bluuebunny/crossref_metadata_2025_split huggingface dataset:
Introducing a Linear Gamma Variate Fit to Measure Pulmonary Perfusion with Electrical Impedance Tomography
Authors: Schuderer Felix , Kircher Michael , Dössel Olaf , Stender Birgit , Bluth Thomas , Gama de Abreu Marcelo 
Published: February 2021

Abstract
The indicator dilution method (IDM) is one approach to measure pulmonary perfusion using Electrical Impedance Tomography (EIT). To be able to calculate perfusion parameters and to increase robustnes, it is necessary to approximate and then to separate the components of the measured signals. The component referring to the passage of the injected bolus through the pixels can be modeled as a gamma variate function, its parameters are often determined using nonlinear optimization algorithms. In this paper, we introduce a linear approach that enables higher robustnes and faster computation, and compare the linear and nonlinear fitting approach on data of an animal study.
EEG-Based Classification of the Driver Alertness State
Authors: Golz Martin , Thomas Sebastian , Schenka Adolf 
Published: February 2021

Abstract
GMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.
A Concept for Context Awareness in Smart Environments
Authors: Bauer Jochen , Hechtel Michael , Holzwarth Martin , Sessner Julian , Franke Jörg , Wittenberg Thomas , Waldhör Klemens , Schena Annamaria , Falanga Annarita , Ristok Bruno , Böhle Florian 
Published: February 2021

Abstract
All aspects of daily life increasingly include digitization. So-called „smart home“ technologies, as well as „wearables“, are gaining attention from more and more dwellers. Therefore, sensor-based, individualized, AI-based services for improved post-intervention monitoring and therapy accompaniment will become feasible and possible if these systems offer a related context-awareness. This paper provides an approach on how to sense and interpret specific contexts with the help of wearables, smartwatches, smart home sensors, and emotion detection software.
ForeSight - AI-based Smart Living Platform Approach
Authors: Bauer Jochen , Hechtel Michael , Konrad Christoph , Holzwarth Martin , Mayr Andreas , Schneider Sven , Franke Jörg , Hoffmann Hilko , Zinnikus Ingo , Feld Thomas , Runge Mathias , Hinz Oliver 
Published: February 2021

Abstract
In the upcoming years, the internet of things (IoT) will enrich daily life. The combination of artificial intelligence (AI) and highly interoperable systems will bring contextsensitive multi-domain services to reality. This paper describes a concept for an AI-based smart living platform with open- HAB, a smart home middleware, andWeb of Things (WoT) as key components of our approach. The platform concept considers different stakeholders, i.e. the housing industry, service providers, and tenants. These activities are part of the Fore- Sight project, an AI-driven, context-sensitive smart living platform.
Contactless Interactive Fall Detection and Sleep Quality Estimation for Supporting Elderly with Incipient Dementia
Authors: Siedel Roman , Scheck Tobias , Perez Grassi Ana C. , Seuffert Julian B. , Apitzsch André , Yu Jingrui , Nestler Norbert , Heinz Danny , Lehmann Lars , Goy Anne , Hirtz Gangolf 
Published: February 2021

Abstract
In recent years, the demographic change in conjunction with a lack of professional caregivers led to retirement homes reaching capacity. The Alzheimer Disease International stated that over 50 million people suffered from dementia in 2019 worldwide and twice the amount will presumably be effected in 2030. The field of Ambient Assisted Living (AAL) tackles this problem by facilitating technical system-aided everyday life. AUXILIA is such an AAL system and does not only support elderly people with dementia in an early phase, but also monitors their activities to provide behaviour analysis results for care attendants, relatives and physicians. Moreover, the system is capable of recognizing emergency situations like human falls. Furthermore, sleep quality estimation is employed to be able to draw conclusions about the current behaviour of an affected person. This article presents the current development state of AUXILIA.
Evaluation of semi-supervised learning using sparse labeling to segment cell nuclei
Authors: Bruch Roman , Rudolf Rüdiger , Mikut Ralf , Reischl Markus 
Published: February 2021

Abstract
The analysis of microscopic images from cell cultures plays an important role in the development of drugs. The segmentation of such images is a basic step to extract the viable information on which further evaluation steps are build. Classical image processing pipelines often fail under heterogeneous conditions. In the recent years deep neuronal networks gained attention due to their great potentials in image segmentation. One main pitfall of deep learning is often seen in the amount of labeled data required for training such models. Especially for 3D images the process to generate such data is tedious and time consuming and thus seen as a possible reason for the lack of establishment of deep learning models for 3D data. Efforts have been made to minimize the time needed to create labeled training data or to reduce the amount of labels needed for training. In this paper we present a new semisupervised training method for image segmentation of microscopic cell recordings based on an iterative approach utilizing unlabeled data during training. This method helps to further reduce the amount of labels required to effectively train deep learning models for image segmentation. By labeling less than one percent of the training data, a performance of 90% compared to a full annotation with 342 nuclei can be achieved.
Optimization of online particle counting with a 3D-printed bubble trap
Authors: Kurzhals Anja , Brandt-Wunderlich Christoph , Borowski Finja , Schmitz Klaus-Peter , Grabow Niels , Schmidt Wolfram , Gurung Jyoti 
Published: February 2021

Abstract
Particulate evaluation is needed for the approval of cardiovascular devices. Air bubbles lead to higher particle counts when light obscuration method (LOM) is used. The aim of the study was to test a custom made bubble trap that removes air bubbles (2 - 100 μm) from a flow circuit prior to online particle counting. Artificially generated air bubbles were counted with an online particle counter with and without the bubble trap. Air bubbles were reduced by about 71 % to 91 % by using the bubble trap.
Sulfated Hyaluronan coating of polyurethanebased implant materials
Authors: Kemkemer Ralf , Athanasopulu Kiriaki , Caltzidou Dimitra , Sekishita Asuka , Kutuzova Larysa , Lorenz Günter , Tanaka Masaru 
Published: February 2021

Abstract
Thermoplastic polycarbonate urethane elastomers (TPCU) are potential implant materials for treating degenerative joint diseases thanks to their adjustable rubberlike properties, their toughness, and their durability. We developed a water-containing high-molecular-weight sulfated hyaluronic acid-coating to improve the interaction of TPCU with the synovial fluid. It is suggested that trapped synovial fluid can act as a lubricant that reduces the friction forces and thus provides an enhanced abrasion resistance of TPCU implants. Aims of this work were (i) the development of a coating method for novel soft TPCU with high-molecular sulfated hyaluronic acid to increase the biocompatibility and (ii) the in vitro validation of the functionalized TPCUs in cell culture experiments.
Permeability and wettability of bioresorbable nanofiber nonwoven membranes
Authors: Großmann Swen , Oldenburg Jan , Ott Robert , Stiehm Michael , Schmitz Klaus-Peter , Siewert Stefan , Illner Sabine , Grabow Niels 
Published: February 2021

Abstract
Nanofiber nonwoven membranes produced by electrospinning provide the possibility to adjust mechanical material parameters as well as simultaneously the biologically relevant properties - a fundamental aspect in developing new implants and medical devices. Wettability and permeability are also of great importance, as they have a decisive influence on the release of drugs, cell attachment, degradability and finally the nutrient supply of the surrounding tissue. Within this work the wettability and permeability of several electrospun poly-L-lactide nonwovens, including different additives, were investigated and a correlation to membrane morphology was found. A potential modification of the permeability by the fluid viscosity was also investigated. The results form a fundamental building block in the development of permeable biodegradable implants and medical devices.
Evaluation of HRV estimation algorithms from PPG data using neural networks
Authors: Wittenberg Th. , Koch R. , Pfeiffer N. , Lang N. , Struck M. , Amft O. , Eskofier B. 
Published: February 2021

Abstract
Heart rate variability (HRV) is a powerful measure to gain information on the activation of the central nervous system and is thus a strong indicator for the overall health and emotional state of a person. Currently, the gold standard for HRV analysis is the examination of R-peaks in electrocardiograms (ECG), which requires a placement of electrodes on the torso. This is often impracticable, especially for the use in daily routines or 24/7 measurements. Photoplethysmograms (PPG) are an alternative to ECG assessment and are easier to acquire, e.g. by using fitness trackers or smart watches. Nevertheless, PPG data is more susceptible to motion artifacts. Hence, goal of this work is to develop and evaluate an artificial neural network (ANN) approach to estimate the R-peak locations in complex PPG signals. Public data collections were used as benchmark to compare our ANN-based approach to state-of-the-art methods. Results show that ANNs can improve HRV estimation during motion. HRV estimations from baseline methods (decision-tree based and automatic multiscalebased peak detection) were compared with the best performing neural network (3L-GRU) using the TROIKA dataset with respect to reference parameters obtained from a manual selection of the peaks in ECG data. In most cases, the neural network based HRV estimation was closer to the reference HRV compared to baseline methods (lower μ and σ ) Also, σ is smaller for the best performing ANN approach across most HRV parameters. Inclusion of another PPG or acceleration channel did not affect HRV estimation. Although, the neural network learning approach outperforms conventional methods, the examined PPG-based HRV estimation has still accuracy limitations. Nonetheless, the proposed estimation approach opens up new directions for further improvement.
Acoustic sensing of tissue-tool interactions – potential applications in arthroscopic surgery
Authors: Sühn Thomas , Pandey Ajay , Friebe Michael , Illanes Alfredo , Boese Axel , Lohman Christoph 
Published: February 2021

Abstract
Arthroscopic surgery is a technically challenging but common minimally invasive procedure with a long learning curve and a high incidence of iatrogenic damage. These damages can occur due to the lack of feedback and supplementary information regarding tissue-instrumentcontact during surgery. Deliberately performed interactions can be used however to obtain clinically relevant information, e.g. when a surgeon uses the tactile feedback to assess the condition of articular cartilage. Yet, the perception of such events is highly subjective. We propose a novel proximally attached sensing concept applied to arthroscopic surgery to allow an objective characterization and utilization of interactions. It is based on acoustic emissions which originate from tissue-instrument-contact, that propagate naturally via the instrument shaft and that can be obtained by a transducer setup outside of the body. The setup was tested on its ability to differentiate various conditions of articular cartilage. A femoral head with varying grades of osteoarthritic cartilage was tapped multiple times ex-vivo with a conventional Veress needle with a sound transducer attached at the outpatient end. A wavelet-based processing of the obtained signals and subsequent analysis of distribution of spectral energy showed the potential of tool-tissue-interactions to characterize different cartilage conditions. The proposed concept needs further evaluation with a dedicated design of the palpation tool and should be tested in realistic arthroscopic scenarios.
HealthTEC Innovation Design - a proposal for a novel Master degree program based on Unmet Clinical Need, global Healthcare Challenges, and 21st century skills
Authors: Friebe Michael 
Published: February 2021

Abstract
The effectiveness, efficiency, availability, agility, and equality of global healthcare systems are in question. The COVID-19 pandemic have further highlighted some of these issues and also shown that healthcare provision is in many parts of the world paternalistic, nimble, and often governed too extensively by revenue and profit motivations. The 4th industrial revolution - the machine learning age - with data gathering, analysis, optimisation, and delivery changes has not yet reached Healthcare / Health provision. We are still treating patients when they are sick rather then to use advanced sensors, data analytics, machine learning, genetic information, and other exponential technologies to prevent people from becoming patients or to help and support a clinicians decision. We are trying to optimise and improve traditional medicine (incremental innovation) rather than to use technologies to find new medical and clinical approaches (disruptive innovation). Education of future stakeholders from the clinical and from the technology side has not been updated to Health 4.0 demands and the needed 21st century skills. This paper presents a novel proposal for a university and innovation lab based interdisciplinary Master education of HealthTEC innovation designers.
Investigation of vibration parameters for needle insertion force reduction
Authors: Rehling Dennis , Liu Jan , Stewart Kent W. , Pott Peter P. , Schiele Frank 
Published: February 2021

Abstract
Many medical interventions in therapy and diagnostics require needle insertion into tissue. Common complications such as increased pain and formation of haematoma are caused by wrong needle positioning. It has been shown that pain experience and needle positioning can be improved by a reduction of insertion force, which can be achieved by vibrating the needle axially. An experimental setup has been designed to investigate the influences of different combinations of vibration frequencies (10, 100, and 200 Hz) and vibration amplitudes (20, 100, and 500 μm) during needle insertion into thin sheets of polyethylene terephthalate (PET). A customary 20 W loudspeaker was used to generate the vibration. The results indicate a maximum reduction of 73 % in puncture force and up to a 100 % reduction in shaft friction force. However, the additional vibration force generated by the vibration movement has to be high enough to generate positive effects in terms of force reduction.
Exponential spline method for singularly perturbed third-order boundary value problems
Authors: Wakjira Yohannis Alemayehu , Duressa Gemechis File 
Published: February 2021

Abstract
The exponential spline function is presented to find the numerical solution of third-order singularly perturbed boundary value problems. Convergence analysis of the method is briefly discussed, and it is shown to be sixth order convergence. To validate the applicability of the method, some model problems are solved for different values of the perturbation parameter, and the numerical results are presented both in tables and graphs. Furthermore, the present method gives more accurate solution than some methods existing in the literature.
Effect of shielding gas combination on microstructure and mechanical properties of MIG welded stainless steel 316
Authors: Açar İsmail , Gülenç Behçet 
Published: February 2021

AbstractThe quality of welded joints depends on the most optimal welding parameters and the selection of shielding gas type. The shielding gas was selected for joining stainless steels through gas metal arc welding methods by considering properties such as chemical-metallurgical interaction of shielding gas and the molten weld metal during the welding process, heat transmission capability of the gas and cost. In this study, the effect of different shielding gas combinations on the mechanical and microstructural properties of 316 austenitic stainless steel joined by the metal inert gas (MIG) welding method was investigated. In the welding process, pure argon (100 % Ar), 98.5 % Ar + 1.5 % H2and 95 % Ar + 5 % H2were used as shielding gases. Tensile, hardness, and bending tests were conducted to determine mechanical properties of the welded samples. In addition, metallographic examinations were carried out to detect the macrostructural and microstructural properties of weld zones. According to the results obtained from the study, the highest tensile strength was obtained from the joints welded using 100 % Ar shielding gas. When the addition of H2into the Ar gas increased, the tensile strength of the welded samples decreased. As a result of the tensile test, fractures occurred in the base metal in all welded samples. In all welding parameters, the hardness of the weld metal was lower as compared to the heat affected zone (HAZ) and the base metal. As a result of the bending test, crack and tearing defects were found in the weld zone.
Italian preconsonantal s-voicing is not regressive voice assimilation
Authors: Huszthy Bálint 
Published: February 2021

AbstractIn the literature of laryngeal phonology Romance languages are considered voice languages, exhibiting a binary distinction between a voiced lenis and a voiceless fortis set of obstruents. Voice languages are characterised by regressive voice assimilation (RVA) due to the phonological activity of [voice]. Italian manifests a process similar to RVA, called preconsonantal s-voicing; that is, /s/ becomes voiced before voiced consonants. Since /sC/ is the only obstruent cluster in Italian phonotactics, Italian seems to fulfil the requirements for being a prototypical voice language. However, this paper argues that s-voicing is not an instance of RVA, at least from a synchronic phonological point of view. RVA and Italian preconsonantal s-voicing essentially differ at every level of a synchronic comparison: in the input, in the trigger, in the domain of application and in the frequency of the processes. In Italian only sibilant fricatives may undergo voicing before consonants; however, other obstruents (which mostly appear in loanwords) do not assimilate for [voice]. Italian preconsonantal s-voicing does not take place at the word boundary or at morpheme boundaries, and it seems to be optional is new loanwords; thus, it is not a postlexical process like RVA. The synchronic differences between the two phenomena are analysed in Classical Optimality Theory. The laryngeal system of Italian prefers faithfulness over markedness, which means that non-/sC/ obstruent clusters surface with underlying voice values; while the voicing of /s/ before voiced consonants is seen as phonetic and not phonological.
Classification and Recognition Model of Water Saturation Level of Rock Based on Near-Infrared Spectroscopy
Authors: Zhang Fang , Zhang Xiulian , Hu Chen , Li Yingjun , Wang Zhenwei , Tao Zhigang , He Manchao 
Published: February 2021

Abstract
In geotechnical engineering, the water saturation of rock cannot be obtained in real time and is lossless. To solve this problem, a continuous classification representation method of water saturation of rock is proposed herein, the classification and recognition theory of the water saturation level of rock is established, and near-infrared spectrum acquisition experiments of rock under different water saturation levels are carried out. Based on the near-infrared spectrum, the partial least square (PLS) method is used to establish the recognition model, and the model is applied to the real-time identification of the saturation level in the gravel water absorption process. The results are as follows: (1) In this paper, the method of hierarchical representation of water saturation of rock is proposed, which solves the limited extrapolation ability and extrapolation precision problems of the preparation accuracy of water saturation of the rock sample and the classification-learning algorithm and thus provides the completeness and feasibility for the identification of water saturation of rock. (2) The classification recognition theory and method based on near-infrared spectroscopy to set up the water saturation level of rock have better recognition precision and can realize the water saturation level of the rock in real time and nondestructively, and (3) when the PLS method is used to establish the recognition model, the appropriate threshold parameters are selected to eliminate the abnormal samples, and multiple spectral segments are used in the modeling, which can greatly improve the recognition accuracy of the model.
A review of the fishes and fisheries of Minas Basin and Minas Passage, Nova Scotia, and their potential risk from tidal power development
Authors: Dadswell Michael J. , Rulifson Roger A. 
Published: February 2021

A total of 85 species of fish are known or suspected from Minas Basin and Minas Passage, Nova Scotia, Canada. This systematic review details their seasonal occurrence, habitat, abundance, migratory behavior, fisheries and potential impact from tidal power development. The fish assemblage is a mixture of species common to the Bay of Fundy and the Atlantic coast of Nova Scotia as well as numerous warm- and cold-water visitors seldom found elsewhere in Canada. Minas Basin fisheries exploit some species especially those that migrate through the Basin during summer. Fishes were captured or observed using angling, seines, benthic long lines, drift and fixed gill nets, intertidal fish weirs, bottom trawls and sightings while on vessels. Fishes are categorized with respect to their taxonomic diversity, seasonal occurrence, status, fisheries and the potential impact from tidal lagoons and propeller turbines resulting from development of tidal power in Minas Basin and Minas Passage. Keywords: Bay of Fundy, habitat, species status, propeller turbines, taxonomic relationship, tidal lagoons.