bluuebunny/crossref_metadata_2025 huggingface dataset:
Quality Teaching in Ekiti State Secondary Schools: An Application of Ordinal Logistic Regression
Authors: Adarabioyo Mumini Idowu , Adigun Abimbola , Adejuwon Samuel
Published: December 2024

This study investigates the quality of teaching in secondary schools in Ekiti State, Nigeria, using ordinal logistic regression to examine the impact of teacher demographics and teaching practices on student performance. The research focuses on teachers from six selected secondary schools within Ado-Ekiti, comprising a sample of 295 educators. Data were collected through a structured questionnaire administered via Google Forms and shared on WhatsApp platforms of the respondents. The study analyzes factors such as age, gender, educational background, teaching experience, lesson planning, technology integration, teaching methods, assessment practices, feedback provision, and professional development. Descriptive statistics and ordinal logistic regression were employed to assess the significance of these variables on student performance outcomes. Results indicate that older teachers and female educators positively influence student performance, while frequent integration of technology and rigid lesson planning negatively impact outcomes. The study also finds that regular feedback and active student participation significantly enhance performance. However, attending professional development workshops and the availability of teaching resources showed unexpected negative associations with student performance, suggesting potential issues with the applicability or effectiveness of these resources and training. The study highlights the importance of adaptive teaching methods, supportive administrative practices, and active student engagement in improving educational quality. These findings provide valuable insights for policy makers and educators aiming to enhance teaching effectiveness and student learning in secondary schools in Ekiti State.
A atuação da educação matemática no desenvolvimento de aluno com Transtorno do Espectro Autista (TEA) em atendimento especial
Authors: Guerra Gabrielli de Almeida , Silva Meirielle Cristina da , Gobira Gilmar Celestino
Published: December 2024

O processo de ensino-aprendizagem da educação matemática se renova a cada ano, pois novos desafios surgem no meio escolar, é crucial que o mediador ao qual ensina a disciplina deve estar atento à necessidade de seus alunos, cada um aprende de uma forma, alguns se destacam na prática outros na teoria, porém o importante ao final é a exatidão do sucesso do aprendizado. Nos últimos anos o número de crianças e adolescentes diagnosticadas com Transtorno do Espectro Autista vem aumentando no mundo, sendo assim a escola junto com o professor deve estar preparado para lhe dar com essa demanda, tendo em vista que o autismo se manifesta de forma diferente em cada indivíduo. A escola tem como principal papel formar pessoas para a vida, ao analisar essa afirmação é notório proporcionar um ambiente acolhedor, com estrutura capaz de desenvolver a educação positiva, que consiga acolher indivíduos com deficiência, transtornos e em vulnerabilidade social, somente com todos os espaços preparados e profissionais qualificados esse desenvolvimento é eficaz. Diante dos fatos o ensino da matemática aplicado no atendimento especializado institucional deve visar acolher e expandir os conhecimentos do aluno autista, quando aplicada com acolhimento e capacidade se desenvolve um fluxo de aprendizado e o aluno se sente pertencente ao ambiente que este incluído.
Variación del factor de forma en cuatro distancias de plantación de hule (Hevea brasiliensis Muell. Arg.)
Authors: Méndez Rolando Misael Tlaxcala , Ramírez Martin Aquino , Mendoza María del Carmen Pablo , Herrera Mayte López
Published: December 2024

El conocimiento sobre el efecto de las densidades de plantación en la geometría de los fustes es importante para el proceso de aserrío, debido a que los fustes más cilíndricos tienen rendimiento mayor de madera aserrada. Para ello, es necesario evaluar la forma de los fustes mediante modelos alométricos forestales. El objetivo del presente estudio fue estimar y comparar los factores de forma (ff) en cuatro distancias de plantación (DP) de Hevea brasiliensis Muell. Arg. (clon IAN-754) con 40 años de edad en Tezonapa, Veracruz, México. Para esto, se realizó un muestreo no destructivo en 123 árboles monopódicos. Se ajustó el modelo lineal de Spurr ponderado para cumplir con los supuestos de normalidad y homocedasticidad. Además, se complementó con una prueba de adicionalidad incorporando variables indicadoras en los parámetros de los interceptos y de las pendientes (ff). Los resultados indican que las DP 3 x 6 m, 4 x 8 m y 5 x 10 m no presentaron diferencias significativas en el intercepto por adición a la DP 2 x 2 m (p>0.05). Respecto al parámetro de la pendiente, la DP 2 x 2 m adoptó una geometría similar al paraboloide (ff=0.518) y expresó diferencias significativas con las DP 3 x 6 m (ff=0.446), 4 x 8 m (ff=0.445) y 5 x 10 m (ff=0.396), (p<0.05). Para concluir, las altas densidades originan el desarrollo de fustes más cilíndricos pero de tamaño menor, conforme disminuyen las densidades los fustes tienden a la conicidad, sin embargo, son de tamaño mayor.
Evaluación agroindustrial de híbridos de caña de azúcar (Saccharum spp.) en el Sur del estado de Jalisco, México
Authors: Preciado Jeovani Francisco Cervantes , Cilva Marcelino Álvarez , Arenas Marianguadalupe Hernandez , Mexicano Maurilio Mendoza
Published: December 2024

Ante el reducido número de variedades comerciales de caña de azúcar, y su inminente deterioro que incide en reducción de su productividad en el estado de Jalisco, en un suelo franco arcillo - arenoso y en condiciones de riego de auxilio, se estableció un grupo de nueve nuevos híbridos de caña de azúcar en fase agroindustrial. El objetivo fue evaluar y seleccionar su adaptación y comportamiento de campo e industrial, utilizando los testigos comerciales Mex 69-290 y CP 72-2086 como control. Las características evaluadas fueron: altura de planta, población de tallos molederos, estimación del rendimiento de campo a los 13 meses de edad de la planta y porcentaje de sacarosa en caña. Se realizaron análisis de varianza y prueba de Tukey con un nivel de confianza del 5%. Los resultados obtenidos indicaron que durante el ciclo soca y en condiciones locales del suelo, el híbrido ColMex 05-542 (C1) mostró el mayor rendimiento estimado de campo (139.3 t ha-1) y el mayor valor para el azúcar teórico por hectárea (29.59 t), a pesar de que obtuvo valores bajos de porcentaje de sacarosa (Pol) (14,47%). Con respecto al porcentaje de sacarosa, los híbridos ColMex 05-134 (15.7%), ColMex 05-107 (15.7) y el testigo CP 72-2086 (15.6%) obtuvieron los registros más altos a los 13 meses de edad de la planta, con valores superiores al control comercial Mex 69-290 (13.3%). Una limitación importante en el ensayo fue la presencia y daño no cuantificado de la enfermedad raya roja en la mayoría de los híbridos evaluados. Y con base en los análisis se concluye que el nuevo hibrido ColMex 05-542 (C1) superó en rendimiento agroindustrial a las variedades comerciales.
Computational analysis of hydrogen bubble formation and dynamics in electrolytic systems using COMSOL
Authors: Debbah Djobeir , Rebai Billel , Fatmi Hakim , Lakhemissi Touam , Tidjani Messas , Mamen Belgacem , Kaghouche Bessem , Aziz Mohamed Walid
Published: December 2024

This study explores the numerical modeling of hydrogen bubble dynamics in electrolytic processes, utilizing COMSOL Multiphysics software. The focus is on the development of precise computational models to simulate the processes of bubble formation, growth, and movement in water electrolysis systems, which are crucial for optimizing hydrogen production. Using 2D axisymmetric modeling, the research applies several interface-capturing techniques, including phase field, level set, and moving mesh methods, to accurately capture the behavior of hydrogen bubbles in various operational conditions. By analyzing these dynamics, the study aims to improve the understanding of bubble-related phenomena in electrolysis, such as formation patterns, bubble size, and the terminal velocities of rising hydrogen bubbles. Additionally, the effects of density differences between hydrogen and water are examined to assess their impact on the overall efficiency of electrolysis. The results indicate that the moving mesh method offers the best performance in accurately modeling bubble dynamics, providing insights that can contribute to the optimization of electrolysis processes for efficient hydrogen production.
Combined effect of removing corner columns and vertical openings in an L-shaped shear wall
Authors: Salem Merabti
Published: December 2024

This study examines several buildings braced with L-shaped shear walls, with and without corner columns. In this case, the L-shaped shear wall consists of two walls, each with vertical rectangular openings. At the same time, three wall thicknesses 15cm, 20 cm, and 25 cm were used in buildings of 4, 7, and 11 stories. A comparison was made between walls with and without columns based on tensile stresses and dynamic shear forces at the base of the buildings. The maximum displacements at the tops of the buildings were also compared between those braced with shear walls with and without corner columns. The results show that the analyzed parameters for the L-shaped shear walls, following the removal of the column, were strongly influenced by both the number of stories and the percentage of openings in the wall. Permissible openings in L-shaped walls without corner columns will be proposed based on the three wall thicknesses and the number of stories examined.
Deep learning-based diabetic retinopathy diagnosis using transfer learning with VGG19 and InceptionV3
Authors: Aiche Ishaq , Brik Youcef , Attallah Bilal
Published: December 2024

Diabetes mellitus is a primary contributor to the development of diabetic retinopathy (DR), a condition characterized by the formation of retinal lesions and progressive vision impairment. Left untreated, DR can culminate in blindness, underscoring the critical importance of early detection and intervention. Despite the absence of a definitive cure for DR, available treatments aim to preserve existing vision. The conventional manual diagnosis of DR through the evaluation of retina fundus images by ophthalmologists is labor-intensive, expensive, and susceptible to human error. As an alternative, computer-aided diagnostic techniques, particularly those based on deep learning, have gained traction for their potential to enhance diagnostic accuracy and reduce errors. Recently, Convolutional Neural Networks (CNNs) have demonstrated exceptional efficacy in medical image analysis due to their capability to extract local image features with high precision. In this study, we propose a multi-level fine-tuned deep learning approach for the classification of diabetic retinopathy using two distinct pre-trained models: VGG19 and InceptionV3. Our findings reveal that the proposed InceptionV3 network outperformed other models as well as the state-of-the-art methods, achieving a classification accuracy of 98.09% in the binary classification of DR images.
Studying the effects of temperature on InGaP/InGaAs/Ge triple junction solar cells using PyAMS Software
Authors: Dhiabi Fathi , Benelmir Okba , Saadoune Achour
Published: December 2024

Multi-junction solar cells, particularly those utilizing III–V semiconductor materials, offer high conversion efficiency by employing multiple P-N junctions with different band gap energies to capture specific wavelengths of sunlight. Among these, the triple-junction photovoltaic cell, comprising Indium Gallium Phosphide (InGaP), Indium Gallium Arsenide (InGaAs), and Germanium (Ge) subcells, stands out for its efficiency. However, temperature variations significantly impact the performance of these subcells, necessitating a comprehensive understanding of their thermal behavior. In this study, we employ PyAMS (Python for Analog and Mixed Signals) software to model and simulate the behavior of InGaP/InGaAs/Ge sub cells under varying temperature conditions. Through comparison with experimental data, we validate our model and analyze key performance parameters, including short circuit current density (Jsc), open circuit voltage (Voc), fill factor (FF), and overall efficiency (η). Our findings reveal minimal changes in short circuit current density with temperature, while the open circuit voltage exhibits a substantial decrease beyond 60°C when exposed to concentrated illumination of 10 Suns, significantly impacting fill factor and efficiency. By elucidating the thermal behavior of three-junction solar cells, our study contributes valuable insights for designing and implementing cooling systems, thereby enhancing the performance and reliability of photovoltaic systems in practical applications.
Investigation of electronic and optical properties of InAsSb/GaAs quantum well solar cell (QWSC)
Authors: Taleb Soufyane , Khaouani Mohamed , Lagraa Ibrahim , Azzououm Ahmed Bahi
Published: December 2024

This paper provides a detailed theoretical analysis of a newly developed InAsSb/GaAs quantum well solar cell (QWSC). The study investigates how critical parameters, including the number and thickness of InAsxSb1-x quantum well (QW) layers, the GaAs barrier width, and operating temperature, affect the cell's electrical and optical characteristics. The performance metrics analyzed include current-voltage density (J-V), power-voltage (P-V), and external quantum efficiency (EQE). Results indicate that at 250 K, an optimal configuration of 20 quantum wells, each with a thickness of 4 nm, combined with a 60 nm barrier width, enhances the solar cell's performance significantly. This design achieves a 35.34% increase in short-circuit current and an 82.56% improvement in efficiency compared to a standard p-i-n solar cell. Additionally, the structure extends the absorption band for low-energy photons from 880 nm to 1000 nm. These findings underscore the potential of the proposed QWSC design in advancing photovoltaic technologies by improving efficiency and expanding spectral absorption capabilities, making it a promising candidate for next-generation solar cell applications.
Relationship between the octane number and the chemical composition of gasoline: study by FTIR spectroscopy
Authors: Moussa Nawel , Bourahla Sarra , Delali Halima , Nemchi Fadela , Belhakem Mostefa
Published: December 2024

The oil refining process undergoes various heat treatments, during which several cuts are obtained, including cuts of gasoline of different chemical composition. Determination of physicochemical properties is essential for our fuel classification. In the present study and by exploiting a rarely used spectroscopic method in the field of production and characterization of fuels, FTIR Fourier transform infrared spectroscopy (a method characterized by its reliability and speed), we identified a correlation between the molecular composition of the fuel (translated by chemical number) and its octane number NO (a quite important parameter for species classification). The results of AR1 aromaticity index show a strong linear relationship between the latter and its NO octane number. In addition, NO also occurs in many modes depending on two indicators DOC (degree of condensation) and AR1. Indeed, the ACL aliphatic chain length index varies depending on the quality of the fuel and precisely on the subsequent fuel production process. Furthermore, this method illustrates the molecular composition of the fuel, the correlation determined between the chemical indices and the octane number offering the possibility of predicting this physico-chemical parameter.
Effect of thermal annealing on the C(V) characteristic of MSOS structure
Authors: Salah Mahi Mohamed , Nasri Abdelfatah
Published: December 2024

In this study, we explore the capacitance C(V) characteristics of metal/polysilicon/silicon oxide/monosilicon (MSPOS) structures. The obtained C(V) curves allow us to analyze the behavior of the capacitance at high frequency as well as the effect of thermal annealing on these characteristics. These reveal particular characteristics of MOS and MOSP structures. The study shows that, under different bias voltages, the obtained curves differ depending on whether the bias is positive or negative. When applying positive voltages, we observe a certain configuration of the curves, while with negative voltages, another configuration appears, always indicating a combination of the C(V) characteristics specific to MOS and MOSP structures. For a substrate doped at a concentration ND = 2.1018 cm-3, we observed a significant variation of the capacitance under the effect of thermal annealing. This thermal treatment, by modifying the crystalline structure of polysilicon, allows the modification of the MOS capacitance. It influences in particular the growth of polysilicon grains as well as the grain boundaries. The results of this study highlight the effect of thermal annealing on polysilicon-based MOS structures. These observations highlight the effect of thermal annealing on the properties of MSPOS structures, by providing a detailed view of how bias voltages and thermal treatment interact to modulate the capacitance characteristics.
Enhancing the mechanical properties and performance of bio-composites reinforced with palm fibers
Authors: Mahmoudi Houdaifa , Bennouna Mohammed Salah , Khalfi Mehdi , Khlif Mohamed
Published: December 2024

In recent years, plant fiber composites have seen growing use in engineering applications and are extensively marketed as industrial goods. In this paper, a novel composite material is developed from palm bark fibers (locally called leaf) and characterized. The study was carried out using long fibers with unidirectional orientation and others randomly oriented. Also, the effect of water immersion on fibers mechanical characteristics was determined, some fibers were immersed in water for a year while others were kept dry. It was found that the mechanical properties of the biocomposite (stress, strain, Young’s modulus, Poisson ratio and shear modulus) increase when the density of fibers increase, they are high for composites with immersed fibers. The findings were promising, the investigated mechanical properties and the light weight of the performed composite allow it to be used in many fields, such as the automotive field, marine and aviation industry, home and office furniture, and other fields. It enables us to minimize palm waste disposal and utilize it in an eco-friendly manner, rather than burning it and exacerbating global warming.
Data-driven approach for the classification of gas turbine faults
Authors: Bouakkaz Messaoud , Bouras Hichem , Fenghour Nadir
Published: December 2024

Gas turbines (GTs) play a crucial role in the production of electricity. Extreme working conditions can lead to deterioration in GTs' performance, resulting in the occurrence of various issues. This study proposes an approach to deal with this issue by combining a layered recurrent neural network (LRNN) with principal component analysis (PCA). This approach aims to reduce the dimensionality of data and computational complexity effectively while enhancing the accuracy of gas turbine fault classification. The methodology outlined consists of two steps. The first step is to apply PCA to the dataset that was collected from the gas turbine. By transforming the data into a lower-dimensional space, PCA helps to eliminate redundant information and improve computational efficiency. Next, LRNN is employed to detect and classify faults in the gas turbine. The LRNN’s structure enables it to capture complex patterns and relationships in the data, which enhances the accuracy of fault classification. This study is based on historical data collected from a gas turbine power station, consisting of 8200 samples of 34 measured variables. The operating parameters contain data such as temperature and pressure. Each data point's relationship to a specific turbine scenario reveals if it is healthy or one of the four faulty scenarios. The results showed that by combining the LRNN with PCA, the gas turbine fault classification achieved good performance in terms of accuracy, precision and neural network model performances, while also showcasing the faster convergence speed of the LRNN when trained on PCA instead of the original dataset.
Deep-RNN based model for short-time forecasting photovoltaic power generation using IoT
Authors: Abdelhamid Nedioui Mohammed , Khechekhouche Ali , Mostefa Kara , Brahim Lejdel , Talal Guia
Published: December 2024

Global warming is one of the most significant issues of the century due to climate change caused by increased carbon emissions resulting from the exploitation of fossil fuels. Consequently, renewable energies are considered an alternative that promotes cleaner production and offers a substantial reduction in carbon emissions. Therefore, accurately forecasting photovoltaic (PV) power generation is crucial for controlling and distributing electrical inventory and ensuring the stability and reliability of power systems. In this paper, we develop a model for forecasting short-term PV power generation based on deep Recurrent Neural Networks (deep-RNNs). To improve efficiency, our model uses weather and PV generation dataset on-site collected  in real-time using IoT technology. Specifically, by leveraging deep-RNN, particularly the long short-term memory network (LSTM) and gated recurrent units (GRU), which excel at capturing long-term dependencies in time series data, this article proposes a combination of LSTM and GRU models to take advantage of both in different weather conditions. The results of the experiments show that the LSTM-GRU model that has been proposed performs better in PV power forecasting than both the LSTM and GRU models together.
Optimal reconfiguration of a distribution network integrated photovoltaic and wind systems using blood-sucking leech optimizer
Authors: Sadaoui Fares , Bouhadouza Boubekeur , Hachemi Ahmed Tidjani
Published: December 2024

This paper presents a planning strategy for integrating renewable distributed generation (DG) units into a distribution network, incorporating network reconfiguration to enhance the network's technical, economic, and environmental performance. Utilizing a novel meta-heuristic algorithm, the Blood-Sucking Leech Optimizer (BSLO), the study addresses a multi-objective optimization problem aimed at determining the optimal placement and sizing of DG units, as well as the most effective network topology. This approach seeks to minimize active power losses, improve voltage profiles, reduce installation costs, and lower greenhouse gas emissions. The model accounts for variable load demands, climatic factors (such as ambient temperature, solar irradiation, and wind speed), and fluctuating energy prices, reflecting realistic operating conditions. Tested on the IEEE 69-bus distribution network, the BSLO algorithm demonstrated rapid convergence to the global optimum by effectively balancing exploration and exploitation phases. Compared to other meta-heuristic methods, such as the Grey Wolf Optimizer, Gorilla Troops Optimizer, Walrus Optimization Algorithm, and Artificial Hummingbird Algorithm, the BSLO consistently achieved superior accuracy and faster convergence, resulting in higher precision and optimization efficiency. The optimal deployment of two PV generators and two wind turbines, combined with selective line switch openings, resulted in an 87.66% reduction in active power losses, a 73.30% decrease in voltage deviation, a 51.91% reduction in overall system costs, and a 62.74% decrease in greenhouse gas emissions compared to the base case.
Study of hygrothermal environment impact on the vibration behavior of thick nanocomposite beams reinforced with multilayer Graphene Nanoplatelet
Authors: Guenifed Abdelhalim Farouk , Bensaid Ismail , Saimi Ahmed
Published: December 2024

This paper aims to investigate the free vibration of a multilayer piezo-electric beam strengthened with functionally graded graphene platelets (FG-GPLRC) and subjected to a consistent increase in temperature and humid external loads. Graphene platelets (GPLs) are supposed to be dispersed either uniformly or layerwise form in the polymeric matrix, with a variety of patterns configurations taken into consideration. The rule of mixing is employed to evaluate Poisson's ratio and mass density features. In order to estimate the efficient Young's modulus, the modifier Halpin-Tsai model has been employed. The whole system of governing equations for motion were achieved by exploiting Hamilton’s concept based on Timoshenko beam theory (TBT). After that, these equations were solved using the Navier analytical solution-based Fourier series with high accuracy. Next, to examine the effects of several elements, including graphene weight percentage and their distribution shape, length by thickness ratio, externally provided thermal-humidity fields, on the dynamic of nanocomposite reinforced beams.
Effect of sewing on the mechanical characteristics of composite sandwich materials
Authors: Missoum Lakhdar , Abdeldjebar Rabiâ , Mouddene Bachir , Labbaci Boudjemaa , Lahmar Lahbib , Idder Abdelghani , Frih Ahmed
Published: December 2024

Sandwich composite are used in numerous structural applications, with demonstrated weight savings over conventional metals and solid composite materials. The increasing use of sandwich composites in defense structures, particularly those which may be exposed to shock loading, demands for a thorough understanding of their response to suc highly transient loadings. The present work is a comparative study of the mechanical characteristics of the presence of reinforced transverse seams sewn at an angle of 45 ° and without seams on the Behavior of sandwich structures subjected to mechanical stresses. The experimental results show an improvement in performance in terms of the energy required for crack initiation and propagation. In addition, the presence of the seams results in atypical behavior that results in a series of characteristic damage and, on the other hand, the seamless structure exhibits a delamination phenomenon. Finally, a comparison of the analytical results with the experiment is carried out.
Development of a DC motor control system by Xilinx system generator
Authors: Salah Mahi Mohamed , Nasri Abdelfatah
Published: December 2024

This study introduces a numerical approach utilizing a Xilinx System Generator to develop a DC motor control system, showcasing a significant ability to regulate and enhance motor speed. We developed the proposed system in the MATLAB/SIMULINK environment, utilizing a collection of blocks generated by the Xilinx System Generator. We developed and simulated a proportional-integral (PI) controller and pulse width modulation (PWM). Subsequent to simulation, we can transfer the HDL code from the Xilinx System Generator block to a Field Programmable Gate Array (FPGA) board using Vivado. The system can produce accelerated PWM signals, thereby enhancing the control of the DC motor. Because of the FPGA board's superior processing capabilities, it was crucial to design and simulate the control system using Sysgen. We proposed the FPGA board to address this issue, facilitating the efficient and rapid execution of the control signals. This work represents a significant advancement in the enhancement of DC motor control systems. It shows how well Matlab/SIMULINK and Xilinx System Generator can work together to make PI and PWM controllers. These findings present compelling possibilities for future applications in power electronics and industrial automation.
Numerical solution of fractional integro-differential equations using ultraspherical polynomials
Authors: Dilmi Mustapha
Published: December 2024

In this work we will discuss the fractional integro-differential (FIDE) equations of the Fredholm and Volterra type, where we begin by presenting some special concepts about the definition of the fractional integral and the fractional derivative of Riemann-Liouville and Caputo, while addressing the Gegenbauer (Ultraspherical) polynomials and its special differential equation, mentioning the primary polynomials, in addition to presenting some theories such as the Krasnoselskii fixed point theorem, which guarantees the existence and uniqueness of solutions to fractional integro-differential equations (FIDE). To find the approximate solution of fractional integro-differential equations, we relied on orthogonal polynomials of the ultraspherical type (Gegenbauer), which have a great advantage in solving integral equations and integro-differential equations. These polynomials, with the application of fractional Caputo derivatives with the series method, in addition to fixing the values and changing the degree of the polynomials used with fixing the values of , lead to the equation becoming a linear algebraic system of equations. By solving this linear system, the approximate solution is obtained in the form of a Gegenbauer (ultraspherical) series. The change in the degree of the polynomials used will inevitably show the effectiveness and speed of the method through the examples that will be presented, where the approximate solutions obtained are compared with the exact solutions.
Impact of several seismic scenarios on the dynamic responses of self-steady structures of different heights
Authors: Abdellah Boudina , Benyamina Smain , Salem Merabti , Guettiche Abdelhaq , Chadouli Rachid
Published: December 2024

Seismic load resistance is a crucial aspect of safety for self-stabilizing reinforced concrete (RC) structures, which are particularly vulnerable to damage caused by destructive earthquakes. These structures, often used in high seismic risk areas, require special attention to ensure their stability and durability under significant dynamic demands. This study focuses on the dynamic responses of low- and medium-rise RC buildings subjected to various seismic response spectra, including those from historic earthquakes such as El-Centro, Kobe, and Boumerdès (recorded at the Dar El-Beida station in Algeria), as well as the spectrum provided by the Algerian seismic code RPA 99/version 2003. The analysis examines three structures with 1, 3, and 6 stories to evaluate the influence of these spectra on key parameters: shear forces, maximum displacements, the ratio between dynamic and static base shear, and overturning moments. These investigations contribute to a deeper understanding of structural behavior under diverse seismic scenarios. The findings emphasize the need to complement the RPA99/v2003 code with studies based on real earthquakes to better address various structural periods and ensure optimal seismic design practices.
A swelling ‎pressure prediction model based on boruta feature selection mila region
Authors: Lekouara Laid , Bahloul Ouassila , Tebbi Fatima Zohra , Rabehi Rachid
Published: December 2024

Swelling soils are problematic soil types that are prevalent across the globe. It ‎was noted that the costs ‎associated with damages caused by distended soils are ‎relatively high and this issue cannot be ignored. ‎Swelling pressure is a ‎fundamental parameter in the prediction of the swelling capacity of expansive ‎soils. In ‎machine learning, feature selection methods allow us to reduce computation time, enhance prediction accuracy, ‎and gain a deeper comprehension of the ‎data. In this paper, the Boruta algorithm is used to remove iteratively ‎the features ‎which are proved by a statistical test to be less relevant from 15 geotechnical ‎variables to predict ‎swelling pressure. The remaining variables are ‎inputs of a neural networks model (ANN). Results based on R ‎squared ‎determination coefficient, RMSE, MAPE, MSE, and RRSE show an ‎improvement of the neural model ‎by considering selected features by the Boruta ‎algorithm compared to the one without feature selection.‎ This approach highlights the effectiveness of feature selection in enhancing machine learning models for geotechnical applications.
Simulating groundwater flow and contaminant transport
Authors: Chetti Ahmed
Published: December 2024

This study presents a comprehensive simulation of groundwater flow and contaminant transport using advanced numerical modeling techniques. The research focuses on understanding the complex interactions between groundwater movement and pollutant dispersion across various geological settings. By applying the governing equations of fluid dynamics and solute transport, the model incorporates essential parameters such as hydraulic conductivity, porosity, and dispersivity, enabling the accurate prediction of contaminant behavior over time. Simulations were carried out under a range of scenarios to assess the effects of differing recharge rates and contaminant source configurations on groundwater quality. The results provide critical insights into the mechanisms governing contaminant migration, highlighting the importance of soil properties, boundary conditions, and flow dynamics in influencing transport behavior. Moreover, the study demonstrates how variations in geological features and pollutant sources can significantly alter contaminant dispersal patterns. This work contributes to the development of robust groundwater management strategies and emphasizes the role of simulation tools in assessing environmental risks tied to groundwater contamination.
Impact of replacing dune sand with ground date palm on the performance of cement mortar: dispersion analysis
Authors: Abdelhafid Boukellouda , Houssam Slimanou , Toufik Kheffache , Nasser Chelouah
Published: December 2024

This study investigates the effects of substituting dune sand with ground date palm at varying proportions of 0.5% to 2% on the mechanical and thermal properties of cement mortar. The findings indicate that this substitution enhances both the thermal and mechanical performance of the mortar, thereby improving its insulation capabilities. Notably, significant variations in compressive strength are observed, particularly at an early age of 7 days, with a reduction in variability as the mortar matures. Additionally, a high dispersion in measurement results is recorded. The application of the Weibull model facilitates the quantification of this variability and the assessment of material reliability, emphasizing the necessity of considering these fluctuations in the evaluation of sustainable construction materials. This approach not only underscores the potential of utilizing ecological resources but also paves the way for innovative practices in the construction sector. Overall, the study highlights the promising implications of incorporating alternative materials for enhancing the performance and sustainability of cement-based composites.
Utilizações e aplicações de processos de automação na produção e distribuição de energias renováveis
Authors: Agostino Cássio Bizan , Oliveira Guilherme Pereira de , Silva Mateus Tonus Fernandes , Santos Matheus Carricondo dos , Germolhato Vinicius de Lima , Filipak Lucas Rafael
Published: December 2024

O artigo analisa a importância das tecnologias e inovações para o progresso da geração de energia sustentável, destacando a importância dessas ferramentas para maximizar seu potencial. Os autores citam diversos exemplos de tecnologias atualmente em uso, que oferecem oportunidades de melhoria, bem como propõem métodos inovadores que podem ser aplicados. A avaliação é baseada em estudos realizados em artigos científicos e fontes especializadas, proporcionando uma perspectiva abrangente e bem fundamentada. Os resultados indicam que a implementação dessas inovações pode aumentar significativamente a eficácia da produção de energia renovável. A longo prazo, essas ações mostram potencial para substituir as fontes de energia elétricas convencionais, que ainda são predominantes no cenário global. Nesse cenário, a pesquisa enfatiza a importância de investir consistentemente no avanço tecnológico como uma forma de atender à crescente demanda por energia, conservar os recursos naturais e reduzir os impactos ambientais. Assim, a conexão entre o progresso sustentável e a inovação na ciência é enfatizada.
Natural light and visual comfort in algerian collective housing: insights from a post-occupancy evaluation
Authors: Salma Makhlouf , Mohammed Cheraitia
Published: December 2024

Natural light is often considered one of the most effective ways to reduce energy consumption in buildings, while ensuring the comfort and well-being of occupants and improving the quality of the indoor environment. Natural light allows people to gather visual information about their environment, making it easier to perform visual tasks safely and comfortably. In this context, this study conducted a post-occupancy evaluation (POE) to assess occupants' satisfaction with the quality of the lighting environment in their living spaces. The ultimate goal of this study was to assess occupants' satisfaction with the lighting environment, and to evaluate their interaction with natural light in their inhabited spaces. The results show that the majority of occupants express dissatisfaction with natural light, which leads them to rely mainly on artificial lighting. Contributing factors are the size of windows, the limited quality of external views and constraints such as the presence of trees and the need for privacy. This occupant-centred research is consistent with an emerging paradigm that emphasises sustainability, ecology, adaptability and the continuous improvement of occupied spaces. The study highlights the need to rethink residential design to optimise daylighting, with the aim of improving occupant comfort and reducing reliance on artificial lighting.
Groundwater potential zones delineation in Oued Zdin basin using GIS, RS and hierarchical analysis process
Authors: Elkhalil Taibi Brahim , Chakali Ahmed Nadjib , Chichoune Malika , Roukh Zin El Abidin , Setti M'Hammed , Lensari Ahmed Mokhtar , Sardou Abdelhadi
Published: December 2024

Water scarcity poses a significant challenge, particularly in regions with limited rainfall such as Algeria, where groundwater plays a crucial role in supporting both daily life and economic activities. This research aims to evaluate the groundwater potential in the Oued Zdin basin in northern Algeria by utilizing advanced geomatics methods, particularly the Analytic Hierarchy Process (AHP). Through the integration of Remote Sensing (RS) and Geographic Information Systems (GIS), the study incorporates multiple datasets, including rainfall patterns, topography, geology, drainage networks, land use, and hydrological data to assess areas with high groundwater potential. By applying AHP, the study assigns relative importance to these factors, creating a groundwater potential map that classifies the region into very high, high, low, and poor potential zones. The results indicate that 7% of the basin has very high potential for groundwater recharge, 33% has high potential, while 56% is categorized as low potential, and 4% falls under poor potential. The accuracy of the results is validated through comparison with existing well data, which aligns with the identified high-potential zones. The research demonstrates that combining GIS, RS, and AHP is an effective approach for mapping groundwater potential, offering valuable insights for sustainable water resource management in areas experiencing water scarcity. This methodology presents a scalable model that can be applied to similar regions facing groundwater challenges.
Assessing groundwater quality in northern Algeria using geochemical and index approaches
Authors: Samira Zamiche , Azzeddine Reghais
Published: December 2024

This study aims to enhance understanding of the hydrogeochemical behavior of the aquifer by utilizing a water quality index in conjunction with hydrogeochemical analysis. The focus is on identifying the factors that control its chemical composition and influence its suitability for domestic use. Water samples were collected from twenty boreholes and analyzed for major ions, pH, and electrical conductivity during the dry and wet seasons. During the wet season, the water quality index indicated generally good chemical quality, with an average score of 96.12. This was categorized as Excellent (5%), Good (55%), and Poor (40%). In contrast, the dry season showed a slight deterioration in water quality, with an average score of 102.57, reflecting a distribution of Excellent (10%), Good (35%), and Poor (55%). Boreholes located in the western and central parts of the aquifer exhibited better chemical quality compared to those in the eastern part, which are considered unsuitable for human consumption due to their poor quality. The mineralization process is primarily attributed to the dissolution of dolomitic and evaporitic formations, resulting in various chemical facies. The dominant facies identified were calcium and magnesium bicarbonate (85%), followed by sodium bicarbonate (7.5%), calcium sulfate (5%), and sodium chloride (2.5%). This study underscores the critical need for sustainable management practices to protect groundwater quality in the Mitidja plain amid ongoing environmental challenges. Addressing these issues is essential for ensuring safe drinking water availability for local communities.
Bearing faults diagnostic in permanent magnets synchronous motor
Authors: Ferradj Mohammed , Bouchetata Nadir , Benouzza Noureddine
Published: December 2024

With the widespread use of MSAPs in fields such as robotics, automotive, aerospace, medical equipment, renewable energy, etc., the preventive diagnosis of faults affecting these types of motors has become essential to avoid unplanned shutdowns that could lead to considerable financial losses. Even though the permanent magnet synchronous motor is well known for its robustness and efficiency, however it suffers from electrical or mechanical faults. Spectral analysis has become a very important tool for the detection of these faults and we will use this method to determine the frequencies characterizing the fault in MSAPs. It is important to note that the exact distribution of failures can vary depending on the specific motor and its operating conditions. The characteristic frequencies of the bearing failure are a function of the geometric parameters of the bearing and the motor rotation frequency. Unlike the induction motor, the rotation frequency of the PMSM is fixe, which makes it easier to locate the characteristic frequencies of the bearing fault. This work presents the application of spectral analysis for the diagnosis of the bearing defect and more specifically, that of the outer ring. The experimental results obtained show the interest and effectiveness of spectral analysis for the detection of bearing defects.
Comparative analysis of groundwater vulnerability in the Oued Agrioun watershed, northeast Algeria: DRASTIC, GOD, and SINTACS approaches with ROC AUC validation
Authors: Idir Sofiane , Khemmoudj Kaddour , Bouali Horiya
Published: December 2024

This study focuses on the Oued Agrioun watershed, a semi-arid region in northeastern Algeria, with the aim of mapping the vulnerability to pollution in order to improve groundwater management. To achieve this, a comprehensive database was compiled from a variety of sources, including geological, hydrogeological, geomorphological, and climatic data, as well as satellite imagery. Three different methods are applied using Geographic Information Systems (GIS): DRASTIC, SINTACS, and GOD. The GOD and SINTACS methods resulted in four main classes of vulnerability: low, medium, high, and very high. In contrast, the DRASTIC model identified three classes: low (2.04%), medium (45.04%) and high (52.93%). ROC curve validation using nitrate concentrations indicates that the SINTACS and GOD methods provide better predictive accuracy than DRASTIC, suggesting that a combination of these approaches provides a more robust assessment of the vulnerability of subterranean waters. This study provides valuable data to guide future policy decisions aimed at protecting groundwater resources in the Oued Agrioun watershed.This study focuses on the Oued Agrioun watershed, a semi-arid region in northeastern Algeria, with the aim of mapping vulnerability to pollution in order to improve groundwater management. To achieve this, a comprehensive database was compiled from a variety of sources, including geological, hydrogeological, geomorphological, and climatic data, as well as satellite imagery. Three different methods are applied using Geographic Information Systems (GIS): DRASTIC, SINTACS, and GOD. The GOD and SINTACS methods resulted in four main classes of vulnerability: low, medium, high, and very high. In contrast, the DRASTIC model identified three classes: low (2.04%), medium (45.04%), and high (52.93%).  Validation by ROC curve using nitrate concentrations indicates that the SINTACS and GOD methods provide better predictive accuracy than DRASTIC, suggesting that a combination of these approaches provides a more robust assessment of the vulnerability of groundwater resources. This study provides valuable data to guide future policy decisions aimed at protecting groundwater resources in the Oued Agrioun watershed.
Computational characterization of structural, electronic, elastic and magnetic properties of double half-Heusler alloy Fe2MnCoGe2
Authors: Cheriet Abderrahmane , Boudia Keltouma , Hamdi Reguia , Sofrani Fatima , Khelfaoui Friha
Published: December 2024

Using the augmented plane wave method based on density functional theory and implemented in the WIEN2k code, we study the structural, elastic, electronic, and magnetic properties of FeCoGe, FeMnGe, and the parent half-Heusler (HH) alloys, as well as their derivative Fe2CoMnGe2 double half-Heusler (DHH) compounds. By analysing the stability of the HH structure in both the ferromagnetic and non-magnetic phases of FeCoGe and FeMnGe alloys, we determine that the latter phase of type II and type III FeCoGe and FeMnGe arrangements is the most stable. The Fe2CoMnGe2 DHH alloy, which is derived from the magnetic and structural ground states of FeCoGe and FeMnGe HH alloys, is found to be most stable in the type II arrangement. Among these materials, the FeMnGe HH alloy demonstrates greater resistance to reversible deformation by shear strain, indicated by its higher Young's modulus, while FeCoGe shows the best overall resistance to deformation. The electronic structures of FeCoGe, FeMnGe, and Fe2CoMnGe2 compounds reveal metallic behavior in the spin-up channel and semiconducting behavior in the spin-down channel. Their half-metallic gaps are 0.437 (0.181) eV, 0.543 (0.224) eV, and 0.353 (0.035) eV, respectively, with Fe2CoMnGe2 DHH retaining half-metallicity despite a lower band gap. The total magnetic moments for FeCoGe, FeMnGe, and Fe2CoMnGe2 are found to be 3, 1, and 4 μB, respectively. Given their half-metallic properties, these alloys show potential as candidates for spintronic applications.
Comparative performance analysis of different controllers for five-phase induction motor drive
Authors: Bessaad Taieb , Benbouali Abderrahmen , Taleb Rachid , Bouchaib Ali
Published: December 2024

In many situations where it is necessary to reduce the total power consumption per phase while maintaining high overall system dependability, multiphase machines are widely acknowledged as an appealing substitute for traditional three-phase machines. Recent years have seen an acceleration in the field's advances, and a significant body of knowledge has been produced. Fuzzy controllers are an effective method for managing complex processes. However, its characteristic changes across wide variations in parameters' ranges. Our research addresses this problem by using a fuzzy adaptive controller. To improve vector controlled induction Motor drive robustness and guarantee that the five-phase asynchronous machine operates consistently even in the face of disruptions (abrupt changes in the load, rotor resistance variations, and rotor inertia variations, and other factors.), control behavior model (CBM) is applied. The simulation's findings demonstrate that fuzzy adaptive control performs well and is more reliable (Rotor resistance Rr = 200%Rrn, Lm = 0.8*Lmn and J = 200%Jn) when compared to the conventional fuzzy Logic Control and as a classic controller.