Miracle-dz/UniFAR-FTUs huggingface dataset:
CORRELATING BIOCHEMICAL, RADIOLOGICAL AND CYTOLOGICAL OUTCOMES OF PLEURAL EFFUSION

Introduction: The research emphasises on the association bond of correlation of biochemical radiological and cytological outcomes of pleural effusion Material and Methods: The samples for the study were collected from the diagnostic centre, out-patient department and in-patient department of Department of Biochemistry, Internal Medicine, Respiratory Medicine at Rabindranath Tagore Medical College, Udaipur, Rajasthan The research was carried out on a total of 100 patients . The observations took about a year’s time. The research was carried out on a total of 100 patients, Results: 100 patients with pleural effusion were studied of which 60.00% were cases of tuberculous effusion and 40.00% were cases of non tuberculous effusion. This was reflective of the high prevalence of tuberculosis in the area being studied. The remaining 40 cases were of malignant effusion (15 cases), Transudative effusion (13 cases), synpneumonic effusion (8 cases) and 4 cases of empyema. In patients of age more than 40 years, malignant effusion was more common; It was found more common in lower socioeconomic class such people live in crowded, unhygienic conditions. The commonest symptoms were cough (81.32%) and breathlessness (78.76%), followed by fever 69.20%, weight loss 64.64%, chest pain 45.50%, loss of appetite 63.30% and hemoptysis 15.80%. Conclusion: Thus it can be concluded that ADA is elevated in tubercular pleural effusion. Exudatives had decreased glucose but increased protein, LDH and cholesterol compared to transudatives Pleural fluid cytology revealed elevated Lymphocytes in tubercular and polymorphs in acute infections Keywords: Lactate dehydrogenase, Adenosine Deaminase biochemical, radiological and cytological outcomes of pleural effusion
Genital Tract Morphopathology of Red Sokoto and West African Dwarf Does in Makurdi

This study was conducted with the aim to record pathological conditions of the female genital tract of Red Sokoto (RS) and West African Dwarf (WAD) does with the objective of comparing the prevalence of genital tract pathology between the two breeds. Of the 211 (RS) and 221 (WAD) genital tract collected and examined, 104 (49.3%) of RS and 97 (43.3%) of WAD showed different types of pathological conditions. Highest occurrence of pathological conditions found in RS was in the uterus (38.4%), followed by those in the ovary (30.8%), cervix (6.6%), salpinx (6.2%) and vagina (1.4%) while in WAD, pathological conditions were highest in the uterus (28.1%), and followed by ovary (26.8%), salpinx (5.0%), cervix (2.2%) and vagina (0.5%). In RS and WAD does, respectively, endometritis (11.9% and 7.1%) was the most common pathology followed by ovaro-bursal adhesion (10.4% and 5.4%), inactive ovaries (4.3% and 4.9%), cystic Graffian follicles (4.2% and 2.2%), paraovarian cyst (3.3% and 3.1%), uterine haemorrhage (3.8% and 2.7%), and ovarian hypoplasia (2.4% and 4.5%). Other pathological conditions found in RS and WAD does include cervical lesions: cervicitis (2.8% and 1.8%), haemorrhage (3.2% and 0.5%); uterine lesions: hydrometra (1.9% and 1.3%), pyometra (2.8% and 1.8%), mucometra (2.8% and 3.6%), metritis (2.4% and 1.3%), post parturient metritis (2.8% and 1.8%), caruncular atrophy (1.4% and 0.5%), endometrial hyperplasia (1.0% and 1.8%), melanosis (1.9% and 1.8%), post parturient emphysematous metritis (1.4% and 2.2%), perimetritis (1.4% and 2.2%), uterine congestion (1.9% and 2.2%), oedema (1.0% and 0.5%) and Cysticercus tenuicolis cyst (0.5% and 0.5%); esophageal lesions: salpingitis (1.9% and 3.1%), hydrosalpinx (1.0% and 0.5%), tubo-bursal cyst (3.3% and 0.9%), ovarian lesions: cystic corpora lutea (2.8% and 1.8%), oophoritis (0.5% and 1.8%), and Cysticercus tenuicolis cyst (1.0% and 0.9%). Hyperaemia (0.5%) and calcification of cervical caudal rugae (0.5%) occurred in RS only. Genital lesions were not statistically different (P > 0.05) between RS and WAD does. In the two breeds, the 13 - 24 months age group had high prevalence of genital lesions. The current study has shown that endometritis and ovaro-bursal adhesions are the common female genital tract pathological conditions in Red Sokoto and West African Dwarf does. The high prevalence of female genital tract abnormalities may suggest that genital diseases are of serious limitation to goat production in Makurdi.
Destination images projected in the sport event website: A case of Ironman 70.3 Qujing

Abstract In the recent decades, many cities have attempted to promote destination images by holding sport events. However, little scholarly attention has been paid to the destination images communicated to the public through event promotion. This research aims to bridge the gap through a case study of the triathlon event, Ironman 70.3 Qujing. Data were collected from the official website of Ironman. Deductive thematic analyses disclose that the website projects an event image, which has some similarities and differences with the destination image. The projected images are event-bound; and this feature leaves out some critical components of destination images, such as cultural landscape and experience quality. The study results have implications for the marketing of event destinations, especially the emerging and small destinations.
Impacts of mineral resources and fossil fuels on sovereign credit ratings and sovereign borrowing costs: evidence from cross-country panel data

This research aims to investigate the impacts of mineral resources and fossil fuels on sover-eign credit ratings and sovereign borrowing costs. Each type of resource is separately inves-tigated for comparison purposes. The government bond yield is the proxy for sovereign costs and 10 year bonds presents long-term debts while 1-year bonds are proxies for short-term debt. The panel data set consists of 110 countries from multiple sources over the period from 1998 to 2015. To achieve the objectives, the study applies panel data regression analysis such as Fixed Effects as the main methodology, Random Effects and Random-Effects or-dered probit estimation as robustness checks. The empirical results imply that not every min-eral resource and fossil fuel variables has a significant impact on sovereign ratings or govern-ment borrowing costs. Mineral rents per GDP is the only variable having positive impacts on credit ratings while gas rents, oil rents and total extractive activity have negative influ-ences. In a sub-sample of high-income countries, none of the mineral resource or fossil fuels are significantly correlated to the ratings. The empirical regression on long-term government bond yields suggests that only oil rents and total extractive activity per GDP influenced 10-year bond yields negatively and significantly. While mineral rents per GDP and total extrac-tive activity per GDP positively affect on 1-year bond yields for high-income countries. The findings imply that rich-mineral resource and high-income countries have to pay higher in-terest rates for their short-term loans. Countries with higher oil rents per GDP tend to pay lower bond yields in the long-term.
Audio or written feedback? The secondary school English student experience

ABSTRACT This paper uses student voice to explore whether and why audio feedback can be a preferred form of formative assessment to written marking in the English classroom. The research is a qualitative case study of the views of 28 questionnaire respondents and seven interviewees aged 14–15 years old with regards to their experiences of audio feedback. The results suggest that audio feedback was overwhelmingly preferred and perceived as a more effective form of feedback, largely due to its social and relational character. This paper shows that audio feedback can provide effective formative assessment, particularly through its relational nature, which can be capitalised on in the English secondary school classroom. Furthermore, it suggests that teacher training courses could equip new professionals with the skills needed for delivering effective audio feedback.
Optimization of Zero Emission Hydrogen Fuel Cell Ferry Design, with Comparisons to the SF-BREEZE

Zero emission hydrogen fuel cell technology has the potential to drastically reduce total “well-to-waves” maritime emissions. Through realistic design studies of five commercially-relevant passenger vessels, this study examines the most cost-effective entry points in the US fleet for deploying today’s available technology, and includes analysis of resulting well-to-waves emission profiles. The results show that per-passenger mile vessel energy use is directly correlated to increased emissions, capital costs, and operating costs. As a consequence, low speed, large capacity vessels offer a cost-effective starting place today. Increases in vessel efficiency through such measures as hull design and light-weighting can have large impacts in reducing cost and emissions of these systems. Overall this work showed all five vessel types to be feasible with today’s hydrogen fuel cell technology and presents more options to fleets that are committed to reducing maritime emissions in cost effective ways.
Conditions for the development of Vietnamese business and entrepreneurship in Poland

This paper outlines the history of the development of Vietnamese business and Vietnamese businessmen and entrepreneurs community in Poland. This special period of history of development was to be divided into two main phases. In addition, this article has analyzed the factors affecting international entrepreneurship spirit and international entrepreneurship capacity of this Vietnamese business community here in Poland. These factors are well identified in the overview part of international startups and international entrepreneurship study. The study showed that these factors include: foreign market appeal, government policies, compatibility of business practices, and adaptability to foreign markets. In the research part of this article, these factors were taken to be reanalyzed for the Vietnamese business community in Poland and showed why Vietnamese people choose Poland to be a good place for family settlement and for doing long-term business. Moreover, the success factors and success barriers behind their business development compared to the community of Chinese businessmen and entrepreneurs who came to Poland just only several years later have also been clearly identified. The research results show that, to strengthen the position and to raise the business awareness of Vietnamese business community in Poland, to strengthen it into a more developed and unified community for the purpose of future sustainable development, Vietnamese embassy in Poland with all of its networks of partnership, Vietnam's foreign trade promotion and trade representative agencies, and the diverse Vietnamese associations in Poland, both for profit and non-profit, need to play an interactive role to support Vietnamese businessmen and entrepreneurs to understand indigenous business practices, to overcome language and culture barriers in a bid to better integrate with the Polish business and social environment.
Mitigation potential and environmental impact of centralized versus distributed BECCS with domestic biomass production in Great Britain

New contingency policy plans are expected to be published by the United Kingdom government to set out urgent actions, such as carbon capture and storage, greenhouse gas removal and the use of sustainable bioenergy to meet the greenhouse gas reduction targets of the 4th and 5th Carbon Budgets. In this study, we identify two plausible bioenergy production pathways for bioenergy with carbon capture and storage (BECCS) based on centralized and distributed energy systems to show what BECCS could look like if deployed by 2050 in Great Britain. The extent of agricultural land available to sustainably produce biomass feedstock in the centralized and distributed energy systems is about 0.39 and 0.5 Mha, providing approximately 5.7 and 7.3 MtDM/year of biomass respectively. If this land‐use change occurred, bioenergy crops would contribute to reduced agricultural soil GHG emission by 9 and 11 MtCO2eq /year in the centralized and distributed energy systems respectively. In addition, bioenergy crops can contribute to reduce agricultural soil ammonia emissions and water pollution from soil nitrate leaching, and to increase soil organic carbon stocks. The technical mitigation potentials from BECCS lead to projected CO2 reductions of approximately 18 and 23 MtCO2 /year from the centralized and distributed energy systems respectively. This suggests that the domestic supply of sustainable biomass would not allow the emission reduction target of 50 MtCO2 /year from BECCS to be met. To meet that target, it would be necessary to produce solid biomass from forest systems on 0.59 or 0.49 Mha, or alternatively to import 8 or 6.6 MtDM/year of biomass for the centralized and distributed energy system respectively. The spatially explicit results of this study can serve to identify the regional differences in the potential capture of CO2 from BECCS, providing the basis for the development of onshore CO2 transport infrastructures.
PRAM: a novel pooling approach for discovering intergenic transcripts from large-scale RNA sequencing experiments

Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by joint examination of large collections of RNA-seq datasets has emerged as one such analysis. Current methods for transcript discovery rely on a ‘2-Step’ approach where the first step encompasses building transcripts from individual datasets, followed by the second step that merges predicted transcripts across datasets. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a novel ‘1-Step’ approach named Pooling RNA-seq and Assembling Models (PRAM) that builds transcript models from pooled RNA-seq datasets. We demonstrate in a computational benchmark that ‘1-Step’ outperforms ‘2-Step’ approaches in predicting overall transcript structures and individual splice junctions, while performing competitively in detecting exonic nucleotides. Applying PRAM to 30 human ENCODE RNA-seq datasets identified unannotated transcripts with epigenetic and RAMPAGE signatures similar to those of recently annotated transcripts. In a case study, we discovered and experimentally validated new transcripts through the application of PRAM to mouse hematopoietic RNA-seq datasets. Notably, we uncovered new transcripts that share a differential expression pattern with a neighboring gene Pik3cg implicated in human hematopoietic phenotypes, and we provided evidence for the conservation of this relationship in human. PRAM is implemented as an R/Bioconductor package and is available at https://bioconductor.org/packages/pram.
Unsupervised Machine Learning Based Scalable Fusion for Active Perception

The fusion of sensor data is an essential requirement for active perception in autonomous robots in order to accurately perceive an environment, make appropriate adjustments that improve the understanding of the environment and generate actionable insights. Despite a large body of literature in this domain, only a handful of research focuses on unsupervised machine learning, which is increasingly important in unlabeled, unknown environments. This paper proposes a scalable self-organizing neural architecture for environmental perception based on multimodal fusion using unsupervised machine learning. Inspired by the biological counterpart, the neural architecture consists of topographic maps across each modality and incorporates a novel scalable self-organization process that handles high volume, high-velocity sensor data. The co-occurrence relationships across modalities are captured in cross-modal connections and the reverse Hebbian projection is used for multimodal representation. This proposed unsupervised machine learning-based scalable fusion technique was implemented on Apache Hadoop and Apache Spark. It was validated using an extensive multimodal human activity sensor dataset to demonstrate efficient representation and multimodal fusion for active perception. Note to Practitioners—Heterogeneous sensors are increasingly used to capture different aspects of an event or an environment. This paper presents a multimodal fusion mechanism to fuse such heterogeneous sensor data to support the accurate perception of the surrounding. The proposed mechanism uses natural regularities in patterns observed across multiple modalities and enriches each modality with information from other co-occurring modalities to achieve a multimodal clustering. The cluster can be used for online prediction and offline action planning of the robot by matching the current sensory inputs. The algorithm uses unsupervised machine learning, and hence, can be used with any multimodal dataset without being restricted by the labeling of the dataset. In addition, we adapt the algorithm for multiple big data platforms so that it is effective and pragmatic for large-scale sensor data fusion tasks.
Deactivation of Escherichia coli with different volumes in drinking water using cold atmospheric plasma

This experimental study aimed to evaluate the potential of cold atmospheric plasma jet to deactivate Escherichia coli from drinking water. We studied the effect of the volume of water samples on the performance of plasma jet on deactivation of E. coli of 1, 500, 1,000, 1,500, and 2,000 cubic centimetres. The results of deactivation of E. coli in 500 and 1,000 cc water samples were the same as one cc of a water sample and we observed 8‐log reduction of E. coli using 50 W. In 1,500 and 2,000 cc water samples at 8 min using a power of 50 W, 4.5 and 2.9 log reduction of E. coli was achieved and while we used 20 W, 2.5 and 1.8 log reduction of E. coli bacteria was performed. This indicated that the increasing volume of water above 1,500 cc caused the reduction of the efficiency of E. coli removal. Also, increasing power caused to increase E. coli removal efficiency. In addition, we monitored changes in pH values and temperature during experiments. Using 20 W, the temperature was increased (natural temperature of the water was 22 °C) 2 °C after 8 min while applying 50 W, the temperatures were raised 5 °C. pH of the water after 8 min in the 1,000 cc water sample, with an input power of 20 W, decreased from 7.1 to 5.5; while the input power was 50 W, pH changed from 7.1 to 4.3. With an increase in plasma irradiation time, the number of E. coli had a significant decrease per min while using in samples of 1 cc. After 8 min, we observed 4‐log reductions of E. coli with the input power of 20 W and 8‐log reduction of bacteria with the input power of 50 W. In 1,500 and 2,000 cc of water samples using plasma radiation for 8 min, 2.5 log and 1.8 log reduction of E. coli was achieved, respectively. This means that an increasing volume of water above 1,500 cc needs more power and time to deactivate E. coli from the water.
Validation of Activity Trackers in a Laboratory Setting with Young Adults

Validation of Activity Trackers in a Laboratory Setting with Young Adults Brandon Leslie Lewis College of Nursing, BYU Master of Science Background: Objectively tracking sedentary behavior (SB) and physical activity (PA) is becoming increasingly important as research continues to show the negative effects with increasing SB and decreasing PA. Objectives: The purpose of this study was to evaluate three commercial activity trackers with young adults regarding how they accurately measure SB and PA behaviors in a laboratory setting. Methods: 50 college-aged participants wore three wrist-based activity trackers (Fitbit Surge, Apple Watch, and Basis Peak) and two ActiGraph accelerometer devices during a series of SB and PA behaviors for five-minute intervals in a laboratory setting. The activity trackers were evaluated against direct observation and the ActiGraph devices, placed on the hip and wrist, which are consistent with the National Health and Nutrition Examination Survey (NHANES) standards of measure. Results: Overall accuracy during the SBs compared to direct observation was high, with Apple (99.0%), Basis (99.0%), and Fitbit (94.9%) performing similar to the Hip ActiGraph (95.1%) and markedly better than the Wrist ActiGraph (58.6%). Overall significant correlations (p ≤ 0.05) during the PAs were higher with the Wrist ActiGraph (66.7%) than with the Hip ActiGraph (8.3%). The Wrist and Hip ActiGraphs significantly correlated in three out of four SBs, but not in any PA behaviors. Discussion: Activity trackers are reliable when determining sedentary behavior, tend to overestimate step count during light walking, and underestimate activity level when biking. Also, the Wrist ActiGraph consistently underestimated both SB and PA step count compared to the Hip ActiGraph. While some variability is seen in the validity of the activity trackers, each activity tracker studied has its strengths and weaknesses. Understanding these strengths and limitations helps healthcare professionals more accurately interpret recorded data based on the patient specific device.
Effective Prediction Model for Heart Disease Using Machine Learning Algorithm

— Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures from large amounts of data stored in databases and data warehouses. The most basic definition of data mining is the analysis of large data sets to discover patterns and use those patterns to forecast or predict the likelihood of future events. Data Mining is one of the most critical aspects of automated disease diagnosis and disease prediction. It involves developing data mining algorithms and techniques to analyze medical data. In present, heart disease has excessively increased and heart diseases are becoming one of the most fatal diseases in several countries. In this paper, heart patient datasets are investigate for building classification models in order to predict heart diagnosis. This paper implements feature model construction and comparative analysis for improving prediction accuracy of heart disease in three phases. In first phase, normalization algorithms are applied on the heart patient datasets collected from UCI repository. In second phase, by the use of HCR-PSO (Highly Co-Related Attribute Practical Swarm Optimization) feature selection, using a subset (data) of heart patient from whole normalized heart patient datasets is obtained which comprises only significant attributes and then applying selected classification algorithms on obtained, significant subset of attributes.Third phase, classification algorithm KNN, Random forest, J48, SVM, Bayesian network and MLP algorithm is considered as the better performance algorithm, because it gives higher accuracy in respective to other classification analytical Model before applying HCR-PSO feature selection. But, J48 algorithm is considered as the better performance algorithm after applying HCR-PSO feature selection. In third phase, the results of classification algorithms with and MAE and RMSE validation metric are compared with each other. The results obtained from our experiments indicate that J48 algorithm outperformed all other techniques with the help of feature selection with an accuracy of 94