Dual Axis Solar Tracking System in Perlis, Malaysia

Sunlight is an abundant source of energy and this energy can be harnessed successfully using solar photovoltaic panels and convert it into electrical energy. However, the conversion efficiency of a normal PV panel is low. One of the main reasons is the power output of a PV panel is dependent directly on the light intensity. As the position of the sun is changing continuously from time to time, the absorption efficiency of an immobile solar panel would be significantly less at a certain time of the day and year. Therefore, to maximize the energy generation and improve the efficiency, a solar tracker comes into play. This paper presents the design and construction of an inexpensive active dual axis solar tracking system for tracking the movement of the sun to get the maximum power from the solar panels. It uses Light Dependent Resistors (LDR) to sense the position of the sun which is communicated to an Arduino Uno microcontroller. An algorithm is implemented to control DC geared motor’s movements which maintaining the solar PV panel position so that it will perpendicularly facing towards the sun at all the effective time. Performance for both fixed and dual-axis solar tracker was compared. Evaluation results show that the dual-axis solar tracking system performs 44.7% better than the fixed solar tracking system

Human upper-airway respiratory airflow: In vivo comparison of computational fluid dynamics simulations and hyperpolarized 129Xe phase contrast MRI velocimetry

Computational fluid dynamics (CFD) simulations of respiratory airflow have the potential to change the clinical assessment of regional airway function in health and disease, in pulmonary medicine and otolaryngology. For example, in diseases where multiple sites of airway obstruction occur, such as obstructive sleep apnea (OSA), CFD simulations can identify which sites of obstruction contribute most to airway resistance and may therefore be candidate sites for airway surgery. The main barrier to clinical uptake of respiratory CFD to date has been the difficulty in validating CFD results against a clinical gold standard. Invasive instrumentation of the upper airway to measure respiratory airflow velocity or pressure can disrupt the airflow and alter the subject’s natural breathing patterns. Therefore, in this study, we instead propose phase contrast (PC) velocimetry magnetic resonance imaging (MRI) of inhaled hyperpolarized 129Xe gas as a non-invasive reference to which airflow velocities calculated via CFD can be compared. To that end, we performed subject-specific CFD simulations in airway models derived from 1H MRI, and using respiratory flowrate measurements acquired synchronously with MRI. Airflow velocity vectors calculated by CFD simulations were then qualitatively and quantitatively compared to velocity maps derived from PC velocimetry MRI of inhaled hyperpolarized 129Xe gas. The results show both techniques produce similar spatial distributions of high velocity regions in the anterior-posterior and foot-head directions, indicating good qualitative agreement. Statistically significant correlations and low Bland-Altman bias between the local velocity values produced by the two techniques indicates quantitative agreement. This preliminary in vivo comparison of respiratory airway CFD and PC MRI of hyperpolarized 129Xe gas demonstrates the feasibility of PC MRI as a technique to validate respiratory CFD and forms the basis for further comprehensive validation studies. This study is therefore a first step in the pathway towards clinical adoption of respiratory CFD

Erratum: Search for gravitational waves from binary black hole inspiral, merger, and ringdown (Physical Review D - Particles, Fields, Gravitation and Cosmology 2011; 83(12):122005-1-122005-20)

This paper was published online on 6 June 2011 with an omission in the Collaboration author list. S. Dwyer has been added as of 12 April 2012. The Collaboration author list is incorrect in the printed version of the journal.J. Abadie... D. J. Hosken... J. Munch... D. J. Ottaway... P. J. Veitch...et al. (LIGO Scientific Collaboration, VIRGO Collaboration

Erratum: All-sky search for gravitational-wave bursts in the first joint LIGO-GEO-Virgo run (Physical Review D - Particles, Fields, Gravitation and Cosmology - 2010: 81(10) 102001-1-102001-20)

This paper was published online on 5 May 2010 with an omission in the Collaboration author list. S. Dwyer has been added as of 12 April 2012. The Collaboration author list is incorrect in the printed version of the journalJ. Abadie... D. J. Hosken... J. Munch... D. J. Ottaway... P. J. Veitch...et al. (LIGO Scientific Collaboration, VIRGO Collaboration

Youth futures and a masculine development ethos in the regional story of Uttarakhand

Research on the Uttarakhand region, which became a new state in 2000, has focused largely on agrarian livelihoods, religious rituals, development demands, ecological politics and the role of women in regional social movements. This essay discusses another dimension of the regional imaginary—that of a masculine development ethos. Based on ethnographic research and print media sources, this essay focuses on stories, politics, mobilities and imaginations of young men in the years immediately after the achievement of statehood. Despite increased outmigration of youth in search of employment, many young men expressed the dream of maintaining livelihoods in the familiar towns and rural spaces of Uttarakhand, describing their home region as a source of power and agency. In rallies and in print media, young (mostly upper caste) men expressed their disillusionment with the government and the promises of statehood, arguing that their aspirations for development and employment were left unfulfilled. Gendered stories of the region, told in Hindi in rallies and print media, contained references to local places, people and historical events and were produced through local connections and know-how, fostering a regional youth politics. The article argues that Uttarakhand as a region is shaped by the politics of local actors as well as embodied forms of aspiration, affiliation and mobility.IS

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.Peer Reviewe

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Genetic Sharing with Cardiovascular Disease Risk Factors and Diabetes Reveals Novel Bone Mineral Density Loci.

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity