forecasting method outperforms persistence, local training, and standard FL in RMSE and MAE across multiple horizons. Simulations on IEEE 14-bus and 30-bus systems further verify fast convergence, accurate anomaly localization, and robust operation, indicating the effectiveness of the integrated forecasting–dispatch framework for smart industrial grids with high wind penetration. Full article

Background: This study aimed to explore the synergistic hypoglycemic effect and mechanism of 1-deoxynojirimycin (DNJ) in mulberry leaves and theaflavins (TFs) in black tea. Methods: The synergistic inhibition of α-glucosidase and α-amylase by DNJ-TFs was evaluated using enzyme assays and the Chou–Talalay model. Insulin-resistant (IR) HepG2 cells and high-fat diet (HFD)-induced type 2 diabetes mellitus mice were treated with DNJ, TFs, or DNJ-TFs, determining the efficacy of drug combinations by measuring glycolipids and inflammatory factors. Network pharmacology and molecular docking were used to identify key target genes and signaling pathways, and CETSA experiments were used to verify the binding of drugs to targets. Key genes were further verified by immunofluorescence, Western blot, and Real-time PCR. Results: DNJ-TFs synergistically suppressed α-glucosidase (CI = 0.85) and α-amylase (CI = 0.76). In HepG2 cells, DNJ-TFs ameliorated palmitic acid-induced IR by promoting glucose uptake, attenuating lipid accumulation, and regulating glycolipid metabolism. In HFD mice, DNJ-TFs counteracted hyperglycemia, dyslipidemia, systemic inflammation and oxidative stress, elevated HOMA-IR, and hepatic steatosis. Network pharmacology integrated with experimental validation identified PTGS2 and MMP9 as key binding targets of DNJ and TFs. Furthermore, DNJ-TFs could inhibit the increase in liver TNFα protein and the decrease in p-AKT, p-GSKα, p-GSKβ, and GLUT2 protein caused by high fat, both in vivo and in vitro. Conclusions: DNJ and TFs exert synergistic glucose-lowering effects by targeting PTGS2/MMP9 and regulating the TNFα/AKT/GSK3/GLUT2 axis, providing a promising natural therapeutic strategy for diabetes management. Full article

The internal temperature of a vehicle is influenced by various factors such as the external environment (temperature, solar radiation, and humidity) and the air conditioning habits of the driver. Even when the air conditioning system is set to a specific temperature, the internal temperature can vary depending on the time, weather, and driver’s manipulation of the system. In this study, we developed and evaluated a deep learning-based vehicle cabin temperature prediction system using CAN (Controller Area Network) data collected from the vehicle and temperature data from thermometers installed on the roof and seats of an electric vehicle (EV). The models used in the temperature prediction system were evaluated by applying various deep learning architectures that consider the characteristics of time series data, and their accuracy was measured using the mean absolute percentage error (MAPE) metric. Additionally, a low-pass filter was applied to the prediction results, which reduced the MAPE from 4.2798% to 4.1433%, indicating an improvement in prediction accuracy. Among the deep learning models, the model with the highest performance achieved an MAPE of 3.5287%, corresponding to an approximate error of 0.88 °C at an actual temperature of 25 °C. The results of this study contribute significantly to enhancing the accuracy and reliability of EV interior temperature predictions, enabling more precise simulations, and improving the thermal comfort and energy efficiency of EVs. The proposed temperature-prediction system is expected to contribute to the comfort of EV users and overall performance of vehicles, thereby strengthening the role of EVs as a sustainable means of transportation. Full article

Aberrations in fibroblast growth factor receptors (FGFRs) constitute a key oncogenic mechanism across multiple solid tumors, influencing tumor initiation, therapeutic response, and clinical outcomes. This review synthesizes current knowledge on the molecular biology, signaling networks, and tumor-specific distribution of FGFR alterations, including amplifications, point mutations, and gene fusions. The mechanistic basis of FGFR-driven tumor progression is discussed, including activation of downstream signaling pathways, crosstalk with other receptor tyrosine kinases, and regulation of the tumor microenvironment, angiogenesis, and immune escape. Recent development of selective FGFR inhibitors—such as pemigatinib, erdafitinib, and futibatinib—has translated mechanistic insights into measurable clinical benefits in genomically defined patient populations. However, acquired resistance remains a major challenge, driven by secondary mutations, activation of bypass pathways, and intratumoral heterogeneity. Integration of multi-omics profiling, liquid biopsy monitoring, and biomarker-guided patient selection is essential to optimize therapeutic efficacy and overcome resistance. This review also highlights emerging therapeutic modalities, such as antibody–drug conjugates and nanotechnology-based delivery systems, which may improve target specificity and prolong therapeutic durability. By integrating molecular, translational, and clinical evidence, this review aims to establish a comprehensive framework for precision oncology strategies targeting FGFR-driven malignancies. Full article