nils-herrmann/clean_scirepeval huggingface dataset:
Mineral Nutrition of Aeroponically Grown Subtropical and Temperate Crops in the Tropics with Manipulation of Root-Zone Temperature at Different Growth Irradiances

Plant growth and productivity are often limited by high root-zone temperatures (RZT) which restricts the growth of subtropical and temperate crops in the tropics. High RZT temperature coupled with low growth irradiances during cloudy days which mainly lead to poor root development and thus causes negative impact on the mineral uptake and assimilation. However, certain subtropical and temperate crops have successfully been grown aeroponically in the tropics by simply cooling their roots while their aerial portions are subjected to hot fluctuating ambient temperatures. This review first discusses the effects of RZT and growth irradiance on root morphology and its biomass, the effect of RZT on uptake and transport of several macro nutrients such as N [nitrogen, mainly nitrate, (NO3 )], P (H2PO4 , phosphate), K (potassium) and Ca (calcium), and micro nutrient Fe (iron) under different growth irradiances. The impact of RZT and growth irradiance on the assimilation of NO3 (the form of N nutrient given to the aeroponically grown plants) and the site of NO3 assimilation are also addressed. _____________________________________________________________________________________________________________
Phenolic glycosides from Curculigo orchioides Gaertn.

Five new chlorophenolic glucosides, curculigine E (1), curculigine F (2), curculigine G (3), curculigine H (5), curculigine I (6) and one new phenolic glycoside, orcinoside H (4), together with eight known phenolic glycosides (7-14) were isolated from the Curculigo orchioides Gaertn. Their structures were established by spectroscopic techniques (IR, UV, MS, 1D and 2D NMR). The isolated phenolic glycosides were evaluated for antiosteoporotic activity against MC3T3-E1 cell line using MTT assays. Compounds 1, 2, 3, and 5 showed moderate antiosteoporotic activity with the proliferation rate of 10.1-14.1%.
OPC-Free and Minimally Irregular IC Design Style

Advancements in IC manufacturing technologies allow for building very large devices with billions of transistors and with complex interactions between them encapsulated in a huge number of design rules. To ease designers' efforts in dealing with electrical and manufacturing problems, regular layout style seems to be a viable option. In this paper we analyze regular layouts in an IC manufacturability context and define their desired properties. We introduce the OPC-free IC design methodology and study properties of cells designed for this layout style that have various degrees of regularity.
An edge-region force guided active shape approach for automatic lung field detection in chest radiographs

Automatic and accurate lung field segmentation is an essential step for developing an automated computer-aided diagnosis system for chest radiographs. Although active shape model (ASM) has been useful in many medical imaging applications, lung field segmentation remains a challenge due to the superimposed anatomical structures. We propose an automatic lung field segmentation technique to address the inadequacy of ASM in lung field extraction. Experimental results using both normal and abnormal chest radiographs show that the proposed technique provides better performance and can achieve 3-6% improvement on accuracy, sensitivity and specificity compared to traditional ASM techniques.
The joint Simon effect: a review and theoretical integration

The social or joint Simon effect has been developed to investigate how and to what extent people mentally represent their own and other persons' action/task and how these cognitive representations influence an individual's own behavior when interacting with another person. Here, we provide a review of the available evidence and theoretical frameworks. Based on this review, we suggest a comprehensive theory that integrates aspects of earlier approaches-the Referential Coding Account. This account provides an alternative to the social interpretation of the (joint) go-nogo Simon effect (aka the social Simon effect) and is able to integrate seemingly opposite findings on joint action.
Analytical mapping of opinion mining and sentiment analysis research during 2000-2015

The new transformed read-write Web has resulted in a rapid growth of user generated content on the Web resulting into a huge volume of unstructured data. A substantial part of this data is unstructured text such as reviews and blogs. Opinion mining and sentiment analysis (OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 20 0 0–2016. For the scientometric mapping, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, types of authorship of papers on OMSA, collaboration patterns in publications on OMSA, most productive countries, institutions, journals and authors, citation patterns and an year-wise citation reference network, and theme density plots and keyword bursts in OMSA publications during the period. A somewhat detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexicon-based) used in these publications, levels (document, sentence or aspect-level) of sentiment analysis work done and major application areas of OMSA. The paper presents a detailed analytical mapping of OMSA research work and charts the progress of discipline on various useful parameters.
ADMM based scalable machine learning on Spark

Most machine learning algorithms involve solving a convex optimization problem. Traditional in-memory convex optimization solvers do not scale well with the increase in data. This paper identifies a generic convex problem for most machine learning algorithms and solves it using the Alternating Direction Method of Multipliers (ADMM). Finally such an ADMM problem transforms to an iterative system of linear equations, which can be easily solved at scale in a distributed fashion. We implement this framework in Apache Spark and compare it with the widely used Machine Learning LIBrary (MLLIB) in Apache Spark 1.3.
Movement Imitation with Nonlinear Dynamical Systems in Humanoid Robots

This article presents a new approach to movement planning, on-line trajectory modification, and imitation learning by representing movement plans based on a set of nonlinear differential equations with well-defined attractor dynamics. In contrast to non-autonomous movement representations like splines, the resultant movement plan remains an autonomous set of nonlinear differential equations that forms a control policy (CP) which is robust to strong external perturbations and that can be modified on-line by additional perceptual variables. The attractor landscape of the control policy can be learned rapidly with a locally weighted regression technique with guaranteed convergence of the learning algorithm and convergence to the movement target. This property makes the system suitable for movement imitation and also for classifying demonstrated movement according to the parameters of the learning system. We evaluate the system with a humanoid robot simulation and an actual humanoid robot. Experiments are presented for the imitation of three types of movements: reaching movements with one arm, drawing movements of 2-D patterns, and tennis swings. Our results demonstrate (a) that multi-joint human movements can be encoded successfully by the CPs, (b) that a learned movement policy can readily be reused to produce robust trajectories towards different targets, (c) that a policy fitted for one particular target provides a good predictor of human reaching movements towards neighboring targets, and (d) that the parameter space which encodes a policy is suitable for measuring to which extent two trajectories are qualitatively similar.
Kerfuffle: a web tool for multi-species gene colocalization analysis

The evolutionary pressures that underlie the large-scale functional organization of the genome are not well understood in eukaryotes. Recent evidence suggests that functionally similar genes may colocalize (cluster) in the eukaryotic genome, suggesting the role of chromatin-level gene regulation in shaping the physical distribution of coordinated genes. However, few of the bioinformatic tools currently available allow for a systematic study of gene colocalization across several, evolutionarily distant species. Furthermore, most tools require the user to input manually curated lists of gene position information, DNA sequence or gene homology relations between species. With the growing number of sequenced genomes, there is a need to provide new comparative genomics tools that can address the analysis of multi-species gene colocalization. Kerfuffle is a web tool designed to help discover, visualize, and quantify the physical organization of genomes by identifying significant gene colocalization and conservation across the assembled genomes of available species (currently up to 47, from humans to worms). Kerfuffle only requires the user to specify a list of human genes and the names of other species of interest. Without further input from the user, the software queries the e! Ensembl BioMart server to obtain positional information and discovers homology relations in all genes and species specified. Using this information, Kerfuffle performs a multi-species clustering analysis, presents downloadable lists of clustered genes, performs Monte Carlo statistical significance calculations, estimates how conserved gene clusters are across species, plots histograms and interactive graphs, allows users to save their queries, and generates a downloadable visualization of the clusters using the Circos software. These analyses may be used to further explore the functional roles of gene clusters by interrogating the enriched molecular pathways associated with each cluster. Kerfuffle is a new, easy-to-use and publicly available tool to aid our understanding of functional genomics and comparative genomics. This software allows for flexibility and quick investigations of a user-defined set of genes, and the results may be saved online for further analysis. Kerfuffle is freely available at http://atwallab.org/kerfuffle , is implemented in JavaScript (using jQuery and jsCharts libraries) and PHP 5.2, runs on an Apache server, and stores data in flat files and an SQLite database.
High Resolution CCD Polarization Imaging Sensor

We present a novel polarization imaging sensor by monolithically integrating aluminum nanowire optical filters with an array of CCD imaging elements. The CCD polarization image sensor is composed of 1000 by 1000 imaging elements with 7.4m pixel pitch. The image sensor has a dynamic range of 65dB and signal-to-noise ratio of 45dB. The CCD array is covered with an array of pixel-pitch matched nanowire polarization filters with four different orientations offset by 45. The complete imaging sensor is used for real-time reconstruction of the shape of various objects.
Infant botulism.

Although the worldwide incidence of infant botulism is rare, the majority of cases are diagnosed in the United States. An infant can acquire botulism by ingesting Clostridium botulinum spores, which are found in soil or honey products. The spores germinate into bacteria that colonize the bowel and synthesize toxin. As the toxin is absorbed, it irreversibly binds to acetylcholine receptors on motor nerve terminals at neuromuscular junctions. The infant with botulism becomes progressively weak, hypotonic and hyporeflexic, showing bulbar and spinal nerve abnormalities. Presenting symptoms include constipation, lethargy, a weak cry, poor feeding and dehydration. A high index of suspicion is important for the diagnosis and prompt treatment of infant botulism, because this disease can quickly progress to respiratory failure. Diagnosis is confirmed by isolating the organism or toxin in the stool and finding a classic electromyogram pattern. Treatment consists of nutritional and respiratory support until new motor endplates are regenerated, which results in spontaneous recovery. Neurologic sequelae are seldom seen. Some children require outpatient tube feeding and may have persistent hypotonia.
Can I relate? A review and guide for nurse managers in leading generations.

The purpose of this review is to help the nurse leader develop an understanding of the five generations currently in the health care workforce by providing defining characteristics, general behaviours, and strategies for the nurse manager to employ for each generational cohort.



Generations are groups of people born during the same 15-20 year time period who share similar experiences before adulthood, which shape long-term behaviours. Key descriptors and characteristics are provided.



The current generational cohorts in the health care workforce are Traditionalists (born between 1922 and 1945), baby boomers (born between 1946 and 1964), Generation X (born between 1965 and 1979), millennials (born between 1980 and 1995), and Generation Z (born after 1995).



Health care teams often comprise members of three or more generations. Intergenerational differences in team members can result in challenges; however, different perspectives provided by multiple generations can be used advantageously to strengthen the team's efficiency and outcomes.



There are strengths in each generation. Key differences can be harnessed to build stronger teams through comprehensive communication strategies, customized reward systems, and workplace flexibility. Examples are provided for each area.



Managers can use intergenerational differences to create a rich environment that bridges generational differences and fosters workforce cohesion.
Determination of Dopamine-β-hydroxylase Activity in Human Serum Using UHPLC-PDA Detection

Dopamine-β-hydroxylase (DBH, EC 1.14.17.1) is an enzyme with implications in various neuropsychiatric and cardiovascular diseases and is a known drug target. There is a dearth of cost effective and fast method for estimation of activity of this enzyme. A sensitive UHPLC based method for the estimation of DBH activity in human sera samples based on separation of substrate tyramine from the product octopamine in 3 min is described here. In this newly developed protocol, a Solid Phase Extraction (SPE) sample purification step prior to LC separation, selectively removes interferences from the reaction cocktail with almost no additional burden on analyte recovery. The response was found to be linear with an r2 = 0.999. The coefficient of variation for assay precision was < 10% and recovery > 90%. As a proof of concept, DBH activity in sera from healthy human volunteers (n = 60) and schizophrenia subjects (n = 60) were successfully determined using this method. There was a significant decrease in sera DBH activity in subjects affected by schizophrenia (p < 0.05) as compared to healthy volunteers. This novel assay employing SPE to separate octopamine and tyramine from the cocktail matrix may have implications for categorising subjects into various risk groups for Schizophrenia, Parkinson’s disease as well as in high throughput screening of inhibitors.
Safety and Efficacy of Banaba–Moringa oleifera–Green Coffee Bean Extracts and Vitamin D3 in a Sustained Release Weight Management Supplement

This 60-day, 30-subject pilot study examined a novel combination of ingredients in a unique sustained release (Carbopol matrix) tablet consumed twice daily. The product was composed of extracts of banaba leaf, green coffee bean, and Moringa oleifera leaf and vitamin D3. Safety was assessed using a 45-measurement blood chemistry panel, an 86-item self-reported Quality of Life Inventory, bone mineral density, and cardiovascular changes. Efficacy was assessed by calculating a body composition improvement index (BCI) based on changes in dual energy X-ray absorptiometry measured fat mass (FM) and fat-free mass (FFM) as well as between the study group (SG) and a historical placebo group. No changes occurred in any blood chemistry measurements. Positive changes were found in the Quality of Life (QOL) inventory composite scores. No adverse effects were observed. Decreases occurred in FM (p = 0.004) and increases in FFM (p = 0.009). Relative to the historical placebo group, the SG lost more FM (p < 0.0001), gained more FFM (p = <0.0001), and had a negative BCI of -2.7 lb. compared with a positive BCI in the SG of 3.4 lb., a 6.1 discordance (p = 0.0009). The data support the safety and efficacy of this unique product and demonstrate importance of using changes in body composition versus scale weight and BMI.
Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case

In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most influential algorithms that have been widely used in the bioinformatics community. These top data mining and machine learning algorithms cover classification, clustering, regression, graphical model-based learning, and dimensionality reduction. The goal of this study is to guide the focus of scalable computing experts in the endeavor of applying new storage and scalable computation designs to bioinformatics algorithms that merit their attention most, following the engineering maxim of “optimize the common case”.
Crash Simulation of an F 1 Racing Car Front Impact Structure

1 motorsport is a platform for maximum race car driving performance resulting from high-tech developments in the area of lightweight materials and aerodynamic design. In order to ensure the driver’s safety in case of high-speed crashes, special impact structures are designed to absorb the race car’s kinetic energy and limit the decelerations acting on the human body. These energy absorbing structures are made of laminated composite sandwich materials like the whole monocoque chassis and have to meet defined crash test requirements specified by the FIA. This study covers the crash behaviour of the nose cone as the F1 racing car front impact structure. Finite element models for dynamic simulations with the explicit solver LS-DYNA are developed with the emphasis on the composite material modelling. Numerical results are compared to crash test data in terms of deceleration levels, absorbed energy and crushing mechanisms. The validation led to satisfying results and the overall conclusion that dynamic simulations with LS-DYNA can be a helpful tool in the design phase of an F1 racing car front impact structure.
Inferring preferences in ontology-based recommender systems using WOWA

In content-based semantic recommender systems the items to be considered are defined in terms of a set of semantic attributes, which may take as values the concepts of a domain ontology. The aim of these systems is to suggest to the user the items that fit better with his/her preferences, stored in the user profile. When large ontologies are considered it is unrealistic to expect to have complete information about the user preference on each concept. In this work, we explain how the Weighted Ordered Weighted Averaging operator may be used to deduce the user preferences on all concepts, given the structure of the ontology and some partial preferential information. The parameters of the WOWA operator enable to establish the desired aggregation policy, which ranges from a full conjunction to a full disjunction. Different aggregation policies have been analyzed in a case study involving the recommendation of touristic activities in the city of Tarragona. Several profiles have been compared and the results indicate that different aggregation policies should be used depending on the type of user. The amount of information available in the ontology must be also taken into account in order to establish the parameters of the proposed algorithm.
User privacy and data trustworthiness in mobile crowd sensing

Smartphones and other trendy mobile wearable devices are rapidly becoming the dominant sensing, computing and communication devices in peoples' daily lives. Mobile crowd sensing is an emerging technology based on the sensing and networking capabilities of such mobile wearable devices. MCS has shown great potential in improving peoples' quality of life, including healthcare and transportation, and thus has found a wide range of novel applications. However, user privacy and data trustworthiness are two critical challenges faced by MCS. In this article, we introduce the architecture of MCS and discuss its unique characteristics and advantages over traditional wireless sensor networks, which result in inapplicability of most existing WSN security solutions. Furthermore, we summarize recent advances in these areas and suggest some future research directions.
Gamifying software development scrum projects

Despite the wide adoption of agile methodologies, software development teams still struggle to meet time, budget and scope. These challenges are partially explained by practitioners' lack of motivation to apply agile techniques in practice. Some researchers have already proposed to tackle this problem with gamification, i.e. the use of game elements and game-design in non-game contexts. However, very few gamification proposals were evaluated with Scrum teams in practice. In this paper, we present a software tool based on gamification to make Scrum techniques more fun and engaging for practitioners. As a result, practitioners should increase their motivation to apply Scrum techniques in practice, which in turn might result in more and better software. This paper describes the first iteration of a larger research effort that follows the Design Science Research methodology. In this first research iteration, a prototype was developed, and a project team is currently using the prototype to evaluate the real impact of gamification on Scrum adoption in a real-world organization.
SherLock vs Moriarty: A Smartphone Dataset for Cybersecurity Research

In this paper we describe and share with the research community, a significant smartphone dataset obtained from an ongoing long-term data collection experiment. The dataset currently contains 10 billion data records from 30 users collected over a period of 1.6 years and an additional 20 users for 6 months (totaling 50 active users currently participating in the experiment).
 The experiment involves two smartphone agents: SherLock and Moriarty. SherLock collects a wide variety of software and sensor data at a high sample rate. Moriarty perpetrates various attacks on the user and logs its activities, thus providing labels for the SherLock dataset.
 The primary purpose of the dataset is to help security professionals and academic researchers in developing innovative methods of implicitly detecting malicious behavior in smartphones. Specifically, from data obtainable without superuser (root) privileges. To demonstrate possible uses of the dataset, we perform a basic malware analysis and evaluate a method of continuous user authentication.
Pedestrian Detection using Infrared images and Histograms of Oriented Gradients

This paper presents a complete method for pedestrian detection applied to infrared images. First, we study an image descriptor based on histograms of oriented gradients (HOG), associated with a support vector machine (SVM) classifier and evaluate its efficiency. After having tuned the HOG descriptor and the classifier, we include this method in a complete system, which deals with stereo infrared images. This approach gives good results for window classification, and a preliminary test applied on a video sequence proves that this approach is very promising