Modern phenotyping techniques yield vast amounts of data that are challenging to manage and analyze. When thoroughly examined, this type of data can reveal genotype-to-phenotype relationships and meaningful connections among individual traits. However, efficient data mining is challenging for experimental biologists with limited training in curating, integrating, and exploring complex datasets. Additionally, data transparency, accessibility, and reproducibility are important considerations for scientific publication. The need for a streamlined, user-friendly pipeline for advanced phenotypic data analysis is pressing. In this article we present an open-source, online platform for multivariate analysis (MVApp), which serves as an interactive pipeline for data curation, in-depth analysis, and customized visualization. MVApp builds on the available R-packages and adds extra functionalities to enhance the interpretability of the results. The modular design of the MVApp allows for flexible analysis of various data structures and includes tools underexplored in phenotypic data analysis, such as clustering and quantile regression. MVApp aims to enhance findable, accessible, interoperable, and reproducible data transparency, streamline data curation and analysis, and increase statistical literacy among the scientific community.
This paper provides a comprehensive analysis of Australian net energy consumption between 2004–05 and 2014–15. Results from environmentally-extended input-output (EEIO) analysis show that the Transport sector has the largest direct effect on net energy consumption in industrial sectors, which decreased by about 35% for net energy consumption per million $AUD in the period. The Export sector has the largest direct net energy consumption while Households consumption results in the largest net energy consumption embodied in different categories of Final demand. The structural decomposition analysis (SDA) decomposes the change of net energy consumption into five drivers, in which net energy intensity mainly reduces Australian net energy consumption by about 8000 Petajoules, while the level effect of Final demand increases it by about 10,000 Petajoules. Analysis of forward and backward linkages highlights the Manufacturing sector as the key industrial sector with the largest energy consumption reduction potential via minor changes in its input and Final demand. This indicates that more attention should be given to the reduction of energy demand from the consumption patterns of Households consumption, the improvement of energy intensity, and the application of cleaner technologies in the Transport and Manufacturing sectors. The Australian Environmental-Economic Accounts is combined with Australian input-output tables to construct the EEIO tables for net energy consumption. The combination of economic and environmental data sets provides a depth of understanding their potential to inform environmental policy decisions. The novelty of the research is the combination of economic and energy data sets, the application of EEIO model, the implementation of the additive SDA method, and the use of forward and backward linkages for the Australian energy system.
A formal mathematical definition of chattering is proposed. Chattering phenomena are classified into three types. In particular, the first type is harmless and cannot be avoided. Chattering properties of various control approaches are considered. The dangerous second and third types of chattering phenomena are proved to be removable by proper use of high-order sliding-modes (HOSM). Fast stable actuators and sensors only generate the first type of chattering in HOSM systems and practically never affect the sliding motion. Computer simulation confirms the theoretical results.
The windings of power synchronous machines are often parallel connected to obtain the desired machine voltage and current ratings. Ideally, the split-phase currents are equal in symmetrical windings, so as to avoid parasitic circulation of current in the parallel branches. However, the symmetry is broken in case of rotor eccentricity, and the split-phase currents become unbalanced. Part I of this paper analyzed the theoretical behavior of the unbalanced currents by using symmetrical components. A new fault diagnosis method was shown, based on a combined space-vector/fast Fourier transformation (FFT) analysis of signatures in the split-phase currents. This Part II applies the split-phase current signature analysis to a laboratory 17-kVA synchronous generator with artificial faults. Method performances have been evaluated with mixed static/dynamic type fault, in no-load and loaded conditions. The experiments are matched with time-stepping finite-element simulations, which help explain the effect of saturation and load on the diagnosis accuracy. The feasibility of installation of current probes in practical machines is also discussed.
Meta-analysis is a method to obtain a weighted average of results from various studies. In addition to pooling effect sizes, meta-analysis can also be used to estimate disease frequencies, such as incidence and prevalence. In this article we present methods for the meta-analysis of prevalence. We discuss the logit and double arcsine transformations to stabilise the variance. We note the special situation of multiple category prevalence, and propose solutions to the problems that arise. We describe the implementation of these methods in the MetaXL software, and present a simulation study and the example of multiple sclerosis from the Global Burden of Disease 2010 project. We conclude that the double arcsine transformation is preferred over the logit, and that the MetaXL implementation of multiple category prevalence is an improvement in the methodology of the meta-analysis of prevalence.
The state-of-art in the lipidomic analysis is summarized here to provide the overview of available sample preparation strategies, mass spectrometry (MS)-based methods for the qualitative analysis of lipids, and the quantitative MS approaches for high-throughput clinical workflows. Major challenges in terms of widely accepted best practices for lipidomic analysis, nomenclature, and standards for data reporting are discussed as well.
This work shows a method to quantify rotor eccentricities in synchronous machines by exploiting the unbalance caused in the split-phase currents. The paper first develops a machine model comprehensive of eccentricities and parallel circuits in the stator, by using symmetrical components. Then, the model is used for formal calculation of the unbalanced currents. Finally, the equations are reversed to obtain eccentricity degrees from current measurements. Practical formulas are given for fault assessment, only requiring machine line voltage and synchronous reactance. The method can be applied on load. This paper provides full details of the theory underlying the method. The theory also clarifies some aspects about split-phase currents, not deepened before. It is proven that the air gap flux modulation due to eccentricities, acting through additional 2(p ±1) -pole flux waves in 2p-pole machines, stimulates additional currents, which circulate in the stator and turn into 2(p ±1)-pole rotating space vectors in the complex domain. Vector trajectories have shape and amplitude dictated by eccentricity type and degree, respectively. This study is limited to 2p-pole machines with p ≥ 2. The theory is corroborated by simulations of a practical 1950-kVA generator in this paper. Experimental proofs and simulations of a laboratory 17-kVA machine are provided in a sequel of this paper.
This paper focuses on direct torque control (DTC) for three-phase AC electric drives. A novel model predictive control scheme is proposed that keeps the motor torque, the stator flux, and (if present) the inverter's neutral point potential within given hysteresis bounds while minimizing the switching frequency of the inverter. Based on an internal model of the drive, the controller predicts several future switch transitions, extrapolates the output trajectories, and chooses the sequence of inverter switch positions (voltage vectors) that minimizes the switching frequency. The advantages of the proposed controller are twofold. First, as underlined by the experimental results in the second part of this paper, it yields a superior performance with respect to the industrial state of the art. Specifically, the switching frequency is reduced by up to 50% while the torque and flux are kept more accurately within their bounds. Moreover, the fast dynamic torque response is inherited from standard DTC. Second, the scheme is applicable to a large class of (three-phase) AC electric machines driven by inverters.
In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific linear transforms. Specifically, our MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, the multiple linear transforms are achieved analytically and simultaneously through generalized eigenvalue decomposition. Furthermore, inspired by the observation that different views share similar data structures, a constraint is introduced to enforce the view-consistency of the multiple linear transforms. The proposed method is evaluated on three tasks: face recognition across pose, photo versus. sketch face recognition, and visual light image versus near infrared image face recognition on Multi-PIE, CUFSF and HFB databases respectively. Extensive experiments show that our MvDA achieves significant improvements compared with the best known results.
Background: The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. Results: We introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Conclusions: Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.