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Journal articles on the topic 'Environment index'

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Author: Grafiati
Published: 8 September 2023

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1

Grimm, Sandra. "Environment Annual Index." Environment: Science and Policy for Sustainable Development 28, no. 10 (December 1986): 35–45. http://dx.doi.org/10.1080/00139157.1986.9928841.

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2

Fast, Ellen, Paul V. Haynos, Barbara T. Richman, and Allison Torres. "ENVIRONMENT® 2001 Annual Index." Environment: Science and Policy for Sustainable Development 43, no. 10 (December 2001): 39–53. http://dx.doi.org/10.1080/00139150109605153.

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3

Zhao, Ming-tian, Shuang-jiu Xiao, Xu-bo Yang, and Li-zhuang Ma. "Variable refractive index in environment matte." Journal of Zhejiang University-SCIENCE A 7, no. 7 (July 2006): 1160–67. http://dx.doi.org/10.1631/jzus.2006.a1160.

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4

Sharma, Rahul. "Indian Deep-sea Environment Experiment (INDEX):." Deep Sea Research Part II: Topical Studies in Oceanography 48, no. 16 (January 2001): 3295–307. http://dx.doi.org/10.1016/s0967-0645(01)00041-8.

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5

Kleeman, Walter B. "1991 Steelcase Worldwide Office Environment Index." Facilities 10, no. 1 (January 1992): 13–19. http://dx.doi.org/10.1108/eum0000000002173.

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6

Abuamer, Dr Fatena F. R. "The Environmental Index of Kuwait: Ways and Chances of Development." Webology 19, no. 1 (January 20, 2022): 1862–71. http://dx.doi.org/10.14704/web/v19i1/web19125.

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This research aims to identify factors affecting the Kuwait Environment Performance Index by identifying the most important environmental problems in Kuwait, identifying the Global Environmental Performance Index and identifying Kuwait's position in the 2020 Global Environmental Performance Index (GII) and studying the most important factors of improving the environmental performance index in Kuwait.
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7

Xing, Congcong, Jing Li, Xiuren Li, and Bei Zhao. "Evaluation Index System of Marine Ecological Environment." E3S Web of Conferences 293 (2021): 01005. http://dx.doi.org/10.1051/e3sconf/202129301005.

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Based on the model of ecological environment evolution in the process of the economic development, this paper constructed the evaluation index system of marine ecological environment. The results were shown as follows: the evaluation index system of marine ecological environment consists of 1 object layer, 3 element layers (state of marine ecological environment pressure of marine ecological environment and response of marine ecological environment), and 15 factor layers. Index weight was calculated through combination weighting approach of subjective and objective evaluation method of analytic hierarchy process(AHP) and entropy method. The evaluation indicator system and evaluation method have general applicability and strong operability.
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8

Kanda, Manabu, Norikazu Yanagimoto, and Masaaki Ukaji. "New Thermal Sensation Index for Outdoor Environment." Doboku Gakkai Ronbunshu, no. 545 (1996): 1–10. http://dx.doi.org/10.2208/jscej.1996.545_1.

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9

Grover, Sandeep, V. P. Agrawal, and I. A. Khan. "Human resource performance index in TQM environment." International Journal of Management Practice 1, no. 2 (2005): 131. http://dx.doi.org/10.1504/ijmp.2005.007132.

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10

Hussain, H., M. R. Intan Suhana, T. H. Law, and M. S. Ahmad farhan. "Development of composite road environment safety index." Journal of Transportation Safety & Security 8, no. 1 (November 7, 2014): 1–17. http://dx.doi.org/10.1080/19439962.2014.976692.

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11

Scott, Åsa. "Environment–accident index: validation of a model." Journal of Hazardous Materials 61, no. 1-3 (August 1998): 305–12. http://dx.doi.org/10.1016/s0304-3894(98)00137-x.

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12

Noormalitasari, Audina Rizka, and Andryan Setyadharma. "Determinants of Environment Quality Index In Indonesia." Efficient: Indonesian Journal of Development Economics 4, no. 2 (June 30, 2021): 1174–87. http://dx.doi.org/10.15294/efficient.v4i2.45107.

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The purpose of this study was to determine the effect of poor people, slum households with proper sanitation and electricity sources, Gini ratio, open employment, micro, and small industrial production, and forest fires on the environmental quality index in 33 provinces in Indonesia 2012-2018. The type of data in this research uses secondary data obtained from Badan Pusat Statistik (BPS) and Kementrian Lingkungan Hidup dan Kehutanan (KLHK) in 2012-2018. The results showed that poor people have a negative and significant effect on EQI, Slum Households have a negative and significant effect on EQI, Households with Proper Sanitation have a positive and significant effect on EQI, Households with lighting sources from electricity have a positive but not significant effect on EQI, Gini Ratio has a negative and significant effect on EQI, the Open Unemployment Rate has a negative and significant effect on EQI, Forest has no influence on EQI, and IMK Production has a positive and significant effect on EQI. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh penduduk miskin, rumah tangga kumuh dengan sanitasi layak dan sumber listrik, rasio Gini, lapangan kerja terbuka, produksi industri mikro, dan kecil, dan kebakaran hutan terhadap indeks kualitas lingkungan di 33 provinsi di Indonesia tahun 2012 -2018. Jenis data dalam penelitian ini menggunakan data sekunder yang diperoleh dari Badan Pusat Statistik (BPS) dan Kementerian Lingkungan Hidup dan Kehutanan (KLHK) tahun 2012-2018. Hasil penelitian menunjukkan bahwa masyarakat miskin berpengaruh negatif dan signifikan terhadap EQI, Rumah Tangga Kumuh berpengaruh negatif dan signifikan terhadap EQI, Rumah Tangga dengan Sanitasi Layak berpengaruh positif dan signifikan terhadap EQI, Rumah tangga dengan sumber penerangan dari listrik berpengaruh positif tetapi tidak berpengaruh signifikan terhadap EQI, Rasio Gini berpengaruh negatif dan signifikan terhadap EQI, Tingkat Pengangguran Terbuka berpengaruh negatif dan signifikan terhadap EQI, Hutan tidak berpengaruh terhadap EQI, dan IMK Production berpengaruh positif dan signifikan terhadap EQI.
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13

TAKEDA, Shinichi, Yoshio HANZAWA, Koichi OGAWA, and Toru TONEGAWA. "A study of the improvement of thermal environment used the living environment index." ENVIRONMENTAL SYSTEMS RESEARCH 18 (1990): 112–17. http://dx.doi.org/10.2208/proer1988.18.112.

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14

Rydin, Y., S. Maguire, and P. Austin. "Dilemmas in developing a sustainable built environment index." Proceedings of the Institution of Civil Engineers - Urban Design and Planning 163, no. 4 (December 2010): 159–68. http://dx.doi.org/10.1680/udap.2010.163.4.159.

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FENG Rao-hui, 冯饶慧, 余健辉 YU Jian-hui, and 佘卫龙 SHE Wei-long. "Environment Refractive Index Sensor Based on Microfiber Coupler." ACTA PHOTONICA SINICA 39, no. 1 (2010): 37–41. http://dx.doi.org/10.3788/gzxb20103901.0037.

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16

Cepel, Martin, Andrius Stasiukynas, Anna Kotaskova, and Jan Dvorsky. "BUSINESS ENVIRONMENT QUALITY INDEX IN THE SME SEGMENT." Journal of Competitiveness 10, no. 2 (June 30, 2018): 21–40. http://dx.doi.org/10.7441/joc.2018.02.02.

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17

Narayanaswamy, Venkataraman, and Kanesan Muthusamy. "Sustainability Index Benchmarking in a Semiconductor Manufacturing Environment." Advanced Materials Research 383-390 (November 2011): 3377–81. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.3377.

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Sustainability is becoming an important tool towards our better future. More and more companies are looking into sustainability as their prime focus. Sustainability encompasses responsibilities towards social, energy, water, pollution and economic factors. Sustainability is the ability to sustain. Sustainability Index evaluation criteria and checklist has been created for a typical semiconductor manufacturing industries and the sustainability index has been compared with similar industry. Based on the benchmarking conducted for two plants one of them ranked at 75% indicating high on sustainability index. Benchmarking on sustainability index is a useful measure for shareholders, customers and employees.
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18

Regush, L. A., E. V. Alekseeva, O. R. Veretina, A. V. Orlova, and Ju S. Pezhemskaya. "Immersion Index in the Internet Environment: Questionary Standardization." Psychological-Educational Studies 13, no. 3 (2021): 31–50. http://dx.doi.org/10.17759/psyedu.2021130303.

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The article presents the results of standardization of the questionary "Immersion Index in the Internet environment", intended for the adolescents aged 12 to 17 years. The questionary was based on theoretical analysis of the phenomenon of "immersion in the Internet environment", which allowed us to build its theoretical model and suggest that this phenomenon can be described through digital competence, digital consumption and emotional attitude to the digital environment. The standardization was carried out in two stages, with the participation of a total of 1142 teenagers from St. Petersburg. The first version of the questionnaire, tested for convergent validity, was adjusted in accordance with the results obtained. At the second stage, the questionnaire underwent the procedures of factorization, determination of reliability (Cronbach's Alpha coefficient), testing distribution normality, and age norms. The article presents a standardized version of the questionnaire "Immersion Index in the Internet environment" and instructions for processing the results.
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19

Johnson, Brian D., and Kenneth R. Meyer. "Managing Yield Curve Risk in an Index Environment." Financial Analysts Journal 45, no. 6 (November 1989): 51–59. http://dx.doi.org/10.2469/faj.v45.n6.51.

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20

Fergusson, A. "Environment Canada's Children's UV Index Sun Awareness Programme." Radiation Protection Dosimetry 91, no. 1 (September 2, 2000): 317–22. http://dx.doi.org/10.1093/oxfordjournals.rpd.a033228.

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21

Rasmussen, Jessica L., Gail Steketee, Randy O. Frost, David F. Tolin, and Timothy A. Brown. "Assessing Squalor in Hoarding: The Home Environment Index." Community Mental Health Journal 50, no. 5 (December 1, 2013): 591–96. http://dx.doi.org/10.1007/s10597-013-9665-8.

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22

Wang, Haiqing, Li Li, and Jie Wang. "Research on Evaluation Index of Mine Environment Rehabilitation." Journal of Physics: Conference Series 1575 (June 2020): 012190. http://dx.doi.org/10.1088/1742-6596/1575/1/012190.

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23

Cornachione Kula, Maria, Priniti Panday, and Brandon Parrish. "A wellbeing index based on an enabling environment." International Journal of Social Economics 35, no. 3 (February 15, 2008): 174–87. http://dx.doi.org/10.1108/03068290810847851.

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24

MENG, Bi-Ping, Teng-Jiao WANG, Hong-Yan LI, and Dong-Qing YANG. "Regional Bitmap Index: A Secondary Index for Data Management in Could Computing Environment." Chinese Journal of Computers 35, no. 11 (2012): 2306. http://dx.doi.org/10.3724/sp.j.1016.2012.02306.

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25

Ali, Mahwish. "COVID-19 and Environment." Life and Science 1, supplement (December 22, 2020): 4. http://dx.doi.org/10.37185/lns.1.1.165.

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Environmental and demographic factors played an important role in the transmission of COVID-19,. This review discusses the potential impact of climatic variables such as temperature, humidity, Air Quality Index (AQI), air pollutants, wastewater and different surfaces on the spread of SARS-COVID-2 virus. Different studies have demonstrated the significant effect of ambient rise in temperature and humidity on Corona cases. However, air quality index and air pollutants are more significantly associated with the mortality rate of corona patients. Furthermore, COVID-19 can survive longer on smooth surfaces as compare to the rough surfaces. The presence of the virus is also detected in stool samples of the patient and wastewater but no study has shown transmission of disease through drinking contaminated water. Hence, meteorological and environmental factors have significant impact on the occurrence of the virus and its spread.
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26

Tausch, Arno, and Almas Heshmati. "The Effects of Globalization on the Environment." UKH Journal of Social Sciences 2, no. 1 (June 30, 2018): 25–40. http://dx.doi.org/10.25079/ukhjss.v2n1y2018.pp25-40.

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In multiple standard OLS regression models, we test the effects of 26 standard predictor variables, including the ‘four freedoms’ of goods, capital, labour and services, on the following indicators of sustainable development: avoiding net trade of ecological footprint gha per person, Carbon emissions per million US dollars GDP, CO2 per capita, Environmental Performance Index (EPI), Global footprint per capita, Happy Life Years, Happy Planet Index, and ln (number of people per mill inhabitants 1980-2000 killed by natural disasters per year+1). Our research shows that the apprehensions of quantitative globalization critical research are fully vindicated by the significant negative environmental effects of the foreign savings rate. High foreign savings are indeed a driver of global footprint, and are a blockade against a satisfactory Happy Planet Index performance. The New International Division of Labour (NIDL)-model (Froebel et al., 1980) is one of the prime drivers of high CO2 per capita emissions. MNC penetration, the master variable of most quantitative dependency theories, blocks environmental performance (EPI-Index) and several other socially important processes. Worker remittances have a significant positive effect on the Happy Planet Index, and Happy Life Years.
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Surdacka, Anna, Krystyna Strzykała, and Anna Rydzewska. "Changeability of Oral Cavity Environment." European Journal of Dentistry 01, no. 01 (January 2007): 014–17. http://dx.doi.org/10.1055/s-0039-1698305.

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ABSTRACTObjectives: In dentistry, the results of in vivo studies on drugs, dental fillings or prostheses are routinely evaluated based on selected oral cavity environment parameters at specific time points. Such evaluation may be confounded by ongoing changes in the oral cavity environment induced by diet, drug use, stress and other factors. The study aimed to confirm oral cavity environment changeability.Methods: 24 healthy individuals aged 20–30 had their oral cavity environment prepared by having professional hygiene procedures performed and caries lesions filled. Baseline examination and the examination two years afterwards, evaluated clinical and laboratory parameters of oral cavity environment. Caries incidence was determined based on DMFT and DMFS values, oral cavity hygiene on Plaque Index (acc. Silness & Löe) and Hygiene Index (acc. O`Leary), and the gingival status on Gingival Index (acc. Löe & Silness) and Gingival Bleeding Index (acc. Ainamo & Bay). Saliva osmolarity, pH and concentrations of Ca2+, Pi, Na+, Cl-, total protein, albumins, F- and Sr2+ were determined.Results: The results confirmed ongoing changeability of the oral cavity environment. After 2 years of the study reduction in oral cavity hygiene parameters PLI and HI (P<0.1), and gingival indices as well as lower saliva concentration of Ca2+ (P<.001), Pi (P<.06), K+ (P<.04), Sr2+ (P<.03), Na+ (P<.1), against the baseline values, were observed. Total protein and albumin saliva concentrations were also significantly lower.Conclusion: Physiological oral cavity environment is subject to constant, individually different, changes which should be considered when analysing studies that employ oral cavity environment parameters. (Eur J Dent 2007;1:14-17)
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Prahmawaty, Raden Faradhiva, and Sapto Punomo Putro. "Struktur Komunitas Makrobentos Pada Kawasan Budidaya dan Non Budidaya Di Pulau Tembelas, Kabupaten Karimun Kepuluan Riau." Bioma : Berkala Ilmiah Biologi 20, no. 1 (July 23, 2018): 66. http://dx.doi.org/10.14710/bioma.20.1.66-74.

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The macrobenthos community structure of living organisms in aquatic base, can function as a bioindicator of changes in aquatic environments, are sensitive to any disturbance of environmental changes within a waters. This research aims to determine the effect of different structures of macrobenthos communities in the cultivation environment and noncultivation environment. The research was conducted for 4 months on Tembelas island in 2 areas that were monoculture cultivation and reference area. Abiotic data obtained were substrate grain analysis and total organic analysis of carbon and nitrogen, as well as abiotic environmental parameters such as temperature, pH, salinity and dissolved oxygen. The biotic data obtained in the form of macrobenthos species that were found and the amount in the area was then calculated using non parametric index that were the level of diversity, eveness and abundance, and T test. The identification result showed the number of species found as many as 24 species in the monoculture location and 42 species at the reference location. Dominant species found in monoculture locations were Costoanachis sp and Anodontia sp. dominant species found in the reference sites were Nuculana sp and Ellobium sp. Shannon-Wiener (H') diversity index value at monoculture location was 2,43, while reference index value index had bigger index value 2,83. The abiotic quality of monoculture and reference aquatic environments was categorized as good. Based on the result of the research, it can be concluded that reference environment had better macrobenthos diversity value than monoculture environment. Keywords: Makrobenthos, Monoculture, Reference Environment
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Tian, Hong Xia, Xue We Cui, Jing Wang, and Ying Jie Wang. "Design on Text Retrieval Algorithm in Cloud Computing Environment." Applied Mechanics and Materials 742 (March 2015): 726–29. http://dx.doi.org/10.4028/www.scientific.net/amm.742.726.

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This paper presents a lightweight index does not suspend services online update program, and demonstrate the performance of the index update program from the theoretical analysis and experimental data in two ways. A new method of MapReduce existing index methodology based on this design and further discussion are done in the paper, in the index MapReduce and Hadoop MapReduce feasibility aspects, the design flaws through experimentation.
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Lourenço, João Luiz Resende, Rodrigo Barros Rocha, Marcelo Curitiba Espindula, Enrique Anastácio Alves, Alexsandro Lara Teixeira, and Fábio Medeiros Ferreira. "Genotype × Environment Interaction in the Coffee Outturn Index of Amazonian Robusta Cultivars." Agronomy 12, no. 11 (November 17, 2022): 2874. http://dx.doi.org/10.3390/agronomy12112874.

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The coffee outturn index, understood as the relation between the cherry coffee fruit harvested from the field and its respective processed grains, is an important component of the Coffea canephora bean yield. The aim of this study was to quantify the coffee outturn index of the Amazon Robusta cultivars grown in irrigated and dryland Western Amazon environments. According to the maturation cycle of each clone, washed samples of cherry coffee were collected considering a completely randomized factorial design for characterization of the effects of genotypes, environments, and the genotype × environment (G×E) interaction. The contrasting environments of Porto Velho, RO, and Ouro Preto do Oeste, RO, Brazil, represent most of the coffee growing sites located in Am and Aw climate types, cultivated in Red and Yellow Oxisols, typical of the Western Amazon. The weight reductions through drying exhibited a G×E interaction of the complex type, which is characterized by a change in the ordering of genotypes from one environment to another, whereas the weight reductions through hulling exhibited a G×E interaction predominantly of the simple type. The reduction in weight due to drying was more affected by the environment than reduction in weight after pulping. The clones BRS1216, BRS 3220, and BRS3137 had the highest outturn index estimates and the clones BRS2314, BRS3213, and BRS2336 had the lowest outturn index estimates. The clones BRS2299, BRS3210, BRS3193, and BRS2357 had performance near the mean value of this group of genotypes. Considered together, the genotypes had a mean outturn of 24.41%, with an amplitude from 22.5% to 27.2%.
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Kang, Jinlong, Christophe Varnier, Ahmed Mosallam, Noureddine Zerhouni, Fares Ben Youssef, and Nannan Shen. "Tool Compatibility Index: Indicator Enables Improved Tool Selection for Well Construction." PHM Society European Conference 7, no. 1 (June 29, 2022): 239–44. http://dx.doi.org/10.36001/phme.2022.v7i1.3346.

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In the area of well construction, the tool reliability and the field environment are two contributing factors that influence drilling job efficiency and success. Either using high specification tools in low-risk environmental or applying tools of low reliability in harsh environments is inadvisable. Thus, how to select a suitable tool fitting the environment of an approaching drilling job is of great significance for tool planning. However, today, the tool selection decision is not optimized because it is often based on partial data availability and understanding. This paper presents an indicator called tool compatibility index, which can support improved tool selection decision making. This index takes part reliability, part criticality, and field environment into consideration, and gives a score indicating the compatibility of the tool to a specific environment. Moreover, the tool compatibility index is computed based on a weighted average method, which is computation simple and can be easily deployed. This work is part of a long-term project aiming to construct a risk-based decision advisor for drilling and measurement tools.
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32

Wang, Yuan, Guoyin Cai, Liuzhong Yang, Ning Zhang, and Mingyi Du. "Monitoring of urban ecological environment including air quality using satellite imagery." PLOS ONE 17, no. 8 (August 25, 2022): e0266759. http://dx.doi.org/10.1371/journal.pone.0266759.

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Rapid urbanisation has highlighted problems in the urban ecological environment and stimulated research on the evaluation of urban environments. In previous studies, key factors such as greenness, wetness, and temperature were extracted from satellite images to assess the urban ecological environment. Although air pollution has become increasingly serious as urbanisation proceeds, information on air pollution is not included in existing models. The Sentinel-5P satellite launched by the European Space Agency in 2017 is a reliable data source for monitoring air quality. By making full use of images from Landsat 8, Sentinel-2A, and Sentinel-5P, this work attempts to construct a new remote sensing monitoring index for urban ecology by adding air quality information to the existing remote sensing ecological index. The proposed index was tested in the Beijing metropolitan area using satellite data from 2020. The results obtained using the proposed index differ greatly in the central urban region and near large bodies of water from those obtained using the existing remote sensing monitoring model, indicating that air quality plays a significant role in evaluating the urban ecological environment. Because the model constructed in this study integrates information on vegetation, soil, humidity, heat, and air quality, it can comprehensively and objectively reflect the quality of the urban ecological environment. Consequently, the proposed remote sensing index provides a new approach to effectively monitoring the urban ecological environment.
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Reisi, Marzieh, Mozhgan Ahmadi Nadoushan, and Lu Aye. "Local walkability index: assessing built environment influence on walking." Bulletin of Geography. Socio-economic Series 46, no. 46 (December 20, 2019): 7–21. http://dx.doi.org/10.2478/bog-2019-0031.

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AbstractWalking is a more sustainable transport mode, and governments around the world are trying to deliver highly walkable areas to their people. Due to its importance, walkability has been a research topic in recent years. Vast empirical studies have reported evidence related to the influence of built environment on walking as a major physical activity. Considering the recent literature, this study developed a framework to quantify walkability by applying a set of indicators related to built environment. The indicators were normalised, weighted and integrated into an overall walkability index. The research was conducted on Chaharbagh Street, which is a major and ancient street in the Isfahan metropolitan area, Iran. The proposed framework would be helpful in investigations of whether a specific area is an appropriate option for a car-free plan based on its built environment features. The outcome of the study could be applied to understand issues related to pedestrian infrastructure and to propose corrective actions.
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Verkhovskaya, Olga R., and Ekaterina A. Aleksandrova. "The motivation index of entrepreneurial activity and institutional environment." St Petersburg University Journal of Economic Studies 34, no. 4 (2018): 511–33. http://dx.doi.org/10.21638/spbu05.2018.402.

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35

Baszanowska, Emilia, and Zbigniew Otremba. "Fluorometric Index for Sensing Oil in the Sea Environment." Sensors 17, no. 6 (June 2, 2017): 1276. http://dx.doi.org/10.3390/s17061276.

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36

Bengani, R., and M. Gadhia. "APPRAISING THE WATER POLLUTION INDEX OF AN ESTUARIAL ENVIRONMENT." International Journal of Engineering Applied Sciences and Technology 7, no. 4 (August 1, 2022): 131–37. http://dx.doi.org/10.33564/ijeast.2022.v07i04.018.

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The WPI is an important tool to summarize a large number of water quality data into simple terms which is one of the most effective ways to interrelate information on water quality trends. The information generated can help to guide policy makers on effective restoration, conservation and management of water resources. In this technique the weightage for various water quality parameters is assigned to be inversely proportional to the recommended standards for the corresponding parameters. Estuaries represent productive aquatic habitats and are characterized by dynamic biogeochemical processes. Water quality loss in estuaries is, therefore, one of the worst forms of water pollution. For the analysis of water parameters, the water samples were collected and preserved in pre-rinsed plastic bottles at monthly intervals during June 2011–May 2013 for the period of two years from the Dumas vicinity of Tapi estuary. Temperature and pH were analyzed in situ and other parameters were analyzed as per standard references. The WPI in the studied area in the first year ranged between 1.2334 to 7.8266 whereas it varied from 1.797 to 6.439 in second year of the study. The higher value of WPI was observed during the month of December and January in the first year and in the month of December in the second year indicating high polluted nature of the estuary. The overall study revealed the polluted nature of the water ranging from mild to severe during different months in the studied area. It was observed that estuarine environment was polluted by the impact of anthropogenic as well as industrial activities. This study is important as understanding the ecological status of the estuarine habitats is imperative for successful environmental management and sustainable development.
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Kumar, S., and S. Kumar. "A GENERLIZATION OF GINI SIMPSON INDEX UNDER FUZZY ENVIRONMENT." Advances in Mathematics: Scientific Journal 9, no. 8 (August 15, 2020): 5443–54. http://dx.doi.org/10.37418/amsj.9.8.15.

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38

Horie, Takehito, Tomonari Okada, and Akira Watanuki. "SUITABILITY INDEX FOR COASTAL ENVIRONMENT RESTORATION IN ENCLOSED BAY." Coastal Engineering Proceedings 1, no. 33 (October 9, 2012): 2. http://dx.doi.org/10.9753/icce.v33.posters.2.

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HSI (Habitat Suitability Index) models are a numerical index that describes the habitat quality for individual species of aquatic organism. This models are composed of SI (suitability index) models, which show the cause-and-effect relationships between biomass of target species and the variation of individual water environmental factors. Fish are often used as the environmental indicator species of HSI models, because fish are strongly related to marine algae, sea grass, water quality, and so forth, and fish are predator high in the food chain for the aquatic system. In order to restore the marine environment of Tokyo Bay, in Japan(Fig.1), some SI models have been proposed. However, we have few precision SI models on target fish. Therefore, the objective of this study is to reveal the relationship between seasonal or daily variations of water environment components in Tokyo bay or in offshore sea area of the Pacific coast of Tokyo metro (Fig.1) and the total mass of fish living in Tokyo bay in order to create precision SI models of fish.
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Libório, Matheus Pereira, Petr Ekel, Patrícia Bernardes, and Gláucia Maria Vasconcellos Vale. "South American business environment cost index: reforms for Brazil." International Journal of Business Environment 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijbe.2021.10043256.

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Ekel, Petr, Patrícia Bernardes, Gláucia Maria Vasconcellos Vale, and Matheus Pereira Libório. "South American business environment cost index: reforms for Brazil." International Journal of Business Environment 13, no. 2 (2022): 212. http://dx.doi.org/10.1504/ijbe.2022.121973.

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Jo, Hye-Jin. "Development of GIS Road Environment Index and Its Application." Journal of korea Planners Association 49, no. 8 (December 31, 2014): 123. http://dx.doi.org/10.17208/jkpa.2014.12.49.8.123.

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Koohsari, Mohammad, Andrew Kaczynski, Tomoya Hanibuchi, Ai Shibata, Kaori Ishii, Akitomo Yasunaga, Tomoki Nakaya, and Koichiro Oka. "Physical Activity Environment and Japanese Adults’ Body Mass Index." International Journal of Environmental Research and Public Health 15, no. 4 (March 26, 2018): 596. http://dx.doi.org/10.3390/ijerph15040596.

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HORI, Masahiro. "Assessment Index and Measurement Methods of Chemical Environment Pollution." Journal of the Society of Mechanical Engineers 117, no. 1145 (2014): 218–21. http://dx.doi.org/10.1299/jsmemag.117.1145_218.

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Cao, Peng, and Haitao Xu. "A Novel Big Data Index Architecture for Programming Environment." Journal of Physics: Conference Series 1617 (August 2020): 012056. http://dx.doi.org/10.1088/1742-6596/1617/1/012056.

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Cull, Robert, Sergio Navajas, Ippei Nishida, and Renate Zeiler. "A New Index of the Business Environment for Microfinance." World Development 70 (June 2015): 357–88. http://dx.doi.org/10.1016/j.worlddev.2014.11.023.

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Madaan, Divya, A. Kapoor, and V. K. Sharma. "Ultrahigh Sensitivity Plasmonic Refractive-Index Sensor for Aqueous Environment." IEEE Photonics Technology Letters 30, no. 2 (January 15, 2018): 149–52. http://dx.doi.org/10.1109/lpt.2017.2779142.

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Peiravian, Farideddin, Sybil Derrible, and Farukh Ijaz. "Development and application of the Pedestrian Environment Index (PEI)." Journal of Transport Geography 39 (July 2014): 73–84. http://dx.doi.org/10.1016/j.jtrangeo.2014.06.020.

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Zhang, Xiu-ju, Ning Qiu, Wen-Rong Zhao, Huan An, and Julius Lissa Tommy. "Water environment early warning index system in Tongzhou District." Natural Hazards 75, no. 3 (October 2, 2014): 2699–714. http://dx.doi.org/10.1007/s11069-014-1456-1.

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Subash, Thanappan, P. Vincent, S. Karuppasamy, and B. K. Kushnappa. "Analysis of Nutrient Index of Soil for Green Environment." Asian Journal of Chemistry 29, no. 10 (2017): 2311–15. http://dx.doi.org/10.14233/ajchem.2017.20809.

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孙, 凤海. "Research on Industrial Park Investment Environment Evaluation Index System." Sustainable Development 05, no. 03 (2015): 67–73. http://dx.doi.org/10.12677/sd.2015.53010.

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