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Background: Type 2 diabetes mellitus (T2DM) poses a significant health-care challenge and is a primary contributor to both mortality and morbidity. Diabetic autonomic neuropathy (DAN) is a frequently occurring complication associated with diabetes mellitus. Autonomic nerve damage leading to cardiac autonomic dysfunction and peripheral sympathetic neuropathy can lead to changes in heart rate variability (HRV) and galvanic skin response (GSR), respectively. Aims and Objectives: The study was designed and done to evaluate the impact of T2DM on HRV and GSR for assessing DAN. Materials and Methods: A total of 100 individuals diagnosed with T2DM (cases) and 100 healthy individuals (controls) participated in this study. Physio Pac (PC-2004) was used to record the necessary data. Statistical analysis was performed using the non-parametric Mann--Whitney test to see the comparison of the cases and controls. In addition, to measure the statistical relationship between the duration of the disease and the HRV and GSR parameters, Spearman's rank correlation coefficient was utilized. The study received approval from the Ethical and Research Committee at the Government Medical College, Patiala. Results: Significant statistical differences were observed between the cases and controls in all time domain measures, as well as GSR and the majority of frequency domain measures of HRV. The correlation analysis revealed a strong association between the duration of T2DM and a decrease in HRV as well as an increase in GSR. Conclusion: The findings from the present study provide evidence that T2DM has an impact on HRV, which suggests the presence of cardiac autonomic neuropathy among diabetic patients, leading to an increased risk of cardiovascular morbidity. In addition, the study indicates that reduced sudomotor function, as assessed by galvanic skin response (GSR), serves as a predictive measure for autonomic neuropathy. Therefore, these tests can serve as valuable tools in facilitating the assessment of DAN. [ABSTRACT FROM AUTHOR]

Clinical procedures, which require a large number of personnel and medical resources, receive the majority of the current focus on thyroid nodule diagnosis. An automated thyroid ultrasound nodule identification system is built using image texture data and convolutional neural networks in this study. The following are the major phases: The underlying stages in building a ultrasound thyroid knob dataset incorporate gathering positive and negative examples, normalizing pictures, and portioning the knob region. Second, a texture features model is built by selecting features, reducing the dimensionality of the data, and extracting texture features. Third, deep neural networks in move learning are utilized to create an element model of the knob in an image. The convolutional brain network highlight model and the surface component model were combined to create the brand-new knob include model known as the Feature Fusion Network. The Feature Fusion Network is used to prepare and improve performance over a single organization in order to create a demonstrative model for deep neural networks that can adapt to a variety of knob features. 1874 clinical ultrasonography thyroid knobs were gathered for this investigation. The musical normal F-score considering Accuracy and Review is utilized as an assessment metric. With an F-score of 92.52 percent, the study’s findings suggest that the Element Combination Organization can differentiate between benign and harmful thyroid knobs. As far as execution, this methodology performs better compared to standard ML procedures and convolutional neural networks.

Mobile wallets have gained widespread popularity as a convenient, secure, and user-friendly payment method embraced by consumers. However, the pace of mobile wallet adoption has exhibited variations across different markets, primarily due to a range of factors. In this study, we present an all-encompassing examination of existing literature, aiming to pinpoint the fundamental elements influencing the sustainable acceptance of mobile wallets by consumers. Through an exhaustive analysis of 80 research papers published between 2010 and 2022, we discern the prevailing factors that hold sway over the adoption of mobile wallets. Our scrutinization highlights that factor such as perceived sustainability, usefulness, ease of use, security, social influence, trustworthiness, and compatibility stand out as the most formidable propellants of mobile wallet adoption. Furthermore, our investigation uncovers hurdles that hinder the wider acceptance of mobile wallets, encompassing insufficient awareness, perceived intricacies, and lingering uncertainties regarding the technology’s sustainability. Our in-depth evaluation underscores the necessity of comprehending consumer perspectives and dispositions towards mobile wallets to galvanize their adoption sustainably. The culmination of our inquiry involves a discourse on the implications drawn from our discoveries, catering to researchers and practitioners vested in fostering the sustainable adoption of mobile wallets.

Heat pipe is used to extract heat generated from a surface and applicable to cooling of the many components. Some of the main applications of heat pipes are space crafts, computer systems, permafrost cooling, heat exchangers and thermal storage sub systems. In this work, an experiment will be carried out with an indirect heat composite pipe located with different orientation, heat input and mass flow. Thermal resistance, heat transfer coefficient and thermal efficiency are determined, which are applicable for laptop cooling and solar heating. Heat input 25 to 150 watts, with orientation angles 0, 30, 45, 90 degrees, mass flow rate are 0.01kg/sec, 0.02kg/sec and 0.03kg/sec are considered.