Whole cell biosensors always face the challenge of low stability of biological components and short storage life. This paper reports the effects of poly(2-hydroxyethyl methacrylate) (pHEMA) immobilization on a whole cell fluorescence biosensor for the detection of heavy metals (Cu, Pb, Cd), and pesticides (dichlorophenoxyacetic acid (2,4-D), and chlorpyrifos). The biosensor was produced by entrapping the cyanobacterium Anabaena torulosa on a cellulose membrane, followed by applying a layer of pHEMA, and attaching it to a well. The well was then fixed to an optical probe which was connected to a fluorescence spectrophotometer and an electronic reader. The optimization of the biosensor using several factors such as amount of HEMA and drying temperature were undertaken. The detection limits of biosensor without pHEMA for Cu, Cd, Pb, 2,4-D and chlorpyrifos were 1.195, 0.027, 0.0100, 0.025 and 0.025 µg/L respectively. The presence of pHEMA increased the limits of detection to 1.410, 0.250, 0.500, 0.235 and 0.117 µg/L respectively. pHEMA is known to enhance the reproducibility of the biosensor with average relative standard deviation (RSD) of ±1.76% for all the pollutants tested, 48% better than the biosensor without pHEMA (RSD = ±3.73%). In storability test with Cu 5 µg/L, the biosensor with pHEMA performed 11.5% better than the test without pHEMA on day-10 and 5.2% better on day-25. pHEMA is therefore a good candidate to be used in whole cell biosensors as it increases reproducibility and enhances biosensor storability.
According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.
Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imbalance on developing an effective SCADA-based IDS. To investigate the impact of various data balancing techniques, we chose two unbalanced datasets, the Morris power dataset, and CICIDS2017 dataset, including random sampling, one-sided selection (OSS), near-miss, SMOTE, and ADASYN. For binary classification, convolutional neural networks were coupled with long short-term memory (CNN-LSTM). The system's effectiveness was determined by the confusion matrix, which includes evaluation metrics, such as accuracy, precision, detection rate, and F1-score. Four experiments on the two datasets demonstrate the impact of the data imbalance. This research aims to help security researchers in understanding imbalanced datasets and their impact on DL SCADA-IDS.
The fabrication of an electrochemical sensor based on an iron oxide/graphene modified glassy carbon electrode (Fe3O4/rGO/GCE) and its simultaneous detection of dopamine (DA) and ascorbic acid (AA) is described here. The Fe3O4/rGO nanocomposite was synthesized via a simple, one step in-situ wet chemical method and characterized by different techniques. The presence of Fe3O4 nanoparticles on the surface of rGO sheets was confirmed by FESEM and TEM images. The electrochemical behavior of Fe3O4/rGO/GCE towards electrocatalytic oxidation of DA was investigated by cyclic voltammetry (CV) and differential pulse voltammetry (DPV) analysis. The electrochemical studies revealed that the Fe3O4/rGO/GCE dramatically increased the current response against the DA, due to the synergistic effect emerged between Fe3O4 and rGO. This implies that Fe3O4/rGO/GCE could exhibit excellent electrocatalytic activity and remarkable electron transfer kinetics towards the oxidation of DA. Moreover, the modified sensor electrode portrayed sensitivity and selectivity for simultaneous determination of AA and DA. The observed DPVs response linearly depends on AA and DA concentration in the range of 1-9 mM and 0.5-100 µM, with correlation coefficients of 0.995 and 0.996, respectively. The detection limit of (S/N = 3) was found to be 0.42 and 0.12 µM for AA and DA, respectively.
Clenbuterol (CLB) is an antibiotic and illegal growth promoter drug that has a long half-life and easily remains as residue and contaminates the animal-based food product that leads to various health problems. In this work, electrochemical immunosensor based on poly(3,4-ethylenedioxythiophene)/graphene oxide (PEDOT/GO) modified screen-printed carbon electrode (SPCE) for CLB detection was developed for antibiotic monitoring in a food product. The modification of SPCE with PEDOT/GO as a sensor platform was performed through electropolymerization, while the electrochemical assay was accomplished while using direct competitive format in which the free CLB and clenbuterol-horseradish peroxidase (CLB-HRP) in the solution will compete to form binding with the polyclonal anti-clenbuterol antibody (Ab) immobilized onto the modified electrode surface. A linear standard CLB calibration curve with R² = 0.9619 and low limit of detection (0.196 ng mL-1) was reported. Analysis of milk samples indicated that this immunosensor was able to detect CLB in real samples and the results that were obtained were comparable with enzyme-linked immunosorbent assays (ELISA).
An attractive electrochemical sensor of poly(3,4-ethylenedioxythiophene)/reduced graphene oxide electrode (PrGO) was developed for an electrochemical technique for uric acid (UA) detection in the presence of ascorbic acid (AA). PrGO composite film showed an improved electrocatalytic activity towards UA oxidation in pH 6.0 (0.1 M PBS). The PrGO composite exhibited a high current signal and low charge transfer resistance (Rct) compared to a reduced graphene oxide (rGO) electrode or a bare glassy carbon electrode (GCE). The limit of detection and sensitivity of PrGO for the detection of UA are 0.19 μM (S/N = 3) and 0.01 μA/μM, respectively, in the range of 1-300 μM of UA.
We present a ternary blend-based bulk heterojunction ITO/PEDOT:PSS/PFO-DBT: MEH-PPV:PC71BM/LiF/Al photodetector. Enhanced optical absorption range of the active film has been achieved by blending two donor components viz. poly[2,7-(9,9-di-octyl-fluorene)-alt-4,7-bis(thiophen-2-yl)benzo-2,1,3-thiadiazole] (PFO-DBT) and poly(2-methoxy-5(2'-ethylhexyloxy) phenylenevinylene (MEH-PPV) along with an acceptor component, i.e., (6,6)-phenyl-C71 hexnoic acid methyl ester. The dependency of the generation rate of free charge carriers in the organic photodetector (OPD) on varied incident optical power density was investigated as a function of different reverse biasing voltages. The photocurrent showed significant enhancement as the intensity of light impinging on active area of OPD is increased. The ratio of Ilight to Idark of fabricated device at -3 V was ~3.5 × 10(4). The dynamic behaviour of the OPD under on/off switching irradiation revealed that sensor exhibits quick response and recovery time of ∼800 ms and 500 ms, respectively. Besides reliability and repeatability in the photoresponse characteristics, the cost-effective and eco-friendly fabrication is the added benefit of the fabricated OPD.
An electrochemical cell using an organic compound, copper (II) phthalocyanine-tetrasulfonic acid tetrasodium salt (CuTsPc,) has been fabricated and investigated as a solution-based temperature sensor. The capacitance and resistance of the ITO/CuTsPc solution/ITO chemical cell has been characterized as a function of temperature in the temperature range of 25-80 °C. A linear response with minimal hysteresis is observed. The fabricated temperature sensor has shown high consistency and sensitive response towards a specific range of temperature values.
Palm kernel cake (PKC) is the solid residue following oil extraction of palm kernels and useful to fatten animals either as a single feed with only minerals and vitamins supplementation, or mixed with other feedstuffs such as corn kernels or soy beans. The occurrence of mycotoxins (aflatoxins, ochratoxins, zearalenone, and fumonisins) in feed samples affects the animal's health and also serves as a secondary contamination to humans via consumption of eggs, milk and meats. Of these, aflatoxin B₁ (AFB₁) is the most toxically potent and a confirmed carcinogen to both humans and animals. Methods such as High Performance Liquid Chromatography (HPLC) and Liquid Chromatography-Mass Spectrometry (LC-MS/MS) are common in the determination of mycotoxins. However, these methods usually require sample pre-treatment, extensive cleanup and skilled operator. Therefore, in the present work, a rapid method of electrochemical immunosensor for the detection of AFB₁ was developed based on an indirect competitive enzyme-linked immunosorbent assay (ELISA). Multi-walled carbon nanotubes (MWCNT) and chitosan (CS) were used as the electrode modifier for signal enhancement.N-ethyl-N'-(3-dimethylaminopropyl)-carbodiimide (EDC) andN-hydroxysuccinimide (NHS) activated the carboxyl groups at the surface of nanocomposite for the attachment of AFB₁-BSA antigen by covalent bonding. An indirect competitive reaction occurred between AFB₁-BSA and free AFB₁ for the binding site of a fixed amount of anti-AFB₁ antibody. A catalytic signal based on horseradish peroxidase (HRP) in the presence of hydrogen peroxide (H₂O₂) and 3,3',5,5'-tetramethylbenzidine (TMB) mediator was observed as a result of attachment of the secondary antibody to the immunoassay system. As a result, the reduction peak of TMB(Ox)was measured by using differential pulse voltammetry (DPV) analysis. Based on the results, the electrochemical surface area was increased from 0.396 cm² to 1.298 cm² due to the electrode modification with MWCNT/CS. At the optimal conditions, the working range of the electrochemical immunosensor was from 0.0001 to 10 ng/mL with limit of detection of 0.1 pg/mL. Good recoveries were obtained for the detection of spiked feed samples (PKC, corn kernels, soy beans). The developed method could be used for the screening of AFB₁ in real samples.
Mobile agent (MA), a part of the mobile computing paradigm, was recently proposed for data gathering in Wireless Sensor Networks (WSNs). The MA-based approach employs two algorithms: Single-agent Itinerary Planning (SIP) and Multi-mobile agent Itinerary Planning (MIP) for energy-efficient data gathering. The MIP was proposed to outperform the weakness of SIP by introducing distributed multi MAs to perform the data gathering task. Despite the advantages of MIP, finding the optimal number of distributed MAs and their itineraries are still regarded as critical issues. The existing MIP algorithms assume that the itinerary of the MA has to start and return back to the sink node. Moreover, each distributed MA has to carry the processing code (data aggregation code) to collect the sensory data and return back to the sink with the accumulated data. However, these assumptions have resulted in an increase in the number of MA's migration hops, which subsequently leads to an increase in energy and time consumption. In this paper, a spawn multi-mobile agent itinerary planning (SMIP) approach is proposed to mitigate the substantial increase in cost of energy and time used in the data gathering processes. The proposed approach is based on the agent spawning such that the main MA is able to spawn other MAs with different tasks assigned from the main MA. Extensive simulation experiments have been conducted to test the performance of the proposed approach against some selected MIP algorithms. The results show that the proposed SMIP outperforms the counterpart algorithms in terms of energy consumption and task delay (time), and improves the integrated energy-delay performance.
In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP).
Recognizing traffic signs is an essential component of intelligent driving systems' environment perception technology. In real-world applications, traffic sign recognition is easily influenced by variables such as light intensity, extreme weather, and distance, which increase the safety risks associated with intelligent vehicles. A Chinese traffic sign detection algorithm based on YOLOv4-tiny is proposed to overcome these challenges. An improved lightweight BECA attention mechanism module was added to the backbone feature extraction network, and an improved dense SPP network was added to the enhanced feature extraction network. A yolo detection layer was added to the detection layer, and k-means++ clustering was used to obtain prior boxes that were better suited for traffic sign detection. The improved algorithm, TSR-YOLO, was tested and assessed with the CCTSDB2021 dataset and showed a detection accuracy of 96.62%, a recall rate of 79.73%, an F-1 Score of 87.37%, and a mAP value of 92.77%, which outperformed the original YOLOv4-tiny network, and its FPS value remained around 81 f/s. Therefore, the proposed method can improve the accuracy of recognizing traffic signs in complex scenarios and can meet the real-time requirements of intelligent vehicles for traffic sign recognition tasks.
Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode.
Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing's challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system's ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.
Lately, wearable applications featuring photonic on-chip sensors are on the rise. Among many ways of controlling and/or modulating, the acousto-optic technique is seen to be a popular technique. This paper undertakes the study of different multilayer structures that can be fabricated for realizing an acousto-optic device, the objective being to obtain a high acousto-optic figure of merit (AOFM). By varying the thicknesses of the layers of these materials, several properties are discussed. The study shows that the multilayer thin film structure-based devices can give a high value of electromechanical coupling coefficient (k2) and a high AOFM as compared to the bulk piezoelectric/optical materials. The study is conducted to find the optimal normalised thickness of the multilayer structures with a material possessing the best optical and piezoelectric properties for fabricating acousto-optic devices. Based on simulations and studies of SAW propagation characteristics such as the electromechanical coupling coefficient (k2) and phase velocity (v), the acousto-optic figure of merit is calculated. The maximum value of the acousto-optic figure of merit achieved is higher than the AOFM of all the individual materials used in these layer structures. The suggested SAW device has potential application in wearable and small footprint acousto-optic devices and gives better results than those made with bulk piezoelectric materials.
Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.
Although wireless sensor networks (WSNs) have been the object of research focus for the past two decades, fault diagnosis in these networks has received little attention. This is an essential requirement for wireless networks, especially in WSNs, because of their ad-hoc nature, deployment requirements and resource limitations. Therefore, in this paper we survey fault diagnosis from the perspective of network operations. To the best of our knowledge, this is the first survey from such a perspective. We survey the proactive, active and passive fault diagnosis schemes that have appeared in the literature to date, accenting their advantages and limitations of each scheme. In addition to illuminating the details of past efforts, this survey also reveals new research challenges and strengthens our understanding of the field of fault diagnosis.
This work reviews design aspects of liquid metal antennas and their corresponding applications. In the age of modern wireless communication technologies, adaptability and versatility have become highly attractive features of any communication device. Compared to traditional conductors like copper, the flow property and lack of elasticity limit of conductive fluids, makes them an ideal alternative for applications demanding mechanically flexible antennas. These fluidic properties also allow innovative antenna fabrication techniques like 3D printing, injecting, or spraying the conductive fluid on rigid/flexible substrates. Such fluids can also be easily manipulated to implement reconfigurability in liquid antennas using methods like micro pumping or electrochemically controlled capillary action as compared to traditional approaches like high-frequency switching. In this work, we discuss attributes of widely used conductive fluids, their novel patterning/fabrication techniques, and their corresponding state-of-the-art applications.
Microwave breast imaging has been reported as having the most potential to become an alternative or additional tool to the existing X-ray mammography technique for detecting breast tumors. Microwave antenna sensor performance plays a significant role in microwave imaging system applications because the image quality is mostly affected by the microwave antenna sensor array properties like the number of antenna sensors in the array and the size of the antenna sensors. In this paper, a new system for successful early detection of a breast tumor using a balanced slotted antipodal Vivaldi Antenna (BSAVA) sensor is presented. The designed antenna sensor has an overall dimension of 0.401λ × 0.401λ × 0.016λ at the first resonant frequency and operates between 3.01 to 11 GHz under 10 dB. The radiating fins are modified by etching three slots on both fins which increases the operating bandwidth, directionality of radiation pattern, gain and efficiency. The antenna sensor performance of both the frequency domain and time domain scenarios and high-fidelity factor with NFD is also investigated. The antenna sensor can send and receive short electromagnetic pulses in the near field with low loss, little distortion and highly directionality. A realistic homogenous breast phantom is fabricated, and a breast phantom measurement system is developed where a two antennas sensor is placed on the breast model rotated by a mechanical scanner. The tumor response was investigated by analyzing the backscattering signals and successful image construction proves that the proposed microwave antenna sensor can be a suitable candidate for a high-resolution microwave breast imaging system.
Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.