Higher Order PWM for Modeling Transcription Factor Binding Sites

Traditional Position Weight Matrices (PWMs) that are used to model Transcription Factor Binding Sites (TFBS) assume independence among different positions in the binding site. In reality, this may not necessarily be the case. A better way to model TFBS is to consider the distribution of dinucleotides or trinucleotides instead of just mononucleotides, thus taking neighboring nucleotides into account. We can therefore, extend the single nucleotide PWM to a dinucleotide PWM or an even higher-order PWM to correctly estimate the dependencies among the nucleotides in a given sequence. The purpose of this project is to develop an algorithm to implement higher-order PWMs to detect the TFBS and other biological motifs in DNA, RNA, and proteins

Estimation of variance of time to recruitment for a two grade manpower system with two sources of depletion and two types of policy decisions

Abstract: A marketing organization consisting of two grades with policy and transfer decisions forming two different sources of depletion is considered. The policy decisions are classified into two different types according to the intensity of attrition. In this paper the problem of time to recruitment for this organization is analysed when the breakdown threshold, a level of maximum allowable manpower depletion, has three components namely normal threshold of depletion of manpower, threshold of frequent breaks of existing workers and threshold of backup or reservation of manpower sources. A mathematical model is constructed and using a univariate policy of recruitment based on shock model approach, analytical results are obtained to estimate the variance of the time to recruitment by considering different forms of the breakdown threshold for the cumulative loss of manpower in the organization. The results are numerically illustrated by assuming specific distributions and relevant findings are presented. Key Words: two grade manpower system, two sources of depletion of manpower, two types of policy decisions, breakdown threshold of depletion with three components, shock model approach, univariate policy of recruitment and performance measures AM

Stochastic Models on Time to Recruitment in a Two Grade Manpower System using Different Policies of Recruitment

In this paper a two grade organization in which depletion of manpower occurs due to its policy decisions is considered. Two mathematical models are constructed employing two different univariate recruitment policies, based on shock model approach. The mean and variance of the time to recruitment are obtained for both the models under different conditions. The analytical results are numerically illustrated and relevant conclusions are presented

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes

Solar irradiance resource and forecasting: a comprehensive review

10.1049/iet-rpg.2019.1227IET RENEWABLE POWER GENERATION14101641-165

Robust Data-driven Sparse Estimation of Power Flow Sensitivities for Smart Grid Monitoring and Operation

This paper proposes a novel data-driven framework for robust sparse DF estimation that can handle the collinearity of high-dimensional synchrophasor measurements in large-scale smart grids. It does not require power flow models, thereby facilitating power flow sensitivity analysis with adaptiveness to operating-point changes and data uncertainties from renewable generations.</p

A Hybrid CFS Filter and RF-RFE Wrapper-Based Feature Extraction for Enhanced Agricultural Crop Yield Prediction Modeling

The innovation in science and technical knowledge has prompted an enormous amount of information for the agrarian sector. Machine learning has risen with massive processing techniques to perceive new contingencies in agricultural development. Machine learning is a novel onset for the investigation and determination of unpredictable agrarian issues. Machine learning models actualize the need for scaling the learning model&rsquo;s performance. Feature selection can impact a machine learning model&rsquo;s performance by defining a significant feature subset for increasing the performance and identifying the variability. This paper explains a novel hybrid feature extraction procedure, which is an aggregation of the correlation-based filter (CFS) and random forest recursive feature elimination (RFRFE) wrapper framework. The proposed feature extraction approach aims to identify an optimal subclass of features from a collection of climate, soil, and groundwater characteristics for constructing a crop-yield forecasting machine learning model with better performance and accuracy. The model&rsquo;s precision and effectiveness are estimated (i) with all the features in the dataset, (ii) with essential features obtained using the learning algorithm&rsquo;s inbuilt &lsquo;feature_importances&rsquo; method, and (iii) with the significant features obtained through the proposed hybrid feature extraction technique. The validation of the hybrid CFS and RFRFE feature extraction approach in terms of evaluation metrics, predictive accuracies, and diagnostic plot performance analysis in comparison with random forest, decision tree, and gradient boosting machine learning algorithms are found to be profoundly satisfying

Optimal Distributed Generation Allocation using Evolutionary Algorithms in Meshed Network

Integration of various types of distributed generation (DG) into the existing power grid requires prediction of allowable DG penetration levels for a stable and reliable operation of the grid. The two major classification of the DGs namely synchronous based DGs (SDG) and inverter based DGs (IDG) exhibit different impacts on the grid which may limit their penetration level. Hence, this paper proposes a two-phase approach for maximizing the penetration of DGs complying with the collective grid constraints such as voltage harmonics and relay coordination limits exhibited by IDGs and SDGs respectively. The proposed method is tested on a standard IEEE 14-bus system with different DG penetration levels. Various case studies are conducted on the test system to demonstrate the efficacy of the proposed approach. The results obtained show the choice of the suitable optimization algorithm and the impacts of the constraints considered for optimal sizing and placement of the DGs

Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability

The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation