Do Remittances Promote Economic Growth? New Evidence from India

This study investigates the empirical relationship between remittances and economic growth of India, placing special attention on the non-linearity of this association. Previous studies on India have ignored the non-linear nature of the remittance-growth nexus. The study employs methods from the ARDL model framework to explore the non-linearity and establishes that remittances do not exhibit any growth effect in lower quantiles and up to 0.50, but the impact increases monotonically, getting more pronounced as the quantile increases. In other words, inward remittances must exceed a threshold to start affecting economic growth positively. It is argued that this behaviour of the remittances is the consequence of a combination of factors like patterns of utilisation (or, misutilisation) of the receipts, India’s trade balance, a weak industrial sector, the lack of entrepreneurial opportunities, the lack of financial inclusion, and the exploitation of poor migrant workers

Supporting Treatment Adherence Readiness through Training (START) for patients with HIV on antiretroviral therapy: study protocol for a randomized controlled trial.

BackgroundFew HIV antiretroviral adherence interventions target patients before they start treatment, assess adherence readiness to determine the timing of treatment initiation, or tailor the amount of adherence support. The Supporting Treatment Adherence Readiness through Training (START) intervention, based on the information-motivation-behavioral skills model of behavior change, is designed to address these gaps with the inclusion of (1) brief pill-taking practice trials for enhancing pretreatment adherence counseling and providing a behavioral criterion for determining adherence readiness and the timing of treatment initiation and (2) a performance-driven dose regulation mechanism to tailor the amount of counseling to the individual needs of the patient and conserve resources. The primary aim of this randomized controlled trial is to examine the effects of START on antiretroviral adherence and HIV virologic suppression.Methods/designA sample of 240 patients will be randomized to receive START or usual care at one of two HIV clinics. Primary outcomes will be optimal dose-taking adherence (>85 % prescribed doses taken), as measured with electronic monitoring caps, and undetectable HIV viral load. Secondary outcomes will include dose-timing adherence (>85 % prescribed doses taken on time) and CD4 count. Primary endpoints will be month 6 (short-term effect) and month 24 (to test durability of effect), though electronic monitoring will be continuous and a fully battery of assessments will be administered every 6 months for 24 months.DiscussionIf efficacious and cost-effective, START will provide clinicians with a model for assessing patient adherence readiness and helping patients to achieve and sustain readiness and optimal treatment benefits.Trial registrationClinicalTrials.gov identifier NCT02329782 . Registered on 22 December 2014

Automatic identification of epileptic and background EEG signals using frequency domain parameters

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yule-Walker and Burg's methods, to extract the power density spectrum from representative signal samples. Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. The classification results are documented with confusion matrices and compared with receiver operating characteristic curves. We found that Burg's method for spectrum estimation together with a support vector machine classifier yields the best classification results. This combination reaches a classification rate of 93.33%, the sensitivity is 98.33% and the specificy is 96.67%

Improved parental dietary quality is associated with children’s dietary intake through the home environment

Background Improving access to supermarkets has been shown to improve some dietary outcomes, yet there is little evidence for such effects on children. Relatedly, there is a dearth of research assessing the impact of a structural change (i.e. supermarket in a former food desert) on the home environment and its relationship with children’s diet. Objective Assess the relative impact of the home environment on children’s diet after the introduction of a new supermarket in a food desert. Methods Among a randomly selected cohort of households living in a food desert, parental diet was assessed before and after the opening of a full-service supermarket. The home environment and children’s intake of fruits and vegetables was measured at one point – after the store’s opening. Structural equation models were used to estimate the pathways between changes in parental dietary quality at follow-up and children’s dietary intake through the home environment. ResultsParental dietary improvement after the supermarket opened was associated with having a better home environment (β = 0.45, p = 0.001) and with healthier children’s dietary intake (β = 0.46, p Conclusions Policy solutions designed to improve diet among low-resource communities should take into account the importance of the home environment

Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

Results from a natural experiment: initial neighbourhood investments do not change objectively-assessed physical activity, psychological distress or perceptions of the neighbourhood

Abstract Background Few studies have assessed objectively measured physical activity (PA), active transportation, psychological distress and neighborhood perceptions among residents of a neighborhood before and after substantial improvements in its physical environment. Also, most research-to-date has employed study designs subject to neighborhood selection, which may introduce bias in reported findings. We built upon a previously enrolled cohort of households from two low-income predominantly African American Pittsburgh neighborhoods, matched on socio-demographic composition including race/ethnicity, income and education. One of the two neighborhoods received substantial neighborhood investments over the course of this study including, but not limited to public housing development and greenspace/landscaping. We implemented a natural experiment using matched intervention and control neighborhoods and conducted pre-post assessments among the cohort. Our comprehensive assessments included accelerometry-based PA, active transportation, psychological distress and perceptions of the neighborhood, with assessments conducted both prior to and following the neighborhood changes. In 2013, we collected data from 1003 neighborhood participants and in 2016, we re-interviewed 676 of those participants. We conducted an intent to treat analysis, with a difference-in-difference estimator using attrition weighting to account for nonresponse between 2013 and 2016. In addition, we derived an individual-level indicator of exposure to neighbourhood investment and estimated effect of exposure to investment on the same set of outcomes using covariate-adjusted models. Results We observed no statistically significant differences in activity, psychological distress, satisfaction with one’s neighborhood as a place to live or any of the other measures we observed prior to and after the neighborhood investments between the intervention and control neighborhoods or those exposed vs not exposed to investments. Conclusions Using this rigorous study design, we observed no significant changes in the intervention neighborhood above and beyond secular trends present in the control neighborhood. Although neighborhood investment may have other benefits, we failed to see improvement in PA, psychological distress or related outcomes in the low-income African American neighborhoods in our study. This may be an indication that improvements in the physical environment may not directly translate into improvements in residents’ physical activity or health outcomes without additional individual-level interventions. It is also possible that these investments were not dramatic enough to spur change within the three year period. Additional studies employing similar design with other cohorts in other settings are needed to confirm these results. Trial registration Trial Registration is not applicable since we did not prospectively assign individuals to a health-related intervention.https://deepblue.lib.umich.edu/bitstream/2027.42/148333/1/12966_2019_Article_793.pd

Impact of remittances on economic growth in developing countries: The role of openness

The paper examines the empirical relationship between remittances and economic growth for a sample of 62 developing countries over the time period 1990–2014. Remittances seem to promote growth only in the ‘more open’ countries. That is because remittances are in themselves not sufficient for growth. The extent of the benefit depends on domestic institutions and macroeconomic environment in the receiving country. Unlike the ‘less open’ countries, ‘more open’ countries have better institutions and better financial markets to take advantage of the remittances income and channelise them into profitable investments which, in turn, accelerates the rate of economic growth in these countries.N/