bluuebunny/crossref_metadata_2025 huggingface dataset:
Master-Slave Approach for a Multi-terminal VSC-HVDC Systems Connected Offshore Wind Farm
Authors: Kazi Mohamed Amine , Majdoul Radouane , Machkour Nadia , El-alami Adnane , Baraka Ibrahim
Published: September 2021

The world is facing today the global challenge of energy transition since countries need more and more energy to grow their economy on a planet where resources are limited and poorly distributed. The integration of renewable energies and especially offshore wind energy into high voltage direct current (VSC-HVDC) transmission systems demonstrates great flexibility and reliability. In this paper, a control strategy for a multi-terminal VSC-HVDC system based on Master-Slave approach is proposed to automatically share the real power variation and stabilize the DC bus voltage in presence of abnormal operating conditions.
Simulation of the Heat Laser of the Selective Laser Sintering Process of the Polyamide12
Authors: Yaagoubi Hanane , Abouchadi Hamid , Taha Janan Mourad
Published: September 2021

Laser sintering sintering is one of the most widely used 3D printing technologies, in which it transforms 3D models into authentic parts with generally excellent workmanship, the test today is to ensure the unmatched nature of the item produced, therefore hypothetically to understand and predict the thermal history in this process, the thermal models must be exact and fair, In this article, the consideration will be focused on the different models of heat flux diffusion, in the bibliography, some formulas Numbers that describe the transport of the heat source out of the powder bed have been found. A comparison between its laser source models will be established. The re-modeling takes place in MATLAB using the parameters of polyamide12.
A patient's temperature remote control system based on NODEMCU ESP8266
Authors: El Kasmi Anas , Abouricha Mostafa , Boulezhar Abdelkader
Published: September 2021

With the prompt development in wireless communication, in addition to the evolution of wireless patient monitoring system, and to reduce the death of a big number of patients who endear as a result of heart failure or sudden high temperature in children. This paper proposed a sophisticated method of observation human body parameters like temperature measurements with employ at NodeMcu ESP8266 unit in the Internet of Things (IoT). This prototype is based on NodeMcu module (a static access point that provides the WIFI network, a server, a client and a mobile access point attached to the remote surveillance object) programmed under Arduino IDE and communicating between them via the HTTP protocol.
Prelimenary results of red lesion segmentation in WCE images
Authors: Charfi Said , El Ansari Mohamed , Ellahyani Ayoub , El Jaafari Ilyas
Published: September 2021

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has encouraged the researchers to provide automated diagnostic technics for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, we present the prelimenary results of red lesion detection in WCE images using Dense-Unet deep learning segmentation model. To this end, we have used a dataset containing two subsets of anonymized video capsule endoscopy images with annotated red lesions. The first set, used in this work, has 3,295 non-sequential frames and their corresponding annotated masks. The results obtained by the proposed scheme are promising.
WMLP: Web-based Multi-Layer protocols for Emergency Data Transmission in Mobile Ad Hoc Network
Authors: Saravanan K. , Anthoniraj S. , Kumarganesh S. , Senthil Kumar T. , Martin Sagayam K.
Published: September 2021

Mobile Ad Hoc Network (MANET) is an Emergency transportable Ad-Hoc network, which is a significant module of the Internet of Effects and has been extensively practiced in smart transportation systems in new existences. In this proposed system using Web-based Multi-Layer protocols for Emergency Data Transmission (WMLP), Dynamic multi-priority message order management method it is used to reduce the data transmission delay in entire networks. To fulfill the real-time limitations of emergency data, we proposed WMLP, a novel Networks web-like transmission mechanism for emergency data. We create a spiderweb-like perfect to limit the exact area. Source nodes send out demand terminus nodes send confirmed host in this controlled area to develop well-organized broadcast paths. The dynamic multi-priority file administration method it is managed some of packets level by level. In this protocols mining, necessary packs and giving the first importance emergency messages are function and considered for transmission failed packages also. When transmitting packets, we are using restricted containers strategy to choose the next hop.
Comparing word embedding models for Arabic aspect category detection using a deep learning-based approach
Authors: Bensoltane Rajae , Zaki Taher
Published: September 2021

Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.
Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges
Authors: Babu Achsha , Andrew Onesimu J. , Martin Sagayam K.
Published: September 2021

Artificial Intelligence (AI) has the ability to process huge datasets, disclose human essence computationally, and perform like humans as technology advances. Because of the necessity for precise diagnosis and improved patient care, AI technology has greatly influenced the healthcare industry. In the domains of dentistry and medicine, artificial intelligence has yet to come a long way. As a result, dentists must be aware of the potential implications for a profitable clinical practise in the future. In this paper, we present the current applications of AI in dentistry. The different types of AI techniques are introduced and summarized. The state-of-the-art literature is studied analysed. A comparative analysis on the different AI techniques in dentistry is presented. Further, the research challenges in the field of dentistry and future directions are also provided.
Segmentation as a base for digital marketing strategies in blood service: A cluster analysis for classifying healthy regional subjects
Authors: Khomenko Liliia , Saher Liudmyla , Letunovska Nataliia , Jasnikowski Adam
Published: September 2021

The work aims to analyse the youth by lifestyle and divide it into segments for more effective interaction in advertising campaigns. The object of the study is young people aged from 18 to 35 living in Sumy, Ukraine. It was performed a cluster analysis using the k-means method in the program Statistica 10 to divide into segments. There were selected 5 clusters. The main parameter for segment division is the age and previous experience of donation or its absence. There are also important: types of family, existence of children, the type of employment, the number of far trips for rest. Demographics is important first and, to a lesser extent, lifestyle. For two clusters it is recommended to use the recruitment strategy; for others - retention strategy with aspects considered in this work. The clusters of respondents identified by the authors also provide an opportunity to divide the residents of the study region in relation to the health problems of the location of their residence in relation to life and activities.
The role of the information technology staff in the digitalization process: A comparative research with ILO data
Authors: Arslan Aysel , Bektaş Çetin
Published: September 2021

In the competitive environment of the 21st century, businesses, economies, and countries that attach importance to technology and use technology in the best way are prosperous. Today, businesses lead a life based on technology. Most of the activities conducted by professionals working in their information and communication technology (ICT) department are implemented online and offline via computers. This process, called digitalization, is very significant for their sustainability. In this study, the number of experts employed in ICT in Turkey between 2015-2019 will be examined and compared with Germany's employment in the same period. The study aims to evaluate whether IT specialists' employment in the businesses in Turkey, which undergoes a process of digitalization, is sufficient. It is considered that the study will contribute to the steps taken towards digitalization. The study relies on the ILO data (International Labour Organization). The study uses a case research design, one of the qualitative research methods, and analysed the data by using the content analysis method.
Barriers to energy sustainability: A case study from Ukraine
Authors: Goncharuk Anatoliy , Khudolei Veronika , Stanislavyk Olena , Yatsyshyn Vadym , Semenchuk Yuliia
Published: September 2021

Sustainability goals imply not only economic and environmental components, but also a social one. The energy business around the world is profitable and economically viable, but it does not always bring appropriate benefits to a society. Besides, some governments, instead of protecting the interests of the population, turn a blind eye and generate injustice in relation to some parts of society in collaboration with energy companies. This paper examines one of the cases when the authorities pursue an unfair policy and contribute to the growth of inequality in society through regulating the natural gas tariffs. The paper considers a case of natural gas consumption and its unfair taxation by the Ukrainian government, which the authors called “gas tax”. A statistical analysis of this case revealed that honest middle-income taxpayers (middle class) are at the most disadvantageous position because they have to pay for utilities at the highest price. The paper also discusses a number of challenges and ethical issues related to the payment of gas tax. All these challenges should become arguments for revising the gas tax, making it less painful for the people and fair.
Two-person game with hesitant fuzzy payoff: An application in MADM
Authors: Jana Jishu , Roy Sankar Kumar
Published: September 2021

Hesitant Fuzzy Set (HFS) permits the membership function having a collection of probable values which are more effective for modelling the real-life problems. Multiple Attribute Decision Making (MADM) process apparently assesses multiple conflicting attribute in decision making. In traditional decision making problems, each player is moving independently whereas in reality it is seen that each player aims to maximize personal profit which causes a negative impact on other player. MADM problem treats with candidate to the best alternative corresponding to the several attributes. Here, we present an MADM problem under hesitant fuzzy information and then transforming it into two-person matrix game, referred to herein as MADM game. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of the prominent approach for solving the MADM problems. In this work, we develop the TOPSIS based on Ordered Weighted Aggregation (OWA) operator and hybrid hesitant fuzzy normalized Euclidean distance.Please check whether short title on odd pages have been set correctly. Then the two approaches, namely Hybrid Hesitant Fuzzy Ordered Weighted Aggregation-TOPSIS (HHFOWA-TOPSIS) and the Linear Programming Problem (LPP) are applied to solve the formulated MADM game. For solving MADM game, we apply LPP by considering the various values of α,ψ, and HHFOWA-TOPSIS for finding the optimal alternative according to their scores.Please provide missing AMS classification codes. An investment selection problem is included to explain the feasibility and superiority of our formulated approaches. A comparison analysis is drawn among the obtained results which are derived from the two approaches. LPP and HHFOWA-TOPSIS provide the best alternative for the proposed problem. Finally, conclusions about our findings and outlooks are described.
Effective neutron detection using vertical-type BGaN diodes
Authors: Nakano Takayuki , Mochizuki Ken , Arikawa Takuya , Nakagawa Hisaya , Usami Shigeyoshi , Honda Yoshio , Amano Hiroshi , Vogt Adrian , Schütt Sebastian , Fiederle Michael , Kojima Kazunobu , Chichibu Shigefusa F. , Inoue Yoku , Aoki Toru
Published: September 2021

In this study, vertical-type thick BGaN PIN diodes were successfully fabricated to improve the neutron capture rate of BGaN diodes by improving the BGaN epitaxial growth technique. In this technique, 5-μm-thick epitaxial growth was achieved using trimethylboron as a B metal-organic source, which suppressed the gas-phase reaction. The α-particle energy spectrum, the neutron pulse signal, and the residual energy of particles emitted from a neutron capture reaction were measured using the fabricated BGaN diodes by performing radiation detection measurements. The detected signal position in the neutron detection signal spectrum was similar to that of 2.3 MeV α-particles. These results indicate that vertical-type BGaN diodes can be used as effective neutron detectors.
Remarkable CO2 photoreduction activity using TiO2 nanotube arrays under favorable photothermal conditions driven by concentrated solar light
Authors: Zhang Zekai , Wang Ying , Cui Guokai , Lu Hanfeng , Abanades Stéphane
Published: September 2021

The photoreduction of carbon dioxide (CO2) to solar fuels is meaningful in many fields relevant to CO2 emission control, solar energy conversion and storage, carbon-neutral fuel production, CO2 conversion, and carbon cycle closure. The CO2 photoreduction on TiO2 nanotube arrays using concentrated sunlight under favorable photothermal conditions is considered in this work. TiO2 is a typical catalyst for the process while the reaction rate is still kept at rather poor level, partly because of the mild reaction conditions employed. In this study, the TiO2 photocatalyst was shaped as nanotube arrays and the current process limitations were tackled by raising the incident light intensity and reaction temperature by using concentrating solar light (concentration ratio between 200 and 800), while jointly increasing the achievable H2O and CO2 partial pressure. The results showed that the rate of CO2 photoreduction on the TiO2 nanotube array was increased by hundred times. The use of concentrated solar light heightens the hydrocarbons production rate to thousand μmol g−1 h−1 and enriches hydrocarbons products to CH4, C2H4, and C2H6. The favorable effects on enhancing the catalyst performance were ascribed to the intensification of reaction conditions. This noticeable breakthrough may represent an important step forward in the deployment of CO2 photoreduction technologies.
Percolation of rigid fractal carbon black aggregates
Authors: Coupette Fabian , Zhang Long , Kuttich Björn , Chumakov Andrei , Roth Stephan V. , González-García Lola , Kraus Tobias , Schilling Tanja
Published: September 2021

We examine network formation and percolation of carbon black by means of Monte Carlo simulations and experiments. In the simulation, we model carbon black by rigid aggregates of impenetrable spheres, which we obtain by diffusion-limited aggregation. To determine the input parameters for the simulation, we experimentally characterize the micro-structure and size distribution of carbon black aggregates. We then simulate suspensions of aggregates and determine the percolation threshold as a function of the aggregate size distribution. We observe a quasi-universal relation between the percolation threshold and a weighted average radius of gyration of the aggregate ensemble. Higher order moments of the size distribution do not have an effect on the percolation threshold. We conclude further that the concentration of large carbon black aggregates has a stronger influence on the percolation threshold than the concentration of small aggregates. In the experiment, we disperse the carbon black in a polymer matrix and measure the conductivity of the composite. We successfully test the hypotheses drawn from simulation by comparing composites prepared with the same type of carbon black before and after ball milling, i.e., on changing only the distribution of aggregate sizes in the composites.
Liquid state properties of SEI components in dimethoxyethane
Authors: Kamphaus Ethan P. , Balbuena Perla B.
Published: September 2021

The solid-electrolyte interphase (SEI) layer is a critical constituent of battery technology, which incorporates the use of lithium metals. Since the formation of the SEI is difficult to avoid, the engineering and harnessing of the SEI are absolutely critical to advancing energy storage. One problem is that much fundamental information about SEI properties is lacking due to the difficulty in probing a chemically complex interfacial system. One such property that is currently unknown is the dissolution of the SEI. This process can have significant effects on the stability of the SEI, which is critical to battery performance but is difficult to probe experimentally. Here, we report the use of ab initio computational chemistry simulations to probe the solution state properties of SEI components LiF, Li2O, LiOH, and Li2CO3 in order to study their dissolution and other solution-based characteristics. Ab initio molecular dynamics was used to study the solvation structures of the SEI with a combination of radial distribution functions, discrete solvation structure maps, and vibrational density of states, which allows for the determination of free energies. From the change in free energy of dissolution, we determined that LiOH is the most likely component to dissolve in the electrolyte followed by LiF, Li2CO3, and Li2O although none were favored thermodynamically. This indicates that dissolution is not probable, but Li2O would make the most stable SEI with regard to dissolution in the electrolyte.
Selectively doped barium ferrite ceramics with giant permittivity and high tunability under extremely low electric bias
Authors: Lv Shuang , Wang Min , Tian Wei , Li Guyao , Wang Zongrong , Ma Ning , Du Piyi
Published: September 2021

Tunable materials have been extensively studied due to their potential applications in many electrical devices. High tunability has been practically achieved in a number of ferroelectric materials such as perovskite phases under usually high DC electric field of 10–100 kV/cm. In this work, single phased M-type barium ferrite ceramics with colossal permittivity accompanied by defect pair dipoles and giant tunability under super low DC bias were successfully prepared by the sol-gel method. Results show that Zr4+ ions substituted for Fe3+ in the spinel phase of ferrites. The concentration of Fe2+ increased from 37.23% to 43.22% and subsequently decreased to 36.72% with increasing Zr4+ ions from 0 to 0.1 and then continuously to 0.3, respectively. The highest content of Fe2+ was ∼43.22% and thus the maximum concentration of Fe2+/Fe3+ pair dipoles formed between Fe2+ generated and Fe3+ nearby appeared in the ferrites with Zr4+ doping of 0.1. Not only in Zr4+ doped ferrites but also in the ferrites with doping other high valent ions, Fe2+/Fe3+ pair dipoles formed and controlled permittivity. Giant permittivity of above 30 k appeared in the ferrites with Zr4+ content of 0.1–0.3 and was controlled by external bias to form tunability. The activation energy of modulation of defect pair dipoles was only ∼0.182 eV, which is 85% lower than 1.2 eV of traditional perovskite BaTiO3. High dielectric tunability of more than 65% with only a low DC electric field of <25 V/cm was obtained in BaFe11.9Zr0.1O19 ferrites, which was in high contrast to conventional ferroelectrics where a high DC bias of dozens of kV/cm was required. Similarly, dielectric tunability of ∼40% with a low electric field of <40 V/cm was exhibited in Nb5+ or Ti4+ doped barium ferrites. Such a high tunability controlled by an extremely low bias field in barium ferrite ceramics doped by the target ions might be promised for novel applications in tunable devices.
Role of local chemical fluctuations in the melting of medium entropy alloy CoCrNi
Authors: Jian Wu-Rong , Wang Liang , Bi Wenbo , Xu Shuozhi , Beyerlein Irene J.
Published: September 2021

Medium- to high-entropy alloys are characterized by fluctuations in chemical composition, i.e., lattice distortion (LD) and chemical short-range ordering (CSRO). However, their roles in the melting of these alloys are still unclear. Using a combination of molecular dynamics and x-ray diffraction simulations, we investigate the effects of LD and CSRO on the melting of single crystalline CoCrNi, a medium entropy alloy (MEA). LD accelerates the melting process and reduces the melting temperature by lowering the energy barrier of the nucleation of amorphous clusters and then promoting the formation of amorphization. By contrast, CSRO divides CoCrNi MEA into CoCr clusters and Ni segregated regions, localizing LD to the CoCr regions and the boundaries between CoCr clusters and Ni regions. Such a LD localization reduces the area for the nucleation of the amorphous clusters in the ordered MEA at the start of melting, resulting in a lower free energy and, thus, a much higher melting temperature than the random MEA without CSRO. As the temperature rises in the ordered MEA, the degree of CSRO decreases slightly in the beginning and then experiences a rapid reduction in the last stage of melting.
Efficient hydrogen evolution from water over thin film photocathode composed of solid solutions between ZnSe and Cu(In, Ga)Se2 with composition gradient
Authors: Minegishi Tsutomu , Yamaguchi Shingi , Sugiyama Masakazu
Published: September 2021

Solid solutions between ZnSe and Cu(In, Ga)Se2 (ZnSe:CIGS) have promising properties as photocathodes for solar hydrogen production from water, such as a long absorption edge of about 900 nm and a large driving force for reaction, >0.9 V, originated by a deep valence band maximum of 1.0–1.1 V vs normal hydrogen electrode (NHE). However, their performance is limited with an incident photon-to-current conversion efficiency (IPCE) of 65% at 400 nm and a half-cell solar-to-hydrogen energy conversion efficiency (HC-STH) of 3.6% so far. Te addition during ZnSe:CIGS thin film deposition by vacuum co-evaporation clearly decreased the optimal deposition temperature from 450 to 380 °C and resulted in columnar shaped grains of submicrometer size in diameter and with almost no grain boundary between the film surface and the backside electrode of Mo, which is significantly larger than the case of without Te addition. Interestingly, a Ga/In compositional ratio gradient was further introduced to the depth profile, which can facilitate charge separation. Structural characterizations using XRD and cross-sectional transmission electron microscopy revealed that the composition gradient was mainly formed by the diffusion of In through grain boundaries in the Ga-rich layer, and thus, the mixing between the Ga-rich and In-rich layers was more significant at a lower deposition temperature owing to the smaller grain size. The photocathode fabricated from the ZnSe:CIGS thin film with the composition gradient showed a very high IPCE of 89% at 540 nm and 0 V vs the reversible hydrogen electrode, and HC-STH of 3.7%, which is higher than values reported thus far. In conclusion, it is clarified that the potential gradient, which can be introduced by composition gradient, is beneficial for photoelectrodes and photocatalysts to achieve higher performance.
An investigation of high entropy alloy conductivity using first-principles calculations
Authors: Raghuraman Vishnu , Wang Yang , Widom Michael
Published: September 2021

The Kubo–Greenwood equation, in combination with the first-principles Korringa–Kohn–Rostoker coherent potential approximation (KKR-CPA) can be used to calculate the DC residual resistivity of random alloys at T = 0 K. We implemented this method in a multiple scattering theory based ab initio package, MuST, and applied it to the ab initio study of the residual resistivity of the high entropy alloy AlxCoCrFeNi as a function of x. The calculated resistivities are compared with experimental data. We also predict the residual resistivity of refractory high entropy alloy MoNbTaVxW. The calculated resistivity trends are also explained using theoretical arguments.
Media portrayals of the Hong Kong Occupy Central Movement’s social actors
Authors: Ho Janet , Chiu Ming Ming
Published: September 2021

We investigated how two English-language newspapers – Hong Kong’s South China Morning Post (SCMP) and the mainland China Daily (CD) – portrayed key social actors (police, students, protesters, and governments) during the Occupy Central/Yellow Umbrella movement. We examined emotional valence, arousal, and dominance characterizations in 1,180 news articles via a multilevel, multivariate outcome regression and critical discourse analysis. The findings reveal that emotional sentiments associated with students and protesters in SCMP were generally more positive than in CD but that this was reversed for the police and the government. Whereas SCMP deployed personal stories to construct a humanized image of protesters and students, CD relied on expert authority, rhetorical questions, and imagined scenarios to convey empathy towards Hong Kong residents, creating a villainized image of protesters. Our mixed-methods approach reveals how SCMP and CD portrayed students differently via the discursive frames of “optimistic dreamers” and “powerless scapegoats,” respectively.
The role of the body in descriptions of emotions
Authors: Ponsonnet Maïa , Laginha Kitty-Jean
Published: September 2021

This article presents the first systematic typological study of emotional expressions involving body parts at the scale of a continent, namely the Australian continent. The role of body parts in figurative descriptions of emotions, a well-established phenomenon across the world, is known to be widespread in Australian languages. This article presents a typology of body-based emotional expressions across a balanced sample of 67 languages, where we found that at least 30 distinct body parts occur in emotional expressions. The belly is by far the most frequent, and a dozen others also have significant representation. The study shows how the properties of these body parts – e.g., whether they are internal organs or visible facial parts – partly determine which historical scenarios led to their linguistic associations with emotions, and in turn, their semantic and figurative properties.
The body and the verb
Authors: Kofod Frances , Crane Anna
Published: September 2021

This paper explores the figurative expression of emotion in Gija, a non-Pama-Nyungan language from the East Kimberley in Western Australia. As in many Australian languages, Gija displays a large number of metaphors of emotion where miscellaneous body parts – frequently, the belly – contribute to the figurative representation of emotions. In addition, in Gija certain verbal constructions describe the experience of emotion via metaphors of physical impact or damage. This second profile of metaphors is far less widespread, in Australia and elsewhere in the world, and has also attracted far fewer descriptions. This article explores both types of metaphors in turn. Body-based metaphors will be discussed first, and we will highlight the specificity of Gija in this respect, so as to offer data that can be compared to other languages, in Australia and elsewhere. The second part of the article will present verbal metaphors. Given that this phenomenon is not yet very well undersood, this account aims to take a first step into documenting a previously unexplored domain in the language thereby contributing to the broader typology that this issue forms a part of. Throughout the text, we also endeavour to connect the discussion of metaphors with local representations and understanding of emotions.
Ear and belly in Warlpiri descriptions of cognitive and emotional experience
Authors: Laughren Mary , Ponsonnet Maïa
Published: September 2021

Like most other Australian languages, Warlpiri – a Pama-Nyungan language of the Ngumpin-Yapa group – is rich in figurative expressions that include a body-part noun. In this article we examine the collocations involving two body parts:langa‘ear’, which mostly relates to cognition; andmiyalu‘belly’, which mostly relates to emotion. Drawing on an extensive Warlpiri database, we analyse the semantic, figurative and syntactic dimensions of these collocations. We note how reflexive variants of certain collocations impose a non-literal aspectual reading, as also observed in Romance and Germanic languagesinter alia. The article also highlights differences between the range of body-based emotion metaphors found in Warlpiri, and that reported for the non-Pama-Nyungan languages of Australia. We hypothesize that these differences sometimes reflect grammatical differences. In particular, Warlpiri allows body-part nouns in syntactic functions that rarely found in non-Pama-Nyungan languages, due to the prevalence of body-part noun incorporation in the latter group.
Advanced learners of Spanish abroad
Authors: Soulé María Victoria , Pérez-Vidal Carmen
Published: September 2021

This study examines the progress made by a group of ERASMUS students in their use of the Spanish Preterite and Imperfect during a stay abroad. Advanced learners of Spanish (N = 12) and native speakers (N = 12) completed an impersonal narrative-based forced-choice test (INT) and an explicit knowledge questionnaire (EKQ). Results from the INT partially confirm the findings of previous studies which indicate that the expression of grounding distinguishes learners from native speakers. Our data show that only the foreground clearly distinguishes between the two groups, not the background. Responses to the EKQ reveal that learners use a variety of mechanisms for their selection of past tense morphology: formal instruction, translation into their L1, knowledge of other L2s and conversation with native speakers. This article suggests further research be undertaken to examine the impact of the learning context on students’ use of past tense morphology.
Problematizing heritage language identities
Authors: Pozzi Rebecca , Reznicek-Parrado Lina
Published: September 2021

Heritage speaker identities have traditionally been a relevant topic of inquiry among scholars of heritage language pedagogy. Nevertheless, there is little research on Spanish heritage language identities in a study abroad context. Additionally, most existing studies on this topic focus on heritage speakers of Mexican descent studying in Mexico (e.g., de Félix & Cavazos Peña, 1992; McLaughlin, 2001; Riegelhaupt & Carrasco, 2000). This study examines heritage language identities in a non-heritage context by exploring the experiences of three heritage speakers of Mexican descent studying in Mendoza, Argentina. By focusing on a non-heritage context, we move away from the presumption that all heritage speakers seek to (re)claim a specific ethnic identity through language study (see Leeman, 2015). Instead, our qualitative analysis illustrates the diverse ways heritage speakers in a non-heritage context construct, contest, and negotiate their identities with respect to linguistic awareness, negotiation between varieties, and perceptions of their abilities.
Fluctuations in mental well-being during Study Abroad
Authors: Dewaele Jean-Marc , Dewaele Livia
Published: September 2021

This mixed-methods study focuses on the effect of Study Abroad (SA) on the mental well-being of 33 Anglophone students who spent between four and twelve months in Francophone countries. It investigates the relationship between well-being and personality traits. Statistical analyses revealed no significant change in well-being between the start, the middle and the end of the SA. A closer look at individual patterns showed large fluctuations, with half of participants scoring higher and the other half scoring lower between the start and the middle of the SA. The narratives of three participants whose well-being scores increased most were not very different from the three participants whose well-being scores decreased most, and only (lower) Emotional Stability was linked with the increase in well-being. At the group level, well-being was not significantly linked to personality traits. The apparent stability of well-being during SA seems to be the result of upward and downward patterns cancelling each other out.
Numerical simulation of ro-vibrational spectra for diatomic molecules using the Numerov matrix method
Authors: Sharma Aditi , Sastri O S K S
Published: September 2021

In this paper, the Numerov matrix method is implemented in a pedagogic manner for simulation of a CO molecule. First, the Numerov algorithm is implemented in the spreadsheet environment ‘Gnumeric’ to obtain coarse vibrational, rotational and ro-vibrational spectrum. Then, the code is written in Scilab so that simulation can be performed for smaller step-sizes so as to achieve greater accuracy for frequencies in ro-vibrational fine-structure spectrum. The simulated vibrational frequencies have been obtained to an accuracy of 0.01% with respect to the experimental ones. The values of rotational constant (B

e
) and anharmonicity constant (

ω

¯

e

x

e

) have been obtained to accuracies of 0.6 and 1.22% respectively. This procedure can be easily integrated as a simulation activity for determination of ro-vibrational spectra of various heterogenous diatomic molecules at undergraduate level.
Valid versus invalid radiation cancer risk assessment methods illustrated using Swiss population data
Authors: Hafner Luana , Walsh Linda
Published: September 2021

After the nuclear accident in Fukushima, the public interest in radiation related cancer-risk assessment increased. However, interpretations of results from epidemiological studies and comprehension of cancer risk assessment methods can be unclear and involve questions about correctness and validity of the approaches. To shed some light on this potential lack of clarity, valid versus invalid radiation cancer risk assessments methods are illustrated here using Swiss population data. This involves a comparison of the cancer risk assessment method based on collective dose and the cumulative risk assessment method, where the latter is recommended with regard to uncertainties and risk of misinterpretation. Further, risk assessment in different dose ranges is discussed and it is concluded that below 100 mSv it cannot be appropriately stated that an adequate strength of evidence of a causal relationship between cancer and radiation is provided, because of the large uncertainties in this dose range. However, the linear non-threshold (LNT) model can be used to model the dose response, because it represents a prudent and parsimonious model, that fits the data well and lies within the given uncertainties. Additionally, treatments of uncertainties in the risk models are illustrated. The EU-project CONFIDENCE software is applied here to obtain example radiation related lifetime cancer risks for exposures of 20 mSv and 5 mSv. Furthermore, the impact of different dosimetry errors on the uncertainties in the cancer lifetime risk calculation is analysed, by including different standard deviations (SD) and by comparing the sampling of the doses from a normal and a lognormal distribution. Using the normal distribution, for females exposed to 20 mSv, the 95% confidence interval (CI) on the cancer lifetime risk increases, when compared to using a SD of 4 mSv, by a factor of 1.5 using a SD of 8 mSv and by a factor of 1.7 using a SD of 10 mSv. The corresponding factors for males for the same exposure are 1.3 and 1.5 respectively. For exposure to 5 mSv, the 95% CIs on the risk increase by a factor of 1.2 for females and 1.4 for men for a SD of 2 mSv using the normal distribution compared to the lognormal distribution and by a factor of 1.5 and 1.8 for a SD of 3 mSv compared to a SD of 1 mSv respectively. Furthermore, differences in the resulting 95% CI on the risk, using different distributions for the dose sampling are visible.
Magnetic moment predictions of odd-A nuclei with the Bayesian neural network approach *
Authors: Yuan Zilong , Tian Dachuan , Li Jian , Niu Zhongming
Published: September 2021

The Bayesian neural network approach has been employed to improve the nuclear magnetic moment predictions of odd-A nuclei. The Schmidt magnetic moment obtained from the extreme single-particle shell model makes large root-mean-square (rms) deviations from data, i.e., 0.949

and 1.272

for odd-neutron nuclei and odd-proton nuclei, respectively. By including the dependence of the nuclear spin and Schmidt magnetic moment, the machine-learning approach precisely describes the magnetic moments of odd-A nuclei with rms deviations of 0.036

for odd-neutron nuclei and 0.061

for odd-proton nuclei. Furthermore, the evolution of magnetic moments along isotopic chains, including the staggering and sudden jump trend, which are difficult to describe using nuclear models, have been well reproduced by the Bayesian neural network (BNN) approach. The magnetic moments of doubly closed-shell

nuclei, for example, isoscalar and isovector magnetic moments, have been well studied and compared with the corresponding non-relativistic and relativistic calculations.
Logistic Map: Stability and Entrance to Chaos
Authors: Chen Shaoqiu , Feng Shiya , Fu Wenjun , Zhang Yingying
Published: September 2021

Chaos and nonlinear dynamics have taken a crucial place in the mathematics, physics, and engineering worlds. The main focus of this paper is about one famous map in the dynamical system that has an extreme sensitivity to the initial conditions, the logistic map.We first discuss the behaviours of the logistic map under different µ: convergence to 0 when√μ ∈ (0,1), convergence to 1−1/µ when µ ∈ (1,3), 2-cycle when µ ∈ (3,1 + 6), further period doubling and eventual chaos, which is in good accordance with our simulation. In the end, we proved three relevant results: the criteria for stability of cycle, the Coppel Theorem, and the famous slogan “period three implies chaos.”
Development and characterization of a large area silicon pad array for an electromagnetic calorimeter
Authors: Mukhopadhyay S. , Chandratre V.B. , Muhuri S. , Singaraju R.N. , Saini J. , Nayak T.K.
Published: September 2021

We present the research and development work of the first version of a 6×6 array of silicon pad detectors, carried out in India, for the proposed forward calorimeter (FOCAL) as part of the ALICE collaboration upgrade program at CERN. The primary motivation is to develop a large area silicon pad array realizing the challenging requirements of high-energy physics experiments such as low leakage current, high breakdown voltage and evaluate its performance as an active layer in the prototype silicon tungsten (Si-W) electromagnetic (EM) calorimeter. Towards these goals, a 36-pad silicon sensor with an individual pad size of ∼1 cm2 is designed on a 4-inch high resistivity N-type wafer and fabricated at Bharat Electronics Limited, Bangalore. The sensors have been used to assemble the prototype Si-W calorimeters and were successfully tested with high-energy particle beams. The design and development of the large area silicon sensor and its characterization using radioactive sources in the laboratory and high-energy particle beams are reported in this paper.
Elastodynamic model for flapping-wing micro aerial vehicle
Authors: Fang Xin , Wu Jianghao , Du Feng
Published: September 2021

Lightweight design is key to high efficiency and long durability of micro air vehicle (MAV), while it will inevitably reduce the stiffness of the structures and affect the motion of the mechanism. In this study, an elastodynamic model for flapping-wing MAV (FMAV) is established to unveil the effect of elastic deformation of transmission mechanism on the flapping motion. Based on kineto-elastostatic analysis, an elastodynamic model of the transmission mechanism is built, which reveals that the inertial force of the transmission mechanism for typical FMAV is much smaller than the force transmitted. Thus, the inertial force can be ignored, and analytical formula between the deformation of transmission mechanism and the flapping angle is derived. Finite element method (FEM) simulations are conducted to validate the analytical formula, and the results show that the flapping angle obtained from the analytical formula matches well with FEM simulations. The proposed elastodynamic model and analytical formula will provide theoretical guidance for designing and optimizing FMAV with desired transmission mechanism and flapping motion.
LiDAR-driven spiking neural network for collision avoidance in autonomous driving
Authors: Shalumov Albert , Halaly Raz , Tsur Elishai Ezra
Published: September 2021

Facilitated by advances in real-time sensing, low and high-level control, and machine learning, autonomous vehicles draw ever-increasing attention from many branches of knowledge. Neuromorphic (brain-inspired) implementation of robotic control has been shown to outperform conventional control paradigms in terms of energy efficiency, robustness to perturbations, and adaptation to varying conditions. Here we propose LiDAR-driven neuromorphic control of both vehicle's speed and steering. We evaluated and compared neuromorphic PID control and online learning for autonomous vehicle control in static and dynamic environments, finally suggesting proportional learning as a preferred control scheme. We employed biologically plausible basal-ganglia and thalamus neural models for steering and collision-avoidance, finally extending them to support a null controller and a target-reaching optimization, significantly increasing performance.
Efficient pathways to zero-carbon energy use by water supply utilities: an example from London, UK
Authors: Majid Aman , Mortazavi-Naeini Mohammad , Hall Jim W
Published: September 2021

Urban water utilities are increasing their use of energy-intensive technologies such as desalination and long-distanced pumped transfers. Under pressure to reduce their energy-related carbon emissions to zero, water utilities have devised a variety of energy management strategies, including the purchase of renewable energy and self-generation of electricity using locally installed renewables. These strategies will incur different costs for the utility, whilst some may have implications for the reliability of water supply systems. Yet the trade-offs between costs, water security and energy sustainability remain unexplored. Here, we present a regional scale analysis to test competing energy strategies, mapping pathways to zero carbon energy and water security. Results from a case-study of the London water system show a balanced approach that allows for some energy self-generation, using biogas, solar and wind, while also purchasing green energy credits from the electricity supply grid can best navigate this trade-off. Balanced investment plans can accommodate energy-intensive water supply techniques such as long-distance transfers, desalination and effluent reuse while meeting energy targets. By becoming energy generators and also adopting more flexible arrangements for energy use, water utilities could become significant players in energy markets.
Anderson 𝑡-modules with thin 𝑡-adic Galois representations
Authors: Maurischat Andreas
Published: September 2021

Pink has given a qualitative answer to the Mumford-Tate conjecture for Drinfeld modules in the 90s. He showed that the image of the

p

\mathfrak {p}

-adic Galois representation is

p

\mathfrak {p}

-adically open in the motivic Galois group for any prime

p

\mathfrak {p}

. In contrast to this result, we provide a family of uniformizable Anderson

t
t

-modules for which the Galois representations of their

t
t

-adic Tate-modules are “far from” having

t
t

-adically open image in their motivic Galois groups. Nevertheless, the image is still Zariski-dense in the motivic Galois group which is in accordance to the Mumford-Tate conjecture. For the proof, we explicitly determine the motivic Galois group as well as the Galois representation for these

t
t

-modules.
Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests
Authors: Çubukçu Hikmet Can , Topcu Deniz İlhan , Bayraktar Nilüfer , Gülşen Murat , Sarı Nuran , Arslan Ayşe Hande
Published: September 2021

Objectives

The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results.

Methods

We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription–polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil.

Results

The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study’s data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%).

Conclusions

ML models presented in this study can be used as clinical decision support tools to contribute to physicians’ clinical judgment for COVID-19 diagnoses.