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One of the critical issues in medical data analysis is accurately predicting a patient’s risk of heart disease, which is vital for early intervention and reducing mortality rates. Early detection allows for timely treatment and continuous monitoring by healthcare providers, which is essential but often limited by the inability of medical professionals to provide constant patient supervision. Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. Doctors cannot constantly have contact with patients, and heart disease detection is not always accurate. By offering a more solid foundation for prediction and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) could help physicians with the prediction and detection of HD. This study aims to use different feature selection strategies to produce an accurate ML algorithm for early heart disease prediction. We have chosen features using chi-square, ANOVA, and mutual information methods. The three feature groups chosen were SF-1, SF-2, and SF-3. The study employed ten machine learning algorithms to determine the most accurate technique and feature subset fit. The classification algorithms used include support vector machines (SVM), XGBoost, bagging, decision trees (DT), and random forests (RF). We evaluated the proposed heart disease prediction technique using a private dataset, a public dataset, and different cross-validation methods. We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate inconsistent data and discover the machine learning algorithm that achieves the most accurate heart disease predictions. Healthcare providers might identify early-stage heart disease quickly and cheaply with the proposed method. We have used the most effective ML algorithm to create a mobile app that instantly predicts heart disease based on the input symptoms. The experimental results demonstrated that the XGBoost algorithm performed optimally when applied to the combined datasets and the SF-2 feature subset. It had 97.57% accuracy, 96.61% sensitivity, 90.48% specificity, 95.00% precision, a 92.68% F1 score, and a 98% AUC. We have developed an explainable AI method based on SHAP approaches to understand how the system makes its final predictions. [ABSTRACT FROM AUTHOR]

Elliptical curves are dedicated for several security applications including Radio Frequency Identification (RFID) devices, smart cards, bankcards, etc. To guarantee effective security of such applications, these cryptographic systems require effective resistance to various types of physical attack. Differential Power-Analysis (DPA) attacks were considered the most efficient attacks against scalar multiplication calculation algorithms. In this paper, we propose a countermeasure method against the DPA attacks, for a scalar multiplication algorithm that is basically secure against Simple Power Analysis (SPA) and safe-error attacks. Our proposal is intended for Elliptic Curves Cryptosystems (ECC) algorithms dedicated to low cost applications. We first introduce the different types of side-channel attacks that ECC-based cryptographic algorithms can suffer, as well as their countermeasure methods existing in the literature. We then present an optimized hardware implementation of the most effective scalar multiplication algorithm against SPA and safe-error attacks. Finally, we present our proposed DPA countermeasure method and its effectiveness against other extensions of DPA attacks. Our proposed method is similar to the Basic Random Initial Point (BRIP) method except that the latter is only applicable for the left-to-right algorithm. The proposed method is based on the randomization of processed data during the computation of the scalar multiplication algorithm and prevents vulnerability to Zero-value Point Attack (ZPA), Refined Power analysis (RPA) attack and double attack. In the last part of our paper, we present comparative analysis in terms of computational cost between our proposed method and other countermeasure algorithms presented in the literature, such as Montgomery-ladder, the BRIP algorithm, the left-to-right algorithm and the Co-Z Mont-Ladder algorithm. [ABSTRACT FROM AUTHOR]

Background: Aim is to analyze alterations in third and fourth corneal higher-order aberrations using a ray tracing aberrometer (iTrace) induced by SMILE (small incision lenticule extraction) refractive surgery on Algerian patients; and also to investigate how corneal aberrations vary with spherical equivalent (SE), corneal central thickness (CCT), and age. Design Prospective, non-randomized case series that were carried out at the Chiali Smile Center, Algiers, Algeria. Participants: A total of 219 eyes from 110 myopic patients, with and without astigmatism, successfully concluded the ultimate follow-up. Participants exhibited an average age of 32.48 years, with preoperative mean corneal high-order aberrations (CHOA) measuring 0.005 ± 0.036 μm. Methods: A tracking trend analysis of high-order aberrations (HOAs) was conducted on the three groups of patients, assessing total corneal high-order aberrations after SMILE over periods of two, six, and twelve months. Results: A substantial increase in total corneal high-order aberrations following SMILE, with a P value < 0.05. Conversely, there was a noteworthy and significant decrease in vertical trefoil for all groups (P value = 0.000). Interestingly, the corneal higher-order aberrations were more pronounced after two months compared to the six and twelve-month intervals. Spherical aberration demonstrated a significant increase with the extent of preoperative myopia and preoperative spherical equivalent both before and after SMILE surgery (P < 0.05), particularly in cases of high myopia. Moreover, there were significant reductions in preoperative and postoperative vertical coma (P = 0.047, P = 0.004, respectively) corresponding to the degree of preoperative myopia, with a more substantial decrease in vertical coma observed in cases of low myopia. Conclusion: In the post-SMILE period, a notable increase in vertical coma and spherical aberrations was observed, accompanied by a significant decrease in vertical trefoil across all eyes. These aberration changes were identified to be closely associated with the spherical equivalent. [ABSTRACT FROM AUTHOR]

To accurately determine the Schottky barrier characteristics and elucidate the consequent impacts, it is imperative to possess a comprehensive understanding of the conduction pathways within a Schottky barrier. The objective of this study was to examine the structural and electrical characteristics of a Schottky junction composed of an aluminum (Al) electrode, a p-type copper zinc tin sulfide (CZTS) thin film, and a molybdenum (Mo) substrate. The structural characterization of the material was conducted through the utilization of SEM analysis and EDX Spectra. On the other hand, the electrical characterization was accomplished by analyzing the current–voltage (I–V) characteristics. A significant part of the study is dedicated to validating the obtained CZTS solar cell parameters by simulating the I–V characteristic using extracted parameters and comparing them to experimental data. A comparative analysis of experimental and fitted I–V characteristics is presented, showcasing the effectiveness of the GA method in parameter extraction. The findings of this research contribute to the broader understanding of solar cell parameter optimization and offer a robust methodology for future applications in the field. [ABSTRACT FROM AUTHOR]

Plasma-liquid interactions yield numerous physicochemical phenomena, rendering them promising for various applications. Plasma-based technology is proposed for water treatment due to its high efficiency in removing contaminants unattainable by conventional techniques. In this study, we employ an argon microwave plasma jet (MWPJ) to investigate methylene blue (MB) degradation. We observe a significant enhancement in the MB degradation rate in a covered system, attributed to increased air humidity promoting hydroxyl radicals (OH) production, which degrade approximately 95% of MB. Furthermore, the injection of O2 gas into the solution under the plasma generates more hydrogen peroxide (H2O2), around 30 mg/L compared to approximately 20 mg/L without injection, although the MB degradation efficiency is reduced. We evaluate MB degradation under various solution properties, revealing that increasing electrical conductivity decreases the MB degradation rate until it becomes independent for conductivities > 10,000 µS/cm. In these latter conditions, a non-conventional temporal evolution of solution conductivity was observed: a decrease during the first tens of minutes followed by a continuous increase for longer treatment time. Conversely, solution acidity minimally affects the MB degradation rate. The MWPJ is characterized by optical emission spectroscopy, showing stability over time and under various solution properties. The energy yield (Y50%) consistently demonstrates superior performance of the MWPJ in a closed environment compared to an open-to-air environment. Although its efficiency is relatively low compared to other systems, we anticipate improvements through parameter adjustments. [ABSTRACT FROM AUTHOR]

This study aims to use beeswax, a readily available and cost-effective organic material, as a novel phase change material (PCM) within blends of low-density polyethylene (LDPE) and styrene-b-(ethylene-co-butylene)-b-styrene (SEBS). LDPE and SEBS act as support materials to prevent beeswax leakage. The physicochemical properties of new blended phase change materials (B-PCM) were determined using an X-ray diffractometer and an infrared spectrometer, confirming the absence of a chemical reaction within the materials. A scanning electron microscope was used for microstructural analysis, indicating that the interconnection of the structure allowed better thermal conductivity. Thermal gravimetric analysis revealed enhanced thermal stability for the B-PCM when combined with SEBS, especially within its operating temperature range. Analysis of phase change temperature and latent heat with differential scanning calorimetry showed no major difference in the melting point of the various PCM blends created. During the melting/solidification process, the B-PCMs possess excellent performance as characterized by W70/P30 (112.45 J.g−1) > W70/P20/S10 (94.28 J.g−1) > W70/P10/S20 (96.21 J.g−1) of latent heat storage. Additionally, the blends tend to reduce supercooling compared to pure beeswax. During heating and cooling cycles, the B-PCM exhibited minimal leakage and degradation, especially in blends containing SEBS. In comparison to the rapid temperature drop observed during the cooling process of W70/P30, the temperature decline of W70/P30 was slower and longer, as demonstrated by infrared thermography. The addition of LDPE to the PCM reduced melting time, indicating an improvement in the thermal energy storage reaction time to the demand. According to the obtained findings, increasing the SEBS concentration in the composite increased the thermal stability of the resulting PCM blends significantly. Despite the challenges mentioned earlier, SEBS proved to be an effective encapsulating material for beeswax, whereas LDPE served well as a supporting material. Leak tests were performed to find the ideal mass ratio, and weight loss was analyzed after multiple cycles of cooling and heating at 70 °C. The morphology, thermal characteristics, and chemical composition of the beeswax/LDPE/SEBS composite were all examined. Beeswax proves to be a highly effective phase change material for storing thermal energy within LDPE/SEBS blends. [ABSTRACT FROM AUTHOR]

The development of composite materials with fewer interface problems always helps to obtaining good properties. Two types of insulating biopolymers (7% w/w) were incorporated into a conductive matrix (polyaniline) during polymerization. The synthesis method, inspired by the synthesis of polyaniline and the coating of chitosan for the development of flexible electrodes, produces thermosets that are more thermally stable than the matrix until 350 °C. While one (gum) reduces the electrical conductivity, the other (lignin) increases the basic value of the raw material (~ 0.72 S Cm–1) by a factor of 10. The physicochemical and morphological analyses show the formation of the polymer on the surface of the biopolymer with the appearance of new chemical bonds by interactions between hydroxyls of the biopolymer and the nitrogen of amine function from the polyaniline. [ABSTRACT FROM AUTHOR]

The rise of water effluents containing emerging contaminants that resist conventional chemical and physical treatments makes the treatment of wastewater more complex. Plasma-based treatment methods have great potential to degrade many of the emerging contaminants, including dyes. In this study, using pulsed nanosecond discharges, we investigate the degradation of methylene blue (MB) dye in water by generating plasma in Ar-O2 gas bubbles in water. The scalability of the setup is studied by producing discharges in a one electrode setup (a needle-to-plate configuration) and in a four electrodes setup (four needles-to-wire configuration). The discharge was characterized by electrical measurements (current and voltage waveforms) and optical emission spectroscopy. We find that the discharge properties are stable during the 30 min of processing, with and without the presence of MB in solution at low electrical conductivity. The production rate of H2O2 in the one electrode setup was measured in 0% and 70% O2, and it was found to be ∼2.3 and 2.9 mg/Lmin, respectively. In the four electrodes setup, H2O2 production rate was lower: ∼1.2 and 1.9 mg/Lmin in 0% and 100% O2. Degradation of MB was assessed in both setups for (i) different % of O2 in the gas mixture, (ii) different MB initial concentration, and (iii) different initial water conductivity. In the one electrode setup, a high MB degradation (> 85%) was generally achieved in all conditions, but a better performance is noted in high O2 percentage (> 50%) at low initial water conductivity. At low MB concentration and low electrical conductivity, the performance of the four electrodes setup was better than the one electrode setup. [ABSTRACT FROM AUTHOR]

Women frequently have less financial literacy and less access to formal financial products than males, despite the fact that both males and females need to be sufficiently financially literate to engage in economic activities and make wise financial decisions for themselves and their families. As a result, women have unique and additional needs in terms of financial literacy. Enhancing financial literacy is one of the ways and means of empowering women which will help them to make sound financial decisions whether short term or long term. The study adopted exploratory and empirical research and aimed at investigating the level of financial literacy among women using five basic financial literacy questions and eleven advanced financial literacy questions. From the descriptive statistics, it is evident that the basic financial literacy level is satisfactory. As far as advanced financial literacy level is concerned, it has taken a back seat. From the multiple regressions, we were able to find that age, education, income, present financial situation, and ability to meet monthly commitments are major influencing variables of financial literacy among women. There was no evidence that supported the influence of marital status and other psychological variables on the financial literacy score. Another noteworthy contribution of the study is that the FinTech was introduced as a moderating variable and it was revealed that its impact on the financial literacy of Saudi women is marginal. [ABSTRACT FROM AUTHOR]

The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy. [ABSTRACT FROM AUTHOR]

Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system. The results obtained indicate that the DRASTIC Vulnerability Index (VI) ranges from 91 to 141 and is classified into two categories: low and moderate vulnerability. However, the correlation between groundwater total dissolved solids (TDS) and the Vulnerability Index is relatively weak (r < 0.5). Indeed, the original DRASTIC index needs some improvements. To improve it, some adjustments are required, notably by incorporating the TDS-groundwater salinization risk (GSR) indicator. The seven parameters of the original DRASTIC model were used as inputs for the artificial intelligence models, while the GSR values were used as outputs. Performance indicators, such as the correlation coefficient (r) and the Willmott Agreement Index (d), showed that the ANN model outperformed the SVR and MLR models. Indeed, during the training phase, the ANN model obtained r values equal to 0.89 and d values of 0.4, demonstrating the superiority, robustness, and accuracy of ANN-based methodologies over the original DRASTIC model. The findings could provide valuable information to guide management of groundwater contamination risks, especially in arid regions. [ABSTRACT FROM AUTHOR]

This paper presents an analytical formulation to investigate functionally graded porous beams' nonlinear thermal buckling performance under various boundary conditions. The current model incorporates innovative cinematic techniques with the focus on the stretching effect and the iteration techniques. The material properties of the porous FG beams are temperature-dependent and vary according to a simple power-law distribution. The validity of the present theory' results is confirmed by comparing them with those obtained by other researchers. The findings demonstrate that the critical buckling temperature in TD and TID ranges from 1.03 to 1.27% for a uniform distribution and 1 to 1.47% for linear and non-linear distributions. Conversely, for regular porosity variation, the critical buckling temperatures fluctuate between 0.99 and 1.74%, and between 0.99 and 1.59% for porosity variation. Furthermore, the influence of boundary conditions becomes more pronounced when the nonlinear temperature difference is high. [ABSTRACT FROM AUTHOR]

This paper proposes a new fault-tolerant control approach for gas turbines, based on fuzzy techniques that allow real-time observation of their behavior. The approach consists of detecting and locating faults, and then determining the appropriate control action to keep the turbine in stable operation. This improves the efficiency and lifespan of the turbine, and reduces maintenance costs with optimal planning. The state space model of the turbine is subjected to a diagnostic procedure based on Type-1 and Type-2 fuzzy models, using the operational data of the different operating points studied. The Luenberger observer is used in the fault detection mechanism, to characterize turbine component malfunctions by comparing the observed real behaviors and the fuzzy models. The results show that the fault-tolerant fuzzy control has ensured the stability and availability of the turbine during fault occurrence, with good performance in detection, isolation and reconfiguration. [ABSTRACT FROM AUTHOR]

In the Arabian Gulf (called also Persian Gulf; hereafter 'the Gulf'), Jana and Karan Islands are recognized as one of the most Important Bird Areas in the region. Many migratory breeding seabirds, like the Greater Crested Tern Thalasseus bergii, White-cheeked Tern Sterna repressa and Bridled Tern Onychoprion anaethetus, depend on these islands during the breeding season. However, these aquatic wildlife species are suffering from intensified urban and industrial coastal development and various contamination events including wars and related oil spills. In this study, we used these three piscivorous top predator birds to analyse the levels of 19 trace elements (TEs; i.e. Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Sr, V and Zn) in 15 muscular tissue samples from Jana and Karan Islands. PERMANOVA analysis showed no difference in contamination profile between sites nor between species probably due to their spatial and ecological proximity and therefore similar levels of exposure to TEs. Comparing these levels with existing literature, our results showed no particular concern for all elements, except for Al (maximum values recorded = 116.5 µg g−1 d.w.) and, in two samples, Ba (33.67 µg g−1 d.w.) and Pb (5.6 µg g−1 d.w.). The results can be considered as an initial step for supplementary evaluations with a larger number of samples and specified time intervals for the collection of specimens. This study provided baseline information on the pollution status of these two ecologically important sites which require a continuous biomonitoring programme. [ABSTRACT FROM AUTHOR]

The non-consumable tungsten electrode plays a key-role in the tungsten inert gas (TIG) welding process, governing the behavior of the electrical arc that is essential for generating the necessary heat in welding. This study is focused on varying of electric parameters in the purpose to identify the appropriate conditions that promote electrode integrity. The post weld cathode analysis revealed that both the input current intensity and its frequency affect the overall morphology and microstructural and mechanical performance of the refractory electrode. Noticeable physical alterations occur with increased current, leading to larger grains and a reduction in microhardness values. In addition, it causes apparent defects such as perforation and superficial deformation. However, applying higher frequencies mitigates these defects significantly. Based on these findings, it seems crucial to take into consideration the interactive effect of current applied and its frequency to ensure better electrode conservation, thereby enhancing the efficiency of the welding process. [ABSTRACT FROM AUTHOR]

Aquifer quality monitoring and scoping are essential for both groundwater control and water provision in agricultural areas. The Irrigation Geothermal Water Quality Index (IGWQI) was investigated based on 6 variables (EC, SAR, Cl, Na, HCO3, and T) for 41 water samples using conventional method and predicted with support vector machine (SVM) and classification tree and regression (CART) computer models as an indicator to evaluate the suitability of geothermal water for irrigation. It was found that the use of such parameters allows for more accurate identification of unsuitable groundwater areas and reduces estimation bias, particularly if the dataset is limited (6 variables). In this study, the SVM and CART models were developed and validated to assess the groundwater quality of the EL Hamma aquifer. The values predicted by the SVM and CART approaches are in extremely good agreement with the measured IGWQI, with a mean coefficient of determination (R2) close to 1, about 0.91 and 0.99, respectively, thus ensuring practicality and confidence for the artificial intelligent model. Findings showed that three classes of groundwater quality were identified: 12% are good, 56% are satisfactory, and 32% are classified as unsuitable water. The poor water quality for irrigation is shown in the eastern parts of the study area, which is nearly similar to that of the water temperature, averaging between 35 and 57 °C. This rise in water temperature is controlled by the fault that connects the deep compartments of the system and the shallow aquifer. IGWQI delineation and mapping is an excellent tool for the protection and safeguarding of groundwater resources, notably in arid areas where groundwater is the sole source of life, as well as an important tool in decision-making for groundwater planning and use. [ABSTRACT FROM AUTHOR]

The three-dimension high-efficiency video coding standard (3D-HEVC) finalized comes with a significant increase in complexity caused by the integration of depth map coding technology. This complexity is primarily triggered by the quad-tree partition of the Intra Coding Units (CU) in the depth map. A new technique utilizing deep learning is proposed, in this paper, to tackle the issue of excessive complexity aiming to predict efficiently the CU partition structure. The proposed method involves building a dataset of CU partition structure information for a depth map, creating a Multi-Deep Convolutional Neural Network (MD-CNN) model using this dataset, and then incorporating the model into the 3D-HEVC test platform. This approach reduces the 3D-HEVC video encoder complexity by 48.29% without affecting robustness, compression efficiency and video quality. [ABSTRACT FROM AUTHOR]

This study fulfils a thermo-hygro-mechanical analysis of the bending behavior of FG plates resting on different elastic foundation models. A quasi-3D high-order shear deformation theory with five unknowns is used herein to perform this analysis. The impact of shear deformation and stretching effect are included in the formulation of the used approach. The result of the change in material characteristics and the volumetric fraction of components on the bending response of FG plates in a thermo-hygro-mechanical environment is analyzed and discussed. The principle of virtual displacements is used to obtain the equilibrium equations, and the Navier-type solution is applied to solve the resulting equations. The results show that the increase in thermal load and moisture concentration causes a rapid deflection increase. Furthermore, the Winkler parameter influences the shear stresses more than the deflections. [ABSTRACT FROM AUTHOR]

Elliptic curve cryptosystems (ECC) are well suited to devices with limited memory and processing capabilities, such as smart cards, as well as devices with limited power, such as wireless handheld devices. This is because, for considerably smaller key sizes, elliptic curves over huge finite fields provide the same level of security as other cryptosystems like RSAECC-based circuits are vulnerable to physical attacks aimed at getting the secret key, despite its security. Fault injection attacks are a sort of physical attack that involves inserting a fault into a circuit during a cryptographic operation to alter its behavior. In this regard, we introduce a new fault detection approach based on time redundancy for the Montgomery Elliptic Curve Scalar Multiplication algorithm. We divided the ECC design into three blocks with registers placed between them. When compared to the original Montgomery ECSM algorithm, our solution takes roughly 11.65% more occupied slices overhead but yields a frequency gain of 51.27%. [ABSTRACT FROM AUTHOR]

In southern Tunisia especially in Gabes region, irrigated agriculture is the major economic sector. The intensive exploitation of groundwater have been gradually rising, leading the water quality to deteriorate. To ensure sustainable development and preserve water, soil, and land cover resources, a careful estimation of the irrigation water quality, which influence on the regional agriculture activities. The objective of this study is to investigate the irrigation water suitability based on hydrogeological, hydrochemical, and analytic hierarchy approach (AHP) and water quality index (WQI). Results highlighted that TDS of the groundwater ranged from 0.4 to 6 g/L. The most abundant chemical species in the water were Ca2+, Cl−, SO42−, and Na+, with mean concentration of 460 mg/L, 1320 mg/L, 1450 mg/L, and 1200 mg/L, respectively. The largest amounts were discovered along the shoreline and in deep aquifers embedded in Cretaceous sediments. AHP has used as multicriteria decision analysis, to weighting groundwater quality criteria, and groundwater suitability for irrigation has been mapped. Based on the Quality index "WQI", the water samples are categorized into three types: excellent, acceptable, and worthless. A 45% of sampled water is not suitable for irrigation use. However, groundwater quality is increasingly poor towards the coastal areas and north of the study area. This study concluded that the method adopted in assessing the groundwater quality in arid coastal areas led to important results and could be helpful scholar to improve their research especially in similar areas. It can also assist decision makers in taking proactive solutions to protect and preserve groundwater resources from pollution risks. [ABSTRACT FROM AUTHOR]

Necrotizing enterocolitis (NEC) is the leading cause of gastrointestinal-related death in premature infants. Its etiology is multifactorial, with intestinal dysbiosis playing a major role. Probiotics are a logical preventative therapy for NEC, however their benefits have been inconsistent. We previously developed a novel probiotic delivery system in which planktonic (free-living) Limosilactobacillus reuteri (Lr) is incubated with biocompatible dextranomer microspheres (DM) loaded with maltose (Lr-DM-maltose) to induce biofilm formation. Here we have investigated the effects of Lr-DM-maltose in an enteral feed-only piglet model of NEC. We found a significant decrease in the incidence of Definitive NEC (D-NEC), death associated with D-NEC, and activated microglia in the brains of piglets treated with Lr-DM-maltose compared to non-treated piglets. Microbiome analyses using 16S rRNA sequencing of colonic contents revealed a significantly different microbial community composition between piglets treated with Lr-DM-maltose compared to non-treated piglets, with an increase in Lactobacillaceae and a decrease in Clostridiaceae in Lr-DM-maltose-treated piglets. Furthermore, there was a significant decrease in the incidence of D-NEC between piglets treated with Lr-DM-maltose compared to planktonic Lr. These findings validate our previous results in rodents, and support future clinical trials of Lr in its biofilm state for the prevention of NEC in premature neonates. [ABSTRACT FROM AUTHOR]

Phlebotomine sand flies (Diptera: Phlebotominae) belonging to the genus Phlebotomus are vectors of pathogens such as arboviruses, bacteria, and parasites (Leishmania). Species of the genus Sergentomyia (Se.) transmit Sauroleishmania (Reptile Leishmania) and feed on cold-blooded vertebrates; recently, they have been incriminated in mammalian Leishmania transmission. In addition, they have been reported to feed on warm-blooded vertebrates. This study aimed to (i) screen wild-caught Sergentomyia species for the detection of mammalian Leishmania and (ii) identify the blood meal origin of engorged females. The sand flies were collected using centers for disease control and prevention (CDC) traps, mounted and identified morphologically. Only females of the genus Sergentomyia were screened for Leishmania infection using PCR targeting the 18S ribosomal DNA locus. For positive specimens, Leishmania parasites were typed using nested PCR targeting ribosomal internal transcribed spacer 1 followed by digestion with HaeIII. The PCR-RFLP results were confirmed through sequencing. Blood meal identification was performed through PCR amplification of the vertebrate cytochrome b gene using degenerate primers followed by sequencing. In total, 6026 sand fly specimens were collected between 2009 and 2018. Among these, 511 belonged to five species of Sergentomyia genus: Se. minuta (58.51%), Se. fallax (18.01%), Se. clydei (14.68%), Se. dreyfussi (6.26%), and Se. antennata (2.54%). A total of 256 female Sergentomyia sp. specimens were screened for Leishmania infection. Seventeen (17) were positive (6.64%). Two Leishmania species were identified. Leishmania major DNA was detected in five specimens; this included three Se. fallax, one Se. minuta, and one Se. dreyfussi collected from Tunisia. Leishmania infantum/L. donovani complex was detected in four Se. minuta and three Se. dreyfussi specimens collected from Tunisia. In addition, we identified the blood meal origin of five engorged Se. minuta specimens collected from Tunisia. Sequencing results revealed two blood sources: humans (n = 4) and reptiles (n = 1) indicating possible role of Sergentomyia species in the transmission of human Leishmania. In addition, these species could be involved in the life cycle of L. infantum/L. donovani complex and L. major. The results of the blood meal origin showed that Sergentomyia fed on both cold- and warm-blooded vertebrates. These findings enable a better understanding of the behavior of this sand fly genus. Further studies should focus on the role of Sergentomyia in human Leishmania transmission and possible control of this disease. [ABSTRACT FROM AUTHOR]

Ischemia/reperfusion injury (IRI) is an inevitable complication of liver surgery and transplantation. The purpose of this study was to examine the beneficial effects of diclofenac on hepatic IRI and the mechanism behind it. Wistar rats' livers were subjected to warm ischemia for 60 min followed by 24 h of reperfusion. Diclofenac was administered intravenously 15 min before ischemia at 10, 20, and 40 mg/kg body weight. To determine the mechanism of diclofenac protection, the NOS inhibitor L-Nitro-arginine methyl ester (L-NAME) was administered intravenously 10 min after diclofenac injection (40 mg/kg). Liver injury was evaluated by aminotransferases (ALT and AST) activities and histopathological analysis. Oxidative stress parameters (SOD, GPX, MPO, GSH, MDA, and PSH) were also determined. Then, eNOS gene transcription and p-eNOS and iNOS protein expressions were evaluated. The transcription factors PPAR-γ and NF-κB in addition to the regulatory protein IκBα were also investigated. Finally, the gene expression levels of inflammatory (COX-2, IL-6, IL-1β, IL-18, TNF-α, HMGB-1, and TLR-4) and apoptosis (Bcl-2 and Bax) markers were measured. Diclofenac, at the optimal dose of 40 mg/kg, decreased liver injury and maintained histological integrity. It also reduced oxidative stress, inflammation, and apoptosis. Its mechanism of action essentially depended on eNOS activation rather than COX-2 inhibition, since pre-treatment with L-NAME abolished all the protective effects of diclofenac. To our knowledge, this is the first study demonstrating that diclofenac protects rat liver against warm IRI through the induction of NO-dependent pathway. Diclofenac reduced oxidative balance, attenuated the activation of the subsequent pro-inflammatory response and decreased cellular and tissue damage. Therefore, diclofenac could be a promising molecule for the prevention of liver IRI. [ABSTRACT FROM AUTHOR]

Groundwater resources are increasingly under demand to support human activities and economic development. This naturally clean resource is threatened by human and natural pollutions. Protecting groundwater has become a challenge for both urban and rural communities. This work aims to develop a simple and effective approach to assess the risk of aquifer pollution and the delineation of risk zones. To do so, an aquifer risk index (ARI) was presented as an overall indicator of the risk of groundwater pollution. The ARI is calculated using the groundwater vulnerability index (GVI) and the normalized water quality index (WQI). The multi-linear regression (MLR) model was used to assign weights to each parameter. This model has been applied and validated in the Koutine area of southern Tunisia. The results revealed that the groundwater in the study area is characterized by low and moderate risk. Fifty-six percent (56%) of the total study area presents a low risk. The remaining 44% of the study area is classified as medium risk, primarily in the Wadi and Lowland plains. ARI considers the intrinsic characteristics of the aquifer and groundwater quality when assessing the risk of pollution. The approach developed for assessing groundwater risks is simple to implement, realistic, and efficient. AVI is a parametric approach that uses aquifer data without losing information. This method can assist academics and groundwater resource managers in delineating risk areas to protect groundwater resources from pollution. [ABSTRACT FROM AUTHOR]

Despite the high coercive field (Ec = 7.3 kV/mm) and the high conductivity (2.1 × 10–7 (Ωm)−1) of (Bi0.5Na0.5)TiO3 (BNT) ceramics, their microstructure and electric properties can be tailored by appropriate substitutions at the A and/or B-sites. In this paper, we offer a comprehensive review of the up-to-date advances on doped lead-free BNT ceramics, highlighting the role of the nature and concentration of dopants on the stoichiometry, microstructure, density, dielectric properties, and piezoelectric properties of the solid solutions obtained after doping. For each composition presented in this review, the synthesis method and the sintering technique of the ceramics are also given because of the well-known connection between powder processing (its morphology), ceramics densification (its density), and the macroscopic properties of the final product. A substitution mechanism for each dopant that explains the role of the dopant on the structure and electrical properties of the BNT matrix is also described in detail. Our review provides knowledge for the development of new solid solutions of BNT. [ABSTRACT FROM AUTHOR]

Purpose: The multi-step sequential infiltration technique or sintering process usually produces porosities in functionally graded structures. It is confirmed that the porosity significantly influences the static responses of FGM beams, but its influence on their thermodynamic response is still worth studying. Methods: To highlight this influence, the dynamic behavior of simply-supported porous FG beams with effective temperature-dependent material properties is examined by using a novel integral three variable quasi-3D high-order shear deformation theory for the first time. Notably, different thermal gradients varying along the thickness are considered. The governing differential equations of motion have been established based on Hamilton's principle and solved by employing the Navier-type closedform solution. Results: The present theoretical results are validated with the existing literature, and excellent agreement is identified between the results. Besides, material temperature dependence, power-law index, porosity parameter, temperature rising, and slenderness ratio effects are discussed. Results show that dynamic behavior using temperature-dependent and independent material properties would produce different natural frequencies. With the rise of porosity, the natural frequency decreases significantly at high temperatures. Conclusions: The beam with a higher slenderness ratio is more sensitive to the stretching effect. Finally, to improve the thermodynamic behavior of such structures, ceramic constituents with a lower thermal expansion coefficient would be recommended. [ABSTRACT FROM AUTHOR]

Cloud Computing is a paradigm allowing access to physical and application resources online via the Internet. These resources are virtualized using virtualization software to make them available to users as a service. Virtual machines (VMs) migration technique provided by virtualization technology impacts the performance of the cloud. It is a significant concern in this environment. When allocating resources, the distribution of VMs is unbalanced, and their movement from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper addresses the VMs migration issue by applying a machine learning model to reduce the VMs migration number and energy consumption. The proposed algorithm (named VMLM) is based on improving VM's migration process and selection. It has been benchmarked with JVCMMD and EVSP solutions. The simulation results demonstrate the efficiency of our proposal, which includes two phases the machine learning preparing stage and the VMs migration stage. [ABSTRACT FROM AUTHOR]

In the present work, a new online fuzzy tuning linear quadratic regulator (FT-LQR) is proposed for balancing and trajectory tracking of the rotary single inverted pendulum. The beauty of the proposed approach is that it tunes the LQR weighting matrices Q and R online by using a fuzzy controller. The idea of the proposed controller is inspired by the designer's choice of Q and R matrices, where according to the system response, the designer changes the values of the two matrices to achieve the best performance. However, it is not easy to define these two matrices in an integrated manner. Classic methods such as trial and error require intensive efforts, time-consuming and do not guarantee the desired performance. Therefore, this empirical knowledge was applied to a fuzzy controller to adjust the weighting matrices according to the required performance and depending on the system's state. The FT-LQR is designed through simulation, and further experimental validations using a laboratory prototype verify the correctness of the proposed approach. Moreover, it can ensure a faster response, good stability, and better robustness to external disturbances in the different operating conditions. [ABSTRACT FROM AUTHOR]

This paper is focused on the study of the grid synchronization of an equivalent wind farm (WF) equipped with doubly fed induction generators (DFIGs) using a nonlinear integral backstepping control (IBSC), in order to improve and accelerate the synchronization of the DFIGs to the electrical grid. In this study, the equivalent model of the WF is obtained by using the semi-aggregation method. This method can be performed by simplifying the mechanical system and by aggregating the electrical system of the wind turbines of the same row. The main contribution of this study is to ensure a good synchronization between the equivalent WF and the electrical grid. This synchronization will be achieved by controlling the rotor currents of the equivalent DFIGs. In this work, the rotor current loops of each equivalent DFIG are controlled by using a nonlinear IBSC algorithm and the proportional-integral (PI) controller. This comparative study between the two controllers is validated by a simulation in Matlab/Simulink environment. The results obtained show that the synchronization between the equivalent WF and the electrical grid is correctly achieved. However, the nonlinear IBSC algorithm has more efficiency and better performance compared to the PI controller. [ABSTRACT FROM AUTHOR]

In this paper we study solutions, possibly unbounded and sign-changing, of a weighted static Choquard equation involving the Grushin operator. Under some appropriate assumptions on the parameters, we prove various Liouville-type theorems for weak solutions under the assumption that they are stable or stable outside a compact set of R n . First, we establish the standard integral estimates via stability property to derive the nonexistence results for stable weak solutions. Next, by means of the Pohozaev identity, we deduce the Liouville-type theorem for weak solutions which are stable outside a compact set. [ABSTRACT FROM AUTHOR]

Glassware cleaning is an essential part of pharmaceutical industry's quality process, although, to date, no precise methodology is recommended by the pharmaceutical regulations. This study focuses on developing a detailed validation procedure for laboratory glassware cleaning and applying it routinely in quality control labs. The first step was the determination of the worst-case among raw materials controlled on a pharmaceutical site by using the failure mode effects analysis (FMEA) approach based on water solubility (WS), flask volume (FV), concentration (C), detectability (D) according to the absorptivity, and the probability (P) of the occurrence of the risk based on raw material analysis frequency. The adopted manual cleaning method was validated on three series of six flasks. Absorbances at λmax of cleaned and worst-case flasks were compared to the limit of detection (LOD). Methylparaben was found to be the worst-case. Cleaning validation was carried out on 50 mL flasks containing aqueous solutions at 0.11 g/L after a contact time of 20 min under stirring. A prewash with tap water, a proper wash with a 95-V/5-V water/detergent solution (quaternary ammonium), and a final wash with purified water were carried out. The absorbances of all the cleaned flasks were lower than those of the worst-case LOD (< 0.0112). The selected cleaning procedure has been successfully validated. The proposed approach precisely describes the steps to be followed and can be adapted by other pharmaceutical industries as part of their laboratory glassware cleaning validation methodology. [ABSTRACT FROM AUTHOR]

The concept of external glass FRP composite confinement is a current process for strengthening concrete beams subjected to static loads. End anchorage glass FRP composites of 80 mm width and 90–130 mm length with different thicknesses (2.4 and 4.8 mm) have been fixed at the bottom of beams with bolts of various diameters (6 and 10 mm). For this purpose, the behavior of beams strengthened with bolt-end anchoring glass fiber polymer composites (BEGFPC) has been analyzed. It is concluded that the load capacity of the BEGFPC beams is improved by increasing the end-anchorage glass FRP composite thickness (about 98–188%). In addition, the BEGFPC system with bolts of 6 mm diameter has significantly improved the flexibility of beams. In contrast, the 10 mm bolts in diameter give a high ultimate load, whatever their quantity. Therefore, combining bolts with diameters of 6 and 10 mm would be the best solution for increasing the ultimate load and ductility of the retrofitted beams. Depending on the number and bolts' arrangement, there is also an enhancement in the crack patterns by changing from intermediate flexural failure to shear failure in beams. [ABSTRACT FROM AUTHOR]

Despite the many existing fibrillation processes for the production of cellulose nanofibrils (CNFs), their exact destructuration effects on fibers remain poorly understood. This work reports the use of disk refining, twin-screw extrusion (TSE), ultra-fine grinding and homogenization for CNF production, alone or in combinations. The same raw material (birch fibers) and pretreatment (enzymatic hydrolysis) were used for comparable process conditions, with a systematic measurement of the energy consumption and a calculation of the process theoretical shear rates. The modifications of cellulose fiber and nanofibril properties were assessed by morphological analysis, optical microscopy, transmission electron microscopy (TEM), X-ray diffraction (XRD), nuclear magnetic resonance (NMR), tensile tests and multi-scale quality index analysis. The water retention value (WRV) and degree of polymerization (DP) were also measured. All processes resulted in 8–14 nm-wide CNFs as well as larger CNF bundles. Residual fiber fragments were observed after refining and TSE. The latter resulted in the most significant decrease of crystallinity index (CI), DP and mechanical properties, while CNFs produced by ultra-fine grinding and homogenization displayed the best mechanical and optical properties. These processes were then combined with a preliminary disk refining step, to assess the influence of the morphology of the initial fibers on their fibrillation efficiency. Such combinations had a negative effect for TSE and homogenization. Lower properties were obtained for these samples, compared to CNFs produced by the processes used individually. In contrast, the combination of disk refining and ultra-fine grinding was found to greatly enhance the resulting CNF properties, with a significant increase in optical transmittance. These results provide an insight on the destructuration effects of each process, and pave the way to further research on the physical phenomena at work. The cellulose fibers destructuration effects of four mechanical fibrillation processes were evaluated in detail. The relevance or irrelevance of their combinations was assessed, the best results being obtained with processes with similar geometries. [ABSTRACT FROM AUTHOR]

Groundwater is the main source to answer the irrigation supply in several arid and semi-arid areas. In the present work, groundwater quality for irrigation purposes in the arid region of Menzel Habib (Tunisia) for thirty-six groundwater samples is assessed considering the application of different conventional water quality indicators, particularly, electrical conductivity (EC), sodium absorption ratio (SAR), soluble sodium percentage (SSP), magnesium adsorption ratio (MAR), Kelly ratio (KR), and permeability index (PI). The results obtained indicate a variability for EC: 3.06 to 14.98 mS.cm−1; SAR: 4.08 to 19.30; SSP: 35.78 to 71.53%; MAR: 34.19 to 56.01; PI: 38.47 to 72.74; and KR: 0.56 to 2.47. These results suggest that groundwater from Menzel Habib aquifer system is classified between excellent to unsuitable according to the applied water quality indices. Furthermore, the groundwater samples are also plotted in the Richards diagram classification system, based on the relation between SAR and EC, suggesting that almost groundwater samples present a harmful quality. Moreover, fuzzy logic model has been proposed and created to assess groundwater quality for irrigation. The membership functions are constructed for six significant parameters such as EC, SAR, SSP, MAR, KR, and PI and the rules are, then, fired to get a simple Fuzzy Irrigation Water Quality Index (FIWQI). The obtained groundwater quality results suggest that 3% of the samples from Menzel Habib region are considered as "good" for irrigation, 3% are classified as "good to permissible", 33% with a "permissible" quality, 36% "permissible to unsuitable", while 25% of groundwater present an "unsuitable" quality. Thus, the use of fuzzy logic techniques has more reliable and robust results by overcoming the uncertainties in the decision-making attributed to the conventional methods by the creation of new classes (excellent to good, good to permissible, and permissible to unsuitable) in addition to the classes proposed by Richards diagram classification (excellent, good, permissible, and unsuitable) to assess the groundwater quality suitability for irrigation purposes. [ABSTRACT FROM AUTHOR]

Olive oil mill wastewater (OMW) is a major waste stream generated in olive oil industry. It is highly polluted due to phenolic compounds. The present study focused on the physicochemical properties of OMW as well as the quantitative and qualitative effects of two extraction methods of phenolic compounds which were liquid–liquid and maceration methods. Spectrophotometry and high-performance liquid chromatography-electrospray ionization–mass spectrometry (HPLC–ESI–MS) were adopted to quantify the phytochemical contents and the phenolic compounds. The extract obtained by the maceration method showed the highest yields of total polyphenol, flavonoid, and tannin contents. The LC–MS results revealed the presence of 16 phenolic compounds in the macerated, and only 12 phenolic compounds were found in the extract of the second method. Quinic acid was identified as the most abundant compound. Moreover, the macerated extracts possessed the highest antioxidant potential as evidenced by their strong ferric reducing antioxidant power (FRAP) and their 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonate) (ABTS) radical scavenging activities. The phytochemical contents, as well as the antioxidant potentials of OMW after extraction using maceration, were significantly greater than using liquid–liquid method. Therefore, maceration seemed to be the most effective method for extracting phenolic compounds from OMW. The OMW constitute a rich source of natural phenolic compounds that could be used as a potential source of natural antioxidants. [ABSTRACT FROM AUTHOR]

Scene classification is a crucial research problem in remote sensing (RS) that has attracted many researchers recently. It has many challenges due to multiple issues, such as: the complexity of remote sensing scenes, the classes overlapping (as a scene may contain objects that belong to foreign classes), and the difficulty of gaining sufficient labeled scenes. Deep learning (DL) solutions and in particular convolutional neural networks (CNN) are now state-of-the-art solution in RS scene classification; however, CNN models need huge amounts of annotated data, which can be costly and time-consuming. On the other hand, it is relatively easy to acquire large amounts of unlabeled images. Recently, Self-Supervised Learning (SSL) is proposed as a method that can learn from unlabeled images, potentially reducing the need for labeling. In this work, we propose a deep SSL method, called RS-FewShotSSL, for RS scene classification under the few shot scenario when we only have a few (less than 20) labeled scenes per class. Under this scenario, typical DL solutions that fine-tune CNN models, pre-trained on the ImageNet dataset, fail dramatically. In the SSL paradigm, a DL model is pre-trained from scratch during the pretext task using the large amounts of unlabeled scenes. Then, during the main or the so-called downstream task, the model is fine-tuned on the labeled scenes. Our proposed RS-FewShotSSL solution is composed of an online network and a target network both using the EfficientNet-B3 CNN model as a feature encoder backbone. During the pretext task, RS-FewShotSSL learns discriminative features from the unlabeled images using cross-view contrastive learning. Different views are generated from each image using geometric transformations and passed to the online and target networks. Then, the whole model is optimized by minimizing the cross-view distance between the online and target networks. To address the problem of limited computation resources available to us, our proposed method uses a novel DL architecture that can be trained using both high-resolution and low-resolution images. During the pretext task, RS-FewShotSSL is trained using low-resolution images, thereby, allowing for larger batch sizes which significantly boosts the performance of the proposed pipeline on the task of RS classification. In the downstream task, the target network is discarded, and the online network is fine-tuned using the few labeled shots or scenes. Here, we use smaller batches of both high-resolution and low-resolution images. This architecture allows RS-FewshotSSL to benefit from both large batch sizes and full image sizes, thereby learning from the large amounts of unlabeled data in an effective way. We tested RS-FewShotSSL on three RS public datasets, and it demonstrated a significant improvement compared to other state-of-the-art methods such as: SimCLR, MoCo, BYOL and IDSSL. [ABSTRACT FROM AUTHOR]

The square root operation is indispensable in a myriad of computational science and engineering applications. Various computational techniques have been devised to approximate its value. In particular, convergence methods employed in this regard are highly affected by the initial approximation of the seed value. Research shows that the provision of an initial approximation with higher accuracy yields fewer additional iterations to calculate the square root. In this article, we propose two novel algorithms. The first one presents a seed generation technique that depends on bit manipulation and whose output is to be used as an initial value in the calculation of square roots. The second one describes a quadrature-based square rooting method that utilizes a rectangle as the plane figure for squaring. We provide error estimation of the former using the vertical parabola equation and employ a suitable lookup table, for the latter, to store needed cosine values. The seed generation approach produces a significant reduction in the number of iterations of up to 84.42% for selected convergence methods. The main advantages of our proposed square rooting algorithm lie in its high accuracy and in its requirement of just a single iteration. Our proposed algorithm also provides for lower computational latency, measured in the number of clock cycles, compared to Newton–Raphson's and Bakhshali's square rooting methods. [ABSTRACT FROM AUTHOR]

In Tunisia, despite the implementation of national strategies to prevent some of cardiovascular risk factors, these diseases still represent the leading cause of death. The current study aimed to determine the prevalence of cardiovascular risk factors and assess the screening and health education practices related to these factors among the adults of Sousse (Tunisia). A cross-sectional study was conducted in 1000 households in Sousse, Tunisia. To collect data, a team of trained medical doctors administered by interview a pre-tested questionnaire and performed blood pressure and anthropometric measures at the participants' homes. In total, 1977 adults participated in the study. Their mean age was 39.8 (± 13.8) years. The Females/Males sex ratio was 1.5. Weight excess and lack of physical activity were found in 65.4% and 60.7% of participants. Screening for obesity and android obesity were reported by 36.6% and 5.7% of health services users. Advices from a healthcare professional to practice the recommended physical activity and eat healthily were reported by less than a third of these latter. Screening and health education practices related to cardiovascular risk factors should be reinforced in the Tunisian healthcare facilities. The implementation of multisectoral actions is necessary at the national level in order to obtain an environment that promotes a healthy lifestyle. [ABSTRACT FROM AUTHOR]

The present article aims to assess the phytotoxic effects of copper and zinc oxide nanoparticles (Cu NPs, ZnO NPs) on mung bean (Vigna radiata L.) and their possible risk on food quality and safety. We also study the molecular mechanisms underlying the toxicity of nanosized Cu and ZnO. Seeds of mung bean were germinated under increasing concentrations of Cu NPs and ZnO NPs (10, 100, 1000, 2000 mg/L). We analyzed levels of free amino acids, total soluble sugars, minerals, polyphenols and antioxidant capacity. Our results showed that depending on the concentrations used of Cu NPs and ZnO NPs, the physiology of seed germination and embryo growth were modified. Both free metal ions and nanoparticles themselves may impact plant cellular and physiological processes. At 10 mg/L, an improvement of the nutritive properties, in terms of content in free amino acids, total soluble sugars, essential minerals, antioxidant polyphenols and flavonoids, was shown. However, higher concentrations (100–2000 mg/L) caused an alteration in the nutritional balance, which was revealed by the decrease in contents and quality of phenolic compounds, macronutrients (Na, Mg, Ca) and micronutrients (Cu, Fe, Mn, Zn, K). The overall effects of Cu and ZnO nanoparticles seem to interfere with the bioavailability of mineral and organic nutrients and alter the beneficial properties of the antioxidant phytochemicals, mineral compounds, phenolic acids and flavonoids. This may result in a potential hazard to human food and health, at some critical doses of nanofertilizers. This study may contribute in the guidelines to the safe use of nanofertilizers or nanosafety, for more health benefit and less potential risks. [ABSTRACT FROM AUTHOR]

This paper is devoted to the investigation of the Weyl and the essential S-spectra of a bounded right quaternionic linear operator in a right quaternionic Hilbert space. Using the quaternionic Riesz projection, the S-eigenvalue of finite type is both introduced and studied. In particular, we have shown that the Weyl and the essential S-spectra do not contain eigenvalues of finite type. We have also described the boundary of the Weyl S-spectrum and the particular case of the spectral theorem of the essential S-spectrum. [ABSTRACT FROM AUTHOR]

Behavior recognition in video sequences is an interesting task in the computer vision field. Human action recognition, allows machines to analyze and comprehend several human activities from input data sources, such as sensors, and multimedia contents. It has been widely applied in public security, video surveillance, robotics and video games control, and industrial automation fields. However, in these applications, gestures are formed by complex temporal sequences of motions. This representation imposes many problems such as variations in action/gesture performers style, body shape and size and even the way that the same signer realize the motion, and various problems related to the scene environment like illumination changes and background complexity. Moreover, processing on large-scale continuous gesture data make these problems more challenging due to the unknown boundaries limit of each gesture embedded in the continuous sequence. In this work, we present a novel deep architecture for isolated actions and large scale continuous hand gesture recognition using RGB, depth, and gray-scale video sequences. The proposed approach, called End-to-End Deep Gesture Recognition Process (E2E-DGRP), considers a new representation of the data by extracting gesture characteristics. This is followed by an analysis of each sequence to detect and recognize the corresponding actions. Firstly, segmentation module spot each start and end gesture boundaries and segment continuous gesture sequences into several isolated gestures based on motion indicator method. Then, a set of discriminating features is generated characterizing motion location, motion velocity and motion orientation using a proposed method named "Deep Signature". Generated features are fed to an additional neural network layer formed by a Softmax classifier which is an output layer for gesture classification purpose. The high performance of our approach is obtained by the experiments carried out on three well-known datasets KTH and Weizman for isolated actions in RGB and grayscale modality and Chalearn LAP ConGD dataset for continuous gesture in depth. A first experimental study shows that, the recognition results obtained by our method outperform those obtained by previously published approaches and it achieves 97.5 and 98.7% in terms of accuracy with KTH and Weizmann datasets, respectively. A second experimental study, conducted using the Chalearn LAP ConGD data set, shows that our strategy outperforms other methods in terms of precision reaching 0.6661 as mean Jaccard Index. [ABSTRACT FROM AUTHOR]

Southern Gabes aquifer is part of coastal Jeffara plain located in southeastern Tunisia. It consists a semi-arid area in which groundwater is the main source to water supply for several socio-economic sectors. Southern Gabes aquifer suffers from excessive abstraction and heavy anthropogenic pressures that make local groundwater resources threatened by pollution risks. This study aims to assess groundwater vulnerability, evaluate, and delineate groundwater risk regions. For this, a 17 water samples were carried out in the study area and chemical compositions were analyzed. A well-known AVI model has been used to assess aquifer vulnerability and new algorithms of sensitivity to pollution index (PSI) and risk groundwater to pollution index (RGPI) were implemented and used to assess, classify, and map groundwater pollution risk. Results reveal that study area suffers from high risk. Forty one percent of the total surface of study area has a very high risk. Nonetheless, only 30% of study area has a low to insignificant risk to pollution which necessitates taking severe precautions to protect the southern Gabes aquifer system. The method used in this study seems giving more precise results compared to conventional approaches. Moreover, this method allows assessing the pollution risk with flexible and reliable algorithm even with limited dataset. Hence, the poor natural protective capacity of study area needs a rapid intervention by local authorities in order to develop proactive solutions to protect and preserve groundwater resources from pollution risks and establish a long-term program for groundwater resources sustainable development. [ABSTRACT FROM AUTHOR]

We study the quasilinear Schrödinger type equation - Δ p u + | x | a | u | r - 1 u = | x | b | u | q - 1 u in R N , where q > p - 1 ≥ r ≥ 1 and a , b ∈ R . By combining integral estimates and a Pohozaev type identity, we prove that stable and finite Morse index solutions to the equation must be zero under various assumptions on a, b, p, q, r and N. Our results are new even in the case a = b = 0 . [ABSTRACT FROM AUTHOR]