nils-herrmann/clean_scirepeval huggingface dataset:
Growth of the Antarctic sea ice diatom Navicula cf. normaloides Cholnoky at different temperatures and salinities

The Antarctic continent is separated and isolated by the Antarctic Circumpolar Current from more northern water masses since millions of years, and hence characterised by low and stable temperatures throughout the whole year. Due to this long isolation a large number of endemic marine algae and animals have developed in this southern polar region, which probably might respond negatively to the predicted climate change as already observed around the Antarctic Peninsula. For algae the degree of endemism in the Antarctic flora had been well analysed for macroalgae, while for microalgae such as phytoplankton, sea ice algae or microphytobenthos only few data exist until now. From an ecophysiological standpoint the evaluation and characterisation of the temperature requirements for growth represents a widely used approach to identify algae endemic to Antarctica because of their adaptation to low temperatures. Since such data on polar diatoms are rare, an Antarctic isolate of the genus Navicula was incubated under a range of temperatures (1, 5, 15, 20, 25 degC) and the growth response followed. In addition, since salinity represents also a variable parameter along the ice edges of Antarctica, growth was measured under various salinities between 5 and 50 S A Navicula cf. normaloides Cholnoky grew under all salinities between 5 and 50 S A, but with different rates indicating a broad tolerance range. This diatom grew between 1degC and 15 degC with an optimum at 5 degC, but at 20 degC and 25 degC growth was completely inhibited. Hence this Navicula species can be described as a stenothermal/psychrotolerant species, because it has a broader temperature tolerance and prefers higher temperatures compared to other micro and macroalgae endemic to Antarctica. Navicula cf. normaloides Cholnoky exhibited an unexpectedly broad temperature tolerance, and hence might certainly not be considered as an endemic species, although it can well cope with cold to temperate conditions.
Successful changes in management accounting systems: A healthcare case study

This paper explores how a change in the management accounting systems (MAS) of healthcare organisations was implemented in a manner acceptable to those involved. The study employed a longitudinal case study of a university hospital in southern Italy, and was informed by Broadbent and Laughlin's Middle Range Theory (MRT) The findings revealed that the change in the MAS was successful due to the involvement of professionals in the ongoing process of change. This involvement reduced their natural tendency to resist, and increased the commitment of the various groups of professionals to the new business culture.
Business Failure and Entrepreneurship: Emergence, Evolution and Future Research

Business failure research has been the focus of renewed interest in the entrepreneurship field. It is complex, being both a sign of economic vibrancy and the source of great individual trauma. An understanding of these complexities is important to academics, practitioners and regulators. This monograph provides a review of the literature to date. It charts the emergence of business failure research in the finance literature through to its recent development within the contemporary entrepreneurship field. The multidiscipline nature of business failure research is explored through incorporation of studies from accountancy, information systems, social psychology, general management, economics and entrepreneurship. Research on the topic is diverse; the lack of a universally accepted definition of failure coupled with the absence of an underpinning theory has resulted in an expansive range of studies. The aim of this review is to provide a comprehensive and critical review of business failure research, bridge the gap between the various perspectives, and develop a cohesive understanding of the phenomena, upon which future studies can be based.
Business intelligence systems use in performance measurement capabilities: Implications for enhanced competitive advantage
Venue: Int. J. Account. Inf. Syst.

The purpose of this study is to better understand how the quality of a Business Intelligence (BI) system improves the diagnostic and interactive dimensions of management control systems (MCS) thereby enhancing performance measurement capabilities, which in turn are positively associated with competitive advantage. Integrating theory from performance measurement, organizational learning and the knowledge based view of the firm, a theoretical model is developed that considers three concepts of BI quality (infrastructure integration, functionality, and self service) and the roles they play in enhancing diagnostic and interactive performance measurement capabilities. Data collected via survey from 324 CEOs and CFOs provides support for the theorized effects of BI quality on performance measurement capabilities. These capabilities in turn are positively associated with competitive advantage.
Basic Queueing Theory

Queueing Theory is one of the most commonly used mathematical tool for the performance evaluation of systems. The aim of the book is to present the basic methods, approaches in a Markovian level for the analysis of not too complicated systems. The main purpose is to understand how models could be constructed and how to analyze them. It is intended not only for students of computer science, engineering, operation research, mathematics but also those who study at business, management and planning departments, too. It covers more than one semester and has been tested by graduate students at Debrecen University over the years. It gives a very detailed analysis of the involved queueing systems by giving density function, distribution function, generating function, Laplace transform, respectively. Furthermore, Java applets are provided to calculate the main performance measures immediately by using the pdf version of the book in a WWW environment. I have attempted to provide examples for the better understanding and a collection of exercises with detailed solution helps the reader in deepening her/his knowledge.
Rehabilitation Needs of Chronic Female Inpatients Attending Day care in a Tertiary Care Psychiatric Hospital
Venue: Indian journal of psychological medicine

Female patients hospitalized for a long duration in psychiatric hospitals are a special population with unique needs.  To assess rehabilitation needs of chronic female inpatients attending psychiatric rehabilitation services in a tertiary care psychiatric hospital.  Rehabilitation needs of nine chronic female inpatients were assessed with an interview schedule developed by expert consensus. The needs were elicited from the patients. Perspectives of nursing staff, vocational instructors, and treating psychiatrists were also sought.  Most patients expressed the need for more incentives for working in day care, variety in food and grooming items. The nursing staff felt many patients could be placed outside, and the family members should come more frequently to meet them. Vocational instructors felt that patients need more incentives, variety in food and work. Treating psychiatrists said that major barriers in discharging and placing them were nonavailability or poor involvement of family members. Services like supported housing, supported education and supported employment are necessary to cater to their complex needs.
Convolutional neural network architectures for predicting DNA protein binding
Venue: Bioinform.

Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications.  We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif based tasks. We show the benefits of CNNs in learning rich higher order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology.  All the models analyzed are available at http:/cnn.csail.mit.edu.  gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection

Fast and reliable industrial inspection is a main challenge in manufacturing scenarios. However, the defect detection performance is heavily dependent on manually defined features for defect representation. In this contribution, we investigate a new paradigm from machine learning, namely deep machine learning by examining design configurations of deep Convolutional Neural Networks (CNN) and the impact of different hyper parameter settings towards the accuracy of defect detection results. In contrast to manually designed image processing solutions, deep CNN automatically generate powerful features by hierarchical learning strategies from massive amounts of training data with a minimum of human interaction or expert process knowledge. An application of the proposed method demonstrates excellent defect detection results with low false alarm rates.
Examining the dynamic relationships between exposure tasks and cognitive restructuring in CBT for SAD: Outcomes and moderating influences.
Venue: Journal of anxiety disorders

Meta analyses indicate that cognitive behavioral therapy (CBT) for social anxiety disorder (SAD) leads to substantial symptom alleviation. Although there is an emphasis on engaging in exposure and cognitive restructuring during treatment, the longitudinal relationship between skill use and symptom alleviation is not well understood. Furthermore, treatment response may be attenuated by pre existing patient vulnerabilities. This study examined the longitudinal association of skill use (i.e. exposure (EX) thought record use (TR) symptom reduction and the potential moderating influence of perfectionism during a 12 session, manual based CBT for SAD intervention for 100 patients (51% female) meeting DSM IV criteria for SAD. Results obtained from Latent Difference Score (LDS) models indicated that the frequency of both EX and TR skill use led to subsequent symptom alleviation; however, this varied based on the type of skill used. Further, although both EX and TR interventions were associated with subsequent symptom reduction, the association of EX and subsequent symptom alleviation was greater than the association of TR and subsequent symptom alleviation. Finally, higher pre treatment perfectionism was associated with greater initial skill use, followed by significantly reduced skill use in later sessions. These preliminary results suggest that perfectionistic individuals demonstrate differential engagement in EX and TR interventions during treatment.
A framework for radial data comparison and its application to fingerprint analysis
Venue: Appl. Soft Comput.

Graphical abstractDisplay Omitted HighlightsWe define restricted radial equivalence functions and radial similarity functions for comparing radial data.Using these functions, we propose a fingerprint Template based Singular Point Detection algorithm.In this algorithm, we exploit the high visibility of singular points in the Orientation Map.We experimentally compare the results of our algorithm with those obtained from other algorithms in the literature. This work tackles the comparison of radial data, and proposes comparison measures that are further applied to fingerprint analysis. First, we study the similarity of scalar and non scalar radial data, elaborated on previous works in fuzzy set theory. This study leads to the concepts of restricted radial equivalence function and Radial Similarity Measure, which model the perceived similarity between scalar and vectorial pieces of radial data, respectively. Second, the utility of these functions is tested in the context of fingerprint analysis, and more specifically, in the singular point detection. With this aim, a novel Template based Singular Point Detection method is proposed, which takes advantage of these functions. Finally, their suitability is tested in different fingerprint databases. Different Similarity Measures are considered to show the flexibility offered by these measures and the behavior of the new method is compared with well known singular point detection methods.
Controlling Shear Stress in 3D Bioprinting is a Key Factor to Balance Printing Resolution and Stem Cell Integrity.
Venue: Advanced healthcare materials

A microvalve based bioprinting system for the manufacturing of high resolution, multimaterial 3D structures is reported. Applying a straightforward fluid dynamics model, the shear stress at the nozzle site can precisely be controlled. Using this system, a broad study on how cell viability and proliferation potential are affected by different levels of shear stress is conducted. Complex, multimaterial 3D structures are printed with high resolution. This work pioneers the investigation of shear stress induced cell damage in 3D bioprinting and might help to comprehend and improve the outcome of cell printing studies in the future.
Analyzing microtomography data with Python and the scikit image library
Venue: Advanced Structural and Chemical Imaging

The exploration and processing of images is a vital aspect of the scientific workflows of many X ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. scikit image is an open source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements. scikit image users benefit from a rich scientific Python ecosystem that contains many powerful libraries for tasks such as visualization or machine learning. scikit image combines a gentle learning curve, versatile image processing capabilities, and the scalable performance required for the high throughput analysis of X ray imaging data.
Mapping for control in an underwater environment using a dynamic inverse sonar model
Venue: OCEANS 2016 MTS/IEEE Monterey

Autonomous Underwater Vehicles (AUVs) are commonly used in oceanographic applications such as seafloor survey and underwater iceberg profiling. During the surveys, the vehicles are intended to follow the variation of the seafloor or iceberg surface at a constant stand off distance in order to maintain a consistent sensor footprint. Mechanical scanning sonar are usually chosen for measuring the distance from the vehicle to the object. Due to the wide beamwidth of the sonar, the uncertainty from the unknown direction of the received echo will affect the accuracy of the resulting environmental map. On the mobile robots, a static inverse sonar model is introduced to compensate for such uncertainty for range finders. As an improvement, we present a dynamic inverse sonar model accounting for the trend of the surveying environment, i. e. the terrain elevation. A vehicle attached occupancy map is introduced for estimating the terrain elevation, while a global occupancy map is created to present the surrounding environment. The proposed technique is first simulated in mapping a vertical underwater column. The resulting global map is found to be more accurate in presenting the original underwater features compared to the results from a static inverse sonar model. The technique is further applied to a set of sonar data collected from an iceberg profiling trial.
Three dimensional Scan Registration using Curvelet Features in Planetary Environments
Venue: J. Field Robotics

Topographic mapping in planetary environments relies on accurate three dimensional 3D scan registration methods. However, most global registration algorithms relying on features such as fast point feature histograms and Harris 3D show poor alignment accuracy in these settings due to the poor structure of the Mars like terrain, and the variable resolution, occluded, sparse range data that are difficult to register without some a priori knowledge of the environment. In this paper, we propose an alternative approach to 3D scan registration using the curvelet transform that performs multiresolution geometric analysis to obtain a set of coefficients indexed by scale coarsest to finest, angle, and spatial position. Features are detected in the curvelet domain to take advantage of the directional selectivity of the transform. A descriptor is computed for each feature by calculating the 3D spatial histogram of the image gradients, and nearest neighbor based matching is used to calculate the feature correspondences. Correspondence rejection using random sample consensus identifies inliers, and a locally optimal singular value decomposition based estimation of the rigid body transformation aligns the laser scans given the reprojected correspondences in the metric space. Experimental results on a publicly available dataset of a planetary analogue indoor facility, as well as simulated and real world scans from Neptec Design Group's IVIGMS 3D laser rangefinder at the outdoor CSA Mars yard, demonstrate improved performance over existing methods in the challenging sparse Mars like terrain.
Building a Virtual Assistant for Raspberry Pi: The practical guide for constructing a voice controlled virtual assistant

Build a voice controlled virtual assistant using speech to text engines, text to speech engines, and conversation modules. This book shows you how to program the virtual assistant to gather data from the internet (weather data, data from Wikipedia, data mining) play music; and take notes. Each chapter covers building a mini project/module to make the virtual assistant better. You'll develop the software on Linux or OS X before transferring it to your Raspberry Pi, ready for deploying in your own home automation or Internet of Things applications. Building a Virtual Assistant for Raspberry Pi walks you through various STTs and TTSs and the implementation of these components with the help of Python. After that you will start implementing logic for handling user queries and commands, so that the user can have conversations with Melissa. You will then work to improve logic handling to detect what the user wants Melissa to do. You will also work on building some useful applications/modules for Melissa, which will allow you to gain interesting information from Melissa such as the time, weather information, and data from Wikipedia. You will develop a music playing application as well as a note taking application for Melissa, laying the foundations for how Melissa can be further extended. Finally, you will learn how to deploy this software to your Raspberry Pi and how you can further scale Melissa to make her more intelligent, interactive and how you can use her in other projects such as home automation as well. What You'll Learn Design the workflow and discover the concepts of building a voice controlled assistant Develop modules for having conversations with the assistant Enable the assistant to retrieve information from the internet Build utilities like a music player and a note taking application for the virtual assistant Integrate this software with a Raspberry Pi Who This Book Is For This book is for anyone who has built a home automation project with Raspberry Pi and now want to enhance it by making it voice controlled. The book would also interest students from computer science or related disciplines.
Analysis of Genetic Diversity and Population Structure of Sesame Accessions from Africa and Asia as Major Centers of Its Cultivation
Venue: Genes

Sesame is an important oil crop widely cultivated in Africa and Asia. Understanding the genetic diversity of accessions from these continents is critical to designing breeding methods and for additional collection of sesame germplasm. To determine the genetic diversity in relation to geographical regions, 96 sesame accessions collected from 22 countries distributed over six geographic regions in Africa and Asia were genotyped using 33 polymorphic SSR markers. Large genetic variability was found within the germplasm collection. The total number of alleles was 137, averaging 4.15 alleles per locus. The accessions from Asia displayed more diversity than those from Africa. Accessions from Southern Asia (SAs) Eastern Asia (EAs) and Western Africa (WAf) were highly diversified, while those from Western Asia (WAs) Northern Africa (NAf) and Southeastern Africa (SAf) had the lowest diversity. The analysis of molecular variance revealed that more than 44% of the genetic variance was due to diversity among geographic regions. Five subpopulations, including three in Asia and two in Africa, were cross identified through phylogenetic, PCA, and STRUCTURE analyses. Most accessions clustered in the same population based on their geographical origins. Our results provide technical guidance for efficient management of sesame genetic resources in breeding programs and further collection of sesame germplasm from these different regions.
Genetic variability and population structure of grey wolf (Canis lupus) in Serbia
Venue: Russian Journal of Genetics

Results of previous morphometric and genetic analyses of grey wolf (Canis lupus L. population from Serbia indicated different patterns of population subdivision. In order to explore population structure, level of genetic variability, genetic drift, inbreeding and signals of bottleneck for grey wolves from Serbia, we applied highly polymorphic genetic markers (microsatellites) Obtained data are valuable in determination of conservation units and creation of appropriate management plans. We have amplified 18 highly polymorphic microsatellites, in a total sample of 75 grey wolves, from different localities across Serbia and multilocus genotypes were analyzed using appropriate software. Observed values of the basic genetic parameters (HO 0.69; HE 0.75) indicated moderate level of genetic variability, similar to genetic variability in other populations belonging to the Dinaric Balkan population of grey wolf. In STRUCTURE analysis, although DK was estimated to be at first peak K 2, and second peak K 4, CLUMPAK analyses showed that there's no structuring for any of assumed K, and therefore the population of grey wolf from Serbia may be considered as one continuous population and treated as one conservation unit in future management plans. Signals of bottleneck haven't been observed (Wilcoxon test two phase mutation model p 0.247; and stepwise mutation model p 0.815)