bluuebunny/crossref_metadata_2025_split huggingface dataset:
Globally $\pmb{+}$-regular varieties and the minimal model program for threefolds in mixed characteristic
Authors: Bhatt Bhargav , Ma Linquan , Patakfalvi Zsolt , Schwede Karl , Tucker Kevin , Waldron Joe , Witaszek Jakub 
Published: May 2023

AbstractWe establish the Minimal Model Program for arithmetic threefolds whose residue characteristics are greater than five. In doing this, we generalize the theory of global$F$F-regularity to mixed characteristic and identify certain stable sections of adjoint line bundles. Finally, by passing to graded rings, we generalize a special case of Fujita’s conjecture to mixed characteristic.
Deep learning implementations in mining applications: a compact critical review
Authors: Azhari Faris , Sennersten Charlotte C. , Lindley Craig A. , Sellers Ewan 
Published: May 2023

AbstractDeep learning is a sub-field of artificial intelligence that combines feature engineering and classification in one method. It is a data-driven technique that optimises a predictive model via learning from a large dataset. Digitisation in industry has included acquisition and storage of a variety of large datasets for interpretation and decision making. This has led to the adoption of deep learning in different industries, such as transportation, manufacturing, medicine and agriculture. However, in the mining industry, the adoption and development of new technologies, including deep learning methods, has not progressed at the same rate as in other industries. Nevertheless, in the past 5 years, applications of deep learning have been increasing in the mining research space. Deep learning has been implemented to solve a variety of problems related to mine exploration, ore and metal extraction and reclamation processes. The increased automation adoption in mining provides an avenue for wider application of deep learning as an element within a mine automation framework. This work provides a compact, comprehensive review of deep learning implementations in mining-related applications. The trends of these implementations in terms of years, venues, deep learning network types, tasks and general implementation, categorised by the value chain operations of exploration, extraction and reclamation are outlined. The review enables shortcomings regarding progress within the research context to be highlighted such as the proprietary nature of data, small datasets (tens to thousands of data points) limited to single operations with unique geology, mine design and equipment, lack of large scale publicly available mining related datasets and limited sensor types leading to the majority of applications being image-based analysis. Gaps identified for future research and application includes the usage of a wider range of sensor data, improved understanding of the outputs by mining practitioners, adversarial testing of the deep learning models, development of public datasets covering the extensive range of conditions experienced in mines.
BOUNCE: memory-efficient SIMD approach for lightweight integer compression
Authors: Hildebrandt Juliana , Habich Dirk , Lehner Wolfgang 
Published: May 2023

AbstractInteger compression plays an important role in columnar database systems to reduce the main memory footprint as well as to speedup query processing. To keep the additional computational effort of (de)compression as low as possible, the powerful Single Instruction Multiple Data (SIMD) extensions of modern CPUs are heavily applied. While a scalar compression algorithm usually compresses a block of N consecutive integers, the state-of-the-art SIMDified implementation scales the block size to $$k \cdot N$$

k
·
N

with k as the number of elements which could be simultaneously processed in an SIMD register. On the one hand, this scaling SIMD approach improves the performance of (de)compression. But on the other hand, it can lead to a degradation of the memory footprint of the compressed data. Within this article, we analyze this degradation effect for various integer compression algorithms and present a novel SIMD concept to overcome that effect. The core idea of our novel SIMD concept called BOUNCE is to concurrently compress k different blocks of size N within SIMD registers, guaranteeing the same compression ratio as scalar variant. As we are going to show, our proposed SIMD idea works well on various Intel CPUs and may offer a new generalized SIMD concept to optimize further algorithms.
Tailoring LPSO phases in Mg–Y–Zn alloys to govern hydrogenation kinetics
Authors: Nicholson Kyle S. , Skripnyuk Vladimir , Xu Chunjie , Gao Xiang , Rabkin Eugen , Hodgson Peter D. , Lapovok Rimma 
Published: May 2023

AbstractA series of Mg–Y–Zn alloys with varying long-period stacking ordered (LPSO) phase fractions were prepared through control of alloy content, heat-treatment, and single-pass extrusion. The effect of LPSO phase volume fraction and microstructure refinement on the hydrogen absorption/desorption properties of ball-milled powders prepared from the extruded alloys was experimentally assessed. The hydrogen absorption and desorption kinetics scaled with the LPSO phase volume fraction, though the results of this study suggest that the scaling is not linear. Variations in the LPSO phase fraction and alloy content did not alter the (de)hydrogenation equilibrium pressure, indicating there is no significant change in thermodynamics of hydrogenation. Hydrogen absorption experiments on thin foils made from the extruded Mg–Y–Zn alloy with a high LPSO phase fraction demonstrated that the LPSO structures decompose into Mg phase, lamellar Mg/Mg–Zn structures and YH2 particles at hydrogen partial pressures sufficient to form YH2. This study shows that the hydrogen absorption/desorption kinetics in the Mg–Y–Zn alloys can be controlled by tailoring the LPSO phases using conventional metallurgical techniques.
Graphical Abstract
Exome sequence analysis of rare frequency variants in Late-Onset Alzheimer Disease
Authors: Sundarrajan Sudharsana , Venkatesan Arthi , Kumar S Udhaya , Gopikrishnan Mohanraj , Tayubi Iftikhar Aslam , Aditya M , Siddaiah Gowrishankar Bychapur , George Priya Doss C. , Zayed Hatem 
Published: May 2023

AbstractAlzheimer disease (AD) is a leading cause of dementia in elderly patients who continue to live between 3 and 11 years of diagnosis. A steep rise in AD incidents is observed in the elderly population in East-Asian countries. The disease progresses through several changes, including memory loss, behavioural issues, and cognitive impairment. The etiology of AD is hard to determine because of its complex nature. The whole exome sequences of late-onset AD (LOAD) patients of Korean origin are investigated to identify rare genetic variants that may influence the complex disorder. Computational annotation was performed to assess the function of candidate variants in LOAD. The in silico pathogenicity prediction tools such as SIFT, Polyphen-2, Mutation Taster, CADD, LRT, PROVEAN, DANN, VEST3, fathmm-MKL, GERP +  + , SiPhy, phastCons, and phyloP identified around 17 genes harbouring deleterious variants. The variants in the ALDH3A2 and RAD54B genes were pathogenic, while in 15 other genes were predicted to be variants of unknown significance. These variants can be potential risk candidates contributing to AD. In silico computational techniques such as molecular docking, molecular dynamic simulation and steered molecular dynamics were carried out to understand the structural insights of RAD54B with ATP. The simulation of mutant (T459N) RAD54B with ATP revealed reduced binding strength of ATP at its binding site. In addition, lower binding free energy was observed when compared to the wild-type RAD54B. Our study shows that the identified uncommon variants are linked to AD and could be probable predisposing genetic factors of LOAD.
NoC-based hardware software co-design framework for dataflow thread management
Authors: Mazumdar Somnath , Scionti Alberto , Zuckerman Stéphane , Portero Antoni 
Published: May 2023

AbstractApplications running in a large and complex manycore system can significantly benefit from adopting the dataflow model of computation. In a dataflow execution environment, a thread can run only if all its required inputs are available. While the potential benefits are large, it is not trivial to improve resource utilization and energy efficiency by focusing on dataflow thread execution models (i.e., the ways specifying how the threads adhering to a dataflow model of computation execute on a given compute/communication architecture). This paper proposes and implements a hardware-software co-design-based dataflow threads management framework. It works at the Network-on-Chip (NoC) level and consists of three stages. The first stage focuses on a fast and effective thread distribution policy. The next stage proposes an approach that adds reconfigurability to a 2D mesh NoC via customized instructions to manage the dataflow thread distribution. Finally, a 2D mesh and ring-based hybrid NoC is proposed for better scalability and higher performance. This work can be considered a primary reference framework from which extensions can be carried out.
Measures of frequency and effect in clinical research
Authors: D’Arrigo Graziella , Gori Mercedes , Pitino Annalisa , Tsalikakis Dimitrios G. , Liakopoulos Vassilios , Roumeliotis Stefanos , Tripepi Giovanni 
Published: May 2023

AbstractThe assessment of risk and effect size of a specific endpoint associated to the presence/absence of a certain exposure is a hallmark in clinical and epidemiological research. In fact, before recommending any treatment, it is mandatory to investigate the magnitude of the benefits and harms between the exposure under investigation (e.g. a given treatment) and a specific disease or event. To do this, clinicians and statisticians use absolute (risk differences, number needed to treat, likelihood to be helped or harmed) and relative (risk ratio, incidence rate ratio, hazard ratio and odds ratio) measures of effect. Herein, using a series of clinical examples, we aim to present a step by step methodologic approach of measures of effect in the area of nephrology and urology.
The Expanded Exercise Addiction Inventory (EAI-3): Towards Reliable and International Screening of Exercise-Related Dysfunction
Authors: Granziol Umberto , Griffiths Mark D. , Zou Liye , Yang Peiying , Herschel Hannah K. , Junker Annika , Akimoto Takayuki , Stoll Oliver , Alpay Merve , Aydın Zeynep , Zandonai Thomas , Di Lodovico Laura , Lichtenstein Mia Beck , Trott Mike , Portman Robert M. , Schipfer Melanie , Cook Brian , Cerea Silvia , Egorov Aleksei Y. , Cantù-Berrueto Abril , de la Vega Marcos Ricardo , Fernandes Paula Texeira , Landolfi Emilio , Demetrovics Zsolt , Tóth Eliza E. , Solmi Marco , Szabo Attila 
Published: May 2023

Abstract
Exercise addiction (EA) refers to excessive exercise, lack of control, and health risks. The Exercise Addiction Inventory (EAI) is one of the most widely used tools in its assessment. However, the cross-cultural psychometric properties of the EAI could be improved because it misses three pathological patterns, including guilt, exercise despite injury, and experienced harm. Therefore, the present study tested the psychometric properties of the expanded EAI (EAI-3) in a large international sample. The EAI-3 was administered to 1931 physically active adult exercisers speaking five languages (Chinese, German, Italian, Japanese, and Turkish) and other measures for obsessive–compulsive behavior, eating disorders, and personality traits. The assessment structure and reliability of the EAI-3 were tested with factorial analyses and through measurement invariance across languages and sex. Finally, a cutoff point for dysfunction-proneness was calculated. The EAI-3 comprised two factors, reflecting the positive and pathological sides of exercise. The structure had excellent reliability and goodness-of-fit indices and configural and metric invariances of the scale were supported. However, three items caused violations in scalar invariance. The results of partial measurement invariance testing suggested an adequate fit for the data. Following sensitivity and specificity analysis, the EAI-3’s cutoff score was 34 out of a maximum score of 48. This preliminary study suggests that the EAI-3 is a promising tool for screening EA in an international sample, with a robust and reliable structure comparable across languages and sex. In addition, the proposed cutoff could pave the way toward a consensus on a threshold to screen for EA.
An Improved Cellular Automata Solidification Model Considering Kinetic Undercooling
Authors: Liang Xiaohui , Bos Cornelis , Hermans Marcel , Richardson Ian 
Published: May 2023

AbstractA cellular automata (CA) model has been developed for solidification simulation considering the kinetic undercooling at the interface. The state-of-the-art model incorporates a decentered growth algorithm to suppress the grid anisotropy and a generalized height function method to calculate the curvature accurately. To develop a CA model which is independent of the mesh size, a new diffusion term is proposed to handle the diffusion between the interface cells and liquid cells. The developed CA model is employed to simulate the single-dendritic solidification of an Al–3Cu (wt pct) alloy. The simulated tip velocities agree with the prediction of the Kurz–Giovanola–Trivedi (KGT) model. Further studies show that the developed CA model converges to an equilibrium model with increasing kinetic mobility values. Moreover, it is found that the virtual liquid cell assumption which is commonly used in existing CA models may lead to a deviation in the mass balance. The mass balance error has been resolved by redistributing solutes from neighboring liquid cells in each time step. The developed CA model could be potentially used in solidification simulations with a high undercooling, which is common in welding and additive manufacturing.
Geochemical Survey and Estimation of Baseline Concentrations of Major and Trace Elements in Stream Water, Uttar Pradesh, India: Implication for Environmental Studies
Authors: Kumar Harish , Naim Nikhat , Ali Danish 
Published: May 2023

Abstract
Stream water plays an important role in the development of habitats. It is one of the important sources of water used for drinking and irrigation in the region. Before using, it is necessary to test the quality of all types of surface water available for various use. A geochemical study of stream water has been conducted in parts of Pratapgarh, Raebareli, and Allahabad districts of Uttar Pradesh to assess its suitability for domestic purposes and establish the baseline concentration of major ions, trace and REEs. 20 samples of stream water from different locations of 1st or 2nd order streams were collected during post-monsoon period, from 18th to 31st October 2021. Different hydrochemical parameters such as pH, electrical conductivity (EC), temperature, dissolved oxygen (DO), salinity, total dissolved solids (TDS), major cations and anions were analysed. In order to obtain maximum quality and purity of water, the stream water samples were analyzed by ICP-MS analytical method in the chemical lab for 47 trace elements including REEs contents. The obtained values of each parameter have been compared with the standard values recommended by BIS and WHO. The value of each element except uranium has been found to be within the desirable and permissible limits prescribed by BIS (2012) and WHO (2006). Selenium and arsenic have been found to be marginally exceed the permissible limits. The pH values of all stream waters in the study area range from 7.14 to 8.86 with an average value of 7.86 indicating an alkaline nature of stream water. The concentration of uranium in stream water varied from 1.5 ppb to 48.67 ppb with a mean value of 7.9 ppb. It is observed that wherever concentration of uranium in the samples increased, the pH as well as the bicarbonate level increased accordingly. Correlation coefficient revealed that uranium is strongly correlated with bicarbonate (r=0.67), moderately with sodium (r=6.1), and low positively correlated with the concentration of pH (r=0.34), EC (r=0.52), and salinity (r=0.52), indicates the same origin. Water quality index (WQI) of water was determined for suitability of drinking purpose, revealed that 65% water samples fall in excellent category and 30% water samples fall in good category while only 5% water sample are in poor category. Trilinear plot of sample shows that most of the stream water samples are Ca-Mg-HCO3 type.
Non-hydrostatic layer-averaged approximation of Euler system with enhanced dispersion properties
Authors: Escalante C. , Fernández-Nieto E. D. , Garres-Díaz J. , Morales de Luna T. , Penel Y. 
Published: May 2023

AbstractA new family of non-hydrostatic layer-averaged models for the non-stationary Euler equations is presented in this work, with improved dispersion relations. They are a generalisation of the layer-averaged models introduced in Fernández-Nieto et al. (Commun Math Sci 16(05):1169–1202, 2018), named LDNH models, where the vertical profile of the horizontal velocity is layerwise constant. This assumption implies that solutions of LDNH can be seen as a first order Galerkin approximation of Euler system. Nevertheless, it is not a fully (x, z) Galerkin discretisation of Euler system, but just in the vertical direction (z). Thus, the resulting model only depends on the horizontal space variable (x), and therefore specific and efficient numerical methods can be applied (see Escalante-Sanchez et al. in J Sci Comput 89(55):1–35, 2021). This work focuses on particular weak solutions where the horizontal velocity is layerwise linear on z and possibly discontinuous across layer interfaces. This approach allows the system to be a second-order approximation in the vertical direction of Euler system. Several closure relations of the layer-averaged system with non-hydrostatic pressure are presented. The resulting models are named LIN-NH$$_k$$

k

models, with $$k=0,1,2$$

k
=
0
,
1
,
2

. Parameter k indicates the degree of the vertical velocity profile considered in the approximation of the vertical momentum equation. All the introduced models satisfy a dissipative energy balance. Finally, an analysis and a comparison of the dispersive properties of each model are carried out. We show that Models LIN-NH$$_1$$

1

and LIN-NH$$_2$$

2

provide a better dispersion relation, group velocity and shoaling than LDNH models.
Direct Conversion of Cellulose into 5-HMF by Transition-Metal Doped Montmorillonite Catalyst in Water
Authors: Tunç Ebru , Hoşgün Emir Zafer , Hoşgün Halit L. , Bozan Berrin 
Published: May 2023

AbstractCatalysts are very important in the use of cellulose, the main component of biomass, as a raw material for the large-scale production of liquid fuels and chemicals. 5-Hydroxymethylfurfural (HMF) is an extremely important intermediate in the fine chemical industry. HMF can be synthesized by acid-catalyzed dehydration of fructose, glucose, cellulose, or sucrose. The conversion of cellulose to HMF is challenging due to its chemical structure. The objective of the present study was to devise a more facile synthesis method using transition metal-doped montmorillonite catalysts (10Cr-Mnt, 10Cu-Mnt, 10Fe-Mnt, and 10Zn-Mnt) by wet impregnation. Samples were characterized by X-ray powder diffraction, specific surface area, and NH3-TPD analyses. The synthesized catalysts were used for the conversion of cellulose to 5-HMF in an aqueous medium. Among the metals studied, Cr showed the greatest catalytic activity. With the use of this catalyst, efficient conversion of cellulose to 5-HMF was achieved, affording a conversion yield of 93.47% and 5-HMF yield of 9.07% within 6 h at 200°C. The study described here could be useful for the efficient conversion of cellulose into 5-HMF, as well as into other biomass-derived chemicals.
What do contracts do to facilitate relationships?
Authors: Itoh Hideshi 
Published: May 2023

AbstractI demonstrate that transacting parties may expend resources on ex ante contracting, which may not be legally enforceable, to help build and maintain their long-term relationships. I first introduce three legal concepts, namely scaffolding, managerial provisions, and formal relational contracts, which highlight the recent trend towards more detailed contracts. These concepts indicate that the role of detailed contracts is not to improve judicial contract enforcement, but to enhance clarity and alignment of interests and to reduce renegotiation costs, which ultimately support the parties’ relationships. I then proceed to present and analyze a simple reduced-form model, which demonstrates that the parties’ efforts for ex ante contracting are not necessarily monotonic with the level of alignment of their interests. Furthermore, I discuss recent contracting practices in Japan and attempt to provide an explanation for the lack of change observed in these practices.
Interfering small ubiquitin modifiers (SUMO) improves the thermotolerance of apple by facilitating the activity of MdDREB2A
Authors: Liu Zeyuan , Bian Ningning , Guo Jianyan , Zhao Shuang , Khan Abid , Chu Baohua , Ma Ziqing , Niu Chundong , Ma Fengwang , Ma Ming , Guan Qingmei , Li Xuewei 
Published: May 2023

AbstractHeat stress, which is caused by global warming, threatens crops yield and quality across the world. As a kind of post-translation modification, SUMOylation involves in plants heat stress response with a rapid and wide pattern. Here, we identified small ubiquitin modifiers (SUMO), which affect drought tolerance in apple, also participated in thermotolerance. Six isoforms of SUMOs located on six chromosomes in apple genome, and all the SUMOs were up-regulated in response to heat stress condition. The MdSUMO2 RNAi transgenic apple plants exhibited higher survival rate, lower ion leakage, higher catalase (CAT) activity, and Malondialdehyde (MDA) content under heat stress. MdDREB2A, the substrate of MdSUMO2 in apple, was accumulated in MdSUMO2 RNAi transgenic plants than the wild type GL-3 at the protein level in response to heat stress treatment. Further, the inhibited SUMOylation level of MdDREB2A in MdSUMO2 RNAi plants might repress its ubiquitination, too. The accumulated MdDREB2A in MdSUMO2 RNAi plants further induced heat-responsive genes expression to strengthen plants thermotolerance, including MdHSFA3, MdHSP26.5, MdHSP18.2, MdHSP70, MdCYP18-1 and MdTLP1. In summary, these findings illustrate that interfering small ubiquitin modifiers (SUMO) in apple improves plants thermotolerance, partly by facilitating the stability and activity of MdDREB2A.
Pandemic Populism: Explaining Support for the People's Party of Canada in the 2021 Federal Election
Authors: Medeiros Mike , Gravelle Timothy B. 
Published: May 2023

AbstractThe COVID-19 pandemic has seen opponents of public health mandates deploy a range of populist and anti-elite arguments. The 2021 Canadian federal election was an exceptional “pandemic election” in which the COVID-19 health crisis took centre stage. But the election campaign also saw the populist People's Party of Canada (PPC) rise to prominence by opposing pandemic-related public health restrictions. While the party failed to win a seat, it did manage to triple its vote share (1.6 per cent to 4.9 per cent). It is unclear, however, what factors led to the rise in support for the PPC. To explore this issue, we draw on an original post-election survey (n = 18,950) and focus on populist attitudes and opposition to COVID-19-related public health restrictions. Results from regression models and structural equation models (SEMs) indicate that opposition to public health restrictions was a much stronger factor than populism in shaping support for the PPC.