bluuebunny/crossref_metadata_2025_split huggingface dataset:
Collaborative experience between scientific software projects using Agile Scrum development
Authors: Baxter Amanda L. , BenZvi Segev Y. , Bonivento Walter , Brazier Adam , Clark Michael , Coleiro Alexis , Collom David , Colomer‐Molla Marta , Cousins Bryce , Delgado Orellana Aliwen , Dornic Damien , Ekimtcov Vladislav , ElSayed Shereen , Gallo Rosso Andrea , Godwin Patrick , Griswold Spencer , Habig Alec , Hill Remington , Horiuchi Shunsaku , Howell D. Andrew , Johnson Margaret W. G. , Jurić Mario , Kneller James P. , Kopec Abigail , Kopper Claudio , Kulikovskiy Vladimir , Lamoureux Mathieu , Lang Rafael F. , Li Shengchao , Lincetto Massimiliano , Lindstrom Lindy , Linvill Mark W. , McCully Curtis , Migenda Jost , Milisavljevic Danny , Nelson Spencer , Novoseltseva Rita , O'Sullivan Erin , Petravick Donald , Pointon Barry W. , Raj Nirmal , Renshaw Andrew , Rumleskie Janet , Sonley Tom , Tapia Ron , Tseng Jeffrey C. L. , Tunnell Christopher D. , Vannoye Godefroy , Vigorito Carlo F. , Virtue Clarence J. , Weaver Christopher , Weil Kathryn E. , Winslow Lindley , Wolski Rich , Xu Xun‐ Jie , Xu Yiyang ,   The SCiMMA and SNEWS Collaborations
Published: July 2022

AbstractDeveloping sustainable software for the scientific community requires expertise in software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for software engineering practices in the scientific community, and the complexity of developing for evolving scientific contexts. While open‐source software can partially address these concerns, it can introduce complicating dependencies and delay development. These issues can be reduced if scientists and software developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi‐Messenger Astrophysics project. The collaboration addressed the difficulties of open‐source software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting a user‐group while maintaining core development. These issues were mitigated by creating a second Agile Scrum framework in parallel with the developers' ongoing Agile Scrum process. This Agile collaboration promoted communication, ensured that the scientists had an active role in development, and allowed the developers to evaluate and implement the scientists' software requirements. The collaboration provided benefits for each group: the scientists actuated their development by using an existing platform, and the developers utilized the scientists' use‐case to improve their systems. This case study suggests that scientists and software developers can avoid scientific computing issues by collaborating and that Agile Scrum methods can address emergent concerns.
Bahiana, a new Euphorbiaceae (Acalyphoideae) genus from seasonally dry forest in northeastern Brazil, corroborated by molecular and morphological evidence
Authors: Carrión Juan F. , dos Santos Francisco A.R. , Cordeiro Inês , Amorim André M. 
Published: July 2022

AbstractAfter discovering an unknown Euphorbiaceae with an unusual combination of morphological characters, molecular phylogenetic analyses were conducted to evaluate the phylogenetic position of the plant. Results of the analyses support the recognition of a resolved lineage sister to a clade comprising Caryodendron, which is sister to the Adenophaedra + Bernardia clade in subfamily Acalyphoideae. The phylogenetic position of this taxon is also supported by morphology, justifying the description of a new genus and species, Bahiana pyriformis, from the Caatinga seasonally dry forest, in Bahia State, Brazil. We compare morphological characters and discuss the phylogenetic relationships of these four genera, here treated as the Bernardia clade, as well as the possible relationship this clade has with genus Avellanita and tribe Plukenetieae.
MiR‐1246 is responsible for lung cancer cells‐derived exosomes‐mediated promoting effects on lung cancer stemness via targeting TRIM17
Authors: Liu Zhengcheng , Zhao Wei , Yang Rusong 
Published: July 2022

AbstractThe stemness of lung cancer cells contributes to drug resistance, tumor occurrence, progression, and recurrence; however, the underlying mechanisms are still fragmentary. In the present study, it was found that exosomes from cisplatin‐resistant cells and spheres derived from lung cancer cells enhanced the stemness of the parental lung cancer cells. Then we screened the upregulated miRNAs in spheres derived from lung cancer cells and cisplatin‐resistant lung cancer cells/exosomes compared to that in the parental lung cancer cells. It was found that miR‐1246 was remarkably enriched in cisplatin‐resistant lung cancer cells/exosomes and spheres. Additionally, inhibition of miR‐1246 attenuated the stemness of lung cancer cells induced by exosomes from cisplatin‐resistant cells and spheres. Furthermore, TRIM17 was identified to the direct target of miR‐1246 in lung cancer cells. Our findings suggest that exosomal miR‐1246 could be as a potential target for lung cancer treatment.
Controlled drainage and subirrigation suitability in the United States: A meta‐analysis of crop yield and soil moisture effects
Authors: Singh Navdeep , Kogan Clark , Chaudhary Siddharth , Rajagopalan Kirti , LaHue Gabriel T. 
Published: July 2022

AbstractControlled drainage and subirrigation (CDSI) is an important water management strategy in many regions, but the conditions under which CDSI is most likely to increase crop yield and soil moisture are not fully understood. A meta‐analysis, consisting of 154 pairwise observations from replicated and randomized trials in 30 peer‐reviewed primary research articles on CDSI (6 controlled drainage, 24 CDSI, analyzed together due to data scarcity), was conducted to study the responses of yield and soil moisture to CDSI, and investigate how crop type, soil texture, and cumulative growing season precipitation (PGS) influence these responses. Based on the yield response to these moderating factors, we used a fuzzy‐logic approach to map potentially suitable locations for CDSI in the conterminous United States. On average, CDSI increased yield by 8.0% (95% CI = 1.8–14.7%) compared with conventional free drainage. The yield response to CDSI did not differ among crops. However, a greater yield response to CDSI was observed in medium‐textured soils (19.4% increase; 95% CI = 12.4–27.0%) than in coarse‐ or fine‐textured soils. The positive effect of CDSI on yield increased with decreasing PGS in coarse‐ and medium‐textured soils. There was no clear effect of CDSI on soil moisture, nor did any moderators influence this relationship, though this may be attributed to the scarcity of studies on CDSI reporting soil moisture. The fuzzy‐logic‐based approach revealed that while potentially suitable areas are mostly concentrated in the well‐studied U.S. Midwest, these areas also exist in other regions where CDSI may warrant further study.
Antenatal Risk Assessment for Preterm Birth: Summary Guidance for Healthcare Providers
Authors: Gillespie Bronwen , Mitchell Caroline 
Published: July 2022

AbstractThis chapter serves as a summary for healthcare providers for the five key areas of guidance relevant to pregnancy care for preterm birth. First of all, brief evidence statements are covered for the five areas: demographics and patient history, pregnancy dating, infection, nutrition and alcohol, tobacco and other substance use. This is followed by a section on risk assessment and recommended interventions. Although those areas may be routinely enquired about during antenatal booking and registration in most contexts, in this chapter we are highlighting the way this information can be used to help to identify the risk of preterm birth.
Pregnancy Dating Guidance
Authors: Anumba Dilly OC 
Published: July 2022

AbstractAccurate estimation of pregnancy duration is crucially important for the definition of term and preterm birth and informs care provision throughout pregnancy. However, this remains a challenge in many resource-limited context largely because the reliability of recall of the last menstrual period is low, and access to early ultrasound to measure foetal crown-rump length and other foetal biometry is highly limited. This chapter outlines a practical simplified approach to estimating pregnancy duration early following engagement with ANC. This forms the basis of subsequent care provision to correctly identify preterm labour/birth, improve epidemiological data on preterm low birth weight incidence, and enable proper triaging of care and its provision.
Weather Classification with Transfer Learning - InceptionV3, MobileNetV2 and ResNet50
Authors: Młodzianowski Patryk 
Published: July 2022

AbstractWeather recognition is a common problem for many branches of industry. For example self-driving cars need to precisely evaluate weather in order to adjust their driving style. Modern agriculture is also based on the analysis of current meteorological conditions. One of the solutions may be a system detecting weather from image. Because any special sensors are needed, the system should be really cheap. Thanks to transfer learning it is possible to create image classification solutions using a small dataset. In this paper three weather recognition models are proposed. These models are based on InceptionV3, MobileNetV2 and ResNet50 architectures. Their efficiency is compared and described.
Realization of a Real-Time Decision Support System to Reduce the Risk of Diseases Caused by Posture Disorders Among Computer Users
Authors: Gumuskaynak Enes , Toptas Faruk , Aslantas Recep , Balki Fatih , Sarp Salih 
Published: July 2022

AbstractNowadays, diseases caused by posture disorders are becoming more common. This situation reduces the working efficiency of people, especially computer users. This study aims to provide prevention of diseases caused by posture disorders faced by computer users and realize an application software to reduce disease risks. With this realized application, computer users’ movements are monitored through the camera, and the situations that may pose a risk of disease for the users are determined. Realized application software is a decision support system. This decision support system provides users suggestions to change their position according to their instant postures and supports them to work more efficiently. The user data is collected by processing the images taken from a camera using the developed computer vision algorithm. Two-dimensional (2D) human exposure estimation is performed with the obtained data. The situations that can decrease the working efficiency are specified with the data obtained from exposure estimation using the developed model. As a result of these findings, increasing the working efficiency is provided by informing the user in real-time about the situation that may decrease the working efficiency.
On Two Approaches to Clustering of Cross-Sections of Rotationally Symmetric Objects
Authors: Baczyńska Paulina , Kaliszewska Agnieszka , Syga Monika 
Published: July 2022

AbstractWe analyze two approaches to clustering 2D shapes representing cross-sections of rotationally symmetrical objects. These approaches are based on two ways of shape representation - contours and silhouettes - and a number of similarity measures which are based on a combination of Procrustes analysis (PA) and Dynamic Time Warping (DTW) as well as on binary matrix analysis. The comparison of efficiency of the proposed approaches is performed on datasets of archaeological ceramic vessels.
Engaging Electricity Users in Italy, Denmark, Spain, and France in Demand-Side Management Solutions
Authors: Bohdanowicz Zbigniew , Kowalski Jarosław , Kobyliński Paweł 
Published: July 2022

AbstractThis paper presents the process of uncovering the motivations and barriers for adopting innovative solutions to increase the flexibility of electricity demand among individual consumers. Currently, efforts are being made to decarbonize electricity production with distributed solar and wind renewable energy installations. Such a shift in energy production also requires significant changes on the consumption side, in particular making demand more flexible to match the current situation in the power grid. The challenge in designing demand-side solutions is to accurately identify the needs of individual users so that they are motivated to take advantage of new solutions. Using data from a quantitative survey of electricity consumers in four countries (Italy, Denmark, Spain, France) on energy literacy, values and attitudes towards energy saving and technology, a cluster analysis was carried out which identified five types of electricity users. The segments defined in this way were the basis for conducting qualitative creative workshops with experts dealing with modern solutions in the field of energy and with individual electricity users. Subsequently, this information was supplemented with theoretical knowledge from the field of economic psychology regarding decision making, cognitive processes and motivation. This method allowed, already at the early stage of innovation design, to identify motivations and barriers specific to individual groups of users. The designers of innovative solutions received valuable clues as to how new technologies should be designed in order to ensure that they are well aligned with the habits, needs and rhythm of daily routines of the users.
A Coarse-to-Fine Multi-class Object Detection in Drone Images Using Convolutional Neural Networks
Authors: Aburasain R. Y. , Edirisinghe E. A. , Zamim M. Y. 
Published: July 2022

AbstractMulti-class object detection has a rapid evolution in the last few years with the rise of deep Convolutional Neural Networks (CNNs) learning based, in particular. However, the success approaches are based on high resolution ground level images and extremely large volume of data as in COCO and VOC datasets. On the other hand, the availability of the drones has been increased in the last few years and hence several new applications have been established. One of such is understanding drone footage by analysing, detecting, recognizing different objects in the covered area. In this study conducted, a collection of large images captured by a drone flying at a fixed altitude in a desert area located within the United Arab Emirates (UAE) is given and it is utilised for training and evaluating the CNN networks to be investigated. Three state-of-the-art CNN architectures, namely SSD-500 with VGGNet-16 meta-architecture, SSD-500 with ResNet meta-architecture and YOLO-V3 with Darknet-53 are optimally configured, re-trained, tested and evaluated for the detection of three different classes of objects in the captured footage, namely, palm trees, group-of-animals/cattle and animal sheds in farms. Our preliminary experiments revealed that YOLO-V3 outperformed SSD-500 with VGGNet-16 by a large margin and has a considerable improvement as compared to using SSD-500 with ResNet. Therefore, it has been selected for further investigation, aiming to propose an efficient coarse-to-fine object detection model for multi-class object detection in drone images. To this end, the impact of changing the activation function of the hidden units and the pooling type in the pooling layer has been investigated in detail. In addition, the impact of tuning the learning rate and the selection of the most effective optimization method for general hyper-parameters tuning is also investigated. The result demonstrated that the multi-class object detector developed has precision of 0.99, a recall of 0.94 and an F-score of 0.96, proving the efficiency of the multi-class object detection network developed.
Use of Virtual Reality in Psychology
Authors: Hakim Arhum , Hammad Sadaf 
Published: July 2022

AbstractThe field of psychology is advancing with incorporation of immersive technologies and Virtual Environments (VE) in research, treatment, assessment and learning etc. Virtual Reality (VR) is one of the focused sub-domains of immersive technologies that is being used vastly in psychology. This also comes under the paradigm of cyberpsychology. The advancements reported in this area bring the need to understand the benefits provided by virtual reality with respect to human interaction and behavior etc. The published literature available discusses the development and evolution of cyber psychology specifically with respect to virtual reality. To the best of our knowledge the latest developments due to virtual reality in various sub fields of psychology have not been collectively documented so far. This paper provides highlights of the developments and transformations linked with the usage of virtual reality with recommendations about future studies and provides the reader a broader and informed view of this cross-disciplinary area. Moreover, the paper also provides limitations that exist, and considerations required while using VR for a specific treatment or experimentation.
Google Translate Facilitates Conference Abstracts’ Acceptance, But Not Invitations to Deliver an Oral Presentation
Authors: Toczyski Piotr , Banerski Grzegorz , Biele Cezary , Kowalski Jarosław , Paradowski Michał B. 
Published: July 2022

AbstractRemoving the language barrier could bring great benefits not only to the scientific community. Therefore, it is necessary to strive to improve both the tools and procedures in which these tools are used, to ensure a reliable exchange of knowledge. The authors try to find out whether the existing and widely available technology (Google Translate) contributes to the facilitation of knowledge sharing among scientists. Humanity has been trying to construct and improve the technology of universal real-time translation for a long time. For many, it was inspired by scifi works, in which, probably, this idea appeared already in the 1940s (see Leinster’s “First contact”). This is an important topic because the language of science has long since become English, and for most of the scientific community it is not the mother tongue. Furthermore, we are now talking about the English languages of the world, or “world Englishes”, not to mention those who say “the language of science is bad English”. The paper tells a story which on the one hand constitutes a thoughtful anecdote, on the other may offer a good introduction to a serious scientific study. As it stands now, the main argument for including it is the story itself, with which we encourage further studies to scale our ideas in terms of a broader sample and comparability.
A Decade of Artificial Intelligence Research in the European Union: A Bibliometric Analysis
Authors: Frankowska Agata , Pawlik Bartosz 
Published: July 2022

AbstractIn recent years, the body of research on artificial intelligence (AI) has grown rapidly. As the European Union strives for excellence in AI development, this study aims to establish the publication achievements in the field among its member states between 2010 and 2019. We applied clustering and principal component analysis (PCA) on a set of bibliometric data concerning research publications on AI obtained from Scopus. The results reveal that while the union’s most populous countries—the United Kingdom, Germany, France, Spain, and Italy—were the most prolific producers of AI publications between 2010 and 2019, the highest impact was noted for publications that originated in the Nordic and Benelux countries, as well as in Austria and Ireland. Analysis confirms that the division between ‘old’ and ‘new’ member states has endured: the nations that joined the EU after 2004 recorded the lowest results in scientific output and impact in the AI field. This study can assist research agencies and researchers in developing a broad grasp of the current state of AI research.
Deep Neural Networks Approach to Microbial Colony Detection—A Comparative Analysis
Authors: Majchrowska Sylwia , Pawłowski Jarosław , Czerep Natalia , Górecki Aleksander , Kuciński Jakub , Golan Tomasz 
Published: July 2022

AbstractCounting microbial colonies is a fundamental task in microbiology and has many applications in numerous industry branches. Despite this, current studies towards automatic microbial counting using artificial intelligence are hardly comparable due to the lack of unified methodology and the availability of large datasets. The recently introduced AGAR dataset is the answer to the second need, but the research carried out is still not exhaustive. To tackle this problem, we compared the performance of three well-known deep learning approaches for object detection on the AGAR dataset, namely two-stage, one-stage, and transformer-based neural networks. The achieved results may serve as a benchmark for future experiments.
Changing Tides: Temporal Dimensions of Low-Cost, High-Skill Fisheries in the Central Visayas, Philippines
Authors: Knudsen Magne 
Published: July 2022

AbstractAt the southern mouth of the Tañon Strait—the body of water that separates the islands of Cebu and Negros in the Visayas region of the Philippines—small-scale fishing has always been challenging. Strong and complex sea currents make it difficult for fishers to utilise certain fishing gears. With a significant decline in the resource base and new regulations of the fisheries in recent decades, only fishers with advanced skills and fine-grained place-specific and calendric knowledge of the marine environment are able to catch enough fish, and the right kind of fish, to secure a decent return. Drawing on insights from cultural ecology, the chapter examines the skills and knowledges that fishers draw on to catch fish in this challenging environment. It gives particular attention to the temporal dimensions of the biocultural knowledge complex, showing how fishers’ knowledge of the links between sea currents, the lunar cycle and monsoon winds play into their decisions about where, when and how to fish. In addition to its direct livelihood significance, calendric knowledge also serves as a resource in the formation of identity asmananagat(fisherman) and authority and status within the fishing community. To further explain why some fishers are able to use their knowledge to make fishing a viable and legitimate livelihood and others are not, the last part of the chapter uses insights from political ecology to address issues of power and dynamics of exclusion in the fisheries.
The Sundanese Traditional Ecological Calendar and Socio-cultural Changes: Case Study from Rancakalong of West Java, Indonesia
Authors: Iskandar Johan , Iskandar Budiawati S. 
Published: July 2022

AbstractIn the past, the Sundanese farmers of West Java, Indonesia, managed wet-rice (sawah) farming usingpranata mangsa, the traditional ecological calendar. They cultivated rice varieties that were adapted to local environmental conditions. Thepranata mangsahelped in determining the appropriate time to undertake rice farming activities, including preparation of land, planting, and harvesting. All these activities were accompanied by traditional ceremonies. Various natural indicators, such as the constellations of stars, leaf fall of certain plant species, sprouting of tubers, and call of insects, were used to determine the months (mangsa) of the ecological calendar. In addition, the calendar and the embedded traditional knowledge also helped in managing rice pests, prudent utilisation of water in irrigation, and effective utilisation of the social capital of villagers, through communal activities. However, after the Green Revolution, traditional rice cultivation practices changed, leading to the neglect of thepranata mangsa. Revitalising thepranata mangsawith inputs from formal scientific knowledge would help the community practise ecologically sound and economically viable agriculture that is adapted to the local environment and culture.