Search Results (191)

Pb (II) contamination in wastewater represents a grave threat to the environment and ecosystems. Consequently, there is an urgent need to prepare low-cost and highly efficient Pb (II) adsorbents. To address this need, abundant and low-cost natural silica-based desert sand (DS) was innovatively utilized as a carrier to develop efficient and selective Pb (II) adsorbents. Modified desert sand (MDS) was first prepared via 1 M HCl pretreatment for 2 h and subsequent KH550 silane modification. Pb (II)-imprinted composites (Pb (II)-IIP@MDS) were then fabricated via ion-imprinted polymerization, using Pb (II) as the template ion and N-hydroxymethacrylamide (NHMA)/hydroxyethyl methacrylate (HEMA) as dual functional monomers with a molar ratio of 1:1. The synthesized Pb (II)-IIP@MDS was comprehensively characterized by X-ray photoelectron spectrometer (XPS), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FT-IR). The adsorption capacity, selectivity, and reusability of this material for lead ions were evaluated through three experiments conducted within the optimized pH range of 6–7, with error bars indicated. In adsorption isotherm experiments, the initial Pb (II) concentration ranged from 50 to 500 mg·L⁻¹, conforming to the Langmuir model (R² = 0.992), with a theoretical maximum adsorption capacity reaching 107.44 mg·g⁻¹; this indicates that the adsorbate forms a monolayer adsorption on the homogeneous imprinted sites. Kinetics data indicate that the process best fits a quasi-first-order kinetic model (R² ≥ 0.988), while the favorable quasi-second-order kinetic fit (R² ≥ 0.982) reflects the synergistic effect of physical diffusion and ion-imprinting chemistry, reaching equilibrium within 120 min. Thermodynamic parameters (ΔH⁰ = 12.51 kJ·mol⁻¹, ΔS⁰ = 101.19 J·mol⁻¹·K⁻¹, ΔG⁰ < 0) confirmed endothermic, entropy-increasing, spontaneous adsorption. In multicomponent systems, Pb (II)-IIP@MDS showed distinct Pb (II) selectivity. It retained 80.3% adsorption efficiency after eight cycles. This work provides a promising strategy for fabricating low-cost, high-performance Pb (II) adsorbents, and Pb (II)-IIP@MDS stands as a practical candidate for the remediation of Pb (II)-contaminated wastewater. Full article

As cyber threats such as denial-of-service (DoS) attacks continue to rise, network intrusion detection systems (NIDS) have become essential components of cybersecurity defense. Although machine learning is widely applied to network intrusion detection, its performance often deteriorates due to the extreme class imbalance present in real-world data. This imbalance causes models to become biased and unable to detect critical attack instances. To address this issue, we propose MCH-Ensemble (Minority Class Highlighting Ensemble), an ensemble framework designed to improve the detection of minority attack classes. The method constructs multiple balanced subsets through random under-sampling and trains base learners, including decision tree, XGBoost, and LightGBM models. Features of correctly predicted attack samples are then amplified by adding a constant value, producing a boosting-like effect that enhances minority class representation. The highlighted subsets are subsequently combined to train a random forest meta-model, which leverages bagging to capture diverse and fine-grained decision boundaries. Experimental evaluations on the UNSW-NB15, CIC-IDS2017, and WSN-DS datasets demonstrate that MCH-Ensemble effectively mitigates class imbalance and achieves superior recognition of DoS attacks. The proposed method achieves enhanced performance compared with those reported previously. On the UNSW-NB15 and CIC-IDS2017 datasets, it achieves improvements in accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) by ~1.2% and ~0.61%, ~9.8% and 0.77%, ~0.7% and ~0.56%, ~5.3% and 0.66%, and ~0.1% and ~0.06%, respectively. In addition, it achieves these improvements by ~0.17%, ~1.66%, ~0.11%, ~0.88%, and ~0.06%, respectively, on the WSN-DS dataset. These findings indicate that the proposed framework offers a robust and accurate approach to intrusion detection, contributing to the development of reliable cybersecurity systems in highly imbalanced network environments. Full article

Wheat (Triticum aestivum L.) is a key crop in Spain, especially in Castilla and León Region. However, there are few studies evaluating predictive models based on spectral indices and multivariate analysis to estimate yield in direct seeding (DS) and conventional seeding (CS) systems. This study addresses this need by implementing a split-plot experimental design in the city of Palencia, Spain, analyzing crop physiological data and nine spectral indices derived from multispectral aerial images captured by drones. The analysis included multivariate techniques such as Principal Component Analysis (PCA) and Random Forest (RF), supplemented with statistical tests, ROC curves, and prediction analysis. The results showed that the RF model successfully classified treatments with 93.75% accuracy and a Kappa index of 0.875, highlighting performance, nitrogen, and protein as key variables. Among the vegetation indices, the Soil-Adjusted Vegetation Index (SAVI) and the Advanced Vegetation Index (AVI) were the most relevant in the flowering stage, with ROC curve values of 0.7778 and 0.8025, respectively. Spearman’s correlations confirmed a significant relationship between these indices and key physiological variables, allowing to distinguish between DS and CS systems. The RF-based prediction model for performance showed R² values above 91% in the indices with the highest correlation. However, predictive capacity was higher in DS, suggesting that conditions inherent in non-mechanized handling significantly influence model performance. This highlights the importance of using non-destructive procedures to estimate production, enabling the development of adaptive and sustainable strategies that contribute to efficient agricultural production, since it is possible to anticipate crop yields before harvest, optimizing resources such as fertilizers and water. Full article

Background: High-throughput metabolomics studies have promoted the discovery of candidate biomarkers linked to atherosclerosis (AS). This narrative systematic review summarises metabolomics studies conducted in (1) individuals with subclinical AS (assessed by imaging techniques such as carotid intimal media thickness, IMT, and coronary artery calcium, CAC), (2) patients with established atherosclerotic plaques, and (3) individuals with AS risk factors. Methods: The systematic search was conducted in the PubMed database according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. The inclusion criteria were as follows: (i) publication date between 2009 and 2024; (ii) identification of potential biomarkers for AS in subjects with a diagnosis of AS or with one or more traits characteristic of the disease (i.e., CAC or IMT); (iii) identification of potential AS biomarkers in subjects with atherogenic clinical conditions (i.e., Down’s syndrome, DS, polycystic ovarian syndrome, PCOS, and systemic lupus erythematosus, SLE); (iv) metabolomic studies; and (iv) studies in human samples. Exclusion criteria comprised the following: (i) studies on lipid metabolic diseases unrelated to AS, (ii) “omics” results not derived from metabolomics, (iii) reviews and studies in animal models or cell cultures, and (iv) systematic reviews and meta-analyses. Of 90 eligible studies screened, 24 met the inclusion criteria. Results: Across subclinical and overt AS, consistent disturbances were observed in amino acid, lipid, and carbohydrate metabolism. Altered profiles included branched-chain amino acids (BCAAs), aromatic amino acids (AACs) and derivatives (e.g., kynurenine–tryptophan pathway), bile acids (BAs), androgenic steroids, short-chain fatty acids (FAs)/ketone intermediates (e.g., acetate, 3-hydroxybutyrate, 3-HB), and Krebs cycle intermediates (e.g., citrate). Several metabolites (e.g., glutamine, lactate, 3-HB, phosphatidylcholines, PCs/lysophosphatidylcholines, lyso-PCs) showed reproducible associations with vascular phenotypes (IMT/CAC) and/or clinical AS. Conclusions: The identification of low-weight metabolites altered in both subclinical and overt AS suggests their potential as candidate biomarkers for early AS diagnosis. Given the steady increase in deaths from cardiovascular disease, a manifestation of advanced AS, this finding could have significant clinical relevance. Full article

With the increasing demand for safety monitoring of offshore engineering structures, traditional single-modality sensing and centralized data processing models face challenges such as insufficient real-time performance and weak anti-interference abilities in complex marine environments. This research proposes an intelligent monitoring system based on multimodal sensor fusion and edge computing, aiming to achieve high-precision real-time diagnosis of offshore structure damage. The research plans to construct multimodal sensors through sensors such as stress change sensors, vibration sensors, ultrasonic sensors, and fiber Bragg grating sensors. A distributed wireless sensor network will be adopted to realize the transmission of sensor data, reduce the complexity of wiring, and meet the requirements of high humidity and strong corrosion in the marine environment. At the edge computing layer, lightweight deep learning models (such as multi-branch Transformer) and D-S evidence theory fusion algorithms will be deployed to achieve real-time feature extraction of multi-source data and damage feature fusion, supporting the intelligent identification of typical damages such as cracks, corrosion, and deformation. Experiments will simulate the coupled working conditions of wave impact, seismic load, and corrosion to verify the real-time performance and accuracy of the system. The expected results can provide a low-latency and highly robust edge-intelligent solution for the health monitoring of offshore engineering structures and promote the deep integration of sensor networks and artificial intelligence in Industry 4.0 scenarios. Full article

This paper presents the design and implementation of a custom-built LiFePO₄ battery monitoring system that offers real-time visibility into the status of individual battery cells. The system is based on a Battery Management System (BMS) architecture and is implemented by measuring the voltage, current, and temperature of each cell in a multi-cell pack. These key parameters are essential for ensuring safe operation, prolonging battery life, and optimizing energy usage in off-grid or mobile power systems. The system architecture is based on an ESP32 microcontroller that interfaces with INA219 and DS18B20 sensors to continuously measure individual cell voltage, current, and temperature. Data are transmitted wirelessly via Wi-Fi to a remote time-series database for centralized storage, analysis, and visualization. Experimental validation, conducted over a 15-day period, demonstrated stable system performance and reliable data transmission. Analytically, the findings indicate that utilizing an advanced smart charger for precise cell balancing and improving the physical layout for cooling led to superior thermal performance. Even when load current nearly tripled to 110 mA, the system maintained a stable cell operating temperature range of 29.8 °C to 30.3 °C. This result confirms significantly reduced cell stress compared to previous iterations, which is critical for enhancing battery health and lifespan. The application of this project aimed to demonstrate how a combination of open hardware components and lightweight network protocols can be used to create a robust, cost-effective battery monitoring solution suitable for integration into smart energy systems or remote IoT infrastructures. Full article

To address the limitations of single-sensor perception in inland vessel monitoring and the lack of robustness of traditional tracking methods in occlusion and maneuvering scenarios, this paper proposes a hierarchical multi-target tracking framework that fuses Light Detection and Ranging (LiDAR) data with Automatic Identification System (AIS) information. First, an improved adaptive LiDAR tracking algorithm is introduced: stable trajectory tracking and state estimation are achieved through hybrid cost association and an Adaptive Kalman Filter (AKF). Experimental results demonstrate that the LiDAR module achieves a Multi-Object Tracking Accuracy (MOTA) of 89.03%, an Identity F1 Score (IDF1) of 89.80%, and an Identity Switch count (IDSW) as low as 5.1, demonstrating competitive performance compared with representative non-deep-learning-based approaches. Furthermore, by incorporating a fusion mechanism based on improved Dempster–Shafer (D-S) evidence theory and Covariance Intersection (CI), the system achieves further improvements in MOTA (90.33%) and IDF1 (90.82%), while the root mean square error (RMSE) of vessel size estimation decreases from 3.41 m to 1.97 m. Finally, the system outputs structured three-level tracks: AIS early-warning tracks, LiDAR-confirmed tracks, and LiDAR-AIS fused tracks. This hierarchical design not only enables beyond-visual-range (BVR) early warning but also enhances perception coverage and estimation accuracy. Full article

When drilling inclined and horizontal sections, a significant part of the drill string is in a compressed state which leads to a loss of stability and longitudinal bending. Modeling of the stress–strain state (SSS) of the drill string (DS), including prediction of its stability loss, is carried out using modern software packages; the basis of the software’s mathematical apparatus and algorithms is represented by deterministic statically defined formulae and equations. At the same time, a number of factors such as the friction of the drill string against the borehole wall, the presence of tool joints, drill string dynamic operating conditions, and the uncertainty of the position of the borehole in space cast doubt on the accuracy of the calculations and the reliability of the predictive models. This paper attempts to refine the actual behavior of the drill string in critical and post-critical conditions. To study the influence of dynamic conditions in the well on changes in the SSS of the DS due to its buckling, the following initial data were used: a drill pipe with an outer diameter of 88.9 mm and tool joints causing pipe deflection under gravitational acceleration of 9.81 m/s² placed in a horizontal wellbore with a diameter of 152.4 mm; axial vibrations with an amplitude of variable force of 15–80 kN and a frequency of 1–35 Hz; lateral vibrations with an amplitude of variable impact of 0.5–1.5 g and a frequency of 1–35 Hz; and an increasing axial load of up to 500 kN. A series of experiments are conducted with or without friction of the drill string against the wellbore walls. The results of computational experiments indicate a stabilizing effect of friction forces. It should be noted that the distance between tool joints and their diametrical ratio to the borehole, taking into account gravitational acceleration, has a stabilizing effect due to the formation of additional contact force and bending stresses. It was established that drill string vibrations may either provide a stabilizing effect or lead to a loss of stability, depending on the combination of their frequency and vibration type, as well as the amplitude of variable loading. In the experiments without friction, the range of critical loads under vibration varied from 85 to >500 kN, compared to 268 kN as obtained in the reference experiment without vibrations. In the presence of friction, the range was 150 to >500 kN, while in the reference experiment without vibrations, no buckling was observed. Based on the results of this study, it is proposed to monitor the deformation rate of the string during loading as a criterion for identifying buckling in the DS stress–strain state monitoring system. Full article

Background and Objectives: Individuals with Down syndrome (DS) represent a specific and vulnerable population requiring improvements in public health and social policies to ensure equal opportunities, longer life expectancy, and better quality of life. Accurate epidemiological and demographic indicators are essential for planning and evaluating interventions. This study aims to assess the prevalence of DS in Croatia from 2014 to 2024, analyzing demographic characteristics and regional distribution. A comparative analysis with international data and a review of national policies related to persons with disabilities and DS are also included. Materials and Methods: Data on the prevalence of DS were collected from the National Registry of Persons with Disabilities, where reporting individuals with DS is mandatory for the realization of legal rights. This ensures high data quality and representativeness. Prevalence per 1000 live births was calculated based on data from the national birth database and the registry. Results: The overall prevalence of DS in Croatia increased from 3.7 to 5.3 per 10,000 population during the observed period, while prevalence among live-born infants ranged from 1.1 to 1.5 per 1000. Males were slightly more represented (52.5%). The most common comorbidities included congenital heart defects. The mean age of individuals with DS was 28 years, with 12 individuals recorded as being older than 65 years and one individual aged 85. Conclusions: The DS prevalence in Croatia is comparable to data from European Union countries. The observed increase in prevalence and in the total number of individuals with disabilities highlights the need for continuous development and adaptation of national policies. As a signatory of the Convention on the Rights of Persons with Disabilities, Croatia is actively working to improve its legislative framework and support systems to ensure equal rights and enhance quality of life for individuals with DS. Full article

Background/Objectives: Real-world data about clinical characteristics and edoxaban performance in patients with heart failure (HF) and atrial fibrillation (AF) are lacking. The EMAYIC study aimed to assess and compare the profile and cardiovascular outcomes in those patients according to HF subtypes based on left ventricular ejection fraction (LVEF). Methods: Multicentre, prospective (follow-up: 12 months), observational study. Consecutive adult patients were included at cardiology and internal medicine clinics across Spain with HF (NT-proBNP > 600 pg/mL) and AF, receiving edoxaban as per routine clinical practice. Incidence of major or clinically relevant non-major (CRNM) bleeding and composite of incidence of stroke or systemic embolism (SE) were assessed according to HF subtypes: reduced (HFrEF, LVEF < 40%), mildly reduced (HFmrEF, LVEF40–49%), and preserved (HFpEF, LVEF ≥ 50%) left ventricular ejection fraction. Results: Between March 2021 and January 2022, 497 patients were enrolled (HFrEF: 30.4%, HFmrEF: 17.3%, HFpEF: 52.3%). The median age was 76.3 years, 57.9% were male, and the mean CHA2DS2-VASc score was 4. A 60 mg edoxaban dose was prescribed in 70% of patients. The observed rate of bleeding was 6.6% (95% CI: 4.5–9.3%), without differences across HF subtypes (HFrEF: 7.5%, HFmrEF: 3.6%, HFpEF: 7.1%; p = 0.474). Intracranial bleeding occurred in one patient (HFrEF). Stroke occurred in seven patients (1.5%) (HFrEF: 3, HFmrEF: 1, HFpEF: 3), two cases of which were fatal (HFrEF: 1, HFpEF: 1). No SE events were reported. Cardiovascular death occurred in 19 patients (4.1%) (HFrEF: 4.8%, HFmrEF: 3.6%, HFpEF: 3.8%; p = 0.871). Conclusions: This study evidences a low incidence of major or CRNM bleeding in patients with HF and AF treated with edoxaban, regardless of HF subtype. Low rates of stroke (1.5%) and SE events (0%) were assessed. Full article

The article presents a multicriterial approach to evaluating the efficiency of the energy transition in EU countries, emphasizing the relationship between resource efficiency and the results of transition. The study uses a data analysis methodology (DEA) to evaluate how effectively countries use resources (inputs), such as energy consumption, investment and innovative development, to achieve the desired results (outputs), including the renewable energy sources, reduction of CO₂ and labour trends. The use of DEA with Python 3.10 software made it possible to obtain objective performance and compare them with the energy transition index (ETI). The DEA and ETI based efficiency matrix has identified four clusters of countries: high efficiency and high transition readiness; high efficiency and low transition readiness; low efficiency and high transition readiness; low efficiency and transition readiness. Validation by means of a solution (DS) confirmed the reliability of the results. The conclusions emphasize that the higher efficiency of resource use does not automatically meet the higher transition indicators, which indicates the need to improve management, innovation spread and investment distribution. The study helps to develop evidence policy by offering a system for monitoring and comparative analysis of the efficiency of the energy transition in EU countries. Full article

This paper presents a method for recognizing sign language gestures corresponding to fruit names using electromyography (EMG) signals. The proposed system focuses on classification using a limited number of EMG channels, aiming to reduce classification process complexity while maintaining high recognition accuracy. The dataset (DS) contains EMG signal data of 46 hearing-impaired people and descriptions of fruit names, including apple, pear, apricot, nut, cherry, and raspberry, in sign language (SL). Based on the presented DS, gesture movements were classified using five different classification algorithms—Random Forest, k-Nearest Neighbors, Logistic Regression, Support Vector Machine, and neural networks—and the algorithm that gives the best result for gesture movements was determined. The best classification result was obtained during recognition of the word cherry based on the RF algorithm, and 97% accuracy was achieved. Full article

Automated violence detection in images presents a technical and scientific challenge that demands specialized methods to enhance classification systems. This study introduces an approach for automatically identifying relevant samples to improve the performance of neural network models, specifically DenseNet121, with a focus on violence classification in images. The proposed methodology begins with an initial training phase using a balanced dataset (DS1, 6000 images). Based on the model’s output scores ($ou{t}_N$), three confidence levels are defined: Safe ($ou{t}_N\ge 0.9+\sigma$ or $ou{t}_N\le 0.1-\sigma$), Border ($0.5-\sigma \le ou{t}_N\le 0.5+\sigma$), and Average ($0.4-\sigma \le ou{t}_N\le 0.6+\sigma$). These levels correspond to scenarios with low, moderate, and high prediction error probabilities, respectively, where $\sigma$ is an adjustable threshold. The Border subset exhibits symmetry around the decision boundary ($ou{t}_N=0.5$), capturing maximally uncertain samples, while the Safe regions reflect functional asymmetries in high-confidence predictions. Subsequently, these thresholds are applied to a second dataset (DS2, 5600 images) to extract specialized subsets for retraining ($D{S}_{Safe}$, $D{S}_{Border}$, and $D{S}_{Average}$). Finally, the model is evaluated using an independent test set ($D{S}_{test}$, 4400 images), ensuring complete data isolation. The experimental results demonstrate that the confidence-based subsets offer competitive performance despite using significantly fewer samples. The Average subset achieved an F1-Score of 0.89 and a g-mean of 0.93 using only 20% of the data, making it a promising alternative for efficient training. These findings highlight that strategic sample selection based on confidence thresholds enables effective training with reduced data, offering a practical balance between performance and efficiency when symmetric uncertainty modeling is exploited. Full article

Low-Altitude and Slow-Speed Small (LSS) targets pose significant challenges to air defense systems due to their low detectability and complex maneuverability. To enhance defense capabilities against low-altitude targets and assist in formulating interception decisions, this study proposes a new threat assessment algorithm based on multisource data fusion under visible-light detection conditions. Firstly, threat assessment indicators and their membership functions are defined to characterize LSS targets, and a comprehensive evaluation system is established. To reduce the impact of uncertainties in weight allocation on the threat assessment results, a combined weighting method based on bias coefficients is proposed. The proposed weighting method integrates the analytic hierarchy process (AHP), entropy weighting, and CRITIC methods to optimize the fusion of subjective and objective weights. Subsequently, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Dempster–Shafer (D-S) evidence theory are used to calculate and rank the target threat levels so as to reduce conflicts and uncertainties from heterogeneous data sources. Finally, the effectiveness and reliability of the two methods are verified through simulation experiments and measured data. The experimental results show that the TOPSIS method can significantly discriminate threat values, making it suitable for environments requiring rapid distinction between high- and low-threat targets. The D-S evidence theory, on the other hand, has strong anti-interference capability, making it suitable for environments requiring a balance between subjective and objective uncertainties. Both methods can improve the reliability of threat assessment in complex environments, providing valuable support for air defense command and control systems. Full article

In this article, the classification of signals arising from the movements of the lower limb of the leg (LLL) based on electromyography (EMG) (walking, sitting, up and down the stairs) was carried out. In the data collection process, 25 athletes aged 15–22 were involved, and two types of data sets (DS-dataset) were formed using FreeEMG and Biosignalsplux devices. Six important time and frequency domain features were extracted from the EMG signals—RMS (Root Mean Square), MAV (Mean Absolute Value), WL (Waveform Length), ZC (Zero Crossing), MDF (Median Frequency), and SSCs (Slope Sign Changes). Several classification algorithms were used to detect and classify movements, including RF (Random Forest), NN (Neural Network), SVM (Support Vector Machine), k-NN (k-Nearest Neighbors), and LR (Logistic Regression) models. Analysis of the experimental results showed that the RF algorithm achieved the highest accuracy of 98.7% when classified with DS collected via the Biosignalsplux device, demonstrating an advantage in terms of performance in motion recognition. The results obtained from the open systems used in signal processing enable real-time monitoring of athletes’ physical condition, which plays a crucial role in accurately and rapidly determining the degree of muscle fatigue and the level of physical stress experienced during training sessions, thereby allowing for more effective control of performance and timely prevention of injuries. Full article