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Although Multi-Layer Ceramic Capacitors (MLCC) are known for its better frequency performance and voltage handling capacity, but under various environmental conditions, its reliability becomes a challenging issue. In modern era of integration, the failure of one component can degrade or shutdown the whole electronic device. The lifetime estimation of MLCC can enhance the reuse capability and furthermore, reduces the e-waste, which is a global issue.

The residual lifetime of MLCC is estimated using empirical method i.e. Military handbook MILHDBK2017F, statistical method i.e. regression analysis using Minitab18.1 as well as intelligent technique i.e. artificial neural networks (ANN) using MATLAB2017b. ANN Feed-Forward Back-Propagation learning with sigmoid transfer function [3–10–1–1] is considered using 73% of available data for training and 27% for testing and validation. The design of experiments is framed using Taguchi’s approach L16 orthogonal array.

After exploring the lifetime of MLCC, using empirical, statistical and intelligent techniques, an error analysis is conducted, which shows that regression analysis has 97.05% accuracy and ANN has 94.07% accuracy.

An intelligent method is presented for condition monitoring and health prognostics of MLCC, which warns the user about its residual lifetime so that faulty component can be replaced in time.

Accessibility to a precise tool for healthcare management and self-precaution among diabetic patients is an absolute necessity. This paper aims to develop and validate diabetes-related awareness instrument (DRAI) – an instrument that measures diabetics awareness about risk factors and prevention strategies.

The reliability and validity of the DRAI were tested with a sample of 112 diabetics. The construct validity of the DRAI was measured using exploratory and confirmatory factor analysis. Item discrimination, reliability, usefulness and validity of the items were determined by performing Cronbach's alpha, item difficulty and discrimination index analysis.

The study finds DRAI – a reliable and valid instrument to assess diabetics awareness towards diabetes mellitus, its associated risk factors and prevention strategies. The value of Cronbach's alpha for all three constructs was above the threshold level of 0.70. Under exploratory factor analysis, “Kaiser–Meyer–Olkin” test value of 0.805 exhibits a meritorious sample adequacy and “Bartlet's test of Sphericity” was statistically significant with p = 0.032. Therefore, results of confirmatory factor analysis (CFA) revealed that all fitness indices of the model to be excellent fit.

The present instrument can help to determine whether the individual is susceptible to diabetes, timely prevention and reduction in the incidence of diabetes mellitus.

DRAI is the first of its kind tool to assess the awareness and knowledge about diabetes-related risk factors and prevention strategies in such a demographically diverse population of India.

Capacitors are one of the most common passive components on a circuit board. From a tiny toy to substantial satellite, a capacitor plays an important role. Untimely failure of a capacitor can destruct the entire system. This research paper targets the reliability assessment of tantalum capacitor, to reduce e-waste and enhance its reusable capability.

The residual lifetime of a tantalum capacitor is estimated using the empirical method, i.e. military handbook MILHDBK2017F, and validated using an experimental approach, i.e. accelerated life testing (ALT). The various influencing acceleration factors are explored, and experiments are designed using Taguchi's approach. Empirical methods such as the military handbook is used for assessing the reliability of a tantalum capacitor, for ground and mobile applications.

After exploring the lifetime of a tantalum capacitor using empirical and experimental techniques, an error analysis is conducted, which shows the validity of empirical technique, with an accuracy of 95.21%.

The condition monitoring and health prognostics of tantalum capacitors, for ground and mobile applications, are explored using empirical and experimental techniques, which warns the user about its residual lifetime so that the faulty component can be replaced in time.

An electrolytic capacitor is extensively used as filtering devices in various power supplies and audio amplifiers. Low cost and higher value of capacitance make it more well known. As environmental stress and electrical parameters increase, capacitors degrade on accelerated pace. The paper aims to discuss these issues.

This paper focusses on the impact of thermal stress on electrolytic capacitors using accelerated life testing technique. The failure time was calculated based on the change in capacitance, equivalent series resistance and weight loss. The experimental results are compared with the outcome of already available life monitoring methods, and the accuracy level of these methods is accessed.

The results of all the three methods are having maximum 55 per cent accuracy. To enhance the accuracy level of theoretical methods, modifications have been suggested. A new method has been proposed, whose outcome is 92 per cent accurate with respect to experimentally obtained outcomes.

To assess the capacitor’s reliability using an experimental and modified theoretical method, failure prediction can be done before it actually fails.

A new method has been proposed to access the lifetime of capacitor.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

This study aims to review the relationship between neurological processes and financial behavior from an interdisciplinary perspective. Individual decision-making is influenced by cognitive and affective biases; hence, it becomes pertinent to understand the origin of these biases. Neurofinance is an emerging field of finance budding from neuroeconomics and explains the relationship between human brain activity and financial behavior, drawn from interdisciplinary fields, including neurology, psychology and finance.

This conceptual paper extensively reviews the extant literature and performs meta-analysis to attain its research objectives.

The paper highlights the use of neuroimaging techniques in mapping the brain areas to help understand the processes in the higher cognitive areas of brain. The paper raises some new questions regarding individual preferences and choices while making financial or non-financial decisions.

The special focus on dysfunctions arising in brain because of injury and their impact on decision-making is also a key point in this paper and is summarized using meta-analytic forest plot. The existing literature provides instances where emotional processing is altered by injury in brain and may lead to more advantageous decisions, especially in risky situations.