bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ensemble based methods for leapfrog integration in the simplified parameterizations, primitive‐equation dynamics model
Authors: Nino‐Ruiz Elias D. , Consuegra Ortega Randy S. , Lucini Magdalena 
Published: January 2023

AbstractThis paper presents efficient and practical implementations of sequential data assimilation methods for the Simplified Parameterizations, primitive‐Equation DYnamics (SPEEDY) Model, a well‐known numerical model, into the data assimilation community for climate prediction. In the SPEEDY model, the time evolution of dynamics is performed via the second‐order Leapfrog integration scheme; this time integrator relies on two steps: the position and the velocity. The computational implementation of SPEEDY blends the time integrator and the spatial discretization of dynamics to accelerate algebraic computations. Thus, there is no access to the right‐hand side function of the ordinary differential equations governing the time evolution of model dynamics. Consequently, the SPEEDY model is often treated as a black box wherein positions and velocities work as inputs and outputs. Since observations in operational data assimilation only match position states, we can exploit augmented vector states to propagate analysis innovations from positions to velocities. For this purpose, we formulate three variants of ensemble‐based filters and perform numerical experiments to assess their accuracies. We consider two scenarios for the experiments: an ideal case wherein positions and velocities can be observed and a more realistic one wherein measurements are only accessible for position states. Besides, we discuss the effects of the ensemble size on the accuracies of our formulations and, even more, the typical case in which velocities are not updated across assimilation steps. The results reveal that all filter formulations' accuracies remain the same in terms of Root‐Mean‐Square‐Error by neglecting observations from velocities (a realistic scenario) even for cases wherein the number of measurements decreases to 6% of model components. Furthermore, for all discussed filter implementations, the propagation of analysis increments from position to velocities improves up to 100% the performance of filter implementations wherein velocities are not updated, a typical operational scenario.
Event‐triggered asynchronous distributed model predictive control with variable prediction horizon for nonlinear systems
Authors: Wang Pengbiao , Ren Xuemei , Zheng Dongdong 
Published: January 2023

AbstractIn this article, we develop an event‐triggered asynchronous distributed model predictive control (ETADMPC) algorithm with the adaptive prediction horizon for distributed nonlinear systems with weakly dynamics couplings, bounded disturbances, and system constraints. First, we focus on designing a novel adaptive event‐triggered mechanism with Zeno‐free phenomenons in order to reduce computational burdens, whose triggering threshold can adapt to the real‐time changes of the system and make necessary adjustments. Then, a robust time‐varying tightened state constraint is tailored for the optimization problem with respect to distributed model predictive control, and it can provide robustness to external disturbances and system coupling parts. And an adaptive prediction horizon update scheme is deliberately designed to decrease the length of the prediction horizon when the system state is close to the terminal set, reducing the computational complexity in the optimization problem. Furthermore, we strictly prove that under the given sufficient conditions, the proposed ETADMPC algorithm is recursively feasible and the closed‐loop system is stable. Finally, a numerical example is provided to show that our scheme can achieve satisfactory control performances with less calculation and a shorter calculation time than the existing results.
The emerging roles of circRNAs in traits associated with livestock breeding
Authors: Sun Ping , Chen Mengjie , Sooranna Suren R. , Shi Deshun , Liu Qingyou , Li Hui 
Published: January 2023

AbstractMany indicators can be used to evaluate the productivity and quality of livestock, such as meat and milk production as well as fat deposition. Meat and milk production are measures of livestock performance, while fat deposition affects the taste and flavor of the meat. The circRNAs, are non‐coding RNAs, that are involved in the regulation of all these three traits. We review the functions and mechanisms of circRNAs in muscle and fat development as well as lactation to provide a theoretical basis for circRNA research in animal husbandry. Various phenotypic changes presented in livestock may be produced by different circRNAs. Our current concern is how to use the roles played by circRNAs to our advantage to produce the best possible livestock. Hence, we describe the advantages and disadvantages of knockout techniques for circRNAs. In addition, we also put forward our thoughts regarding the mechanism and network of circRNAs to provide researchers with novel ideas of how molecular biology can help us advance our goals in animal farming.This article is categorized under:
RNA in Disease and Development > RNA in Disease
RNA Interactions with Proteins and Other Molecules > Protein‐RNA Interactions: Functional Implications
RNA Interactions with Proteins and Other Molecules > Protein‐RNA Recognition
RNA Interactions with Proteins and Other Molecules > RNA‐Protein Complexes
Machine Learning Weather Analogs for Near-Surface Variables
Authors: Hu Weiming , Cervone Guido , Young George , Delle Monache Luca 
Published: January 2023

AbstractNumerical weather prediction models and high-performance computing have significantly improved our ability to model near-surface variables, but their uncertainty quantification still remains a challenging task. Ensembles are usually produced to depict a series of possible future states of the atmosphere, as a means to quantify the prediction uncertainty, but this requires multiple instantiation of the model, leading to an increased computational cost. Weather analogs, alternatively, can be used to generate ensembles without repeated model runs. The analog ensemble (AnEn) is a technique to identify similar weather patterns for near-surface variables and quantify forecast uncertainty. Analogs are chosen based on a similarity metric that calculates the weighted multivariate Euclidean distance. However, identifying optimal weights for similarity metric becomes a bottleneck because it involves performing a constrained exhaustive search. As a result, only a few predictors were selected and optimized in previous AnEn studies. A new machine learning similarity metric is proposed to improve the theoretical framework on how weather analogs are identified. First, a deep learning network is trained to generate latent features using all the temporal multivariate input predictors. Analogs are then selected in this latent space, rather than the original predictor space. The proposed method does not require prior predictor selection and an exhaustive search, thus presenting a significant computational benefit and scalability. It is tested for surface wind speed and solar irradiance forecasts in Pennsylvania from 2017 to 2019. Results show that the proposed method is capable of handling a large number of predictors, and it outperforms the original similarity metric in RMSE, bias, and CRPS. Since the data-driven transformation network is trained using the historical record, the proposed method has been found to be more flexible for searching through a longer record.
Understanding the Impact of Home Confinement on Children and Young People with ADHD and ASD During the COVID-19 Pandemic
Authors: Hall Charlotte L. , Partlett Christopher , Valentine Althea Z. , Pearcey Samantha , Sayal Kapil 
Published: January 2023

AbstractTo understand whether the mental health of children and young people (CYP) with and without attention-deficit/hyperactivity disorder (ADHD) and/or autism spectrum disorder (ASD) were differentially affected by COVID-19. We analysed data (n = 6507) from the Co-Space study, a UK web-based longitudinal survey. CYP with ADHD (n = 160;2.5%), ASD (n = 465;7%), and ADHD + ASD (n = 155;2.4%) were compared with a reference group (n = 5727;88%) using parent-completed questionnaires [Strengths and Difficulties Questionnaire (SDQ) & Pandemic Anxiety Scale (PAS)]. Baseline to 1-month follow-up differences were compared using linear regression models. CYP with ADHD and/or ASD had higher scores at baseline than other CYP. At follow-up, CYP with ASD showed small but significant improvements in symptoms (SDQ), compared with the reference group. CYP with ASD experienced a worsening of disease anxiety (PAS) and CYP with ADHD a deterioration in functional impairment. These findings indicate a mixed pattern of pandemic-related impact for CYP with ADHD and/or ASD.
The influence of using video media on basic movement skills in kindergarten
Authors: Mohammad Mona , Boushehry Heyam Reda 
Published: January 2023

Abstract This study aimed to measure the effects of video media on kindergarten children basic movement skills. Educators use many strategies to assist their children in acquiring basic movement skills in kindergartens; The style of teaching encompasses the style of guided discovery, with a component and entire method approach supported by visual medias. This research used an experimental design with convenience sampling of 40 children from two classes in kindergarten. The experimental study is an investigation into the effects of video media in children’s and other supervised subjects on fundamental movement abilities. Twenty children assigned to an experimental treatment, in which the researchers studied findings on children development on basic movement skills. Other children (N = 20) were assigned to a control group. Instrumentation for the Research-based on observation to gather data, the observation made by a researcher to find items that are needed. Moreover, The Bruininks-Oseretsky Second Edition Motor Capability Test ((Bruiniks and Bruiniks, Bruininks-Oseretsky Test of Motor Proficiency> Second (Edition manual), Pearson Assessment, 2005)) and The Test of Gross Motor Development (TGMD) used in this research. Blinding teaching methods in kindergarten physical education using visual media showed increase the basic mobility skills of the kindergarten sample. Physical Education teachers using blinding methods in classroom provide their special children with more effective learning skills in the Basic Movement Skills in the kindergarten. In addition, video media tools showed improvement in basic movement skills of school lay-up children. The outcome of this research provided advice to practitioners and researchers on how to incorporate video in their teaching with a view to promoting the development of important motor skills in children.
On predicting school dropouts in Egypt: A machine learning approach
Authors: Selim Kamal Samy , Rezk Sahar Saeed 
Published: January 2023

AbstractCompulsory school-dropout is a serious problem affecting not only the education systems, but also the developmental progress of any country as a whole. Identifying the risk of dropping out, and characterizing its main determinants, could help the decision-makers to draw eradicating policies for this persisting problem and reducing its social and economic negativities over time. Based on a substantially imbalanced Egyptian survey dataset, this paper aims to develop a Logistic classifier capable of early predicting students at-risk of dropping out. Training any classifier with an imbalanced dataset, usually weaken its performance especially when it comes to false negative classification. Due to this fact, an extensive comparative analysis is conducted to investigate a variety of resampling techniques. More specifically, based on eight under-sampling techniques and four over-sampling ones, and their mutually exclusive mixed pairs, forty-five resampling experiments on the dataset are conducted to build the best possible Logistic classifier. The main contribution of this paper is to provide an explicit predictive model for school dropouts in Egypt which could be employed for identifying vulnerable students who are continuously feeding this chronic problem. The key factors of vulnerability the suggested classifier identified are student chronic diseases, co-educational, parents' illiteracy, educational performance, and teacher caring. These factors are matching with those found by many of the research previously conducted in similar countries. Accordingly, educational authorities could confidently monitor these factors and tailor suitable actions for early intervention.
You Don’t Have to Re-invent the Wheel to Implement Technology Activities in Early Childhood Education
Authors: Eliasson Sara , Peterson Louise , Lantz-Andersson Annika 
Published: January 2023

AbstractThis study on technology education in preschool aims to explore how technology activities in preschool are enacted and what knowledge, related to the five dimensions of the nature of technology, is made possible for the children to learn when intersubjectivity is established in the interaction between the participants. The empirical data encompass three video-documented technology activities, involving five children and one preschool teacher. Drawing on the five dimensions of the nature of technology by DiGironimo, the participants’ interactions were analysed using interaction analysis. The results showed that the teacher, through well-defined and sensitive orchestration, enacted goal-oriented activities by allowing a play-oriented approach, and that intersubjectivity on technology was established related to four of the five dimensions of technology. The lack of knowledge related to the historical dimension of technology suggests further scrutiny and is discussed as essential in ECE technology education for contemporary children, growing up in high-tech societies.
On the Phase Space of Fourth-Order Fiber-Orientation Tensors
Authors: Bauer Julian Karl , Schneider Matti , Böhlke Thomas 
Published: January 2023

AbstractFiber-orientation tensors describe the relevant features of the fiber-orientation distribution compactly and are thus ubiquitous in injection-molding simulations and subsequent mechanical analyses. In engineering applications to date, the second-order fiber-orientation tensor is the basic quantity of interest, and the fourth-order fiber-orientation tensor is obtained via a closure approximation. Unfortunately, such a description limits the predictive capabilities of the modeling process significantly, because the wealth of possible fourth-order fiber-orientation tensors is not exploited by such closures, and the restriction to second-order fiber-orientation tensors implies artifacts. Closures based on the second-order fiber-orientation tensor face a fundamental problem – which fourth-order fiber-orientation tensors can be realized? In the literature, only necessary conditions for a fiber-orientation tensor to be connected to a fiber-orientation distribution are found. In this article, we show that the typically considered necessary conditions, positive semidefiniteness and a trace condition, are also sufficient for being a fourth-order fiber-orientation tensor in the physically relevant case of two and three spatial dimensions. Moreover, we show that these conditions are not sufficient in higher dimensions. The argument is based on convex duality and a celebrated theorem of D. Hilbert (1888) on the decomposability of positive and homogeneous polynomials of degree four. The result has numerous implications for modeling the flow and the resulting microstructures of fiber-reinforced composites, in particular for the effective elastic constants of such materials. Based on our findings, we show how to connect optimization problems on fourth-order fiber-orientation tensors to semi-definite programming. The proposed formulation permits to encode symmetries of the fiber-orientation tensor naturally. As an application, we look at the differences between orthotropic and general, i.e., triclinic, fiber-orientation tensors of fourth order in two and three spatial dimensions, revealing the severe limitations inherent to orthotropic closure approximations.
The nutrition-environment nexus assessment of Thai Riceberry product for supporting environmental product declaration
Authors: Mungkung Rattanawan , Dangsiri Sarocha , Satmalee Prajongwate , Surojanametakul Vipa , Saejew Katreeya , Gheewala Shabbir H. 
Published: January 2023

AbstractRiceberry rice has a special characteristic of being specially bred with high nutrients. High-value added products from riceberry are being promoted targeting health-conscious consumers. To provide supporting information for sustainable food systems, environmental footprinting was applied for evaluating the environmental performance of a ready-to-eat product of riceberry rice mixed with kidney red bean called “Riceberry + KU”, developed by Kasetsart University in Thailand. Based on sold unit as the unit of analysis, the carbon footprint of Riceberry + KU was 5.24 gCO2e per 300 g. Interestingly, white rice had the highest carbon footprint and riceberry rice as well as the riceberry product had at least 30% lower values. However, using nutrient-based scores, although white rice still had the highest carbon footprint, Riceberry + KU had 80% lower and the riceberry rice had 65% lower values. This resulted from the highest nutritional levels found in Riceberry + KU along with the lower greenhouse gas emissions from riceberry field during the cultivation stage. Similar trends were found in the other impact categories assessed by using life cycle assessment as well. To provide appropriate information to consumers for making more sustainable food choices, the environmental performance based on nutrient quality resulting from different farming systems and processing methods should be used to derive recommendations for moving toward sustainable food systems. The nutrition-environment nexus assessment could be very useful for supporting consumers toward making more sustainable food choices.
Enhancement of thermoelectric power factor in Cu2Se superionic conductor via high energy electron beam irradiation
Authors: Mangavati Suraj , Gurukrishna K , Rao Ashok , Petwal Vikash Chandra , Verma Vijay Pal , Dwivedi Jishnu 
Published: January 2023

AbstractThe modification in the Cu2Se thermoelectric system by electron beam irradiation has been carried out in this work. Samples were prepared using the solid-state reaction technique. The prepared samples were irradiated with various energy dosages viz. 50, 100, and 150 kGy. XRD studies reveal that the synthesized samples crystallized in a monoclinic structure. The micro-hardness of the samples decreased with an increase in irradiation dosage. The sample irradiated at 100 kGy dose exhibits the lowest electrical resistivity, moderate Seebeck coefficient, and highest power factor.
Bumble Bee Avoidance of Argentine Ants and Associated Chemical Cues
Authors: Miner Michelle Constanza , Wilson Rankin Erin E. 
Published: January 2023

AbstractBees provide pollination services in both agricultural and natural ecosystems. However, invasive ants that exploit floral nectar in these landscapes can compete with bees for resources, with implications for pollinator resource acquisition, plant fitness, and, ultimately, ecosystem function. While interference competition has been described between bees and nectivorous, non-pollinator antagonists, the behaviors and sensory mechanisms involved remain largely unresolved. Here, we studied the mechanisms by which invasive Argentine ants influence bee nectar foraging behavior. In a series of laboratory assays, we assessed the foraging behavior of bumble bees (Bombus impatiens) in response to live Argentine ants (Linepithema humile) or to a subset of ant chemical cues. Bees were clearly deterred by live ants at a nectar source: they consumed less, fed less frequently, and fed for a shorter duration when live ants were present. Bees were also deterred by a combination of olfactory and gustatory ant chemical cues, consistent with both innate and learned avoidance behavior. Naïve and ant-experienced bees were deterred through chemosensation of ants, feeding less from nectar infused with ant chemicals as compared to nectar lacking ants or their associated cues. Some ant-experienced bees showed a unique behavior, displaying aggression toward ants as well as in response to ant chemicals. The marked effects of this invasive ant on bee foraging behavior—through physical interaction and chemical cues—highlights Argentine ants as a serious pest whose control should be considered when developing pollinator conservation and management strategies.
Towards trust-based governance of health data research
Authors: Bak Marieke A. R. , Ploem M. Corrette , Tan Hanno L. , Blom M. T. , Willems Dick L. 
Published: January 2023

AbstractDevelopments in medical big data analytics may bring societal benefits but are also challenging privacy and other ethical values. At the same time, an overly restrictive data protection regime can form a serious threat to valuable observational studies. Discussions about whether data privacy or data solidarity should be the foundational value of research policies, have remained unresolved. We add to this debate with an empirically informed ethical analysis. First, experiences with the implementation of the General Data Protection Regulation (GDPR) within a European research consortium demonstrate a gap between the aims of the regulation and its effects in practice. Namely, strictly formalised data protection requirements may cause routinisation among researchers instead of substantive ethical reflection, and may crowd out trust between actors in the health data research ecosystem; while harmonisation across Europe and data sharing between countries is hampered by different interpretations of the law, which partly stem from different views about ethical values. Then, building on these observations, we use theory to argue that the concept of trust provides an escape from the privacy-solidarity debate. Lastly, the paper details three aspects of trust that can help to create a responsible research environment and to mitigate the encountered challenges: trust as multi-agent concept; trust as a rational and democratic value; and trust as method for priority setting. Mutual cooperation in research—among researchers and with data subjects—is grounded in trust, which should be more explicitly recognised in the governance of health data research.
The cascading failure of check dam systems during the 28 July 2022 Emamzadeh Davood flood in Iran
Authors: Motagh Mahdi , Akhani Hossein 
Published: January 2023

AbstractOn July 18, 2022, an unexpected rainfall and flash flood struck the Emamzadeh Davood village in northwestern Tehran, the capital city of Iran, claiming the life of at least 23 people. In this brief communication, we report results from a recent investigation carried out by field surveys and remote sensing data, highlighting the role of anthropogenic factors and catastrophic failures in a series of check dams in intensifying the impacts of the 2022 Emamzadeh Davood event.