Novel Sustainable Cellulose Acetate Based Biosensor for Glucose Detection

In this study,green zinc oxide(ZnO)/polypyrrole(Ppy)/cellulose acetate(CA)film has been synthesized via solvent casting.This film was used as supporting material for glucose oxidase(GOx)to sensitize a glucose biosensor.ZnO nanoparticles have been prepared via the green route using olive leaves extract as a reductant.ZnO/Ppy nanocomposite has been synthesized by a simple in-situ chemical oxidative polymerization of pyrrole(Py)monomer using ferric chloride(FeCl3)as an oxidizing agent.The produced materials and the composite films were characterized using X-ray diffraction analysis(XRD),scanning electron microscope(SEM),Fourier transform infrared(FTIR)and thermogravimetric analysis(TGA).Glucose oxidase was successfully immobilized on the surface of the prepared film and then ZnO/Ppy/CA/GOx composite was sputtered with platinum electrode for the current determination at different initial concentrations of glucose.Current measurements proved the suitability and the high sensitivity of the constructed biosensor for the detection of glucose levels in different samples.The performance of the prepared biosensor has been assessed by measuring and comparing glucose concentrations up to 800 ppm.The results affirmed the reliability of the developed biosensor towards real samples which suggests the wide-scale application of the proposed biosensor.

EJUSTCO:Monte Carlo radiation transport code hybrid with ANN model for gamma-ray shielding simulation

Gamma ray shielding is essential to ensure the safety of personnel and equipment in facilities and environments where radiation exists.The Monte Carlo technique is vital for analyzing the gamma-ray shielding capabilities of materials.In this study,a simple Monte Carlo code,EJUSTCO,is developed to cd simulate gamma radiation transport in shielding materials for academic purposes.The code considers the photoelectric effect,Compton(incoherent)scattering,pair production,and photon annihilation as the dominant interaction mechanisms in the gamma radiation shielding problem.Variance reduction techniques,such as the Russian roulette,survival weighting,and exponential transformation,are incorporated into the code to improve computational efficiency.Predicting the exponential transformation parameter typically requires trial and error as well as expertise.Herein,a deep learning neural network is proposed as a viable method for predicting this parameter for the first time.The model achieves an MSE of 0.00076752 and an R-value of 0.99998.The exposure buildup factors and radiation dose rates due to the passage of gamma radiation with different source energies and varying thicknesses of lead,water,iron,concrete,and aluminum in single-,double-,and triple-layer material systems are validated by comparing the results with those of MCNP,ESG,ANS-6.4.3,MCBLD,MONTEREY MARK(M),PENELOPE,and experiments.Average errors of 5.6%,2.75%,and 10%are achieved for the exposure buildup factor in single-,double-,and triple-layer materials,respectively.A significant parameter that is not considered in similar studies is the gamma ray albedo.In the EJUSTCO code,the total number and energy albedos have been computed.The results are compared with those of MCNP,FOTELP,and PENELOPE.In general,the EJUSTCO-developed code can be employed to assess the performance of radiation shielding materials because the validation results are consistent with theoretical,experimental,and literary results.

Fundamentals of Direct Inverse CFD Modeling to Detect Air Pollution Sources in Urban Areas

This paper presents the fundamentals of direct inverse modeling using CFD simulations to detect air pollution sources in urban areas. Generally, there are four techniques used for detecting pollution sources: the analytical technique, the optimization technique, the probabilistic technique, and the direct technique. The study discusses the potentialities and limits of each technique, where the direct inverse technique is focused. Two examples of applying the direct inverse technique in detecting pollution source are introduced. The difficulties of applying the direct inverse technique are investigated. The study reveals that the direct technique is a promising tool for detecting air pollution source in urban environments. However, more efforts are still needed to overcome the difficulties explained in the study.

用于高性能硫化镉敏化太阳能电池对电极的硫化铜/还原氧化石墨烯纳米复合材料的合成(英文)

用一釜水热合成法制备了硫化铜/还原氧化石墨烯纳米复合材料,改变前驱体中石墨烯含量,得到具有不同石墨烯含量的纳米复合材料。所制备的纳米复合材料首先和聚偏氟乙烯粘结剂混合,再涂覆在SnO2 _(x-)Fx基体上,得到以CdS敏化TiO2为负极的量子点太阳能电池的对电极,并与传统的Cu2 S/Cu对电极进行比较。用场发射扫描电子显微镜、X-射线衍射、拉曼光谱、循环伏安和阻抗谱技术表征了纳米复合材料对电极的微观结构和性能。结果表明:硫化铜/还原氧化石墨烯纳米复合材料优于Cu2 S/Cu对电极。前驱体中石墨烯的含量显著影响了硫化铜纳米晶的化学计量比和形貌。当前驱体石墨烯含量在中等水平下,获得了具有更多供S2_(-x)离子还原的活性位的优化的硫化铜/还原氧化石墨烯纳米复合材料。以此优化的纳米复合材料为对电极制备的量子点太阳能电池在100 mW/cm2的光照强度下具有高的、稳定的和可重复的_(2.)36%的能量转化效率,高于用Cu2 S/Cu为对电极的能量转化效率。此性能的提升归因于硫化铜纳米晶和导电的还原氧化石墨烯之间的协同作用,还原氧化石墨烯充当共催化剂和导电促进剂,降低对电极的内阻并加快多硫化物的还原。

Design Procedure and Simulation of a Novel Multi-Modal Tactile Display Device for Biomedical Applications

Tactile display is recently attracting much attention in the field of human computer interaction. There is a strong need for such a device especially for application in which the touch feeling is lost, such as surgeons willing to feel the tissue hardness during laparoscopic surgeries. In this paper, a novel multi-modal tactile display device which can display both surface shape and stiffness of an object is introduced. The conceptual design is built upon using two springs, made of Shape Memory Alloys-SMA, to control both shape and stiffness. The design parameters of this device are selected based on the spatial resolution of human finger and the stiffness range of the soft tissue. The display device is simulated using Finite Element Method, FEM, to study the effect of design parameters on the resulting stiffness. The results showed that the device can display stiffness of an object independent of its shape display. Simulation results confirmed that the stiffness display is stable when applying force by the finger during indentation for feeling stiffness, since the total stiffness error does not exceed 1.2%.

Design Methodology of a Micro-Scale 2-DOF Energy Harvesting Device for Low Frequency and Wide Bandwidth

A detailed design methodology of a micro-scale 2-DOF energy harvesting device that can harvest human motion energy of low frequency and wide bandwidth is developed. Based on the concept of the 2-DOF vibration absorber, device parameters are selected to harvest energy at low frequency of 1-10 Hz and wide bandwidth with ±20% of the mean frequency, which matches the human motion. The device dimensions are limited to 40 × 30 × 10 mm3 to fit with the human wrist size. Then, a finite element model is developed to investigate the system performance with the selected parameters. When subjected to harmonic excitation of 1 g, the proposed 2-DOF device is able to provide a power of at least 10 μW in between the two close resonant peaks of 4 Hz and 6 Hz, which is the target frequency range. The device shows very high power per square frequency compared with the reported harvesters.

Exponential Spline Solution for Singularly Perturbed Boundary Value Problems with an Uncertain—But—Bounded Parameter

In this paper, we develop a new numerical method which is based on an exponential spline and Shishkin mesh discretization to solve singularly perturbed boundary value problems, which contain a small uncertain perturbation parameter. The proposed method uses interval analysis principle to deal with the uncertain parameter and the Monte Carlo Simulations (MCS) are used to validate the solution and the accuracy of the proposed method. Furthermore, sensitivity analysis has been conducted using different methods to assess how much the solution is sensitive to the changes of the perturbation parameter. Numerical results are provided to show the applicability and efficiency of the proposed method, which is ε-uniform convergence of almost second order.

Simulation of Graphene Piezoresistivity Based on Density Functional Calculations

The piezoresistive effect in graphene ribbon has been simulated based on the first-principles electronic-state calculation for the development of novel piezoresistive materials with special performances such as high flexibility and low fabrication cost. We modified theoretical approach for piezoresistivity simulation from our original method for semiconductor systems to improved procedure applicable to conductor systems. The variations of carrier conductivity due to strain along with the graphene ribbon models (armchair model and zigzag model) have been calculated using band carrier densities and their corresponding effective masses derived from the one-dimensional electronic band diagram. We found that the armchair-type graphene nano-ribbon models have low conductivity with heavy effective mass. This is a totally different conductivity from two-dimensional graphene sheet. The variation of band energy diagrams of the zigzag-type graphene nano-ribbon models due to strain is much more sensitive than that of the armchair models. As a result, the longitudinal and transverse gauge factors are high in our calculation, and in particular, the zigzag-type graphene ribbon has an enormous potential material with high piezoresistivity. So, it will be one of the most important candidates that can be used as a high-performance piezoresistive material for fabricating a new high sensitive strain gauge sensor.

The Study on Motion of a Rigid Body Carrying a Rotating Mass

The free motion of a rigid body carrying a rotating mass without change of the centroid (this system may be called one-rotor gyrostat) is discussed. Equations of motion are derived: first integrals as a vectorial equation which contained the right vector of an angular velocity of the given rotor with respect to the carrier body and the turn-tensor of this body;a scalar relation between rotation angle of the given rotor with respect to the carrier body and the angular velocity of the carrier body. Only two of these parameters are independent variables. To get equations and to exclude the singular points in the solutions, it is necessary to determine the turn-tensor of the carrier body in the most suitable form. To this end the representation theorem of the turn-tensor and some additional arguments are used. As a final result, we enabled to get two complicated differential equations of the first order. In particular case, the exact solution is represented. Excluding the singular points numerical solutions are determined.

Novel design of a compact tunable dual band wireless power transfer(TDB-WPT)system for multiple WPT applications

In this study we present the design and realization of a tunable dual band wireless power transfer(TDB-WPT)coupled resonator system.The frequency response of the tunable band can be controlled using a surface-mounted varactor.The transmitter(Tx)and the receiver(Rx)circuits are symmetric.The top layer contains a feed line with an impedance of 50Ω.Two identical half rings defected ground structures(HR-DGSs)are loaded on the bottom using a varactor diode.We propose a solution for restricted WPT systems working at a single band application according to the operating frequency.The effects of geometry,orientation,relative distance,and misalignments on the coupling coefficients were studied.To validate the simulation results,the proposed TDB-WPT system was fabricated and tested.The system occupied a space of 40 mm×40 mm.It can deliver power to the receiver with an average coupling efficiency of 98%at the tuned band from 817 to 1018 MHz and an efficiency of 95%at a fixed band of 1.6 GHz at a significant transmission distance of 22 mm.The results of the measurements accorded well with those of an equivalent model and the simulation.

A new technique for fault diagnosis in transformer insulating oil based on infrared spectroscopy measurements

Condition monitoring of the insulating system within power transformers has a massive importance according to the electrical utilities.Dissolved gas analysis(DGA)is frequently used for this purpose.However,DGA lacks the necessary level of accuracy to identify all equipment faults,particularly in their initial stages of degradation.Also,it does not have the capability for real-time monitoring and relies on manual sampling and laboratory testing,causing potential delays in fault identification.Additionally,the interpretation of DGA data necessitates specialised expertise,which may pose difficulties for smaller entities that have limited access to resources.Therefore,the contribution of this research is to use infrared spectroscopy measurements as a new effective technique substituting the DGA method for fault diagnosis in insulating oil.The inception faults that were considered in this study were the electrical fault(discharges of high energy)and the thermal fault(300°C<Temperature<700°C).Regarding that,two test cells were crafted especially for serving the simulation processes inside the laboratory for both types of inception faults.Subsequently,six samples of pure paraffinic mineral oil were taken to be degraded in the laboratory.Following that,all of them besides another sample that were not subjected to any kind of faults were taken to be examined by Fourier transform infrared(FTIR)spectroscopy to obtain an overview of the oil's behaviour in each fault case.After that,the FTIR analysis was initially verified utilising the DGA method.Then,for further affirmation,the dielectric dissipation factor(DDF)for all samples was measured.In the final analysis,the verification tests provide experimental evidence about the outperformance of this new optical technique in detecting the transformer's inception faults in addition to proving its potential for being a superior alternative to the well-known traditional diagnostic techniques.

跟网型和构网型逆变器的阻抗无源化方法综述

相比传统同步机主导的电力系统,由并网逆变器所主导的新能源并网发电系统动态特性发生了根本性变化。近年,国内外报道了多次新能源并网发电系统振荡事件,振荡现象具有宽频域特征,动态行为具有非线性、时变性和复杂性。并网逆变器的端口阻抗无源性是保证并网发电系统稳定的充分条件,适用于复杂电网下的稳定性优化。文中针对跟网型和构网型逆变器,分别从控制器优化设计、串并联虚拟阻抗和其他无源化方法这三个方面,对两类逆变器的中高频阻抗无源化改善方法进行了总结和梳理,最后探讨了当前研究的不足与现存的挑战。

A new approach to learning and recognizing leaf diseases from individual lesions using convolutional neural networks

Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area.Most studies have focused on recognizing diseases from images of whole leaves.This approach limits the resulting models’ability to estimate leaf disease severity or identify multiple anomalies occurring on the same leaf.Recent studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition accuracy.In those studies,however,the lesions were laboriously cropped by hand.This study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this problem.These datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network(CNN)models,respectively.We report that GoogLeNet’s disease recognition accuracy improved by more than 15%when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves.A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this paper.The proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union(mIoU)score of 0.8448 and 0.6257 for the leaf and lesion pixel classes,respectively.In terms of mean boundary F1 score,the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes,respectively.Lastly,a fully automatic algorithm for leaf disease recognition from individual lesions is proposed.The algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease recognition.The proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.

Application of artificial intelligence techniques in modeling attenuation behavior of ionization radiation: a review

Introduction Shielding of ionizing radiations,which are gamma rays,neutrons,and X-rays,can be achieved by attenuating its intensity using different materials.Protection is therefore crucial in ensuring the safety of lives and essential equipment in areas such as nuclear power plants,radiotherapy facilities,space exploration,and others.Artificial Intelligent technologies have become desirable in modeling shielding materials’attenuation behavior due to their unique advantages.Objective The overview aims to present the recent application of AI technologies in modeling the radiation attenuation behavior of materials.Methods A total of 41 relevant articles were obtained using Scopus and web of science databases.The search was restricted to articles and conference papers published within the last two decades.Results From the overview,it was realized that AI techniques can predict the attenuation properties of shielding materials and optimize the shield design.The methods can be grouped into predictive models which are:fuzzy logic,Support Vector Regression,Neural Networks,and optimization models which include Genetic algorithms,Ant Colony,and Particle Swarm Optimization.Neural networks are the most robust and widely used technique.The predictive models are used in predicting parameters such as attenuation coefficient,buildup factor,shield thickness,and radiation dose rates,whiles the optimization techniques are employed in single and multi-objective attenuator designs.Conclusion In the overview,the accuracies and complexities of the various AI techniques have been discussed giving insight into their prospects.The AI techniques are easy to model compared to conventional methods and can save computational time when coupled with conventional statistical and deterministic models or employed as a standalone technique.

Dissolved gas analysis and dissipation factor measurement of mineral oil-based nanofluids under thermal and electrical faults

Mineral oil is the most frequent insulating liquid used in oil-immersed transformers for electrical insulation and heat dissipation.However,oil-based nanofluids are becoming more popular in scientific research as they have proved to have better dielectric and thermal characteristics.When applying these nanofluids into actual transformers,they would be exposed to thermal and electrical stresses.Thus,The aim of the authors is to investigate the generation pattern of dissolved gases in nanofluids under thermal and electrical faults and to assess the applicability of traditional Dissolved Gas Analysis(DGA)techniques if oil-based nanofluids are used in transformers.Oil-based nanofluid samples were prepared using a magnetic stirrer and an ultrasonic homogeniser and then subjected to simulated thermal and electrical faults in the laboratory using properly sealed test cells.Three types of metal oxides,Silicon dioxide,Titanium dioxide,and Aluminium oxide nanoparticles,have been used to prepare nanofluids with 0.02 g/L and 0.04 g/L concentrations.The gases released and dissolved into oil due to the simulated faults were analysed and compared to traditional mineral oil as a benchmark.The dielectric dissipation factor was obtained and analysed for all samples.According to the findings,the presence and concentration of nanoparticles were shown to influence the amount of gases produced.As a result,this research is crucial in guaranteeing that traditional DGA techniques can be employed in transformers that use oil-based nanofluids.

Recent advances in image processing techniques for automated leaf pest and disease recognition – A review

Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme weather.However,this is expensive,time consuming and in some cases impractical.To counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research topic.In this paper,we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques.We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques.In particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras.We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted features.Researchers have reported high recognition accuracies on particular datasets but in many cases,the performance of those systems deteriorated significantly when tested on different datasets or in field conditions.Nevertheless,progress made so far has been encouraging.Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy,recall,precision,specificity,F1-score,training duration and storage requirements are also presented.Subsequently,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments.We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.

ANFIS-based Sensor Fusion System of Sit-to-stand for Elderly People Assistive Device Protocols

This paper describes the analysis and design of an assistive device for elderly people under development at the EgyptJapan University of Science and Technology(E-JUST) named E-JUST assistive device(EJAD).Several experiments were carried out using a motion capture system(VICON) and inertial sensors to identify the human posture during the sit-to-stand motion.The EJAD uses only two inertial measurement units(IMUs) fused through an adaptive neuro-fuzzy inference systems(ANFIS) algorithm to imitate the real motion of the caregiver.The EJAD consists of two main parts,a robot arm and an active walker.The robot arm is a 2-degree-of-freedom(2-DOF) planar manipulator.In addition,a back support with a passive joint is used to support the patient s back.The IMUs on the leg and trunk of the patient are used to compensate for and adapt to the EJAD system motion depending on the obtained patient posture.The ANFIS algorithm is used to train the fuzzy system that converts the IMUs signals to the right posture of the patient.A control scheme is proposed to control the system motion based on practical measurements taken from the experiments.A computer simulation showed a relatively good performance of the EJAD in assisting the patient.

Experimental and numerical investigation of the abrasive waterjet machining of aluminum-7075-T6 for aerospace applications

The machining of hard-to-cut materials with a high degree of precision and high surface quality is one of the most critical considerations when fabricating various state-of-the-art engineered components.In this investigation,a comprehensive three-dimensional model was developed and numerically simulated to predict kerf profiles and material removal rates while drilling the aluminum-7075-T6 aerospace alloy.Kerf profile and material removal prediction involved three stages:jet dynamic flow modeling,abrasive particle tracking,and erosion rate pre-diction.Experimental investigations were conducted to validate the developed model.The results indicate that the jet dynamic characteristics and flow of abrasive particles alter the kerf profiles,where the top kerf diameter increases with increasing jet pressure and standoff distance.The kerf depth and hole aspect ratio increase with jet pressure,but decrease with standoff distance and machining time.Crosssectional profiles were characterized by progressive edge rounding and parabolic shapes.Defects can be minimized by utilizing high jet pressure and small standoff distance.The material removal rate increases with increasing jet pressure,abrasive particle size,and exposure time,but decreases with increasing standoff distance.