Data-oriented Approach Research Articles

Clustering Universities According to Performance Indicators and Determination of Organizational Culture Types in Clusters

This study aims to reveal the organizational culture types of state universities in Kazakhstan, to define the dominant organizational culture type and to interpret the organizational culture types in terms of performance indicators. In order to achieve this goal, statistical data collected from 29 state universities were classified using hierarchical cluster analysis and Ward’s Technique. In this context, firstly, the concept of organizational culture and its effects on organizational performance were explained in the light of literature information, then cluster analysis was conducted on universities in terms of institutional size and performance. In addition, since a data-oriented approach was preferred in this study, universities were classified based on objective data instead of predetermined criteria. Finally, SPSS 22.0 program and Organizational Culture Assessment Tool were used to analyze the validity of the obtained data and the relationships between the variables, and as a result, the organizational culture types of universities were revealed, the dominant organizational culture type among them was determined and suggestions were given based on empirical findings in the study. It is expected that the results of this study will bring different perspectives on the effect of organizational culture on performance indicators in state universities in Kazakhstan.

Microcystis abundance is predictable through ambient bacterial communities: A data-oriented approach

The number of cyanobacterial harmful algal blooms (cyanoHABs) has increased, leading to the widespread development of prediction models for cyanoHABs. Although bacteria interact closely with cyanobacteria and directly affect cyanoHABs occurrence, related modeling studies have rarely utilized microbial community data compared to environmental data such as water quality. In this study, we built a machine learning model, the multilayer perceptron (MLP), for the prediction of Microcystis dynamics using both bacterial community and weekly water quality data from the Daechung Reservoir and Nakdong River, South Korea. The modeling performance, indicated by the R2 value, improved to 0.97 in the model combining bacterial community data with environmental factors, compared to 0.78 in the model using only environmental factors. This underscores the importance of microbial communities in cyanoHABs prediction. Through the post-hoc analysis of the MLP models, we revealed that nitrogen sources played a more critical role than phosphorus sources in Microcystis blooms, whereas the bacterial amplicon sequence variants did not have significant differences in their contribution to each other. Similar to the MLP model results, bacterial data also had higher predictability in multiple linear regression (MLR) than environmental data. In both the MLP and MLR models, Microscillaceae showed the strongest association with Microcystis. This modeling approach provides a better understanding of the interactions between bacteria and cyanoHABs, facilitating the development of more accurate and reliable models for cyanoHABs prediction using ambient bacterial data.

A Study of Corporate Social Responsibility: Infosys Limited

This research paper provides an in-depth analysis of corporate social responsibility initiatives undertaken by Infosys Limited using a secondary data-oriented approach. It draws on a broad range of sources, such as corporate reports, media reports, and academic literature, to gain a comprehensive understanding of the scope and impact of the company's CSR efforts. The purpose of this secondary data analysis is to identify the primary themes, tactics, and results of Infosys Ltd's CSR activities. By examining the company's dedication to ethical practices, ecological sustainability, and social engagement, the research provides insight into the wider implications of Infosys' CSR activities. The findings of this paper contribute to a more nuanced comprehension of Infosys's CSR practices and their impact on the company and the broader business community.

Reassessing the transport properties of fluids: A symbolic regression approach.

The viscosity and thermal conductivity coefficients of the Lennard-Jones fluid are extracted through symbolic regression (SR) techniques from data derived from simulations at the atomic scale. This data-oriented approach provides closed form relations that achieve fine accuracy when compared to well-established theoretical, empirical, or approximate equations, fully transparent, with small complexity and high interpretability. The novelty is further outlined by suggesting analytical expressions for estimating fluid transport properties across the whole phase space, from a dilute gas to a dense liquid, by considering only two macroscopic properties (density and temperature). In such expressions, the underlying physical mechanisms are reflected, while, at the same time, it can be a computationally efficient alternative to costly in time and size first principle and/or molecular dynamics simulations.

A Machine Learning-Based Approach to Identifying Key Innovation Determinants for Industrial Firms: The Case of the Pharmaceutical Industry in China

With abundant resources consumed by innovation practices, it is of strategic importance for industrial firms to identify key determinants of innovation performance from both internal and external environments for better and effective innovation management. To this end, this article develops a new machine learning-based approach to examine a variety of factors influencing innovation performance. In particular, a two-process modeling approach is developed to build machine learning models efficiently for identifying key internal and external innovation factors. With extensive experiments using data collected from China's pharmaceutical industry, the approach identifies a set of key internal and external innovation factors and their best configurations in terms of two commonly used innovation performance measures. The findings enable pharmaceutical firms and local governments in China to manage innovation efforts and formulate management strategies for promoting industrial innovation. The article contributes to innovation management research by providing an objective and data-oriented approach for comprehensively exploring internal and external innovation factors and establishing their relationships with expected innovation performance. Although the approach is exemplified by China's pharmaceutical industry and the results cannot be generalized, it has general applicability to other industries or countries.

A machine learning-driven framework for the property prediction and generative design of multiple principal element alloys

Multi-principal element alloys (MPEAs), inclusive of so-called high entropy alloys (HEAs), represent an innovative class of metallic materials that reveal unique properties and potentially broad applicability. However, the compositional complexity of MPEAs presents challenges in discerning physical mechanisms that control properties, and in harnessing such mechanisms to drive the design of new alloys. An ability to design metallic alloys that possess user-defined requisite properties has emerged as a critical area of interest within the field of materials science and engineering. This research illustrates how the integration of data science, machine learning (ML), and generative design strategies can evolve alloy design to a predictive, data-oriented approach. A comprehensive workflow utilising machine learning for feature analysis, property prediction, and generative design of novel MPEA generation is proposed. This workflow facilitates the examination and comparison of the predictive capabilities of ML models in determining the mechanical properties of MPEAs, providing insights into the influence of different design parameters. Additionally, by integrating a generative adversarial model, the prediction of novel MPEAs and anticipate their mechanical behaviours is revealed.

Defining Routes for Emergency Response from Climate Events: a Data-oriented Approach

Defining Routes for Emergency Response from Climate Events: a Data-oriented Approach

Real time quality monitoring of electrical distribution network affected by heatwaves: a data-oriented approach

Real time quality monitoring of electrical distribution network affected by heatwaves: a data-oriented approach

Towards a taxonomy of waste management research: An application of community detection in keyword network

Our global environment is facing significant waste and waste management challenges, despite minor differences in definitions and what constitutes waste. In recent decades, waste has been the subject of a great deal of research since civilized society needs to manage waste efficiently and effectively. As a result of all of these issues, researchers have focused on improving waste management. To do so, a taxonomy based on the research conducted on waste management will help them. Research taxonomies and visualizations help us find the most important areas of research in waste management. There are a few researchers who taxonomize research waste according to their methodology, but there is no quantitative and data-oriented approach. By using community detection and social network analysis, this study aims to quantify and systematize waste area taxonomy.There have been 9095 articles published on waste management since 2000, and these articles were retrieved from the WOS database to create a keyword network made up of 17,808 members (unique keywords) and 79,763 links in this study. To discover the research areas of waste management, the community detection technique was used in the keyword network, and 10 main communities that represent the main research areas of waste management were discovered and named based on waste management characteristics and waste types. These 10 communities include more than 90% of all waste management research.In the next step and to achieve a hierarchical taxonomy of waste management studies, the community detection algorithm was implemented in ten communities and by discovering sub-communities, a waste management taxonomy with 10 main branches and 26 sub-branches was presented.

Efficient multi-level hp-finite elements in arbitrary dimensions

We present an efficient algorithmic framework for constructing multi-level hp-bases that uses a data-oriented approach that easily extends to any number of dimensions and provides a natural framework for performance-optimized implementations. We only operate on the bounding faces of finite elements without considering their lower-dimensional topological features and demonstrate the potential of the presented methods using a newly written open-source library. First, we analyze a Fichera corner and show that the framework does not increase runtime and memory consumption when compared against the classical p-version of the finite element method. Then, we compute a transient example with dynamic refinement and derefinement, where we also obtain the expected convergence rates and excellent performance in computing time and memory usage.

Data-oriented description of texture-dependent anisotropic material behavior

In metallurgical processes, as for example cold rolling or deep drawing of sheet metal, it is frequently observed that the crystallographic texture, and with it the anisotropic mechanical properties of a material, evolve dynamically. Hence, to describe such processes, it is necessary to model the functional dependence of anisotropic material parameters on the texture, which itself can vary locally with the different plastic strain histories. In this work, we present a new data-oriented approach to parametrize the anisotropic yield function Barlat Yld2004-18p from micromechanical simulations for different textures. This is accomplished by applying supervised machine learning (ML) methods to express the relationship between different crystallographic textures and the material parameters of the yield function. The crystallographic textures are chosen to vary continuously between a random texture on the one hand side, and a unimodal Goss or Copper texture the other. These crystallographic textures are rather common in sheet metal forming. In this way, furthermore, the transition from isotropic plasticity to a rather severe case of anisotropy can be modeled, which is thought to mimic the dynamical evolution of the texture in a metallurgical process. It is found that a regularization strategy is necessary to circumvent the known non-uniqueness between Yld2004-18p parameters and the resulting plastic yield behavior. After this regularization, a unique relationship between the material parameters and the yield onset is established, making it possible to train different ML models with excellent accuracy and generalization properties to anisotropic plastic material behavior. The trained ML models are able to reliably predict the coefficients of unknown textures even with a small amount of training data and, thus, to correctly represent the yield behavior resulting from the various textures. The proposed method represents an efficient extension of the description of anisotropic plastic yielding as it establishes a data-oriented way to explicitly consider microstructural parameters in the material description, which opens new pathways to formulate material models that include the process history.

Modeling a Born-Digital Factoid Prosopography using the TEI and Linked Data

Although the TEI has traditionally been used for encoding text, its combination of structured and semi-structured data has made it a compelling choice for born-digital, linked-data resources as well. Our intent here is to demonstrate the advantages it offers for digital prosopographies along with a model that can be used for them. Syriac Persons, Events, and Relations (SPEAR) is a born-digital prosopography project in the field of Syriac studies. Where traditional prosopographies focused on prose descriptions of individual persons of significance, SPEAR follows recent developments in research methodologies that instead produce prosopographical factoids. Factoids are structured data about persons drawn from the analysis of historical texts. Most factoid prosopographies use relational databases to model data. Instead, SPEAR uses a customized TEI schema to model factoids that can be queried and visualized in an XML database as well as serialized in HTML for human viewers and in RDF for data sharing. The TEI’s provisions for structured and semi-structured data make it ideal for encoding data from heterogeneous historical source material. Moreover, its linking capabilities connect SPEAR data to related data sets. By modeling prosopographical factoids, and not the source texts themselves, SPEAR offers an example of how a born-digital, data-oriented approach to using the TEI can circumvent some of the challenges posed by the tree structure of XML. It also disrupts traditional understandings of data and stand-off markup through combining linked open data approaches with the use of the TEI.

Politeness in early Ptolemaic papyri: a frame-based approach

This paper deals with the expression of linguistic politeness in early Hellenistic documentary papyri, through a data-oriented approach exploiting the notion of “frame” (cf. Terkourafi 2001), which accounts for politeness in terms of regular correlations between forms and contexts. The study focuses on a subset of epistolary types, i.e., ordinary business letters, recommendations and petitions, considering those situational and expressive features that make them distinct frames of interaction. Deviances from the frame of the petition within the corpus are then taken into account and evaluated in terms of impoliteness or rudeness.

Discovering temporal scientometric knowledge in COVID-19 scholarly production.

The mapping and analysis of scientific knowledge makes it possible to identify the dynamics and/or growth of a particular field of research or to support strategic decisions related to different research entities, based on bibliometric and/or scientometric indicators. However, with the exponential growth of scientific production, a systematic and data-oriented approach to the analysis of this large set of productions becomes increasingly essential. Thus, in this work, a data-oriented methodology was proposed, combining Data Analysis, Machine Learning and Complex Network Analysis techniques, and Data Version Control (DVC) tool, for the extraction of implicit knowledge in scientific production bases. In addition, the approach was validated through a case study in a COVID-19 manuscripts dataset, which had 199,895 articles published on arXiv, bioRxiv, medRxiv, PubMed and Scopus databases. The results suggest the feasibility of the proposed methodology, indicating the most active countries and the most explored themes in each period of the pandemic. Therefore, this study has the potential to instrument and expand strategic decisions by the scientific community, aiming at extracting knowledge that supports the fight against the COVID-19 pandemic.

A data-based model for condition monitoring and maintenance planning for protective coating systems for wind tower structures

The application of protective coating systems is the major measure against the corrosion of steel structures for onshore wind turbines. The organic coatings are, however, susceptible to atmospheric exposure and tend to deteriorate during the operation. At the same time, onshore turbines become more powerful and require taller and more resistant tower structures. The inspection and condition monitoring of protective coating systems on large onshore turbines (in excess of 120 m height) is a demanding and time-consuming procedure and requires high human effort. The rapid developments in digitization and data analysis offer opportunities to notably increase the efficiency of monitoring processes and to develop (semi-)automated standardized procedures. The paper describes a data-oriented approach to utilize digital data for the monitoring and maintenance planning of surface protection systems of large onshore wind turbines. The proposed approach includes the following steps: the segmentation of an existing wind power structure into a number of reference areas based on an In-situ Virtual Twin; the definition of a local deterioration degree for each individual reference area; the annotation of image data; the use of heterogenous multi-modal data (image data, geodetical data, meteorological data, profile scanning data) as the sources for condition assessment and monitoring. An example procedure is exercised for a tower structure of an onshore wind power turbine in order to illustrate the practical relevance of the approach.

결측치가 있는 실험계획 데이터의 분석 방법에 관한 소고

A data-oriented approach is proposed to analyze experiments designed data with missing values resulting from the invalidity of the design points. The procedure consists of representing available data in a normal probability plot, identifying better design points and the factor levels common to these points, and then deciding on optimal conditions in the experimental region or the direction of improvement. Proposed approach is illustrated with analyses of a fractional factorial design and a robust design data. The procedure is expected to be useful to the practitioners implementing design of experiments to product/process development.

Distributed in-memory data management for workflow executions.

Complex scientific experiments from various domains are typically modeled as workflows and executed on large-scale machines using a Parallel Workflow Management System (WMS). Since such executions usually last for hours or days, some WMSs provide user steering support, i.e., they allow users to run data analyses and, depending on the results, adapt the workflows at runtime. A challenge in the parallel execution control design is to manage workflow data for efficient executions while enabling user steering support. Data access for high scalability is typically transaction-oriented, while for data analysis, it is online analytical-oriented so that managing such hybrid workloads makes the challenge even harder. In this work, we present SchalaDB, an architecture with a set of design principles and techniques based on distributed in-memory data management for efficient workflow execution control and user steering. We propose a distributed data design for scalable workflow task scheduling and high availability driven by a parallel and distributed in-memory DBMS. To evaluate our proposal, we develop d-Chiron, a WMS designed according to SchalaDB’s principles. We carry out an extensive experimental evaluation on an HPC cluster with up to 960 computing cores. Among other analyses, we show that even when running data analyses for user steering, SchalaDB’s overhead is negligible for workloads composed of hundreds of concurrent tasks on shared data. Our results encourage workflow engine developers to follow a parallel and distributed data-oriented approach not only for scheduling and monitoring but also for user steering.

How did governmental interventions affect the spread of COVID-19 in European countries?

BackgroundTo reduce the transmission of the severe acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. We examined how the effective-contact rate (ECR), the mean number of daily contacts for an infectious individual to transmit the virus, among European citizens evolved during this wave over the period with implemented measures, disregarding a priori information on governmental measures.MethodsWe developed a data-oriented approach that is based on an extended Susceptible-Exposed-Infectious-Removed (SEIR) model. Using the available data on the confirmed numbers of infections and hospitalizations, we first estimated the daily total number of infectious-, exposed- and susceptible individuals and subsequently estimated the ECR with an iterative Poisson regression model. We then compared change points in the daily ECRs to the moments of the governmental measures.ResultsThe change points in the daily ECRs were found to align with the implementation of governmental interventions. At the end of the considered time-window, we found similar ECRs for Italy (0.29), Spain (0.24), and Germany (0.27), while the ECR in the Netherlands (0.34), Belgium (0.35) and the UK (0.37) were somewhat higher. The highest ECR was found for Sweden (0.45).ConclusionsThere seemed to be an immediate effect of banning events and closing schools, typically among the first measures taken by the governments. The effect of additionally closing bars and restaurants seemed limited. For most countries a somewhat delayed effect of the full lockdown was observed, and the ECR after a full lockdown was not necessarily lower than an ECR after (only) a gathering ban.

Data-Oriented View for Convolutional Coding With Adaptive Irregular Constellations

Current wireless systems offer various use-cases where conventional channel capacity focused performance criteria might not apply. Following the similar perspective, convolutional encoding can find more room due to its low complexity and low decoding delay. Besides, it has been also shown that error performance of a convolutional encoder can be improved further by using adaptive irregular constellations. A new performance measure, data-oriented approach, was recently proposed for the transmission of small data packets, i.e. mission-critical IoT applications, over fading channels. In this letter, delay performance gain resulting from convolution coding optimized irregular constellations is investigated. Then, we derive a new performance criterion based on delay and finite block length constraints. Based on this criterion, we design irregular constellations together with convolutional coding for short packet transmission.

Ancient evolution of hepadnaviral paleoviruses and their impact on host genomes.

Hepadnaviruses (family Hepadnaviviridae) are reverse-transcribing animal viruses that infect vertebrates. DNA sequences derived from ancient hepadnaviruses have been identified in the germline genome of numerous vertebrate species, and these ‘endogenous hepatitis B viruses’ (eHBVs) reveal aspects of the long-term coevolutionary relationship between hepadnaviruses and their vertebrate hosts. Here, we use a novel, data-oriented approach to recover and analyse the complete repertoire of eHBV elements in published animal genomes. We show that germline incorporation of hepadnaviruses is exclusive to a single vertebrate group (Sauria) and that the eHBVs contained in saurian genomes represent a far greater diversity of hepadnaviruses than previously recognized. Through in-depth characterization of eHBV elements, we establish the existence of four distinct subgroups within the genus Avihepadnavirus and trace their evolution through the Cenozoic Era. Furthermore, we provide a completely new perspective on hepadnavirus evolution by showing that the metahepadnaviruses (genus Metahepadnavirus) originated >300 million years ago in the Paleozoic Era and have historically infected a broad range of vertebrates. We also show that eHBVs have been intra-genomically amplified in some saurian lineages, and that eHBVs located at approximately equivalent genomic loci have been acquired in entirely distinct germline integration events. These findings indicate that selective forces have favoured the accumulation of hepadnaviral sequences at specific loci in the saurian germline. Our investigation provides a range of new insights into the long-term evolutionary history of reverse-transcribing DNA viruses and shows that germline incorporation of hepadnaviruses has played a role in shaping the evolution of saurian genomes.