bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Vision Enhancement and Feature Fusion Multiscale Detection Network
Authors: Qian Chengwu , Qian Jiangbo , Wang Chong , Ye Xulun , Zhong Caiming 
Published: February 2024

AbstractIn the field of object detection, there is often a high level of occlusion in real scenes, which can very easily interfere with the accuracy of the detector. Currently, most detectors use a convolutional neural network (CNN) as a backbone network, but the robustness of CNNs for detection under cover is poor, and the absence of object pixels makes conventional convolution ineffective in extracting features, leading to a decrease in detection accuracy. To address these two problems, we propose VFN (A Vision Enhancement and Feature Fusion Multiscale Detection Network), which first builds a multiscale backbone network using different stages of the Swin Transformer, and then utilizes a vision enhancement module using dilated convolution to enhance the vision of feature points at different scales and address the problem of missing pixels. Finally, the feature guidance module enables features at each scale to be enhanced by fusing with each other. The total accuracy demonstrated by VFN on both the PASCAL VOC dataset and the CrowdHuman dataset is better than that of other methods, and its ability to find occluded objects is also better, demonstrating the effectiveness of our method.The code is available at https://github.com/qcw666/vfn.
On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis
Authors: Zhao Yulai , Liu Zepeng , Zhang Hongxu , Han Qingkai , Liu Yang , Wang Xuefei 
Published: February 2024

AbstractThis paper proposes a novel on-line rotor system condition monitoring approach using nonlinear data-driven modelling and model frequency analysis. First, the dynamic process model of the vibration transmission path between the vibration measurement points of two fulcrum structures is established by utilizing nonlinear data-driven modelling. Then, the unique frequency properties are extracted from the established model to reveal, in real time, the health condition of the rotor system. Finally, using the frequency properties as features, the unsupervised learning technology is applied to the on-line monitoring of the rotor system. Compared to conventional condition monitoring methods, the proposed approach can output an early warning 26 min before a shaft fracture occurs, without generating false alarms. Consequently, this approach can greatly enhance diagnostic accuracy, demonstrating its potential to contribute to the advancement of rotor system condition monitoring techniques.
Microlensing of Strongly Lensed Quasars
Authors: Vernardos G. , Sluse D. , Pooley D. , Schmidt R. W. , Millon M. , Weisenbach L. , Motta V. , Anguita T. , Saha P. , O’Dowd M. , Peel A. , Schechter P. L. 
Published: February 2024

AbstractStrong gravitational lensing of quasars has the potential to unlock the poorly understood physics of these fascinating objects, as well as serve as a probe of the lensing mass distribution and of cosmological parameters. In particular, gravitational microlensing by compact bodies in the lensing galaxy can enable mapping of quasar structure to $<10^{-6}$
<

10

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6

arcsec scales. Some of this potential has been realized over the past few decades, however the upcoming era of large sky surveys promises to bring this promise to full fruition. In this article, we review the theoretical framework of this field, describe the prominent current methods for parameter inference from quasar microlensing data across different observing modalities, and discuss the constraints so far derived on the geometry and physics of quasar inner structure. We also review the application of strong lensing and microlensing to constraining the granularity of the lens potential, i.e. the contribution of the baryonic and dark matter components, and the local mass distribution in the lens, i.e. the stellar mass function. Finally, we discuss the future of the field, including the new possibilities that will be opened by the next generation of large surveys and by new analysis methods now being developed.
The implicit decision theory of non-philosophers
Authors: Greene Preston , Latham Andrew J. , Miller Kristie , Nielsen Michael 
Published: February 2024

AbstractThis paper empirically investigates whether people’s implicit decision theory is more like causal decision theory or more like a non-causal decision theory (such as evidential decision theory). We also aim to determine whether implicit causalists, without prompting and without prior education, make a distinction that is crucial to causal decision theorists: preferring something as a news item and preferring it as an object of choice. Finally, we investigate whether differences in people’s implicit decision theory correlate with differences in their level of future bias: the preference for positively valenced events to be located in the future and not the past, and negatively valenced events to be located in the past and not the future. We find that people are overwhelmingly non-causalists. We also find that implicit causalists do not make the distinction between news items and objects of choice, and that there is little to no correlation between people’s future bias and their implicit decision theory. We end by discussing the philosophical upshots of this work.
Objectivity, shared values, and trust
Authors: Metzen Hanna 
Published: February 2024

AbstractThis paper deals with the nature of trust in science. Understanding what appropriate trust in science is and why it can reasonably break down is important for improving scientists’ trustworthiness. There are two different ways in which philosophers of science think about trust in science: as based on objectivity or as based on shared values. Some authors argue that objectivity actually grounds mere reliance, not genuine trust. They draw on a distinction that philosophers of trust following Annette Baier have made with respect to interpersonal trust, where genuine trust has some normative element to it that is missing in cases of mere reliance. However, I will show that there are plural forms of trust in science: Both objectivity and shared values ground trust in science in a normative sense. I will link the trust in science debate to the literature on interpersonal trust. This is significant in that, beyond references to Baier, there is little connection between both areas of philosophy. In a pluralist reading, different trust accounts help to identify the normative elements involved in different kinds of trust in science.
Susceptibility to postbiotics - enterocins of methicillin-resistant Staphylococcus aureus strains isolated from rabbits
Authors: Zábolyová Natália , Lauková Andrea , Pogány Simonová Monika 
Published: February 2024

AbstractThere is a major problem with the rising occurrence of highly virulent and multiply-resistant strains, including methicillin-resistant Staphylococcus aureus (MRSA), because of their difficult treatment. This study aimed to evaluate the antibacterial and antibiofilm effect of new enterocins (Ent) against potential pathogenic MRSA strains isolated from rabbits. Staphylococci were identified with PCR and screened for methicillin/oxacillin/cefoxitin resistance (MR) using the disk diffusion method and the PBP2’ Latex Agglutination Test Kit. Enzyme production, hemolysis, DNase activity, slime production, and biofilm formation were tested in MRSA strains. The susceptibility of MRSA to eight partially-purified enterocins (Ent) produced by E. faecium and E. durans strains was checked using agar spot tests. The antibiofilm activity of Ents was tested using a quantitative plate assay. Out of 14 MRSA, PBP testing confirmed MR in 8 strains. The majority of MRSA showed DNase activity and β-hemolysis. Slime production and moderate biofilm formation were observed in all strains. MRSA were susceptible to tested Ents (100–12,800 AU/mL; except Ent4231). The antibiofilm effect of Ents (except Ent4231) was noted in the high range (64.9–97.0%). These results indicate that enterocins offer a promising option for the prevention and treatment of bacterial infections caused by biofilm-forming MRSA.
Antibacterial activity of plant-derived compounds and cream formulations against canine skin bacteria
Authors: Strompfová Viola , Štempelová Lucia , Wolaschka Tomáš 
Published: February 2024

AbstractAn urgent need to find alternative antimicrobial compounds effective in the prevention and treatment of skin infections led us to study the inhibitory activity of eight plant-derived bioactive compounds (betulin, curcumin, glycyrrhizic acid, guaiazulene, piperine, quercetin, quinine, tannic acid) against 14 canine skin isolates (11 Gram-positive and three Gram-negative bacteria) selected based on antibiotic resistance and virulence features. The minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC) were determined using the broth microdilution method. In detail, the results for the eight different plant compounds showed their inhibitory activity in the concentration range from 0.04 to more than 16 mg/ml (MIC) and from 0.25 to more than 16 mg/ml (MBC). The most potent compounds appear to be tannic acid, followed by quinine and curcumin (MIC 0.04–16.0 mg/ml). The most susceptible strain to the tested agents in general was Bacillus cereus AE13, while Enterococcus faecium AA14 was the most resistant strain (the highest MICs) among the tested bacteria. The two most potent plant-derived compounds (tannic acid and quinine) were tested in mixture in different ratios (1:1, 1:2, 2:1). The lowest MIC and MBC values were observed for the 1:2 ratio, which was used for preparation of creams with different cream bases. One of the cream formulations (cream F) was effective up to 63.0 mg/ml (MIC) with a microbial inactivation time of 1–6 h according to the tested strain. This study provides evidence that some plant-derived compounds could have an antimicrobial effect against canine skin bacteria, the strength of which is bacterial strain dependent.
Investigation of Clean Energy Production in Drinking Water Networks
Authors: Süme Veli , Daneshfaraz Rasoul , Kerim Ali , Abbaszadeh Hamidreza , Abraham John 
Published: February 2024

AbstractViable renewable energy sources must be low-cost, easy to install, clean, and reliable if they are to meet the energy demand of urban or rural areas. Water is able to provide efficient sources of electrical energy among alternative renewable energy sources. Using modern hydraulic techniques, drinking water can be transported to the areas where it is needed. Studies on obtaining energy from drinking water networks are quite limited. Although not as necessary as water in terms or human health, electrical power is still critically important in many applications (lighting, heating, cooling, etc.). Consequently, many countries are developing energy using various renewable energy sources. In this study, a water distribution network in the Armağan Village of Maçka district of Trabzon province were used to obtain electrical energy. There are 47 pressure breaking valves (PBVs) in this network and the broken energy height is 236.35 m. The same energy loss can be achieved by installing microturbines instead of PBVs, and electricity generation can thus be achieved with a clean energy source. It was found that 84.12kWh of energy was obtained from each PBV, with a total of 3950kWh. The most suitable microturbine was selected according to sound microturbine selection criteria. From this study, it is revealed that electricity can be produced by using a Microturbine instead of Pressure Breaker Valves (PBVs).
Neural networks with functional inputs for multi-class supervised classification of replicated point patterns
Authors: Pawlasová Kateřina , Karafiátová Iva , Dvořák Jiří 
Published: February 2024

AbstractA spatial point pattern is a collection of points observed in a bounded region of the Euclidean plane or space. With the dynamic development of modern imaging methods, large datasets of point patterns are available representing for example sub-cellular location patterns for human proteins or large forest populations. The main goal of this paper is to show the possibility of solving the supervised multi-class classification task for this particular type of complex data via functional neural networks. To predict the class membership for a newly observed point pattern, we compute an empirical estimate of a selected functional characteristic. Then, we consider such estimated function to be a functional variable entering the network. In a simulation study, we show that the neural network approach outperforms the kernel regression classifier that we consider a benchmark method in the point pattern setting. We also analyse a real dataset of point patterns of intramembranous particles and illustrate the practical applicability of the proposed method.
Numerical and Experimental Investigation for Application of CoNiCrAlY Coatings by HVAF
Authors: Bobzin K. , Heinemann H. , Jasutyn K. 
Published: February 2024

AbstractA relatively small change in oxide content and microstructure in MCrAlY coatings (M = Co, Ni) can affect the functionality of the coating for oxidation protection or as a bond coat. The objective of this study is to fabricate a CoNiCrAlY coating with low porosity and low oxide content. The high velocity air-fuel (HVAF) process, with its relatively low process temperature, is particularly suitable for the deposition of spray materials that are susceptible to oxidation or degradation at high temperature. A CFD simulation model of HVAF process is developed to determine the process parameters for fabricating CoNiCrAlY coating. The simulation results are validated by particle diagnostics, thereby establishing a comprehensive understanding of the underlying process. To assess the coating microstructure, XRD and EDS analyses as well as observation of cross sections of the coatings are conducted. The results highlight the influence of various factors, such as the variation of carrier gas and particle size distribution, on the quality of the coatings. Consequently, the utilization of simulation-based process parameter development is well supported by the coating fabrications, offering valuable insights into the processes prior to implementation.
Numerical study of novel OME1−6 combustion mechanism and spray combustion at changed ambient environments
Authors: Wiesmann Frederik , Qiu Zeyan , Han Dong , Strauβ Lukas , Rieβ Sebastian , Wensing Michael , Lauer Thomas 
Published: February 2024

Abstract
For a climate-neutral future mobility, the so-called e-fuels can play an essential part. Especially, oxygenated e-fuels containing oxygen in their chemical formula have the additional potential to burn with significantly lower soot levels. In particular, polyoxymethylene dimethyl ethers or oxymethylene ethers (PODEs or OMEs) do not contain carbon-carbon bonds, prohibiting the production of soot precursors like acetylene (C2H2). These properties make OMEs a highly interesting candidate for future climate-neutral compression-ignition engines. However, to fully leverage their potential, the auto-ignition process, flame propagation, and mixing regimes of the combustion need to be understood. To achieve this, efficient oxidation mechanisms suitable for computational fluid dynamics (CFD) calculations must be developed and validated. The present work aims to highlight the improvements made by developing an adapted oxidation mechanism for OME1−6 and introducing it into a validated spray combustion CFD model for OMEs. The simulations were conducted for single- and multi-injection patterns, changing ambient temperatures, and oxygen contents. The results were validated against high-pressure and high-temperature constant-pressure chamber experiments. OH*-chemiluminescence measurements accomplished the characterization of the auto-ignition process. Both experiments and simulations were conducted for two different injectors. Significant improvements concerning the prediction of the ignition delay time were accomplished while also retaining an excellent agreement for the flame lift-off length. The spatial zones of high-temperature reaction activity were also affected by the adaption of the reaction kinetics. They showed a greater tendency to form OH* radicals within the center of the spray in accordance with the experiments.
A copula formulation for multivariate latent Markov models
Authors: Russo Alfonso , Farcomeni Alessio 
Published: February 2024

AbstractWe specify a general formulation for multivariate latent Markov models for panel data, where outcomes are possibly of mixed-type (categorical, discrete, continuous). Conditionally on a time-varying discrete latent variable and covariates, the joint distribution of outcomes simultaneously observed is expressed through a parametric copula. We therefore do not make any conditional independence assumption. The observed likelihood is maximized by means of an expectation–maximization algorithm. In a simulation study, we argue how modeling the residual contemporary dependence might be crucial in order to avoid bias in the parameter estimates. We illustrate through an original application to assessment of poverty through direct and indirect indicators in a cohort of Italian households.
Modeling the air quality impact of aircraft emissions: is area or volume the appropriate source characterization in AERMOD?
Authors: Pandey Gavendra , Venkatram Akula , Arunachalam Saravanan 
Published: February 2024

AbstractModeling dispersion of aircraft emissions is challenging because aircraft are mobile sources with varying emissions rates at different elevations depending on the operating mode. Aircraft emissions during landing and take-off cycle (LTO) influence air quality in and around the airport, and depending on the number of aircraft operations and location of the airport, this influence may be significant. AERMOD (v22112) incorporates a variety of conventional source types to characterize the intended emissions source, leaving the question of which conventional source type(s) best characterizes aircraft activities across the four modes of LTO cycle, unanswered. Currently, the publicly released version of FAA’s Aviation Environmental Design Tool (version 3e) models aircraft emissions as a set of AREA sources for all flight segments. A research version of AEDT allows users to model aircraft sources—both fixed wing and rotorcraft—as a series of VOLUME sources in AERMOD. However, both source treatments do not account for plume rise of aircraft jet exhaust. This paper compares AERMOD’s performance in describing SO2 concentrations associated with airport sources by comparing model results from the two source options during the summer campaign of the Air Quality Source Apportionment study conducted at the Los Angeles International Airport. We conclude that both VOLUME source and AREA treatments overestimate the highest observed SO2 concentrations despite not accounting for background sources. The VOLUME source option reduces this overestimation by using a higher initial plume spread than the AREA option does, and through the inclusion of meander. Our results suggest the need to include the plume rise of jet exhaust when using AERMOD for airport air quality studies.
Comprehensive Analysis of a Japanese Pedigree with Biallelic ACAGG Expansions in RFC1 Manifesting Motor Neuronopathy with Painful Muscle Cramps
Authors: Izumi Rumiko , Warita Hitoshi , Niihori Tetsuya , Furusawa Yoshihiko , Nakano Misa , Oya Yasushi , Kato Kazuhiro , Shiga Takuro , Ikeda Kensuke , Suzuki Naoki , Nishino Ichizo , Aoki Yoko , Aoki Masashi 
Published: February 2024

AbstractCerebellar ataxia, neuropathy, and vestibular areflexia syndrome (CANVAS) is an autosomal recessive multisystem neurologic disorder caused by biallelic intronic repeats in RFC1. Although the phenotype of CANVAS has been expanding via diagnostic case accumulation, there are scant pedigree analyses to reveal disease penetrance, intergenerational fluctuations in repeat length, or clinical phenomena (including heterozygous carriers). We identified biallelic RFC1 ACAGG expansions of 1000 ~ repeats in three affected siblings having sensorimotor neuronopathy with spinocerebellar atrophy initially presenting with painful muscle cramps and paroxysmal dry cough. They exhibit almost homogeneous clinical and histopathological features, indicating motor neuronopathy. Over 10 years of follow-up, painful intractable muscle cramps ascended from legs to trunks and hands, followed by amyotrophy and subsequent leg pyramidal signs. The disease course combined with the electrophysical and imagery data suggest initial and prolonged hyperexcitability and the ensuing spinal motor neuron loss, which may progress from the lumbar to the rostral anterior horns and later expand to the corticospinal tract. Genetically, heterozygous ACAGG expansions of similar length were transmitted in unaffected family members of three successive generations, and some of them experienced muscle cramps. Leukocyte telomere length assays revealed comparatively shorter telomeres in affected individuals. This comprehensive pedigree analysis demonstrated a non-anticipating ACAGG transmission and high penetrance of manifestations with a biallelic state, especially motor neuronopathy in which muscle cramps serve as a prodromal and disease progress marker. CANVAS and RFC1 spectrum disorder should be considered when diagnosing lower dominant motor neuron disease, idiopathic muscle cramps, or neuromuscular hyperexcitability syndromes.
Hydraulic performance of bottom intake velocity caps using PIV and OpenFOAM methods
Authors: Hajebi Zahra , Firozjaei Mahmood Rahmani , Naeeni Seyed Taghi Omid , Akbari Hassan 
Published: February 2024

AbstractThe objective of this investigation is to obtain a more profound understanding of the effective parameters of the velocity caps for bottom intake systems, utilizing particle image velocimetry (PIV) and OpenFOAM. Observations indicate a higher probability of surface vortex formation in square types compared to circular ones, with the vortex being formed downstream of the caps. Additionally, the flow pattern reveals that the flow whirls in a more favorable path into the circular caps as opposed to the square ones. Through both experimental and numerical comparisons of three shapes (rhombus, square, and circle), it becomes evident that the circular type outperforms the other types in terms of discharges through the intake, showing an improvement of about 8%. The results indicate that flow depth and height of the velocity caps are positively effective parameters for the flow rate, with respective influences of 90% and 30%. In contrast, the interaction between the flow and caps intensifies with an increase in the distance of the intake opening from the bed, which plays a negative influence on the flow rate. Enhancing the number of blades in caps proves to be the optimal approach for generating a smoother flow with minimal impact on the flow rate. Numerical simulations show a 50% reduction in cap height leads to a significant 33% decrease in flow rate. Additionally, rotating the square cap by 45° into a rhombus aligned with the flow direction results in a 7% discharge flow rate increase.