Communications Internationales

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    Synthesis, Characterization, and in Silico ADMET Evaluation of Transition Metal Complexes Based on Ortho-Phenylenediamine and Its Derivatives
    (ISRES, 2025) Kichou, Noura; Guechtouli, Nabila; Taferguennit, Manel; Ighilahriz, Karima
    A series of cobalt (II), nickel (II), and zinc(II) complexes were synthesized using orthophenylenediamine and its two substituted derivatives (methyl- and nitro-ortho-phenylenediamine) as ligands. These complexes were isolated and characterized using various analytical techniques, including Elemental analysis, infrared (IR) and UV-Visible spectroscopy, gravimetry, and conductimetry. Conductimetric analysis revealed that all the complexes exhibit a non-electrolytic behavior in solution, indicating the absence of free ions in the medium. IR spectroscopic studies allowed the identification of the coordination modes of the ligands to the metal centers. Comparison of the IR spectra of the complexes with those of the free ligands highlighted the involvement of the amine (-NH₂) groups in coordination with the metal, confirming their role as the primary coordination sites. UV-Visible spectroscopic analysis was used to determine the geometry of the complexes. The observed absorption bands are characteristic of an octahedral coordination around the metal ions, which is consistent with the expected electronic transitions for these systems. In recent years, the integration of computational methodologies has considerably enhanced the ability to predict the toxicity and pharmacokinetic behavior of bioactive compounds, thereby streamlining the early stages of drug discovery. Within this framework, the present study investigates the ADMET profiles - Absorption, Distribution, Metabolism, Excretion, and Toxicity as well as the drug-likeness properties of the synthesized ligands and their corresponding transition metal complexes.
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    Verified Path Indexing
    (pringer Science and Business Media Deutschland GmbH, 2025) Chaabani, Mohamed; Robillard, Simon
    The indexing of syntactic terms is a key component for the efficient implementation of automated theorem provers. This paper presents the first verified implementation of a term indexing data structure, namely a formalization of path indexing in the proof assistant Isabelle/HOL. We define the data structure, maintenance operations, and retrieval operations, including retrieval of unifiable terms, instances, generalizations and variants. We prove that maintenance operations preserve the invariants of the structure, and that retrieval operations are sound and complete
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    Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions
    (Avestia Publishing, 2025) Ferroudji, Hicham; Al-Ammari, Wahib A.; Barooah, Abinash; Hassan, Ibrahim; Hassan, Rashid; Hassan, Rashid; Gomari, Sina Rezaei; Rahman, Mohammad Azizur
    Multiphase flows are crucial to the oil and gas industry since most petroleum companies produce and transport both gas and oil simultaneously. Pipeline leaks are frequently caused by corrosion, aging, and metal deterioration. After an incident, the energy sector not only loses money but also raises environmental and safety concerns. Therefore, developing a successful tool for instantaneous leakage identification in pipelines becomes crucial. In the current work, a leaky pipeline carrying multiphase flow is numerically simulated using Ansys-Fluent under plug flow conditions. The obtained numerical results were validated against experimental data collected from an experimental setup. After that, Probability Density Function (PDF), Wavelet Transform (WT), and Empirical Mode Decomposition (EMD) methods were applied to the obtained time series signals. On the other hand, the analysis is complemented by the application of several machine learning models like Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). For instance, it is observed that the Empirical Mode Decomposition exhibits better performance in leakage identification
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    Vortex characteristics of two rotating immiscible fluids
    (2023) Brahma, Kenza; Saci, Rachid; Mansouri, Kacem
    Hydrodynamic and behavior of laminar confined axisymmetric flows driven by the rotating top disk in cylindrical cavity have been studied numerically. The vertical cavity, is filled with two superposed immiscible incompressible fluids. The top more viscous liquid drives the lower heavier fluid via the interface shear. The study, identified and highlighted a flow topology of types of axisymmetric recirculation regions; depending upon the effects of the disk rotation rate. This work confirms partly previous experimental observations and provides additional quantitative findings; particularly in the vicinity of the interface. The findings are in good accord with the experiments and show that vortex size increases with increasing rotation rate. The basic flow is made up of two clockwise circulation cells, separated by a thin layer of anticlockwise circulation (TCL). The gap thickness of TCL decreases with increasing rotation rate however, the interface high increases as rotation rate increases
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    Detection of knee osteoarthritis based on wavelet and random forest model
    (2021) Messaoudene, Khadidja; Harrar, Khaled
    The most recurrent kind of osteoarthritis is Knee osteoarthritis (KOA). Doctors encounter difficulties for a precise diagnosis through its features and to the naked eye. In this paper, we propose a new approach for the classification of KOA by combining the discrete wavelet decomposition (DWT) and random forest classifier from knee X-ray images. A total of 50 images from patients suffering or not from osteoarthritis were used in this study. The suggested technique includes image enhancement using the Gaussian filter followed by Haar wavelet transform. Five texture features namely, contrast, entropy, correlation, energy, and homogeneity were extracted from the transformed image, and these attributes were used to differentiate the radiographs into two groups: normal (KL 0) or affected with osteoarthritis (KL2). Four classifiers including random forest, SVM, RNN, and Naïve Bayes were tested and compared. The results obtained reveal that random forest achieved the highest performance in terms of accuracy (ACC = 88%) on X-Ray images of the Osteoarthritis Initiative (OAI) dataset.
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    Axial-Torsional Vibrations Interaction Analysis and Decoupling in Drill String Systems
    (2024) Meddah, Sabrina; Tadjer, Sid Ahmed; Kidouche, Madjid
    Rotary drilling system is an important and crucial electromechanical system used in petroleum industry, it is used to drill holes and extract oil and gas from targeted reservoirs beneath the surface. The rate of penetration (ROP) is one of the quantitative metrics used to assess the performance of the drilling system. However, the appearance of unwanted vibrations generally leads to decrease of this performance and increase the nonproductive time (NPT), in addition to drill string damages and wears. These vibrations are classified according to their propagation direction into three types: Axial, Lateral and Torsional. Many researches have been dedicated to designing robust controller to mitigate such vibrations separately. Nevertheless, vibrations often occur simultaneously, with interactions between them. This interaction can have a direct influence on the robustness of the designed controllers. Thus, in order to design a robust controller that mitigate the most frequent vibrations (Axial and torsional), it is necessary to analyze the interaction between them and decouple them before application of any controller. The main objective of this study is to analyze the interaction between the axial and torsional vibrations in the Two-input two-output (TITO) drill string model and to design appropriate decouplers for the system. Based on the obtained results, we demonstrate a significant interaction between the torsional and axial vibrations, and proved that the introduced decouplers have omitted these interaction terms with a minimum influence on the whole dynamic of drill-string system. Therefore, applying this decoupling process is highly recommended to enhance the robustness of the controller in mitigating axial and torsional vibrations simultaneously.
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    Open-Switches fault diagnosis and fault tolerant direct torque control of voltage source inverter fed induction motor
    (Springer Nature, 2024) Boubou, Fouad Eddine; Nedjai, Abd Elouahed; Ammar, Abdelkarim
    Fault diagnosis and fault tolerance are considered essential features in critical industrial applications in order to maintain the necessary levels of availability and dependability. The components that are most frequently affected by failures in closed-loop controlled power converters are semiconductors and sensors. This work focuses on fault tolerant direct torque control (DTC) of induction motor drive systems under inverter open-switch failure. This system detects and isolates the fault, and then ensures the system’s operation under the new conditions. The solution was accomplished using a new reduced switch converter. This system modifies the DTC switching table using available stator voltage vectors in two-phase mode with a Four Switch Three Phase Inverter (FSTPI) topology to maintain the performance of the motor as in the Six Switch Three Phase Inverter (SSTPI) mode. The effectiveness of the fault detection method and fault tolerant control algorithm have been investigated using MATLAB/Simulink software.
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    An accelerated aquila optimizer for maximum power point tracking of PV systems under partial shading conditions
    (EDP Sciences, 2024) Belmadani, Hamza; Merabet, Oussama; Khettab, Sofiane; Maindola, Meenakshi; Bajaj, Mohit; Oubelaid, Adel
    In this work, an improved version of the recent Aquila Optimizer was designed for Maximum Power Point Tracking. The new algorithm was tested on a standalone PV system under several complex partial shading scenarios. A comparative study was conducted to evaluate efficiency, robustness, and convergence speed against the PSO, and the standard AO algorithms. The results indicate that the proposed Accelerated Aquila Optimizer (AAO) generally outperformed its competitors, particularly in terms of convergence time.
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    Decomposition of Hydrogen Peroxide in Presence of DimethylglyoximatoNickel Complexes as Catalysts: Catalase-Like Activity
    (ISRES Publishing, 2024) Kichou, Noura; Guechtouli, Nabila; Merrad, Anissa; Hank, Zakia
    The development in coordination chemistry in recent years raise hopes that synthetically produced metal complexes could mimic many biochemical systems widely found in nature. There is a certain analogy between nature and organometallic systems. A large number of biological metal complexes are known, including oxygen carriers like hemoglobin in the blood, which contains a ferrous ion; respiratory enzymes; those involved in protein hydrolysis; and vitamin B12, which is only active in the presence of cobalt in the trivalent state. The Nickel (III), in addition to Fe-S clusters, was an essential component in hydrogenases. Since then, nickel (III) complexes have been used as models for studying the catalytic function of certain enzymes (hydrogenases). In this context, a study on the catalytic ability of dimethylglyoximato-nickel complexes as peroxiredoxases in the dismutation or oxidation of hydrogen peroxide was conducted. The results were discussed, commented upon, and a reaction mechanism was proposed. The results seem encouraging, regarding the effect of the complexation on catalase-like activity.
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    Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization
    (Elsevier B.V., 2024) Yahyaoui, Zahra; Hajji, Mansour; Mansouri, Majdi; Kouadri, Abdelmalek; Bouzrara, Kais; Nounou, Hazem
    The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.