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Résumé: Cette étude vise à étudier les compétences liées à l’utilisation des TIC chez les futurs PEP algériens en formation initiale au sein du Centre de formation ouvert au niveau de la wilaya de BBA par la direction de l’éducation pour habiliter les enseignants récemment intégrés, 132 futurs enseignants de langues française ont été interrogés. L’analyse des données collectées a laissé apparaitre des niveaux de confort significatifs dans l’utilisation de la technologie, des réseaux sociaux et de l’information médiatisée. Cependant, un effort de formation considérable reste à réaliser pour que ces futurs enseignants puissent utiliser les TIC à des fins pédagogiques. Mots-clés : formation initiale ; compétences numériques ; usage technique ; usage éducatif ; TIC

Artisanal gold mining in Chami is an important lever in the country economy, owing to the benefits it brings, but it is also likely to have a negative impact on various environmental compartments. Contamination of surface and ground water by trace metals elements (ETMs) was studied in the vicinity of the Chami gold processing center. The aim of the study was to assess the water contamination by trace metals (ETMs) in the town of Chami. Nine water samples (process and boreholes) were analyzed for (Co, Cr, Cu, Ni, Pb, Zn and Hg). The analysis was carried out by the DMA 80 technique for Hg at the ONISPA laboratory in Nouakchott and the other ETM at the Kenitra geosciences laboratory by the ICP-MS technique. The results of the analyses show that the ETM concentrations are high in process water samples and low in drilling water samples. Pollution indices and coefficients of variation indicate no contamination or anthropogenic traces in the borehole samples, but high levels of Hg and Ni in the process water. These results show the accumulation of metals in significant quantities in the process waters, especially Hg, proving that the waters of the mining waste basins at the Chami processing site are becoming sources of environmental pollution by mercury, which can be released into the environment by several pathways.

Supported cell membrane coatings meet many requirements set to bioactive nanocarriers and materials, provided sidedness and fluidity of the natural membrane are maintained upon coating. However, the properties of a support-surface responsible for maintaining correct sidedness and fluidity are unknown. Here, we briefly review the properties of natural membranes and membrane-isolation methods, with focus on the asymmetric distribution of functional groups in natural membranes (sidedness) and the ability of molecules to float across a membrane to form functional domains (fluidity). This review concludes that hydrophilic sugar-residues of glycoproteins in the outer-leaflet of cell membranes direct the more hydrophobic inner-leaflet towards a support-surface to create a correctly-sided membrane coating, regardless of electrostatic double-layer interactions. On positively-charged support-surfaces however, strong, electrostatic double-layer attraction of negatively-charged membranes can impede homogeneous coating. In correctly-sided membrane coatings, fluidity is maintained regardless of whether the surface carries a positive or negative charge. However, membranes are frozen on positively-charged, highly-curved, small nanoparticles and localized nanoscopic structures on a support-surface. This leaves an unsupported membrane coating in between nanostructures on planar support-surfaces that is in dual-sided contact with its aqueous environment, yielding enhanced fluidity in membrane coatings on nanostructured, planar support-surfaces as compared with smooth ones.

This work demonstrates the development and effectiveness of a novel diagnostic device that uses virtual reality (VR) and micro-heating touch feedback to measure the size and temperature of lung tumors. This cutting-edge technology helps with the accurate diagnosis and localization of tumors by providing users with a warm sensation when they touch a 3D rendering of a tumor inside a VR environment. By integrating thermal feedback with immersive VR, we enhance the accuracy and intuitiveness of tumor detection, potentially contributing to better diagnostic results and enabling more efficient treatment planning. The integration of thermal feedback not only enhances the realism of the virtual interaction but also makes it easier for the user to locate tumors precisely based on temperature changes. This new method offers a more thorough and interactive way to assess the identification and localization of lung cancers, which is a significant improvement in medical diagnostics. Rapid identification and detection are crucial as they can significantly enhance survival probabilities, especially since early detection is often challenging. This paper presents the development of a novel device for lung tumor analysis, employing VR and thermal technologies to provide thermal touch feedback. At the core of this device is the innovative use of microheater-based technology, integrating new techniques for thermal sensation. Through the use of a VR interface, our microheater device was examined. Users were able to interact with 3D-rendered lung tumors and experience a heat sensation that was different from the surrounding lung tissue. The MEMS microheater demonstrated a response time of approximately 16 seconds, significantly faster than the 200 seconds required by traditional Peltier devices. The preliminary results show that our microheater device offers optimal performances in terms of response time and allows low energy consumption (range of milliwatts), compared to the Peltier actuator. The results further demonstrate that our microheater’s optimized thermal touch feedback produces precise and safe temperatures for tumor detection.

The proliferation of high-dimensional data in many advanced database applications is a result of today’s technological advancements. These data points that correspond to objects are often without a precise description, which make their representation uncertain. While the concept of data streaming is not new, its practical uses are only recently emerging. This research focuses on continuous range data—a type of uncertain data common in database applications—that do not have explicit representations of their exact values. Furthermore, the identification of skyline objects—one of the popular database applications—becomes more challenging when skylines are to be identified from a collection of continuously generated input data streams where objects might have imprecise values. This makes it imperative to determine which approach has the optimal accuracy for estimating or predicting the uncertain values and at the same time able to handle a massive streams of data that are continuously generated and analyze them almost instantly to provide accurate and timely responses. Given this, the following techniques are selected—Linear Regression (LR), k-Nearest Neighbour (k-NN), Random Forest (RF), Decision Trees (DT), and Centre and Range Method (CRM) and their effectiveness is evaluated in terms of execution time, precision, recall, F1-score, and root mean square error (RMSE). Additionally, in order to verify the accuracy of each prediction technique, the predicted data derived from its model is used to derive skyline objects, which are subsequently compared to the actual skyline results. An inaccurate prediction of a continuous range value would result in incorrect set of skyline objects.

Abstract Background Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer, with a high mortality rate and poor prognosis. Mutated or dysregulated transcription factors (TFs) are significantly associated with carcinogenesis. The aim of this study was to develop a TF-related prognostic risk model to predict the prognosis and guide the treatment of HCC patients. Methods RNA sequencing data were obtained from the TCGA database. The ICGC and GEO databases were used as validation datasets. The consensus clustering algorithm was used to classify the molecular subtypes of TFs. Kaplan‒Meier survival analysis and receiver operating characteristic (ROC) analysis were applied to evaluate the prognostic value of the model. The immunogenic landscape differences of molecular subtypes were evaluated by the TIMER and xCell algorithms. Autodock analysis was used to predict possible binding sites of trametinib to TFs. RT‒PCR was used to verify the effect of trametinib on the expression of core TFs. Results According to the differential expression of TFs, HCC samples were divided into two clusters (C1 and C2). The survival time, signaling pathways, abundance of immune cell infiltration and responses to chemotherapy and immunotherapy were significantly different between C1 and C2. Nine TFs with potential prognostic value, including HMGB2, ESR1, HMGA1, MYBL2, TCF19, E2F1, FOXM1, CENPA and ZIC2, were identified by Cox regression analysis. HCC patients in the high-risk group had a poor prognosis compared with those in the low-risk group (p

In the realm of education, the timely identification of potential challenges, such as learning difficulties leading to dropout risks, and the facilitation of personalized learning, emphasizes the crucial importance of early grade prediction. This study seeks to connect predictive modeling with educational outcomes, particularly focusing on addressing these challenges in computer science higher education programs. To address these issues, nonlinear dynamic systems, notably Recurrent Neural Networks (RNNs), have demonstrated efficacy in unraveling the intricate relationships within student learning traces, surpassing the constraints of traditional time series methods. However, the challenge of vanishing gradient issues hampers RNNs, leading to a significant decrease in gradient values during weight matrix multiplication. To solve this challenge, we introduce an innovative loss function, the MSECosine loss function crafted by seamlessly combining two established loss functions: Mean Square Error (MSE) and LogCosh. In assessing the performance of this novel loss function, we employed two self-collected datasets comprising learning management system (LMS) and assessment records from a higher education computer science program. These datasets serve as the testing ground for four deep time series models: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and CNN-LSTM. Employing 29 meticulously designed feature sets representing combination of demography, learning activities and assessment, LSTM emerges as the preeminent model which is consistent with our expectation that RNN is the best suited approach. Building on this groundwork, we solve the vanishing gradient issue and boost the LSTM model’s performance by integrating the proposed MSECosine loss function, resulting in an enhanced model termed eLSTM. Experimental results underscore the noteworthy achievements of the eLSTM model, emphasizing an accuracy of 0.6191% and a substantially reduced error rate of 0.1738. The proposed MSECosine loss function performance in addressing the vanishing gradient issue yields two times better than compared to standard loss functions. These outcomes surpass those of alternative approaches, highlighting the instrumental role of the MSECosine loss function in refining eLSTM for more accurate predictions in course grade prediction, as well as the feature set that captures early grade prediction.

The generalization of the classical FitzHugh–Nagumo model provides a more accurate description of the physical phenomena of neurons by incorporating both nonlinearity and fractional derivatives. In this article, we present a numerical method for solving the time-fractional FitzHugh–Nagumo equation (TFFNE) in the sense of the Atangana–Baleanu fractional derivative using B-spline functions. The proposed method employs a finite difference scheme to discretize the fractional derivative in time, while θ\theta -weighted scheme is used to discretize the space directions. The efficiency of the scheme is demonstrated through numerical results and rate of convergence. The convergence order and error norms are studied at different values of the noninteger parameter, temporal directions, and spatial directions. Finally, the effectiveness of the proposed methodology is examined through the analysis of three applications.

Wireless sensor networks are a collection of tiny sensor devices powered by energy-scarce batteries. Depending on the nature of the application, those tiny devices might be deployed in areas that make it difficult to recharge or change their battery. Therefore, it is important to optimize the energy consumption of these devices to prolong their lifetime for as long as possible. Clustering is regarded as the most efficient approach to extend the lifetime of wireless sensor networks. This study proposes a novel method integrating cluster head selection and cluster formation to obtain as long a network lifetime as is possible. We employ a linear programming model to resolve the issue of cluster head selection and a Hierarchical Agglomerative Clustering (HAC) method to form clusters in the network. We evaluated HAC on different sets of inputs where we varied the position of the sink from the corner of the network to the center. HAC outperformed other existing clustering techniques for the first node death (FND) and half node death (HND) lifetime definitions in the cases where the sink is at the corner of the network. In the cases where the sink is located at the center of the network, HAC outperformed the other existing techniques for all definitions of network lifetime.

This article suggested a solution to a flow control problem governed by a class of nonlinear systems called bilinear systems. The problem was initially well-posed, and after it was established that an optimal control solution existed, its characteristics were stated. After that, we demonstrated how to use various bounded feedback controls to make a plate equation's flow close to the required profile. As an application, we resolved the plate equation-governed partial flow control issue. The findings bring up a variety of system applications, which can be employed in engineering advancement.

Le présent article porte sur une enquête ayant trait à l’enseignement/apprentissage du texte littéraire au cycle d’enseignement moyen, et plus précisément, en classe de la 2e année moyenne. À travers un questionnaire auto-administré destiné à 289 enseignants de ce niveau, nous tenterons de comprendre tant leurs attitudes eu égard à l’exploitation du document littéraire en classe de FLE, que les difficultés de leurs apprenants. Ainsi, nous jetterons la lumière – à partir des avis des enseignants -, sur les nouveaux apports du manuel scolaire de la 2e année moyenne, selon la révision du programme de la 2e génération. Cette étude nous a montré, malgré les difficultés dont souffrent encore certains apprenants, que le manuel scolaire de la 2e génération comporte un aspect socioculturel plus riche et a été plus allégé que celui de la 1ère génération. Plusieurs textes de cultures algériennes y ont été intégrés, et conséquemment, l’apprenant pourra être en immersion de sa culture. Enfin, cette recherche contribue à faire des propositions fructueuses pour une exploitation profitable du texte littéraire en classe de FLE

This paper presents a new optimization technique based on the hybridization of two meta-heuristic methods, Jellyfish Search (JS) and Moth Flame Optimizer (MFO), to solve the Optimal Power Flow (OPF) problem. The JS algorithm offers good exploration capacity but lacks performance in its exploitation mechanism. To improve its efficiency, we combined it with the Moth Flame Optimizer, which has proven its ability to exploit good solutions in the search area. This hybrid algorithm combines the advantages of both algorithms. The performance and precision of the hybrid optimization approach (JS-MFO) were investigated by minimizing well-known mathematical benchmark functions and by solving the complex OPF problem. The OPF problem was solved by optimizing non-convex objective functions such as total fuel cost, total active transmission losses, total gas emission, total voltage deviation, and the voltage stability index. Two test systems, the IEEE 30-bus network and the Mauritanian RIM 27-bus transmission network, were considered for implementing the JS-MFO approach. Experimental tests of the JS, MFO, and JS-MFO algorithms on eight well-known benchmark functions, the IEEE 30-bus, and the Mauritanian RIM 27-bus system were conducted. For the IEEE 30-bus test system, the proposed hybrid approach provides a percent cost saving of 11.4028%, a percent gas emission reduction of 14.38%, and a percent loss saving of 50.60% with respect to the base case. For the RIM 27-bus system, JS-MFO achieved a loss percent saving of 50.67% and percent voltage reduction of 62.44% with reference to the base case. The simulation results using JS-MFO and obtained with the MATLAB 2009b software were compared with those of JS, MFO, and other well-known meta-heuristics cited in the literature. The comparison report proves the superiority of the JS-MFO method over JS, MFO, and other competing meta-heuristics in solving difficult OPF problems.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image-processing methodologies. Leveraging the capabilities of ASTER image, band ratio (BR) images, and principal component analysis (PCA) alongside the power of 3D convolutional neural networks (3D-CNNs), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture accepting input as a 7 × 7 × C image patch, where C represents the combined number of selected ASTER image bands, principal component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image band combinations, including the proposed band combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside ASTER bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected misclassification rates. Surprisingly, utilizing solely ASTER bands as input parameters yielded higher accuracy rates (OA = 93.13%, AA = 91.96%, kappa = 90.91%) compared with scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, kappa = 82.60%) or the integration of BR images to PC bands (OA = 87.78%, AA = 82.39%, kappa = 83.81%). However, the amalgamation of ASTER bands with selected PC bands showed slight improvements in accuracy (OA = 94.65%, AA = 92.93%, kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of ASTER bands, PC bands, and BR images (proposed band combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA = 94.87%, kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the ASTER bands, PC bands, and BR images, with the ASTER bands proving pivotal for optimal skarn classification, the PC bands enhancing intrusions classification accuracy, and the BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.