Database Maintenance Research Articles

Evaluate the accuracy of vehicle type on road surface condition survey using roadroid application

Poor road surface conditions influence the continuity of sustainable transportation such as congestion, the assessment stages road surface conditions are by evaluating the existing road conditions. One of the pavement performance parameters that can be determined objectively is International Roughness Index (IRI). Nowadays, the cheap and reliable technology used to measure IRI value is Roadroid which is an Android application developed from a company in Sweden by using a built-in vibration sensor on a smart phone which is placed on the vehicle and the old manual method is called Surface Distress Index (SDI) which is based on visual observations using the RCS method. With the variation of flexible and rigid pavement consist of good and bad road conditions and using motorcycle, sedan and sport utility vehicle, it is found that the correlation between the IRI value using Roadroid and SDI value using visual observation shows high correlation where the highest r2 values by using Sport Utility Vehicle (SUV) compare to small car and motorcycle. It concludes that Roadroid can be used as database for road maintenance planning purposes.

The Polito Surface Wave flat-file Database (PSWD): statistical properties of test results and some inter-method comparisons

The compilation and maintenance of experimental databases are of crucial importance in all research fields, allowing for researchers to develop and test new methodologies. In this work, we present a flat-file database of experimental dispersion curves and shear wave velocity profiles, mainly from active surface wave testing, but including also data from passive surface wave testing and invasive methods. The Polito Surface Wave flat-file Database (PSWD) is a gathering of experimental measurements collected within the past 25 years at different Italian sites. Discussion on the database content is reported in this paper to evaluate some statistical properties of surface wave test results. Comparisons with other methods for shear wave velocity measurements are also considered. The main novelty of this work is the homogeneity of the PSWD in terms of processing and interpretation methods. A common processing strategy and a new inversion approach were applied to all the data in the PSWD to guarantee consistency. The PSWD can be useful for further correlation studies and is made available as a reference benchmark for the validation and verification of novel interpretation procedures by other researchers.

GIS for USU campus transportation asset management

Transportation infrastructures consume a large number of resources, and managing infrastructure assets is a big issue in the public sector. Transportation asset management is important to obtain optimal performance as a result of maintaining, upgrading, operating assets, and making decisions. This paper proposes a concept to support campus management to maintain transportation assets by using the GIS database for maintenance and rehabilitation concerns. Universitas Sumatera Utara (USU) was one of the biggest universities in North Sumatera, consist of 16 schools and faculties with total area + 120 ha. Transportation infrastructure at the USU Campus includes roads, bus stops, pedestrian facilities, and parking areas. Transportation asset management based on GIS could combine with the budgeting system that supporting the decision-makers to recognize leads better outcomes for organizations.

Research on Safety of Operation and Maintenance of Utility Tunnel based on BIM and Computer Technology

The purpose of this study is to ensure the safety of operation and maintenance of utility tunnel. First of all, the framework of safety management system in operation and maintenance phase of utility tunnel is constructed by combining BMI technology, and the Access database tool integrates the operation and maintenance data of utility tunnel with BIM technology to establish a BIM-based utility tunnel operation and maintenance database. On this basis, the BIM model of an utility tunnel is constructed by using Autodesk Revit modeling software. Finally, the application of the safety management model in visualization, safety education and fire simulation of operation and maintenance system of utility tunnel is verified.

Content-based image retrieval system for HRCT lung images: assisting radiologists in self-learning and diagnosis of Interstitial Lung Diseases

Content-based Image Retrieval (CBIR) is a technique that can exploit the wealth of the data stored in a repository and help radiologists in decision making by providing references to the image in hand. A CBIR system for High-Resolution Computed Tomography (HRCT) lung images depicting signs of Interstitial Lung Diseases (ILDs) can be built and used as a self-learning tool for budding radiologists. The study of a few lung image retrieval systems available in the literature identifies some important issues that need to be taken care of. In most of the works, the creation of the reference database involves painstaking manual activity, which is time-consuming and needs skilled labor. A lot of human interventions are required, particularly for the proper delineation of the region of interest (ROI) that represents pathology in each of the images in a database. In most cases, the size of the ROIs representing different disease findings are fixed (i.e., either a fixed size square or circle), which at times may not be a proper representation of the disease pattern and as a consequence, it might limit the system’s performance. Until date, a few learning-based approaches have been developed for content-based image retrieval of HRCT lung images, which either learn the similarity using a classifier or get trained through relevance feedback. For medical image analysis, the availability of labelled data for learning makes these learning-based retrieval systems meaningful as it enhances their performance in contrast to their simple distance-based counterpart. The objective of this paper is to develop a CBIR system for ILDs that is reliable and needs minimal human intervention. The paper evaluates the performance of three popular segmentation algorithms. It identifies the best for the effective and automated delineation of an arbitrary region of interest (AROI) depicting the sign of ILDs on HRCT images of the thorax in contrast to the manual delineation of fixed size ROI. This minimizes the manual effort for the creation and maintenance of the reference database, as well as the manual delineation of AROI during query formation. Moreover, AROI created through the automated clustering is found to have a better representation of disease patterns. Three recently proposed general-purpose learning based CBIR techniques are implemented and tested for retrieval of HRCT lung images depicting the sign of ILDs. The best method is suggested after careful evaluation of all the competing techniques.

Basra: the influence of territories, rich for crude oil, on the development of Iraqi governorates

Introduction. The article presents the results of the analysis of changes in the planning structure of an urbanized area, addresses land use for crude oil recovery purposes in the Basra governorate, and explores the influence, produced by this activity on urban development and the environment using the city of Basra as an example. Crude oil recovery reduces the size of natural areas. The practical aspect of the scientific research entails the development of an information system designated for land management and the maintenance of databases focused on the study of urban planning tasks to ensure well-balanced development of areas, rich for crude oil in the Basra governorate with regard for a structured regional planning pattern as one of the most important objectives of Iraq. Growing residential areas, on the one hand, and expanding areas occupied by oil recovery and industrial enterprises, on the other hand, have been a relevant post-war problem since 2003. The environmental disorder, caused by substantial changes in land use, involves various disruptions, which translate into grave problems of the urban environment experienced by the governorate of Basra.
 Materials and methods. Official data, submitted by the Basra administration, tables, documents and cartographic materials were used to perform this research, let alone the open source information, including maps that contain statistical calculations made using the ArcGIS10.x software package and Microsoft Excel. The co-authors also performed a retrospective analysis of the Basra territory using remote sensing, which allowed them to determine the boundaries of the city and to track its spatial transformation.
 Results. The retrospective analysis has identified changes in the city borders. High economic growth, boosted by oil production and developing industrial territories, drives the need for well-balanced territories focused on sustainable development, especially in the historical part of Basra which is suffering from degradation.
 Conclusions. The problem of unbalanced use of urbanized territories and environmental sustainability of cities, affected by wars and military operations, requires the development of regulations governing the delimitation of borders within the framework of a land-use plan to be developed.

Exploring the Influence of Focal Loss on Transformer Models for Imbalanced Maintenance Data in Industry 4.0

Abstract Harnessing data from historical maintenance databases may be challenging, as they tend to rely on text data provided by operators. Thus, they often include acronyms, jargon, typos, and other irregularities that complicate the automated analysis of such reports. Furthermore, maintenance datasets may present highly imbalanced distributions: some situations happen more often than others, which hinders the effective application of classic Machine Learning (ML) models. Hence, this paper explores the use of a recent Deep Learning (DL) architecture called Transformer, which has provided cutting-edge results in Natural Language Processing (NLP). To tackle the class imbalance, a loss function called Focal Loss (FL) is explored. Results suggests that when all the classes are equally important, the FL does not improve the classification performance. However, if the objective is to detect the minority class, the FL achieves the best performance, although by degrading the detection capacity for the majority class.

Design and implementation of international agricultural and biological engineering expert management system based on WEB mode

Agriculture and biological engineering are the foundation of agricultural modernization, and related countries have also issued relevant policies to guide the development of agriculture and biotechnology. Agricultural and biological engineering experts are the intellectual resources in the field. At present, there is less research on the management and maintenance of agricultural and biological engineering experts, and there is a lack of software systems in this area. In order to realize the management and maintenance of agricultural and biological engineering expert information, a service platform for the international agricultural and biological engineering expert system based on the B/S framework has been developed. The background of the system used C# as the development language, and the foreground used JavaScript technology and Bootstrap. The software adopted ASP.NET MVC as the web development framework and used the Entity Framework to operate the SQL Server background database. The system has the function of searching and querying agricultural and biological engineering expert information according to keywords, and implements the functions of adding, deleting, and modifying data records, and the function of generating spreadsheets and importing spreadsheet data. The development of this system provides effective management tools for the maintenance and construction of agricultural and biological engineering expert databases and lays a good foundation for the construction of agricultural and biological engineering think tanks. Keywords: agricultural and bioengineering engineering, expert management, WEB mode, conditional search, think tanks, Asp.net MVC, bootstrap, entity framework technology DOI: 10.25165/j.ijabe.20201306.5759 Citation: Shen J B, Tan L W, Wang Y K. Design and implementation of international agricultural and biological engineering expert management system based on WEB mode. Int J Agric & Biol Eng, 2020; 13(6): 195–200.

Сетевые технологии в подготовке старшеклассников к ведению баз данных

The article shows the importance of network technologies in teaching schoolchildren. The analysis of textbooks of informatics on the subject of teaching a topic related to the maintenance of databases is given, and the features of its presentation are highlighted. On the example of the implementation of the project “Entering the University”, it is shown how students of grade 11 can be taught to work with network databases. In particular, create and maintain them, use them in professional activities. A step-by-step description of the actions of schoolchildren when creating a network database is given (creating a table structure, fi lling the database with information, selecting information through the formation of a query, working with reports). A business game through which students better understand the purpose of database management systems is described. Examples of a possible violation of information security when working with documents of applicants in the university selection committee are given.

Scenarios for Life Cycle Studies of Bridge Concrete Structure Maintenance

The sustainability of structures during their construction and service life has become a widespread topic of interest. To ensure the sustainability of bridges, maintenance databases can be analyzed to determine the status changes and required maintenance of existing bridges. The results of this analysis can then be used to predict the environmental impacts and costs incurred during ongoing maintenance of new bridges to prepare accordingly for the future. To prepare for future events, this study utilizes the US National Bridge Inventory to analyze changes in the condition rating of bridge decks and substructures according to their service years, and suggests maintenance scenarios for the service life of bridge deck and substructure concrete by investigating the maintenance activities according to service years. The factors for applying the scenarios in Korea and conceptual equations for life cycle studies which apply the scenarios are discussed for further study in the life cycle assessment field of bridges.

Algorithm and Simulation of Association Rules of Drug Relationship Based on Network Model

Studying drug relationships can provide deeper information for the construction and maintenance of biomedical databases and provide more important references for disease treatment and drug development. The research model has expanded from the previous focus on a certain drug to the systematic analysis of the pharmaceutical network formed between drugs. Network model is suitable for the study of the nonlinear relationship of the pharmaceutical relationship by modeling the data learning. Association rule mining is used to find the potential correlations between the various sets of massive data. Therefore, based on the network model, this research proposed an algorithm for drug interaction under improved association rules, which achieved accurate analysis and decision-making of drug relationship. Meanwhile, this research applied the established association rule algorithm to discuss the relationship between Chinese medicine and mental illness medicine and conducted the algorithm research and simulation analysis of the association relationship. The results showed the association rule algorithm based on the network model constructed was better than other association algorithms. It had reliability and superiority in decision-making in improving the drug-drug relationship. It also promoted the rational use of medicines and played a guiding role in pharmaceutical research. This provides scientific research personnel with research basis and research ideas for disease-related diagnosis.

CERRADO KNOWLEDGE PLATFORM: A SOCIAL AND ENVIRONMENTAL MANAGEMENT TOOL TO CONSERVE BRAZILIAN SAVANNAS

Abstract. In the last decade, the access to geographic information through Web platforms has grown substantially in Brazil, and is now a strategic condition for the social and environmental governance of large biomes such as Cerrado (savanna) and Amazonia. This paper aims to present the first version of the Cerrado Knowledge Platform, designed within the scope of the Critical Ecosystem Partnership Fund (CEPF) for the Brazilian savanna. The Platform aims to provide geospatial and census data, organize and systematize the accumulated knowledge about the Cerrado, and also highlight the actions of researches and social networks in this region. It was developed based on three components: (1) protocols and data formats; (2) adaptation of computational tools for social and environmental analysis and monitoring, in order to notify possible threats to the ecosystem (e.g. burning and deforestation); (3) training and maintenance database component. Although still in a beta version, our platform already has some active features, including access to dynamic land use maps, deforestation, and aerial imagery provided by Unmanned Aerial Vehicles (UAVs). With its enhancement and constant data input from partners, we expect the Cerrado Knowledge Platform can better assist the management of land use and land cover of Cerrado, with a perspective of maintaining key areas for biodiversity conservation.

Effective Maintenance by Reducing Failure-Cause Misdiagnosis in Semiconductor Industry (SI)

Increasing demand diversity and volume in semiconductor industry (SI) have resulted in shorter product life cycles. This competitive environment, with high-mix low-volume production,requires sustainable production capacities that can be achieved by reducing unscheduled equipment breakdowns. The fault detection and classification (FDC) is a well-known approach, used in the SI, to improve and stabilize the production capacities. This approach models equipment as a single unit and uses sensors data to identify equipment failures against product and process drifts. Besides its successful deployment for years, recent increase in unscheduled equipment breakdown needs an improved methodology to ensure sustainable capacities. The analysis on equipment utilization, using data collected from a world reputed semiconductor manufacturer, shows that failure durations as well as number of repair actions in each failure have significantly increased. This is an evidence of misdiagnosis in the identification of failures and prediction of its likely causes. In this paper, we propose two lines of defense against unstable and reducing production capacities. First, equipment should be stopped only if it is suspected as a source for product and process drifts whereas second defense line focuses on more accurate identification of failures and detection of associated causes. The objective is to facilitate maintenance engineers for more accurate decisions about failures and repair actions, upon an equipment stoppage. In the proposed methodology, these two lines of defense are modeled as Bayesian network (BN) with unsupervised learning of structure using data collected from the variables (classified as symptoms) across production, process, equipment and maintenance databases. The proofs of concept demonstrate that contextual or statistical information other than FDC sensor signals, used as symptoms, provide reliable information (posterior probabilities) to find the source of product/process quality drifts, a.k.a. failure modes (FM), as well as potential failure and causes. The reliability and learning curves concludes that modeling equipment at module level than equipment offers 45% more accurate diagnosis. The said approach contributes in reducing not only the failure durations but also the number of repair actions that has resulted in recent increase in unstable production capacities and unscheduled equipment breakdowns.

Extracting procedures of key data from a structural maintenance database

Maintenance continues for structure’s life cycle, which usually costs a lot. Inspection and/or monitoring are widely implemented to investigate the conditions of the structures. Existing databases are sometimes referred to for understanding the performance of structure with inspection/monitoring data. Since structural performance is related to various irregularly time-shifted factors, it is complicated to analyze a database efficiently. To improve the situation, the key data selection (KDS) method was proposed to extract key data from a database in this paper. The KDS method is first introduced in the context of conventional maintenance procedure. Afterwards, the detailed principle, implementation procedure, and possible applications of the KDS method are explained. Verification is performed with the data from the natural vibration of a bridge girder produced by vehicles (normal conditions) and from the lateral displacement of a bridge girder during a typhoon (accidental conditions). The results showed that the method can lessen the amount of data without changing their tendency and keep the error within an acceptable range. As a result, the KDS method is simple, reliable, and capable of reducing data under normal and accidental conditions.

Data transfer between digital models of built assets and their operation & maintenance systems

The operation and maintenance of built assets is crucial for optimising their whole life cost and efficiency. Historically, however, there has been a general failure in the transfer information between the design-and-construct (D&C) and operate-and-maintain (O&M) phases of the asset lifecycle. The recent steady uptake of digital technologies, such as Building Information Modelling (BIM) in the D&C phase has been accompanied by an expectation that this would enable better transfer of information to those responsible for O&M. Progress has been slow, with practitioners being unsure as to how to incorporate BIM into their working practices. Three types of challenge are identified, related to communication, experience and technology. In examining the last aspect, it appears that a major problem has been that of interoperability between building information models and the many computer-aided facilities management (CAFM) systems in use. The successful and automatic transfer of information from a building model to an FM tool is, in theory, achievable through the medium of the Industry Foundation Classes (IFC) schema. However, this relies upon the authoring of the model in terms of how well its structure permits the identification of relevant objects, their relationships and attributes. The testing of over 100 anonymised building models revealed that very few did; prohibiting their straightforward mapping to the maintenance database we had selected for the test. An alternative, hybrid approach was developed using an open-source software toolkit to identify objects by their geometry as well as their classification, thus enabling their automatic transfer. In some cases, manual transfer proved necessary. The implications are that while these problems can be overcome on a case-by-case basis, interoperability between D&C and O&M systems will not become standard until it is accommodated by appropriate and informed authoring of building models.

A Time Series Prediction Approach Based on Hybrid Tuning for Database Performance Indicator in AIOps

One of the most important applications of the intelligent operation and maintenance of a cloud database is its trend prediction of key performance indicators (KPI), such as disk use, memory use, etc. We propose a method named AutoPA4DB (Auto Prophet and ARIMA for Database) to predict the trend of the KPIs of the cloud database based on the Prophet model and the ARIMA model. Our AutoPA4DB method includes data preprocessing, model building, parameter tuning and optimization. We employ the weighted MAPE coverage to measure its accuracy and use 6 industrial datasets including 10 KPIs to compare the AutoPA4DB method with other three time-series trend prediction algorithms. The experimental results show that our AutoPA4DB method performs best in predicting monotonic variation data, e.g.disk use trend prediction. But it is unstable in predicting oscillatory variation data; for example, it is acceptable in memory use trend prediction but has poor accuracy in predicting the number of database connection trends.

Improving the quality of nursing care in Austria: 10 years of success.

AimsWe provide more updated and comprehensive insights, including descriptions of changes that have taken place in the quality of pressure injury care provided in hospitals over a 10‐year period.BackgroundVarious nursing quality measurements do not present a comprehensive view on nursing‐sensitive quality indicators or place a focus on one specific care problem.MethodsIt is a repeated cross‐sectional multicentre study conducted annually on 1 day including comprehensive data regarding nursing‐sensitive care problems and quality indicators on the structure, process and outcome levels.ResultsThe prevalence of pressure injuries decreased over the years from 4.4% to 2.9%, and the frequency of interventions increased.ConclusionThe Nursing Quality Measurement 2.0 initiative shows considerable improvements over a 10‐year period. Therefore, the maintenance of such nursing databases should be treated as a prerequisite to providing high‐quality nursing care and safe nursing practice. One main benefit of creating and maintaining such databases is that allow users to screen for improvements, for example in pressure injury care. These observations can be used to develop marketing strategies and/or to empower and engage nursing staff.Implications for Nursing ManagementParticipation in such quality measurements allows the comparison of data collected in wards and institutions in many different countries, enabling them to set appropriate benchmarks. Furthermore, the results can be compared over a period of time, highlighting systematic changes, trends or improvements (e.g., due to implemented innovations).

Сетевые технологии в подготовке старшеклассников к ведению баз данных

The article shows the importance of network technologies in teaching schoolchildren. The analysis of textbooks of informatics on the subject of teaching a topic related to the maintenance of databases is given, and the features of its presentation are highlighted. On the example of the implementation of the project “Entering the University”, it is shown how students of grade 11 can be taught to work with network databases. In particular, create and maintain them, use them in professional activities. A step-by-step description of the actions of schoolchildren when creating a network database is given (creating a table structure, fi lling the database with information, selecting information through the formation of a query, working with reports). A business game through which students better understand the purpose of database management systems is described. Examples of a possible violation of information security when working with documents of applicants in the university selection committee are given.

Dietary Supplement and Food Contaminations and Their Implications for Doping Controls.

A narrative review with an overall aim of indicating the current state of knowledge and the relevance concerning food and supplement contamination and/or adulteration with doping agents and the respective implications for sports drug testing is presented. The identification of a doping agent (or its metabolite) in sports drug testing samples constitutes a violation of the anti-doping rules defined by the World Anti-Doping Agency. Reasons for such Adverse Analytical Findings (AAFs) include the intentional misuse of performance-enhancing/banned drugs; however, also the scenario of inadvertent administrations of doping agents was proven in the past, caused by, amongst others, the ingestion of contaminated dietary supplements, drugs, or food. Even though controversial positions concerning the effectiveness of dietary supplements in healthy subjects exist, they are frequently used by athletes, anticipating positive effects on health, recovery, and performance. However, most supplement users are unaware of the fact that the administration of such products can be associated with unforeseeable health risks and AAFs in sports. In particular anabolic androgenic steroids (AAS) and stimulants have been frequently found as undeclared ingredients of dietary supplements, either as a result of cross-contaminations due to substandard manufacturing practices and missing quality controls or an intentional admixture to increase the effectiveness of the preparations. Cross-contaminations were also found to affect therapeutic drug preparations. While the sensitivity of assays employed to test pharmaceuticals for impurities is in accordance with good manufacturing practice guidelines allowing to exclude any physiological effects, minute trace amounts of contaminating compounds can still result in positive doping tests. In addition, food was found to be a potential source of unintentional doping, the most prominent example being meat tainted with the anabolic agent clenbuterol. The athletes’ compliance with anti-doping rules is frequently tested by routine doping controls. Different measures including offers of topical information and education of the athletes as well as the maintenance of databases summarizing low- or high-risk supplements are important cornerstones in preventing unintentional anti-doping rule violations. Further, the collection of additional analytical data has been shown to allow for supporting result management processes.

Fingerprint variation detection by unlabeled data for indoor localization

Indoor localization has attracted increasing attention due to demands for location awareness, where GPS (global positioning system) does not work. Fingerprint-based indoor localization is one of standard approaches, but it does not gain recognition as a practical technique due to labor-intensive database maintenance. To protect the degradation of localization accuracy caused by the environmental variations such as relocation of APs (access points) and broken-down APs, this paper proposes a new unlabeled maintenance approach to detect variations of fingerprint pattern, where a massive number of unlabeled data can be easily collected in contrast to costly labeled data. The proposed variation detection algorithm, in offline phase, is based on feature extraction and machine learning techniques. In online phase, KL divergence by information theory is used to measure similarity between existing database and up-to-date data. For experimental study, Wi-Fi RSSI (received signal strength indicator) data including labeled data and unlabeled data are collected from the multi-story office building. The proposed algorithm validates (1) clear relationship between KL divergence metric and positioning accuracy, (2) ability of distinguishing minor and major variations, and (3) accurate variation detection based on unlabeled data.