Search Results (8)

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

The National Institute of Earth Physics (NIEP) in Romania has upgraded its seismic monitoring stations into multifunctional platforms equipped with advanced devices for measuring gas emissions, magnetic fields, telluric fields, solar radiation, and more. This enhancement enabled the integration of a seismic forecasting system designed to extend the alert time of the existing warning system, which previously relied solely on seismic data. The implementation of an Operational Earthquake Forecast (OEF) aims to expand NIEP’s existing Rapid Earthquake Early Warning System (REWS) which currently provides a warning time of 25–30 s before an earthquake originating in the Vrancea region reaches Bucharest. The AFROS project (PCE119/4.01.2021) introduced fundamental research essential to the development of the OEF system. As a result, real-time analyses of radon and CO₂ emissions are now publicly available at afros.infp.ro, dategeofizice. The primary monitored area is Vrancea, known for producing the most destructive earthquakes in Romania, with impacts extending to neighboring countries such as Bulgaria, Ukraine, and Moldova. The structure and methodology of the monitoring network are adaptable to other seismic regions, depending on their specific characteristics. All collected data are stored in an open-access database available in real time, geobs.infp.ro. The monitoring methods include threshold-based event detection and seismic data analysis. Each method involves specific technical nuances that distinguish this monitoring network as a novel approach in the field. In conclusion, experimental results indicate that the Gutenberg-Richter law, combined with gas emission measurements (radon and CO₂), can be used for real-time earthquake forecasting. This approach provides warning times ranging from several hours to a few days, with results made publicly accessible. Another key finding from several years of real-time monitoring is that the value of fundamental research lies in its practical application through cost-effective and easily implementable solutions—including equipment, maintenance, monitoring, and data analysis software. Full article

Thermal infrared remotely sensed data, by capturing the thermal radiation characteristics emitted by the Earth’s surface, plays a pivotal role in various domains, such as environmental monitoring, resource exploration, agricultural assessment, and disaster early warning. However, the acquisition of thermal infrared hyperspectral remotely sensed imagery necessitates more complex and higher-precision sensors, which in turn leads to higher research and operational costs. In this study, a novel Convolutional Neural Network (CNN)–Transformer combined block, termed CTBNet, is proposed to address the challenge of thermal infrared multispectral image spectral reconstruction. Specifically, the CTBNet comprises blocks that integrate CNN and Transformer technologies (CTB). Within these CTBs, an improved self-attention mechanism is introduced, which not only considers features across spatial and spectral dimensions concurrently, but also explicitly extracts incremental features from each channel. Compared to other algorithms, the proposed method more closely aligns with the true spectral curves in the reconstruction of hyperspectral images across the spectral dimension. Through a series of experiments, this approach has been proven to ensure robustness and generalizability, outperforming some state-of-the-art algorithms across various metrics. Full article

Studying vegetation change and its interaction with climate change is essential for regional ecological protection. Previous studies have demonstrated the impact of climate change on regional vegetation in South America; however, studies addressing the fragile ecological environment in Argentina are limited. Therefore, we assessed the vegetation dynamics and their climatic feedback in five administrative regions of Argentina, using correlation analysis and multiple regression analysis methods. The Normalized Difference Vegetation Index 3rd generation (NDVI3g) from Global Inventory Monitoring and Modeling Studies (GIMMS) and climatic data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) were processed. The NDVI of the 1982–2015 period in Argentina showed a downward trend, varying from −1.75 to 0.69/decade. The NDVI in Northeast Argentina (NEA), Northwest Argentina (NWA), Pampas, and Patagonia significantly decreased. Precipitation was negatively correlated with the NDVI in western Patagonia, whereas temperature and solar radiation were positively correlated with the NDVI. Extreme precipitation and drought were essential causes of vegetation loss in Patagonia. The temperature (73.09%), precipitation (64.02%), and solar radiation (73.27%) in Pampas, Cuyo, NEA, and NWA were positively correlated with the NDVI. However, deforestation and farming and pastoral activities have caused vegetation destruction in Pampas, NEA, and NWA. Environmental protection policies and deforestation regulations should be introduced to protect the ecological environment. The results of this study clarify the reasons for the vegetation change in Argentina and provide a theoretical reference for dealing with climate change. Full article

Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods. Full article

Ground-level ozone (O₃) is one of the most significant forms of air pollution around the world due to its ability to cause adverse effects on human health and environment. Understanding the variation and association of O₃ level with its precursors and weather parameters is important for developing precise forecasting models that are needed for mitigation planning and early warning purposes. In this study, hourly air pollution data (O₃, CO, NO₂, PM₁₀, NmHC, SO₂) and weather parameters (relative humidity, temperature, UVB, wind speed and wind direction) covering a ten year period (2003–2012) in the selected urban areas in Malaysia were analyzed. The main aim of this research was to model O₃ level in the band of greatest solar radiation with its precursors and meteorology parameters using the proposed predictive models. Six predictive models were developed which are Multiple Linear Regression (MLR), Feed-Forward Neural Network (FFANN), Radial Basis Function (RBFANN), and the three modified models, namely Principal Component Regression (PCR), PCA-FFANN, and PCA-RBFANN. The performances of the models were evaluated using four performance measures, i.e., Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Index of Agreement (IA), and Coefficient of Determination (R²). Surface O₃ level was best described using linear regression model (MLR) with the smallest calculated error (MAE = 6.06; RMSE = 7.77) and the highest value of IA and R² (0.85 and 0.91 respectively). The non-linear models (FFANN and RBFANN) fitted the observed O₃ level well, but were slightly less accurate compared to MLR. Nonetheless, all the unmodified models (MLR, ANN, and RBF) outperformed the modified-version models (PCR, PCA-FFANN, and PCA-RBFANN). Verification of the best model (MLR) was done using air pollutant data in 2018. The MLR model fitted the dataset of 2018 very well in predicting the daily O₃ level in the specified selected areas with the range of R² values of 0.85 to 0.95. These indicate that MLR can be used as one of the reliable methods to predict daytime O₃ level in Malaysia. Thus, it can be used as a predictive tool by the authority to forecast high ozone concentration in providing early warning to the population. Full article

In this paper, we proposed an unsupervised machine-learning-based framework to automate the process of extracting suspicious gamma dose rate incidents from the real unlabeled raw historical data measured in the German Radiation Early Warning Network and identify the underlying events behind each. This raised the research problem of clustering unlabeled time series data with varying lengths and scales. Based on the many evaluations, we demonstrated that the state-of-the-art’s most popular time series clustering models were not suitable to perform this task. This motivated us to introduce our own approach. Through this approach we were able to perform online classification for gamma dose rate incidents of varying lengths and scales. Full article

In this paper, we propose a new method for choosing the most suitable time-series classification method that can be applied to online gamma dose rate incidents. We referred to the historical incidents measured in the German Radiation Early Warning Network and clustered them into several classes before testing existing classification methods. This raises the research problem of the online classification of time-series data with varying scales and lengths. Referring to the state-of-the-art methods, we found that no specific classification method can fit our data all the time. This motivated us to introduce our own approach. Full article