nils-herrmann/clean_scirepeval huggingface dataset:
International Undergraduate Medical Student in China and the Potentiality of Using PBL Method in the Integrated Organ/System Curriculum
Year: 2020

The advance of the medical field in the 21st century has influenced the development of many innovations, including in medical education. PBL (problem based learning) is one of the pedagogical approaches that has been studying and implementing worldwide to attract medical students' attention with their learning style and learning environment. China is the most significant "receiver" country of foreign students in Asia, and clinical medicine is one of the most chosen majors for undergraduate. In China, PBL has been implementing in some schools, but mostly with the local students and only a few implementations to the international students. International medical students in China faced a variety of challenges during their studies. In this review paper, we give the solution to adapt to the challenges by focusing on the implementation the integrated organ/system curriculum/syllabus with the PBL method to the school system with the support of the school stakeholders and students' role.
PENGARUH DIMENSI KUALITAS PELAYANAN JASA DAN KINERJA KARYAWAN TERHADAP KEPUASAN KONSUMEN DALAM MENGGUNAKAN JASA PENGIRIMAN BARANG PADA PT. JNE BANDAR LAMPUNG TAHUN 2017
Year: 2018

Seiring perkembangan zaman, pada saat ini kegiatan bisnis khususnya dalam pemasaran mengalami peningkatan. Salah satu cara yang dilakukan perusahaan dalam menghadapi persaingan yang ada yaitu dengan memberikan kepuasan kepada konsumen. Adanya pelayanan kualitas yang baik akan memberikan kepuasan bagi konsumennya. Tujuan penelitian ini adalah untuk mengetahui pengaruh kualitas pelayanan dan kinerja karyawan terhadap kepuasan konsumen dalam menggunakan jasa pengiriman barang pada PT JNE Bandar Lampung tahun 2018. Populasi dalam penelitian ini adalah seluruh konsumen jasa PT JNE Bandar Lampung. Teknik pengambilan sampel yaitu accidental sampling dengan menggunakan rumus Joseph F. Hair didapat sampel sebanyak 90 responden. Metode penelitian yang digunakan dalam penelitian ini adalah metode deskriptif verifikatif dengan pendekatan ex post facto dan survey. Pengumpulan data dilakukan dengan menggunakan kuesioner. Berdasarkan pengujian hipotesis dengan melihat hasil koefisien regresi linier multipel diperoleh diperoleh thitung untuk variabel Bukti Fisik (Tangible) sebesar 8,548 ttabel sebesar 1,9873, diperoleh thitung untuk Kehandalan (Reliability) sebesar 10,35,6303 ttabel sebesar 1,9873, Diperoleh thitung untuk variabel Daya Tanggap (Responsiveness) sebesar 8,010 ttabel sebesar 1,9873, Diperoleh thitung untuk variabel Empati (emphaty) sebesar 12,285 ttabel sebesar 1,9873, Diperoleh thitung untuk variabel Jaminan (assurance) sebesar 8,449 t tabel sebesar 1,9873, Diperoleh t hitung untuk variabel Kinerja Karyawan sebesar 7,506 ttabel sebesar 1,9873. Selain itu, dengan uji F dilihat bahwa kualitas pelayanan jasa memiliki pengaruh terhadap kepuasan pelanggan dengan hasil Fhitung Ftabel atau 54,652> 2,21 maka H0 ditolak dan H1 diterima. Hasil analisis data menunjukkan bahwa ada pengaruh yang positif dan signifikan kualitas pelayanan jasa (bukti fisik, keandalan, daya tanggap, jaminan dan empati) dan kinerja karyawan terhadap kepuasan konsumen. Kata kunci: kepuasan konsumen, kinerja karyawan, kualitas pelayanan jasa.
Anomaly Detection in Social Media Using Recurrent Neural Network
Venue: ICCS
Year: 2019

In today's information environment there is an increasing reliance on online and social media in the acquisition, dissemination and consumption of news. Specifically, the utilization of social media platforms such as Facebook and Twitter has increased as a cutting edge medium for breaking news. On the other hand, the low cost, easy access and rapid propagation of news through social media makes the platform more sensitive to fake and anomalous reporting. The propagation of fake and anomalous news is not some benign exercise. The extensive spread of fake news has the potential to do serious and real damage to individuals and society. As a result, the detection of fake news in social media has become a vibrant and important field of research. In this paper, a novel application of machine learning approaches to the detection and classification of fake and anomalous data are considered. An initial clustering step with the K Nearest Neighbor (KNN) algorithm is proposed before training the result with a Recurrent Neural Network (RNN) The results of a preliminary application of the KNN phase before the RNN phase produces a quantitative and measureable improvement in the detection of outliers, and as such is more effective in detecting anomalies or outliers against the test dataset of 2016 US Presidential Election predictions.
Fast SARS CoV 2 detection protocol based on RNA precipitation and RT qPCR in nasopharyngeal swab samples
Venue: medRxiv
Year: 2020

The SARS CoV 2 pandemic has evolved far more aggressively in countries lacking a robust testing strategy to identify infected individuals. Given the global demand for fast and reliable diagnosis to determine the carrier individuals, a stock out scenario for a number of essential reagents/kits used along the diagnostic process has been foreseen by many organizations. Having identified the RNA extraction step as one of the key bottlenecks, we tested several alternatives that avoid the use of commercial kits for this step. The analysis showed that 2 propanol precipitation of the viral RNA, followed by one step RT qPCR results in a sensitivity and specificity comparable to that provided currently by automatized systems such as the COBAS 6800 system. Therefore, this simple protocol allows SARS CoV 2 testing independently of commercial kit providers in a time and cost effective manner. It can be readily implemented in research and/or diagnostic laboratories worldwide, provided that patient confidentiality and researcher safety are ensured. Scaling up the testing capabilities of hospitals and research facilities will identify larger numbers of infected individuals to paint a clear picture of the COVID 19 prevalence, a pre requisite for informed policy decision making.
Equilibrium, dynamic surface tensions and application properties of fatty alcohol ether carboxylic ester surfactants
Year: 2020

A series of fatty alcohol ether carboxylic ester (AECE) surfactants were mainly synthesized from succinic anhydride and different fatty alcohol ethers (AEO) with green and atom economic process. The chemical constitutions of AECE were characterized by Fourier transform infrared (FTIR) 1H nuclear magnetic resonance (1HNMR) and matrix assisted laser desorption/ionization time of flight mass spectra (MALDI TOF MS) The equilibrium and dynamic surface tensions (DST) of AECE surfactants were investigated, and Ward Tordai equation was employed to analyze the adsorption and diffusion process of surfactants by calculating the relevant parameters (n, t* ti tm R 1/2) as well as the effective diffusion coefficients (D long and D short) from DST curves. Equilibrium surface tension results showed that AECE surfactants have strong surface activities, and their equilibrium surface tension and critical micelle concentration values are less than 30 mN/m and 0.4 mmol/L, respectively. According to DST parameters, nonionic surfactant AEC5E H is more favorable to diffuse and adsorb to the air/water interface than others. In addition, the movement of surfactant molecules is also related to the concentration of solutions, and a reasonable concentration can be selected in practical applications based on the experimental results. The outcomes of the diffusion coefficients illustrated that the adsorption mechanism is the mixed diffusion kinetic adsorption. Moreover, the application performances results showed that the ionic surfactants (AEC3E Na and AEC5E Na) are superior to the nonionic surfactants (AEC3E H and AEC5E H) in terms of wetting, foam and detergency, while the emulsifying ability is different for various oil phases.
Immediate loading with single implant crowns: a systematic review and meta analysis.
Venue: The International journal of prosthodontics
Year: 2009

A systematic review and meta analysis of studies that specifically compared immediate to conventional loading of single implant crowns was conducted and the overall treatment effect was estimated.  MEDLINE, the Cochrane Controlled Trials Register, and bibliographies of relevant primary and review articles were searched. Randomized and nonrandomized controlled studies that compared immediate with conventional loading of single implant crowns were selected according to strict criteria. From the 105 articles screened, five studies with 248 implants were analyzed. The meta analysis was prepared in accordance with the Quality of Reporting of Meta analyses (QUOROM) statement. Descriptive and outcome data were extracted using specially designed data extraction forms. The data were entered into MIX software for meta analysis using a fixed effects model, relative risk, and 95% confidence interval (CI) RESULTS Immediate loading of single implant crowns was associated with a significantly higher risk of implant failure (relative risk: 5.07, 95% CI: 2.00 to 12.84, P .001) Pooling of randomized controlled trials showed similar results, although the difference was not statistically significant. Immediate nonocclusal loading was also associated with worse outcomes when compared to conventional loading (relative risk: 4.76, 95% CI: 1.74 to 13.02, P .002)  This systematic review and meta analysis shows that better outcomes are currently achieved using conventional loading of single implants with crowns, as opposed to immediately loaded ones, which are at a higher risk of failure. Further adequately powered clinical trials are needed. Caution with immediate loading of implants with crowns as a standard of care for single tooth replacement is recommended.
Solving high dimensional global optimization problems using an improved sine cosine algorithm
Venue: Expert Syst. Appl.
Year: 2019

The sine cosine algorithm (SCA) is a relatively novel population based optimization technique that has been proven competitive with other algorithms and it has received significant interest from researchers in different fields. However, similar to other population based algorithms, SCA tends to be trapped in local optima and unbalanced exploitation. Additionally, to our limited knowledge, the present SCA and its variants have not been applied to the high dimensional global optimization problems. This paper presents an improved version of the SCA (ISCA) for solving high dimensional problems. A modified position updating equation by introducing inertia weight is proposed to accelerate convergence and avoid falling into the local optima. In addition, to balance the exploration and exploitation of the SCA, we present a new nonlinear conversion parameter decreasing strategy based on the Gaussian function. The effectiveness of the proposed ISCA is evaluated using 24 benchmark high dimensional (D 30, 100, 500, 1000, and 5000) functions, large scale global optimization problems from the IEEE CEC2010 competition, and several real world engineering applications. The comparisons show that the proposed ISCA can better escape from local optima with faster convergence than both the traditional SCA and other population based algorithms.
Crosslingual Embeddings are Essential in UNMT for distant languages: An English to IndoAryan Case Study
Venue: MTSUMMIT
Year: 2021

Recent advances in Unsupervised Neural Machine Translation (UNMT) has minimized the gap between supervised and unsupervised machine translation performance for closely related language pairs. However and the situation is very different for distant language pairs. Lack of overlap in lexicon and low syntactic similarity such as between English and IndoAryan languages leads to poor translation quality in existing UNMT systems. In this paper and we show that initialising the embedding layer of UNMT models with cross lingual embeddings leads to significant BLEU score improvements over existing UNMT models where the embedding layer weights are randomly initialized. Further and freezing the embedding layer weights leads to better gains compared to updating the embedding layer weights during training. We experimented using Masked Sequence to Sequence (MASS) and Denoising Autoencoder (DAE) UNMT approaches for three distant language pairs. The proposed cross lingual embedding initialization yields BLEU score improvement of as much as ten times over the baseline for English Hindi and English Bengali and English Gujarati. Our analysis shows that initialising embedding layer with static cross lingual embedding mapping is essential for training of UNMT models for distant language pairs.
Predicting Hospital Readmission via Cost Sensitive Deep Learning
Venue: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Year: 2018

With increased use of electronic medical records (EMRs) data mining on medical data has great potential to improve the quality of hospital treatment and increase the survival rate of patients. Early readmission prediction enables early intervention, which is essential to preventing serious or life threatening events, and act as a substantial contributor to reduce healthcare costs. Existing works on predicting readmission often focus on certain vital signs and diseases by extracting statistical features. They also fail to consider skewness of class labels in medical data and different costs of misclassification errors. In this paper, we recur to the merits of convolutional neural networks (CNN) to automatically learn features from time series of vital sign, and categorical feature embedding to effectively encode feature vectors with heterogeneous clinical features, such as demographics, hospitalization history, vital signs, and laboratory tests. Then, both learnt features via CNN and statistical features via feature embedding are fed into a multilayer perceptron (MLP) for prediction. We use a cost sensitive formulation to train MLP during prediction to tackle the imbalance and skewness challenge. We validate the proposed approach on two real medical datasets from Barnes Jewish Hospital, and all data is taken from historical EMR databases and reflects the kinds of data that would realistically be available at the clinical prediction system in hospitals. We find that early prediction of readmission is possible and when compared with state of the art existing methods used by hospitals, our methods perform significantly better. For example, using the general hospital wards data for 30 day readmission prediction, the area under the curve (AUC) for the proposed model was 0.70, significantly higher than all the baseline methods. Based on these results, a system is being deployed in hospital settings with the proposed forecasting algorithms to support treatment.
Massive Open Online Course Evaluation Methods: Systematic Review
Venue: Journal of medical Internet research
Year: 2020

Massive open online courses (MOOCs) have the potential to make a broader educational impact because many learners undertake these courses. Despite their reach, there is a lack of knowledge about which methods are used for evaluating these courses.  The aim of this review was to identify current MOOC evaluation methods to inform future study designs.  We systematically searched the following databases for studies published from January 2008 to October 2018: (1) Scopus, (2) Education Resources Information Center, (3) IEEE (Institute of Electrical and Electronic Engineers) Xplore, (4) PubMed, (5) Web of Science, (6) British Education Index, and (7) Google Scholar search engine. Two reviewers independently screened the abstracts and titles of the studies. Published studies in the English language that evaluated MOOCs were included. The study design of the evaluations, the underlying motivation for the evaluation studies, data collection, and data analysis methods were quantitatively and qualitatively analyzed. The quality of the included studies was appraised using the Cochrane Collaboration Risk of Bias Tool for randomized controlled trials (RCTs) and the National Institutes of Health National Heart, Lung, and Blood Institute quality assessment tool for cohort observational studies and for before after (pre post) studies with no control group.  The initial search resulted in 3275 studies, and 33 eligible studies were included in this review. In total, 16 studies used a quantitative study design, 11 used a qualitative design, and 6 used a mixed methods study design. In all, 16 studies evaluated learner characteristics and behavior, and 20 studies evaluated learning outcomes and experiences. A total of 12 studies used 1 data source, 11 used 2 data sources, 7 used 3 data sources, 4 used 2 data sources, and 1 used 5 data sources. Overall, 3 studies used more than 3 data sources in their evaluation. In terms of the data analysis methods, quantitative methods were most prominent with descriptive and inferential statistics, which were the top 2 preferred methods. In all, 26 studies with a cross sectional design had a low quality assessment, whereas RCTs and quasi experimental studies received a high quality assessment.  The MOOC evaluation data collection and data analysis methods should be determined carefully on the basis of the aim of the evaluation. The MOOC evaluations are subject to bias, which could be reduced using pre MOOC measures for comparison or by controlling for confounding variables. Future MOOC evaluations should consider using more diverse data sources and data analysis methods.
Pengaruh Stres Kerja dan Lingkungan Kerja terhadap Kinerja Karyawan di Bank Perkreditan Rakyat Syariah (BPRS) Al Mabrur Ponorogo
Year: 2019

ABSTRAK Destiantari, Anggita Nindy. 2019. "Pengaruh Stres Kerja dan Lingkungan Kerja terhadap Kinerja Karyawan di Bank Perkreditan Rakyat Syariah (BPRS) Al Mabrur Ponorogo." Skripsi. Jurusan Perbankan Syariah, Fakultas Ekonomi dan Bisnis Islam, Institut Agama Islam Negeri (IAIN) Ponorogo. Pembimbing Mansur Azis, M.S.I. Kata Kunci Beban Kerja, Sumber Daya Manusia, Perbankan, Kuantitatif. Penelitian dalam skripsi ini dilatarbelakangi oleh kondisi lembaga keuangan syariah di Indonesia yang semakin berkembang dan menimbulkan persaingan yang sangat ketat. Sumber daya manusia berperan penting dalam perusahaan. Beban kerja yang berlebihan pada karyawan dapat berdampak buruk pada perusahaan, lingkungan yang kurang nyaman akan membuat karyawan tidak optimal dalam bekerja. Oleh sebab itu, perusahaan harus menyelesaikan permasalahan yang dapat menurunkan kinerja karyawan. Rumusan masalah dari skripsi ini adalah: 1) Apakah Stres kerja berpengaruh terhadap kinerja karyawan di BPRS Al Mabrur Ponorogo 2) Apakah lingkungan kerja berpengaruh terhadap kinerja karyawan di BPRS Al Mabrur Ponorogo 3) Apakah stres kerja dan lingkungan kerja berpengaruh terhadap kinerja karyawan di BPRS Al Mabrur Ponorogo. Jenis penelitian yang digunakan adalah penelitian kuantitatif. Metode pengumpulan data dilakukan melalui penyebaran kuisioner. Peneliti mengangkat sampel dari populasi sebanyak 34 responden, data yang diperoleh kemudian diolah dengan menggunakan IBM SPSS 19 dan dianalisis menggunakan analisis regresi linier berganda. Analisis ini meliputi uji validitas dan reliabilitas, pengujian hipotesis meliputi uji asumsi klasik, uji t, uji F dan koefisien determinasi Berdasarkan hasil penelitian dapat disimpulkan 1) Berdasarkan uji t Stres Kerja tidak berpengaruh secara signifikan terhadap kinerja karyawan BPRS Al Mabrur Ponorogo. Hal tersebut mengindikasikan bahwa kinerja karyawan tidak dipengaruhi oleh stres kerja 2) Berdasarkan uji t lingkungan kerja berpengaruh secara positif dan signifikan terhadap kinerja karyawan BPRS Al Mabrur Ponorogo. Hal ini mengindikasikan bahwa semakin baik lingkungan kerja, maka semakin baik pula kinerja karyawan. 3) Berdasarkan uji F stres kerja dan lingkungan kerja secara bersama sama berpengaruh terhadap keputusan kinerja karyawan BPRS Al Mabrur Ponorogo.
Using Conceptual Metaphor and Functional Grammar to Explore How Language Used in Physics Affects Student Learning.
Year: 2007

This paper introduces a theory about the role of language in learning physics. The theory is developed in the context of physics students' and physicists' talking and writing about the subject of quantum mechanics. We found that physicists' language encodes different varieties of analogical models through the use of grammar and conceptual metaphor. We hypothesize that students categorize concepts into ontological categories based on the grammatical structure of physicists' language. We also hypothesize that students over extend and misapply conceptual metaphors in physicists' speech and writing. Using our theory, we will show how, in some cases, we can explain student difficulties in quantum mechanics as difficulties with language.
The Bhagavad Gita as an Antidote to Duality: A Challenge to the Orthodoxy of Current Decisions Theory
Year: 2019

This chapter revisits decision making theory and challenges four major premises. These premises have been identified by James March as the bedrock of decision sciences. Currently these four premises are elucidated as dialectics in the extant literature. This chapter suggests that according to the Bhagavad Gita the current "closure" to the four premises achieved in the literature would be inadequate to satisfy the dharmic calling of all the parties involved in decision making situations. Drawing from the Bhagavad Gita we challenge the four divisive dialectics and provide a unitive closure to them. This is achievable by decision makers through a process of transcendence while not rejecting the importance of rooting decisions in the phenomenal plane.
Photoluminescent cup holder lighting
Year: 2015

According to one aspect of the present invention, an illuminated cup holder assembly is disclosed. The assembly includes an upper portion comprising a lower matching part with a first diameter, and a base portion which comprises an upper matching portion with a second diameter. A light ring is substantially arranged between the lower matching part and the upper matching part in an assembled configuration. The cup holder assembly includes a photoluminescent part configured to convert a first wavelength to a second wavelength to illuminate at least a portion of the cup holder assembly.
Efecto del reemplazo de los despojos de trigo con paja de soja en la respuesta de crecimiento, perfil sanguineo y rendimiento de la carcasa de pollos de engorde
Venue: Archivos de Zootecnia
Year: 2019

Se realizo un estudio de alimentacion de 5 semanas para evaluar la idoneidad de la paja de soja (SC) como reemplazo de los despojos de trigo en las dietas de pollos de engorde. Un total de trescientos sesenta (360) 21 dias de edad, pollos de engorde se pesaron individualmente y se asignaron aleatoriamente a 1 de 3 tratamientos dieteticos con 8 plumas repetidas y 15 aves por repeticion en un diseno completamente aleatorizado. Los tratamientos dieteticos consistieron en dietas basadas en maiz y soja que contenian 0, 50 y 100% de SC como reemplazo de los despojos de trigo. Los datos generados sobre el rendimiento de crecimiento, los indices de sangre, el rendimiento de la canal y las propiedades sensoriales de los pollos de engorde de acabado se analizaron mediante ANOVA usando PROC GLM de SAS (2007) Los resultados mostraron que un mayor peso final (P 0.05) Las aves alimentadas con SC al 100% tenian un peso vivo (P