bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of Bilateral Assistance for Hemiparetic Gait Post-Stroke Using a Powered Hip Exoskeleton
Authors: Pan Yi-Tsen , Kang Inseung , Joh James , Kim Patrick , Herrin Kinsey R. , Kesar Trisha M. , Sawicki Gregory S. , Young Aaron J. 
Published: August 2022

AbstractHemiparetic gait due to stroke is characterized by an asymmetric gait due to weakness in the paretic lower limb. These inter-limb asymmetries increase the biomechanical demand and reduce walking speed, leading to reduced community mobility and quality of life. With recent progress in the field of wearable technologies, powered exoskeletons have shown great promise as a potential solution for improving gait post-stroke. While previous studies have adopted different exoskeleton control methodologies for restoring gait post-stroke, the results are highly variable due to limited understanding of the biomechanical effect of exoskeletons on hemiparetic gait. In this study, we investigated the effect of different hip exoskeleton assistance strategies on gait function and gait biomechanics of individuals post-stroke. We found that, compared to walking without a device, powered assistance from hip exoskeletons improved stroke participants’ self-selected overground walking speed by 17.6 ± 2.5% and 11.1 ± 2.7% with a bilateral and unilateral assistance strategy, respectively (p < 0.05). Furthermore, both bilateral and unilateral assistance strategies significantly increased the paretic and non-paretic step length (p < 0.05). Our findings suggest that powered assistance from hip exoskeletons is an effective means to increase walking speed post-stroke and tuning the balance of assistance between non-paretic and paretic limbs (i.e., a bilateral strategy) may be most effective to maximize performance gains.
On-line estimators for ad-hoc task execution: learning types and parameters of teammates for effective teamwork
Authors: Shafipour Yourdshahi Elnaz , do Carmo Alves Matheus Aparecido , Varma Amokh , Soriano Marcolino Leandro , Ueyama Jó , Angelov Plamen 
Published: August 2022

AbstractIt is essential for agents to work together with others to accomplish common objectives, without pre-programmed coordination rules or previous knowledge of the current teammates, a challenge known as ad-hoc teamwork. In these systems, an agent estimates the algorithm of others in an on-line manner in order to decide its own actions for effective teamwork. A common approach is to assume a set of possible types and parameters for teammates, reducing the problem into estimating parameters and calculating distributions over types. Meanwhile, agents often must coordinate in a decentralised fashion to complete tasks that are displaced in an environment (e.g., in foraging, de-mining, rescue or fire control), where each member autonomously chooses which task to perform. By harnessing this knowledge, better estimation techniques can be developed. Hence, we present On-line Estimators for Ad-hoc Task Execution (OEATE), a novel algorithm for teammates’ type and parameter estimation in decentralised task execution. We show theoretically that our algorithm can converge to perfect estimations, under some assumptions, as the number of tasks increases. Additionally, we run experiments for a diverse configuration set in the level-based foraging domain over full and partial observability, and in a “capture the prey” game. We obtain a lower error in parameter and type estimation than previous approaches and better performance in the number of completed tasks for some cases. In fact, we evaluate a variety of scenarios via the increasing number of agents, scenario sizes, number of items, and number of types, showing that we can overcome previous works in most cases considering the estimation process, besides robustness to an increasing number of types and even to an erroneous set of potential types.
Towards explainable interactive multiobjective optimization: R-XIMO
Authors: Misitano Giovanni , Afsar Bekir , Lárraga Giomara , Miettinen Kaisa 
Published: August 2022

AbstractIn interactive multiobjective optimization methods, the preferences of a decision maker are incorporated in a solution process to find solutions of interest for problems with multiple conflicting objectives. Since multiple solutions exist for these problems with various trade-offs, preferences are crucial to identify the best solution(s). However, it is not necessarily clear to the decision maker how the preferences lead to particular solutions and, by introducing explanations to interactive multiobjective optimization methods, we promote a novel paradigm of explainable interactive multiobjective optimization. As a proof of concept, we introduce a new method, R-XIMO, which provides explanations to a decision maker for reference point based interactive methods. We utilize concepts of explainable artificial intelligence and SHAP (Shapley Additive exPlanations) values. R-XIMO allows the decision maker to learn about the trade-offs in the underlying problem and promotes confidence in the solutions found. In particular, R-XIMO supports the decision maker in expressing new preferences that help them improve a desired objective by suggesting another objective to be impaired. This kind of support has been lacking. We validate R-XIMO numerically, with an illustrative example, and with a case study demonstrating how R-XIMO can support a real decision maker. Our results show that R-XIMO successfully generates sound explanations. Thus, incorporating explainability in interactive methods appears to be a very promising and exciting new research area.
Systematic assessment of coronary calcium detectability and quantification on four generations of CT reconstruction techniques: a patient and phantom study
Authors: Dobrolinska M. M. , van Praagh G. D. , Oostveen L. J. , Poelhekken K. , Greuter M. J. W. , Fleischmann D. , Willemink M. J. , de Lange F. , Slart R. H. J. A. , Leiner T. , van der Werf N. R. 
Published: August 2022

AbstractIn computed tomography, coronary artery calcium (CAC) scores are influenced by image reconstruction. The effect of a newly introduced deep learning-based reconstruction (DLR) on CAC scoring in relation to other algorithms is unknown. The aim of this study was to evaluate the effect of four generations of image reconstruction techniques (filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), and DLR) on CAC detectability, quantification, and risk classification. First, CAC detectability was assessed with a dedicated static phantom containing 100 small calcifications varying in size and density. Second, CAC quantification was assessed with a dynamic coronary phantom with velocities equivalent to heart rates of 60–75 bpm. Both phantoms were scanned and reconstructed with four techniques. Last, scans of fifty patients were included and the Agatston calcium score was calculated for all four reconstruction techniques. FBP was used as a reference. In the phantom studies, all reconstruction techniques resulted in less detected small calcifications, up to 22%. No clinically relevant quantification changes occurred with different reconstruction techniques (less than 10%). In the patient study, the cardiovascular risk classification resulted, for all reconstruction techniques, in excellent agreement with the reference (κ = 0.96–0.97). However, MBIR resulted in significantly higher Agatston scores (61 (5.5–435.0) vs. 81.5 (9.25–435.0); p < 0.001) and 6% reclassification rate. In conclusion, HIR and DLR reconstructed scans resulted in similar Agatston scores with excellent agreement and low-risk reclassification rate compared with routine reconstructed scans (FBP). However, caution should be taken with low Agatston scores, as based on phantom study, detectability of small calcifications varies with the used reconstruction algorithm, especially with MBIR and DLR.
Market Structure and Instability Artifacts in Heterogeneous Agent Models: Lessons from Implicit Discretizations of Stiff Equations
Authors: Baumann Michael Heinrich , Baumann Michaela , Grüne Lars , Herz Bernhard 
Published: August 2022

AbstractWe consider a standard heterogeneous agent model (HAM) that is widely used to analyze price developments in financial markets. The model is linear in log-prices and, in its basic setting, populated by fundamentalists and chartists. As the number of fundamentalists increases and exceeds a specific threshold, oscillations occur whose amplitude might even grow exponentially over time. From an economic perspective to adequately interpret such instability results it is indispensable to ensure that the characteristics and specific building blocks of the HAM are not at odds with the underlying structure of financial markets, in particular the specific trading rules. We expect that in markets with (almost) only fundamentalist traders prices might in the most extreme case oscillate, but never explode. In addition, if limit orders are available, prices should converge monotonically. Finally, if price bubbles occur in financial markets with fundamentalist traders, they should only result from the interactions between fundamentalists and the other traders, e.g., chartists, but not from fundamentalists’ decisions alone. From a mathematical perspective we show that the instability result common to the standard approach can be related to a “hidden” explicit discretization of a stiff ordinary differential equation contained in the model. Replacing this explicit discretization by an implicit one improves the model as it removes this artifact, bringing the model’s prediction in line with standard theory. The refined model still allows for price overshoots, bubbles, and crashes. However, in the implicit model these instabilities are caused by chartists and not by an unintended artifact.
SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams
Authors: Sun Yibin , Pfahringer Bernhard , Gomes Heitor Murilo , Bifet Albert 
Published: August 2022

AbstractMost research in machine learning for data streams has focused on classification algorithms, whereas regression methods have received a lot less attention. This paper proposes Self-Optimising K-Nearest Leaves (SOKNL), a novel forest-based algorithm for streaming regression problems. Specifically, the Adaptive Random Forest Regression, a state-of-the-art online regression algorithm is extended like this: in each leaf, a representative data point – also called centroid – is generated by compressing the information from all instances in that leaf. During the prediction step, instead of letting all trees in the forest participate, the distances between the input instance and all centroids from relevant leaves are calculated, only k trees that possess the smallest distances are utilised for the prediction. Furthermore, we simplify the algorithm by introducing a mechanism for tuning the k values, which is dynamically and automatically optimised based on historical information. This new algorithm produces promising predictive results and achieves a superior ranking according to statistical testing when compared with several standard stream regression methods over typical benchmark datasets. This improvement incurs only a small increase in runtime and memory consumption over the basic Adaptive Random Forest Regressor.
Nursing student´s attitudes toward e-learning: a quantitative approach
Authors: Sánchez Alina de las Mercedes Martínez , Karaksha Abdullah 
Published: August 2022

AbstractThis article seeks to determine the attitudes of undergraduate nursing students toward e-learning at the (X). A quantitative, non-experimental, descriptive, and exploratory approach was the procedural methodology selected in this study. A suitable sample of sophomore nursing scholars (n = 71) was registered. A total of 58 students returned the questionnaire (82.8% were females). Students who have previous computer training were significantly more confident in connecting to the internet than those with no prior computer training (t = 2.1, p < 0.05). Students who had prior experience in e-learning predicted they would feel significantly more nervous when working with computers than those who did not have this prior experience (t = 2.3, p < 0.05). In general, our investigation uncovered a differently favorable view of nursing students towards e-learning, however, some negative attitudes were also recorded. Factors likes students` motivation and personalities, backgrounds and feelings related to the control of their educational process must be considered in the application of e-learning.
Teach to use CAD or through using CAD: An interview study with technology teachers
Authors: Brink Helen , Kilbrink Nina , Gericke Niklas 
Published: August 2022

AbstractToday, many technology teachers in compulsory technology education teach design and design processes using a digital design tool, such as computer aided design (CAD). Teaching involving CAD is a relatively new element and not very much is known about what teachers intend pupils to learn in compulsory education. Thus, the aim of this study is to investigate technology teachers’ experiences in order to gain insight into their teaching practices involving CAD. A phenomenographic approach was used and twelve semi-structured interviews with lower secondary technology teachers were conducted. The interviews were analyzed and categories of description were hierarchically organized into the phenomenographic outcome space. The results show that teachers have different experiences of the intended learning outcomes when CAD is used in teaching, and four hierarchical categories emerged: (1) Handling the software, (2) Using ready-made models, (3) Manufacturing and creating printed models, and (4) Designing. The four categories describe teaching to use CAD and/or through using CAD. Further, the hierarchical categories indicate a teaching progression and the categories can be used as a basis for further discussions among teachers, teacher educators and researchers to develop CAD pedagogies within compulsory technology education.
Effects of experimental rewilding on butterflies, bumblebees and grasshoppers
Authors: Garrido P. , Naumov V. , Söderquist L. , Jansson A. , Thulin C.-G. 
Published: August 2022

Abstract
Grassland ecosystems are species-rich habitats that are rapidly declining globally posing serious concerns for biodiversity conservation. This situation is particularly relevant in agricultural areas in Europe. As traditional management practices and livestock grazing regimes ceased, rewilding could be a potential avenue to tackle current biodiversity declines. To test this hypothesis, we set up a 3-year experiment where 12 horses were introduced in three 10-hectare enclosure replicates (four horses per enclosure). Horses were kept without supplementary feeding to mimic ecosystem functions of wild horses. We applied Generalized Linear Mixed Effects Models and a backward stepwise model selection procedure to elucidate factors that modulate insect richness induced by grazing. Our results show that plant species richness, the proportion of flowers and plant height play a significant role for butterfly and bumblebee richness, while the opposite effect was detected for grasshoppers. However, the effect on grasshoppers was counterbalanced by increased grasshopper species richness in habitats adjacent to horse latrines.

Implications for insect conservation
Rewilding with horses may offset current biodiversity declines by maintaining important functional links between plants and pollinators in grassland ecosystems. Horse grazing can however have different effects on diverse functional groups of insects. Application of integrative landscape scale approaches may be needed to elucidate the effects of rewilding for certain functional groups such as grasshoppers. With current biodiversity declines, up-scaling rewilding research and practice might be crucial to mitigate the pervasive effects on insects as their services and functions are critical for our existence.
Attractivity of various artificial light sources to caddisfly (Trichoptera) species and its importance in their sampling and conservation
Authors: Szanyi Kálmán , Nagy Antal , Varga Zoltán , Potish Ludvid , Szanyi Szabolcs 
Published: August 2022

AbstractThe artificial light sources are useful tools for sampling night active insects, however, they also possess potential environmental risks in their habitats. To test their applicability and evaluate environmental risk for caddisflies (Trichoptera), the attractivity of different portable light traps working with LED, UV and mixed-white light sources was studied and compared with attractivity of traditionally used mercury-vapour lamp (in Jermy-type light trap), which is tool of sampling and street-light. Analysing 1135 caught individuals of 19 species light sources emitting different wavelength spectra showed different attractivity and selectivity on caddisflies both on species and family levels. Attractivity of mercury-vapour lamp was generally lower than the other tested light sources. We found that the most attractive wavelength range for caddisflies is between 360 and 407 nm. One of the tested LED and mixed-white lamps together could cover this spectrum and a high and wide spectral peak of mixed-white light source between 375 and 391 nm resulted additional catches considering both species and number of individuals. Lamps emitting between 360 and 407 nm may be both a useful tool for sampling caddisflies and dangerous source of light pollution along lowland water courses where the sampled species are common and widespread.
High-temperature phase transformations of hydroxylapatite and the formation of silicocarnotite in the hydroxylapatite–quartz–lime system studied in situ and in operando by Raman spectroscopy
Authors: Böhme Nadine , Hauke Kerstin , Dohrn Matthias , Neuroth Manuela , Geisler Thorsten 
Published: August 2022

AbstractSilica-/calcium phosphate ceramics are of high interest in various aspects. On the one hand, they play an important role in medical applications due to their excellent biocompatibility. Therefore, detailed knowledge of the formation and stability properties of the high-temperature products ensures production under controlled conditions. On the other hand, they were identified as sinter deposits in industrial kilns, where it can indicate problems caused by too high combustion temperatures during the thermal combustion processes. Here, we report the results of two Raman heating studies to ~ 1300 °C in 10 °C-steps with nano-crystalline hydroxylapatite (HAp) and tricalcium phosphate (TCP), and a Raman heating study of natural silicocarnotite (to ~ 1200 °C, 50 °C-steps). The Raman experiments were complemented with thermal analyses. The Raman spectra of nano-crystalline HAp recorded at high temperatures revealed the stepwise loss of adsorbed water and surface-bound OH groups until ~ 570 °C. Significant loss of structural OH started at ~ 770 °C and was completed at ~ 850 °C, when HAp transformed to β-TCP. Between ~ 1220 and ~ 1270 °C, β-TCP was found to transform to α-TCP. The room temperature Raman spectrum of silicocarnotite is characterized by an intense v1(PO4) band at 951 ± 1 cm−1 that shifts to ~ 930 cm−1 at ~ 1200 °C. Using hyperspectral Raman imaging with a micrometer-scale spatial resolution, we were able to monitor in operando and in situ the solid-state reactions in the model system Ca10(PO4)6(OH)2-SiO2-CaO, in particular, the formation of silicocarnotite. In these multi-phase experiments, silicocarnotite was identified at ~ 1150 °C. The results demonstrate that silicocarnotite can form by a reaction between β-TCP and α′L-Ca2SiO4, but also between β-TCP and CaSiO3 with additional formation of quartz.
The Role of Religion and Religiosity in Health-Promoting Care for the Body During the Lockdowns caused by the COVID-19 Pandemic in Egypt, Poland and Romania
Authors: Lipowska Małgorzata , Modrzejewski Arkadiusz , Sawicki Artur , Helmy Mai , Enea Violeta , Liu Taofeng , Izydorczyk Bernadetta , Radtke Bartosz M. , Sajewicz-Radtke Urszula , Wilczyńska Dominika , Lipowski Mariusz 
Published: August 2022

AbstractThe coronavirus pandemic (COVID-19), as a widespread health threat, has triggered an increase in health-related behaviours, both pro-and anti-health, especially with regard to diet and physical activity. One of the factors modifying the intensity of such activities may be the religious doctrine and religiosity with which a person is associated. A total of 1502 people (1147 women) from countries that feature one dominant religion, took part in the study. Participants represented Sunni Islam (Egypt, n = 798), Roman Catholicism (Poland, n = 443) and Orthodox Christianity (Romania, n = 261). The Coronavirus Anxiety Scale, the Eating Attitudes Test and the Inventory of Physical Activity Objectives were used in the study. Fear of COVID-19 is associated with engagement in pro-health activity, although not to such a significant extent as might be expected. The type of religion in question was revealed to moderate this relationship, but the intensity of religiosity was not found to serve as a moderator.
Moving beyond landscape resistance: considerations for the future of connectivity modelling and conservation science
Authors: Unnithan Kumar Siddharth , Turnbull Jonathon , Hartman Davies Oscar , Hodgetts Timothy , Cushman Samuel Alan 
Published: August 2022

AbstractLandscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has emerged as a central focus of landscape ecology and conservation science. Connectivity modelling now encompasses an enormous body of work across ecological theory and application. The dominant connectivity models in use today are based on the framework of ‘landscape resistance’, which is a way of measuring how landscape structure influences movement patterns. However, the simplistic assumptions and high degree of reductionism inherent to the landscape resistance paradigm severely limits the ability of connectivity algorithms to account for many fundamental aspects of animal movement, and thus greatly reduces the effectiveness and relevance of connectivity models for conservation theory and practice. In this paper, we first provide an overview of the development of connectivity modelling and resistance surfaces. We then discuss several key drivers of animal movement which are absent in resistance-based models, with a focus on spatiotemporal variation, human and interspecies interactions, and other context-dependent effects. We look at a range of empirical studies which highlight the strong impact these effects have on movement and connectivity predictions. But we also provide promising avenues of future research to address this: we discuss newly emerging technologies and interdisciplinary work, and look to developing methodologies, models and conversations which move beyond the limiting framework of landscape resistance, so that connectivity models can better reflect the complexities and richness of animal movement.
A radical theory for graphs that do not admit loops
Authors: Veldsman Stefan 
Published: August 2022

AbstractThere is a well-developed theory of connectednesses and disconnectednesses (= radical theory) for the category of graphs that admit loops. Here it is shown that such a theory for the category of graphs that do not allow loops degenerates to the trivial case for all the Hoehnke radicals, but there are non-trivial connectednesses (KA-radical classes) and disconnectednesses (KA-semisimple classes). Moreover, the connectednesses and disconnectednesses always come as complementary pairs.