Search Results (19)

UAV-based inspections are an effective way to ensure railway safety and have gained significant attention. However, images captured during complex weather conditions, such as rain, snow, or fog, often suffer from severe degradation, affecting image recognition accuracy. Existing algorithms for removing rain, snow, and fog have two main limitations: they do not adaptively learn features under varying weather complexities and struggle with managing complex noise patterns in drone inspections, leading to incomplete noise removal. To address these challenges, this study proposes a novel framework for removing rain, snow, and fog from drone images, called WeatherClean. This framework introduces a Weather Complexity Adjustment Factor (WCAF) in a parameterized adjustable network architecture to process weather degradation of varying degrees adaptively. It also employs a hierarchical multi-scale cropping strategy to enhance the recovery of fine noise and edge structures. Additionally, it incorporates a degradation synthesis method based on atmospheric scattering physical models to generate training samples that align with real-world weather patterns, thereby mitigating data scarcity issues. Experimental results show that WeatherClean outperforms existing methods by effectively removing noise particles while preserving image details. This advancement provides more reliable high-definition visual references for drone-based railway inspections, significantly enhancing inspection capabilities under complex weather conditions and ensuring the safety of railway operations. Full article

The synthesis of realistic fog in images is critical for applications such as autonomous navigation, augmented reality, and visual effects. Traditional methods based on Koschmieder’s law or GAN-based image translation typically assume homogeneous fog distributions and rely on oversimplified scattering models, limiting their physical realism. In this paper, we propose a physics-driven approach to fog synthesis by discretizing the Radiative Transfer Equation (RTE). Our method models spatially inhomogeneous fog and anisotropic multi-scattering, enabling the generation of structurally consistent and perceptually plausible fog effects. To evaluate performance, we construct a dataset of real-world foggy, cloudy, and sunny images and compare our results against both Koschmieder-based and GAN-based baselines. Experimental results show that our method achieves a lower Fréchet Inception Distance ($-10\%$ vs. Koschmieder, $-42\%$ vs. CycleGAN) and a higher Pearson correlation ($+4\%$ and $+21\%$, respectively), highlighting its superiority in both feature space and structural fidelity. These findings highlight the potential of RTE-based fog synthesis for physically consistent image augmentation under challenging visibility conditions. However, the method’s practical deployment may be constrained by high memory requirements due to tensor-based computations, which must be addressed for large-scale or real-time applications. Full article

This research addresses visibility challenges in surveillance systems under foggy conditions through a novel synthetic fog generation method leveraging the GridNet dehazing architecture. Our approach uniquely reverses GridNet, originally developed for fog removal, to synthesize realistic foggy images. The proposed Fog Generator Model incorporates perceptual and dark channel consistency losses to enhance fog realism and structural consistency. Comparative experiments on the O-HAZY dataset demonstrate that dehazing models trained on our synthetic fog outperform those trained on conventional methods, achieving superior Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) scores. These findings confirm that integrating high-performance dehazing networks into fog synthesis improves the realism and effectiveness of fog removal solutions, offering significant benefits for real-world surveillance applications. Full article

Background/Objectives: Iron deficiency has been suggested as a potential mechanism for attention-deficit hyperactivity disorder (ADHD) development due to involvement in neurotransmitter synthesis and transporter expression. As iron deficiency is particularly common in women of reproductive age, often due to heavy menstrual bleeding (HMB), we aimed to explore the relationship between iron deficiency, HMB and ADHD in women. Methods: We screened women (18–49 years) at university and local sporting events in Western Australia. To screen for ADHD, section A of the Adult ADHD Self-Report Scale-V1.1 (ASRS-V1.1) and the Adult Concentration Inventory were used to assess cognitive disengagement syndrome (CDS) symptoms. Risk factors for iron deficiency, such as HMB, commonly reported symptoms and a fingerpick haemoglobin concentration (Hb) (Hemocue Hb801) were recorded. Results: Of the 405 completed questionnaires, the mean age was 24.8 ± 10.1 years, the mean Hb was 136.8 ± 12.4 g/L and 6.4% of women were anaemic. Symptoms suggestive of ADHD were reported by 174/405 (43%) women, and 128/405 (32%) women reported HMB. There was a greater prevalence of HMB reported in those experiencing symptoms suggestive of ADHD (39% vs. 26%, p = 0.01). Symptoms of fatigue, dizziness, brain fog, anxiety, heart palpitations, headaches, restless legs and depression were more common in patients with symptoms suggestive of ADHD (p ≤ 0.01) and HMB (p < 0.05). Anaemia status did not influence ADHD status (p = 0.87) nor CDS scores (15.7 ± 7.0 vs. 13.8 ± 6.1, p = 0.17). Conclusions: There is an apparent relationship between those with symptoms reported in ADHD, HMB and iron deficiency. Further exploration is required to determine whether there is a causative relationship. Full article

Metabolic dysfunction-associated Fatty Liver Disease (MAFLD) has emerged as one of the leading cardiometabolic diseases. Friend of GATA2 (FOG2) is a transcriptional co-regulator that has been shown to regulate hepatic lipid metabolism and accumulation. Using meta-analysis from several different biobank datasets, we identified a coding variant of FOG2 (rs28374544, A1969G, S657G) predominantly found in individuals of African ancestry (minor allele frequency~20%), which is associated with liver failure/cirrhosis phenotype and liver injury. To gain insight into potential pathways associated with this variant, we interrogated a previously published genomics dataset of 38 human induced pluripotent stem cell (iPSCs) lines differentiated into hepatocytes (iHeps). Using Differential Gene Expression Analysis and Gene Set Enrichment Analysis, we identified the mTORC1 pathway as differentially regulated between iHeps from individuals with and without the variant. Transient lipid-based transfections were performed on the human hepatoma cell line (Huh7) using wild-type FOG2 and FOG2^S657G and demonstrated that FOG2S^657G increased mTORC1 signaling, de novo lipogenesis, and cellular triglyceride synthesis and mass. In addition, we observed a significant downregulation of oxidative phosphorylation in FOG2^S657G cells in fatty acid-loaded cells but not untreated cells, suggesting that FOG2^S657G may also reduce fatty acid to promote lipid accumulation. Taken together, our multi-pronged approach suggests a model whereby the FOG2^S657G may promote MAFLD through mTORC1 activation, increased de novo lipogenesis, and lipid accumulation. Our results provide insights into the molecular mechanisms by which FOG2^S657G may affect the complex molecular landscape underlying MAFLD. Full article

A simple method is presented for the continuous generation of carbon nanotube forests stably anchored on stainless-steel surfaces using a reactive-roll-to-roll (RR2R) configuration. No addition of catalyst nanoparticles is required for the CNT-forest generation; the stainless-steel substrate itself is tuned to generate the catalytic growth sites. The process enables very large surfaces covered with CNT forests to have individual CNT roots anchored to the metallic ground through primary bonds. Fog water harvesting is demonstrated and tested as one potential application using long CNT-covered wires. The RR2R is performed in the gas phase; no solution processing of CNT suspensions is used, contrary to usual R2R CNT-based technologies. Full or partial CNT-forest coverage provides tuning of the ratio and shape of hydrophobic and hydrophilic zones on the surface. This enables the optimization of fog water harvesters for droplet capture through the hydrophobic CNT forest and water removal from the hydrophilic SS surface. Water recovery tests using small harp-type harvesters with CNT-forest generate water capture of up to 2.2 g/cm²·h under ultrasound-generated fog flow. The strong CNT root anchoring on the stainless-steel surfaces provides opportunities for (i) robustness and easy transport of the composite structure and (ii) chemical functionalization and/or nanoparticle decoration of the structures, and it opens the road for a series of applications on large-scale surfaces, including fog harvesting. Full article

This paper deals with the properties and testing of newly prepared organic pigments based on melamine cyanurate containing magnesium or zinc cations depending on their composition and anticorrosive properties in model coatings. Organic pigments based on melamine cyanurate with Mg²⁺ in the form of a complex differing in the ratio of melamine and cyanurate units were prepared. Furthermore, a pigment based on melamine citrate with magnesium cation Mg²⁺, a pigment based on melamine citrate with magnesium cation, and a pigment based on melamine cyanurate with zinc cation were prepared. The properties of Mg-containing organic pigments were also compared with those of selected magnesium-containing inorganic oxide-type pigments. The above-synthesized pigments were characterized by inductively coupled plasma-optical emission spectroscopy, elemental analysis, scanning electron microscopy, and X-ray diffraction. In addition, the basic parameters that are indicative of the applicability of the pigments in the binders of anti-corrosion coatings were determined. The anti-corrosive properties of the tested pigments were verified after application to the epoxy-ester resin-based paint binder in three different concentrations: at pigment volume concentrations of 0.10%, 0.25%, and 0.50%. The anticorrosive effectiveness of pigmented organic coatings was verified by cyclic corrosion tests in a salt electrolyte fog (NaCl + (NH₄)₂SO₄) in an atmosphere containing SO₂ and by the electrochemical technique of linear polarization. Finally, the effect of the structure of the pigments on the mechanical resistance of the organic coatings was investigated. The results obtained showed that the new organic pigments exhibit anticorrosive properties, and at the same time, differences in performance were found depending on the structure of the pigments tested. Specifically, the results of cyclic corrosion tests and the electrochemical technique of linear polarization clearly demonstrated that synthesized pigments of the organic type based on melamine cyanurate containing magnesium or zinc cations ensure the anti-corrosion efficiency of the tested organic coatings. The highest anti-corrosion efficiency was achieved by the system pigmented with synthesized melamine cyanurate with magnesium cation (C₁₂H₁₆MgN₁₈O₆), whose anti-corrosion efficiency was comparable to the anti-corrosion efficiency of the tested inorganic pigment MgFe₂O₄, which was prepared by high-temperature solid-phase synthesis. In addition, these organic coatings achieved high mechanical resistance after being tested using the most used standardized mechanical tests. Full article

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is characterized by severe flu-like symptoms, which can progress to life-threatening systemic inflammation and multiorgan dysfunction. The nervous system is involved in over one-third of patients, and the most common neurological manifestations concern the central nervous system, such as headache, fatigue, and brain fog. The activation of innate, humoral, and cellular immune responses, resulting in a cytokine storm and endothelial and mitochondrial dysfunctions, are the main pathophysiological mechanisms of SARS-CoV-2 infection. Citicoline is an exogenous source of choline and cytidine involved in intracellular phospholipid synthesis, which improves blood flow, brain activity, and mitochondrial dysfunction. This report will present the case of a non-hospitalized, 59-year-old female. After a mild form of SARS-CoV-2 infection, the patient developed cognitive disturbances such as forgetfulness and anomia. The multidimensional neuropsychological assessment revealed an impairment in episodic memory with borderline performance in executive and visuospatial functioning. Cognitive rehabilitation and treatment with citicoline 1000 mg/daily led to a marked improvement in symptoms after six months. Early identification of the neurological sequelae of the Coronavirus Disease 2019 (COVID-19) and timely rehabilitation interventions are required in non-hospitalized long-hauler patients with COVID-19. Long-term treatment with citicoline should be considered as potentially effective in improving cognitive functioning in subjects with Post COVID-19 Neurological Syndrome. Full article

Nighttime image dehazing presents unique challenges due to the unevenly distributed haze caused by the color change of artificial light sources. This results in multiple interferences, including atmospheric light, glow, and direct light, which make the complex scattering haze interference difficult to accurately distinguish and remove. Additionally, obtaining pairs of high-definition data for fog removal at night is a difficult task. These challenges make nighttime image dehazing a particularly challenging problem to solve. To address these challenges, we introduced the haze scattering formula to more accurately express the haze in three-dimensional space. We also proposed a novel data synthesis method using the latest CG textures and lumen lighting technology to build scenes where various hazes can be seen clearly through ray tracing. We converted the complex 3D scattering relationship transformation into a 2D image dataset to better learn the mapping from 3D haze to 2D haze. Additionally, we improved the existing neural network and established a night haze intensity evaluation label based on the idea of optical PSF. This allowed us to adjust the haze intensity of the rendered dataset according to the intensity of the real haze image and improve the accuracy of dehazing. Our experiments showed that our data construction and network improvement achieved better visual effects, objective indicators, and calculation speed. Full article

Accurate identification and extraction of lake boundaries are the basis of the accurate assessment of lake changes and their responses to climate change. To reduce the effects of lake ice and snow cover, mountain shadows, cloud and fog shielding, alluvial and proluvial deposits, and shoals on the extraction of lake boundaries on the Tibetan Plateau, this study developed an RNSS water index to increase the contrast between the lake and similar surface objects of the spectral curve, and constructed a new method flow for lake extraction on the Tibetan Plateau based on image synthesis, topographic-spectral feature indexes, and machine learning algorithms. The lake extraction effects of three common machine learning classification algorithms were compared: the Cart decision tree, random forest (RF), and gradient boosting decision tree (GBDT). The results show that the new lake extraction method based on topographic-spectral characteristics and the GBDT classification method had the highest extraction accuracy for Tibetan Plateau lakes in 2016 and 2021. Its overall accuracy, Kappa coefficient, user’s accuracy, and producer’s accuracy for 2016 and 2021 were 99.81%, 0.887, 83.55%, 94.67% and 99.88%, 0.933, 89.18%, 98.24%, respectively, and the total area of lake extraction was the most consistent with the validation datasets. The three classification methods can effectively extract lakes covered by ice and snow, and the extraction effect was ranked as GBDT > RF > Cart. The lake extraction effect under mountain shadow was ranked as Cart > GBDT > RF, and the lake extraction effect under alluvial deposits and shoals was ranked as GBDT > RF > Cart. The results may provide technical support for extracting lakes from long time series and reveal the impact of climate change on Tibetan Plateau lakes. Full article

In this study, cellulose nanocrystals hollow microspheres (HMs) were fabricated through Pickering emulsion polymerization, in which hydrophobically modified cellulose nanocrystals (CNCs) acted as Pickering stabilizers. The hollow interior core was prepared by solvent evaporation. This manuscript describes the synthesis of HMs in detail. The hollow structure and nanoscale size of HMs were verified using TEM. The resultant HMs could easily coat self-forming films on the surface of PET fabrics. Additionally, these coatings exhibited superior breathability and moisture permeability properties with a high one-way transport index of 936.33% and a desirable overall moisture management capability of 0.72. Cellulose nanocrystal hollow microsphere coatings could be used as a moisture-wicking functionality agent for finishing fabrics, oil–water separation, and fog harvesting. Full article

Superhydrophobic surfaces are important in many applications owing to their special properties such as self-cleaning, anti-icing, antibacterial, and anti-fogging. In this paper, a micro/nano hierarchical superhydrophobic surface with a low roll-off angle was created on 304 stainless steel. The water contact angle was measured to be 152° with a roll-off angle of 7.3°. Firstly, microscale bumps were created by femtosecond laser irradiation. Secondly, zinc oxide (ZnO) nanowires were fabricated on the laser-induced bumps using a hydrothermal synthesis method. Results show that after laser treatment and ZnO nanostructuring, the stainless steel surface became superhydrophobic. However, the roll-off angle of this hierarchical structure surface was larger than 90°. To reduce the surface activity, trimethoxy silane hydrophobic coating was applied. A 7.3° roll-off angle was achieved on the coated surface. The underlying mechanism was discussed. The hydrophobic ZnO structured surface can help prevent bacterial contamination from water, which is important for implants. Thus, for biomedical applications, the antibacterial property of this hierarchical surface was examined. It was found that the antibacterial property of sample surfaces with ZnO nanowires were significantly increased. The optical density (OD) of Escherichia coli (E. coli) attached to the original surface was 0.93. For the micro-structured surface (with bumps), the OD was 0.9, and for the hierarchical surface (with bump & nanowires), it was 0.54. For nanostructured ZnO nanowire surface, the OD was only 0.09. It demonstrates good antibacterial properties of ZnO nanowires. Full article

The role of sensors such as cameras or LiDAR (Light Detection and Ranging) is crucial for the environmental awareness of self-driving cars. However, the data collected from these sensors are subject to distortions in extreme weather conditions such as fog, rain, and snow. This issue could lead to many safety problems while operating a self-driving vehicle. The purpose of this study is to analyze the effects of fog on the detection of objects in driving scenes and then to propose methods for improvement. Collecting and processing data in adverse weather conditions is often more difficult than data in good weather conditions. Hence, a synthetic dataset that can simulate bad weather conditions is a good choice to validate a method, as it is simpler and more economical, before working with a real dataset. In this paper, we apply fog synthesis on the public KITTI dataset to generate the Multifog KITTI dataset for both images and point clouds. In terms of processing tasks, we test our previous 3D object detector based on LiDAR and camera, named the Spare LiDAR Stereo Fusion Network (SLS-Fusion), to see how it is affected by foggy weather conditions. We propose to train using both the original dataset and the augmented dataset to improve performance in foggy weather conditions while keeping good performance under normal conditions. We conducted experiments on the KITTI and the proposed Multifog KITTI datasets which show that, before any improvement, performance is reduced by 42.67% in 3D object detection for Moderate objects in foggy weather conditions. By using a specific strategy of training, the results significantly improved by 26.72% and keep performing quite well on the original dataset with a drop only of 8.23%. In summary, fog often causes the failure of 3D detection on driving scenes. By additional training with the augmented dataset, we significantly improve the performance of the proposed 3D object detection algorithm for self-driving cars in foggy weather conditions. Full article

Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes. Full article

In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc. Full article