École Normale Supérieure Casablanca

Diarrheic shellfish toxins (DSTs), especially okadaic acid (OA) and its related compounds, are lipophilic marine biotoxins mainly synthesized by dinoflagellates of the genera Dinophysis and Prorocentrum. These compounds bioaccumulate in filter-feeding shellfish like mussels and clams, posing a considerable public health risk due to their strong gastrointestinal effects when contaminated seafood is consumed. This review offers a thorough overview of the current understanding of OA-group toxins with a focus on the molecular mechanisms of toxicity, including cytoskeletal disruption, apoptosis, inflammation, oxidative stress, and mitochondrial dysfunction. Additionally, their ecological impacts on aquatic organisms and patterns of bioaccumulation are explored. Recent advances in detection methods and regulatory frameworks are discussed, highlighting the necessity for robust monitoring systems to safeguard seafood safety. Enhanced knowledge of the toxicity, distribution, and fate of DSP (diarrheic shellfish poisoning) is essential for improving risk assessment and managing marine biotoxins. Despite methodological advances, gaps remain regarding chronic exposure and species-specific detoxification pathways.

The growing demand for sustainable and efficient water treatment solutions underscores the importance of high-quality activated carbon (AC) derived from renewable resources. In this study, AC was produced from sugarcane bagasse using sulfuric acid as an activating agent. A hybrid approach combining experimental design and advanced computational modeling was employed to optimize the production process and model the relationship between operational parameters and AC yield. A Box-Behnken design was used to systematically investigate the effects of four key variables: temperature, activation time, raw material-to-activating agent ratio, and acid concentration. The generated experimental data were used to develop and compare predictive models based on response surface methodology (RSM), support vector machine (SVM), and artificial neural networks (ANNs). All models demonstrated strong predictive capabilities, with ANN achieving R=0.989±0.003, outperforming SVM (R=0.950±0.004), while RSM showed a slightly higher overall fit (R=0.996±0.002). This study demonstrates that integrating experimental design with machine learning techniques enhances both the precision and efficiency of process optimization. The proposed approach offers a robust, scalable, and sustainable pathway for producing high-quality AC from agricultural waste, with significant potential for industrial applications in environmental remediation and water purification technologies.

The Distributed No-Wait Flow Shop Scheduling Problem (DNWFSSP) is a difficult combinatorial optimization problem with significant industrial applications. It involves scheduling tasks in several distributed factories while ensuring that operations take place without intermediate waiting times. The main objective is to minimize makespan, which has a direct impact on production efficiency. Due to the NP-hard nature of this problem, exact methods become impractical for large instances. To address this, we propose two metaheuristics: The Artificial Bee Colony (ABC) algorithm and Migratory Bird Optimization (MBO). The results obtained demonstrate that MBO is a powerful approach for solving optimization problems in terms of both calculation time and solution quality. These results highlight the potential of inspired algorithms for solving complex scheduling problems.

ABSTRACT We discuss, in this article, two major topics. First, we address the study and investigation of a generalized fractional optimal control problem (FOCP) involving a fractional derivative defined with respect to another function. We consider a cost functional, to be minimized, and explore an optimal control solution together with its corresponding trajectory. For this aim, we derive the necessary optimality conditions for the considered problem, from which we deduce the expression of optimal control. Second, we provide a numerical scheme allowing us to solve the equations derived from the optimality conditions. Further, we compare the obtained numerical results with already existing ones so that we can show the efficiency of our proposed approach in comparison with the others previously available in the literature.

Abstract This work focused on investigating the effect of oxidants and their interaction with the monomer (m‐phenylenediamine) (mPD) on performance of the resulting composite membrane. The synthesized poly(m‐phenylenediamine) (PmPD) and poly(vinyl alcohol) (PVA) were deposited onto flat ceramic support made from pozzolan and micronized phosphate. The difference between the two composite membranes is the oxidant used for the chemical polymerization of mPD monomer. The PmPD used to develop the first membrane in this work was synthesized using ammonium persulphate (APS) oxidant. The second membrane was developed in a previous study using ferric chloride (FeCl3) as oxidant. Although PmPD‐based membranes have been explored, few studies have systematically compared the influence of different oxidants on membrane performance, especially for dye removal. This study addresses that gap by evaluating how APS and FeCl3 affect membrane characteristics and dye rejection efficiency. The effect of oxidants on membrane properties such as microstructure, wettability, permeability, and filtration performances was investigated. The composite membranes were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X‐ray analysis, and X‐ray diffraction technique. The morphology analysis shows that using APS leads to the formation of uniform microparticles compared to FeCl3 oxidant. It was proven that the use of APS in the polymerization of the mPD enhances the rejection of the membrane accompanied by with a decrease in permeate flux. It removed up to 99.7% of Congo red under optimal conditions (ΔP = 3 bar, C = 600, and pH = 4).

The explosive growth of the Internet of Things (IoT) has introduced significant security and privacy challenges, as connected devices are highly susceptible to cyberattacks. Conventional safeguards often fail to meet the specific demands of IoT networks. To address this gap, the present study proposes a method that integrates machine learning with blockchain technology to enhance IoT security. An intrusion detection system (IDS) powered by ML algorithms provides a stronger, decentralized defense against emerging threats, while a blockchain layer secures device-to-device communication. Experiments conducted on a simulated IoT environment with synthetically generated attacks demonstrate that a Random Forest model achieved 98.77<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy in intrusion detection. The blockchain component further established a decentralized, tamper-proof communication channel, significantly improving overall network security and safeguarding user privacy.

The present work aims to investigate the structural, magnetic and electronic properties of ternary intermetallic MnZnSb using density functional theory (DFT) and Monte Carlo simulation (MCs). The obtained ground state results reveal that MnZnSb is stable in the ferromagnetic state with a metallic nature and high magnetic anisotropy. The calculated total and local magnetic moments are 2.96 μ B and 3.05 μ B for MnZnSb. Ferromagnetic behaviour was confirmed using the Stoner criterion. The elastic constants are used to verify the mechanical stability of the MnZnSb compound, demonstrating that the compound is mechanically stable and has a brittle character with low Debye temperature. The magnetic behaviour of MnZnSb is studied using MCs and the obtained results show that undergoes a transition from ferromagnetic to paramagnetic state at a critical temperature ( T C ) of 320 K. The computed magnetocaloric effect indicates that the compound exhibits large values of relative cooling power at 14 T, around 502.2 J/K.