bluuebunny/crossref_metadata_2025_split huggingface dataset:
How to select candidates for an undergraduate degree in psychology? Combining high-school GPA and admission test score
Authors: Sorgente Angela , Pietrabissa Giada , Antonietti Alessandro , Bonanomi Andrea , Castelnuovo Gianluca , Lanz Margherita , Tagliabue Semira , Traficante Daniela 
Published: June 2024

AbstractThe current study investigated whether the grade point average (GPA) of high school courses and the score on the test for admission to a bachelor’s degree in Psychology—assessing logical reasoning, reading comprehension, and general knowledge—can predict student academic success (AS) and to what extent. Archival data from 5147 candidates (82.4% female) collected over six years (2010–2016) were used. For 2559 of these, indicators of AS (graduation marks, average weighted marks, completion of the degree on time, and dropout) were also available. A series of hierarchical regressions were performed, in which the high school GPA was included as the first predictor, and the scores of the three sections of the admission test were included as the second set of predictors. Findings indicated high school GPA as the best predictor of AS. However, admission test scores predicted a portion of AS that the high school GPA was not able to account for. This result indicates that the administration of the admission test, which evaluates logical reasoning, reading comprehension, and general knowledge, can add relevant information to the high school GPA, thus increasing the efficacy in the selection of candidates, who can have the best chance to earn a bachelor’s degree in psychology.
Study of caesium adsorption onto alluvial sediments from the Italian Po Plain
Authors: Giacobbo F. , Pezzoli F. , Cydzik I. , Da Ros M. , Dapiaggi M. , Giudici M. 
Published: June 2024

Abstract
The study investigates the adsorption processes of caesium onto alluvial sediments from the Po Plain (northern Italy). Understanding these adsorption processes is crucial for assessing the safety of low- and intermediate-level radioactive waste repositories, including the proposed Italian repository. Adsorption kinetics and equilibrium experiments on sandy samples were conducted with the aim of evaluating how even small differences in clay content and mineralogy can affect kinetics and equilibrium adsorption behaviour. The obtained data were compared with literature studies and confirmed the significant affinity of caesium for sandy sediments, even for a mud content of less than 5%. Kinetics analysis revealed that a pseudo-second-order model best described the process, suggesting two-site occupancy adsorption kinetics attributed to the presence of illite and characterised by various different sites for caesium adsorption. Samples with higher clay and micaceous minerals content, cation exchange capacity and specific surface area exhibit faster kinetics and higher affinity for caesium. The study shows a significant variation in partition coefficient values, ranging from 57 to 750 mg L−1. This finding emphasises the importance of sediment composition in caesium adsorption, which is crucial for developing accurate environmental protection and safety assessment models.
A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders
Authors: Magalhães Jorge , Jorge Tomás , Silva Rúben , Guedes António , Ribeiro Diogo , Meixedo Andreia , Mosleh Araliya , Vale Cecília , Montenegro Pedro , Cury Alexandre 
Published: June 2024

AbstractWayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness (OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using (healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages: (1) data collection, (2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder (SAE), (3) data fusion based on the Mahalanobis distance, and (4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses (not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.
Aggregation operators of complex fuzzy Z-number sets and their applications in multi-criteria decision making
Authors: Köseoğlu Ali , Altun Fatma , Şahin Rıdvan 
Published: June 2024

AbstractFuzzy sets (FSs) are a flexible and powerful tool for reasoning about uncertain situations that cannot be adequately expressed by classical sets. However, these sets fall short in two areas. The first is the reliability of this tool. Z-numbers are an extension of fuzzy numbers that improve the representation of uncertainty by combining two important components: restriction and reliability. The second is the problems that need to be solved simultaneously. Complex fuzzy sets (CFSs) overcome this problem by adding a second dimension to fuzzy numbers and simultaneously adding connected elements to the solution. However, they are insufficient when it comes to problems involving these two areas. We cannot express real-life problems that need to be solved at the same time and require the reliability of the information given with any set approach given in the literature. Therefore, in this study, we propose the complex fuzzy Z-number set (CFZNS), a generalization of Z-numbers and CFS, which fills this gap. We provide the operational laws of CFZNS along with some properties. Additionally, we define two essential aggregation operators called complex fuzzy Z-number weighted averaging (CFZNWA) and complex fuzzy Z-number weighted geometric (CFZNWG) operators. Then, we present an illustrative example to demonstrate the proficiency and superiority of the proposed approach. Thus, we process multiple fuzzy expressions simultaneously and take into account the reliability of these fuzzy expressions in applications. Furthermore, we compare the results with the existing set operations to confirm the advantages and demonstrate the efficiency of the proposed approach. Considering the simultaneous expression of fuzzy statements, this study can serve as a foundation for new aggregation operators and decision-making problems and can be extended to many new applications such as pattern recognition and clustering.
Pose estimation algorithm based on point pair features using PointNet +  +
Authors: Chen Yifan , Li Zhenjian , Li Qingdang , Zhang Mingyue 
Published: June 2024

AbstractThis study proposes an innovative deep learning algorithm for pose estimation based on point clouds, aimed at addressing the challenges of pose estimation for objects affected by the environment. Previous research on using deep learning for pose estimation has primarily been conducted using RGB-D data. This paper introduces an algorithm that utilizes point cloud data for deep learning-based pose computation. The algorithm builds upon previous work by integrating PointNet +  + technology and the classical Point Pair Features algorithm, achieving accurate pose estimation for objects across different scene scales. Additionally, an adaptive parameter-density clustering method suitable for point clouds is introduced, effectively segmenting clusters in varying point cloud density environments. This resolves the complex issue of parameter determination for density clustering in different point cloud environments and enhances the robustness of clustering. Furthermore, the LineMod dataset is transformed into a point cloud dataset, and experiments are conducted on the transformed dataset to achieve promising results with our algorithm. Finally, experiments under both strong and weak lighting conditions demonstrate the algorithm's robustness.
An edge-assisted group authentication scheme for the narrowband internet of things
Authors: Zhao Guosheng , Chen Huan , Wang Jian 
Published: June 2024

AbstractAs an emerging wireless communication technology, narrowband Internet of Things is widely used in various scenarios, but it also brings new security problems, especially the group authentication between terminal devices and servers. In narrowband Internet of Things, terminal devices are often deployed in unattended environments, which are vulnerable to various attacks and controlled by adversaries, resulting in identity information leakage. Secondly, a large number of terminal devices send authentication request information to the server at the same time, which is easy to cause network signaling congestion, and the centralized calculation mode of the server will cause a large delay in the authentication process. To solve these problems, we propose an edge-assisted group authentication scheme for narrowband Internet of Things. Firstly, a lightweight encryption algorithm LCHAOSAES was used to encrypt the terminal device identity to prevent the terminal device identity information leakage. Then, the edge computing method was used to add an edge network layer to the NB-IoT system architecture to assist in completing the group authentication of massive terminal devices, and the edge assistance could realize decentralization and reduce the load of centralized processing of the server. The Burrows Abadi Needham logic verification, informal security analysis and performance analysis show that the proposed scheme is robust under malicious attacks such as replay attack, man-in-the-middle attack and insider attack, and has less signaling overhead and delay.
Protein–protein-interaction assay coupled with proteome profiler and immune activation flow cytometry assay validate immune activation in 3D oral cancer-immune co-culture model
Authors: Chakraborty Rajdeep , Khodlan Pallavi , Liu Fei 
Published: June 2024

AbstractImmune evasion is one of the hallmarks of cancer progression. Immunotherapy failed to produce appreciable treatment response in aggressive oral cancer cases. It is imperative to understand the molecular biological aspects of cancer-immune cell interactions and the reason for the lower anti-tumour reactivity of immune cells. Given that patient biopsy samples are heterogeneous, we constructed cancer-immune cell co-culture models using various oral cancer cells. In this paper, we present a simplified data-based method pipeline to identify the protein-interaction partners of proliferation mechanism proteins. The protocol pipeline makes use of data generated from broad-scale discovery and co-immunoprecipitation-based proteomics approaches, immune protein marker flow cytometry, and proteome profiler assays. Co-analyses of these data highlight genes of interest and provide in-depth insights into the functions of these genes, reveal the immune activation profile, provide a real-time visualization of immune cells attacking cancer cells, and assess the viability of the different cancer-immune cell co-culture models. Overall, this approach carries potential to enable detailed insights into the molecular underpinnings of oral cancer research and may also be used in research on other types of cancers.
China’s role in sovereign debt restructuring
Authors: Nishizawa Toshiro 
Published: June 2024

AbstractAs the world’s largest bilateral lender to low- and middle-income countries (LMICs), China faces challenges dealing with some sovereign borrowers in debt distress under the Belt and Road Initiative (BRI). The current global sovereign debt landscape suggests that the fundamental challenge of sovereign debt restructuring in the developing world is not China, but rather how to equitably address unsustainable debt owed to multiple categories of creditors. Nonetheless, by offering early-stage debt relief, China can alleviate the burden on borrowers who disproportionately owe significant debt to China and prevent trapping itself in unpaid debts. China’s policy decision will determine whether it can avoid the same mistake that Western creditors made of eventually losing all financial claims on many debt-distressed, heavily indebted poor countries (HIPCs) by the mid-2000s. Furthermore, a considerate approach by China is key to achieving the dual goals of mitigating LMICs’ debt distress and vulnerabilities to climate change by applying a climate-centered approach toward sustainable socio-economic development. China can contribute to creating better global debt governance, which should go hand in hand with global environmental governance and have critical economic, political, and social implications.
An index for measuring degree of departure from symmetry for ordinal square contingency tables
Authors: Ando Shuji , Momozaki Tomotaka , Masusaki Yuta , Tomizawa Sadao 
Published: June 2024

AbstractFor the analysis of square contingency tables with the same row and column ordinal classifications, this study proposes an index for measuring the degree of departure from the symmetry model using new cumulative probabilities. The proposed index is constructed based on the Cressie and Read’s power divergence, or the weighted average of the Patil and Taillie’s diversity index. This study derives a plug-in estimator of the proposed index and an approximate confidence interval for the proposed index. The estimator of the proposed index is expected to reduce the bias more than the estimator of the existing index, even when the sample size is not large. The proposed index is identical to the existing index under the conditional symmetry model. Therefore, assuming the probability structure in which the conditional symmetry model holds, the performances of plug-in estimators of the proposed and existing indexes can be simply compared. Through numerical examples and real data analysis, the usefulness of the proposed index compared to the existing index is demonstrated.
Monkeypox: genetic, clinical, molecular, diagnostic, and therapeutic perspectives
Authors: Wambani Josephine , Were Tom , Okoth Patrick 
Published: June 2024

AbstractMonkeypox is a DNA virus that belongs to the orthopox virus family with two distinct clades known as West African and Congo Basin. This virus was initially discovered in crab-eating Macaques in 1958 and in 1970s it spread among people in the Democratic Republic of the Congo. Initially, there were several outbreaks of the disease reported in Africa and other regions of the world. The simultaneous spread in 19 countries in 2022 prompted severe worries. The monkeypox virus is closely related to smallpox, which was responsible for the highest fatality rate in human history, and a preconceived thought combined with fear is enough to make us shiver. Furthermore, the virus is often mistaken for a sexually transmitted infection or the Varicella zoster virus. The emergence of outbreaks outside of Africa is indicative of the disease’s global footprint. Increased detection and monitoring of monkey pox cases as well as understanding the disease’s ever-changing epidemiology is critical. Furthermore, intensive research is yet to identify the exact small mammal reservoir for monkeypox virus. Identifying the exact reservoir may aid in the identification of previously unknown high-risk activities for getting orthopoxvirus infections. Finally, a better understanding of the potential/suspected monkeypox viral transmission pathways is required so that public health officials can develop and implement interventions to lower the risk of human infection. This review focuses on the genetic, clinical, molecular, diagnostic, and therapeutic perspectives of monkeypox.
Learnable differencing center for nighttime depth perception
Authors: Yan Zhiqiang , Zheng Yupeng , Fan Deng-Ping , Li Xiang , Li Jun , Yang Jian 
Published: June 2024

AbstractDepth completion is the task of recovering dense depth map from sparse ones, usually with the help of color images. Existing image guided methods perform well on daytime depth perception self-driving benchmarks, but struggle in nighttime scenarios with poor visibility and complex illumination. To address these challenges, we propose a simple yet effective learnable differencing center network (LDCNet). The key idea is to use recurrent inter-convolution differencing (RICD) and illumination affinitive intra-convolution differencing (IAICD) to enhance the nighttime color images and reduce the negative effects of the varying illumination, respectively. RICD explicitly estimates global illumination by differencing two convolutions with different kernels, treating the small-kernel-convolution feature as the center of the large-kernel-convolution feature in a new perspective. IAICD softly alleviates the local relative light intensity by differencing a single convolution, where the center is dynamically aggregated based on neighboring pixels and the estimated illumination map in the RICD. On both nighttime depth completion and depth estimation tasks, extensive experiments demonstrate the effectiveness of our LDCNet, reaching the state of the art.
Assessment of Gravity Waves From Tropopause to Thermosphere and Ionosphere in High‐Resolution WACCM‐X Simulations
Authors: Liu H.‐L. , Lauritzen P. H. , Vitt F. , Goldhaber S. 
Published: June 2024

AbstractA new version of NCAR Whole Atmosphere Community Climate Model with thermosphere/ionosphere extension (WACCM‐X) has been developed. The main feature of this version is the species‐dependent spectral element (SE) dynamical core solved on a cubed sphere grid, eliminating the polar singularity and enabling simulations at high‐resolutions. Molecular viscosity and diffusion in the horizontal direction are also included. The Conservative Semi‐Lagrangian Multi‐Tracer Transport Scheme (CSLAM) is employed for the species transport. An efficient regridding scheme based on the Earth System Modeling Framework is used to map fields between the physics mesh and geomagnetic grid. Simulations have been performed at coarse (∼200 km and 0.25 scale height) and high (∼25 km and 0.1 scale height) resolutions. Spatial distribution of the resolved gravity waves from the high‐resolution simulations compares well with available observations in the middle and upper atmosphere. Analysis of the scale dependence of the gravity wave energy density and momentum flux shows that, while larger scale waves are dominant energetically at most latitudes, smaller scale waves contribute significantly to the total momentum flux, especially at mid‐high latitudes. The waves in the thermosphere are shown to be strongly modulated by the large‐scale wind through Doppler shift and molecular damping, and they cause large neutral atmosphere and plasma perturbations.
Major Modes of Climate Variability Dominate Nonlinear Antarctic Ice‐Sheet Elevation Changes 2002–2020
Authors: King Matt A. , Christoffersen Poul 
Published: June 2024

AbstractWe explore the links between elevation variability of the Antarctic Ice Sheet (AIS) and large‐scale climate modes. Using multiple linear regression, we quantify the time‐cumulative effects of El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) on gridded AIS elevations. Cumulative ENSO and SAM explain a median of 29% of the partial variance and up to 85% in some coastal areas. After spatial smoothing, these signals have high spatial correlation with those from GRACE gravimetry (r∼ = 0.65 each). Much of the signal is removed by a firn densification model but inter‐model differences exist especially for ENSO. At the lower parts of the Thwaites and Pine Island glaciers, near their grounding line, we find the Amundsen Sea Low (ASL) explains ∼90% of the observed elevation variability. There, modeled firn effects explain only a small fraction of the variability, suggesting significant height changes could be a response to climatological ice‐dynamics.
Maternal obesity and offspring cardiovascular remodelling — the effect of preconception and antenatal lifestyle interventions: a systematic review
Authors: Burden Samuel J. , Alshehri Rahaf , Lamata Pablo , Poston Lucilla , Taylor Paul D. 
Published: June 2024

Abstract
Background
Preconception or antenatal lifestyle interventions in women with obesity may prevent adverse cardiovascular outcomes in the child, including cardiac remodelling. We undertook a systematic review of the existing data to examine the impact of randomised controlled trials of lifestyle interventions in pregnant women with obesity on offspring cardiac remodelling and related parameters of cardiovascular health.

Methods
This review was registered with PROSPERO (CRD42023454762) and aligns with PRISMA guidelines. PubMed, Embase, and previous reviews were systematically searched. Follow-up studies from randomised trials of lifestyle interventions in pregnant women with obesity, which included offspring cardiac remodelling or related cardiovascular parameters as outcome measures, were included based on pre-defined inclusion criteria.

Results
Eight studies from five randomised controlled trials were included after screening 3252 articles. Interventions included antenatal exercise (n = 2), diet and physical activity (n = 2), and preconception diet and physical activity (n = 1). Children were <2-months to 3–7-years-old, with sample sizes ranging between n = 18–404. Reduced cardiac remodelling, with reduced interventricular septal wall thickness, was consistently reported. Some studies identified improved systolic and diastolic function and a reduced resting heart rate. Risk of bias analyses rated all studies as ‘fair’ (some risk of bias). A high loss-to-follow-up was a common limitation.

Conclusion
Although there is some evidence to suggest that lifestyle interventions in women with obesity may limit offspring cardiac remodelling, further high-quality longitudinal studies with larger sample sizes are required to confirm these observations and to determine whether these changes persist to adulthood.