bluuebunny/crossref_metadata_2025_split huggingface dataset:
Static Worst-Case Analyses and Their Validation Techniques for Safety-Critical Systems
Authors: Wägemann Peter 
Published: February 2022

AbstractThe reliable operation of systems with both timing and energy requirements is a fundamental challenge in the area of safety-critical embedded systems. In order to provide guarantees for the execution of tasks within given resource budgets, these systems demand bounds of the worst-case execution time (WCET) and the worst-case energy consumption (WCEC). While static WCET analysis techniques are well established in the software development process of real-time systems nowadays, these program analysis techniques are not directly applicable to the fundamentally different behavior of energy consumption and the determination of the WCEC. Besides the missing approaches for WCEC bounds, the domain of worst-case analyses generally faces the problem that the accuracy and validity of reported analysis bounds are unknown: Since the actual worst-case resource consumption of existing benchmark programs cannot be automatically determined, a comprehensive validation of these program analysis tools is not possible.This summary of my dissertation addresses these problems by first describing a novel program analysis approach for WCEC bounds, which accounts for temporarily power-consuming devices, scheduling with fixed real-time priorities, synchronous task activations, and asynchronous interrupt service routines. Regarding the fundamental problem of validating worst-case tools, this dissertation presents a technique for automatically generating benchmark programs. The generator combines program patterns so that the worst-case resource consumption is available along with the generated benchmark. Knowledge about the actual worst-case resource demand then serves as the baseline for evaluating and validating program analysis tools. The fact the benchmark generator helped to reveal previously undiscovered software bugs in a widespread WCET tool for safety-critical systems underlines the relevance of such a structured testing technique.
A Quest for Equity: Labour Standards on the Transnational Move
Authors: Mückenberger Ulrich 
Published: February 2022

AbstractThrough the presentation and discussion of some data from the current legal–empirical research of our Collaborative Research Center (CRC) research project “Worlds of Labour”, it is shown that we are in the midst of a worldwide labour regulation development in transition from patriarchal to gender-egalitarian rules. Equal rights’ drivers in the gender field encompass actors representing states/international organisations (IOs) setting equalising rules and social actors raising voice for equal rights. To the latter belong, nationally and globally, workers and workers’ organisations and actors not directly involved as social partners, trying to intervene into employment affairs in order to trigger equality. Epistemic communities, transnational advocacy and norm-building networks, international non-governmental organisations (INGOs) and the like collaborate with norm-building states and IOs (“women caucuses”) propagating egalitarian rules. This multiplicity of example setters and emulators helped equality values gain momentum, worldwide.
Hypoxisch-hypoglykämischer Hirnschaden infolge einer Nebennierenrindenkrise – eine wichtige Differenzialdiagnose bei unspezifischer Symptomatik
Authors: Mayer C. , Hackenbroch C. , Mayer S. , Helm M. , Müller T. 
Published: February 2022

ZusammenfassungDie akute Nebennierenrindeninsuffizienz (NNRI) ist eine seltene Erkrankung, welche aufgrund ihrer diffusen Symptomatik oft schwer zu diagnostizieren ist. Symptome wie allgemeine Schwäche, Übelkeit/Erbrechen, Müdigkeit, Hyponatriämie, Hyperkaliämie, Hypoglykämie sowie ausgeprägte Hypotonie bis zum Schock können in der Diagnosestellung einer Nebennierenrindenkrise wegweisend sein. Die Grunderkrankung der NNRI ist ebenso von nicht immer eindeutigen Symptomen geprägt und je nach primärer oder sekundärer Form unterschiedlich ausgeprägt.Nach der Diagnosestellung sind die Patientenschulung, die richtige Dosierung der Substitutionsmedikation und die Adhärenz der Patienten entscheidend für den weiteren Verlauf, insbesondere für die Vermeidung einer Nebennierenkrise. Diese stellt eine Ursache der erhöhten Mortalität betroffener Patienten dar, was in der aktuellen Literatur häufig beschrieben ist. Jedoch zeigt sich in der Literaturrecherche, dass der Verlauf solcher Krisen bzw. die Kausalität von letalen Verläufen selten thematisiert wird, obwohl das Wissen um die Erkrankung und ein damit verbundenes, rasches Handeln v. a. für in der Notaufnahme und auf der Intensivstation tätige Ärzte von großer Bedeutung sind.Berichtet wird über eine Patientin, die bei bekannter sekundärer NNRI das Vollbild einer Nebennierenkrise entwickelte. Trotz rascher Diagnosestellung sowie Therapieeinleitung war infolge von prolongierter Hypoxie und Hypoglykämie ein massiver Hirnschaden unabwendbar. Der Fallbericht zeigt die mögliche Symptomatik, v. a. eine massive Hypoglykämie und einen initial katecholaminrefraktären Schock. Außerdem zeigt er die Schwere des Krankheitsbildes und stellt die Bedeutung einer schnellen Therapie trotz erschwerter Diagnosestellung dar.
Pfändungsschutz für Tiere
Authors: Herberger Marie 
Published: February 2022

ZusammenfassungDie aktuellen Entwicklungen durch das Gesetz zur Verbesserung des Schutzes von Gerichtsvollziehern
vor Gewalt (GvSchuG) sind Anlass, den dadurch mit Wirkung zum 1.1.2022 neu geregelten Pfändungsschutz
für Tiere darzustellen und auf seine innere Stimmigkeit hin zu untersuchen.
Entrustable professional activities versus competencies and skills: Exploring why different concepts are often conflated
Authors: ten Cate Olle , Schumacher Daniel J. 
Published: February 2022

AbstractDespite explanations in the literature, a returning question in the use of entrustable professional activities (EPAs) is how to distinguish them from competencies and skills. In this article, we attempt to analyze the causes of the frequent confusion and conflation of EPAs with competencies and skills, and argue why the distinction is important for education, qualification and patient safety. ‘Tracheotomy’, ‘lumbar puncture’, ‘interprofessional collaboration’ for example are colloquially called ‘skills’, but its is a person’s ability to perform these activities that is the actual skill; the EPA is simply the activity itself. We identify two possible causes for the confusion. One is a tendency to frame all educational objectives as EPAs. Many objectives of medical training can be conceptualized as EPAs, if ‘the ability to do X’ is the corresponding competency; but that does not work for all. We offer ways to deal with objectives of training that are not usefully conceptualized as EPAs. A more fundamental cause relates to entrustment decisions. The permission to contribute to health care reflects entrustment. Entrustment decisions are the links or pivots between a person’s readiness for the task and the actual task execution. However, if entrustment decisions do not lead to increased autonomy in the practice of health care, but only serve to decide upon the advancement to a next stage of training, EPAs can become the tick boxes learners feel they need to collect to ‘pass’. Gradually, then, EPAs can loose their original meaning of units of practice for which one becomes qualified.
Diets for Tamarixia triozae adults before releasing in augmentative biological control
Authors: Chen Chen , He Xiong Z. , Zhou Peng , Wang Qiao 
Published: February 2022

AbstractThe effectiveness of augmentative biological control using parasitoids often depends on their physiological state and the pest population density at the time of release. Tamarixia triozae (Burks) (Hymenoptera: Eulophidae) is a primary host-feeding parasitoid of a serious invasive pest Bactericera cockerelli (Šulc) (Hemiptera: Triozidae). Here we investigated the effects of adult diets (honey, water, yeast, and hosts) and timing of their provision on T. triozae fitness and oviposition patterns, providing knowledge for enhancement of its biological control potential. Adults fed with honey for four days with no access to hosts or with water or yeast for one day followed by host feeding for three days had similar longevity and lifetime pest killing ability. Adults fed with only water for one day before release had significantly greater intrinsic rate of increase, shorter doubling time, and higher daily fecundity peak. Adults fed with honey or yeast for one day followed by host feeding for three days significantly flattened their daily oviposition curves. These findings have several implications for augmentative biological control using T. triozae. First, honey diet may allow at least four days for successful shipment of host-deprived adults without compromising biological control effectiveness. Second, the release of host-deprived adults with one-day water feeding may achieve rapid pest suppression when the pest population density is high. Finally, releasing host-deprived adults with one-day honey or yeast feeding followed by three-day host feeding can increase their establishment success and reduce the risk of massive removal of hosts when the pest population density is low.
Super-resolution microscopy reveals stochastic initiation of replication in Drosophila polytene chromosomes
Authors: Kolesnikova Tatyana D. , Pokholkova Galina V. , Dovgan Viktoria V. , Zhimulev Igor F. , Schubert Veit 
Published: February 2022

AbstractStudying the probability distribution of replication initiation along a chromosome is a huge challenge. Drosophila polytene chromosomes in combination with super-resolution microscopy provide a unique opportunity for analyzing the probabilistic nature of replication initiation at the ultrastructural level. Here, we developed a method for synchronizing S-phase induction among salivary gland cells. An analysis of the replication label distribution in the first minutes of S phase and in the following hours after the induction revealed the dynamics of replication initiation. Spatial super-resolution structured illumination microscopy allowed identifying multiple discrete replication signals and to investigate the behavior of replication signals in the first minutes of the S phase at the ultrastructural level. We identified replication initiation zones where initiation occurs stochastically. These zones differ significantly in the probability of replication initiation per time unit. There are zones in which initiation occurs on most strands of the polytene chromosome in a few minutes. In other zones, the initiation on all strands takes several hours. Compact bands are free of replication initiation events, and the replication runs from outer edges to the middle, where band shapes may alter.
Explanatory pragmatism: a context-sensitive framework for explainable medical AI
Authors: Nyrup Rune , Robinson Diana 
Published: February 2022

AbstractExplainable artificial intelligence (XAI) is an emerging, multidisciplinary field of research that seeks to develop methods and tools for making AI systems more explainable or interpretable. XAI researchers increasingly recognise explainability as a context-, audience- and purpose-sensitive phenomenon, rather than a single well-defined property that can be directly measured and optimised. However, since there is currently no overarching definition of explainability, this poses a risk of miscommunication between the many different researchers within this multidisciplinary space. This is the problem we seek to address in this paper. We outline a framework, called Explanatory Pragmatism, which we argue has two attractive features. First, it allows us to conceptualise explainability in explicitly context-, audience- and purpose-relative terms, while retaining a unified underlying definition of explainability. Second, it makes visible any normative disagreements that may underpin conflicting claims about explainability regarding the purposes for which explanations are sought. Third, it allows us to distinguish several dimensions of AI explainability. We illustrate this framework by applying it to a case study involving a machine learning model for predicting whether patients suffering disorders of consciousness were likely to recover consciousness.
Data-Driven Modeling of Linear Dynamical Systems with Quadratic Output in the AAA Framework
Authors: Gosea Ion Victor , Gugercin Serkan 
Published: February 2022

AbstractWe extend the Adaptive Antoulas-Anderson () algorithm to develop a data-driven modeling framework for linear systems with quadratic output (). Such systems are characterized by two transfer functions: one corresponding to the linear part of the output and another one to the quadratic part. We first establish the joint barycentric representations and the interpolation theory for the two transfer functions of systems. This analysis leads to the proposed algorithm. We show that by interpolating the transfer function values on a subset of samples together with imposing a least-squares minimization on the rest, we construct reliable data-driven models. Two numerical test cases illustrate the efficiency of the proposed method.
Chronic-pharma: New Platform for Chronic Patients Pharmacotherapy Optimization
Authors: Villalba-Moreno Angela María , Galván-Banqueri Mercedes , Rodríguez-Pérez Aitana , Toscano-Guzmán María Dolores , López-Hermoso Clara , Sánchez-Fidalgo Susana , Santos-Ramos Bernardo , Alfaro-Lara Eva Rocío 
Published: February 2022

AbstractWe describe the technological development of a web platform named CHRONIC-PHARMA that integrates three prescription support tools for patients with chronic diseases: Anticholinergic Burden Calculator (ABC), LESS-CHRON criteria and TRIGGER-CHRON. They focus on the optimization and evaluation of pharmacotherapy in patients with chronic diseases, resulting in a useful, single platform that can facilitate the review of pharmacotherapy and improve the safety of chronically ill patients. This is achieved by estimating and reducing the anticholinergic risk (ABC), detecting opportunities for deprescribing drugs and monitoring its success (LESS-CHRON criteria), as well as calculating the risk of adverse drug events (TRIGGER-CHRON). The platform is freely accessible online (https://chronic-pharma.com/) as well as through a mobile application, and therefore easily accessible among the healthcare community.
Naturally occurring plant-based anticancerous candidates as prospective ABCG2 inhibitors: an in silico drug discovery study
Authors: Ibrahim Mahmoud A. A. , Abdelrahman Alaa H. M. , Badr Esraa A. A. , Almansour Nahlah Makki , Alzahrani Othman R. , Ahmed Muhammad Naeem , Soliman Mahmoud E. S. , Naeem Mohamed Ahmed , Shawky Ahmed M. , Sidhom Peter A. , Mekhemer Gamal A. H. , Atia Mohamed A. M. 
Published: February 2022

Abstract
ATP-binding cassette transporter G2 (ABCG2) is an efflux transporter related to the clinical multidrug resistance (MDR) phenomenon. Identifying ABCG2 inhibitors could help discover extraordinary curative strategies for carcinoma remediation. Hitherto, there is no medication drug inhibiting ABCG2 transporter, notwithstanding that a considerable number of drugs have been submitted to clinical-trial and investigational phases. In the search for unprecedented chemical compounds that could inhibit the ABCG2 transporter, an in silico screening was conducted on the Naturally Occurring Plant-based Anticancer Compound-Activity-Target (NPACT) database containing 1574 compounds. Inhibitor-ABCG2 binding affinities were estimated based on molecular docking and molecular minimization (MM) calculations and compared to a co-crystallized inhibitor (BWQ) acting as a reference inhibitor. Molecular dynamics (MD) simulations pursued by molecular mechanics-generalized Born surface area (MM-GBSA) binding energy estimations were further executed for compounds with MM-GBSA//MM binding energies lower than BWQ (calc. − 60.5 kcal/mol). NPACT00968 and NPACT01545 demonstrated auspicious inhibitory activities according to binding affinities (ΔGbinding) over the 100 ns MD simulations that were nearly one and a half folds compared to BWQ (− 100.4, − 94.7, and − 62.9 kcal/mol, respectively). Throughout the 100 ns MD simulations, structural and energetical analyses unveiled outstanding stability of the ABCG2 transporter when bound with NPACT00968 and NPACT01545. In silico calculations hold a promise for those two inhibitors as drug candidates of ABCG2 transporter and emphasize that further in vitro and in vivo experiments are guaranteed.

Graphical abstract
Cross-Instrument Comparison of MapCam and OVIRS on OSIRIS-REx
Authors: Golish D. R. , Simon A. A. , Reuter D. C. , Ferrone S. , Clark B. E. , Li J.-Y. , DellaGiustina D. N. , Drouet d’Aubigny C. , Rizk B. , Lauretta D. S. 
Published: February 2022

AbstractTwo of the instruments onboard the OSIRIS-REx spacecraft, the MapCam color imager and the OVIRS visible and infrared spectrometer, observed the surface of asteroid (101955) Bennu in partially overlapping wavelengths. Significant scientific advances have been enabled by using data from these two instruments in tandem, but a robust statistical understanding of their relationship is needed for future analyses to cross-compare their data as accurately and sensitively as possible. Here we present a cross-instrument comparison of data acquired by MapCam and OVIRS, including methods and results for all global and site-specific observation campaigns in which both instruments were active. In our analysis, we consider both the absolute radiometric offset and the relative (normalized) variation between the two instruments; we find that both depend strongly on the photometric and instrumental conditions during the observation. The two instruments have a large absolute offset (>15%) due to their independent radiometric calibrations. However, they are very consistent (relative offset as low as 1%) when each instrument’s response is normalized at a single wavelength, particularly at low phase angles where shadows on Bennu’s rough surface are minimized. We recommend using the global datasets acquired at 12:30 pm local solar time for cross-comparisons; data acquired at higher phase angles have larger uncertainties.
Using a Multi-GPU node to accelerate the training of Pix2Pix neural networks
Authors: Lupión M. , Sanjuan J. F. , Ortigosa P. M. 
Published: February 2022

AbstractGenerative adversarial networks are gaining importance in problems such as image conversion, cross-domain translation and fast styling. However, the training of these networks remains unclear because it often results in unexpected behavior caused by non-convergence, model collapse or overly long training, causing the training task to have to be supervised by the user and vary with each dataset. To increase the speed of training in Pix2Pix (image-to-image translation) networks, this work incorporates multi-GPU training using mixed precision, along with optimizations in the GPU image input process. In addition, in order to make the training unsupervised and to terminate it when the best transformations are performed, an early stopping method using the peak signal noise ratio (PSNR) metric is proposed.
Queue congestion prediction for large-scale high performance computing systems using a hidden Markov model
Authors: Park Ju-Won , Kwon Min-Woo , Hong Taeyoung 
Published: February 2022

AbstractTo share limited, large-capacity resources, the high-performance computing field provides services by allocating available resources to jobs through batch job schedulers. Therefore, it is natural that a queue waiting time occurs until the resources are available if resources are not sufficient. The prediction of queue waiting time is very useful to improve overall resource utilization. However, the queue waiting time is very difficult to predict because it is significantly affected by the many factors such as applied scheduling algorithm and characteristics of the executed job. In this study, a method of predicting queue waiting time using only the historical log data created by the batch job scheduler is examined. Specifically, a method of predicting queue waiting time based on a hidden Markov model is proposed. It has the following three stages. First, outliers are removed by applying the outlier detection algorithm using a statistics-based parametric method. Second, the parameters of the hidden state are estimated using the observed queue waiting time sequence based on the historical job log. Third, the queue waiting interval at time$$t+1$$t+1is provided using the estimated parameters at timet. Comparing the prediction accuracy with those of the other prediction methods, experimental results show that the proposed algorithm improves the prediction accuracy by up to 60%.
Power Muirhead mean in spherical normal fuzzy environment and its applications to multi-attribute decision-making
Authors: Temel Tansu , Aydemir Salih Berkan , Hoşcan Yaşar 
Published: February 2022

AbstractThis study aims to propose the power Muirhead mean (PMM) operator in the spherical normal fuzzy sets (SNoFS) environment to solve multiple attribute decision-making problems. Spherical normal fuzzy sets better characterize real-world problems. On the other hand, the Muirhead mean (MM) considers the relationship between any number of criteria of the operator. Power aggregation (PA) reduces the negative impact of excessively high or excessively low values on aggregation results. This article proposes two new aggregation methods: spherical normal fuzzy power Muirhead mean (SNoFPMM) and spherical normal fuzzy weighted power Muirhead mean (SNoFWPMM). Also, these operators produce effective results in terms of their suitability to real-world problems and the relationship between their criteria. The proposed operators are applied to solve the problems in choosing the ideal mask for the COVID-19 outbreak and investment company selection. However, uncertainty about the effects of COVID-19 complicates the decision-making process. Spherical normal fuzzy sets can handle both real-world problems and situations involving uncertainty. Our approach has been compared with other methods in the literature. The superior aspects and applicability of our strategy are also mentioned.
Nerve optic segmentation in CT images using a deep learning model and a texture descriptor
Authors: Ranjbarzadeh Ramin , Dorosti Shadi , Jafarzadeh Ghoushchi Saeid , Safavi Sadaf , Razmjooy Navid , Tataei Sarshar Nazanin , Anari Shokofeh , Bendechache Malika 
Published: February 2022

AbstractThe increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems can assist the physician to decrease the time of ICP diagnosis. The accurate detection of the nerve optic regions, with drawing a precise slope line behind the eyeball and calculating the diameter of nerve optic, are the main aims of this research. First, the Fuzzy C-mean (FCM) clustering is employed for segmenting the input CT screening images into the different parts. Second, a histogram equalization approach is used for region-based image quality enhancement. Then, the Local Directional Number method (LDN) is used for representing some key information in a new image. Finally, a cascade Convolutional Neural Network (CNN) is employed for nerve optic segmentation by two distinct input images. Comprehensive experiments on the CT screening dataset [The Cancer Imaging Archive (TCIA)] consisting of 1600 images show the competitive results of inaccurate extraction of the brain features. Also, the indexes such as Dice, Specificity, and Precision for the proposed approach are reported 87.7%, 91.3%, and 90.1%, respectively. The final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with the other methods. Therefore, this method can be used for early diagnose of ICP and preventing the occurrence of serious health problems in patients.