Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins. In this study, we developed nine combinations by integrating three normalization methods, locally weighted linear regression (LOESS), variance stabilization normalization (VSN), and robust linear regression (RLR) with three imputation methods: k-nearest neighbors (k-NN), local least-squares (LLS), and singular value decomposition (SVD). We utilized statistical measures, including the pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD), to assess intragroup and intergroup variation. The combinations yielding the lowest values corresponding to each statistical measure were chosen as the data set’s suitable normalization and imputation methods. The performance of this approach was tested using two spiked-in standard label-free proteomics benchmark data sets. The identified combinations returned a low NRMSE and showed better performance in identifying spiked-in proteins. The developed approach can be accessed through the R package named ’lfproQC’ and a user-friendly Shiny web application (https://dabiniasri.shinyapps.io/lfproQC and http://omics.icar.gov.in/lfproQC), making it a valuable resource for researchers looking to apply this method to their data sets.

The Hyperconverged Infrastructure (HCI) Software market has emerged as a transformative force in the realm of data center management, merging computing, storage, and networking into a singular, software-driven solution. This innovative approach enables businesses to streamline their IT infrastructure, enhance scalab

The recent accelerated growth in the computing power has generated popularization of experimentation with dynamic computer models in various physical and engineering applications. Despite the extensive statistical research in computer experiments, most of the focus had been on the theoretical and algorithmic innovations for the design and analysis of computer models with scalar responses. In this article, we propose a computationally efficient statistical emulator for a large-scale dynamic computer simulator (i.e., simulator which gives time series outputs). The main idea is to first find a good local neighborhood for every input location, and then emulate the simulator output via a singular value decomposition (SVD) based Gaussian process (GP) model. We develop a new design criterion for sequentially finding this local neighborhood set of training points. Several test functions and a real-life application have been used to demonstrate the performance of the proposed approach over a naive method of choosing local neighborhood set using the Euclidean distance among design points. The supplementary material, which contains proof of the theoretical results, detailed algorithms, additional simulation results, and R codes, are available online.

The Single Point Weighing Sensor market plays a crucial role in various industries, including food and beverage, pharmaceuticals, and manufacturing, by providing precise weight measurements essential for quality control and process efficiency. These sensors, designed to measure the weight of objects on a singular po

The Integrated Microwave Assembly (IMA) market is evolving as a critical component in several high-tech applications, spanning telecommunications, aerospace, defense, and consumer electronics. As a technology that combines various microwave components such as amplifiers, filters, and antennas into a singular, compac

1. ENTITIES. A singular entity is considered to be any habitable area of the municipality, inhabited or exceptionally uninhabited, clearly differentiated within it, and which is known by a specific name that identifies it without the possibility of confusion. A municipality may consist of one or more population entities. If there are no clearly differentiated habitable areas in a municipality, the municipality is considered to be a single entity. NUCLEUS. In this data set, subdivisions of the singular population entities are collected according to the distribution of either the buildings that are housing or the population. Population nucleus. It is the set of at least ten buildings, which are forming streets, squares or other urban roads, as well as isolated constructions that are less than 200 meters from said set or provided that the population of right exceeds 50 inhabitants. Disseminated. It is the set of buildings or dwellings of a singular entity of population that cannot be included in the concept of nucleus. NEIGHBOURHOODS. In this data set, subdivisions of municipalities with a population greater than 10,000 inhabitants are collected. These subdivisions remain stable for time series studies. The creation of these territorial entities is due to the fact that other entities or are very large in some cases and do not allow establishing homogeneous variables or vary greatly in a short time. DISTRICT. The Census District is the intramunicipal grouping of census sections. The municipality shall always have at least one district, even if it is a single section. SECTION. The census section is the partition of the districts of the municipality that is preferably defined by easily identifiable limits, such as natural accidents of the land, permanent constructions and roads, and that has a size between 1,000 and 3,500 residents, except in the case that the entire municipality has a lower population - in this case it would be of district and single section -. It should be borne in mind that a census section may contain one or more population entities.

Tax assessment, supplementary tax refund issuance, single household number, amount, singular distribution, 5th percentile declaration statistical table. Unit: %

The Single Line LiDAR (Light Detection and Ranging) market has emerged as a critical technology in various industries, revolutionizing how we capture and analyze spatial data. Single Line LiDAR primarily utilizes a singular laser beam to generate precise topographical maps and 3D models, making it an invaluable tool

The event type denotes whether an event is unfolding over a non-singular period of time like the (o)ngoing snow storm, if there is an incisive, temporally (s)ingular happening like the winning move in the golf event, or if there is (n)o particular event. Suffixes for ranges and time steps stand for (m)inute, (h)our, (d)ay, (y)ear. The fraction of messages posted in the peak hour, compared with hourly fractions 12 hours prior and afterwards, is denoted by and can be interpreted as the immediacy of the response to singular events. Exponents and measure the best least-squares fit slopes between and , and between and , respectively. The symbol denotes the imposed length limitation in the respective medium. Although the fraction of peak hour messages is highest for the golf event, correlation is stronger in the presidential election forum, possibly due to the length limitation in data set 1. All other correlations are consistent with the type of event, i.e. correlations are less strong when there is only an ongoing event. We checked for robustness of the parameters in Section S4 in File S1.

The assembly of pyrotechnic grain demands high precision and stability in robotic arm motion control due to the small shell apertures and stringent assembly accuracy requirements. Inverse kinematics is a core technology in robotic arm motion control. This paper constructs a robotic arm inverse kinematics model by reformulating the inverse kinematics problem as a constrained optimization problem and employs a multi-strategy improved Secretary Bird Optimization Algorithm (ISBOA) to achieve high-precision solutions. Aiming at the problems of restricted solution set exploration, easy to fall into local optimization and slow convergence when solving the inverse kinematics of multi-DOF robotic arm by SBOA, this paper introduces the oppositional variational perturbation, golden sine development and evolutionary strategy to optimize the formation of ISBOA, and verifies its effectiveness through numerical experiments. Simulation experiments using 4, 6, and 7-DOF robotic arms are conducted, with inverse solution results analyzed via PCA dimensionality reduction and K-means clustering, demonstrating the superiority of ISBOA in inverse solution diversity. Finally, a MATLAB-CoppeliaSim-UR16e experimental platform is developed to compare ISBOA with traditional analytical and Newton iterative method. Results are analyzed in terms of assembly accuracy, singular position handling, grasping success rate, and assembly success rate, confirming ISBOA’s advantages in pyrotechnic grain assembly and its potential for engineering applications.