nils-herrmann/clean_scirepeval huggingface dataset:
The Study on the Interpersonal Meanings of Modality in Micro blogging English News Discourse by the case of "Donald Trump's Muslim Entry Ban"
Year: 2018

News is a kind of writing style, which is so valuable that many linguists choose it to study. This thesis aims to conduct a systemic analysis of modality type, value and orientation under the framework of Halliday's Systemic functional Grammar in order to explore the interpersonal meanings of modality in English news discourse. The research data is drawn from micro blogging official platforms, among which 20 pieces of news discourse in all are selected to establish a small type of corpus. All the 20 pieces of news discourse are taken from the micro blogging in 1.20 to 2.20 of 2017. All the news is about "Donald Trump's Muslim Entry Ban" (A ban made by Donald Trump, which claimed that Muslim can't enter America) Meanwhile, both qualitative and quantitative research methods are adopted to discover the distribution of modality in micro blogging news discourse and its interpersonal meanings, and hence to deepen people's cognition and understanding on micro blogging news discourse. Through a detailed analysis, the study has a lot of findings. We found that modality language is widely used in micro blogging news. From the perspective of modality type, reporters prefer to use finite modal adjunct such as will in the type of modulation to show their emotional attitude of the target thing. From the perspective of modality value, we can see that median value is the most popular among three values for reporters, and "will" and "would" are the most popular expressions that express the speaker's expectations, willingness and determination or the reporter's views, attitudes on the possibility of a certain event. Meanwhile, from the perspective of modality orientation, the speaker or writer tends to use implicit objective orientation in order to show objectiveness of the news discourse and get rid of writers' responsibilities. This paper attempts to analyze modality in micro blogging English news discourse from the perspective of interpersonal functions which aims to provide a new method for discourse analysis and acts as a beneficial complementary to modality language analysis.
PENTINGNYA KESEHATAN MASYARAKAT, EDUKASI DAN PEMBERDAYAAN PEREMPUAN UNTUK MENGURANGI STUNTING DI NEGARA BERKEMBANG
Year: 2020

ABSTRAK Latar belakang: Negara berkembang tidak hanya rentan terhadap penyakit infeksius, tetapi terhadap tumbuh kembang anak. Stunting dianggap sebagai masalah kesehatan masyarakat utama di antara anak anak secara global. Indonesia merupakan negara ke lima di antara negara lain dengan beban stunting anak pada usia dibawah lima tahun. Faktor penyebab terjadinya stunting tidak hanya karena malnutrisi pasca melahirkan tapi asupan nutrisi selama kehamilan dan sanitasi lingkungan yang bersih untuk mencegah infeksi.  Studi ini bertujuan untuk mengetahui faktor resiko terjadinya stunting serta pentingnya kesehatan masyarakat, edukasi dan pemberdayaan wanita untuk menurunkan angka stunting di negara berkembang.  Metode penelitian ini adalah literature review. Kami menggunakan risk factor, public health, education dan women's empowerment sebagai kata kunci dalam database online seperti Science Direct, Proquest, PubMed, Google Cendekia dan situs web lainnya. Hasil: Stunting merupakan masalah kesehatan global yang disebabkan oleh multifaktor mulai dari ibu, anak, lingkungan tempat tinggal hingga fasilitas kesehatan dan tenaga kesehatan. Tenaga kesehatan masyarakat berperan dalam memberikan informasi dan edukasi terkait faktor resiko stunting, gizi selama kehamilan hingga pasca melahirkan. Edukasi yang baik akan meningkatkan pengetahuan ibu, sehingga dapat menerapkan asupan nutrisi baik kepada anak. Hal ini menunjukkan bahwa pemberdayaan perempuan adalah faktor utama pencegahan stunting, karena ibu adalah memegang peranan penting dalam keluarga.  Banyak faktor risiko yang mempengaruhi stunting. Stunting disebabkan oleh faktor multidimensi dan tidak hanya disebabkan oleh faktor malnutrisi yang dialami ibu hamil dan anak balita, akan tetapi tenaga kesehatan masyarakat dalam memberikan informasi dan edukasi serta upaya pemberdayaan wanita, khususnya ibu sangat penting dalam menurunkan stunting. Kata kunci: stunting, faktor risiko, kesehatan masyarakat, pemberdayaan perempuan
Mechanical Recycling of PET Waste from Non Woven Fabrics by Reactive Extrusion with Chain Extenders
Year: 2014

Mechanical recycling of poly (ethylene terephthalate) (PET) is an important industrial activity with direct effect for environmental saving. However, recycled PET (R PET) undergoes progressive degradation during each recycling process, leading to considerable loss of properties such as mechanical, thermal and melting strength. Chain extenders have been successfully used to increase molecular weight of R PET, improving process ability and mechanical performance of the material. The aims of this work was to evaluate the performance of the compounds polymeric methylene diphenyldiisocyanate (PMDI) and bis (2,4 di t butylphenol) pentaerythritoldiphosphite (Irgafos A(r) 126) for potential use as chain extenders when compared to the traditional chain extender pyromelliticdianhydride (PMDA) Tensile testing, differential exploratory calorimetry, viscometry and dynamic rheometry were used to evaluate changes in mechanical properties, crystallinity, molecular weight and rheological properties of R PET. PMDI showed effective action on increase in molecular weight and improvements in mechanical and rheological properties of R PET, while Irgafos 126 causes depreciation of properties of the R PET after initially to increase the molecular weight of the polymer. Thus, the use of PDMI as chain extension can represent an important alternative for mechanical recycling of highly degraded PET.
Automobile pollution control using catalysis
Year: 2020

The emissions of pollutants from vehicles are generally low but the numbers of vehicles increasing on the road therefore the environmental pollutions are also increases. About 35% of CO, 30% of HC and 25% percent of NOx produced into the atmosphere is from the transportation sector. These pollutants have adverse effects on the environment and human health. The emissions from vehicles are generally depends upon the air fuel ratio. The control techniques for exhaust gas emissions are engine modifications, fuel pretreatment, fuel additives, exhaust gas recirculation (EGR) positive crankcase ventilation (PCV) and an application of catalytic converters. A catalytic converter is a device that converts more toxic exhaust gas pollutants into less toxic pollutants. There are different types of catalysts used in the automobile exhaust gas treatment like noble metal and base metals catalysts etc. The catalytic converter was effective and consistent for reducing the noxious tailpipe emissions so that it was developed for use in the trucks, buses, cars, motorcycles and other construction equipped. This paper will discuss about the different types of recent developments in catalysis for automobile exhaust pollution control.
Countries with High Registered Nurse (RN) Concentrations Observe Reduced Mortality Rates of Coronavirus Disease 2019 (COVID 19)
Venue: SSRN
Year: 2020

The novel coronavirus disease 2019 (COVID 19) pandemic has generated many questions on its screening, diagnosis and management. One important question emerging from this discussion of regional differences pertains to the healthcare workforce not just in absolute numbers but also in terms of skill mix. As nurses comprise over half the skilled healthcare workforce worldwide, it is important to consider them as part of a health systems strengthening approach to quality improvement. We sought to investigate the influence of the registered nurse (RN) concentration and the impact on COVID 19 mortality between countries worldwide.  We conducted an observational cohort study to examine the association between COVID 19 mortality rates and concentrations of registered nurses. Data on nursing concentrations by country provided from the World Health Organization were merged with March 2020 COVID 19 mortality rates. Descriptive statistics, geographical information systems (GIS) mapping and multilevel linear regression with mixed effects were applied to examine this association with regional clustering of countries.  In total, 172 countries were included in the analysis. These results indicated that for every 1.0 increases in RN per 1,000 individuals in a country, there was a decrease in COVID 19 mortality of 1.98 per 1 million individuals (p=0.003) INTERPRETATION High nursing concentrations are strongly associated with reduced mortality rates of COVID 19. The key to address the needs of current and future COVID 19 outbreaks, or for that matter, any infectious disease outbreak of this magnitude is to prepare a highly trained, versatile nursing workforce.  William Padula is supported by grants from the U.S. National Institutes of Health (KL2 TR001854) and the Bill Melinda Gates Foundation.
Research on the application of Cloud Computing in Power User Electric Energy Data Acquisition System.
Year: 2016

As the State Grid Corporation of China is improving the Power User Electric Energy Data Acquisition System so that it can collect all necessary electric energy information from all power consumers, the number of acquisition devices is increasing sharply with large amounts of data flooding into the system. Current Power User Electric Energy Data Acquisition Systems have met many bottlenecks. To find effective solutions for these problems, this paper first examines the architecture and demand of Power User Electric Energy Data Acquisition Systems. And, then, by further analyzing advanced technologies in Cloud Computing, which is a highly effective approach for processing Big Data, research about the utilization of Cloud Computing technology for improving Power User Electric Energy Data Acquisition Systems, and related challenges, are carried out. The work of this paper can provide guidance for construction of future Power User Electric Energy Data Acquisition Systems.
Experimental IR Meets Multilinguality, Multimodality, and Interaction: 10th International Conference of the CLEF Association, CLEF 2019, Lugano, Switzerland, September 9 12, 2019, Proceedings
Venue: CLEF
Year: 2019

2019 marks the 20 birthday for CLEF, an evaluation campaign activity which has applied the Cranfield evaluation paradigm to the testing of multilingual and multimodal information access systems in Europe. This paper provides a summary of the motivations which led to the establishment of CLEF, and a description of how it has evolved over the years, the major achievements, and what we see as the next challenges.
Jio sparks Disruption 2.0: infrastructural imaginaries and platform ecosystems in 'Digital India'
Year: 2019

In recent years, Reliance Jio's offer of 4G services, guaranteeing free voice calls and 'unlimited' data streaming, lead to disruption in the Indian telecom market with other cellular operators losing their revenue and customer base. To comprehensively analyze this churn in the Indian telecom industry and its impact on mobile phone customers, the article argues for observing the entanglement of infrastructural and platform related discourses at three levels of operation: Jio's strategies to capture the Indian telecom market and the responses by the leading incumbent service provider (Airtel) ordinary citizens' phone use practices and infrastructural encounters, and the government's vision for India's digital future. Connecting pipes to platforms, Jio made infrastructural investments (in spectrum, cell towers, and fiber optics networks) to promote its suite of apps (JioTV, JioChat, and JioMoney) Ordinary citizens relate their access/proximity to telecom infrastructure (cell antennas) to their ability to effectively use apps on their phones. 'Digital India' vision purportedly facilitated infrastructural growth to create platforms that would support demonetization and facilitate transparent governance. Through such a three pronged analysis, I conceptualize 'infrastructural imaginaries' that are coproduced by states and citizens, and lie at the intersection of structured state policy/corporate initiatives and lived experiences/affective encounters of ordinary citizens.
Users' guides to the medical literature: how to use an article about mortality in a humanitarian emergency
Venue: Conflict and health
Year: 2008

The accurate interpretation of mortality surveys in humanitarian crises is useful for both public health responses and security responses. Recent examples suggest that few medical personnel and researchers can accurately interpret the validity of a mortality survey in these settings. Using an example of a mortality survey from the Democratic Republic of Congo (DRC) we demonstrate important methodological considerations that readers should keep in mind when reading a mortality survey to determine the validity of the study and the applicability of the findings to their settings.
Progress in Gene Prediction: Principles and Challenges
Year: 2013

Bioinformatics is a promising and innovative research field in 21 st century. Automatic gene prediction has been an actively researched field of bioinformatics. Despite a high number of techniques specifically dedicated to bioinformatics problems as well as many successful applications, we are in the beginning of a process to massively integrate the aspects and experiences in the different core subjects such as biology, medicine, computer science, engineering, chemistry, physics, and mathematics. Presently, a large number of gene identification tools are based on computational intelligence approaches. Here, we have discussed the existing conventional as well as computational methods to identify gene(s) and various gene predictors are compared. The paper includes some drawbacks of the presently available methods and also, the probable guidelines for future directions are discussed.
Performance optimization of the power user electric energy data acquire system based on MOEA/D evolutionary algorithm
Year: 2017

The power user electric energy data acquire system (PUEEDAS) is an important part of smart grid. This paper builds a multi objective optimization model for the performance of the PUEEADS from the point of view of the combination of the comprehensive benefits and cost. Meanwhile, the Chebyshev decomposition approach is used to decompose the multi objective optimization problem. We design a MOEA/D evolutionary algorithm to solve the problem. By analyzing the Pareto optimal solution set of multi objective optimization problem and comparing it with the monitoring value to grasp the direction of optimizing the performance of the PUEEDAS. Finally, an example is designed for specific analysis.The power user electric energy data acquire system (PUEEDAS) is an important part of smart grid. This paper builds a multi objective optimization model for the performance of the PUEEADS from the point of view of the combination of the comprehensive benefits and cost. Meanwhile, the Chebyshev decomposition approach is used to decompose the multi objective optimization problem. We design a MOEA/D evolutionary algorithm to solve the problem. By analyzing the Pareto optimal solution set of multi objective optimization problem and comparing it with the monitoring value to grasp the direction of optimizing the performance of the PUEEDAS. Finally, an example is designed for specific analysis.
Capsule Networks for Hyperspectral Image Classification
Venue: IEEE Transactions on Geoscience and Remote Sensing
Year: 2019

Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton's capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral spatial capsules, which are able to learn HSI spectral spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well known HSI data sets and several state of the art classification methods, reveal that our HSI classification approach based on spectral spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.
Influence of Cutting Speed and Feed Rate on the Wear of an S Type Mixed Ceramic Tool in Turning of AISI 4140 Hardened Steel
Venue: Tehnicki vjesnik Technical Gazette
Year: 2019

In this study, the performance and the useful life of a mixed ceramic tool with S type edge microgeometry are evaluated for the case of dry turning of AISI 4140 steel hardened to 55 HRC. The influence of the cutting speed and feed rate of the cutting parameters on the tool failure was investigated. The experiments were designed and analysed using the complete factorial design technique. Our results indicate that the cutting speed and feed rate values exert significant influence on the tool lifespan. Crater and flank wear occur in all cutting conditions. The predominant tool wear mechanisms are diffusion, abrasion, and adhesion, which are also known as attrition. The tool life curve and equation were established for the machining conditions of this investigation to achieve the subsequent optimisation of this process.
The driven readers amongst private higher educationinstitution students: a case study
Year: 2017

The development of reading habits and the detrimental effects of its decline are intriguing issues to the proponents of reading. Findings or related research around the globe have resulted in discomforting outcomes, giving an indication that the habit is now facing a challenging period. However, an interesting phenomenon observable is that despite the unfavourable circumstances, there are individuals who are avid readers. This case study attempts to gather in depth information on what motivates and influences private higher education institution students to become avid readers. Employing qualitative methods, information from two informants were analysed for essential clues on how motivation to read could be catalysed and sustained. Eleven major themes, namely, enjoyment, selfdevelopment, self initiative, social support, reading friendly environment, unfriendly reading environment, reading motivates reading, curiosity motivates reading, author attraction, alliteracy awareness and spiritually motivated, surfaced from the analysis and they could prove invaluable towards the development of strategies to inculcate the reading habit.
Unsupervised seismic facies analysis via deep convolutional autoencoders
Year: 2018

ABSTRACTOne of the most important goals of seismic stratigraphy studies is to interpret the elements of the seismic facies with respect to the geologic environment. Prestack seismic data carry rich information that can help us get higher resolution and more accurate facies maps. Therefore, it is promising to use prestack seismic data for the seismic facies recognition task. However, because each identified object changes from the poststack trace vectors to a prestack trace matrix, effective feature extraction becomes more challenging. We have developed a novel data driven offset temporal feature extraction approach using the deep convolutional autoencoder (DCAE) As an unsupervised deep learning method, DCAE learns nonlinear, discriminant, and invariant features from unlabeled data. Then, seismic facies analysis can be accomplished through the use of conventional classification or clustering techniques (e.g. K means or self organizing maps) Using a physical model and field prestack seismic surveys, we c.
A 14 Marker Multiplexed Imaging Panel for Prognostic Biomarkers and Tumor Heterogeneity in Head and Neck Squamous Cell Carcinoma
Venue: Frontiers in Oncology
Year: 2021

Recent advances made in treatment for head and neck squamous cell carcinoma (HNSCC) highlight the need for new prediction tools to guide therapeutic strategies. In this study, we aimed to develop a HNSCC targeting multiplex immunohistochemical (IHC) panel that can evaluate prognostic factors and the intratumor heterogeneity of HNSCC. To identify IHC based tissue biomarkers that constitute new multiplex IHC panel, a systematic review and meta analysis were performed to analyze reported IHC biomarkers in laryngeal and pharyngeal SCC in the period of 2008 2018. The Cancer Genome Atlas (TCGA) and Reactome pathway databases were used to validate the prognostic and functional significance of the identified biomarkers. A 14 marker chromogenic multiplex IHC panel including identified biomarkers was used to analyze untreated HNSCC tissue. Forty five high quality studies and thirty one candidate tissue biomarkers were identified (N 7062) Prognostic validation in TCGA laryngeal and pharyngeal SCC cohort (N 205) showed that b catenin, DKK1, PINCH1, ADAM10, and TIMP1 were significantly associated with poor prognosis, which were related to functional categories such as immune system, cellular response, cell cycle, and developmental systems. Selected biomarkers were assembled to build a 14 marker panel, evaluating heterogeneity and polarized expression of tumor biomarkers in the tissue structures, which was particularly related to activation of Wnt/b catenin pathway. Integrated IHC analysis based on a systemic review and meta analysis provides an in situ proteomics tool to assess the aggressiveness and intratumor heterogeneity of HNSCC.