nils-herrmann/clean_scirepeval huggingface dataset:
Responding to the health needs of women from migrant and refugee backgrounds Models of maternity and postpartum care in high income countries: A systematic scoping review.
Venue: Health social care in the community
Year: 2020

Pregnant women from migrant and refugee backgrounds living in high income countries (HIC) are at increased risk of adverse perinatal outcomes compared with women born in the host country. Women from migrant and refugee background have perinatal healthcare needs that are recognised internationally as a public health priority. The aim of this study was to identify, appraise and synthesise available evidence on the effectiveness of models of care in pregnancy or first 12 months postpartum for women from migrant and refugee backgrounds living in HIC. Care models were mapped in terms of (a) effectiveness at improving service access, (b) effectiveness at improving maternal and infant health outcomes, (c) acceptability and appropriateness from the perspective of women and (d) acceptability and appropriateness from the perspective of service providers. Using systematic scoping review methodology, qualitative, quantitative, and mixed methods research published in English 2008 2019 were included. The databases MEDLINE, Embase, Emcare, PubMed, Scopus, CINAHL, PsycINFO, Web of Science, Google Scholar, Cochrane Database of Systematic Reviews and Joanna Briggs Institute were searched between 27 February 2019 and updated 27 December 2019. Qualitative and quantitative data were analysed narratively. Seventeen studies, involving 1,499 women and 203 service providers, were included. A diverse range of interventions were identified, including bilingual/bicultural workers, group antenatal care and specialised clinics. All identified interventions were acceptable to women, and improved access, however, few provided evidence of improved perinatal outcomes. Gaps identified for future research include the use of qualitative and quantitative approaches to ascertain the experiences of women, their families, service providers and impact on perinatal outcomes. Synthesis of the included studies indicates the key elements of acceptable and accessible models, which were as follows: culturally responsive care, continuity of care, effective communication, psychosocial and practical support, support to navigate systems, flexible and accessible services.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS E LEARNING PADA MATA KULIAH PERKEMBANGAN PESERTA DIDIK DI JURUSAN PENDIDIKAN TEKNIK ELEKTRONIKA UNIVERSITAS NEGERI MAKASSAR
Year: 2019

ABSTRAK Penelitian ini bertujuan untuk mengetahui: (1) mengembangan media pembelajaran berbasis e learning dengan menggunakan aplikasi Google classroom pada mata kuliah perkembangan peserta didik di Jurusan Pendidikan Teknik Elektronika Universitas Negeri Makassar. (2) mengetahui tingkat kelayakan media pembelajaran berbasis e learning dengan menggunakan Google classroom pada mata kuliah perkembangan peserta didik di Jurusan Pendidikan Teknik Elektronika Universitas Negeri Makassar. Jenis penelitian yang digunakan adalah Research and Development yang bertujuan untuk mengembangkan dan menghasilkan suatu produk. Desain pengembangan yaitu menggabungkan dua model pengembangan yang di modifikasi yakni model pengembangan 4D yang terdiri dari 4 tahapan dan model pengembangan ADDIE yang terdiri dari 5 tahap yang di modifikasi menjadi 4 tahapan yaitu: tahap analysis, tahap desaign, tahap development, dan tahap evaluasi. Instrumen Penelitian menggunakan lembar penilaian untuk Ahli Media dan Mahasiswa. Adapun hasil penelitian pengembangan media pembelajaran pada mata kuliah perkembangan peserta didik ahli media 1 memperoleh presentase 97% di nyatakan sangat layak. Ahli media 2 memperoleh presentase sebesar 96% dinyatakan sangat layak. Hasil uji coba mahasiswa dalam uji coba satu satu memperoleh presentase 91% dinyatakan sangat layak, hasil uji coba kelompok kecil memperoleh presentase 88% dinyatakan sangat layak dan hasil uji coba kelompok besar memperoleh presentase sebesar 89% di nyatakan sangat layak. Dari hasil penelitian media pembelajaran perkembangan peserta didik yang dikembangkan memiliki kategori "sangat layak" untuk digunakan atau diimplementasikan dalam pembelajaran perkembangan peserta didik.  Media Pembelajaran, Google Classroom dan Perkembangan Peserta Didik.
The Effect of Liquidity Risk, Financing Risk, and Operational Risk toward Indonesian Sharia Bank's Financing with Bank Size as a Moderating Variable
Year: 2018

Islamic banking is growing rapidly in Indonesia, so it needs to be done a lot of studies on sharia banking especially about the influence of risks to sharia financing. The purpose of this study is to analyze of the influence of liquidity risk, financing risk, and operational risk with bank size as moderating variable. This research uses financial statement of Sharia Commercial Bank for 2012 2016 period. By using purposive sampling method, 12 Sharia Commercial Bank were chosen as samples in this study. The data use in this study is panel data. Those data was collected from Sharia Commercial Bank's website. Data analyzed by using moderated regression analysis. The result shows that liquidity risk, financing risk, and operational risk significantly influenced the financing of Indonesian sharia banking with bank size as it's moderating variable.
Emerging roles of liposomal bupivacaine in anesthesia practice
Venue: Journal of anaesthesiology, clinical pharmacology
Year: 2017

Despite advances in drug technology and improvements in technology, including peripheral nerve catheters and ultrasound, postoperative pain is still a significant problem in the clinical setting worldwide. Postoperative pain can have a critical negative impact with regard to physiological consequences to the body and therefore, the role of liposomal bupivacaine as an extended release bupivacaine with approximately 72 h of duration may have far reaching and significant impact in clinical practice. Liposomal bupivacaine has a DepoFoam multivesicular liposome technology with particle suspension in an isotonic aqueous solution and consists of tiny lipid based particles, which contain discrete water filled chambers dispersed through a lipid matrix. Other advantages include a reduction in opioid consumption, while not requiring a catheter or any other device, as well as easy dilution with saline. This review summarizes current research with this novel agent in postsurgical pain, and discusses potential roles in chronic pain states. Further studies are warranted for its use in epidural and intrathecal administration. Moreover, this review will explore the expansion of liposomal bupivacaine's current clinical role.
Description and performance of track and primary vertex reconstruction with the CMS tracker
Year: 2014

A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged particle trajectories in proton proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For tt events under typical 2011 pileup conditions, the average trackreconstruction efficiency for promptly produced charged particles with transverse momenta of pT 0.9 GeV is 94% for pseudorapidities of |e| 0.9 and 85% for 0.9 |e| 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100% For isolated muons of pT 100 GeV emitted at |e| 1.4, the resolutions are approximately 2.8% in pT, and respectively, 10 mm and 30 mm in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10 12 mm in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung. Published in the Journal of Instrumentation as doi:10.1088/1748 0221/9/10/P10009. c (c) 2014 CERN for the benefit of the CMS Collaboration. CC BY 3.0 license See Appendix A for the list of collaboration members ar X iv :1 40 5. 65 69 v2 ph ys ic s. in sde t] 2 8 O ct 2 01 4
Match Based Candidate Network Generation for Keyword Queries over Relational Databases
Venue: 2018 IEEE 34th International Conference on Data Engineering (ICDE)
Year: 2018

Several systems for processing keyword queries over relational databases rely on the generation and evaluation of Candidate Networks (CNs) i.e. networks of joined relations that when processed as SQL queries, provide a relevant answer to the input keyword query. Although the evaluation of CNs has been extensively addressed in the literature, the problem of generating CNs has received much less attention. We propose a novel approach for generating CNs, wherein the possible matches for the query in the database are efficiently enumerated at first. These query matches are then used to guide the CN generation process, avoiding the exhaustive search procedure used by the current state of art approaches. We experimentally show that our approach allows the generation of a compact set of CNs that results in superior quality answers and demands less resources in terms of processing time and memory.
A blood based biomarker panel for stratifying current risk for colorectal cancer
Venue: International journal of cancer
Year: 2010

Colorectal cancer (CRC) is often curable and preventable using current screening modalities. Unfortunately, screening compliance remains low, partly due to patient dissatisfaction with faecal/endoscopic testing. Recent guidelines advise CRC screening should begin with risk stratification. A blood based test providing clinically actionable CRC risk information would likely improve screening compliance and enhance clinical decision making. We analyzed 196 gene expression profiles to select candidate CRC biomarkers. qRT PCR was performed on 642 samples to develop a 7 gene biomarker panel using 112 CRC/120 controls (training set) and 202 CRC/208 controls (independent, blind test set) Panel performance characteristics and disease prevalence (0.7% were then used to develop a scale assessing an individual's current risk of having CRC based on his/her gene signature. A 7 gene panel (ANXA3, CLEC4D, LMNB1, PRRG4, TNFAIP6, VNN1 and IL2RB) discriminated CRC in the training set (area under the receiver operating characteristic curve (ROC AUC) 0.80; accuracy, 73% sensitivity, 82% specificity 64% The independent blind test set confirmed performance (ROC AUC, 0.80; accuracy, 71% sensitivity, 72% specificity, 70% Individual gene profiles were compared against the population results and used to calculate the current relative risk for CRC. We have developed a 7 gene, blood based biomarker panel that can stratify subjects according to their current relative risk across a broad range in an average risk population. Across the continuous spectrum of risk as defined by the current relative risk scale, it is possible to identify clinically meaningful reference points that can assist patients and physicians in CRC screening decision making.
Invasion trajectory of Pacific oysters in the northern Wadden Sea
Venue: Marine biology
Year: 2017

Invasion trajectories of introduced alien species usually begin with a long establishment phase of low abundance, often followed by exponential expansion and subsequent adjustment phases. We review the first 26 years of feral Pacific oysters Crassostrea gigas around the island of Sylt in the Wadden Sea (North Sea, NE Atlantic) and reveal causal conditions for the invasion phases. Sea based oyster farming with repeated introductions made establishment of feral oysters almost inevitable. Beds of mussels Mytilus edulis on mud flats offered firm substrate for attachment and ideal growth conditions around low tide level. C. gigas mapped on to the spatial pattern of mussel beds. During the 1990s, cold summers often hampered recruitment and abundances remained low but oyster longevity secured persistence. Since the 2000s, summers were often warmer and recruitment more regular. Young oysters attached to adult oysters and abundances of >1000 m 2 were achieved. However, peak abundance was followed by recruitment failure. The population declined and then was also struck by ice winters causing high mortality. Recovery was fast >2000 m 2) but then recruitment failed again. We expect adjustment phase will proceed with mean abundance of about 1000 m 2 but density dependent (e.g. diseases) and density independent (e.g. weather anomalies) events causing strong fluctuations. With continued global warming, feral C. gigas at the current invasion fronts in British estuaries and Scandinavian fjords may show similar adjustment trajectories as observed in the northern Wadden Sea, and also other marine introductions may follow the invasion trajectory of Pacific oysters.
Self Supported Stainless Steel Nanocone Array Coated with a Layer of Ni Fe Oxides/(Oxy)hydroxides as a Highly Active and Robust Electrode for Water Oxidation.
Venue: ACS applied materials interfaces
Year: 2018

Highly efficient, robust, and cheap water oxidation electrodes are of great significance for large scale production of hydrogen by electrolysis of water. Here, a self supported stainless steel (SS) nanocone array coated with a layer of nanoparticulate Ni Fe oxides/(oxy)hydroxides was fabricated by a facile, low cost, and easily scalable two step process. The construction of a nanocone array on the surface of an AISI 304 SS plate by acid corrosion greatly enlarged the specific surface area of the substrate, and the subsequent formation of a layer of Ni Fe oxides/(oxy)hydroxides featuring the NiFe2O4 spinel phase on the nanocone surface by electrodeposition of [Ni(bpy)3]2+ significantly enhanced the intrinsic activity and the stability of the SS based electrode. The as prepared electrode demonstrated superior activity for the oxygen evolution reaction (OER) in 1 M KOH, with 232 and 280 mV overpotentials to achieve 10 and 100 mA cmgeo 2 current densities, respectively. The high activity of the electrode was maintained over 340 h of chronopotentiometric test at 20 mA cmgeo 2, and the electrode also showed good stability over 100 h of electrolysis at high current density (200 mA cm 2) More important for practical application, the used SS based electrode can be easily regenerated with the original OER activity. The superior activity of this SS based electrode stems from synergistic combination of high conductivity of the SS substrate, a large electrochemically active surface area of the nanocone array, and a uniformly coated nanoparticulate Ni Fe oxide/(oxy)hydroxide layer with an optimal Ni/Fe ratio.
Cyberbullying detection solutions based on deep learning architectures
Year: 2020

Cyberbullying is disturbing and troubling online misconduct. It appears in various forms and is usually in a textual format in most social networks. Intelligent systems are necessary for automated detection of these incidents. Some of the recent experiments have tackled this issue with traditional machine learning models. Most of the models have been applied to one social network at a time. The latest research has seen different models based on deep learning algorithms make an impact on the detection of cyberbullying. These detection mechanisms have resulted in efficient identification of incidences while others have limitations of standard identification versions. This paper performs an empirical analysis to determine the effectiveness and performance of deep learning algorithms in detecting insults in Social Commentary. The following four deep learning models were used for experimental results, namely: Bidirectional Long Short Term Memory (BLSTM) Gated Recurrent Units (GRU) Long Short Term Memory (LSTM) and Recurrent Neural Network (RNN) Data pre processing steps were followed that included text cleaning, tokenization, stemming, Lemmatization, and removal of stop words. After performing data pre processing, clean textual data is passed to deep learning algorithms for prediction. The results show that the BLSTM model achieved high accuracy and F1 measure scores in comparison to RNN, LSTM, and GRU. Our in depth results shown which deep learning models can be most effective against cyberbullying when directly compared with others and paves the way for future hybrid technologies that may be employed to combat this serious online issue.
Dye Sensitized Solar Cell Based Photovoltaic Thermal for Ethanol Distillation: A Narrative Review
Year: 2021

The potential for solar energy in Indonesia is abundant and attractive to be developed into one of the leading renewable energy sources. Photovoltaic or solar cells are devices that can be used to convert solar energy directly into electrical energy. A dye sensitized solar cell (DSSC) is an exciting type of solar cell to be developed because it is cheap, easy, and can use natural dyes based on plant extracts. However, the performance of DSSC degrades when it is at high operating temperatures. In this article, a narrative review is presented to improve the efficiency of DSSC by integrating with a solar collector in the form of a photovoltaic/thermal (PV/T) system. The solar collector will act as a heat absorbent from the DSSC, and the heat energy obtained will be used to distill ethanol. Optimization in the ethanol distillation system can take advantage of CuO as a nanoparticle in the working fluid or better know as nanofluid.
Analyzing Dynamical Brain Functional Connectivity as Trajectories on Space of Covariance Matrices
Venue: IEEE Transactions on Medical Imaging
Year: 2020

Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time series data, over a set of brain regions, as a trajectory on the space of covariance matrices, or symmetric positive definite matrices (SPDMs) We use a recently developed metric on the space of SPDMs for quantifying differences across FC observations, and for clustering and classification of FC trajectories. To facilitate large scale and high dimensional data analysis, we propose a novel, metric based dimensionality reduction technique to reduce data from large SPDMs to small SPDMs. We illustrate this comprehensive framework using data from the Human Connectome Project (HCP) database for multiple subjects and tasks, with task classification rates that match or outperform state of the art techniques.
Convex Optimization based Controller Design for Stochastic Nonlinear Systems using Contraction Analysis
Venue: 2019 IEEE 58th Conference on Decision and Control (CDC)
Year: 2019

This paper presents an optimal feedback tracking controller for a class of Ito stochastic nonlinear systems, the design of which involves recasting a nonlinear system equation into a convex combination of multiple non unique State Dependent Coefficient (SDC) models. Its feedback gain and controller parameters are found by solving a convex optimization problem to minimize an upper bound of the steady state tracking error. Multiple SDC parametrizations are utilized to provide a design flexibility to mitigate the effects of stochastic noise and to ensure that the system is controllable. Incremental stability of this controller is studied using stochastic contraction analysis and it is proven that the controlled trajectory exponentially converges to the desired trajectory with a non vanishing error due to the linear matrix inequality state dependent algebraic Riccati equation constraint. A discrete time version of stochastic contraction analysis with respect to a state and time dependent metric is also presented in this paper. A simulation is performed to show the superiority of the proposed optimal feedback controller compared to a known exponentially stabilizing nonlinear controller and a PID controller.
Caracteristicas individuales y familiares de los adolescentes en violencia filio parental: la agresividad fisica, la cohesion familiar y el conflicto interparental como variables explicativas.
Year: 2016

El objetivo del presente estudio fue analizar las diferencias en caracteristicas sociodemografi cas y familiares, en salud mental y agresividad de 34 adolescentes inmersos en una dinamica de violencia fi lio parental (VFP) y un grupo contraste de 81 adolescentes que no ejercian violencia contra sus progenitores. Los resultados mostraron diferencias entre ambos grupos en variables como nivel de escolarizacion, estructura familiar, niveles de agresividad fisica e ira, cohesion, adaptabilidad y comunicacion familiar y confl icto interparental, pero no asi en los niveles de agresividad verbal y hostilidad, ansiedad, somatizacion, panico o depresion. Asi mismo los resultados encontrados no apoyaron la hipotesis de que una edad de inicio mas temprana de la VFP o una mayor duracion de la violencia sin tratamiento se asocien a una mayor gravedad de la VFP. Finalmente, la VFP global se asocio de manera signifi cativa con una mayor agresividad fisica, una menor cohesion familiar y un alto sentido de autoculpa del adolescente en relacion al confl icto interparental.
Relationship between Electricity Energy Consumption and GDP: Evidence from India
Year: 2015

The paper examines whether electricity energy consumption drives economic growth or vice versa in the Indian context using the annual data covering the period from 1970 1971 to 2011 2012. KPSS tests reveal that both the series, after logarithmic transformation, are non stationary at level and stationary at first difference. Applying, two step Engle Granger technique and Granger causality/ Block exogeneity Wald test, the study suggests that it is the electricity energy consumption that fuels economic growth both in short run and long run. It rejects the neo classical hypothesis and empirically proves that electricity consumption is a limiting factor on economic growth. Using dynamic OLS(DOLS) method, the elasticity of electricity consumption on economic growth is estimated at 0.86 and the elasticity of economic growth on eletrcity consumption is estimated at 1.19. Based upon the elasticity, the energy requirement and energy generation is projected at 1436 BU and 1766 BU at the end of 12th Plan (at end of 2016 2017) period.
SPEED ESTIMATION AND CONTROL OF INDUCTION MOTOR USING MODERN PREDICTIVE CONTROL
Year: 2020

This paper uses model predictive control that is Superior to traditional drive transmission to handle sensorless speed control of induction motor driven by a single stage sun. The proposed system, solar Photovoltaic (PV) array connected with Voltage Source Inverter (VSI) and predictive control system, MRAS. A MPPT algorithm based on perturbation and observation is used for harnessing the full power from a PV series. System predictive control strategy is a predictive control approach to standard optimisation. For less dependency on model parameters, MPC can achieve sufficient control efficiency and has been widely used in process control systems. MRAS speed estimation method was implemented to achieve sensorless operation to boost the device reliability and cost reduction of Hardware. In MATLAB Simulink environment the desired configuration is modelled and simulated.