bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of smoking on nasal mucociliary clearance time in Kano metropolis, Nigeria
Authors: Mahmud Ahmad , Salisu Abubakar D. , Kolo Emmanuel S. , Hasheem Muhammad G. , Bello‐Muhammad Nafisatu , Tukur Ahmad R. , Nuhu Yasir J. , Jalo Rabiu I. 
Published: June 2022

AbstractBackgroundMucociliary clearance is an important defense mechanism in human upper and lower respiratory airways. Impairment of this process by certain conditions such as cigarette smoking can predispose to chronic infection and neoplasm of the nose and paranasal sinuses.MethodsThis was a cross‐sectional study conducted in Kano metropolis, Nigeria. Eligible adults were enrolled, a saccharine test was conducted, and the nasal mucociliary clearance time was assessed. Analysis of the result was carried out using Statistical Product and Service Solutions version 23.0.ResultsThere were 225 participants categorized into 75 active smokers (33.3%), 74 passive smokers (32.9%), and 76 nonsmokers (33.8%, living in a smoking‐free zone). The age range of the participants was between 18 and 50 years, with a mean age of (31.2 ± 5.6) years. All participants were males. There were 139 (61.8%) of Hausa‐Fulani ethnic group, 24 (10.7%) Yoruba, 18 (8.0%) Igbo, and 44 (19.5%) other ethnic groups. Findings in this study showed that the average mucociliary clearance time among active smokers was prolonged ([15.25 ± 6.20] min) compared to passive ([11.41 ± 4.25] min) and nonsmokers ([9.17 ± 2.76] min) respectively, with a statistical significance (F = 33.59, P < 0.001). Binary logistic regression revealed that the number of cigarettes smoked per day was an independent predictor of prolonged mucociliary clearance time (P = 0.008, odds ratio = 0.44, 95% confidence interval = 0.24–0.80).ConclusionActive cigarette smoking is associated with prolonged nasal mucociliary clearance time. The number of cigarette sticks smoked per day was found to be an independent predictor of prolonged mucociliary clearance time.
Surgical wound classification in otolaryngology: A state‐of‐the‐art review
Authors: Bernstein Jeffrey D. , Bracken David J. , Abeles Shira R. , Orosco Ryan K. , Weissbrod Philip A. 
Published: June 2022

AbstractObjectiveTo describe the issues related to the assignment of surgical wound classification as it pertains to Otolaryngology—Head & Neck surgery, and to present a simple framework by which providers can assign wound classification.Data SourcesLiterature review.ConclusionSurgical wound classification in its current state is limited in its utility. It has recently been disregarded by major risk assessment models, likely due to inaccurate and inconsistent reporting by providers and operative staff. However, if data accuracy is improved, this metric may be useful to inform the risk of surgical site infection. In an era of quality‐driven care and reimbursement, surgical wound classification may become an equally important indicator of quality.
Ln3MBi5 (Ln=Pr, Nd, Sm; M=Zr, Hf): Intermetallics with Hypervalent Bismuth Chains
Authors: Khoury Jason F. , Song Xiaoyu , Schoop Leslie M. 
Published: June 2022

AbstractWe report five new isostructural compounds in the Ln3MBi5 (Ln=Pr, Nd, Sm; M=Zr, Hf) family, and compare them to the recently reported Sm3ZrBi5 analogue. Ln3MBi5 crystallizes in the P63/mcm space group, hosting the anti‐Hf5Sn3Cu structure type. The one‐dimensional structure consists of hypervalent Bi2− chains and face‐sharing MBi6 octahedra that form chains along the c axis. A framework of Ln3+ cations charge balances and separates the two motifs. The Bi−Bi and M−Bi bond lengths decrease as the Ln cation becomes smaller across the period, but there is almost no diﬀerence in either bond length when the identity of M changes, as expected because Zr and Hf have the exact same radii due to lanthanide contraction. We present a structural stability map showing the limits of cation stability in the structure, with La3+ and U3+ as the largest cations and Sm3+ as the smallest cation. X‐ray photoelectron spectra suggest ambiguous valence states in the Ln3MBi5 structure depending on the identity of the M atom. This work further expands the Ln3MBi5 family and sheds new light on how their bonding behavior may vary based on chemical composition.
Preparation of RGO film based BiVO4 (040) composites with photocatalytic properties
Authors: Qian Jiaqi , Cao Liwen , Hu Xiaolin , Li Yao , Wang Rong , Shen Mengru , Qu Jiangang 
Published: June 2022

AbstractGraphene oxide (GO) film was modified by silane coupling agent KH‐560, then bismuth vanadate (BiVO4) was successfully loaded on the modified GO film via hydrothermal method to obtain reduced graphene oxide (RGO) film based BiVO4 composite. The effects of hydrothermal reaction time on BiVO4 morphology and photocatalytic properties were investigated by scanning electron microscopy (SEM), X‐ray diffraction pattern (XRD), ultraviolet visible light absorption spectroscopy (UV‐vis) and photocatalytic activity. The results show that BiVO4 is a porous structure growing towards (040) crystal plane on the composite, and when the synthesis time is 6 h, the degradation rate for Reactive Black 5 by the composite can reach 95 % within 90 min. Furthermore, the composite prepared at the optimal time was characterized by transmission electron microscope (TEM), energy dispersive X‐ray spectroscopy (EDS) and X‐ray photoelectron spectroscopy (XPS). The microphysical process of the separation and combination of photogenerated electron hole pairs was successfully demonstrated by the variations of photoinduced current density with time (i‐t curve) and electrochemical impedance spectroscopies (EIS). Finally, the photocatalytic mechanism was proposed by free radical capture experiment.
Detecting and Learning the Unknown in Semantic Segmentation
Authors: Chan Robin , Uhlemeyer Svenja , Rottmann Matthias , Gottschalk Hanno 
Published: June 2022

AbstractSemantic segmentation is a crucial component for perception in automated driving. Deep neural networks (DNNs) are commonly used for this task, and they are usually trained on a closed set of object classes appearing in a closed operational domain. However, this is in contrast to the open world assumption in automated driving that DNNs are deployed to. Therefore, DNNs necessarily face data that they have never encountered previously, also known asanomalies, which are extremely safety-critical to properly cope with. In this chapter, we first give an overview about anomalies from an information-theoretic perspective. Next, we review research in detecting unknown objects in semantic segmentation. We present a method outperforming recent approaches by training for high entropy responses on anomalous objects, which is in line with our theoretical findings. Finally, we propose a method to assess the occurrence frequency of anomalies in order to select anomaly types to include into a model’s set of semantic categories. We demonstrate that those anomalies can then be learned in an unsupervised fashion which is particularly suitable in online applications.
Safety Assurance of Machine Learning for Perception Functions
Authors: Burton Simon , Hellert Christian , Hüger Fabian , Mock Michael , Rohatschek Andreas 
Published: June 2022

AbstractThe latest generation of safety standards applicable to automated driving systems require both qualitative and quantitative safety acceptance criteria to be defined and argued. At the same time, the use of machine learning (ML) functions is increasingly seen as a prerequisite to achieving the necessary levels of perception performance in the complex operating environments of these functions. This inevitably leads to the question of which supporting evidence must be presented to demonstrate the safety of ML-based automated driving systems. This chapter discusses the challenge of deriving suitable acceptance criteria for the ML function and describes how such evidence can be structured in order to support a convincing safety assurance case for the system. In particular, we show how a combination of methods can be used to estimate the overall machine learning performance, as well as to evaluate and reduce the impact of ML-specific insufficiencies, both during design and operation.
The Good and the Bad: Using Neuron Coverage as a DNN Validation Technique
Authors: Gannamaneni Sujan Sai , Akila Maram , Heinzemann Christian , Woehrle Matthias 
Published: June 2022

AbstractVerification and validation (V&V) is a crucial step for the certification and deployment of deep neural networks (DNNs). Neuron coverage, inspired by code coverage in software testing, has been proposed as one such V&V method. We provide a summary of different neuron coverage variants and their inspiration from traditional software engineering V&V methods. Our first experiment shows that novelty and granularity are important considerations when assessing a coverage metric. Building on these observations, we provide an illustrative example for studying the advantages of pairwise coverage over simple neuron coverage. Finally, we show that there is an upper bound of realizable neuron coverage when test data are sampled from inside the operational design domain (in-ODD) instead of the entire input space.
Joint Optimization for DNN Model Compression and Corruption Robustness
Authors: Varghese Serin , Hümmer Christoph , Bär Andreas , Hüger Fabian , Fingscheidt Tim 
Published: June 2022

AbstractModern deep neural networks (DNNs) are achieving state-of-the-art results due to their capability to learn a faithful representation of the data they are trained on. In this chapter, we address two insufficiencies of DNNs, namely, the lack of robustness to corruptions in the data, and the lack of real-time deployment capabilities, that need to be addressed to enable their safe and efficient deployment in real-time environments. We introduce hybrid corruption-robustness focused compression (HCRC), an approach that jointly optimizes a neural network for achieving network compression along with improvement in corruption robustness, such as noise and blurring artifacts that are commonly observed. For this study, we primarily consider the task of semantic segmentation for automated driving and focus on the interactions between robustness and compression of the network. HCRC improves the robustness of the network by 8.39% absolute mean performance under corruption (mPC) on the Cityscapes dataset, and by 2.93% absolute mPC on the Sim KI-A dataset, while generalizing even to augmentations not seen by the network in the training process. This is achieved with only minor degradations on undisturbed data. Our approach is evaluated over two strong compression ratios (30% and 50%) and consistently outperforms all considered baseline approaches. Additionally, we perform extensive ablation studies to further leverage and extend existing state-of-the-art methods.
Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?
Authors: Gottschalk Hanno , Rottmann Matthias , Saltagic Maida 
Published: June 2022

AbstractWhile automated driving is often advertised with better-than-human driving performance, this chapter reviews that it is nearly impossible to provide direct statistical evidence on the system level that this is actually the case. The amount of labeled data needed would exceed dimensions of present-day technical and economical capabilities. A commonly used strategy therefore is the use of redundancy along with the proof of sufficient subsystems’ performances. As it is known, this strategy is efficient especially for the case of subsystems operating independently, i.e., the occurrence of errors is independent in a statistical sense. Here, we give some first considerations and experimental evidence that this strategy is not a free ride as the errors of neural networks fulfilling the same computer vision task, at least in some cases, show correlated occurrences of errors. This remains true, if training data, architecture, and training are kept separate or independence is trained using special loss functions. Using data from different sensors (realized by up to five 2D projections of the 3D MNIST dataset) in our experiments is more efficiently reducing correlations, however not to an extent that is realizing the potential of reduction of testing data that can be obtained for redundant and statistically independent subsystems.
Confidence Calibration for Object Detection and Segmentation
Authors: Küppers Fabian , Haselhoff Anselm , Kronenberger Jan , Schneider Jonas 
Published: June 2022

AbstractCalibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for object detection and segmentation models in this chapter. We introduce the concept of multivariate confidence calibration that is an extension of well-known calibration methods to the task of object detection and segmentation. This allows for an extended confidence calibration that is also aware of additional features such as bounding box/pixel position and shape information. Furthermore, we extend the expected calibration error (ECE) to measure miscalibration of object detection and segmentation models. We examine several network architectures on MS COCO as well as on Cityscapes and show that especially object detection as well as instance segmentation models are intrinsically miscalibrated given the introduced definition of calibration. Using our proposed calibration methods, we have been able to improve calibration so that it also has a positive impact on the quality of segmentation masks as well.
Uncertainty Quantification for Object Detection: Output- and Gradient-Based Approaches
Authors: Riedlinger Tobias , Schubert Marius , Kahl Karsten , Rottmann Matthias 
Published: June 2022

AbstractSafety-critical applications of deep neural networks require reliable confidence estimation methods with high predictive power. However, evaluating and comparing different methods for uncertainty quantification is oftentimes highly context-dependent. In this chapter, we introduce flexible evaluation protocols which are applicable to a wide range of tasks with an emphasis on object detection. In this light, we investigate uncertainty metrics based on the network output, as well as metrics based on a learning gradient, both of which significantly outperform the confidence score of the network. While output-based uncertainty is produced by post-processing steps and is computationally efficient, gradient-based uncertainty, in principle, allows for localization of uncertainty within the network architecture. We show that both sources of uncertainty are mutually non-redundant and can be combined beneficially. Furthermore, we show direct applications of uncertainty quantification by improving detection accuracy.
Chapter 5 Using Bluetooth for contact tracing
Authors: Elmokashfi Ahmed , Kvalbein Amund 
Published: June 2022

AbstractBluetooth data is used as the main method for contact tracing with Smittestopp. When two active devices are within Bluetooth range, they will record the ID of the paired device, along with information about the received signal quality. In this chapter, we describe how this method is implemented in Smittestopp, and how Bluetooth data is processed and analysed, to determine if an encounter between two users should be considered a qualified contact with a risk of contamination.We show that distance estimation based on Bluetooth signals is challenging due to differences between devices, lack of information on transmit power and varying environmental factors. Based on this experience, we propose a simple rule for identifying contacts based on received signal strength combined with information about the operating system type.
Pseudo-Goldstone dark matter model with CP violation
Authors: Darvishi Neda , Grzadkowski Bohdan 
Published: June 2022

Abstract
We consider an explicitly CP-violating model with two Higgs doublets and one complex singlet scalar. The singlet S is charged under a global U(1) symmetry which is softly broken by a mass term μ2S2 + H.c.. Imaginary part of S is a stable dark matter candidate which at the tree level, in the limit of zero momentum transfer, decouples from nucleons naturally satisfying all existing direct detection limits on dark matter scattering cross-section. It is explicitly shown that within this framework in the alignment limit CP violation is still present in contrast to a popular version of a 2-Higgs doublet model with softly broken Z2 symmetry. In this context, we investigate dark matter implications of the model both with and without CP violation in the scalar sector. In particular, dark matter relic abundance is calculated and the possibility for its indirect detection is discussed.
Optimal Limit Order Book Trading Strategies with Stochastic Volatility in the Underlying Asset
Authors: Aydoğan Burcu , Uğur Ömür , Aksoy Ümit 
Published: June 2022

AbstractIn quantitative finance, there have been numerous new aspects and developments related with the stochastic control and optimization problems which handle the controlled variables of performing the behavior of a dynamical system to achieve certain objectives. In this paper, we address the optimal trading strategies via price impact models using Heston stochastic volatility framework including jump processes either in price or in volatility of the price dynamics with the aim of maximizing expected return of the trader by controlling the inventories. Two types of utility functions are considered: quadratic and exponential. In both cases, the remaining inventories of the market maker are charged with a liquidation cost. In order to achieve the optimal quotes, we control the inventory risk and follow the influence of each parameter in the model to the best bid and ask prices. We show that the risk metrics including profit and loss distribution (PnL), standard deviation and Sharpe ratio play important roles for the trader to make decisions on the strategies. We apply finite differences and linear interpolation as well as extrapolation techniques to obtain a solution of the nonlinear Hamilton-Jacobi-Bellman (HJB) equation. Moreover, we consider different cases on the modeling to carry out the numerical simulations.
Initial High-Temperature Oxidation Behavior of Fe–Mn Binaries in Air: The Kinetics and Mechanism of Oxidation
Authors: Aghaeian S. , Sloof W. G. , Mol J. M. C. , Böttger A. J. 
Published: June 2022

AbstractHigh-temperature oxidation of steels can be relatively fast when exposed to air. Consequently, elucidating the effect of different parameters on the oxidation mechanism and kinetics is challenging. In this study, short-time oxidation was investigated to determine the oxidation mechanism, the affecting parameters, and the linear-to-parabolic growth transition of different Fe–Mn alloys in various oxygen partial pressures (10–30 kPa) and gas flow rates (26.6 and 53.3 sccm) in a temperature range of 950–1150 °C. Oxidation kinetics was investigated using a thermogravimetric analyzer (TGA) under controlled atmosphere. Linear oxide growth was observed within the first 20 minutes of oxidation. The linear rate constant was significantly increased by increasing the oxygen partial pressure or the flow rate of the oxidizing gas. The morphology of the oxide layer was determined by scanning electron microscopy (SEM). The crystal structure of the oxides formed was followed by in-situ X-ray diffraction (XRD), confirming that the growing layer consists of wustite mainly, which upon slow cooling to room temperature, transformed into magnetite. Energy-dispersive X-ray spectroscopy (EDS) showed that the atomic ratio of Fe+Mn to O was ~ 1.03:1 in the oxide scale, corresponding to Fe(Mn)O formation. Based on the characterization and a model for linear growth kinetics, it is concluded that the oxidation rate is controlled by the diffusion of oxidizing molecules through the gas layer to the sample’s surface. The findings led to a better understanding of initial oxidation behavior and provided a pathway for improved insight into the high-temperature oxidation behavior for more complex alloys.
Number of books at home as an indicator of socioeconomic status: Examining its extensions and their incremental validity for academic achievement
Authors: Heppt Birgit , Olczyk Melanie , Volodina Anna 
Published: June 2022

AbstractThe present study investigates the incremental validity of the traditional books-at-home measure and selected extensions (i.e., number of children’s books and number of ebooks) for explaining students’ academic achievement as measured by their academic language comprehension. Using multiple linear regressions, we additionally explore the role of the source of information (i.e., whether information is given by parents or children). Based on cross-sectional data of a German sample of 2353 elementary school children from Grades 2 through 4, we found that parents’ information on the number of books and children’s books contributed to students’ academic language comprehension over and above parental occupation and education. Children’s information on the number of books did not further increase the amount of explained variance, and the effects were smaller than those for parents’ information. Yet, when investigated separately, both parents’ and children’s information on the number of books and children’s books at home predicted students’ academic language comprehension and mediated the relationship between more distal structural features of socioeconomic status (i.e., parents’ occupational status and education) and the outcome variable. No effect emerged for the number of ebooks. Our findings point to the robustness of the traditional books-at-home measure when used in parent questionnaires.
Faces in disguise. Masks, concealment, and deceit
Authors: Gramigna Remo 
Published: June 2022

AbstractThe present study investigates and thematizes the interrelation between face masking, concealment, and deceit. It starts from the premise that the significance of disguise and deceit in the history of ideas should be reversed as these methods of the management of human appearance are not only regarded as coercive methods to manipulate and exert power over others but also as tactics skillfully used by the weak in order to outmaneuver those who are in a position of power. The study traces the matrix of simulation and dissimulation as forming the structure of deceit, it reviews some of the main theories of disguise within the field of semiotics, and it singles out two main dimensions of disguise, one geared upon dynamism and the other based on the static features of the face. This study suggests that classifications of masks elaborated in semiotic theory hitherto are useful but insufficient to encompass the full scope of such phenomenon. For this reason, the study provides a new typology of masks.