Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumer Expenditures Observed on Social Media: Material Versus Experiential Purchases

The visibility of consumer expenditure is a key concept in theories of consumer behavior. This study extends previous research from the offline context to the online space with the following research objectives: a) to rank a full range of expenditure categories by their frequencies of being observed on social media; b) to explain the mechanisms that underlay the observed frequencies of the expenditure categories with two predictors. One is the expenditure type, namely experiential versus material expenditures, and the other is the observers’ need for status. An online survey was conducted to rank a range of expenditure categories by their frequencies of being observed by people on their social media networks. Regression analysis was conducted to test the effects of the predictors. The results indicate that what is frequently observed online differs from what is easily observable offline. Observed frequencies are related to the nature of the expenditures. The more experiential a purchase is, the higher its observed frequency on social media. Moreover, people's need for status has differing impacts on the observed frequencies, depending on the nature of the expenditures. To the best of our knowledge, this is one of the first studies that examines a virtually entire basket of consumer expenditures as observed on social media. Another contribution of the study is the incorporation of predictors to explain the pattern of observed frequencies across expenditure categories and respondents, which has received scarce empirical investigation.
Optimization of Spark Data Skew in Big Data Environment

In order to solve the problem of uneven data distribution in the shuffle stage of Spark distributed platform, this paper first analyzes the causes of data skew in Spark platform, and then establishes a skew model that can quantify the degree of data skew. Based on quantifiable skew model, a shuffle partition scheme is proposed to optimize data skew in Spark distributed platform. The working node of the partitioning scheme first samples the output data of the Map stage, summarizes the data of each working node to the master node to predict the data size of all working nodes, and then pre-partitions the intermediate data according to the load balancing partitioning algorithm and Hash partitioning algorithm to obtain a pre-partitioning table. The master node distributes the pre-partitioning table to each working node. Finally, the working node partitions all the intermediate data according to the pre-partitioning table. The experimental results under different skew conditions show that the shuffle partition scheme proposed in this paper is universal and efficient, and can effectively deal with the data skew problem of Spark distributed platform.
AI in Rehabilitation Medicine: Opportunities and Challenges

Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient’s outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.
A Case-based Channel Selection Method for EEG Emotion Recognition Using Interpretable Transformer Networks

Optimizing the selection of features and channels with satisfactory emotion classification accuracy is important for electroencephalograph (EEG)-based emotion recognition. Here, we propose a novel case-based channel selection method based on interpretable transformer networks for emotion recognition. Gradients and relevance of the prediction of the transformer model were used for backpropagation to produce the attention relevance map. Critical channels were selected by ranking the attention relevance value. We test the effectiveness of the proposed method on the multi-channel EEG emotional datasets SEED. The results demonstrated that the method can pick out the critical channels effectively with relatively high classification accuracy. The locations of selected channels converged on the frontal and temporal areas which were consistent with neuroscience findings. Thus, the proposed method has a potential to reduce computation time and assist source localization in cognitive neuroscience studies.
A Multidimensional Data Aggregation Scheme Based on Edge Federated Learning and Blockchain for Smart Grid

Smart grid is a new generation of energy infrastructure. Data aggregation is a common method for assessing and protecting electricity consumption in smart grids. Since the existing data aggregation scheme focuses on centralization, it is easy to lead to user privacy leakage. The security of data acquisition and transmission is of great concern. In practice, smart grid data is multi-dimensional and needs to be aggregated. Federated learning is a collaborative machine learning approach. Due to the limited computing power of smart meters, data training efficiency becomes an issue. Therefore, we propose a multi-dimensional data aggregation solution with blockchain and edge federated learning in smart grid. In this scheme, edge federated learning is used to protect parameters and improve the training efficiency. PBFT and blockchain are adopted to prevent user privacy data leakage. The solution enables the secure aggregation of fine-grained data from the smart grid to defend against a wide range of attacks, while supporting fault tolerance, traceability, and tamper resistance. Performance evaluation shows that the computational cost of this scenario is less than two-fifths of the existing solution, and the communication cost is only two-thirds of the existing cost. The reduction in computing and communication costs greatly improves performance.
A model study of warming-induced phosphorus–oxygen feedbacks in open-ocean oxygen minimum zones on millennial timescales

Abstract. Observations indicate an expansion of oxygen minimum zones (OMZs) over the past 50 years, likely related to ongoing deoxygenation caused by reduced oxygen solubility, changes in stratification and circulation, and a potential acceleration of organic matter turnover in a warming climate. The overall area of ocean sediments that are in direct contact with low-oxygen bottom waters also increases with expanding OMZs. This leads to a release of phosphorus from ocean sediments. If anthropogenic carbon dioxide emissions continue unabated, higher temperatures will cause enhanced weathering on land, which, in turn, will increase the phosphorus and alkalinity fluxes into the ocean and therefore raise the ocean's phosphorus inventory even further. A higher availability of phosphorus enhances biological production, remineralisation and oxygen consumption, and might therefore lead to further expansions of OMZs, representing a positive feedback. A negative feedback arises from the enhanced productivity-induced drawdown of carbon and also increased uptake of CO2 due to weathering-induced alkalinity input. This feedback leads to a decrease in atmospheric CO2 and weathering rates. Here, we quantify these two competing feedbacks on millennial timescales for a high CO2 emission scenario. Using the University of Victoria (UVic) Earth System Climate Model of intermediate complexity, our model results suggest that the positive benthic phosphorus release feedback has only a minor impact on the size of OMZs in the next 1000 years. The increase in the marine phosphorus inventory under assumed business-as-usual global warming conditions originates, on millennial timescales, almost exclusively (> 80 %) from the input via terrestrial weathering and causes a 4- to 5-fold expansion of the suboxic water volume in the model.
Unsupervised Stock Clustering Based on Adversarial Learning and Its Application in Quantitative Investment

Quantitative investment is one of the primary trading methods in stock markets. Quantitative models based on artificial intelligence technologies have overcome the limitations and drawbacks of traditional quantitative models, providing richer sources of revenue. Supervised learning-based quantitative models rely on various forms of stock labels, which can generate good returns but cannot reveal common features among groups of stocks with potentially high-yield trends. This paper proposes unsupervised learning on stock datasets using clustering to explore potential features within high-dimensional stock time-series datasets. This approach aims to obtain useful information from limited data and optimize existing trading. Finally, we used backtesting results to validate the effectiveness of the model in the real stock market.
An Object Detection Algorithm Based on Multiple Attention Mechanisms

In the field of autonomous driving, a novel approach is proposed for detecting and recognizing surrounding vehicles targets. The proposed method combines multiple attention mechanisms within a 3D object detection algorithm, drawing inspiration from the PointPillars network. Firstly, an additional local attention mechanism layer is incorporated into the point cloud pillar feature extraction module. This augmentation allows for a more comprehensive fusion of surrounding point cloud feature during the point cloud feature extraction process. Secondly, a channel attention mechanism module is introduced to the 2D image backbone network. The resulting features are subsequently inputted into the SSD algorithm for object detection. The improved algorithm's effectiveness has been demonstrated through an experimental evaluation on the popular KITTI dataset. The results show a significant improvement in average mean accuracy compared to the original algorithm, indicating that the attention mechanisms successfully enhance the detection and recognition performance of surrounding vehicles in autonomous driving scenarios.
Possible Seasonal Pattern of Ebola Virus Disease in the Democratic Republic of the Congo

The Democratic Republic of the Congo has two seasons (dry and rain). However this allocation across the country is totally different in terms of length. First, in the north of Equator, the rainy seasons start from April until the end of June then from September to the end of October. The dry seasons start from the beginning of November until the end of March (called the major dry season) and the beginning of July until the end of August (the minor dry season). Second, in the south of Equator, the rhythm of seasons is exactly inversed. The rainy season starts from October to May and the dry season (mid-May until the end of September). Rains are almost constant along the year.
Obesity and Miscarriage

Abstract Obesity affects nearly 40% of reproductive-aged women and has serious implications for women's overall and reproductive health. Women with an elevated body mass index (BMI) have higher rates of anovulation and irregular menses, lower success with fertility treatment, and significantly higher rates of pregnancy complications, such as hypertension/preeclampsia, gestational diabetes, and preterm delivery. Many studies have also shown an association between obesity and early pregnancy loss. However, the causal association between BMI and miscarriage has not been elucidated, likely due to the multifactorial effects that BMI may have on early pregnancy development. In addition, BMI as an isolated variable fails to capture other relevant confounding health risk factors, such as nutrition, physical activity, and insulin resistance. In this review, we will summarize the current literature demonstrating the association between BMI and miscarriage, highlight the research that attempts to explain the association, and finally provide data on therapeutic interventions to improve reproductive outcomes in women suffering from obesity and early pregnancy loss.
An investigation into the possibilities of sex and age determination of Eurasian woodcock (Scolopax rusticola L.) based on biometric parameters, using conditional inference trees and minimal important differences

Abstract. Morphometric characteristics of Eurasian woodcock collected during spring hunting (March) in Hungary between 2010 and 2014 were investigated to evaluate the accuracy of methods for determining the sex of live birds. We analysed the size dimorphism of biometric traits by sex, age, and sex and age, with sex determination (n = 13,226) performed by destructive methods and age determination based on wing examination (n = 8,905). Using the minimal important differences (MID) method, we demonstrated that, during spring migration, adult females have significantly greater mass and bill length than juvenile females and adult males, as well as a significant difference in body length compared to juvenile females. No biologically relevant differences were demonstrated between the sexes or age classes for other morphometric parameters. Conditional inference trees were applied to test whether body size parameters could be used to separate the age and sex of individuals. Based on posterior probabilities (55.4%), we suggest that biometric parameters no longer provide a sufficiently reliable method to separate age classes during the spring migration. Separation of sexes showed the best results for adult birds, with bill length (85.4%) and body mass (85.2%) proving the best predictors. The inclusion of additional morphometric variables (tarsus, tail, body and wing length) in the model did not increase the reliability of sex segregation, confirming the results obtained using MID, i.e. that there is no statistically verifiable biologically relevant difference between adult male and female birds for these parameters. A methodological innovation in this study was using MIDs for comparisons to determine biological thresholds for differences, the procedure helping to exclude Type I errors and determine biological significance.
Acquisition and evaluation of sedimentologic paleomagnetic and geochemical time -series data from Flathead Lake Montana: Implications for late Pleistocene and Holocene paleoclimate

ii PREFACE. iii FORW ARD. iii ACKNOWLEDGEMENTS. v TABLE OF CONTENTS. ix LIST OF TA B LES. xiv LIST OF FIGU RES. xv LIST OF PLA TES. xx CHAPTER 1: High-Resolution Particle Size Analysis of Naturally Occurring Very FineGrained Sediment Through Laser Diffractometry. 1 A bstract. 2 Introduction. 3 Grain Size as a an Environmental P ro xy . 3 Laser Diffractometry. 6 Experimental D esign . 9 Instrument Operation . 9 Statistical Representation o f D istribution . 9 Sample Preparation . 11 Dispersion and Sonication. 11 ix Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Subsampling and Aliquot Introduction. 12 Optimizing Machine Settings and Initial Experimental Parameters . 14 Obscuration. 14 Pump Speed . 15 Optical P roperties . 15 Index of R efraction. 15 A bsorption. 16 Results and Interpretations. 17 Instrument Precision and Accuracy . 17 Dispersion and Sonication . 17 Aliquot Introduction . 18 Obscuration . 22 Pump S p eed . 23 Index o f Refraction and Absorption. 24 Acknowledgements. 31 R eferences. 32 CHAPTER 2: Assessment of Paleomagnetic Secular Variation Correlation and Establishment of a Chronostratigraphic Model for Late Pleistocene and Holocene Lacustrine Sediments of Core FL-00-9P from Flathead Lake, M ontana. 34 A bstract. 35 x Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Introduction 35 Initial Chronologic Controls. 39 Top o f C o re . 39 Middle o f C o re . 41 Bottom o f C o re . 42 Initial Chronology. 44 Refinement of Initial Age M odel. 45 Bulk Carbon D ating . 45 Paleomagnetic Secular V ariation. 47 Declination Rotation . 51 Correlation D a ta . 59 Graphical Correlations. 60 Tie Point Uncertainty. 62 Interpolation M ethods . 73 Reference Source Uncertainty. 78 Final Model Considerations. 84 Chronostratigraphic Model and Uncertainty. 87 Sedimentologic Model Validation. 90 References. 91
The effects of managerial response on future review valence and volume: the moderating role of rote response

Hotel managers are increasingly using rote responses to customer reviews in an effort to improve efficiency and reduce costs. However, the lack of customization in these responses may diminish their positive impact on subsequent review valence and volume. Through panel data analysis of 108,351 reviews and 76,783 managerial responses from TripAdvisor, this study found that managerial responses have a significant positive effect on subsequent review outcomes while a higher level of rote response will weaken this effect. These findings offer valuable insights for hotel booking platforms and managers seeking to optimize their online review management strategies.
LiDAR Remote Sensing of Vegetation Biomass

Accurate estimates of vegetation biomass are critical for calibrating and validating biogeochemical models (Hurtt et al. 2010), quantifying carbon fluxes from land use and land cover change (Shukla et al. 1990; Houghton et al. 2001), and supporting the United Nations Framework Convention on Climate Change (UNFCCC) program to reduce deforestation and forest degradation (Reducing Emissions from Deforestation and Forest Degradation) (Asner 2009). For instance, it was argued that at least half of the uncertainty in the estimates of emissions of carbon from land use change results from uncertain estimates of biomass density (Houghton 2005; Houghton et al. 2009). CONTENTS
A randomized multiplex CRISPRi-Seq approach for the identification of critical combinations of genes

Identifying virulence-critical genes from pathogens is often limited by functional redundancy. To rapidly interrogate the contributions of combinations of genes to a biological outcome, we have developed a multiplex, randomized CRISPR interference sequencing (MuRCiS) approach. At its center is a new method for the randomized self-assembly of CRISPR arrays from synthetic oligonucleotide pairs. When paired with PacBio long-read sequencing, MuRCiS allowed for near-comprehensive interrogation of all pairwise combinations of a group of 44 Legionella pneumophila virulence genes encoding highly conserved transmembrane proteins for their role in pathogenesis. Both amoeba and human macrophages were challenged with L. pneumophila bearing the pooled CRISPR array libraries, leading to the identification of several new virulence-critical combinations of genes. lpg2888 and lpg3000 were particularly fascinating for their apparent redundant functions during L. pneumophila human macrophage infection, while lpg3000 alone was essential for L. pneumophila virulence in the amoeban host Acanthamoeba castellanii. Thus, MuRCiS provides a method for rapid genetic examination of even large groups of redundant genes, setting the stage for application of this technology to a variety of biological contexts and organisms.
Qualitative Assessment of a Smartphone-Based Mobile Health Tool to Guide Diarrhea Management in Bangladesh

ABSTRACT. Diarrheal diseases are a major cause of morbidity and mortality in children worldwide and a significant contributor to antimicrobial resistance. In the absence of laboratory diagnostics to establish diarrhea etiology, electronic clinical decision support tools can help physicians make informed treatment decisions for children with diarrhea. In Bangladesh, we assessed the feasibility and acceptability of an electronic Diarrhea Etiology Prediction algorithm (DEP tool) embedded into a rehydration calculator, which was designed to help physicians manage children with diarrhea, including decisions on antibiotic use. A team of Bangladeshi anthropologists conducted in-depth interviews with physicians (N = 13) in three public hospitals in Bangladesh about their experience using the tool in the context of a pilot trial. Physicians expressed positive opinions of the DEP tool. Participants perceived the tool to be simple and easy to use, with structured guidance on collecting and entering clinical data from patients. Significant strengths of the tool were as follows: standardization of protocol, efficiency of clinical decision-making, and improved clinical practice. Participants also noted barriers that might restrict the widespread impact of the tool, including physicians’ reluctance to use an electronic tool for clinical decision-making, increasing work in overburdened healthcare settings, unavailability of a smartphone, and patients’ preferences for antibiotics. We conclude that an electronic clinical decision support tool is a promising method for improving diarrheal management and antibiotic stewardship. Future directions include developing and implementing such a tool for informal healthcare physicians in low-resource settings, where families may first seek care for pediatric diarrhea.
Performance of Landfills Under Seismic Loading

SYNOPSIS: The record of performance of landfills in earthquakes is excellent. However, the advent of geosynthetic liner and cover systems has increased the susceptibility of modern landfills to seismically-induced instability and deformations. Analyses used to assess the performance of landfills in earthquakes include site response, limit equilibrium stability, and Newmark deformation analyses. Well documented case histories of the behavior of landfills subject to seismic loading are necessary to improve knowledge of the parameters required for these analyses and thereby enhance the reliability of seismic performance evaluations for landfills.
New kinematic and geochronologic evidence for the Quaternary evolution of the Central Anatolian fault zone (CAFZ)

As the kinematics of active faults that bound the Anatolian plate are well studied, it is now essential to improve our understanding of the style and rates of intraplate deformation to constrain regional strain partitioning and improve seismic risk assessments. One of these internal structures, the Central Anatolian fault zone (CAFZ), was originally defined as a regionally significant left‐lateral “tectonic escape” structure, stretching for 700 km in a NE direction across the Anatolian plate. We provide new structural, geomorphic, and geochronologic data for several key segments within the central part of the CAFZ that suggest that the sinistral motion has been overstated. The Ecemiş fault, the southernmost part of the CAFZ, has a late‐Quaternary minimum slip rate of 1.1 ± 0.4 mm a−1, slower than originally proposed. Farther north, the Erciyes fault has fed a linear array of monogenetic vents of the Erciyes stratovolcano and 40Ar/39Ar dating shows a syneruptive stress field of ESE‐WNW extension from 580 ± 130 ka to 210 ± 180 ka. In the Erciyes basin, and central part of the CAFZ, we mapped and recharacterized the Erkilet and Gesi faults as predominantly extensional. These long‐term geological rates support recent GPS observations that reveal ESE‐WNW extension, which we propose as the driver of faulting since 2.73 ± 0.08 Ma. The slip rates and kinematics derived in this study are not typical of an “escape tectonic” structure. The CAFZ is a transtensional fault system that reactivates paleotectonic structures and accommodates E‐W extension associated with the westward movement of Anatolia.