Fabrication and Characterization of Lanthanide-TiO2 Nanotube Composites

Titanium dioxide (TiO2) doped with neodymium (Nd) and/or Gadolinium (Gd) rare-earth elements were fabricated into nanotubes via the hydrothermal method in a KOH solution and in-situ doping. Titanium dioxide nanotubes (TNTs) and in-situ Nd-doped and/or Gd-doped TNTs were characterized with transmission and scanning electron microscopy, energy-dispersive X-ray analysis, X-ray diffraction, Raman spectroscopy, and Fourier-transform infrared spectroscopy. Morphologies indicated a network of aggregated nanotubes. The phase and composition analyses revealed that the lanthanide TNTs had anatase phases with Nd and/or Gd nanoparticles in the TNT lattice. The nanoparticles were uniformly deposited on the surface because of hydroxyl groups on the TNT surfaces, resulting in a very high loading density. The outer diameter and the length of the TNTs increased with doping. The mechanisms for the formation of multiwall TNTs are discussed.

Magnetic,dielectric and electrical modulus of Ce^(3+) doped cobalt-magnesium ferrite nanoparticles

In this work,the magnetic,dielectric properties and electric modulus of Ce^(3+) substituted cobaltmagnesium(Co_(0.7)Mg_(0.3)Ce_(x)Fe_(2-x)O_4)(labeled as CMCF) ferrite nanoparticles were investigated in detail.Saturation magnetization decreases from 50.05 to 34.87 emu/g for further substituting Ce^(3+) ions.Meanwhile,coercivity increases from 738.22 Gs for the CMCFO sample to 912.10 Gs for the CMCF2 sample,then decreases monotonically to 762.1 Gs for the CMCF5 sample.The cerium content and particle size play important roles in controlling the magnetization and coercivity of the CMCF nanoparticles.All CMCF nanoferrites are suitable for microwave applications since their high-frequency response ranges from 7.72 to 11.07 GHz.The CMCF nanoferrites' dielectric parameter dispersion exhibits normal behavior.The pristine Co-Mg nanoferrite only has ε' value of 28.25,but the nanoferrite MCMF2 has ε' value of365.03,with an enhancing ratio of 1192%.The conduction mechanism of the MCMF nanoferrites was determined by fitting the σ_(ac) results via the Jonscher power law.At 653 K,large polaron tunnelling is thought to be responsible for this conduction process,which is followed by electron barrier hopping at higher temperatures.Cole-Cole diagrams at different temperatures,assuring the contributions of the grains and their boundaries at lower temperatures(653 K) and only the grains at higher temperatures.Based on our results,the CMCF nanoferrites hold magnetic and semiconducting nature,which can be used in magnetic devices and dielectrics in lower-frequencies or conductors in higher-frequencies.

Functional Activity of Some Growth Regulators on Yield Components and Endogenous Hormones of Cowpea Plants (<i>Vigna sinensis</i>L.)

The effect of seed presoaking with different concentrations of growth bio-regulators (indole acetic acid, gibberellic acid and kinetin) on productivity and some biochemical and physiological aspects of yielded seeds of cowpea (Vigna sinensis L.) was investigated. Generally, application of growth regulators stimulated yield and yield quality of cowpea plants as compared to control plants through inducing a massive increase in number of pods/plants, seed biomass, pod length and number of seeds. In addition, results of this study showed that these growth regulators increased protein content and total soluble sugars in cowpea yielded seeds. Finally, it is evident from the present data that application of kinetin appeared to be the most effective hormone in stimulated productivity endogenous hormones and biochemical aspects in yielded seeds of cowpea plants.

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things(IoT)has gained more popularity in research because of its large-scale challenges and implementation.But security was the main concern when witnessing the fast development in its applications and size.It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats.Additionally,machine learning(ML)techniques optimally use a colossal volume of data generated by IoT devices.Deep Learning(DL)related systems were modelled for attack detection in IoT.But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations.This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network(AAA-ODBN)Enabled Ransomware Detection in an IoT environment.The presented AAAODBN technique mainly intends to recognize and categorize ransomware in the IoT environment.The presented AAA-ODBN technique follows a three-stage process:feature selection,classification,and parameter tuning.In the first stage,the AAA-ODBN technique uses AAA based feature selection(AAA-FS)technique to elect feature subsets.Secondly,the AAA-ODBN technique employs the DBN model for ransomware detection.At last,the dragonfly algorithm(DFA)is utilized for the hyperparameter tuning of the DBN technique.A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm.The experimental values indicate the significant outcome of the AAA-ODBN model over other models.

Electrical Properties of the Al/CuInSe₂Thin Film Schottky Junction

The Schottky diode (Al/p-CuInSe2/FTO) was fabricated by simple deposition of pure Aluminum on the front side of the CuInSe2 thin film. We have investigated its electrical characteristics by measuring the current-voltage (I-V), the capacitance-voltage (C-V) and the electrical impedance in the range of temperature (300 K - 425 K). At room temperature, this heterostructure has shown non-ideal Schottky behavior with 3.98 as ideality factor and 38 μA/cm2 as a reverse saturated current density. The C-V measured at 100 kHz has shown non-linear behavior and an increase with temperature. Similarly, we have estimated, at room temperature, the carrier doping density, the built-in potential and the depletion layer width which are of about 8.66 × 1015 cm﹣3, 1.12 V and 0.37 μm respectively. By the impedance spectroscopy technique, we have found a decrease with temperature of all the serial resistance Rs, the parallel resistance Rp and the capacitance Cp. The frequency dependence of the imaginary part of this impedance was carried out to characterize the carrier transport properties in the heterostructure. From the Arrhenius diagram, we have estimated the activation energy at 460 meV. An equivalent electrical circuit was used for modeling these results.

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

Fisheries studies and stock evaluation of shrimp scad,Alepes djedaba(Teleostei:Carangidae)caught from Arabian Gulf

Objective:To evaluate the stock of Alepes djedaba(A.djedaba)by describing the length composition,growth parameters,mortality rates of A.djedaba captured in Arabian Gulf off Saudi Arabia and adopting yield per recruit and biomass per recruit models.Methods:A random sample of 490 fish representing a moderate range of total lengths(16.5-32.4cm)and weights(60-410 g)were sampled in Arabian Gulf off Dammam,Saudi Arabia during the period from August 2008 to July 2009.LFD5 software was used for estimation of growth parameters.Total mortality was calculated using the length converted catch curve.Natural mortality was estimated using Pauly and David's formula.Fishing mortality was computed by subtracting natural mortality from total mortality.Per recruit analysis was made using Beverton and Holt model.Results:Length-frequency analysis revealed four peaks and the length range from 22 cm to 27 cm dominated the catch,constituting about 71%of the catch.Values of the von Bertalanffy growth parameters were computed using LFD5 software as follows:the asymptotic length(L_(∞))=41.71 cm,curvature parameter(K)=0.36 year^(-1),and hypothetic age at zero length(t_(0))=-0.76 year.The total mortality(Z)was estimated as 2.07 year^(-1),and natural mortality was 0.8 year^(-1).Fishing mortality F=1.27 year^(-1),which was higher than F_(0.1)(0.3 year^(-1)),F_(SB(50))(0.59 year^(-1))and FS_(B(40))(0.86 year^(-1)).Atthe current levels of fishing and natural mortality,the biomass per recruit is 34%of the virgin biomass.Conclusions:These may indicate an overexploitation state of the fisheries of A.djedaba in Arabian Gulf.

Secondary antiviral metabolites from fungi with special reference to coronaviruses

Profound inspection of the life forms on the earth teaches how to be the complexity of interrelationships among the various systems.Because of the emergence of novel viruses all the time and the inadequate of vaccines and antivirals,viral contagions are amongst the most causative diseases affecting people worldwide.Fungi exemplify a massive source of bioactive molecules as,many fungal secondary metabolities like Oxoglyantrypine,Carneic acid F,Scedapin C,Asteltoxin E,Phomanolide,Norquinadoline A and Quinadoline B have antiviral activity.This review deals with how secondary metabolites of fungi can help in the war against viruses in general and especially Coronaviruses moreover several pieces of literature pointed out that many clusters of fungi in different biotopes are waiting to be exploited.

Inequalities for the Gaussian hypergeometric function

we study the monotonicity of certain combinations of the Gaussian hypergeometric functions F(-1/2,1/2;1;1- xc) and F(-1/2- δ,1/2 + δ;1;1- xd) on(0,1) for given 0 < c 5d/6 < ∞ andδ∈(-1/2,1/2),and find the largest value δ1 = δ1(c,d) such that inequality F(-1/2,1/2;1;1- xc) <F(-1/2- δ,1/2 + δ;1;1- xd) holds for all x ∈(0,1). Besides,we also consider the Gaussian hypergeometric functions F(a- 1- δ,1- a + δ;1;1- x3) and F(a- 1,1- a;1;1- x2) for given a ∈ [1/29,1) and δ∈(a- 1,a),and obtain the analogous results.