International Winter Wheat nurseries data: Facultative and Winter Wheat Observation Nurseries and International Winter Wheat yield trials for semi-arid and irrigated conditions

This data paper describes the content of 16 datasets collected under the International Winter Wheat Improvement Program (IWWIP), an alliance between Turkey-CIMMYT-ICARDA (TCI), during the 2015–2016, 2016–2017, 2017–2018 and 2018–2019 seasons. Data was collected from the Facultative and Winter Wheat Observation Nursery (FAWWON) and the International Winter Wheat Yield Trials (IWWYT) conducted under semi-arid and irrigated conditions across different countries. Data on all nurseries was collected during the growing season by IWWIP's team and cooperators in their local environments. It was compiled at the end of the wheat season by IWWIP's team. Multi-locational data can be used to select advanced lines that fit to collaborators’ growing environment. The selected germplasm can either be used as a parent in their breeding programs or be released as a variety in their country

Extended Multilingual Protest News Detection -- Shared Task 1, CASE 2021 and 2022

We report results of the CASE 2022 Shared Task 1 on Multilingual Protest Event Detection. This task is a continuation of CASE 2021 that consists of four subtasks that are i) document classification, ii) sentence classification, iii) event sentence coreference identification, and iv) event extraction. The CASE 2022 extension consists of expanding the test data with more data in previously available languages, namely, English, Hindi, Portuguese, and Spanish, and adding new test data in Mandarin, Turkish, and Urdu for Sub-task 1, document classification. The training data from CASE 2021 in English, Portuguese and Spanish were utilized. Therefore, predicting document labels in Hindi, Mandarin, Turkish, and Urdu occurs in a zero-shot setting. The CASE 2022 workshop accepts reports on systems developed for predicting test data of CASE 2021 as well. We observe that the best systems submitted by CASE 2022 participants achieve between 79.71 and 84.06 F1-macro for new languages in a zero-shot setting. The winning approaches are mainly ensembling models and merging data in multiple languages. The best two submissions on CASE 2021 data outperform submissions from last year for Subtask 1 and Subtask 2 in all languages. Only the following scenarios were not outperformed by new submissions on CASE 2021: Subtask 3 Portuguese \& Subtask 4 English.Comment: To appear in CASE 2022 @ EMNLP 202

SU-NLP at SemEval-2022 Task 11: complex named entity recognition with entity linking

This paper describes the system proposed by Sabanci University Natural Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an unsupervised entity linking pipeline that detects potential entity mentions with the help of Wikipedia and also uses the corresponding Wikipedia context to help the classifier in finding the named entity type of that mention. The proposed pipeline significantly improved the performance, especially for complex entities in low-context settings

GIS-based optimal site selection for the solar-powered hydrogen fuel charge stations

Energy consumption which is the most critical input in daily life is increasing constantly because of the growth of the world population and development of the devices. The transportation industry which is responsible for almost half of all worldwide emissions is switching to innovative devices run on renewable energy. Renewable-powered charging stations are important for reducing the environmental impact of vehicles. The objective of the study was to determine the best hydrogen charge station locations and rates which are supplied by coupled solar power plants. The GIS-based assessment was carried out with proper data from data sources such as Copernicus Land Monitoring (CLC 2018), and Global Solar Atlas (GSA). For the determination of suitable regions for solar power plants, eight sub-criteria were assessed under technical (C1), accessibility (C2), and environmental (C3) main criteria. Afterward, the obtained result map layer and suitability of the six determined water bodies, located in the study region, were investigated by assuming the buffer zone value. Results showed that the study area had suitable regions of 38.1&nbsp;km2&nbsp;indicating that %22.4 of it was convenient for the solar power plant investments. While Cihanbeyli Lake does not have the highest suitable region, the biggest suitable area inside the buffer zone of lakes was determined as the Ağcaşayar Dam. The maximum annual solar production level is obtained as 1,919 MWh/year in the Ağcaşayar Dam, with a hydrogen production of 34,933 tons/year. Consequently, the Ağcaşayar Dam is the best suitable destination for a hydrogen fuel station in Kayseri.</p

Evaluation of High Performance Liquid Chromatography and Liquid Chromatography-Tandem Mass Spectrometry Methods for 25 (OH) D-3 Assay

Background: This study was designed to compare the performances of HPLC (High Performance Liquid Chromatography) and LC-MS/MS (Liquid Chromatography-Tandem Mass Spectrometry) methods in 25 (OH) D3 testing