Search Results (243)

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

Background: Optical Coherence Tomography (OCT) is a high-resolution imaging technique capable of quantifying Blood Flow at Depth (BD) and the Attenuation Coefficient (AC). However, the clinical relevance of these parameters in burn assessment remains unclear. This study investigated whether OCT-derived metrics can differentiate between superficial and deep pediatric hand burns. Method: This prospective, single-center study analyzed 73 OCT scans from 37 children with thermal hand injuries. A structured algorithm was used to evaluate AC and BD. Results: The mean AC was 1.61 mm⁻¹ (SD ± 0.48), with significantly higher values in deep burns (2.11 mm⁻¹ ± 0.53) compared to superficial burns (1.49 mm⁻¹ ± 0.38; p < 0.001), reflecting increased optical density in more severe burns. BD did not differ significantly between burn depths, although superficial burns more often showed visible capillary networks. Conclusions: This is the first study to assess both AC and BD using OCT in pediatric hand burns. AC demonstrated potential as a diagnostic marker for burn depth, whereas BD had limited utility. Image quality limitations highlight the need for technical improvements to enhance OCT’s clinical application. Full article

In this study, multifunctional nanocomposite membranes were fabricated using biopolymeric polylactic acid (PLA) and cellulose acetate (CA) composites via electrospinning. The hydrophobic nanocomposite membranes were reinforced with varying concentrations of silicon dioxide (silica/SiO₂) nanoparticles. The developed PLA–CA–SiO₂ nanofibrous membranes are characterized using field emission scanning electron microscopy (FE- energy-dispersive SEM), energy-dispersive X-ray (EDX), elemental mapping, X-ray diffraction analysis (XRD), Fourier-transform infrared spectroscopy (FT–IR), thermal gravimetric analysis (TGA), and differential scanning calorimetry (DSC) techniques. Various physical and mechanical properties of the bio-nanocomposite membrane, such as tensile testing, infrared thermal imaging, ultraviolet–visible spectroscopy (UV–Vis), water contact angle, hydrostatic pressure resistance, and breathability are also investigated. The analysis revealed that a small concentration of silica nanoparticles improves the morphological, mechanical, and thermal characteristics of nanocomposite membranes. The addition of silica nanoparticles improves the UV (A & B), visible and infrared blocking efficiency while also enhancing the waterproofness of protective textiles. The PLA–CA–SiO₂ biopolymer nanocomposite membrane has a fibrous microstructure and demonstrated the tensile strength of 11.2 MPa, a Young’s modulus of 329 MPa, an elongation at break of 98.5%, a hydrostatic pressure resistance of 27 kPa, and a water contact angle of 143.7°. The developed electrospun composite membranes with improved properties provide strong potential to replace petroleum-based membranes with biopolymer-based alternatives, promising improved and wider usage for bio-related applications. Full article

Infrared-visible image fusion quality is critical for nighttime perception in autonomous driving and surveillance but suffers severe degradation under extreme low-light conditions, including irreversible texture loss in visible images, thermal boundary diffusion artifacts, and overexposure under dynamic non-uniform illumination. To address these challenges, a Decomposition–Disentanglement–Dynamic Compensation framework, D3Fusion, is proposed. Firstly, a Retinex-inspired Decomposition Illumination Net (DIN) decomposes inputs into enhanced images and degradative illumination maps for joint low-light recovery. Secondly, an illumination-guided encoder and a multi-scale differential compensation decoder dynamically balance cross-modal features. Finally, a progressive three-stage training paradigm from illumination correction through feature disentanglement to adaptive fusion resolves optimization conflicts. Compared to State-of-the-Art methods, on the LLVIP, TNO, MSRS, and RoadScene datasets, D3Fusion achieves an average improvement of 1.59% in standard deviation (SD), 6.9% in spatial frequency (SF), 2.59% in edge intensity (EI), and 1.99% in visual information fidelity (VIF), demonstrating superior performance in extreme low-light scenarios. The framework effectively suppresses thermal diffusion artifacts while mitigating exposure imbalance, adaptively brightening scenes while preserving texture details in shadowed regions. This significantly improves fusion quality for nighttime images by enhancing salient information, establishing a robust solution for multimodal perception under illumination-critical conditions. Full article

The Karnak Temples are considered one of Egypt’s most significant archaeological sites, dating back to the Middle Kingdom (c. 2000–1700 BC) and were continuously expanded until the Ptolemaic period (305–30 BC). As the second most visited UNESCO World Heritage archaeological site in Egypt after the Giza Pyramids, Karnak faces severe deterioration processes due to prolonged exposure to environmental impacts, mechanical damage, and historical interventions. This study employs a multidisciplinary approach integrating non-destructive testing (NDT) methods to assess the physical and mechanical condition and degradation mechanisms of scattered sandstone blocks at the site. Advanced documentation techniques, including Reflectance Transformation Imaging (RTI), photogrammetry, and Infrared Thermography (IRT), were used to analyze surface morphology, thermal stress effects, and weathering patterns. Ultrasonic Pulse Velocity (UPV) testing provided internal structural assessments, while spectral and gloss analysis quantified chromatic alterations and surface roughness. Additionally, the Karsten Tube test determined the water absorption behavior of the sandstone, highlighting variations in porosity and susceptibility to salt crystallization. In this sense, the results indicate that climatic factors such as extreme temperature fluctuations, wind erosion, and groundwater infiltration contributed to sandstone deterioration. Thermal cycling leads to microcracking and granular disintegration, while high capillary water absorption accelerates chemical weathering processes. UPV analyses showed substantial internal decay, with low-velocity zones correlating with fractures and differential cementation loss. Finally, an interventive conservation plan was proposed. Full article

Background: Canine cranial cruciate ligament (CCL) rupture is a common orthopedic condition leading to stifle joint dysfunction, discomfort, and reduced mobility. Diagnosis typically involves radiography, computed tomography (CT), and magnetic resonance imaging (MRI). In this study, we conducted a retrospective analysis to evaluate the use of infrared thermography in assessing local temperature and thermal patterns in dogs with acute-onset lameness due to CCL rupture compared to those with intact ligaments. Methods: The study involved 12 dogs with cranial cruciate ligament rupture and nine dogs with intact ligaments. The stifle area of all dogs was clipped and scanned using a FLIR E50 thermographic camera. Two regions of interest (ROI), designated El1 and Bx1, were analyzed with FLIR Tools software 5.X by comparing the average of the maximum and of the mean temperature values between the two groups. Results: Thermal imaging revealed differences between the two groups of dogs, which were further supported by significantly higher temperatures in the El1 (lateral aspect of the stifle joint) and Bx1 (cranial aspect of the stifle joint) areas in the study group compared to the control group using a comparative analysis—two-sample t-test. In the El1 area, the study group showed a temperature increase of 1.8 °C compared to the control group, while in the Bx1 area, the difference was 1.76 °C. Conclusions: Infrared thermography shows potential to differentiate dogs with acute-onset lameness due to CCL rupture from dogs with intact ligaments, but further studies are needed to assess its accuracy in distinguishing it from other stifle pathologies. Full article

Nutrition plays a fundamental role in promoting health and preventing chronic diseases, with bioactive food components offering a therapeutic potential in biomedical applications. Among these, edible oils are recognised for their functional properties, which contribute to disease prevention and metabolic regulation. The proposed study aims to evaluate the quality of four bioactive oils (olive oil, sunflower oil, tomato seed oil, and pumpkin seed oil) by analysing their thermal behaviour through infrared (IR) imaging. The study designed a customised electronic system to acquire thermographic signals under controlled temperature and humidity conditions. The acquisition system was used to extract thermal data. Analysis of the acquired thermal signals revealed characteristic heat absorption profiles used to infer differences in oil properties related to stability and degradation potential. A hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units was used to classify and differentiate the oils based on stability, thermal reactivity, and potential health benefits. A signal analysis showed that the AI-based method improves both the accuracy (achieving an F1-score of 93.66%) and the repeatability of quality assessments, providing a non-invasive and intelligent framework for the validation and traceability of nutritional compounds. Full article

The effect of periodical heatwaves and related thermal stratification in freshwater aquatic ecosystems has been a hot research issue. A large dataset of samples was generated from samples exposed to temporary thermal stratification in mesocosms mimicking shallow eutrophic freshwater lakes. Temperature regimes were based on IPCC climate warming scenarios, enabling simulation of future warming conditions. Surface oxygen levels reached 19.37 mg/L, while bottom layers dropped to 0.07 mg/L during stratification. Analysis by FlowCAM revealed dominance of Cyanobacteria under ambient conditions (up to 99.2%), while Cryptophyta (up to 98.9%) and Chlorophyta (up to 99.9%) were predominant in the A2 and A2+50% climate scenarios, respectively. We identified temperature changes and shifts in nutrient concentrations, particularly phosphate, as critical factors in microbial community composition. Furthermore, five distinct Microcystis morphospecies identified by FlowCAM-based analysis were associated with different microbial clusters. The combined use of imaging flow cytometry, which differentiates phytoplankton based on morphological parameters, and nanopore long-read sequencing analysis has shed light into the dynamics of microbial communities associated with different Microcystis morphospecies. In our observations, a peak of algicidal bacteria abundance often coincides with or is followed by a decline in the Cyanobacteria. These findings highlight the importance of species-level classification in the analysis of complex ecosystem interactions and the dynamics of algal blooms in freshwater bodies in response to anthropogenic effects and climate change. Full article

Herein, electrospun nanofibers composed of polyaniline (PANI), polyethylene oxide (PEO), and Luffa cylindrica (LC) cellulose, reinforced with titanium dioxide (TiO₂) nanoparticles, were synthesized via electrospinning to investigate the effect of TiO₂ nanoparticles on PANI/PEO/LC nanocomposites and the effect of conductivity on nanofiber morphology. Cellulose extracted from luffa was added to the PANI/PEO copolymer solution, and two different ratios of TiO₂ were mixed into the PANI/PEO/LC biocomposite. The morphological, vibrational, and thermal characteristics of biocomposites were systematically investigated using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). As anticipated, the presence of TiO₂ enhanced the electrical conductivity of biocomposites, while the addition of Luffa cellulose further improved the conductivity of the cellulose-based nanofibers. FTIR analysis confirmed chemical interactions between Luffa cellulose and PANI/PEO matrix, as evidenced by the broadening of the hydroxyl (OH) absorption band at 3500–3200 cm⁻¹. Additionally, the emergence of characteristic peaks within the 400–1000 cm⁻¹ range in the PANI/PEO/LC/TiO₂ spectra signified Ti–O–Ti and Ti–O–C vibrations, confirming the incorporation of TiO₂ into the biocomposite. SEM images of the biocomposites reveal that the thickness of nanofibers decreases by adding Luffa to PANI/PEO nanofibers because of the nanofibers branching. In addition, when blending TiO₂ nanoparticles with the PANI/PEO/LC biocomposite, this increment continued and obtained thinner and smother nanofibers. Furthermore, the incorporation of cellulose slightly improved the crystallinity of the nanofibers, while TiO₂ contributed to the enhanced crystallinity of the biocomposite according to the XRD and DCS results. Similarly, the TGA results supported the DSC results regarding the increasing thermal stability of the biocomposite nanofibers with TiO₂ nanoparticles. These findings demonstrate the potential of PANI/PEO/LC/TiO₂ nanofibers for advanced applications requiring conductive and structurally optimized biomaterials, e.g., for use in humidity or volatile organic compound (VOC) sensors, especially where flexibility and environmental sustainability are required. Full article

This study investigates the hydrogen permeability of injection-molded polyamide 6 (PA6) nanocomposites reinforced with organo-modified montmorillonite (OMMT) at varying concentrations (1, 2.5, 5, and 10 wt. %) for potential use as Type IV composite-overwrapped pressure vessel (COPV) liners. While previous work examined their mechanical properties, this study focuses on their crystallinity, morphology, and gas barrier performance. The precise inorganic content was determined using thermal gravimetry analysis (TGA), while differential scanning calorimetry (DSC), wide-angle X-ray diffraction (WAXD), and scanning electron microscopy (SEM) were used to characterize the structural and morphological changes induced by varying filler content. The results showed that generally higher OMMT concentrations promoted γ-phase formation but also led to increased agglomeration and reduced crystallinity. The PA6/OMMT-1 wt. % sample stood out with higher crystallinity, well-dispersed clay, and low hydrogen permeability. In contrast, the PA6/OMMT-2.5 and -5 wt. % samples showed increased permeability, which corresponded to WAXD and SEM evidence of agglomeration and DSC results indicating a lower degree of crystallinity. PA6/OMMT-10 wt. % showed the most-reduced hydrogen permeability compared to all other samples. This improvement, however, is attributed to a tortuous path effect created by the high filler loading rather than optimal crystallinity or dispersion. SEM images revealed significant OMMT agglomeration, and DSC analysis confirmed reduced crystallinity, indicating that despite the excellent barrier performance, the compromised microstructure may negatively impact mechanical reliability, showing PA6/OMMT-1 wt. % to be the most balanced candidate combining both mechanical integrity and hydrogen impermeability for Type IV COPV liners. Full article

This study addresses the constitutive relationship of the welded coarse-grained heat-affected zone (CGHAZ) in 80-mm-thick DH36 marine steel plates. By integrating quasi-static tensile testing, digital image correlation (DIC) technology, and metallographic analysis, we systematically investigated the mechanical property differences and underlying mechanisms between the CGHAZ and base metal (BM). High-precision DIC technology enabled strain field characterization at the microscale in the CGHAZ, while the Ramberg-Osgood model was adopted to establish a dual-material constitutive equation. The results demonstrate that grain coarsening induced by welding thermal cycles significantly influenced the mechanical responses: the CGHAZ exhibited enhanced tensile strength but reduced plastic compatibility due to decreased grain boundary density. Notably, gradient differences in elastic modulus (CGHAZ: 184 GPa vs. BM: 213 GPa) and yield strength (CGHAZ: 363 MPa vs. BM: 373 MPa) between the BM and CGHAZ necessitate strict differentiation in engineering design. This work overcomes the limitations of oversimplified CGHAZ properties in conventional design approaches, providing a novel methodology for strength assessment and lightweight design of marine structures. The findings offer critical theoretical insights and practical guidelines for enhancing the reliability of offshore engineering equipment. Full article

Background/Objectives: Patients with Parkinson’s disease (PD) often have cold hands and experience frostbite. The diagnostic criteria for multiple system atrophy (MSA) also describe cold and discolored hands; however, in our clinical experience, the hands are relatively warm. These symptoms are thought to be caused by autonomic dysfunction; however, the detailed mechanisms and differences in cold hands between MSA and PD remain unclear. We aimed to identify an appropriate cold stimulation test to differentiate patients with PD and MSA using finger surface temperature (FST). Methods: We included a total of 34 patients, 27 with PD and 7 with MSA diagnosed at least 5 years after disease onset. After 15 min in a room with constant temperature and humidity, the patient’s hand was placed in cold water at 4 °C for 10 s as the cold water stress test (10sec-CWST). FST was captured using a thermal imaging camera every minute for 15 min, and the recovery of FST was analyzed. The association between the clinical characteristics of each patient and the degree of FST recovery was examined. Results: All patients completed the 10sec-CWST without adverse events. Patients with PD showed a significantly slower recovery of FST after 7 min compared to those with MSA, with a maximum difference at 11 min (PD: 8.1 ± 0.6 °C; MSA: 10.5 ± 0.3 °C; p < 0.01). FST recovery at 11 min was negatively correlated with the degree of resting hand tremor (r = −0.585, p < 0.01). Conclusions: FST after 10sec-CWST may be a safe and efficient test to differentiate PD and MSA. Full article

Many researchers focus on improving reproductive health in sows and ensuring successful breeding by accurately identifying the optimal time of ovulation through estrus detection. One promising non-contact technique involves using computer vision to analyze temperature variations in thermal images of the sow’s vulva. However, variations in camera distance during dataset collection can significantly affect the accuracy of this method, as different distances alter the resolution of the region of interest, causing pixel intensity values to represent varying areas and temperatures. This inconsistency hinders the detection of the subtle temperature differences required to distinguish between estrus and non-estrus states. Moreover, failure to maintain a consistent camera distance, along with external factors such as atmospheric conditions and improper calibration, can distort temperature readings, further compromising data accuracy and reliability. Furthermore, without addressing distance variations, the model’s generalizability diminishes, increasing the likelihood of false positives and negatives and ultimately reducing the effectiveness of estrus detection. In our previously proposed methodology for estrus detection in sows, we utilized YOLOv8 for segmentation and keypoint detection, while monocular depth estimation was used for camera calibration. This calibration helps establish a functional relationship between the measurements in the image (such as distances between labia, the clitoris-to-perineum distance, and vulva perimeter) and the depth distance to the camera, enabling accurate adjustments and calibration for our analysis. Estrus classification is performed by comparing new data points with reference datasets using a three-nearest-neighbor voting system. In this paper, we aim to enhance our previous method by incorporating the mean pixel intensity of the region of interest as an additional factor. We propose a detailed four-step methodology coupled with two stages of evaluation. First, we carefully annotate masks around the vulva to calculate its perimeter precisely. Leveraging the advantages of deep learning, we train a model on these annotated images, enabling segmentation using the cutting-edge YOLOv9 algorithm. This segmentation enables the detection of the sow’s vulva, allowing for analysis of its shape and facilitating the calculation of the mean pixel intensity in the region. Crucially, we use monocular depth estimation from the previous method, establishing a functional link between pixel intensity and the distance to the camera, ensuring accuracy in our analysis. We then introduce a classification approach that differentiates between estrus and non-estrus regions based on the mean pixel intensity of the vulva. This classification method involves calculating Euclidean distances between new data points and reference points from two datasets: one for “estrus” and the other for “non-estrus”. The classification process identifies the five closest neighbors from the datasets and applies a majority voting system to determine the label. A new point is classified as “estrus” if the majority of its nearest neighbors are labeled as estrus; otherwise, it is classified as “non-estrus”. This automated approach offers a robust solution for accurate estrus detection. To validate our method, we propose two evaluation stages: first, a quantitative analysis comparing the performance of our new YOLOv9 segmentation model with the older U-Net and YOLOv8 models. Secondly, we assess the classification process by defining a confusion matrix and comparing the results of our previous method, which used the three nearest points, with those of our new model that utilizes five nearest points. This comparison allows us to evaluate the improvements in accuracy and performance achieved with the updated model. The automation of this vital process holds the potential to revolutionize reproductive health management in agriculture, boosting breeding success rates. Through thorough evaluation and experimentation, our research highlights the transformative power of computer vision, pushing forward more advanced practices in the field. Full article

The development and characterization of a sustainable carboxymethylcellulose (CMC)-based material hosting Egyptian blue (EB) as a luminophore with emission in both the visible and NIR regions is herein presented and discussed, demonstrating its potential to be applied in a variety of applications, such as bioimaging, sensing, and security marking. Solution casting was used to synthesize the films, with citric acid (CA) as a crosslinking agent. Fully characterization was performed using attenuated total reflection (ATR) and coherent anti-Stokes Raman scattering (CARS) spectroscopy, zeta potential, UV–Vis, and photoluminescence (PL) spectroscopy, and thermal analysis techniques, such as thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The results confirm the effective crosslinking of CMC with CA within CMC–EB–CA films with 1.5 and 3% of EB. The introduction of EB retained its usual NIR emission with λ_em max = ~950 nm reaching quantum yield values in the range of 11.2–12.8% while also enabling a stable dispersion within the CMC matrix, as confirmed using CARS imaging and zeta potential. Additionally, the CMC films exhibited the characteristic clustering-triggered emission (CTE) in the blue region at 430 nm with a slight increase in luminescence quantum yield (Φ) from 5.8 to 6.1% after crosslinking with citric acid. Full article

This study investigates the relationship between Green View Index (GVI) and street thermal environment in Hangzhou’s main urban area during summer, quantifying urban greenery’s impact on diurnal/nocturnal thermal conditions to inform urban heat island mitigation strategies. Multi-source data (3D morphological metrics, LCZ classifications, mobile measurements) were integrated with deep learning-derived street-level GVI through image analysis. A random forest-multiple regression hybrid model evaluated spatiotemporal variations and GVI impacts across time, street orientation, and urban-rural gradients. Key findings include: (1) Urban street Ta prediction model: Daytime model: R² = 0.54, RMSE = 0.33 °C; Nighttime model: R² = 0.71, RMSE = 0.42 °C. (2) GVI shows significant inverse association with temperature, A 0.1 unit increase in GVI reduced temperatures by 0.124°C during the day and 0.020 °C at night. (3) Orientation effects: North–south streets exhibit strongest cooling (1.85 °C daytime reduction), followed by east–west; northeast–southwest layouts show negligible impact; (4) Canyon geometry: Low-aspect canyons (H/W < 1) enhance cooling efficiency, while high-aspect canyons (H/W > 2) retain nocturnal heat despite daytime cooling; (5) Urban-rural gradient: Cooling peaks in urban-fringe zones (10–15 km daytime, 15–20 km nighttime), contrasting with persistent nocturnal warmth in urban cores (0–5 km); (6) LCZ variability: Daytime cooling intensity peaks in LCZ3, nighttime in LCZ6. These findings offer scientific evidence and empirical support for urban thermal environment optimization strategies in urban planning and landscape design. We recommend dynamic coupling of street orientation, three-dimensional morphological characteristics, and vegetation configuration parameters to formulate differentiated thermal environment design guidelines, enabling precise alignment between mitigation measures and spatial context-specific features. Full article