bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tree species admixture increases ecosystem service provision in simulated spruce- and beech-dominated stands
Authors: Mey Reinhard , Zell Jürgen , Thürig Esther , Stadelmann Golo , Bugmann Harald , Temperli Christian 
Published: July 2022

AbstractClimate-adaptive forest management aims to sustain the provision of multiple forest ecosystem services and biodiversity (ESB). However, it remains largely unknown how changes in adaptive silvicultural interventions affect trade-offs and synergies among ESB in the long term. We used a simulation-based sensitivity analysis to evaluate popular adaptive forest management interventions in representative Swiss low- to mid-elevation beech- and spruce-dominated forest stands. We predicted stand development across the twenty-first century using a novel empirical and temperature-sensitive single-tree forest stand simulator in a fully crossed experimental design to analyse the effects of (1) planting mixtures of Douglas-fir, oak and silver fir, (2) thinning intensity, and (3) harvesting intensity on timber production, carbon storage and biodiversity under three climate scenarios. Simulation results were evaluated in terms of multiple ESB provision, trade-offs and synergies, and individual effects of the adaptive interventions. Timber production increased on average by 45% in scenarios that included tree planting. Tree planting led to pronounced synergies among all ESBs towards the end of the twenty-first century. Increasing the thinning and harvesting intensity affected ESB provision negatively. Our simulations indicated a temperature-driven increase in growth in beech- (+ 12.5%) and spruce-dominated stands (+ 3.7%), but could not account for drought effects on forest dynamics. Our study demonstrates the advantages of multi-scenario sensitivity analysis that enables quantifying effect sizes and directions of management impacts. We showed that admixing new tree species is promising to enhance future ESB provision and synergies among them. These results support strategic decision making in forestry.
A survey on deep reinforcement learning for audio-based applications
Authors: Latif Siddique , Cuayáhuitl Heriberto , Pervez Farrukh , Shamshad Fahad , Ali Hafiz Shehbaz , Cambria Erik 
Published: July 2022

AbstractDeep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels of understanding of the real world. Currently, deep learning (DL) is enabling DRL to effectively solve various intractable problems in various fields including computer vision, natural language processing, healthcare, robotics, to name a few. Most importantly, DRL algorithms are also being employed in audio signal processing to learn directly from speech, music and other sound signals in order to create audio-based autonomous systems that have many promising applications in the real world. In this article, we conduct a comprehensive survey on the progress of DRL in the audio domain by bringing together research studies across different but related areas in speech and music. We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain. We conclude by presenting important challenges faced by audio-based DRL agents and by highlighting open areas for future research and investigation. The findings of this paper will guide researchers interested in DRL for the audio domain.
What Makes Learners Overestimate Their Text Comprehension? The Impact of Learner Characteristics on Judgment Bias
Authors: Golke Stefanie , Steininger Tim , Wittwer Jörg 
Published: July 2022

AbstractIt is frequently assumed that learner characteristics (e.g., reading skill, self-perceptions, optimism) account for overestimations of text comprehension, which threaten learning success. However, previous findings are heterogenous. To circumvent a key problem of previous research, we considered cognitive, metacognitive, motivational, and personality characteristics of learners (N = 255) simultaneously with regard to their impact on the judgment biases in prediction and postdiction judgments about factual and inference questions. The main results for the factual questions showed that men, lower reading skill, working memory capacity, and topic knowledge, yet higher self-perceptions of cognitive and metacognitive capacities yielded stronger overestimations for prediction judgments. For inference questions, a lower reading skill, higher self-perceptions of metacognitive capacities, and a higher self-efficacy were related to stronger overestimations for prediction and postdiction judgments. A higher openness was a risk factor for stronger overestimations when making predictions for the inference questions. The findings demonstrate that learner characteristics are a relevant source of judgment bias, which should be incorporated explicitly in theories of judgment accuracy. At the same time, fewer learner characteristics were actually relevant than previous research suggests. Moreover, which learner characteristics impact judgment bias also depends on task requirements, such as factual versus inference questions.
Storylines in public news media about mathematics education and minoritized students
Authors: Andersson Annica , Ryan Ulrika , Herbel-Eisenmann Beth , Huru Hilja Lisa , Wagner David 
Published: July 2022

AbstractPublic media both reflects and shapes societal perceptions and attitudes. Teachers and others around students in mathematics classrooms have expectations for the students, projected with what appears in these media. We are most concerned about the expectations placed on students who are identified with minoritized groups—particularly students who are Indigenous or migrated to Norway. We investigate how minoritized group contexts and mathematics education appear together in Norwegian news media texts. Our analysis uses the notion of storylines to describe the expectations about minoritized groups that news media project. We found seven entangled storylines: “the majority language and culture are keys to learning and knowing mathematics,” “mathematics is language- and culture-neutral,” “minoritized groups’ mathematics achievements are linked to culture and gender,” “extraordinary measures are needed to teach students from minoritized groups mathematics,” “students from minoritized groups underachieve,” “students from minoritized groups put in extraordinary effort and time to learn mathematics,” and “minoritized mathematics students are motivated by gratitude.”
Covid-19 infection risk on US domestic airlines
Authors: Barnett Arnold , Fleming Keith 
Published: July 2022

AbstractWorking with recent data and research findings, we estimate the probability that an air traveler in economy class would have contracted Covid-19 on a US domestic jet flight over the nine-month period June 2020 to February 2021. The estimates take account of the rates of confirmed Covid-19 infections in the US, flight duration, fraction of seats occupied, and some demographic differences between US air travelers and US citizens as a whole. Based on point estimates, the risk of contracting Covid-19 in-flight exceeded 1 in 1000 on a fully-loaded two-hour flight at the height of the pandemic over the nine months, but was about 1 in 6000 on a half-full flight when the pandemic was at a low ebb. However, these estimates are subject to substantial uncertainty, with the 10th percentiles of various risk distributions only about 1/7 as large as the medians, and the 90th percentiles about four times as large. Based on seat-occupancy levels on US flights for each month over June 2020 to February 2021, the median risk estimate for that period is 1 in 2250, while the mean risk estimate is 1 in 1450. Indirect effects arose because those who contracted Covid-19 on US airplanes could in turn infect others.
The assessment of Intolerance of uncertainty in youth: An examination of the Intolerance of Uncertainty Scale-Revised in Italian nonclinical boys and girls
Authors: Bottesi Gioia , Iannattone Sara , Carraro Eleonora , Lauriola Marco 
Published: July 2022

AbstractIntolerance of Uncertainty (IU) is a transdiagnostic factor involved in several psychological disorders. Adolescence is characterized by elevated uncertainty and psychopathological vulnerability, but insufficient attention has been paid to IU at this age. This study aimed to investigate the factor structure and psychometric properties of the Intolerance of Uncertainty Scale-Revised (IUS-R) in Italian preadolescents and adolescents. 862 Italian students (57.3% girls) aged 11–17 (M = 14.8 ± 1.91) completed the IUS-R and measures of internalizing and externalizing symptoms, and psychological well-being. To test the factor structure of the IUS-R, one-factor, two-factor, and bifactor models were compared; measurement invariance, reliability, and validity were also addressed. Results showed that the bifactor model outperformed alternative factor models, and a general factor was needed to model the IUS-R. Bifactor model indices supported using the total score to assess IU reliably. Configural and metric invariance by age and sex were fully supported, while the IUS-R achieved partial scalar invariance. Significant correlations emerged for the IUS-R total score with psychopathological constructs, while no relationships with psychological well-being were found. Compared to adult normative data, higher total IUS-R scores were detected, suggesting that IU may be a phase-specific characteristic of adolescence. Our findings support using the IUS-R to measure IU across the lifespan. The recommended use of the total score and its associations with psychopathological dimensions highlight the transdiagnostic nature of IU in adolescence. Therefore, including IU when implementing interventions to prevent maladaptive outcomes in teenagers would be beneficial.
The Role of Culture and Contextual Risk for Maternal Parenting and Children’s Behavior Regulation in Chile and Germany
Authors: Deffaa Mirjam , Weis Mirjam , Muñoz Lorena , Trommsdorff Gisela 
Published: July 2022

AbstractChildren’s behavior regulation development takes place in diverse sociocultural settings. In this study, we take a multilayer ecological perspective and examine cross-cultural as well as intra-cultural similarities and differences in relations between different aspects of contextual risks (i.e., family and neighborhood risk), maternal restrictive control, and children’s behavior regulation in Chile and Germany. One hundred sixty-seven mothers of primary school children in Chile and 109 mothers in Germany (total sample M (child age) = 10.01 years) completed questionnaires on family risk, parenting practices, and their child’s behavior regulation. Mothers in Germany rated children’s behavior regulation significantly higher than mothers in Chile. Further, in both cultural contexts (Chile, Germany), the higher the family risk, the higher was the use of maternal restrictive control and the lower the child’s behavior regulation. In Chile, after including maternal restrictive control, the relation between family risk and children’s behavior regulation remained significant. In Germany, in contrast, there was no direct significant relation between family risk and children’s behavior regulation, instead we found a significant indirect pathway via maternal restrictive control. Further, we investigated the moderating role of neighborhood risk, as distal contextual risk, for the relation between family risk and maternal restrictive control as well as for the relation between maternal restrictive control and children’s behavior regulation. We found no significant overall moderated mediation effect. However, findings in Chile and Germany revealed a conditional indirect effect indicating that family risk and behavior regulation were indirectly related via maternal restrictive control only when neighborhood risk was high. This underlines the need for an integrative consideration of the cultural context as well as family risk and neighborhood risk when investigating the role of maternal parenting for children’s behavior regulation development.
A novel tracking system for the iron foundry field based on deep convolutional neural networks
Authors: Beck Michael , Layh Michael , Nebauer Markus , Pinzer Bernd R. 
Published: July 2022

AbstractIn modern manufacturing the ability of retracing produced components is crucial for quality management and process optimization. Tracking is essential, especially for analyzing the influence of the production parameters on the final quality of the castings. In the iron foundry industry, common marking methods, such as a datamatrix code, cannot be used due to harsh environmental conditions and the rough surface of the cast parts. This work presents a new coding and reading system that guarantees unique marking in the casting process.The coding is built up over several beveled pins and is read out using an optical 2D handheld scanner. With a deep convolutional neural network approach of object detection and classification, a stable image processing algorithm is presented. With a first prototype a reading accuracy of 99.86% for each pin was achieved with an average scanning time of 0.43 s. The presented code is compatible with existing foundry processes, while the handheld scanner is intuitive and reliable. This allows immediate benefits for process optimization.
DC sputter deposited TiO2 layers on FTO: towards a maximum photoelectrochemical response of photoanodes
Authors: Kim Hyesung , Wang Yue , Denisov Nikita , Wu Zhenni , Kment Štěpán , Schmuki Patrik 
Published: July 2022

Abstract
In this work, we examine the photoelectrochemical response of TiO2 layers prepared by reactive DC sputter deposition on conductive glass (FTO). We show that adequate conditioning of the FTO by a very thin (few nm thick) Ti interlayer and optimizing the sputter parameters, compact TiO2 anatase layers can be produced that reach incident photocurrent conversion efficiencies (IPCE) maxima peaking at 75%. This is outperforming many of the best titania photoanode structures (including high surface area 1D and 3D titania structures). The key role of the interlayer is to promote the crystallization of titania in the anatase form during the annealing process (as opposed to rutile in the interlayer-free case). Without this interlayer, an IPCE maxima of ≈43% is obtained for otherwise identically processed electrodes. The present work thus describes a most simple straightforward approach for fabricating compact, high-efficiency TiO2 (anatase) photoanodes.

Graphical abstract
Long-term SARS-CoV-2 Asymptomatic Carriage in an Immunocompromised Host: Clinical, Immunological, and Virological Implications
Authors: Spinicci Michele , Mazzoni Alessio , Coppi Marco , Antonelli Alberto , Salvati Lorenzo , Maggi Laura , Basile Gregorio , Graziani Lucia , Di Lauria Nicoletta , Di Pilato Vincenzo , Kiros Seble Tekle , Beccastrini Enrico , Saccardi Riccardo , Angileri Manuela , Cecchi Michele , Cusi Maria Grazia , Rossolini Gian Maria , Annunziato Francesco , Bartoloni Alessandro , Parronchi Paola 
Published: July 2022

Abstract

Purpose
SARS-CoV-2 infection in immunocompromised hosts is challenging, and prolonged viral shedding can be a common complication in these patients. We describe the clinical, immunological, and virological course of a patient with eosinophilic granulomatosis with polyangiitis, who developed the status of long-term asymptomatic SARS-CoV-2 carrier for more than 7 months.

Methods
Over the study period, the patient underwent 20 RT-PCR tests for SARS-CoV-2 detection on nasopharyngeal swabs. In addition, viral cultures and genetic investigation of SARS-CoV-2 were performed. As for immunological assessment, serological and specific T-cell testing was provided at different time points.

Results
Despite the patient showing a deep drug-induced B and T adaptive immunity impairment, he did not experience COVID-19 progression to severe complications, and the infection remained asymptomatic during the follow-up period, but he was not able to achieve viral clearance for more than 7 months. The infection was finally cleared by SARS-CoV-2-specific monoclonal antibody treatment, after that remdesivir and convalescent plasma failed in this scope. The genetic investigations evidenced that the infection was sustained by multiple viral subpopulations that had apparently evolved intra-host during the infection.

Conclusion
Our case suggests that people with highly impaired B- and T-cell adaptive immunity can prevent COVID-19 progression to severe complications, but they may not be able to clear SARS-CoV-2 infection. Immunocompromised hosts with a long-term infection may play a role in the emergence of viral variants.
Historical social contracts and their legacy: a disaggregated analysis of the medieval republics
Authors: Buonanno P. , Cervellati M. , Lazzaroni S. , Prarolo G. 
Published: July 2022

AbstractWe study the comparative political economy within the territories ruled by the medieval republics in Northern Italy. Building on the historical evidence, we conceptualize the emergence of more inclusive vs. extractive institutions in these sovereign polities as driven by the interests of local rulers and their need to build state capacity. We provide novel insights on the role of geography and historical contingencies in the development of public governance, individual attitudes, and social-inclusion, which mutually supported each other and persisted over time. We measure the origin, territorial evolution, and disappearance of all the sovereign polities that ruled over the Italian peninsula during the period of 1000-1800 AD. The empirical analysis connects contemporary socio-economic outcomes across spatially disaggregated northern Italian localities, at the municipality level, to local political history. We distinguish between so-called “communal” and “maritime republics” and show that the intensity and stability of exposure to the different types of republican rule in pre-industrial times continues to shape local public good provision and individual fiscal compliance, and has left a tangible imprint on today’s population diversity.
Fascistville: Mussolini’s new towns and the persistence of neo-fascism
Authors: Carillo Mario F. 
Published: July 2022

AbstractThis paper explores the link between infrastructures built by autocratic regimes and political values in the wake of the transition to democracy and in the long run. In Fascist Italy (1922–43), Mussolini founded 147 “New Towns” (Città di Fondazione). Exploring municipality-level data before and after their construction, I document (1) that the New Towns enhanced local electoral support for the Fascist Party and (2) that the effect persisted through democratization, enhancing local support for Italy’s neo-fascist party, which endured until recent times. Placebo estimates of New Towns planned but not built and spatial regression discontinuity design both support a causal interpretation of this pattern. Survey respondents near the New Towns currently exhibit preferences for a stronger leader in politics, for nationalism, and for the fascists as such. The effect is greater for individuals who lived under the Fascist Regime and is transmitted across generations inside the family. The findings suggest that authoritarian leaders may exploit public investment programs to induce a favorable view of their ideology, which persists across institutional transitions and over the long term.
Optimising portfolio diversification and dimensionality
Authors: Barkhagen M. , García S. , Gondzio J. , Kalcsics J. , Kroeske J. , Sabanis S. , Staal A. 
Published: July 2022

AbstractA new framework for portfolio diversification is introduced which goes beyond the classical mean-variance approach and portfolio allocation strategies such as risk parity. It is based on a novel concept called portfolio dimensionality that connects diversification to the non-Gaussianity of portfolio returns and can typically be defined in terms of the ratio of risk measures which are homogenous functions of equal degree. The latter arises naturally due to our requirement that diversification measures should be leverage invariant. We introduce this new framework and argue the benefits relative to existing measures of diversification in the literature, before addressing the question of optimizing diversification or, equivalently, dimensionality. Maximising portfolio dimensionality leads to highly non-trivial optimization problems with objective functions which are typically non-convex and potentially have multiple local optima. Two complementary global optimization algorithms are thus presented. For problems of moderate size and more akin to asset allocation problems, a deterministic Branch and Bound algorithm is developed, whereas for problems of larger size a stochastic global optimization algorithm based on Gradient Langevin Dynamics is given. We demonstrate analytically and through numerical experiments that the framework reflects the desired properties often discussed in the literature.
Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment
Authors: Abohamama A. S. , El-Ghamry Amir , Hamouda Eslam 
Published: July 2022

AbstractIoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms.
Graphical Abstract