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Background: Pharmacy intravenous admixture service (PIVAS) center has emerged as an important department of hospitals as it can improve occupational protection and ensure the safety and effectiveness of intravenous infusions. However, there is little research on the standardized capability and risk evaluation of PIVAS by using modern information technology. In this research, we established Regional Pharmacy Intravenous Admixture Services Data Reporting and Analysis Platform (RPDRAP) to improve quality control ability for PIVAS management. RPDRAP including evaluation matrix for quality control monitoring. The construction of platform is based on guidelines for the Construction and Management of PIVAS and management specifications of PIVAS in China. Methods: RPDRAP was established in 2018. This platform comprises a data collection system and a data analysis system. The data collection system consists of 67 data items. Data collection relied on online platforms through data acquisition module. The collected data were analyzed using a model with 20 indicators within the data analysis system. Fifteen hospitals, public comprehensive healthcare facilities with more than 500 beds, participated in the platform's application evaluation. Results: The study revealed significant differences in PIVAS total score, supervisors, and workload between 2020 and 2022. The platform's application results demonstrated improvements in personnel management, work efficiency, and infection control within these PIVAS. Although statistical significance was observed in only 8 out of the 25 items, most of the scores showed an increase, with a small portion remaining unchanged and no decline in scores. Conclusions: This platform can be recommended for PIVAS homogeneous and regional efficient management. The use of this platform not only improves the quality control ability of PIVAS but also enables the management department to quickly grasp the current situation and characteristics of each PIVAS through standardized data collection and analysis. [ABSTRACT FROM AUTHOR]

The data that everyday consumers produce is becoming more and more important to the economy. Yet, as this data imbues tech corporations with tremendous wealth and power, we, the data producers, have no say as to how our data is collected or how it is used. The reign of data analytics to pursue profit above all else has led to a conflagration of data harms perpetuated against already marginalized groups. What is needed in this moment is a tool that equalizes the bargaining power between platforms and users, to give consumers meaningful control over the data they produce. In the early 20th century, labor organizers called for industrial democracy: the ability for workers to have substantial say over the conditions of their labor. For today’s datafied information economy, this Note instead calls for the need for informational democracy: the ability of consumers, as data producers, to exert meaningful control over the data that their lives engender. This Note advocates for data unions as one such tool to achieve informational democracy. It conceptualizes data unions as democratically elected organizations that aggregate data to create collective bargaining units to negotiate with platforms as to allowed uses for data. First, the Note gives an overview of how today’s economy creates both value and harm out of data processing. Then, it argues that due to the specific nature of this value and harm creation, data unions are uniquely situated regulatory tools that can enact meaningful consumer control. [ABSTRACT FROM AUTHOR]

Recording a stellar occultation is one powerful method that gives direct information about the physical properties of the occulting Solar system object. In order to obtain reliable and accurate results, simultaneous observations from different locations across-track of the projected path are of great importance. However, organizing all the observing stations, aggregating, and analysing the data is time-consuming and not that easy. We have developed a web portal named Occultation Portal (OP) to manage all those occultation observation campaigns from a central server. With this portal, the instrumental and observational information of all observers participating in a stellar occultation campaign and the concerned data are archived systematically in a standard format. The researchers can then visualize the archived data on an event basis. The investigators can also extract the light curve for each data set with the added reduction pipeline to the portal base. This paper describes in detail the portal structure and the developed features. [ABSTRACT FROM AUTHOR]

With the continuous deepening and development of tobacco manufacturing informatization construction, tobacco manufacturing enterprises have the characteristics of strong production capacity, high degree of production automation, and advanced enterprise-level management information system. In order to cooperate with the market and brand strategy of the tobacco industry, the State Tobacco Monopoly Administration has carried out serious strategic thinking on informatization and customized the overall plan and strategic goals. In recent years, China's informatization construction has made great progress, and both industries and enterprises have felt various benefits from the application of information technology. In the deployment and development of mobile services, it is often local and municipal mobile companies that, in order to meet the different needs of the market, immediately carry out corresponding services, resulting in one service for each municipal subsidiary in the same province or one platform for a class of services. Situation is good for quickly meeting user needs at that time. The exchange and communication of enterprise personnel information are becoming more and more frequent, and the collection of enterprise information through mobile has become a new way of enterprise information collection. The work orders, process parameters, documents, notifications, and instructions generated by the planning layer are sent to the production control layer to respond, guide, and trigger the production site events. The ultimate goal of mobile data collection is to gather the collected data into the enterprise production data center, which plays an important role in mobile devices, mobile performance, mobile planning, mobile balancing, mobile prediction, and so on. Based on the unified mobile application data collection method, this paper analyzed and analyzed the design of application architecture in the tobacco enterprise platform and further described the business functions, security performance, and system characteristics of the platform. [ABSTRACT FROM AUTHOR]

Although multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth’s surface, nonidealities and estimation imperfections between records and investigation models can limit its actual information extraction ability. In this article, we aim at predicting the maximum information extraction that can be reached when analyzing a given data set. By means of an asymptotic information theory-based approach, we investigate the reliability and accuracy that can be achieved under optimal conditions for multimodal analysis as a function of data statistics and parameters that characterize the multimodal scenario to be addressed. Our approach leads to the definition of two indices that can be easily computed before the actual processing takes place. Moreover, we report in this article how they can be used for operational use in terms of image selection in order to maximize the robustness of the multimodal analysis, as well as to properly design data collection campaigns for understanding and quantifying physical phenomena. Experimental results show the consistency of our approach. [ABSTRACT FROM AUTHOR]

Big data has the characteristics of rapid data flow, massive data scale, dynamic data system, and various data types, and it has become increasingly apparent in improving innovation and entrepreneurship data analysis, trend prediction, and decision support. In this paper, the authors analyze the economic function data and entrepreneurship analysis based on machine learning. The support vector pair is very sensitive to the choice of parameters, and the parameters obtained using the genetic algorithm will greatly improve the accuracy of the model prediction. When using the genetic algorithm to find parameters, the cv method is used for verification. By applying big data technologies and platforms, it can provide strong data support to establish entrepreneurship education; integrate and integrate various types of innovation and entrepreneurship data, improve the quality of data collection.At the same time, through big data mining and analysis, accurately determine market demand hotspots and innovation and entrepreneurship trends, and promote scientific planning of innovation and entrepreneurship strategies. The research results show that this research model can be applied to actual projects in the future, and help investors better understand the changes of market economy. [ABSTRACT FROM AUTHOR]

Background: Translational data analytics aims to apply data analytics principles and techniques to bring about broader societal or human impact. Translational data analytics for environmental health is an emerging discipline and the objective of this study is to describe a real-world example of this emerging discipline.Methods: We implemented a citizen-science project at a local high school. Multiple cohorts of citizen scientists, who were students, fabricated and deployed low-cost air quality sensors. A cloud-computing solution provided real-time air quality data for risk screening purposes, data analytics and curricular activities.Results: The citizen-science project engaged with 14 high school students over a four-year period that is continuing to this day. The project led to the development of a website that displayed sensor-based measurements in local neighborhoods and a GitHub-like repository for open source code and instructions. Preliminary results showed a reasonable comparison between sensor-based and EPA land-based federal reference monitor data for CO and NOx.Conclusions: Initial sensor-based data collection efforts showed reasonable agreement with land-based federal reference monitors but more work needs to be done to validate these results. Lessons learned were: 1) the need for sustained funding because citizen science-based project timelines are a function of community needs/capacity and building interdisciplinary rapport in academic settings and 2) the need for a dedicated staff to manage academic-community relationships. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to identify details of technological, organizational and people (TOP) factors affecting maintenance technicians' use of computerized maintenance management systems (CMMS) in manual collection of asset data. Design/methodology/approach: In addition to TOP factor details, results from six case studies in Finland, India and the Caribbean are presented. Interviews and observations clarify the role of TOP factors in CMMS use in industrial maintenance. Findings: In total, 17 detailed TOP factors are identified and criteria for analyzing CMMS contexts with them are defined. Analyzing the cases with these factors reveals that technicians who collect good quality data have received good training and instructions for the CMMS, are competent, and understand how manually collected data benefits them in their own work. However, even these sites struggle with the usability of the CMMS. Research limitations/implications: The 17 TOP factors and the criteria for CMMS evaluation extend understanding on context and usability in manual data collection. Case study method does not imply the relative importance of the TOP factors, which calls for future research using quantitative methods. Practical implications: Management can use the criteria to analyze the context of manual data collection for improvements, e.g., in CMMS usability. Originality/value: Insights from industrial environments and a new way of studying contextual factors of CMMS use are presented. The results extend a data quality research framework with details to manual data collection and define the TOP factors in CMMS context. [ABSTRACT FROM AUTHOR]

We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. This article has been retracted by Hindawi following an investigation undertaken by the publisher [[1]]. [Extracted from the article]

This research note discusses opportunities and challenges of using online survey engines for social network analysis (SNA) data collection and assessing sample size and representativeness. The discussion is based on a case study of SNA data collection for a pilot research on health-relevant policy networks in Canada. Our approach demonstrates how to use generic online survey tools in long-term snowball sampling campaigns and how to compute population size estimates through capture–mark–recapture formulas, thus solving a significant methodological challenge of collecting snowball data for unbounded networks. [ABSTRACT FROM AUTHOR]

This article addresses methodological issues related to the consequences of researchers' range of insider identities that emerge over the course of completing subsequent stages of qualitative migration research projects. Taking on a temporal approach to the insider status evolving over the course of field entry, data collection, data analysis, and dissemination, this article engages with nuanced insider positionalities. These range from apparent, to trespassing, to distanced, and to ambassadorial insiderness. Exploring a specific case of Polish mobility, this article assumes a methodological focus and argues that being "on the inside" of the migration research field may go beyond gender, ethnicity, and social status when it is linked to a project's life cycle. [ABSTRACT FROM AUTHOR]

As a direct consequence of production systems' digitalization, high‐frequency and high‐dimensional data has become more easily available. In terms of data analysis, latent structures‐based methods are often employed when analyzing multivariate and complex data. However, these methods are designed for supervised learning problems when sufficient labeled data are available. Particularly for fast production rates, quality characteristics data tend to be scarcer than available process data generated through multiple sensors and automated data collection schemes. One way to overcome the problem of scarce outputs is to employ semi‐supervised learning methods, which use both labeled and unlabeled data. It has been shown that it is advantageous to use a semi‐supervised approach in case of labeled data and unlabeled data coming from the same distribution. In real applications, there is a chance that unlabeled data contain outliers or even a drift in the process, which will affect the performance of the semi‐supervised methods. The research question addressed in this work is how to detect outliers in the unlabeled data set using the scarce labeled data set. An iterative strategy is proposed using a combined Hotelling's T2 and Q statistics and applied using a semi‐supervised principal component regression (SS‐PCR) approach on both simulated and real data sets. [ABSTRACT FROM AUTHOR]

The behavior of the drilling force and torque when machining Pinus Radiata wood was studied, utilizing two different drills: one for wood and another for metal, highlighting as variables of great interest, the moisture content of the wood specimens and the granular orientation of the Pinus Radiata. Regarding data collection, a dynamic platform was used that has two piezoelectric force and torque sensors, respectively. For the analysis of the data obtained by the sensors, the LabVIEW software was used, where the variables involved in the drilling processes were modeled. The specific cutting pressure of the tests for each content of the safety and orientation of the fibers was estimated using the different types of cutting tools. Among the results, the behavior of the different fibers is specifically observed when machining them with the different drills, where the tangential orientation fiber has a greater resistance to the cut, which results in an increase of the specific cutting pressure in comparison with the other orientations of the fiber. [ABSTRACT FROM AUTHOR]

Mineral exploration and mining are data-driven industries. Here, we emphasize the role of “ data upcycling ” as a significant contributor to modern exploration. Upcycling can take three basic forms, all aimed at enhancing the veracity of data: (1) re-collection of data, (2) collection of complimentary data, and (3) assessment and innovative portrayal/integration of data. Upcycling of previously collected (or legacy) data allows information to be integrated into modern datasets. This paper offers perspectives and examples on how data upcycling benefits mineral exploration, with short case studies from Western Australia highlighting the role of government, service providers and resource explorers. [ABSTRACT FROM AUTHOR]

Versatile controllers for accurate, fast, and real-time synchronized acquisition of large-scale data are useful in many areas of science, engineering, and technology. Here, we describe the development of a controller software based on a technique called queued state machine for controlling the data acquisition (DAQ) hardware, continuously acquiring a large amount of data synchronized across a large number of channels (>400) at a fast rate (up to 20 kHz/channel) in real time, and interfacing with applications for real-time data analysis and display of electrophysiological data. This DAQ controller was developed specifically for a 384-channel pediatric whole-head magnetoencephalography (MEG) system, but its architecture is useful for wide applications. This controller running in a LabVIEW environment interfaces with microprocessors in the MEG sensor electronics to control their real-time operation. It also interfaces with a real-time MEG analysis software via transmission control protocol/internet protocol, to control the synchronous acquisition and transfer of the data in real time from >400 channels to acquisition and analysis workstations. The successful implementation of this controller for an MEG system with a large number of channels demonstrates the feasibility of employing the present architecture in several other applications. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to empirically analyse the influence ofcolour distanceandfont sizeon map readability. We utilized eye-tracking to complement the classical usability metrics; thus, we studied performance metrics such aseffectiveness(i.e. success, accuracy),efficiency(i.e. time to answer, task completion time), and selected eye tracking metricsfixation frequency, fixation durationandscanpath speedas well as conducting anarea-of-interest (AOI) analysisto understand the performance and strategy issues that may be influenced by colour distance and font size during map reading. The user experiment was carried out in a controlled laboratory where participants were asked to conduct a visual search task and mark the correct answer with a mouse click on a static map on a computer screen. Collected data was analysed through descriptive and inferential statistics. Task completion times for the five tested colour distances show that as the colour distances grow larger, the relative differences in task completion times become statistically significant; empirically confirming our intuition that larger colour distances are better for map readability. The comparison of the scanpath speeds for the tested font sizes suggests that the medium font size leads to a more efficient search. [ABSTRACT FROM PUBLISHER]

3D desktop-based virtual environments provide a means for displaying quantitative data in context. Data that are inherently spatial in three dimensions may benefit from visual exploration and analysis in relation to the environment in which they were collected and to which they relate. We empirically evaluate how effectively and efficiently such data can be visually analyzed in relation to location and landform in 3D versus 2D visualizations. In two experiments, participants performed visual analysis tasks in 2D and 3D visualizations and reported insights and their confidence in them. The results showed only small differences between the 2D and 3D visualizations in the performance measures that we evaluated: task completion time, confidence, complexity, and insight plausibility. However, we found differences for different datasets and settings suggesting that 3D visualizations or 2D representations, respectively, may be more or less useful for particular datasets and contexts. [ABSTRACT FROM PUBLISHER]

For grid-connected photovoltaic (PV) system, monitoring is considered as a crucial aspect for observing the stability and performance of the system. The simplest method is to have the data collected and transmitted across data cables. Due to the cost and technical limitations of the data cable, the monitoring station needs to be located reasonably close to the monitored plant. Apart being inconvenient, the use of data cable often adds capital and maintenance cost to the system. In this research project, a Zigbee-based wireless monitoring system is developed for online monitoring of a grid-connected photovoltaic system. Parameters like temperature, irradiation, PV power output and grid inverter power output are monitored. The implementation process, including design and development of the hardware and software, is explained in detail. A user-friendly web-application is also developed, such that the monitored data is easily accessible via internet. To validate the performance, the system has been implemented on 1.25 kW p grid-connected photovoltaic system. [ABSTRACT FROM AUTHOR]

The article describes case studies on packet capture and analysis using a support tool for batch input and analysis of a large volume of captured data. The features of the tool are described, such as capture-data analysis, time-offset output and log and graph display functions. Results of the case studies are discussed, including the reason for complicated Internet protocol (IP) faults.

Data collected for recommendation purposes might be distributed among various e-commerce sites, which can collaboratively provide more accurate predictions. However, because of privacy concerns, they might not want to work together. If privacy measures are provided, they may decide to become involved in prediction generation processes. We propose privacy-preserving schemes eliminating e-commerce sites' privacy concerns for providing predictions on distributed data. We investigate how to achieve naïve Bayesian classifier-based recommendations when data are distributed horizontally or vertically among multiple parties, even competing ones, without greatly violating their confidentiality. We analyze our schemes in terms of privacy and additional costs and show that they do not deeply violate online vendors' secrecy and they cause insignificant overhead costs. We also perform experiments on real data, evaluate our outcomes, and provide suggestions. Our empirical results show that our schemes produce more accurate predictions. [ABSTRACT FROM AUTHOR]

The article discusses the features of computered-controlled uniaxial tensile test machine and its supporting equipment to study the inelastic behaviors of rubber-like materials. It states that the major features of uniaxial machines include the simplicity of its mechanical system, its cost, and its flexible software which was developed by utilizing LabView data acquisition and control software. Inelastic behaviors of rubber-like materials and details on processing the data are also cited.

Understanding manure nitrate-nitrogen (NO3-N) movement in soil is essential to determine manure placement for efficient use of the NO3-N in the manure. A two-dimensional model for simulating NO3-N movement in soils following manure injection was developed. The transport domain for modelling NO3-N movement was defined by two hypothetical planes midway between centrelines of two consecutive manure bands in the vertical plane. The Hydrus-2D software package was used in computation of Richards flow and convection-dispersion (CDE) solute transport equations. The model was calibrated and validated with previous field data of injecting manure at a tool spacing of 0.9 m and 3.6 litres of manure per meter of manure band. Soil hydraulic and transport parameters were inversely optimised using one set of the field data. The model was validated against another set of the field data. The model results indicated that little manure NO3-N applied to soil moved laterally beyond 0.20 m from manure band. When compared with the field measurements, the model predicted well the trend of soil NO3-N distribution in the lateral direction between manure bands; however, underestimations and over-estimations were noticed in most cases. The relative errors between the predictions and field measurements ranged from 13.4 to 75.8%. [ABSTRACT FROM AUTHOR]

• This technique supports engineers by providing automated capabilities to make decisions in the field. • Improving consistency and accelerating decisions, an imperative after a major disruptive event. • Automated post-event data collection to facilitate the data analysis process and reduce bias in the results. In post-event reconnaissance missions, engineers and researchers collect perishable information about damaged buildings in the affected geographical region to learn from the consequences of the event. A typical post-event reconnaissance mission is conducted by first doing a preliminary survey, followed by a detailed survey. The objective of the preliminary survey is to develop an understanding of the overall situation in the field, and use that information to plan the detailed survey. The preliminary survey is typically conducted by driving slowly along a pre-determined route, observing the damage, and noting where further detailed data should be collected. This involves several manual, time-consuming steps that can be accelerated by exploiting recent advances in computer vision and artificial intelligence. The objective of this work is to develop and validate an automated technique to support post-event reconnaissance teams in the rapid collection of reliable and sufficiently comprehensive data, for planning the detailed survey. The focus here is on residential buildings. The technique incorporates several methods designed to automate the process of categorizing buildings based on their key physical attributes, and rapidly assessing their post-event structural condition. It is divided into pre-event and post-event streams, each intending to first extract all possible information about the target buildings using both pre-event and post-event images. Algorithms based on convolutional neural networks (CNNs) are implemented for scene (image) classification. A probabilistic approach is developed to fuse the results obtained from analyzing several images to yield a robust decision regarding the attributes and condition of a target building. We validate the technique using post-event images captured during reconnaissance missions that took place after hurricanes Harvey and Irma. The validation data were collected by a structural wind and coastal engineering reconnaissance team, the National Science Foundation (NSF) funded Structural Extreme Events Reconnaissance (StEER) Network. [ABSTRACT FROM AUTHOR]