Search

Tarfah Al-Warhi,1 Ahmed M El Kerdawy,2,3 Mohamed A Said,4 Amgad Albohy,5 Zainab M Elsayed,6 Nada Aljaeed,1 Eslam B Elkaeed,7 Wagdy M Eldehna,8,9 Hatem A Abdel-Aziz,10 Miral A Abdelmoaz11 1Department of Chemistry, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia; 2Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Cairo University, Cairo, Egypt; 3Department of Pharmaceutical Chemistry, School of Pharmacy, Newgiza University (NGU), Newgiza, Cairo, Egypt; 4Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Egyptian Russian University, Badr City, Cairo, 11829, Egypt; 5Department of Pharmaceutical Chemistry, Faculty of Pharmacy, The British University in Egypt (BUE), El-Sherouk City, Cairo, 11837, Egypt; 6Scientific Research and Innovation Support Unit, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, Egypt; 7Department of Pharmaceutical Sciences, College of Pharmacy, Almaarefa University, Riyadh, 13713, Saudi Arabia; 8School of Biotechnology, Badr University in Cairo, Badr City, Cairo, 11829, Egypt; 9Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt; 10Department of Applied Organic Chemistry, National Research Center, Dokki, Giza, 12622, Egypt; 11Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Sinai University, Kantra, EgyptCorrespondence: Wagdy M Eldehna, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt, Tel +201068837640, Email wagdy2000@gmail.comIntroduction: Epidermal growth factor receptor (EGFR) regulates several cell functions which include cell growth, survival, multiplication, differentiation, and apoptosis. Currently, EGFR kinase inhibitors are of increasing interest as promising targeted antitumor therapeutic agents.Methods: Different thiazolyl-pyrazoline derivatives ( 7a-o) were synthesized and were first tested for anti-proliferative effect towards the A549 lung cancer cell line and the T-47D breast cancer cell line in MTT assay. Thereafter, thiazolyl-pyrazolines ( 7b, 7g, 7l, and 7m) were subsequently evaluated for their PK inhibition for EGFR. Moreover, representative promising derivatives ( 7g and 7m) in cytotoxic and PK inhibition assays were tested to investigate their impact on the apoptosis and cell cycle phases in T-47D cells in order to explore more insights into the antitumor actions of the target thiazolyl-pyrazolines. Furthermore, docking studies were accomplished to evaluate the patterns of binding of thiazolyl-pyrazolines 7b, 7g, 7l, and 7m in the EGFR active pocket (PDB ID: 1M17).Results: Testing the thiazolyl pyrazoline compounds 7a-o on A549 and T-47D cell lines showed IC50 arrays between 3.92 and 89.03 μM, and between 0.75 and 77.10 μM, respectively. Also, the tested thiazolyl-pyrazolines ( 7b, 7g, 7l, and 7m) demonstrated significant sub-micromolar EGFR inhibitory actions with IC50 values 83, 262, 171 and 305 nM, respectively, in comparison to erlotinib (IC50 =57 nM).Discussion: Generally, it was observed that the tested thiazolyl pyrazolines showed more potent antiproliferative activity toward breast cancer cells T-47D than toward lung cancer cell lines A549. In particular, thiazolyl pyrazolines 7g and 7m showed the best activity against A549 cells (IC50 = 3.92 and 6.53 μM) and T-47D cells (IC50 = 0.88 and 0.75 μM). Compounds 7g and 7m provoked a sub-G1 phase arrest and cell apoptosis which are in agreement with the expected outcome of EGFR inhibition. Finally, the molecular docking of 7g and 7m in the active site of EGFR revealed a common binding pattern similar to that of erlotinib which involves the accommodation of the 1,3 thiazol-4-one ring and pyrazoline ring of target compounds in the binding region of erlotinib’s quinazoline ring and anilino moiety.Keywords: antitumor, EGFR inhibitors, pyrazolo, molecular docking, breast cancer, lung cancer

In recent years, the proliferation of IoT technologies and the widespread adoption of wireless sensors across various critical infrastructures such as power plants, service monitoring systems, space and earth exploration missions, and water treatment facilities have resulted in the generation of vast quantities of multivariate time series data. Within this context, unsupervised anomaly detection has emerged as a pivotal yet challenging problem in time series research, necessitating machine learning models capable of identifying rare anomalies amidst massive datasets. Traditionally, unsupervised methods have approached this issue by learning representations of primary patterns within sequences and detecting deviations through reconstruction errors. However, the effectiveness of this approach is often limited due to the intricate dynamics and diverse patterns inherent in these dynamic systems. Moreover, many existing unsupervised anomaly detection techniques fail to fully exploit inter-feature relationships within multivariate time series data, thereby overlooking a crucial criterion for accurate detection. To address these shortcomings, this paper introduces a novel unsupervised method for multivariate time series anomaly detection based on normalized flows and autoencoders. Central to our approach is the incorporation of a channel shuffling mechanism during training, enhancing the model’s capacity to discern inter-channel patterns and anomalies. Concurrently, the application of normalized flows within the autoencoder framework serves to constrain the latent space, effectively isolating anomalies and improving detection accuracy. Experimental validation conducted on two large-scale public datasets demonstrates the efficacy of the proposed method compared to established benchmarks, highlighting its superior performance.

The objective of this study is to build a pulse combustion simulation model in order to determine the design parameters that influence the pulse combustor's optimized performance. The Helmholtz valveless pulse combustor type is the simple design of pulse combustors. It has many advantages over conventional combustion, so it has been used in a variety of applications. There are many parameters that need to be studied in order to design this combustor type to achieve optimal performance. This research presents numerical and experimental investigations of pulse combustion. FLUENT software is used to conduct numerical simulations of the design parameters and pulse combustor performance. These simulations are then experimentally validated and confirmed to be accurately optimized in order to be applicable. The performance of the pulse combustor is studied for different inlet and tailpipe length and combustion chamber volume. This research accurately predicts how the frequency and pressure amplitude vary as the lengths of the inlet and tailpipe increase, as well as the combustion chamber volume. [ABSTRACT FROM AUTHOR]

Architecture quality is complex due to a number of factors. Some of these factors implicitly engage in deep reflection on architectural space, then they end up having a significant impact on the user’s architectural experience. For the purposes of this paper, experience is considered as any contact between the user and the built environment through the architectural space, where the impact will be the result of both objective and subjective factors. Among these subjective aspects, we focus on the emotional dimension of the user's experience. This theoretical article will address this issue using an inductive approach. The main purpose is to examine the role of each of the following elements: firstly, the architect's perception of architectural space, in particular in relation to the traditional duel between form/space or geometry/architecture; secondly, the impact of emotion which is thought to be most closely related to human nature in promoting architectural quality; and finally, we will attempt to define the methodology that will enable the architect to address this emotional dimension in such a technical practice. The desired and appropriate methodological frame is intended to provide a scientific solution to the potential conflict between subjective and objective factors from the disciplines and participants involved. To do so, it should represent the first level (level-0 methodological framework) where the user-centered approach takes precedence and coordinates between architects, environmental psychologists and neuroscientists to prepare the quantitative and qualitative data needed to support the next level (level-1: architectural design), which is primarily the responsibility of the architect.

This study examines translation strategies and the impact of translation strategies on translated texts produced by master's students of the English program at the campus of UNDIKMA Mataram. This study used a qualitative approach that included recording methods, specifically internal data recording. The data collected were 10 products translated from English to Indonesian by the requester. The English translation includes 390 words. They are allowed to use traditional or offline/electronic dictionaries when translating text. The study used several translation strategies, which are translation methods (literal translation and oblique translation) and translation processes (borrowing, calque translation, literal translation, modulation, transposition, equivalence, and translation). Transform). The results show that master's students tend to use local strategies and literal translation methods in translation. This affects the quality of the translation, making it less natural in the target language. In addition, translation errors also occur due to wrong/incorrect word selection, leading to failure to convey the message in the text source. The result of this research expected will bring valuable contributions to future research and courses serving translation activities.

Résumé : Cet article vise à stimuler le débat sur ce que certains considèrent comme une baisse du niveau de français des élèves comoriens. Cette observation est unilatérale car, au contraire, enrichis et stimulés par les moyens à leur disposition et les opportunités offertes par les innovations technologiques, ces élèves ont un niveau de langue française bien meilleur que leurs aînés en 1980. Cette baisse de niveau perçue émane de la vision traditionnelle qui accorde toute l'importance au français écrit en abandonnant délibérément le côté des compétences orales. Une langue se parle d'abord avant d'être écrite. Il est donc nécessaire de revoir la manière dont les compétences en langue française des élèves comoriens sont évaluées. Peu contextualisé, l'enseignement du français évolue lentement dans la mesure où l'enseignant modèle encore ses pratiques sur un système déjà incompatible avec les réalités actuelles. Le français est la langue de l'administration et la première langue officielle du pays. Les difficultés rencontrées dans son apprentissage peuvent être remédiées sur plusieurs facteurs, notamment la réhabilitation des programmes d'enseignement, la formation des enseignants et la lutte contre l'absentéisme. Il est également nécessaire de savoir modérer les tempéraments afin de mieux repenser l'éducation. Mots-clés : enseignement, apprentissage, éducation, baisse de niveau, action, remédiation.

Abstract The most rapidly expanding type of solar cells are the Perovskite Solar Cells (PSCs), because of its high device performance, ease of synthesis, high open-circuit voltage, and affordability. Despite these advantages, the development of perovskite-based solar cells continues to be impeded by the issues with perovskite stability and the utilization of the hazardous heavy element lead (Pb). The study emphasizes on the bifacial structure that maintains the conventional absorber layer and electron transport layer (ETL) in the optimized PSC structure. This study employs SCAPS software for device simulation to comprehensively analyze how various parameters affect the performance of solar cells. Additionally, doping concentration variation in both ETL and HTL are explored. The simulation reveals that changing device structure from monofacial to bifacial significantly influences PSC performance, demonstrating that optimizing individual layers effectively enhances overall solar cell performance. The optimized structure achieves impressive PSC performance metrics through parametric analysis, such as voltage (VOC) of 1.18 V, fill factor (FF) of 82.24%, current density (JSC) of 27.12 mA/cm2, power conversion efficiency (PCE) of 27.90% for an incident solar spectrum from the ETL side, and power conversion efficiency (PCE) of 19.86% for an incident solar spectrum from the HTL side, the calculated bifaciality factor (BF) for this structure is 71.18%.

Smart healthcare is one of the promising areas of the Internet of Things (IoT), particularly in the case of the Covid-19 pandemic. Real-time patient monitoring and remote diagnostics facilitate better medical services to preserve human lives using Internet of Medical Things (IoMT) technology. Regardless of the numerous benefits, IoMT devices are susceptible to sophisticated cyber-attacks at a breakneck pace, which lead to tampering with healthcare data and threaten patients' lives. In a similar context, the 2022 ransomware cyber-attack on Versailles André-Mignot Hospital compromised the healthcare system and disclosed tremendous amounts of patient information. Towards this direction, most researchers have solely developed either machine learning or Deep learning algorithms to identify network traffic anomalies. Motivated by the above challenges, an effort has been made in this paper to design a Recursive Feature Elimination (RFE) integrated with machine learning paradigms and a Ridge regression merged into deep learning models for implementing accurate anomaly intrusion detection based on the real-time dataset WUSTL-EHMS. Among the paradigms used, the proposed approach confirms that the RFE-based Decision Tree (DT) outperforms state-of-the-art techniques with a training accuracy of 99 % and a testing accuracy of 97.85 % while maintaining a reduction of FAR to 0.03. In a nutshell, it has been proven that the suggested framework can be deployed to build anomaly intrusion detection, reinforcing IoMT against widespread cyber-attacks and safeguarding the integrity of advanced healthcare systems.

The Mw 6.8 Adassil earthquake that occurred in the High Atlas on September 8, 2023, was a catastrophic event that provided a rare opportunity to study the mechanics of deep crustal seismicity. This research aimed to decipher the rupture characteristics of the Adassil earthquake by analyzing teleseismic waveform data in conjunction with interferometric synthetic aperture radar (InSAR) observations from both ascending and descending orbits. Our analysis revealed a reverse fault mechanism with a centroid depth of approximately 28 km, exceeding the typical range for crustal earthquakes. This result suggests the presence of cooler temperatures in the lower crust, which facilitates the accumulation of tectonic stress. The earthquake exhibited a steep reverse mechanism, dipping at 70°, accompanied by minor strike-slip motion. Within the geotectonic framework of the High Atlas, known for its volcanic legacy and resulting thermal irregularities, we investigated the potential contributions of these factors to the initiation of the Adassil earthquake. Deep seismicity within the lower crust, away from plate boundaries, calls for extensive research to elucidate its implications for regional seismic hazard assessment. Our findings highlight the critical importance of studying and preparing for significant seismic events in similar geological settings, which would provide valuable insights into regional seismic hazard assessments and geodynamic paradigms.

AbstractWe report on the development of metal oxide films with potential applications in energy conversion devices. Using an electron beam evaporator in an oxygen atmosphere, titanium, tin, nickel, molybdenum and aluminum oxides in room temperature were reactively deposited. The deposited films were characterized optically and structurally using ultraviolet–visible (UV–Vis) spectrometry, ellipsometry, X-ray photoelectron spectroscopy (XPS), contact angle and scanning electron microscopy. A systematic comparison between these filters confirmed that stacking layers with titanium dioxide (TiO2) and nickel oxide (NiO) are the best candidate for PV modules as they demonstrate higher transmission in the visible range. The morphological analysis confirms the formation of compact, uniform, and defect-free metal films, as validated by field emission scanning electron microscopy (FESEM). Additionally, contact angle measurements were conducted to assess the wettability of the metal surfaces. All oxide films exhibited semi-hydrophilic characteristics, indicating their ability to repel water from the surface and suggesting improved stability. The stoichiometry was impacted by the varying oxygen pressures at which each oxide was deposited. The transmittance of the TiO2 and NiO are more than 80% in the visible range. The results investigate potential applications as antireflection coatings, high reflectance mirrors and selective filters.

Objective:. We aimed to evaluate the prevalence of highly detailed ventral hernia repair (VHR) operative reports and associations between operative report detail and postoperative outcomes in a medico-legal dataset. Background:. VHR are one of the most common surgical procedures performed in the United States. Previous work has shown that VHR operative reports are poorly detailed, however, the relationship between operative report detail and patient outcomes is unknown. Methods:. This is a retrospective cross-sectional observational study. Operative reports describing VHR were obtained from a medical-legal database. Medical records were screened and data was extracted including clinical outcomes, such as surgical site infection (SSI), hernia recurrence, and reoperation and the presence of key details in each report. Highly detailed operative reports were defined as having 70% of recommended details. The primary outcome was the prevalence of highly detailed VHR operative reports. Results:. A total of 1011 VHR operative reports dictated by 693 surgeons across 517 facilities in 50 states were included. Median duration of follow-up was 4.6 years after initial surgery. Only 35.7% of operative reports were highly detailed. More recent operative reports, cases with resident involvement, and contaminated procedures were more likely to be highly detailed (all P < 0.05). Compared to poorly detailed operative reports, cases with highly detailed reports had fewer SSIs (13.2% vs 7.5%, P = 0.006), hernia recurrence (65.8% vs 55.4%, P = 0.002), and reoperation (78.9% vs 62.6%, P = 0.001). Conclusions:. In this medico-legal dataset, most VHR operative reports are poorly detailed while highly detailed operative reports were associated with lower rates of complications. Future studies should examine a nationally representative dataset to validate our findings.

In the medical field, efforts are currently focused on the development of devices implanted in the human body, to build a high-performance infrastructure and a working tool adapted to patients' needs. One such implanted device is the pacemaker, which is playing an increasingly important role in today's medicine. Moreover, in the general context of medical activities, we speak of telemedicine. This involves the transmission of medical information (images, reports, recordings) via antennae implanted or fixed on the human body, to obtain a remote diagnosis, specialist advice, constant patient monitoring, and a therapeutic decision. This paper presents a study and design of a circular flexible patch antenna with half-circle-shaped gaps on its sides and a slot on the opposite part of its feed; it is implemented on a bio-sourced type substrate intended for industrial, scientific, and medical applications. In addition, it is fed by microstrip technology, which features notches for impedance matching. In addition, this antenna offers improved performance compared with the literature.

Recently Direct Torque Control is widely appreciated compared to other conventional control methods due to its numerous advantages, notably its speed and precision. However, despite its qualities, it often encounters torque ripples that limit its operational effectiveness. These variations can be attributed to the use of hysteresis comparators, leading to variable frequency operation and undesirable speed overshoots. To address these challenges and enhance overall motor control, this article introduces a new approach based on neural networks. Direct Torque Control method is specifically designed for Doubly Fed Induction Motors and utilizes an Artificial Neural Network. Unlike conventional methods, this approach eliminates the need for speed controllers, commutation tables, and hysteresis comparators, thus providing a more integrated and efficient solution. Simulations conducted in the Matlab/Simulink environment have demonstrated the significant advantages of this approach with a higher performance enhancement. Not only were torque ripples reduced, but speed overshoot was completely eliminated. Furthermore, significant reductions in Total Harmonic Distortion values of stator and rotor currents were achieved, indicating an overall improvement in system performance.

Electric vehicles (EVs) are unquestionably the future of transportation due to their numerous advantages, such as their ability to address environmental issues, decrease dependence on fossil fuel, and enhance energy security. Attaining optimum performance, efficiency, and safety in EVs requires accurate control over both the speed and torque of the electric motor used. The Doubly Fed Induction Motor (DFIM) is classified as a motor that offers precise control over both its speed and torque. Additionally, it has some features that render it suitable for EV applications. This study contributes to advancing the field of new control systems for a DFIM used in EV applications. A key novelty of this work is the introduction of the Backstepping-Direct Torque Control (BSC-DTC) system for DFIM in EV applications. The suggested controller accurately tracks the specified speed set by the EV driver (reference speed) without any deviation, which is a key characteristic in modern EVs. Furthermore, the torque of the DFIM is regulated using the developed DTC method, BSC-DTC. To assess the capability of the BSC-DTC to effectively monitor the reference speed and achieve torque control, a comparison with conventional DTC is provided. Furthermore, an experimental implementation was conducted using the DS1104 board developed by dSPACE to validate the simulation results, highlighting the practical feasibility of the proposed system. Comprehensive modeling of the EV drivetrain was conducted and simulated utilizing the MATLAB-Simulink environment. The simulation findings demonstrate that the suggested control system offers superior performance for the EV system in terms of accurate reference speed tracking with zero tracking error and a slight overshoot of 0.1 km/h, as well as dynamic torque response. However, ripples in the electromagnetic torque provided by the motor have been observed. To address this issue, another innovative aspect of the work involves focusing future efforts on implementing control approaches based on artificial intelligence methodologies, opening new research and innovation avenues.

Identifying stator imbalance problems in induction machines (IMs) is crucial to preserving operating efficiency, reliability, and safety. This identification contributes to the IM’s overall health, prolongs its lifespan, and helps organizations meet regulatory and performance standards. Moreover, it enables the execution of maintenance tasks when required, improving resource use and decreasing shutdown periods. This research presents a Non-Destructive Technique (NDT) that utilizes a thermography-based machine learning algorithm for diagnosing Stator Imbalance Defects (SID) within IMs experimentally. This technique relies on assessing temperature distribution on the outer surface of the IM stator. Then, the evaluation of various statistical variables that provide valuable insights into the normality of temperature distribution within the designated area referred to as the Region of Interest (ROI) is presented. Initially, thermal images of the stator are captured under various operating conditions, including a healthy state, SID presence, and phase missing. Then, all thermal images are converted into thermographic data matrices for statistical analysis using the Machine Learning Algorithm Gaussian Naïve Bayes (GNB). This analysis aimed at classifying SID in IMs arising from deviations in the normalcy of temperature distribution within the ROI. To demonstrate the efficacy of the suggested GNB approach, a comparison with the Subspace Discriminant Classifier and Cubic Support Vector Machine is provided. The GNB algorithm exhibits exceptional accuracy of 95.2% in classification tasks while maintaining a minimal processing time, irrespective of the operational duration of the IM.

The dynamic and heterogeneous nature of fog computing environments presents significant challenges for efficient resource management, especially in the context of workflow optimization. To address this problem, we propose a novel particle swarm optimization (PSO)-based algorithm for workflow time and energy optimization in heterogeneous fog computing environments. Our algorithm leverages the collective intelligence of particles to efficiently explore the solution space and adapt to the dynamic and uncertain nature of fog computing resources. We evaluate the performance of our algorithm using simulations and experiments, demonstrating significant improvements in workflow completion time, energy consumption, and resource utilization compared to existing PSO-based algorithms and state-of-the-art methods. Our main contributions are twofold: a novel PSO-based algorithm that effectively addresses the challenges of workflow optimization in fog computing environments and empirical evidence of its efficacy and potential impact on real-world scenarios. Our results provide valuable insights for practitioners and researchers in the field of fog computing, demonstrating the feasibility of efficient resource management in dynamic and heterogeneous environments.

Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization problems. Genetic Algorithm (GA) is widely popular due to its logical approach, broad applicability, and ability to tackle complex issues encountered in engineering systems. However, GA is known for its high implementation cost and typically requires a large number of iterations. On the other hand, Particle Swarm Optimization (PSO) is a relatively new heuristic technique inspired by the collective behaviors of real organisms. Both GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches family. While they are often seen as competitors, their efficiency heavily relies on the parameter values chosen and the specific optimization problem at hand. In this study, we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud architecture. Through extensive experiments and performance evaluations, the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim simulator. The comparative analysis sheds light on the strengths and limitations of each algorithm, providing valuable insights for researchers and practitioners in the field.

Abstract Zinc oxide (ZnO) nanorods and ZnO nanostructures composited with silver (Ag) and multi-walled carbon nanotubes (MWCNTs) have been synthesized by a simple impregnation–calcination method and have been shown to be suitable for use as antimicrobial agents. The preparation method used for composite materials is very simple, time-effective, and can be used for large-scale production. Several analytical techniques, including X-ray diffraction (XRD), scanning electron spectroscopy (SEM), energy dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM) and Fourier transmission infrared spectroscopy (FTIR), have been used to characterize the prepared ZnO-Ag-MWCNT composite materials. The effects on structural, morphological, and antimicrobial properties of (ZnO)100-x (Ag)x nanocomposites at various weight ratios (x = 0, 5, 10, 30, and 50 wt%) have been investigated. The antimicrobial properties of ZnO composited with Ag nanoparticles and MWCNTs towards both gram-positive and gram-negative bacteria species were studied. The effect of raw MWCNTs and MWCNTs composited with ZnO and Ag on the cell morphology and chemical composition of Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) was studied by SEM and EDS, respectively. It was found that composite materials made of ZnO-Ag-MWCNTs exhibit greater antibacterial activities toward the microorganisms E. coli and S. aureus than ZnO-Ag, which could be beneficial for efficient antimicrobial agents in water and air treatment applications.

Pada bulan Januari 2023, terjadi KLB Hepatitis A di Pondok Pesantren (Ponpes) X yang terletak di Kabupaten Rokan Hilir Provinsi Riau dan telah menginfeksi banyak santri. Studi ini bertujuan untuk mengidentifikasi faktor risiko terjadinya KLB hepatitis A di Ponpes X. Studi ini menggunakan desain kasus-kontrol dengan perbandingan 1:1. Total sampel berjumlah 232 (116 sampel per kelompok). Populasi penelitian adalah semua siswa dan penghuni Ponpes X. Kasus adalah orang yang memenuhi kriteria klinis hepatitis A menurut diagnosis dokter. Kontrol adalah orang yang tidak mengalami gejala klinis hepatitis A, dipilih menggunakan metode consecutive sampling dengan matching berdasarkan umur dan jenis kelamin. Data dianalisis secara univariat, bivariat menggunakan uji chi-square, dan multivariat menggunakan uji conditional logistic regression. Hasil studi ini menemukan bahwa indeks kasus mulai merasakan gejala pada tanggal 20 Oktober 2022. Kurva epidemi menunjukkan pola continuous common source. Model akhir uji regresi logistik model prediksi mendapatkan dua variabel signifikan yaitu riwayat kontak (OR=9,92, CI 95%: 3,31-29,72, p=

The Direct Torque Control (DTC) was previously designed to achieve high performance of induction motor (IM) by accurately and independently controlling its flux and torque. However, this classical DTC generates several disadvantages such as strong fluctuations in the torque and stator flux as well as quite significant switching losses. For these reasons, many modern techniques have been proposed in the literature to improve it. On the other hand, as the inadequacy of the parameters seriously affects the efficiency and stability of the induction motor, it is paramount to accurately estimate the stator resistance, for this, robust observers are necessary suggested to be employed. This paper proposes developed structure of DTC strategy using twelve sectors combined with Backstepping speed regulator for the external loop and robust model reference adaptive system based on active power principle to estimate the stator resistance. In addition, several simulations under Matlab/Simulink and experiment results using the dSPACE DS1104 board are discussed to confirm improvements in control and speed estimation.

In this article, we try to explain that although the Renaissance was a renaissance and a transformation that was not and is not desirable to the church, the preparations for this transformation have been gradually provided at least since the 13th century AD within the church itself. Certainties of the church itself, such as the belief in the Trinity, the infallibility of the Pope, the Aristotelian view of religion, various readings, at the top of all of them, the appearance of Martin Luther, which led to the establishment of the Protestant Church, translations from Islamic culture centered on Avicenna and Averroes was taught in Latin and was taught in the church itself. The consequences of these developments in the centuries leading to the Renaissance, such as anthropocentrism, the originality of reason, the controversy over the truth of the church and secularism, are exactly opposite to the medieval worldview. Medieval philosophers were planted and nurtured, and in the Renaissance, in various contexts that we have highlighted the philosophical dimension in this writing, it bore fruit. The establishment of naturalism in theology by the Catholic Church is only one example of these cases. Other cases we have tried to briefly explain in this article. In the end, the roots of some components of modernity should be sought in the same completely intertwined theological-philosophical complex in seminary tradition.

Sustainable development projects require careful balancing of economic interests and ecological needs. The case of Skikda, a city in northeast Algeria, located on the Mediterranean coast, illustrates the challenges connected with such a development. The ancient city coexists with a young hydrocarbon port and industrial pole that serves as a transfer hub in the flow of petroleum between hinterland and sea. The installation of the port and petrochemical refining plants on the banks of the estuary of the Safsaf River presents many challenges to local citizens and the ecosystem, including pollution of the water system, groundwater, and river water, and damage to the area’s ancient heritage. This study argues that we need new and less polluting forms of intermodality between hinterland and seaport to make urban mobility more sustainable. It asks whether and how the existing rivers and wadis (river channels that are dry except during rainy periods) can be transformed into artificial canals for river navigation to improve the transport fluidity and sustainability of Skikda. To answer this question, the study adopts a prospective approach using the MICMAC scenario method. This approach entails, first, presenting and evaluating the potentialities of the existing rivers of Skikda using QGIS, and second, discussing and proposing scenarios for transforming these rivers into urban waterways, that is, artificial canals for inland navigation. The prospect of inland waterway transport in Skikda may be a radical scenario, yet, despite its hydraulic capacity and advantages, this system is not receiving attention in Algeria. We suggest that water transport can breathe sustainable blue life into a vulnerable industrial port city, transforming its challenges into opportunities.

The influence of the removal of carbonates on the ceramic properties made of Moroccan red clay as a main material and tea waste as pore-forming agent, has been investigated. ATD-TG, dilatometry, dimensional changes, weight loss, water absorption, open porosity, bulk density, flexural and indirect tensile strengths were assessed at different firing temperatures within the range of 900–1150 °C. SEM was used to evaluate the microstructures of the fired samples, while XRD was used to study the phase evolution. At varying firing temperatures, the materials which contains the carbonate (CC) exhibit greater weight loss, water absorption, and porosity compared to the materials without carbonate (NCC). On the other hand, NCC materials has greater mechanical strength than that CC materials. The microstructural analysis conducted through SEM revealed distinct changes in the fired samples when comparing observations at 1100 °C to those at 900 and 1000 °C. At 1100 °C, there was a noticeable increase in particle contacts and layered structures, which can be attributed to the presence of dehydroxylated clay minerals (illite and chlorite), quartz particles, and pores formed during firing. Subsequently, at 1150 °C/ 2 h, further alterations in the microstructures were observed due to a higher degree of vitrification in the fired samples. This resulted in material consolidation, interparticle and neck contacts, leading to the formation of vitrified bridges. Firing has resulted in the formation of closed and open pores of varying sizes, in the vitrified structures associated with anorthite, hematite, and quartz, a thin crystal precipitation of small particles was observed. This alteration in microstructure made it possible to conclude that the flexural and indirect tensile strength increased during firing, reaching its pinnacle at 1150 °C. As a result of the firing process, the ceramic supports underwent heightened sintering, leading to a gradual decline in open porosity. The present inquiry proved to be intriguing as it led to a deeper understanding of the utilization of Moroccan red clay as a ceramic’s raw material.

AbstractVarious metal layers grown by e-beam evaporator have been studied to be used as metal contacts for scalable perovskite photovoltaic (PV) devices. The evaporated films consisting of gold (Au), silver (Ag), nickel (Ni), titanium (Ti), tin (Sn), copper (Cu), and molybdenum (Mo) were grown on glass substrates at room temperature with an optimized thickness. Later, the measured optical properties such as transmission and absorptance of such films were used computationally to extract the optimum device performance using SCAPS-1D software. Among all the layers, Ti-based perovskite solar cells outperform other metal contacts with a power conversion efficiency of (>27%). The films were characterized optically, topologically, structurally, and morphologically using ultraviolet—visible (UV—Vis) spectrometry, atomic force microscopy, x-ray photoelectron spectroscopy (XPS), three-dimensional (3D) profilometry, and scanning electron microscopy. The morphological data confirm the growth of compact, uniform, and defect-free metal films as confirmed by the field emission scanning electron microscopy. Contact angle measurement was also performed to determine the wettability of metal surfaces. Both Au and Ni films were found semi-hydrophilic which shows the adaptability of better stability through repelling water from the surface. The computational analysis confirms that screening of suitable metal back contact is necessary to increase device performance and stability significantly.

AbstractWe report on several state of the art thin films coating technologies including physical vapor deposition (PVD) and solution process deposition techniques. Such techniques have their own significance to develop the energy efficiency devices. Choosing the right technique has become very critical in recent days as the scaling varies significantly with the technology. Thus, it becomes obvious to pick-up the right deposition procedure according to the needs where substrate size, film thickness, and required surface roughness are critically involved. Among all techniques, PVD offers various advantages, which include full control over growth, pristine film quality, large-scale fabrication, stacking of thin films, co-deposition flexibility, and controlling of the deposition temperature. In addition, such techniques can be adapted in small-scale research based labs with capping in terms of budget. Such vapor deposition processes also result in pin-hole free, homogeneous and continuous thin films which are highly desirable parameters for many energy related applications. Solution process techniques such as dip, spin, blade, and roll coating techniques are also discussed to provide an overall view. This report aims to give in-depth cross sectional review of all deposition techniques considering both positive and negative sides. PVD growth processes are very efficient where pattering of films are very significantly required. Hence, in this work, we have captured many deposition techniques where the properties of the films are very crucial to end up with different morphologies. The goal is to compare various techniques as a key parameter to control the fine formation of thin films. We are convinced that our report will show substantial importance to a wider group of materials scientists, chemists, physicists, and to the wider photovoltaic community to pick the best-suited vehicle for thin film growth purposes.

Abstract The Tell Atlas of Algeria has a huge potential for hydrothermal energy from over 240 thermal springs with temperatures up to $$98^\circ$$ 98 ∘ C in the Guelma area. The most exciting region is situated in the northeastern part which is known to have the hottest hydrothermal systems. In this work, we use a high-resolution gravity study to identify the location and origin of the hot water, and how it reaches the surface. Gravimetric data analysis shows the shapes of the anomalies arising due to structures at different subsurface depths. The calculation of the energy spectrum for the data also showcases the depths of the bodies causing anomalies. 3D-Euler deconvolution is applied to estimate the depths of preexisting tectonic structures (faults). These preprocessing steps assist with assessing signal attenuation that impacts the Bouguer anomaly map. The residual anomaly is used in a three-dimensional inversion to provide a subsurface density distribution model that illustrates the locations of the origin of the dominant subsurface thermal systems. Overall, the combination of these standard processing steps applied to the measurements of gravity data at the surface provides new insights about the sources of the hydrothermal systems in the Hammam Debagh and Hammam Ouled Ali regions. Faults that are key to the water infiltrating from depth to the surface are also identified. These represent the pathway of the hot water in the study area.

In the early stages of building design, decisions are made about the building’s form and envelope, but designers rarely base their decisions on sophisticated energy simulations, even though these features are critical to a building’s energy performance. This paper employs three methods—empirical, parametric, and uncertainty—to assess the interconnectedness of building form, envelope, orientation, and occupancy regarding thermal comfort and energy consumption for heating and cooling a residential building across three regions: Gdyel (mediterranean climate), Oum El Bouaghi, and Constantine (semi-arid climate). The study variables include indoor air temperature, relative humidity, and energy consumption. The initial findings stem from an experiment conducted in an apartment on the top floor of a building in Gdyel, which allowed us to record the evolution of the variables mentioned throughout the year and validate the parametric results of the multi-zone model created in TRNSYS16 software. This study showed that for the considered climates, a compact form is more suitable; it was found that the top floor with SF = 0.57 needs about 30% to 54% more energy than the inter-floor with SF = 0.21. In addition, the heating and cooling methods and habits adopted by Algerian households are responsible for 18% to 35% on the top floor and the inter-floor, respectively.

The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9.

In wastewater treatment, the application of ceramic membranes has gained significant attention due to their potential for highly effective filtration. These membranes are typically produced using methods such as spin coating, dip-coating, and spray coating. However, these techniques have limitations when applied to large-scale industrial applications due to their high cost, time consuming process, and difficulty of control. Moreover, obtaining a complete and uniform coating typically requires several attempts. This research demonstrates the highly effective treatment of tannery wastewater using cost-effective porous ceramic membranes, by incorporating bio-based materials, such as tea waste, as porosifying additives into the main material, which is Moroccan red clay. Various analytical techniques, including X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF), Thermogravimetric Analysis (TDA-TG), Scanning Electron Microscopy (SEM), and the Archimedes principle test, were used to investigate the properties of these ceramic membranes. The impact of the pore-forming agents on the membranes physical and mechanical properties, such as open porosity, bulk density, average pore diameter, flexural and indirect tensile strength, was evaluated. Filtration tests demonstrated effective removal of turbidity, suspended matter and chemical oxygen demand from the wastewater. The optimized membrane exhibited a permeability of 1249 L/h.m2.bar and turbidity removal efficiencies of 99.76% for seawater and 99.16% for tannery wastewater.

This study evaluates the possibilities to produce new materials, starting from Moroccan oil shales, for different applications. More specifically, the authors aimed to demonstrate that the organic fraction of the oil shales could be used as a precursor of carbon foams and graphitizable carbons, after appropriate chemical treatments resulting in the âmaturationâ of this organic phase. First, the researchers studied the optimization of experimental conditions and the identification of various parameters influencing the yield and composition of oils obtained by the supercritical extraction of Moroccan oil shale. The effect of various experimental parameters, such as mineral matter, thermal treatment temperature (T), treatment duration (t) and solvent type, was studied. The experimental results obtained show clearly that the organic matter contained in the Moroccan Tarfaya oil shale (sub-layer R3 ) can be recovered by phenol under the following optimal conditions: T = 390 °C, t = 2.5 hours using phenol as a solvent with an amount of 15 g for each 10 g of sub-layer R3 carbonate-free oil shale (RH). The results reveal that the yield and composition of the oil obtained by extraction with phenol is markedly different from those obtained by extraction with toluene, quinoline or without solvent. Furthermore, the phenol has a very signiï¬cant role in increasing the recovery yield and the degree of maturation of the obtained oil. It was shown that phenol was a suitable extraction solvent to produce graphitizable carbon at a relatively low temperature, below 1800 °C.

The integration of wind energy systems into the electric grid has become inevitable despite the many problems associated with this integration. Most of these problems are due to variations in wind speed. The problems are for example oscillations in the power generated, which implies the lack of guarantee of obtaining the maximum energy and the ripple in the electromechanical torque of the generator. This work aims at mitigating these problems for wind energy conversion system-driven doubly-fed induction generator (DFIG), which is the main wind turbine utilized for energy applications. This mitigation is performed through direct reactive and active powers control of the DFIG using an artificial neural network. A DSP (Digital Signal Processor-dSPACE DS1104) was used to experimentally test the proposed strategy. The dynamic performances of the controlled generator are analyzed by using the designed intelligent control strategy in the case of variable wind speeds and upon sudden change of the active power demand. Based on the obtained experimental results, it can be said that the designed intelligent control strategy outperforms traditional methods like direct power (DPC) and vector control in terms of reducing the current harmonics, and torque ripples, and enhancing dynamic response.

The 5th generation of mobile communications, currently being rolled out, aims to improve network performance and efficiency over the older generations. With mmWaves, densified cell deployment is necessary for 5G networks, increasing capacity and improving network coverage. This imposes a considerable increase in the energy consumption of the 5G stations, which not only increases operating expenses for operators but also burdens the environment. Optimizing the energy consumption of 5G networks would be necessary to curb the energy curve. In this context, this paper presents a new algorithm called Energy Consumption Optimization Algorithm (ECOA), which combines cell selection and standby techniques to optimize energy consumption while preserving network performance. A comparison is conducted between ECOA and standard cell selection modes to evaluate the performance of the conventional approach. Our algorithm exhibits good performance, particularly in high-density, high-load scenarios. For instance, in a site with 25 Pico base stations serving 500 users, our algorithm achieves an average throughput of 23 Mb/s per user while consuming 1750.75 W of energy. This represents a 2.44% increase in energy consumption compared to the optimal solution.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can engender multi-system inflammatory syndrome. Its main symptoms are cardiovascular and thromboembolic problems that can develop into severe complications. The present case is about a 55-year-old patient who was admitted for critical ischemia of the right lower limb and necrosis of the right forefoot. The patient was infected with coronavirus disease 2019 (COVID-19) one month before her admission. The patient also has cardiovascular risks including type 2 diabetes and hypertension. The performance of ultrasounds revealed a thrombus in the right atrium and the pulmonary artery, and arteriography detected an occlusion of the right popliteal joint for which she had an endovascular recanalization and amputation of the right forefoot. This case highlights that SARS-CoV-2 infection could be considered a serious cardiovascular disease requiring cardiovascular explorations to initiate hospital management and avoid severe complications.

Silica aerogels are the most efficient thermal insulation materials, this property is related essentially to their internal porosity consisting of micro and mesoporous structure. However, their weak mechanical properties and the high cost of drying methods limit the application of these materials. Herein, we propose a facile approach to the development of super-insulating material based on silica aerogel reinforced with alfa nonwoven. The sol-gel process followed by vacuum compression molding was used for composite manufacturing. SEM observations, IR, and Raman spectroscopies have confirmed the good adhesion between the aerogel and nonwoven. Mechanical tests, insulation property, fire resistance, and hydrophobicity are reported and the preliminary results confirm that the aerogel incorporation into the multiscale pores of the nonwoven improves the physical properties of the resulted composite and enhances hydrophobicity without affecting the low thermal conductivity.

Graphene aerogels are widely used in the oil–water system as they possess high internal surface area and super-oleophilic properties. However, they tend to absorb water along with oil, and to overcome this problem; surface coatings are generally employed using expensive fluoro-silane compounds. It leads to an increase in production costs and environmental concerns. Herein, we report super-hydrophobic 3D graphene aerogels as selective oil sorbent for oil–water​ separation. The reduction of oxygen-containing functional groups on the surface of graphene aerogels has been studied and characterized with FTIR. The thermal treatment of up to 700 °C was carried out using an in-house flow system. The gases used to reduce graphene oxide aerogel are H2and N2with an optimized ratio of 5:95. The presence of H2significantly decreased the oxygen-containing functional groups in graphene aerogel. The increase in the C/O ratio results in higher uptake capacity due to higher surface area and pore volume. The thermal reduction yields a C/O ratio of 24:1, slightly higher than most reported values.

Direct Torque Control (DTC) presents an optimal solution to control the behaviors of the alternative motors, compared to other controls, because of several advantages offered by this technique, the speed overshoots, fluxes, and torque ripples remain the major factors which minimize the DTC robustness. The regulation speed in DTC is carried out by the classic Proportional Integrator Derivative (PID), which is known for its higher robustness in linear systems, except that in the case of non-linear systems, the PID controller gives poor reactions to variations in the system’s parameters. The best solutions adopted in this situation are often based on optimization algorithms that generate the controller’s gains in each period where there is an internal or external perturbation, adapting the behaviors of the PID against the system’s nonlinearity. For that reason, this work is focused on the theoretical studies and experimental validation on dSPACE Board DS1104 of the new proposed approach based on PID speed regulation, optimized by the Ant Colony Optimization algorithm (ACO) for DTC, applied to both sides of the Doubly Fed Induction Motor (DFIM), to overcome the previous drawbacks cited at the beginning. The new combined ACO-DTC strategy has been studied for optimizing the gains of the PID controller by using a cost function such as Integral Square Error (ISE). The proposed approach is implemented on Matlab/Simulink to validate the objectives adopted by this strategy. The simulation and experimental results extracted from Matlab and ControlDesk have proved the efficiency of the proposed ACO-DTC with the system’s nonlinearity, which attribute different enhancements in the global system performance.

Smoking is an established risk factor for various pathologies including lung cancer. Electronic cigarettes (e-cigs) and heated tobacco products (HTPs) have appeared on the market in recent years, but their safety or, conversely, their toxicity has not yet been demonstrated. This study aimed to compare the metabolome of human lung epithelial cells exposed to emissions of e-cigs, HTPs, or 3R4F cigarettes in order to highlight potential early markers of toxicity. BEAS-2B cells were cultured at the air–liquid interface and exposed to short-term emissions from e-cigs set up at low or medium power, HTPs, or 3R4F cigarettes. Untargeted metabolomic analyses were performed using liquid chromatography coupled with mass spectrometry. Compared to unexposed cells, both 3R4F cigarette and HTP emissions affected the profiles of exogenous compounds, one of which is carcinogenic, as well as those of endogenous metabolites from various pathways including oxidative stress, energy metabolism, and lipid metabolism. However, these effects were observed at lower doses for cigarettes (2 and 4 puffs) than for HTPs (60 and 120 puffs). No difference was observed after e-cig exposure, regardless of the power conditions. These results suggest a lower acute toxicity of e-cig emissions compared to cigarettes and HTPs in BEAS-2B cells. The pathways deregulated by HTP emissions are also described to be altered in respiratory diseases, emphasizing that the toxicity of HTPs should not be underestimated.

Nigella sativa (NS) is a native herb consumed habitually in several countries worldwide, possessing manifold therapeutic properties. Among them, anti-inflammatory features have been reported, presumably relating to mechanisms involved in the nuclear factor kappa-B pathway, among others. Given the observed association between neuroimmune factors and mental illness, the primary aim of the present study was to examine the effects of chronic NS use on manic-like behavior in rats, as well as analyze levels of brain inflammatory mediators following NS intake. Using male and female rats, baseline tests were performed; thereafter, rats were fed either regular food (control) or NS-containing food (treatment) for four weeks. Following intervention, behavioral tests were induced (an open field test, sucrose consumption test, three-chamber sociality test, and amphetamine-induced hyperactivity test). Subsequently, brain samples were extracted, and inflammatory mediators were evaluated, including interleukin-6, leukotriene B4, prostaglandin E2, tumor necrosis factor-α, and nuclear phosphorylated-p65. Our findings show NS to result in a marked antimanic-like effect, in tandem with a positive modulation of select inflammatory mediators among male and female rats. The findings reinforce the proposed therapeutic advantages relating to NS ingestion.

Abstract Medieval mosques in Algeria represent an important architectural heritage that deserves to be identified, studied and preserved. Considering the period spanning the 7th to 15th centuries, this study investigated medieval mosques in Algeria, spatially and socially, to identify the architectural genotype and to establish whether such mosques present the same topological model that governs their spatial properties. This study adopted a new approach to spatial analysis of mosques that could be applied to improve understanding of other religious buildings. Space syntax as an architectural analysis tool can be used as a qualitative method for drawing justified graphs and comparing them visually and quantitatively, calculating syntactic measures and integrating visibility graph analysis to identify spatial types, reveal architectural genotypes and explain social logic. The findings demonstrate how space syntax with topological analysis and syntactic measures could be used to provide new understanding for architects, students and all those interested in heritage, architecture and design, by uncovering hidden structures and revealing the social logic embodied in the spatial configurations of mosques.

Cette étude vise la connaissance des représentations des étudiants de la première année Master du département de français de l’université de Saïda vis-à-vis de la possibilité du maintien de l’activité de l’oral à l’ère de la COVID-19. Dans le cadre formel de cette enquête, il a été évalué chez ces étudiants, la maîtrise des outils numériques mis en place dans le but d’assurer une continuité de formation. De même, un intérêt particulier a été accordé à l’exploitation de l’image comme support didactique pour un enseignement / apprentissage de la compétence orale. Au cours de cette enquête, les étudiants ont pris conscience de l’intégration de ces moyens numériques, qui sont en cours d’adaptation dans leur vie estudiantine. L’image joue un rôle très important dans le déclenchement de situations verbales authentiques en permettant le développement de la compétence orale.

Now more than ever, optimizing resource allocation in cloud computing is becoming more critical due to the growth of cloud computing consumers and meeting the computing demands of modern technology. Cloud infrastructures typically consist of heterogeneous servers, hosting multiple virtual machines with potentially different specifications, and volatile resource usage. This makes the resource allocation face many issues such as energy conservation, fault tolerance, workload balancing, etc. Finding a comprehensive solution that considers all these issues is one of the essential concerns of cloud service providers. This paper presents a new resource allocation model based on an intelligent multi-agent system and reinforcement learning method (IMARM). It combines the multi-agent characteristics and the Q-learning process to improve the performance of cloud resource allocation. IMARM uses the properties of multi-agent systems to dynamically allocate and release resources, thus responding well to changing consumer demands. Meanwhile, the reinforcement learning policy makes virtual machines move to the best state according to the current state environment. Also, we study the impact of IMARM on execution time. The experimental results showed that our proposed solution performs better than other comparable algorithms regarding energy consumption and fault tolerance, with reasonable load balancing and respectful execution time.