Abdelmalek Essaâdi University (Arabic: جامعة عبدالمالك السعدي) is a Moroccan public university created in 1989. It is considered to be the main university in the North of the Morocco. This university is made up of 15 institutions, including schools and establishments spread across the Tanger-Tétouan-AL Hoceima region and in the city of Larache. It covers the fields of science, law, economics, humanities and letters and technology. In 2016, the University established the CONFUCIUS Institute for Chinese Language Learning, the third nationwide institute..
Approximate nearest-neighbor (ANN) indexes accelerate candidate retrieval, but comparisons at unrelated retrieval fidelity can produce misleading system choices. We evaluate exact Flat, HNSW, IVF-Flat, IVF-PQ, and Flat-PQ on four public interaction datasets and separate user-to-item from item-to-item retrieval. The CPU-only protocol calibrates tunable indexes against exact Flat at agreement-recall targets of 0.90, 0.95, and 0.98. A held-out-item rank proxy is reported separately from exact-neighbor fidelity. Because the archived pipeline learns item representations from the complete filtered interaction matrix before constructing leave-one-out queries, this proxy is transductive and is not an estimate of out-of-sample recommender quality. The study combines 40 primary conditions with query-level discordance, corrected paired inference over three nonredundant outcomes, exposure proxies, SVD-weighting sensitivity, and storage-and-fidelity synthetic scaling. HNSW and IVF-Flat reached all targets on the calibration samples, whereas the tested IVF-PQ configuration reached 0.90 in two of eight scenarios and never reached 0.95. Expanded-sample evaluation at the selected 0.95 setting retained the target in seven scenarios for HNSW and six for IVF-Flat, revealing finite-sample transfer uncertainty. HNSW and IVF-Flat changed the held-out item’s top-10 rank for only 0.03% and 0.11% of queries on average, compared with 5.23% for IVF-PQ and 6.01% for Flat-PQ. The corrected 20,000-resample analysis rejected 18 of 96 Holm-adjusted comparisons, predominantly for Recall@100; every rejection favored exact Flat. The results support constraint-first index selection in which the agreement threshold is derived from tolerated degradation in an application-specific downstream or online service metric, not treated as a universal technical constant.
Variations in start, end, duration, and intensity of main pollen seasons represent a growing public issue, particularly in light of the rising prevalence of pollen sensitization among populations. However, the assessment of long-term pollen trends depends heavily on how the main pollen season is defined. In this study, we analyzed airborne pollen data in Tétouan (NW of Morocco) over the period 2008–2024, applying eight different main pollen season definitions—including two percentage-based, four threshold-based, a moving mean, and a logistic method—to four major allergenic taxa. Linear trends were assessed using the nonparametric Mann–Kendall method with Theil–Sen slope estimates. Our analysis revealed a significant delay in the onset of the main pollen season for Poaceae and Cupressaceae. For Poaceae, this delay was detected using the threshold method (definition 4) and moving mean method, whereas for Cupressaceae it was identified using the threshold methods (definitions 3 and 4) and logistic method. The observed trends in main pollen season end dates varied depending on the definition applied, although negative trends—indicating an earlier end to the season—were predominant. Specifically, advances of 1.5 days for Olea (threshold definition 3), 1.57 days for Poaceae (moving mean method), and 5.8 days for Quercus (99
Digital transformation has become a defining determinant of supply-chain competitiveness. For Morocco—strategically positioned between Europe and Africa—upgrading logistics, manufacturing, and agri-export systems is critical for economic competitiveness. Despite clear momentum in adopting frontier technologies, fragmented evidence and persistent frictions (skills gaps, capital constraints, uneven readiness) stall scaling, particularly for SMEs. This study employs a PRISMA–ScR scoping review of 300 sources with bibliometric mapping, complemented by an ecosystem scan of 903 Moroccan startups and an exploratory field probe (N=7). Substantial performance improvements arise when technologies (IoT, AI/ML, blockchain, digital twins) are deployed as an integrated stack supported by enabling infrastructure layers—shared data schemas, interoperable edge–cloud connectivity, security and identity management, and MLOps/TwinOps. Results reveal uneven regional readiness; SME adoption driven by efficiency and cost but constrained by skills, capex, and security; an urban-centred startup ecosystem; and a tractable policy-to-practice pathway aligned with Digital Morocco 2030. An implementation framework linking technologies to operational levers and measurable KPIs is proposed, extending Industry 4.0/5.0 theory and Dynamic Capabilities Theory to SME-dominant emerging economies and informing government programmes and managerial roadmaps.
In the context of sustainable landfill leachate management, this study aims to evaluate the efficiency of advanced oxidation processes (AOPs) for the treatment of synthetic leachate, with a particular focus on reducing Chemical Oxygen Demand (COD) and mineral ions (NH4+, NO3−, NO2−, PO43−). Two processes were investigated: heterogeneous photocatalysis using titanium dioxide (TiO2) under different irradiation systems (UV-A and UV-C). Preliminary dark adsorption tests established an equilibrium time of 5–6 h and identified an optimal catalyst dose of 1 g/L. The results also indicated that adsorption efficiency decreased with increasing initial pollutant concentrations. Photocatalytic experiments revealed that heterogeneous photocatalysis under UV-C irradiation (75 W) yielded the highest performance, achieving a COD removal efficiency of 88.3
Renewable energy systems have become indispensable in the pursuit of sustainable development, with photovoltaic (PV) technologies emerging as one of the most accessible and scalable solutions. In this context, the present work introduces a new metaheuristic optimization algorithm, known as the Artificial Protozoa Optimizer (APO), which is tailored to improve maximum power point tracking (MPPT) in PV systems.Compared to established techniques such as Perturb and Observe (P&O), Particle Swarm Optimization (PSO), and Fuzzy Logic Control (FLC), APO has shown notable improvements in terms of efficiency, reaching values as high as 99 % in simulation tests.The algorithm is specifically designed to minimize power fluctuations and energy losses, which translates into enhanced system stability and energy yield.Through extensive simulations, APO has demonstrated its capacity to overcome common MPPT challenges, such as slow convergence, sensitivity to rapid environmental variations, and oscillations near the optimal point.The simulated setup includes a buck converter interfaced with a battery, enabling an effective energy storage mechanism. All scenarios were tested using MATLAB/Simulink, under both uniform and non-uniform irradiation profiles to assess the algorithm's robustness in dynamic conditions.
