bluuebunny/crossref_metadata_2025_split huggingface dataset:
Die Krise bekämpfen, das Wirtschaftsmodell neu justieren
Authors: Grimm Veronika , von Rüden Christina 
Published: February 2023

AbstractTackling the crisis, readjusting the economic model: Rising energy prices, historically high inflation rates and ongoing supply chain bottlenecks are challenging Germany’s long-standing industrial base. On top of that, entrepreneurs and the state alike are faced with new geopolitical tensions forcing them to re-evaluate existing (trade) relationships. In order to strengthen Germany’s attractiveness as an industrial location in the long term and to emerge from this crisis more resilient to economic and geopolitical changes, its economic model needs to be readjusted. In this article, we thus discuss key areas of action, including how to secure energy supply, how to diversify international relationships, as well as how to provide sustainable and targeted support to firms that does not prevent structural change.
Umweltfreundliche Öffentliche Beschaffung fördern mit People-Public-Private-Partnerships
Authors: Ohlig Sebastian , Schmidt Linus , Lichter Chiara , Harting Moritz , Herrmann Lena , Krieger Bastian 
Published: February 2023

ZusammenfassungEine Umweltfreundliche Öffentliche Beschaffung kann die Anreize von Unternehmen erhöhen in die Entwicklung und Adaption von umweltfreundlichen Produkten und Dienstleistungen zu investieren. People-Public-Private-Partnerships integrieren die engagierte Bevölkerung und ihre Expertise als Endnutzende zur Unterstützung der Beschaffenden in den Beschaffungsprozess. Insbesondere aufgrund des zunehmenden öffentlichen Interesses an umweltrelevanten Themen ist das Potenzial für die Einführung von People-Public-Private-Partnerships innerhalb der Umweltfreundlichen Öffentlichen Beschaffung hoch. Dieser Lösungsansatz wurde von Schüler:innen des St. Raphael Gymnasiums in Heidelberg für den Schülerwettbewerb YES! Young Economic Summit analysiert und weiterentwickelt.
Assessing genetic diversity patterns at neutral and adaptive loci to inform population reinforcement of an endangered migratory vulture
Authors: Bounas Anastasios , Saravia-Mullin Victoria , Méndez Maria , Arkumarev Volen , Aghajanyan Lusine , Ararat Korsh , Buechley Evan , Dobrev Vladimir , Dobrev Dobromir , Efrat Ron , Klisurov Ivaylo , Kret Elzbieta , Skartsi Theodora , Oppel Steffen , Petrov Rusko , Şekercioğlu Çağan H. , Vaidl Anton , Donázar José A. , Nikolov Stoyan C. , Sotiropoulos Konstantinos 
Published: February 2023

AbstractOne of the primary goals of conservation translocation programs should be the maintenance of both population demographic stability and genetic diversity. Here, we provide genetic management recommendations to inform a population reinforcement of the declining Egyptian Vulture population in the Balkans. Specifically, we examined whether the number of released individuals is sufficient to prevent genetic diversity loss due to random genetic drift and what the origin of the individuals should be that comprise the captive breeding pool. To this aim, we estimated and assessed genetic diversity levels and genetic structure of Egyptian Vulture populations across much of the species’ range using both neutral and non-neutral candidate loci involved in migration. We then evaluated the effects of the currently proposed population management scheme and candidate source populations on retaining allelic diversity. Our results show low differentiation values among populations and absence of genetic structure which point to past high gene flow. Furthermore, there was no predicted significant impact of different source populations on the genetic diversity of the recipient Balkan population. We also found that the declining Egyptian Vulture population in the Balkans still retains high levels of genetic diversity and therefore genetic diversity restoration is not currently needed. However, without any management, diversity is likely to decrease fast because of increased genetic drift as the population size continues to decline. Population reinforcement with nine birds per year for 20 years would provide sufficient demographic support for the population to retain > 85% of rare allelic diversity. Birds originating from the Balkans would ensure ecological and behavioral similarity and thus would be the best option for reinforcement. Nevertheless, our results demonstrate that to prevent further population contraction and loss of adaptive alleles, releasing individuals of different origin would also be appropriate.
Reverse channel selection for commercial product returns under time-to-market and product value considerations
Authors: Hwang Sung Ook , Üster Halit , Savaskan-Ebert R. Canan 
Published: February 2023

AbstractThe advent of mobile channels have changed retail business models, the choice of retail mix, and shopper behavior. As consumers do not differentiate among the channels where they try, purchase and/or take delivery of their product, they also expect maximum flexibility in the product returns process. On average, retailers forecasted returns to reach about 16.6% of the total merchandise that customers purchased in 2021, according to the National Retail Federation, which is an increase from an average return rate of 10.6% in 2020. The resulting cost of returns amounted to $761 billion worth of merchandise in 2021 (Repko in A more than $761 billion dilemma: retailers’ returns jump as online sales grow. https://www.cnbc.com/2022/01/25/retailers-average-return-rate-jumps-to-16point6percent-as-online-sales-grow-.html. Accessed 17 June 2022, 2022). For retailers and manufacturers, integration of different reverse channels is extremely important to deliver the seamless experience demanded by today’s discerning consumer while ensuring the profitable handling of the returned products as well as ensuring the environmental sustainability of the retailing operations. Regardless of which channel receives a return, the reverse logistics network should have the flexibility and the capability to remarket or to recover the value in the returned product in a cost efficient and timely manner that maximizes firm profitability. To the best of our knowledge, this paper is one of the first studies that develops a linear programming model with profit maximization objective to help determine how to optimally decide the returned product touch point(s) in the reverse logistics network. Unlike the extant literature, our model explicitly incorporates the marginal value of time for returns, product characteristics as well as the underling reverse logistics network configuration in return channel selection strategy. We present a comprehensive analysis on how and to what extent the return channel selection is dependent on the product characteristics such as time-based value decay rate, defective rates, and disposal rates as well as the network structure. Using data from HP and Bosch Power tools operations as well as real geographical US data, we show that our decision model can effectively help determine the reverse logistics network and the type of facility where a product is returned as a function of product characteristics and economic parameters. Our work emphasizes that product returns and waste reduction, improved firm sustainability and profitability can co-exist through effective reverse logistics planning.
Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging
Authors: Sun Xiaowu , Cheng Li-Hsin , Plein Sven , Garg Pankaj , Moghari Mehdi H. , van der Geest Rob J. 
Published: February 2023

AbstractPurpose: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow. Methods: A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. The method was evaluated using velocity vector end-point error (EPE) and angle error. Additionally, the E/A ratio and diastolic function classification derived from the predicted velocities were compared to those derived from 4D flow. Results: For intra-cardiac pixels with a velocity > 5 cm/s, our method achieved an EPE of 8.65 cm/s and angle error of 41.27°. For pixels with a velocity > 25 cm/s, the angle error significantly degraded to 19.26°. Although the averaged blood flow velocity prediction was under-estimated by 26.69%, the high correlation (PCC = 0.95) of global time-varying velocity and the visual evaluation demonstrate a good agreement between our prediction and 4D flow data. The E/A ratio was derived with minimal bias, but with considerable mean absolute error of 0.39 and wide limits of agreement. The diastolic function classification showed a high accuracy of 86.9%. Conclusion: Using a deep learning-based algorithm, intra-cardiac blood flow velocities can be predicted from long-axis cine MRI with high correlation with 4D flow derived velocities. Visualization of the derived velocities provides adjunct functional information and may potentially be used to derive the E/A ratio from conventional CMR exams.
Dormancy, stemness, and therapy resistance: interconnected players in cancer evolution
Authors: Francescangeli Federica , De Angelis Maria Laura , Rossi Rachele , Cuccu Adriano , Giuliani Alessandro , De Maria Ruggero , Zeuner Ann 
Published: February 2023

AbstractThe biological complexity of cancer represents a tremendous clinical challenge, resulting in the frequent failure of current treatment protocols. In the rapidly evolving scenario of a growing tumor, anticancer treatments impose a drastic perturbation not only to cancer cells but also to the tumor microenvironment, killing a portion of the cells and inducing a massive stress response in the survivors. Consequently, treatments can act as a double-edged sword by inducing a temporary response while laying the ground for therapy resistance and subsequent disease progression. Cancer cell dormancy (or quiescence) is a central theme in tumor evolution, being tightly linked to the tumor’s ability to survive cytotoxic challenges, metastasize, and resist immune-mediated attack. Accordingly, quiescent cancer cells (QCCs) have been detected in virtually all the stages of tumor development. In recent years, an increasing number of studies have focused on the characterization of quiescent/therapy resistant cancer cells, unveiling QCCs core transcriptional programs, metabolic plasticity, and mechanisms of immune escape. At the same time, our partial understanding of tumor quiescence reflects the difficulty to identify stable QCCs biomarkers/therapeutic targets and to control cancer dormancy in clinical settings. This review focuses on recent discoveries in the interrelated fields of dormancy, stemness, and therapy resistance, discussing experimental evidences in the frame of a nonlinear dynamics approach, and exploring the possibility that tumor quiescence may represent not only a peril but also a potential therapeutic resource.
Explaining deep convolutional models by measuring the influence of interpretable features in image classification
Authors: Ventura Francesco , Greco Salvatore , Apiletti Daniele , Cerquitelli Tania 
Published: February 2023

AbstractThe accuracy and flexibility of Deep Convolutional Neural Networks (DCNNs) have been highly validated over the past years. However, their intrinsic opaqueness is still affecting their reliability and limiting their application in critical production systems, where the black-box behavior is difficult to be accepted. This work proposes EBAnO, an innovative explanation framework able to analyze the decision-making process of DCNNs in image classification by providing prediction-local and class-based model-wise explanations through the unsupervised mining of knowledge contained in multiple convolutional layers. EBAnO provides detailed visual and numerical explanations thanks to two specific indexes that measure the features’ influence and their influence precision in the decision-making process. The framework has been experimentally evaluated, both quantitatively and qualitatively, by (i) analyzing its explanations with four state-of-the-art DCNN architectures, (ii) comparing its results with three state-of-the-art explanation strategies and (iii) assessing its effectiveness and easiness of understanding through human judgment, by means of an online survey. EBAnO has been released as open-source code and it is freely available online.
222Rn and 226Ra concentrations in selected shallow circulation groundwaters from the Fore-Sudetic Monocline area
Authors: Maciejewski Piotr , Kowalska Agata 
Published: February 2023

AbstractNatural radioactive isotopes occur in various components of the natural environment, including groundwater. The general population, not always aware of possible threats, can use its resources. The activity concentration of some of the radioactive isotopes should be monitored, especially in those intakes from which it is possible to obtain water for human consumption, e.g. in domestic wells. The conducted research was innovative due to the fact that in many countries, including Poland, there are no regulations on waters exploited from home wells using as a drinking water source. As the groundwaters from this area have not been examined for radon (222Rn) and radium (226Ra) occurrence yet, the goal of this research was to perform screening tests in this part of the Fore-Sudetic Monocline. For this purpose, the authors have measured the concentration of 222Rn and 226Ra activity in groundwater collected from this geological unit located in south-western Poland. 222Rn and 226Ra occurrence was researched, and specific electrolytic conductivity, redox potential, pH and temperature were measured in 52 groundwater samples. 222Rn activity concentration ranged from 0.18 to 19.78 Bq/dm3. Only in three cases, 226Ra activity concentration reached a value above the lower detection limit of the applied method, i.e. 0.05 Bq/dm3 (max. 0.77 Bq/dm3). The authors present completely new data on the occurrence of these radioactive isotopes in the waters of the first aquifer in the Fore-Sudetic Monocline, which is not without significance for the health of consumers of these waters.
Who is controlling whom? Reframing “meaningful human control” of AI systems in security
Authors: Christen Markus , Burri Thomas , Kandul Serhiy , Vörös Pascal 
Published: February 2023

AbstractDecisions in security contexts, including armed conflict, law enforcement, and disaster relief, often need to be taken under circumstances of limited information, stress, and time pressure. Since AI systems are capable of providing a certain amount of relief in such contexts, such systems will become increasingly important, be it as decision-support or decision-making systems. However, given that human life may be at stake in such situations, moral responsibility for such decisions should remain with humans. Hence the idea of “meaningful human control” of intelligent systems. In this opinion paper, we outline generic configurations of control of AI and we present an alternative to human control of AI, namely the inverse idea of having AI control humans, and we discuss the normative consequences of this alternative.
The Markovian Shot-noise Risk Model: A Numerical Method for Gerber-Shiu Functions
Authors: Pojer Simon , Thonhauser Stefan 
Published: February 2023

AbstractIn this paper, we consider discounted penalty functions, also called Gerber-Shiu functions, in a Markovian shot-noise environment. At first, we exploit the underlying structure of piecewise-deterministic Markov processes (PDMPs) to show that these penalty functions solve certain partial integro-differential equations (PIDEs). Since these equations cannot be solved exactly, we develop a numerical scheme that allows us to determine an approximation of such functions. These numerical solutions can be identified with penalty functions of continuous-time Markov chains with finite state space. Finally, we show the convergence of the corresponding generators over suitable sets of functions to prove that these Markov chains converge weakly against the original PDMP. That gives us that the numerical approximations converge to the discounted penalty functions of the original Markovian shot-noise environment.
Stability analysis of chaotic systems from data
Authors: Margazoglou Georgios , Magri Luca 
Published: February 2023

AbstractThe prediction of the temporal dynamics of chaotic systems is challenging because infinitesimal perturbations grow exponentially. The analysis of the dynamics of infinitesimal perturbations is the subject of stability analysis. In stability analysis, we linearize the equations of the dynamical system around a reference point and compute the properties of the tangent space (i.e. the Jacobian). The main goal of this paper is to propose a method that infers the Jacobian, thus, the stability properties, from observables (data). First, we propose the echo state network (ESN) with the Recycle validation as a tool to accurately infer the chaotic dynamics from data. Second, we mathematically derive the Jacobian of the echo state network, which provides the evolution of infinitesimal perturbations. Third, we analyse the stability properties of the Jacobian inferred from the ESN and compare them with the benchmark results obtained by linearizing the equations. The ESN correctly infers the nonlinear solution and its tangent space with negligible numerical errors. In detail, we compute from data only (i) the long-term statistics of the chaotic state; (ii) the covariant Lyapunov vectors; (iii) the Lyapunov spectrum; (iv) the finite-time Lyapunov exponents; (v) and the angles between the stable, neutral, and unstable splittings of the tangent space (the degree of hyperbolicity of the attractor). This work opens up new opportunities for the computation of stability properties of nonlinear systems from data, instead of equations.
Content validation of the SF-36v2® Health Survey Acute for use in hypoparathyroidism
Authors: Brod Meryl , Waldman Laura Tesler , Shu Aimee D. , Smith Alden 
Published: February 2023

Abstract
Purpose
The purpose of this study was to conduct cognitive debriefing (CD) interviews with adults diagnosed with chronic hypoparathyroidism (HP) to assess the content validity of the SF-36v2® Health Survey Acute (SF-36v2) measure in this population.

Methods
CD interviews were conducted with adults with HP in the United States (US). Interviews were conducted by a trained moderator using a semi-structured interview guide, employing a think-aloud method in conjunction with verbal probing. Participants were asked whether each item was understandable, relevant, important, and sensitive to change in relation to HP. Additionally, comprehension of instructions, response options, and the appropriateness of a 1-week recall period was assessed.

Results
Sixteen adults with HP participated in individual CD telephone interviews. All items in the SF-36v2 were reported to be understood, relevant, important, and sensitive to change by at least half, and in most cases, by a strong majority of study participants. Most of the study sample confirmed comprehension of the instructions and the entire sample understood all response options.

Conclusion
The study findings show that the items in the SF-36v2® are applicable to adults with HP. The overall high levels of endorsement of items provide strong evidence of the measure’s content validity for this population. The SF-36v2 is therefore recommended for usage in clinical trials examining adults with HP, although it is recommended that this generic measure be supplemented with disease-specific instruments such as the recently developed Hypoparathyroidism Patient Experience Scale—Symptom (HPES-Symptom) and Hypoparathyroidism Patient Experience Scale—Impact (HPES-Impact) measures.
Temporality in Co-generative Processes: Reframing Time in Territorial Complexity
Authors: Karlsen James , Hildebrandt Clare 
Published: February 2023

AbstractAction research provides fertile grounds for co-generation of knowledge in complex contexts and to be present in the becoming of the process. Time and temporality warrant explanation and distinction, and in retrospective a process can be described with temporal phases, such as planning, observation, action, reflection, evaluation, and modification. Such a description may appear rational, sequential, and linear. However, an action research process is not that! This paper explores the various positionalities of the action researcher, as an insider in a process of becoming, showing how time and temporality can be made explicit in the evolution of an action research process. Our contributions to the action research literature concerning co-generation of knowledge when addressing territorial complexity are: (1) an explicit awareness of temporality provides the opportunity for research on evolvement of processes from the inside, (2) presence in the becoming of a process means there is a unique possibility for reflection and iteration, (3) research in the present tense allows for insight into unexpected developments that create the foundation for future action, as an alternative to retrospective process evaluation, and (4) modelling the process creates a narrative which tells the story of evolution of the process over time.
On predictive inference for intractable models via approximate Bayesian computation
Authors: Järvenpää Marko , Corander Jukka 
Published: February 2023

AbstractApproximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based statistical models whose likelihood function cannot be evaluated. In this paper we instead investigate the feasibility of ABC as a generic approximate method for predictive inference, in particular, for computing the posterior predictive distribution of future observations or missing data of interest. We consider three complementary ABC approaches for this goal, each based on different assumptions regarding which predictive density of the intractable model can be sampled from. The case where only simulation from the joint density of the observed and future data given the model parameters can be used for inference is given particular attention and it is shown that the ideal summary statistic in this setting is minimal predictive sufficient instead of merely minimal sufficient (in the ordinary sense). An ABC prediction approach that takes advantage of a certain latent variable representation is also investigated. We additionally show how common ABC sampling algorithms can be used in the predictive settings considered. Our main results are first illustrated by using simple time-series models that facilitate analytical treatment, and later by using two common intractable dynamic models.
CamDec: Advancing Axis P1435-LE video camera security using honeypot-based deception
Authors: Sikos Leslie F. , Valli Craig , Grojek Alexander E. , Holmes David J. , Wakeling Samuel G. , Cabral Warren Z. , Karie Nickson M. 
Published: February 2023

AbstractThe explosion of online video streaming in recent years resulted in advanced services both in terms of efficiency and convenience. However, Internet-connected video cameras are prone to exploitation, leading to information security issues and data privacy concerns. The proliferation of video-capable Internet of Things devices and cloud-managed surveillance systems further extend these security issues and concerns. In this paper, a novel approach is proposed for video camera deception via honeypots, offering increased security measures compared to what is available on conventional Internet-enabled video cameras.