Babasaheb Bhimrao Ambedkar University

In this article, the sensing behaviors of undoped titanium dioxide (TiO2) and CdS-doped TiO2 (CdS-TiO2) thick films are discussed. Sensing pastes of 2 wt% CdS-TiO2 and undoped TiO2 were prepared in the laboratory and used to fabricate thick film gas sensors on an alumina substrate. The crystal structures of TiO2 and CdS-TiO2 samples were characterized by XRD and atomic force microscopy (AFM). The results indicated that the grain size and RMS roughness parameter were reduced by adding CdS contents. The sensing behaviors of the fabricated devices were studied at varying concentrations (0–5000 ppm) of different hydrocarbon gases, such as LPG, methanol, ethanol, toluene, and benzene, in ambient air at 300 K. The effect of humidity levels on the sensing properties of the sensors was also investigated. The sensor response value of CdS-TiO2 for benzene was found to be 2.25 times higher than that of TiO2-based sensing devices. Thus, CdS doping significantly enhanced the response and recovery times of the sensor. The TiO2 film exhibited response and recovery times of 65 s and 180 s, respectively. In contrast, when doped with CdS, the response times were reduced to 15 s and 103 s, respectively, when exposed to benzene at a concentration of 5000 ppm at 300 K. The sensing mechanism has been discussed and the experimental results were validated using a model based on the Frenkel–Poole theory of electronic emission and catalytic oxidation. The obtained results demonstrate that TiO2 structures doped with low concentrations of CdS exhibit superior sensitivity and selectivity to benzene gas under low humidity levels at room temperature (300 K).

Cybercriminals, fraudsters, and hackers are using the online payment transaction system as a deadly weapon posing a significant threat to customers in the form of online payment transaction fraud. Cybercriminals exploit user vulnerabilities using sophisticated techniques, including advanced digital technology, machine learning, and artificial intelligence to perform fraudulent transactions. The aim of this study is to upgrade the security of the Existing Online Payment Transaction System (EOTS) with a focus on protecting end users and significantly mitigating fraudulent transactions to a great extent. To achieve this aim, a Three-Defense Wall Authentication Framework (TDWAF) that works at the authentication level, a Fraud Detection Decision Support System Framework (FDDSSF) that works at the fraud detection level, and a Deep Authentication Fraud Detection Model (DAFDM) that works at the fraud detection level have presented. In this context, the verification of the authenticity of the two frameworks and one model has been shown in this chapter. Standard performance metrics have been used to test the accuracy, precision, specificity, F1-score, G-mean, and AUC-ROC, and two different datasets have been used. The accuracy results of the proposed frameworks and model have shown remarkable achievements in mitigating fraudulent transactions.

Online payment transaction system using plastic money cards participates in the country’s economic growth and provide convenience and security for the authorized user. The aim of the complete study is to propose a robust and secure online payment transaction system, including a three-defense wall authentication framework, a fraud detection-decision support system framework, and a deep autoencoder fraud detection model to identify and mitigate fraudulent online payment transactions. This chapter presents recommendations for implementing the proposed methodologies and also provides the limitations of the proposed methodologies. This chapter explores the future scope of the study.

Online payment transaction fraud using plastic money card is a very crucial topic because of global financial implications. Although, the share percentage of online payment transaction fraud is small, however, the global card fraud amount reaches amount trillions. As digital technology evolves, the fraud patterns of cybercriminals, hackers, or fraudsters evolve. The primary objective of cybercriminals, hackers, or fraudsters is to obtain monetary gains. Financial institutions and governments are actively working to identify and mitigate online payment transaction fraud by implementing numerous security measures. The chapter aims to protect online payment transactions against fraudulent transactions. In pursuing the same, the study finds the genesis behind online payment transaction fraud before addressing the issue and finding the solution to the problem. The reason behind studying online payment transaction fraud is to find a solution to online payment transaction fraud in the future and it should be capable enough to handle almost any online payment transaction fraud scenario. Therefore, this study analyzes the diverse patterns of fraudulent transactions and strategies behind committing online transaction fraud, aiming to find a robust solution for mitigating online payment transaction fraud in future and also spread the knowledge of online payment transaction fraud among users.

The global digital growth promotes online payment transactions with plastic money card. The growth of online payment transactions attracts cybercriminals, fraudsters, and hackers to perform fraudulent online payment transactions. There are two different security measures to prevent fraudulent transactions that can be implemented including multifactor authentication and fraud detection system. Therefore, there is a need for fraud detection system to mitigate the fraudulent online payment transaction. The aim of the study is to identify and mitigate fraudulent online payment transactions. The study proposes and implements a deep autoencoder fraud detection model to detect fraudulent transactions. The training and testing of the model are performed using two datasets. The first is the primary dataset with 16 features and 880 instances and the second is the credit card fraud dataset with 31 features and 284,807, which is publicly available. Before the training and testing of the model, data anomalies from the dataset are removed using data processing and feature engineering to make the dataset compatible with the model. This study also implements a synthetic minority oversampling technique to make the dataset balanced because both datasets are imbalanced.

The growth of digital technology with information and communication technology is on the rise. Similarly, the rise of fraudulent online payment transactions with plastic money card occurs and attracts cybercriminals because of less chance to catch, high money gain, less hard work and no physical presence. In 2021, the global economy experienced a loss of $32.34 billion due to fraudulent activities associated with plastic money card transactions. The change in technology promotes the change in transaction patterns of authorized users and fraudsters. Therefore, there is need to update the fraud detection system for fraudulent online payment transactions is very essential. The aim of the study is to identify and mitigate fraudulent online payment transactions. In order to achieve the aim of the study, the study proposes and implements a fraud detection-decision support system framework along the transaction authentication process. The idea behind the proposed framework is to compare the current and previous transaction patterns of the authorized user and generate a safe score for a current transaction. A primary dataset with 16 (transaction pattern parameters) features and 880 instances are used to assess the validity of the framework.

Online payment transactions pose a risk of fraudulent transactions. In addition to security measures, fraudsters perform unauthorized online payment transactions. Because, at regular intervals, fraudsters change their fraudulent transaction strategies and come up with more advanced fraudulent techniques, especially attacks on the user’s end. This chapter presents the two proposed frameworks and one proposed model for identifying and mitigating fraudulent online payment transactions. The first framework includes the multifactor authentication technique. The multifactor authentication framework aims to restrict fraudsters from executing fraudulent online payment transactions under any circumstances. The second framework comprises the fraud detection system that relies on the decision support system. The goal of the decision support system framework is to identify anomalies in the current transaction by comparative analysis with the previous transaction pattern of an authorized user. The third model incorporates the deep autoencoder based on a data-driven approach. The deep autoencoder model aims to grab the anomalies in the current transaction by comparing it to the previous transaction pattern of an authorized user. This chapter illustrates the basic foundation of data collection in relation to the proposed two frameworks and one model for identifying and mitigating fraudulent online payment transactions.

Online payment transaction with plastic money card is a basic need of everyone around the world and increases the productivity of the system. Despite of the security measures, online payment transaction attracts fraudsters to perform fraudulent transactions. This chapter presents and implements a three defense wall authentication framework to identify and mitigate fraudulent transaction based on multifactor authentication techniques. The goal of the proposed framework is to restrict fraudsters under any circumstances, even if the fraudster possesses all the necessary information for conducting a fraudulent transaction. The proposed framework uses four different dynamic authentication features such as transaction request, the CEOTP (Customer End One Time Password), location of the mobile device with the transaction device and the OTP (One Time Password) at three different authentication level of online transaction system. Validation of the proposed framework uses the dataset of online payment transaction which is collected during the execution of the proposed framework.

Online payment transaction system is playing a crucial role in the economic development of countries. It indicates the growth in revenue generation and reduction of black money. This chapter aims to know the genesis of online payment transaction system using plastic money card. The development and enhancement in security for online payment transaction system due to the rise in plastic money card fraud with change in innovative and sophisticated fraudulent patterns of cybercriminals, fraudsters, or hackers is the reason behind the study. This chapter presents the growth of the online payment transaction system from start to present and the drivers behind its development, including digital technological development, COVID-19, and the requirements of customers. This chapter elaborates on the worldwide adoption of online payment transactions along with its consequences in terms of online payment transaction fraud. This chapter illustrates the online payment transaction system using plastic money cards and various options to perform transactions. This chapter also presents the various security measures of online payment transaction system and plastic money card. Lastly, this chapter demonstrates the reason behind adopting online payment transactions, including significance, requirements, and benefits.

The utilization of multilevel inverters (MLI) has expanded in recent years and emerged as a transformative technology in the field of power electronics. In MLI, level shift (LS) and phase shift (PS) pulse width modulation (PWM) techniques are widely used to achieve enhanced MLI performance. However, each technique has its own set of limitations. PSPWM tends to result in high switching losses across all switches, while LSPWM faces challenges with uneven power distribution among the cascaded modules. These two drawbacks of PWM techniques are considered and proposed a new PWM technique that overcomes these drawbacks. The proposed PWM’s harmonic spectrum and power-sharing capability have been compared with conventional LS and PSPWM techniques. The proposed PWM ensures balanced power distribution with switching losses reduced to 50% across both H-bridge module, whereas PSPWM experiences 100% switching losses on both modules, and LSPWM suffers from uneven losses; 13.4% on HB-I and 86.6% on HB-II. Moreover, the performance of the proposed PWM technique is demonstrated for shunt active power filters. The proposed and conventional PWM techniques are compared for grid current harmonic distortion, input power factor, and active filtering efficiency. The proposed PWM technique achieves superior performance, reducing total harmonic distortion (THD) in the grid current to 1.10%, compared to 1.14% for LSPWM and 1.16% for PSPWM. It also achieves the highest active filtering efficiency at 23.92%, surpassing both LSPWM and PSPWM. The performance of the proposed PWM over the conventional PWM techniques has been evaluated and simulated using MATLAB/Simulink 2021b to confirm its superior performance.

In this paper, we theoretically investigate the optical properties of defective one-dimensional crystals as a biosensor for the early detection of breast tumors. The theoretical model of defective 1D photonic crystal consists of Si/SiO2 layers and a defect layer consists of the dispersion of normal breast tissue and breast cancer tissue into the water as analyte material. The effective refractive index of the defect layer is based on the Maxwell–Garnett model at different filling fractions of cancer cells. A transfer matrix method is used to calculate the transmission spectra and other optical properties like sensitivity, Q-factor, and figure of merit in the THz region. Additionally, the detection of cancer cells using a design biosensor is measured by shifting the transmission peak with incident angle, defect layer thickness, and filling fraction concentration of the analyte. The present theoretical design may have potential application as a biosensor to detect breast cancer even with the minimal amount of cancer cells.

Lepidopteran insects are novel source of omega‐3 fatty acids as revealed by the studies recently. In the current study, we assessed the effects of four different drying temperatures (50, 60, 70, and80 ºC) of eri silkworm (Philosamia ricini) pupal oil (EPO)abbreviated asEPO50, 60, 70, and 80 followed by conducting the characterization and antibacterial efficacy studies. Gas chromatography‐mass spectroscopy (GC‐MS) and Fourier transform infrared (FTIR) were used for chemical profiling of EPOs. The agar‐well diffusion method was performed to evaluate the antibacterial activities. The trend of this study clearly revealed that enhanced drying percentage of temperatures showed raising saturated fats and reduced unsaturated fats. However, the best suitable temperature was found at 60 ºC with high unsaturated fatty acids, including Omega‐3. Further, at higher temperature, FTIR analysis revealed an increasing complexity of functional groups with potential bioactive compounds. EPOs were found to have significant antibacterial activity against S. typhi and S. aureus at all tested (10, 20, 30, and 40 µL) volumes used in the study.

India has been identified as one of the most susceptible nations to the impacts of climate change. The present study made an attempt to examine the adaptive capacity of farmers living in the Gangetic Plains of India. This study creates an adaptive capacity index for four Gangetic Plains: the lower Gangetic plain, the middle Gangetic plain, the upper Gangetic plain, and the Trans Gangetic plain. It does this by using data from the 77th round of the national sample survey organisation (NSSO), the 2011 census, and the agricultural census of 2015–16, as well as an indicator approach. A total of 29 indicators covers six dimensions: physical resource capacity, financial resource capacity, human resource capacity, social resource capacity, livelihood diversity capacity, and information accessibility. The results show that the trans-Gangetic Plain region has the highest adaptive capacity, while the lower Gangetic Plain region has the lowest adaptive capacity to deal with climate change. Based on the cross-index analysis, the trans-Gangetic plain region had higher adaptive capacity due to higher physical resource capacity, financial resource capacity, human resource capacity, and information accessibility capacity. This paper suggests enhancing the adaptive capacity of farmers in the Gangetic Plains to facilitate the implementation of more effective adaptation measures. We can achieve this by improving the availability of accurate and timely weather information, upgrading irrigation facilities, increasing accessibility to institutional credit, establishing a robust extension service network to disseminate information on changing climate conditions and effective farm management techniques, and promoting livelihood diversification.

Short-read sequencing technology has emerged as a preferred tool to analyse the bacterial composition of a niche by targeting hypervariable regions of the 16S rRNA gene. It targets the short hypervariable regions of the 16S rRNA gene and uncovers the taxonomic profile and their associated pathways. QIIME 2 is preferred and ready-to-use pipelines that perform stepwise analysis of massive short reads of 16S rRNA genes. This wrapper comprises several tools that include quality checking, denoising, taxonomic classification, alignment, and diversity analysis. However, it demands huge bioinformatic analysis practices which are quite challenging to many microbiologists working in the field of traditional microbiology. This paper, therefore, aims to make microbiologists familiar with the steps of computational analysis for processing 16S rRNA-based sequences. Here, we are presenting stepwise processing of NGS sequences using the QIIME 2 platform along with their analyses, which include installing QIIME 2, importing and processing data, quality checks, taxonomy assignments, and diversity analysis. Besides, the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) has also been illustrated to understand the correlation between metabolic and physiological footprints of the different species observed during microbiome analysis. Therefore, this paper can be used as a handy toolkit for those researchers who are less familiar with its associated bioinformatic analysis.

In this analysis, the collective impact of temperature-dependent thermal conductivity and viscosity variations on the convective instability of a Jeffrey fluid in a rotating layer of cellular porous material is examined using an improved Jeffrey–Darcy model. This study has significant implications for cellular foams made from plastics, ceramics and metals, in which radiative heat transmission can be taken as a diffusion practice. Utilizing the linear stability concept and Galerkin method, approximate analytical and numerical solutions accurate to one decimal place are offered. The analysis reveals that the effect of the thermal conductivity variation factor and the rotation factor is to postpone the convective wave, whereas the viscosity variation factor and the Jeffrey factor have a dual effect in the form of rotation. The range of the convective cell is reduced with cumulating thermal conductivity variation factor, viscosity variation factor, Jeffrey factor and rotation factor. In the absence of rotation, the range of the convective cell is not dependent on the Jeffrey factor or the viscosity variation factor. Furthermore, the outcomes are matched with the existing literature for the specific case of this investigation.

The textile industry significantly contributes to environmental pollution through the discharge of dye-laden wastewater. Among these dyes, methyl orange, an azo dye, is particularly challenging to remove due to its stability and potential toxicity. This study explores an integrated treatment approach combining constructed wetland-based remediation with bacterial treatment to effectively degrade methyl orange and other pollutants in textile wastewater. Textile wastewater was collected from Bhadohi Nagar Palika, Uttar Pradesh, India, and subjected to physicochemical analysis. The initial wastewater exhibited a pH of 9.6 ± 0.12, biochemical oxygen demand (BOD) of 1008 ± 0.75 mg/l, and chemical oxygen demand (COD) of 2035 ± 0.61 mg/l, all exceeding Central Pollution Control Board (CPCB) standards. An 80-liter constructed wetland system planted with Typha latifolia and Phragmites karka was implemented, followed by bacterial treatment. The integrated system reduced the pH to 7.21 ± 0.38, BOD to 54 ± 0.25 mg/l, and COD to 134 ± 0.74 mg/l. Fourier transform-infrared (FTIR) analysis and gas chromatography-mass spectrometry (GC-MS) confirmed significant reductions in organic pollutants and dye components. Additionally, 16S rRNA gene analysis identified key bacterial strains contributing to biodegradation, including Enterococcus faecium and Bacillus subtilis . Further, the treatment system also achieved a notable shift in methyl orange dye absorbance from 538 nm to 201 nm, indicating substantial decolorization. These findings demonstrate the potential of combined wetland and bacterial treatment for effective remediation of textile wastewater.