Previous Research Sample Clauses

Previous Research. A variety of research has been published over the last ten years on foam and blowing agent usage, characteristics and impacts, but only a very few were specific to California, and none have taken a holistic ‘bottom-up’ approach to the identification of foam based emissions in the State. Caleb has taken account of California specific research by CARB on estimated foam bank size and distribution, on estimated emissions from foam banks, on Appliances end-of-life fate and on in-state TRU/Reefer populations (CARB, 2008). Beyond California, there have been a variety of studies in the USA relevant to foam blowing agent use, banks and emissions, such as on Polyurethane blowing agents, (Skeist Inc. 2004), and on a US high GWP inventory (US EPA, 2001a). Internationally, there have been a series of studies completed on characterizing banks, emissions and management options, on defining a global emission function for blowing agents (AFEAS, 2000) and on the collection and treatment of unwanted ODS (ICF International, 2008). The studies were completed for the United Nations Environment Programme (UNEP), the Intergovernmental Panel on Climate Change (IPCC), and the Technical and Economic Assessment Panel (TEAP) of UNEP. There have also been studies specific to the European situation, including a study on regulatory options (Milieu 2007), and a study on characterizing building foam banks and emissions in the United Kingdom (BRE 2010). Xxxxx was, in whole or in part, responsible for much of this research and has reviewed and drawn upon the work as part of the Literature Review process. This review process has continued throughout this project in order to keep updated with the latest findings.
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Previous Research. Rigorous description of rGyalrong languages starts among Chinese linguists in the 1940s with Xxx Xxxx (Kin 1949). A large-scale linguistic survey of rGyalrongic was conducted in the 1950s, and some of the results are published in Xxx Xxxxxxxxx’s magnum opus Lin (1993), along with his own research. More recently, important work was done by Xxxxxxx X.-S. Xxx, his student Xxx Xxx-xxxx, and Xxxxxxxxx Xxxxxxx, with his book (Xxxxxxx 2008) on all the rGyalrong group, concentrating on the Japhug of gDongbrgyad. Zbu rGyalrong is the least documented language in the group. No text has been published in this language. Xxx (1993) recorded the vocabulary of the dialect of the village of Zhongre. Xxxxxxx
Sample 1
Previous Research. A variety of research has been published over the last ten years on foam and blowing agent usage, characteristics and impacts, but only a very few were specific to California, and none have taken a holistic ‘bottom-up’ approach to the identification of foam based emissions in the State. Caleb has taken account of California specific research by CARB2 on estimated foam bank size & distribution, on estimated emissions from foam banks, on Appliances end-of-life fate and on In-State TRU/Reefer populations. Beyond California, there have been a variety of studies in the USA relevant to foam blowing agent use, banks & emissions, such as on Polyurethane blowing agents, (SKEIST Inc. 2004), and on a US high GWP inventory (US-EPA 2001). Internationally, there have been a series of studies completed for UNEP/IPCC/TEAP on characterizing banks, emissions and management options, on defining a global emission function for blowing agents (AFEAS 2000) and on the collection and treatment of unwanted ODS (ICF International 2008). There have also been studies specific to the European situation, including on regulatory options (Milieu 2007), on characterizing building foam banks & emissions in the United Kingdom (BRE 2010). Xxxxx was, in whole or in part, responsible for much of this research and has reviewed and drawn upon the work as part of the Literature Review process. This review process has continued throughout this project in order to keep updated with the latest findings.
Sample 1
Previous Research. ‌ Although there is limited prior work addressing NLP and IR in the archaeology domain, there are some examples of related research in the literature. Almost all of those studies have focused on grey literature as the source material, presumably because it has the greatest potential for computational techniques. One of the earliest applications of IR in archaeology was done by Xxxxxxxx (1983), who did a study on information needs of users of a sites and monuments record. As this was back in 1983, the information was stored on physical 5 by 8 inch record cards, ordered by grid coordinates. Even though the situation was very different to our current situation, the problem is the same: the metadata (grid coordinates) were not good enough for information retrieval, as users want a way to cross reference or search through the data (text on cards). Xxxxxxxx sent out surveys by post asking archaeology professionals on their opinion on the use of computers for record manipulation, and found that 63% already did, or were hoping to do so in the future, meaning 37% of respondents did not see any value in using computers for this task. Eventually they concluded that “A computer-based recording system gives the potential to relieve problems of lack of space, lost data, inaccuracies in recording and to provide a flexible and efficient retrieval system, therefore relieving staff time for other work” (Xxxxxxxx, 1983, p. 43), which is basically also the main aim of this project. It seems not much
Sample 1
Previous Research. 31 algorithm to perform NER. This showed promising results, but unfortunately the technique has not been evaluated fully yet. Building on her work, Talks (2019) added more entity types and did an extensive evaluation with users. All the research described above has been on the English language, and re- search on Dutch and other languages is much less prevalent. For Dutch, there are two main examples: the OpenBoek project and the experiments on Dutch texts in the above mentioned ARIADNE project. The OpenBoek project (Xxxxxxxx & Xxxxxx, 2008; Xxxxxxxx & Brandsen, 2010) aimed to create a full text search engine combined with entity search, on about 2,000 reports. They used Memory Based Learning to automatically label time periods and locations, which were searchable together with the full text in a web application based on the SMART system (Salton, 1971). While the search engine showed promising results, unfortunately this web application has gone offline not too long after the funding for the project ended. The ARIADNE project – besides the work on English texts described above – also experimented with Dutch and Swedish grey literature. For Dutch, they applied a rules based technique using the General Architecture for Text Engineer- ing (GATE) framework (Xxxxxxxxxx et al., 1995). The rules were mainly based on thesauri, but they found many issues with the thesauri and gold standard, making effective NER with this approach difficult. Very recently, Xxxxxxx et al. (2021) experimented with Text Mining and IR as part of their research on urban farming and ruralisation in the Netherlands. They extracted text from a number of PDFs, created a term document matrix and compared this with a list of keywords related to the topic of urban farming, to automatically assess the relevance of a large number of documents for a number of topics. In a slightly different direction, recent work by Xxxxx et al. describes research on Dutch archaeological texts from Belgium, looking at theoretical trends over time. They successfully manage to use Text Mining to find these trends, and chart the decrease in text quality due to developer-led archaeology. Similarly, Xxxxxxx et al. (2020) used topic modelling techniques on large-scale English data to see if there are patterned ways in which archaeologists write about bone. Almost no research has been done on multilingual techniques, but Xxxxxxx- xxxxxxx et al. (2015) present some interesting results for NER on English, German and French ...
Sample 1
Previous Research. In this section I talk about some articles I have found interesting for my research study during my review of previously research in this area. In general, there are many studies about how we can improve our safety procedures practices, and I have found two main approaches in this area: researchers, trying to create and simulate their own environment. Hopefully, I do not need to create new one, because I will use Second Life as my environment. Secondly, researchers were focusing on procedures and advantages or disadvantages applying these procedures in virtual world. But, none of them were using Second Life yet. Let me introduce some of the articles.
Sample 1
Previous Research. Background research for the archaeological survey of the US 301 project began with the development of a predictive model for the occurrence of prehistoric and historic archaeological resources within a broad study area that encompassed several proposed highway alignments (Xxxxxxxx et al. 2006). For the occurrence of prehistoric archaeological resources, the model relied primarily on the cost distance to various sources of water and the presence of micro- drainage divides. For the occurrence of historic resources, the model was based on the locations of extant historic structures, structures shown on historic maps, and proximity to early roads. This was followed by a Phase Ia survey for US 301 Section 1, in which the predictive model was evaluated and applied specifically to the project area (Hay et al. 2009). This resulted in the division of the US 301 Section 1 project area into 31 survey segments, based on zones of high, medium, or low probability for prehistoric and historic archaeological resources (see Figures 2a-2f). Background and archival research was conducted in conjunction with the Phase Ia survey, to trace the history of ownership of the historic properties within the project area. At that time, only two of the 12 identified historic properties through which Section 1 passes were successfully traced back to original land grants. The others were generally traced back to mid- nineteenth century ownerships, with the aid of names shown on the 1849 and 1868 atlases (Rea and Price 1849, Beers 1868), but could not be traced further back for various reasons.
Sample 1
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Previous Research. Although there is limited prior work addressing NLP and IR in the archaeology domain, there are some examples of related research in the literature. Almost all of those studies have focused on grey literature as the source material, presumably because it has the greatest potential for computational techniques. One of the earliest applications of IR in archaeology was done by Xxxxxxxx (1983), who did a study on information needs of users of a sites and monuments record. As this was back in 1983, the information was stored on physical 5 by 8 inch record cards, ordered by grid coordinates. Even though the situation was very different to our current situation, the problem is the same: the metadata (grid coordinates) were not good enough for information retrieval, as users want a way to cross reference or search through the data (text on cards). Xxxxxxxx sent out surveys by post asking archaeology professionals on their opinion on the use of computers for record manipulation, and found that 63% already did, or were hoping to do so in the future, meaning 37% of respondents did not see any value in using computers for this task. Eventually they concluded that “A computer-based recording system gives the potential to relieve problems of lack of space, lost data, inaccuracies in recording and to provide a flexible and efficient retrieval system, therefore relieving staff time for other work” (Xxxxxxxx, 1983, p. 43), which is basically also the main aim of this project. It seems not much has changed in the last 40 years in that respect. At the end of the 20th century, computer systems became increasingly com- mon place, and in the last 20 years a number of projects have used Text Mining techniques on archaeological texts. Xxxxxx et al. (2008) created a full work- flow allowing experts to extract information from text, but in a quite specialised way on small collections, and is not meant for searching through large corpora. Xxxxx & Xxxxx (2010) experimented with extracting archaeological events and converting them to Resource Description Framework (RDF) triples, to increase the interconnectivity between data silos. Going more in the direction of IR, the Archaeotools project used a combina- tion of rules based and machine learning approaches to automatically generate location, time period, and subject metadata for a small selection of a thousand reports, with moderate success. This generated metadata could then be used for searching in a facetted interface (Xxxxxxx et...
Sample 1
Previous Research. In the other fields of the social sciences, the previous research concerning immigration from former Soviet republics has concentrated on social networks, stigmatization experiences, integration, professional experiences and migration of mothers of young children. Therefore, this field has been broadly studied already. The viewpoint of the most of the previous studies made in Finland, has been either to study the number of immigrants and to clarify their backgrounds by using migration xxxxxxxx0 or to research the acculturation process of the immigrants8. Also studies concerning the attitudes of Finns against foreigners9, the willingness of Estonian people to emigrate10 and Estonian people's stigmatization experiences11 have been made. In the field of cross-cultural psychology, for example Xxxx X. Xxxxx has studied the psychological consequences of acculturation and created concepts to study acculturation and adaptation.12 Finnish immigration research has traditionally concentrated on how immigrants have done and acted in Finland and among Finns but less attention has been paid on the communities 7 E.g. Kyntäjä & Kulu 1998. 8 E.g. Liebkind 1994, Perhoniemi & Xxxxxxxxxx-Lahti 2006, Xxxxxxxxxx 2006.
Sample 1

Related to Previous Research

  • Technology Research Analyst Job# 1810 General Characteristics

  • Research Support (a) Having regard to the resources reasonably available for such purposes, the Operator will cooperate with AHS to provide such participation by its Staff as may be reasonable in relation to the carrying out of research within the Province. (b) The Operator agrees to promptly notify AHS in the event that it undertakes or agrees to participate in any form of clinical trial, research project, instrument use, or similar activity which in any way relates to the Services provided under this Agreement. The Operator shall, upon request, provide AHS with written evidence of Client disclosure and consent to research.

  • Research Matters By entering into this Agreement, the Placement Agent does not provide any promise, either explicitly or implicitly, of favorable or continued research coverage of the Company and the Company hereby acknowledges and agrees that the Placement Agent’s selection as a placement agent for the Offering was in no way conditioned, explicitly or implicitly, on the Placement Agent providing favorable or any research coverage of the Company. In accordance with FINRA Rule 2711(e), the parties acknowledge and agree that the Placement Agent has not directly or indirectly offered favorable research, a specific rating or a specific price target, or threatened to change research, a rating or a price target, to the Company or inducement for the receipt of business or compensation.

  • Research Primary Investigator as part of a multi-site study (25 points) • Co-Investigator as part of a multi-site study (20 points) • Primary Investigator of a facility/unit based research study (15 points) • Co-Investigator of a facility/unit based research study (10 points) • Develops a unit specific research proposal (5 points) • Conducts a literature review as part of a research study (5 points)

  • Collaboration 31.1 If the Buyer has specified in the Order Form that it requires the Supplier to enter into a Collaboration Agreement, the Supplier must give the Buyer an executed Collaboration Agreement before the Start date. 31.2 In addition to any obligations under the Collaboration Agreement, the Supplier must: 31.2.1 work proactively and in good faith with each of the Buyer’s contractors 31.2.2 co-operate and share information with the Buyer’s contractors to enable the efficient operation of the Buyer’s ICT services and G-Cloud Services

  • Research Use The Requester agrees that if access is approved, (1) the PI named in the DAR and (2) those named in the “Senior/Key Person Profile” section of the DAR, including the Information Technology Director and any trainee, employee, or contractor1 working on the proposed research project under the direct oversight of these individuals, shall become Approved Users of the requested dataset(s). Research use will occur solely in connection with the approved research project described in the DAR, which includes a 1-2 paragraph description of the proposed research (i.e., a Research Use Statement). Investigators interested in using Cloud Computing for data storage and analysis must request permission to use Cloud Computing in the DAR and identify the Cloud Service Provider (CSP) or providers and/or Private Cloud System (PCS) that they propose to use. They must also submit a Cloud Computing Use Statement as part of the DAR that describes the type of service and how it will be used to carry out the proposed research as described in the Research Use Statement. If the Approved Users plan to collaborate with investigators outside the Requester, the investigators at each external site must submit an independent DAR using the same project title and Research Use Statement, and if using the cloud, Cloud Computing Use Statement. New uses of these data outside those described in the DAR will require submission of a new DAR; modifications to the research project will require submission of an amendment to this application (e.g., adding or deleting Requester Collaborators from the Requester, adding datasets to an approved project). Access to the requested dataset(s) is granted for a period of one (1) year, with the option to renew access or close-out a project at the end of that year. Submitting Investigator(s), or their collaborators, who provided the data or samples used to generate controlled-access datasets subject to the NIH GDS Policy and who have Institutional Review Board (IRB) approval and who meet any other study specific terms of access, are exempt from the limitation on the scope of the research use as defined in the DAR.

  • Research Use Reporting To assure adherence to NIH GDS Policy, the PI agrees to provide annual Progress Updates as part of the annual Project Renewal or Project Close-out processes, prior to the expiration of the one (1) year data access period. The PI who is seeking Renewal or Close-out of a project agree to complete the appropriate online forms and provide specific information such as how the data have been used, including publications or presentations that resulted from the use of the requested dataset(s), a summary of any plans for future research use (if the PI is seeking renewal), any violations of the terms of access described within this Agreement and the implemented remediation, and information on any downstream intellectual property generated from the data. The PI also may include general comments regarding suggestions for improving the data access process in general. Information provided in the progress updates helps NIH evaluate program activities and may be considered by the NIH GDS governance committees as part of NIH’s effort to provide ongoing stewardship of data sharing activities subject to the NIH GDS Policy.

  • Research Program The term “Research Program” shall mean the research program to be undertaken by TSRI under the direction and control of the Principal Investigator as expressly set forth on Exhibit A hereto.

  • Research Project The findings of any research project, which would change the provisions of this Agreement will not be implemented until such changes are negotiated and agreed to by the parties.

  • Manufacturing Technology Transfer Upon AbbVie’s written request with respect to a given Collaboration CAR-T Product and Licensed Product, Caribou shall effect a full transfer to AbbVie or its designee (which designee may be an Affiliate or a Third Party Provider) of all Materials and Know-How Controlled by Caribou relating to the then-current process for the Manufacture of such Collaboration CAR-T Product and any corresponding Licensed Products (each, a “Manufacturing Process”). Caribou shall provide, shall cause its Affiliates to provide, and shall use Commercially Reasonable Efforts to assist AbbVie in causing all Third Party Providers to provide, all reasonable assistance requested by AbbVie to enable AbbVie (or its Affiliate or designated Third Party Provider, as applicable) to implement each Manufacturing Process at the facilities designated by AbbVie. If requested by AbbVie, such assistance shall include facilitating the entering into of agreements with applicable Third Party suppliers relating to such Collaboration CAR-T Product and any corresponding Licensed Products. Without limitation of the foregoing, in connection with the Manufacturing Process and related transfer: (a) Caribou shall, and shall cause its Affiliates to, make available to AbbVie (or its Affiliate or designated Third Party Provider, as applicable), and shall use Commercially Reasonable Efforts to assist AbbVie in causing all Third Party Providers to make available to AbbVie, from time to time as AbbVie may request, all Materials and Manufacturing-related Know-How Controlled by Caribou relating to each Manufacturing Process, including methods, reagents and processes and testing/characterization Know-How, and all documentation constituting material support, performance advice, shop practice, standard operating procedures, specifications as to Materials to be used, and control methods, that are necessary or reasonably useful to enable AbbVie (or its Affiliate or designated Third Party manufacturer, as applicable) to use and practice such Manufacturing Process; (b) Caribou shall cause all appropriate employees and representatives of Caribou and its Affiliates, and shall use Commercially Reasonable Efforts to assist AbbVie in causing all appropriate employees and representatives of Third Party Providers, to meet with employees or representatives of AbbVie (or its Affiliate or designated Third Party Provider, as applicable) at the applicable manufacturing facility at mutually convenient times to assist with the working up and use of each Manufacturing Process and with the training of the personnel of AbbVie (or its Affiliate or designated Third Party Provider, as applicable) to the extent necessary or reasonably useful to enable AbbVie (or its Affiliate or designated Third Party Provider, as applicable) to use and practice such Manufacturing Process; (c) Without limiting the generality of this Section 4.4.2, Caribou shall cause all appropriate analytical and quality control laboratory employees and representatives of Caribou and its Affiliates, and shall use Commercially Reasonable Efforts to assist AbbVie in causing all appropriate analytical and quality control laboratory employees and representatives of Third Party Providers, to meet with employees or representatives of AbbVie (or its Affiliate or designated Third Party Provider, as applicable) at the applicable manufacturing facility and make available all necessary equipment, at mutually convenient times, to support and execute the provision of all applicable analytical methods and the validation thereof (including all applicable Know-How, Information and Materials Controlled by Caribou, and sufficient supplies of all primary and other reference standards); (d) Caribou shall, and shall cause its Affiliates to, take such steps, and shall use Commercially Reasonable Efforts to assist AbbVie in causing Third Party Providers take such steps, as are necessary or reasonably useful to assist AbbVie (or its Affiliate or designated Third Party Provider, as applicable) in obtaining any necessary licenses, permits or approvals from Regulatory Authorities with respect to the Manufacture of the applicable Collaboration CAR-T Products and corresponding Licensed Products at the applicable facilities; and (e) Caribou shall, and shall cause its Affiliates to, provide, and shall use Commercially Reasonable Efforts to assist AbbVie in causing Third Party Providers to provide, such other assistance as AbbVie (or its Affiliate or designated Third Party Provider, as applicable) may reasonably request to enable AbbVie (or its Affiliate or designated Third Party Provider, as applicable) to use and practice each Manufacturing Process and otherwise to Manufacture the applicable Collaboration CAR-T Products and corresponding Licensed Products.

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