Automated Data Collection System Research Articles

NRP: A Multi-Source, Heterogeneous, Automatic Data Collection System for Infants in Neonatal Intensive Care Units

NRP: A Multi-Source, Heterogeneous, Automatic Data Collection System for Infants in Neonatal Intensive Care Units

Lactation performances in primiparous Holstein cows following short and normal gestation lengths.

Despite decades of research, little is known regarding physiologic temporal limits for initiation of lactation in pregnant non-lactating cattle the aim of this study was to compare the lactation performances in primiparous Holstein cows after a short gestation length (GL) or abortion to those after a normal GL. The data were collected using an automated data collection system. The 94 herds evaluated were located in Belgium, France, Italy, the Netherlands and Germany. Data from a wide range of physiological cow-life events including birth and calving events, reproduction events (insemination, pregnancy checks, and abortions), and milking events were collected. The GL was defined as the interval between the last insemination and the subsequent calving (or abortion) within a range of 150-297 days. Animals were categorized into one of five categories based on GL quantiles (C-I to C-V). Lactation curve parameters including the scale, ramp, and decay were estimated using the Milkbot model. Then, the derived 305-day milk yield (M305-d), peak yield, and time to peak were compared between different GL categories. Of 13,732 lactations, 15 (0.11%) were found with a GL shorter than 210 days (ranging from 158 to 208 days). The 305-day milk yield was significantly lower in the C-I (7,566 ± 186) and C-II groups (7,802 ± 136 kg), compared to the C-III (8,254 ± 116 kg), C-IV (8,148 ± 119 kg), and C-V (8,255 ± 117 kg) groups. The same trends were found for the scale and peak yield of the lactation; the lowest scale were found for the C-I (31.5 ± 0.73) and C-II (32.8 ± 0.53) groups, and the highest were found for the C-III (34.5 ± 0.46), C-IV (34.9 ± 0.45), and C-V (35.0 ± 0.45) groups. Peak yield increased significantly from C-I (27.8 ± 0.66 kg) and C-II group (28.8 ± 0.48 kg) to the C-III (30.2 ± 0.42 kg) and further to the C-IV (30.6 ± 0.40 kg) and C-V (30.6 ± 0.41 kg) groups. Moreover, primiparous cows in the C-II GL category showed a higher milk yield persistency (decay of 1.30E-4 ± 3.55E-5) compared to those belonging to the C-IV (decay of 1.38E-4 ± 2.51E-5) and C-V (decay of 1.38E-4 ± 2.58E-5) group. In conclusion, results showed that primiparous cows with a shorter GL produced significantly less 305-day milk and peak yields, had a higher lactation persistency, and showed a lower upward slope of the lactation curve compared to those with a normal GL.

Правовые проблемы использования виртуального пространства метавселенной в образовательном процессе вуза

Introduction. On the one hand, traditional forms, methods and means of teaching are used in the educational process of a university, on the other hand, education cannot be static, it must meet modern requirements and challenges. Artificial intelligence technologies are rapidly developing in society, and their introduction into education will provide students with a modern, high-quality education. The Russian higher education system needs digital transformation; it requires the introduction of the latest learning technologies into the educational process to ensure competitiveness in the global education market. Theoretical analysis. To qualitatively improve the learning process at a university and train highly qualified personnel, it is necessary to use the latest teaching technologies, such as augmented and virtual reality. Virtual reality technologies in the form of virtual campuses and laboratories make it possible to organize the learning process in a highly motivated way for students, to achieve the effect of complete immersion in the processes and phenomena being studied. Currently, there is positive experience in using virtual space as an interactive teaching method when modeling practice-oriented cases. Еmpirical analysis. It was revealed that the problems of using the virtual space of the metaverse in the process of university education lie in the legal field. Legal problems of the metaverse include problems of cybersecurity, confidentiality of personal data, problems with automated data collection systems Big Data and others. One of the main problems is that of information security, the solution to which is the development of domestic software, local standards in the field of using virtual space technologies as part of the educational process of the university. Results. The author has proven the need to improve the current legislation of higher education in accordance with the specifics of the use of the virtual space of the metaverse by the educational process of the university; to develop local regulatory legal acts regulating the specifics of using metaverses when training a specific specialist.

Automatic OEE Data Collection and Alert System for Food Industry

The constant demand for food and beverages to sustain human life drives fierce competition among manufacturers, focusing on product excellence in terms of timeliness, quality, and pricing. The key to competitiveness depends in optimizing manufacturing processes by efficiently utilizing company resources. To ensure the overall optimization and reliable flow of manufacturing processes, a systematic evaluation process must be used, Overall Equipment Efficiency (OEE) stands out as a prominent performance measurement metric in manufacturing process efficiency. OEE serves as a valuable diagnostic tool, exposing areas for improvement and losses transparently. Accurate OEE measurement necessitates the implementation of an automated data collection system with minimum human dependencies, human intervention, and conducting on-the-fly calculations to informed the stakeholder/user. Data quality and accuracy in OEE measurement is very critical. Low quality and accuracy data could lead to false decision. OEE categorizes losses into six groups loss to pinpoint significant factors for potential improvement. Once OEE could be maintain at high level with high data accuracy and right improvement point, an optimum manufacturing process, and cost effective in manufacturing expenses will be achieve. Base on the result comparison for OEE result before and after the system implementation, positive improvement in OEE could reach 8.06%. This scenario be adopted by other company, and could become a model for 1st phase journey in company digital transformation.

Speed Behavior of Heterogeneous Traffic on Two-Lane Rural Roads in Malaysia

Highway geometry is a significant factor that affects the efficiency and safety of highway systems. The present study aims to investigate the speed behavior of various vehicle classes on the horizontal alignment of two-lane rural roads. An automatic data collection system based on a pressure sensor was employed to collect the speed of each individual vehicle, vehicle type, and headway at seven sites in each travel direction. The 85th percentile speed under free-flow conditions was used to observe the relationship between the operating speeds of various vehicle classes at consecutive curve points and the effect of the travel direction, time of day, and curve radius on the operating speed of the vehicle. A one-way ANOVA was employed to evaluate whether there is a significant difference in speed on horizontal curves. Then, a Tukey post hoc test was used to assess the significance of the difference in speed across four classes of vehicles. The results revealed that the horizontal curve affects the operating speed for all vehicle classes. A curve radius of less than 500 m, the travel direction, and the time of day are significant variables that affect the speed of all vehicle classes. The findings from this study can provide insight to transportation engineers for safer road design of horizontal curves and to assess traffic safety based on actual speed behavior.

Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems

This research explores the application of archived data from Automated Data Collection Systems (ADCS) to transport planning with a focus on bus passenger travel behavior, including Origin-Destination (OD) inference, using London as a case study. It demonstrates the feasibility and ease of applying trip-chaining to infer bus passenger OD from smart card transactions and Automatic Vehicle Location (AVL) data and is the first known attempt to validate the results by comparing them with manual passenger survey data. With the inferred OD matrices, the variations of weekday and weekend bus route OD patterns are examined for planning purposes. Moreover, based on the inferred OD matrices and the AVL data, alighting times for bus passengers also can be estimated. Bus journey stages, therefore, can easily be linked. By comparing the interchange time and the connecting bus route’s headway, it provides a way to evaluate bus connections.

Analysis of Roadway Grade Estimations From Global Positioning Systems and Barometer Measurements

Roadway grade is used as an input variable for several highway analysis tasks, including evaluating curve margin-of-safety, applying safety prediction models, assessing the adequacy of sight distance, and maintaining state roadlog databases. However, collecting accurate grade data is often expensive owing to the need to obtain field measurements or review as-built plan sets. Agencies would benefit from the development of a method to compute roadway grade from an automated data collection system. This paper documents a comparison of roadway grade estimations computed from global positioning system (GPS) and barometric altimeter data streams obtained during test drives on highway segments of interest. To calibrate the estimation method, the authors obtained ground-truth grade measurements collected from the field and then modeled those measures as responses in a time series model with the two data stream types as explanatory variables. The authors found that for several applications, the elevation data obtained from GPS were adequate to obtain reasonable estimates, but such estimations could be improved with a supplemental data stream from a barometer.

Implementasi Scrapping Data Untuk Sentiment Analysis Pengguna Dompet Digital dengan Menggunakan Algoritma Machine Learning

Analysis of social media is an effective tool for understanding people's attitudes, preferences and opinions. For a company, social media analysis can help companies to make decisions about needs, attitudes, opinions or trends about potential customers or potential customers. Hootsuite's Wearesocial research results show that out of 150 million Indonesians, 88% are actively using social media. As a result, a lot of information that was once difficult to obtain is now very easy to obtain. With so much data, data collection can be made easier by using an automated data collection system. The use of Machine Learning Algorithm as a method is used to find out how far the level of search from social media data collected is to get accurate results related to changes in behavior, lifestyle, and community activities in dealing with this pandemic. Scraping data is done using Twitter social media data with various hashtags that support opinion mining contained in the community. The results obtained are the analysis of gopay users having a positive sentiment level of 79,6%, negative sentiment of 20,4% from total of 250 data. Meanwhile, Linkaja users have a positive sentiment level of 62,1%, 37,9% negative comments from 250 data comments taken. The test results also carried out a process of calculating the level of accuracy with recall and precision, namely 87% for Gopay and 89% for LinkAja.

Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area.

Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system.

BackgroundThe American Society for Clinical Oncology recently launched the minimal common oncology data elements project to facilitate cancer data interoperability. However, clinical data are often unrecorded in an organized way, and converting them into a structured format can be time-consuming. Clinical Data Warehouse (CDW) is a database that consolidates data from different clinical sources. However, the clinical data extracted from this database include not only structured data but also natural language generated during clinical practice. Therefore, applying these data to a clinical study is challenging because they are unstructured, and unformatted to allow essential content to be found. This study determined how best to organize a huge amount of clinical data to evaluate the upper aerodigestive tract cancers’ clinical features and outcomes, including cancer of the head and neck, esophagus, lung, thymus, and mesothelioma.MethodsThe Real-time autOmatically updated data warehOuse in healThcare (ROOT) uses six main regions to describe the journey of cancer patients. This study, developed an algorithm optimized for each disease category using natural language processing of unstructured data and data capture of structured data. Data from patients diagnosed at the Samsung Medical Center from 2008–2020 were used.ResultsComprehensive clinical data for 67,617 patients across six tumor types: 28,954 with non-small-cell lung cancer, 2,540 with small-cell lung cancer, 30,035 with head and neck cancer, 4,950 with esophageal cancer, 966 with thymic cancer, and 172 with mesothelioma were collected. Additionally, the results of a longitudinal molecular study, including epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) tests, and next-generation sequencing (NGS), were included. Scattered information was integrated and automatically built up to match the cohort, allowing users to capture the most updated test results and treatment outcomes.ConclusionsThis landmark study documented the successful construction of a real-time updating system for medical big data, based on the CDW program.

CORR Insights®: Is Self-reported Return to Duty an Adequate Indicator of Return to Sport and/or Return to Function in Military Patients?

CORR Insights®: Is Self-reported Return to Duty an Adequate Indicator of Return to Sport and/or Return to Function in Military Patients?

Combined evidence model to enhance pavement condition prediction from highly uncertain sensor data

Despite the significant advancements in data collection technology and the increased use of automated data collection systems, pavement condition assessment mainly relies on ground-based monitoring. The increasing availability of low-cost sensors and remote sensing technologies provide opportunities to explore more efficient approaches. These data, however, are often characterized by high uncertainties. This study explores the application of Evidence Theory to incorporate highly uncertain sensor data. The capabilities of the proposed approach are assessed in a case study comparing the estimated pavement condition derived from the proposed Evidence Theory approach and the traditional approaches of Markov Deterioration Process and the Bayesian approach proposed in the Partially Observable Markov Decision Process. This paper also explores how to incorporate aspects such as reliability and conflict in the combination of multiple sensors data and discusses the impacts on the results of various parameters and methods in the application of Evidence Theory. The results show that the proposed Evidence Theory approach produces errors that are 44% lower than any of the other methods. The sensitivity analysis exploring different treatments of conflict and the introduction of reliability measures show the importance of an adequate calibration of Evidence Theory parameters in real life applications.

Guidelines for de novo phasing using multiple small-wedge data collection.

Intense micro-focus X-ray beamlines available at synchrotron facilities have achieved high-quality data collection even from the microcrystals of membrane proteins. The automatic data collection system developed at SPring-8, named ZOO, has contributed to many structure determinations of membrane proteins using small-wedge synchrotron crystallography (SWSX) datasets. The `small-wedge' (5-20°) datasets are collected from multiple crystals and then merged to obtain the final structure factors. To our knowledge, no systematic investigation on the dose dependence of data accuracy has so far been reported for SWSX, which is between `serial crystallography' and `rotation crystallography'. Thus, herein, we investigated the optimal dose conditions for experimental phasing with SWSX. Phase determination using anomalous scattering signals was found to be more difficult at higher doses. Furthermore, merging more homogeneous datasets grouped by hierarchical clustering with controlled doses mildly reduced the negative factors in data collection, such as `lack of signal' and `radiation damage'. In turn, as more datasets were merged, more probable phases could be obtained across a wider range of doses. Therefore, our findings show that it is essential to choose a lower dose than 10 MGy for de novo structure determination by SWSX. In particular, data collection using a dose of 5 MGy proved to be optimal in balancing the amount of signal available while reducing the amount of damage as much as possible.

Penerapan Sistem Informasi Pendataan Penerima Dana Program Keluarga Harapan pada Gampong Beurawe Kecamatan Kuta Alam berbasis Web GIS

Administratively, Gampong Beurawe is one of the villages in Kuta Alam District, Banda Aceh City. The Department of Social Affairs is in charge of collecting data and providing assistance to people in need of social welfare services. Data collection is done manually in the form of archives and files, this causes inefficiencies in space, security, time and costs. The Social Service has difficulties in conveying information on the Family Hope Program (PKH) which includes data on poor households, education data and health data, to the wider community and related parties, causing data information to be unclear. So we need an automatic data collection system to help collect data on the hopeful family program in Gampong Beurawe, Kuta Alam District. In the geographic information system itself, there are two important elements, namely Geographical Data which is used as a reference for attribute data. The design of the map that will be displayed in this application uses map info and google maps. This PKH acceptance mapping information system in Beurawe Village, Kuta Alam District, Banda Aceh City is to assist the Social Service, especially the social assistance and security department in the storage and delivery of information data (PKH) so that it can provide clear information about the program. Data information system in the form of maps, graphs and tables.

Development of Bengkulu Traditional Cake Marketing through E-Commerce Media

Ummi shop is a company engaged in the sale of traditional cakes. The sales system used today is a conventional system, where the buyer must come directly to the Garsel Shop to see the products offered. With this system, it will take the consumer's time so that it is difficult to obtain information about the specifications of the product being offered. E-Commerce or in Indonesian, namely electronic commerce is the distribution, purchase, sale, marketing of goods and services through electronic systems such as the internet or television, www, or other computer networks. E-commerce can involve electronic funds transfer, electronic data exchange, automated inventory management systems, and automated data collection systems. At this time, Ummi Store has not used the e-commerce facility so that it is difficult to offer products to consumers who are outside the city. In addition, it can also have an impact that affects the product sales process, namely the ignorance of consumers who are outside the city of the existence of Ummi Shops. The process of selling the Ummi Store will only be carried out if there are consumers who come to the store, while many consumers who are outside the city cannot come or do not know the existence of the Ummi Store. To build this e-commerce system, the tools used to describe the system model are in the form of a data flow diagram (FlowMap), context diagrams, and Data Flow Diagrams (DFD), as well as in database design using a data dictionary and Entity Relationship Diagram (ERD). With the existence of e-commerce is expected to make it easier for consumers to obtain information about product specifications offered by Ummi Stores. Make it easy for Ummi shops to offer or sell their products to consumers who are outside the city. Consumers who are outside the city can find out the existence of Ummi stores by using this e-commerce service and can order without having to come to the store. Make it easy for consumers to process payments because there are several options in the payment process. With a good product data processing or management system that is equipped with security, it will support the performance of the system.

Body Temperature Detection and Data Collection during COVID-19

At present, COVID-19 is raging in many countries, which poses a great threat to people's life, health, social and economic development. Body temperature screening is very helpful for early detection of potential infected persons and blocking the spread of the epidemic. The paper mainly introduces the body temperature detection and data acquisition system, and designs a system with the function of non-contact temperature measurement and automatic data collection system. This system is suitable for the company or school where the flow of people is large and the temperature needs to be measured quickly, and upload information in real time.

Pajak E-Commerce, Pemecahan dan Solusinya

Electronic or e-trade trading (English: Electronic Commerce, also e-commerce) is a spread, purchase, sales, marketing goods and services through an electronic system such as internet or television, www, or other computer networks. Ecommerce can involve electronic fund transfers, electronic data exchange, auto inventory management systems, and automatic data collection systems. The Information Technology industry sees this e-commerce activity as the application and application of e-business (e-business) related to commercial transactions, such as: electronic fund transfer, SCM (E-Marketing), e-marketing), online marketing, online transaction processing (online transaction processing), electronic data interchange / edi), etc.E-commerce is part of e-business, where e-business coverage is wider, not just a commerce but includes also co-entrance business partners, customer service, job vacancies etc. Seeing very fast developments of the e-commerce growth in the world including in Indonesia, it is necessary an effective strategy for the tax authority in the reacting. One of the things that need to be concern is this very rapid growth should be maintained so that there is no distortion as a result of taxation policy. During this time, the tax aspect in e-commerce has been the highlight of tax authorities in the world, especially whether there should be a new tax impression on this transaction and also how to align the existing tax rules with e-commerce development.Transactions through digital media or e-commerce in Indonesia need rebuilding from the side of the legislation (Cyberlaw) so that in the future do not miss the dispute in it runs. This happens because the difficulty of tracking the transactions used through the e-commerece is either B-to-B (business to business) or B-to-C (business to consumers). For example the relationship between the supplier with the factory, how the apparatus is to oversee the existence of transactions or not between the two through the e-commerce the solution of solving the problem is the government must take steps in solving the unstable internet infrastructure issues and frequent reliable transportation, the licensing procedure of customs to dedicate to the duty procedure from dawn to the negative trade for the other value to increase the efficacy to make the rules for the epimal services that are still under the regulations of the same bank as well as the public of the transactions of online transactions, plan and conduct the introduction of certification systems, a number of large players should be able to open the way of foreign investment, including doing some major plans for foreign investors, of course with clear regulations and the government must provide ease of rules for small and medium business to gain access to investors, and giving taxes to the company in the new company.

Modified soil surface temperature forecasting method for automated data collection systems

There are presents a developed method for predicting temperature of soil surface for next 24 hours with using data of air temperatures at height of 2 meters. The proposed method is a modification of the Mikhalevsky method, recognized in agrometeorology when predicting radiation freezes. Only dry-bulb thermometer temperature data and relative air humidity are available for application in automated systems. We have therefore obtained formulas for the calculation of dry thermometer temperature. A separate study was to test the simplified formula for calculating saturated vapour pressure above water in the temperature range 0-60 °C. The study showed the complete correspondence of the saturated vapour pressure above water value calculated by different methods in the range 0-60°C. The relative error in the given temperature range is not exceed 0.35%. Thanks to obtained equations, the value of dry-bulb thermometer temperature for the first decade of May 2020 was calculated. The temperature and humidity of the air were measured by the automatic station every hour, and the value dry-bulb thermometer temperature were obtained from psychrometric tables. The absolute error in the calculation of value dry-bulb thermometer according to the proposed formulas do not exceed 1°C in the temperature range 0-16°C.

Real-time autOmatically updated data warehOuse in healThcare (ROOT: An Innovative and Automated Data Collection System) (Preprint)

Real-time autOmatically updated data warehOuse in healThcare (ROOT: An Innovative and Automated Data Collection System) (Preprint)

Creation of a Phenol/Water Phase Diagram Using a Low-Cost Automated System and Remote Transmission

A remote experiment was developed and carried out to create a phenol/water phase diagram using a low-cost automated data collection system and an online video broadcast. Both the data and video from the experiment were transmitted in real-time. The remote lab experiment was evaluated using qualitative and quantitative questionnaires filled out by the students. The phenol/water phase diagram obtained is entirely consistent, with precision and accuracy compatible with what is expected to be achieved in conventional lab classes. The activity evaluation showed excellent acceptance by the students by doing this lab activity remotely.