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Optimisation thermique d'un mur composite par l’intégration d'un PCM pour l'amélioration du confort thermique des bâtiments.
(2026) ZENASNI Feriel; ZERGA Ramyz Ahmed; NEHARI Taieb
This thesis focuses on the thermal optimization of a composite wall incorporating a phase
change material (PCM) in order to improve thermal comfort in buildings and reduce energy
consumption. PCMs can store and release heat in the form of latent heat during solid/liquid phase
transition, which helps limit indoor temperature fluctuations.
The study is based on a numerical simulation carried out using ANSYS Fluent and the
enthalpy-porosity method to model heat transfer and phase change phenomena. Several parameters
were analyzed, including the type of PCM, its thickness, and its combination with passive
insulation techniques. The results show that integrating PCMs improves the thermal inertia of
walls, reduces heat flux, and enhances indoor thermal comfort while decreasing heating and
cooling energy demands.
Supplémentation en vitamine D3, chrome et régime sans sucre chez les patients diabétiques : EPSP el amria
(2026) Hadjoudja Kheira; Meryem ABI-AYAD
This work aims to evaluate the impact of a combined vitamin D3 and chromium
supplementation in patients with type 2 diabetes. The study consisted of assessing and
comparing two key metabolic biomarkers — fasting glucose and glycated hemoglobin (HbA1c)
— before and after the supplementation period.
The results revealed an improvement in glycemic parameters among the monitored patients.
Indeed, the mean fasting glucose decreased from 1.37±0.53 g/L (among 14 patients evaluated
before the intervention) to 1.14 ± 0.35 g/L (among 10 patients evaluated after the
supplementation). Furthermore, a notable reduction in the mean HbA1c was recorded, dropping
from 7.17± 1.71 % to 6.33 ± 1.44 %.
These data suggest a favorable and beneficial effect of vitamin D3 and chromium
supplementation on glycemic control in type 2 diabetic patients, although postsupplementation
HbA1c values remain close to the prediabetes zone according to the ADA criteria.
Conception d'un capteur intelligent de recul pour un véhicule
(2026) BENCHERIF Tarek; MOULFI Mouad; BENCHERIF Kaddour
The reversing radar, based on a smart sensor and designed to monitor reversing or assist with
parking, is a system that allows the driver to estimate the distance between their vehicle and
an obstacle during low-speed maneuvers.
This project aims to create an automotive reversing radar using an Arduino board and
Processing, capable of detecting both stationary and moving objects. The work involves a
general study of reversing radar theory, electronic boards, and the Processing programming
language, using an Arduino YUN Rev2 board, an ultrasonic sensor, a buzzer, resistors, and
LEDs. The electronic circuit design is carried out using Proteus ISIS and ARES software,
with the prototype visualized in Fritzing. Following positive simulation results, the project
will proceed to practical implementation. At the end of this project, a reversing radar device
will be designed and tested on real-world targets.
Evaluation des propriétés technologiques de souches de lactobacillus pour starters laitiers
(2026) BENDAHMANE Fatima Zohra; BENEDDIF Fatima Zohra; DAHMANE Hana; DJOUAD Mokhtar Eddine
A total of 46 strains related to the genus Lactobacillus sensu lato were isolated and characterized based on
their morphological, physiological, and biochemical properties.
The strains S3, S13, S17, and S27 were identified using the API 50 CHL system as Lactobacillus brevis,
Lactobacillus curvatus, Lactobacillus plantarum, and Lactobacillus paracasei subsp. paracasei,
respectively, revealing an interesting microbiological diversity within the studied samples.
The evaluation of technological properties showed that the strains exhibited good tolerance to NaCl
concentrations of 2% and 4%, as well as a predominant production of β-galactosidase. Strains S23, S2, S3,
and S4 demonstrated significant proteolytic activity, while positive lipolytic activity was observed in
approximately 55% of the tested strains. In contrast, no exopolysaccharide production was detected.
The assessment of functional properties revealed variable resistance to acidic conditions (pH 2 and pH 3)
and bile salts, with strains S31 and S34 showing the highest tolerance. Furthermore, the strains exhibited
stronger antimicrobial activity against Staphylococcus aureus than against Escherichia coli, indicating their
ability to inhibit certain undesirable microorganisms.
These results highlight the microbiological richness of local raw milk and emphasize the potential interest of
certain strains for applications in the dairy industry. However, further studies are needed to deepen the
characterization of these isolates and confirm their potential for practical use.
Deep RL with Optimization-Based Refinement for Mobility Prediction in Smart Cities Using MEC and V2X Communication
(2026) BENKRAMA Yassine; BENZERBADJ Ali
The rapid growth of smart cities and the proliferation of Vehicle-to-Everything (V2X)
communication networks have created an urgent need for traffic prediction systems that are
simultaneously accurate, adaptive to non-stationary conditions, and deployable within the
stringent latency constraints of Multi-Access Edge Computing (MEC) platforms. This thesis
proposes and validates a hybrid two-stage framework for real-time directional traffic trend
prediction. In the first stage, a Proximal Policy Optimization (PPO) agent equipped with a
Temporal Convolution Network encoder and a multi-head self-attention spatial integration
module is trained offline on a 200,000-timestep synthetic V2X dataset generated over the real
urban road network of Nördlingen, Germany, learning to classify the dominant directional
traffic surge (North, East, South, or West) over a 60-minute prediction horizon from 15
roadside unit observations. In the second stage, the frozen PPO policy is augmented at
inference time by a Genetic Algorithm (GA) that continuously optimises a 4-dimensional
logit bias vector against a sliding window of recent prediction outcomes, correcting for
systematic directional errors introduced by abrupt traffic regime shifts without any gradient-
based retraining. Experimental evaluation against supervised LSTM, ARIMA, Kalman filter,
and random baselines demonstrates that the PPO+GA system achieves 78.0% accuracy and
79.26% Macro-F1 on the static test set, and maintains 46.8% accuracy across four abrupt
regime transitions a 50% relative improvement over the frozen LSTM (31.2%). A closed-loop
validation in the SUMO traffic simulator on the Belgrade road network, with 74 signalized
intersections, yields a 33.3% vehicle throughput gain and a 10.4% average speed
improvement over fixed-time signal control without any retraining, demonstrating cross-
network generalization. All results confirm that the proposed framework meets the
computational requirements of real-time MEC deployment with an inference latency of 54.59
ms, well within the 60-second operational window.