Abdelmalek Essaâdi University

In order to estimate the signal parameters accurately for mobile systems, it is necessary to estimate a system's propagation characteristics through a medium. Propagation analysis provides a good initial estimate of the signal characteristics. The ability to accurately predict radio-propagation behavior for wireless personal communication systems, such as cellular mobile radio, is becoming crucial to system design. Since site measurements are costly, propagation models have been developed as a suitable, low-cost, and convenient alternative. Channel modeling is required to predict path, loss and to characterize the impulse response of the propagating channel. The path loss is associated with the design of base stations, as this tells us how much a transmitter needs to radiate to service a given region. Channel characterization, on the other hand, deals with the fidelity of the received signals, and has to do with the nature of the waveform received at a receiver. The objective here is to design a suitable receiver that will receive the transmitted signal, distorted due to the multipath and dispersion effects of the channel, and that will decode the transmitted signal. An understanding of the various propagation models can actually address both problems. This paper begins with a review of the information available on the various propagation models for both indoor and outdoor environments. The existing models can be classified into two major classes: statistical models and site-specific models. The main characteristics of the radio channel - such as path loss, fading, and time-delay spread - are discussed. Currently, a third alternative, which includes many new numerical methods, is being introduced to propagation prediction. The advantages and disadvantages of some of these methods are summarized. In addition, an impulse-response characterization for the propagation path is also presented, including models for small-scale fading, Finally, it is shown that when two-way communication ports can be defined for a mobile system, it is possible to use reciprocity to focus the energy along the direction of an intended user without any explicit knowledge of the electromagnetic environment in which the system is operating, or knowledge of the spatial locations of the transmitter and the receiver.

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. The main objective of this paper is to see in which precision a Machine learning algorithm can predict and how much the epochs can improve our model.

Performance Factors Analysis (PFA) is considered one of the most important Knowledge Tracing (KT) approaches used for constructing adaptive educational hypermedia systems. It has shown a high prediction accuracy against many other KT approaches. While, the desire to estimate more accurately the student level leads researchers to enhance PFA by inventing several advanced extensions. However, most of the proposed extensions have exclusively been improved in a pedagogical sense, as the improvements have mostly been limited to the analysis of students’ behaviour during their learning process. In contrast, Machine Learning provides many powerful methods that could be efficient to enhance, in the technical sense, the prediction of student performance. Our goal is to focus on the exploitation of Ensemble Learning methods as an extremely effective Machine Learning paradigm used to create many advanced solutions in several fields. In this sense, we propose a new PFA approach based on different models (Random Forest, AdaBoost, and XGBoost) in order to increase the predictive accuracy of student performance. Our models have been evaluated on three different datasets. The experimental results show that the scalable XGBoost has outperformed the other evaluated models and substantially improved the performance prediction compared to the original PFA algorithm.

Interleukin-18 (IL-18) is a potent pro-inflammatory cytokine involved in host defense against infections and regulates the innate and acquired immune response. IL-18 is produced by both hematopoietic and non-hematopoietic cells, including monocytes, macrophages, keratinocytes and mesenchymal cell. IL-18 could potentially induce inflammatory and cytotoxic immune cell activities leading to autoimmunity. Its elevated levels have been reported in the blood of patients with some immune-related diseases, including rheumatoid arthritis, systemic lupus erythematosus, type I diabetes mellitus, atopic dermatitis, psoriasis, and inflammatory bowel disease. In the present review, we aimed to summarize the biological properties of IL-18 and its pathological role in different autoimmune diseases. We also reported some monoclonal antibodies and drugs targeting IL-18. Most of these monoclonal antibodies and drugs have only produced partial effectiveness or complete ineffectiveness in vitro , in vivo and human studies. The ineffectiveness of these drugs targeting IL-18 may be largely due to the loophole caused by the involvement of other cytokines and proteins in the signaling pathway of many inflammatory diseases besides the involvement of IL-18. Combination drug therapies, that focus on IL-18 inhibition, in addition to other cytokines, are highly recommended to be considered as an important area of research that needs to be explored.

Higher reversible capacity of 333 mA h g⁻¹ in Na cell is demonstrated for hard carbon derived from HCl-treated argan.

SARS-CoV-2 is a novel human coronavirus responsible for the Coronavirus disease 2019 (COVID-19) pandemic. Pneumonia and acute respiratory distress syndrome are the major complications of COVID-19. SARS-CoV-2 infection can activate innate and adaptive immune responses and result in massive inflammatory responses later in the disease. These uncontrolled inflammatory responses may lead to local and systemic tissue damage. In patients with severe COVID-19, eosinopenia and lymphopenia with a severe reduction in the frequency of CD4+ and CD8+ T cells, B cells and natural killer (NK) cells are a common feature. COVID-19 severity hinges on the development of cytokine storm characterized by elevated serum levels of pro-inflammatory cytokines. Moreover, IgG-, IgM- and IgA-specific antibodies against SARS-CoV-2 can be detected in most patients, along with the viral RNA, forming the basis for assays that aid in patient diagnosis. Elucidating the immunopathological outcomes due to COVID-19 could provide potential targets for immunotherapy and are important for choosing the best clinical management by consultants. Currently, along with standard supportive care, therapeutic approaches to COVID-19 treatment involve the use of antiviral agents that interfere with the SARS-CoV-2 lifecycle to prevent further viral replication and utilizing immunomodulators to dampen the immune system in order to prevent cytokine storm and tissue damage. While current therapeutic options vary in efficacy, there are several molecules that were either shown to be effective against other viruses such as HIV or show promise in vitro that could be added to the growing arsenal of agents used to control COVID-19 severity and spread.

Sentiment analysis becomes more popular in the research area. It allocates positive or negative polarity to an entity or items by using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our work focuses on the Sentiment analysis resulting from the product reviews using original techniques of text’s search. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we proposed hybrid approach to identify product reviews offered by Amazon. The results show that the proposed system approach outperforms these individual classifiers in this amazon dataset.

Abstract Pinus pinaster bark, an abundant by-product from the timber industry, has been studied as a potential low-cost adsorbent for the removal of Congo red (CR) dye from wastewaters. Surface morphological and physico-chemical characteristics of pine bark were analysed using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), determination of the point of zero charge (pH PZC ) and elemental analysis. Assays were performed to determine the wavelength for the maximum absorbance and the stability with time of CR solutions depending on concentration and/or pH, which resulted to be a very significant parameter. Adsorption studies were conducted on batch mode to study the effect of contact time (till 7 days), pH (2–9), adsorbent dosage (1–10 g L −1 ) and temperature (25–60 °C). The bark adsorption capacity at equilibrium varied between 0.3 and 1.6 mg g −1 and the equilibrium adsorption percentage between 23.4 and 100% depending on adsorbent dosage, temperature and pH at an initial CR concentration of 5 mg L −1 . Kinetic data for the removal of CR by pine bark were best fitted by the pseudo-second-order kinetic model. The equilibrium data fitted well with the Freundlich model. Thermodynamic analysis indicated that the adsorption process is exothermic and spontaneous.

Matricaria chamomilla L. is a famous medicinal plant distributed worldwide. It is widely used in traditional medicine to treat all kinds of diseases, including infections, neuropsychiatric, respiratory, gastrointestinal, and liver disorders. It is also used as a sedative, antispasmodic, antiseptic, and antiemetic. In this review, reports on M. chamomilla taxonomy, botanical and ecology description, ethnomedicinal uses, phytochemistry, biological and pharmacological properties, possible application in different industries, and encapsulation were critically gathered and summarized. Scientific search engines such as Web of Science, PubMed, Wiley Online, SpringerLink, ScienceDirect, Scopus, and Google Scholar were used to gather data on M. chamomilla. The phytochemistry composition of essential oils and extracts of M. chamomilla has been widely analyzed, showing that the plant contains over 120 constituents. Essential oils are generally composed of terpenoids, such as α-bisabolol and its oxides A and B, bisabolone oxide A, chamazulene, and β-farnesene, among other compounds. On the other hand, M. chamomilla extracts were dominated by phenolic compounds, including phenolic acids, flavonoids, and coumarins. In addition, M. chamomilla demonstrated several biological properties such as antioxidant, antibacterial, antifungal, anti-parasitic, insecticidal, anti-diabetic, anti-cancer, and anti-inflammatory effects. These activities allow the application of M. chamomilla in the medicinal and veterinary field, food preservation, phytosanitary control, and as a surfactant and anti-corrosive agent. Finally, the encapsulation of M. chamomilla essential oils or extracts allows the enhancement of its biological activities and improvement of its applications. According to the findings, the pharmacological activities of M. chamomilla confirm its traditional uses. Indeed, M. chamomilla essential oils and extracts showed interesting antioxidant, antibacterial, antifungal, anticancer, antidiabetic, antiparasitic, anti-inflammatory, anti-depressant, anti-pyretic, anti-allergic, and analgesic activities. Moreover, the most important application of M. chamomilla was in the medicinal field on animals and humans.

<p class="Abstract"><span lang="EN-GB">In this competitive scenario of the educational system, the higher education institutes use data mining tools and techniques for academic improvement of the student performance and to prevent drop out. The authors collected data from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. Four classification methods, the J48, PART, Random Forest and Bayes Network Classifiers were used. The data mining tool used was WEKA. The high influential attributes were selected using the tool. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in our dataset. The results showed that random forest outperforms the other classifiers based on accuracy and classifier errors. Apriori algorithm was also used to find the association rule mining among all the attributes and the best rules were also displayed.<em></em></span></p>

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world's oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits.

The aim of this study was to investigate antibacterial activity of Origanum compactum essential oils collected at three phenological stages on Escherichia coli and Bacillus subtilis. The antibacterial activity was evaluated using the agar-well diffusion assay. The MIC and MBC values were determined using the micro-dilution assay. The investigation of the antibacterial action was carried out by the evaluation of the effect of O. compactum essential oils on the antibacterial kinetic growth, the integrity of cell membrane and permeability of the cell membrane. The anti-quorum sensing activity was tested by the inhibition of the biofilm formation. The findings of this study showed that O. compactum essential oil has potent antibacterial activities against E. coli and B. subtilis. The lowest inhibition value against B. subtilis was obtained with O. compactum essential oil at the post-flowering stage (MIC = MBC = 0.0312% (v/v)). The antibacterial mechanisms of O. compactum essential oils are related to the disturbing of the cell membrane integrity and the increasing of the membrane permeability, which leads to the leakage of genetic materials (DNA and RNA). Moreover, O. compactum essential oils inhibited the formation of the biofilms, a phenotype that has been known to be quorum sensing regulated.

Deserts and arid regions are generally perceived as bare and rather homogeneous areas of low diversity. The Sahara is the largest warm desert in the world and together with the arid Sahel displays high topographical and climatic heterogeneity, and has experienced recent and strong climatic oscillations that have greatly shifted biodiversity distribution and community composition. The large size, remoteness and long-term political instability of the Sahara-Sahel, have limited knowledge on its biodiversity. However, over the last decade, there have been an increasing number of published scientific studies based on modern geomatic and molecular tools, and broad sampling of taxa of these regions. This review tracks trends in knowledge about biodiversity patterns, processes and threats across the Sahara-Sahel, and anticipates needs for biodiversity research and conservation. Recent studies are changing completely the perception of regional biodiversity patterns. Instead of relatively low species diversity with distribution covering most of the region, studies now suggest a high rate of endemism and larger number of species, with much narrower and fragmented ranges, frequently limited to micro-hotspots of biodiversity. Molecular-based studies are also unravelling cryptic diversity associated with mountains, which together with recent distribution atlases, allows identifying integrative biogeographic patterns in biodiversity distribution. Mapping of multivariate environmental variation (at 1 km × 1 km resolution) of the region illustrates main biogeographical features of the Sahara-Sahel and supports recently hypothesised dispersal corridors and refugia. Micro-scale water-features present mostly in mountains have been associated with local biodiversity hotspots. However, the distribution of available data on vertebrates highlights current knowledge gaps that still apply to a large proportion of the Sahara-Sahel. Current research is providing insights into key evolutionary and ecological processes, including causes and timing of radiation and divergence for multiple taxa, and associating the onset of the Sahara with diversification processes for low-mobility vertebrates. Examples of phylogeographic patterns are showing the importance of allopatric speciation in the Sahara-Sahel, and this review presents a synthetic overview of the most commonly hypothesised diversification mechanisms. Studies are also stressing that biodiversity is threatened by increasing human activities in the region, including overhunting and natural resources prospection, and in the future by predicted global warming. A representation of areas of conflict, landmines, and natural resources extraction illustrates how human activities and regional insecurity are hampering biodiversity research and conservation. Although there are still numerous knowledge gaps for the optimised conservation of biodiversity in the region, a set of research priorities is provided to identify the framework data needed to support regional conservation planning.

This paper investigates the hydraulic issues of pumping well optimization in saltwater‐intruded coastal aquifers. The well field includes freshwater outflow, pumping wells, and a recharge canal. The objective is to maximize the total pumping rate subject to the constraint of no intrusion of the saltwater front into the wells. Analytical solutions are presented for one‐well, two‐well, and one‐well‐with‐recharge‐canal problems. For problems involving multiple pumping wells, a structured messy genetic algorithm is used to search for the optimal solution.

Lead (Pb) is the most common heavy metal contaminant in the environment. Pb is not an essential element for plants, but they absorb it when it is present in their environment, especially in rural areas when the soil is polluted by automotive exhaust and in fields contaminated with fertilizers containing heavy metal impurities. To investigate lead effects on nutrient uptake and metabolism, two plant species, spinach (Spinacia oleracea) and wheat (Triticum aestivum), were grown under hydroponic conditions and stressed with lead nitrate, Pb(NO3)2, at three concentrations (1.5, 3, and 15 mM). Lead is accumulated in a dose-dependent manner in both plant species, which results in reduced growth and lower uptake of all mineral ions tested. Total amounts and concentrations of most mineral ions (Na, K, Ca, P, Mg, Fe, Cu and Zn) are reduced, although Mn concentrations are increased, as its uptake is reduced less relative to the whole plant's growth. The deficiency of mineral nutrients correlates in a strong decrease in the contents of chlorophylls a and b and proline in both species, but these effects are less pronounced in spinach than in wheat. By contrast, the effects of lead on soluble proteins differ between species; they are reduced in wheat at all lead concentrations, whereas they are increased in spinach, where their value peaks at 3 mM Pb. The relative lead uptake by spinach and wheat, and the different susceptibility of these two species to lead treatment are discussed.

In light of the recent discovery by the ATLAS and CMS experiments at the Large Hadron Collider (LHC) of a Higgs-like particle with a narrow mass range of 125-126 GeV, we perform an updated analysis on one of the popular scalar dark matter models, the Inert Higgs Doublet Model (IHDM). We take into account in our likelihood analysis of various experimental constraints, including recent relic density measurement, dark matter direct and indirect detection constraints as well as the latest collider constraints on the invisible decay width of the Higgs boson and monojet search at the LHC. It is shown that if the invisible decay of the standard model Higgs boson is open, LHC as well as direct detection experiments like LUX and XENON100 could put stringent limits on the Higgs boson couplings to dark matter. We find that the most favoured parameter space for IHDM corresponds to dark matter with a mass less than 100 GeV or so. In particular, the best-fit points are at the dark matter mass around 70 GeV where the invisible Higgs decay to dark matter is closed. Scalar dark matter in the higher mass range of 0.5-4 TeV is also explored in our study. Projected sensitivities for the future experiments of monojet at LHC-14, XENON1T and AMS-02 one year antiproton flux are shown to put further constraints on the existing parameter space of IHDM.

Lead (Pb) is an environmental pollutant extremely toxic to plants and other living organisms including humans. To assess Pb phytotoxicity, experiments focusing on germination of wheat seeds were germinated in a solution containing Pb (NO(3))(2) (0.05; 0.1; 0.5; 1g/L) during 6 days. Lead accumulation in seedlings was positively correlated with the external concentrations, and negatively correlated with morphological parameters of plant growth. Lead increased lipid peroxidation, enhanced soluble protein concentrations and induced a significant accumulation of proline in roots. Esterase activity was enhanced in the presence of lead, whereas α-amylase activity was significantly inhibited. Antioxidant enzymes activities, such as, ascorbate peroxidase, peroxidase, superoxide dismutase, catalase and glutathione S-transferase were generally significantly increased in the presence of lead in a dose-dependent manner. The present results thus provide a model system to screen for natural compounds able to counteract the deleterious effects of lead.

Water-soluble sulfated polysaccharides isolated from two red algae Sphaerococcus coronopifolius (Gigartinales, Sphaerococcaceae) and Boergeseniella thuyoides (Ceramiales, Rhodomelaceae) collected on the coast of Morocco inhibited in vitro replication of the Human Immunodeficiency Virus (HIV) at 12.5 μg/mL. In addition, polysaccharides were capable of inhibiting the in vitro replication of Herpes simplex virus type 1 (HSV-1) on Vero cells values of EC₅₀ of 4.1 and 17.2 μg/mL, respectively. The adsorption step of HSV-1 to the host cell seems to be the specific target for polysaccharide action. While for HIV-1, these results suggest a direct inhibitory effect on HIV-1 replication by controlling the appearance of the new generations of virus and potential virucidal effect. The polysaccharides from S. coronopifolius (PSC) and B. thuyoides (PBT) were composed of galactose, 3,6-anhydrogalactose, uronics acids, sulfate in ratios of 33.1, 11.0, 7.7 and 24.0% (w/w) and 25.4, 16.0, 3.2, 7.6% (w/w), respectively.

The paper present an evaluation of a grid-connected photovoltaic (PV) system installed on the roof of a government building located in Tangier, Morocco. The experimental data was recorded from 1st January 2015 to December 2015 based on real time observation. The aim is to encourage the use of solar PV system for government, commercial and residence building in Morocco based on the obtained results. The system is made up of 20 modules of 250 Wp and one inverter of 5 kW. The assessed parameters of the PV installation includes energy output, final yield, modules temperature, efficiency module, performance ratio (PR) and others. The PV park supplied the grid with 6411.3 kWh during the year 2015. The final yield (Yf) ranged from 1.96 to 6.42 kWh/kWp, the performance ratio (PR) ranged from 58% to 98% and the annual capacity factor was found to be 14.84%. The final yield of PV installation is compared with other final yields of solar PV systems located at other places. Finally various power losses are given through a diagram loss.

AIMS: To study cellular damage induced by Cinnamomum verum essential oil in Pseudomonas aeruginosa ATCC 27853 and Staphylococcus aureus ATCC 29213. METHODS AND RESULTS: The effect of cinnamon bark essential oil on these two strains was evaluated by plate counts, potassium leakage, flow cytometry and transmission electron microscopy (TEM). Exposure to this oil induced alterations in the bacterial membrane of Ps. aeruginosa, which led to the collapse of membrane potential, as demonstrated by bis-oxonol staining, and loss of membrane-selective permeability, as indicated by efflux of K(+) and propidium iodide accumulation. Thus, respiratory activity was inhibited, leading to cell death. In Staph. aureus, cells treated with the oil entered a viable but noncultivable (VNC) state. The oil initially caused a considerable decrease in the metabolic activity and in the replication capacity of these bacterial cells. The loss of membrane integrity appeared later, as indicated by bis-oxonol and Propidium iodide (PI) staining. Data provided by TEM showed various structural effects in response to cinnamon essential oil. In Ps. aeruginosa cells, coagulated cytoplasmic material was observed, and intracellular material was seen in the surrounding environment, while oil-treated Staph. aureus showed fibres extending from the cell surface. CONCLUSIONS: Cinnamon essential oil damages the cellular membrane of Ps. aeruginosa, which leads to cell death. There is evidence of VNC Staph. aureus after exposure to the oil. SIGNIFICANCE AND IMPACT OF THE STUDY: Cinnamon essential oil shows effective antimicrobial activity and health benefits and is therefore considered a potential food additive. To use this oil as a natural food preservative, especially in combination with other preservation methods, a thorough understanding of the mechanism through which this oil exerts its antibacterial action is required.