The effect of socioeconomic status, depression, and diabetes symptoms severity on diabetes patient's life satisfaction in India.

Evidence suggests that diabetes is on the rise in India, affecting many people's life satisfaction. Comprehensive estimation of life satisfaction among diabetes patients does not exist in the country. This study examined the effects of socioeconomic status, depression, and diabetes symptoms severity on the life satisfaction of diabetes patients by controlling various demographic variables. It was a cross-sectional study comprising 583 diabetes patients from Punjab, India. Patients were interviewed using a multi-stage purposeful random sampling method. Descriptive analysis and partial least squares structural equation modelling were used in the study to test the hypotheses. Results revealed that socioeconomic status, depression and diabetes symptoms severity significantly influence the life satisfaction of diabetes patients. A 1% drop in diabetes symptoms severity corresponds to a 0.849% increase in life satisfaction, whereas a 1% decrease in depression results in a 0.898% increase in life satisfaction. Patients with higher diabetes symptoms severity were coping with common mental disorders. Women reported higher diabetes symptoms severity and depression than men, resulting in lower life satisfaction. An experimental evaluation of the effects of socioeconomic status, depression and diabetes symptoms severity, and numerous demographic factors on life satisfaction was reported. The findings will help policymakers understand the problem associated with life satisfaction among diabetes patients in the country.

Gender differential in the pattern and severity of perceived symptoms among diabetes patients in India.

Objective: To analyze the pattern of diabetes symptoms and to estimate the association between diabetes symptom severity (level of discomfort perceived by a patient due to diabetes symptoms) among different socio-demographic variables for both women and men. Methods: Primary cross-sectional data of 583 diagnosed patients (51.3% and 48.7%, women and men, respectively) were collected from Punjab, India. Frequency percentage distribution and negative binomial regressions (NBR) were used for analysis. Results: More men were asymptomatic compared to women. Both genders perceived increased hunger, thirst, and frequent urination in their early stages of diabetes. More women than men have experienced hormonal change as their first symptom with a higher severity level. NBR analyzed the association between discomfort perceived by both genders due to symptoms among different socio-demographic categories. Urban patients (incidence rate ratio-IRR: 0.90) were significantly (p = 0.056) less likely to perceive discomfort than their rural counterparts, whereas men (IRR: 0.93) reported more significant discomfort than women (IRR: 0.88) in the urban area. Literate patients [Up to class 10 (IRR: 0.87), (p = 0.013) and 11-above (IRR: 0.85), (p = 0.022) categories] were significantly less likely to perceive discomfort. In all education categories, women professed more significant discomfort than men. Conclusion: Given the differences in symptoms between the two genders, this paper will help comprehend the disease development process and limit the possibilities of misdiagnosis. This study will assist in identifying the order of the symptoms among both genders.

From Memristive Materials to Neural Networks.

The information technologies have been increasing exponentially following Moore's law over the past decades. This has fundamentally changed the ways of work and life. However, further improving data process efficiency is facing great challenges because of physical and architectural limitations. More powerful computational methodologies are crucial to fulfill the technology gap in the post-Moore's law period. The memristor exhibits promising prospects in information storage, high-performance computing, and artificial intelligence. Since the memristor was theoretically predicted by L. O. Chua in 1971 and experimentally confirmed by HP Laboratories in 2008, it has attracted great attention from worldwide researchers. The intrinsic properties of memristors, such as simple structure, low power consumption, compatibility with the complementary metal oxide-semiconductor (CMOS) process, and dual functionalities of the data storage and computation, demonstrate great prospects in many applications. In this review, we cover the memristor-relevant computing technologies, from basic materials to in-memory computing and future prospects. First, the materials and mechanisms in the memristor are discussed. Then, we present the development of the memristor in the domains of the synapse simulating, in-memory logic computing, deep neural networks (DNNs) and spiking neural networks (SNNs). Finally, the existent technology challenges and outlook of the state-of-art applications are proposed.

A Unified Capacitive-Coupled Memristive Model for the Nonpinched Current-Voltage Hysteresis Loop.

The concept of the memristor, a resistor with memory, was proposed by Chua in 1971 as the fourth basic element of electric circuitry. Despite a significant amount of effort devoted to the understanding of memristor theory, our understanding of the nonpinched current-voltage (I-V) hysteresis loop in memristors remains incomplete. Here we propose a physical model of a memristor, with a capacitor connected in parallel, which explains how the nonpinched I-V hysteresis behavior originates from the capacitive-coupled memristive effect. Our model replicates eight types of characteristic nonlinear I-V behavior, which explains all observed nonpinched I-V curves seen in experiments. Furthermore, a reversible transition from a nonpinched I-V hysteresis loop to an ideal pinched I-V hysteresis loop is found, which explains the experimental data obtained in C15H11O6-based devices when subjected to an external stimulus (e.g., voltage, moisture, or temperature). Our results provide the vital physics models and materials insights for elucidating the origins of nonpinched I-V hysteresis loops ascribed to capacitive-coupled memristive behavior.

Tunneling of photon-generated carrier in the interface barrier induced resistive switching memory behaviour.

The resistive switching effect is a great physical phenomenon that the resistance of a material can be reversibly changed by applying an electric pulse, which is useful to constructing the nonvolatile resistance random access memory (RRAM) in the next generation of memory system. In this work, a sandwich structure (ITO/WO3/AZO) was prepared by using WO3 film (∼300 nm) as the dielectric layer meanwhile indium tin oxide (ITO) as the top electrode and aluminium-doped zinc oxide (AZO) as the bottom electrode. An enhanced resistive switching memory behavior was observed in the sample processed by light-illumination. Furthermore, the set voltage (Vset) and reset voltage (Vreset) are increased but the HRS/LRS resistance ratio is decreased with the increasing of illumination time for 600 °C annealed sample. Through further analysis, a physical model on the tunneling of photon-generated carrier in the Schottky barrier layer driven by electric pulse is proposed to explain the enhanced resistive switching memory behavior. The suggested mechanism is highly pivotal for the resistive switching phenomenon to be properly applied in the nonvolatile RRAM device.