Common Format Research Articles

A Study for Digital Transformation Based on Collaboration Master Plan for Shipbuilding & Marine Engineering Industry

In the shipbuilding and marine industry, digital transformation activities are promoted primarily by large shipyards. However, bottlenecks are observed across value chains, and digital transformation effects are reducing because of the cost and technical challenges encountered by supplies. In this study, we proposed a win-win cooperation model for large, small, and medium-sized companies using digital transformation based on the characteristics of the shipbuilding and marine industry through case studies. We investigated the digital transformation progress in German and Korean small and medium-sized enterprises (SMEs). In addition, we identified information-sharing methods and management challenges encountered in enterprise resource planning and manufacturing execution systems in the collaboration process of pipes, panels, blocks, etc. of SMEs that are suppliers of a Korean shipyard, and clarified communication by building a platform based on a common format between shipyards and suppliers. Further, we proposed a standard model of a digital transformation system for enhancing the collaboration between large companies and suppliers and proposed a basic plan including strategies to efficiently and effectively build a digital transformation system based on the standard model.

Step-Nucleation In Situ Self-Repair to Prepare Rollable Large-Area Ultrathin MOF Membranes.

Ultrathin membranes with ultrahigh permeance and good gas selectivity have the potential to greatly decrease separation process costs, but it requires the practical preparation of large area membranes for implementation. Metal-organic frameworks (MOFs) are very attractive for membrane gas separation applications. However, to date, the largest MOF membrane area reported in the literature is only about 100 cm2 . In the present study, a new step-nucleation in situ self-repair strategy is proposed that enables the preparation of large-area (2400 cm2 ) ultrathin and rollable MOF membranes deposited on an inexpensive flexible polymer membrane support layer for the first time, combining a polyvinyl alcohol (PVA)-metal-ion layer and a pure metal-ion layer. The main role of the pure metal-ion layer is to act as the main nucleation sites for MOF membrane growth, while the PVA-metal-ion layer acts as a slow-release metal-ion source, which supplements MOF crystal nucleation to repair any defects occurring. Membrane modules are necessary components for membrane applications, and spiral-wound modules are among the most common module formats that are widely applied in gas separation. A 4800 cm2 spiral-wound membrane modulewas successfully prepared, demonstrating the practical implementation of large-area MOF membranes.

ColabFit exchange: Open-access datasets for data-driven interatomic potentials.

Data-driven interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational materials science and chemistry for performing atomic-scale simulations. Despite this, apart from a few notable exceptions, there is a distinct lack of well-organized, public datasets in common formats available for use with IP development. This deficiency precludes the research community from implementing widespread benchmarking, which is essential for gaining insight into model performance and transferability, and also limits the development of more general, or even universal, IPs. To address this issue, we introduce the ColabFit Exchange, the first database providing open access to a large collection of systematically organized datasets from multiple domains that is especially designed for IP development. The ColabFit Exchange is publicly available at https://colabfit.org, providing a web-based interface for exploring, downloading, and contributing datasets. Composed of data collected from the literature or provided by community researchers, the ColabFit Exchange currently (September 2023) consists of 139 datasets spanning nearly 70 000 unique chemistries, and is intended to continuously grow. In addition to outlining the software framework used for constructing and accessing the ColabFit Exchange, we also provide analyses of the data, quantifying the diversity of the database and proposing metrics for assessing the relative diversity of multiple datasets. Finally, we demonstrate an end-to-end IP development pipeline, utilizing datasets from the ColabFit Exchange, fitting tools from the KLIFF software package, and validation tests provided by the OpenKIM framework.

Prediction of key milk biomarkers in dairy cows through milk mid-infrared spectra and international collaborations

At the individual cow level, suboptimum fertility, mastitis, negative energy balance, and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were (1) to assess the potential of milk mid-infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6 phosphate [glucose-6P], free glucose), ketosis (β-hydroxybutyrate [BHB] and acetone), mastitis (N-acetyl-β-d-glucosaminidase activity [NAGase] and lactate dehydrogenase), and fertility (progesterone); (2) to test alternative methodologies to partial least squares (PLS) regression to better account for the specific asymmetric distribution of the biomarkers; and (3) to create robust models by merging large datasets from 5 international or national projects. Benefiting from this international collaboration, the dataset comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents, whereas the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. Partial least squares regression was used as the reference basis, and compared with a random modification of distribution associated with PLS (random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS), and support vector machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low versus high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation dataset. The remaining 80% of herds were used as the calibration dataset. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose, and lactate dehydrogenase (coefficient of determination in external herd validation [R2v] = 0.48, 0.58, 0.28, and 0.24, respectively). For other molecules, PLS-random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase, and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15, respectively). Hence, PLS and SVM based on the entire dataset provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis, and mastitis in dairy cows, which in turn have major influences on their fertility and survival.

Left Ventricular Geometric Patterns in Newly Diagnosed Hypertension: An Echocardiographic Study

Objective: To investigate patterns of left ventricular (LV) geometric patterns in patients diagnosed with new-onset hypertension using transthoracic echocardiography. The LV diastolic function was also evaluated in these patients. Materials and Methods: The present study was a cross-sectional study that clinically evaluated patients diagnosed with new-onset hypertension at Burapha University Hospital. To classify LV geometric patterns, electrocardiogram, and transthoracic echocardiography to measure LV mass index and relative wall thickness were performed. Other relevant assessments were also conducted, including the diastolic function. Results: Fifty-five patients diagnosed with new-onset hypertension were enrolled, their mean age was 55.3 years, with a standard deviation of 11.8 years. Of all participants, 70.9% (95% CI 57.1 to 82.4) had concentric remodeling, 16.4% (95% CI 7.8 to 28.8) had concentric hypertrophy, 10.9% (95% CI 4.1 to 22.3) had normal geometry and 1.8% (95% CI 0.1 to 9.7) had eccentric hypertrophy. Of all participants, 81.8% were detected to have abnormal LV diastolic dysfunction. Abnormal relaxation pattern was the most common format. Conclusion: In the present study, approximately 10.9% of patients diagnosed with new-onset hypertension had normal LV geometry, whereas 89.1% had abnormal geometry in different patterns. Concentric remodeling was found to be the predominant abnormal geometrical format. Understanding LV geometric patterns helps clinicians stratify risk, predict prognosis, and make informed decisions about treatment strategies for these patients. Keywords: Hypertension; Left ventricular geometric pattern; Diastolic dysfunction

Pynapple, a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high-dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience.

An empirical investigation into social and platform endorsement of user-generated content in the online Q&A community

Online communities employ recognition mechanisms to signal their acknowledgment of and support for the contributions of their members. These endorsements usually take the form of platform conferment, such as badges, or user interactions via social media, such as Facebook’s “Like” button. Although previous research has shown that both types of endorsements can significantly impact users’ online contributions, little attention has been paid to the relative effectiveness of and interaction between these two common endorsement formats. By analyzing a sample collected from China’s largest online social Q&A platform and conducting multiple robustness checks, we demonstrate that while both social and platform endorsements positively impact users’ online contributions, the formers are largely more effective than the latter in driving user contributions. Moreover, we find a complementary relationship between social and platform endorsements, suggesting that the combination of the two can further stimulate content generation. Our findings offer practical insights for content providers seeking to encourage content generation in online communities.

Transparent Rule Enablement Based on Commonization Approach in Heterogeneous IoT Edge Networks.

The paradigm of the Internet of Things (IoT) and edge computing brings a number of heterogeneous devices to the network edge for monitoring and controlling the environment. For reacting to events dynamically and automatically in the environment, rule-enabled IoT edge platforms operate the deployed service scenarios at the network edge, based on filtering events to perform control actions. However, due to the heterogeneity of the IoT edge networks, deploying a consistent rule context for operating a consistent rule scenario on multiple heterogeneous IoT edge platforms is difficult because of the difference in protocols and data formats. In this paper, we propose a transparent rule enablement, based on the commonization approach, for enabling a consistent rule scenario in heterogeneous IoT edge networks. The proposed IoT Edge Rule Agent Platform (IERAP) deploys device proxies to share consistent rules with IoT edge platforms without considering the difference in protocols and data formats. Therefore, each device proxy only considers the translation of the corresponding platform-specific and common formats. Also, the rules are deployed by the corresponding device proxy, which enables rules to be deployed to heterogeneous IoT edge platforms to perform the consistent rule scenario without considering the format and underlying protocols of the destination platform.

Host, parasite and drug determinants of clinical outcomes following treatment of visceral leishmaniasis: a protocol for individual participant data meta-analysis

IntroductionVisceral leishmaniasis (VL) is a parasitic disease with an estimated 30 000 new cases occurring annually. There is an observed variation in the efficacy of the current first-line therapies across...

Impact of Lateral Gap on Flow Distribution, Backwater Rise, and Turbulence Generated by a Logjam

AbstractLogjams may form at natural obstructions and are also used as nature‐based solutions for river restoration and natural flood management. Previous research has described backwater rise due to logjams that span the full channel cross‐section and logjams with a gap between the lower edge of the logjam and the bed. Logjams that fill the channel depth, but not its width, leaving a lateral gap between the logjam and the channel bank, are also common natural formations and the focus of this study. The flow distribution between the logjam and the lateral gap, backwater rise, and wake turbulence are key factors in determining the ecologic and flood risk impact of a logjam. Specifically, relative to a channel‐spanning logjam, the introduction of a lateral gap can reduce backwater rise and increase the potential for trapping particles, such as nutrients or microplastics, within the wake region, but may also promote erosion in the gap. The choice of logjam and gap widths can be used to maximize flow and habitat diversity in rivers, while reducing erosion risk. We present experimental results demonstrating that the flow distribution between the logjam and the lateral gap can be predicted by assuming equal resistance through the logjam and gap sections. Further, we show that backwater rise can be determined from the predicted discharge through the logjam using a momentum balance developed for channel‐spanning logjams. Finally, turbulence generated within the jam was observed directly downstream of the logjam, and, for the densities considered, increased with jam density.

Disaster Information Sharing Technology Among Heterogeneous Information Systems Through SIP4D-ZIP Framework

“SIP4D-ZIP,” developed by the authors, is a common data framework for sharing disaster information among systems operated by different organizations. In past large-scale disaster responses, it has often been necessary to share information that could not be predicted in advance with several organizations. Even if such information could be converted into electronic data, the valuable information was sometimes not fully utilized due to the lack of a common format for describing the meaning of the data or lack of the data format that can be read by different systems. To solve such problems, we developed SIP4D-ZIP as a common data framework that enables data to be exchanged between systems without prior arrangement. We demonstrate how SIP4D-ZIP can solve the problems found in actual disaster response, which are the background of the design concept of SIP4D. Furthermore, we explain in detail how SIP4D-ZIP works and show that it can automate the processes required to share information between systems. We believe that designing both the information source system and the utilization system based on SIP4D-ZIP will enable highly versatile and automatic mutual sharing of disaster information and promote digital transformation in disaster response.

Introducing the BlendedICU dataset, the first harmonized, international intensive care dataset

Objective:This study introduces the BlendedICU dataset, a massive dataset of international intensive care data. This dataset aims to facilitate generalizability studies of machine learning models, as well as statistical studies of clinical practices in the intensive care units. Methods:Four publicly available and patient-level intensive care databases were used as source databases. A unique and customizable preprocessing pipeline extracted clinically relevant patient-related variables from each source database. The variables were then harmonized and standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Format. Finally, a brief comparison was carried out to explore differences in the source databases. Results:The BlendedICU dataset features 41 timeseries variables as well as the exposure times to 113 active ingredients extracted from the AmsterdamUMCdb, eICU, HiRID, and MIMIC-IV databases. This resulted in a database of more than 309000 intensive care admissions, spanning over 13 years and three countries. We found that data collection, drug exposure, and patient outcomes varied strongly between source databases. Conclusion:The variability in data collection, drug exposure, and patient outcomes between the source databases indicated some dissimilarity in patient phenotypes and clinical practices between different intensive care units. This demonstrated the need for generalizability studies of machine learning models. This study provides the clinical data research community with essential data to build efficient and generalizable machine learning models, as well as to explore clinical practices in intensive care units around the world.

Systematic review of free and open source software (FOSS) employed in ecomorphological studies with recommendations for user-friendly developments

Morphometrics is an important aspect of biological research that integrates in various ways with other branches of biology like genetics, ecology, and evolution to determine an organism's form and function. It is one of the most frequently used methods to study ecomorphology (the interaction of morphological features of organisms with environmental variables). In recent years, research in the field of morphometrics has achieved revolutionary developments from studying simple morphological variations to visualizing shape differences, thanks to free and open source software (FOSS). These FOSS not only incorporate various steps of morphometric methods such as image processing, digitization, data acquisition and statistical analyses but also help in determining the influence of ecological factors on an organism's morphology. In this article, we discussed the applications, benefits, and drawbacks of FOSS frequently employed in current ecomorphological studies. We observed that in many morphological studies, the integration of ecological data is completely missing, which is a prerequisite in determining the influential factors influencing an organism's morphology. We believe that the main reason behind the lack of integration of ecological information in these studies is the dearth of proper knowledge about the usage of presently available FOSS. To move forward, we, therefore, propose software developers to design user-friendly FOSS which will be compatible with all operating systems and can integrate all maneuvers of ecomorphological methods with common data file formats. This will help researchers to employ an ecomorphological approach to resolve various questions in biological research.

An Automated Method for Extracting and Analyzing Railway Infrastructure Cost Data

The capability of extracting information and analyzing it so that it is in a common format is essential for performing predictions, comparing projects through cost benchmarking, and having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of data make this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway infrastructure cost data. To validate the suggested approach, data from 23 real historical projects from the client network rail were extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing cost benchmarking to be performed. The presented method proves the benefits of data extraction for gathering, analyzing, and benchmarking each project in an efficient manner, and to develop a deeper understanding of the relationships and the relevant factors that matter in infrastructure costs.

Integrative transcriptomics and metabolomics analysis of body color formation in the common carp

The coloration of aquatic animals influences their adaptability to environment and economic values. The skin color of common carp (Cyprinus carpio) is various between different strains. Hebao red carp is famous for its bright red color, which is different from the brown skin color of wild-type common carp. To reveal the different components and molecular mechanism between skins with red and brown colors, LC-MS and transcript analysis were conducted. 614 significantly different metabolites (SDM) and 392 differentially expressed genes (DEG) were detected between skins of Hebao red carp (HB) and Yellow River carp (YR). There are 35 KEGG pathways shared by DEGs and SDMs, among which microbial metabolism in diverse environments (ko01120), carbon metabolism (ko01200) and metabolic pathways (ko01100) were significantly enriched by DEGs and SDMs. Pathways related to melanin synthesis and retinal metabolism were down-regulated in HB, such as adh8a, adh8b, cyp3a, dhrs3b, mart1, hgd, hpd and maiA, which may induce the disappear of melanocytes and accumulation of carotenoid. The content of lutein in HB skin was significantly higher than in YR. This study shows retinol and carotenoid metabolism is vital in the common carp body color formation, and will help understand the mechanism of color determination in common carp.

3D real property in vertical mixed-use developments. A comparative analysis of common property and management aspects in selected jurisdictions – The case of British Columbia, Denmark and Sweden

Jurisdictions around the world are experiencing an increasing demand for formation of three-dimensional (3D) real property units, especially in the presence of vertical mixed-use developments. Since traditional real property formation is only in two dimensions (2D), jurisdictions are on different steps (levels) of transforming 2D real property legislation into 3D real property legislation. The objective of this article is to analyse and compare law and practice in jurisdictions that are on different steps of developing 3D real property legislation, but otherwise comparable in terms of societal development status. The predominant focus in academic literature is on formation of private 3D property rights. This paper has a somewhat different approach, focussing on formation and management of common property. The formation of common property is usually unavoidable in a vertical mixed-use development, e.g. due to a high degree of interdependence between layered and intertwined 3D property units. Legal aspects regarding common property formation and management have not been rigorously compared internationally. This article presents a comparative study including British Columbia (Canada), Denmark and Sweden. Each jurisdiction represents a unique step on the 3D transformation staircase where legal aspects regarding common property and management in each jurisdiction is analysed and presented. The results of the study can be beneficial for researchers and legislators as a tool to analyse 3D real property legislation.

Transcoding unicode characters with AVX‐512 instructions

AbstractIntel includes in its recent processors a powerful set of instructions capable of processing 512‐bit registers with a single instruction (AVX‐512). Some of these instructions have no equivalent in earlier instruction sets. We leverage these instructions to efficiently transcode strings between the most common formats: UTF‐8 and UTF‐16. With our novel algorithms, we are often twice as fast as the previous best solutions. For example, we transcode Chinese text from UTF‐8 to UTF‐16 at more than 5 GiB using fewer than 2 CPU instructions per character. To ensure reproducibility, we make our software freely available as an open‐source library. Our library is part of the popular Node.js JavaScript runtime.

A Method to Enable Automatic Extraction of Cost and Quantity Data from Hierarchical Construction Information Documents to Enable Rapid Digital Comparison and Analysis

Context: Despite the effort put into developing standards for structuring construction costs and the strong interest in the field, most construction companies still perform the process of data gathering and processing manually. This provokes inconsistencies, different criteria when classifying, misclassifications, and the process becomes very time-consuming, particularly in large projects. Additionally, the lack of standardization makes cost estimation and comparison tasks very difficult. Objective: The aim of this work was to create a method to extract and organize construction cost and quantity data into a consistent format and structure to enable rapid and reliable digital comparison of the content. Methods: The approach consisted of a two-step method: firstly, the system implemented data mining to review the input document and determine how it was structured based on the position, format, sequence, and content of descriptive and quantitative data. Secondly, the extracted data were processed and classified with a combination of data science and experts’ knowledge to fit a common format. Results: A large variety of information coming from real historical projects was successfully extracted and processed into a common format with 97.5% accuracy using a subset of 5770 assets located on 18 different files, building a solid base for analysis and comparison. Conclusions: A robust and accurate method was developed for extracting hierarchical project cost data to a common machine-readable format to enable rapid and reliable comparison and benchmarking.

A common format for representing spatial location in visual and motor working memory.

Does the mind rely on similar systems of spatial representation for both perception and action? Here, we assessed the format of location representations in two simple spatial localization tasks. In one task, participants simply remembered the location of an item based solely on visual input. In another, participants remembered the location of a point in space based solely on kinesthetic input. Participants' recall errors were more consistent with the use of polar coordinates than Cartesian coordinates in both tasks. Moreover, measures of spatial bias and performance were correlated across modalities. In a subsequent study, we tested the flexibility with which people use polar coordinates to represent space; we show that the format in which the information is presented to participants influences how that information is encoded and the errors that are made as a result. We suggest that polar coordinates may be a common means of representing location information across visual and motor modalities, but that these representations are also flexible in form.

Determination of optimum process parameters for minimum punch force in deep drawing using numerical simulation

This article represents the most common sheet formation method, deep drawing. This technology is employed in the production of the automotive and aerospace industries. There are various aspects that have an impact on the deep drawing process; these factors are known as the deep drawing process parameters. Finite element analysis and Taguchi Techniques are coupled to find out the optimum deep drawing parameters. Six process parameters such as die corner radius, punch corner radius, punch speed, friction between blank and die, friction between blank and blank holder and friction between blank and punch are used for analysis. Punch force is considered as response variable. The Taguchi method is employed to determine the optimal process parameters for the deep drawing process. Based on SN ratio analysis, optimum deep drawing parameters for punch force were found to be die corner radius of 7mm, punch corner radius of 7mm, punch speed of 0.2mm/s, friction between blank and die of 0.08, friction between blank and blank holder of 0.08 and friction between blank and punch of 0.10. From ANOVO, it was found that die corner radius was the most significant process parameter influencing minimum punch force.