nils-herrmann/clean_scirepeval huggingface dataset:
Factors influencing alcohol use among female in school adolescents in the Western Cape, South Africa
Year: 2020

The increasing levels and more frequent use of alcohol among females, especially those in younger age groups, is a worldwide concern. An in depth understanding of this phenomenon is, however, limited. This study aimed to explore the factors influencing alcohol use among female adolescent students as guided by the ecological systems theory of Bronfenbrenner. Data were collected from 25 female high school students in Grade 9 in the Western Cape, South Africa, using five individual semi structured interviews and two focus group discussions with each group comprising 10 students. Thematic analysis was used to analyse the data. The findings of the study indicated that participants begin drinking at an early age and due to low self esteem, which makes them prone to peer influence. Female adolescents are also influenced by various factors residing within the microsystem (family members and peers) mesosystem (lack of parental attention, weak mother to daughter relationship, influence of peers and dating partners) exosystem (media advertisement and lifestyle of celebrities) macrosystem (change in societal roles) and chronosystem (transition to higher education) Interventions are needed in schools to help female adolescents resist peer pressure and cope with the changing environment of high school. Also, parents should be educated on the influence of their drinking behaviour on their children in order for them to be more cautious when drinking at home. Adolescent students will benefit from educational materials on the harmfulness of alcohol use.
Low Power Binary Neuron Circuit With Adjustable Threshold for Binary Neural Networks Using NAND Flash Memory
Venue: IEEE Access
Year: 2020

Recent studies have demonstrated that binary neural networks (BNN) could achieve a satisfying inference accuracy on representative image datasets. BNN conducts XNOR and bit counting operations instead of high precision vector matrix multiplication (VMM) significantly reducing the memory storage. In this work, an analog bit counting scheme is proposed to decrease the burden of neuron circuits with a synaptic architecture utilizing NAND flash memory. A novel binary neuron circuit with a double gate positive feedback (PF) device is demonstrated to replace the sense amplifier, adder, and comparator, thereby reducing the burden of the complementary metal oxide semiconductor (CMOS) circuits and power consumption. By using the double gate PF device, the threshold voltage of the neuron circuits can be adaptively matched to the threshold value in the algorithms eliminating the accuracy degradation introduced by the process variation. Thanks to the super steep SS characteristics of the PF device, the proposed neuron circuit with the PF device has an off state current of 1 pA, representing 105 times improvement compared to the neuron circuit with a conventional metal oxide semiconductor field effect transistor (MOSFET) device. A system simulation of a hardware based BNN shows that the low variance conductance distribution (8.4 of the synaptic device and the adjustable threshold of the neuron circuit implement a highly efficient BNN with a high inference accuracy.
Optimizing Reconfigurable Recurrent Neural Networks
Venue: 2020 IEEE 28th Annual International Symposium on Field Programmable Custom Computing Machines (FCCM)
Year: 2020

This paper proposes a novel latency hiding hardware architecture based on column wise matrix vector multiplication to eliminate data dependency, improving the throughput of systems of RNN models. In addition, a flexible checkerboard tiling strategy is introduced to allow large weight matrices, while supporting element based parallelism and vector based parallelism. These optimizations improve the exploitation of the available parallelism to increase run time hardware utilization and boost inference throughput. Furthermore, a quantization scheme with fine tuning is proposed to achieve high accuracy. Evaluation results show that the proposed architecture can enhance performance and energy efficiency with little accuracy loss. It achieves 1.05 to 3.35 times better performance and 1.22 to 3.92 times better hardware utilization than a state of theart FPGA based LSTM design, which shows that our approach contributes to high performance FPGA based LSTM systems.
A framework integrating interval valued hesitant fuzzy DEMATEL method to capture and evaluate co creative value propositions for smart PSS
Venue: Journal of Cleaner Production
Year: 2019

Through analysing evolution of smart PSS, it is a challenge to capture and evaluate co creative value propositions (CVPs) for smart PSS. This study thus proposes a framework integrating IVHF DEMATEL method enhanced by an operator of AVL (average vector length) on CVPs. Given that smart PSS involves multi stakeholders and its value is co created, stakeholder types are proposed to define stakeholder value flow network (SVFN) Based on quality characteristics of smart PSS's core elements, dimensions and criterions of CVPs are courageously proposed to align specific CVPs captured from a SVFN. Since different knowledges and skills of experts, their weights are assigned by the operator of intuitionistic fuzzy number. Compared to other techniques handling uncertainty of expert judgement, IVHF element is more powerful in term of hesitancy. To evaluate the interdependent CVPs, IVHF DEMATEL method is employed to derive the total relation of CVPs. Moreover, an operator of AVL is firstly proposed to prioritize the CVPs, also is feasible and simpler than multiple operators of modified CFCS. Furthermore, influence dependence (I D) map with cut offs of hesitant interval and interval value is utilized to give much richer interpretations on evaluation results. Finally, an illustrative case of smart fridge service system is demonstrated to justify this proposal methodological framework.
A Graph Based Knowledge and Reasoning Representation Approach for Modeling MongoDB Data Structure and Query
Venue: 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
Year: 2019

MongoDB document store is the most popular of NoSQL systems. For managing and retrieving data, having a representation of its inherent knowledge structures proves to be significant for modeling purposes. In this paper, we present a modeling method for MongoDB data structure and query based on a graph based knowledge and reasoning (GBKR) model, due to Chein and Mugnier. The expressive power of the GBKR model makes it particularly suitable for conceptual modeling of semi structured data. We propose a common interface for data structure modeling and queries in MongoDB by using knowledge graphs. For a proof of concept we have developed a software tool implementing these functions.
Calculation of the 4f1 4f05d1 transitions in Ce3+ doped systems by Ligand Field Density Functional Theory
Year: 2013

We present a recipe for the calculation of the optical properties of Ce 3+ doped systems. The model implies the use of ligand field phenomenology in conjunction with Density Functional Theory (DFT) The particular procedures enable the reliable prediction of the 4f 1 4f 0 5d 1 transitions in Cs 2 NaYCl 6 :Ce 3+ The analysis of the doping of Ce 3+ into the host is accomplished by band structure calculations. The calculated multiplet energy levels are in agreement with the experimental observation, the outlined treatment being, to the best of our knowledge, unprecedented clear and conclusive application of DFT for the rather complex problems of structure and spectroscopy of cerium doped systems.
Dual time and wavelength multiplexed photonic reservoir computing
Venue: OPTO
Year: 2021

Photonics based AI processors have the potential to outperform digital AI accelerators in terms of throughput, latency, and efficiency, and meet the growing demands of AI edge computing. One such class of processors, known as photonic reservoir computers, are a promising candidate for performing real time edge computing, but their performance has been limited by the small number of effective nodes in the reservoir. We discuss our advances in developing a next generation photonic reservoir with dual time and wavelength multiplexing. This allows for a reservoir with increased size and complexity, enabling it to successfully perform complex classification and prediction tasks.
A novel temporal and spatial panorama stream processing engine on IoT applications
Year: 2020

Nowadays, more and more streaming data are generated with the development of Internet of Things. Although, streaming data show great application values in practical scenarios, raw streams from terminal sensors are quite massive, heterogeneous and complex, and those features make it difficult for applications to deal with them. In order to simplify streaming data processing for applications, a novel temporal and spatial panorama stream processing engine is proposed. This stream processing engine forms an effective link between bottom sensors and upper stream based applications, and provides configurable, flexible, available and usable stream services for various upper applications. With support of Internet plus and domain data, raw streaming data are formatted thorough data fusion and are represented in temporal, spatial, logic and storage views. Fusion data are encapsulated with multiple strategies and methods of clustering models on every dimension. According to configurable strategies, encapsulated data are extracted, partitioned and distributed as the form of dynamical variable gratitude data blocks into stream channels. Our engine is applied to a practical application scenario. Case study of dynamic power distribution application based on people crowd streaming data proves the customized service capabilities of our engine. And the outstanding performance of the engine is shown further by experiments evaluation in this case.
Distribution of stress state in the Nankai subduction zone, southwest Japan and a comparison with Japan Trench
Year: 2016

To better understand the distribution of three dimensional stress states in the Nankai subduction zone, southwest Japan, we review various stress related investigations carried out in the first and second stage expeditions of the Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) by the Integrated Ocean Drilling Program (IODP) and compile the stress data. Overall, the maximum principal stress s1 in the shallower levels 1 km below seafloor or in underlying accretionary prism) with s1 becoming horizontal is also suggested at all deeper drilling sites. We also make a comparison of the stress state in the hanging wall of the frontal plate interface between Site C0006 in the Nankai and Site C0019 in the Japan Trench subduction zone drilled after the 2011 Mw 9.0 Tohoku Oki earthquake. In the Japan Trench, a comparison between stress state before and after the 2011 mega earthquake shows that the stress changed from compression before the earthquake to extension after the earthquake. As a result of the comparison between the Nankai Trough and Japan Trench, a similar current stress state with trench parallel extension was recognized at both C0006 and C0019 sites. Hypothetically, this may indicate that in Nankai Trough it is still in an early stage of the interseismic cycle of a great earthquake which occurs on the decollement and propagates to the toe (around site C0006)
An Effective Similar Pixel Reconstruction of the High Frequency Cloud Covered Areas of Southwest China
Venue: Remote. Sens.
Year: 2019

With advantages of multispatial resolutions, a high retrieval accuracy, and a high temporal resolution, the satellite derived land surface temperature (LST) products are very important LST sources. However, the greatest barrier to their wide application is the invalid values produced by large quantities of cloudy pixels, especially for regions frequently swathed in clouds. In this study, an effective method based on the land energy balance theory and similar pixels (SP) method was developed to reconstruct the LSTs over cloudy pixels for the widely used MODIS LST (MOD11A1) The southwest region of China was selected as the study area, where extreme drought has frequently occurred in recent years in the context of global climate change and which commonly exhibits cloudy and foggy weather. The validation results compared with in situ LSTs showed that the reconstructed LSTs have an average error 1.00 K (0.57 K at night and 0.14 K during the day) and an RMSE 3.20 K (1.90 K at night and 3.16 K in the daytime) The experiment testing the SP interpolation indicated that the spatial structure of the LST has a greater effect on the SP performance than the size of the data missing area, which benefits the LST reconstruction in the area frequently covered by large clouds.
Highly Efficient Deep Blue OLEDs using a TADF Emitter with a Narrow Emission Spectrum and High Horizontal Emitting Dipole Ratio.
Venue: Advanced materials
Year: 2020

New blue (DBA SAB) and deep blue (TDBA SAF) thermally activated delayed fluorescence (TADF) emitters are synthesized for blue emitting organic light emitting diodes (OLEDs) by incorporating spiro biacridine and spiro acridine fluorene donor units with an oxygen bridged boron acceptor unit, respectively. The molecules show blue and deep blue emission because of the deep highest occupied molecular energy levels of the donor units. Besides, both emitters exhibit narrow emission spectra with the full width at half maximum (FWHM) of less than 65 nm due to the rigid donor and acceptor units. In addition, the long molecular structure along the transition dipole moment direction results in a high horizontal emitting dipole ratio over 80% By combining the effects, the OLED utilizing DBA SAB as the emitter exhibits a maximum external quantum efficiency (EQE) of 25.7% and 1931 Commission Internationale de l'eclairage (CIE) coordinates of (0.144, 0.212) Even a higher efficiency deep blue TADF OLED with a maximum EQE of 28.2% and CIE coordinates of (0.142, 0.090) is realized using TDBA SAF as the emitter.
COVID 19 and tourism spaces of vulnerability in South Africa
Year: 2020

The COVID 19 pandemic will exert a devastating and destructive impact on the South African tourism economy with its ramifications felt countrywide Nevertheless, the negative local impacts of the pandemic will be particularly harsh for those parts of South Africa where tourism is a critical sector in the local economy The objective in this article is to identify the tourism spaces of vulnerability in South Africa Use is made of the IHS Global Insight data base for 2016 to analyse at a local authority scale the most vulnerable localities to the impacts of the COVID 19 pandemic Indicators used to isolate tourism spaces of vulnerability are total tourism spend;tourism spend as a proportion of local GDP;domestic and international tourism (trips and bednights);and, leisure, business and VFR (visiting friends and relatives) travel The analysis discloses those local authorities that are the most vulnerable to the downturn/collapse of tourism as a whole as well as to the hollowing out of specific forms of tourism, namely domestic as opposed to international travel, leisure as opposed to business or VFR travel (c) 2020 AJHTL /Author(s)
Salt Stress Represses Soybean Seed Germination by Negatively Regulating GA Biosynthesis While Positively Mediating ABA Biosynthesis
Venue: Front. Plant Sci.
Year: 2017

Soybean is an important and staple oilseed crop worldwide. Salinity stress has adverse effects on soybean development periods, especially on seed germination and post germinative growth. Improving seed germination and emergence will have positive effects under salt stress conditions on agricultural production. Here we report that NaCl delays soybean seed germination by negatively regulating gibberellin (GA) while positively mediating abscisic acid (ABA) biogenesis, which leads to a decrease in the GA/ABA ratio. This study suggests that fluridone (FLUN) an ABA biogenesis inhibitor, might be a potential plant growth regulator that can promote soybean seed germination under saline stress. Different soybean cultivars, which possessed distinct genetic backgrounds, showed a similar repressed phenotype during seed germination under exogenous NaCl application. Biochemical analysis revealed that NaCl treatment led to high MDA (malondialdehyde) level during germination and the post germinative growth stages. Furthermore, catalase, superoxide dismutase, and peroxidase activities also changed after NaCl treatment. Subsequent quantitative Real Time Polymerase Chain Reaction analysis showed that the transcription levels of ABA and GA biogenesis and signaling genes were altered after NaCl treatment. In line with this, phytohormone measurement also revealed that NaCl considerably down regulated active GA1, GA3, and GA4 levels, whereas the ABA content was up regulated; and therefore ratios, such as GA1/ABA, GA3/ABA, and GA4/ABA, are decreased. Consistent with the hormonal quantification, FLUN partially rescued the delayed germination phenotype caused by NaCl treatment. Altogether, these results demonstrate that NaCl stress inhibits soybean seed germination by decreasing the GA/ABA ratio, and that FLUN might be a potential plant growth regulator that could promote soybean seed germination under salinity stress.
Slotted Permanent Magnet Machines: General Analytical Model of Magnetic Fields, Torque, Eddy Currents, and Permanent Magnet Power Losses Including the Diffusion Effect
Venue: IEEE Transactions on Magnetics
Year: 2016

This paper presents an exact analytical subdomain model of slotted permanent magnet (PM) machines, taking into account the diffusion effect. The model gives the magnetic fields, both the eddy currents and the power losses in the PM, the instantaneous and average torque. In the model, eddy currents are not assumed to be resistance limited, contrary to what is done in the conventional exact subdomain models of the slotted machines. The model is hence general and valid both at low and high frequencies. It shows that it is possible to calculate the power losses in the PMs, only knowing the fields in the air gap. The model takes into account the presence of tooth tips. The comparison with conventional exact subdomain models shows that the model presented in this paper is more accurate. The model is validated using the finite element methods.
Recombinase polymerase amplification with polymer flocculation sedimentation for rapid detection of Staphylococcus aureus in food samples.
Venue: International journal of food microbiology
Year: 2020

Currently, rapid, sensitive, and convenient visual detection methods for Staphylococcus aureus (S. aureus) are scarce. In this study, a novel detection method based on recombinase polymerase amplification (RPA) and polymer flocculation sedimentation (PFS) was developed. Twelve effective primer combinations derived from four forward primers F1, F2, F3, F4, and three reverse primers R1, R2, R3 targeting the nuc gene of S. aureus were designed and screened by a polymerase chain reaction and RPA methods. RPA reaction conditions, including temperature, time, and volume as well as PEG8000 and NaCl concentrations range, were optimized. Moreover, the specificity and sensitivity of the RPA PFS assay were further analyzed. Finally, the potential use of the RPA PFS assay was evaluated using artificially S. aureus contaminated food samples, including pork, beef, shrimp, fish, cheese, cabbage, leftover rice, egg, milk, and orange juice. Results showed that the SA5 (F2/R2) combination was the optimal primer candidate. The optimal temperature range, the shortest time and the minimal volume of RPA reaction were 40 42 degC, 10 min and 10 mL, respectively and the optimal PEG8000/NaCl concentrations were 0.2 g/mL and 2.5 M, respectively, for the adsorption between magnetic beads and RPA products. The RPA PFS method could detect as little as 13 fg genomic DNA of S. aureus and was also specific for five target S. aureus as well as twenty seven non target foodborne bacteria. The limit of detection of RPA PFS for S. aureus in artificially contaminated food samples was 38 CFU/mL (g) Besides, RPA PFS has directly been judged by the naked eye and has totally taken less than 20 min. In short, the assay RPA PFS developed in this study is a rapid, sensitive, and specific visual detection method for S. aureus.
Stock Market Reactions to India's 2016 Demonetization: Implications for Tax Evasion, Corruption, and Financial Constraints
Year: 2017

On November 8, 2016, the Indian government made a surprise announcement that certain currency notes (representing 86% of the currency then in circulation) would no longer be legal tender (although they could be deposited in banks over a limited period) The stated reason for this sudden "demonetization" was to combat tax evasion and corruption associated with "unaccounted for" cash. We compute abnormal returns for different subsamples of firms defined by industry, ownership structure, and other characteristics on the Indian stock market around this event. There is little evidence that sectors thought to be associated with greater tax evasion or corruption experienced significantly different returns. However, we find substantial positive returns for banks and for state owned enterprises (SOEs) These effects persist over longer time horizons, implying that the initial reactions do not show any indication of subsequent reversal. The bank results appear to indicate a market expectation of a persistent increase in financial depth. We also find a pattern of higher returns for industries that are characterized by a greater dependence on external finance, possibly suggesting an expectation of an easing of financial constraints. The returns for SOEs are more puzzling and we canvass a number of potential explanations, while leaving further examination of this finding to future research.