Search Results (2,066)

Adverse weather events have a negative impact on the productivity of construction site activities. Understanding these effects is essential for developing realistic construction schedules. The influence of weather is shaped by both environmental factors (climate, geography, topography) and construction-related aspects such as technologies, materials, equipment, and site exposure. This paper proposes a model to quantify the influence of adverse weather by estimating monthly intervals of expected days with reduced construction productivity, based on data regarding specific weather events, including precipitation, wind, extreme temperatures, snow cover, fog, and high humidity. Data analysis employs the inclusion–exclusion principle, a combinatorial technique, alongside confidence interval estimation, a standard statistical approach. The model was applied in three Croatian cities to demonstrate its practicality and accuracy. Contractors with extensive on-site experience reviewed the results, providing insights into weather-sensitive activities and organizational practices. Full article

Reduced visibility caused by foggy weather has a significant impact on transportation systems and driving safety, leading to increased accident risks and decreased operational efficiency. Traditional methods rely on expensive physical instruments, limiting their scalability. To address this challenge in a cost-effective manner, we propose a two-stage network for visibility estimation from stereo image inputs. The first stage computes scene depth via stereo matching, while the second stage fuses depth and texture information to estimate metric-scale visibility. Our method produces pixel-wise visibility maps through a physically constrained, progressive supervision strategy, providing rich spatial visibility distributions beyond a single global value. Moreover, it enables the detection of patchy fog, allowing a more comprehensive understanding of complex atmospheric conditions. To facilitate training and evaluation, we propose an automatic fog-aware data generation pipeline that incorporates both synthetically rendered foggy images and real-world captures. Furthermore, we construct a large-scale dataset encompassing diverse scenarios. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both visibility estimation and patchy fog detection. Full article

Artificial intelligence-based image processing is critical for sensor fusion and image transformation in mobility systems. Advanced driver assistance functions such as forward monitoring and digital side mirrors are essential for driving safety. Degradation due to raindrops, fog, and high-dynamic range (HDR) imbalance caused by lighting changes impairs visibility and reduces object recognition and distance estimation accuracy. This paper proposes a diffusion framework to enhance visibility under multi-degradation conditions. The denoising diffusion probabilistic model (DDPM) offers more stable training and high-resolution restoration than the generative adversarial networks. The DDPM relies on large-scale paired datasets, which are difficult to obtain in raindrop scenarios. This framework applies the Palette diffusion model, comprising data augmentation and raindrop-removal modules. The data augmentation module generates raindrop image masks and learns inpainting-based raindrop synthesis. Synthetic masks simulate raindrop patterns and HDR imbalance scenarios. The raindrop-removal module reconfigures the Palette architecture for image-to-image translation, incorporating the augmented synthetic dataset for raindrop removal learning. Loss functions and normalization strategies improve restoration stability and removal performance. During inference, the framework operates with a single conditional input, and an efficient sampling strategy is introduced to significantly accelerate the process. In post-processing, tone adjustment and chroma compensation enhance visual consistency. The proposed method preserves fine structural details and outperforms existing approaches in visual quality, improving the robustness of vision systems under adverse conditions. Full article

To address the issues of small-target missed detection, false alarms from cloud/fog interference, and low computational efficiency in traditional wildfire detection for transmission line corridors, this paper proposes a YOLOv11_MDS detection model by integrating Multi-Scale Convolutional Attention (MSCA) and Distribution-Shifted Convolution (DSConv). The MSCA module is embedded in the backbone and neck to enhance multi-scale dynamic feature extraction of flame and smoke through collaborative depth strip convolution and channel attention. The DSConv with a quantized dynamic shift mechanism is introduced to significantly reduce computational complexity while maintaining detection accuracy. The improved model, as shown in experiments, achieves an mAP@0.5 of 88.21%, which is 2.93 percentage points higher than the original YOLOv11. It also demonstrates a 3.33% increase in recall and a frame rate of 242 FPS, with notable improvements in detecting small targets (pixel occupancy < 1%). Generalization tests demonstrate mAP improvements of 0.4% and 0.7% on benchmark datasets, effectively resolving false/missed detection in complex backgrounds. This study provides an engineering solution for real-time wildfire monitoring in transmission lines with balanced accuracy and efficiency. Full article

There are various mechanisms through which water droplets can be present in compressor flows, e.g., rain ingestion in aeroengines or overspray fogging used in heavy-duty gas turbines to boost power output. For the latter, droplet evaporation within the compressor leads to a cooling of the flow as well as to a shift in the fluid properties, which is beneficial to the overall process. However, due to their inertia, the majority of droplets are deposited in the first stages of a multistage compressor. While this phenomenon is generally considered in CFD computations of droplet-laden flows, the subsequent re-entrainment of collected water, the formation of new droplets, and the impact on the overall evaporation are mostly neglected because of the additional computational effort required, especially with regard to the modeling of films formed by the deposited water. The work presented here shows an approach that allows for the integration of the process of droplet deposition and re-entrainment based on relatively simple correlations and experimental observations from the literature. Thus, the two-phase flow in multistage compressors can be modelled and analyzed very efficiently. In this paper, the models and assumptions used are described first, then the results of a study performed based on a generic multistage compressor are presented, whereby the various models are integrated step by step to allow an assessment of their impact on the droplet evaporation throughout the compressor and overall performance. It can be shown that evaporation becomes largely independent of droplet size when deposition on both rotor and stator and subsequent re-entrainment of collected water is considered. In addition, open issues with regard to the future improvement of models and correlations of two-phase flow phenomena are highlighted based on the results of the current investigation. Full article

 COVID-19 infection is often accompanied by psychological symptoms, which may persist long after the end of the infection (long COVID). The symptoms include fatigue, cognitive impairment, and anxiety. The reason for these long-term effects is currently unclear. Therapeutic approaches have included cognitive rehabilitation therapy, physical activity, and serotonin reuptake inhibitors (SSRIs) if depression co-exists. The neuropsychological evaluation of subjects with suspected cognitive issues is essential for the correct diagnosis. Most of the COVID-19 studies used the Montreal Cognitive Assessment (MoCA) or the Mini Mental State Examination (MMSE). However, MoCA scores can be confusing if not interpreted correctly. For this reason, we have developed an original technique to map cognitive domains and motor performance on various brain areas in COVID-19 patients aiming at improving the follow-up of long-COVID-19 symptoms. To this end, we retrospectively reanalyzed data from a cohort of 40 patients hospitalized for COVID-19 without requiring intubation or hemodialysis. Cognitive function was tested during hospitalization and six months after. Global cognitive function and cognitive domains were retrieved using MoCA tests. Laboratory data were retrieved regarding kidney function, electrolytes, acid–base, blood pressure, TC score, and P/F ratio. The dimensionality of cognitive functions was represented over cortical brain structures using a transformation matrix derived from fMRI data from the literature and the Cerebroviz mapping tool. Memory function was linearly dependent on the P/F ratio. We also used the UMAP method to reduce the dimensionality of the data and represent them in low-dimensional space. Six months after hospitalization, no cases of severe cognitive deficit persisted, and the number of moderate cognitive deficits reduced from 14% to 4%. Most cognitive domains (visuospatial abilities, executive functions, attention, working memory, spatial–temporal orientation) improved over time, except for long-term memory and language skills, which remained reduced or slightly decreased. The Cerebroviz algorithm helps to visualize which brain regions might be involved in the process. Many patients with COVID-19 continue to suffer from a subclinical cognitive deficit, particularly in the memory and language domains. Cerebroviz’s representation of the results provides a new tool for visually representing the data.  Full article

Synthetic aperture radar (SAR) provides all-weather and all-day imaging capabilities and can penetrate clouds and fog, playing an important role in ship detection. However, small ships usually contain weak feature information in such images and are easily affected by noise, which makes detection challenging. In practical deployment, limited computing resources require lightweight models to improve real-time performance, yet achieving a lightweight design while maintaining high detection accuracy for small targets remains a key challenge in object detection. To address this issue, we propose a novel lightweight network for accurate small-ship recognition in SAR images, named S2M-Net. Specifically, the Space-to-Depth Convolution (SPD-Conv) module is introduced in the feature extraction stage to optimize convolutional structures, reducing computation and parameters while retaining rich feature information. The Mixed Local-Channel Attention (MLCA) module integrates local and channel attention mechanisms to enhance adaptation to complex backgrounds and improve small-target detection accuracy. The Multi-Scale Dilated Attention (MSDA) module employs multi-scale dilated convolutions to fuse features from different receptive fields, strengthening detection across ships of various sizes. The experimental results show that S2M-Net achieved mAP50 values of 0.989, 0.955, and 0.883 on the SSDD, HRSID, and SARDet-100k datasets, respectively. Compared with the baseline model, the F1 score increased by 1.13%, 2.71%, and 2.12%. Moreover, S2M-Net outperformed other state-of-the-art algorithms in FPS across all datasets, achieving a well-balanced trade-off between accuracy and efficiency. This work provides an effective solution for accurate ship detection in SAR images. Full article

Water scarcity is an escalating global challenge, driven by climate change and population growth. With only 2.5% of Earth’s freshwater readily accessible, there is an urgent need to explore sustainable alternatives. Textile-based fog collectors are advanced tools which have shown great potential and have gained remarkable attention across the world. This review critically evaluates emerging technologies for freshwater generation, including desalination (thermal and reverse osmosis (RO)), fog and dew harvesting, atmospheric water extraction, greywater reuse, and solar desalination systems, e.g., WaterSeer and Desolenator. Key performance metrics, e.g., water yield, energy input, and water collection efficiency, are summarized. For instance, textile-based fog harvesting devices can yield up to 103 mL/min/m², and modern desalination systems offer 40–60% water recovery. This work provides a comparative framework to guide future implementation of water-scarcity solutions, particularly in arid and semi-arid regions. Full article

Polyurethane-coated fabrics are widely employed as tarpaulin materials. However, due to the long duration and large space requirements of natural exposure tests, studies on fabric degradation remain scarce. To systematically investigate the natural aging patterns and mechanisms of polyurethane-coated fabrics, this study conducted 24-month natural aging tests in three representative regions: Xishuangbanna (tropical monsoon climate), Xiamen (subtropical maritime monsoon climate), and Jinan (temperate monsoon climate). Changes in appearance, mechanical properties, surface morphology, elemental composition, and microstructure were thoroughly analyzed. The results indicated that gloss decreased by over 60%, the color difference exceeded 5.8, and tear strength was reduced by more than 50%. SEM, ATR-FTIR, and XPS analyses revealed that hydrolysis and oxidation occurred in the coating, leading to coating thinning, fiber exposure, and even damage. In Xishuangbanna, high temperature, high humidity, and strong solar radiation are responsible for the most severe degradation of fabrics. High temperature, humidity, and salt fog synergistically accelerated the aging process. In Jinan, significant thermal strain contributed to deterioration, and fabrics exhibited the mildest degradation. This multi-region natural exposure study realistically simulates in-service aging behavior, providing important validation for accelerated laboratory aging methods, product reliability improvement, and service-life modeling. Full article

Long COVID (LC), also known as post-acute sequelae of COVID-19 infection (PASC), is a heterogeneous and debilitating chronic disease that currently affects 10 to 20 million people in the U.S. and over 420 million people globally. With no approved treatments, the long-term global health and economic impact of chronic LC remains high and growing. LC affects children, adolescents, and healthy adults and is characterized by over 200 diverse symptoms that persist for months to years after the acute COVID-19 infection is resolved. These symptoms target twelve major organ systems, causing dyspnea, vascular damage, cognitive impairments (“brain fog”), physical and mental fatigue, anxiety, and depression. This heterogeneity of LC symptoms, along with the lack of specific biomarkers and diagnostic tests, presents a significant challenge to the development of LC treatments. While several biological abnormalities have emerged as potential drivers of LC, a causative factor in a large subset of patients with LC, involves reservoirs of virus and/or viral RNA (vRNA) that persist months to years in multiple organs driving chronic inflammation, respiratory, muscular, cognitive, and cardiovascular damages, and provide continuous viral antigenic stimuli that overstimulate and exhaust CD4⁺ and CD8⁺ T cells. In this review, we (i) shed light on persisting virus and vRNA reservoirs detected, either directly (from biopsy, blood, stool, and autopsy samples) or indirectly through virus-specific B and T cell responses, in patients with LC and their association with the chronic symptomatology of LC; (ii) explore potential mechanisms of inflammation, immune evasion, and immune overstimulation in LC; (iii) review animal models of virus reservoirs in LC; (iv) discuss potential T cell immunotherapeutic strategies to reduce or eliminate persistent virus reservoirs, which would mitigate chronic inflammation and alleviate symptom severity in patients with LC. Full article

Nighttime sea fog exhibits high frequency and prolonged duration, posing significant risks to maritime navigation safety. Current detection methods primarily rely on the dual-infrared channel brightness temperature difference technique, which faces challenges such as threshold selection difficulties and a tendency toward overestimation. In contrast, the VIIRS Day/Night Band (DNB) offers exceptional nighttime visible-like cloud imaging capabilities, offering a new solution to alleviate the overestimation issues inherent in infrared detection algorithms. Recent advances in artificial intelligence have further addressed the threshold selection problem in traditional detection methods. Leveraging these developments, this study proposes a novel generative adversarial network model incorporating attention mechanisms (SEGAN) to achieve accurate nighttime sea fog detection using DNB data. Experimental results demonstrate that SEGAN achieves satisfactory performance, with probability of detection, false alarm rate, and critical success index reaching 0.8708, 0.0266, and 0.7395, respectively. Compared with the operational infrared detection algorithm, these metrics show improvements of 0.0632, 0.0287, and 0.1587. Notably, SEGAN excels at detecting sea fog obscured by thin cloud cover, a scenario where conventional infrared detection algorithms typically fail. SEGAN emphasizes semantic consistency in its output, endowing it with enhanced robustness across varying sea fog concentrations. Full article

We present a deep learning model for the recognition of speech commands in the English language. The dataset is based on the Google Speech Commands Dataset by Warden P., version 0.01, and it consists of ten distinct commands (“left”, “right”, “go”, “stop”, “up”, “down”, “on”, “off”, “yes”, and “no”) along with additional “silence” and “unknown” classes. The dataset is split in a speaker-independent manner, with 70% of speakers assigned to the training set and 15% to the test set and validation set. All audio clips are sampled at 16 kHz, with a total of 46 146 clips. Audio files are converted into Mel spectrogram representations, which are then used as input to a deep learning model composed of a four-layer convolutional neural network followed by two fully connected layers. The model employs Rectified Linear Unit (ReLU) activation, the Adam optimizer, and dropout regularization to improve generalization. The achieved testing accuracy is 96.05%. Micro- and macro-averaged precision, recall, and F1-score of 95% are reported to reflect class-wise performance, and a confusion matrix is also provided. The proposed model has been deployed on a Raspberry Pi 5 as a Fog computing device for real-time speech recognition applications. Full article

Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse weather conditions, leading to sensor interference, reduced visibility, and inconsistent reliability. This study evaluates the effectiveness and limitations of ARAS technologies in rain, fog, and snow, focusing on how sensor performance, algorithms, techniques, and dataset suitability influence system reliability. A thematic analysis was conducted, selecting studies focused on ARAS in adverse weather conditions based on specific selection criteria. The analysis shows that while ARAS offers substantial safety benefits, its accuracy declines in challenging environments. Existing datasets, algorithms, and techniques were reviewed to identify the most effective options for ARAS applications. However, more comprehensive weather-resilient datasets and adaptive multi-sensor fusion approaches are still needed. Advancing in these areas will be critical to improving the robustness of ARAS and ensuring safer riding experiences across diverse environmental conditions. Full article

The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions. Full article

This paper presents a comparative mathematical analysis of electrode configurations used in active fog water harvesting systems based on electrostatic ionization. The study begins with a brief overview of fog formation and typology. It also addresses the global relevance of fog as a decentralized water resource. It also outlines the main methods and collector designs currently employed for fog water capture, both passive and active. The core of the work involves solving the Laplace equation for various electrode geometries to compute electrostatic field distributions and analyze field line density patterns as a proxy for potential water collection efficiency. The evaluated configurations include centered rod–cylinder, symmetric parallel multi-rod, and asymmetric wire–plate layouts, with emphasis on identifying spatial regions of high field line convergence. These regions are interpreted as likely trajectories of charged droplets under Coulombic force influence. The modeling approach enables preliminary assessment of design efficiency without relying on time-consuming droplet-level simulations. The results serve as a theoretical foundation prior to the construction of electrode layouts in the portable HygroCatch experimental harvester and provide insight into how field structure correlates with fog water harvesting performance. Full article