Photovoltaic module segmentation and thermal analysis tool from thermal images

The growing interest in the use of clean energy has led to the construction of increasingly large photovoltaic systems. Consequently, monitoring the proper functioning of these systems has become a highly relevant issue.In this paper, automatic detection, and analysis of photovoltaic modules are proposed. To perform the analysis, a module identification step, based on a digital image processing algorithm, is first carried out. This algorithm consists of image enhancement (contrast enhancement, noise reduction, etc.), followed by segmentation of the photovoltaic module. Subsequently, a statistical analysis based on the temperature values of the segmented module is performed.Besides, a graphical user interface has been designed as a potential tool that provides relevant information of the photovoltaic modules.Comment: 7 pages, 12 Figure

Machine Learning Approaches for the Prediction of Obesity using Publicly Available Genetic Profiles

This paper presents a novel approach based on the analysis of genetic variants from publicly available genetic profiles and the manually curated database, the National Human Genome Research Institute Catalog. Using data science techniques, genetic variants are identified in the collected participant profiles then indexed as risk variants in the National Human Genome Research Institute Catalog. Indexed genetic variants or Single Nucleotide Polymorphisms are used as inputs in various machine learning algorithms for the prediction of obesity. Body mass index status of participants is divided into two classes, Normal Class and Risk Class. Dimensionality reduction tasks are performed to generate a set of principal variables - 13 SNPs - for the application of various machine learning methods. The models are evaluated using receiver operator characteristic curves and the area under the curve. Machine learning techniques including gradient boosting, generalized linear model, classification and regression trees, K-nearest neighbours, support vector machines, random forest and multilayer neural network are comparatively assessed in terms of their ability to identify the most important factors among the initial 6622 variables describing genetic variants, age and gender, to classify a subject into one of the body mass index related classes defined in this study. Our simulation results indicated that support vector machine generated high accuracy value of 90.5%

Climate- and Eustasy-Driven Cyclicity in Pennsylvanian Fusulinid Assemblages, Donets Basin (Ukraine)

A model of cyclic recurrence (~ 0.6–1.2 myr) of three fusulinid assemblages in the Middle Pennsylvanian siliciclastic–carbonate succession of the Donets Basin is proposed. Each cycle records progressive turnover of assemblages in shallow marine environments in response to sea-level and regional climate change. A Hemifusulina-assemblage (A), adapted to cooler and reduced salinity seawater records the onset of sea level rise accompanied by humid climatic conditions. Sea level high stand is captured by the Beedeina–Neostaffella–Ozawainella–Taitzehoella (or Beedeina-dominated) assemblage (B), characteristic of relatively deeper-water environments. The B assemblage is successively replaced by the most diverse population of the warm-water Fusulinella-dominated assemblage (C). This assemblage, which occurs in the upper limestones of each fusulinid cycle records the onset of sea level fall accompanied by a shift to drier conditions and likely increased seawater salinity. The proposed model permits robust interbasinal correlation of the Pennsylvanian successions of the Tethyan realm. Fusulinids of the A and C2 assemblages are the most provincial and therefore the most useful for paleogeographic reconstructions. Specifically, they delineate originally contiguous regions that subsequently were dispersed hundreds to thousands of kilometers, whereas fusulinids of the B assemblage hold the highest potential for global correlation. Extinction at the Moscovian–Kasimovian transition of fusulinid genera of the A and B assemblages, which inhabited predominately cooler and normal salinity (perhaps hyposaline) waters, can be explained by the onset of global warming in the earliest Late Pennsylvanian. Fusulinid assemblages define various types of distribution patterns that differ by tectonic setting of the studied basins suggesting that fusulinid assemblage patterns hold potential for reconstructing the paleogeography and tectonic evolution of Pennsylvanian basins of eastern Laurasia

Semi-Classical Mechanics in Phase Space: The Quantum Target of Minimal Strings

The target space $M_{p,q}$ of $(p,q)$ minimal strings is embedded into the phase space of an associated integrable classical mechanical model. This map is derived from the matrix model representation of minimal strings. Quantum effects on the target space are obtained from the semiclassical mechanics in phase space as described by the Wigner function. In the classical limit the target space is a fold catastrophe of the Wigner function that is smoothed out by quantum effects. Double scaling limit is obtained by resolving the singularity of the Wigner function. The quantization rules for backgrounds with ZZ branes are also derived.Comment: 16 pages, 6 figure

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes

Gynaecologists and obstetricians visually interpret cardiotocography (CTG) traces using the International Federation of Gynaecology and Obstetrics (FIGO) guidelines to assess the wellbeing of the foetus during antenatal care. This approach has raised concerns among professionals with regards to inter- and intra-variability where clinical diagnosis only has a 30\% positive predictive value when classifying pathological outcomes. Machine learning models, trained with FIGO and other user derived features extracted from CTG traces, have been shown to increase positive predictive capacity and minimise variability. This is only possible however when class distributions are equal which is rarely the case in clinical trials where case-control observations are heavily skewed in favour of normal outcomes. Classes can be balanced using either synthetic data derived from resampled case training data or by decreasing the number of control instances. However, this either introduces bias or removes valuable information. Concerns have also been raised regarding machine learning studies and their reliance on manually handcrafted features. While this has led to some interesting results, deriving an optimal set of features is considered to be an art as well as a science and is often an empirical and time consuming process. In this paper, we address both of these issues and propose a novel CTG analysis methodology that a) splits CTG time-series signals into n-size windows with equal class distributions, and b) automatically extracts features from time-series windows using a one dimensional convolutional neural network (1DCNN) and multilayer perceptron (MLP) ensemble. Collectively, the proposed approach normally distributes classes and removes the need to handcrafted features from CTG traces

Deep Learning Classification of Polygenic Obesity using Genome Wide Association Study SNPs

In this paper, association results from genome-wide association studies (GWAS) are combined with a deep learning framework to test the predictive capacity of statistically significant single nucleotide polymorphism (SNPs) associated with obesity phenotype. Our approach demonstrates the potential of deep learning as a powerful framework for GWAS analysis that can capture information about SNPs and the important interactions between them. Basic statistical methods and techniques for the analysis of genetic SNP data from population-based genome-wide studies have been considered. Statistical association testing between individual SNPs and obesity was conducted under an additive model using logistic regression. Four subsets of loci after quality-control (QC) and association analysis were selected: P-values lower than 1x10-5 (5 SNPs), 1x10-4 (32 SNPs), 1x10-3 (248 SNPs) and 1x10-2 (2465 SNPs). A deep learning classifier is initialised using these sets of SNPs and fine-tuned to classify obese and non-obese observations. Using a deep learning classifier model and genetic variants with P-value < 1x10-2 (2465 SNPs) it was possible to obtain results (SE=0.9604, SP=0.9712, Gini=0.9817, LogLoss=0.1150, AUC=0.9908 and MSE=0.0300). As the P-value increased, an evident deterioration in performance was observed. Results demonstrate that single SNP analysis fails to capture the cumulative effect of less significant variants and their overall contribution to the outcome in disease prediction, which is captured using a deep learning framework

A Genetic Analytics Approach for Risk Variant Identification to Support Intervention Strategies for People Susceptible to Polygenic Obesity and Overweigh

Obesity is a growing epidemic that has increased steadily over the past several decades. It affects significant parts of the global population and this has resulted in obesity being high on the political agenda in many countries. It represents one of the most difficult clinical and public health challenges worldwide. While eating healthy and exercising regularly are obvious ways to combat obesity, there is a need to understand the underlying genetic constructs and pathways that lead to the manifestation of obesity and their susceptibility metrics in specific individuals. In particular, the interpretation of genetic profiles will allow for the identification of Deoxyribonucleic Acid variations, known as Single Nucleotide Polymorphism, associated with traits directly linked to obesity and validated with Genome-Wide Association Studies. Using a robust data science methodology, this paper uses a subset of the TwinsUK dataset that contains genetic data from extremely obese individuals with a BMI≥40, to identify significant obesity traits for potential use in genetic screening for disease risk prediction. The approach posits a framework for methodical risk variant identification to support intervention strategies that will help mitigate long-term adverse health outcomes in people susceptible to obesity and overweight

A Comment on Quantum Distribution Functions and the OSV Conjecture

Using the attractor mechanism and the relation between the quantization of $H^{3}(M)$ and topological strings on a Calabi Yau threefold $M$ we define a map from BPS black holes into coherent states. This map allows us to represent the Bekenstein-Hawking-Wald entropy as a quantum distribution function on the phase space $H^{3}(M)$. This distribution function is a mixed Husimi/anti-Husimi distribution corresponding to the different normal ordering prescriptions for the string coupling and deviations of the complex structure moduli. From the integral representation of this distribution function in terms of the Wigner distribution we recover the Ooguri-Strominger-Vafa (OSV) conjecture in the region "at infinity" of the complex structure moduli space. The physical meaning of the OSV corrections are briefly discussed in this limit.Comment: 27 pages. v2:reference and footnote adde

Association Mapping Approach into Type 2 Diabetes using Biomarkers and Clinical Data

The global growth in incidence of Type 2 Diabetes (T2D) has become a major international health concern. As such, understanding the aetiology of Type 2 Diabetes is vital. This paper investigates a variety of statistical method-ologies at various level of complexity to analyse genotype data and identify bi-omarkers that show evidence of increase susceptibility to T2D and related traits. A critical overview of several selected statistical methods for population-based association mapping particularly case-control genetic association analysis is pre-sented. A discussion on a dataset accessed in this paper that includes 3435 female subjects for cases and controls with genotype information across 879071 Single Nucleotide Polymorphism (SNPs) is presented. Quality control steps into the dataset through pre-processing phase are performed to remove samples and markers that failed the quality control test. Association analysis is discussed to address which statistical method can be appropriate to the dataset. Our genetic association analysis produces promising results and indicated that Allelic asso-ciation test showed one SNP above the genome-wide significance threshold of 5×10−8 which is rs10519107 (Odds Ratio (OR)=0.7409,P−Value (P)=1.813×10−9), While, there are several SNPs above the suggestive association threshold of 5×10−6 these SNPs could worth further investigation. Furthermore, Logistic Regression analysis adjusted for multiple confounder factors indicated that none of the genotyped SNPs has passed genome-wide significance threshold of 5×10−8 . Nevertheless, four SNPs (rs10519107, rs4368343, rs6848779, rs11729955) have passed suggestive association threshold

Sequence Stratigraphy and Onlap History of the Donets Basin, Ukraine: Insight into Carboniferous Icehouse Dynamics

The degree to which Permo-Carboniferous cyclothemic successions archive evidence for long-term variations in ice volume during the Late Paleozoic Ice Age is insufficiently resolved. Here we develop the sequence stratigraphy and onlap-offlap history for a 33-my interval of the Carboniferous using the U-Pb calibrated succession of the Donets Basin, Ukraine, in order to assess the relationship between sea-level, high-latitude changes in glacial extent, and climate. Integrated subsurface and outcrop data permit meter-scale correlation of 242 biostratigraphically constrained limestones and coals, and in turn individual cyclothems, across ~250 km of the Donets Basin. Rapid uniform subsidence and basinwide continuity of marker beds indicate Pennsylvanian deposition under relatively stable tectonic conditions. Three scales of sequences (avg. durations of ~140 ky, ~480 ky and 1.6 my) are recognized on the basis of stratigraphic stacking patterns and basinwide architecture of marine to terrestrial facies assemblages. The hierarchy of sequences and the geographic and stratigraphic positions of shifts in base-level sensitive facies across the Donets ramp permit the construction of an onlap-offlap history at a sub-400 ky scale. Major sea-level lowstands occur across the mid-Carboniferous boundary and during the early Moscovian. These lowstands coincide with glacial maxima inferred from high-latitude glacigenic deposits. The middle to late Pennsylvanian is characterized by a stepwise onlap, culminating in an earliest Gzhelian highstand, suggesting contraction of Carboniferous ice sheets prior to the initiation of Early Permian glaciation. The stratigraphic position of climate sensitive facies within individual Donets cyclothems indicates a turnover from seasonal sub-humid or semi-arid climate to everwet conditions during the late lowstand and maximum ice sheet accumulation. Comparison of the stratigraphic and aerial distribution of coals and evaporites in the Donets Basin with the onlap-offlap history further indicates everwet conditions during lowstands and inferred glacial maxima and drier climate during onlap and inferred ice sheet contraction at the intermediate (~0.8 to 1.6 my) and long (106 yr) time-scales. Taken together, the relationship between inferred climate and glacioeustasy suggests a likely teleconnection between high-latitude ice sheet behavior and low-latitude atmospheric dynamics