CV Method Research Articles

A facile one-step electrosynthesis of polypyrrole/nano-SbOx composite for supercapacitors

The study depicts a one-step electrochemical synthesis of composite based on polypyrrole (PPy) and SbOx (Sb2O3, Sb2O5) on the graphite surface for the supercapacitor electrode in an acetonitrile solution including pyrrole monomer and Sb(BF4)3. PPy/SbOx composite on graphite plate is characterized using XPS, XRD, TEM, BET, and FESEM by comparing it with PPy homopolymer prepared in the same way. SbOx forms in nano-size and homogeneously encapsulates in PPy due to their simultaneous synthesis. The average pore size of the composite coating is 23.9 nm. The coated PG electrodes are tested in an H2SO4 electrolyte using CV, EIS, and GCD methods. SbOx nanoparticles enhance the capacitive performance of PPy due to the contribution of its catalytic and pseudocapacitive properties. The specific capacitance of the PPy/SbOx-coated PG electrode is 389.5 F g−1 at 5 A g−1 for 1.0 mg cm−2 loading. PPy/SbOx is synthesized in the presence of carboxymethyl cellulose (CMC) to provide stability to the coating. An asymmetric supercapacitor cell is fabricated by assembling the PPy/SbOx/CMC-coated and activated carbon-based graphite paper electrodes (10 mg cm−2 loading). While the potential window expands to 1.4 V, it delivers an energy density of 8.3 Wh kg−1, a power density of 3.5 kW kg−1, and a coulombic efficiency of 90% at 5 A g−1. Post-mortem analysis reveals that structural degradation of PPy in the composite limits long-term stability.

Benzotiyofen@Pd as an efficient and stable catalyst for the electrocatalytic oxidation of hydrazine

• New organic molecule was synthesized and investigated its anode catalyst performance. • CV, CA, and EIS methods are used to determination of the hydrazine oxidation activities. • The specific activity of organic catalyst is found as 25.811 mA/cm 2 . • Pd metal is electrochemically deposited on the organic catalyst, the catalytic activity increased the 80.930 mA/cm 2 . • This activity is higher than the current expensive metal anode catalyst. An efficient methods for the synthesis of 2-(2,5-dimethylphenyl)-3-iodobenzo[ b ]thiophene ( 4 ) is described, and investigated its anode catalyst performance by using electrochemical methods (CV, CA and EIS). When 2-(2,5-dimethylphenyl)-3-iodobenzo[ b ]thiophene ( 4 ) is applied, the specific activity is found as 25.811 mA/cm 2 . Interestingly, when Palladium (Pd) is electrochemically deposited on the benzothiophene derivative, the catalytic activity increased the 80.930 mA/cm 2 . This result is highest than the current metal based anode catalyst. Moreover, EIS and CA measurements display that Pd doped benzothiophene organic catalyst have high stability, and give the low charge transfer resistance. Energy dispersive X-ray (SEM-EDX), electron microscopy, TEM are used for the determination of its surface morphology. As a result, 2-(2,5-dimethylphenyl)-3-iodobenzo[ b ]thiophene ( 4 ) may be alternative electro-catalysts in fuel cell applications.

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach.

Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.

Effect of the Potentiostatic and Potentiodynamic Dealloying on the Structure and Chemical Distribution of Ag Atoms in Au-Enriched Nanoparticles

Recently, nanoporous gold (np-Au) prepared by dealloying has emerged as an efficient and durable catalyst for electrochemical oxidation of small alcohols. [1] An additional increase of surface-to-volume ratio by nano-sizing of AuxAg1-x master alloy might improve the catalytic methanol oxidation reaction (MOR). Only few research groups have recently shown that the morphology, structure and elemental distribution of dealloyed nanoparticles (NPs) depend on the various parameters such as initial particle size and composition.[2-4] Starting from AuxAg1-x alloy NPs, it is, however, unclear how the different dealloyed Au-rich NP motifs such as core-shell, hollow or porous NPs and the Ag distribution influence the catalytic MOR performance.In this work, we have investigated the effects of potentiodynamic and potentiostatic dealloying methods on the structure and chemical distribution of the residual Ag atoms for dealloyed Au-rich NPs. First, two size types of Ag-rich AuxAg1-x alloy NPs were prepared by one pot colloidal route and wet impregnation route. Larger Au23Ag77 NPs (size of 77 ± 26 nm) form a homogeneous disordered alloy, while the Au14Ag86 NPs with a size of 12 ± 5 nm show a strong enrichment of Ag atoms at the particle surface, referred to as core-shell arrangement. Subsequently, the as-prepared NPs with two sizes (12 nm and 77 nm) were electrochemically dealloyed using a potentiodynamic (cyclic voltammetry, CV) or potentiostatic (chronoamperometry, CA) method in 0.1 M HClO4. We observed that the CV method leads to the formation of pores inside the initial 77 nm NPs. Very interestingly, high-resolution STEM-EDX shows the appearance of Ag-rich regions near to the pore network of these dealloyed NPs. Unlike, after the dealloying by CV method the initial 12 nm NPs are still dense and solid, forming a thin Au-rich particle shell. After the exposure to air, the internal Ag atoms tend to segregate to the particle surface. In the CA method, we pointed out that the applied anodic potential has a strong influence of the surface diffusion behavior of the remaining Au atoms. Below a critical potential of 1.3 V vs. RHE, the dealloying process is kinetically hindered by the passivation of Au surface atoms. This critical potential in the CA method is strongly different to the critical dissolution potential of Ag atoms for AgxAu1-x NPs obtained from the CV profile (around 1.05 V vs. RHE). However, when the applied potential is larger than the critical potential, e.g. 1.6 V vs. RHE, the difference in the obtained structure and composition of dealloyed 77 nm NPs is negligible between potentiodynamic and potentiostatic approaches. On the other hand, the potentiostatic dealloying at 1.6 V vs. RHE leads to higher surface content of residual Ag for dealloyed NPs with 12 nm compared to potentiodynamic. This increased enrichment of the residual Ag atoms near the particle surface is also supported by XPS.In summary, we correlate the structural parameters (initial particle size, composition/distribution) of dealloyed AgxAu1-x NPs with the electrochemical dealloying methods (potentiostatic vs. potentiodynamic). Tailoring the surface diffusion rate of the Au atoms and the Ag distribution helps to improve the catalytic performance for MOR.

The Novel Potentiometric Approach to Antioxidant Capacity Assay Based on the Reaction with Stable Radical 2,2'-diphenyl-1-picrylhydrazyl.

For the first time, new possibilities of using the DPPH• as a signal-forming oxidant molecule with potentiometric detection are shown. The CV method confirmed the presence of a quasi-reversible potential-determining system DPPH•/DPPH-H under experimental conditions. This fact makes it possible to use DPPH• as the model of the oxidizing agent for obtaining an analytical signal by the potentiometry method. The potentiometric approach makes it possible to obtain the value of the Nernst slope and the antioxidant capacity in one experiment. It consists of an antioxidant supplement and two consecutive DPPH• supplements. In this case, the calculation of the Nernst slope is carried out by introducing the second addition of the oxidizing agent and constructing a calibration curve against the reaction background with an antioxidant. Solutions of individual antioxidants α-tocopherol, quercetin, (±)-catechin hydrate, and α-lipoic acid were studied by the developed approach. A high correlation with the results of spectrophotometric measurements is shown. At the same time, the potentiometry method is devoid of the concentration limitations of the spectrophotometric method, which was confirmed. In the study of plant materials extracts, a high correlation of antioxidant capacity, obtained by potentiometric and spectrophotometric methods, was shown only for objects whose color did not contribute to the DPPH• absorption. The versatility of the potentiometric method for studying objects of any color was shown.

Addition of SiO2 to the operation of a polyimide cathode in a sodium battery

The effect of the addition of SiO2 nanoparticles on the properties of a polyimide cathode was explored by CV, XPS, and galvanostatic cycling methods. The capacity and average cycling potential of the cell increased in the presence of SiO2. By quantum chemical modeling, it was shown that SiO2 nanoparticles served as a framework for polyimide, which retained its fixed structure upon metalation with sodium.

Detection of Methylparaben in Cosmetics by Poly L–Lysine/Reduced Graphene Oxide-Based Sensor

In this study, a PoL/RGO material was successfully synthesized and employed to modify the working electrode for determining MPB in medication products through voltammetric techniques. The structure of the nanocomposite was characterized by UV–vis and FT-IR spectrum and its application to the MPB electrochemical detection was tested by the CV and DPV techniques. In the result, the modified PoL-RGO/GCE electrode exhibited a superior response toward MPB by applying the DPV method, compares to using the bare GCE, with a limit of detection (LOD), a limit of quantification (LOQ) is 0.20 μM and 0.70 μM, respectively and the concentration ranging from 1 to 200 μM. In addition, the repeatability (RSD of 2.2, 1.6, 1.4 for 5, 50 and 100 μM MPB, respectively), and the reproducibility (RSD of 4.7%) of the technique were examined as well. This illustrates the performance of the electrochemical sensor was statistically investigated by the CV and DPV methods demonstrating accuracy comparable to other analytical methods as well as indicating that MPB can be determined in cosmetics with high recovery ranging from 97% to 104.3%.

Preparation and Properties Research of Manganese Dioxide-polyaniline Electrode

Hydrogen energy is the cleanest energy at present because its combustion product and no carbon emission. Electrolysis of water to produce hydrogen is generally considered to be the most promising method to produce hydrogen in large quantities. But electrolysis of water to produce hydrogen is without the use of a catalyst. Manganese dioxide is an efficient and green catalyst. MnO2 dioxide-polyaniline electrode was prepared by CV method and CV/CA method respectively. The properties of dioxide-polyaniline were studied with the linear sweep cyclic voltammetry test, electrochemical impedance test and tafel slope test, and the results were compared with those of MnO2 electrode. It was found the MnO2 dioxide-polyaniline electrode properties were outstanding. It was further showed that performance of manganese dioxide-polyaniline electrode was superior by preparation of CV/CA with the testes of XPS and SEM and stability test.

Ultrasensitive Electrochemical Detection of Carcinoembryonic Antigen with a Label‐Free Immunosensor Using Gold Nanoparticle‐Decorated Poly(pyrrole‐co‐3,4‐ethylenedioxythiophene)

AbstractIn this study, an electrochemical immunosensor was developed using gold nanoparticles (AuNPs), 3,4‐ethylenedioxythiophene (EDOT), and pyrrole (Py) to detect carcinoembryonic antigen (CEA) sensitively. Firstly, the electrocopolymerization of PPy and EDOT was carried out on a pencil graphite electrode (PGE) by the chronoamperometry method (p(Py‐co‐EDOT)/PGE). Then, gold nanoparticles were electrochemically deposited on the prepared p(Py‐co‐EDOT)/PGEs by the chronoamperometry method. The characterizations of the developed label‐free immunosensor (AuNPs/p(Py‐co‐EDOT)/PGE) were carried out using CV, EIS, XPS, SEM, EDX, and FTIR methods. Antibody concentration, antibody immobilization time, and immunocomplex time were optimized using differential pulse voltammetry (DPV). After optimal conditions are determined, the DPV peak current of the immunosensor decreases linearly with increasing CEA concentration of 0.001–100 ng mL−1. The detection limit was calculated as 0.741 pg mL−1 from the calibration graph (S/N=3). The developed sensor showed excellent anti‐interference ability against ascorbic acid, bovine serum albumin, dopamine, and glucose. The applicability of the developed sensor to real samples was investigated, and good recovery values were obtained. As a result, the AuNPs/p(Py‐EDOT)/PGE provides low‐cost, selective, and rapid detection of CEA. Our findings suggest the present immunosensor is a good candidate for application in clinical screening.

Study on spatio-temporal changes and driving factors of carbon emissions at the building operation stage- A case study of China

Global climate change has become one of the greatest challenges to human development, the study of the dynamic evolution of carbon emissions at the building operation stage and its driving mechanism is of great significance for the reasonable control of carbon emissions, and helpful to provide more scientific and reasonable decision support for proposing an implementable carbon neutral pathway. According to the geospatial differences of carbon emissions at the building operation stage, this paper constructs a research framework on the influencing factors and driving mechanisms of carbon emissions at the building operation stage based on CV, kernel density, Geodetector and other methods. The study shows that first, that the carbon emissions at the building operation stage are unequally distributed in space in provinces of China, and the building carbon emissions vary greatly in structure among different regions. Second, different factors vary greatly in influence. Third, there are great differences in the driving mechanisms of carbon emissions over time, but factors such as urban population and refrigerator ownership are both key influencing factors that have a comprehensive influence on the spatio-temporal changes of carbon emissions. Fourth, all the factor pairs are bifactor-enhanced or non-linearly enhanced, and they can be classified into three types of high, medium, and low levels. Fifth, a carbon neutral pathway map for the building sector is proposed based on the spatial distribution of carbon emissions and its driving mechanism, to provide reference for foreign regions with similar conditions.

In situ Growth of Ni(OH)2 Nanoparticles on 316L Stainless Steel Foam: An Efficient Three‐dimensional Non‐enzymatic Glucose Electrochemical Sensor in Real Human Blood Serum Samples

AbstractFor the first time, nickel hydroxide nanoparticles (Ni(OH)2 NPs) grown on 316L stainless steel foam were used as a non‐enzymatic electrochemical glucose sensor. The Ni(OH)2/SSF‐316L was elaborated by applying a simple ultrafast CV method without nickel salts addition. Ni(OH)2/SSF‐316L was characterized by SEM and XRD. The electrochemical behavior was investigated by CV, EIS, and amperometric measurements. The fabricated sensor revealed higher sensitivity 1062 μA mM−1cm−2, wide linear range from 1.0 μM to 4.0 mM with a low detection limit of 2.0 μM and good selectivity. In addition, real sample analysis was performed for controlled glucose in real blood serum.

Heterostructures of mixed metal oxides (ZnMnO3/ZnO) synthesized by a wet-chemical approach and their application for the electrochemical detection of the drug chlorpromazine

Materials development using heterostructures is generally considered an efficient method for developing electrocatalysts . However, it still faces many challenges, such as instability and accurate phase mixing ratio of metals. On the other hand, the excessive consumption of drugs such as chlorpromazine (CPMZ) leads to several serious human diseases. Therefore, the accurate detection and determination of CPMZ is essential. In this view, a highly efficient electrochemical sensor based on a heterostructure of ZnMnO 3 /ZnO NPs/GCE was fabricated to detect the psychotic drug CPMZ. The heterostructure of cubic ZnMnO 3 (ZMO) and hexagonal zinc oxide nanoparticles (ZMO/ZnO NPs) was synthesized by a simple co-precipitation method followed by thermal calcination . The as-prepared material crystallinity and average crystal size (26 nm) were analyzed by p-XRD. The morphology of the ZMO/ZnO NPs was analyzed by SEM and HR-TEM, which revealed the formation of non-uniform spherical and hexagonal nanoparticles with a size of ∼10-30 nm. FT-IR and XPS were used to investigate the functional groups and oxidation states of the ZMO/ZnO NPs. The electrochemical properties of the ZMO/ZnO NPs/GCE were analyzed by EIS , CV , and DPV methods. ZMO/ZnO/GCE has improved electrochemical activity for detecting CPMZ. In addition, the ZMO/ZnO/GCE electrochemical probe has two linear ranges (0.05–125.55 μM and 185.55–1625 μM) and good sensitivity (0.760 μA μM −1 cm −2 ) with a low detection limit of 0.019 μM. Analysis of real samples was performed in human blood serum and urine, and the recoveries of CPMZ were 96–100% and 98–100%, respectively.

Grid Search CV Implementation in Random Forest Algorithm to Improve Accuracy of Breast Cancer Data

Breast cancer is the most common cancer in women and is the second leading cause of global death. Disease diagnosis plays an important role in determining treatment strategies related to patient safety. Therefore, we need machine learning to predict disease. This paper aims to determine the best parameter values in breast cancer data using the Grid Search CV method and classify breast cancer data using the random forest algorithm. In addition, the paper aims to compare the accuracy values generated using the Grid Search CV and without the Grid Search CV. The method used to analyze breast cancer data in researchers is the Random Forest (RF) classification algorithm. In addition to using the Random Forest algorithm, this study also uses the Grid Search CV method. Grid Search CV is a method used to determine the optimal model parameters so that the classifier can predict the test data reliably. This study indicates that the highest accuracy value is obtained in the random forest algorithm using the grid search method of 0.9545. In contrast, the accuracy of the random forest algorithm without using the grid search method is 0.9480. For further research, it is suggested to develop a breast cancer dataset using the grid search cv method with other algorithms, such as Logistic Regression, Xgboost, and SVM. We can also use the same algorithm with different datasets to prove that the grid search cv method can increase accuracy.

Deep learning-assisted smartphone-based molecularly imprinted electrochemiluminescence detection sensing platform: Protable device and visual monitoring furosemide

A novel, portable, and smartphone-based molecularly imprinted polymer electrochemiluminescence (MIP-ECL) sensing platform was constructed for sensitive and selective determination of furosemide (FSM). In this platform, MoSe2 nanoparticles/starch-derived biomass carbon (MoSe2/BC) nanocomposites as imprinted material, lucigenin (Luc) as the energy donor, CdS quantum dots (CdS QDs) were used as the luminophore (energy acceptor), and molecularly imprinted polymer (MIP) as the specificity recognition element to construct a MIP-ECL sensing system based on electroluminescence resonance energy transfer (ECL-RET) mechanism, which enhanced the sensitivity and the specificity of this system. Imprinted materials were characterized by SEM, TEM, XRD, FT-IR, etc. and the recognition performance of MIP was characterized using CV, EIS, and ECL methods. The elution and re-sorption of template molecules can be used as a switch to control ECL based on the signal that can be quenched by FSM. Interestingly, deep learning based on convolutional neural networks realizes batch processing of ECL signals. Additionally, this developed MIP-ECL method was established by using the traditional ECL analyzer detector for the assay of FSM with a detection limit of 4 nM in the range of 0.010 μM–100 μM. Besides, the consumer smartphone sensing platform based on deep learning showed an outstanding linear response between the R-value of the picture and the concentration of furosemide in the range of 1–70 μM with a detection limit of 0.25 μΜ, which is much lower than that the reported for other detection methods. More importantly, due to the transferability of deep learning, the smartphone-based MIP-ECL systems can facilitate the real-time monitoring of biochemical analytes in multiple fields.

Development of novel predictive equations for local flow asymmetry within control valves using a distribution parameter-based method under multiphase conditions

When designing control valves for multiphase applications using conventional valve sizing methodologies, it is assumed that the flow properties such as mixture density and volume fraction of phases are uniform throughout the valve trim. In reality, however, the properties may be different due to the difference in the gas and liquid densities as well as velocities within the valve which cause phase stratification. Furthermore, complex geometric effects can also cause phase non-uniformity within the valve. Current valve sizing methodologies are based on global parameters such as overall valve coefficient (Cv) which do not consider local phase non-uniformities within the trim during the design phase. Among these methodologies, it has previously been shown that the harmonised Cvs method is the most accurate method of sizing control valves under multiphase flow conditions across a wide range of flow regimes. The current study provides additional equations to be used with the harmonised Cvs method to ensure that the local flow quality compliance is achieved, which seems to be the major weakness of the existing method. In order to obtain local gas and liquid flow rates, pressure drops and phase fractions, well-validated computational fluid dynamics (CFD) simulations were carried out. Two valve opening positions of 60% and 100% were considered each with 5, 10, and 15% inlet air volume fractions to simulate real life conditions. The results show that there is severe non-uniformity in the local gas and liquid distributions within the valve trim. To quantify the phase non-uniformities observed, a distribution parameter (Co) based on the drift-flux model was used. The harmonised Cv method was used to calculate the local Cvs and the local equivalent area factor (Ψ). A new equation was derived that considers the local variation of the Ψ factor within the trim and which incorporates the distribution parameter. Based on the foregoing analyses, additional flow distribution equations have been proposed that when used with the harmonised Cvs valve design method for multiphase flow applications will ensure that the deviation in performance of the valve is minimal from the design conditions.

Electrochemical determination of quercetin using glassy carbon electrode modified with WS2/GdCoO3 nanocomposite.

AWS2/GdCoO3 nanocomposite was successfully prepared using hydrothermal-assisted synthesis. Our prepared WS2/GdCoO3 nanocomposite was fabricated on a glassy carbon electrode (GCE) for the detection of quercetin (QCT). TheWS2/GdCoO3 nanomaterial was characterized by powder XRD, micro-Raman, FT-IR, XPS, FE-SEM, and HR-TEM, which proved that WS2 nanoplates were finely dispersed on the surface of the GdCoO3 nanoflakes. The electrocatalytic performance of WS2/GdCoO3 was investigated by the EIS technique, and it exhibited a small semi-circle, which confirms that it has a large active surface area and high electrical conductivity. The electrochemical behavior of QCT at the WS2/GdCoO3 sensor was explored by using the CV and DPV methods. Theproposed electrochemical sensor exhibited excellent electrochemical response toward QCT with a wide linear range of 0.001 to 329µM, low limit of detection (LOD) of 0.003µM, and limit of quantification (LOQ) of 0.0101µM. The sensor also displayed excellent selectivity, sensitivity, reproducibility, and stability. Additionally, theWS2/GdCoO3 sensor was utilized for the detection of QCT in apple juice and grape juice samples, and it exhibited good recovery results.

High potential of yellow potato (Solanum Tuberosum L.) peel waste as porous carbon source for supercapacitor electrodes

Yellow potato peel contains chemical components such as protein, fiber, starch and sugar which is composed atom carbon chains bonding. Heating potato peel at high temperatures can disrupt the bonding of the carbon atoms of the constituents, vaporizing volatile compounds, thereby producing high carbon fixed. This study preparation yellow potato peel-based carbon electrodes through a single-stage integrated pyrolysis with carbonization from room temperature to 600 °C in N2 gas atmosphere followed by physical activation to a temperature of 850°C in CO2 gas environment. The impregnation of ZnCl2 at different concentrations was optimized as an independent variable precursor to produce porous activated carbon for energy storage devices. The difference in concentration of 0.1M, 0.3M and 0.5M ZnCl2 can increase the porosity, structure of amorphous carbon and the resulting high electrochemical performance. Electrochemical properties were characterized using cyclic voltammetry and galvanostatic charge discharge methods in an aqueous electrolyte of 1M H2SO4 at a voltage of 0-1000 mV and a scanning rate of 1 mV s−1. Furthermore, the resulting specific capacitance increased from 82.82 F g−1, 195.66 F g−1 and 147.03 F g−1 based on the effect of the concentration of the chemical activator ZnCl2. While the specific capacitance obtained using the GCD method shows higher numbers, namely 145.13 F g−1, 223.25 F g−1 and 174.08 F g−1. Energy density 27.18 Wh kg−1 and power density 97.93 W kg−1 from cv method. The simple approach of activated carbon from potato peel waste is expected to produce an economical and simple porous carbon electrode for high performance energy storage application.

Continuous Human Activity Recognition With Distributed Radar Sensor Networks and CNN–RNN Architectures

Unconstrained human activities recognition with a radar network is considered. A hybrid classifier combining both CNNs and RNNs for spatial-temporal pattern extraction is proposed. The two-dimensional CNNs (2D-CNNs) are first applied to the radar data to perform spatial feature extraction on the input spectrograms. Subsequently, gated recurrent units with bidirectional implementations are used to capture the long- and short-term temporal dependencies in the feature maps generated by the 2D-CNNs. Three NN-based data fusion methods were explored and compared to utilize the rich information provided by the different radar nodes. The performance of the proposed classifier was validated rigorously using the K-fold CV and L1PO method. Unlike competitive research, the dataset with continuous human activities with seamless inter-activity transitions that can occur at any time and unconstrained moving trajectories of the participants has been collected and used for evaluation purposes. Classification accuracy of about 90.8% is achieved for nine-class HAR by the proposed classifier with the halfway fusion method.

Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework

In this era, people using vehicles is getting increased day by day. As pedestrians leading a dog for a walk, or hurrying to their workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. To plan, monitor and also control these vehicles is becoming a big challenge. In the article, we have come up with a solution to the above problem using the video surveillance considering the video data from the traffic cameras. Using computer vision and deep learning technology we will be able to recognize violations of rules. This article will describe modern CV and DL methods to recognize vehicle on the road and traffic violations of rules by them. Implementation of methods can be done using OpenCV Python as a tool. Our proposed solution can recognize vehicles, track their speed and help in counting the objects precisely.

Label-free electrochemical detection of genetic damage induced by the interaction of a novel potential aminoanthraquinone-derived antitumor agent with DNA modified electrode

In this work, an efficient electrochemical biosensor for the detection of DNA damage induced by the interaction of a novel antitumor agent AD-NU was investigated by CV, DPV and SWV methods. The GCE was modified with calf thymus DNA, G-quadruplexes, poly dA and poly dG, respectively. Among all the candidates, compound AD-NU was the optimum ligand because of the minimum energy when binding to DNA or TOP II by molecular docking. The electrochemical analysis revealed the redox mechanism of AD-NU on the GCE surface, which included two consecutive reversible processes of 9,10-diquinone and irreversible redox of nitroso group. The DNA damage caused by AD-NU was investigated by comparing the intensity changes of dGuo and dAdo oxidation peaks in DNA strands, and monitoring the appearance of peak for oxidative product 8-oxoGua. Further confirmation for the interaction between AD-NU and DNA was performed by UV–vis absorption and fluorescence titration, which implied the intercalation of AD-NU into DNA base pairs with a high affinity constant (Kb = 13.7 ×105 M−1). The electrophoresis also revealed the potency of AD-NU to cleave and unwind DNA strands. Finally, AD-NU was proved to have good antiproliferative activity toward tumor cells, with IC50 values of 3.0 μmol∙L−1 for A549 cell lines and 3.3 μmol∙L−1 for Hela cell lines, superior to MTZ reference.