bluuebunny/crossref_metadata_2025_split huggingface dataset:
A hierarchic isogeometric hyperelastic solid-shell
Authors: Leonetti Leonardo , Verhelst Hugo M. 
Published: March 2024

AbstractThe present study aims to develop an original solid-like shell element for large deformation analysis of hyperelastic shell structures in the context of isogeometric analysis (IGA). The presented model includes a new variable to describe the thickness change of the shell and allows for the application of unmodified three-dimensional constitutive laws defined in curvilinear coordinate systems and the analysis of variable thickness shells. In this way, the thickness locking affecting standard solid-shell-like models is cured by enhancing the thickness strain by exploiting a hierarchical approach, allowing linear transversal strains. Furthermore, a patch-wise reduced integration scheme is adopted for computational efficiency reasons and to annihilate shear and membrane locking. In addition, the Mixed-Integration Point (MIP) format is extended to hyperelastic materials to improve the convergence behaviour, hence the efficiency, in Newton iterations. Using benchmark problems, it is shown that the proposed model is reliable and resolves locking issues that were present in the previously published isogeometric solid-shell formulations.
Long short-term memory prediction of user’s locomotion in virtual reality
Authors: Mayor Jesus , Calleja Pablo , Fuentes-Hurtado Felix 
Published: March 2024

AbstractNowadays, there is still a challenge in virtual reality to obtain an accurate displacement prediction of the user. This could be a future key element to apply in the so-called redirected walking methods. Meanwhile, deep learning provides us with new tools to reach greater achievements in this type of prediction. Specifically, long short-term memory recurrent neural networks obtained promising results recently. This gives us clues to continue researching in this line to predict virtual reality user’s displacement. This manuscript focuses on the collection of positional data and a subsequent new way to train a deep learning model to obtain more accurate predictions. The data were collected with 44 participants and it has been analyzed with different existing prediction algorithms. The best results were obtained with a new idea, the use of rotation quaternions and the three dimensions to train the previously existing models. The authors strongly believe that there is still much room for improvement in this research area by means of the usage of new deep learning models.
Long-term effects of SARS-CoV-2 infection in patients with and without chemosensory disorders at disease onset: a psychophysical and magnetic resonance imaging exploratory study
Authors: Cecchini Maria Paola , Pizzini Francesca Benedetta , Boschi Federico , Marcon Alessandro , Moro Lucia , Gordon Elizabeth , Guizard Nicolas , Cavedo Enrica , Ricatti Maria Jimena , Veronese Sheila , Tamburin Stefano , Tinazzi Michele , Mansueto Giancarlo , Sbarbati Andrea 
Published: March 2024

AbstractA preserved sense of smell and taste allows us to understand many environmental “messages” and results in meaningfully improvements to quality of life. With the COVID-19 pandemic, it became clear how important these senses are for social and nutritional status and catapulted this niche chemosensory research area towards widespread interest. In the current exploratory work, we assessed two groups of post-COVID-19 patients who reported having had (Group 1) or not (Group 2) a smell/taste impairment at the disease onset. The aim was to compare them using validated smell and taste tests as well as with brain magnetic resonance imaging volumetric analysis. Normative data were used for smell scores comparison and a pool of healthy subjects, recruited before the pandemic, served as controls for taste scores. The majority of patients in both groups showed an olfactory impairment, which was more severe in Group 1 (median UPSIT scores: 24.5 Group 1 vs 31.0 Group 2, p = 0.008), particularly among women (p = 0.014). No significant differences emerged comparing taste scores between Group 1 and Group 2, but dysgeusia was only present in Group 1 patients. However, for taste scores, a significant difference was found between Group 1 and controls (p = 0.005). No MRI anatomical abnormalities emerged in any patients while brain volumetric analysis suggested a significant difference among groups for the right caudate nucleus (p = 0.028), although this was not retained following Benjamini–Hochberg correction. This exploratory study could add new information in COVID-19 chemosensory long-lasting impairment and address future investigations on the post-COVID-19 patients’ research.
Eutrophication hotspots, nitrogen fluxes and climate impacts in estuarine ecosystems: A model study of the Odra estuary system
Authors: Pein Johannes , Staneva Joanna 
Published: March 2024

AbstractThe Odra estuary in the southern Baltic Sea comprises the Odra (Szczecin) Lagoon, the Pomeranian Bay and a number of other shallow water areas and channels. Known for its abundance of fish, eutrophication in the Odra Lagoon is a pressing issue for science and environmental management representing a global problem: What determines the seasonal variability of nitrogen and nitrogen turnover in shallow water areas, and how does seasonal variability change due to climatic changes such as warming and sea level rise? How do such changes affect nutrient exports to the regional ocean? This study employs a high-resolution unstructured model system to investigate physical-biogeochemical interactions, nitrogen turnover, and conditions leading to nitrogen export to the Baltic Sea within the Odra estuary. The research comprises hindcast and a climatic experiment with modified water level and temperature inputs. The model reproduces the thermohaline dynamics of brackish shallow water areas, phytoplankton blooms and the variability of inorganic nitrogen. The simulations identify the dynamic partitioning of the Odra Lagoon into the highly eutrophic, lake-like Small Lagoon and more frequently flushed, zooplankton-rich Great Lagoon. Although the two years of the hindcast simulation feature very different boundary conditions in terms of river forcing, comparable patterns of seasonal nitrogen export emerge. In a climate change experiment with increased sea levels and global temperatures, the system appears sensitive, but remains stable with regard to nutrient transport and is therefore predictable. The climate change experiment reveals enhanced primary producer biomass concentrations, suggesting heightened eutrophication. While in the shallow waters of Odra Lagoon oxygen concentration remains relatively stable, oxygen depletion intensifies as the lagoon outflows enter the Pomeranian Bay. This phenomenon is linked to increased denitrification within the stratified Odra plume. Deeper, meandering channels, such as Swina, demonstrate resilience to oxygen reduction, influenced by sea level rise and enhanced currents. Based on the temporal-spatial high-resolution coupled, validated simulations, it is possible to develop tailor-made management solutions without having to run expensive and complicated observation campaigns in the shallow waters with complex topography.
Characterization and decontamination of deposited dust: a management regime at a museum
Authors: Hameed A. A. Abdel , El-Gendy S. , Saeed Y. 
Published: March 2024

AbstractDeposited dust represents a nutritional niche for microflora. Inhibiting microflora-associated deposited dust is a critical approach to manage cultural heritage buildings. Knowledge on the effectiveness of commercial disinfection on microflora in a real field environment is limited. The present study aims to: (1) characterize deposited dust composition, and (2) assess the effectiveness of several commercial biocides/and an air ionizer on microflora-associated floor surface and air before and after treatment. Deposited dust was collected using a dust collector and microbial air sampling was conducted via a volumetric impactor sampler. Susceptibility of microorganisms to biocide/ionizer was performed in a naturally ventilated unoccupied room with a floor area of 18 m2. One-treatment protocol, a daily disinfection mode, was applied to each biocide/ionizer. The surface floor was adjacently sprayed by a biocide, and the ionizer was turned on for 30 min. Indoor deposited dust rates varied between 0.75 and 8.7 mg/m2/day with indoor/outdoor ratio of ~ 1:100. Ion concentrations of NH4+, Cl−, SO42− and NO3− were higher indoor than outdoor. The concentration of microorganisms-associated deposited dust averaged 106 CFU/g; 105 CFU/g and 104 CFU/g for bacteria, fungi and actinomycetes, respectively. A total of 23 fungal taxa were identified, with Aspergillus flavus, Asp. fumigatus and Asp. niger were the predominant taxa. Biocides quickly reduced floor surface and airborne microbial loads. The biocidal effect was time limited, as microflora loads increased again after ~ 4 days of the treatment protocol. Benzalkonium chloride (BAC) out-performed other biocides, showed a relatively permanent microbial inhibiting effect. The air ionizer reduced airborne microorganisms and increased surface floor ones. Characterizing of deposited dust (rate and composition) and choice an appropriate biocide may effectively reduce biodeterioration. Further real field treatment trials under various microenvironmental conditions are needed to determine the effectiveness of disinfection treatment.
Machine learning-based test smell detection
Authors: Pontillo Valeria , Amoroso d’Aragona Dario , Pecorelli Fabiano , Di Nucci Dario , Ferrucci Filomena , Palomba Fabio 
Published: March 2024

AbstractTest smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these detectors is still limited and dependent on tunable thresholds. We design and experiment with a novel test smell detection approach based on machine learning to detect four test smells. First, we develop the largest dataset of manually-validated test smells to enable experimentation. Afterward, we train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we compare the ML-based approach with state-of-the-art heuristic-based techniques. The key findings of the study report a negative result. The performance of the machine learning-based detector is significantly better than heuristic-based techniques, but none of the learners able to overcome an average F-Measure of 51%. We further elaborate and discuss the reasons behind this negative result through a qualitative investigation into the current issues and challenges that prevent the appropriate detection of test smells, which allowed us to catalog the next steps that the research community may pursue to improve test smell detection techniques.
Enhancing dietary diversity and food security through the adoption of climate-smart agricultural practices in Nigeria: a micro level evidence
Authors: Omotoso Abeeb Babatunde , Omotayo Abiodun Olusola 
Published: March 2024

AbstractThe paranoid belief that climate change will gradually reduce the ability of the world to meet the demand for food serves as the rationale for Nigerian smallholder farmers’ advocacy of climate-smart agriculture (CSA) techniques. The study investigates the effects of CSA practice on the food security status of rural farming households in Nigeria. A multi-stage sampling technique was employed in selecting 480 rural farming households across three selected states from Southwest, Nigeria. Data were analyzed using descriptive and inferential statistics. Obtained results showed that 59.79% of the respondents were food insecure while severe and depth of food insecure among the farming households were 0.0711 and 0.1913 respectively. The result of the household dietary diversity score revealed the diverse consumption-ability of the respondents and the contributions of CSA practice in their farming system. This implies that households engaged in climate-smart farming are more likely to achieve higher levels of food consumption score, dietary diversity, and food security. The probit regression revealed that the food security status among rural farming households was significantly influenced by household heads’ gender, farm size, and contact with extension agents as well as adopted CSA practices such as crop diversification, agroforestry, and use of Fadama land for agricultural activities. This research concludes that CSA lowered the probability of food insecurity among rural farming households in Nigeria. Accordingly, the study suggests that the government and the key players should encourage the use of CSA practices in order to ensure agricultural sustainability and food security in agrarian communities by reducing the impact of climate change.
Work, Heat and Internal Energy in Open Quantum Systems: A Comparison of Four Approaches from the Autonomous System Framework
Authors: Seegebrecht Anja , Schilling Tanja 
Published: March 2024

AbstractWe compare definitions of the internal energy of an open quantum system and strategies to split the internal energy into work and heat contributions as given by four different approaches from the autonomous system framework. Our discussion focuses on methods that allow for arbitrary environments (not just heat baths) and driving by a quantum mechanical system. As a simple application we consider an atom as the system of interest and an oscillator field mode as the environment. Three different types of coupling are analyzed. We discuss ambiguities in the definitions and highlight differences that appear if one aims at constructing environments that act as pure heat or work reservoirs. Further, we identify different sources of work (e.g. coherence, correlations, or frequency offset), depending on the underlying framework. Finally, we give arguments to favour the approach based on minimal dissipation.
Transcriptome profiling of longissimus dorsi during different prenatal stages to identify genes involved in intramuscular fat deposition in lean and obese pig breeds
Authors: Chen Jun Feng , Wang Jing , Chai Jin , Jin Wei , Ren Qiao Ling , Ma Qiang , Lu Qing Xia , Sun Jia Jie , Mo De Lin , Zhang Jia Qing , Xing Bao Song 
Published: March 2024

Abstract

Background
There was significant difference in muscle development between fat-type and lean-type pig breeds.

Methods and results
In current study, transcriptome analysis and bioinformatics analysis were used to compare the difference in longissimus dorsi (LD) muscle at three time-points (38 days post coitus (dpc), 58 dpc, and 78 dpc ) between Huainan (HN) and Large white (LW) pig breeds. A total of 24500 transcripts were obtained in 18 samples, and 2319, 2799, and 3713 differently expressed genes (DEGs) were identified between these two breeds at 38 dpc, 58 dpc, and 78 dpc, respectively. And the number and foldchange of DEGs were increased, the alternative splice also increased. The cluster analysis of DEGs indicated the embryonic development progress of LD muscle between these two breeds was different. There were 539 shared DEGs between HN and LW at three stages, and the top-shared DEGs were associated with muscle development and lipid deposition, such as KLF4, NR4A1, HSP70, ZBTB16 and so on.

Conclusions
The results showed DEGs between Huainan (HN) and Large white (LW) pig breeds, and contributed to the understanding the muscle development difference between HN and LW, and provided basic materials for improvement of meat quality.
The assessment of probabilistic seismic risk using ground-motion simulations via a Monte Carlo approach
Authors: Rudman Archie , Douglas John , Tubaldi Enrico 
Published: March 2024

AbstractAccurately characterizing ground motions is crucial for estimating probabilistic seismic hazard and risk. The growing number of ground-motion models, and increased use of simulations in hazard and risk assessments, warrants a comparison between the different techniques available to predict ground motions. This research aims at investigating how the use of different ground-motion models can affect seismic hazard and risk estimates. For this purpose, a case study is considered with a circular seismic source zone and two line sources. A stochastic ground-motion model is used within a Monte Carlo analysis to create a benchmark hazard output. This approach allows the generation of many records, helping to capture details of the ground-motion median and variability, which a ground motion prediction equation may fail to properly model. A variety of ground-motion models are fitted to the simulated ground motion data, with fixed and magnitude-dependant standard deviations (sigmas) considered. These include classic ground motion prediction equations (with basic and more complex functional forms), and a model using an artificial neural network. Hazard is estimated from these models and then we extend the approach to a risk assessment for an inelastic single-degree-of-freedom-system. Only the artificial neural network produces accurate hazard results below an annual frequency of exceedance of 1 × 10–3 years−1. This has a direct impact on risk estimates—with ground motions from large, close-to-site events having more influence on results than expected. Finally, an alternative to ground-motion modelling is explored through an observational-based hazard assessment which uses recorded strong-motions to directly quantify hazard.
Patient experiences in ulcerative colitis: conceptual model and review of patient-reported outcome measures
Authors: Kim Chong , Brown Fiona L. , Burk Caroline , Anatchkova Milena , Sargalo Nashmel , Kaushik Ankita 
Published: March 2024

Abstract
Purpose
To identify symptoms and their impacts on daily functioning and health-related quality of life (HRQoL) experienced by adult patients with ulcerative colitis (UC) and evaluate patient-reported outcome (PRO) measures for UC clinical studies.

Methods
A conceptual model of symptoms and impacts of UC were developed from a literature review. PRO measures were identified from the literature, clinical trials databases, health technology assessment submissions, and regulatory label claims, and were selected for conceptual analysis based on disease specificity and use across information sources. PRO measures covering the most concepts when mapped against the conceptual model were assessed for gaps in psychometric properties using Food and Drug Administration (FDA) guidance and consensus-based standards for the selection of health measurement instruments (COSMIN) criteria.

Results
The conceptual model grouped the 52 symptom concepts and 72 proximal and distal impacts into eight, two, and five dimensions, respectively. Of 65 PRO measures identified, eight underwent conceptual analysis. Measures covering the most concepts and assessed for psychometric properties were the Inflammatory Bowel Disease Questionnaire, Symptoms and Impacts Questionnaire for UC, UC-PRO symptoms modules, UC-PRO impact modules, and Crohn’s and UC Questionnaire; all had good or excellent support for content validity. The UC-PRO Signs and Symptoms fully met FDA guidance and COSMIN criteria for content validity and most psychometric properties.

Conclusion
Existing PRO measures assess concepts relevant to patients with UC, but all PRO measures reviewed require further psychometric evaluation to demonstrate they are fit for purpose.
What is a mathematical structure of conscious experience?
Authors: Kleiner Johannes , Ludwig Tim 
Published: March 2024

AbstractSeveral promising approaches have been developed to represent conscious experience in terms of mathematical spaces and structures. What is missing, however, is an explicit definition of what a ‘mathematical structure of conscious experience’ is. Here, we propose such a definition. This definition provides a link between the abstract formal entities of mathematics and the concreta of conscious experience; it complements recent approaches that study quality spaces, qualia spaces, or phenomenal spaces; and it provides a general method to identify and investigate structures of conscious experience. We hope that ultimately this work provides a basis for developing a common formal language to study consciousness.
Ballistic and Blast-Relevant, High-Rate Material Properties of Physically and Chemically Crosslinked Hydrogels
Authors: Bremer-Sai E. C. , Yang J. , McGhee A. , Franck C. 
Published: March 2024

Abstract
Background
Hydrogels are one of the most ubiquitous polymeric materials. Among them gelatin, agarose and polyacrylamide-based formulations have been effectively utilized in a variety of biomedical and defense-related applications including ultrasound-based therapies and soft tissue injury investigations stemming from ballistic and blast exposures. Interestingly, while in most cases accurate prediction of the mechanical response of these surrogate gels requires knowledge of the underlying finite deformation, high-strain rate material properties, it is these properties that have remained scarce in the literature.

Objective
Building on our prior works using Inertial Microcavitation Rheometry (IMR), here we present a comprehensive list of the high-strain rate (> 10$$^3$$

3

1/s) mechanical properties of these three popular classes of hydrogel materials characterized via laser-based IMR, further showing that the choice in finite-deformation, rate-dependent constitutive model can be informed directly by the type of crosslinking mechanism and resultant network structure of the hydrogel, thus providing a chemophysical basis of the the choice of phenomenological constitutive model.

Methods
We analyze existing experimental gelatin IMR datasets and compare the results with prior data on polyacrylamide.

Results
We show that a Neo-Hookean Kelvin-Voigt (NHKV) model can suitably simulate the high-rate material response of dynamic, physically crosslinked hydrogels like gelatin, while the introduction of a strain-stiffening parameter through the use of the quadratic Kelvin-Voigt (qKV) model was necessary to appropriately model chemically crosslinked hydrogels such as polyacrylamide due to the nature of the static,covalent bonds that comprise their structure.

Conclusions
In this brief we show that knowledge of the type of underlying polymer structure, including its bond mobility, can directly inform the appropriate finite deformation, time-dependent viscoelastic material model for commonly employed tissue surrogate hydrogels undergoing high strain rate loading within the ballistic and blast regimes.