University of Ouargla

Sentiment analysis (SA) is a widely recognized and increasing field of research in the science of natural language processing (NLP). A wide range of methods exist by which individuals express their sentiments and emotions. Sarcasm is occasionally employed with sentiments, particularly when expressing intense emotions. Sarcasm is characterized by using positive language to express a negative intention. In current research, these two aspects are often treated as separate tasks. However, recent advancements in deep learning algorithms have greatly improved the efficiency of standalone classifiers for both sentiment and sarcasm tasks. Despite these improvements, a major challenge remains: correctly classifying sarcastic sentences as negative. Furthermore, there has been an important increase in the number of research efforts focused on Arabic dialects. In this research paper, we explore both Sentiment and Sarcasm within multi-dialect Arabic language corpora to set up a highly accurate sentiment classification and sarcasm detection. To be more specific, we develop a system of classification that employs a Multi-Task Learning (MTL) algorithm using a pre-trained Arabic language model to accurately determine sentiment classification and sarcasm detection. Considering this, we claim that having the ability to identify sarcasm will improve the accuracy of sentiment classification. The performance of our approach showed notable results, surpassing the performance of previously developed models described in the literature on all of the three datasets, for sentiment classification with up to an F1-score of 73.96% on ArSarcasm$_{senti}$ dataset and up to an F1-score of 59.46% on ArSentD-Lev dataset. Moreover on sarcasm detection task our model got an F1-score of 76.42% on ArSarcasm$_{sarcasm}$ dataset outperforming all other models.

Medicinal plants possess the potential to yield bioactive compounds that offer significant health benefits; positioning them as valuable and promising sources for the development of innovative pharmaceutical products. This study aims to comprehensively assess the in vitro and in vivo pharmacological effects of the aqueous extract of the plant Atractylis aristata (AEAA) as well as assessments of its phytochemical composition. UPLC‐ESI‐MS/MS analysis of AEAA revealed a variety of bioactive compounds, including flavonoids and phenolic acids. In antioxidant assays, AEAA demonstrated considerable activity, with IC50 values of 0.269±0.05 mg/mL for DPPH scavenging and 0.0376±0.003 mg/mL for hydrogen peroxide radical inhibition. AEAA exhibited strong anti‐inflammatory activity in vitro, with an IC50 value of 2.563 mg/mL in the BSA denaturation test. In vivo, AEAA reduced carrageenan‐induced paw edema by 56.51 %, in comparison to an 83.58 % reduction with Ibuprofen®. Antibacterial testing showed AEAA′s broad‐spectrum activity, with the highest inhibition against Bacillus subtilis (34 mm zone of inhibition). Additionally, AEAA induced significant sedative effects, reducing locomotor activity by 48.98 %. These findings underscore the diverse pharmacological potential in addressing oxidative stress, inflammation, microbial infections, and anxiety of A. aristata, which can be attributed to its rich phytochemical profile.

In this study, we investigate the thermodynamic properties of an ideal Quark-Gluon Plasma (QGP) at a vanishing chemical potential, under the influence of quantum gravitational effects, specifically incorporating the Linear-Quadratic Generalized Uncertainty Principle (LQGUP). We analyze the impact of LQGUP on key thermodynamic quantities, including the grand canonical potential, pressure, energy density, entropy, speed of sound, and the bulk viscosity’s response to changes in the speed of sound. Furthermore, we extend our analysis to examine the time evolution of the universe’s temperature in the presence of LQGUP effects.

This study examines the distribution of pore pressure (PP) and fracture gradient (FG) within intervals of lost circulation encountered during drilling operations in the Ordovician reservoir (IV-3 unit) of the Tin Fouye Tabankort (TFT) field, located in the Illizi Basin, Algeria. The research further aims to determine an optimized drilling mud weight to mitigate mud losses and enhance overall operational efficiency. PP and FG models for the Ordovician reservoir were developed based on data collected from five vertical development wells. The analysis incorporated multiple datasets, including well logs, mud logging reports, downhole measurements, and Leak-Off Tests (LOTs). The findings revealed an average overburden gradient of 1.03 psi/ft for the TFT field. The generated pore pressure and fracture gradient (PPFG) models indicated a sub-normal pressure regime in the Ordovician sandstone IV-3 reservoir, with PP values ranging from 5.61 to 6.24 ppg and FG values between 7.40 and 9.14 ppg. The analysis identified reservoir depletion due to prolonged hydrocarbon production as the primary factor contributing to the reduction in fracture gradient, which significantly narrowed the mud weight window and increased the likelihood of lost circulation. Further examination of pump on/off cycles over time, coupled with shallow and deep resistivity variations with depth, confirmed that the observed mud losses were predominantly associated with induced fractures resulting from the application of excessive mud weight during drilling operations. Based on the established PP and FG profiles, a narrow mud weight window of 6.24–7.40 ppg was recommended to ensure the safe and efficient drilling of future wells in the TFT field and support the sustainability of drilling operations in the context of a depleted reservoir.

This work reports the preparation of high-performance electrocatalytic electrodes based on structured copper deposits. The metal catalysts are synthesized by potentiostatic electrodeposition on a stainless steel substrate under hydrogen co-generation. This deposition technique generates evolving gases that lead to the formation of copper deposits in the form of dendrites, revealed by scanning electron microscopy. These catalysts exhibit high electrocatalytic activity for formaldehyde electrooxidation reaction (FOR) in alkaline media, showing a peak overpotential of − 0.6 V (peak current of 2.3 mA cm⁻²) in 0.1 M KOH + 2.0 M HCHO. The results show that the electroactivity of the catalysts depends on the value of the electrodeposition overpotential, with the best performance observed at − 0.8 V. Besides, the anodic peak of the catalyst based on dendrite microstructures for formaldehyde oxidation is 2.3 times higher than that of smooth copper obtained without hydrogen co-generation and exhibit good durability results.

This paper offers an experimental approach that integrates acoustic emission (AE) monitoring with machine learning (ML) to identify damage mechanisms and predict the mechanical properties of 3D-printed biocomposites. Specimens were fabricated using a bio-filament composed of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers and were subjected to tensile and flexural tests. The processed dataset, comprising six normalized features (cumulative rise, duration, count, frequency, energy, and amplitude) was used to train four ML models: Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT) implemented in Python using libraries such as scikit-learn, pandas, and numpy. The prediction models were developed using an 80/20 train–test split and further validated by 5-fold cross-validation, with performance evaluated by R-squared (R²) and Mean Squared Error (MSE) metrics. Our results demonstrate robust prediction capabilities, with the RFR model achieving the highest accuracy (R² > 0.98 and MSE as low as 0.013 for tensile stress prediction). Additionally, unsupervised clustering using K-means was applied to group AE signals into distinct clusters corresponding to different damage modes. This comprehensive methodology not only enhances our understanding of damage evolution in composite materials but also establishes a data-driven framework for non-destructive evaluation and structural health monitoring.

A system of nonlinear wave equations in viscoelasticity with variable exponents is considered. It is assumed that the kernel included in the integral term of the equations depends on both the time and the spatial variables. Using the Faedo–Galerkin method and the contraction mapping principle, a theorem of unique solvability of the problem is proved. In addition, under appropriate variable assumptions, an estimate of the stability of the solution to the problem of determining the kernel is obtained. The study is based on Komornik’s inequality. We expand the class of nonlinear boundary value problems that can be investigated by well-known methods.

Nickel-based composite coatings, incorporating nanoparticles such as Y2O3, have gained significant attention due to their superior mechanical, tribological, and anticorrosion properties. These coatings are extensively used across industries including transportation, energy, and manufacturing. The study of composite coatings dates back to 1928 when copper-graphite coatings were first developed for self-lubricating automotive engine surfaces. However, recent advancements in electrodeposition have demonstrated that adding inert Y2O3 particles to metallic nickel coatings results in notable improvements in performance, far surpassing the properties of pure metal or alloy coatings. This review provides a comprehensive analysis of the latest developments in nickel-based composite coatings, focusing on the electrodeposition process, key parameters, and the optimization of functional properties. It also explores the various applications of these coatings and evaluates the significant advancements in the field. Furthermore, a comparison of Y2O3 nanoparticles with other materials highlights their unique advantages and identifies promising areas for future research and applications. This work not only summarizes existing knowledge but also underscores the potential of Y2O3-based composite coatings to address critical challenges in surface engineering, paving the way for future innovations.

This paper presents an adaptive fault-tolerant control strategy tailored for fixed-wing unmanned aerial vehicles (UAV) operating under adverse conditions such as icing. Using radial basis function neural networks and nonlinear dynamic inversion, the proposed framework effectively handles simultaneous actuator and sensor faults with arbitrary nonlinear dynamics caused by environmental effects, model uncertainties and external disturbances. A nonlinear disturbance observer is incorporated for accurate sensor fault detection and estimation, thereby enhancing the robustness of the control system. The integration of the radial basis function neural network enables an adaptive estimation of the faults, ensuring accurate fault compensation and system stability under challenging conditions. The observer is optimised to minimise the deviation of the closed-loop dynamics eigenvalues from the assigned eigenvalues and to approach unity observer steady-state gain. The stability of the control architecture is mathematically proven using Lyapunov analysis, and the performance of the approach is validated through numerical simulations on a six Degrees of Freedom fixed-wing unmanned aerial vehicles model. The results show superior performance and robustness to challenging fault scenarios. This research provides a comprehensive fault management solution that enhances the safety and reliability of unmanned aircraft operations in extreme environments.

With the increasing use of telemedicine in healthcare, the security and integrity of medical images during transmission have become critical. This paper presents a novel blind watermarking scheme Local Binary Pattern–Discrete Wavelet Transform (LBP–DWT) for medical images based on Local Binary Patterns and the Discrete.Wavelet Transform in frequency domain. We take advantage of the LBP, which is computationally fast, to improve the watermark's resistance to the different kinds of attacks, and maintain the overall visual quality of the watermarked images. In addition, the DWT could offer a high trade-off between robustness and imperceptibility due to the multi-resolution analysis it provides. During embedding, the LL band (approximation coefficients) of the DWT is selected and divided into 3 × 3 blocks. The resulting LBP codes are then XORed with the embedding bits and hidden in the corresponding blocks using the Least Significant Bit technique. Note that the Arnold transform is used during the embedding step to scramble the watermark, which is then vectorized based on the ZigZag fashion to improve the security of the proposed scheme. To evaluate the performance of the proposed method, extensive experiments are conducted on a dataset of medical images. The watermarked images are tested against various attacks, including compression, noise addition, and cropping. The obtained results demonstrate the effectiveness of the proposed techniques.

Coiled tubing (CT) plays a pivotal role in oil and gas well intervention operations due to its advantages, such as flexibility, fast mobilization, safety, low cost, and its wide range of applications, including well intervention, cleaning, stimulation, fluid displacement, cementing, and drilling. However, CT is subject to fatigue and mechanical damage caused by repeated bending cycles, internal pressure, and environmental factors, which can lead to premature failure, high operational costs, and production downtime. With the development of CT properties and modes of application, traditional fatigue life prediction methods based on analytical models integrated in the tracking process showed, in some cases, an underestimate or overestimate of the actual fatigue life of CT, particularly when complex factors like welding type, corrosive environment, and high-pressure variation are involved. This study addresses this limitation by introducing a comprehensive machine learning-based approach to improve the accuracy of CT fatigue life prediction, using a dataset derived from both lab-scale and full-scale fatigue tests. We incorporated the impact of different parameters such as CT grades, wall thickness, CT diameter, internal pressure, and welding types. By using advanced machine learning techniques such as artificial neural networks (ANNs) and Gradient Boosting Regressor, we obtained a more precise estimation of the number of cycles to failure than traditional models. The results from our machine learning analysis demonstrated that CatBoost and XGBoost are the most suitable models for fatigue life prediction. These models exhibited high predictive accuracy, with R² values exceeding 0.94 on the test set, alongside relatively low error metrics (MSE, MAE and MAPE), indicating strong generalization capability. The results of this study show the importance of the integration of machine learning for CT fatigue life analysis and demonstrate its capacity to enhance prediction accuracy and reduce uncertainty. A detailed machine learning model is presented, emphasizing the capability to handle complex data and improve prediction under diverse operational conditions. This study contributes to more reliable CT management and safer, more cost-efficient well intervention operations.

This study evaluated the potential of Hmira dates for ethanol production. Several analyses were conducted to determine the physicochemical and biochemical characteristics of the Hmira dates. Accordingly, the moisture content of Humira dates reached 26.33%, with the remaining 73.67% being dry matter. Hmira dates contained 97.71% organic material and just 2.29% ash. The total Sugars were 71%, the protein content was 2.98%, and the pH value was 6.30. The dates underwent extraction to facilitate ethanol production before fermentation using Saccharomyces cerevisiae yeast. Key factors influencing the fermentation were analyzed, revealing a peak ethanol concentration of 78% and a production rate of 102.57 g/L.h. The optimum conditions for ethanol production were substrate concentration of 25%, pH 6, fermentation temperature of 35 °C, and duration of 72 h. The use of biofuels, particularly in Algeria, could contribute to a cleaner environment and offer a sustainable energy future for the nation. Graphical Abstract

Chaos theory, with its unique blend of randomness and ergodicity, has become a powerful tool for enhancing metaheuristic algorithms. In recent years, there has been a growing number of chaos-enhanced metaheuristic algorithms (CMAs), accompanied by a notable scarcity of studies that analyze and organize this field. To respond to this challenge, this paper comprehensively analyzes recent advances in CMAs from 2013 to 2024, proposing a novel classification scheme that systematically organizes prevalent and practical approaches for integrating chaos theory into metaheuristic algorithms based on their strategic roles. In addition, a list of 27 standard chaotic maps is explored, and a summary of the application domains where CMAs have demonstrably improved performance is provided. To experimentally demonstrate the capability of chaos theory to enhance metaheuristic algorithms that face common issues such as susceptibility to local optima, non-smooth transitions between global and local search phases, and decreased diversity, we developed a chaotic variant of the recently proposed RIME optimizer, which also encounters these challenges to some extent. We tested C-RIME on the CEC2022 benchmark suite, rigorously analyzing numerical results using statistical metrics. Non-parametric statistical tests, including the Friedman and Wilcoxon signed-rank tests, were also used to validate the findings. The results demonstrated promising performance, with 14 out of 21 chaotic variants outperforming the non-chaotic variant, whereas the piecewise map-based variant achieved the best results. In addition, C-RIME outperformed ten state-of-the-art metaheuristic algorithms regarding solution quality and convergence speed.

Soil salinity has a negative impact on the microbial populations and their activities in hot arid lands. This study aimed to evaluate and compare the microbial abundance and activity in non-saline (NS) and saline (SS) soils, focusing on the impact of salinity on the mineralization of soil endogenous carbon and nitrogen in the region of Ouargla (southern Algeria). The mineralization of organic C and N was estimated by respirometric test (CO2 release) and the extraction of two forms of mineral nitrogen (NH4+–N and NO3––N), respectively. The experiment was conducted on incubations of soil samples under controlled parameters (28 ± 1 °C and 80% of water holding capacity). Numeration of microbial densities was performed either on solid medium of extract agar soil, oxytetracycline agar (OGA) and Katinsky medium, respectively for bacterial microflora, fungal microflora and actinomycetes (Mycelial bacteria) or in liquid medium for certain functional groups involved in mineralization of carbon and nitrogen. After 56 days of incubation, both soils showed a low potential for mineralization of carbon and nitrogen. The cumulative amounts of CO2–C released are 62.53 and 50.03mg 100 g–1of dry soil, respectively for the non-saline and saline soils. Regarding nitrogen mineralization, the cumulative quantities of ammoniacal NH4+–N and nitric NO3––N, nitrogen released were 0.53 and 0.49 mg 100 g–1 of dry soil and 1.19 and 0.91 mg 100 g–1 of dry soil, for the non-saline and saline soils, respectively for the two forms of mineral nitrogen. The reduction rates of the two forms of mineral nitrogen are 7.54 and 23.50%, for NH4+–N and NO3––N, respectively. The microbial groups studied revealed a predominance of fungal microflora in saline soil. In contrast, a high sensitivity of nitrifying germs to salinity was reported. Findings indicate that despite the lower microbial abundance and activity recorded for both soils, they respond to salinity differentially depending on the type of microbial species present in the soil as well as the nature of the microbial activity itself. On the one hand, the microbial diversity recorded in both soils demonstrates an appreciable potential for adaptation of microorganisms to the hard ecological conditions characterizing arid regions, in particularly the high salinity of the soil.

Extreme Edge Computing (EEC) promotes sustainable computing by reducing reliance on centralized data centres and decreasing their environmental impact. By using extreme edge devices to handle computing requests, the EEC reduces the energy demands for data transmission and execution, thereby reducing carbon footprints. However, EEC introduces challenges due to the mobile, heterogeneous, and resource-limited nature of these devices. Additionally, tasks are often complex and interdependent, complicating offloading and workload orchestration. The dynamicity of EEC systems, where both task generation and resources can be mobile, alongside task inter-dependencies, escalates the complexity of task offloading and workload management. To tackle these complexities, task partitioning emerges as a viable strategy. Moreover, in dynamic edge computing scenarios, resource demand remains unpredictable, emphasizing the critical need to optimize resource utilization efficiently. In this paper, we investigate the problem of tasks with inter-dependencies offloading in an EEC environment where mobile and resource-constrained edge devices are employed as computing resources. In this regard, a partitioning-based Deep Reinforcement Learning (DRL) for Dependent sub-Task Orchestration (DeTOrch) model is proposed. DeTOrch uses a state-of-the-art partitioning method for decomposing tasks and proposes a novel mobility task-orchestration mechanism to minimize the task completion time and maximize the use of edge devices’ resource. The simulation results show that the proposed model can significantly improve the task success rate and decrease task completion time. In addition, in various scenarios with different levels of mobility, the proposed model outperforms the baselines while utilizing the resource of edge devices.

This study presents a fresh perspective on the existence, uniqueness, and stability of solutions for initial value problems involving variable-order differential equations with finite delay. Departing from conventional techniques that utilize generalized intervals and piecewise constant functions, we introduce a novel fractional operator tailored for this specific problem. Our methodology integrates sophisticated mathematical analysis, including the Schauder fixed-point theorem and Banach’s contraction principle, with an examination of the Ulam–Hyers stability of the problem. The strength of our approach is in its simplicity, requiring fewer restrictive assumptions. We conclude with a practical application to illustrate our findings. These results are valuable for understanding complex dynamical systems with time delays, offering applications in diverse fields such as engineering, economics, and medicine, and enhancing numerical methods for solving delay equations.

The present study has investigated the binding interactions of four N‐ferrocenylmethyl‐nucleobase derivatives: N1‐ferrocenylmethyladenine (FcMeAd), N1‐ferrocenylmethyl‐cytosine (FcMeCy), N1‐ferrocenylmethylthymine (FcMeTh), and N6,9‐bis(ferrocenyl‐methyl)adenine ((FcMe)2Ad), with human hemoglobin (HHb) and bovine serum albumin (BSA). A combination of absorption spectroscopy, cyclic voltammetry, molecular docking, and molecular dynamics simulations is employed to investigate these interactions. The obtained results demonstrated that these derivatives can bind to the target proteins, inducing conformational changes with the binding affinity order: FcMeCy > FcMeTh > FcMeAd > (FcMe)2Ad. Molecular docking studies identified the preferred binding sites and modes, revealing that hydrogen and hydrophobic predominantly facilitate the binding to BSA and HHb, also exhibiting π–π stacking interactions with FcMeCy and FcMeAd. Dynamic simulations of the FcMeCy‐BSA and FcMeCy‐HHb complexes, selected based on both experimental and theoretical results, further confirmed their stability within the protein binding pockets. The RMSD, RMSF, rGyr, SASA, H‐bonds, MolSA, and PSA parameters consistently indicated that FcMeCy maintains stability in the receptor binding site throughout the 100 ns simulation.