Search Results (1,661)

Background/Objectives: The classification of psychological disorders has gained significant importance due to recent advancements in signal processing techniques. Traditionally, research in this domain has focused primarily on binary classifications of disorders. This study aims to classify five distinct states, including one control group and four categories of psychological disorders. Methods: Our investigation will utilize algorithms based on Granger causality and local graph structures to improve classification accuracy. Feature extraction from connectivity matrices was performed using local structure graphs. The extracted features were subsequently classified employing K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, and Naïve Bayes classifiers. Results: The KNN classifier demonstrated the highest accuracy in the gamma band for the depression category, achieving an accuracy of 89.36%, a sensitivity of 89.57%, an F1 score of 94.30%, and a precision of 99.90%. Furthermore, the SVM classifier surpassed the other machine learning algorithms when all features were integrated, attaining an accuracy of 89.06%, a sensitivity of 88.97%, an F1 score of 94.16%, and a precision of 100% for the discrimination of depression in the gamma band. Conclusions: The proposed methodology provides a novel approach for analyzing EEG signals and holds potential applications in the classification of psychological disorders. Full article

For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface). Currently, although there are many target detection algorithms, they all require a large amount of data for training. However, datasets for low-resolution face detection are scarce, leading to poor detection performance of the models. This paper aims to solve Retinaface’s weak face recognition capability in low-resolution scenarios and its potential inaccuracies in face bounding box localization when faces are at extreme angles or partially occluded. To this end, Spatial Depth-wise Separable Convolutions are introduced. Retinaface-Resnet is designed for face detection and localization, while adaface is employed to address low-resolution face recognition by using feature norm approximation to estimate image quality and applying an adaptive margin function. Additionally, a multi-object tracking algorithm is used to solve the problem of moving occlusion. Experimental results demonstrate significant improvements, achieving an accuracy of 96.12% on the WiderFace dataset and a recognition accuracy of 84.36% in practical laboratory applications. Full article

Background: Over the past decade, TNF inhibitors such as Infliximab and Adalimumab have become central to Inflammatory Bowel Diseases treatment, greatly enhancing patient outcomes. However, immunogenicity—where anti-drug antibodies diminish effectiveness—remains an issue, often requiring dose changes or combination therapies. Pharmacogenomics is increasingly applied in IBD to personalise treatment, especially since genetic factors like the HLA-DQA1*05 variant heighten the immunogenicity risk with IFX. This study aims to examine the relationship between the HLA-DQA1*05 variant and response loss or antibody development in patients regularly monitored on IFX or ADA. Methods: Sixty-five paediatric IBD patients were enrolled, with therapeutic drug monitoring (TDM) of IFX and ADA, conducted using immunoenzymatic assays. The presence of the HLA-DQA1*05 T>C allele variant was also tested using a Biomole HLA-DQA1 Real-time PCR kit. Results: The HLA-DQA1*05 rs2097432 T>C allele was present in 54% of patients on IFX and 69% of those on ADA. No statistically significant differences were found between HLA carriers and non-carriers across any of the three analysed groups: IFX, ADA and the overall anti-TNFα. Conclusions: Our study suggests that the HLA-DQA1*05 allele does not increase the risk of secondary loss of response to anti-TNF therapy, likely because most patients were on a combination of anti-TNF agents and immunomodulators, which can lower anti-drug antibody production. Testing for HLA-DQA105 can aid in personalising treatment and optimising therapy to minimise immunogenicity risks. Full article

Background/Objectives: The introduction of anti-tumor necrosis factor-α (anti-TNF-α) agents, particularly infliximab (IFX) and adalimumab (ADA), has significantly expanded the therapeutic arsenal for inflammatory bowel disease (IBD). While these biologics have demonstrated substantial efficacy, they are associated with a spectrum of potential adverse events (AEs). This study aims to evaluate and document these AEs to facilitate optimal patient selection and monitoring strategies of patients undergoing these therapies. Methods: This retrospective, single-center study examined pediatric IBD patients receiving anti-TNF-α therapy at the “Grigore Alexandrescu” Emergency Hospital for Children in Bucharest, Romania, from January 2015 to October 2024. AEs were categorized into non-infectious complications (acute infusion reactions, anti-drug antibody formation), dermatological effects (erythema nodosum, vasculitis), neurological effects (Guillain–Barré syndrome), and infections. AEs were analyzed in relation to the specific anti-TNF-α agent administered and comprehensively characterized. Results: Of 40 patients enrolled, 22 (55%) had Crohn’s disease (CD). The median (IQR) age at diagnosis was 14.8 years [10.8–15.9]. IFX was used in 34 (85%) patients while 6 (15%) patients received either ADA or IFX/ADA sequential therapy. Twenty-seven AEs were documented in 19 (47.5%) patients, the most prevalent being antidrug antibody formation (44.4%), infections (22.2%), and acute infusion reactions (22.2%). All ADA-exposed patients experienced at least one AE, compared to 41.2% (n = 14) patients treated with IFX, p = 0.01. Conclusions: AEs were observed in approximately half of the study cohort, with anti-drug antibody formation emerging as the most frequent complication. ADA therapy was associated with a significantly higher rate of AEs compared to IFX. These findings underscore the critical importance of vigilant monitoring for patients undergoing anti-TNF-α therapy in pediatric IBD management. Full article

In advanced driver-assistance systems (ADASs), the misalignment of the mounting angle of the automotive radar significantly affects the accuracy of object detection and tracking, impacting system safety and performance. This paper introduces the Automotive Radar Alignment Detection Network (AutoRAD-Net), a novel model that leverages complex-valued convolutional neural network (CV-CNN) to address azimuth misalignment challenges in automotive radars. By utilizing complex-valued inputs, AutoRAD-Net effectively learns the physical properties of the radar data, enabling precise azimuth alignment. The model was trained and validated using mounting angle offsets ranging from −3° to +3° and exhibited errors no greater than 0.15° across all tested offsets. Moreover, it demonstrated reliable predictions even for unseen offsets, such as −1.7°, showcasing its generalization capability. The predicted offsets can then be used for physical radar alignment or integrated into compensation algorithms to enhance data interpretation accuracy in ADAS applications. This paper presents AutoRAD-Net as a practical solution for azimuth alignment, advancing radar reliability and performance in autonomous driving systems. Full article

The widespread use of thiamethoxam has led to pesticide residues that have sparked global concerns regarding ecological and human health risks. A pressing requirement exists for a detection method that is both swift and sensitive. Herein, we introduced an innovative fluorescence biosensor constructed from alendronic acid (ADA)-modified upconversion nanoparticles (UCNPs) linked with magnetic nanoparticles (MNPs) via aptamer recognition for the detection of thiamethoxam. Through base pairing, thiamethoxam-specific aptamer-functionalized MNPs (apt-MNPs) were integrated with complementary DNA-functionalized UCNPs (cDNA-UCNPs) to create the MNPs@UCNPs fluorescence biosensor. Thiamethoxam specifically attached to apt-MNPs, leading to their separation from cDNA-UCNPs, which in turn led to a reduction in fluorescence intensity at 544 nm following separation by an external magnetic field. The change in fluorescence intensity (ΔI) was directly correlated with the concentration of thiamethoxam, enabling the quantitative analysis of the pesticide. With optimized detection parameters, the biosensor was capable of quantifying thiamethoxam within a concentration span of 0.4–102.4 ng·mL⁻¹, and it achieved a detection limit as minute as 0.08 ng·mL⁻¹. Moreover, leveraging the swift magnetic concentration properties of MNPs, the assay duration could be abbreviated to 25 min. The research exhibited a swift and precise sensing platform that yielded promising results in samples of cucumber, cabbage, and apple. Full article

Solar-induced chlorophyll fluorescence (SIF), as a direct indicator of vegetation photosynthesis, offers a more accurate measure of plant photosynthetic dynamics than traditional vegetation indices. However, the current SIF satellite products have low spatial resolution, limiting their application in fine-scale agricultural research. To address this, we leveraged MODIS data at a 1 km resolution, including bands b1, b2, b3, and b4, alongside indices such as the NDVI, EVI, NIRv, OSAVI, SAVI, LAI, FPAR, and LST, covering October 2018 to May 2020 for Shandong Province, China. Using the Random Forest (RF) model, we downscaled SIF data from 0.05° to 1 km based on invariant spatial scaling theory, focusing on the winter wheat growth cycle. Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R² = 0.931, RMSE = 0.052 mW/m²/nm/sr, and MAE = 0.031 mW/m²/nm/sr for 2018–2019 and R² = 0.926, RMSE = 0.058 mW/m²/nm/sr, and MAE = 0.034 mW/m²/nm/sr for 2019–2020. The downscaled SIF products showed a strong correlation with TanSIF and GOSIF products (R² > 0.8), and consistent trends with GPP further confirmed the reliability of the 1 km SIF product. Additionally, a time series analysis of Shandong Province’s wheat-growing areas revealed a strong correlation (R² > 0.8) between SIF and multiple vegetation indices, underscoring its utility for regional crop monitoring. Full article

Urbanization poses significant challenges to ecosystems, resources, and human well-being, necessitating sustainable planning. Urban vegetation, particularly trees, provides critical ecosystem services such as carbon sequestration, air quality improvement, and biodiversity conservation. Traditional tree assessments are resource-intensive and time-consuming. Recent advances in remote sensing, especially high-resolution multispectral imagery and object-based image analysis (OBIA), offer efficient alternatives for mapping urban vegetation. This study evaluates and compares the efficacy of Sentinel-2 and Pléiades satellite imagery in classifying tree species within historic urban parks in Rome—Villa Borghese, Villa Ada Savoia, and Villa Doria Pamphilj. Pléiades imagery demonstrated superior classification accuracy, achieving an overall accuracy (OA) of 89% and a Kappa index of 0.84 in Villa Ada Savoia, compared to Sentinel-2’s OA of 66% and Kappa index of 0.47. Specific tree species, such as Pinus pinea (Stone Pine), reached a user accuracy (UA) of 84% with Pléiades versus 53% with Sentinel-2. These insights underscore the potential of integrating high-resolution remote sensing data into urban forestry practices to support sustainable urban management and planning. Full article

Toll-like receptor 4 (TLR4) has been implicated in the production of uncontrolled inflammation within the body, known as the cytokine storm. Studies that employ machine learning (ML) in the prediction of potential inhibitors of TLR4 are limited. This study introduces AICpred, a robust, free, user-friendly, and easily accessible machine learning-based web application for predicting inhibitors against TLR4 by targeting the TLR4-myeloid differentiation primary response 88 (MyD88) interaction. MyD88 is a crucial adaptor protein in the TLR4-induced hyper-inflammation pathway. Predictive models were trained using random forest, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), k-nearest neighbours (KNN), and decision tree models. To handle imbalance within the training data, resampling techniques such as random under-sampling, synthetic minority oversampling technique, and the random selection of 5000 instances of the majority class were employed. A 10-fold cross-validation strategy was used to evaluate model performance based on metrics including accuracy, balanced accuracy, and recall. The XGBoost model demonstrated superior performance with accuracy, balanced accuracy, and recall scores of 0.994, 0.958, and 0.917, respectively, on the test. The AdaBoost and decision tree models also excelled with accuracies ranging from 0.981 to 0.992, balanced accuracies between 0.921 and 0.944, and recall scores between 0.845 and 0.891 on both training and test datasets. The XGBoost model was deployed as AICpred and was used to screen compounds that have been reported to have positive effects on mitigating the hyperinflammation-associated cytokine storm, which is a key factor in COVID-19. The models predicted Baricitinib, Ibrutinib, Nezulcitinib, MCC950, and Acalabrutinib as anti-TLR4 compounds with prediction probability above 0.90. Additionally, compounds known to inhibit TLR4, including TAK-242 (Resatorvid) and benzisothiazole derivative (M62812), were predicted as bioactive agents within the applicability domain with probabilities above 0.80. Computationally inferred compounds using AICpred can be explored as potential starting skeletons for therapeutic agents against hyperinflammation. These predictions must be consolidated with experimental screening to enhance further optimisation of the compounds. AICpred is the first of its kind targeting the inhibition of TLR4-MyD88 binding and is freely available at http://197.255.126.13:8080. Full article

The accurate prediction of the spatial distribution of soil organic carbon (SOC) and the identification of the mechanisms underlying its spatial differentiation are of paramount significance for the conservation and utilization of land and regional sustainable development. A total of 512 soil samples were collected from Wuchang and Shuangcheng County in Harbin City, Heilongjiang Province, China, which served as the study area. Six machine learning models, including Random Forest (RF), AdaBoost, Support Vector Regression (SVR), weighted average, Stacking, and Blending, were utilized to predict the spatial distribution of SOC and analyze its spatial differentiation. The result reveals that 12 environmental variables, including soil type, bulk density, pH, average annual precipitation, average annual temperature, net primary productivity (NPP), land use type, normalized difference vegetation index (NDVI), slope, elevation, soil parent material, and distance to rivers, are effective influencing factors on SOC in the study area. It turns out that the Stacking model, with an R² of 0.4327, performed the best in this study, followed by the weighted average, Blending, RF, AdaBoost, and SVR models; a heterogeneous integrated learning model may be more robust than an individual learner. The predicted SOC content is generally lower in the northwestern arable land and higher in the southeastern forest land. In addition, SOC differentiation shows that forest land and grass land with dark brown soil or swamp soil, soil covering igneous and metamorphic rocks with various minerals, higher elevation and slope, and suitable water-thermal and soil intrinsic conditions for aerobic microbial activity benefit the enrichment of SOC in the study area. The enrichment and depletion of SOC are jointly influenced by pedogenesis, microbial activity, and biodiversity. Full article

Joint communication and sensing (JCS) is becoming an important trend in 6G, owing to its efficient utilization of spectrums and hardware resources. Utilizing echoes of the same signal can achieve the object location sensing function, in addition to the V2X communication function. There is application potential for JCS systems in the fields of ADAS and unmanned autos. Currently, the NR-V2X sidelink has been standardized by 3GPP to support low-latency high-reliability direct communication. In order to combine the benefits of both direct communication and JCS, it is promising to extend existing NR-V2X sidelink communication toward sidelink JCS. However, conflicting performance requirements arise between radar sensing accuracy and communication reliability with the limited sidelink spectrum. In order to overcome the challenges in the distributed resource allocation of sidelink JCS with a full-duplex, this paper has proposed a novel consecutive-collision mitigation semi-persistent scheduling (CCM-SPS) scheme, including the collision detection and Q-learning training stages to suppress collision probabilities. Theoretical performance analyses on Cramér–Rao Lower Bounds (CRLBs) have been made for the sensing of sidelink JCS. Key performance metrics such as CRLB, PRR and UD have been evaluated. Simulation results show the superior performance of CCM-SPS compared to similar solutions, with promising application prospects. Full article

Many real-world expensive industrial and engineering multi-objective optimization problems (MOPs) are driven by historical, experimental, or simulation data. In such scenarios, due to the expensive cost and time required, we are only left with a small amount of labeled data to perform the optimization. These offline data-driven MOPs are usually solved by multi-objective evolutionary algorithms (MOEAs) with the help of surrogate models constructed from offline historical data. The key challenge in developing these data-driven MOEAs is that they have to replace multiple conflicting fitness functions by approximating these objective functions, which may produce cumulative approximation errors and misguide the search. In order to build a reliable surrogate model from a small amount of multi-output offline data and solve the DDMOPs, we have proposed an adaptive model selection method with a reliable individual-based model management-driven MOEA. The proposed algorithm dynamically selects between DNN and XGBoost by comparing their k-fold cross-validation MAE error, which can capture the true generalization ability of the surrogates on unseen data. Then, the selected surrogate is updated with a reliable individual selection strategy, where the individual who is closest, both in the decision and objective space, to the most preferred solution among labeled offline data is chosen. As a result, these two strategies guide the underlying MOEA to the Pareto optimal solutions. The empirical results of the ZDT and DTLZ benchmark test suite validate the use of the three state-of-the-art offline DDMOEAs, showing that our algorithm is able to achieve highly competitive results in terms of convergence and diversity for 2–3 objectives. Finally, our algorithm is applied to an offline data-driven multi-objective problem—transonic airfoil (RAE 2822) shape optimization—to validate its efficiency on real-world DDMOPs. Full article

Lawsonia intracellularis is a Gram-negative, intracellular bacterium that can infect several animal species. In pigs, the bacteria cause porcine proliferative enteropathy, or ileitis. The wide spread of the pathogen produces a large impact on pig production worldwide. Saliva is a source of biomarkers that can help to monitor changes in the immune system after vaccination. The purpose of this study was to study the changes in haptoglobin (Hp), immunoglobulin G (IgG), and adenosine deaminase (ADA) in saliva after vaccination against Lawsonia intracellularis. In addition, productivity parameters were analysed to evaluate if vaccination and changes in salivary analytes could be associated with changes in these parameters. The pigs vaccinated against Lawsonia showed an improvement in the productive parameters and a reduction in food conversion and frequency of diseases. In addition, they showed lower values of Hp (p = 0.011), IgG (p < 0.01), and ADA (p < 0.003) in saliva during the first two months of the fattening period compared to non-vaccinated pigs. It could be concluded that in our experimental conditions, the vaccination against Lawsonia intracellularis produced a significant decrease in biomarkers of the immune response in saliva compared with the non-vaccinated pigs. This would indicate a reduction in the activation of the immune system, which could be postulated to be due to the increased defence ability of the organism against pathogens. This reduced activation of the immune system can lead to better food conversion and an increase in the productive parameters of these pigs. Overall, this report opens a new window for the possible use of saliva for non-invasive evaluation of the immune system after vaccination in pigs. Full article

Fault detection and classification in transmission lines are critical for maintaining the reliability and stability of electrical power systems. Quick and accurate fault detection allows for timely intervention, minimizing equipment damage, and reducing downtime. This study addresses the challenge of effective fault classification, particularly when dealing with smaller, more practical datasets. Initially, the study examined the performance of conventional machine learning algorithms on a comprehensive dataset of 7681 samples, demonstrating high accuracy owing to the inherent symmetry of sinusoidal voltage and current signals. However, the true efficacy of these algorithms was evaluated by minimizing the dataset to 231 training samples, with the remainder being used for testing. A novel Multi-Target Ensemble Classifier was developed to improve classification accuracy. The proposed algorithm achieved an impressive overall accuracy of 0.829165, outperforming traditional methods, including the K-Nearest Neighbors Classifier, support vector classification, random forest classifier, decision tree classifier, AdaBoost classifier, gradient boosting classifier, and Gaussian NB. This research highlights the importance of efficient fault classification techniques in power systems and proposes a superior solution in the form of a multitarget ensemble classifier. Full article

Background/Objectives: Colorectal cancer (CRC) remains a major global health burden, necessitating innovative preventive approaches. Artemisia annua (A. annua), known for its extensive pharmacological properties, has shown potential in cancer therapy. This study investigates the chemopreventive efficacy of methanolic extract of A. annua (MEA) in an azoxymethane (AOM)-induced murine model of CRC, with a focus on its antioxidant, biomarker modulation, and pro-apoptotic activities. Methods: MEA was obtained via cold solvent extraction, yielding 39%, and demonstrated potent in vitro cytotoxicity against HCT116 and RKO colon cancer cell lines, with IC50 values of 20 µg/mL and 15 µg/mL, respectively. Swiss albino mice were treated with MEA beginning two weeks before AOM induction, with treatment continuing for 21 weeks. Survival was monitored for 40 weeks. Key outcomes included serum biomarker levels (ADA, GGT, CD73, LDH), antioxidant enzyme activities (SOD, CAT, GPx1, MDA), reactive oxygen species (ROS) modulation, apoptosis induction, and histopathological evaluation. Results: MEA significantly improved survival rates, reduced AOM-induced weight loss, and modulated cancer biomarkers, with marked reductions in ADA, GGT, CD73, and LDH levels. Antioxidant defenses were restored, as evidenced by increased SOD, CAT, and GPx1 activities and decreased MDA levels. ROS levels were significantly reduced, and apoptosis in colonic cells was effectively induced. Histopathological analysis revealed substantial mitigation of CRC-associated morphological abnormalities. Conclusions: MEA exhibits robust chemopreventive properties, demonstrating its potential to reduce oxidative stress, modulate key biomarkers, and induce apoptosis in CRC. These findings position MEA as a promising natural candidate for CRC prevention and therapy, warranting further exploration for clinical application. Full article