bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Virtual Reconstruction of the Batavia Shipwreck in Its Landscape
Authors: McCarthy John , van Duivenvoorde Wendy 
Published: June 2023

AbstractWe present a digital reconstruction of a shipwreck in a landscape context, illustrating the value of combining and synthesizing spatial data from a wide variety of sources to provide new insights on a heavily studied archaeological site. The shipwreck used for this case study is Batavia, one of the best-known shipwrecks in the world due to its rarity as an early Dutch East Indiaman, its association with the early days of the discipline of maritime archaeology, and with the dramatic events that followed the wrecking of the ship on an isolated reef off the west coast of Australia in 1629. The digital reconstruction of this shipwreck site leverages LiDAR, ship-construction manuals, and contemporary and modern ship models with underwater photogrammetry to provide new insights on the aftermath of the wrecking, site taphonomy on the seabed, and to recreate the visual aspects of the site.
Ist mein Ziel wirklich „mein“ Ziel?
Authors: Ertl Sonja , Xie Leilei , Kücherer Benjamin , Hartinger Andreas 
Published: June 2023

ZusammenfassungIn Lernentwicklungsgesprächen werden für gewöhnlich Ziele für das weitere Lernen vereinbart. Der vorliegende Beitrag geht den Fragen nach, in welchem Umfang die Schüler:innen in den Zielvereinbarungsprozess eingebunden werden und ob die Art der Ziele dabei eine Rolle spielt. Dazu wurden 63 Videoaufnahmen von Lernentwicklungsgesprächen in der Jahrgangsstufe 2, in denen insgesamt 105 Ziele vereinbart wurden, mittels quantitativer Inhaltsanalyse ausgewertet. Es zeigt sich, dass die Kinder überwiegend in den Prozess der Zielvereinbarung eingebunden sind – jedoch mehr in der Phase des Zielvorschlags und weniger in der Phase der Anpassung/Festlegung des Ziels. Zudem sind die Kinder eher bei fachbezogenen als bei überfachlichen Zielen eingebunden. Die Ergebnisse werden diskutiert und weitere Forschungsdesiderata werden aufgezeigt.
Evaluation potentiality of Rhizophora mucronata plantation for pollutants remediation on the Red Sea Coast, Egypt
Authors: Aboulsoud Yasmin I. E. , Elkhouly Ahmed A. 
Published: June 2023

AbstractThe planted Rhizophora mucronata was evaluated in two plant ages (one year and eight years) as a biological tool for reducing the mobility of heavy metals in sediments in Safaga and Hamata, Red Sea Coast, Egypt. It is an important region for tourism and nature reserves; however, this area suffers from various anthropogenic contaminants. The ability of mangrove plantations to reduce sediment contamination through bioaccumulation, phytostabilization, or phytoextraction must be clarified through the investigation of metal behavior in mangrove plants and sediments. All of the studied heavy metals had significantly higher concentrations in the Safaga site's sediments. Elder plants had much lower levels of heavy metals in their sediments than younger plants, also rhizosphere samples were less contaminated than non-rhizosphere ones. The order of remediation efficiency was Mo > Ni > Mn ≥ Co > Al > Cu > Zn ≥ Cr > Fe > V, where the highest % was 99.25, 58.97, 42.64, 42.48, 41.91, 39.47, 37.93, 37.01, 36.89, and 29.44, respectively. R. mucronata parts were more significantly contaminated with Co, Cr, Cu, Mo and Zn in Safaga site, while at the Hamata site, they were more significantly contaminated with Al, Fe, Mn, Ni, and V. The elder plants accumulated higher concentrations than younger ones and the contents of heavy metals in plant samples followed the order of root > aerial roots > shoot. Bioconcentration factor (BCF) values representing the accumulation efficiency of R. mucronata were Ni > Mo > Zn > Cu > Cr > Co > Mn ≥ Al > V > Fe, where their highest values were 17.74, 7.89, 3.95, 3.84, 2.66, 1.91, 1.67, 1.66, 1.6, 1.18, respectively. BCF values exceeded one for all metals and values of translocation factor (TF) were less than unity in all cases, thus Rhizophora mucronata can be considered as a good phytostabilizer of ten studied heavy metals able to reduce their mobility through accumulation by roots, thereby reducing off-site contamination.
MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types
Authors: Xu Yanhua , Wojtczak Dominik 
Published: June 2023

AbstractInfluenza poses a significant threat to public health, particularly among the elderly, young children, and people with underlying diseases. The manifestation of severe conditions, such as pneumonia, highlights the importance of preventing the spread of influenza. An accurate and cost-effective prediction of the host and antigenic subtypes of influenza A viruses is essential to addressing this issue, particularly in resource-constrained regions. In this study, we propose a multi-channel neural network model to predict the host and antigenic subtypes of influenza A viruses from hemagglutinin and neuraminidase protein sequences. Our model was trained on a comprehensive data set of complete protein sequences and evaluated on various test data sets of complete and incomplete sequences. The results demonstrate the potential and practicality of using multi-channel neural networks in predicting the host and antigenic subtypes of influenza A viruses from both full and partial protein sequences.
Solving Kolmogorov PDEs without the curse of dimensionality via deep learning and asymptotic expansion with Malliavin calculus
Authors: Takahashi Akihiko , Yamada Toshihiro 
Published: June 2023

AbstractThis paper proposes a new spatial approximation method without the curse of dimensionality for solving high-dimensional partial differential equations (PDEs) by using an asymptotic expansion method with a deep learning-based algorithm. In particular, the mathematical justification on the spatial approximation is provided. Numerical examples for high-dimensional Kolmogorov PDEs show effectiveness of our method.
A Comprehensive Survey on Machine Learning using in Software Defined Networks (SDN)
Authors: Faezi Sahar , Shirmarz Alireza 
Published: June 2023

AbstractThese days, Internet coverage and technologies are growing rapidly, hence, it makes the network more complex and heterogeneous. Software defined network (SDN) revolutionized the network architecture and simplified the network by separating the control and data plane. On the other hand, machine learning (ML) and its derivations have made the systems more intelligent. Many pieces of research papers have addressed ML and SDN. In this survey, we collected the papers published in Springer, Elsevier, IEEE, and ACM and addressed SDN and ML between 2016 and 2023. The research papers are organized based on the solutions, evaluation parameters, and evaluation environments to help those working on SDN and ML for improving the target functional or non-functional parameters. The research papers will be analyzed to extract the solutions, evaluation parameters and environments. The extracted solutions, evaluation parameters and environments will be clustered in this review paper. The research gap and future research directions will be stated in this work. This survey is completely useful for those who working on SDN and want to improve the functional and non-functional parameters using machine learning.
An Explainable Artificial Intelligence Framework for the Predictive Analysis of Hypo and Hyper Thyroidism Using Machine Learning Algorithms
Authors: Hossain Md. Bipul , Shama Anika , Adhikary Apurba , Raha Avi Deb , Uddin K. M. Aslam , Hossain Mohammad Amzad , Islam Imtia , Murad Saydul Akbar , Munir Md. Shirajum , Bairagi Anupam Kumar 
Published: June 2023

AbstractThe thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body’s metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to predict hypothyroidism and hyperthyroidism. Moreover, we identified the most significant features, which can be used to detect thyroid diseases more precisely. After completing the pre-processing and feature selection steps, we applied our modified and original data to several classification models to predict thyroidism. We found Random Forest (RF) is giving the maximum evaluation score in all sectors in our dataset, and Naive Bayes is performing very poorly. Moreover selecting the feature by using the feature importance method RF provides the best accuracy of 91.42%, precision of 92%, recall of 92% and F1-score of 92%. Further, by analyzing the characteristics and behavior of the dataset, we identified the most important features (TSH, T3, TT4, and FTI) of the dataset. In terms of accuracy and other performance evaluation criteria, this study could advocate the use of effective classifiers and features backed by machine learning algorithms to detect and diagnose thyroid disease. Finally, we did some explainability analysis of our best classifier to understand the internal black-box of our machine learning model and datasets. This study could further pave the way for the researcher as well as healthcare professionals to analyze thyroid disease in real time applications.
Alaska Backcountry Expeditionary Hunting Promotes Sustained Muscle Protein Synthesis
Authors: Coker Robert H. , Ruby Brent C. , Coker Melynda S. , Bartlett Larry , Kowalski Brandon , Goropashnaya Anna V. , Bateman Terry , Shankaran Mahalakshmi , Hellerstein Marc , Evans William J. 
Published: June 2023

Introduction We have previously described negative energy balance (ie, −9.7±3.4 MJ/d) and weight loss (Δ−1.5 ± 0.7 kg) influenced by high levels of energy expenditure (ie, 17.4±2.6 MJ/d) during remote expeditionary hunting in Alaska. Despite negative energy balance, participants retained skeletal muscle. The purpose of this pilot study was to measure skeletal muscle protein synthesis and examine molecular markers of skeletal muscle protein metabolism under similar conditions of physical and nutrient stress. Methods The “virtual biopsy method” was used to evaluate integrated fractional synthetic rates (FSRs) of muscle protein from blood samples in 4 participants. Muscle biopsies were taken to measure molecular markers of muscle protein kinetics (ie, FSTL1, MEF2, MYOD1, B2M, and miR-1-3p, -206, -208b, 23a, and 499a) using real-time polymerase chain reaction. Results Our findings in 4 participants (2 females [28 and 62 y of age; 66.2 and 71.8 kg body weight; 25.5 and 26.7 kg/m2 body mass index] and 2 males [47 and 56 y of age; 87.5 and 91.4 kg body weight; 26.1 and 28.3 kg/m2 body mass index]) describe mean muscle FSRs of serum carbonic anhydrase (2.4%) and creatine kinase M-type (4.0%) and positive increments in molecular regulation. Conclusions Preservation of skeletal muscle under conditions of physical and nutrient stress seems to be supported by positive inflection of skeletal muscle FSR and molecular activation.
On the properties of phase heads in raising and passive clauses: DP movement and Transfer options
Authors: Fernández-Salgueiro Gerardo 
Published: June 2023

AbstractThis short article proposes to eliminate asymmetries between the CP and vP phases by arguing for a more uniform clause structure in which both phase heads, C and v, are always present in a derivation but may be removed from the workspace by Transfer. I argue that C is present in the derivation of raising clauses but is removed from the workspace after DP movement yields intersecting sets, in the sense of Epstein et al. (2012, 2015).
Two Decades of Industrial Disputation at an Indian Auto Plant: Lean Production versus Local Cultural Values
Authors: Mathew Sagi , Burgess John 
Published: June 2023

AbstractThis case study finds that disrespect by international expatriate managers towards local employees triggered long-term industrial unrest in the Indian subsidiary of the global car maker Toyota. Whilst innovative production models and their tools provide economic advantage to the company, the interaction of the application of the lean production model within the context of host country institutions often creates workplace disputation and unrest due to unilateralism and managerial hegemonies that overrides local customs and norms. The power of multinational enterprises to override or ignore institutional resistance and to inflict disrespect towards the local workforce can result in worker resistance, a lack of trust, and ongoing industrial unrest. This case study demonstrates how a lack of respect of local customs and workers grievances had long-lasting consequences in terms of the subsequent conflict and a poor industrial relations climate at the production plant in India.
Institutional Dynamics of State-Minority Relations: The Case of Roma Communities in Slovakia
Authors: Csabay Jakub 
Published: June 2023

AbstractThe study addresses the absence of a comprehensive institutional analysis framework in the academic literature on state-minority relations. It does so by employing a framework of analysis based on Skocpol’s analysis of structural factors and Ostrom’s multi-level institutional analysis to understand the processes of radical and incremental institutional change. The article is empirically grounded in a case study of Roma communities in Slovakia. More specifically, it maps and analyzes the evolution and change of institutional frameworks of state-minority relations in the context of Roma communities in Slovakia from the 1960s to 2020. Drawing from archival materials, interview findings, and document analysis, this article shows how post-socialist Slovakia radically redefined and diversified its institutional framework for Roma communities at different institutional levels, which subsequently continued to change at an incremental pace. Overall, the study aspires to offer a more dynamic institutional approach to the study of state-minority relations, which are currently dominated by more static regime- and rights-based approaches, and to contribute with a prospectively useful framework for understanding the developments of state-minority relations in the broader post-Soviet space and beyond.
A core component of psychological therapy causes adaptive changes in computational learning mechanisms
Authors: Dercon Quentin , Mehrhof Sara Z. , Sandhu Timothy R. , Hitchcock Caitlin , Lawson Rebecca P. , Pizzagalli Diego A. , Dalgleish Tim , Nord Camilla L. 
Published: June 2023

AbstractBackgroundCognitive distancing is an emotion regulation strategy commonly used in psychological treatment of various mental health disorders, but its therapeutic mechanisms are unknown.Methods935 participants completed an online reinforcement learning task involving choices between pairs of symbols with differing reward contingencies. Half (49.1%) of the sample was randomised to a cognitive self-distancing intervention and were trained to regulate or ‘take a step back’ from their emotional response to feedback throughout. Established computational (Q-learning) models were then fit to individuals' choices to derive reinforcement learning parameters capturing clarity of choice values (inverse temperature) and their sensitivity to positive and negative feedback (learning rates).ResultsCognitive distancing improved task performance, including when participants were later tested on novel combinations of symbols without feedback. Group differences in computational model-derived parameters revealed that cognitive distancing resulted in clearer representations of option values (estimated 0.17 higher inverse temperatures). Simultaneously, distancing caused increased sensitivity to negative feedback (estimated 19% higher loss learning rates). Exploratory analyses suggested this resulted from an evolving shift in strategy by distanced participants: initially, choices were more determined by expected value differences between symbols, but as the task progressed, they became more sensitive to negative feedback, with evidence for a difference strongest by the end of training.ConclusionsAdaptive effects on the computations that underlie learning from reward and loss may explain the therapeutic benefits of cognitive distancing. Over time and with practice, cognitive distancing may improve symptoms of mental health disorders by promoting more effective engagement with negative information.
Fermenting a place in history: The first outbreak of Escherichia coli O157 associated with kimchi in Canada
Authors: Smith Courtney R. , Bond Heather , Kearney Ashley , Chau Kelvin , Chui Linda , Gerrie Monica , Honish Lance , Oukouomi Lowé Yves , Mah Victor , Manore Anna J. W. 
Published: June 2023

Abstract
A Canadian outbreak investigation was initiated in January 2022 after a cluster of cases of Shiga-toxin-producing Escherichia coli (STEC) O157 was identified through whole genome sequencing (WGS). Exposure information was collected through case interviews. Traceback investigations were conducted, and samples from case homes, retail, and the manufacturer were tested for STEC O157. Fourteen cases were identified in two provinces in Western Canada, with isolates related by 0–5 whole genome multi-locus sequence typing allele differences. Symptom onset dates ranged from 11 December 2021 to 7 January 2022. The median age of cases was 29.5 (range 0–61); 64% were female. No hospitalisations or deaths were reported. Of 11 cases with information available on fermented vegetable exposures, 91% (10/11) reported consuming Kimchi Brand A during their exposure period. The traceback investigation identified Manufacturer A in Western Canada as the producer. One open and one closed sample of Kimchi Brand A tested positive for STEC O157, with isolates considered genetically related by WGS to the outbreak strain. Napa cabbage within the kimchi product was hypothesised as the most likely source of contamination. This paper summarises the investigation into this STEC O157 outbreak associated with kimchi, the first reported outside of East Asia.
Integrating Classroom and Community with Undergraduate Civically Engaged Research
Authors: Schmitt Sam , Mehlhaff Isaac D. , Ommundsen Emily Cottle 
Published: June 2023

ABSTRACTIn addition to interest in undergraduate research, political science increasingly recognizes the value of civically engaged research for various educational, professional, and civic outcomes. With limited time and steep tradeoffs, instructors must find ways to cleverly combine undergraduate research experiences with the broader normative civic-education responsibilities of political science and higher education. This article presents a course design that allows instructors to leverage their classroom for both civic education and undergraduate research without the need for previously developed community partnerships that are common to most engaged research and learning. Our approach brings together undergraduate research and community engagement through course design.
Feasibility and initial impact of single-session internet-delivered acceptancevschange skills for emotions for stress- and trauma-related problems: a randomized controlled trial
Authors: McLean Caitlin L. , Ruork Allison K. , Ramaiya Megan K. , Fruzzetti Alan E. 
Published: June 2023

AbstractBackground:Current psychological trauma-focused interventions have left a gap for individuals who may not be ready for trauma-focused treatment and/or who present with other forms of clinically significant distress, such as subthreshold post-traumatic stress disorder (PTSD). Emotion regulation is a possible transdiagnostic mechanism of change that may promote and maintain some of the varied mental health problems related to trauma exposure.Aims:This study examines the feasibility and initial impact of two brief emotion regulation skill trainings targeting different processes hypothesized to reduce trauma-related problems, compared with an active control.Method:Subjects (n= 156) were randomized to receive one of three brief internet-based trainings: (1) skill training on accepting emotions, (2) skill training on changing emotions, or (3) stress psychoeducation (control). Participants completed measures of emotion regulation, mindfulness, and affect intensity 24 hours pre- and immediately post-training.Results:Results suggested that a brief internet-based skills training programme was feasible and acceptable, with 91.9% completing the training programme to which they were randomized. Results showed that participants in all conditions demonstrated significant decreases in emotion regulation problems over time; yet these improvements did not vary by condition. Participants in the Change condition with higher PTSD symptoms were significantly more likely to have greater increases in positive affect compared with those with lower PTSD symptoms.Conclusions:Although the three conditions did not show different outcomes, all three brief internet-delivered trainings were feasible. Results provide direction for future studies to evaluate the delivery of emotion regulation skills in individuals with trauma-related distress.
Local contextual factors of child stunting found via shared values of stakeholder groups: an exploratory case study in Kaffrine, Senegal
Authors: Moreno Juan Manuel , Chapman Annabel J , Ebido Chike C , Sougou Ndèye Marième , Diallo Amadou H , Tening Rahel Neh , Dial Fatou Binetou , Massonnié Jessica , Firoozmand Mahsa , Niang Cheikh El Hadji Abdoulaye , Heffernan Claire , Harder Marie K 
Published: June 2023

AbstractObjective:This work aims to demonstrate an original approach to identify links between locally situated shared values and contextual factors of stunting. Stunting results from multi-factorial and multi-sectoral determinants, but interventions typically neglect locally situated lived experiences, which contributes to problematic designs that are not meaningful for those concerned and/or relatively ineffective.Design:This case study investigates relevant contextual factors in two steps: by first facilitating local stakeholder groups (n 11) to crystallise their shared-values-in-action using a specialised method from sustainability studies (WeValue_InSitu (WVIS)). Secondly, participants (n 44) have focus group discussions (FGD) about everyday practices around child feeding/food systems, education and/or family life. Because the first step strongly grounds participants in local shared values, the FGD can reveal deep links between contextual factors and potential influences on stunting.Setting:Kaffrine, Senegal, an ‘Action Against Stunting Hub’ site. December 2020.Participants:Eleven stakeholder groups of mothers, fathers, grandmothers, pre-school teachers, community health workers, farmers, market traders and public administrators.Results:Local contextual factors of stunting were identified, including traditional beliefs concerning eating and growing practices; fathers as decision-makers; health worker trust; financial non-autonomy for women; insufficient water for preferred crops; merchants’ non-access to quality produce; religious teachings and social structures affecting children’s food environment.Conclusions:Local contextual factors were identified. Pre-knowledge of these could significantly improve effectiveness of intervention designs locally, with possible applicability at other sites. The WVIS approach proved efficient and useful for making tangible contextual factors and their potential links to stunting, via a lens of local shared values, showing general promise for intervention research.