The Earth Air Heat Exchanger (EAHE) is a promising passive technique that utilises shallow geothermal energy to improve the thermal comfort in buildings. EAHE has the potential to minimize the amount of electrical energy used by traditional air conditioning systems. The aim of this research is to examine the thermal performance of the EAHE under continuous operation. A transient numerical model was developed using the implicit finite difference method. Afterwards, the thermal performance was evaluated by using the means of derating factor. In addition, an experimental setup is realised in Biskra University (Algeria) to take measurements during cooling period. According to numerical calculations, the high thermal performance of EAHE is dependant on high thermal conductivity of soil and low air velocity. The values of the derating factor in the studied cases ranged from 0% to 35% that can mislead the design of the EAHE if ignored. The experimental findings revealed that for 3.5 m/s of air velocity, the maximum air temperature drop can reach up 19 °C. It is noticed that the initial 33 m of the pipe can provide 91% of the whole reduction in air temperature. In extreme real cases, the maximum air temperature increasing does not exceed 0.85 °C during all 95 h. Consequently, ambient temperature decreases during night operation and then cools the heated subsoil and assists the soil to recover its cooling capacity.
In this paper, the effect of steel fibers recovered from pneumatic waste, dune sand (D), and its granulometric correction on the thermal conductivity of dune sand concrete was studied. Three types of steel fibers were used (f1, f2, and f3) having the lengths of 20 mm, 30 mm, and 40 mm, respectively, and diameters of 0.28 for f1 and f2 and 0.9 mm for f3, incorporated in the concrete with a volume fraction of 1 %. River sand (R) was used for the correction of dune sand granulometry. The proportions of 50 % D mixed with 50 % R and 40 % D mixed with 60 % R were adopted for the mixtures M2 and M3, respectively. The concretes made with only dune sand have a lower thermal conductivity, compared to the mixtures M2 and M3.The results obtained also showed that, when the concrete density decreases, the thermal conductivity decreases. The thermal conductivity of concretes without fibers is lower compared with fiber-reinforced concretes. Other parameters have an influence on this property, namely the diameter, the length, and the aspect ratio (l/d).
Medical data is transferred between hospitals and healthcare providers via telemedicine to improve patient care. This transfer exposes medical data to a number of security risks, and while most existing security solutions, such as cryptographic approaches, protect data from unauthorized access, this protection is only effective when the data is encrypted. In this context, we present a frequency-domain watermarking method for hiding electronic patient records in their associated elec-trocardiogram (ECG) signals in this paper. The signal is transformed into a 2D picture in this approach, and the frequency content of the image is extracted using the integer wavelet transform. Finally, the acquired coefficients are treated through Schur decomposition, and the watermark bits are integrated by altering the least significant bit of the generated Eigen values. We used the ECG data from the MIT-BIH Arrhythmia Database to test the suggested method. Based on the results of the experiments, we can infer that using the Integer wavelet transform allows for the generation of a watermarked signal that is practically identical to the host signal. The watermark's resistance to several attacks commonly used in watermarking is further confirmed by the robustness results.
This paper proposes a sequential rules-based recommendation system, called STS-Rec. It addresses the main drawbacks of sequential patterns mining approaches for POI (Point of interest) recommendation by considering both temporal and social influences to perform short-term recommendations. STS-Rec first transforms mobility data into location sequences. Then, it incrementally mines sequential recommendation rules in these sequences. In contrast with standard sequential recommenders, the proposal (1) discovers rules that tolerate locations’ order variations by loosening the strict ordering constraint of location sequences, (2) builds a tree-based model to incrementally mine recommendation rules, and (3) supports short and long-term POI recommendation by using a user-defined window by extracting patterns that appear within a maximum number of consecutive locations. To take the temporal influence into account, STS-Rec adapts its mining strategy to include the temporal context in location data. Hence, the conventional rule mining problem is redefined to mine time-extended recommendation rules. An experimental evaluation conducted on two large-scale real check-in datasets from Gowalla and Brightkite shows that the proposed model outperforms two state-of-the-art sequential models in terms of accuracy and coverage.
Groundwater in the Touggourt region—or as its named, Oued Righ—in southeastern Algeria, is the only source of irrigation. To assess its suitability for agricultural purposes, we collected 72 samples from wells at this region, physical and chemical measurements were carried out for each water sample, and calculations of the sodium adsorption ratio (SAR), permeability index (PI), soluble sodium percent (SSP), residual sodium carbonate (RSC), magnesium hazard ratio (MHR) and Kelley’s ratio (KR) were carried out, as these indices are often used to assess the suitability of groundwater for irrigation uses. Based on the irrigation water quality index (IWQI) values, a spatial distribution map for each parameter using the inverse interpolation technique (IDW) was produced by Geographical Information System (GIS). According to the IWQI map, about 35% of the water samples analyzed fall into the Severe Restriction category (SR), making it unsuitable for irrigation under normal circumstance. Again, the remaining 65% of the groundwater has a high restriction (HR) for use. Groundwater in the study area could be used for irrigation in highly permeable soils where salt-tolerant crops are grown. Adequate drainage and continuous monitoring of water quality are recommended.
A new electrochemical sensor based on inactivated Escherichia coli (E. coli) was prepared for the determination of lead ions (Pb²⁺) in real samples. The biorecognition element was obtained by inactivation of the E. coli bacteria. The biosensing platform was constructed by mixing bovine serum albumin (BSA) with inactivated Escherichia coli (E. coli*) deposited on the surface of platinum (Pt) electrode modified with conductive poly(vinylidene fluoride) (PVDF)/multiwall carbon nanotubes (MWCNT)-gold nanoparticles MWCNTs-PVDF-AuNPs/E. coli*-BSA. The impedance characterization of the nanocomposite reveals performance improvements in the charge transfer process (Ret = 20 Ω) compared to the use of Pt bare electrode (Ret = 115 Ω). Moreover, the bioelectrochemical reaction between the released-enzymes from inactivated E. coli* and the inhibitor (Pb²⁺) was confirmed and successfully demonstrates the inhibition system. The electrochemical detection of Pb²⁺ was performed by square wave voltammetry (SWV) with a detection limit of 0.13 μg/l and a quantification limit of 0.43 μg/l. The sensor was applied to detect lead ions in real samples like waste-water and lipstick with an excellent selectivity against copper ions. The biosensor is highly sensitive (30.1 μA/ppb.cm²) with good stability and reliable reproducibility with a relative standard deviation (RSD = 0.23%) for monitoring lead ions and seems operational for monitoring other hazardous materials. Graphical Abstract
The most important asset for a person is their health and wellbeing. While it is possible to keep one's health at its best, it is common for people to have health shocks (HSs; negative shocks to an individual's health). In this study, using Chinese Health and Nutrition Survey (CHNS) panel data, we studied the impact of different HSs on income using new modified methods. Thus, we considered the substantial links among different HSs, levels of education, and insurance types, as well as their impact on people's wealth defined by their income. The core aim of this study is to help devise and guide new policies to reduce the effect of these HSs through the proper use of education and insurance channels. In this research, we used simple pooled OLS regression to measure the different causality estimates of HSs, education, and insurance, as well as their interactions. Obtained through the use of up-to-date panel data, the study results are consistent with previous research using different HS and education measures. The findings of this research suggest revising previous policies concerning education levels and health insurance schemes.
In recent years, the optimization problem using meta-heuristic algorithms has been widely used in medical image registration and was a solution in diagnosing many diseases and tumors. Given the great success achieved by the sine cosine algorithm (SCA) and particle swarm optimization (PSO) algorithms in many medical images analysis, and the use of the computed tomography (CT) scan images for diagnosing COVID-19 patients, we propose an improved sine cosine algorithm (ISCA) resulting from the hybridization of the SCA and PSO algorithms to register the CT images of the lung of the people infected by COVID-19. Simulation results show that the proposed approach can achieve high accuracy and robust recording compared to the SCA method.
Zygophyllum album L. known as "Agga" in vernacular Algerian dialect is one of great traditional remedies used by the native populations, as a treatment for skin diseases, indigestions and diabetes. This medicinal specie is drought resistant and salt tolerant, it plays an important role against desertification, reforestation of degraded lands and soil rehabilitation. Therefore, knowledge of environmental requirements for its seed germination is necessary for plant establishment in field conditions to colonize new territories. The main objective of this paper is to evaluate the influence of salinity level and the type of salts on seed germination characteristics (germination percentage and salinity tolerance index) of this shrub found in the Sahara of Algeria. The test was conducted in a phytotron at a temperature of 25 °C and in the dark for 10 days. The seeds germinated in Petri dishes soaked with aqueous solutions of (NaCl, CaCl2 or Na2SO4) at the same concentrations of 0, 100, 150, 200, 250, 300, 350 and 400 mM. Statistical analysis revealed that germination characteristics were significantly affected by salinity level and salt compositions. Seeds of Z. album were non dormant, exhibited highest germination percentage (100%) under distilled water and were moderately salt tolerant. Both germination percentage and germination velocity gradually decreased with increasing salinity, irrespective of applied salt types. The salts that induced the inhibition of germination were salt specific and decreasing in following sequence: Na2SO4 > CaCl2 > NaCl. Our results support the statements that different salts exhibit both osmotic stress and ionic toxicity on seed germination.
Nowadays, the rapid spread of various tick-borne viruses has caused various diseases in the animal population of livestock and poultry, in which the human population is not safe. Crimean-Congo hemorrhagic fever is one of the common diseases between animals and humans that causes many deaths of both populations in the large areas of the world every year. Identification and control methods of this epidemic have led virologists to study the dynamics and behavior of these viruses in different transmission cycles in recent years based on mathematical models. In this paper, we present an advanced mathematical model of transmission cycle of viruses of the Crimean-Congo hemorrhagic fever between livestock, ticks and humans in a fractal-fractional system of six initial value problems. In fact, we extend the standard integer-order model to a two-parametric six-compartmental fractal-fractional hybrid model with pawer-law type kernels. To study the existence of solution for such a system, we first use a special family of contractions titled ϕ−ψ-contractions and also in the next step, we use the Leray–Schauder fixed point theorem. The Banach contraction principle helps us to prove the uniqueness of solutions. We try to investigate the stability behaviors of the solutions in the context of the Ulam’s criterion, and then use Lagrange polynomials to obtain a numerical algorithm to find the approximate solutions of the mathematical model of Crimean-Congo hemorrhagic fever. Finally, by changing the values of the fractal dimension and fractional order in a closed interval, we analyze the convergence and stability of the solutions graphically. We see that all solutions have stable behaviors and at smaller fractal dimensions, decay and growth rates in susceptible and infected groups are slower, and vice versa. The accurate results of fractal-fractional operators in mathematical modeling motivate us to use them in different models.
In this paper, the economic dispatch problem (EDP) is defined as determining the output power generated by the unit or units to supply the specified load in a manner that will minimize the volume of gas combusted (VGC). It is a kind of management for electrical energy in the power system in way to operate their generators as economically as possible. This problem is solved under some equality and inequality constraints. The equality constraints are the active power flow balance equations, while the inequality constraints are the minimum and maximum power output of each unit. The voltage levels and security are assumed to be constant. This paper presents a static and dynamic economic dispatch study in electrical power systems using the Cuckoo Search Algorithm (CSA). This method has been tested on the western Algerian electrical power system. The study results are quite encouraging showing the good applicability of CSA for EDP. After a theoretical introduction of the problem formulation and the Cuckoo Search Algorithm, a description of the western Algerian electrical power system is presented, followed by a discussion of the simulation results.
Herein, for the first time, we report a novel one-step green reduction approach of reduced graphene oxide decorated with silver nanoparticles (rGO-AgNPs) that is both cost-effective and ecologically friendly using Allium sativum extract. The rGO-AgNPs were then coupled with a molecularly imprinted polymer to create a highly sensitive selective electrochemical sensor to detect lactic acid, a key agent in cancer cell screening. The scanning electron microscope, X-ray diffraction, and Fourier-transform infrared spectroscopy were used to study the morphology and surface characteristics of the synthesized nanomaterials. In contrast, cyclic voltammetry was used to characterize the modified electrodes in the presence of [Fe(CN)6]3-/4-. The electrochemical detection of LA was indirectly performed using AuE/rGO-AgNPs/MIP and based on the signal generated from [Fe(CN)6]3-/4- redox peaks which were steadily decreased with the increase of LA concentration due to the interruption of electron transfer paths suggesting high sensitivity towards the target molecule. The current variation ΔI(I BLANK - I LA) was directly proportional to LA concentration between 10 and 250 μM with a very low detection limit of 0.726 μM (S/N = 3). The developed LA sensor revealed an excellent selectivity with strong stability. Furthermore, the LA sensor robustness was tested against different pH mediums, and good results were obtained; the practical use of the developed sensor was validated in human serum samples revealing the strong potential use in cancer screening and monitoring.
Trace fossils provide detailed palaeoenvironmental and palaeoecological information of both ancient and modern sedimentary systems. During middle Miocene times the Aures Massif located in the northeastern part of Algeria, was affected by, at least, one marine transgression. The latter led to the installation of a carbonate platform, which is placed, for the first time, in a Mediterranean context. In the Rhassira basin, the Middle Miocene marine succession is characterised by carbonate platform deposits dominated by rhodolith beds, typical of those known throughout the Mediterranean area. This succession can be divided into many units separated by discontinuities interpreted here as omission surfaces. The Djebel Arhane section shows two omission surfaces characterised by a pre-omission suite (firmground) represented by Balanoglossites burrows for the first surface and Gastrochaenolites ornatus burrows/borings for the second one, and an omission suite (hardground) as evidenced by the bioerosive structures Trypanites and Caulostrepsis, in both surfaces, respectively. Gastrochaenolites ornatus traces were formed and preserved in firm, compact, semi-lithified and fine-grained substrates (firm- to hardground), indicating the Glossifungites ichnofacies. They show bioglyphs which have been formed during contraction of the posterior adductor muscles. These suggest that their tracemakers were represented by suspension-feeding bivalves, most probably Pholadidae or Mytilidae, which rotated during penetration. The fill of these traces is composed of marine deposits related to a transgressive lag. The omission suite is divided into two ichnocoenoses: (i) pre-lithification burrows/borings, and (ii) post-lithification borings. This is the first report of the ichnotaxon G. ornatus from Algeria.
This study aims to characterize camel production systems in the Ouargla region, located in the northern part of the Algerian Sahara. Data were collected from 70 camel farmers using questionnaires. We studied the socioeconomic status of breeders; as well as herd structure and composition, breeding management, and dromedary products and income. Subsequently, a typology was established using multiple correspondence factorial analysis (MCA) and hierarchical ascendant classification (HAC), which identified four groups of farmers. The First group comprises 11 fattener breeders with low educational levels, having large or medium-sized meat herds with high commercial objectives. The second Group includes phoeniculture-camel farms that consist of 22 camel milk producers, 7 of whom were regular milk sellers. The third Group consists of 5 multi-active farmers; with a large market of young camels (for meat), hair, and milk. Fourth group is composed of 32 agro-breeders and public servants who maintain small herds with low commercial management.
In this paper, we propose an unsupervised lightweight network with a single layer for ear print recognition. We refer to this method by MDFNet because it relies on gradient Magnitude and Direction alongside with responses of data-driven Filters. At first, we align ear using Convolution Neural Network (CNN) and Principal Component Analysis (PCA). MDFNet starts by generating a filter bank from training images using PCA. This is followed by a twofold layer, which comprises two operations namely convolution using learned filters and computation of gradient image. To prevent over-fitting, a binary hashing process is done by combining different filter responses into a single feature map. Then, we separately construct histograms for each of gradient magnitude and direction according to the feature map. These histograms are then normalized, using power-L2 rule, to cope with illumination disparity. Several fusion rules are evaluated to combine the two histograms. The main novelty of MDFNet lies in its simple architecture, effectiveness, the good compromise between processing time and performance it provides along with its high robustness to occlusion. We conduct extensive experiments on three public datasets namely AWE, AMI and IIT Delhi II. Experimental results demonstrate the effectiveness of MDFNet, which achieves high recognition rates (82.5%, 97.67% and 98.96%, respectively), and outperformed several state of the art methods with a high robustness to occlusion. Experiments revealed also the actual need for considering ear alignment.
According to the World Health Organization, lower-income countries suffer from adverse health issues more than higher-income countries. Information and communication technologies (ICT) have the potential to resolve these issues. Previous research has analyzed the theoretical and empirical causal effects of ICT on infant mortality at country-specific and global levels for a short period of time. However, the causes and results could be different in low-income countries. The objective of this paper was to examine the deficiencies through the use of panel data from 27 low-income countries from 2000-2017. We applied the predictive mean matching technique to supplement the missing data and then used panel data techniques (i.e., fixed effects (FE) and pooled common correlated effects (PCCE)), and system-GMM to estimate the causal effects. We compared the consistency and the possible heterogeneity of previous results using a set of robust techniques and empirical tests. We found that internet access and, to a lesser extent, cellular mobile subscriptions, two of the three ICT variables used in our research, had a significant positive effect on reducing infant mortality in low-income countries. In conclusion, governments and policymakers of low-income countries should consider the availability of internet-related ICT innovations and make them nationally accessible to reduce health crises such as the infant mortality rate.
Medical data is transferred between hospitals and healthcare providers via telemedicine to improve patient care. This transfer exposes medical data to a number of security risks. While most existing security solutions, such as cryptographic techniques , protect data from unauthorized access, this protection is only effective when the data is encrypted. In this context, we present a frequency-domain watermarking method for hiding electronic patient records in their associated ECG signals in this paper. The signal is transformed into a 2D image in this method, and the frequency content of the image is extracted using the integer wavelet transform. Finally, the acquired coefficients are subjected to Schur decomposition, and the watermark bits are integrated by altering the least significant bit of the generated Eigen values. We used the ECG data from the MIT-BIH Arrhythmia Database to test the suggested method. Based on the outcomes of the studies, we can conclude that using the Integer wavelet transform allows for the generation of a watermarked signal that is nearly identical to the host signal. The watermark's resistance to various attacks commonly used in watermarking is also confirmed by the robustness results.
The brown Tunisian seaweed Halopteris scoparia was used as a feedstock for producing renewable bioethanol, biogas, and biodiesel to demonstrate the proof of concept for the North African energy sector. A quantitative and qualitative quantification of H. scoparia composition using different colorimetric methods was completed to highlight its bioconversion potential. These substrate inputs were subjected to anaerobic fermentation by Saccharomyces cerevisiae to produce bioethanol. The materials were also used to generate bio-hydrogen and volatile fatty acids during dark fermentation by a bacterial consortium and using the oleaginous yeast Yarrowia lipolytica. The lipids were extracted and trans-esterified to Fatty Acid Methyl Esters (FAMEs), and their profiles were then analyzed with gas chromatography (GC). A significant ratio of the bioethanol, e.g., 0.35 g ethanol/g DW substrate, was produced without pretreatment, consistent with the theoretical Gay-Lussac yield. The production of the biohydrogen and lipids were up to 1.3 mL H2/g DW substrate and 0.04 g/g DW substrate, respectively, from the raw biomass. These results were higher than those reported for other well-studied seaweeds such as L. japonica. Overall, this work contributes to the current investigations in Tunisia for producing alternative energies from algae and finding new solutions to the current energy situation and environmental challenges in Maghreb.
Cognitive radio has been nominated as a key technology for the internet of things (IoT), due to its intelligent functionalities ensuring continuous connectivity for IoT objects. Spectrum prediction, as one of the core CR functions, has emerged as a leading tool to alleviate the spectrum scarcity problem. Spectrum prediction minimizes sensing and decision‐making delays, and thereby it reduces collisions with primary users and guarantees safe access for secondary users (SUs). Thus, it became an inseparable part of many new spectrum allocation and mobility methods. In this work, the proposed cognitive radio IoT model consists of local sensors (LS) that perform sensing instead of SUs, and a cognitive base station that receives sensing results from different LSs to predict the next occupancy information and allocate frequencies for SUs. The predictor is followed by a channel extraction block for efficient spectrum allocation. Then, a low complexity spectrum prediction and preallocation system based on optimized neural network architecture is presented. Two nonlinear neural network models, time delay neural network and nonlinear autoregressive with exogenous input, that are trained on a real spectral occupancy dataset, are optimized using the Bayesian optimization algorithm then compared. The best predictor forecasts the next occupancy rate of multiple channels simultaneously based on three dimensions, area, time, and frequency. Performance evaluation was conducted through accuracy, mean squared error (MSE), and regression fit. The highest prediction accuracy was 93.5%, the regression coefficient was 0.98, and a reduced MSE of 0.0013 obtained. Results show that the considered scheme is efficient in forecasting the spectrum availability of different bands within the IoT spectrum resources.
The current work concentrated on the green synthesis of silver nanoparticles (AgNPs) through the use of aqueous Citrus limon 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.
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