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This paper responds to recent calls for deeper scrutiny of the institutional contexts of citizen science. In the last few years, at least two dozen civil society organizations in New York and Pennsylvania have begun monitoring the watershed impacts of unconventional natural gas drilling, also known as “fracking.” This study examines the institutional logics that inform these citizen monitoring efforts and probes how relationships with academic science and the regulatory state affect the practices of citizen scientists. We find that the diverse practices of the organizations in the participatory water monitoring field are guided by logics of consciousness-raising, environmental policing, and science. Organizations that initiate monitoring projects typically attempt to combine two or more of these logics as they develop new practices in response to macro-level social and environmental changes. The dominant logic of the field remains unsettled, and many groups appear uncertain about whether and how their practices might have an influence. We conclude that the impacts of macro-level changes, such as the scientization of politics, the rise of neoliberal policy ideas, or even large-scale industrial transformations, are likely to be experienced in field-specific ways.

Seepage of the dam is an important safety problem, which may cause internal erosion of the structure. In the field of seepage monitoring in civil engineering, the distributed optical fiber sensing technology based on the temperature tracing method has been paid more attention due to its unique advantages of high sensitivity, good stability and high resolution. The purpose of this paper is to make a review of the existing related research, so as to facilitate the later scholars to understand and further study more systematically.

In this paper, three kinds of commonly used distributed fiber temperature measurement technologies are introduced. Based on the working principle, monitoring system, theoretical analysis, experimental research and engineering application of the fiber seepage monitoring technology, the present situation of dam seepage monitoring based on distributed fiber is reviewed in detail and their advantages and disadvantages are compared.

The thermal monitoring technology of seepage measurement depends on the accuracy of optical fiber temperature measurement (including the accuracy of the system and the rationality of the discrimination method), the correct installation of optical fiber and the quantitative analysis of temperature data. The accuracy of the current monitoring system can basically meet the existing measurement requirements, but the correct installation of optical fiber and the calibration of temperature data need to be further studied for different discrimination methods, and this field has great research value.

At present, there are many applications and research studies of optical fiber sensing in the field of structural health monitoring, and there are also reviews of related aspects. However, there is little or no review only in the field of seepage monitoring. This paper summarizes the research and application of optical fiber sensing in the field of seepage monitoring. The possibility of the gradient method to find its new prospect with the development of monitoring systems and the improvement of temperature resolution is discussed. The idea of extending the seepage monitoring method based on distributed optical fiber thermal monitoring technology to other monitoring fields is also given in the paper.

The purpose of this article is to suggest that Fraby‐Perot optic sensor is a practical measurement gage to monitor the strain of great structures such as railway bridges.

A remote strain monitoring system based on F‐P optic fiber and virtual instrument is designed to monitor the strains of a railway bridge.

The application results show that the Fraby‐Perot optical fiber sensors can accurately measure strain and they are suitable for the long‐term and automatic monitoring. In addition, the system has several advantages over conventional structural instruments including fast response, ability of both static and dynamic monitoring, absolute measurement, immunity to interferences such as lightning strikes, electromagnetic noise and radio frequency, low attenuation of light signals in long fiber optic cables.

Health monitoring of structures is getting more and more recognition all over the world because it can minimize the cost of reparation and maintenance and ensure the safety of structures. A strain monitoring system based on F‐P optic fiber sensor was developed according to the health monitoring requirements of Wuhu Yangtze River Railway Bridge, which is the first cable‐stayed bridge with a maximum span of 312 m carrying both railway and highway traffic in China. It has run stably in the monitoring field more than two years and fulfilled the monitoring requirement very well. Now the system has been transplanted successfully to the Zhengzhou Yellow Railway Bridge for strain monitoring. So the work can be referenced by other similar health monitoring projects.

Long‐term, real‐time monitoring of strain using FP fiber optic sensors in railway bridge is an innovation. A remote strain data acquisition and real‐time processing are another character of the system. The work studied can be referenced by other structures monitoring, such as tunnel, concrete bridges, concrete and earth dams.

An extensive research base supports the positive effects of self-monitoring interventions on a number of student outcomes, both academic and behavioral. While the vast majority of this research base relied on traditional paper-and-pencil forms of self-monitoring, advances in technology have created significant opportunities to develop technology-based self-monitoring (TBSM) systems that may offer a number of benefits in terms of efficiency and data management, storing, and graphing. Technology-based self-management applications have evolved and been studied extensively in health-related fields, but research and development for such applications is only beginning in the field of education. In this chapter we (1) provide a brief overview of the literature on traditional forms of self-monitoring, (2) examine how educators and educational researchers may apply lessons learned about TBSM from the medical field, (3) summarize emerging literature on TBSM for students with or at risk for emotional/behavioral disorders in particular, and (4) offer suggestions for future research and development in TBSM.

This study aims to analyze the dynamic monitoring of deformation damage of steel structure buildings in long-term use. Although the steel structure building has the advantage of high structural strength, it will be deformed after being affected by factors such as corrosion and impact during long-term use, and which will affect building safety, especially the public building facilities. The dynamic monitoring of its security is an indispensable means.

This paper briefly introduced the principle of building information modeling (BIM)-based steel structure building information monitoring and the dynamic information monitoring system based on this principle. Then the monitoring system was used to analyze an operational steel structure suspension bridge in Xinxiang City, Henan Province, China, and compared it with the monitoring system based on back propagation (BP) neural network.

The results showed that the fitting degree of the dynamic deformation displacement data processed by BIM-based monitoring system was higher than that processed by BP-based monitoring system. Based on the comprehensive comparison of the dynamic data of all monitoring points, the BIM-based monitoring system had higher accuracy of deformation displacement monitoring and reliability of structural safety evaluation.

In summary, the BIM-based steel structure building monitoring system can effectively monitor the dynamic information of steel structure information.

This paper aims to provides a brief review of the dashboard literature, an account of the development of performance dashboards for field data collection at Westat, and more specifically for the first cycle of the Programme for the International Assessment of Adult Competencies (PIAAC). A brief concluding section offers suggestions for improvements in survey dashboards for PIAAC’s next cycle.

To manage field work at Westat, the authors create views into various paradata databases and present them in a dashboard, showing key performance indicators at a glance. Users can drill down from the dashboard into underlying databases to investigate potential problems. The US PIAAC dashboard is a monitoring system that supports daily review of many activities. For example, it provides overnight alerts to the field supervisor when global positioning system (GPS) data from an interviewer’s smartphone shows the interview occurred far from the respondent’s home.

Performance dashboards may represent best practice for monitoring field activities. Paradata sources and systems vary greatly across the PIAAC countries, but a multitude of process data exists in every country and can be used to create quality indicators and a monitoring system. PIAAC can establish standards/guidelines to improve visualization of quality metrics and management data, regardless of the local survey infrastructure.

The core of the paper is a case study of the experiences on the US PIAAC implementation of dashboards to monitor survey quality, production and costs, with special attention to the issue of fabrication.

A behavioural approach to information retrieval system design is outlined based on the derivation of a behavioural model of the information seeking patterns of academic social scientists. The information seeking patterns of a variety of academic social scientists were broken down into six characteristics: starting, chaining, browsing, differentiating, monitoring, and extracting. These characteristics constitute the principal generic features of the different individual patterns, and together provide a flexible behavioural model for information retrieval system design. The extent to which these characteristics are available on existing systems is considered, and the requirements for implementing the features on an experimental system are set out.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

This study aims to study the implementation of lean thinking at the strategic level of an Italian manufacturing company. Companies implementing continuous improvement (CI) projects in their production processes often take the monitoring phase for granted. This research deploys an A3 lean thinking project in the monitoring phase of strategic KPIs upon completion of several ongoing improvement projects.

The research methodology is action research aiming at disseminating the problems that the company is facing. The study relies on the lean action plan developed by Womack and Jones (2003): Planning for lean and Lean action. Lean planning consists of the following steps: find a change agent; get the knowledge; find a lever. Lean action uses the A3 lean approach.

The company reached high-performance improvements due to the proposed lean action plan.

This study contributes by presenting a lean action plan in the monitoring phase, highlighting the importance of the lean thinking-monitoring continuum in reducing time waste for faster diagnosis and using action research to analyze and instill reflective learning.

The research relies on the A3 methodology to showcase the benefits that a mature paradigm, often coined to production, still has unexplored potentials.

This paper aims to illustrate the main changes during martial law for conducting financial intelligence (monitoring), in particular, in the system of combating legalisation (laundering) of illegally gained income.

The research methodology includes general scientific methods of cognition. The authors used it to research the theoretical, organisational and legal aspects of financial intelligence (monitoring) in normal situations and during martial law. The authors also use the doctrinal legal research method to analyse and describe the legislation connected to the financial intelligence (monitoring) during martial laws.

This paper is an original work written by authors that discusses financial intelligence (monitoring) during martial law. The following changes in the conduction of financial intelligence (monitoring) were highlighted: suspension of scheduled and unscheduled on-site inspections; postponement of the entry into force of the provisions of some regulations on the submission of reports in electronic form; and the norms amending to remove administrative barriers in matters of financial intelligence (monitoring) in martial law. The authors also noted that certain rules for the functioning of the financial intelligence (monitoring) system were being clarified. Thus, the financial intelligence (monitoring) regime is being weakened to some extent.

In connection with the introduction of martial law in the territory of Ukraine on 24 February 2022, the most important changes that have taken place in the banking and financial sectors of the economy should be mentioned. In particular, this applies to the issues of the regulation on the statutory and regulatory levels. Certain vital points regarding the mechanism and procedures for conducting financial intelligence (monitoring) also needed to be changed, on a need-to-know basis to adapt to the needs of Ukrainian society during the military aggression of the Russian Federation. This paper is devoted to the study of financial intelligence (monitoring) during martial law. The authors identified the main changes in the procedure for conducting financial intelligence (monitoring) by both the National Bank of Ukraine (one of the main entities) and the legislator (by amending the current regulatory framework).