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PITAYA : Evaluation de la conduite culturale du Pitaya (Hylocereus undatus) : itinéraire technique, paramètres morphométriques et état sanitaire dans la Wilaya de Ain Témouchent.
(2026) Kechkeche Abdelali; Aguili Mohamed Amine; Zenasni Youcef; Abdellaoui Hadjira
Dragon fruit (Pitaya) is an emerging fruit crop with increasing economic and nutritional
value. In Algeria, its cultivation remains limited. This study aims to assess the adaptation
potential of this species to the agro-climatic conditions of the Aïn Témouchent Province and
to investigate its technical cultivation practices.
The experimental protocol involved six groups of the same variety, distributed between two
series (B1 and B2) and three cutting types (Alpha, Beta and Gamma), subjected to a uniform
fertilization program. Planting was carried out on 27/10/2025 for B1 and on 11/02/2026 for
B2. After an initial phase under greenhouse conditions, the plants were transferred outdoors
under a shade net, with progressive repotting and a pest management program.
The results showed a flowering rate of 31.8% for B2, compared with 20.7% for B1. Thirteen
flowering plants out of 51 were recorded, with three fertilized flowers and five successive
flowering waves. The observations also highlighted the importance of controlling
environmental conditions, particularly solar exposure and fertilization, in plant development.
These preliminary results indicate a potential for dragon fruit adaptation to local conditions.
However, multi-year trials incorporating control treatments, replications, and statistical
analyses are still required to confirm these findings and to assess yield, fruit quality, as well as
the crop's water and nutritional requirements.
Réalisation d’une presse pour la récupération plastique
(2026) MEKHISSI Abdelkader; BENARBIA Kheireddine; Elahmar Kadi
Etude d’activité anti-oxydante et activité antibactérienne d’une plante médicinale de la région d’Ain Témouchent
(2026) MAZOUZI Amar; DERRAG Zaineb
This work aims to study the antimicrobial and antioxidant activity of the essential oil of a
medicinal and aromatic plant: the bay laurel (Laurus nobilis). The essential oil was extracted by
hydrodistillation.
The antioxidant activity of the studied oil using the DPPH method showed a very promising
antioxidant effect. This is a value very close to the IC50 of ascorbic acid (vitamin C), while the
antioxidant power is stronger than that of BHT.
The FRAP test results for bay leaf oil confirmed and agreed with the DPPH test results, revealing
that Laurus nobilis oil has a very high reducing capacity, far superior to BHT and very close to
ascorbic acid, confirming its status as a first-class antioxidant oil.
The antimicrobial activity was studied against five strains: three Gram-negative bacterial strains
(Escherichia coli, Citrobacter freundii, and Klebsiella pneumoniae), two Gram-positive bacterial strains
(Enterococcus faecalis and Bacillus cereus), and one fungal strain (Candida albicans).
With the exception of the bacterium Klebsiella pneumoniae, the essential oil of L. nobilis showed
excellent inhibitory activity against all other tested strains, with efficacy ranging from moderately
sensitive to extremely sensitive. It should also be noted that this oil proved particularly active
against Gram-negative bacteria, especially Escherichia coli, more so than against Gram-positive
bacteria and yeasts.
These results highlight the great potential of essential oils as natural alternatives to conventional
antimicrobial agents; however, further research is needed to understand the mechanisms of
action and explore the potential therapeutic applications of essential oils.
Prévision de la demande électrique journalière d'une maison à l'aide de modèles de séries temporelles et de Machine Learning
(2026) REBHI Rim Chames El Houda; BELABANE Hayet Kaouter Zahra; DORBANE Abdelhakim
This Master's end-of-study project focuses on forecasting residential electricity consumption
using Machine Learning techniques. The primary objective is to develop predictive models
capable of accurately estimating electricity demand based on historical consumption data and
relevant explanatory variables. Following a comprehensive literature review, four prediction
models—Linear Regression, Random Forest, XGBoost, and CatBoost—were developed,
implemented, and comparatively evaluated. The performance of these models was assessed
using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error
(RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination
(R2). The experimental results demonstrate the effectiveness of machine learning techniques
in improving forecasting accuracy. The best-performing model achieved an MSE of
**0.408103**, an RMSE of **0.638829**, an MAE of **0.480083**, and an **R2 score of
0.986685**, indicating excellent predictive performance. These findings highlight the
potential of machine learning to support efficient energy management, accurate electricity
demand forecasting, and improved electrical grid operation.
Compensation de la puissance réactive dans les charges non-linéaires et aléatoires
(2026) ABLAOUI Ikram; HAMLET Rym; BERRACHED Djelloul
This thesis focuses on the optimization of reactive power compensation in industrial electrical
networks, particularly those subjected to non-linear and random loads. Following a theoretical
study of static compensation devices, we have developed robust calculation methods for filter-
compensators (D.C.F). Our work, based on comparative and statistical analysis, allows for the
adjustment of technical parameters to reduce voltage distortion, improve wave quality, and
minimize installation costs. These solutions provide efficient and economically viable tools
for the metallurgy and energy sectors.