ENTREPRENEURSHIP DEVELOPMENT IN DELHI THROUGH MICROFINANCE

Microfinance can play a pivotal role in encouraging entrepreneurship and self-employment in an urban region like Delhi which does not fit the classic definition of a poor agricultural society, but is still plagued by the identical problems of poverty and lack of risk-bearing capacities amongst the poorer members of the society. These hyper-urban areas are often left out of the development policy loop as a majority government and private initiatives seek to target the rural, agrarian regions of the country. The objective of the study is to analyse the impact of microfinance activities of SHGs and community groups on the development of entrepreneurship in Delhi. The data extracted from the reports by NABARD reveals that the Self-Help groups (SHGs) and other institutions employing microfinance as a tool of encouraging entrepreneurship and income augmentation have a minimal presence in Delhi. The state machinery and the private sector both need to undertake definite, result-oriented measures to better address the issues of urban poverty prevalent in Delhi.

Evaluation of two mobile health apps in the context of smoking cessation: qualitative study of cognitive behavioral therapy (CBT) versus non-CBT-based digital solutions.

BACKGROUND: Mobile health (mHealth) apps can offer users numerous benefits, representing a feasible and acceptable means of administering health interventions such as cognitive behavioral therapy (CBT). CBT is commonly used in the treatment of mental health conditions, where it has a strong evidence base, suggesting that it represents an effective method to elicit health behavior change. More importantly, CBT has proved to be effective in smoking cessation, in the context of smoking-related costs to the National Health Service (NHS) having been estimated to be as high as £2.6bn in 2015. Although the evidence base for computerized CBT in mental health is strong, there is limited literature on its use in smoking cessation. This, combined with the cost-effectiveness of mHealth interventions, advocates a need for research into the effectiveness of CBT-based smoking cessation apps. OBJECTIVE: The objective of this study was, first, to explore participants' perceptions of 2 mHealth apps, a CBT-based app, Quit Genius, and a non-CBT-based app, NHS Smokefree, over a variety of themes. Second, the study aimed to investigate the perceptions and health behavior of users of each app with respect to smoking cessation. METHODS: A qualitative short-term longitudinal study was conducted, using a sample of 29 smokers allocated to one of the 2 apps, Quit Genius or Smokefree. Each user underwent 2 one-to-one semistructured interviews, 1 week apart. Thematic analysis was carried out, and important themes were identified. Descriptive statistics regarding participants' perceptions and health behavior in relation to smoking cessation are also provided. RESULTS: The thematic analysis resulted in five higher themes and several subthemes. Participants were generally more positive about Quit Genius's features, as well as about its design and information engagement and quality. Quit Genius users reported increased motivation to quit smoking, as well as greater willingness to continue using their allocated app after 1 week. Moreover, these participants demonstrated preliminary changes in their smoking behavior, although this was in the context of our limited sample, not yet allowing for the finding to be generalizable. CONCLUSIONS: Our findings underscore the use of CBT in the context of mHealth apps as a feasible and potentially effective smoking cessation tool. mHealth apps must be well developed, preferably with an underlying behavioral change mechanism, to promote positive health behavior change. Digital CBT has the potential to become a powerful tool in overcoming current health care challenges. The present results should be replicated in a wider sample using the apps for a longer period so as to allow for generalizability. Further research is also needed to focus on the effect of greater personalization on behavioral change and on understanding the psychological barriers to the adoption of new mHealth solutions

The generalized 3-edge-connectivity of lexicographic product graphs

The generalized $k$-edge-connectivity $\lambda_k(G)$ of a graph $G$ is a generalization of the concept of edge-connectivity. The lexicographic product of two graphs $G$ and $H$, denoted by $G\circ H$, is an important graph product. In this paper, we mainly study the generalized 3-edge-connectivity of $G \circ H$, and get upper and lower bounds of $\lambda_3(G \circ H)$. Moreover, all bounds are sharp.Comment: 14 page

Whole genome sequence analysis of the TALLYHO/Jng mouse

Background: The TALLYHO/Jng (TH) mouse is a polygenic model for obesity and type 2 diabetes first described in the literature in 2001. The origin of the TH strain is an outbred colony of the Theiler Original strain and mice derived from this source were selectively bred for male hyperglycemia establishing an inbred strain at The Jackson Laboratory. TH mice manifest many of the disease phenotypes observed in human obesity and type 2 diabetes. Results: We sequenced the whole genome of TH mice maintained at Marshall University to a depth of approximately 64.8X coverage using data from three next generation sequencing runs. Genome-wide, we found approximately 4.31 million homozygous single nucleotide polymorphisms (SNPs) and 1.10 million homozygous small insertions and deletions (indels) of which 98,899 SNPs and 163,720 indels were unique to the TH strain compared to 28 previously sequenced inbred mouse strains. In order to identify potentially clinically-relevant genes, we intersected our list of SNP and indel variants with human orthologous genes in which variants were associated in GWAS studies with obesity, diabetes, and metabolic syndrome, and with genes previously shown to confer a monogenic obesity phenotype in humans, and found several candidate variants that could be functionally tested using TH mice. Further, we filtered our list of variants to those occurring in an obesity quantitative trait locus, tabw2, identified in TH mice and found a missense polymorphism in the Cidec gene and characterized this variant’s effect on protein function. Conclusions: We generated a complete catalog of variants in TH mice using the data from whole genome sequencing. Our findings will facilitate the identification of causal variants that underlie metabolic diseases in TH mice and will enable identification of candidate susceptibility genes for complex human obesity and type 2 diabetes

Effect of Voluntary and Involuntary Joint Movement on EEG Signals

710-712For rehabilitation persons suffering from neuro-musculoskeletal disorders technology has given enough power. This can be done through active orthotic devices. For optimal control and assistance of these devices, there is a need of bio-controlled closed loop assistance system such as BCI. It is a complex interaction when working in an assist as needed mode and synchronization between voluntary and involuntary movements of the joint is required. In this context, the aim of this study is to investigate the EEG dynamics associated with both voluntary and involuntary movements of the ankle joint. Frontal and primary motor cortex position are considered for this study. The results determine that the neural signals governing these two types of activities are different. Studies show that the gamma band is prominent in attention which also supports our hypothesis in the case of voluntary and involuntary movements. Similarly, we further extend this experiment for voluntary and involuntary gait cycle using exoskeleton. These synchronized voluntary and involuntary movements signal may be used in the brain-computer interface to restore human gait function

Heuristics for Hierarchical Partitioning with Application to Model Checking

Given a collection of connected components, it is often desired to cluster together parts of strong correspondence, yielding a hierarchical structure. We address the automation of this process and apply heuristics to battle the combinatorial and computational complexity. We define a cost function that captures the quality of a structure relative to the connections and favors shallow structures with a low degree of branching. Finding a structure with minimal cost is NP -complete. We present a greedy polynomial-time algorithm that approximates good solutions incrementally by local evaluation of a heuristic function. We argue for a heuristic function based on four criteria: the number of enclosed connections, the number of components, the number of touched connections and the depth of the structure. We report on an application in the context of formal verification, where our algorithm serves as a preprocessor for a temporal scaling technique, called "Next" heuristic [2]. The latter is applicable in reachability analysis and is included in a recent version of the Mocha model checking tool. We demonstrate performance and benefits of our method and use an asynchronous parity computer and an opinion poll protocol as case studies.