nils-herrmann/clean_scirepeval huggingface dataset:
An Experimental Study on Combustion and Emission Analysis of Four Cylinder 4 Stroke Gasoline Engine Using Pure Hydrogen and LPG at Idle Condition
Year: 2016

Fluctuation in oil prices and stricter exhaust emission norms were the main reasons wakening every researcher to search for suitable and feasible alternative fuels for automotive use. Among the available option gaseous fuels find their best position because of their compatible physical chemical properties and ecofriendly nature than present fossil fuels. Hydrogen's combustion properties like high energy content, high heating value, wide range of flammability and low ignition energy with almost least toxic emissions are favorable to use in an IC engine as an alternative fuel. Liquid petroleum gas (LPG) has lower carbon content, higher calorific value, octane number and flame propagation speed will improve the emission results compared to gasoline fuel. This paper describes an experimental results carried out to evaluate the combustion and emission performance of a Maruti Suzuki make, spark ignited four cylinders, four stroke engines at idle condition by using pure hydrogen, LPG and gasoline. The engine was adjoined with Electronic Control Unit (ECU) assisting hydrogen and LPG injector system keeping gasoline line unchanged. Tests were carried out by using compressed hydrogen gas regulated by two stage pressure reduction from cylinder to atmospheric value and by using vaporizer pressure for LPG. For comparison engine was run first by gasoline and then by pure hydrogen and LPG. Study revealed that there was increment of 13% cylinder pressure for pure hydrogen and decrement of 4.5% cylinder pressure for LPG when compared to gasoline. The burn duration for pure hydrogen, LPG and gasoline were found to be increasing respectively which infers that hydrogen has very short combustion duration and gasoline higher. It was observed that toxic emissions like Carbon monoxide (CO) Hydrocarbons (HC) and Oxides of Nitrogen (NO x were improved for pure hydrogen than LPG and gasoline.
Secretory laccase 1 in Bemisia tabaci MED is involved in whitefly plant interaction
Venue: Scientific Reports
Year: 2017

The whitefly Bemisia tabaci is a phloem feeding pest that lives predominantly on herbaceous species and causes serious damage to hosts. Whitefly saliva is thought to contain proteins that modulate plant defences and facilitate feeding. A predicted secreted protein, laccase 1 (LAC1) was found in the salivary gland transcriptome of B. tabaci and might be existed in the watery saliva of B. tabaci. As LAC1 has a potential role in detoxification of secondary plant compounds in insects, we speculated that it may participate in the insect's response to plant defences. Here, we cloned the complete cDNA of LAC1 and found that (1) LAC1 was highly expressed in the salivary gland (SG) and midgut; (2) LAC1 transcript level in head (containing SG) was 2.1 times higher in plant fed than in diet fed whiteflies and 1.6 times higher in the head and 23.8 times higher in the midgut of whiteflies that fed on jasmonic acid (JA) sprayed plants than on control plants; and (3) silencing LAC1 decreased the survival rate of plant fed whiteflies but had a marginal effect on whiteflies raised on an artificial diet. These results indicate that LAC1 enables whiteflies to overcome the chemical defences of host plants and might act as an effector in saliva.
Gene networks in cancer are biased by aneuploidies and sample impurities
Year: 2019

Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, among other data sources, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. They are often applied to gene expression of human cancers. However, in contrast to the evaluations, these data sets often contain fewer samples, more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtaining reliable networks, and (2) even when taking these into account, we should expect between 20 and 80% of edges to be caused by copy number changes and cell mixtures rather than transcription factor regulation.
Invariant Feature Extraction for Finger Vein Matching Using Fuzzy Logic Inference
Venue: SocProS
Year: 2015

Finger vein is a new member in the biometrics family. In the recent years, as a biometric trait finger vein has been achieved success and attracted lots of attention from the researchers. Current finger vein recognition algorithms provide high accuracy in good quality images. Most of the finger vein recognition systems utilize based on minutiae points. In this paper, we propose a method for finger vein matching using scale invariant feature transformation (SIFT) key points which are determining the rules using fuzzy inference system and the matching scores are delivered by Euclidean distance. The fuzzy logic system provides the more adequate description of the proposed algorithm. The algorithm has been formulated based on SIFT key points which examine n number of images. MATLAB 7:14:0 is used to implement this technique.
Functional Analysis, Sobolev Spaces and Partial Differential Equations
Year: 2010

Preface. 1. The Hahn Banach Theorems. Introduction to the Theory of Conjugate Convex Functions. 2. The Uniform Boundedness Principle and the Closed Graph Theorem.  Spectral Decomposition of Self Adjoint Compact Operators. 7. The Hille Yosida Theorem. 8. Sobolev Spaces and the Variational Formulation of Boundary Value Problems in One Dimension. 9. Sobolev Spaces and the Variational Formulation of Elliptic Boundary Value Problems in N Dimensions. 10.
Phenotypic Analysis of Mutants of Ergosterol Biosynthesis Genes (ERG3 and ERG4) in the Red Yeast Xanthophyllomyces dendrorhous
Venue: Frontiers in Microbiology
Year: 2020

Xanthophyllomyces dendrorhous synthesizes astaxanthin, a carotenoid used in aquaculture. Astaxanthin is synthesized from metabolites of the mevalonate pathway, which are also precursors for sterols biosynthesis. The interruption of the CYP61 gene, which is involved in the synthesis of ergosterol (mutant CBS.cyp61 resulted in a phenotype that overproduces carotenoids due to the activation of the SREBP pathway. In this work, we constructed other mutants of ergosterol biosynthesis in this yeast to evaluate whether they have the same phenotype as mutant CBS.cyp61 By bioinformatic analysis, the ERG3 and ERG4 genes of X. dendrorhous were identified, and each gene was deleted in the wild type strain. Mutants CBS.Derg3 and CBS.Derg4 did not produce ergosterol; CBS.Derg3 primarily accumulated episterol, and CBS.Derg4 primarily accumulated ergosta 5,7,22,24(28) tetraenol. The transcription levels of the HMGS gene of the mevalonate pathway were evaluated by RT qPCR, which showed a slight increase in CBS.Derg4, but the transcription levels were still 10 fold lower than in strain CBS.cyp61 Both CBS.Derg3 and CBS.Derg4 did not overproduce carotenoids, even though they do not produce ergosterol. Thus, the results of this study indicate that the absence of ergosterol does not activate the SREBP pathway in X. dendrorhous, but rather it depends on other alterations in sterol composition.
Random Forest Based Feature Selection of Macroeconomic Variables for Stock Market Prediction
Venue: American Journal of Applied Sciences
Year: 2019

A firm's equity price on the stock market is reported to be closely related to the Macroeconomic Variable (MVs) of the country in which the firm trades. For this reason, researchers, market traders, financial analysts and forecasters to examine the association between MVs and stock price have carried out numerous studies, using time series statistical analysis methods like Autoregressive Integrated Moving Average (ARIMA) Autoregressive Moving Average (ARMA) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) However, these techniques are reported to suffer from limited predictive power and restrictive assumptions. Besides, in pursuit of ways to remedy these paucities and limitations within these techniques, some researchers have examined uncountable machine learning techniques for measuring the stock markets trends and making trading decisions using macroeconomic variables. On the other hand, a higher percentage of these studies paid attention to the stock index prediction and neglected the diversity of MVs that influence different sector indices. In addressing the issues above, this study seeks to examine the degree of significance between different sectors stock price and MVs and predict a 30 day head stock price using Random Forest (RF) with an improve leave one out cross validation tactic and Long Short Term Memory Recurrent Neural Network (LSTMRNN) An empirical analysis of the proposed model over the Ghana Stock Exchange (GSE) exhibits high prediction accuracy and better mean absolute error compared with other time series techniques. It can, therefore, be inferred from the fallouts that the proposed stock market prediction with MVs, provides an efficient approach to automatic identification and extraction of MVs that affect diverse sector stock and offer an accurate prediction of a stock's future price.
The impact of national culture and social capital on corporate social responsibility attitude among immigrants entrepreneurs
Year: 2016

Although significant attention has been given to the phenomenon of immigrant entrepreneurship, little is known about the adoption of corporate social responsibility (CSR) in immigrant firms. Beyond the traditional social, cultural and regulatory challenges in the last years immigrant entrepreneurs have had to cope with the wide growing interest towards CSR, with raising expectations about firm's environmental and social behaviors. This study provides initial insights on this topic by studying the effect of social capital and national culture on the immigrant entrepreneur's attitude to adopt CSR in three areas: social, health and safety and environment. Sixty semi structured interviews were conducted among immigrant entrepreneurs in the Italian Province of Milan; the first one for number of immigrant entrepreneurs. Data collected were analyzed through OLS regression. Findings shows that power distance and individualism/collectivism have a negative relation with all the three CSR areas, while uncertainty avoidance is negatively related only to environmental effort. We find out a robust relation between national culture and environmental and social practices and the health and safety area. The study concludes with practical implications and suggestions for future research. The paper is one of the first studies that address the topic of CSR among immigrant entrepreneurs. Understanding the commitment of this firms is relevant for the debate around CSR and SMEs, mainly because immigrant entrepreneurs are increasing their relevance in the developed countries.
Burnout Syndrome in an International Setting
Year: 2013

Burnout is a global concern, and work related stress is recognized worldwide as a major challenge to workers' health and the functioning of their organizations. Epidemiological studies performed in western and developing countries have found a high prevalence of burnout syndrome and associated mental illness among health workers, teachers, caregivers, other professionals, and others. Unsatisfactory work may lead to long term emotional exhaustion, depersonalization, and low levels of personal accomplishment. Emotional exhaustion is considered to be the most important dimension of burnout syndrome. Work related stress can affect employee satisfaction, work productivity, and mental and physical health; it also can increase rates of absenteeism and affect family roles and function. Professional distress can provoke serious mental manifestations such as anxiety and depression disorders, leading to divorce, alcoholism, substance abuse and suicide. Perception of adverse psychosocial factors in the workplace may be related to an elevated risk of subsequent major depressive disorder. Burnout rates in society range from 2.4 to 72 whereas the prevalence of depressive symptoms is variable and may affect between 3 and one third of workers. High staff turnover, increased absenteeism, and decreased productivity cause an elevated financial cost.
GOBO projection based high speed three dimensional shape measurement
Venue: Commercial Scientific Sensing and Imaging
Year: 2016

Pattern projection based three dimensional (3 D) measurement systems are well established in various application fields. However, for some time, there has been an increasing demand in terms of a higher measurement speed in order to analyze fast processes. We introduce a GOBO slide based projector (GOes Before Optics) providing high speed, high radiant flux, and eye safe incoherent light at low cost. We present its setup as well as the generation and change of the projected patterns. We explain the 3 D measurement principle and show first measurements of an inflating airbag and a rope skipper conducted at a 3 D frame rate of more than 1.3 kHz.
Low Rank and Sparse Recovery of Human Gait Data
Venue: Sensors
Year: 2020

Due to occlusion or detached markers, information can often be lost while capturing human motion with optical tracking systems. Based on three natural properties of human gait movement, this study presents two different approaches to recover corrupted motion data. These properties are used to define a reconstruction model combining low rank matrix completion of the measured data with a group sparsity prior on the marker trajectories mapped in the frequency domain. Unlike most existing approaches, the proposed methodology is fully unsupervised and does not need training data or kinematic information of the user. We evaluated our methods on four different gait datasets with various gap lengths and compared their performance with a state of the art approach using principal component analysis (PCA) Our results showed recovering missing data more precisely, with a reduction of at least 2 mm in mean reconstruction error compared to the literature method. When a small number of marker trajectories is available, our findings showed a reduction of more than 14 mm for the mean reconstruction error compared to the literature approach.
Differentiation of Progressive Supranuclear Palsy: clinical, imaging and laboratory tools
Venue: Acta neurologica Scandinavica
Year: 2013

Progressive supranuclear palsy (PSP) is the most common atypical parkinsonian syndrome comprising two main clinical subtypes: Richardson's syndrome (RS) characterized by prominent postural instability, supranuclear vertical gaze palsy and frontal dysfunction; and PSP parkinsonism (PSP P) which is characterized by an asymmetric onset, tremor and moderate initial therapeutic response to levodopa. The early clinical features of PSP P are often difficult to discern from idiopathic Parkinson's disease (PD) and other atypical parkinsonian disorders, including multiple system atrophy (MSA) and corticobasal syndrome (CBS) In addition, rare PSP subtypes may be overlooked or misdiagnosed if there are atypical features present. The differentiation between atypical parkinsonian disorders and PD is important because the prognoses are different, and there are different responses to therapy. Structural and functional imaging, although currently of limited diagnostic value for individual use in early disease, may contribute valuable information in the differential diagnosis of PSP. A growing body of evidence shows the importance of CSF biomarkers in distinguishing between atypical parkinsonian disorders particularly early in their course when disease modifying therapies are becoming available. However, specific diagnostic CSF biomarkers have yet to be identified. In the absence of reliable disease specific markers, we provide an update of the recent literature on the assessment of clinical symptoms, pathology, neuroimaging and biofluid markers that might help to distinguish between these overlapping conditions early in the course of the disease.
Pemisahan Emulsi CPO (Crude Palm Oil) Air pada Horizontal T Junction ke Atas 60@ dan 90@
Venue: FLUIDA
Year: 2019

Emulsi CPO air merupakan aliran dua fase yang tidak dapat menyatu dan bila dialirkan dalam pipa akan membentuk pola aliran. Pemisahan fase cair cair dilakukan pada pola aliran strata (stratified) yang terbentuk dalam pipa horizontal. Pemisahan fase cair cair dilakukan pada sistem horizontal t junction. Tujuan penelitian ini untuk memperoleh pemisahan fase CPO (crude palm oil) air pada horizontal t junction ke atas dengan sudut 60@ dan 90@ Pola aliran dan pemisahan fase diamati pada horizontal t junction ke atas dengan sudut 60@ dan 90@ pipa aklirik transparan dengan diameter dalam 25,4 mm. Kecepatan superfacial CPO (SCPO) air (SW) yang digunakan bervariasi pada rentang 0,10 0,35 m/s dengan interval 0,05 m/s. Pencatatan data menggunakann perangkat keras Ns.One/32 berbasis mikrokontroler AVR Atmega 32. Pemisahan fase terbaik untuk horizontal t junction 60@ terjadi pada SW 0,1 m/s dan SCPO 0,11 m/s, sedangkan pemisahan fase terbaik untuk horizontal t junction 90@ terjadi pada SW 0,1 m/s dan SCPO 0,20 m/s.
Unsupervised Deep Representation Learning and Few Shot Classification of PolSAR Images
Year: 2020

Deep learning and convolutional neural networks (CNNs) have made progress in polarimetric synthetic aperture radar (PolSAR) image classification over the past few years. However, a crucial issue has not been addressed, i.e. the requirement of CNNs for abundant labeled samples versus the insufficient human annotations of PolSAR images. It is well known that following the supervised learning paradigm may lead to the overfitting of training data, and the lack of supervision information of PolSAR images undoubtedly aggravates this problem, which greatly affects the generalization performance of CNN based classifiers in large scale applications. To handle this problem, in this paper, learning transferrable representations from unlabeled PolSAR data through convolutional architectures is explored for the first time. Specifically, a PolSAR tailored contrastive learning network (PCLNet) is proposed for unsupervised deep PolSAR representation learning and few shot classification. Different from the utilization of optical processing methods, a diversity stimulation mechanism is constructed to narrow the application gap between optics and PolSAR. Beyond the conventional supervised methods, PCLNet develops an unsupervised pre training phase based on the proxy objective of instance discrimination to learn useful representations from unlabeled PolSAR data. The acquired representations are transferred to the downstream task, i.e. few shot PolSAR classification. Experiments on two widely used PolSAR benchmark datasets confirm the validity of PCLNet. Besides, this work may enlighten how to efficiently utilize the massive unlabeled PolSAR data to alleviate the greedy demands of CNN based methods for human annotations.
Tax Aware Portfolio Construction via Convex Optimization
Venue: J. Optim. Theory Appl.
Year: 2021

We describe an optimization based tax aware portfolio construction method that adds tax liability to standard Markowitz based portfolio construction. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot held. To avoid wash sales (in which some realized capital losses are disallowed) we assume that we trade monthly, and cannot simultaneously buy and sell the same asset. The tax aware portfolio construction problem is not convex, but it becomes convex when we specify, for each asset, whether we buy or sell it. It can be solved using standard mixed integer convex optimization methods at the cost of very long solve times for some problem instances. We present a custom convex relaxation of the problem that borrows curvature from the risk model. This relaxation can provide a good approximation of the true tax liability, while greatly enhancing computational tractability. This method requires the solution of only two convex optimization problems: the first determines whether we buy or sell each asset, and the second generates the final trade list. In our numerical experiments, our method almost always solves the nonconvex problem to optimality, and when it does not, it produces a trade list very close to optimal. Backtests show that the performance of our method is indistinguishable from that obtained using a globally optimal solution, but with significantly reduced computational effort.
Open hardware portable dual probe heat pulse sensor for measuring soil thermal properties and water content
Venue: Comput. Electron. Agric.
Year: 2017

A portable DPHP device based on Arduino platform is presented.The DPHP device showed an accuracy comparable to TDR in assessing soil water content.The DPHP device allows the investigation of smaller volume samples. A portable probe based on open hardware architecture for the assessment of soil thermal properties and water content using the DPHP method is presented. The mean percentage errors for assessment of volumetric heat capacity and thermal conductivity were 4.6% and 8.9% respectively, computed after sensor spacing calibration in agar stabilized water.The DPHP probe has been tested for soil thermal properties assessment and compared to TDR probe for the water content estimation in four different soils. The DPHP probe showed an accuracy comparable to TDR in estimating water content, with DPHP showing lower values of error index in 3 samples out of 4. The DPHP can be used to investigate smaller volume of soil than TDR probe can do however, TDR is much faster than DPHP in measure acquisition.
The ongoing challenge of novel psychoactive drugs of abuse. Part I. Synthetic cannabinoids (IUPAC Technical Report)
Year: 2018

In the past decade, the world has experienced a large increase in the number of novel compounds appearing on the illicit drug market for recreational purposes. Such substances are designed to circumvent governmental regulations; the illegal drug manufacturers take a known psychoactive compound reported in the scientific literature and slightly modify its chemical structure in order to produce analogues that will mimic the pharmacological activity of the original substance. Many of these novel substances are sold via the Internet. Among the various chemical classes, synthetic cannabinoid receptor modulators, commonly referred to as "synthetic cannabinoids" have been at the forefront, as demonstrated by the frequency of drug seizures, numerous severe toxic effects, and fatalities associated with some of these substances. This review presents the chemical structures of relevant synthetic cannabinoids and describes their mechanism of action, pharmacological features, metabolic pathways, and structure activity relationships. It illustrates the approaches used in forensic testing, both for bulk analysis (drug seizures) and for analytical toxicology (biological matrices) and discusses aspects of regulation surrounding this drug class. This report is intended to provide pertinent information for the purposes of informing scientific, medical, social, and governmental bodies about this ever evolving recreational drug class and the challenges it poses worldwide.