Eco-toxicity and health risk assessment of polycyclic aromatic hydrocarbons in surface sediments of Burullus Lake in Egypt

Polycyclic aromatic hydrocarbons(PAHs)are ubiquitous environmental contaminants of growing concern due to their potential ecological and human health risks.This study presents a comprehensive assessment of PAHs contamination in the surface sediments of Burullus Lake,a vital and second largest delta lake in Egypt.The aim was to evaluate the eco-toxicity and potential health risks associated with the presence of these compounds.Surface seven sediment samples were collected from various drains in the southern part of Burullus Lake.Soxhlet extraction method was employed to extract PAHs(16PAHs)from the sediment sample.Analytically,target compounds were located using HPLC.The results showed that samples contained PAHs levels ranging from 0.038×10^(-6)to 0.459×10^(-6),which is considered heavily polluted by the European standard for PAHs pollution.Additionally,there was no apparent source of PAHs in the ElKhashah drain or the Brinbal Canal,as HPLC found none of the compounds.The most prevalent compound in sediment samples along the study area was fluoranthene.The diagnostic indices in the present study indicated that the hydrocarbons in the region originated from pyrolytic and man-made sources along the drains of Burullus Lake.The principal component analysis(PCA)and diagnostic ratios revealed that coal combustion and pyrolytic sources were responsible for the PAHs contamination in the surface sediments.The non-carcinogenic risk(HI),which is the product of the HQs for the adult and child populations,respectively,was calculated.HI values under 1,therefore,demonstrated that they had no carcinogenic effects on human health.TEQs and MEQs in the sediments of Burullus Lake do not have a cancer-causing impact on people.For the safety of nearby wildlife,aquatic life,and people,all activities that raise petroleum hydrocarbon levels in Burullus Lake must be adequately regulated and controlled.According to the ecological risk assessment,there is little chance that PAHs will be found in the sediments of Burullus Lake.This study underscores the urgent need for effective pollution control measures and regular monitoring of PAHs levels in Burullus Lake sediments to protect the aquatic ecosystem and public health.It also highlights the importance of considering eco-toxicity and human health risks in integrated risk assessments of PAHs-contaminated environments.

Quantitative and Qualitative Responses of Hydroponic Tomato Production to Different Levels of Salinity

From economic and nutritional points of view,tomato is,historically,considered one of the most important crops.Without significant yield reduction,most commercial cultivars of tomato crops are sensitive to moderate levels of salinity.However,high levels of salt stress can negatively affect the yield and quality of tomato fruits.Therefore,this study was conducted to evaluate the yield and fruit quality of three tomato cultivars(Forester,Ghandowra-F1,and Feisty-Red)cultivated hydroponically,under three different levels of nutrient solution salinity.Evaluation of tomato fruits was performed based on quantity(number and weight of fruits,and total fruit yield),physical quality(color andfirmness),and chemical quality(refractometric index“Brix”,pH,EC,K^(+),Na^(+),and NO_(3)-).Experiments were conducted using three salinity levels of the nutrient solution with electrical conductivity values(dSm^(-1))of 2.5(control),6.0,and 9.5.Results showed that the studied tomato cultivars were significantly influenced by high salinity(9.5 dSm^(-1))in comparison to the low(2.5 dSm^(-1))and medium(6.0 dSm^(-1))levels of salinity.On average,the highest fruit weight per plant of 1944.84 g and total fruit yield of 4.42 kgm^(-2) were observed at the low salinity level;however,no significant differences were obtained in the two yield factors(single fruit weight and total fruit yield)for the low and medium salinity levels.On the other hand,a significant reduction in tomato yield(31%)was associated with the high salinity level compared to the yield at low and medium salinity levels.Results of physical quality parameters showed highly significant differences among all salinity levels.On average,the maximum value of color change(1.72)was associated with the medium salinity level,and the maximum value offirmness(9.61 Ncm^(-1))was recorded at the high salinity level.Salinity levels and tomato cultivars introduced significant differences in chemical quality parameters;however,no significant differences in these parameters were attributed to the low and medium salinity levels.The maximum value of pH was recorded for the combination of medium salinity and Forester cultivar.Moreover,the maximum values of Brix,EC,K^(+),Na^(+),and NO_(3)^(-) were recorded for the Ghandowra-F1 cultivar at the high salinity level.Unlike the Feisty-Red,the performance of the Forester and Ghandowra-F1 cultivars was found to be acceptable at the tested medium salinity level(6.0 dSm^(-1)).

An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

Markers of Heart, Lung and Dorsal Aorta Damage of Mother Rats and Their Neonates Post Therapeutic Treatment with Doxorubicin, Cisplatin and 5-Flurouracil

Aim: Recently, there is an increased average of developing cancers. Though, the chemotherapeutic-treatment is unfavorable during pregnancy due to its harmful effects on developing fetuses, physicians have two ways to minimize these effects either by termination of the pregnancy or minimizing its side effects. The present work aimed to illustrate the susceptibility of cardiac, lung and dorsal aorta function to the widely applicable drugs doxorubicin and cisplatin as well as 5-flurouracil. Materials and Methods: Mother albino rats were arranged into four-groups (control, doxorubicin, cisplatin and 5-flurouracil-treated groups). Each pregnant rat received intraperitoneal administration of 0.2 mg/kg body weight at 10th and 14th day of gestation and sacrificed at parturition (two doses). At parturition, serum of mother rats used to assess troponin I, heat shock protein 70, 8-hydroxydeoxyguanosine, vascular endothelial growth factor and adhesion molecules (ICAM-1 & VCAM-1). Isoenzyme electrophoresis of alkaline and acid phosphatases, glucose-6-phosphate dehydrogenase and lactic dehydrogenase were estimated in serum, myocardium and dorsal aorta of mother rats. The myocardium and lung were processed for histopathological investigations for both mothers and their offspring. Single strand (comet assay) and double strand DNA damage were carried out in heart and dorsal aorta of mother rats. Results: The present finding revealed that there are detected alterations of myocardial markers and lung amino acid metabolism as well as disruption of myocardial isoenzymes. DNA damage of myocardium and dorsal aorta were observed. Conclusions: The authors concluded that the metabolic activity of heart and lung is highly susceptible to doxorubicin and cisplatin treatment compared to 5-flurouracil and the therapeutic doses must be degraded.

Estimation of the Maximum Annual Loads Modeling for Kingdom of Bahrain

The present paper proposes the impact of the air temperature on electricity demand as expected. It is clear that the annual maximum load is recorded versus the years starting by the year 2009 up to 2012. At present, the graph fitting technique is applied with some mathematical and computational tools based on the actual values of the years 2009 up to 2012 considering the lower values, the higher values and the average values of the annual maximum loads for Kingdom of Bahrain. For the three scenarios, the models are obtained by curve fitting technique. As well, the model of actual loads is obtained finally which has mostly the closest values obtained.

Characteristics,composition and functional properties of seeds,seed cake and seed oil from different Brassica carinata genotypes

Carinata(Brassica carinata)is an oilseed crop with potential in biofuel production and livestock feed.The present research was conducted to evaluate twenty Brassica carinata genotypes used in breeding programs to develop commercial production of Brassica carinata with low levels of erucic acid.The following characters were evaluated:oil contents,fatty acid profiles,physicochemical characteristics of oils,as well as the content of protein,total phenolic compounds,and the antiradical activity of Brassica carinata defatted seed cake.The oil analysis indicated desirable quality,including low percentages of free fatty acids(FFA),high amounts of unsaponifiable matter(USM),and low values for primary and secondary oxidation products that were under the regulated limits.The oil contents of the samples varied between 27.0 and 39.2%.A total of 16 fatty acid compositions were identified in different samples.As compared to the commercial canola oil,it was observed that Brassica carinata 14C genotype had quite similar fatty acid composition.Total phenolics and protein contents of Brassica carinata cake extract varied from 43.2 to 132 mg/100 g and from 24.6 to 35.4%,respectively.It can be noted that Brassica carinata genotype 14C with low erucic acid(EA)and high oleic acid contents consider as an edible oil cultivar.Regarding EA,the genotypes 3C,10C,and 17C with low EA content could be recommended as a basis for further breeding programs.Brassica carinata cake from different genotypes might be a valuable source of natural antioxidants due to their antioxidant capacities and free radical scavenging potential.

Analysis of Flow Structure in Microturbine Operating at Low Reynolds Number

In this paper,three-dimensional flows in laminar subsonic cascades at relatively low Reynolds numbers(Re<2500)are presented,based on numerical calculations.The stator and rotor blade designs are those for a MEMS-based Rankine microturbine power-plant-on-a-chip with 109-micron chord blades.Blade passage calculations in 3D were done for different Reynolds numbers,tip clearances(from 0 to 20%)and incidences(0◦to 15◦)to determine the impact of aerodynamic conditions on the flow patterns.These conditions are applied to a blade passage for a stationary outer casing.The 3D blade passage without tip clearance indicates the presence of two large symmetric vortices due to the interaction between hub/casing boundary layers and the blade.Opening the tip clearance introduces the tip vortex,which tends to become dominant above a tip clearance of 10%.In addition to providing a description and understanding of the 3D flow in a MEMS microturbine,these results suggest the importance of considering 3D flows in the design of microturbomachinery,even though the geometry is dominantly 2D.

Wave Propagation Model in a Human Long Poroelastic Bone under Effect of Magnetic Field and Rotation

This article is aimed at describing the way rotation and magnetic field affect the propagation of waves in an infinite poroelastic cylindrical bone.It offers a solution with an exact closed form.The authors got and examined numerically the general frequency equation for poroelastic bone.Moreover,they calculated the frequencies of poroelastic bone for different values of the magnetic field and rotation.Unlike the results of previous studies,the authors noticed little frequency dispersion in the wet bone.The proposed model will be applicable to wide-range parametric projects of bone mechanical response.Examining the vibration of surface waves in rotating cylindrical,long human bones under the magnetic field can have an impact.The findings of the study are offered in graphs.Then,a comparison with the results of the literature is conducted to show the effect of rotation and magnetic field on the wave propagation phenomenon.It is worth noting that the results of the study highly match those of the literature.

Phytochemical Analysis,Estimation of Quercetin,and in Vitro Anti-Diabetic Potential of Stevia Leaves Samples Procured from Two Geographical Origins

The current study used RP-HPLC to compare phytochemicals and estimate the bioactive constituents found in Stevia rebaudiana Bert.(SRB)leaves collected from two different geographical sources.SRB leaves were collected from Bangalore,Karnataka,India,and Reduit,Mauritius.Extracts were prepared using ethanol and aqueous solvents.Proximate analysis was used to evaluate moisture content,ash values,crude fibers,and extractive values.Following that,preliminary phytochemical screening was done on both ethanol leaves extracts,and subsequently total flavonoid content was determined.In addition,TLC chromatograms and RP-HPLC studies were performed on both plant extracts to determine the presence of flavonoid components in both leaves extracts,followed by in vitro anti-diabetic activity was performed with alpha amylase and alpha glucosidase enzymes against Acarbose as standard.Results revealed that both the extracts from two different geographical sources varied significantly with the yield,content of chemicals,and presence of quercetin(flavonoid)content when estimated through the RP-HPLC standardized method.Glycosides,flavonoids,proteins,steroids,saponins,terpenoids,and phenols were found in various concentrations during phytochemical screening.Among both zones,the ethanol leaves extract of SRB taken from Mauritius had a greater content of phytochemicals and a higher yield than other extracts due to the soil nature.The Mauritius sample had greater total flavonoid levels as well as more quercetin(0.92±0.011)than the other extracts.Following that,ethanol extract inhibited enzymes(alpha amylase,alpha glucosidases)more than aqueous extract,and this inhibition was dose dependent.Among them,the Mauritius ethanol sample showed higher anti-diabetic efficacy than the Indian sample,but this difference was not significant.Overall,SRB ethanol leaves extracts outperformed other leaves extracts in terms of yield,phytoconstituents,and total flavonoids.Overall,both SRB samples had high quercetin levels and possessed anti-diabetic potential,but they were greater in the Mauritius sample,demonstrating that plant traits are influenced by geographic location.

Green Synthesis of Novel 5-Arylazo-2-[(2S, 3S, 4R, 5R)-3, 4, 5-Trihydroxy-6-(Hydroxymethyl) Tetrahydro-2H-Pyran-2-Yloxy]-4, 6-Dimethyl 3-Nicotinonitrile

Introduction: Pyridone derivatives played important roles in the last decade to approach many and different functionalities, especially as antitumor, antibacterial, anti-fungal, and many of pharmacological activities. Methodology: Novel compounds of 5-Arylazo-2-[(substituted)-3, 4, 5-trihydroxy-6-(hydroxymethyl) tetrahydro-2H-pyran-2-yloxy]-4, 6-dimethylnicotinonitrile, (3a-e), generally called (fluroarylazopyridine glucosides) were synthesised via green protocol, microwave. Results: The compounds were investigated by (IR, 1HNMR, 13CNMR and mass spectrometry). Where some of pharmacological activities like antibacterial and antifungal studies had been investigated and characterized. It was found that 3a-d had characterized by high activities as antibacterial and antifungal. Where microwave synthetic methods were more efficient, gave higher products quantity, and more saving for time requirement and for using of much more solvents.

Eco-Friendly Production of Silver Nanoparticles from Peel of Tangerine for Degradation of Dye

Green chemistry methods for production of nanoparticles have many advantages, such as ease of use, which makes the methods desirable and economically viable. The aim of the present work was to green synthesise silver nanoparticles (SNPs) using aqueous tangerine peel extract in different ratios (2:1, 1:1, 1:2). The formed SNPs were characterised using ultraviolet-visible (UV-Vis) spectrophotometry, and transmission electron microscopy (TEM). The UV-Vis spectra showed that the highest absorbance was observed when the ratio of peel tangerine extract to silver nitrate solution was 1:2. The transmission electron micrographs showed the formation of poly dispersed nanoparticles. It was found that the average diameter of the nanoparticles was 30.29 ± 5.1 nm, 16.68 ± 5.7 nm, and 25.85 ± 8.4 nm, using a tangerine peel solution and silver nitrate solution ratio of 2:1, 1:1, and 1:2, respectively. The formed SNPs were evaluated as catalysts for methyl orange dye degradation, and the results confirmed that SNPs can speed up the degradation of the dye.

Peak Load Modeling for Kingdom of Bahrain

Deriving some models to estimate the electrical demand for future for the Kingdom of Bahrain is carried out in the present study. The ambient temperature is taken into the account as well as the time factor (Year). The model was developed in away describing the electric power demand during a summer period. The estimated values of the maximum electrical load is obtained and evaluated on actual peak load data of the Kingdom of Bahrain.

Annual Maximum Loads Estimation Modeling for Kingdom of Bahrain

The present paper proposes the impact of the air temperature on electricity demand as expected. The annual maximum load is recorded versus the years starting by the year 2009. At present, the graph fitting was applied with some mathematical and computational tools considering the lower values, the higher values and the average values of the annual maximum loads of Kingdom of Bahrain. For the three scenarios, the models are obtained by curve fitting technique. As well, the model of actual loads is obtained finally which has mostly the closest values obtained.

Diagnosis and Recommendation Integrated System Assessment of the Nutrients Limiting and Nutritional Status of Tomato

Tomato is an important field crop,and nutritional imbalances frequently reduce its yield.Diagnosis and Recommendation Integrated System(DRIS),uses ratios for nutrient deficiency diagnosis instead of absolute concentration in plant tests.In this study,local DRIS norms for the field tomatoes were established and the nutrient(s)limiting tomatoes yield were determined.Tomato leaves were analyzed for nutrients,to identify nutritional status using the DRIS approach.One hundred tomatoes fields were selected from Chatter Plain Khyber Pakhtunkhwa and the Sheikupura Punjab Pakistan.The first fully matured leaf was sampled,rinsed,dried and ground for analyzing P,K,Ca,Mg,Cu,Fe,Mn and Zn using an Inductively Coupled Plasma Atomic Emission Spectrophotometer(ICP AES).Plant tissue N and S were measured by the combustion method.The tomatoes yields were recorded at each location.The data were divided into high-yielding(≥3.79 kg/10 plant)and low-yielding(<3.79 kg/10 plant)populations and norms were computed using standard DRIS procedures.High-yielding plant population had a statistically greater mean S and Fe than the low-yielding population.The average balance index,the sum of functions,for S and Fe were−11.04 and−5.17 which reflected deficiency of S and Fe.Plant nutrients norms established may optimize plant nutrition in field tomatoes for high yield.

Adaptive Power Control Aware Depth Routing in Underwater Sensor Networks

Underwater acoustic sensor network(UASN)refers to a procedure that promotes a broad spectrum of aquatic applications.UASNs can be practically applied in seismic checking,ocean mine identification,resource exploration,pollution checking,and disaster avoidance.UASN confronts many difficulties and issues,such as low bandwidth,node movements,propagation delay,3D arrangement,energy limitation,and high-cost production and arrangement costs caused by antagonistic underwater situations.Underwater wireless sensor networks(UWSNs)are considered a major issue being encountered in energy management because of the limited battery power of their nodes.Moreover,the harsh underwater environment requires vendors to design and deploy energy-hungry devices to fulfil the communication requirements and maintain an acceptable quality of service.Moreover,increased transmission power levels result in higher channel interference,thereby increasing packet loss.Considering the facts mentioned above,this research presents a controlled transmission power-based sparsity-aware energy-efficient clustering in UWSNs.The contributions of this technique is threefold.First,it uses the adaptive power control mechanism to utilize the sensor nodes’battery and reduce channel interference effectively.Second,thresholds are defined to ensure successful communication.Third,clustering can be implemented in dense areas to decrease the repetitive transmission that ultimately affects the energy consumption of nodes and interference significantly.Additionally,mobile sinks are deployed to gather information locally to achieve the previously mentioned benefits.The suggested protocol is meticulously examined through extensive simulations and is validated through comparison with other advanced UWSN strategies.Findings show that the suggested protocol outperforms other procedures in terms of network lifetime and packet delivery ratio.

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis.Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously.Traditionally,physicians examine the characteristics of tongue prior to decision-making.In this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed.This model can reduce the physical harm made to the patients.Several tongue image analytical methodologies have been proposed earlier.However,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis model.With this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI model.The proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal process.Besides,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)algorithm.At last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color images.The design of OGSO algorithm for SqueezeNet model shows the novelty of the work.To assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several measures.The resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

Bayesian Analysis in Partially Accelerated Life Tests for Weighted Lomax Distribution

Accelerated life testing has been widely used in product life testing experiments because it can quickly provide information on the lifetime distributions by testing products or materials at higher than basic conditional levels of stress,such as pressure,temperature,vibration,voltage,or load to induce early failures.In this paper,a step stress partially accelerated life test(SSPALT)is regarded under the progressive type-II censored data with random removals.The removals from the test are considered to have the binomial distribution.The life times of the testing items are assumed to follow lengthbiased weighted Lomax distribution.The maximum likelihood method is used for estimating the model parameters of length-biased weighted Lomax.The asymptotic confidence interval estimates of the model parameters are evaluated using the Fisher information matrix.The Bayesian estimators cannot be obtained in the explicit form,so the Markov chain Monte Carlo method is employed to address this problem,which ensures both obtaining the Bayesian estimates as well as constructing the credible interval of the involved parameters.The precision of the Bayesian estimates and the maximum likelihood estimates are compared by simulations.In addition,to compare the performance of the considered confidence intervals for different parameter values and sample sizes.The Bootstrap confidence intervals give more accurate results than the approximate confidence intervals since the lengths of the former are less than the lengths of latter,for different sample sizes,observed failures,and censoring schemes,in most cases.Also,the percentile Bootstrap confidence intervals give more accurate results than Bootstrap-t since the lengths of the former are less than the lengths of latter for different sample sizes,observed failures,and censoring schemes,in most cases.Further performance comparison is conducted by the experiments with real data.

Zubair Lomax Distribution:Properties and Estimation Based on Ranked Set Sampling

In this article,we offer a new adapted model with three parameters,called Zubair Lomax distribution.The new model can be very useful in analyzing and modeling real data and provides better fits than some others new models.Primary properties of the Zubair Lomax model are determined by moments,probability weighted moments,Renyi entropy,quantile function and stochastic ordering,among others.Maximum likelihood method is used to estimate the population parameters,owing to simple random sample and ranked set sampling schemes.The behavior of the maximum likelihood estimates for the model parameters is studied using Monte Carlo simulation.Criteria measures including biases,mean square errors and relative efficiencies are used to compare estimates.Regarding the simulation study,we observe that the estimates based on ranked set sampling are more efficient compared to the estimates based on simple random sample.The importance and flexibility of Zubair Lomax are validated empirically in modeling two types of lifetime data.

Sentinel-2 Satellite Imagery Application to Monitor Soil Salinity and Calcium Carbonate Contents in Agricultural Fields

The estuary tides affect groundwater dynamics;these areas are susceptible to waterlogging and salinity issues.A study was conducted on two fields with a total area of 60 hectares under a center pivot irrigation system that works with solar energy and belong to a commercial farm located in Northern Sudan.To monitor soil salinity and calcium carbonate in the area and stop future degradation of soil resources,easy,non-intrusive,and practical procedures are required.The objective of this study was to use remote sensing-determined Sentinel-2 satellite imagery using various soil indices to develop prediction models for the estimation of soil electrical conductivity(EC)and soil calcium carbonate(CaCO_(3)).Geo-referenced soil samples were collected from 72 locations and analyzed in the laboratory for soil EC and CaCO_(3).The electrical conductivity of the soil saturation paste extract was represented by average values in soil dataset samples from two fields collected from the topsoil layer(0 to 15 cm)characteristic of the local salinity gradient.The various soil indices,used in this study,were calculated from the Sentinel-2 satellite imagery.The prediction was determined using the root mean square error(RMSE)and cross validation was done using coefficient of determination.The results of regression analysis showed linear relationships with significant correlation between the EC analyzed in laboratory and the salinity index-2“SI2”(Model-1:R^(2)=0.59,p=0.00019 and root mean square error(RMSE=1.32%)and the bare soil index“BSI”(Model-2:R^(2)=0.63,p=0.00012 and RMSE=6.42%).Model-1 demonstrated the best model for predicting soil EC,and validation R^(2)and RMSE values of 0.48%and 1.32%,respectively.The regression analysis results for soil CaCO_(3)determination showed linear relationships with data obtained in laboratory and the bare soil index“BSI”(Model-3:R^(2)=0.45,p=0.00021 and RMSE=1.29%)and the bare soil index“BSI”&Normalized difference salinity index“NDSI”(Model-4:R^(2)=0.53,p=0.00015 and RMSE=1.55%).The validation confirmed the Model-3 results for prediction of soil CaCO_(3)with R^(2)and RMSE values of 0.478%and 1.29%,respectively.Future soil monitoring programs might consider the use of remote sensing data for assessing soil salinity and CaCO_(3)using soil indices results generated from satellite image(i.e.,Sentinel-2).

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

The ability to detect and localize the human eye is critical for use in security applications and human identification and verification systems.This is because eye recognition algorithms have multiple challenges,such as multi-pose variations,ocular parts,and illumination.Moreover,the modern security applica-tions fail to detect facial expressions from eye images.In this paper,a Speeded-Up Roust Feature(SURF)Algorithm was utilized to localize the face images of the enrolled subjects.We highlighted on eye and pupil parts to be detected based on SURF,Hough Circle Transform(HCT),and Local Binary Pattern(LBP).Afterward,Deep Belief Neural Networks(DBNN)were used to classify the input features results from the SURF algorithm.We further determined the correctly and wrongly classified subjects using a confusion matrix with two class labels to classify people whose eye images are correctly detected.We apply Stochastic Gradient Descent(SGD)optimizer to address the overfitting problem,and the hyper-parameters arefine-tuned based on the applied DBNN.The accuracy of the proposed system is determined based on SURF,LBP,and DBNN classifier achieved 95.54%for the ORL dataset,94.07%for the BioID,and 96.20%for the CASIA-V5 dataset.The proposed approach is more reliable and more advanced when compared with state-of-the-art algorithms.