bluuebunny/crossref_metadata_2025_split huggingface dataset:
Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model
Authors: Srikrishnan Vivek , Guan Yawen , Tol Richard S. J. , Keller Klaus 
Published: February 2022

AbstractProbabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here, we use a simple integrated assessment model, calibrated to century-scale data and expert assessments of baseline emissions, global economic growth, and population growth, to make probabilistic projections of carbon emissions through 2100. Under a variety of assumptions about fossil fuel resource levels and decarbonization rates, our projections largely agree with several emissions projections under current policy conditions. Our global sensitivity analysis identifies several key economic drivers of uncertainty in future emissions and shows important higher-level interactions between economic and technological parameters, while population uncertainties are less important. Our analysis also projects relatively low global economic growth rates over the remainder of the century. This illustrates the importance of additional research into economic growth dynamics for climate risk assessment, especially if pledged and future climate mitigation policies are weakened or have delayed implementations. These results showcase the power of using a simple, transparent, and calibrated model. While the simple model structure has several advantages, it also creates caveats for our results which are related to important areas for further research.
Physical home learning environments for digitally-supported learning in academic continuing education during COVID-19 pandemic
Authors: Keser Aschenberger Filiz , Radinger Gregor , Brachtl Sonja , Ipser Christina , Oppl Stefan 
Published: February 2022

AbstractSince the outbreak of the COVID-19 pandemic in 2020, digital technologies for distance learning have been used in educational institutions worldwide, raising issues about social implications, technological development, and teaching and learning strategies. While disparities regarding access to technical equipment and the internet (‘the digital divide’) have been the subject of previous research, the physical learning environment of learners participating in online learning activities has hardly been investigated. In this study, the physical-spatial conditions of learning environments, including technical equipment for distance learning activities and their influence on adult learners in academic continuing education during initial COVID-19 restrictions, were examined. Data were collected with an online survey sent to all students enrolled in an Austrian continuing education university, together with a small number of semi-structured interviews. A total of 257 students participated in the survey during the 2020 summer semester. Our findings provide insights in two infrequently-studied areas in learning environment research: the physical learning environment for online learning and the learning environment in academic continuing education. The study illustrates that students in academic continuing education have spacious living conditions and almost all the equipment necessary for digitally-supported learning. According to gender and household structure, significant differences were found regarding technical equipment, ergonomic furniture and availability of a dedicated learning place. In their learning sessions during the restrictions, students reported low stress levels and positive well-being. The more that they perceived that their physical learning environment was meeting their needs, the higher were their motivation and well-being and the lower was their stress. Their learning experience was further improved by the extent to which they had a separate and fixed learning place that did not need to be coordinated or shared with others. The study contributes to the literature on creating conducive learning environments for digitally-supported online learning for adult learners.
Foliar stable isotope ratios of carbon and nitrogen in boreal forest plants exposed to long-term pollution from the nickel-copper smelter at Monchegorsk, Russia
Authors: Manninen Sirkku , Zverev Vitali , Kozlov Mikhail V. 
Published: February 2022

AbstractLong-term exposure to primary air pollutants, such as sulphur dioxide (SO2) and nitrogen oxides (NOx), alters the structure and functions of forest ecosystems. Many biochemical and biogeochemical processes discriminate against the heavier isotopes in a mixture; thus, the values of δ13C and δ15N (i.e. the ratio of stable isotopes 13C to 12C and that of 15 N to 14 N, respectively) may give insights into changes in ecosystem processes and identify the immediate drivers of these changes. We studied sources of variation in the δ13C and δ15N values in the foliage of eight boreal forest C3 plants at 10 sites located at the distance of 1–40 km from the Monchegorsk nickel-copper smelter in Russia. From 1939‒2019, this smelter emitted over 14,000,000 metric tons (t) of SO2, 250,000 t of metals, primarily nickel and copper, and 140,000 t of NOx. The δ13C value in evergreen plants and the δ15N value in all plants increased near the smelter independently of the plant mycorrhizal type. We attribute the pollution-related increase in the foliar δ13C values of evergreen species mainly to direct effects of SO2 on stomatal conductance, in combination with pollution-related water stress, which jointly override the potential opposite effect of increasing ambient CO2 concentration on δ13C values. Stomatal uptake of NOx and root uptake of 15N-enriched organic N compounds and NH4+ may explain the increased foliar δ15N values and elevated foliar N concentrations, especially in the evergreen trees (Pinus sylvestris), close to Monchegorsk, where the soil inorganic N supply is reduced due to the impact of long-term SO2 and heavy metal emissions on plant biomass. We conclude that, despite the uncertainties in interpreting δ13C and δ15N responses to pollution, the Monchegorsk smelter has imposed and still imposes a great impact on C and N cycling in the surrounding N-limited subarctic forest ecosystems.
Sports Betting Advertising: A Systematic Review of Content Analysis Studies
Authors: Killick Elizabeth A. , Griffiths Mark D. 
Published: February 2022

AbstractA systematic review of empirical studies that used quantitative, qualitative, or a mixed-methods approach to content analysis sports betting advertising was conducted. Study attributes were examined including the main findings, key themes, research design, focus of analysis, type of interpretation, use of theory, media type, inter-coder reliability measures, and recommendations for future studies. The findings showed that the studies (N=15) were all published relatively recently (since 2012), predominantly focused on the content of television advertisements, and there was a general lack of reporting on inter-coder reliability. The narratives most commonly identified within sports betting advertisements were friendship and humour. Advertisements were found to be embedded into professional sports games, and this type of advertising occurred more frequently when compared to commercial advertising during televised sporting events. In order to enhance the sports betting advertising literature, researchers need to ensure that they are meeting scientific standards, especially when it comes to reliability measures. Such studies can add to an understanding of how sports betting advertisements are presented to the public and infer what the potential impact of the availability and exposure of these advertisements may be.
On finite mixtures of Discretized Beta model for ordered responses
Authors: Simone Rosaria 
Published: February 2022

AbstractThe paper discusses the specification of finite mixture models based on the Discretized Beta distribution for the analysis of ordered discrete responses, as ratings and count data. The ultimate goal of the paper is to parameterize clusters of opposite and intermediate response outcomes. After a thorough discussion on model interpretation, identifiability and estimation, the proposal is illustrated on the wake of a case study on the probability to vote for German Political Parties and with a comparative discussion with the state of the art.
Dynamics of Weighted Composition Operators and Their Adjoints on the Fock Space
Authors: Mengestie Tesfa 
Published: February 2022

AbstractWe study the cyclic structures of the weighted composition operators and their adjoints on the Fock space $${\mathcal {F}}_2$$

F
2

. A complete characterization of cyclicity which depends on the derivative of the symbol for the composition operator and non-vanishing structure of the weight function is provided. It is further shown that the space fails to support supercyclic adjoint weighted composition operators. As a tool in proving our main results, we also identified eigenvectors of the weighted composition operators in the space which is interest of its own.
A Phase-Field Study on Internal to External Oxidation Transition in High-Temperature Structural Alloys
Authors: Wang Rui , Ji Yanzhou , Cheng Tianle , Xue Fei , Chen Long-Qing , Wen You-Hai 
Published: February 2022

AbstractStructural alloys applied at high temperatures rely on an external dense layer of oxide scale for protection. As some reactive alloy components are selectively oxidized internally, understanding how these dispersed metal oxide particles reach the surface to form a protective scale—the so-called internal to external oxidation transition—is crucial for designing these alloys. While the literature is replete with experimental studies on oxidation of alloys, there is a lack of computational studies in this realm due to the complex nature of coupled reaction and diffusion processes in multicomponent multi-phase alloy systems. In this work, we apply a recently developed phase-field model to simulate the oxidation processes under different compositions and nucleation scenarios to gain insights into how a continuous oxide scale can be established. The results show that while alloy composition is critical for internal to external oxidation transition, the oxide nuclei size, shape and distribution also have significant impact on the transition kinetics.
PACAP and VIP Mitigate Rotenone-Induced Inflammation in BV-2 Microglial Cells
Authors: Broome Sarah Thomas , Musumeci Giuseppe , Castorina Alessandro 
Published: February 2022

AbstractRotenone is a commercial pesticide commonly used to model Parkinson’s disease (PD) due to its ability to induce dopaminergic degeneration. Studies have confirmed that rotenone causes microglial activation, which seems to contribute to the toxic effects seen in rodent models. Pituitary adenylate cyclase–activating polypeptide (PACAP) and vasoactive intestinal peptide (VIP) are two structurally related neuropeptides that have robust neuroprotective and anti-inflammatory properties. However, their ability to regulate microglial activity in response to rotenone is not fully understood. Using rotenone as an inflammatory stimulus, we tested whether PACAP or VIP could mitigate microglial activation in BV2 microglial cells. Rotenone dose-dependently reduced cell viability and the percentage of apoptotic cells. It also increased the release of nitric oxide (NO) in culture media and the expression of microglial activation markers and pro-inflammatory markers, including CD11b, MMP-9 and IL-6, and heightened the endogenous levels of PACAP and its preferring receptor PAC1. Co-treatment with PACAP or VIP prevented rotenone-induced increase of NO, CD11b, MMP-9 and IL-6. These results indicate that both PACAP and VIP are able to prevent the pro-inflammatory effects of rotenone in BV2 cells, supporting the idea that these molecules can have therapeutic value in slowing down PD progression.
Graphical Abstract
Thermal treatment of Kalabsha kaolin deposits, south of the Western Desert, Egypt: contribution to geopolymer production as green building materials
Authors: El-Moghny Mohamed W. Abd , El-Desoky Hatem M. , El-Nagar Abdeen M. , El-Hafez Nabil A. Abd , Sharaka Hossam K. , Abouellaban Sayed A. 
Published: February 2022

AbstractKaolin samples were collected to study the mineralogical, texture, chemical composition, thermal behavior, and treatment and also the possibility to convert kaolinite mineral into metakaolin and use it as a natural pozzolanic and/or as geopolymer materials. Kaolinite is the main constituent of clay mineral, which related to many of the chemical weathering of parent rocks and complete leaching of alkali elements under warm, humid to semi-arid climatic conditions. However, quartz, anatase, and iron oxide are considered non-clay minerals. Cement substituted by metakaolin under different ratios in the concrete industry was carried out to enhance the physical and mechanical properties of the concrete. The best results are detected at 10% and 15% of cement replacement by metakaolin. Geopolymer production using Metakaolin (MK) was mixed with NaOH and Na2O-3SiO2to replace the Portland cement in the concrete industry and the obtained results are suitable and match with the green building material concept.
Comparison of modified Woo’s solar phase scintillation model with ESA’s BepiColombo superior solar conjunction measurement data for X-band and Ka-band
Authors: Chen Shun-Ping , Villalvilla Jose 
Published: February 2022

AbstractSolar phase scintillation is of major interest in deep space mission operations for designing a communications system capable of transmitting signals when the path is close to the Sun, but also for improving the navigation under these conditions. The basis of this work is Woo’s solar phase scintillation theory which reported a useful model for the spectral power density of phase scintillations of a signal traveling through a medium close to the Sun. In this paper, Woo’s solar phase scintillation formula is used for the simulation of the spectral density of phase fluctuations induced by the Sun on coherent signals (uplink and downlink carriers are coherently related by a fractional ratio implemented by the spacecraft’s transponder), for the two downlink frequency bands supported by the ESA deep space mission BepiColombo. In particular, the model has been compared to measurement data collected at low Sun Earth Probe (SEP) angles during the superior solar conjunction campaign in March 2021 (in its cruise phase to Mercury). The comparison shows a good match between the phase noise spectral densities of the modified formula and measurement data, therefore, confirming that the formula can be used for the planning and verification of the future near Sun measurements.
IT Capability, Organisational Learning and Innovation Performance of Firms in Kenya
Authors: Otioma Chuks 
Published: February 2022

AbstractThis paper explores the role that IT capability (IT-c) plays in firm innovation performance through the channel of organisational learning (OL) in Kenya. It frames OL in two dimensions: explorative and exploitative OL. The former entails seeking new knowledge, which mainly exists outside the firm’s competence. The latter is an activity or process that builds on existing competence and knowledge in the firm. Using mediation analysis of 481 firms drawn from the World Bank Enterprise Survey (2013) and Innovation Follow-up Survey 2014, it demonstrates that IT-c has a significant effect on innovation performance of firms (capability to simultaneously improve products, processes, organising and market development). It finds that the mediative role of OL in the relationship between IT-c and innovation performance is realised mainly through explorative learning, whilst enabling the firm exploit existing (in-house) knowledge base. The paper puts forward some managerial, policy and further research suggestions.
National and Higher Education Institutions (HEIs) IP Policies: Comparison of Indian HEIs’ IP Policies from a Global Perspective
Authors: Sattiraju Vijay , Ligade Virendra S. , Muragundi Pradeep , Pandey Ravi , Janodia Manthan D. 
Published: February 2022

AbstractUntil the end of the eighteenth century, Higher Education Institutions (HEIs) were restricted to generate knowledge for free access to industries which they use for making commercial products. Though HEIs have the potential to contribute to the national economy from their creative and commercially viable R&D output through commercialization of research and Intellectual Property Rights, it was not given the needed importance due to lack of supportive policies. In many developed countries, innovation strategies, Science, Technology, and Innovation (STI) and national Intellectual Property Rights (IPR) policies provided opportunities to HEIs to own and commercialize knowledge they generate. National IPR policy and formulation and implementation of HEI IP policies became crucial for effective implementation, promotion of innovation, generation, and protection of IP in the HEIs. Efficiency of HEI in commercializing research is dependent on national IP policy framework aligning vision and goals of HEI IP policy with national IP policy, objectives, ownership, and revenue sharing mechanism. These are the key indicators for comparing the institutions’ innovation and IP capacity. This paper uses these indicators to discuss the impact of different countries’ (developed, developing, and least developed) national IPR policy frameworks on top-performing HEIs of those countries and to address the research question, how national and HEI IP policies have worked in achieving envisaged economic growth by improving innovation output of the HEIs. It further compares Indian HEI IP policies with global HEIs. We found that there is a greater need for local governments and HEIs to make their incentive strategies and policies in line with their corresponding national frameworks for facilitating the collaboration and commercialization of innovations. Implementing a robust IP policy framework at state/region and HEI level plays a pivotal role in creating an innovation ecosystem supportive to envisaged national economic growth.
Three new pyrrole alkaloids from the endophytic fungus Albifimbria viridis
Authors: Wei Pan-Pan , Ji Jia-Cheng , Ma Xu-Jun , Li Zheng-Hui , Ai Hong-Lian , Lei Xin-Xiang , Liu Ji-Kai 
Published: February 2022

AbstractThree new pyrrole alkaloids albifipyrrols A–C (1–3), were isolated from the endophytic fungus Albifimbria viridis collected from the Chinese medicinal plant. Their structures were elucidated by extensive NMR and HRESIMS spectrometric analyses. All compounds were evaluated for immunosuppressive activity. Fortunately, compound 2 exhibits certain inhibition specifically against the LPS-induced proliferation of B lymphocyte cells with IC50 value 16.16 μM.
Graphical Abstract
Automated defect detection in digital radiography of aerospace welds using deep learning
Authors: Tyystjärvi Topias , Virkkunen Iikka , Fridolf Peter , Rosell Anders , Barsoum Zuheir 
Published: February 2022

AbstractAerospace welds are non-destructively evaluated (NDE) during manufacturing to identify defective parts that may pose structural risks, often using digital radiography. The analysis of these digital radiographs is time consuming and costly. Attempts to automate the analysis using conventional computer vision methods or shallow machine learning have not, thus far, provided performance equivalent to human inspectors due to the high reliability requirements and low contrast to noise ratio of the defects. Modern approaches based on deep learning have made considerable progress towards reliable automated analysis. However, limited data sets render current machine learning solutions insufficient for industrial use. Moreover, industrial acceptance would require performance demonstration using standard metrics in non-destructive evaluation, such as probability of detection (POD), which are not commonly used in previous studies. In this study, data augmentation with virtual flaws was used to overcome data scarcity, and compared with conventional data augmentation. A semantic segmentation network was trained to find defects from computed radiography data of aerospace welds. Standard evaluation metrics in non-destructive testing were adopted for the comparison. Finally, the network was deployed as an inspector’s aid in a realistic environment to predict flaws from production radiographs. The network achieved high detection reliability and defect sizing performance, and an acceptable false call rate. Virtual flaw augmentation was found to significantly improve performance, especially for limited data set sizes, and for underrepresented flaw types even at large data sets. The deployed prototype was found to be easy to use indicating readiness for industry adoption.
Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security
Authors: Attkan Ankit , Ranga Virender 
Published: February 2022

AbstractThe recent years have garnered huge attention towards the Internet of Things (IoT) because it enables its consumers to improve their lifestyles and professionally keep up with the technological advancements in the cyber-physical world. The IoT edge devices are heterogeneous in terms of the technology they are built on and the storage file formats used. These devices require highly secure modes of mutual authentication to authenticate each other before actually sending the data. Mutual authentication is a very important aspect of peer-to-peer communication. Secure session keys enable these resource-constrained devices to authenticate each other. After successful authentication, a device can be authorized and can be granted access to shared resources. The need for validating a device requesting data transfer to avoid data privacy breaches that may compromise confidentiality and integrity. Blockchain and artificial intelligence (AI) both are extensively being used as an integrated part of IoT networks for security enhancements. Blockchain provides a decentralized mechanism to store validated session keys that can be allotted to the network devices. Blockchain is also used to load balance the stressing edge devices during low battery levels. AI on the other hand provides better learning and adaptiveness towards IoT attacks. The integration of newer technologies in IoT key management yields enhanced security features. In this article, we systematically survey recent trending technologies from an IoT security point of view and discuss traditional key security mechanisms. This article delivers a comprehensive quality study for researchers on authentication and session keys, integrating IoT with blockchain and AI-based authentication in cybersecurity.
On the strategic value of equifinal choice
Authors: Arrieta Jose P. , Shrestha Yash R. 
Published: February 2022

AbstractManagers are often faced with the need to choose among multiple satisficing options. We call this situationequifinal choiceand argue how it opens an opportunity for managers to choose a new trajectory for their firm—an opportunity for strategic action. Althoughequifinal choicecan exist in any environment, it becomes most consequential when uncertainty is high. Uncertainty weakens the adherence of organizational members to a superordinate goal and the plurality of goals leads political processes to guide the firm’s strategy. Extant view has identified random choice as an unbiased, fair, simple, and swift solution to the problem of equifinal choice. Random choice is also commonly used in machine learning and artificial intelligence systems. As organizations augment their decision making with these systems, there is a threat that they forego these strategic opportunities and randomly choose actions that fail to harness commitment and trust. In thisPoint of Viewarticle, we highlight the problem ofequifinal choice, explain different ways it can be approached, and motivate why strategic choice can be valuable for organizations over and above defaulting to random choice.