The phenomenon of swelling-shrinkage has gained widespread attention in practice owing to the generation of eroded clayey layers of soil that amplify with global climate change and the seasonal water content. This provokes several serious disorders affecting the stability of nearby constructions and consequently generating human loss. The expansive clayey soils show the phenomena of wetting and drying cycles in their natural state (undisturbed soil). Hence, classical oedometric tests are found to be unable to take into account the thermal behavior of naturally swelling soils; this is proven by the resulting asymptotic volumetric behavior, as well as the steady values of the potential of swelling and shrinkage. The main aim of this experimental analysis is to derive a test that considers the significant effect of temperature. Experimental results of an oedometric approach are represented herein for the purpose of investigating the volumetric and hydric behavior of naturally swelling soil in the region of N’Gaous (Eastern Batna province, Algeria) through drying-wetting paths. Innovative expressions are derived for the direct computations of the swelling-shrinkage potential in terms of water content, appearance time and applied loads. It is of interest to mention that those expressions are applicable to other regions in the world with similar soil geotechnical and chemical characteristics and conditions. The cyclic outputs show that the swelling pressure variation with the appearance time is mainly related to the first cycle of swelling shrinkage; as it exhibits a noticeable increase in the swelling potential with the amplification of applied loads until reaching a state of steadiness. The experimental results demonstrate a high degree of reliability and correlation with the soil behavior. Therefore, the swelling shrinkage potentials are expressed innovatively in equations that help predict the soil behavior in expansive regions in order to enhance the safety of nearby foundations.

The growing need for clean energy has made solar panels an essential solution. However, the nonlinear behavior of photovoltaic (PV) systems under varying weather conditions necessitates advanced control strategies to ensure optimal energy harvesting. This paper presents an enhanced Maximum Power Point Tracking (MPPT) approach that integrates the conventional Perturb and Observe (P&O) method with an Indirect Adaptive Fuzzy Controller (IAFC). While P&O is known for its simplicity, it suffers from steady-state oscillations and slow response during environmental changes. To address these issues, the IAFC adaptively adjusts the perturbation step using a Lyapunov-based rule to improve convergence and minimize power fluctuations. The proposed method achieves Maximum Power Point tracking within less than 0.025 s, compared to 0.05 s for the conventional P&O algorithm. This enhances the credibility of our dynamic performance claim. Specifically, unlike prior fuzzy-P&O hybrids with fixed rule sets, our method leverages Lyapunov-based adaptation to dynamically adjust the control action, improving convergence and robustness under changing conditions. We also included a quantitative metric showing a 75% reduction in power fluctuations compared to conventional P&O. Simulation results under varying sunlight conditions demonstrate fast convergence and improved power stability. The proposed IAFC method clearly outperforms classical P&O in tracking accuracy, responsiveness, and overall energy yield.

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 present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.

Urban planning plays a critical role in sustainable city development by guiding urban expansion efficiently. In Algeria, the master plan for development and urban planning (PDAU) designates specific areas for city growth, yet the suitability of these areasfor urban extension often remains unassessed using systematic methods. Most PDAU, including the plan for Setif City established in 2016, rely on planning approaches and data that risk becoming outdated due to rapid urban changes and evolving spatial dynamics. As a result, there is a pressing need to critically assess and validate these designated extension zones using updated, objective analytical tools. This study addresses this gap by applying an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to evaluate land suitability for urban extension in Setif City. Fifteen socio-economic, physical, environmental, and accessibility criteria were applied to assess areas designated in the PDAU. The methodology enables a multi-criteria, data-driven analysis to prioritize zones for sustainable urban growth. About 21.5% of the study area is categorized as very high suitability, and most of these sites are concentrated around the edges of the city.36.7% is classified as high suitability, according to the suitability analysis for future urban expansion. Moderately and poorly suitable areas make up 23.79% and 13% of the total. Merely 5% of the land is deemed to be extremely unsuitable for the extension. The findings support evidence-based urban planning, offering actionable insights for policymakers and urban planners. This study contributes methodologically by demonstrating the effective integration of AHP with GIS in an Algerian context, encouraging replication and further research in similar urban environments.

Wireless sensor networks (WSNs) have recently emerged as a critical technology in various applications, including industrial automation, building monitoring, and military. However, the data generated by these networks are often prone to outliers, which can compromise sensor data quality and reliability. Detecting outliers is paramount to ensure proper network functioning. Traditional detection techniques pose several challenges, such as weak adaptability to the increasing complexity and dynamic environmental changes, limited accuracy, and higher computation costs. To address these challenges, this paper proposes an optimized one-dimensional convolutional neural networks (1D CNN)-based outlier detection approach for WSNs. This approach comprises two key modules: a predictor module and an outlier detector. The predictor module employs a 1D CNN model to forecast forthcoming sensor measurements based on historical data. Bayesian optimization is used to enhance the 1D CNN model’s accuracy. The outlier detector identifies outliers based on the Euclidean distance between the predicted measurements and their corresponding actual values. Experiments on synthetic and real-world datasets reveal that our proposed approach outperforms other existing deep learning-based frameworks in terms of accuracy, F1 score, and false alarm rates.

Introduction/purpose: Algerian natural zeolite (denoted NZ) was modified using hydrochloric acid (HZ) and sodium hydroxide solution (NaZ). This study investigated the impact of acid and alkaline modifications on the adsorption of two cationic textile dyes (BR46 and BY13) from aqueous solutions. Methods: The XRF analysis confirmed that SiO2 is the predominant mineral in all three zeolites. The XRD results revealed that NZ is primarily composed of mordenite, with chabazite and minor quartz content. The MEB-EDX analysis showed slight variations in the Si and Al content for HZ and NaZ, without significantly altering the zeolite’s structure.. The effects of initial dye concentration, contact time and pH were examined in a batch system.

Results: The adsorption on NZ, NaZ and HZ increased with longer contact times, higher initial dye concentrations, and elevated temperatures. Equilibrium was rapidly attained best described using the pseudo-second order kinetic model. Both the Langmuir and the Freundlich isotherm models fit for the adsorption data. Conclusion: The highest dye removal efficiency was observed for NaZ, with 97.62% for BR46 and 98.97% for BY13. The lowest removal rates occurred at pH= 8 for HZ and pH=10 for NZ and NaZ. Adsorption was spontaneous and endothermic.

Introduction purpose: As population growth and industrial expansion continue, surface freshwater reservoirs such as dams have become increasingly vital due to their accessibility and ease of treatment. However, the quality of these water sources has significantly deteriorated, primarily due to the discharge of domestic and industrial wastewater. The proliferation of extensive algal blooms has led to significant challenges in maintaining drinking water quality and raised concerns about public health. This study investigates the impact of various water sources on the physicochemical quality of an Algerian dam over four seasons (December 2020 – October 2021) and explores the factors influencing the occurrence of cyanobacterial blooms to better understand and manage this excessive growth.

Methods: Physicochemical properties and algal composition of the dam water were analyzed monthly to determine nutrient sources and environmental factors affecting cyanobacterial proliferation. Results: The analysis revealed that the Timgad stream and Reboua valley are notable sources of nutrient enrichment. Elevated temperatures and high nutrient loads, particularly total phosphorus (TP), in Timgad dam water facilitate the proliferation of blue-green algae. Additionally, limited nitrogen content favors the dominance of nitrogen-fixing cyanobacteria such as Aphanizomenon and Oscillatoria. The study also highlights that the low flow rate and high nutrient load of the Timgad stream create favorable conditions for cyanobacterial growth. Conclusions: Nutrient inputs, temperature, and hydrological conditions significantly influence cyanobacterial blooms. Understanding these factors is crucial for implementing effective water management strategies to reduce algal proliferation and protect freshwater quality.

This article presents a quantitative and qualitative analysis of causal markers in scientific articles published in France and Algeria. Based on two corpora drawn from Synergie France and Synergie Algérie, the study examines the frequency, distribution, and functions of connectors such as car, donc, puisque, and parce que. The results reveal a common core of markers but distinct preferences:  French authors favor a structured and diversified argumentative style, while Algerian writers adopt a more explicit and pedagogical approach. These differences reflect contrasting academic traditions and highlight the didactic importance of causal markers in the teaching of scientific writing.

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.

 Ensuring the safety and health of assembly line workers is critical to increasing productivity and preventing accidents. This research presents a real-time monitoring system that combines computer vision (AI), wearable Internet of Things (IoT) devices, and cloud-based technologies to detect worker fatigue and health risks. The system calculates eye aspect ratio (EAR) and mouth aspect ratio (MAR) to identify fatigue symptoms such as eye closure and yawning, while wearable IoT devices monitor physiological parameters such as heart rate (HR) and blood oxygen saturation (SpO₂) to detect potential health issues. Alerts are automatically triggered based on pre-defined thresholds, allowing for immediate intervention. All data is processed in real-time with input from wearables and computer vision, and transmitted to a cloud platform for analysis, reporting and storage. This integration of AI-powered computer vision, wearable IoT and cloud connectivity ensures continuous monitoring and provides actionable insights to supervisors, improving workplace safety and operational efficiency. The results of the study demonstrate the effectiveness of this innovative system in identifying fatigue and health issues, reducing accidents and promoting a safer working environment. By using the latest technology, the proposed solution addresses the urgent need for advanced safety measures in demanding work environments.

This study investigates the fatigue degradation and crack propagation in the locking mechanism of PET bottle blow molding machines, focusing on the impact of elliptical cracks on the mechanism’s performance and longevity. The locking mechanism, which plays a vital role in securing the mold during the blow molding process, is subjected to repeated loading, making it susceptible to fatigue damage. Using a combination of finite element analysis (FEA) and experimental methodologies, we examine the stress distribution, deformation, and displacement in the mechanism under operational loads. The study identifies the most stressed component and models the behavior of an elliptical crack located at the center of this component. A stress intensity factor (K) of 3.7553 MPa.mm-0.5 is found, indicating significant risk in the crack region. Fatigue analysis using Goodman’s law predicts a service life of one million cycles with a safety factor of 2.08. These findings highlight the need for targeted design and maintenance strategies to enhance the reliability and durability of PET blow molding machines.

This paper investigates the relationship between the extended spectrum of a bounded linear operator and its group inverse. We also establish a connection between the extended spectrum of the bounded linear operator and that of its Drazin inverse. As part of our analysis, we prove the following equality: σext((BA)D) = σext((AB)D), where (BA)D and (AB)D represent the Drazin inverses of BA and AB, respectively. 2020 Mathematics Subject Classification. 35K15; 35K55; 35K65; 35B40. Key words and phrases. extended spectrum; operator group inverse; Drazin inverse.

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.

This study presents the development and validation of a hybrid cutting force prediction model for ball end milling of aluminum 7075-T6 alloy. The model combines a mechanistic approach with a specific cutting force coefficient (Ks=850 N/mm²) sourced from experimental literature. Cutting forces in the x, y, and z directions are predicted by integrating differential force components with tool geometry and machining parameters. Experimental validation was performed under dry conditions at a spindle speed of 15,000 rpm. In the x-direction, the simulated force was 162.4 N versus an experimental force of 215.4 N; in the y and z-directions, predicted values (65.2 N and 25.3 N) closely matched experimental forces (74.3 N and 28.2 N), respectively. The corresponding mean absolute errors were 18.2% (x), 4.5% (y), and 3.3% (z). The higher error in the x direction highlights limitations in modeling tangential force dynamics, while the y and z predictions align closely with experimental data. Leveraging the experimentally derived Ks, the proposed model offers a practical tool for optimizing machining processes in the aerospace sector, with potential for further refinement in tangential force modeling.

One of the main factors that adversely affect surface quality, dimensional accuracy, and geometric precision during turning processes is workpiece’s deformation. The manufacturer's optimization of the cutting process is crucial. The goal of this work is to model and optimize workpiece’s deflection using statistical analysis. The tangential and radial cutting forces were observed as a function of the cutting parameters: cutting speed (Vc in m/min), advance (f in mm/rev), cutting depth (ap in mm), workpiece hardness (HB), and tool rake angle (An) using a numerical experimental plan (DOE) based on the Taguchi L32 table and the finite element analysis (FEA) tool (Third Wave AdvantEdge). For every test, the cantilever beam equation is used to determine the workpiece's deflection, which is then examined using the statistical approach based on the controllable parameters through cutting forces and the workpiece's overhang ratio (L/d). Prediction models have been found for the quantity of interest.

The usual use of value stream mapping is studying to improve production lines that are already running. In this study, we used value stream mapping and the PDCA cycle on a production line that is still being finished and not yet operational. This work is important and unique because it uses a proactive approach to improve processes. The used method aims to create a waste-free production chain from the start. This is a big plus because it means avoiding losses with high costs and getting a very efficient production line from the start. The findings demonstrate that lean manufacturing tool (VSM) can be used on current and future production lines, and this strategy enhances production line efficiency from the outset by minimising non-value-added activities and maximising value-added activities.

The undifferentiated nasopharyngeal cancer (NPC) is a multifactorial disease mainly due to Epstein-Barr Virus (EBV) infection. The transmembrane tyrosine kinase 'EphA2' and the protease 'Furin' are implicated in the EBV entry into epithelial cells and other physiological processes. To gain insights into the association of single-nucleotide polymorphisms (SNPs) rs4702 and rs6603883 (FURIN and EPHA2 genes, respectively) with the risk and prognosis of the NPC, the genotypes of 471 individuals (228 cases and 243 controls) were assessed alongside risk cofactors (sex, tobacco, alcohol, occupation, and recurrent Ear, Nose and Throat infections) and prognosis cofactors (Tumor stage, local invasion, lymph node involvement, and metastasis) using multivariable logistic regression. We found that only the rs4702 AG/GG genotypes were statistically significantly associated with a reduced risk of cancer, both in the overall population and in men (approximately 50% reduction). The rs4702 GG genotype was also associated with a low frequency of local tumor invasion in the whole population (OR = 0.382, p = 0.017, co-dominant model, and OR = 0.409, p = 0.02, recessive model), but heterozygous women were associated with a higher lymph node involvement (OR = 3.53, p = 0.031, co-dominant model, and OR = 3.62, p = 0.02, overdominant model). The rs6603883 GG genotype was associated, in the dominant model, with distant metastasis in the whole population (OR = 2.5, p = 0.024), with advanced clinical stage in men (OR = 2.22, p = 0.034), and with advanced clinical stage and distant metastasis in patients under 49 years (OR = 3.13, p = 0.009, and OR = 5.15, p = 0.011, respectively). Additionally, men having the rs6603883 GA genotype were associated with lymph node invasion (OR = 2.22, p = 0.027, overdominant model). Our study is the first to demonstrate that FURIN and EPHA2 germline gene polymorphisms are associated with NPC risk (for rs4702) and prognosis (for both rs4702 and rs6603883), with sex-specific differences. These results need to be replicated and further investigated in other populations.

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.

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.

Keywords Acinetobacter baumannii, blaOXA-23, blaOXA-24, blaNDM, Algeri