Effect of Nanostructures Addition and Enhancement of Poly (Vinylidene Difluoride) (PVDF) Energy Harvesting

With concerns in energy crisis and global warming, researchers are actively investigating alternative energy renewable solutions. Among the various methods, piezoelectric transduction stands out due to its impressive electromechanical coupling factor and coefficient. As a result, piezoelectric energy harvesting has garnered significant attention from the scientific community. In this study, we explored methods to enhance the piezoelectric properties of polyvinylidene fluoride (PVDF) through two distinct approaches. The first approach involved applying external high voltages at various stages during the mixture reaction. The goal was to determine whether this voltage application could alter or enhance PVDF’s piezoelectric conformation by improving the alignment of polarized dipoles. In the second part of our study, we investigated the effects of incorporating various nanostructures (including Iron Oxide, Magnesium Oxide, and Zinc Oxide) into PVDF. To analyze changes in PVDF’s crystalline structure, we utilized Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction (XRD) techniques. Additionally, we measured the electric polarization of samples using a Precision LC Meter and examined the morphology of nanofibers through Scanning Electron Microscopy (SEM).

Assessment of Heavy Metal Exposure in Soils of Ihwrekreka Communities, Delta State, Nigeria

Crude oil pollution in the Niger Delta, perpetrated by both local communities and industrial actors, has brought about soil pollution with its consequent ecological, human health and food challenges. The purpose of this research was to examine the concentration and distribution of heavy metals in soil from communities contaminated by crude oil in Niger Delta, and to evaluate the potential health risks to residents from exposure to these contaminants. To achieve this, soil samples were collected from the Ihwrekreka community and analyzed for heavy metal content using inductively coupled plasma mass spectrometry (ICP-MS). The analytical results in mg/kg revealed a significant metals pollution level derived from the oil spill in the soil ranging from 4.85 - 17,078 (Cu), 1.01 - 16.1 (Cd), 0.22 - 36.8 (Cr), 8.28 - 40.9 (Ni), 7.51 - 6474 (Pb), and 8.84 - 12,851 (Zn) respectively. Most of the metals were above the permissible limits of World Health Organization, with Cu, Zn, and Pb as the most contaminating metals. Lead was found to be the main contributor to the hazard index (HI) values for both children and adults in the study area, with its concentration exceeding the permitted limits set by the WHO and the EC. The hazard index (HI) values of Pb, Cu, Zn, Cd, Ni, and Cr were significantly higher than 1. These findings suggest that the release of heavy metals from an oil-contaminated site may pose a risk to human health and the environment.

The Effects of Thermal Radiation and Viscous Dissipation on the Stagnation Point Flow of a Micropolar Fluid over a Permeable Stretching Sheet in the Presence of Porous Dissipation

In this paper,the effects of thermal radiation and viscous dissipation on the stagnation–point flow of a micropolar fluid over a permeable stretching sheet with suction and injection are analyzed and discussed.A suitable similarity transformation is used to convert the governing nonlinear partial differential equations into a system of nonlinear ordinary differential equations,which are then solved numerically by a fourth–order Runge–Kutta method.It is found that the linear fluid velocity decreases with the enhancement of the porosity,boundary,and suction parameters.Conversely,it increases with the micropolar and injection parameters.The angular velocity grows with the boundary,porosity,and suction parameters,whereas it is reduced if the micropolar and injection parameters become larger.It is concluded that the thermal boundary layer extension increases with the injection parameter and decreases with the suction parameter.

Nanoparticle Shape Effect on a Sodium–Alginate Based Cu–Nanofluid under a Transverse Magnetic Field

Sodium-alginate(SA)based nanofluids represent a new generation of fluids with improved performances in terms of heat transfer.This work examines the influence of the nanoparticle shape on a non–Newtonian viscoplastic Cu–nanofluid pertaining to this category.In particular,a stretching/shrinking sheet subjected to a transverse magnetic field is considered.The proposed Cu–nanofluid consists of four different nanoparticles having different shapes,namely bricks,cylinders,platelets,and blades dispersed in a mixture of sodium alginate with Prandtl number Pr=6.45.Suitable similarity transformations are employed to reduce non–linear PDEs into a system of ODEs and these equations and related boundary conditions are solved numerically by means of a Runge–Kutta–Fehlberg(RKF)method.Moreover,analytical solutions are obtained through the application of a MAPLE built–in differential equation solver(Dsolve).The behavior of prominent parameters against velocity and temperature is analyzed.It is found that the temperature increases for all shapes of nanoparticles with the viscoplastic parameter and the Eckert number.

Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance applications.This trend promotes the development of precise and efficient control schemes for individual motors.This research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)inverters.The framework under consideration is developed in four stages.To begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical dynamicmodel.The research presents a modified SVPWM inverter control scheme.By tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are derived.All the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control scheme.The reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data sets.For the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed range.The speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 s.The maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust ripple.Due to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

An Innovative Technique for Constructing Highly Non-Linear Components of Block Cipher for Data Security against Cyber Attacks

The rapid advancement of data in web-based communication has created one of the biggest issues concerning the security of data carried over the internet from unauthorized access.To improve data security,modern cryptosystems use substitution-boxes.Nowadays,data privacy has become a key concern for consumers who transfer sensitive data from one place to another.To address these problems,many companies rely on cryptographic techniques to secure data from illegal activities and assaults.Among these cryptographic approaches,AES is a well-known algorithm that transforms plain text into cipher text by employing substitution box(S-box).The S-box disguises the relationship between cipher text and the key to guard against cipher attacks.The security of a cipher using an S-box depends on the cryptographic strength of the respective S-box.Therefore,various researchers have employed different techniques to construct high order non-linear S-box.This paper provides a novel approach for evolving S-boxes using coset graphs for the action of the alternating group A5 over the finite field and the symmetric group S256.The motivation for this work is to study the symmetric group and coset graphs.The authors have performed various analyses against conventional security criteria such as nonlinearity,differential uniformity,linear probability,the bit independence criterion,and the strict avalanche criterion to determine its high cryptographic strength.To evaluate its image application performance,the proposed S-box is also used to encrypt digital images.The performance and comparison analyses show that the suggested S-box can secure data against cyber-attacks.

Magnetohydrodynamics hemodynamics hybrid nanofluid flow through inclined stenotic artery

The present study aims to perform computational simulations of twodimensional(2D)hemodynamics of unsteady blood flow via an inclined overlapping stenosed artery employing the Casson fluid model to discuss the hemorheological properties in the arterial region.A uniform magnetic field is applied to the blood flow in the radial direction as the magneto-hemodynamics effect is considered.The entropy generation is discussed using the second law of thermodynamics.The influence of different shape parameters is explored,which are assumed to have varied shapes(spherical,brick,cylindrical,platelet,and blade).The Crank-Nicolson scheme solves the equations and boundary conditions governing the flow.For a given critical height of the stenosis,the key hemodynamic variables such as velocity,wall shear stress(WSS),temperature,flow rate,and heat transfer coefficient are computed.

Multi-Attribute Group Decision-Making Method under Spherical Fuzzy Bipolar Soft Expert Framework with Its Application

Spherical fuzzy soft expert set(SFSES)theory blends the perks of spherical fuzzy sets and group decision-making into a unified approach.It allows solutions to highly complicated uncertainties and ambiguities under the unbiased supervision and group decision-making of multiple experts.However,SFSES theory has some deficiencies such as the inability to interpret and portray the bipolarity of decision-parameters.This work highlights and overcomes these limitations by introducing the novel spherical fuzzy bipolar soft expert sets(SFBSESs)as a powerful hybridization of spherical fuzzy set theory with bipolar soft expert sets(BSESs).Followed by the development of certain set-theoretic operations and properties of the proposed model,important problems,including the selection of non-powered dam(NPD)sites for hydropower conversion are discussed and solved under the proposed approach.These problems mainly focus on the need for an efficient tool capable of considering the bipolarity of parameters,complicated ambiguities,and multiple opinions.Supporting the new approach by a detailed comparative analysis,it is concluded that the proposed model is more comprehensive and reliable for multi-attribute group decisionmaking(MAGDM)than the previous tools,particularly considering the bipolarity of parameters under SFSES environment.

Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research

Education quality has undoubtedly become an important local and international benchmark for education,and an institute’s ranking is assessed based on the quality of education,research projects,theses,and dissertations,which has always been controversial.Hence,this research paper is influenced by the institutes ranking all over the world.The data of institutes are obtained through Google Scholar(GS),as input to investigate the United Kingdom’s Research Excellence Framework(UK-REF)process.For this purpose,the current research used a Bespoke Program to evaluate the institutes’ranking based on their source.The bespoke program requires changes to improve the results by addressing these methodological issues:Firstly,Redundant profiles,which increased their citation and rank to produce false results.Secondly,the exclusion of theses and dissertation documents to retrieve the actual publications to count for citations.Thirdly,the elimination of falsely owned articles from scholars’profiles.To accomplish this task,the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new evaluation.The data extracted from GS is processed into structured data,and afterward,it is utilized to generate statistical computations of citations’analysis that contribute to the ranking based on their citations.The research promoted the predictive approach of correlational research.Furthermore,experimental evaluation reported encouraging results in comparison to the previous modi-fication made by the proposed taxonomy.This paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm.

Dramatic changes in anatomical traits of a C4 grass Chrysopogon serrulatus Trin.(Poaceae)over a 1000 m elevational gradient

Chrysopogon serrulatus(false beard-grass)is a dominant component of vegetation in the foothills of the Himalayas.To study whole plant morphology,individuals of C.serrulatus were collected from three plots at each of six locations spanning from 400 to 1,400 m.The population colonizing the highest elevation modifications in different plant organs.Roots showed increased metaxylem number and area.In the stem,especially outside of the vascular tissue,there was intensive sclerification indicative of increased xeromorphy as a survival strategy.At the highest elevation,leaves were wider;aerenchyma formation and increased sclerification were noted in the leaf sheath;and a greater proportion of storage parenchyma was observed in the leaf blade,all indicators of succulence.In contrast,leaves at lower elevations had xeric morphological features such as increased epidermal thickness,sclerification and more developed metaxylem area.In conclusion,shifting of morphological features in below-and above-ground plant parts of C.serrulatus were linked to shifts in environmental factors along this elevation gradient,thus enabling the successful distribution of this species along this elevation gradient.

Atomic Heterointerface Engineering of Nickel Selenide Confined Nickel Molybdenum Nitride for High-Performance Solar-Driven Water Splitting

A heterostructured electrocatalyst of small NiSe_(2) nanoparticles confined NiMoN nanorods(NiSe_(2)-NPs/NiMoN-NRs)is prepared to accelerate both the hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)in a same alkaline medium.The synergistic effects caused by the combination of merits derived from NiSe_(2) and NiMoN phases trigger an optimum electronic structure with high density of state at near Fermi level and enhance adsorption free energy,thereby resulting in excellent catalytic activities and strengthened working stability.The catalyst requires a low overpotential of 58 mV for HER and 241 mV for OER to reach 10 mA cm^(−2) in 1.0 M KOH electrolyte.A twoelectrode electrolyzer based on the developed catalyst shows outstanding cell voltage of 1.51 and 1.46 V to reach 10 mA cm^(−2) in 1.0 M and 30 wt%KOH solution at 25℃ for overall water splitting,respectively.In addition,the solardriven water splitting process delivers a high solar-to-H_(2) conversion efficiency of∼18.4%,impressively unveiling that the developed bifunctional catalyst is highly potential for overall water splitting to produce green hydrogen fuel.

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

Investigations on the Optimization of Contacts Barrier Height for the Improved Performance of ZnO/CdS/CZTS Solar Cells

The numerical simulations were performed using the AMPS-1D simulator to study the effects of the CZTS as an absorber layer and the contacts’barrier height on the performance of four ZnO/CdS/CZTS solar cells.To obtain the best cell performances,the barrier heights of the back and front contacts were adjusted between 0.01,0.77,0.5,and 1.55 eV,respectively.For simulations,we used the lifetime mode,and the device performances were evaluated under AM1.5 illumination spectra.We found that the efficiency,fill factor,and open-circuit voltage were almost constant at a front contact barrier height of less than 0.31 eV.The short-current density was not affected by the front contact barrier height.The back contact material had a significant impact on the CZTS cells parameters.The best performance was obtained for the CZTS550 cell with JSC=29.53 mA/cm2,VOC=1.07 V,FF=0.88,andη=28.08%at barrier heights of 0.31 and 1.55 eV for front and back contacts,respectively.The conduction band offset at the CZTS550/CdS hetero-junction was found to be spike-like with 0.21 eV.The obtained conversion efficiency is comparable to those previously reported in the literature.

Study of engineering electronic structure modulated non-noble metal oxides for scaled-up alkaline blend seawater splitting

Scaled-up industrial water electrolysis equipment that can be used with abundant seawater is key for affordable hydrogen production.The search for highly stable,dynamic,and economical electrocatalysts could have a significant impact on hydrogen commercialization.Herein,we prepared energy-efficient,scalable,and engineering electronic structure modulated Mn-Ni bimetal oxides(Mn_(0.25)Ni_(0.75)O)through simple hydrothermal followed by calcination method.As-optimized Mn_(0.25)Ni_(0.75)O displayed enhanced oxygen and hydrogen evolution reaction(OER and HER)performance with overpotentials of 266 and115 mV at current densities of 10 mA cm^(-2)in alkaline KOH added seawater electrolyte solution.Additionally,Mn-Ni oxide catalytic benefits were attributed to the calculated electronic configurations and Gibbs free energy for OER,and HER values were estimated using first principles calculations.In real-time practical application,we mimicked industrial operating conditions with modified seawater electrolysis using Mn_(0.25)Ni_(0.75)O‖Mn_(0.25)Ni_(0.75)O under various temperature conditions,which performs superior to the commercial IrO_(2)‖Pt-C couple.These findings demonstrate an inexpensive and facile technique for feasible large-scale hydrogen production.

Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

Machine Learning(ML)has changed clinical diagnostic procedures drastically.Especially in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human errors.Enormous studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical procedures.Multi-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical diagnostics.DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets.This paper proposed a novel method that deals with the issue of less data dimensionality.Inspired by the regression analysis,the proposed method classifies the data by going through three different stages.In the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final classifications.Extensive experiments were carried out on the Cleveland heart disease dataset.The results show significant improvement in classification accuracy.It is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Pine wilt disease(PWD)has recently caused substantial pine tree losses in Republic of Korea.PWD is considered a severe problem due to the importance of pine trees to Korean people,so this problem must be handled appropriately.Previously,we examined the history of PWD and found that it had already spread to some regions of Republic of Korea;these became our study area.Early detection of PWD is required.We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD.Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees.To differentiate healthy pine trees from those with PWD,we produced a land cover(LC)map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods,i.e.,artificial neural network(ANN)and support vector machine(SVM).Furthermore,compared the accuracy of two types of Global Positioning System(GPS)data,collected using drone and hand-held devices,for identifying the locations of trees with PWD.We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD.In Anbi,the SVM had an overall accuracy of 94.13%,which is 6.7%higher than the overall accuracy of the ANN,which was 87.43%.We obtained similar results in Wonchang,for which the accuracy of the SVM and ANN was 86.59%and 79.33%,respectively.In terms of the GPS data,we used two type of hand-held GPS device.GPS device 1 is corrected by referring to the benchmarks sited on both locations,while the GPS device 2 is uncorrected device which used the default setting of the GPS only.The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang.However,in Anbi,we obtained better results from GPS device 2 than from GPS device 1.In Anbi,the error in the data from GPS device 1 was 7.08 m,while that of the GPS device 2 data was 0.14 m.In conclusion,both classifiers can distinguish between healthy trees and those with PWD based on LC data.LC data can also be used for other types of classification.There were some differences between the hand-held and drone GPS datasets from both areas.

Clinical impact of surveillance for head and neck cancer in patients with esophageal squamous cell carcinoma

To evaluate the clinical impact of surveillance for head and neck (HN) region with narrow band imaging (NBI) in patients with esophageal squamous cell carcinoma (ESCC).METHODSSince 2006, we introduced the surveillance for HN region using NBI for all patients with ESCC before treatment, and each follow-up. The patients with newly diagnosed stage I to III ESCC were enrolled and classified into two groups as follows: Group A (no surveillance for HN region); between 1992 and 2000), and Group B (surveillance for HN region with NBI; between 2006 and 2008). We comparatively evaluated the detection rate of superficial head and neck squamous cell carcinoma (HNSCC), and the serious events due to metachronous advanced HNSCC during the follow-up.RESULTSA total 561 patients (group A: 254, group B: 307) were enrolled. Synchronous superficial HNSCC was detected in 1 patient (0.3%) in group A, and in 12 (3.9%) in group B (P = 0.008). During the follow up period, metachronous HNSCC were detected in 10 patients (3.9%) in group A and in 30 patients (9.8%) in group B (P = 0.008). All metachronous lesions in group B were early stage, and 26 patients underwent local resection, however, 6 of 10 patients (60%) in group A lost their laryngeal function and died with metachronous HNSCC.CONCLUSIONSurveillance for the HN region by using NBI endoscopy increase the detection rate of early HNSCC in patients with ESCC, and led to decrease serious events related to advanced metachronous HNSCC.

Selection of Adaptive Grass and Frutex and Their Planting Benefits in the Arid-Hot Valleys of Yuanmou

In view of the environment character of the soil and content of the soil organic carbon, 278 species of grass and frutex were introduced from abroad and China, Some were selected by applied production and plantation benefit investigations. The results showed that： In the arid-hot valleys of Yuanmou the selected species showed the characteristics of fast growth and high yield （38 227.00 kg/hm2）, high nutrition value （CP% content of legu- minous grass and frutex were 22.47%）, as well as the strong ad- versity resistance to high air temperature and arid climate （soil humidity was 3.4%）. The selected species not only effectively meliorated soil and increased fertility, but also improved small environment weather.

Phase transformation behaviors and mechanical properties of Ti_(50)Ni_(49)Fe_1 alloy with severe plastic deformation

In this work, transformation behaviors and mechanical properties of cold-rolled shape memory alloy TisoNia9Fel by severe plastic deformation （SPD） were intensively investigated. The phase transformation behaviors, phase analysis, and microstructures were characterized by differential scanning calorimetry （DSC）, X-ray diffraction （XRD）, and transmission electron microscopy （TEM）, respectively. Tensile testing was performed to analyze the effect of SPD on the mechanical properties and shape memory of TisoNi49Fel alloy. When the thickness reduction is beyond 30 %, the martensitic transformation is suppressed. After cold-rolling, the alloy is mainly com- posed of B2 parent phases with some stress-induced martensitic B 19t phases, and high density of dislocations are generated and the grains are obviously refined. The yield stress ab significantly raises from 618 MPa of 0 % cold rolling to 1,338 MPa of 50 % SPD. Shape-memory effect increases from 6.5 % without cold rolling to 8.5 % after 30 % SPD, ascribed to the induced defects in cold rolling. Those results indicate that TisoNi49Fel alloy has improved mechanical properties and potential commercial applications after SPD.

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.