bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advantages of the usage of the Infinity Computer for reducing the Zeno behavior in hybrid system models
Authors: Falcone Alberto , Garro Alfredo , Mukhametzhanov Marat S. , Sergeyev Yaroslav D. 
Published: December 2022

AbstractTo capture the dynamics of modern Cyber-Physical Systems, hybrid system models are introduced to combine their continuous dynamics with the discrete ones. Unfortunately, one important negative issue can affect hybrid system models: the so-called Zeno phenomenon, which results in an infinite number of discrete transitions in a finite amount of time occurring during the model’s simulation that leads to inconsistent results. In this context, the paper investigates the use of a recently proposed numerical algorithm, based on the Infinity Computer methodology, to handle the Zeno phenomenon and evaluate it with respect to standard numerical methods by considering the hybrid system models of two exemplary Cyber-Physical Systems: the Water tanks and the Thermostat.
Evaluation of adaptation measures to counteract rising groundwater levels in urban areas in response to climate change
Authors: Rasmussen Per , Kidmose Jacob , Kallesøe Anders J. , Sandersen Peter B. E. , Schneider Raphael , Sonnenborg Torben O. 
Published: December 2022

AbstractFor an increasing number of urban areas in Denmark and other countries with a temperate climate, large seasonal variations in precipitation, evaporation, and groundwater recharge cause problems with high groundwater levels during winter for private house owners, industry, public institutions, and infrastructure. Several factors contribute to the problem, e.g., an increase in winter precipitation, renovation of old leaky sewer pipes (previously acting as drain systems), and closure of groundwater abstraction for drinking water in urban areas in response to pollution. Four adaptation measures are compared with a detailed hydrological model for the town of Sunds, located in the western part of Denmark. Two ‘grey’, one ‘green’ and one ‘blue’ measure are evaluated. The grey solutions involve (1) installing drainage pipes (a third pipe) alongside the existing sewer pipes, and (2) lowering the water table by groundwater pumping from shallow wells, including storage of water in deeper aquifers for use in the drier summer; the green solution involves planting new forest in and around the town; and the blue solution is to establish a new ditch in the town. A climate model that projects more precipitation, especially in the winter, is used to evaluate the robustness of the different measures in a wetter climate for the northern European area. The hydrological modelling shows that the third pipe is the most effective climate-change adaptation of the four measures tested. The new ditch is an effective solution to lower the water table but with a more limited areal coverage.
The more the merrier? On the optimality of market size restrictions
Authors: von Negenborn Colin 
Published: December 2022

AbstractThis paper provides a novel rationale for the regulation of market size when heterogeneous firms compete. A regulator seeks to maximize total welfare by choosing the number of firms allowed to enter the market, e.g. by issuing a certain number of licenses. Opening up the market for more firms has a two-fold effect: it increases competition and thus welfare, but at the same time, it also attracts more cost-intensive firms, driving down average production efficiency. The regulator hence faces a trade-off between raising beneficial competition and detrimental costs. If goods are sufficiently substitutable, the latter effect can outweigh the former. It is then optimal to restrict the market size, rationalizing a limit to competition. This possibility result holds even in the absence of entry costs, search costs or increasing returns to scale, which previous literature required.
What European Union in the “Age of Uncertainty”? Weathering the Geopolitical Storms in a World of Perpetual Crises
Authors: Ioannides Isabelle 
Published: December 2022

AbstractA world of risks and uncertainties can also be a world of opportunities. How and how quickly the EU adapts to the socio-political, economic, energy and climate transitions at hand, and how it responds to geopolitical shifts and shocks, will determine whether it can sustain its role and standing on the international stage.
Fracking als Chance?
Authors: von Tschirnhaus Maximilian Klemens 
Published: December 2022

ZusammenfassungMit den am 11.2.2017 in Kraft getretenen wasserhaushaltsrechtlichen Regelungen, die das hydraulic
fracturing – Fracking – von unkonventionellen Lagerstätten verbieten, ist eine Einschränkung
der damit verbundenen technologischen Möglichkeiten vorgenommen worden. Die Fracking-Technologie könnte
einen Beitrag zur autonomen Energieversorgung leisten, insbesondere hinsichtlich der Strategie der Bundesregierung
hin zu einem “Reservemarkt” und einer Energiestrategie, die auf erneuerbare Energien und Gas
setzt. Die im zweiten Quartal des Jahres 2022 entstandene Energiekrise durch die Sanktionen gegen die Russische
Föderation und eine Abhängigkeit von russischem Erdgas und Erdöl, belastet die europäische,
und in besonderem Maße die deutsche Wirtschaft und Gesellschaft. Auf der Suche nach Lösungen
wird neben der Aktivierung von Kohlekraftwerken über die Nutzung des Frackings diskutiert. So spricht
sich Bundesfinanzminister Lindner für eine offene Debatte über die Nutzung des Frackings aus.
Dieser Beitrag befasst sich mit der Technologie des Frackings, der wasserhaushaltsrechtlichen Regelungen
ebendieser und unterzieht sie einer kritischen Würdigung der daraus resultierenden Folgen.
Benchmarking the performance of portfolio optimization with QAOA
Authors: Brandhofer Sebastian , Braun Daniel , Dehn Vanessa , Hellstern Gerhard , Hüls Matthias , Ji Yanjun , Polian Ilia , Bhatia Amandeep Singh , Wellens Thomas 
Published: December 2022

AbstractWe present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio optimization problem is formulated as quadratic binary optimization constrained on the number of assets contained in the portfolio. QAOA has been suggested as a possible candidate for solving this problem (and similar combinatorial optimization problems) more efficiently than classical computers in the case of a sufficiently large number of assets. However, the practical implementation of this algorithm requires a careful consideration of several technical issues, not all of which are discussed in the present literature. The present article intends to fill this gap and thereby provides the reader with a useful guide for applying QAOA to the portfolio optimization problem (and similar problems). In particular, we will discuss several possible choices of the variational form and of different classical algorithms for finding the corresponding optimized parameters. Viewing at the application of QAOA on error-prone NISQ hardware, we also analyse the influence of statistical sampling errors (due to a finite number of shots) and gate and readout errors (due to imperfect quantum hardware). Finally, we define a criterion for distinguishing between ‘easy’ and ‘hard’ instances of the portfolio optimization problem.
Hopf bifurcations in dynamics of excitable systems
Authors: De Angelis Monica 
Published: December 2022

AbstractA general FitzHugh–Rinzel model, able to describe several neuronal phenomena, is considered. Linear stability and Hopf bifurcations are investigated by means of the spectral equation for the ternary autonomous dynamical system and the analysis is driven by both an admissible critical point and a parameter which characterizes the system.
Deep limits of residual neural networks
Authors: Thorpe Matthew , van Gennip Yves 
Published: December 2022

AbstractNeural networks have been very successful in many applications; we often, however, lack a theoretical understanding of what the neural networks are actually learning. This problem emerges when trying to generalise to new data sets. The contribution of this paper is to show that, for the residual neural network model, the deep layer limit coincides with a parameter estimation problem for a nonlinear ordinary differential equation. In particular, whilst it is known that the residual neural network model is a discretisation of an ordinary differential equation, we show convergence in a variational sense. This implies that optimal parameters converge in the deep layer limit. This is a stronger statement than saying for a fixed parameter the residual neural network model converges (the latter does not in general imply the former). Our variational analysis provides a discrete-to-continuum $$\Gamma $$
Γ
-convergence result for the objective function of the residual neural network training step to a variational problem constrained by a system of ordinary differential equations; this rigorously connects the discrete setting to a continuum problem.
Modeling multiple latent information graph structures via graph convolutional network for aspect-based sentiment analysis
Authors: Wang Jiajun , Li Xiaoge , An Xiaochun 
Published: December 2022

AbstractAspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of aspects in a sentence. Recently, graph convolution network (GCN) model combined with attention mechanism has been used for ABSA task over graph structures, achieving promising results. However, these methods of modeling over graph structure fail to consider multiple latent information in the text, i.e., syntax, semantics, context, and so on. In addition, the attention mechanism is vulnerable to noise in sentences. To tackle these problems, in this paper, we construct an efficient text graph and propose a matrix fusion-based graph convolution network (MFLGCN) for ABSA. First, the graph structure is constructed by combining statistics, semantics, and part of speech. Then, we use the sequence model combined with the multi-head self-attention mechanism to obtain the feature representation of the context. Subsequently, the text graph structure and the feature representation of context are fed into GCN to aggregate the information around aspect nodes. The attention matrix is obtained by combining sequence model, GCN and the attention mechanism. Besides, we design a filter layer to alleviate the noise problem in the sentence introduced by the attention mechanism. Finally, in order to make the context representation more effective, attention and filtering matrices are integrated into the model. Experimental results on four public datasets show that our model is more effective than the previous models, demonstrating that using our text graph and matrix fusion can significantly empower ABSA models.
Privacy-preserving record linkage using autoencoders
Authors: Christen Victor , Häntschel Tim , Christen Peter , Rahm Erhard 
Published: December 2022

AbstractPrivacy-preserving record linkage (PPRL) is the process aimed at identifying records that represent the same real-world entity across different data sources while guaranteeing the privacy of sensitive information about these entities. A popular PPRL method is to encode sensitive plain-text data into Bloom filters (BFs), bit vectors that enable the efficient calculation of similarities between records that is required for PPRL. However, BF encoding cannot completely prevent the re-identification of plain-text values because sets of BFs can contain bit patterns that can be mapped to plain-text values using cryptanalysis attacks. Various hardening techniques have therefore been proposed that modify the bit patterns in BFs with the aim to prevent such attacks. However, it has been shown that even hardened BFs can still be vulnerable to attacks. To avoid any such attacks, we propose a novel encoding technique for PPRL based on autoencoders that transforms BFs into vectors of real numbers. To achieve a high comparison quality of the generated numerical vectors, we propose a method that guarantees the comparability of encodings generated by the different data owners. Experiments on real-world data sets show that our technique achieves high linkage quality and prevents known cryptanalysis attacks on BF encoding.
Antipsychotic drug—aripiprazole against schizophrenia, its therapeutic and metabolic effects associated with gene polymorphisms
Authors: Stelmach Adriana , Guzek Katarzyna , Rożnowska Alicja , Najbar Irena , Sadakierska-Chudy Anna 
Published: December 2022

AbstractSecond-generation antipsychotics are widely used for the treatment of schizophrenia. Aripiprazole (ARI) is classified as a third-generation antipsychotic drug with a high affinity for dopamine and serotonin receptors. It is considered a dopamine-system stabilizer without severe side effects. In some patients the response to ARI treatment is inadequate and they require an effective augmentation strategy. It has been found that the response to the drug and the risk of adverse metabolic effects can be related to gene polymorphisms. A reduced dose is recommended for CYP2D6 poor metabolizers; moreover, it is postulated that other polymorphisms includingCYP3A4,CYP3A5,ABCB1,DRD2,and5-HTRsgenes influence the therapeutic effect of ARI. ARI can increase the levels of prolactin, C-peptide, insulin, and/or cholesterol possibly due to specific genetic variants. It seems that a pharmacogenetic approach can help predict drug response and improve the clinical management of patients with schizophrenia.
Essential oils of some medicinal plants and their biological activities: a mini review
Authors: Mohamed Amal A. , Alotaibi Bader M. 
Published: December 2022

AbstractMedicinal herbs have an essential function in health and therapy. Bioactives of medicinal plants showed fewer side effects than manufactured drugs and their antioxidant properties are responsible for their different therapeutic properties. Essential oils (EOs) are an aromatic mixture of active components with a strong aroma obtained from aromatic plants. Natural EOs are composed of monoterpenes, sesquiterpenes, oxygenated monoterpenes, oxygenated sesquiterpenes, and phenolics. Because of their volatility, EOs are easily extracted from a variety of natural sources using the steam distillation method. Essential oils are one of the important therapeutic ingredients used in developing new therapeutic vehicles because of their anticancer, antiviral, antidiabetic, anticancer, antibacterial, antioxidant, and aromatherapy capabilities. This review includes different literature to survey the nature of essential oils, therapeutic potentials, and the key components of various essential oils.
Data Augmentation for Small Sample Iris Image Based on a Modified Sparrow Search Algorithm
Authors: Xiong Qi , Zhang Xinman , He Shaobo , Shen Jun 
Published: December 2022

AbstractTraining convolutional neural networks (CNN) often require a large amount of data. However, for some biometric data, such as fingerprints and iris, it is often difficult to obtain a large amount of data due to privacy issues. Therefore, training the CNN model often suffers from specific problems, such as overfitting, low accuracy, poor generalization ability, etc. To solve them, we propose a novel image augmentation algorithm for small sample iris image in this article. It is based on a modified sparrow search algorithm (SSA) called chaotic Pareto sparrow search algorithm (CPSSA), combined with contrast limited adaptive histogram equalization (CLAHE). The CPSSA is used to search for a group of clipping limit values. Then a set of iris images that satisfies the constraint condition is produced by CLAHE. In the fitness function, cosine similarity is used to ensure that the generated images are in the same class as the original one. We select 200 categories of iris images from the CASIA-Iris-Thousand dataset and test the proposed augmentation method on four CNN models. The experimental results show that, compared with the some standard image augmentation methods such as flipping, mirroring and clipping, the accuracy and Equal Error Rate (EER)of the proposed method have been significantly improved. The accuracy and EER of the CNN models with the best recognition performance can reach 95.5 and 0.6809 respectively. This fully shows that the data augmentation method proposed in this paper is effective and quite simple to implement.
Optimal scaling of MCMC beyond Metropolis
Authors: Agrawal Sanket , Vats Dootika , Łatuszyński Krzysztof , Roberts Gareth O. 
Published: December 2022

AbstractThe problem of optimally scaling the proposal distribution in a Markov chain Monte Carlo algorithm is critical to the quality of the generated samples. Much work has gone into obtaining such results for various Metropolis–Hastings (MH) algorithms. Recently, acceptance probabilities other than MH are being employed in problems with intractable target distributions. There are few resources available on tuning the Gaussian proposal distributions for this situation. We obtain optimal scaling results for a general class of acceptance functions, which includes Barker’s and lazy MH. In particular, optimal values for Barker’s algorithm are derived and found to be significantly different from that obtained for the MH algorithm. Our theoretical conclusions are supported by numerical simulations indicating that when the optimal proposal variance is unknown, tuning to the optimal acceptance probability remains an effective strategy.
A new automated national register-based surveillance system for outbreaks in long-term care facilities in Norway detected three times more severe acute respiratory coronavirus virus 2 (SARS-CoV-2) clusters than traditional methods
Authors: Gravningen Kirsten , Nymark Petter , Wyller Torgeir B. , Kacelnik Oliver 
Published: December 2022

AbstractObjective:To develop and test a new automated surveillance system that can detect, define and characterize infection clusters, including coronavirus disease 2019 (COVID-19), in long-term care facilities (LTCFs) in Norway by combining existing national register data.Background:The numerous outbreaks in LTCFs during the COVID-19 pandemic highlighted the need for accurate and timely outbreak surveillance. As traditional methods were inadequate, we used severe acute respiratory coronavirus virus 2 (SARS-CoV-2) as a model to test automated surveillance.Methods:We conducted a nationwide study using data from the Norwegian preparedness register (Beredt C19) and defined the study population as an open cohort from January 2020 to December 2021. We analyzed clusters (≥3 individuals with positive SARS-CoV-2 test ≤14 days) by 4-month periods including cluster size, duration and composition, and residents’ mortality associated with clusters.Results:The study population included 173,907 individuals; 78% employees and 22% residents. Clusters were detected in 427 (43%) of 993 LTCFs. The median cluster size was 4–8 individuals (maximum, 50) by 4-month periods, with a median duration of 9–17 days. Employees represented 60%–82% of cases in clusters and were index cases in 60%–90%. In the last 4-month period of 2020, we detected 107 clusters (915 cases) versus 428 clusters (2,998 cases) in the last period of 2021. The 14-day all-cause mortality rate was higher in resident cases from the clusters. Varying the cluster definitions changed the number of clusters.Conclusion:Automated national surveillance for SARS-CoV-2 clusters in LTCFs is possible based on existing data sources and provides near real-time detailed information on size, duration, and composition of clusters. Thus, this system can assist in early outbreak detection and improve surveillance.
Effect of mode of birth on development of mental disorders in the offspring
Authors: Lerche Anna Skovgaard , Christensen Rune Haubo , Köhler-Forsberg Ole , Nordentoft Merete , Petersen Liselotte Vogdrup , Mortensen Preben Bo , Benros Michael Eriksen 
Published: December 2022

AbstractObjective:Increasing rates of caesarean sections has led to concerns about long-term effects on the offspring’s health, and it has been hypothesised that caesarean section induced differences in the child’s microbiota could potentially increase the risk of mental disorders.Methods:Nationwide Danish cohort study of 2,196,687 births was conducted between 1980 and 2015, with 38.5 million observation-years. Exposure was ‘Caesarean Section’ and outcome was the child’s risk of any mental disorder. Absolute and relative risks (RRs) were estimated using inverse probability weighting to adjust for age, calendar time and confounding variables while accounting for the competing risk of death.Results:Caesarean section (n = 364,908, 16.6%), compared to vaginal birth, was associated with a small RR increase of 8% (RR, 1.08; 95% CI, 1.04–1.13; n = 44,352) for the development of any in-patient psychiatric admission at age 36 for the offspring and with a small absolute risk difference of 0.47% (95% CI, 0.23–0.76). When looking at all in-patient, out-patient and emergency room psychiatric contacts among people born after 1995, the effect was diminished (RR, 1.04; 95% CI, 0.99–1.09; n = 15,211). The risk was comparable when comparing prelabour versus intrapartum caesarean section (RR, 0.98; 95% CI, 0.90–1.08) and acute versus planned caesarean section (RR, 1.00; 95% CI, 0.80–1.29).Conclusion:Birth by caesarean section was associated with only a very slightly increased risk of any in-patient psychiatric admission for the offspring and diminished even further when including all psychiatric contacts. The very small associations observed may be explained by unmeasured confounding and is unlikely to be of substantial clinical relevance.
Mobilizing the Spiritual Resources of the Nation: The 1918 United War Work Campaign
Authors: Petit Jeanne 
Published: December 2022

On September 3, 1918, President Woodrow Wilson released a letter praising organizations that had provided services for soldiers, including the Young Men's Christian Association, the National Catholic War Council, and the Jewish Welfare Board. Through the work of these groups, the president declared, “the moral and spiritual resources of the nation have been mobilized.” Wilson then asked that, instead of the individual fundraising drives they were planning, the groups instead have single drive during the week of November 11, 1918, what he called a “united war work campaign.”The United War Work Campaign provides a window into understanding how a more pluralistic United States emerged through a process that involved competition and sacrifice as well as cooperation. The YMCA leadership fought to have a separate campaign, but they were forced into accommodating the changing religious dynamics of the nation. The Catholic and Jewish groups confronted Protestant establishment assumptions as they offered their own takes on how to appeal to a diverse American public. All the religious groups found themselves in situations where they had to downplay their faith commitments to ensure the campaign's success, and this led to tensions within the organizations and struggles with their co-religionists. Overall, the United War Work revealed the challenges of church-state collaboration and the compromises that were necessary for the emergence of a tri-faith nation.