bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing asphalt mix design using machine learning methods based on RIOCHTrack data
Authors: Cheng Chunru. , Liu Jian , Yaohui Su , Wang Linbing. , Wang Xudong. 
Published: December 2023

Abstract
Traditional mix design is a time-consuming and labor-intensive process ,requiring extensive testing and relying heavily on engineering experience. In order to enhance the speed and efficiency of asphalt concrete mix design process,this study investigated the use of machine learning techniques to predict key parameters of concrete mixture design,such as voids in the mineral aggregate (VMA), voids in the coarse aggregate(VCA), and dry density of the mixture(pd). Four machine learning methods, namely support vector regression, artificial neural network, random forest, and AdaBoost models were trained using data from RIOHTRack. Metircs releatde to asphalt mix design such as gradation, asphalt content, asphalt properties, compaction method, and compaction temperature were used as input variables. Various encoding methods were employed to encode classification variables, with the ordinal encoding method yielding the most favorable results. Through the calculation of different performance scoring metrics, such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), and by plotting the development curve of volume parameters and asphalt content, the most suitable prediction model was selected for each target variable. The analysis revealed that the random forest model (R2 = 0.8595 for pd, R2 = 0.9488 for VMA) demonstrated the best performance in predicting pd and VMA, while the Adaboost model (R2 = 0.9716) was chosen for predicting VCA. By calculating different performance scoring metrics, such as coefficient of determination (R2), root means square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) and plotting the development curve of volume parameters and asphalt content, the final prediction model was selected for each target variable. The analysis revealed that the random forest model (R2 = 0.8595 for pd, R2 = 0.9488 for VMA) demonstrated the best performance in predicting pd and VMA, while the Adaboost model (R2 = 0.9716) was chosen for predicting VCA.
Investigation of turbulent flow field in maglev centrifugal blood pumps of CH-VAD and Heartmate III using large-eddy simulation
Authors: Wu Peng , Xiang Wen-Jing , Zhang Ke-Jia , Du Guan-Ting 
Published: December 2023

Abstract
Maglev bearings can avoid serious blood damage caused by mechanical bearings, and has become the primary trend of blood pumps. Maglev blood pumps allow a relatively large clearance to improve blood washout and reduce the stress inside the clearance so that blood damage can be reduced. Nonetheless, large clearances also lead to high secondary flow and turbulence intensity, causing further blood damage. This study aims to conduct a thorough analysis of flow fields in two typical maglev blood pumps, the CH-VAD and Heartmate III which feature distinct designs of secondary flow path and impeller (semi-open versus closed impeller) using large eddy simulation (LES) with a focus on the secondary flows and their interaction with the main flows. LES was found to be superior to the Reynolds-averaged Navier-Stokes (RANS) method in predicting performance curves. At high flow rate (8L/min), the efficiency of CH-VAD remains high compared with 5 L/min, while the efficiency of Heartmate III drops considerably. The wide clearance in Heartmate III induced high secondary flow and flow loss, leading to an large incidence angle at both working conditions. The high viscous stress inside the clearances is the major cause of flow loss and potential blood damage in CH-VAD. This study shows that Maglev bearings dose not guarantee good blood compatibility, clearances should be designed based on trade-offs among high shear stress inside smaller clearance, and strong recirculations caused by larger clearances. This study provides useful reference for the design and optimization of maglev blood pumps.
Environmental implication of the production of non-centrifugal sugar in the Colombian Andean region
Authors: Sanchez Nestor , Cobo Martha , Rodríguez-Fontalvo David , Ruiz-Pardo Ruth Y. , Roedl Anne 
Published: December 2023

Abstract
Non-centrifugal sugar (Panela) is a natural sweet enhancer obtained from sugarcane. In this process, waste from the agricultural industry is disposed of incorrectly, fossil fuels are used, and the furnaces' efficiency is low, leading to environmental problems. Therefore, this study aims to assess the environmental impacts of Panela production to identify possible hotspots and propose alternatives to reduce the environmental impacts such as acidification, global warming, freshwater eutrophication, and particulate matter emissions. Aspen Plus, interviews, and databases were used to determine life cycle inventory that then was used to calculate environmental impacts. Central results showed that producing 1 kg of Panela results in a carbon footprint of 4.09 kg CO2-eq and removes 6.02 kg CO2 during the growth phase. Moreover, the acidification potential, freshwater eutrophication, and particulate matter emissions were 0.21 molc H+-eq, 0.0077 kg P-eq, and 0.0042 kg PM2.5-eq, respectively. These impacts are mainly due to the harvesting phase, as agro-industrial wastes such as sugarcane press mud, and leaves are disposed of incorrectly. To solve this issue, suitable methods of fertilization are proposed, achieving a reduction in acidification potential and particulate matter emissions by 80%. In addition, greenhouse gas emissions are reduced by 35% if bagasse is not burned. Hence, the combination of both alternatives will improve the sustainable production of Panela. The significance of this study is to show new alternatives to reduce the environmental impact of Panela. Besides, these strategies such as improving agricultural practices and designing new furnaces are aligned with national policies.
To retire or not to retire? A Comprehensive Examination of Retirement Decision Dynamics in Italy
Authors: Fabiani Michele 
Published: December 2023

Abstract

The issue of active aging has become central to the public debate in many Western countries, given the increasing aging of the population and the future challenges associated with it. This phenomenon is tightly intertwined with choices regarding the retirement period, which can be postponed for different reasons by individuals in a society. The purpose of this paper is to understand what personal and family characteristics influence future choices about retirement date in Italy. Utilizing data provided by the Bank of Italy in the Survey on Household Income and Wealth (SHIW), a logistic regression is performed to observe the impact of different covariates on the hypothesized retirement date of individuals close to retirement, over the age of 50 who are still in working status.
Enhanced RTP Method for Nonlinear Statistical Prediction of Ship Structural Response
Authors: Sadaoki Matsui 
Published: December 2023

Abstract

In this study, the ERTP (Enhanced RAO-based Translation Process) method, which is an enhanced version of the RTP method proposed in the previous study, is newly proposed. The RTP method approximates the extreme value distribution in arbitrary irregular waves from the nonlinear response in regular waves based on the idea of the transformation process, in which the nonlinear process is regarded as a transformation of a linear process. However, it was found that the RTP method is inaccurate when the response spectrum has multiple peaks, which is often the case for stresses in ship structures in waves. To solve this problem, the author developed the ERTP method, which defines a nonlinear transformation based on the Lp-norm of the response spectrum. A direct load and structure analysis (DLSA) considering the nonlinearity of wave loads was conducted and confirmed that the proposed method can reasonably estimate the extreme value distribution even for structural elements with a multi-peaked response spectrum with respect to the wave direction.
To study the diagnostic and prognostic value of GADD45α in AML based on bioinformatics and its preliminary experimental validation
Authors: Gui Yonghui , Zhang Nannan , Tong Wenjing , Chen Qiong , Yang Peng 
Published: December 2023

Abstract
Objectives
Exploring the expression status, prognostic value, immune infiltration, potential functions, and mechanisms of GADD45α in AML using bioinformatics.
Methods
Using the GEPIA2 database to analyze the expression levels of GADD45α in AML and other cancers, and to assess the correlation between GADD45α levels and prognosis in AML. Conducting immune regulation gene analysis, immune checkpoint analysis, and immune infiltration analysis of GADD45α in AML in Sangerbox. Obtaining the protein interaction network with GADD45α from the STRING database and conducting GO and KEGG analysis in Sangerbox. By establishing stable overexpression of GADD45α in AML cells, using CCK-8 and Annexin V-FITC/APC to respectively detect the effects of overexpression and knockdown of GADD45α on cell proliferation and apoptosis in AML cells. Validating the diagnostic and prognostic value of GADD45α in AML. All data were statistically analyzed using SPSS 23.0 software.
Results
GADD45α is mostly downregulated in cancer, and is downregulated in AML, which is associated with poor prognosis. We found significant correlation between GADD45α and immune cells in AML in Sangerbox. We also found that GADD45α expression in AML is closely related to 43 checkpoints, including VEGFB, ARG1, KIR2DL1, KIR2DL3, TIGIT, and is closely related to five types of immune cells, including chemokines, receptors, MHC, immune inhibitors, and immune stimulators. This indicates that GADD45α may coordinate the activity of these ICP genes in different signaling pathways and may become an ideal immunotherapy target. We found that GADD45α interacts with TDG, FOXO3, TP53, CDKN1A, CDK1, AURKA, CCNB1, PCNA, MAP2K6, and MAP3K4 proteins, and after GO and KEGG analysis, we found that its biological function may be involved in cell cycle arrest, mitotic G1/S checkpoint, and other processes. KEGG enrichment of GADD45α is related to cell senescence, cell cycle, p53 signaling pathway, and FoxO signaling pathway. Overexpression of GADD45α can inhibit the proliferation of leukemia cells, promote apoptosis, and after GADD45α knockdown, it can promote the proliferation of leukemia cells and inhibit apoptosis.
Conclusions
GADD45α is a prognostic marker for AML, and its expression levels are associated with various immune cell molecules, immune checkpoints, and immune regulatory genes. Detecting GADD45α levels helps predict the prognosis of AML, providing a new method for optimizing and monitoring the effectiveness of anti-AML immunotherapy. Further validation of the GADD45α signaling pathway is more conducive to the discovery of occurrence and development mechanisms, and is more beneficial for patient treatment.
Cardiorespiratory Fitness Mediates the Relationship between Depressive Symptomatology and Cognition in Older but not Younger Adults
Authors: Barros Catarina , Sampaio Adriana , Pinal Diego 
Published: December 2023

Abstract
Background
Aging is commonly associated with emotional, physical, and cognitive changes, with the latter, particularly affecting executive functioning. Further, such changes may interact. For instance, depressive symptomatology is a known risk factor for developing cognitive deficits, especially at older ages. In contrast, an active lifestyle, reflected in high cardiorespiratory fitness (CRF) levels, has proven to protect against adverse effects on cognition across the adult lifespan. Hence, this study aimed to investigate the relationships between depressive symptomatology, CRF, and cognition during critical developmental stages, namely in young adults (YA), when cognitive abilities are at their peak, and in older adults (OA), when they may start to decline.
Methods
Eighty-one OA with ages between 60 and 89 years (M = 70.46; SD = 7.18) and 77 YA with ages between 18 and 34 years (M = 22.54; SD = 3.72) went through (i) a sociodemographic interview, (ii) an emotional assessment, (iii) a battery of cognitive tests, and (iv) a physical evaluation assessing CRF levels, visceral fat and body-mass index.
Results
Results showed that OA exhibited lower general cognitive performance, inhibitory control, cognitive flexibility, memory, and CRF. Depressive symptoms and anxiety were not different among groups, with CRF mediating the relationship between depressive symptoms and cognition in the OA group.
Conclusions
The present study provides valuable insights into the interplay between emotional, physical, and cognitive well-being. Additionally, it calls attention to how lifestyle factors can play a protective role against the adverse effects that depressive symptoms have on cognition, particularly at older ages.
Humanoid Avatars in Individualised Virtual Reality for Supporting Mental Health: Self-Compassion &amp; Cost Minimisation
Authors: Elliott Thomas C. , Henry Julie D. , Baghaei Nilufar 
Published: December 2023

Abstract
Avatars are employed within Virtual Reality environments to interact with participants in training and therapeutic settings. Avatars have been introduced with varying quality mainly due to historical technological limitations with respect to computer graphics and hardware. The VR community may assume that with the development of higher quality and therefore ever more lifelike avatars, implies an advance in avatar performance, yielding ever better human interactions. But is that true? Until now this has not yet been investigated fully. The authors of this paper recognised how contextual evidence suggests that ‘lifelike’ avatars may potentially hinder VR therapeutic outcomes. As a result, an individualised virtual reality self-compassion therapy was replicated but replaced all the ‘lifelike’ avatars with cartoony humanoid avatars and re-measured user experience as well as therapeutic benefit. The results show a dramatic increase in user experience on areas pertaining to feelings of dependability, which demonstrates a reduction in ‘eeriness’ likely caused by the Uncanny Valley phenomenon previously invoked by the too realistic ‘lifelike’ avatars. The data therefore suggests that humanoid avatars are an affordable substitute with better interaction.
Frames: Reduction of Size of Binary Decision Diagrams using an Alternative Representation
Authors: García-Zapata Juan Luis 
Published: December 2023

Abstract

Binary Decision Diagrams (BDDs) have long been a fundamental data structure in computer science and computational logic, due to their compact representation and efficient manipulation of Boolean functions. This paper introduces the concept of frame, an ordered array of partitions of a set that facilitates a structured approach to representing and analyzing Boolean functions. Frames enable a generalization of BDD constructions, providing a more flexible structure. The paper presents a description of frame-based BDDs, together with algorithms for frame change operations and for constructing frames that maximize the efficiency of Boolean functions representation. Additionally, computational complexities associated with these algorithms are discussed. Notably, we propose an algorithm that, for a Boolean function on \(n\) variables, constructs a frame-based BDD with \(n+2\) nodes or less in \(O(n^5)\) functional operations.
MSC Classification: 68R05 , 06E30 , 05A18 , 68W30 , 68R10.
Efficient proxy for Time-Lapse Seismic ForwardModeling using U-Net Encoder-Decoder Approach
Authors: Diniz Michael Macedo , Maleki Masoud , Cirne Marcos , Danaei Shahram , Oliveira João , Schiozer Denis José , Davolio Alessandra , Rocha Anderson 
Published: December 2023

Abstract
The time-lapse seismic (4D seismic) forward modeling provides crucial data for calibrating reservoir models through the reservoir data assimilation method. Unfortunately, conventional 4D seismic forward modeling methodology is time-expensive and entails significant computational resource consumption. To address these drawbacks, in this work, our goal is to develop a proxy model for the 4D seismic forward modeling using a class of machine learning algorithm named U-Net encoder-decoder. We applied the developed proxy model to a benchmark carbonate reservoir using an ensemble of reservoir simulation models from UNISIM IV (a synthetic benchmark based on real data of a Brazilian pre-salt field). Moreover, we aim to introduce seminal strategies for interpreting the proposed proxy model operation, its outputs, and possible correlations between input and output variables. To achieve this, we trained and tested two versions of U-net-based models and applied methods for explainable AI, such as Grad-CAM and Forward Feature Selection. The experiments showed good results when applied to the test dataset. The R-squared values were in the range of 0.7 to 0.9, showing the efficiency of the proxy model to replace the 4D seismic forward modeling. Additionally, the qualitative analysis helped us identify which input properties and regions of the reservoir are more relevant for the model's inference. These results are a valuable preliminary step toward a robust, explainable machine learning-based proxy forward modeling.
Enhancing Grid Tagging Schema with Hybrid Contrastive Learning for Aspect-level Sentiment Triplet Extraction
Authors: Zheng Guangmin , Wang Jin , Yu Liang-Chih , Zhang Xuejie 
Published: December 2023

Abstract
Aspect-based sentiment triplet extraction (ASTE) is a fine-grained opinion mining to jointly extract a triplet consisting of aspect terms, opinion terms, and sentiment polarity. Previous works tried incorporating extra dependency information by stacking complex graph convolution networks (GCN) on transformers to learn the relationship between the aspects and their associated opinion. While the number of existing datasets is still insufficient, the GCN-accompanied redundant parameters in each method require more samples to provide supervised information, leading to the model being trapped by overfitting. Instead of stacking GCNs, this study proposes an enhanced grid tagging scheme with hybrid contrastive learning, exploiting a self-supervised training approach from the negative samples. Specifically, sentiment-aware contrastive learning (SCL) is applied by introducing word-level continuous sentiment information in the valence-arousal space. Syntactic-aware graph contrastive learning (SGCL) is used to guide the language model to gain the ability to capture dependencies. Supervised tagging contrastive learning (STCL) is further adapted to guide the language model to distinguish both positive and negative samples. Hybrid contrastive learning can provide extra supervision information but only introduce tolerable parameters. Extensive experimental results on the ASTE-Data-V2 datasets show that the proposed model significantly outperforms previous baselines.
The pain target NaV1.7 is expressed late during human iPS cell differentiation into sensory neurons as determined in high resolution imaging
Authors: Liu Yi , Balaji Rachna , Toledo Marcelo A. Szymanski , Ernst Sabrina , Hautvast Petra , Zenke Martin , Neureiter Anika , Lampert Angelika 
Published: December 2023

Abstract
Human induced pluripotent stem cells (iPS cells) are efficiently differentiated into sensory neurons. These cells express the voltage-gated sodium channel NaV1.7, which is a validated pain target. NaV1.7 deficiency leads to pain insensitivity, whereas NaV1.7 gain-of-function mutants are associated with chronic pain. Here we used CRISPR/Cas9 genome editing to generate a HA-tag NaV1.7 to follow its expression. We used two differentiation protocols for generation sensory neurons: the classical small molecule approach and a directed differentiation methodology and assessed surface NaV1.7 expression by Airyscan high resolution microscopy. Our results show that maturation of at least 49 days is necessary to observe robust NaV1.7 surface expression in both protocols. A clinically effective NaV1.7-blocker is still missing, and we expect this iPS cell model system to be useful for drug discovery and disease modeling.
Off the script and crypt: the systematic affinities and evolutionary significance of an unexpected Sparassodonta (Mammalia, Metatheria) from Itaborai Basin (early Eocene)
Authors: Rangel Caio César , Carneiro Leonardo Melo , Guimarães Bruno Maurício Graichen , Bampi Hugo , Oliveira Édison Vicente 
Published: December 2023

Abstract
The early Eocene Itaboraí fauna in southeastern Brazil has preserved two of the oldest known sparassodonts in South America, Patene simpsoni and Silvenator brasiliensis. Among the specimens assigned to Patene simpsoni, some differ in their significant smaller size, and in having separated paracone and metacone, flat labial borders of paracone and metacone, labial contact of the centrocrista on second lower molar, and a more elongated and less robust dentary. To test the taxonomic significance of these smaller specimens, we performed a PCA and a t-student test of lower molar measurements. The analyses showed that the smaller specimens do not overlap the morphospace of Patene simpsoni, which is an indicative of a new taxon, herein known as Xenocynus crypticus. The inclusion of Xenocynus in the concept of a phylogenetic analysis recovered that the differential dental features observed on this taxon represent synapomorphies for it in comparison with Patene. Some of Xenocynus dental features are convergent with the ones found on Hondadelphys and Stylocynus, two Miocene taxa. However, the presence of StC and large metaconids on m1-2 in Xenocynus confidently support these similarities as evolutionary convergence with these Miocene taxa. The dental features of X. crypticus suggest an insectivorous-carnivorous diet.
Combined studies of N170/M170 responses to single letters and pseudoletters
Authors: Toussi Nima , Takai Osamu , Bann Sewon , Bann Seho , Rowe Jacob , Hildebrand Andrew-John I. , Herdman Anthony T. 
Published: December 2023

Abstract
To read efficiently, individuals must be able to rapidly identify letters within their visual networks, which occurs through forming line segments into letters and then letters into words. The temporal processes and utilized brain areas that engage in this process are widely thought to be left-lateralized within the brain. However, a range of studies demonstrate that the processing of unfamiliar stimuli, such as pseudoletters, is temporally delayed and bilaterally processed when compared to letters. This study investigated the contributions of both hemispheres and how these interactions impact the temporal dynamics of implicit visual processing of single-letters as compared to unfamiliar pseudoletters (false fonts). The results of 5 “in-house” studies are presented within a meta-analysis (synthesis analysis). Delayed N170 waveforms to pseudoletters as compared to letters were exhibited across all studies. Lateralization of the ERP differences between letter-evoked and pseudoletter-evoked responses were bilaterally distributed, whereas lateralization measure separately for letters and pseudoletters were primarily left-lateralized. As a whole, these in-house studies indicate that ERPs occur earlier in letters relative to pseudoletters, and that interpretation of hemispheric laterality depends on whether the researcher is assessing ERP differences between letters and pseudoletters or the ERP waveforms of the separate letter and pseudoletter conditions.