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Artemisia absinthium L. is a spontaneous medicinal plant grown in Algeria. This work aims to enhance the essential oil of Artemisia absinthium (EOAa) from the region of Sidi Bel Abbes by studying the chemical composition of the species and its antibacterial activity. The essential oil is analyzed by gas chromatography coupled with mass spectrometry. We identified 41 constituents, representing 98.43% of the oil total composition; the main compounds of the essential oil were Camphor (25.47%), followed respectively by β-Thujone (18.23%), Camphene (7.21%), and β-Caryophyllene (6.72%). The antibacterial activity of essential oils was evaluated against five microorganisms using the diffusion disc method. The gram-negative micro-organisms (Candida Albicans ATCC 10231, Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853) had an inhibition zone with an important diameter, between 19 ± 09 mm and 12 ± 06 mm for concentration (100 mg/ml, 50 mg/ml and 25 mg/ml). Furthermore the gram-positive bacteria (Staphylococcus aureus ATCC 25923, Bacillus ATCC 11778) were less sensitive since dilution (25 mg/ml).

The objective of this study was to examine the whole genome of the bacterial strain CIP111899, isolated from the root surface of maize (Zea mays), in order to reveal the presence of genes implicated in enhancing plant growth. The genome-based taxonomy revealed that strain CIP111899 belongs to a new species called Bacillus rhizoplanae. In the second step, the genome of CIP111899 was analyzed on multiple levels using various information tools. This involved examining functional categories associated with genes using analytical techniques, namely, annotation using the RAST server, then identifying growth-promoting genes with the Prokka program, and finally detecting groups of genes responsible for secondary metabolism through antiSMASH analysis. The results of the genomic analysis of strain CIP111899 showed the presence of multiple genes that enhance stress tolerance, such as those encoding enzymes and antioxidants (superoxide dismutase, peroxidases, and catalase). Additionally, various plant growth-promoting genes were identified, including those involved in the solubility of inorganic phosphorus, phytohormone production, and iron uptake. In conclusion, strain CIP111899 has shown promise as a potential agent for promoting plant growth and thereby improving food security due to its genetic composition.

As a part of prospecting bioactive molecules from natural resources, the phytochemical composition and antioxidant properties of extracts of leaves and berries oilcake of Pistacia lentiscus L., collected from two sites (Mechat and Bordj-Ali) in the northeast region of Algeria, were investigated. Dried leaves and berries oilcake obtained after removing the oily part by mean of Soxhlet apparatus were macerated in methanol in order to recover their respective active extracts. The phytochemical content analysis showed that lentisk leaves of both sites were relatively rich in polyphenols, flavonoids, flavonols and proanthocyanidins. The phytochemical content of berries oilcake extract was lower than that of leaves, yet, samples of Bordj-Ali displayed higher values for all assayed phytochemicals compared to those of Mechat. The P. lentiscus leaves extract exhibited a strong radical-scavenging activity (IC50 = 10.46 µg/mL) against 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals and a substantial inhibitory activity against H2O2 free radicals (20.23 - 25.92%). Furthermore, these extracts displayed a very strong reducing power (EC50 = 28.08 µg/mL) and total antioxidant capacity (104.07 - 159.39 mg EAA/g DW). The observed results correlated positively with total phytochemical content strongly plead in favor of valorization of this wild plant as a potential natural source of active biomolecules for food, cosmetics, and medicinal industry sectors.

Artemisia herba-albais a white mugwort harvested in the Bougtobe region of the El-Bayad province. Essential oils were extracted from the plant using hydrodistillation. The antibacterial study ofArtemisia herba-albaessential oils revealed a significant inhibitory action on the growth of tested microorganisms includingBacillus cereus, Staphylococcus aureus. These results demonstrated significant activity compared to the positive control penicillin, amoxicillin, and spiramycin. Overall, the results showed that the tested essential oil possesses interesting antimicrobial activities, and therefore, opens up new perspectives in the field of natural applications as a promising alternative to chemical products and for industrial exploitation.

Reduced quasilinear and nonlinear (gradient-driven) models with scale separations, commonly used to interpret experiments and to forecast turbulent transport levels in magnetised plasmas, are tested against nonlinear models without scale separations (flux-driven). Two distinct regimes of turbulence—either above threshold or near marginal stability—are investigated with Boltzmann electrons. The success of reduced models hinges in particular on the reproduction of nonlinear fluxes. Good agreement between models is found above threshold, whilst reduced models significantly underpredict fluxes near marginality, overlooking mesoscale flow organisation and turbulence self-advection. Constructive prescriptions whereby to improve reduced models are discussed.

This paper focuses on the modelling and control of an electromagnetic suspension using the bond graph tool. This technique combines graphical and relational aspects to establish connections between bond graph elements, particularly in deducing junction relationships and employing bicausal inversion for trajectory tracking controller design. To determine the controller parameters, we utilized a meta-heuristic method called Firefly for optimization, thus avoiding the need for trial and error. The simulation was conducted using 20-Sim software and a Matlab/Simulink script, which yielded improved results. This paper presents an initial contribution that demonstrates the integration of meta-heuristic optimization and the bond graph tool for parameter selection in 20-sim simulation software.

Objective - Co-creation in entrepreneurship education is related to the teacher's ability to innovate and develop new practice among the student-teacher relationship; it is also a new way to enhance creativity and student value creation. In this approach, the teacher is considered the first actor of co-creation; he creates value among co-teaching, he is a facilitator for the launch of a start-up, and he is at the core of the entrepreneurial ecosystem. Thus, teaching with value co-creation, as we define, is a learning process that combines co-creation as a venture creation tool and provides personalised support for business plan competition. In the higher education system, innovation is an indicator of quality in teaching in all discipline. For entrepreneurship education, innovation with co-creation is a pedagogical practice based on project learning experimentation. This practice is efficient when it creates value for the learner in a co-creation process that combines knowledge sharing between the stakeholders. However, selecting the appropriate pedagogical practice is a curial element in the skills development in entrepreneurship education. Indeed, using co-creation as co-teaching implicates a pre-selection of the participant. Methodology - In this study, the author uses a qualitative analysis method of interviews made with teachers implicated in a co-creation approach and students who had participated in start-up competition. The sample was selected based on (Science, Technology, Engineering and Mathematics) STEM student entrepreneur experience. Results and findings - In achieving the objective of this paper, which is to understand the use of co-creation as an innovative pedagogical practice in the area of entrepreneurship education. The results show the value creation of co-creation as a co-teaching practice and a challenging tool of enhancing entrepreneurial spirit. Type of Paper - Empirical Keywords: Co-Creation; Entrepreneurship Education; Co-Teaching. JEL Classification: M31, E24, I29.

Recently, Environmental Customer collaboration has gained a considerable attention among researchers and Industrial enterprises. Many studies highlight that organizations can achieve a good performance level while considering customer collaboration and environmental regulation. However, the literature in the Green Supply Chain Management (GSCM) suggests having a more structured collaboration and information exchange process based between Supply Chain partners on new technologies. Towards this end, a hybrid approach based on Multi Agent Systems and Multi Objective Linear Programming is proposed as mean of automating and facilitating the environmental customer collaboration process. This research shows that MAS can be utilized to reduce the complexity and facilitate communication in the GSCM context. The applicability of the developed MAS approach is demonstrated using an industrial case study in the automotive spare parts sector.

This study aims to assess the effect of five endophytic bacteria on the growth promotion of tomato plants under salt stress conditions. Twenty-five endophytic bacteria were isolated from roots of the native halophyte Stipa tenacissima L. collected from Djelfa (Algeria) and were evaluated for their halotolerant potential. Seven strong halotolerant isolates were selected and characterized for in vitro Plant Growth Promotion (PGP) including the ACC-deaminase activity, inorganic phosphate solubilization and the production of indole-3-acetic acid and siderophores. Five strains showing high PGP traits were investigated for the mitigation of salt stress and PGP of tomato (Solanum esculentum L. cv. Aicha). Screening of the salt-tolerant isolates for PGP traits in saline condition (75 mM NaCl) under Petri-dishes experiment of tomato revealed that three isolates ST09, ST18 and ST19 improved the growth of tomato seedlings by highlighting germination rate, shoot and root lengths and biomass. Under greenhouse conditions at 0 and 75 mM NaCl, inoculation of strains ST09, ST18 and ST19 in tomato seedlings produced an increase in germination (69.23%, 73.07% and 73.07%). The isolate ST19 showed an increase of more than 90% in root elongation and 17.4% increase in biomass when compared to uninoculated tomato seedlings at 75 mM NaCl stress. Phylogenetic analysis based on the 16S rRNA gene showed that the strain ST19 belongs to the genus Kocuria, and is closely related to Kocuria arsenatis |CM1E1|KM874399T (99.29% similarity). Thus, we conclude that the inoculation of tomato plants with the strain ST19 showed its efficiency to mitigate salt stress conditions.

This study introduces a new strategy that combines unsupervised learning (clustering) and transfer learning. Clustering methods are employed to generate synthetic labels for the source dataset (ICAR-2018). The generated dataset is then used for transfer learning to other histopathological datasets (KimiaPath960, CRC, Biomaging− 2015, Breakhis, and Lymphoma). The comparative study based on two clustering algorithms (K-means and multi-objective clustering stream) demonstrates the efficiency of MOC-Stream. The generated synthetic histopathological dataset by this clustering algorithm outperformed the original labeled dataset and the imageNet models in transfer learning.

Hybrid metaheuristics has received a lot of attention lately to solve combinatorial optimization problems. The purpose of hybridization is to create a cooperation between metaheuristics for better solutions. Most proposed works were interested in static hybridization. The objective of this work is to propose a novel dynamic hybridization method (GPBD) that generates the most suitable sequential hybridization between GA, PSO, BAT, and DE metaheuristics, according to each problem. The authors choose to test this approach for solving the best feature selection problem in a wrapper tactic, performed on face image recognition datasets, with the k-nearest neighbor (KNN) learning algorithm. The comparative study of the metaheuristics and their hybridization GPBD shows that the proposed approach achieved the best results. It was definitely competitive with other filter approaches proposed in the literature. It achieved a perfect accuracy score of 100% for Orl10P, Pix10P, and PIE10P datasets.

It is well known that the human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully. In this study, error back propagation neural networks were utilized to predict the working bearing capacity of piles. The data of performed pile load tests are used to verify the applicability of the presented neural network procedure. The results showed that the maximum error of prediction did not exceed 25%. Thus, the use of Neural Networks to predict pile capacity seems to be feasible for practical purpose.

One of the important processes in the data quality field is record linkage (RL). RL (also known as entity resolution) is the process of detecting duplicates that refer to the same real-world entity in one or more datasets. The most critical step during the RL process is blocking, which reduces the quadratic complexity of the process by dividing the data into a set of blocks. By that way, matching is done only between the records in the same block. However, selecting the best blocking keys to divide the data is a hard task, and in most cases, it's done by a domain expert. In this paper, a novel unsupervised approach for an automatic blocking key selection is proposed. This approach is based on the recently proposed meta-heuristic bald eagles search (bes) optimization algorithm, where the problem is treated as a feature selection case. The obtained results from experiments on real-world datasets showed the efficiency of the proposition where the BES for feature selection outperformed existed approaches in the literature and returned the best blocking keys.

The performance of deep excavations is governed by the ground conditions such as soil stiffness and in situ stress conditions. Cantilever secant pile wall (CPW) is one of the retaining systems which are used to support deep excavation. Its lateral deflection has great importance as it shows the behavior of CPW in the soil. Two dimensional analysis (2D) and three dimensional analysis (3D) may be used to get the lateral deflection in numerical solution. This study is focused on 2D and 3D analysis for CPW in soft clay. The finite element software "Abaqus/CAE-v6.9" has been used in the analysis. The 2D study analysis takes into account the different CPW depths and its diameter effect. The construction stages effect on the lateral deflection have been studied also. The 3D study analysis takes into account the different excavation geometry “the length and width of the excavation” effect, also different depths for CPW in 3D analysis have been studied also. The results showed that longer CPW has larger lateral deflection while CPW diameters have no effect on lateral deflection. Later deflection in one construction stage may be double that of previous stage. Also it was found that later deflection in three dimensional (3D) analysis is higher than the 2D analysis by three times. This confirms that 3D analysis of CPW in soft clay leads to more accurate results than 2D analysis.

Deep learning methods are characterized by their capacity to learn data representation compared to the traditional machine learning algorithms. However, these methods are prone to overfitting on small volumes of data. The objective of this research is to overcome this limitation by improving the generalization in the proposed deep learning framework based on various techniques: data augmentation, small models, optimizer selection, and ensemble learning. For ensembling, the authors used selected models from different checkpoints and both voting and unweighted average methods for combination. The experimental study on the lymphomas histopathological dataset highlights the efficiency of the MobileNet2 network combined with the stochastic gradient descent (SGD) optimizer in terms of generalization. The best results have been achieved by the combination of the best three checkpoint models (98.67% of accuracy). These findings provide important insights into the efficiency of the checkpoint ensemble learning method for histopathological image classification.

Drilling and installation of deep foundations typically alter and affect surrounding soils and subsequently affect the adjacent structures. Possible effects on the adjacent structures due to the installation of driven piles have been thoroughly investigated in the literature. However, little attention has been given to other pile installation techniques such as continuous flight auger (CFA). In this paper, unfavorable CFA drilling conditions are discussed and investigated using three-dimensional (3D) finite element analyses. The induced settlements around the piles are calculated to assess the possible adverse effects on surrounding structures. The numerical models are validated using laboratory physical modeling of unfavorable soil drilling conditions reported in the literature. The analyses showed that unfavorable drilling conditions might have a major effect on adjacent structures up to two times the pile length. Parametric numerical modeling has also been conducted to study the influence of different parameters such as soil shear strength parameters, pile length, pile diameter, and soil permeability on the deformation adjacent to CFA pile during construction. The results revealed that soil relative density and permeability have a significant influence on the calculated surface settlement. Finally, several pile group drilling scenarios were investigated to gain insight into the optimum scenario for pile drilling. The analyses show that alternating between drilling piles near the structure and those far from it, may significantly reduce the surface settlement during pile construction.

Deep learning is one of the most commonly used techniques in computer-aided diagnosis systems. Their exploitation for histopathological image analysis is important because of the complex morphology of whole slide images. However, the main limitation of these methods is the restricted number of available medical images, which can lead to an overfitting problem. Many studies have suggested the use of static ensemble learning methods to address this issue. This article aims to propose a new dynamic ensemble deep learning method. First, it generates a set of models based on the transfer learning strategy from deep neural networks. Then, the relevant subset of models is selected by the particle swarm optimization algorithm and combined by voting or averaging methods. The proposed approach was tested on a histopathological dataset for colorectal cancer classification, based on seven types of CNNs. The method has achieved accurate results (94.52%) by the Resnet121 model and the voting strategy, which provides important insights into the efficiency of dynamic ensembling in deep learning.

This article presents a new fine-tuning framework for histopathological images analysis. Despite the most common solutions where the ImageNet models are reused for image classification, this research sets out to perform an intra-domain fine tuning between the trained models on the histopathological images. The purpose is to take advantage of the hypothesis on the efficiency of transfer learning between non-distant datasets and to examine for the first time these suggestions on the histopathological images. The Inception-v3 convolutional neural network architecture, six histopathological source datasets, and four target sets as base modules were used in this article. The obtained results reveal the importance of the pre-trained histopathological models compared to the ImageNet model. In particular, the ICIAR 2018-A presented a high-quality source model for the various target tasks due to its capacity in generalization. Finally, the comparative study with the other literature results shows that the proposed method achieved the best results on both CRC (95.28%) and KIMIA-PATH (98.18%) datasets.

This study aims to address ethical leadership and know the extent of its practice by small and medium enterprises in the state of Djelfa and measure the level of environmental responsibility in these institutions and try to find out the level of the impact of moral leadership on environmental responsibility, and in order to answer the main problem and test the hypotheses has been developed A questionnaire based on the previous studies that touched on the two variables. A random sample of 60 workers was selected from the two high-altitude tannery establishments in the Djelfa and the Sweetly Foundation in the Djelfa. The data was analyzed through SPSS. The results of the study showed a positive impact of ethical leadership in its three dimensions (availability of ethical behaviour, ethical behaviour practices, promote ethical behaviour) on environmental responsibility for small and medium-sized enterprises in Djelfa.

This study aimed to investigate the effect of indole-3-acetic acid (IAA)-producing endophytes from saline environments on survival and growth of tomato under salt stress, using different treatment times. Twenty-five endophytic bacteria were isolated from halophytes (Atriplex halimus L., Tamarix aphylla L.) issued from saline regions (Algeria). Five strains were selected based on tolerance to NaCl. A polyphasic taxonomic study was conducted. The selected strains were assigned to the Bacillus cereus group. Quantification of IAA production was done by high-performance liquid chromatography (HPLC). An in vitro test of growth promotion/protection of tomato ‘Aicha’ was conducted under saline conditions (50 mM, 75 mM, 100 mM, and 125 mM), for different durations of seed biopriming (1h, 3h, and 12h). Endophytes led to a significant amelioration of salt stress in tomato ‘Aicha’ at all salinity levels, depending on treatment times, indicating the necessity of optimizing this crucial factor.

pas d'abstract disponible, + ID der Publikation: hslu_93711 + Art des Beitrages: Buchkapitel/Beitrag in Sammelband + Herausgeberschaft: Emilie Rosenstein; Serge Mimouni + Seiten: 157 - 172 + Sprache: Französisch + Letzte Aktualisierung: 2023-01-18 14:47:36

International audience; Full self-consistent stationary Vlasov-Maxwell solutions of magnetically confined plasmas are built for systems with cylindrical symmetries. The stationary solutions are thermodynamic equilibrium solutions. These are obtained by computing the equilibrium distribution function resulting from maximizing the entropy and closing the equations with source terms that are then computed by using the obtained distribution. This leads to a self-consistent problem corresponding to solving a set of two coupled second order non-linear differential equations. Relevant plasma parameters are introduced and a bifurcation leading to an improvement of plasma confinement is shown. Conversely in the improved confinement setting, we exhibit the emergence of a separatrix in the integrable motion of a charged particles.

The use of halotolerant beneficial plant-growth-promoting (PGP) bacteria is considered as a promising eco-friendly approach to improve the salt tolerance of cash crops. One strategy to enhance the possibility of obtaining stress-alleviating bacteria is to screen salt impacted soils. In this study, amongst the 40 endophytic bacteria isolated from the roots of Sahara-inhabiting halophytes Atriplex halimus L. and Lygeum spartum L., 8 showed interesting NaCl tolerance in vitro. Their evaluation, through different tomato plant trials, permitted the isolate IS26 to be distinguished as the most effective seed inoculum for both plant growth promotion and mitigation of salt stress. On the basis of 16S rRNA gene sequence, the isolate was closely related to Stenotrophomonas rhizophila. It was then screened in vitro for multiple PGP traits and the strain-complete genome was sequenced and analysed to further decipher the genomic basis of the putative mechanisms underlying its osmoprotective and plant growth abilities. A remarkable number of genes putatively involved in mechanisms responsible for rhizosphere colonization, plant association, strong competition for nutrients, and the production of important plant growth regulator compounds, such as AIA and spermidine, were highlighted, as were substances protecting against stress, including different osmolytes like trehalose, glucosylglycerol, proline, and glycine betaine. By having genes related to complementary mechanisms of osmosensing, osmoregulation and osmoprotection, the strain confirmed its great capacity to adapt to highly saline environments. Moreover, the presence of various genes potentially related to multiple enzymatic antioxidant processes, able to reduce salt-induced overproduction of ROS, was also detected.

Cet ouvrage, destiné tant à des chercheur·e·s qu’à des acteurs et actrices de terrain, met en exergue les attentes formulées à l’égard de la formation professionnelle et les lignes de tension qui la traverse. S’inscrivant dans des contextes historiques et nationaux variés (France, Espagne, Suisse), les différents chapitres réunis proposent des regards croisés et interdisciplinaires sur cette filière de formation, souvent considérée comme le « parent pauvre » de la recherche. Ils livrent une analyse des enjeux politiques et économiques de la formation professionnelle et portent une attention particulière aux parcours de formation, aux rapports à l’emploi et à l’insertion, mais aussi aux usages que font les acteurs et les actrices de cette voie professionnelle et de ses diplômes.

Turbulent plasmas notably self-organize to higher energy states upon application of additional free energy sources or modification of edge operating conditions. Mechanisms whereby such bifurcations occur have been actively debated for decades, mostly on the basis of reduced models. Here we unravel a surprising causal chain of events in the onset of an improved confinement state, by applying generic entropy-based and information theoretic measures to the primitive kinetic equations. Interfacial contamination of a large, stable region (the ‘dog’) by locally-borne peripheral turbulent fluctuations (the ‘tail’) is found to be central to explaining transport properties, globally. These results, highly relevant to the quest for magnetic fusion advocate the use of such data-driven methods to many problems in fluids and plasmas where interfacial turbulent contamination is active.

Le manque d’engagement des entreprises italiennes dans l’apprentissage est frequemment correle au cout du dispositif, comparativement a d’autres formules contractuelles. Toutefois, ce resultat interroge : si la concurrence entre contrats est si vive, comment saisir le recours a l’apprentissage lorsqu’il a lieu ? Pour repondre a cette question, l’article part de textes reglementaires et de statistiques touchant le fait apprenti en Italie, qui seront mis en regard d’un materiau d’entretiens aupres d’acteurs de l’apprentissage (responsables regionaux, employeurs et apprentis). Cette demarche permet de reveler plusieurs types d’encastrement au sein desquels le contrat d’apprentissage peut prendre sens : les compromis d’une entreprise familiale du nord du pays ou la perspective d’emigration d’un jeune diplome d’un institut technique. De ce fait, cet article nous renseigne sur les conditions de primo-engagement en Italie.

El presente texto refiere a una problemática que se ha observado en las escuelas de nivel básico, específicamente en el municipio de Atizapán Santa Cruz, en donde los adolescentes se autolesionan cortando brazos y piernas, esta práctica es denominada cutting es una problemática que está en aumento y que suele tener lugar o inicio en la adolescencia. el cutting tiene que ver con la angustia, presión o enojo, tristeza, etc., que el adolescente no sabe expresar de manera sana o funcional y que encuentra por medio de las autolesiones una alternativa para poder expresar lo que no puede decir, por otra partem está la etapa adolescente, la cual ya de por sí tiene sus propios procesos y posibles conflictos debido a las nuevas experiencias y búsqueda de identidad. Sin embargo, la familia suele tener un gran peso en este proceso ya que los adolescentes identificados con cutting refieren vivir problemas en el hogar. El estudio de este tema está basado en los fundamentos psicológicos retomando algunos autores de la materia y relacionando la teoría con lo observado en la institución y las probables razones que incitan a la propagación de la problemática, así como algunas acciones que se han tomado para contrarrestarla.

Feature selection is the process of identifying good performing combinations of significant features among many possibilities. This preprocess improves the classification accuracy and facilitates the learning task. For this optimization problem, the authors have used a metaheuristics approach. Their main objective is to propose an enhanced version of the firefly algorithm as a wrapper approach by adding a recursive behavior to improve the search of the optimal solution. They applied SVM classifier to investigate the proposed method. For the authors experimentations, they have used the benchmark microarray datasets. The results show that the new enhanced recursive FA (RFA) outperforms the standard version with a reduction of dimensionality for all the datasets. As an example, for the leukemia microarray dataset, they have a perfect performance score of 100% with only 18 informative selected genes among the 7,129 of the original dataset. The RFA was competitive compared to other state-of-art approaches and achieved the best results for CNS, Ovarian cancer, MLL, prostate, Leukemia_4c, and lymphoma datasets.

The interplay between toroidal drift-wave turbulence and tokamak profiles is investigated using a wave-kinetic description. The coupled system is used to investigate the interplay between marginally stable toroidal drift-wave turbulence and geodesic acoustic modes (GAMs). The coupled system is found to be unstable. Notably, the most unstable mode corresponds to the resonance between the turbulent wave radial group velocity and the GAM phase velocity. For a low-field-side ballooned drift-wave growth, a background flow shear breaks the symmetry between inwards- and outwards-travelling instabilities. Although this turbulence–GAM coupling may not be the primary driver for avalanches in standard core ion temperature gradient simulations, this mechanism is generic and displays many of the expected features, and should be of interest in several other regimes, which include towards the edge or in the presence of energetic particles.