bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing sustainability in polymer 3D printing via fusion filament fabrication through integration of by-products in powder form: mechanical and thermal characterization
Authors: Castanon-Jano Laura , Palomera-Obregon Paula , Lázaro Mariano , Blanco-Fernandez Elena , Blasón Sergio 
Published: May 2024

AbstractFFF (fused filament fabrication) is a type of 3D printing that utilizes filament for part creation. This study proposes using by-products or waste to replace part of the plastic in FFF filament, reducing environmental impact. The aim is to maintain a simple manufacturing process involving extrusion on a single-screw desktop machine followed by printing. The plastic matrix comprises polylactic acid (PLA) and polyethylene glycol (PETG), with added powdered by-products: seashells, car glass and mill scale (metal). Additives will be incorporated at 10% and 20% by weight, with two grain sizes: up to 0.09 mm and up to 0.018 mm. Mechanical tests (tensile, flexural and hardness) and thermal characterization tests will be conducted. Findings suggest adding 10%w powder of any variety to PETG increases tensile strength up to 48%, with metal powder (mill scale) showing the highest enhancement, even at 20%w, resulting in a 41% increase. Conversely, adding powder to PLA worsens mechanical properties without stiffening the material; instead, the elastic modulus decreases. Metal grain size has minimal impact, with grain sizes lower than 0.09 mm optimal for PLA. Thermal conductivity in polymers blended with powder additives is lower than in virgin polymers, likely due to air void formation, supported by density and microscopic evaluations. This research underscores the potential of utilizing waste materials with a simple FFF filament production to enhance sustainability in 3D printing practices.
A novel in vitro high-content imaging assay for the prediction of drug-induced lung toxicity
Authors: Fitzpatrick Paul A. , Johansson Julia , Maglennon Gareth , Wallace Ian , Hendrickx Ramon , Stamou Marianna , Balogh Sivars Kinga , Busch Susann , Johansson Linnea , Van Zuydam Natalie , Patten Kelley , Åberg Per M. , Ollerstam Anna , Hornberg Jorrit J. 
Published: May 2024

AbstractThe development of inhaled drugs for respiratory diseases is frequently impacted by lung pathology in non-clinical safety studies. To enable design of novel candidate drugs with the right safety profile, predictive in vitro lung toxicity assays are required that can be applied during drug discovery for early hazard identification and mitigation. Here, we describe a novel high-content imaging-based screening assay that allows for quantification of the tight junction protein occludin in A549 cells, as a model for lung epithelial barrier integrity. We assessed a set of compounds with a known lung safety profile, defined by clinical safety or non-clinical in vivo toxicology data, and were able to correctly identify 9 of 10 compounds with a respiratory safety risk and 9 of 9 compounds without a respiratory safety risk (90% sensitivity, 100% specificity). The assay was sensitive at relevant compound concentrations to influence medicinal chemistry optimization programs and, with an accessible cell model in a 96-well plate format, short protocol and application of automated imaging analysis algorithms, this assay can be readily integrated in routine discovery safety screening to identify and mitigate respiratory toxicity early during drug discovery. Interestingly, when we applied physiologically-based pharmacokinetic (PBPK) modelling to predict epithelial lining fluid exposures of the respiratory tract after inhalation, we found a robust correlation between in vitro occludin assay data and lung pathology in vivo, suggesting the assay can inform translational risk assessment for inhaled small molecules.
Diversity and Bioprospection of Gram-positive Bacteria Derived from a Mayan Sinkhole
Authors: Wissner Julian L. , Parada-Fabián José Carlos , Márquez-Velázquez Norma Angélica , Escobedo-Hinojosa Wendy , Gaudêncio Susana P. , Prieto-Davó Alejandra 
Published: May 2024

AbstractWater-filled sinkholes known locally as cenotes, found on the Yucatán Peninsula, have remarkable biodiversity. The primary objective of this study was to explore the biotechnological potential of Gram-positive cultivable bacteria obtained from sediment samples collected at the coastal cenote Pol-Ac in Yucatán, Mexico. Specifically, the investigation aimed to assess production of hydrolytic enzymes and antimicrobial compounds. 16 S rRNA gene sequencing led to the identification of 49 Gram-positive bacterial isolates belonging to the phyla Bacillota (n = 29) and Actinomycetota (n = 20) divided into the common genera Bacillus and Streptomyces, as well as the genera Virgibacillus, Halobacillus, Metabacillus, Solibacillus, Neobacillus, Rossellomorea, Nocardiopsis and Corynebacterium. With growth at 55ºC, 21 of the 49 strains were classified as moderately thermotolerant. All strains were classified as halotolerant and 24 were dependent on marine water for growth. Screening for six extracellular hydrolytic enzymes revealed gelatinase, amylase, lipase, cellulase, protease and chitinase activities in 93.9%, 67.3%, 63.3%, 59.2%, 59.2% and 38.8%, of isolated strains, respectively. The genes for polyketide synthases type I, were detected in 24 of the strains. Of 18 strains that achieved > 25% inhibition of growth in the bacterial pathogen Staphylococcus aureus ATCC 6538, 4 also inhibited growth in Escherichia coli ATCC 35,218. Isolates Streptomyces sp. NCA_378 and Bacillus sp. NCA_374 demonstrated 50–75% growth inhibition against at least one of the two pathogens tested, along with significant enzymatic activity across all six extracellular enzymes. This is the first comprehensive report on the biotechnological potential of Gram-positive bacteria isolated from sediments in the cenotes of the Yucatán Peninsula.
Context-specific adaptation for head fakes in basketball: a study on player-specific fake-frequency schedules
Authors: Güldenpenning Iris , Böer Nils T. , Kunde Wilfried , Giesen Carina G. , Rothermund Klaus , Weigelt Matthias 
Published: May 2024

AbstractIn basketball, an attacking player often plays a pass to one side while looking to the other side. This head fake provokes a conflict in the observing opponent, as the processing of the head orientation interferes with the processing of the pass direction. Accordingly, responses to passes with head fakes are slower and result in more errors than responses to passes without head fakes (head-fake effect). The head-fake effect and structurally similar interference effects (e.g., Stroop effect) are modulated by the frequency of conflicting trials. Previous studies mostly applied a block-wise manipulation of proportion congruency. However, in basketball (and also in other team sports), where different individual opponents can be encountered, it might be important to take the individual frequency (e.g., 20% vs. 80%) of these opponents into account. Therefore, the present study investigates the possibility to quickly (i.e., on a trial-by-trial basis) reconfigure the response behavior to different proportions of incongruent trials, which are contingent on different basketball players. Results point out that participants indeed adapted to the fake-frequency of different basketball players, which could be the result of strategic adaptation processes. Multi-level analyses, however, indicate that a substantial portion of the player-specific adaptation to fake frequencies is accounted by episodic retrieval processes, suggesting that item-specific proportion congruency effects can be explained in terms of stimulus-response binding and retrieval: The head orientation (e.g., to the right) of a current stimulus retrieves the last episode with the same head orientation including the response that was part of this last episode. Thus, from a theoretical perspective, an attacking player would provoke the strongest detrimental effect on an opponent if s/he repeats the same head movement but changes the direction of the pass. Whether it is at all possible to strategically apply this recommendation in practice needs still to be answered.
ESIT – a digital twin of air surveillance infrastructure
Authors: Schreiber Helmut , Bösch Wolfgang , Paulitsch Helmut , Schlemmer Andreas , Schäfer Matthias , Kraft Marc 
Published: May 2024

AbstractCivil air traffic surveillance and management depends heavily on the use of aircraft transponders replying to radio interrogations from cooperative surveillance systems such as secondary surveillance radars, multilateration and collision avoidance systems. A high number of such surveillance systems combined with a high aircraft density can cause a large quantity of interrogations, therefore having a detrimental impact on transponder behavior and sometimes even causing a loss of replies and thus a deterioration in aircraft surveillance. In order to ensure the safe operation of European air traffic management, the European Commission obligates the member states to take measures to ensure that the transponder load in their air spaces does not exceed a safe level. EUROCONTROL, in its function as network manager responsible for supporting the monitoring, management and coordination of the scarce spectrum resource, has contracted the implementation of a European simulation tool to Austro Control in collaboration with SeRo Systems and the Institute of Microwave and Photonic Engineering as subcontractors. This paper examines the development and use of this tool as well as the functionality it provides.
A low-cost, open-source-based optical surgical navigation system using stereoscopic vision
Authors: Tsui Darin , Ramos Kirsten , Melentyev Capalina , Rajan Ananya , Tam Matthew , Jo Mitsuhiro , Ahadian Farshad , Talke Frank E. 
Published: May 2024

AbstractComputer-assisted surgical navigation systems have gained popularity in surgical procedures that demand high amounts of precision. These systems aim to track the real-time positioning of surgical instruments in relation to anatomical structures. Typically, state-of-the-art methods involve tracking reflective 3D marker spheres affixed to both surgical instruments and patient anatomies with infrared cameras. However, these setups are expensive and financially impractical for small healthcare facilities. This study suggests that a fully optical navigation approach utilizing low-cost, off-the-shelf parts may become a viable alternative. We develop a stereoscopic camera setup, costing around $120, to track and monitor the translational movement of open-source based fiducial markers on a positioning platform. We evaluate the camera setup based on its reliability and accuracy. Using the optimal set of parameters, we were able to produce a root mean square error of 2 mm. These results demonstrate the feasibility of real-time, cost-effective surgical navigation using off-the-shelf optical cameras.
A bibliometric analysis of scientific literature on alternate wetting and drying (AWD)
Authors: Vuciterna Rina , Ruggeri Giordano , Corsi Stefano , Facchi Arianna , Gharsallah Olfa 
Published: May 2024

AbstractThe study presents a bibliometric analysis of scientific literature on alternate wetting and drying (AWD) as a water-saving irrigation practice for rice cultivation. Data were collected from the Web of Science, resulting in a database of 439 articles written by 2574 authors in 167 journals. The study reveals the growing importance of AWD in publications from the 90s to mid-2022, with fast growth and reaching its peak in the last 5 years, suggesting that the full potential of AWD remains yet to be realised and explored. Most papers are produced in oriental countries, except the USA and Australia. The trend of keywords in the research on AWD reveals a persistence of certain themes throughout the years while simultaneously showcasing a clear evolution of the topics being addressed. In addition to optimising productivity and agricultural yields, research now encompasses environmental issues and human and crop health, reflecting a broader trend in agriculture and research towards sustainable and environmentally responsible practices. This analysis provides insights into the development and direction of research in AWD, emphasising the need for future research to address the emerging concerns of the impact of AWD on the environment, human and crop health, and economic profitability of AWD adoption.
Approximation in the extended functional tensor train format
Authors: Strössner Christoph , Sun Bonan , Kressner Daniel 
Published: May 2024

AbstractThis work proposes the extended functional tensor train (EFTT) format for compressing and working with multivariate functions on tensor product domains. Our compression algorithm combines tensorized Chebyshev interpolation with a low-rank approximation algorithm that is entirely based on function evaluations. Compared to existing methods based on the functional tensor train format, the adaptivity of our approach often results in reducing the required storage, sometimes considerably, while achieving the same accuracy. In particular, we reduce the number of function evaluations required to achieve a prescribed accuracy by up to over $$96\%$$

96
%

compared to the algorithm from Gorodetsky et al. (Comput. Methods Appl. Mech. Eng. 347, 59–84 2019).
Hierarchical reinforcement learning for handling sparse rewards in multi-goal navigation
Authors: Yan Jiangyue , Luo Biao , Xu Xiaodong 
Published: May 2024

AbstractReinforcement learning (RL) has achieved remarkable advancements in navigation tasks in recent years. However, tackling multi-goal navigation tasks with sparse rewards remains a complex and challenging problem due to the long-sequence decision-making involved. Such multi-goal navigation tasks inherently incorporate a hybrid action space, where the robot needs to select a navigation endpoint first before executing primitive actions. To address the problem of multi-goal navigation with sparse rewards, we introduce a novel hierarchical RL framework named Hierarchical RL with Multi-Goal (HRL-MG). The main idea of HRL-MG is to divide and conquer the hybrid action space, splitting long-sequence decisions into short-sequence decisions. The HRL-MG framework is composed of two main modules: a selector and an actuator. The selector employs a temporal abstraction hierarchical architecture designed to specify a desired end goal based on the discrete action space. Conversely, the actuator utilizes a continuous goal-oriented hierarchical architecture developed to enact continuous action sequences to reach the desired end goal specified by the selector. In addition, we incorporate a dynamic goal detection mechanism, grounded in hindsight experience replay, to mitigate the challenges posed by sparse reward landscapes. We validated the algorithm’s efficacy on both the discrete environment Maze_2D and the continuous robotic environment MuJoCo ‘Ant’. The results indicate that HRL-MG significantly outperforms other methods in multi-goal navigation tasks with sparse rewards.
On the distribution of zeros of analytic functions in angles in $$\mathbf{C} \backslash \{ {0}\} $$
Authors: Fernández Árias A. 
Published: May 2024

AbstractIn this article some results on the value distribution theory of analytic
functions defined in angles of $$\mathbb{C}$$
C
, due mainly to B. Ja. Levin and A. Pfluger,
will be extended to the more general situation where the functions are defined in
angles of $$\mathbb{C}\backslash\{ 0\}$$

C
\
{
0
}

. More precisely, angles $$S ( \theta_{1},\theta_{2}) $$

S
(

θ
1

,

θ
2

)

with vertex at the origin will be
considered and where a singularity at zero is allowed. An special class of these
functions are those of completely regular growth for which it is proved a basic result
which yields an expression of the density of its zeros in terms of the indicator
function.
Modelling the Flow Conditions and Primary Atomization of an Air-Core-Liquid-Ring (ACLR) Atomizer Using a Coupled Eulerian–Lagrangian Approach
Authors: Ballesteros Martínez Miguel Ángel , Becerra Deisy , Gaukel Volker 
Published: May 2024

AbstractThe Air-Core-Liquid-Ring atomizer is a pioneering internal-mixing pneumatic atomization technique designed for energy-efficient spray drying of highly viscous liquid feeds with substantial solid contents. However, it can suffer internal flow instabilities, which may lead to spray droplets with a wide variation in diameter. Experimental investigation of how flow conditions mechanistically determine the resulting droplet sizes is hindered by high velocities near the nozzle outlet. Therefore, this study addressed the issue by implementing a numerical model, employing a coupled Eulerian-Lagrangian approach with adaptive mesh refinement, to simulate the breakup of the liquid into ligaments and droplets. Additionally, Large Eddy Simulation was incorporated to replicate turbulent flow conditions observed in experiments. The numerical model demonstrated significant improvement in predicting liquid film thickness, compared to previous work. Additionally, the simulated droplet size distributions mirrored experimental trends, shifting to smaller sizes as pressure increased. Unfortunately, while reduced, there is a persistent underestimation of the lamella thickness and the droplet sizes at 0.2 MPa. In spite of this, the fact that the error propagates between the two phenomena underscores the effective coupling between Eulerian and Lagrangian approaches.
A Personalised Approach to Identifying
Important Determinants of Well-being
Authors: Ciarrochi Joseph , Sahdra Baljinder , Hayes Steven C. , Hofmann Stefan G. , Sanford Brandon , Stanton Cory , Yap Keong , Fraser Madeleine I. , Gates Kathleen , Gloster Andrew T. 
Published: May 2024

Abstract
Purpose
To develop effective and personalized interventions, it is
essential to identify the most critical processes or psychological drivers that
impact an individual’s well-being. Some processes may be universally
beneficial to well-being across many contexts and people, while others may only
be beneficial to certain individuals in specific contexts.

Method
We conducted three intensive daily diary studies, each with
more than 50 within-person measurement occasions, across three data sets
(n1 = 44; n2 = 37; n3 = 141). We aimed
to investigate individual differences in the strength of within-person
associations between three distinct process measures and a variety of outcomes.
We utilized a unique idiographic algorithm, known as i-ARIMAX (Autoregressive
Integrated Moving Average), to determine the strength of the relationship (Beta)
between each process and outcome within individuals (“i”). All of
the computed betas were then subjected to meta-analyses, with individuals
treated as the “study”.

Results
The results revealed that the process-outcome links varied
significantly between individuals, surpassing the homogeneity typically seen in
meta-analyses of studies. Although several processes showed group-level effects,
no process was found to be universally beneficial when considered individually.
For instance, processes involving social behavior, like being assertive, did not
demonstrate any group-level links to loneliness but still had significant
individual-level effects that varied from positive to negative.

Discussion
Using i-ARIMAX might help reduce the number of candidate
variables for complex within-person analyses. Additionally, the size and pattern
of i-ARIMAX betas could prove useful in guiding personalized
interventions.
Bitcoin Price Prediction Using Sentiment Analysis and Empirical Mode Decomposition
Authors: Arslan Serdar 
Published: May 2024

AbstractCryptocurrencies have garnered significant attention recently due to widespread investments. Additionally, researchers have increasingly turned to social media, particularly in the context of financial markets, to harness its predictive capabilities. Investors rely on platforms like Twitter to analyze investments and detect trends, which can directly impact the future price movements of Bitcoin. Understanding and analyzing Twitter sentiments can potentially provide insights into future Bitcoin price movements and can shed light on how investor sentiment affects cryptocurrency markets. In this study, we explore the correlation between Twitter activity and Bitcoin prices by examining tweets related to Bitcoin price sentiments. Our proposed model consists of two distinct networks. The first network exclusively utilizes historical price data, which is further decomposed into various components using the Empirical Mode Decomposition method. This decomposition helps mitigate the impact of irregular fluctuations on Bitcoin price predictions. Each of these components is then separately processed by Long Short-Term Memory (LSTM) networks. The second network focuses on modeling user sentiments and emotions in conjunction with Bitcoin market data. User opinions are categorized into positive and negative classes and are integrated with historical data to predict the next-day price using LSTM networks. Finally, the outputs of each network are combined to form the ultimate prediction values. Experimental results demonstrate that Twitter sentiment can effectively helps us predict Bitcoin price trends. Furthermore, to validate our proposed model, we compared it with several state-of-the-art methods. The results indicate that our approach outperforms these existing models in terms of accuracy.
The mediating role of self-regulated online learning behaviors: Exploring the impact of personality traits on student engagement
Authors: Kara Ahmet , Ergulec Funda , Eren Esra 
Published: May 2024

AbstractOnline learning environments have become increasingly prevalent in higher education, necessitating an understanding of factors influencing student engagement. This study examines the mediating role of self-regulated online learning in the relationship between five-factor personality traits and student engagement among university students. A sample of 437 university students from educational sciences, social sciences, and health sciences disciplines participated in the study. Data were collected using ‘The Big Five Inventory’ to assess personality traits, the ‘Self-Regulated Online Learning Questionnaire’ to measure self-regulated online learning, and the ‘Student Engagement Scale in the Online Learning Environment’ to evaluate student engagement. Structural equation modeling with bootstrap analysis was employed to analyze the data. The study findings indicate that the five factor personality traits significantly predict self-regulated online learning. Furthermore, self-regulated online learning is a significant predictor of students’ engagement in the online learning environment. Additionally, the five factor personality traits are found to be significant predictors of student engagement in the online learning environment. Lastly, self-regulated online learning plays a partially mediating role in the relationship between the five factor personality traits and student engagement in the online learning environment. This study underscores the importance of considering individual differences in personality traits and fostering self-regulated learning strategies to enhance student engagement in online learning environments. Understanding these dynamics can inform the design of effective interventions aimed at improving student outcomes in online education.