Inspection Information Research Articles

Diagnostic role of hysteroscopia at complex pathology of uterus

The objective: hysteroscopic estimation of complex pathology of uterus in postmenopausal period. Materials and methods. Information of prospective inspection appeared research material 171 patient in a postmenopausal period. All patients passed a complete clinical, laboratory and instrumental inspection. During a deep inspection two clinical groups were formed. In a basic group 89 women entered with the presence of metabolic syndrome; a control group was made by 82 women, not having a metabolic syndrome. Into a basic group two sub-groups of women are selected: 1 – with the isolated form of pathology of эндометрия (30) and 2 – with complex pathology of uterus (59); in a control group observed accordingly: a 1 sub-group is 48 patients with the isolated pathology of эндометрия and 2 a sub-group is 34 women with the complex form of proliferative processes of uterus. In the complex of inspection of women, being under a supervision, entered: careful collection of anamnesis of life and disease, objective inspection, additional methods of inspection: gisteroscopy on the generally accepted methods. Results. At hysteroscopic research the women of basic group (in both sub-groups) had hyperplasia of endometrium more frequent, while in the control group of women, having the isolated pathology of endometrium, more frequent the noted atrophy of endometrium. Coincidence of conclusions of morphological research of sraping endometrium and hysteroscopic researches exposed for 156 (91,22%) women. In the complex inspection of patients simple and extended colposcopy of neck of uterus entered also with a subsequent aiming biopsy. Pathology of exocervix was confirmed by given morphological research of biopsies. Conclusion. Results of the spent researches testifies about high degree information hysteriscopical estimation complex pathologies of a uterus at women postmenopausal period. The established features are necessary for considering by working out of algorithm of diagnostic and treatment-and-prophylactic actions at these patients.

Quality of medical care in Russia

The system of providing medical care belongs to the sphere of economic activity referred to as «range of services» in Russia.The main criterion in the provision of services is the receipt of the final intended effect, which fully satisfies the customer of this service. If the final intended effect is not achieved, then this service cannot be considered as completed.In medical care, there is no final guaranteed result. The purpose of providing medical care is the very process of its provision.The intended result of treatment cannot be guaranteed, but the provider of medical care is obliged to apply all their experience and knowledge to achieve the most useful effect for the patient.Russian legislation provides for the evaluation of the quality of medical care based on the final effect. This approach to solving the issue of medical care improvement quality does not allow the healthcare in Russia to adequately develop since the main emphasis is placed on the administrative command system of control and motivation of doctors to their work. The openness of the medical community to the society leads to an inadequate evaluation of the inevitable medical failures, complications and problems in the field of medical treatment. The absence of medical practice institution in Russia and the prevalence of hired labor of doctors deprive the medical community of one of the main criteria for the development of medicine — the discretion, provided personal responsibility before a patient. In fact, it is impossible to receive high quality medical care under circumstances where a physician is only a hired "addition" to the material and technical base of a medical institution.The solution to the above problems can be found on condition of separating healthcare in Russia into a special sphere of economic activity with its own legal determination, structure, management, legislation. It is necessary to isolate medical community from society as much as possible, to limit free access to special information for public inspection and non-expert accusations. We should rely on the development of medical practice in the country as it fullestly reflects doctor's competence independent on the will of the employer.

Hybrid Method Incorporating a Rule-Based Approach and Deep Learning for Prescription Error Prediction.

Recently, automated detection has been a new approach to address the risks posed by prescribing errors. This study focused on prescription errors and utilized real medical data to supplement the Drug Utilization Review (DUR)-based rules, the current prescription error detection method. We developed a new hybrid method through artificial intelligence for prescription error prediction by utilizing actual detection accuracy improvement to reduce 'warning fatigue' for doctors and improve medical care quality. This study was conducted in the Department of Pediatrics, targeting children sensitive to drugs to develop a prescription error detection system. Based on the DUR prescription history, 15,281 patient-level observations of children from Konyang University Hospital (KYUH)'s common data model (CDM) and DUR were collected and analyzed retrospectively. Among the CDM data, inspection information was interlocked with DUR and reflected as standard information for model development; this included outpatient prescriptions from January 1 to December 31, 2018. Through consultation with pediatric clinicians, rule definitions and model development were conducted for 35 drugs, with 137,802 normal and 1609 prescription errors. We developed a novel hybrid method of error detection in the form of an advanced rule-based deep neural network (ARDNN), which showed the expected performance (precision: 72.86, recall: 81.01, F1 score: 76.72) and reduced alarm pop-up alert fatigue to below 10%. We also created an ARDNN-based comprehensive dashboard that allows doctors to monitor prescription errors with alarm pop-ups when prescribing medications. These results can advance the existing rule-based model by developing a prescription error detection model using deep learning. This method can improve overall medical efficiency and service quality by reducing doctors' fatigue.

Transmission line condition assessment technology based on multi-source Parameter Fusion

Aiming at the shortcomings of current transmission line state evaluation, which refers to the isolation of scalars and the low accuracy of evaluation methods, a transmission line state evaluation technology based on multi-source parameter fusion is proposed. This paper collects and selects characteristic data such as line test inspection information, operation and maintenance records, and power grid operating parameters as indicator quantities. The membership function is used to express the distribution state of the index quantity, and the distribution state is used as the data to establish a comprehensive evaluation model of the line operation state based on the long- and short-term memory network. The data tag is used to train the deep network to establish the mapping relationship between index parameters and operation state. Experiments show that the evaluation accuracy of the proposed method is significantly higher than that of the traditional shallow method, which can provide theoretical guidance for the operation and maintenance of overhead transmission lines.

In-line Inspection(ILI) 기반 확률론적 구조적 파손 예측

Underground water supply pipelines are prone to functional deterioration and damage because of the occurrence and growth of corrosion over time. Therefore, to ensure the safety of such pipelines, their service life should be accurately predicted using precise inspection information. For this purpose, in-line inspection is applied for diagnosing pipelines. In this study, the structural safety of a water supply pipeline in Korea was evaluated by applying Monte-Carlo simulation, which is a stochastic analysis method, to the corrosion information of large-diameter water pipes obtained through a magnetic flux leakage sensor. For this purpose, the most appropriate probability distribution function and parameters were estimated through the statistical analysis of the data obtained from the sensor, and the corrosion growth was estimated using the probability distribution function determined from the simulation results. The safety factors for the internal pressure and external load for each pipe, which depended on the corrosion growth, could be derived probabilistically. Thus, the structural safety deterioration of the pipe over time could be estimated, and an appropriate rehabilitation method was proposed. In addition, the optimum rehabilitation time could be determined. This study will provide guidelines for the rehabilitation of water supply pipelines.

Condition-based maintenance strategies for stochastically dependent systems using Nested Lévy copulas

Nowadays, industrial systems are more and more complex and components are integrated dependently. Without consideration of dependence between components often leads to overestimate the reliability of system and high maintenance cost. In this paper, a multi-component system with hierarchical stochastic dependencies is considered. Components within subsystem are strongly correlated and dependence between subsystems is heterogeneous. Thanks to its capacity of modeling asymmetric dependence structures, the Nested Lévy copula is used in this paper. Based on the multiple sources of dependence among components and hierarchical structures, a novel condition-based maintenance policy is proposed. The inspection scheme as well as maintenance decision is dynamically planned according to the collected inspection information and the influence of stochastic dependence is investigated. The numerical results show the efficiency and robustness of proposed maintenance policy on reducing the inspection and maintenance cost.

A Digital Information Model Framework for UAS-Enabled Bridge Inspection

Unmanned aerial systems (UAS) provide two main functions with regards to bridge inspections: (1) high-quality digital imaging to detect element defects; (2) spatial point cloud data for the reconstruction of 3D asset models. With UAS being a relatively new inspection method, there is little in the way of existing framework for storing, processing and managing the resulting inspection data. This study has proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS). Previous efforts focusing on visualisation functionality have focused on BIM and GIS as separate entities, which has a number of problems associated with it. This methodology has a core focus on the integration of BIM and GIS, providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS. Three-dimensional GIS visualisation allows the user to navigate through a fully interactive environment, where element level inspection information can be obtained through point-and-click operations on the 3D structural model. Two visualisation environments were created: a web-based GIS application and a desktop solution. Both environments develop a fully interactive, user-friendly model which have fulfilled the aims of coordinating and streamlining the BMS process.

Artificial Intelligence Based Structural Assessment for Regional Short- and Medium-Span Concrete Beam Bridges with Inspection Information

The functional and structural characteristics of civil engineering works, in particular bridges, influence the performance of transport infrastructure. Remote sensing technology and other advanced technologies could help bridge managers review structural conditions and deteriorations through bridge inspection. This paper proposes an artificial intelligence-based methodology to solve the condition assessment of regional bridges and optimize their maintenance schemes. It includes data integration, condition assessment, and maintenance optimization. Data from bridge inspection reports is the main source of this data-driven approach, which could provide a substantial amount og condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified. Given the need to manage limited resources and ensure safety and functionality, adequate maintenance schemes for regional bridges are optimized with genetic algorithms. The proposed data-driven methodology is applied to real regional highway bridges. The regional inspection information is obtained with the help of emerging technologies. The established structural deterioration models achieve up to 85% prediction accuracy. The obtained optimal maintenance schemes could be chosen according to actual structural conditions, maintenance requirements, and total budget. Data-driven decision support can substantially aid in smart and efficient maintenance planning of road bridges.

Bridge inspection named entity recognition via BERT and lexicon augmented machine reading comprehension neural model

As an important data source in the field of bridge management, bridge inspection reports contain large-scale fine-grained data, including information on bridge members and structural defects. However, due to insufficient research on automatic information extraction in this field, valuable bridge inspection information has not been fully utilized. Particularly, for Chinese bridge inspection entities, which involve domain-specific vocabularies and have obvious nesting characteristics, most of the existing named entity recognition (NER) solutions are not suitable. To address this problem, this paper proposes a novel lexicon augmented machine reading comprehension-based NER neural model for identifying flat and nested entities from Chinese bridge inspection text. The proposed model uses the bridge inspection text and predefined question queries as input to enhance the ability of contextual feature representation and to integrate prior knowledge. Based on the character-level features encoded by the pre-trained BERT model, bigram embeddings and weighted lexicon features are further combined into a context representation. Then, the bidirectional long short-term memory neural network is used to extract sequence features before predicting the spans of named entities. The proposed model is verified by the Chinese bridge inspection named entity corpus. The experimental results show that the proposed model outperforms other mainstream NER models on the bridge inspection corpus. The proposed model not only provides a basis for automatic bridge inspection information extraction but also supports the downstream tasks such as knowledge graph construction and question answering systems.

BIM-based mixed-reality application for bridge inspection and maintenance

PurposeThe purpose of this study is to develop a building information modelling (BIM)-based mixed reality (MR) application to enhance and facilitate the process of managing bridge inspection and maintenance works remotely from office. It aims to address the ineffective decision-making process on maintenance tasks from the conventional method which relies on documents and 2D drawings on visual inspection. This study targets two key issues: creating a BIM-based model for bridge inspection and maintenance; and developing this model in a MR platform based on Microsoft Hololens.Design/methodology/approachLiterature review is conducted to determine the limitation of MR technology in the construction industry and identify the gaps of integration of BIM and MR for bridge inspection works. A new framework for a greater adoption of integrated BIM and Hololens is proposed. It consists of a bridge information model for inspection and a newly-developed Hololens application named “HoloBridge”. This application contains the functional modules that allow users to check and update the progress of inspection and maintenance. The application has been implemented for an existing bridge in South Korea as the case study.FindingsThe results from pilot implementation show that the inspection information management can be enhanced because the inspection database can be systematically captured, stored and managed through BIM-based models. The inspection information in MR environment has been improved in interpretation, visualization and visual interpretation of 3D models because of intuitively interactive in real-time simulation.Originality/valueThe proposed framework through “HoloBridge” application explores the potential of integrating BIM and MR technology by using Hololens. It provides new possibilities for remote inspection of bridge conditions.

Intrinsic value of food chain data

Intrinsic value of food chain data

P-078. Profile of cesarean delivery care in hypertensive pregnant women

As an important data source in the field of bridge management, bridge inspection reports contain large-scale fine-grained data, including information on bridge members and structural defects. However, due to insufficient research on automatic information extraction in this field, valuable bridge inspection information has not been fully utilized. Particularly, for Chinese bridge inspection entities, which involve domain-specific vocabularies and have obvious nesting characteristics, most of the existing named entity recognition (NER) solutions are not suitable. To address this problem, this paper proposes a novel lexicon augmented machine reading comprehension-based NER neural model for identifying flat and nested entities from Chinese bridge inspection text. The proposed model uses the bridge inspection text and predefined question queries as input to enhance the ability of contextual feature representation and to integrate prior knowledge. Based on the character-level features encoded by the pre-trained BERT model, bigram embeddings and weighted lexicon features are further combined into a context representation. Then, the bidirectional long short-term memory neural network is used to extract sequence features before predicting the spans of named entities. The proposed model is verified by the Chinese bridge inspection named entity corpus. The experimental results show that the proposed model outperforms other mainstream NER models on the bridge inspection corpus. The proposed model not only provides a basis for automatic bridge inspection information extraction but also supports the downstream tasks such as knowledge graph construction and question answering systems.

A widely-applicable structural maintenance decision-analytic modelling approach assisted by information value computation

Improving the quality of maintenance decision-making under uncertainty is important for safety, reliability and cost management of marine assets. It is worth studying how condition inspections can be objective-oriented and inspection information be fully utilized in the maintenance decision-making process. Herein a new maintenance decision-analytic modelling approach assisted by value of information (VoI) computation is proposed and applied to a safety-critical marine structure. Three maintenance decision-making cases and three inspection strategies are tested. Below advantages of the proposed approach represent contribution to the current literature. The approach can yield optimal maintenance decisions in various decision-making cases, while condition based maintenance (CBM) is efficient only in the cases where inspections add value. The approach yields maintenance decisions associated with the same or lower life cycle costs (LCC) than decision rule-based approaches (e.g. CBM), and gives more improved decisions (associated with lower LCC) with more in-depth inspections. The advantages are benefited from VoI computation, decision-analytic modelling, and probabilistic modelling of uncertainty, crack growth and inspection outcomes. Maintenance decisions are derived based on integrated crack information via Bayesian updating, rather than only on inspection results. The approach can be used to optimize inspection and maintenance strategies for structural assets.

Experimental study on the complementary inverse reconstruction of tree growth state data by radar detection and 3D raster scan

Three-dimensional modeling of the growth characteristics of living wood is a difficult problem. In tree growth characteristics of nondestructive testing of data, how to process the image of tree growth defect and how to reconstruct the three-dimensional model of tree growth is the key technology, so the use of radar signal detection feedback internal growth of the trunk, using 3D raster scan reverse image processing techniques reverse reconstruct the 3D shape data of standing timber, realize the radar wave nondestructive test of lumber and 3D scanning and grating surface more stitching way as complementary is the effective solution. In this study, the feedback data of radar wave detection of internal defects of trees and the feedback data of root growth condition of trees were analyzed, so as to complete the digital data collection of invisible internal growth condition of trees. In this study, 3D grating method was used to scan wood specimens for inverse surface fitting modeling, and then the surface model of tree specimens was reconstructed by splicing multi-point markers on the scanned point cloud data, and the multi-surface grouping fitting method was used to successfully reduce the splicing error. Experimental results are obtained by stocking the trunk of the growth of internal defects inspection information, analyzes the testing information after statistical data for the chart, the experimental results of 3D scanning the reverse reconstruction is to obtain the stocking complex 3D data in the form of external information, through the application of this technology, the effective of the radar wave nondestructive testing technology is obtained with 3D scanning grating reverse reconstruction technology application in tree growth data sampling method, this method can empty of wood rotting pathological monitoring, trees outside surface can be three-dimensional characteristics of surface reconstruction. This conclusion is the application of ray detection technology and pattern recognition technology in the field of wood growth monitoring.

Enhancement and Development of Next Generation Data Mining Photolithographic Mechanism

The analytical data of project management was established. In a stereolithography method, the APC system was already implemented in essential dimensions and overlays. Productivity and system efficiency have been enhanced. The new APC, however, is created on the inspection information where the method anomalies are blended with the fluctuation of the system and which have to evaluate very small quantities, and it has the impact cap. The inspection data for the CD, overlay and log information of the acquaintance tool in ainteractive data base have been compiled and processed. We have also investigated how the earlier in this thread problem can be paid and resolved. First of all, in the enormous tool log data we have extracted ties between inspection informationbesides several parameters, particularly factor loadings. We then discussed problems with big relationships and have, thus, gathered valuable knowledge which did not come out of the traditional system. In order to show the stabilising machine fluctuation effect, we developed, along with APC, a second-generationinformation mining system.

The effect of the inspection information sharing policy on quality‐oriented food production in online commerce

Food products are normally classified as credence goods, and their quality cannot be fully ascertained by consumers even after being consumed. Online commerce exacerbates information asymmetry, which breeds a market filled with low‐price and poor‐quality food products. This study aims to examine whether certificate authority's inspection information sharing policy (IIS policy) can improve food quality in online commerce. The results suggest that producers tend to provide high‐quality food products, whereas both the online platform and food producers can earn more profits by adjusting selling price and commission fees under the IIS policy. Meanwhile, consumers also benefit from IIS policy.

SafeOne Machine Learning model to predict industrial incidents in Chemical and Gas Industries

Understanding activity incidents is one of the necessary measures in workplace safety strategy. Analyzing the trends of the activity incident information helps to spot the potential pain points and helps to scale back the loss. Optimizing the Machine Learning algorithms may be a comparatively new trend to suit the prediction model and algorithms within the right place to support human helpful factors. This research aims to make a prediction model spot the activity incidents in chemical and gas industries. This paper describes the design and approach of building and implementing the prediction model to predict the reason behind the incident which may be used as a key index for achieving industrial safety specific to chemical and gas industries. The implementation of the grading algorithmic program including the prediction model ought to bring unbiased information to get a logical conclusion. The prediction model has been trained against incident information that has 25700 chemical industrial incidents with accident descriptions for the last decade. Inspection information and incident logs ought to be chomped high of the trained dataset to verify and validate the implementation. The result of the implementation provides insight towards the understanding of the patterns, classifications, associated conjointly contributes to an increased understanding of quantitative and qualitative analytics. Innovative cloud-based technology discloses the gate to method the continual in-streaming information, method it, and output the required end in a period. The first technology stack utilized in this design is Apache Kafka, Apache Spark, KSQL, Data frames, and AWS Lambda functions. Lambda functions are accustomed implement the grading algorithmic program and prediction algorithmic program to put in writing out the results back to AWS S3 buckets. Proof of conception implementation of the prediction model helps the industries to examine through the incidents and can layout the bottom platform for the assorted protective implementations that continuously advantage the workplace's name, growth, and have less attrition in human resources.

Efficiency of Port State Control inspection regimes: A comparative study

Port State Control (PSC) guarantees that foreign ships do not jeopardize marine safety, security, and the environment when entering foreign ports. To share inspection information and improve the efficiency of PSC, countries in the same regions cooperate and follow memoranda of understanding (MoUs). Globally, a total of ten MoUs govern three different inspection regimes designed to select and inspect substandard ships. In this study, we use a super-slacks-based measure (super-SBM) to evaluate and compare the inspection efficiency of the three inspection regimes implemented by these ten MoUs and relies on the Malmquist production index (MPI) to identify the most applicable regimes. Our analyses produce three main conclusions. First, we use the average scores of the super-SBM in the past 11 years to confirm the efficiency rankings of the ten MoUs. Second, we combine the average efficiency scores of the three inspection regimes to show that the New Inspection Regime (NIR) is more economically efficient than other inspection regimes. Third, we use MPI scores to obtain a better understanding of the efficiency changes in MoUs' productivity over time and confirm that the NIR is more stable than other inspection regimes.

A Public Health Informatics Solution to Improving Food Safety in Restaurants: Putting the Missing Piece in the Puzzle.

Foodborne illnesses remain an important public health challenge in the United States causing an estimated 48 million illnesses, 128,000 hospitalizations, and 3,000 deaths per year. Restaurants are frequent settings for foodborne illness transmission. Public health surveillance - the continual, systematic collection, analysis, and interpretation of reports of health data to prevent and control illness - is a prerequisite for an effective food control system. While restaurant inspection data are routinely collected, these data are not regularly aggregated like traditional surveillance data. However, there is evidence that these data are a valuable tool for understanding foodborne illness outbreaks and threats to food safety. This article discusses the challenges and opportunities for incorporating routine restaurant inspection data as a surveillance tool for monitoring and improving foodborne illness prevention activities. The three main challenges are: 1) lack of a national framework; 2) lack of data standards and interoperability; and 3) limited access to restaurant inspection data. Tapping into the power of public health informatics represents an opportunity to address these challenges. Advancing the food safety system by improving restaurant inspection information systems and making restaurant inspection data available to support decision-making represents an opportunity to practice smarter food safety.

Probabilistic service life prediction updating with inspection information for RC structures subjected to coupled corrosion and fatigue

The performance and remaining service life of deteriorating reinforced concrete (RC) structures decrease with time due to increased critical damage propagation based on coupled corrosion and fatigue. Since damage propagation prediction is highly uncertain, an updating process after each inspection and monitoring is required to improve the accuracy and applicability of the structural performance and service life prediction. This paper proposes a probabilistic approach for updating the service life prediction of deteriorating structures under coupled corrosion-fatigue deterioration processes. The probabilistic parameters involved in the coupled corrosion-fatigue deterioration model are updated using Bayesian inference (BI). In the updating process, the Markov Chain Monte Carlo (MCMC) sampling method is applied to integrate all the possible inspection results associated with no damage detection, corrosion damage, and/or fatigue crack damage. The most appropriate updating parameters of the deterioration model are determined by adopting a comparison-based analysis. Finally, the remaining service life of the deteriorating structures based on the updated coupled damage prediction model is re-estimated. The application of the proposed probabilistic approach is illustrated on an RC bridge girder. This paper shows that (a) the coupled corrosion-fatigue damage can reduce the structural service life compared to corrosion or fatigue damage separately, and (b) the errors and uncertainty in corrosion-fatigue damage prediction can be reduced through the parameter updating integrating multiple inspection outcomes.