Strangeness production in two-particle azimuthal correlations on the near and away side measured with ALICE in pp collisions at 7 TeV

Two-particle azimuthal correlations allow one to study high-$p_{\rm T}$ parton fragmentation without full jet reconstruction. Enhancements of the azimuthal correlations are seen at $\Delta \varphi \approx 0$ and $\Delta \varphi \approx \pi$, resulting from back-to-back jet fragmentation in the parton center-of-mass system. We present the current status of the study of correlations between charged trigger particles and associated strange baryons ($\Lambda$) and mesons (K$_{S}^{0}$) in pp collisions at $\sqrt{s}$ = 7 TeV. A data-driven feeddown correction for $\Lambda$ is also presented, which could allow a more accurate calculation of the primary $\Lambda/$K$_{S}^{0}$ ratio in jets and the underlying event.Comment: 5 pages, 4 figures, Proceedings of the Second Annual Conference on Large Hadron Collider Physics (LHCP 2014), June 2-7, 2014, New Yor

Comparison of the Knowledge in Core Policies of Essential Drug List Among Medical Practitioners and Medical Students in Galle, Sri Lanka

Selection of the best and safest medicine should be one of the national economic policies in a country for good health care services. Introduction of detailed module in rational use of medicine (RUM) to pharmacology syllabus needed prior analysis of the existing knowledge among health care workers. Therefore we assessed the knowledge and attitudes of essential drug list (EDL) on medical practitioners (MPs) and medical students (MSs). Forty two MPs and 120 MSs from hospital and Faculty of Medicine were given a pretested structured questionnaire related to core policies of EDL, contents, criteria for selection and time frame for revision in RUM. Our study showed that only 29% of MPs were confident about their knowledge in EDL and 17% of them had marked it as don’t know. Study was expanded for quantitative analysis of the knowledge on the core policies of EDL on them. Knowledge on contents, criteria for selection and the time frame for revision of EDL were 63%, 83% and 17% in MPs. Of MSs, 87% had sound knowledge in core contents, 32% in criteria for selection and only 50% of MSs were aware about the correct time frame of revision of EDL. Knowledge in contents of EDL was higher in MSs (87%) than MPs(63&). MPs were not aware about EDL preparing criteria such as inclusion of generic names, common ailment, majority ailments of the people (59%, 56% and 56% respectively). In contrast, MSs had > 93% of the knowledge in all three areas. However MSs had poorer knowledge (32%) in criteria for selection of EDL than MPs (83%). Knowledge in time frame for revision of EDL was 17% in MPs and 50% in MSs. We found that MPs in the service were not convinced about their knowledge in EDL. Deficiency was significant in the core contents of the EDL preparation. Therefore we suggest that MPs need repetitive in-service training programme for practicing of RUM in the national health facilities. We need to reiterative programme in the core curriculum regarding the criteria for EDL selection. Though MSs had good knowledge in content of EDL, they are poor in criteria for selection and time frame for revision

Novel starch types

This thesis investigated molecular changes in potato starch achieved through targeted mutations in the starch synthesis pathway. CRISPR/Cas9 was used to induce mutations in starch branching enzyme genes (SBE), with or without mutations in granule bound starch synthase gene (GBSS). The resulting starch was characterised for molecular and functional attributes. Barley starch obtained through conventional cross-breeding was also characterised, to explore potential impacts of fructan synthesis changes on starch synthesis at composition and molecular structure level. Inducing mutations in all alleles of the SBEI and some alleles of the SBEII produced high-amylose starch, while inducing mutations in all alleles of both SBEs resulted in amylose-only starch. GBSS mutations alone yielded a waxy starch phenotype, while introducing GBSS mutations in SBEs mutated background led to non-waxy, low-amylose lines. Mutations in SBEI produced starch with unit chain distributions close to the native variety. Mutations in the GBSS produced starch with building blocks (BB) distribution resembling the native variety. Significant deviations to unit chain and BB distribution were observed when both SBEI and SBEII were mutated. Presence of high proportions of large BB elevated gelatinisation and retrogradation temperatures, while high proportions of short amylopectin chains lowered gelatinisation temperature. Potato lines with diverse genetic backgrounds exhibited variations in pasting profiles, influencing film-forming behaviour. Natural genetic variation-based conventional cross-breeding of barley produced starch with modified structures, as upregulated fructan synthesis resulted in starch with a high proportion of large BB. These novel insights into how alterations in starch synthesis pathway affect starch properties pave the way for tailored starch development

The Level of Green Supply Chain Practices Adoption in Sri Lankan Manufacturing Companies

It is been topical to discuss about Green Supply Chain Management due to increasing environmental issues all over the world. Therefore many scholars have been given their attention to do more research on this area based on their own countries. Through this study researcher expects to strength existing body of knowledge regarding green supply chain management based on the Sri Lankan context. Thus this paper attempts to identify the current level of green supply chain practices adoption in Sri Lankan manufacturing companies and gives comparative analysis of green supply chain practices adoption with Chinese and Japanese manufacturing companies. For this study structured questionnaire was used to collect data from the sample of 125 manufacturing companies in Sri Lanka and data were analyzed using descriptive analysis. In order to have general understanding about the level of green supply chain practices adoption, five stages of adoption level was used as a theoretical base. Findings of this study will be more contributed to strengthen the green supply chain management further for managers, relevant authorities and the government of Sri Lanka.Key words: Environmental Issues, Green Supply Chain Management, Green Supply Chain Practices, Level of Adoption, Manufacturing Companies

Theories Involved in Recognizing and Implementing International Law in Domestic Contexts

The impact of international law on the domestic legal system has never being more visible and potent as we see it today. While this being the case, international law itself does not provide a mechanism as to how international law should be absorbed in to the domestic legal system and that question is left for the country in question to decide. In selecting an appropriate method for introducing international law into the domestic legal system, the theoretical aspirations of monism and dualism has been in the forefront of many books and articles that have been written on the subject, yet it has been rejected as being failing to appraise the actual practises of the countries in question when adopting a particular mechanism for bringing international law into the domestic legal system. This article there for explores the reasons as to why monism and dualism has failed to introduced a methodology in bringing international law into the domestic context and what theoretical explanations could be provided for such failure with a conclusion on a more pragmatic approach in choosing the middle way between monism and dualism. Keywords: Monism, Dualism, International Law, Incorporation, Transformation DOI: 10.7176/JLPG/109-09 Publication date:May 31st 202

Predicting Income and Feasible Loan Amount for a Household Unit (Expenditure Analysis of Badulla District, Sri Lanka)

Expenditures of a household are growing due to many reasons which came up with complexity of basic needs and downturn of economy. Emerging body of knowledge suggest the necessity of expenditure studies in Sri Lanka. It is difficult to carry out an island wide study. Therefore Badulla was selected since it covers variety of living conditions in a limited extent. Six divisional secretariats were selected randomly and those were Badulla, Passara, Uva-Paranagama, Haldummulla, Soranathota and Meegahakivula. Respondents (household heads) were selected by systematic random sampling. They were interviewed by a structured questionnaire. 95% confidence intervals were calculated for mean costs of basic needs. These means were compared by living areas using one way ANOVA. Result implies that living area can influence on amount of cost. The main objective of this study is predicting total income and feasible loan amount per a household unit. Multiple regression analysis showed 70.9% of the variation of total income was accounted by the explanatory variables. Cost for foods, cost for transport and cost for clothing had a significant (p-value<0.05) effect on total income. Moreover, fitted model for predicting feasible loan amount explained 87.0% of total variation. Cost for electricity, cost for water, cost for education, cost for health and cost for social activities were significant (p-value<0.05) variables of the best fitted model. It can be concluded that multiple linear regression model which was fitted for predicting feasible amount of loan performs better than fitted model for predicting total income. This would be useful for administrative divisions and financial sector. Because before a person go for a higher amount of loan it can be explained by taking basic details of him or her what is the feasible amount of loan with his or her economic status. It would be good for loan receiver and loan providers. Key Words: Expenditure, household income, stratified random sampling, multiple linear regression DOI: 10.7176/DCS/9-6-11 Publication date:June 30th 201

SSentiaA: A Self-Supervised Sentiment Analyzer for Classification From Unlabeled Data

In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable. When annotated data are unavailable, the unsupervised tools are exercised, which still lag behind the performance of supervised ML methods by a large margin. Therefore, in this work, we focus on improving the performance of sentiment classification from unlabeled data. We present a self-supervised hybrid methodology SSentiA (Self-supervised Sentiment Analyzer) that couples an ML classifier with a lexicon-based method for sentiment classification from unlabeled data. We first introduce LRSentiA (Lexical Rule-based Sentiment Analyzer), a lexicon-based method to predict the semantic orientation of a review along with the confidence score of prediction. Utilizing the confidence scores of LRSentiA, we generate highly accurate pseudo-labels for SSentiA that incorporates a supervised ML algorithm to improve the performance of sentiment classification for less polarized and complex reviews. We compare the performances of LRSentiA and SSSentA with the existing unsupervised, lexicon-based and self-supervised methods in multiple datasets. The LRSentiA performs similarly to the existing lexicon-based methods in both binary and 3-class sentiment analysis. By combining LRSentiA with an ML classifier, the hybrid approach SSentiA attains 10%–30% improvements in macro F1 score for both binary and 3-class sentiment analysis. The results suggest that in domains where annotated data are unavailable, SSentiA can significantly improve the performance of sentiment classification. Moreover, we demonstrate that using 30%–60% annotated training data, SSentiA delivers similar performances of the fully labeled training dataset

Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object detectors and object instance segmentation models to find the best model to be integrated in a real-time eye movement analysis pipeline. The authors filter gaze data that falls within the polygonal boundaries of detected dynamic AOIs and apply object detector to find bounding-boxes in a public dataset. The results indicate that the dynamic AOIs generated by object detectors capture 60% of eye movements & object instance segmentation models capture 30% of eye movements

Human Interaction With Fake News

https://digitalcommons.odu.edu/reu2022_computerscience/1007/thumbnail.jp

DFS: A Dataset File System for Data Discovering Users

Many research questions can be answered quickly and efficiently using data already collected for previous research. This practice is called secondary data analysis (SDA), and has gained popularity due to lower costs and improved research efficiency. In this paper we propose DFS, a file system to standardize the metadata representation of datasets, and DDU, a scalable architecture based on DFS for semi-automated metadata generation and data recommendation on the cloud. We discuss how DFS and DDU lays groundwork for automatic dataset aggregation, how it integrates with existing data wrangling and machine learning tools, and explores their implications on datasets stored in digital libraries