Relevant bibliographies by topics / Evaluation of indicators

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Evaluation of indicators'

Author: Grafiati
Published: 28 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Evaluation of indicators"

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Min, Qu, Chen Yu, Xiong Xing-Fu, and Wu Jiang. "A framework to identify indicators for evaluating car sharing systems." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4703–21. http://dx.doi.org/10.3233/jifs-200646.

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A comprehensive evaluation indicator system is the basis of car sharing systems (CSS) evaluation. The purpose of this study is to introduce the principles and methods of indicator selection for CSS, and to identify indicators for evaluating car sharing systems due to the reason that the importance of indicators can never be overestimated in CSS evaluation. A framework to identify indicators for evaluating CSS is proposed with four steps. First of all, the structure for indicator selection is established with application of AHP method. Secondly, adequacy check and redundancy check are carried out to ensure the structure is adequate and redundant. Thirdly, underlying individual indicators are proposed according to questionnaires. Fourthly, to ensure the necessity, identification, and feasibility of indicators, we conduct N-I-F check. We carry out a case study of CSS evaluation indicators to validate the proposed framework from four dimensions: economic, environmental, systematic, and social. The proposed framework is quantitative and it is helpful in CSS evaluation to identify proper indicators and find out the best CSS option.
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Zhao, Rongying, and Xu Wang. "Research on impact evaluation of academic journals from multidimensional perspective." Library Hi Tech 38, no. 2 (July 24, 2019): 458–78. http://dx.doi.org/10.1108/lht-03-2019-0067.

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Purpose The purpose of this paper is to introduce altmetric indicators and combine with traditional citation indicators to comprehensively evaluate the impact of academic journals from the perspective of multidimensional and multi-indicator fusion. Design/methodology/approach The authors take international multidisciplinary journals as an example, combining 14 traditional citation indicators of academic journals and introducing 14 altmetric indicators to build a comprehensive evaluation model of the impact of academic journals (academic impact and societal impact). At the same time, the authors systematically construct a journal evaluation indicator system from three dimensions. Then, the indicators data of three dimensions are evaluated by normalized processing, correlation analysis, reliability and validity analysis, PCA and factor analysis. Findings Two-dimensional and three-dimensional analyses can exactly provide some useful information for academic journals’ location in the respective coordinate systems. There are strong positive correlations among the measured indicators in the three dimensions, and each indicator has a significant consistency between whole and internal. The correlation coefficient between FD1 and FD2 is 0.888 with a strong positive correlation. It shows that the traditional citation indicators provided by WoS and Scopus database are highly consistent, and they are comparable and alternative in evaluating the academic impact of journals. The correlation coefficients of FD1, FD2 with FD3 are 0.831 and 0.798. There are strong positive correlations among them, which indicate that the evaluation of journals’ societal impact based on altmetrics indicator can be considered as a potential supplement to academic impact evaluation based on citation and to reflect the multidimensional nature of journals impact in an immediate way. Originality/value Multidimensional and multi-indicator perspective evaluation can provide references for the selection of impact evaluation indicators and model optimization of academic journals, and also provide new ideas for improving the status of the impact evaluation of academic journals.
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Xing, Danxia, and Chun Lu. "Evaluation of the Development Level of Information and Communications Technology in Education Based on the Entire-Array-Polygon Indicator Method: Taking the Questionnaire Survey Data of 13 Cities in Province W as an Example." Best Evidence in Chinese Education 7, no. 2 (March 30, 2021): 987–97. http://dx.doi.org/10.15354/bece.21.ar013.

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The objective assessment of the development level of information and communications technology (ICT) in education can support the government in formulating and implement ICT policies. The article first introduced the Entire-Array-Polygon (EAP) indicator method and then designed an evaluation indicator system which containing five first indicators and 31 secondary indicators. Finally, using the questionnaire survey data of 13 cities in Province W as an example, the EAP indicator method was used to carry out on the evaluation of ICT development level. The study drew the following conclusions: EAP indicator method can objectively assess the development level of ICT; the overall development level of ICT in the 13 cities in Province W is average and above, and most of them are level II. When using the EAP indicator method to assess the development level of ICT, experts do not need to determine the indicator's weight. Also, this method presents the evaluation results more concisely and intuitively, so it can be promoted as an essential method of evaluating on the development level of ICT in education.
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Xu, Dan, Jilong Liu, Dong Liu, Qiang Fu, Mo Li, Muhammad Abrar Faiz, Sicheng Liu, Tianxiao Li, Song Cui, and Ge Yan. "Indicator system optimization model for evaluating resilience of regional agricultural soil–water resource composite system." Water Supply 21, no. 6 (March 25, 2021): 3251–66. http://dx.doi.org/10.2166/ws.2021.090.

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Abstract Resilience is an important indicator for measuring regional sustainable development capacity. The construction of a suitable evaluation indicator system is the premise of evaluating regional sustainable development. In this study, taking the Jiansanjiang Administration of Heilongjiang Province in China as an example, a preliminary selection library of the evaluation indicator system for the resilience of a regional agricultural soil–water resource composite system covering seven subsystems and 59 indicators was established. Selection criteria such as the Dale indicator criteria, subjective and objective combination weighting and principal component analysis were introduced to construct an optimization model for the resilience evaluation indicator system for the ASWRS. First, 14 indicators that were incomplete or incapable were removed. Then, the Dale indicator selection criteria were used to ensure that 14 indicators were selected. The binary fuzzy comparison method and criteria importance through interference correlation method were used to calculate the combination weight. Finally, an indicator system optimization model was established. The indicator system was optimized from 59 to 35 indicators, and the completeness of the indicator system reached 85.75%. The proposed method had obvious advantages in terms of indicator identification and elimination, and it may truly achieve the goal of indicator optimization.
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McKool, Marissa, Kimberley Freire, Kathleen C. Basile, Kathryn M. Jones, Joanne Klevens, Sarah DeGue, and Sharon G. Smith. "A Process for Identifying Indicators With Public Data: An Example From Sexual Violence Prevention." American Journal of Evaluation 41, no. 4 (June 12, 2020): 510–28. http://dx.doi.org/10.1177/1098214019891239.

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Despite advances in the sexual violence (SV) prevention field, practitioners still face challenges with identifying indicators to measure the impact of their prevention strategies. Public data, such as existing administrative and surveillance system data, may be a good option for organizations to examine trends in indicators for the purpose of program evaluation. In this article, we describe a framework and a process for identifying indicators with public data. Specifically, we present the SV Indicator Framework and a five-step indicator review process, which we used to identify indicators for a national SV prevention program. We present the findings of the indicator review and explain how the process could be used by evaluators and program planners within other developing topic areas. Tracking indicators with public data, in conjunction with other evaluation methods, may be a viable option for state-level program evaluations. We discuss limitations and implications for practice and research.
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Syrůček, Jan, Luděk Bartoň, Dalibor Řehák, Jindřich Kvapilík, and Jiří Burdych. "Evaluation of economic indicators for Czech dairy farms." Agricultural Economics (Zemědělská ekonomika) 65, No. 11 (November 20, 2019): 499–508. http://dx.doi.org/10.17221/136/2019-agricecon.

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Milk production is one of the most important areas of the Czech agrarian sector, as evidenced by its 50% share (at 2017 prices) in revenues from livestock production. As for any business, a certain level of profitability is a prerequisite for long-term and sustainable development of dairy farms. This study’s aim was to evaluate the economic efficiency of milk production from both Czech Fleckvieh (C) and Holstein (H) cows based on data collected each year from 48 to 70 Czech dairy farms in the period from 2012 to 2017. Total costs per feeding day and litre of milk, level of profitability, and income over feed costs were calculated. The influences of herd size and milk yield on profitability and break-even points were examined while sensitivity analysis and model calculations were utilised to predict profitability. The farms with higher average milk yields (&gt;7 500 and &gt;9 500 L per lactation for C and H, respectively) had higher costs per feeding day, lower costs per litre of milk, and improved profitability (p &lt; 0.05). Average break-even points were estimated for milk price (0.31 and 0.32 EUR) and milk yield (7 257 and 9 209 L) in C and H herds, respectively.<br />
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Palyvoda, Olena, Oksana Karpenko, Valentyna Vlasova, Nataliia Bondar, and Olga Mishulina. "Evaluation of seaports’ investment attractiveness." Investment Management and Financial Innovations 17, no. 3 (September 18, 2020): 160–74. http://dx.doi.org/10.21511/imfi.17(3).2020.13.

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Ukraine’s European integration requires the involvement of seaports in the international TEN-T network, so it is extremely important to create favorable investment conditions to develop port infrastructure. This study aims to make a comprehensive assessment of the seaports’ investment attractiveness to use it for increasing the efficiency of attracting investment in the development of Ukrainian seaports, which are part of the European transport network. The study was conducted using the Saati method and the method of calculating the integrated indicator of seaports’ investment attractiveness. The integrated indicator includes assessing indicators of business activity in the region and consolidated indicators of financial and property status, logistical attractiveness, and prospects for port development. According to the results of calculations, the seaports of Ukraine were divided into three groups. The ports of Yuzhne, Odesa, Illichivsk, and Mykolaiv have a high level of investment attractiveness. The ratio of investment attractiveness ranges from 3 to 2.6. The ports of Izmail, Mariupol, Oktyabrsk, and Kherson have an average level (ratio from 2.2 to 1), and other ports have a low investment attractiveness (coefficient from 0.9 to 0.7).
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Zagursky, О. M. "System of evaluation of performance indicators of supply chains." Naukovij žurnal «Tehnìka ta energetika» 10, no. 3 (August 15, 2019): 103–9. http://dx.doi.org/10.31548/machenergy2019.03.103.

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An, Lei, and Pei Zhang. "The System of Evaluation Indicators for Urban System Planning." Advanced Materials Research 368-373 (October 2011): 3002–5. http://dx.doi.org/10.4028/www.scientific.net/amr.368-373.3002.

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Evaluation of urban system planning is to build its index system to evaluate the results of planning. First, from scale level, functional structure, spatial structure, network organizations, social development, and environment assessment, these 6 major -level evaluation systems separate into 9 secondary indicator issues and 33 tertiary evaluation indicators. After that, give each indicator a specific weight by AHP and linear algebra knowledge, then build the system of evaluation indicator for urban.
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Vink, Karina, Md Nasif Ahsan, Hisaya Sawano, and Miho Ohara. "Global Water-Related Risk Indicators: Meta-Analysis of Indicator Requirements." Journal of Disaster Research 12, no. 2 (March 16, 2017): 355–67. http://dx.doi.org/10.20965/jdr.2017.p0355.

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Despite a long developmental history of water-related disaster risk indicators, there is still no consensus or reliable system for selecting objective data, no methodological system for choosing and verifying the relevancy of water-related disaster risk indicators, and no linking results back to root causes or addressing possible impacts on policies or actors to instigate change.Global policy documents such as the Sendai Framework for Disaster Risk Reduction (DRR) 2015–2013 [1] emphasize the urgent need for indicators capable of measuring risk reduction. However, developing and determining risk indicators faces many issues. Most disaster risk indices published do not yet include a basic overview of what data was used and how it was collected, let alone provide a systematic explanation of why each indicator was included, and why others were not. This consequently complicates linking the findings to their potential policy impacts. It also complicates the providing of clear-cut recommendations for improving resilience, which is a common intent of disaster risk indices.This study, which focuses on water-related hazards, aims to provide disaster managers with a set of criteria for evaluating existing datasets used in disaster risk indices, index construction methods, and the links back to policy impacts. So far, there has been no comprehensive overview of indicator requirements or scoring systems. Previous studies concerning indicator evaluating metrics [2] have fewer metrics and have not yet addressed the different tiers of requirements, namely objective indicator data quality, methodological/epistemological aspects of index composition, and, most importantly, policy and actors of change (impact requirements). Further testing of these metrics in local studies can lead to the greatly needed scientific justification for indicator selection and can enhance index robustness.The results aid in developing an evaluation system to address issues of data availability and the comparability of commonly used indicator sources, such as the World Bank. Once indicators can be scientifically linked to impacts through policy devices, national governments or other actors can become more likely to claim ownership of the data management of indicators. Future studies should expand this evaluation system to other natural hazards and focus on investigating the links between indicators and DRR in order to further validate indicator selection robustly.
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Dissertations / Theses on the topic "Evaluation of indicators"

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Vuyani, Lingela Gratitude. "A national evaluation of sustainability indicators." Diss., University of Pretoria, 2000. http://hdl.handle.net/2263/30432.

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Please read the abstract in the section 00front of this document
Dissertation (M Inst Agrar ( Sustainable Ecological Management))--University of Pretoria, 2007.
Zoology and Entomology
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Maier, Philipp. "Website Evaluation Model and Key Performance Indicators /." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/04608352001/$FILE/04608352001.pdf.

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Canarslan, Ozgecan. "Evaluation Indicators For Selection Of Sustainable Building Materials." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12609144/index.pdf.

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Environmental issues have gained importance due to global environmental threat, such as depletion of energy resources and the impacts of climate change. The building sector is responsible for almost half of the impacts on the environment. Hence, this study focuses on the importance of environmental impacts of building materials. In this regard, firstly, sustainability indicators for building materials were determined and the environmental impacts of selected building materials were studied. Then, the evaluation system BREEAM and the evaluation software BEES were selected and used to evaluate one block of bachelor flats and one of housing units in ODTUKENT, which is located in the Middle East Technical University campus in Ankara, Turkey. Building materials used for the construction of walls, floors and roofs were evaluated according to the indicators accepted by BREEAM and BEES. The results for both units were compared and it was seen that the block of bachelor flats takes lower ratings than the triplex unit for BREEAM and also lower values for BEES. Therefore, the block of bachelor flats has less environmental impact than the triplex unit. While evaluating the materials an exact match for all the materials used in the case buildings could not be found in these tools. Hence, it was not possible to exact results for these materials. In this regard, countries should determine their own evaluation indicators and develop their evaluation systems.
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O'Connell, Bethesda. "Biosand Water Filter Evaluation: Meta-Evaluation and Pilot Study of Field Use Indicators." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3059.

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Diarrheal diseases are a global public health burden, killing 1.8 million people annually. Diarrhea disproportionately affects children and those in poverty. Most diarrheal cases can be prevented through safe drinking water, basic hygiene and/or sanitation measures, with drinking water interventions having the most impact on reducing diarrheal disease. A meta-evaluation was completed of studies evaluating a specific household water treatment method, the biosand water filter. Results from the meta-evaluation illustrate that biosand water filters improve drinking water quality and reduce diarrheal disease. However, there is no generally agreed upon field method for determining biosand water filter effectiveness that is useable in low-resource communities. A pilot study was conducted of potential field use indicators, including the Colilert coliform Presence/ Absence test, hydrogen sulfide, alkalinity, hardness, pH, and fluorescently-labeled latex microspheres. The study included both laboratory and field testing. The Colilert Presence/ Absence test had the highest correlation to the United States Environmental Protection Agency standard method (IDEXX Quantitrays), but more data is needed before making a recommendation. This study adds to understanding about evaluation of biosand water filters and provides preliminary data to address the need for a field use indicator for biosand water filters.
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Climaco, Carmo. "The use of performance indicators in school self-evaluation." Thesis, Cranfield University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260439.

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Kim, Mark Euno. "Evaluation of performance measures as leading indicators of safety." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015846.

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Cunningham, Scott Woodroofe. "The content evaluation of British scientific research." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364137.

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Lamont, Neil Alastair. "Evaluation of chemical indicators for identifying sources of oil pollution." Thesis, Staffordshire University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414861.

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O'Connell, Bethesda, Megan Quinn, Phillip Scheuerman, and Deborah Slawson. "Biosand Water Filter Evaluation: Pilot Study of Field Use Indicators." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/6809.

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Diarrheal diseases are a global public health burden, killing 1.8 million people annually. Diarrhea disproportionately affects children and those in poverty. Most diarrheal cases can be prevented through safe drinking water, basic hygiene and/or sanitation measures, with drinking water interventions having the most impact on reducing diarrheal disease. A meta-evaluation was completed of studies evaluating a specific household water treatment method, the biosand water filter. Results from the meta-evaluation illustrate that biosand water filters improve drinking water quality and reduce diarrheal disease. However, there is no generally agreed upon field method for determining biosand water filter effectiveness that is useable in low-resource communities. A pilot study was conducted of potential field use indicators, including the Colilert coliform Presence/ Absence test, hydrogen sulfide, alkalinity, hardness, pH, and fluorescently-labeled latex microspheres. The study included both laboratory and field testing. The Colilert Presence/ Absence test had the highest correlation to the United States Environmental Protection Agency standard method (IDEXX Quanti-trays), but more data is needed before making a recommendation. This study adds to understanding about evaluation of biosand water filters and provides preliminary data to address the need for a field use indicator for biosand water filters.
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Tran, Qui Can Cuong. "Empirical evaluation of defect identification indicators and defect prediction models." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2553.

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Context. Quality assurance plays a vital role in the software engineering development process. It can be considered as one of the activities, to observe the execution of software project to validate if it behaves as expected or not. Quality assurance activities contribute to the success of software project by reducing the risks of software’s quality. Accurate planning, launching and controlling quality assurance activities on time can help to improve the performance of software projects. However, quality assurance activities also consume time and cost. One of the reasons is that they may not focus on the potential defect-prone area. In some of the latest and more accurate findings, researchers suggested that quality assurance activities should focus on the scope that may have the potential of defect; and defect predictors should be used to support them in order to save time and cost. Many available models recommend that the project’s history information be used as defect indicator to predict the number of defects in the software project. Objectives. In this thesis, new models are defined to predict the number of defects in the classes of single software systems. In addition, the new models are built based on the combination of product metrics as defect predictors. Methods. In the systematic review a number of article sources are used, including IEEE Xplore, ACM Digital Library, and Springer Link, in order to find the existing models related to the topic. In this context, open source projects are used as training sets to extract information about occurred defects and the system evolution. The training data is then used for the definition of the prediction models. Afterwards, the defined models are applied on other systems that provide test data, so information that was not used for the training of the models; to validate the accuracy and correctness of the models Results. Two models are built. One model is built to predict the number of defects of one class. One model is built to predict whether one class contains bug or no bug.. Conclusions. The proposed models are the combination of product metrics as defect predictors that can be used either to predict the number of defects of one class or to predict if one class contains bugs or no bugs. This combination of product metrics as defect predictors can improve the accuracy of defect prediction and quality assurance activities; by giving hints on potential defect prone classes before defect search activities will be performed. Therefore, it can improve the software development and quality assurance in terms of time and cost
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Books on the topic "Evaluation of indicators"

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Unit, European Social Fund Programme Evaluation. Impact indicators. Dublin: ESF Programme Evaluation Unit, 1994.

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Wilson, Christopher. Indicators for the despairing: Basic steps in indicator development. Toronto: Christopher Wilson Consulting, 1997.

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Calfee, Robert C. Indicators of literacy. Santa Monica, CA: Rand Corp., 1988.

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Pybus, Ronald L. Performance indicators 1988. Trowbridge: Wiltshire County Council Library & Museum Service, 1988.

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Gaudreau, Louise. Evolve through evaluation: Indicators and criteria. Montréal: Comité de gestion de la taxe scolaire de l'Île de Montréal, 2005.

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Roundtable, Community College. Community colleges: Core indicators of effectiveness. Washington, DC: American Association of Community Colleges, National Center for Higher Education, 1994.

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Galway, Gerald. Profile '90: Educational indicators. [St. John's?: The Division?], 1991.

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Galway, Gerald. Profile '90: Educational indicators. [St. John's]: Govt. of Newfoundland and Labrador, Dept. of Education, Division of Evaluation, 1991.

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Environmental Protection Indicators for California (EPIC) Project. Environmental protection indicators for California: Understanding environmental conditions through indicators. [Sacramento]: California Environmental Protection Agency [and] California Resources Agency, 2002.

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Group, GHK. Evaluation of Jobcentre Plus key management indicators. Sheffield: Department for Work and Pensions, 2004.

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Book chapters on the topic "Evaluation of indicators"

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Morgenstern, Wolfgang, Mark S. Tsechkovski, Egbert Nüssel, and Gotthard Schettler. "Overview of the Epidemiological Baseline Indicators." In Baseline Evaluation, 11–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-02686-1_4.

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Chai, Nan. "Sustainability Performance Indicators." In Sustainability Performance Evaluation System in Government, 119–56. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3012-2_5.

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Varanasi, Usha, John E. Stein, Lyndal L. Johnson, Tracy K. Collier, Edmundo Casillas, and Mark S. Myers. "Evaluation of Bioindicators of Contaminant Exposure and Effects in Coastal Ecosystems." In Ecological Indicators, 461–98. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-4659-7_28.

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Oliveira, João Fernando Gomes de, and Tatiana Costa Guimarães Trindade. "Sustainabilty Indicators." In Sustainability Performance Evaluation of Renewable Energy Sources: The Case of Brazil, 45–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77607-1_3.

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Sirgy, M. Joseph. "Re-Evaluation Based on Social Comparison." In Social Indicators Research Series, 135–47. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-015-9904-7_10.

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Sirgy, M. Joseph. "Re-Evaluation Based on Personal History." In Social Indicators Research Series, 103–16. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-015-9904-7_8.

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Sirgy, M. Joseph. "Re-Evaluation Based on Self-Concept." In Social Indicators Research Series, 117–34. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-015-9904-7_9.

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Morgenstern, Wolfgang, Mark S. Tsechkovski, Egbert Nüssel, and Gotthard Schettler. "Evaluation Framework and Epidemiological Indicators for the First Baseline Evaluation." In Baseline Evaluation, 7–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-02686-1_3.

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Morgenstern, Wolfgang, Mark S. Tsechkovski, Egbert Nüssel, and Gotthard Schettler. "Additional Baseline Indicators and Plans for Future Development of the Evaluation Framework." In Baseline Evaluation, 37–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-02686-1_5.

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Brand, H., and R. Milne. "Health Service Indicators im Britischen Gesundheitswesen." In Sozialmedizinische Ansätze der Evaluation im Gesundheitswesen, 323–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-76796-8_33.

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Conference papers on the topic "Evaluation of indicators"

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Tian, Zhigang. "An Evaluation of Wave Impact Indicators." In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-79732.

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Wave impact on offshore structures has been the focus of several studies, due to its significant effect on offshore operations. We evaluate several parameters (wave impact indicators) which can be adopted to indicate the possibility of wave impact on offshore structures due to extreme waves. The indicators can be estimated quickly with given sea states, and thus may provide useful information to offshore structure designers at early design phases. Definitions of three wave impact indicators are presented and discussed. The first indicator, Ψ, is proposed by Stansberg (2008). The second one considered is a wave breaking parameter, μ, originally presented by Song and Banner (2002) in their construction of a wave breaking criterion. Finally, we propose a more generalized impact indicator, βn. The subscript n indicates its dependence on local wave steepness. Our study demonstrates that the three indicators are analytically related. To evaluate these indicators numerically, 2nd order random surface waves are generated with random phase method and Two-Dimensional Fast Fourier Transform (2D FFT). Hilbert analysis of the wave signal reveals that all indicators are able to identify steep and energetic waves that may potentially cause large wave impact loads. Further numerical study demonstrates that the quantitative correlation of wave impact loads to μ is less promising than that to Ψ and βn; while βn provides the best relationship to both local wave impact load and global wave load with its dependence on local wave steepness adjusted (i.e. adjusting n). The correlation is independent of sea states. Estimations and recommendations for thresholds of the two impact indicators (i.e. Ψ and βn with n = 1) are made based on model test results. With proper estimation of the thresholds, both indicators can be applied to predict wave impact and wave impact probability in given sea states.
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Sharapov, Ruslan. "INDICATORS FOR KARST PROCESSES EVALUATION." In 14th SGEM GeoConference on SCIENCE AND TECHNOLOGIES IN GEOLOGY, EXPLORATION AND MINING. Stef92 Technology, 2014. http://dx.doi.org/10.5593/sgem2014/b12/s2.066.

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Shtern, Mark, and Vassilios Tzerpos. "Refining clustering evaluation using structure indicators." In 2009 IEEE International Conference on Software Maintenance (ICSM). IEEE, 2009. http://dx.doi.org/10.1109/icsm.2009.5306306.

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Chen Xiachu, Liao Wenhe, and Liu Sifeng. "Research of evaluation indicators supporting innovation." In 2007 IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2007. http://dx.doi.org/10.1109/gsis.2007.4443475.

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Stevens, Scott H., and _. Hadiyanto. "Indonesia: Coalbed Methane Indicators and Basin Evaluation." In SPE Asia Pacific Oil and Gas Conference and Exhibition. Society of Petroleum Engineers, 2004. http://dx.doi.org/10.2118/88630-ms.

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van Nunen, Ellen, Francesco Esposto, Arash Khabbaz Saberi, and Jan-Pieter Paardekooper. "Evaluation of safety indicators for truck platooning." In 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2017. http://dx.doi.org/10.1109/ivs.2017.7995847.

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Ma, Yingying, Xiaoguang Yang, Meiping Yun, and Hanzhong Pan. "Evaluation Indicators of Urban Traffic Control System." In Second International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41039(345)59.

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Soenen, H. "Performance indicators for low temperature cracking." In Sixth International RILEM Symposium on Performance Testing and Evaluation of Bituminous Materials. RILEM Publications SARL, 2003. http://dx.doi.org/10.1617/2912143772.057.

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Wang, Qiyao, Ahmed Farahat, Kosta Ristovski, Chetan Gupta, and Shuai Zheng. "Evaluation of Event Impact on Key Performance Indicators." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00130.

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Pastrav, Andra, Daniel Tataru, Ancuta Bara, Tudor Palade, and Emanuel Puschita. "Key performance indicators evaluation for LTE networks implementation." In 2014 11th International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2014. http://dx.doi.org/10.1109/isetc.2014.7010782.

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Reports on the topic "Evaluation of indicators"

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Fernandes, R. A., F. Zhou, and H. Song. Evaluation of multiple datasets for producing snow-cover indicators for Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/300853.

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Wreathall, J., J. Fragola, P. Appignani, G. Burlile, and Y. Shen. The development and evaluation of programmatic performance indicators associated with maintenance at nuclear power plants. Office of Scientific and Technical Information (OSTI), May 1990. http://dx.doi.org/10.2172/6927800.

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Wreathall, J., J. Fragola, P. Appignani, G. Burlile, and Y. Shen. The development and evaluation of programmatic performance indicators associated with maintenance at nuclear power plants. Office of Scientific and Technical Information (OSTI), May 1990. http://dx.doi.org/10.2172/6927804.

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Carlson, Dawn E., William G. Cioffi, Mason Jr., McManus Arthur D., Pruitt William F., and Jr Basil A. Evaluation of Serum Visceral Protein Levels as Indicators of Nitrogen Balance in Thermally Injured Patients. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada244867.

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Tingey, Leigha. Post-occupancy Evaluation at the Zoo: Behavioral and Hormonal Indicators of Welfare in Orangutans (Pongo pygmaeus abelii). Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.901.

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Mattson, Earl, Robert Smith, Yoshiko Fujita, Travis McLing, Ghanashyam Neupane, Carl Palmer, David Reed, and Vicki Thompson. Improved Geothermometry Through Multivariate Reaction-path Modeling and Evaluation of Geomicrobiological Influences on Geochemical Temperature Indicators: Final Report. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1177234.

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Hicks, Jacqueline. Defining and Measuring Diplomatic Influence. Institute of Development Studies (IDS), February 2021. http://dx.doi.org/10.19088/k4d.2021.032.

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This review found no sources of straightforward indicators for use in measuring diplomatic influence. The literature and evaluations found to recommend the use of tailor-made evaluations to account for “differences in diplomatic settings, diplomatic activities and policy fields”. They hinge on developing a theory of change alongside questions and evaluation criteria that are context-specific. They rely on assessing intermediate goals as a ‘proxy’ for the immeasurable long-term influence, and causal contributions (contributed to a result) rather than causal attributions (caused a result). It was also frequently mentioned that programme designers tend to design programmes to support diplomatic influence without specific and measurable objectives because influencing processes are by nature non-linear. In these cases, evaluations will be correspondingly unable to provide specific and measurable indicators of achievement.
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Coulson, Saskia, Melanie Woods, Drew Hemment, and Michelle Scott. Report and Assessment of Impact and Policy Outcomes Using Community Level Indicators: H2020 Making Sense Report. University of Dundee, 2017. http://dx.doi.org/10.20933/100001192.

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Making Sense is a European Commission H2020 funded project which aims at supporting participatory sensing initiatives that address environmental challenges in areas such as noise and air pollution. The development of Making Sense was informed by previous research on a crowdfunded open source platform for environmental sensing, SmartCitizen.me, developed at the Fab Lab Barcelona. Insights from this research identified several deterrents for a wider uptake of participatory sensing initiatives due to social and technical matters. For example, the participants struggled with the lack of social interactions, a lack of consensus and shared purpose amongst the group, and a limited understanding of the relevance the data had in their daily lives (Balestrini et al., 2014; Balestrini et al., 2015). As such, Making Sense seeks to explore if open source hardware, open source software and and open design can be used to enhance data literacy and maker practices in participatory sensing. Further to this, Making Sense tests methodologies aimed at empowering individuals and communities through developing a greater understanding of their environments and by supporting a culture of grassroot initiatives for action and change. To do this, Making Sense identified a need to underpin sensing with community building activities and develop strategies to inform and enable those participating in data collection with appropriate tools and skills. As Fetterman, Kaftarian and Wanderman (1996) state, citizens are empowered when they understand evaluation and connect it in a way that it has relevance to their lives. Therefore, this report examines the role that these activities have in participatory sensing. Specifically, we discuss the opportunities and challenges in using the concept of Community Level Indicators (CLIs), which are measurable and objective sources of information gathered to complement sensor data. We describe how CLIs are used to develop a more indepth understanding of the environmental problem at hand, and to record, monitor and evaluate the progress of change during initiatives. We propose that CLIs provide one way to move participatory sensing beyond a primarily technological practice and towards a social and environmental practice. This is achieved through an increased focus in the participants’ interests and concerns, and with an emphasis on collective problem solving and action. We position our claims against the following four challenge areas in participatory sensing: 1) generating and communicating information and understanding (c.f. Loreto, 2017), 2) analysing and finding relevance in data (c.f. Becker et al., 2013), 3) building community around participatory sensing (c.f. Fraser et al., 2005), and 4) achieving or monitoring change and impact (c.f. Cheadle et al., 2000). We discuss how the use of CLIs can tend to these challenges. Furthermore, we report and assess six ways in which CLIs can address these challenges and thereby support participatory sensing initiatives: i. Accountability ii. Community assessment iii. Short-term evaluation iv. Long-term evaluation v. Policy change vi. Capability The report then returns to the challenge areas and reflects on the learnings and recommendations that are gleaned from three Making Sense case studies. Afterwhich, there is an exposition of approaches and tools developed by Making Sense for the purposes of advancing participatory sensing in this way. Lastly, the authors speak to some of the policy outcomes that have been realised as a result of this research.
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Erulkar, Annabel, and Erica Chong. Evaluation of a savings and micro-credit program for vulnerable young women in Nairobi. Population Council, 2005. http://dx.doi.org/10.31899/pgy19.1010.

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Tap and Reposition Youth (TRY) was a four-year initiative undertaken by the Population Council and K-Rep Development Agency to reduce adolescents’ vulnerabilities to adverse social and reproductive health outcomes by improving livelihoods options. The project targeted out-of-school adolescent girls and young women aged 16–22 residing in low-income and slum areas of Nairobi. TRY used a modified group-based micro-finance model to extend integrated savings, credit, business support, and mentoring to out-of-school adolescents and young women. A longitudinal study of participants was conducted with a matched comparison group identified through cross-sectional community-based studies, undertaken at baseline and endline to enable an assessment of changes associated with the project. This report states that 326 participants and their controls were interviewed at baseline and 222 pairs were interviewed at endline. The results suggest that rigorous micro-finance models may be appropriate for a subset of girls, especially those who are older and less vulnerable. The impact on noneconomic indicators is less clear. Additional experimentation and adaptation is required to develop livelihoods models that acknowledge and respond to the particular situation of adolescent girls.
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Michalak, Julia, Josh Lawler, John Gross, and Caitlin Littlefield. A strategic analysis of climate vulnerability of national park resources and values. National Park Service, September 2021. http://dx.doi.org/10.36967/nrr-2287214.

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The U.S. national parks have experienced significant climate-change impacts and rapid, on-going changes are expected to continue. Despite the significant climate-change vulnerabilities facing parks, relatively few parks have conducted comprehensive climate-change vulnerability assessments, defined as assessments that synthesize vulnerability information from a wide range of sources, identify key climate-change impacts, and prioritize vulnerable park resources (Michalak et al. In review). In recognition that funding and planning capacity is limited, this project was initiated to identify geographies, parks, and issues that are high priorities for conducting climate-change vulnerability assessments (CCVA) and strategies to efficiently address the need for CCVAs across all U.S. National Park Service (NPS) park units (hereafter “parks”) and all resources. To help identify priority geographies and issues, we quantitatively assessed the relative magnitude of vulnerability factors potentially affecting park resources and values. We identified multiple vulnerability factors (e.g., temperature change, wildfire potential, number of at-risk species, etc.) and sought existing datasets that could be developed into indicators of these factors. To be included in the study, datasets had to be spatially explicit or already summarized for individual parks and provide consistent data for at least all parks within the contiguous U.S. (CONUS). The need for consistent data across such a large geographic extent limited the number of datasets that could be included, excluded some important drivers of climate-change vulnerability, and prevented adequate evaluation of some geographies. The lack of adequately-scaled data for many key vulnerability factors, such as freshwater flooding risks and increased storm activity, highlights the need for both data development and more detailed vulnerability assessments at local to regional scales where data for these factors may be available. In addition, most of the available data at this scale were related to climate-change exposures, with relatively little data available for factors associated with climate-change sensitivity or adaptive capacity. In particular, we lacked consistent data on the distribution or abundance of cultural resources or accessible data on infrastructure across all parks. We identified resource types, geographies, and critical vulnerability factors that lacked data for NPS’ consideration in addressing data gaps. Forty-seven indicators met our criteria, and these were combined into 21 climate-change vulnerability factors. Twenty-seven indicators representing 12 vulnerability factors addressed climate-change exposure (i.e., projected changes in climate conditions and impacts). A smaller number of indictors measured sensitivity (12 indicators representing 5 vulnerability factors). The sensitivity indicators often measured park or landscape characteristics which may make resources more or less responsive to climate changes (e.g., current air quality) as opposed to directly representing the sensitivity of specific resources within the park (e.g., a particular rare species or type of historical structure). Finally, 6 indicators representing 4 vulnerability factors measured external adaptive capacity for living resources (i.e., characteristics of the park and/or surrounding landscape which may facilitate or impede species adaptation to climate changes). We identified indicators relevant to three resource groups: terrestrial living, aquatic living (including living cultural resources such as culturally significant landscapes, plant, or animal species) and non-living resources (including infrastructure and non-living cultural resources such as historic buildings or archeological sites). We created separate indicator lists for each of these resource groups and analyzed them separately. To identify priority geographies within CONUS,...
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