nils-herrmann/clean_scirepeval huggingface dataset:
Coronavirus (COVID 19) infection induced chilblains: A case report with histopathologic findings
Venue: JAAD Case Reports
Year: 2020

A 23 year old man presented with a 3 day history of acute onset, violaceous, infiltrated, and painful plaques on the toes and lateral aspect of the feet. The appearance of the plaques was preceded by 3 days of a low grade fever (37.78C) and a dry cough. His clinical history was notable for psoriasis, which had been treated with secukinumab for 30 months; however, the secukinumab had been stopped 1 month before to minimize immunosuppression during the COVID 19 pandemic. No additional treatment had been initiated. The patient had many violaceous, infiltrated, and painful plaques on an erythematous background on the dorsal aspect of the toes and the lateral sides of the feet  The hands and fingers were unaffected. The patient did not have a history of Raynaud phenomenon, acrocyanosis, chilblains (perniosis) or photosensitivity. A complete skin examination result was otherwise unremarkable, and dermoscopy showed normal proximal nail folds.
When More Is Less: An Exploratory Study of the Precautionary Reporting Bias and Its Impact on Safety Signal Detection
Venue: Clinical pharmacology and therapeutics
Year: 2018

Concerns have been expressed that large numbers of nonvalue added reports have been accumulating in adverse drug reaction (ADR) databases, for example, via patient support programs. We performed an assessment of the impact of such reports, which we refer to as "precautionary reports," on safety signal detection in the Netherlands. The case narratives of ADR reports of three case products were screened with text mining algorithms to identify those reports that lack a causal relationship with the suspected medicinal product. We demonstrate that precautionary reports impede the optimal use of the pharmacovigilance system by, on the one hand, masking safety signals and, on the other hand, creating spurious signals. The precautionary reporting bias and its suppressing effect on statistical signal detection results in an altered adverse event safety profile. The findings from this study highlight the need for a better alignment between regulatory authorities and marketing authorization holders regarding pharmacovigilance guidelines.
Ultrasonic Non Destructive Testing System of Semi Enclosed Workpiece with Dual Robot Testing System
Venue: Sensors
Year: 2019

With the rapid development of material science, more and more workpieces, especially workpieces with complex curved surfaces, are being made of composite materials. Robotic non destructive testing (NDT) systems for complex curved surface composite material parts are being used more and more. Despite the emergence of such flexible NDT systems, the detection of semi enclosed parts is also a challenge for robotic NDT systems. In order to overcome the problem, this paper establishes an NDT solution for semi enclosed workpieces based on a dual robot system of synchronous motion, in which an extension arm is installed on one of the robots and presents a trajectory planning method that always ensures the extension arm is parallel to the rotary axis of a semi enclosed workpiece and that the ultrasonic probes are perpendicular to the workpiece surface. Trajectory analysis experiments and ultrasonic NDT experiments utilizing the optimal water path distance determined by simulation result of multi Gaussian beam model for two types of semi enclosed workpieces are performed with the dual robot NDT system. Experimental results prove that the dual robot NDT scheme functions well and the planned trajectories are correct. All the hole shaped artificial defects with diameters =3 mm are detected by using 2.25 MHz ultrasonic probes through the transmission testing method. Vivid 3D C scan image of a small diameter cylindrical workpiece based on the testing result is provided for convenience of observation.
Robust medical image segmentation using particle swarm optimization aided level set based global fitting energy active contour approach
Venue: Eng. Appl. Artif. Intell.
Year: 2014

The active contour models have been popularly employed for image segmentation for almost a decade now. Among these active contour models, the level set based Chan and Vese algorithm is a popular region based model that inherently utilizes intensity homogeneity in each region under consideration. However, the Chan and Vese model often suffers from the possibility of getting trapped in a local minimum, if the contour is not properly initialized. This problem assumes greater importance in the context of medical images where the intensity variations may assume large varieties of local and global profiles. In this work we propose a robust version of the Chan and Vese algorithm which is expected to achieve satisfactory segmentation performance, irrespective of the initial choice of the contour. This work formulates the fitting energy minimization problem to be solved using a metaheuristic optimization algorithm and makes a successful implementation of our algorithm using particle swarm optimization (PSO) technique. Our algorithm has been developed for two phase level set implementation of the Chan and Vese model and it has been successfully utilized for both scalar valued and vector valued images. Extensive experimentations utilizing different varieties of medical images demonstrate how our proposed method could significantly improve upon the quality of segmentation performance achieved by Chan and Vese algorithm with varied initializations of contours.
One pot process of 2' deoxyguanylic acid catalyzed by a multi enzyme system
Venue: Biotechnology and Bioprocess Engineering
Year: 2015

Abstract2' Deoxyguanylic acid (deoxyguanosine 5' monophosphate, dGMP) is a substance required by living cells that is used extensively in reagents, fine chemicals and other industrial fields. Traditionally, dGMP is separated from DNA degradation products, which is low yielding and time consuming. Herein, we investigated a novel, one pot multi enzymatic cascade reaction to produce dGMP. This reaction involved purine nucleoside phosphorylase (PNPase) and acetate kinase (ACKase) from Escherichia coli, N deoxyribosytransferase II (NDT II) from Lactobacillus delbrueckii and deoxyguanosine kinase (dGKase) from Bacillus subtilis. During the reaction, the initial guanosine substrate was cleaved into guanine and ribose 1 phosphate by PNPase. Then, deoxyguanosine (dGR) was subsequently produced from a reaction between guanine and thymidine catalysed by NDT II. Finally, the intermediate dGR was phosphorylated to dGMP by dGKase and a cytidine triphosphate (CTP) regeneration system that utilised acetyl phosphate via ACKase. A very small amount of CTP was added because CTP regeneration was efficient to transfer a phosphate group from acetyl phosphate to dGR. After 12 h of incubation, a maximal dGMP yield of up to 76% was obtained based on the addition of 5 mM guanosine and 5 mM thymidine.
Sparse grid, reduced basis Bayesian inversion
Year: 2015

We analyze reduced basis (RB for short) short acceleration of recently proposed sparse Bayesian inversion algorithms for partial differential equations with uncertain distributed parameter, for observation data subject to additive, Gaussian observation noise. Specifically, Bayesian inversion of affine parametric, linear operator families on possibly high dimensional parameter spaces. We consider "high fidelity" Petrov Galerkin (PG) discretizations of these countably parametric operator families: we allow general families of inf sup stable, PG Finite Element methods, covering most conforming primal and mixed Finite Element discretizations of standard problems in mechanics. RB acceleration of the high dimensional, parametric forward response maps which need to be numerically solved numerous times in Bayesian inversion is proposed and convergence rate bounds for the error in the Bayesian estimate incurred by the use of RB are derived. As consequence of recent theoretical results on dimension independent sparsity of parametric responses, and preservation of sparsity for holomorphic parametric problems, we establish new convergence rates of greedy RB compressions for both, the parametric forward maps as well as for the countably parametric posterior densities which arise in Bayesian inversion. We show that the convergence rates for the RB compressions of the parametric forward maps as well as of the countably parametric, sparse Bayesian posterior densities are free from the curse of dimensionality and depend only on the sparsity of the uncertain input data. In particular, we establish the quadratic convergence of the RB compression for the posterior densities with respect to that for the parametric forward maps. Numerical experiments for linear elliptic, affine parametric model problems in two space dimensions with hundreds of parameters are reported which confirm that the proposed adaptive sparse grid reduced basis algorithms indeed exploit sparsity of both, the parametric forward maps as well as the Bayesian posterior density.
German Women's Writing of the Eighteenth and Nineteenth Centuries: Future Directions in Feminist Criticism
Year: 2011

Book synopsis: German women writers of the eighteenth and nineteenth centuries have been the subject of feminist literary critical and historical studies for around thirty years. This volume, with contributions from an international group of scholars, takes stock of what feminist literary criticism has achieved in that time and reflects on future trends in the field. Offering both theoretical perspectives and individual case studies, the contributors grapple with the difficulties of appraising 'non feminist' women writers and genres from a feminist perspective and present innovative approaches to research in early women's writing. This inclusive and cross disciplinary collection of essays will enrich the study of German women's writing of the eighteenth and nineteenth centuries and contribute to contemporary debates in feminist literary criticism.
Bronchiolitis Care in the Hospital.
Venue: Reviews on recent clinical trials
Year: 2017

Viral bronchiolitis is a common cause of hospitalization in young children, but despite a variety of therapeutic options, the mainstay of treatment remains supportive care.  To examine the most recent evidence for supportive care measures and pharmacologic options in the treatment of bronchiolitis in the hospital setting.  MEDLINE search with expert medical librarian for publications on management and therapies for bronchiolitis.  Evidence does not support the use of bronchodilators, racemic epinephrine, deep suctioning, systemic corticosteroids, or antibiotics in the absence of a concomitant bacterial infection, as these treatments do not change the course of illness or shorten length of stay (LOS) Nebulized hypertonic saline is not routinely recommended, though it may provide some benefit for patients with anticipated prolonged LOS. Continuous pulse oximetry should not be routinely used in stable patients as it may be associated with longer LOS. Supplemental oxygen should be used to maintain oxyhemoglobin concentrations =90% a level lower than what many clinicians may have used previously. Current evidence suggests high flow nasal cannula may reduce intubation rate, but its effect on LOS is unclear. Intravenous or nasogastric tube hydration should be used when oral hydration is not sufficient.  Overall, bronchiolitis remains a self limited disease whose mainstay of therapy is supportive care.
D matrix analysis of the Renner Teller effect: an accurate three state diabatization for NH2.
Venue: The Journal of chemical physics
Year: 2006

Some time ago we published our first article on the Renner Teller (RT) model to treat the electronic interaction for a triatomic molecule 2006) The main purpose of that Communication was to suggest considering the RT phenomenon as a topological effect, just like the Jahn Teller phenomenon. However, whereas in the first publication we just summarized a few basic features to support that idea, here in the present article, we extend the topological approach and show that all the expected features that characterize a three (multi) state RT type'3 system of a triatomic molecule can be studied and analyzed within the framework of that approach. This, among other things, enables us to employ the topological D matrix  (2000) to determine, a priori, under what conditions a three state system can be diabatized. The theoretical presentation is accompanied by a detailed numerical study as carried out for the HNH system. The D matrix analysis shows that the two original electronic states 2A1 and 2B1 (evolving from the collinear degenerate Pi doublet) frequently used to study this Renner Teller type system, are insufficient for diabatization. This is true, in particular, for the stable ground state configurations of the HNH molecule. However, by including just one additional electronic state a B state (originating from a collinear Sigma state) it is found that a rigorous, meaningful three state diabatization can be carried out for large regions of configuration space, particularly for those, near the stable configuration of NH2. This opens the way for an accurate study of this important molecule even where the electronic angular momentum deviates significantly from an integer value.
Age of Information Aware Resource Management in UAV Assisted Mobile Edge Computing Systems
Venue: GLOBECOM 2020 2020 IEEE Global Communications Conference
Year: 2020

This paper investigates the problem of age of information (AoI) aware resource awareness in an unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system, which is deployed by an infrastructure provider (InP) A service provider leases resources from the InP to serve the mobile users (MUs) with sporadic computation requests. Due to the limited number of channels and the finite shared I/O resource of the UAV, the MUs compete to schedule local and remote task computations in accordance with the observations of system dynamics. The aim of each MU is to selfishly maximize the expected long term computation performance. We formulate the non cooperative interactions among the MUs as a stochastic game. To approach the Nash equilibrium solutions, we propose a novel online deep reinforcement learning (DRL) scheme, which enables each MU to behave using its local conjectures only. The DRL scheme employs two separate deep Q networks to approximate the Q factor and the post decision Q factor for each MU. Numerical experiments show the potentials of the online DRL scheme in balancing the tradeoff between AoI and energy consumption.
XYLEM DYNAMICS OF DIFFERENT ROOTSTOCK/SCION COMBINATIONS OF APPLE UNDER A HOT, SEMI ARID MEDITERRANEAN CLIMATE
Year: 2014

New apple plantations in Albania have descended from the mountain areas of Eastern Albania to the hot and dry Western Plain demanding an evaluation of different cultivars and rootstocks adaptable for such a climate. Moreover, best practice in terms of water use efficiency (WUE) requires an irrigation regime which is temporally and spatially adjusted for each cultivar within the same orchard. The object of this study was to assess the behavior of these rootstock/scion combinations this climate regime to properly design an irrigation strategy. The study was conducted in a commercial apple orchard, trained in a central leader system, in their 2nd year of growth. Sap flow sensors based on the stem heat balance method (SHB) were installed on the main leader at 30 cm height, from August to September on 2 trees for each of the following rootstock/scion combinations: Golden Delicious/Pajam2, Fuji/Pajam2 and I dared/Pajam2. Soil water potential was measured at two different depths. A portable meteorological station measured meteorological variables enabling the calculation of vapour pressure deficit (VPD) and potential evapotranspiration (PET) Trunk Cross Sectional Area (TCSA) was measured for rootstock, trunk, individual and all branches. Length of branches was measured too. A linear regression analysis was undertaken to study different regression models which better predict sap flow by measuring environmental variables. Irrigation of the orchard was withheld in order to create extreme water stress levels. The results show clearly that cultivar 'Fuji' on Pajam 2 rootstock has lower levels of transpiration compared to other cultivars, as a results of a more favorable ratio between the reproductive and vegetative growth, which makes it more adaptable for this arid area of cultivation. From the regression models studied, it can be concluded that sap flow was best predicted by ET. Global radiation and even air temperature are useful climatic variables for predicting SF reference values because are easily measured.
First report of Tomato leaf curl New Delhi virus affecting zucchini squash in an important horticultural area of southern Italy
Year: 2016

Tomato leaf curl New Delhi virus (ToLCNDV) is a bipartite begomovirus (family Geminiviridae which infects species in the families Cucurbitaceae and Solanaceae (Padidam et al 1995; Mizutani et al 2011) Begomoviruses are transmitted by.
Automation of streetlight using Lora Technology
Year: 2019

Increase in population and the corresponding increase of roads has increased the number of the street light, for the roads and people's safety, which rises investment and energy. Lighting consumes an adequate amount of energy in both outdoor and indoor. However different approaches are being proposed for making systems energy efficient and upgraded with the latest technology. Internet of Things (IoT) has been growing over the last few years in multiple applications and due to a growing need for smart street light, an innovative opportunity arises. Proposed work is to develop a smart street light system. This research gives precise and the best control over energy efficient street lighting system. It gives practical implementation using Long Range (LoRa) and Arduino micro controller using sensors to gather information. The communication part is realized with Low Power Wide Area Network (LP WAN) LoRa technology that combine low power and long range capabilities.
Uvajanje racunalnistva v slovenske srednje sole in modernizacija pouka naravoslovnih predmetov v sedemdesetih letih dvajsetega stoletja
Year: 2006

In the first half of the 1960s Slovenia began to systematically modernise its secondary school programmes. The remodelling first focused on the natural sciences. The Republic of Slovenia was the first member of the then federal state of Yugoslavia as well as of Central and South East Europe to start introducing information technology to secondary schools in 1974. This was even earlier than in the Federal Republic of Germany. It was also the first to publish an information technology school book, "Introduction to Information Technology Science for Secondary Schools" (whose authors were Dr. Ivan Bratko and Dr. Vladislav Rajkovic) Along with this, the overhaul of chemistry and biology classes as well as methods of teaching foreign languages began. The latter focused in particular on English and was carried out in co operation with several Western European and American universities.
Falla hepatica y anestesia: un enfoque practico
Year: 2017

Resumen Introduccion La enfermedad hepatica terminal es un importante problema de salud publica, ademas de un reto interesante para el manejo anestesico de estos pacientes debido a la cantidad de factores de riesgo que presenta. El objetivo principal de este articulo es brindar un enfoque practico con respecto al manejo anestesico de los pacientes con falla hepatica. Materiales y metodos Se realizo una revision exhaustiva de la literatura en bases de datos como UpToDate, PubMed y ScienceDirect. Discusion Se presentan los factores que aumentan el riesgo quirurgico de los pacientes con enfermedad hepatica, asi como las estrategias para disminuirlos ademas de los efectos sistemicos de la misma, el manejo anestesico tanto prequirurgico como las consideraciones intraoperatorias y los cuidados posoperatorios.  La enfermedad hepatica presenta alta morbimortalidad quirurgica; sin embargo, realizando una adecuada valoracion preanestesica se podrian identificar y corregir los factores de riesgo del paciente con la finalidad de realizar un acto quirurgico seguro teniendo en cuenta las alteraciones farmacodinamicas de los medicamentos.
Lifecycle of neural semantic parsing
Year: 2019

Humans are born with the ability to learn to perceive, comprehend and communicate with language. Computing machines, on the other hand, only understand programming languages. To bridge the gap between humans and computers, deep semantic parsers convert natural language utterances into machine understandable logical forms. The technique has a wide range of applications ranging from spoken dialogue systems and natural language interfaces. This thesis focuses on neural network based semantic parsing. Traditional semantic parsers function with a domain specific grammar that pairs utterances and logical forms, and parse with a CKY like algorithm in polynomial time. Recent advances in neural semantic parsing reformulate the task as a sequenceto sequence learning problem. Neural semantic parsers parse a sentence in linear time, and reduce the need for domain specific assumptions, grammar learning, and extensive feature engineering. But this modeling flexibility comes at a cost since it is no longer possible to interpret how meaning composition is performed, given that logical forms are structured objects (trees or graphs) Such knowledge plays a critical role in understanding modeling limitations so as to build better semantic parsers. Moreover, the sequence to sequence learning problem is fairly unconstrained, both in terms of the possible derivations to consider and in terms of the target logical forms which can be ill formed or unexecutable. The first contribution of this thesis is an improved neural semantic parser, which produces syntactically valid logical forms following a transition system and grammar constrains. The transition system integrates the generation of domain general (i.e. valid tree structures and language specific predicates) and domain specific aspects (i.e. domain specific predicates and entities) in a unified way. The model employs various neural attention mechanisms to handle mismatches between natural language and formal language a central challenge in semantic parsing. Training data to semantic parsers typically consists of utterances paired with logical forms. Another challenge of semantic parsing concerns the annotation of logical forms, which is labor intensive. To write down the correct logical form of an utterance, one not only needs to have expertise in the semantic formalism, but also has to ensure the logical form matches the utterance semantics. We tackle this challenge in two ways. On the one hand, we extend the neural semantic parser to a weakly supervised setting within a parser ranker framework. The weakly supervised setup uses training data of utterance denotation (e.g. question answer) pairs, which are much easier to obtain and therefore allow to scale semantic parsers to complex domains. Our framework