nils-herrmann/clean_scirepeval huggingface dataset:
A review: Energy storage system and balancing circuits for electric vehicle application
Year: 2020

The prominent electric vehicle technology, energy storage system, and voltage balancing circuits are most important in the automation industry for the global environment and economic issues. The energy storage system has a great demand for their high specific energy and power, high temperature tolerance, and long lifetime in the electric vehicle market. For reducing the individual battery or super capacitor cell damaging change, capacitive loss over the charging or discharging time and prolong the lifetime on the string, the cell balancing is compulsory. The electric vehicles drive train architecture, overall applicable energy storage system, and the balancing circuit categories as cell to heat, cell to cell, cell to pack, pack to cell, and cell to pack to cell are reviewed. The comparative study has shown the different key factors of market available electric vehicles, different types of energy storage systems, and voltage balancing circuits. The study will help the researcher improve the high efficient energy storage system and balancing circuit that is highly applicable to the electric vehicle.
An IoT Blockchain Architecture Based on Hyperledger Framework for Healthcare Monitoring Application
Venue: 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS)
Year: 2019

Blockchains are one of the most promising technologies in the domain of the Internet of Things (IoT) At the same time, healthcare monitoring is one of IoT applications where many devices are connected, and collect data that need to be stored in a highly secure way. In this context, we focus on IoT Blockchain architectures for healthcare monitoring applications. We start our study by exploring both IoT and blockchain technologies and identify how Fabric Hyperledger is a blockchain framework that fits our application needs. In this paper, we propose a security architecture based on this framework. We validate our approach first at a design level through concrete examples, then by showing some implemented functionalities.
Composite solid electrolytes for all solid state lithium batteries
Venue: Materials Science and Engineering: R: Reports
Year: 2019

Compared to currently used liquid electrolyte lithium batteries, all solid state lithium batteries are safer and possess longer cycle life and have less requirements on packaging and state of charge monitoring circuits. Among various types of solid electrolytes, composite solid electrolytes, which are composed of active or passive inorganic fillers and polymer matrices, have been considered as promising electrolyte candidates for all solid state lithium batteries. Incorporation of inorganic fillers into the polymer matrices has been demonstrated as an effective method to achieve high ionic conductivity and excellent interfacial contact with the electrodes. In this review article, we first summarize the historical development of composite solid electrolytes. Contribution of both inert inorganic fillers and active Li ion conductors to the ionic conductivity, electrochemical stability, and mechanical properties of the composite solid electrolytes are elaborated. Possible mechanisms of conductivity enhancement by inorganic fillers are broadly discussed. Examples of different composite solid electrolyte design concepts, such as inorganic nanoparticle/polymer, inorganic nanofiber/polymer, and other inorganic/polymer composite solid electrolytes, are introduced and their advantages and disadvantages are discussed. Inorganic filler/polymer composite solid electrolytes studied for use in various Li battery systems including Li ion, Li sulfur, and Li metal batteries are evaluated. Promising designs of composite solid electrolytes and cathode materials used in all solid state Li batteries are also introduced. Finally, future perspectives on current requirements of composite solid electrolyte technologies are highlighted.
Responsabilidad social, necesidades psicologicas basicas, motivacion intrinseca y metas de amistad en educacion fisica (Social responsibility, basic psychological needs, intrinsic motivation, and friendship goals in physical education)
Year: 2017

El objetivo de este estudio fue analizar la capacidad de prediccion de la responsabilidad social, las necesidades psicologicas basicas y la motivacion sobre la meta de aproximacion a la amistad a partir de la Teoria de la Autodeterminacion. Una muestra de 402 estudiantes (209 mujeres y 193 hombres; M 14.20; DT 1.52) de entre 12 y 18 anos, procedentes de tres centros de educacion secundaria del norte de Espana cumplimentaron un cuestionario que incluia la subescala de responsabilidad social del Cuestionario de la Responsabilidad Personal y Social (PSQR) la Escala de Necesidades Psicologicas Basicas en Educacion Fisica (BPN PE) la subescala de motivacion intrinseca de la Escala del Locus Percibido de Casualidad (PLOC) y la subescala de aproximacion amistad del Cuestionario de Metas de Amistad (FGQ PE) Los resultados del analisis de regresion lineal multiple reflejaron que la responsabilidad social, las necesidades psicologicas basicas de relacion y autonomia y la motivacion intrinseca predecian significativamente la meta de aproximacion amistad. Estos resultados reflejan la importancia de crear contextos educativos en los que se potencie la responsabilidad social, las relaciones, la autonomia y la motivacion intrinseca de los estudiantes con el objetivo de mejorar sus metas de amistad.  The goal of the present study was to assess social responsibility, basic psychological needs, and motivation towards friendship approach goals within the Self Determination Theory. A total of 404 students from three secondary schools of northern Spain (209 females and 193 males; M 14.20; DT 1.52) aged from 12 to 18 years, completed a questionnaire that included the social responsibility subscale of the Personal and Social Responsibility Questionnaire (PSQR) the Basic Psychological Needs in Physical Education Scale (BPN PE) the subscale of intrinsic motivation of the Perceived Locus of Causality (PLOC) and the subscale of friendship approach of the Friendship Goals Questionnaire in Physical Education (FGQ PE) Results of multiple linear regression analysis showed that social responsibility, relatedness and autonomy, and intrinsic motivation significantly predicted friendship approach goals. These results suggest the importance of creating educational contexts that can foster social responsibility, relatedness, autonomy, as well as students' intrinsic motivation, with the aim to improve their friendship goals.
Book Review: Consumer Democracy: The Marketing of Politics
Year: 2015

Rather than political ideology or other preference oriented decision criteria, Webster argues that structural forces like language and geography play a stronger role in segmenting the marketplace of attention. He shows evidence that people remain interested in local news and information, and notes that the United States, China, and most European nations have media systems that employ local media. Like geography, language is a "scarcely noted" structure defining audiences (p. 162) The book is expansive and ambitious. Webster is convincing on the importance of media measurement and the need for a more comprehensive model. However, this book has its limitations as well. His model seems to speak past questions of media ownership. The horizontal integration of mass media provides opportunities for both the fragmentation and concentration of audiences. NBCUniversal is able to provide a variety of niche programming types across its various networks, subscription channels, and websites (e.g. NBC, Telemundo, USA Network, NBC Sports Network) yet draws together the attention of a broader audience for major events like the various Olympic games, programmed across all of these channels in the United States. Webster's engagement with questions of geography and globalism is incomplete too. This cannot be a major criticism because Webster is engaging a sufficient number of moving parts that something must be sacrificed. There is a disjoint between his theoretical development and empirical references. The theory he develops should be applicable across media systems and cultures. His theoretical explication of audience members points to fundamental psychological research. Most of the systematic data he uses comes from the U.S. case. It would be useful for him to more explicitly consider the limitations of this model, conditioned by assumptions about expression rights and norms, types of media companies, and audience members. Still, this is essential reading for scholars interested in the ever changing media landscape. The Marketplace of Attention should directly inform scholarship on U.S. mass media and provides an excellent point of reference for further theorizing comparative media systems.
Efecto de la fertilizacion organica e inorganica sobre variables agroproductivas y composicion quimica del pasto estrella (Cynodon nlemfuensis)
Year: 2012

In order to evaluate the effect of organic fertilizer (FO: 2000 kg compost*ha 1 and inorganic (FI: 200 kg N 85 kg P*ha 1 on agro productive variables and chemical composition of star grass, is conducted a trial during the dry season in experimental plots located in a tropical dry forest, on a loam soil of medium fertility and under randomized block design with three replications. The variables evaluated were plant height (PH) green (GB) and dry (DB) biomass production, dry matter accumulated (DMA) yields of dry matter in leaves (DMYL) and stems (DMYS) leaf stem relation (LSR) daily growth rate (DGR) crude protein (CP) total ash (TA) macro and microelements present, at 21, 35, 49 and 63 days post fertilization (dpf) The resulting data were subjected to analysis of variance and mean separation by LSD Fisher test. The FI showed a signifi cantly higher average PH (49.8 cm) GB and DB (10952.5 and 3017.5 kg*ha 1 *cut, respectively) except for LSR where no differences between treatments. The DMA was higher in pastures under FO (30.7% while DMYL and DMYS (2513 and 1068 kg*ha 1 respectively) were higher in the FI at 63 dpf, like DBT (MS 33.8 g*m 2 *day 1 The chemical composition of pasture was not affected by sources of fertilizer used, except for iron and zinc content decreased signifi cantly in plants that were fertilized.
CNN Enhanced Graph Convolutional Network With Pixel and Superpixel Level Feature Fusion for Hyperspectral Image Classification
Venue: IEEE Transactions on Geoscience and Remote Sensing
Year: 2021

Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions; hence, the HSI spatial contextual structure can be better modeled. However, to reduce the computational complexity and promote the semantic structure learning of land covers, GCN usually works on superpixel based nodes rather than pixel based nodes; thus, the pixel level spectral spatial features cannot be captured. To fully leverage the advantages of the CNN and GCN, we propose a heterogeneous deep network called CNN enhanced GCN (CEGCN) in which CNN and GCN branches perform feature learning on small scale regular regions and large scale irregular regions, and generate complementary spectral spatial features at pixel and superpixel levels, respectively. To alleviate the structural incompatibility of the data representation between the Euclidean data oriented CNN and non Euclidean data oriented GCN, we propose the graph encoder and decoder to propagate features between image pixels and graph nodes, thus enabling the CNN and GCN to collaborate in a single network. In contrast to other GCN based methods that encode HSI into a graph during preprocessing, we integrate the graph encoding process into the network and learn edge weights from training data, which can promote the node feature learning and make the graph more adaptive to HSI content. Extensive experiments on three data sets demonstrate that the proposed CEGCN is both qualitatively and quantitatively competitive compared with other state of the art methods.
Life role salience and subjective well being among Macedonian employees: Does family supportive organization perception moderate this relationship.
Venue: International journal of occupational medicine and environmental health
Year: 2018

As many studies have shown, one of the most important tendencies of employees nowadays is to achieve work life balance. Organizations should develop various activities and create supportive climate, within the framework of which employees will have opportunities to realize aforementioned goals which in turn would increase work productivity and work motivation. The aim of this paper was to examine how subjective well being (life satisfaction and exhaustion) is associated with commitment to work and family roles under the conditions of strongly and weakly perceived organizational support for family life among health care professionals, teachers and bankers in Macedonia. Marriage duration and the number of children were introduced as control variables.  This cross sectional study was conducted on a sample of 198 full time employed doctors, nurses, teachers and bankers. Research variables were assessed using self reported measures/ questionnaires. Hierarchical multiple linear regression was performed for data analysis.  It was revealed that occupational role commitment contributed to highly expressed life satisfaction, while exhaustion was predicted by marital role commitment. These relationships were stronger among surveyed employees who reported positive family supportive organization perception, but tested moderation effect of this variable was not statistically significant. Findings demonstrated that family supportive organization perception moderated association of the number of children with life satisfaction, that is, participants who perceived an organization as family supportive and had more children were more satisfied with their life in general.  Results highlighted the importance of organizational orientation toward employees, their commitment to work and family roles, and their subjective well being, as characteristics that might contribute to higher work engagement, success and family satisfaction.  Environ Health 2018;31(3):281 291.
Constant Composition Distribution Matching
Venue: IEEE Transactions on Information Theory
Year: 2016

Distribution matching transforms independent and Bernoulli(1/2) distributed input bits into a sequence of output symbols with a desired distribution. Fixed to fixed length, invertible, and low complexity encoders and decoders based on constant composition and arithmetic coding are presented. The encoder achieves the maximum rate, namely, the entropy of the desired distribution, asymptotically in the blocklength. Furthermore, the normalized divergence of the encoder output and the desired distribution goes to zero in the blocklength.
Incorporating Ethics into Artificial Intelligence
Year: 2017

This article reviews the reasons scholars hold that driverless cars and many other AI equipped machines must be able to make ethical decisions, and the difficulties this approach faces. It then shows that cars have no moral agency, and that the term 'autonomous' commonly applied to these machines, is misleading, and leads to invalid conclusions about the ways these machines can be kept ethical. The article's most important claim is that a significant part of the challenge posed by AI equipped machines can be addressed by the kind of ethical choices made by human beings for millennia. Ergo, there is little need to teach machines ethics even if this could be done in the first place. Finally, the article points out that it is a grievous error to draw on extreme outlier scenarios such as the Trolley narratives as a basis for conceptualizing the ethical issues at hand.
Drug Problem or Medicrime? Distribution and Use of Falsified Tramadol Medication in Egypt and West Africa
Year: 2019

Tramadol is a synthetic opioid that is widely used as an analgesic for alleviating pain of moderate to medium intensity. With potency estimated to be about one tenth that of morphine, tramadol is considered as relatively safe with regard to poisonings or dependency. Yet there are increasing reports of widespread non medical consumption of tramadol in North and West Africa. The Egyptian government has requested the UN Commission of Narcotic Drugs to put tramadol under international control. This will have profound implications for the treatment of acute and chronic pain across developing countries where tramadol is often the only available analgesic, because controlled substances are impossible to obtain for health care practitioners. The tramadol sold outside of medical establishments is often adulterated and substandard, part of the massive trade in falsified medicines that is possibly far more devastating than the hedonic use of psychoactive substances. Yet the international machinery in place to control medical products is feeble and the penalties for medicrime are modest next to drug trafficking offences. The article suggests that international controls need to re assess their priorities to focus on human and patient well being. A further shift is needed away from repressive measures against consumers, to tighter regulation in the production and distribution of medications and pharmaceutical substances. This must involve a wide range of stakeholders, including health care practitioners, the pharmaceutical industry, pharmacists, patients associations, and the public at large.
A Simple Approximate Long Memory Model of Realized Volatility
Year: 2008

The paper proposes an additive cascade model of volatility components defined over different time periods. This volatility cascade leads to a simple AR type model in the realized volatility with the feature of considering different volatility components realized over different time horizons and thus termed Heterogeneous Autoregressive model of Realized Volatility (HAR RV) In spite of the simplicity of its structure and the absence of true long memory properties, simulation results show that the HAR RV model successfully achieves the purpose of reproducing the main empirical features of financial returns (long memory, fat tails, and self similarity) in a very tractable and parsimonious way. Moreover, empirical results show remarkably good forecasting performance.
Multipartite entanglement of three trapped ions in a cavity and W state generation
Year: 2008

A scheme to generate three qubit maximally entangled W states, using three trapped ions interacting with the red sideband tuned single mode field of a high finesse cavity, is proposed. For the cavity field initially prepared in a number state, the probability of generating the three ion W state is calculated. By using the ion cavity coupling strengths achieved in experimental realizations, the interaction time needed for W state generation is found to be of the order of 10 us. It is found that for a fixed number of photons in the cavity the nature of entanglement of ionic internal states can be manipulated by an appropriate choice of the initial state phonon number. The ionic qubits in the W like state are found to be entangled to cavity photons. Analytical expressions for global negativity and partial K way negativities (K 2 to 4) are obtained to study the evolution of entanglement distribution as a function of the interaction parameter. Reversible entanglement exchange between different entanglement modes is observed. For specific values of the interaction parameter, the three ions and photon phonon system are found to have 4 partite entanglement, generated by 2 way and 3 way correlations.
ANALISIS PENGARUH VARIABEL MAKRO EKONOMI TERHADAP INDEKS SAHAM SYARIAH INDONESIA
Year: 2019

ABSTRAK Mutaqin, Imamul. 2019. Analisis Pengaruh Variabel Makro Ekonomi Terhadap Indeks Saham Syariah Indonesia, Fakultas Ekonomi dan Bisnis Islam, Program Studi S1 Perbankan Syariah, IAIN Salatiga. Pembimbing Dr. Anton Bawono, S.E. M.Si. Tujuan penelitian ini dilakukan untuk menganalisis respon yang ditunjukan oleh indeks saham syariah Indonesia (ISSI) terhadap shock yang terjadi pada inflasi,Kurs IDR/USD, BI Rate, jumlah uang beredar (JUB) harga minyak dunia dan harga emas dunia. Penelitian ini menggunakan jenis penelitian kuantitatif dengan menggunakan model VAR sebagai analisis data. Penelitian ini menggunakan data sekunder berbentuk time series data bulanan yang meliputi indeks saham syariah Indonesia (ISSI) terhadap shock yang terjadi pada inflasi,Kurs IDR/USD, BI Rate, jumlah uang beredar (JUB) harga minyak dunia dan harga emas dunia. Data yang telah diperoleh kemudian dianalisis menggunakan alat bantu aplikasi Eviews versi 9. Hasil penelitian ini menunjukkan bahwa indeks saham syariah Indonesia (ISSI) merespon negatif dan bersifat permanen terhadap shock yang terjadi pada inflasi, indeks saham syariah Indonesia (ISSI) merespon negatif dan bersifat permanen terhadap shock yang terjadi pada BI Rate, indeks saham syariah Indonesia (ISSI) merespon positif dan bersifat permanen terhadap shock yang terjadi pada harga minyak dunia, indeks saham syariah Indonesia (ISSI) merespon positif dan bersifat permanen terhadap shock yang terjadi pada harga emas dunia, Kata Kunci indeks saham syariah Indonesia, inflasi,Kurs IDR/USD, BI Rate, jumlah uang beredar (JUB) harga minyak dunia, harga emas dunia.
Deep Learning Methods for Credit Card Fraud Detection
Venue: ArXiv
Year: 2020

Credit card frauds are at an ever increasing rate and have become a major problem in the financial sector. Because of these frauds, card users are hesitant in making purchases and both the merchants and financial institutions bear heavy losses. Some major challenges in credit card frauds involve the availability of public data, high class imbalance in data, changing nature of frauds and the high number of false alarms. Machine learning techniques have been used to detect credit card frauds but no fraud detection systems have been able to offer great efficiency to date. Recent development of deep learning has been applied to solve complex problems in various areas. This paper presents a thorough study of deep learning methods for the credit card fraud detection problem and compare their performance with various machine learning algorithms on three different financial datasets. Experimental results show great performance of the proposed deep learning methods against traditional machine learning models and imply that the proposed approaches can be implemented effectively for real world credit card fraud detection systems.