Search Results (17,033)

Metal–organic frameworks (MOFs) have garnered significant attention in recent years for their potential to revolutionize heat exchanger performance, thanks to their high surface area, tunable porosity, and exceptional adsorption capabilities. This review focuses on the integration of MOFs into heat exchangers to enhance heat transfer efficiency, improve moisture management, and reduce energy consumption in Heating, Ventilation and Air Conditioning (HVAC) and related systems. Recent studies demonstrate that MOF-based coatings can outperform traditional materials like silica gel, achieving superior water adsorption and desorption rates, which is crucial for applications in air conditioning and dehumidification. Innovations in synthesis techniques, such as microwave-assisted and surface functionalization methods, have enabled more cost-effective and scalable production of MOFs, while also enhancing their thermal stability and mechanical strength. However, challenges related to the high costs of MOF synthesis, stability under industrial conditions, and large-scale integration remain significant barriers. Future developments in hybrid nanocomposites and collaborative efforts between academia and industry will be key to advancing the practical adoption of MOFs in heat exchanger technologies. This review aims to provide a comprehensive understanding of current advancements, challenges, and opportunities, with the goal of guiding future research toward more sustainable and efficient thermal management solutions. Full article

Ti₃C₂T_x MXene, a novel two-dimensional transition metal carbide with nanoscale dimensions, has attracted significant attention due to its exceptional structural and performance characteristics. This review comprehensively examines various preparation methods for Ti₃C₂T_x MXene, including acid etching, acid–salt composite etching, alkali etching, and molten salt etching. It further discusses several strategies for interlayer exfoliation, highlighting the advantages and limitations of each method. The effects of these techniques on the nanostructure, surface functional groups, interlayer spacing, and overall performance of Ti₃C₂T_x MXene are evaluated. Additionally, this paper explores the diverse applications of Ti₃C₂T_x MXene in ceramic materials, particularly its role in enhancing mechanical properties, electrical and thermal conductivity, as well as oxidation and corrosion resistance. The primary objective of the review is to provide scientific insights and theoretical guidance for the preparation of Ti₃C₂T_x MXene and its further research and innovative applications in ceramic materials, advancing the development of high-performance, multifunctional ceramics. Full article

Green fiscal policy draws worldwide attention from policymakers as a potential mechanism that contributes to sustainable development. However, although many studies have discussed the economic consequences of green fiscal policy, there is still a lack of studies that systematically quantify the productivity impacts of green fiscal policy. Therefore, to fill this gap, China’s Energy Conservation and Emission Reduction Fiscal Policy Pilot (ECER) and difference-in-differences (DID) identification method were chosen to explore the impact of green fiscal policy on green total factor productivity (GTFP). We find that ECER significantly enhances urban GTFP, and it holds after a series of robustness tests. Moreover, we explore the mediating mechanisms that may explain this effect: government environmental regulation, green technology innovation, and industrial structure optimization. Further analysis shows that the positive effect of ECER is more significant when (1) government transparency is high; (2) government financial autonomy is high; (3) government digital transformation is high; (4) the city’s resource endowment is high; and (5) the city’s economic development level is high. Overall, our study provides new insights into the economic consequences of green fiscal policy. Full article

Traditional entropy source evaluation methods rely on statistical analysis and are hard to deploy on-chip or online. However, online detection of entropy source quality is necessary in some applications with high encryption levels. To address these issues, our experimental results demonstrate a significant negative correlation between minimum entropy values and prediction accuracy, with a Pearson correlation coefficient of −0.925 (p-value = 1.07 × 10⁻⁷). This finding offers a novel approach for assessing entropy source quality, achieving an accurate rate in predicting the next bit of a random sequence using neural networks. To further improve prediction capabilities, we also propose a novel deep learning architecture, Fast Fourier Transform-Attention Mechanism-Long Short-Term Memory Network (FFT-ATT-LSTM), that integrates a simplified soft attention mechanism with Fast Fourier Transform (FFT), enabling effective fusion of time-domain and frequency-domain features. The FFT-ATT-LSTM improves prediction accuracy by 4.46% and 8% over baseline networks when predicting random numbers. Additionally, FFT-ATT-LSTM maintains a compact parameter size of 33.90 KB, significantly smaller than Temporal Convolutional Networks (TCN) at 41.51 KB and Transformers at 61.51 KB, while retaining comparable prediction performance. This optimal balance between accuracy and resource efficiency makes FFT-ATT-LSTM suitable for online deployment, demonstrating considerable application potential. Full article

Silver nanoparticles (AgNPs) have gained significant attention in veterinary medicine due to their antimicrobial properties and potential therapeutic applications. Silver has long been recognized for its ability to combat a wide range of pathogens, and when engineered at the nanoscale, silver’s surface area and reactivity are greatly enhanced, making it highly effective against bacteria, viruses, and fungi. This narrative review aimed to summarize the evidence on the antimicrobial properties of AgNPs and their current and potential clinical applications in veterinary medicine. The antimicrobial action of AgNPs involves several mechanisms, including, among others, the release of silver ions, disruption of cell membranes and envelopes, induction of oxidative stress, inhibition of pathogens’ replication, and DNA damage. Their size, shape, surface charge, and concentration influence their efficacy against bacteria, viruses, and fungi. As a result, the use of AgNPs has been explored in animals for infection prevention and treatment in some areas, such as wound care, coating of surgical implants, animal reproduction, and airway infections. They have also shown promise in preventing biofilm formation, a major challenge in treating chronic bacterial infections. Additionally, AgNPs have been studied for their potential use in animal feed as a supplement to enhance animal health and growth. Research suggested that AgNPs could stimulate immune responses and improve the gut microbiota of livestock, potentially reducing the need for antibiotics in animal husbandry. Despite their promising applications, further research is necessary to fully understand the safety, efficacy, and long-term effects of AgNPs on animals, humans, and the environment. Full article

The pore structure plays a crucial role in soil systems. It affects a range of processes essential for soil ecological functions, such as the transport and retention of water and nutrients, as well as gas exchanges. The mechanical and hydrological characteristics of soil are predominantly determined by the three-dimensional pore pore-space structure. A precise analysis of pore structure can help specialists understand how these shapes impact plant root activity, leading to better cultivation practices. X-ray computed tomography provides detailed information without destroying the sample. However, manually delineating pore structure and estimating porosity are challenging tasks. This work proposes an automated method for 3D pore segmentation and characterization using convolutional neural networks with attention mechanisms. The method introduces a novel approach that combines attention at both channel and spatial levels, enhancing the segmentation and property estimation, providing valuable insights for a more detailed study of soil conditions. In experiments conducted with a private dataset, the segmentation results achieved mean Dice values of 99.10% ± 0.0004 and mean IoU values of 98.23% ± 0.0008. Additionally, in tests with Phaeozem Albic, the automatic method provided porosity estimates comparable to those obtained by a method based on integral geometry and morphology. Full article

This study examines the use of ultrafine (~128 nm) microsilica (composed of a mixture of amorphous and microcrystalline silicon dioxide phases) particles, an industrial waste product, as a reinforcing material to create aluminum matrix composites (AMCs) via ultrasonic-assisted stir casting followed by T6 heat treatment. This study aimed to improve the mechanical properties of pure aluminum, which has insufficient strength for most engineering applications. The main objective of this study is to develop environmentally and economically efficient AMCs with improved properties, namely, the balance between strength and ductility, for further application in caliber rolling processes. Attention is also paid to minor reinforcements using a low concentration of microsilica (~0.36%wt), which minimizes the problems with the wettability of the reinforcing material particles. The composites reinforced with ultrafine microsilica exhibited enhanced mechanical performance, including a 59.7% increase in Vickers microhardness and a significant improvement in tensile strength, reaching 73 MPa. Additionally, T6 heat treatment synergistically improved ductility to 60.3% elongation while maintaining high strength, achieving a balanced performance suitable for forming processes. The study results confirm that using microsilica as a reinforcing material is an effective way to improve the performance of aluminum alloys, while minimizing costs and solving environmental problems. Full article

To address the issues of insufficient samples, limited scene diversity, missing perspectives, and low resolution in existing UAV-based pedestrian detection datasets, this paper proposes a novel UAV-based pedestrian detection benchmark dataset named the Novel Surveillance View (NSV). This dataset encompasses diverse scenes and pedestrian information captured from multiple perspectives, and introduces an innovative data mining approach that leverages tracking and optical flow information. This approach significantly improves data acquisition efficiency while ensuring annotation quality. Furthermore, an improved pedestrian detection method is proposed to overcome the performance degradation caused by significant perspective changes in top-down UAV views. Firstly, the View-Agnostic Decomposition (VAD) module decouples features into perspective-dependent and perspective-independent branches to enhance the model’s generalization ability to perspective variations. Secondly, the Deformable Conv-BN-SiLU (DCBS) module dynamically adjusts the receptive field shape to better adapt to the geometric deformations of pedestrians. Finally, the Context-Aware Pyramid Spatial Attention (CPSA) module integrates multi-scale features with attention mechanisms to address the challenge of drastic target scale variations. The experimental results demonstrate that the proposed method improves the mean Average Precision (mAP) by 9% on the NSV dataset, thereby validating that the approach effectively enhances pedestrian detection accuracy from UAV perspectives by optimizing perspective features. Full article

Face detection in resource-constrained environments presents challenges, due to the computational demands of state-of-the-art models and the complexity of real-world conditions, such as variations in scale, pose, and occlusion. This study introduces FeatherFace, a lightweight face-detection architecture with only 0.49 M parameters, designed for high accuracy and efficiency in such environments. Leveraging MobileNet-0.25 as a backbone, FeatherFace incorporates advanced feature-integration strategies, including a bidirectional feature pyramid network (BiFPN), a convolutional block attention module (CBAM), deformable convolutions, and channel shuffling. Evaluated on the WIDERFace dataset, FeatherFace achieves an overall average precision (AP) of 87.2%, with notable performance gains of 4.0% AP on the Hard subset compared with the baseline. Ablation studies highlight the critical role of multiscale feature integration and the strategic placement of attention mechanisms in addressing detection challenges such as small or occluded faces. With its compact design and reduced inference time, FeatherFace bridges the gap between the reliability of computationally intensive models and the need for deploying robust models in highly resource-constrained environments, such as edge devices and embedded systems. This work provides valuable insights for developing robust and lightweight models suited to challenging real-world applications. Full article

An accurate and efficient detection method for harvesting is crucial for the development of automated harvesting robots in short-cycle, high-yield facility tomato cultivation environments. This study focuses on cherry tomatoes, which grow in clusters, and addresses the complexity and reduced detection speed associated with the current multi-step processes that combine target detection with segmentation and traditional image processing for clustered fruits. We propose YOLO-Picking Point (YOLO-PP), an improved cherry tomato picking point detection network designed to efficiently and accurately identify stem keypoints on embedded devices. YOLO-PP employs a C2FET module with an EfficientViT branch, utilizing parallel dual-path feature extraction to enhance detection performance in dense scenes. Additionally, we designed and implemented a Spatial Pyramid Squeeze Pooling (SPSP) module to extract fine features and capture multi-scale spatial information. Furthermore, a new loss function based on Inner-CIoU was developed specifically for keypoint tasks to further improve detection efficiency.The model was tested on a real greenhouse cherry tomato dataset, achieving an accuracy of 95.81%, a recall rate of 98.86%, and mean Average Precision ($mAP$) scores of 99.18% and 98.87% for mAP50 and mAP50-95, respectively. Compared to the DEKR, YOLO-Pose, and YOLOv8-Pose models, the mAP value of the YOLO-PP model improved by 16.94%, 10.83%, and 0.81%, respectively. The proposed algorithm has been implemented on NVIDIA Jetson edge computing devices, equipped with a human–computer interaction interface. The results demonstrate that the proposed Improved Picking Point Detection Network exhibits excellent performance and achieves real-time accurate detection of cherry tomato harvesting tasks in facility agriculture. Full article

Background/Objectives: Colorectal cancer (CRC) remains a leading cause of cancer mortality globally, with KRAS mutations occurring in 30–40% of cases, contributing to poor prognosis and resistance to anti-EGFR therapy. This review explores the biological significance, clinical implications, and therapeutic targeting of KRAS mutations in CRC. Methods: A comprehensive analysis of the existing literature and clinical trials was performed, highlighting the role of KRAS mutations in CRC pathogenesis, their impact on prognosis, and recent advancements in targeted therapies. Specific attention was given to emerging therapeutic strategies and resistance mechanisms. Results: KRAS mutations drive tumor progression through persistent activation of MAPK/ERK and PI3K/AKT signaling pathways. These mutations influence the tumor microenvironment, cancer stem cell formation, macropinocytosis, and cell competition. KRAS-mutant CRC exhibits poor responsiveness to anti-EGFR monoclonal antibodies and demonstrates primary and acquired resistance to KRAS inhibitors. Recent breakthroughs include the development of KRAS G12C inhibitors (sotorasib and adagrasib) and promising agents targeting G12D mutations. However, response rates in CRC remain suboptimal compared to other cancers, necessitating combination therapies and novel approaches, such as vaccines, nucleic acid-based therapeutics, and macropinocytosis inhibitors. Conclusions: KRAS mutations are central to CRC pathogenesis and present a significant therapeutic challenge. Advances in KRAS-targeted therapies offer hope for improved outcomes, but resistance mechanisms and organ-specific differences limit efficacy. Continued efforts in personalized treatment strategies and translational research are critical for overcoming these challenges and improving patient survival. Full article

In the intelligent transportation field, the Internet of Things (IoT) is commonly applied using 3D object detection as a crucial part of Vehicle-to-Everything (V2X) cooperative perception. However, challenges arise from discrepancies in sensor configurations between vehicles and infrastructure, leading to variations in the scale and heterogeneity of point clouds. To address the performance differences caused by the generalization problem of 3D object detection models with heterogeneous LiDAR point clouds, we propose the Dual-Channel Generalization Neural Network (DCGNN), which incorporates a novel data-level downsampling and calibration module along with a cross-perspective Squeeze-and-Excitation attention mechanism for improved feature fusion. Experimental results using the DAIR-V2X dataset indicate that DCGNN outperforms detectors trained on single datasets, demonstrating significant improvements over selected baseline models. Full article

Immunohistochemical (IHC) detection is crucial in diagnosing lung cancer. Interleukin-24 (IL-24) is a valuable marker in IHC analysis, aiding in tumor characterization and prognostication. However, current manual scoring methods are labor-intensive, imprecise, and subjective, leading to inconsistencies among observers. Automated scoring methods also have limitations, such as poor segmentation and lack of interpretability. In this paper, we introduce RMD-Net, a novel scoring network framework specifically designed for IL-24 scoring in lung cancer. The framework incorporates a regional attention mechanism and a multi-channel scoring network. Initially, diagnostic region identification and segmentation are accomplished by integrating the diagnostic regional spatial attention module into the fully convolutional network. Subsequently, we employ the Adaptive Multi-Thresholding algorithm to derive expert, strong feature description maps. Finally, the attention-guided IHC images and expert feature description maps are fed into a multi-channel scoring network. Its backbone includes feature fusion layers and scoring layers to ensure the accuracy and interpretability of the final result. To the best of our knowledge, this is the first system that directly employs lung cancer IL-24 IHC images as input and combines both expert-derived features and deep-learning abstract features to produce clinical scores. Our dataset is sourced from the Institute of Life Sciences and Bioengineering at Beijing Jiaotong University. The experimental results demonstrate that the proposed method achieves an IL-24 score precision of 89.25%, an F1 score of 89.00, and an accuracy of 95.94%, outperforming other state-of-the-art methods. This contribution has the potential to advance clinical diagnosis and treatment strategies for lung cancer. Full article

Neural network models based on GNNs often achieve good results in traffic flow prediction tasks of traffic networks. However, most existing GNN-based methods apply a fixed graph structure to capture spatial dependencies between nodes, and fixed graph structures may not be able to reflect the spatiotemporal changes in node dependencies. To address this, introducing a self-attention mechanism applied to an adaptive adjacency matrix, the neural network architecture is improved based on Graph WaveNet, and a new approach called self-attention dynamic graph wave network (SA-DGWN) is proposed, which can fit the spatiotemporal dependencies of the road network. In an experiment, traffic flow data were extracted based on RFID from certain roads in Nanjing, China. The results show that under the same configuration, compared to Graph WaveNet, MAE, MAPE, and RMSE from the proposed method reduced by 3.08%, 3.68%, and 2.6%, respectively. In addition, for the training data, we explored the impact of temporal feature and sampling periods on the training effect. The additional results indicate that adding hour-minute-second information to the input improved the model’s accuracy, reducing MAE, MAPE, and RMSE by 15.28%, 12.28%, and 14.01%, respectively. Adding day-of-the-week features also brought substantial performance improvements. For different sampling periods, the model performed better overall with a 10 min sampling period compared to 5 min and 15 min periods. For single-step prediction tasks, the longer the sampling period, the better the prediction effect. Full article

With the acceleration of global climate change and urbanization, the urban heat island effect has significantly impacted the quality of life of urban residents. Although numerous studies have focused on macro-scale factors such as air temperature, surface albedo, and green space coverage, relatively little attention has been paid to micro-scale factors, such as shading provided by building facades and tree canopy coverage. However, these micro-scale factors play a significant role in enhancing pedestrian thermal comfort. This study focuses on a city community in China, aiming to assess the thermal comfort of urban streets during the summer. Utilizing high-resolution 3D geographic data and street view images extracted from drone data, this study comprehensively considers the mechanisms affecting the urban street thermal environment and the human comfort requirements for shading and greening. By proposing quantitative indicators from multiple scales and dimensions, this study thoroughly quantifies the impact of the surrounding environment, greening, shading effects, buildings, and road design on the thermal comfort of summer streets. The results show that increasing tree canopy coverage by 10 m can significantly reduce the surrounding temperature, and a building layout extending 200 m can regulate temperature. The distribution of shadows at different times significantly affects thermal comfort, while the sky view factor negatively correlates with thermal comfort. Environments with a high green view index enhance visual comfort. This study reveals the specific contributions of different environmental characteristics to street thermal comfort and identifies factors that significantly impact thermal comfort. This provides a scientific basis for urban green space planning and thermal comfort improvement, holding substantial practical significance. Full article