bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preserving the rule of law in the era of artificial intelligence (AI)
Authors: Greenstein Stanley 
Published: July 2021

AbstractThe study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society.
Development of parametric seismic fragility curves for historical churches
Authors: Marotta Alessandra , Liberatore Domenico , Sorrentino Luigi 
Published: July 2021

AbstractFor both spiritual and cultural reasons, churches are an essential part of the historical heritage of several countries worldwide, including Europe, Americas and Australasia. The extreme damage that occurred during the 2016–2017 Central Italy seismic swarm highlighted once again the noteworthy seismic vulnerability of unreinforced masonry churches, which exhibited several collapses and caused uncountable losses to the Italian artistic heritage. The seismic performance of 158 affected buildings was analyzed in the aftermath of the main shocks. The failure modes activated by the earthquakes were identified making reference to the local mechanisms currently considered in Italy for post-seismic assessment of churches. The structural damage of the investigated buildings, related to 21 mechanisms rather than to an overall global response, was explained resorting to empirical statistical procedures taking into account ground motion intensity and structural details that can worsen or improve the seismic performance. Finally, parametric fragility curves were derived selecting those structural details that mostly influence the damage by means of the likelihood-ratio test. Developed models can be used in future territorial-scale scenario or risk analyses.
Importance of complex blood flow in the assessment of aortic regurgitation severity using phase contrast magnetic resonance imaging
Authors: Truedsson Frida , Polte Christian L. , Gao Sinsia A. , Johnsson Åse A. , Bech-Hanssen Odd , Lagerstrand Kerstin M. 
Published: July 2021

AbstractThis study aimed to investigate if and how complex flow influences the assessment of aortic regurgitation (AR) using phase contrast MRI in patients with chronic AR. Patients with moderate (n = 15) and severe (n = 28) chronic AR were categorized into non-complex flow (NCF) or complex flow (CF) based on the presence of systolic backward flow volume. Phase contrast MRI was performed repeatedly at the level of the sinotubular junction (Ao1) and 1 cm distal to the sinotubular junction (Ao2). All AR patients were assessed to have non-severe AR or severe AR (cut-off values: regurgitation volume (RVol) ≥ 60 ml and regurgitation fraction (RF) ≥ 50%) in both measurement positions. The repeatability was significantly lower, i.e. variation was larger, for patients with CF than for NCF (≥ 12 ± 12% versus ≥ 6 ± 4%, P ≤ 0.03). For patients with CF, the repeatability was significantly lower at Ao2 compared to Ao1 (≥ 21 ± 20% versus ≥ 12 ± 12%, P ≤ 0.02), as well as the assessment of regurgitation (RVol: 42 ± 34 ml versus 54 ± 42 ml, P < 0.001; RF: 30 ± 18% versus 34 ± 16%, P = 0.01). This was not the case for patients with NCF. The frequency of patients that changed in AR grade from severe to non-severe when the position of the measurement changed from Ao1 to Ao2 was higher for patients with CF compared to NCF (RVol: 5/26 (19%) versus 1/17 (6%), P = 0.2; RF: 4/26 (15%) versus 0/17 (0%), P = 0.09). Our study shows that complex flow influences the quantification of chronic AR, which can lead to underestimation of AR severity when using PC-MRI.
Organised food crime: an analysis of the involvements of organised crime groups in the food sector in England and Italy
Authors: Rizzuti Alice 
Published: July 2021

AbstractThe food sector is subject to illegal practices of various types such as adulteration or exploitation of labour. In the media and public discourse, this phenomenon is often associated to activities by organised crime groups. Drawing on a socio-legal empirical study on the perception and conceptualisation of food crime in English and Italian public institutions, this paper unpacks the involvement of organised crime and mafia-type actors in the food sector. Considering data collected through in-depth interviews with representatives of law enforcement and other public authorities, supported by documentary sources, this research points out that, from both an institutional perspective that narrowly conceptualises as food crime as food fraud, as well as from a wider perspective that addresses other practices happening in the food sector, organised crime is involved in food crime. By referring to the English and Italian cases, and by merging different bodies of literature, such as green criminology and enterprise theory, this article advocates for conceptual clarity when referring to the involvement of corporate crime, organised crime and mafia-type groups active in the food sector. In so doing, it presents and reflects upon ‘organised food crime’ as a new socio-legal category and highlights its policy outcomes.
Design, implementation and initial findings of COVID-19 research in the Rotterdam Study: leveraging existing infrastructure for population-based investigations on an emerging disease
Authors: Licher Silvan , Terzikhan Natalie , Splinter Marije J. , Velek Premysl , van Rooij Frank J. A. , Heemst Jolande Verkroost-van , Haarman Annechien E. G. , Thee Eric F. , Geurts Sven , Mens Michelle M. J. , van der Schaft Niels , de Feijter Maud , Pardo Luba M. , Kieboom Brenda C. T. , Ikram M. Arfan 
Published: July 2021

AbstractThe Rotterdam Study is an ongoing prospective, population-based cohort study that started in 1989 in the city of Rotterdam, the Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. It focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. In response to the COVID-19 pandemic, a substudy was designed and embedded within the Rotterdam Study. On the 20th of April, 2020, all living non-institutionalized participants of the Rotterdam Study (n = 8732) were invited to participate in this sub-study by filling out a series of questionnaires administered over a period of 8 months. These questionnaires included questions on COVID-19 related symptoms and risk factors, characterization of lifestyle and mental health changes, and determination of health care seeking and health care avoiding behavior during the pandemic. As of May 2021, the questionnaire had been sent out repeatedly for a total of six times with an overall response rate of 76%. This article provides an overview of the rationale, design, and implementation of this sub-study nested within the Rotterdam Study. Finally, initial results on participant characteristics and prevalence of COVID-19 in this community-dwelling population are shown.
Gamma-ray astrophysics in the MeV range
Authors: De Angelis Alessandro , Tatischeff Vincent , Argan Andrea , Brandt Søren , Bulgarelli Andrea , Bykov Andrei , Costantini Elisa , Silva Rui Curado da , Grenier Isabelle A. , Hanlon Lorraine , Hartmann Dieter , Hernanz Margarida , Kanbach Gottfried , Kuvvetli Irfan , Laurent Philippe , Mazziotta Mario N. , McEnery Julie , Morselli Aldo , Nakazawa Kazuhiro , Oberlack Uwe , Pearce Mark , Rico Javier , Tavani Marco , Ballmoos Peter von , Walter Roland , Wu Xin , Zane Silvia , Zdziarski Andrzej , Zoglauer Andreas 
Published: July 2021

AbstractThe energy range between about 100 keV and 1 GeV is of interest for a vast class of astrophysical topics. In particular, (1) it is the missing ingredient for understanding extreme processes in the multi-messenger era; (2) it allows localizing cosmic-ray interactions with background material and radiation in the Universe, and spotting the reprocessing of these particles; (3) last but not least, gamma-ray emission lines trace the formation of elements in the Galaxy and beyond. In addition, studying the still largely unexplored MeV domain of astronomy would provide for a rich observatory science, including the study of compact objects, solar- and Earth-science, as well as fundamental physics. The technological development of silicon microstrip detectors makes it possible now to detect MeV photons in space with high efficiency and low background. During the last decade, a concept of detector (“ASTROGAM”) has been proposed to fulfil these goals, based on a silicon hodoscope, a 3D position-sensitive calorimeter, and an anticoincidence detector. In this paper we stress the importance of a medium size (M-class) space mission, dubbed “ASTROMEV”, to fulfil these objectives.
Ultra-Reliable and Low-Latency Computing in the Edge with Kubernetes
Authors: Toka László 
Published: July 2021

AbstractNovel applications will require extending traditional cloud computing infrastructure with compute resources deployed close to the end user. Edge and fog computing tightly integrated with carrier networks can fulfill this demand. The emphasis is on integration: the rigorous delay constraints, ensuring reliability on the distributed, remote compute nodes, and the sheer scale of the system altogether call for a powerful resource provisioning platform that offers the applications the best of the underlying infrastructure. We therefore propose Kubernetes-edge-scheduler that provides high reliability for applications in the edge, while provisioning less than 10% of resources for this purpose, and at the same time, it guarantees compliance with the latency requirements that end users expect. We present a novel topology clustering method that considers application latency requirements, and enables scheduling applications even on a worldwide scale of edge clusters. We demonstrate that in a potential use case, a distributed stream analytics application, our orchestration system can reduce the job completion time to 40% of the baseline provided by the default Kubernetes scheduler.
The Impact of Encoding and Transport for Massive Real-time IoT Data on Edge Resource Consumption
Authors: Tusa Francesco , Clayman Stuart 
Published: July 2021

AbstractEdge microservice applications are becoming a viable solution for the execution of real-time IoT analytics, due to their rapid response and reduced latency. With Edge Computing, unlike the central Cloud, the amount of available resource is constrained and the computation that can be undertaken is also limited. Microservices are not standalone, they are devised as a set of cooperating tasks that are fed data over the network through specific APIs. The cost of processing these feeds of data in real-time, especially for massive IoT configurations, is however generally overlooked. In this work we evaluate the cost of dealing with thousands of sensors sending data to the edge with the commonly used encoding of JSON over REST interfaces, and compare this to other mechanisms that use binary encodings as well as streaming interfaces. The choice has a big impact on the microservice implementation, as a wrong selection can lead to excessive resource consumption, because using a less efficient encoding and transport mechanism results in much higher resource requirements, even to do an identical job.
Current and emerging drug targets in heart failure treatment
Authors: Ghionzoli Nicolò , Gentile Francesco , Del Franco Anna Maria , Castiglione Vincenzo , Aimo Alberto , Giannoni Alberto , Burchielli Silvia , Cameli Matteo , Emdin Michele , Vergaro Giuseppe 
Published: July 2021

AbstractAfter initial strategies targeting inotropism and congestion, the neurohormonal interpretative model of heart failure (HF) pathophysiology has set the basis for current pharmacological management of HF, as most of guideline recommended drug classes, including beta-blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and mineralocorticoid receptor antagonists, blunt the activation of detrimental neurohormonal axes, namely sympathetic and renin–angiotensin–aldosterone (RAAS) systems. More recently, sacubitril/valsartan, a first-in-class angiotensin receptor neprilysin inhibitor, combining inhibition of RAAS and potentiation of the counter-regulatory natriuretic peptide system, has been consistently demonstrated to reduce mortality and HF-related hospitalization. A number of novel pharmacological approaches have been tested during the latest years, leading to mixed results. Among them, drugs acting directly at a second messenger level, such as the soluble guanylate cyclase stimulator vericiguat, or other addressing myocardial energetics and mitochondrial function, such as elamipretide or omecamtiv-mecarbil, will likely change the therapeutic management of patients with HF. Sodium glucose cotransporter 2 inhibitors, initially designed for the management of type 2 diabetes mellitus, have been recently demonstrated to improve outcome in HF, although mechanisms of their action on cardiovascular system are yet to be elucidated. Most of these emerging approaches have shifted the therapeutic target from neurohormonal systems to the heart, by improving cardiac contractility, metabolism, fibrosis, inflammation, and remodeling. In the present paper, we review from a pathophysiological perspective current and novel therapeutic strategies in chronic HF.
A Meta-Analytic Structural Equation Model for Understanding Social Commerce Adoption
Authors: Dwivedi Yogesh K , Ismagilova Elvira , Sarker Prianka , Jeyaraj Anand , Jadil Yassine , Hughes Laurie 
Published: July 2021

AbstractSocial commerce (s-commerce) has gained prominence with advances in social media and social networking technologies over the last decade. Prior research has employed diverse theoretical perspectives to understand and explain consumer behavior within s-commerce but has also produced inconsistent results. This study integrates different theoretical perspectives including trust, social support, and social presence. The research portrays an integrated research model involving factors that impact behavioral intention and use behavior of s-commerce consumers whilst synthesizing prior empirical findings. A meta-analytic structural equation modeling (MASEM) method was used to synthesize 189 findings reported in 68 s-commerce studies and to analyze the structural model. Our findings show that trust and informational support have positive effects on behavioral intention while trust and emotional support have positive effects on use behavior. Furthermore, our findings highlight that behavioral intention influences use behavior and mediates the effect of trust and informational support on use behavior. The implications for research and practice are discussed in detail.
Necessity-Rich, Leisure-Poor: The Long-Term Relationship Between Income Cohorts and Consumption Through Age-Period-Cohort Analysis
Authors: Karonen Esa , Niemelä Mikko 
Published: July 2021

AbstractThe main aim of this study is to analyse household consumption patterns in the highest and lowest income quintiles and explore how they have changed over time and generations. Thus, the article explores whether social inclusivity through consumption has truly increased. This study utilises the cross-sectional time-series data of the Finnish Household Expenditure Surveys (HESs), covering the period 1966–2016. We use the Age-Period-Cohort Gap/Oaxaca (APCGO) model with logitrank dependent variables as the main statistical method. Our results indicate that an overall high income is advantageous with respect to income and spending, though the gap between high- and low-income groups has remained stagnant over cohorts. A more in-depth analysis reveals that the expenditure gap, in terms of necessities, food, and groceries consumption, has narrowed. Instead, income elastic-oriented spending on culture and leisure time has significantly increased in the high-income group, where the expenditure gap has expanded 60 percentage points over the cohorts. Simply put, expenditures on necessities have become more inclusive, but low-income groups are increasingly more ‘leisure-poor’. Overall, high-income classes are spending an increasing amount of money on culture and leisure time over cohorts.
Knowledge Acquisition from Pedestrian Flow Analysis using Sparse Mobile Probe Data
Authors: Neto Ranulfo Plutarco Bezerra , Ohno Kazunori , Westfechtel Thomas , Kojima Shotaro , Yamada Kento , Tadokoro Satoshi 
Published: July 2021

AbstractAutonomous vehicles require high-level semantic maps, which contain the activities of pedestrians and cars, to ensure safe navigation. High-level semantics can be obtained from mobile probe sensor data. Analyzing pedestrian trajectories obtained from mobile probe data is an effective approach to avoid collisions between autonomous vehicles and pedestrians. Such analyses of pedestrian trajectories can generate new information such as pedestrian behaviors in violation of traffic regulations. However, pedestrian trajectories obtained from mobile probe data significantly sparse and noisy, making it challenging to analyze pedestrian activity. To address this issue, we propose multiple daily data and graph-based approaches to treat sparse and noisy data for estimating the flow of pedestrians based on mobile probe data. To improve the sparseness of the data, multiple daily data are fused. After that, a pedestrian graph is created to enhance the region’s coverage by connecting the sparse data indicating the flow of pedestrians. This proposed approach successfully obtained pedestrian trajectory data from the sparse and noisy data. Moreover, it was possible to identify the potential locations where pedestrians tend to cross the street by analyzing the pedestrian flow. The results indicate that 83% of well-known regions where pedestrians tend to cross the street corresponded with those extracted using the proposed approach. Furthermore, a high-level semantic map of the regions where pedestrians tend to cross the street along a 1-km road is presented. The trajectory information obtained using the proposed approach is expected to be essential for understanding different scenarios of the interactions between individuals and autonomous vehicles.
Development of the COVID-19 Perinatal Perception Questionnaire (COVID19-PPQ)
Authors: Hulsbosch Lianne P. , Boekhorst Myrthe G. B. M. , Muskens Lotte , Potharst Eva S. , Nyklíček Ivan , Pop Victor J. M. 
Published: July 2021

AbstractThe COVID-19 pandemic affects the mental health status of perinatal women, which makes it important to gain insight into and to effectively measure specific stressors of the COVID-19 pandemic. Therefore, we aimed to develop a COVID-19 Perinatal Perception Questionnaire (COVID19-PPQ). In-depth interviews were conducted during the first national lockdown period with pregnant women, new mothers and perinatal healthcare professionals, resulting in (a) a 27-item pregnancy and (b) a 21-item postpartum scale. Explorative factor analyses (EFA) in sample Ia (N = 154) and Ib (N = 90), and confirmatory factor analyses (CFA) in sample IIa (N = 113) and IIb (N = 81) were conducted to test the psychometric properties of both scales. For the pregnancy scale, EFA suggested a three-factor solution (risk of infection, contact, future), which was confirmed by CFA and resulted in a final nine-item scale. For the postpartum scale, a three-factor solution (first postpartum week, COVID-19 measures, fear for infection) was suggested by EFA and confirmed by CFA, resulting in a final ten-item scale. Symptoms of depression and pregnancy-specific distress were significantly correlated with the pregnancy (sub)scale(s), while symptoms of postpartum depression and anxiety showed significant correlations with the COVID-19 measures and fear for infection subscale. The COVID19-PPQ seems to be a valid instrument for assessment of perinatal COVID-19-related stress perception, showing adequate psychometric properties for both the pregnancy and postpartum scale. Future research should examine the use of this instrument in clinical practice during new episodes of the COVID-19 pandemic.
A Systematic Review of Perinatal Social Support Interventions for Asylum-seeking and Refugee Women Residing in Europe
Authors: Balaam Marie-Clare , Kingdon Carol , Haith-Cooper Melanie 
Published: July 2021

AbstractAsylum-seeking and refugee women currently residing in Europe face unique challenges in the perinatal period. A range of social support interventions have been developed to address these challenges. However, little is known about which women value and why. A critical interpretive synthesis was undertaken using peer reviewed and grey literature to explore the nature, context and impact of these perinatal social support interventions on the wellbeing of asylum-seeking and refugee women. Four types of interventions were identified which had varying impacts on women’s experiences. The impacts of the interventions were synthesised into five themes: Alleviation of being alone, Safety and trust, Practical knowledge and learning, being cared for and emotional support, and increased confidence in and beyond the intervention. The interventions which were most valued by women were those using a community-based befriending/peer support approach as these provided the most holistic approach to addressing women’s needs.