A data-centric method for microservice identification: From data models analysis to source code assignment

The Lampung University repository system is a repository of data related to study, community service, and other scientific works, currently has 37242 documents accessible through repository.lppm.unila.ac.id. Despite the amount of data, its optimal use as an information retrieval remains unrealized, hindering the effective promotion of Lampung University's scientific publication excellence. Recognizing the limitations of existing information retrieval systems that are limited to specific methods for topic identification through clustering, this study aims to develop a retrieval system for Lampung University's repository using Vector Space Model (VSM), K-Means and Latent Semantic Analysis (LSA) that generates clusters and study expertise at the level of study program, faculty and Lampung University. The methodology includes data collection, preprocessing, modeling, evaluation and system deployment. The results show that the number of clusters obtained for the university level is 7 clusters, for the faculty level are 6, 7, 8 and 10 clusters, and for the program level are 3 to 5 clusters. In addition, the finding topic identification indicate that the expertise topics at Lampung University, which are agriculture, soil, education, plants, learning, society, Lampung. This study contributes to optimizing the information retrieval system, promoting academic excellence, and advancing the understanding of study expertise at Lampung University.

Rapid and correct identification of RNA-binding residues based on the protein primary sequences is of great importance. In most prevalent machine-learning-based identification methods; however, either some features are inefficiently represented, or the redundancy between features is not effectively removed. Both problems may weaken the performance of a classifier system and raise its computational complexity. Here, we addressed the above problems and developed a better classifier (RBRIdent) to identify the RNA-binding residues. In an independent benchmark test, RBRIdent achieved an accuracy of 76.79%, Matthews correlation coefficient of 0.3819 and F-measure of 75.58%, remarkably outperforming all prevalent methods. These results suggest the necessity of proper feature description and the essential role of feature selection in this project. All source data and codes are freely available at http://166.111.152.91/RBRIdent.

This paper proposes an identification method of the equivalent inertia for doubly fed induction generator (DFIG) wind turbines. Firstly, a simplified model of DFIG incorporating phase-locked loop (PLL) and virtual inertia control is developed based on the analysis of mathematical model and control strategy of DFIG. On the basis, the equivalent motion equation of DFIG similar to the rotor motion equation of synchronous generators (SGs) is derived considering the dynamic characteristics of PLL and active power control model. According to the equivalent motion equation, the mathematical expression of equivalent inertia of DFIG wind turbines is derived. And then, an extended Kalman filter (EKF) based identification method for the controller parameters in the expression of equivalent inertial is proposed to quantitatively characterize the equivalent inertia of DFIG wind turbines. Finally, the effectiveness and accuracy of the proposed method is verified on a modified 4-machine 2-area system.

Aim of study: We analyzed and evaluated a nonlinear dynamic crop growth model called NICOLET B3, which can predict the dry and fresh matter content of lettuce in greenhouses. Area of study: Calibration was performed using experimental data obtained from the literature. The experiment was carried out in Saltillo, Mexico, and in a greenhouse in Beijing, China. Material and methods: We identified and discussed the feasibility of the studied model with multi-dimensional evaluation criteria. Meanwhile, a sensitivity analysis of input variables was conducted. After that, the least square identification method was used to calibrate the most sensitive parameter values to improve the robustness of the model. Main results: Results demonstrate that: i) the NICOLET B3 model is able to predict the fresh and dry matter production of lettuce with satisfactory accuracy verified (R2 = 0.9939 for fresh matter and R2 = 0.9858 for dry matter); ii) temperature has the most obvious impact on the model performance, compared with photosynthetically active radiation and CO2 concentration; iii) the model could perform well with only two inputs. Research highlights: Simulation results of evaluated NICOLET B3 model have a perfect goodness-of-fit. A method of calibrating parameters of the model and sensitivity analysis of three input variables of the model can facilitate its application.

Online monitoring of underwater wireless power transfer (UWPT) system parameters is of great significance for improving system transmission efficiency and stability. This paper proposes a UWPT system parameter identification method based on the adaptive particle swarm optimization Algorithm (APSO) algorithm. By measuring the front-end port impedance, it enables online identification of important parameters such as parasitic parameters due to eddy current effect, load resistance, and mutual inductance, without bilateral communication. Additionally, it eliminates the need for solving the circuit matrix multi-parameter equations precisely, saving computational resources. The paper conducts a mathematical analysis of the UWPT system's eddy current effect model and constructs an optimization model for parameter identification. Finally, an experimental platform is established to verify the effectiveness of the proposed parameter identification method. The PSO algorithm and the proposed APSO algorithm are used for parameter identification of the UWPT system under different load and coil offset conditions. Experimental results show that under various operating conditions, the APSO algorithm achieves an average identification error of 0.94‰ and an average convergence count of 60. Compared to the traditional PSO algorithm, the APSO algorithm achieves higher accuracy and faster convergence speed in parameter identification.

Identification of elastic materials using wavelet transform

The reuse of free and open source software (FOSS) components is becoming more prevalent. One of the major challenges in finding the right component is finding one that has a license that is e for its intended use. The license of a FOSS component is determined by the licenses of its source code files. In this paper, we describe the challenges of identifying the license under which source code is made available, and propose a sentence-based matching algorithm to automatically do it. We demonstrate the feasibility of our approach by implementing a tool named Ninka. We performed an evaluation that shows that Ninka outperforms other methods of license identification in precision and speed. We also performed an empirical study on 0.8 million source code files of Debian that highlight interesting facts about the manner in which licenses are used by FOSS

Sophisticated methodological approaches and measures for scaling up are key elements of effective evaluation Digital health technologies and services are significant contributors to the transformation of health care delivery. It has been estimated that 80% of technology projects fail1 due to uncertainty, abandonment and lack of organisational willingness to adopt.2 In response to the high failure rate, the discipline of benefits management has emerged, with the aim of measuring and optimising the value of digital health initiatives. The development and application of benefits management has received some attention,3 but owing to the infancy of the discipline there has been limited assessment of methodological frameworks and their application. In this article, we describe the framework that is being used to measure and quantify the benefits of the My Health Record system in Australia. We consider the strengths and limitations of this framework in the context of existing frameworks, and its ability to demonstrate digital health system benefits. We also identify priority areas for further development of digital health benefit evaluation frameworks. Further, we provide an overview of the approach to digital health system benefits evaluation in Australia, in the context of the My Health Record system. A role of the Australian Digital Health Agency has been to lead the development of the National Digital Health Strategy, to lay the foundations for a safe, seamless and secure health system.4 This strategy comprises seven priority areas to be achieved by 2022. A key strategic priority is to provide health information whenever and wherever it is needed, and this is underpinned by the My Health Record system. My Health Record is a secure online summary of health information which can be accessed by people and their health care providers, and is patient-controlled. It is a personal health record, enabling people to access, manage and share their health information with their clinicians using a range of privacy controls. This functionality includes the ability to decline access to specific documents, set a control to restrict access to the entire record, see an audit trail of any organisations that have accessed the record, and block organisations from viewing the record. The record can contain summary information from general practice, hospital, pharmacy and other health care settings. It may also contain results of investigations, plus documents that patients create themselves (such as advance care plans and personal notes). In addition, it can provide access to Medicare documents such as Medicare Benefits Schedule and Pharmaceutical Benefits Scheme information, the Australian Immunisation Register and the Australian Organ Donor Register. A multimethod evaluation framework has been developed to comprehensively evaluate the benefits of the My Health Record system. It draws on approaches that have been used overseas and assesses the range of clinical contexts in which the system is used. Connecting people to their own health information has been shown to produce a range of benefits relating to patient engagement and a 60–80% improvement in their adherence to treatment regimens.5 In addition, enabling self-management has been associated with significant financial savings in terms of health care costs and avoided hospital admissions.6 It is also theorised that enabling clinicians across different health care settings to share information will result in improved patient safety (eg, fewer medication errors), improved care coordination, a reduction in unnecessarily duplicated investigations, and efficiency gains for clinicians in terms of time savings. In Australia, 2–3% of hospital admissions each year relate to medication misadventure, costing an estimated $1.2 billion annually.7 Improved access to medication information from a range of settings through the medicines information view in My Health Record should provide clinicians with more comprehensive information, and it is hoped that this will result in a reduction of medication misadventure events. Moreover, the use of digital health records to enable test results to be shared has been shown to reduce duplicated pathology tests by 18% per week,8 and in primary care settings it has been found that 13.6% of visits were missing important clinical information.9 Several lessons relating to the application of digital health research and evaluation frameworks have been learned. Many different "key measures" have been described, and there is an overall lack of consensus as to the "who, why, how, when and what" that should apply to an evaluation.10 The impact of this has been a failure to capture the complete range of players involved in the successful delivery of a system — players who do not necessarily share identical perspectives. A recent systematic review recommends that future frameworks present better methods for stakeholder identification and have a greater focus on understanding the context in which the system is delivered. From a systems perspective, this includes usability and organisational impact.10 The foundations of Australia's digital health system benefits evaluation framework are based on applying a range of measurement methods to capture a broad variety of outcome measures, reflecting a multistakeholder National Digital Health Strategy which offers different types of benefits to its different stakeholders. Five benefit workstreams have been introduced to evaluate the My Health Record system using qualitative, quantitative and mixed-method designs, as well as behavioural economic and health economic evaluation methods (Box). These workstreams are: Perceived improvement in access to patients' information and reduction in the need to order pathology tests and diagnostic imaging Self-reported experience of being able to view information that was previously unknown, and saving time requesting information Reduction in adverse medicine-related events through having access to a patient's medicines information Reduction in unnecessary duplication of pathology tests and diagnostic imaging The workstreams have been designed with data sources in mind to assist with planning and prioritising evaluation measures. These have been ranked by feasibility11 and impact, taking into account the relative importance of each measure to different stakeholders.10 To support two of these workstreams — customer and market insights, and impact evaluations — we have taken a similar approach to Canada's Clinical Adoption Framework.3 The impact evaluations workstream in particular focuses on outcomes and how these are being realised. For example, it focuses on measuring usability and adoption of digital tools from clinicians and consumers from their inception and throughout their development. The behavioural economics workstream refers to the evaluation methods which aim to measure change in the behaviour of system users (eg, clinicians and patients), organisational issues which can affect adoption, and indicators that would inform change management requirements to improve usability and adoption of digital health services.12 The data analytics workstream enables monitoring of trends in adoption and usage. For example, this workstream is investigating, through data modelling, whether My Health Record use is associated with fewer medication errors and reduced unnecessary duplication of pathology tests, to provide comprehensive results from a range of settings. The health economics evaluations workstream evaluates and forecasts indirect population health outcomes and downstream economic benefits using health economics modelling. For example, it is developing a health economics model to inform the ongoing business case for the My Health Record system, and conducting modelling that will support measurement of current and future benefits. To support an iterative process in product and service design, the Australian Digital Health Agency also undertakes user experience research and agile project management methods.13 The advantage of this approach is that it can deliver user insights which inform continual design and development of new features. Further, conducting field research to observe a range of digital system users in their environment is a way of identifying attributes that can influence the methods and measures used more broadly in our benefits evaluation framework. Current approaches and frameworks that have been developed overseas to support evaluation and benefits measurement of digital health services are at various stages of maturity. An increasing range of methods to evaluate digital health technologies is being supported by publications, which recognise that they are being deployed in complex health systems that require a contextual understanding of users, clinical settings and the policy environment in which they operate. We must not underestimate the challenges of evaluating benefits of digital health system delivery, and new methods to support evaluations continue to be developed and validated.2 Benefits evaluation frameworks offer a platform that can guide researchers and policy makers in generating and translating evidence to support future directions and ongoing investment in digital health services. Recent debates highlight the importance of fostering evaluation designs which combine different research methods, using qualitative, quantitative and co-design principles, as well as process measures1 that we have embedded into our benefits framework. To strengthen our current benefits evaluation framework and overall approach to digital health service evaluation, we are actively introducing methods that ascertain how services can be scaled up, to identify enablers and barriers to implementation across a range of settings.1 In this context, we define scalability as "the ability … to be expanded under real-world conditions to reach a greater proportion of the eligible population, while retaining effectiveness".14 Measuring scalability is not a commonly undertaken process and has been described as poorly understood.15 But the Australian Digital Health Agency is working towards applying these methods to build an understanding of impact. These findings form a picture of how, where and for whom the intervention could have the greatest impact and, conversely, what adaptations are needed for interventions to work across different population groups. A practical example of how this is being undertaken through the Agency is the introduction of and investment in a range of "test bed" studies. Test beds are projects assessing new digital-enabled models of care that are instigated and delivered cooperatively through sustainable and viable partnerships between industry, government and other organisations. Their purpose is to promote innovation to address Australia's highest priority health challenges and generate evidence on how the new approaches improve health outcomes. Currently, there are 15 test beds across Australia which are testing digital infrastructure and integration of digital health into clinical workflows.16 Despite ongoing interest in digital health benefits evaluation frameworks, few examples of their use in evaluation of digital health services have been published. The Australian Digital Health Agency's benefits evaluation framework will be used to justify future funding of digital health and to inform community and clinical education material. Moreover, the findings will be used to inform enhancements of the My Health Record system, ensuring that its progress is relevant and appropriate for clinicians and consumers. We are grateful to Darian Eckersley, from the Australian Digital Health Agency, for advice on product design methods. Many thanks to the broader Research Programs team at the Agency for reviewing parts of this manuscript and commenting on an earlier version. We are all employed by the Australian Digital Health Agency. Commissioned; externally peer reviewed.

The article deals with the issues of data extraction from Web-resources on the example of gathering information on vacancies. There are three main interacting parts of this process: data source, database, and an expert. The main problematic aspects of the data mining process are the availability of several data sources; data representation in different languages; extraction data from different file formats; multiple updating of repetitive operations and data. The advantages and disadvantages of Web Mining methods were analyzed and defined. They are DOM tree analysis, line parsing, usage of regular expressions, XML parsing and visual approach. Method of DOM tree using XPath was applied in the paper. The method of comparator identification for modeling the data extraction process was proposed. The component, which receives the search topic and the search start page, carries out a thematically directed extraction. The comparator compares the extracted word from the page with the words of the search model. The application of the above-mentioned approach is presented for identifying a vacancy on the job search site. The thesaurus of employers' requirements is developed. Words-indicators of the required vacancies are presented in three languages. The parser work was set up. The parser processes the documents and retrieves the data used to fill a particular data model. The developed module works as follows. It begins to work with obtaining an array of necessary pages from the selected Web site. The next step is the analysis of Web page‘s structure. Then it is necessary to get the content of a specific HTML page, which contains the necessary information for its further retrieval and processing. As a result ―vacancy model‖ is developed. The model should include the following elements: vacancy title; date of adding a job to the site; the city where the applicant needs to work; requirements for the candidate; applicant duties; working conditions. Extraction of requirements, liabilities, and conditions was defined as the most problematic area, whereas the same information can be presented in a different way. In order to unify requirement experts were engaged.

Subspace Identification and ARX Modeling

With the development of plant phenomics, the identification of plant diseases from leaf images has become an effective and economic approach in plant disease science. Among the methods of plant diseases identification, the convolutional neural network (CNN) is the most popular one for its superior performance. However, CNN's representation power is still a challenge in dealing with small datasets, which greatly affects its popularization. In this work, we propose a new method, namely PiTLiD, based on pretrained Inception-V3 convolutional neural network and transfer learning to identify plant leaf diseases from phenotype data of plant leaf with small sample size. To evaluate the robustness of the proposed method, the experiments on several datasets with small-scale samples were implemented. The results show that PiTLiD performs better than compared methods. This study provides a plant disease identification tool based on a deep learning algorithm for plant phenomics. All the source data and code are accessible at https://github.com/zhanglab-wbgcas/PiTLiD.

The three-dimensional normalized full gradient (3DNFG) method was developed to identify multiple objects in complex geology. Two synthetic models- one with a single object and the other with multiple objects- were designed to evaluate the performance of 3DNFG. Through the analysis and study of these synthetic models, it was found that the planar position of targets can be easily determined using the 3DNFG response, regardless of whether the model is simple or complex. To accurately estimate the depth of targets, it is crucial to determine the optimal harmonic coefficient. For the simple model, commonly used methods such as the maxima method are feasible. For the multiple-object model, the local optimal harmonic coefficient induced by each target should be identified separately, after which the depth of each target can be determined using the corresponding harmonic coefficient. The 3DNFG methos was then applied to process gravity data collected from the Dachang area in Guangxi. The analysis revealed that the upper interface of the concealed granite at Longxianggai is approximately H = -530 m. Furthermore, the 3DNFG response indicates that the granite intrusion is shallow in several areas, including Longxianggai, Ruomacun, Dafulou, Yangjiaojian, and Tongkeng. These findings are in close agreement with the results obtained from inversion using the controlled random search method. Both synthetic models and the case study demonstrate that the 3DNFG performs well in identifying multiple objects in complex geological environments.

Batch reactor is an irreplaceable process of many industries. An essential task in any industrial process is model identification. Any mathematical model, including transfer functions, state spaces, and differential equations, can be used to depict a process. In some cases, due to our limited knowledge of the model parameters, the process couldn’t be represented mathematically. In these situations, instead of white and gray box technique the black box model technique has been used, in which the process input and output data are subjected to various estimation methods for the model parameter identification. Here, least mean square and recursive least square algorithms are chosen as estimators. This work concentrates on ARX, NARX, and NARMAX model identification of reactor temperature Tr of batch process. The reactor temperature data is collected by solving process differential equation. Those data are subjected to estimation algorithms to find the ARX, NARX, and NARMAX model parameters. Identified model outputs are validated with process reactor temperature Tr data. The best model and estimation algorithm are highlighted from the computational study for future work.

The chronological classification of Jingdezhen blue and white porcelain in past dynasties has high academic and socioeconomic research value. In this study, RGB, non-imaging spectral, and imaging spectral datasets of Jingdezhen blue and white porcelain were constructed with small samples, and the spectral range was between 350-950 nm. The transfer learning classification and identification model of blue and white porcelain was established based on the VGG16 and ResNet50 models. On this basis, Dempster-Shafer (DS) evidence theory was used to construct a chronological classification and identification model of blue and white porcelain imaging spectral data that combined spatial and spectral information. The experimental comparison shows that in the case of small samples, the method incorporating the ResNet50 network model and stepwise discriminant analysis combined with the Long Short-Term Memory (LSTM) algorithm had the best classification effect, and the classification accuracy reached 90.47%. This study provides a new method and idea for the nondestructive identification and classification of cultural relics.

PDF HTML阅读 XML下载 导出引用 引用提醒 高潜水位煤矿区生态风险识别与评价 DOI: 10.5846/stxb201503050430 作者: 作者单位: 中国矿业大学北京,中国矿业大学北京土地复垦与生态重建研究所,中国矿业大学北京土地复垦与生态重建研究所,北京大学经济学院;北京大学经济学院,中国矿业大学北京土地复垦与生态重建研究所 作者简介: 通讯作者: 中图分类号: TD88 基金项目: 国家自然科学基金项目资助（41401609）；中国矿业大学煤炭资源与安全开采国家重点实验室开放基金项目（SKLCRSM13KFB07） Ecological risk identification and assessment for a coal mine with a high groundwater table Author: Affiliation: State Key Laboratory of Coal Resources and Safe Mining,China University of Mining Technology Beijing;China;Institute of Land Reclamation and Ecological Restoration,China University of Mining and Technology Beijing;China;School of economics,Peking university,;China,Institute of Land Reclamation and Ecological Restoration,China University of Mining and Technology Beijing,Institute of Land Reclamation and Ecological Restoration,China University of Mining and Technology Beijing,School of economics,Peking university,Institute of Land Reclamation and Ecological Restoration,China University of Mining and Technology Beijing Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:生态风险评价是生态环境保护与管理的重要研究内容，并广泛运用于流域与较大范围的区域尺度的研究。以区域生态风险评价理论为基础，结合高潜水位煤矿区生态环境以及煤炭开采对生态系统造成的危害的特点，通过分析风险源、风险受体、生态终点以及暴露-响应过程，对高潜水位煤矿区生态风险的识别与评价方法进行了研究，构建了典型高潜水位煤矿区的生态风险识别与评价概念模型与空间分析框架，分析了煤矿区生态风险识别的主要技术手段与方法，并构建了以缓冲为主要手段的综合生态风险评价方法。选择山东东滩煤矿作为研究对象，针对研究区内存在的采煤塌陷、洪涝、污染、景观及社会等生态风险类型，定量评价其空间差异，并提出相应的风险防范措施。案例分析结果表明，研究区综合生态风险重度、中度、一般、轻度分别占到研究区的4.70%，64.00%，24.09%，7.20%。生态风险较高的区域主要位于矿区中西部，为煤矸石山、裸露煤炭堆积与发电厂分布区域；中度风险是研究区主要的风险类型。从降低生态风险保障矿区生态安全角度，在未来矿区规划与生态治理过程中，提出了具体的应对措施，包括：（1）注重源头控制；（2）建立高生态风险区域阻隔带；（3）加强污染的监测与控制；（4）采用边开采边治理技术。建议加强生态风险高区域的阻隔，建立生态缓冲带，减缓对整个矿区的综合影响，构建东滩煤矿生态风险防范的空间结构。 Abstract:Ecological risk assessment is one of the most important subdivisions in eco-environmental protection and management, and it is widely used to evaluate ecological risks on both watershed and regional scales. In this study, we conducted identification and ecological assessment by analyzing risk sources, bearer, ecological endpoints, and the exposure-response process on the basis of regional ecological risk assessment coupled with the unique characteristics of mining ecosystems in high groundwater table areas. The identification and evaluation method for ecological risk was studied, and an ecological assessment conceptual model and spatial analysis framework for a coal mine with high ground water were proposed. Furthermore, a comprehensive ecological risk assessment method was proposed on the basis of the spatial buffering theory. The proposed model identifies and assesses mining-induced ecological risks. The Dongtan coal mine in Shandong Province was selected to test this model and framework. The major ecological risks in this mine include land subsidence, floods, water pollution, landscape, and social issues. The degrees of single and comprehensive ecological risk were evaluated. On the basis of the analysis, ecological risk was quantitatively analyzed and spatial strategies for ecological planning in the coal mine were broached. Severe, moderate, general, and mild comprehensive ecological risks were 4.7%, 64%, 24.09%, and 7.2%, respectively. The high ecological risk was located mid-west of the coal mine with sites for coal waste piles, exposed coal, and power plants. Despite the small area occupied, enough attention should be paid to high ecological-risk areas because of their high potential of air, water, and soil pollution. The moderate ecological risk was the dominant risk-type and was present in 64% of the whole region, where most of the mining subsidence is distributed. Mining subsidence triggers flood, farmland loss, and related social issues. The general and low ecological risks were located in the mining industry square area and coal mine edges (where coal pillars protect the ground infrastructure), and were kept at a distance from pollution sources. On the basis of quantitative and spatial ecological risk assessment, specific recommendations were proposed after considering reduced ecological risk and protection from an ecological security perspective for the mine. The countermeasures and suggestions include:(1) Source control. Coal waste piles, exposed coal, and power plants are the main causes of pollution and are considered as point-source pollution; therefore, we recommend that the coal company should focus on source control, improve the comprehensive utilization of coal waste, eliminate exposed coal, and implement monitoring of water and air emissions in power plants. (2) Establishment of barriers for high ecological-risk areas. High ecological-risk areas were established over a long period and cannot be eliminated immediately; therefore, establishment of ecological buffer zones and barriers for mitigating the effects of high ecological risks is strongly recommended. Specific measures include shelterbelt construction and utilization of geotextiles. (3) Improved monitoring and pollution control. Field sampling was not conducted in this study; the coal company should build a monitoring system to assess high pollution levels and spatial distribution of ecological risk areas and consider the dominant wind direction. (4) Concurrent mining and reclamation, filling materials in high groundwater and plain coal mines, and concurrent mining and reclamation technology are recommended to protect the topsoil and improve land reclamation efficiency. 参考文献 相似文献 引证文献