Psychosocial covariates of physical activity in recently diagnosed Type 2 diabetes patients

Background Regular physical activity can be effective not only in preventing diabetes and managing its complications but also be effective in minimizing the risk of developing other chronic diseases among diabetics. The overall aim of study was to determine probable causes of change in physical activity so as to generate evidences for future interventions and to identify psychosocial covariates of self reported physical activity in recently diagnosed Type 2 diabetes cases. Methods Participants n=478 (239 intervention arm and 239 control arm) of an observational cohort were randomized into the ADDITION Plus trial and were recruited from 36 practices in East Anglia region. Participants were people recently diagnosed with diabetes (screen detected and clinically diagnosed within the preceding 3 years were individually randomized) and were between the age group of 40-69 years, (mean age 59.2 years). The self reported data regarding physical activity was measured at baseline and one year were used. Demographic and psychosocial (treatment control, consequences, anxiety) covariates were assessed at the baseline. Linear univariate and multivariable linear regression analysis was used to quantify the associations between demographic and psychosocial correlates. Results: With regard to the psychosocial correlates(except for participants’ perceptions about the consequences of diabetes), no significant associations with physical activity were found. Treatment control and anxiety failed to predict physical activity. Conclusion The result suggests to further investigate the change in physical activity by including other variables related to demography, other psycho-social and environment influences. Based on the available literature, it is suggested that other factors were found consistently associated with physical activity such as self efficacy, attitude, sensation seeking, family-friend social support, goal orientation, motivation could be studied

A Study on the Emergence of SMEs & e- Commerce

Abstract– The Covid-19 pandemic aggravated the effects of economic slowdown. With the closure of brick-and-mortar stores during lockdowns, restriction on physical movement of consumers and resulting rapid decline in sales businesses were force to turn digital and go online. It was this struggle for survival that forced the small and mid-sized enterprises (SMEs) to enter the international e-commerce arena. This article examines the scenario that propelled the SMEs into the global e-commerce, evaluates the present scenario and explores the way forward

Operations and Supply Chain Optimization – The New Era Model

Purpose:This article is an investigative study of optimization of supply chain and hence operations in the new era of digitization and identifies scope for future studies and further development of ways to achieve operational excellence. Design/ Methodology/ Approach:This research uses literature review and case studies to highlight the evolution of supply chain in the present era. The focus is on operational modifications the took place following the disruptions caused by such factors as technological innovations and global pandemic. This study employs the method of case study to support and substantiate the knowledge.Findings:The COVID-19 pandemic has demonstrated to the world that supply chain is the true lifeline of any business operation. The manufacturers had been hitherto concentrating on long-term planning to decide their future course of action and the organizational strategy. The COVID-19 pandemic exposed the futility and vulnerability of long-term planning and the need for short-term ad-hoc decision-making ability.Recommendation Conclusion:It is necessary to optimize supply chain and operations to build in resilience and flexibility to ensure faster response to unforeseen disruptions. Short term responsiveness is gaining popularity and building resilience must overtake forecasting to ensure shocks such as Covid do not throw businesses completely out of gear

Low Flow Low Gradient Severe Aortic Stenosis: Diagnosis and Treatment

Approximately 40% of patients with aortic stenosis (AS) show discordant Doppler-echocardiographic parameters with aortic valve area (AVA) <1 cm2 and/or index iAVA <0.6 cm2/m2 (consistent with severe AS) and the mean gradient (MG) <40 mmHg, consistent with mild/moderate AS. Accurate diagnosis of true severe low flow low gradient AS versus pseudo-severe aortic stenosis is important for prognosis and optimal timing for intervention. Doppler echocardiography using intravenous low dose dobutamine challenge is widely used for differentiating pseudo-severe from true severe aortic stenosis. However, relying on echocardiography alone may have limitations in accurate diagnosis. Reliable diagnosis using echocardiography is dependent on multiple factors like the angle of interrogation of the aortic jet, the assumption that the LVOT area is circular in cross section, optimal echo windows, the presence of underlying subclinical coronary artery disease prior to dobutamine challenge etc. In this chapter, we describe non-invasive and invasive strategies to assess the aortic valve using dobutamine stress. Direct measurement of gradients across the aortic valve while estimating the change in cardiac output and aortic valve area with increments of dobutamine infusion dose is complementary, safe and useful when conventional echocardiography techniques are inconclusive. Finally, the chapter describes effective strategies of treatment for low gradient severe aortic stenosis, including the role for diagnostic balloon valvuloplasty, in the era of transcatheter valve replacement (TAVR)

Skeleton-based action analysis for ADHD diagnosis

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder worldwide. While extensive research has focused on machine learning methods for ADHD diagnosis, most research relies on high-cost equipment, e.g., MRI machine and EEG patch. Therefore, low-cost diagnostic methods based on the action characteristics of ADHD are desired. Skeleton-based action recognition has gained attention due to the action-focused nature and robustness. In this work, we propose a novel ADHD diagnosis system with a skeleton-based action recognition framework, utilizing a real multi-modal ADHD dataset and state-of-the-art detection algorithms. Compared to conventional methods, the proposed method shows cost-efficiency and significant performance improvement, making it more accessible for a broad range of initial ADHD diagnoses. Through the experiment results, the proposed method outperforms the conventional methods in accuracy and AUC. Meanwhile, our method is widely applicable for mass screening

Signature of a three-dimensional photonic band gap observed on silicon inverse woodpile photonic crystals

We have studied the reflectivity of CMOS-compatible three-dimensional silicon inverse woodpile photonic crystals at near-infrared frequencies. Polarization-resolved reflectivity spectra were obtained from two orthogonal crystal surfaces corresponding to 1.88 pi sr solid angle. The spectra reveal broad peaks with high reflectivity up to 67 % that are independent of the spatial position on the crystals. The spectrally overlapping reflectivity peaks for all directions and polarizations form the signature of a broad photonic band gap with a relative bandwidth up to 16 %. This signature is supported with stopgaps in plane wave bandstructure calculations and with the frequency region of the expected band gap.Comment: 9 pages, 5 figure