This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.
In this paper, we investigate the connections between certain spec-tra arising from Fredholm theory of a generalized Drazin invertible bounded linear operator and those of its generalized Drazin inverse. Furthermore, we analyze the transfer of Browder’s theorem and its generalized form from such an operator to its corresponding generalized Drazin inverse. Applications to left, right, and multiplication operators are also presented.
Background: Bioinformatics, a relatively new discipline, is now widely used to characterize ADMET profiling and the pharmacological characteristics of real bio-molecules found in vegetal, helping in developing new drugs. The actual work aimed to simulate ADMET profile, pharmacological capacities, and cytotoxicity capacities of bioactive molecules identified in Tamarix africana seed extract.
Methods: Anticholinesterase and free radical scavenging capacities with chemical composition and total phenol, total flavonoid, and hydrolyzable tannin of Tamarix africana extract seeds have been quantified. In addition, molecular docking methods were used against cholinesterase's enzymes. LC-MS analyses revealed that the presence of several compounds in T. africana extract were apigenin-7-O-glucoside, diosmin, neohespiridin, and rutin.
Results: Acetone extract from seeds exhibited a large amount of phenolics, flavonoids, and hydrolyzable tannins of 703 ± 3.88 GAE/mg, 266.03 ± 3.09 QE/mg, and 533.77 ± 2.00 TAE/mg, respectively. T. africana extract had a significant inhibitory effect against DPPH (IC50 = 4.50 ± 0.66 µg/mL), which is lower than standards. However, extract had a modest impact against ABTS+ (IC50 = 25.45 ± 1.74 µg/mL). Acetone extract of seeds showed higher IC50 of AChE (IC50 = 102 ± 0.41 µg/mL), while the galantamine showed lower IC50 BChE (IC50 = 4.99 ± 1.33 µg/mL). In silico study revealed that the biocompounds tested have notable cytotoxic effect.
Conclusions: Furthermore, these compounds may be pharmacologically useful. Molecular docking studies demon-strated that among the four tested plant-derived compounds, apigenin-7-O-glucoside, diosmin, neohesperidin, and rutin, apigenin-7-O-glucoside exhibited the highest binding affinity toward acetylcholinesterase (AChE), while diosmin showed the strongest interaction with butyrylcholinesterase (BChE). These findings underscore the therapeutic potential of selected plant bioactives as lead candidates in cholinesterase-targeted drug discovery. T. africana extract can be utilized as a possible source of substitute chemicals.
Let T ∈ B(H) be a bounded linear operator on a Hilbert space H with the polar decomposition T =U|T|. The(f,g)-Aluthge transform of the operator T, denoted by ∆f,g(T), is defined as ∆f,g(T) = f(|T|)Ug(|T|), wheref andg botharenon-negativecontinuousfunctionson[0,∞[suchthatf(x)g(x) = x, for all x ≥ 0. In this paper, firstly, we investigate the relationship between this transform and several classes of operators as quasi-normal, normal, positive, nilpotent and closed range operators. Secondly, we show that under some conditions the (f,g)-Aluthge transform possesses the polar decomposition. Lastly, we provide a characterization of binormal operators from the viewpoint of the polar decomposition and the (f, g)-Aluthge transform. 2020 Mathematics Subject Classification. 47A05; 47B49. Key words and phrases. (f,g)-Aluthge transform; quasinormal operato; Polar decomposition; binormal operators.
This paper compares two approaches for detecting and analyzing acoustic microwaves in piezoelectric materials, specifically in Lithium Niobate (LiNbO3) substrates. The first method focuses on modeling the propagation of acoustic microwaves in piezoelectric structures, utilizing an interdigital transducer (IDT) to excite the electroelastic waves. This method investigates various wave types, such as secondary surface waves, leaky waves, bulk waves, and skimming bulk waves, and applies wavelet transform for efficient detection. Two wavelet functions—Mexican-hat and Morlet—are compared based on their ability to detect acoustic wave singularities, with an emphasis on their efficiency in processing microwave signals. The second method introduces a machine learning approach using support vector machines (SVM) to detect ultrasonic pulses and identify previously undetectable waves. By classifying real and imaginary parts of the coefficient attenuation and acoustic velocity, this method provides more accurate values and facilitates the modeling of ultrasonic pulse propagation. While the wavelet-based approach focuses on signal processing for wave detection, the SVM-based method excels in detecting complex wave patterns that traditional methods may overlook, offering higher precision in ultrasonic pulse modeling and the realization of acoustic microwave devices.
The coupling system of two different sources has always been an important subject of research in the field of electrical grids of any voltage range. In particular, after the connection of the photovoltaic and the public grids, the voltages cannot be distinguished from each other, because after their coupling there is one voltage across the output load. In this article, we take into account the variation of the current when the load varies in order to establish the relationship between the measured current and the output AC voltage, which can be regulated using only the current. For this purpose, we employ two types of controllers, the Proportional-Integral-Derivative (PID) controller and the Artificial Neural Network (ANN) controller,using Matlab/Simulink. Despite the connection of aninverter, which increases the loss rate and the error,the results are encouraging considering that the error rate obtained for the ANN controller, which is 1.49%, is much lower compared to that of the PID controller, which is 2.4%. Based on the results obtained, it can be concluded that the ANN controller is the best choice to perform this simulation.
A numerical investigation is undertaken, employing a 3D conjugated heat transfer model to examine the impact of geometric configurations and hydrodynamical parameters on the overall thermal resistance and pumping power in mini-channels heat sinks. The aim lies in its holistic approach, integrating the non-uniform section of the mini-channel, the impact of the inlet velocity, the energy and exergy analysis, multi-objective optimization and performance evaluation criteria (PEC) evaluations, and the consideration of metal Galinstan and Cu-water nanofluid working fluids. The parametric analysis highlighted metal Galinstan as the best coolant for the five configurations involved in the present study. Furthermore, The PEC results indicate that the best performance is achieved by the Converged-Diverged Mini-channel (CDMC)heat sink. CDMC configuration with metal Galinstan performs well in terms of exergy evaluations and shows a better average temperature distribution with a maximum temperature of about 328K. The optimal inlet velocity (Uin = 0.21 m/s) is determined on the basis of the pumping power and thermal resistance profiles. The optimization process is based on the impact of the mini-channel's maximum width on the PEC. It is shown that the PEC increases with the maximum width of the CDMC and the highest (PEC = 1.31) is obtained at a maximum width of 0.95 mm.
The aim of the present study is to examine the way in which The Daily Telegraph portrays migrants as ‘Others’ by employing a discourse and power dynamics perspective. It attempts to identify and analyse the predominant discursive strategies, social context implications and power dynamics that the newspaper employs to represent this group of individuals. The study uses a descriptive qualitative research approach, along with critical discourse analysis, adopting Fairclough’s three-dimensional framework as a research instrument for analysis. This framework allows for a thorough analysis of the text, and its social context. Consequently, the results gained from the examination, revealed that the Daily Telegraph used various discursive strategies to construct migrants as others in a negative way, employing metaphor, hyperbole, and othering strategies. As regards the discursive practices, social context implications and power dynamics at play, the study showed that migrants are believed to be an uncontrollable "other" that necessitates border control. The marginalisation and exclusion of migrants from the holding society were frequently the result of the recurrent use of negative stereotypes by the daily Telegraph. It is possible that this will lead to unfair policies and the maintenance of power relationships by making these migrants seem different or dangerous.
The hospital environment is characterised by stressful situations and constant pressures. Resilience is essential for healthcare professionals to cope with these challenges and maintain their well-being and effectiveness. This study aims to assess the level of resilience and its relationship with sociodemographic factors among healthcare professionals working in hospitals in the Algerian province of Batna. To this end, a cross-sectional survey based on an anonymous, self-administered questionnaire (Resilience Scale-25) was conducted in healthcare settings of Batna-Algeria. Sociodemographic characteristics and resilience attributes were analyzed using descriptive and inferential statistics. The results indicate, on the one hand, a low level of resilience among healthcare professionals (mean RS-25 score = 115.63) and, on the other, a significant relationship between resilience and sociodemographic factors (p <0.05). Consequently, the hospitals studied need to adopt appropriate strategies to continuously improve healthcare professionals' resilience.
III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.
The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate geometries and strict precision requirements. Conventional inspection methods, such as those using coordinate measuring machines or optical techniques, are often time-consuming and lack adaptability to subtle deviations. Recent deep learning approaches show promise but are typically limited to point-based or scan-to-scan comparisons, which remain sensitive to noise and alignment errors. We propose a voxel-based 3D inspection framework that integrates an XGBoost-guided perturbation model with a 3D convolutional autoencoder (3D CNN-AE). CAD-derived gear models are systematically perturbed with controlled Gaussian deformations to emulate tolerances, defects, and sensor noise, then voxelized for autoencoder training. This enables robust learning of nominal gear geometry distributions. Extensive experiments conducted against PointNet++, a Variational Autoencoder, and a GAN-based reconstruction model demonstrate that our method consistently achieves superior performance across various metrics, including PSNR, SSIM, accuracy, precision, recall, and F1-score. The results highlight the potential of voxel-based learning with data-driven perturbation for scalable and high-accuracy inspection in industrial applications.
Objective The aim of this study was to investigate the distribution and genetic determinants of carbapenemase production and colistin resistance among Acinetobacter baumannii isolates recovered from three health care facilities in the city of Batna, Algeria.
Methods A prospective study was conducted between 2021 and 2022 on 46 Acinetobacter baumannii clinical isolates, which were collected and identified using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Antibiotic susceptibility testing was performed using the disk diffusion method and colistin minimum inhibitory concentrations (MICs) were determined by broth microdilution method. Carbapenemase and colistin resist ance determinants were detected by qPCR.
Results The 46 clinical isolates were mainly from the intensive care unit (52.17%) and the burns unit (17.39%). The strains were collected primarily from pus samples (34.78%) and blood samples (17.39%). Eleven strains were classified as colistin-resistant, with MICs ranging from 4 to 128 μg/mL. The blaOXA-24 gene was detected in 63.04% of the isolates, followed by the blaOXA-23 gene (43.47%). Nine strains were positive for both blaOXA-23-like and blaOXA-24-like genes. The blaNDM gene was detected in eight isolates (17.39%), including two which co-expressed a blaOXA-24 gene. In contrast, all strains were negative for the plasmid-mediated colistin resistance mcr-1 to mcr-5 and mcr-8.
Conclusion Here, we report a high prevalence of carbapenemases-producing A. baumannii isolates in Batna hospi tals. Notably, this study is the first to identify A. baumannii isolates co-producing OXA-24 and NDM carbapenemases and to report the first detection of colistin-resistant A. baumannii co-producing OXA-24 and OXA-23 carbapenemases from a patient in Algeria.
Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.
Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.
Introduction: Obesity is a metabolic disease characterized by abnormal fat accumulation. Physical inactivity can contribute to this accumulation of fat, which reduces cardiorespiratory capacity in obese women. The excess weight can impair both cardiometabolic and mechanical functions. The perimenopausal phase is marked by changes that affect women's body composition. Our aim is to identify the effects of body composition on cardio-respiratory capacities of perimenopausal women living with obesity.
Material and Methods: Our study concerned patients with obesity (BMI ≥ 30 Kg/m2). Body composition analysis was carried out by bioelectric impedancemetry. It allowed us to identify the total fat mass (FM) and the lean mass (LM) in Kg and as a percentage. Cardiorespiratory capacities, oxygen consumption rate (VO2max), heart rate max (HRmax) and metabolic equivalent of task (MET) were assessed using an ergocycle, for a maximal exercise test. The correlations between body composition and cardiorespiratory capacities were sought.
Results: 51 women, average age 41.12 ± 12 years, BMI = 36.9 ± 5.4 Kg/m2, weight = 93.43 ± 14.9 kg including Fat Mass (FM) 41.3 ± 10 kg. The Heart Rate max (HRmax) was 152 ± 17bmp, the VO2max was 16.5 ± 2.08 ml/Kg/min. Negative and statistically significant correlations were found between VO2max and BMI (r = - 0.49, p = 0.02), FM in % (r = - 0.61, p < 0.01). Likewise, HRmax is inversely correlated (r = - 0.71) with age and in a highly significant manner (p < 0.001).
Conclusion: The accumulation of fatty tissue in our series seems to negatively influence cardiorespiratory capacities in perimenopausal women with obesity. Fat mass as a percentage provides better information on the evolution of VO2max. In addition to age, this category of obese seems to present a limitation in effort that must be taken when prescribing an appropriate physical activity.
