All Sciences Abstracts

Determination of the content of heavy metals in residues released from manufacturing procceses of Ni-Cd batteries by atomic absorption spectrometry

Milihate Aliu — 2023-08-12

The residues discharged from industry contain a bulk amount of liquid and solid wastes with substantial quantities of toxic heavy metals. These wastes from the industries is not only polluting the water but also the sediment in the adjunct river and ultimately affecting the aquatic life. The objectiv of this study is to evaluate the heavy metal (i.e., Cd, Cu, Co, Fe, Pb, Mn, Ni and Zn) concentration in solid residue samples released from the manufacturing processes of Ni- Cd batteries. For this purpose samples were digested with aqua regia and analysed for metals by the atomic absorption spectrophotometer (AAS). This study revealed that the metal concentration for Cd, Cu, Co, Fe, Pb, Mn, Ni and Zn were found to be 4200-16500 mg/kg, 140-850 mg/kg, 100-629 mg/kg, 170-630 mg/kg, 150 -920 mg/kg, 30-250 mg/kg, 6400-35000 mg/kg and 110-800 mg/kg respectively. The concentration of heavy metals in residues samples decreased with the following order: Ni > Cd > Pb > Cu > Zn > Co > Fe > Mn. The results of this study provide support for the prevention of human health risks and the control of soil heavy metal pollution. In the industrial area, waste management is one of the difficult and most challenging issue. Sustainable solution is the best fit for dealing with the waste management, because when discharged on the land as well as dumped into the surface water, which ultimately lead to contamination due to accumulation of toxic metals and resultes in a series of problems in living beings, because they cannot be completely degraded.

Econometric study on the impact of digital technologies on GDP in Algeria

Laid Bouallaga — 2023-08-12

The aims of this study are the concept of digital impact economy technologies on the gross domestic product in Algeria in the last twenty years. We link our study to using of the directed error correction model (VECM). This study concluded that there is a single relationship of co-integration in the long term between the impact of digital economy technologies and human resource management through the effect of both fixed-line subscribers and the number of telephone subscribers in the gross domestic product. Results can reach that the contribution of digital economy technologies is for the increased economic growth.

Structural, electronic and magnetic properties RbRuO4 ; A theoretical study

Ahmed Memdouh Younsi — 2023-08-12

Ruthenate oxide materials are significant compounds in magnetic technologies because they have an important element in transition metals. Herein we make a density functional theory (DFT) investigation to study the structural, electronic, and magnetic properties of rubidium ruthenium oxide RbRuO4. Gradient generalized approximation, GGA-PBE, and GGA-WC within Cohen-Sham equations were implemented in the CASTEP code and used in our calculations. Plane-wave pseudo potential method was used to simplify the electrons-Ions interactions. Results showed a ferromagnetic ground state of the material studied here. For the structural properties, RbRuO₄ crystallizes in the orthorhombic structure (Pnma space group). In calculations of lattice parameters, we found a volume of primitive cells between 477 A°3 and 482 A°3 and bulk modulus between 82,12 GPa and 86,65 GPa. Finally, the band structure energies showed that RbRuO₄ had a metallic nature.

Re-entrant honey comb meta-materials configuration and its application in buckling restrained braces: A numerical study

Hamza Basri — 2023-08-12

Evens came up with the term "auxetics" in the 1990s. It is used to describe materials and structures with a negative Poisson's ratio (NPR) that shrink or grow in an unusual way when put under uniaxial compression and tension. These materials have different mechanical properties, such as a high shear bearing capacity, resistance to breaking, ability to absorb energy, and resistance to falling apart. But because auxetics often have holes in them, they are often much less stiff than solid structures. Although these shapes should function well under both static and dynamic loading conditions as energy-absorbing components, the behavior of auxetic metamaterials has been rarely investigated in the field of seismic protection. In this paper, a parametric study was conducted using a metallic damper, such as a buckling restrained brace (BRB) equipped with an auxetic steel core and re-entrant honeycomb cells, with the aim of exploring the performance dissipative of these types of metamaterials. The proposed model of BRB with an auxetic core was modeled and analyzed under cyclic loading using a finite element method. The repetitive re-entrant honeycomb cells are controlled by four parameters: hole ratio (porosity), section weakening rate, concave angle, and angle chamfering radius. Hysteresis behavior and vos mises distribution under large deformation were extracted and discussed. As seen in the results, it was found that the local buckling of the steel core could be improved by the auxetic behavior. The values of 75° angle and 0.5 corner chamfer radius were selected to have the largest effect on the hysteresis curves of BRB model specimens when the axial strain exceeds 1.2%. The dissipative performance of these metamaterials under cyclic tests provides a good basis for further investigations about applying auxetic structures in the field of protective structures.

From Pixels to Bits: A State of the Art of Deep Learning Approaches for Natural Image Compression

Nour El Houda Bourai — 2023-08-12

With the ever-increasing volume of digital imagery and the growing demand for efficient storage and transmission, image compression has become a crucial aspect of multimedia processing. In recent years, deep learning models have emerged as powerful tools for a wide range of computer vision tasks, including image compression. This research article presents a comprehensive state-of-the-art review of natural image compression techniques leveraging deep learning architectures.

This research article presents a state-of-the-art review of natural image compression using deep learning models. With the exponential growth of digital imagery, efficient compression techniques are essential. Deep learning, particularly Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Autoencoders, has shown promise in this area. The paper explores various architectures, discussing their adaptation to image compression tasks. Both lossless and lossy compression approaches are surveyed, considering the trade-off between compression ratios and visual quality. The article identifies challenges, such as computational complexity, scalability, and real-world applications, while suggesting future directions.

Enhancing the water quality of treated effluents from Wastewater Treatment Plants by mitigating emerging pollutants

Nadia OUASFI — 2023-08-13

With the increasing demand for water resources and growing environmental concerns, reclaimed water from wastewater treatment plants has gained prominence as an alternative water source for various non-potable applications. However, one of the major challenges in utilizing reclaimed water is the presence of emerging pollutants, which encompass a wide range of chemical and biological compounds not routinely monitored or regulated in conventional wastewater treatment processes.

This study aims to address the pressing issue of emerging pollutants in reclaimed water by exploring and evaluating innovative strategies for their removal.

Through a comprehensive literature review, cutting-edge technologies and advanced treatment methods will be assessed, focusing on their efficiency in eliminating a spectrum of emerging pollutants from treated effluents.

Moreover, this research seeks to shed light on the potential risks associated with these emerging contaminants and their impact on human health and the environment.

Ultimately, the finding from this study will contribute to the development of sustainable and effective approaches for enhancing the water quality of reclaimed water from wastewater treatment plants.by mitigating the presence of emerging pollutants, stakeholders can confidently promote the safe and reliable use of reclaimed water, thus fostering a more resilient and environmentally conscious water management paradigm.

A comparison of methods of analysis of heavy metals in soil samples of Mitrovica environment, Republic of Kosovo

Milihate Aliu — 2023-08-13

In this work, the distribution of heavy metals in surface soil samples (0-5 cm) from the Mitrovica Region, was studied. The investigated region (301.5 km2) is covered by a sampling grid of 1.4×1.4 km. In total 156 soil samples from 149 locations were collected. Digestion methods, including Aqua Regia Digestion (mixture of HNO3 and HCl and water at 95ºC- the 1DX1 method) and acid digestion: use of concentrated acids such as hydrofluoric acid (HF), hydrochloric acid (HCl), nitric acid (HNO3) and perchloric acid (HClO4) (ISO 14869- 1:2001(E) method), are used to prepare samples for spectroscopic analysis. High-sensitivity spectroscopy techniques such as inductively coupled plasma emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) were applied to measure the concentration of Ni and Co in soil samples. Data analysis and construction of the map were performed using the Statistica (ver. 9), AutoDesk Map (ver. 2008) and Surfer (ver. 9) software. It was found that the average content of Ni and Co in the surface soil for the entire study area is 96 mg/kg (with a range of 7.6-2600 mg/kg) and 22 mg/kg (with a range of 2.7-1600 mg/kg), respectively. The obtained average and median values obtained by ICP-MS are very similar with those obtained by ICP-AES. Namely, the correlation factor for Co and Ni between the results from both methods are 0.92 (for normal distribution), 0.93 (for logarithmic) and 0.94 (for rank), and 0.86 (for normal distribution), 0.94 (for logarithmic) and 0.96 (for rank), respectively. The obtained results show that the high concentrations of the Ni and Co in the surface soil samples may originate from similar sources and their distribution follows the lithology of the study area.

Real-Time Obstacle Detection in 3D Environments Using P300-based EEG Signals

Walid GUETTALA — 2023-09-02

The research introduces a new system for obstacle detection in 3D spaces using P300 eventrelated potential from EEG signals. By incorporating brain-computer interfaces with robotics, this method enhances intuitive human-robot interaction. The system utilizes the Common Spatial Pattern (CSP) algorithm to identify distinct EEG features, improving the safety and adaptability of robots. Tested in a custom 3D simulation environment, the system demonstrated high accuracy and real-time performance, indicating its potential for applications like autonomous navigation and assistive robotics. This study represents a significant step in robotics and brain-computer interfaces, fostering a more natural user interface and propelling advancements in the field.

Leveraging Deep Learning for Speciality Recommendation in Higher Education: An AI-Driven Approach

Abdelhakim NAHILI — 2023-09-02

In an increasingly complex educational environment, choosing a concentration that aligns with one's talents and interests for academic success is essential. This study introduces a novel recommender system based on deep learning that assists students in selecting their Computer Science master's concentration. Utilizing a robust dataset of student performance from the second to the fifth year, the system optimizes data quality through extensive data preprocessing and feature engineering. The model captures intricate relationships between performance in specific modules and success in chosen specialties by employing cutting-edge deep learning techniques. The model was subjected to systematic training, optimization, and evaluation, resulting in impressive precision and recall metrics. The initial implementation of the model has proven effective in providing accurate and personalized recommendations for specializations, thereby facilitating students' decision-making process. Future research directions include expanding the system's predictive capabilities to encompass additional aspects of academic success. This study highlights the potential of deep learning for individualizing educational guidance and enhancing student achievement in higher education.

Predictive Modeling of Acrophobia Severity Using EEG Data

Dounia CHEBANA — 2023-09-02

This study draws upon EEG data collected from four environments from acrophobic patients and uses advanced machine learning algorithms to create a predictive model. It has revealed distinct EEG patterns correlated with severity of acrophobia, leading to an innovative EEG signature associated with this condition and thus potentially aiding early diagnosis and risk evaluation. Furthermore, this research investigates how different environments influence acrophobia symptoms, which could help shape tailored treatment strategies. This work makes strides toward using AI in the assessment and treatment of acrophobia, opening up promising avenues for data-driven personalized therapy plans. It brings value to fields like clinical psychology and neuroinformatics as well as underlining the necessity of further testing on larger, diverse populations.

A study of the effect of changing exchange rates on trade (balance of payments) in Algeria

Laid Bouallaga — 2023-09-02

This study aims to highlight the impact of exchange rate changes on trade in Algeria as an influencing variable in the balance of payments through annual data for the period (2001-2018), and trade is considered a dependent variable while exchange rates represent the dollar and the euro for the Algerian dinar as two independent variables The study concluded that there is no relationship between these two variables due to the presence of other independent variables that were not included in this model, and the short study periods. In addition to the nature of the Algerian economy which depends on one economy, which is the export of hydrocarbons (rentier economy).

A theoretical study of Structural and electronic properties of Al0,125B0,125Ga0,75N; optoelectronic applications

Ahmed Memdouh Younsi — 2023-09-02

Semiconductor materials are highly valued in the electronics industry. Gallium nitride (GaN) from the family of these compounds is particularly important due to its use in various applications, including P-N junction diodes, transistors, and laser components. Our study focused on the effect of doping GaN with aluminum and boron simultaneously, using density functional theory (DFT) calculations with the Gradient Generalized Approximation (GGA-PBE) implemented in the CASTEP code. Our results showed that the volume of the primitive cell within the hexagonal wurtzite structure was 44.62A3 , and a bulk modulus of 163.07 GPa. In analyzing of the band structure, we found that the energy gap of Al0,125B0,125Ga0,75N was 3.75 eV, higher than the band gap of GaN, which was 3.40 eV.

Evaluation of self-compacting concretes (SCC) made with brick powder using non-destructive and destructive tests

Rachid RABEHI — 2023-09-02

The main goal of this study is to see if it is possible to test the physico-mechanical behavior of self-compacting concrete (SCC) with brick powder as a partial replacement for cement using nondestructive and destructive methods. In fact, there were seven different levels of brick powder substitution in this work, namely 5, 10, 15, 20, 25, 30, and 35% by weight, with 3% superplasticizer. The properties in the fresh state and the physico-mechanical properties in the hardened state were studied. In addition, a non-destructive ultrasonic pulse velocity test method evaluated the compressive strength of SCC. Similarly, the relationship between ultrasonic velocity and concrete compressive strength was also estimated after 7, 14, 28, 90, and 180 days of curing. The results of the study indicated that the values of compressive strength and ultrasonic pulse velocity were very low for all levels of substitution at the beginning of treatment, and over the long term the values increased; the highest values were obtained at the value of 20% of the substitution. In the end, a linear relationship was found between the speed of the ultrasound pulse and the compressive strength for all the SCCs that had different amounts of brick powder instead of cement.

Modeling and simulating direct competition among three microorganisms for two supplementary nutrients

Zerrouak Sadem — 2023-09-02

In our study, we investigated direct competition among three microorganisms for two supplementary nutrients. This investigation was translated into a mathematical model using a fivedimensional equation. We validated the model's stability and identified equilibrium points through MATLAB simulations. The main finding was competitive exclusion, wherein one or both competitors are excluded during direct competition for the supplementary nutrients. Additionally, the study explored the intricate network of interactions resulting from heightened competition for limited resources. By examining various aspects of this competition, the research aimed to uncover the underlying mechanisms shaping the fate of each participant. The experimental setup allowed for comprehensive analysis of competition intricacies. The findings were supported by rigorous stability analysis, clarifying potential outcomes. MATLAB simulations aimed to replicate real-world scenarios and predict competition dynamics over time. The key insight was the concept of competitive exclusion, where intensified competitive pressures lead to the exclusion of one or both microorganisms from the competition. This underscores the pivotal role of resource availability and competitive strategies in shaping the environmental landscape.

The Economic Implications of Geopolitical Tensions Between Russia and Ukraine on the Global Economy

Thameur Oussama — 2023-09-02

This study aims to provide a comprehensive analysis of the implications of the emerging conflict between Russia and Ukraine on the global economy. A multidimensional methodology was adopted, including both quantitative and qualitative analysis of potential consequences and their impact on the economy . The study yielded key findings that indicate that geopolitical tensions between Russia and Ukraine have negatively impacted the global economy through increased volatility in energy and natural resource markets. These repercussions have also led to a decline in confidence in global financial markets and a slowdown in economic growth.. Moreover, developing countries have been significantly affected by the geopolitical tensions, resulting in decreased trade and investment flows to these nations. This has led to economic growth deceleration in these regions and added pressure on fragile economic systems. Based on the economic analysis, the study emphasizes the importance of adopting policies to mitigate the impact of this war and promoting international cooperation to address the economic challenges arising from this conflict. It is essential for international entities to work towards enhancing economic stability and encouraging dialogue among concerned parties. In conclusion, the study underscores the profound repercussions of the war between Russia and Ukraine on the global economy, urging effective international collaboration to address the challenges and maintain stability in the global economic system.

Co-Simulation of Sensor-less Predictive Control of a BLDC Motor using Sliding Mode Observer

Dehmeche Ibrahim — 2023-09-02

This paper presents a novel approach for achieving sensor-less predictive control of a Brushless Direct Current (BLDC) motor using a Sliding Mode Observer (SMO). The increasing demand for efficient and reliable motor control systems has motivated the development of sensor-less techniques that eliminate the need for additional hardware sensors. The proposed method leverages the sliding mode observer's ability to estimate rotor position and speed, enabling the implementation of predictive control strategies. The predictive control scheme optimizes motor performance by anticipating future states and applying control actions accordingly. The paper outlines the theoretical foundation of the sensor-less predictive control strategy, detailing the formulation of the sliding mode observer and its integration into the predictive control framework. The estimation accuracy of the SMO is assessed through simulation studies validations, demonstrating its effectiveness in accurately estimating rotor position and speed under varying operating conditions. In conclusion, the proposed approach offers a viable solution for achieving sensor-less predictive control of BLDC motors, paving the way for more efficient and cost-effective motor control systems in various applications, ranging from industrial automation to electric vehicles [1-3].