University of Ouargla

Abstract Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), random subspace (RSS), additive regression (AR), artificial neural network (ANN), support vector regression (SVR), and locally weighted linear regression (LWLR), were employed to generate WQI prediction in Illizi region, southeast Algeria. Using the best subset regression, 12 different input combinations were developed and the strategy of work was based on two scenarios. The first scenario aims to reduce the time consumption in WQI computation, where all parameters were used as inputs. The second scenario intends to show the water quality variation in the critical cases when the necessary analyses are unavailable, whereas all inputs were reduced based on sensitivity analysis. The models were appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE). The results reveal that TDS and TH are the key drivers influencing WQI in the study area. The comparison of performance evaluation metric shows that the MLR model has the higher accuracy compared to other models in the first scenario in terms of 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, and 3.1708*10–08% for R, MAE, RMSE, RAE, and RRSE, respectively. The second scenario was executed with less error rate by using the RF model with 0.9984, 1.9942, 3.2488, 4.693, and 5.9642 for R, MAE, RMSE, RAE, and RRSE, respectively. The outcomes of this paper would be of interest to water planners in terms of WQI for improving sustainable management plans of groundwater resources.

The current work concentrated on the green synthesis of silver nanoparticles (AgNPs) through the use of aqueous Citruslimon zest extract, optimizing the different experimental factors required for the formation and stability of AgNPs. The preparation of nanoparticles was confirmed by the observation of the color change of the mixture of silver nitrate, after the addition of the plant extract, from yellow to a reddish-brown colloidal suspension and was established by detecting the surface plasmon resonance band at 535.5 nm, utilizing UV-Visible analysis. The optimum conditions were found to be 1 mM of silver nitrate concentration, a 1:9 ratio extract of the mixture, and a 4 h incubation period. Fourier transform infrared spectroscopy spectrum indicated that the phytochemicals compounds present in Citrus limon zest extract had a fundamental effect on the production of AgNPs as a bio-reducing agent. The morphology, size, and elemental composition of AgNPs were investigated by zeta potential (ZP), dynamic light scattering (DLS), SEM, EDX, X-ray diffraction (XRD), and transmission electron microscopy (TEM) analysis, which showed crystalline spherical silver nanoparticles. In addition, the antimicrobial and antioxidant properties of this bioactive silver nanoparticle were also investigated. The AgNPs showed excellent antibacterial activity against one Gram-negative pathogens bacteria, Escherichia coli, and one Gram-positive bacteria, Staphylococcus aureus, as well as antifungal activity against Candida albicans. The obtained results indicate that the antioxidant activity of this nanoparticle is significant. This bioactive silver nanoparticle can be used in biomedical and pharmacological fields.

Carbon nanotubes (CNTs), are safe, biocompatible, bioactive, and biodegradable materials, and have sparked a lot of attention due to their unique characteristics in a variety of applications, including medical and dye industries, paper manufacturing and water purification. CNTs also have a strong film-forming potential, permitting them to be widely employed in constructing sensors and biosensors. This review concentrates on the application of CNT-based nanocomposites in the production of electrochemical sensors and biosensors. It emphasizes the synthesis and optimization of CNT-based sensors for a range of applications and outlines the benefits of using CNTs for biomolecule immobilization. In addition, the use of molecularly imprinted polymer (MIP)-CNTs in the production of electrochemical sensors is also discussed. The challenges faced by the current CNTs-based sensors, along with some the future perspectives and their future opportunities, are also briefly explained in this paper.

The ability of Date palm Leaves powder (DPLP) to remove methylene blue (MB) from aqueous solutions by the biosorption process has been studied. Biosorption studies were carried out at different initial dye concentration, contact time, initial solution pH, biosorbent dosage, the particle size of (DPLP) and temperature. Biosorption data were modeled using Langmuir, Freundlich, Temkin and Dubinin-Radushkevich adsorption isotherms. The results showed that equilibrium was reached within 160 min. The used biosorbent gave the highest adsorption capacity at pH 6.5. Equilibrium data of the biosorption process fitted very well to the Temkin model (R 2 =0.994). The maximum adsorption capacity, Langmuir's qmax, improved from 43.103 to 58.14 mg/g as the temperature increased from 30 to 60 °C. The enthalpy ΔH° and entropy ΔS° values were respectively estimated at 8.098 kJ mol −1 and 12.97 J K −1 mol −1 for the process. Three simplified kinetic models including a pseudo-first-order equation, pseudo-second-order equation and intraparticle diffusion equation were selected to follow the adsorption process. Kinetic parameters, rate constants, equilibrium sorption capacities and related correlation coefficients, for each kinetic model were calculated and discussed. It was shown that the adsorption of methylene blue (MB) could be described by the pseudo-second order equation (R 2 = 0.996), methylene blue is slowly transported via intraparticle diffusion into the particles and is finally retained in micropores, suggesting that the adsorption process is presumable a physisorption.

In recent years, software-based face presentation attack detection (PAD) methods have seen a great progress. However, most existing schemes are not able to generalize well in more realistic conditions. The objective of this competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics. Thirteen teams from academic and industrial institutions across the world participated in this competition. This time typical liveness detection based on physiological signs of life was totally discarded. Instead, every submitted system relies practically on some sort of feature representation extracted from the face and/or background regions using hand-crafted, learned or hybrid descriptors. Interesting results and findings are presented and discussed in this paper.

Anthropogenic climate change is projected to exacerbate midlatitude aridity. Here, we analyze newly developed multi‐century tree‐ring records for a long‐term perspective on drought in Tunisia and Algeria. We use a new set of 13 Cedrus atlantica and Pinus halepensis chronologies with a strong signal for warm‐season drought (May–August) to generate a robust, well‐validated reconstruction of the Palmer Drought Severity Index (PDSI) for the period AD 1456–2002. Key features of the reconstruction reveal the magnitude of pre‐instrumental droughts from the historic record. Remarkably, the most recent drought (1999–2002) appears to be the worst since at least the middle of the 15th century. This drought is consistent with the early signature of a transition to more arid midlatitude conditions, as projected by general circulation models.

The current COVID-19 pandemic, with its numerous variants including Omicron which is 50-70% more transmissible than the previously dominant Delta variant, demands a fast, robust, cheap, and easily deployed identification strategy to reduce the chain of transmission, for which biosensors have been shown as a feasible solution at the laboratory scale. The use of nanomaterials has significantly enhanced the performance of biosensors, and the addition of CNTs has increased detection capabilities to an unrivaled level. Among the various CNT-based detection systems, CNT-based field-effect transistors possess ultra-sensitivity and low-noise detection capacity, allowing for immediate analyte determination even in the presence of limited analyte concentrations, which would be typical of early infection stages. Recently, CNT field-effect transistor-type biosensors have been successfully used in the fast diagnosis of COVID-19, which has increased research and commercial interest in exploiting current developments of CNT field-effect transistors. Recent progress in the design and deployment of CNT-based biosensors for viral monitoring are covered in this paper, as are the remaining obstacles and prospects. This work also highlights the enormous potential for synergistic effects of CNTs used in combination with other nanomaterials for viral detection.

Cellulose is the most abundant renewable resource in nature, it has various industrial applications due to its promising properties. Retama raetam is a wild plant belonging to the Fabaceae family, largely abundant in arid area which makes it a good candidate for industrial utilization. In the present study, highly crystalline cellulose microfibers (77.8% CrI) were extracted from Retama Raetam stems as a novel renewable source. The samples underwent a dewaxing process, then the microfibers were extracted using 7 wt% sodium hydroxide followed by a bleaching treatment. The extracted cellulose microfibers were characterized by Scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray Diffraction and thermo-gravimetric analysis.

biochemical characterization showed differences with BSE and scrapie. Our identification of this prion disease in a geographically widespread livestock species requires urgent enforcement of surveillance and assessment of the potential risks to human and animal health.

In order to optimize the purification of water and sewage water, a new technique of degradation «the heterogeneous photocatalysis» of the organic matter was underlined. As catalyst we chose a semiconductor which is dioxide TiO2 the titanium in the presence of a lamp UV as source of energy. One model substances present in many industrial effluents: the 4-iso propyl phenol was selected. The results of our experiments show that the adsorption of the pollutant (10-4mol/l) on TiO2 supported in absence of radiation UV is negligible. Compared to direct photolysis UV (365 nm), the devolution of the pollutant is definitely faster in the presence of TiO2/UV for the same experimental conditions.

Abstract The fruits of two plants from Algeria ( Quercus and Pistacia lentiscus ) were investigated. The paper reports the chemical characteristics and the fatty acid composition of the oil extracts from the fruits. The black fruits of P. lentiscus has the highest crude fat of 32.8%, followed by the red fruits with 11.7%, and the lowest value of 9% in Quercus (acorn). The acid value was highest in red fruits of P. lentiscus oil (24.0 mg KOH/g), followed by the black fruits oil and lowest in acorn oil. The relatively high iodine value in the oils indicates the presence of many unsaturated bonds. Saponification value was highest in the Quercus ilex oil (166.7 mg KOH/g), while the lowest value was in the black fruits of P. lentiscus oil. Gas‐liquid chromatography revealed that the three dominant fatty acids found are: palmitic C16:0 (16.3–19.5%), oleic C18:1 (55.3–64.9%), linoleic C18:2 (17.6–28.4%). The oils contain an appreciable amount of unsaturated fatty acids (78.8–83.5%).

This paper deals with the development of an analytical model for simulating a polycrystalline Cu(In,Ga)Se2 (CIGS)-based thin film solar cells by a CdS(n)/CIGS(p) heterojunction structure. Consequently, a link between the characteristics of this cell and the material parameters is established. This procedure would help improving the performances of the cell. We have been investigating over this work the contribution of the space charge region in the photocurrent density which seemed to be dominant in comparison to the neutral regions. However, the increases of the buffer layer thickness only reduce the cell performances. The optimum thickness of the absorber layer was about 3 μm, a value from which the efficiency has no significant increase. The increase of the absorber bandgap reduces the optical absorption, which is reflected in the reduction of the photocurrent density. Accordingly the open circuit voltage increases as a result to the linear variation with the band gap. The compromise between these two phenomena would be a band gap of 1.55 eV which is the optimum value for obtaining a high efficiency of about 25%. All these optimization results give helpful indication for a feasible fabrication process.

In response to problems involved in the current crisis of petrol in Algeria, with the decrease in the price of the oil barrel, the rate of growth in domestic electricity demand and with an associated acceleration of global warming, as a result of significantly increased greenhouse gas (GHG) emissions, renewable energy seems today as a clean and strategic substitution for the next decades. However, the greatest obstacles which face electric energy comes from renewable energy systems are often referred to the intermittency of these sources as well as storage and transport problems, the need for their conversion into a versatile energy carrier in its use, storable, transportable and environmentally acceptable are required. Among all the candidates answering these criteria, hydrogen presents the best answer. In the present work, particular attention is paid to the production of hydrogen from wind energy. The new wind map of Algeria shows that the highest potential wind power was found in Adrar, Hassi-R’Mel and Tindouf regions. The data obtained from these locations have been analyzed using Weibull probability distribution function. The wind energy produced in these locations is exploited for hydrogen production through water electrolysis. The objective of this paper is to realize a technological platform allowing the evaluation of emergent technologies of hydrogen production from wind energy using four wind energy conversion systems of 600, 1250, 1500 and 2000 kW rated capacity. The feasibility study shows that using wind energy in the selected sites is a promising solution. It is shown that the turbine “De Wind D7” is sufficient to supply the electricity and hydrogen with a least cost and a height capacity factor. The minimum cost of hydrogen production of 1.214 $/kgH2 is obtained in Adrar.

The integration of a PCM layer into an external building wall diminished the amplitude of the instantaneous heat flux through the wall. The types of PCM, its location in the wall and its amount, have been studied in this paper. A two-dimensional transient heat transfer model has been developed and solved numerically using the commercial Computational Fluid Dynamics (CFD) package Fluent. The numerical results have been verified and validated with an experimental model. The considered model consists of usual brick with square holes used as construction materials for residential buildings in Algeria, some of these square holes are filled with PCM. The results showed that the PCM introduced in square holes can improves considerably the thermal inertia of brick and a combination of the types of PCM, its location in the wall and its amount, is very important for improve reduction of heat gain before it reaches the indoor space.

The feasibility of using palm oil fuel ash (POFA) as a precursor for alkali activation reactions, in combination with glass fibers as a discrete reinforcement, has been investigated. The experimental work was focused on the shear strength (using unconfined compression tests) and the tensile strength (using indirect tensile tests and flexural tests). According to the results, it was found that the peak stress increased and the post-peak behavior was modified from a brittle to a more ductile response depending on the amount of fiber reinforcement in the alkali-activated mixtures. An analysis of the microstructures revealed that the most significant factor contributing to the enhanced behavior of the reinforced mixtures was the interaction between the geo-polymeric matrix and the fiber surface. The present work brings new insights to the soil stabilization industry by providing an effective method for enhancing the properties of soil treated by the alkali activation of POFA (a low-value agro-waste by-product) through the inclusion of glass fibers. This brings advantages over the traditional calcium-based binders (i.e., lime and cement) as their production involves the emission of carbon dioxide, one of the factors significantly contributing to global warming.

This paper deals with site selection problems for hydrogen production plants and aims to propose a structural procedure for determining the most feasible sites. The study area is Adrar province, Algeria which has a promising wind potential. The methodology is mainly composed of two stages: the first stage is to evaluate and select the best locations for wind-powered hydrogen production using GIS and MCDM technique. the AHP is applied to weigh the criteria and compute a LSI to evaluate potential sites, and the second stage is applying different filtration constraints to select the suitable petrol stations for such hydrogen refueling station modification. The result map showed that the entire Adrar province is almost suitable for wind-powered hydrogen production with varying suitability index. The LSI model groups sites into three categories: High suitable areas, Medium suitable areas, and Low suitable. As a result, 2.95 % (12808.97 km2) of the study area has high suitability, 54.59 % (236320.16 km2) has medium suitability, 1.12 %(4842.94 km2) has low suitability and 41.34 % (178950.35 km2) of the study area is not suitable for wind hydrogen production. By applying the constraints, about 4 stations are suitable for wind-powered hydrogen refueling system retrofitting in Adrar province.

Aim . This study investigated the antifungal properties of aqueous extracts obtained from indigenous plants that grow spontaneously in the Northern Sahara of Algeria. The activities of these plants in controlling two fungal species that belong to Fusarium genus were evaluated in an in vitro assay. Materials and Methods . Fresh aerial parts of four plant species ( Artemisia herba alba, Cotula cinerea, Asphodelus tenuifolius , and Euphorbia guyoniana ) were collected for the preparation of aqueous extracts. Two levels of dilution (10% and 20%) of the pure extracts were evaluated against Fusarium graminearum and Fusarium sporotrichioides . Results . The results of this study revealed that the A. herba alba , C. cinerea, A. tenuifolius , and E. guyoniana aqueous extracts are effective at both concentrations of 10% and 20% for the Fusarium mycelia growth inhibition. In particular, A. tenuifolius extract is effective against F. graminearum , whereas F. sporotrichioides mycelium growth is strongly affected by the E. guyoniana 20% extract. The phytochemical characterization of the compositions of the aqueous extracts has revealed that the presence of some chemical compounds (tannins, flavonoids, saponins, steroids, and alkaloids) is likely to be responsible for the antifungal activities sought. Conclusion . The antifungal properties of A. herba alba , C. cinerea , A. tenuifolius , and E. guyoniana make these plants of potential interest for the control of fungi affecting both wheat yield and safety.

Because of their unique physical, chemical, and biological characteristics, conductive nanomaterials have a lot of potential for applications in materials science, energy storage, environmental science, biomedicine, sensors/biosensors, and other fields. Recent breakthroughs in the manufacture of carbon materials, conductive polymers, metals, and metal oxide nanoparticles based electrochemical sensors and biosensors for applications in environmental monitoring by detection of catechol (CC) and hydroquinone (HQ) are presented in this review. To achieve this goal, we first introduced recent works that discuss the effects of phenolic compounds and the need for accurate, inexpensive, and quick monitoring, and then we focused on the use of the most important applications of nanomaterials, such as carbon-based materials, metals, and metal oxides nanoparticles, and conductive polymers, to develop sensors to monitor catechol and hydroquinone. Finally, we identified challenges and limits in the field of sensors and biosensors, as well as possibilities and recommendations for developing the field for better future applications. Meanwhile, electrochemical sensors and biosensors for catechol and hydroquinone measurement and monitoring were highlighted and discussed particularly. This review, we feel, will aid in the promotion of nanomaterials for the development of innovative electrical sensors and nanodevices for environmental monitoring.

.Communicated by Ramaswamy H. Sarma.

We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be NP-hard even with small numbers of robots and tasks. The field of survivors' search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks; (b) Unmanned Aerial Vehicles are the robots; and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve conflicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans; (ii) traveled distances; and (iii) exchanged messages.