Miracle-dz/UniFAR-FTUs huggingface dataset:
Trust Building Mechanisms in Online Health Communities and Their Impact on Information Adoption and Close Relationship Formation

This study conceptually replicates Fan and Lederman’s (2018) work on trust formation mechanisms in online health communities (OHCs). Social capital theory sets the framework for the research. Contextualized in online health communities (OHCs), it is the content contributors’ task to demonstrate trustworthiness by showing the credibility of the posted content in their previous postings. In contrast, the recipients, rather than the contributors, have to initially perceive trustworthiness in the sense of traditional social capital theory. We adopted the model, hypotheses, measurement, and statistical methods from the original study conducted by Fan and Lederman in 2018. Three out of nine hypotheses in our replication are not consistent with the original study results. The inconsistencies primarily lie in the antecedents of two types of trust. We discuss possible explanations for these discrepancies and suggest additional data and statistical tests to validate our replication results.
Research on the Core Competitiveness of Small and Medium-Sized Enterprises Based on Nvivo Text Mining Technology - Data from China

How to obtain and maintain competitive advantage is one of the important topics in strategic management research. Resource basic theory is an important analysis angle of strategic management. Understanding enterprises from the perspective of resources and capabilities can help enterprises to form more lasting competitive advantages. Based on the grounded theory, this paper studies the lighting enterprises in Guzhen town of Zhongshan city, deconstructs the resources from the perspective of the basic theory of resources, and explores the kinds of resources that affect the competitive advantages of enterprises. The research shows that capital resources and relationship resources are important factors that affect the competitive advantages of SMEs
Data Analysis of the Inﬂuence of College Students’ Personality Traits on Positive Mental Health Education Under COVID-19 Epidemic Situation

This study is quantitative research using survey method and mainly uses SPSS software to statistically analyze the questionnaire survey data. The purpose of this study is to analyze the inﬂuence of college students’ personality traits on positive mental health education in colleges and universities under the current epidemic situation by using the empirical research method of statistical analysis of data, so as to provide ideas and empirical research basis for perfecting the positive mental health education model in Chinese colleges and universities. Research method: 843 college students in Shaanxi Province were randomly sam-pled and measured by questionnaire. By using various statistical analysis methods, such as descriptive analysis, t-test, ANOVA, and regression analysis, the following statisticalresultswereobtained:(1)Thereweresigniﬁcantdifferencesinthescores ofpositivementalhealth(well-being)amongthethreefactors:gender,gradeand whetherstudentshadparticipatedinthestudentunionornot(P < 0.05). (2) All ﬁve personality traits have signiﬁcant predictive effects on positive mental health (well-being). Conclusions: Under the epidemic situation, we should pay attention to the positive mental health education of college students, and consciously culti-vate their positive personality traits, so as to help college students face difﬁculties optimistically and improve their well-being during the epidemic situation.
2010 Standards for Delineating Metropolitan and Micropolitan Statistical

This Notice announces OMB’s adoption of 2010 Standards for Delineating Metropolitan and Micropolitan Statistical Areas. The 2010 standards replace and supersede the 2000 Standards for Defining Metropolitan and Micropolitan Statistical Areas. In arriving at its decision, OMB accepted the recommendations of the interagency Metropolitan and Micropolitan Statistical Area Standards Review Committee (the Review Committee) as published in the February 12, 2009 Federal Register. The SUPPLEMENTARY INFORMATION in this Notice provides background information on the standards (Section A), a brief synopsis of the public comments OMB received in response to the February 12, 2009 Federal Register notice (Section B), and OMB’s decisions on the recommendations of the Review Committee (Section C). The 2010 standards appear at the end of this Notice (Section D). The adoption of the 2010 standards will not affect the availability of Federal data for geographic areas such as States, counties, county subdivisions, and municipalities. For the near term, the U.S. Census Bureau will tabulate and publish data from the 2010 Census for all metropolitan, micropolitan, and combined statistical areas in existence at the time of the census. DATES: Effective Date: This Notice is effective immediately. OMB plans to announce delineations of areas based on the 2010 standards and 2010 Census data in 2013. Federal agencies should begin to use the new area delineations to tabulate and publish statistics when the delineations are announced. ADDRESSES: Please send correspondence about OMB’s decision to Katherine K. Wallman, Chief Statistician, Office of Management and Budget, Room 10201, New Executive Office Building, Washington, DC 20503, telephone number (202) 395–3093, fax number (202) 395–7245, or E-mail 2010MetroAreas@omb.eop. gov with the subject 2010 MetroAreas. Electronic Availability: This notice is available on the Internet from the OMB Web site at http://www.whitehouse. gov/ omb/fedreg_default/.
Beyond latent and active: a scoping review of conceptual frameworks and diagnostic criteria for tuberculosis

Background There is growing recognition that tuberculosis (TB) exists as a spectrum of states beyond the current binary classification of latent and active TB. Our aim was to systematically map and synthesize the proposed alternative conceptual frameworks for TB states from the literature. Methods We systematically searched MEDLINE, Embase and EMcare for review articles without date restrictions. We included articles that explicitly described >2 states for TB. We conducted a thematic and frequency analysis for terminologies, conceptual definitions and diagnostic criteria for TB states. Results We identified 37 articles that met our inclusion criteria. All included articles were written after 2009. We identified 8 broad conceptual themes that were used to categorize TB states and to calculate their frequency among included articles. These states were: State 0: Mycobacterium tuberculosis (Mtb) elimination by innate immune response (n=23/37, 62%); State I: Mtb elimination by acquired immune response (n=28/37, 76%); State II: Mtb infection not eliminated but controlled by immune system (n=34/37, 92%); State III: Mtb infection not controlled by the immune system (n=21/37, 57%); State IV: bacteriologically positive without symptoms (n=23/37, 62%); State V: signs or symptoms associated with TB (n=36/37, 97%); State VI: severe or disseminated TB disease (n=11/37, 30%); and State VII: previous history of TB (n=5/37, 14%). We found 27 additional variations within these themes that were labelled as sub-states. Articles varied in the terminology used to describe conceptual states and similar terms were often used to describe different concepts. Diagnostic criteria were provided in 27 articles and were also applied inconsistently. Conclusion Terminologies and definitions for TB states are highly inconsistent in the literature. A consensus on a framework for describing TB disease states is required to standardize communication in scientific publications as well as to inform research, clinical and public health practice for TB elimination.
Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination

Translating prediction models into practice and supporting clinicians' decision-making demand demonstration of clinical value. Existing approaches to evaluating machine learning models emphasize discriminatory power, which is only a part of the medical decision problem. We propose the Applicability Area (ApAr), a decision-analytic utility-based approach to evaluating predictive models that communicate the range of prior probability and test cutoffs for which the model has positive utility; larger ApArs suggest a broader potential use of the model. We assess ApAr with simulated datasets and with three published medical datasets. ApAr adds value beyond the typical area under the receiver operating characteristic curve metric (AUROC) analysis. As an example, in the diabetes dataset, the top model by ApAr was ranked as the 23rd best model by AUROC. Decision makers looking to adopt and implement models can leverage ApArs to assess if the local range of priors and utilities is within the respective ApArs.
Modified Fire Hawks Gazelle Optimization (MFHGO) Algorithm Based Optimized Approach to Improve the QoS Provisioning in Cloud Computing Environment

– This work introduces a method that focuses on enhancing resource allocation in cloud computing environments by considering Quality of Service (QoS) factors. Since resource allocation plays a crucial role in determining the QoS of cloud services, it is important to consider indicators like response time, throughput, waiting time, and makespan. The primary difficulty in cloud computing lies in resource allocation, which can be tackled by proposing a novel algorithm known as Modified Fire Hawks Gazelle Optimization (MFHGO). The proposed approach involves the hybridization of the modified fire hawks algorithm with gazelle optimization to facilitate efficient resource allocation. It aims to optimize several objectives, such as resource utilization, degree of imbalance, completion time, throughput, relative error, and response time. To achieve this, an optimal resource allocation is achieved using the Partitioning around K-medoids (PAKM) clustering approach. The proposed model extends the K-means clustering method. For simulation purposes, the GWA-T-12 Bitbrains dataset is utilized, while the JAVA tool is employed for exploratory analysis. The effectiveness of the proposed resource allocation and clustering approach is demonstrated by comparing it with existing schemes. The proposed work's makespan is 1.45 seconds for 50 tasks, 3.6 seconds for 100 tasks, 3.67 seconds for 150 tasks, and 5.34 seconds for 200 jobs. As a result, the proposed model achieves the smallest makespan value when compared to the previous approaches. The proposed work yielded response times of 105ms for a task length of 100, 376ms for 200, 555ms for 300, 624ms for 400, and 1014ms for 500. These results indicate that the proposed model outperforms current approaches by achieving a faster response time and also attains a bandwidth utilization of 0.80%, 0.90%, and 0.97% for 4, 6, and 16 tasks, respectively, indicating better bandwidth utilization than the other approaches.
Discovering motifs restricted in space-time. (Découverte de motifs fréquents limités en espace-temps)

Many phenomena can be observed and organized as a sequence of observations on a timeline that can be modeled as a time series. A relevant area that is being explored in time series analysis is pattern discovery. Patterns are subsequences of time series related to some special properties or behaviors. A particular pattern that occurs a significant number of times in time series is called motif. Several important phenomena of a time series present different behaviors when observed at points in space (for example, series collected by sensors and IoT) and are best modeled as space-time series. Each time series is associated with a position in space. A space-time pattern is a sequence of events that are limited in space and time. Finding patterns that are frequent and restricted in space and time can allow us to understand how a phenomenon occurs. Several works have been developed to identify motifs in time series. However, studies that address spatiotemporal data techniques have not been identified. In this thesis, we compare different approaches to identifying motifs in time series with their main differences. We propose two methods for automatically identifying space-time restricted motifs in space-time series, the CSA and CSTMP . We experimentally compare the proposed methods with two other alternative methods: the Matrix Profile technique and the ensemble of the Matrix Profile and DBScan techniques. Our results show that CSA and CSTMP are innovative and obtain results that outperform the state-of-the-art techniques for time series and their adaptations for space-time series, being evaluated in two datasets.
Low-Cost Algorithms for the Restricted Assignment Problem on Intervals of Machines

We consider a special case of the Restricted Assignment problem where processing sets of jobs consist in contiguous intervals of machines. This problem is known as the Restricted Assignment problem on Intervals (RAI), and arises, for instance, when partitioning multi-get requests in distributed and replicated NoSQL databases. These databases aim at providing high availability and very low latency, i.e., the algorithms used to schedule requests must have good execution time. In this paper, we propose optimal and approximation algorithms for some variants of the RAI problem that apply to these systems. On the basis of previous work done by Lin et al. ( European Journal of Operational Research , 2004), we present an efficient ( 2 − 1 / m ) -approximation algorithm, where m is the number of machines. We also provide a generalization of the RAI problem on so-called circular intervals, i.e., intervals that appear when machines are arranged in a circular fashion, and we call this new problem the Restricted Assignment problem on Circular Intervals (RACI). We give a procedure that computes an optimal solution when there are K possible processing times, assuming that one already knows an optimal algorithm for the corresponding non-circular problem. This enables us, for instance, to obtain an optimal algorithm with complexity O ( n log n ) for the RACI problem with a fixed number of machines and unitary jobs.
Security of the Sponge Construction with a Random Transformation

The sponge construction is often used for the design of cryptographic hash functions. Some interesting security properties of hash functions are collision, preimage and second preimage security. Recently, a tight bound for preimage security has been proven in the case where this construction uses a permutation. Since tight bounds for collision and second preimage security had been proven already, this implies we have tight bounds for all of the above security properties for this construction. In the case where the sponge uses a transformation instead of a permutation, the collision attack carries over and this proves that the bound for collision security is tight. However, tight first and second preimage bounds have not been proven yet. We will prove those bounds and show that they correspond to the original expectations for the sponge.
Time series prediction and causation

Logistics, the lifeblood of our global economy, is a fast-growing sector with high strategic stakes. The efficiency of global supply chains has major economic impacts along with significant ecological repercussions. The rapid evolution of the sector is based in particular on the gradual and massive introduction of advanced technological solutions (AI, robotics) whose emergence opens up new horizons to explore.
Social Media Stocks Reviews Big Data Management Research

[Purpose/Significance] In order to enhance the big data management of stock reviews by regulators, make management more scientific and efficient, and improve the screening efficiency of big data stock reviews by investors. This paper explores the influencing factors of the perceived usefulness of big data in social media stock reviews. [Methods/Process] Based on the Information adoption theory, this paper constructed a factor theory model of the helpfulness of stock review information through the signals related to reviewers and reviews and the signal environment. Using Tobit regression to empirically test the relationship between various signals and review helpfulness. [Result/Conclusion] The findings suggest that the perceived helpfulness of stock reviews is positively influenced by review images, review information entropy, review professionalism, review bilaterality, financial blogger certification, number of followers of the reviewer, and published at non-trading times.
Self-Supervised Segmentation of 3D Fluorescence Microscopy Images Using CycleGAN

In recent years, deep learning models have been extensively applied for the segmentation of microscopy images to efficiently and accurately quantify and characterize cells, nuclei, and other biological structures. However, typically these are supervised models that require large amounts of training data that are manually annotated to create the ground-truth. Since manual annotation of these segmentation masks is difficult and time-consuming, specially in 3D, we sought to develop a self-supervised segmentation method.Our method is based on an image-to-image translation model, the CycleGAN, which we use to learn the mapping from the fluorescence microscopy images domain to the segmentation domain. We exploit the fact that CycleGAN does not require paired data and train the model using synthetic masks, instead of manually labeled masks. These masks are created automatically based on the approximate shapes and sizes of the nuclei and Golgi, thus manual image segmentation is not needed in our proposed approach.The experimental results obtained with the proposed CycleGAN model are compared with two well-known supervised segmentation models: 3D U-Net [1] and Vox2Vox [2]. The CycleGAN model led to the following results: Dice coefficient of 78.07% for the nuclei class and 67.73% for the Golgi class with a difference of only 1.4% and 0.61% compared to the best results obtained with the supervised models Vox2Vox and 3D U-Net, respectively. Moreover, training and testing the CycleGAN model is about 5.78 times faster in comparison with the 3D U-Net model. Our results show that without manual annotation effort we can train a model that performs similarly to supervised models for the segmentation of organelles in 3D microscopy images.Clinical relevance— Segmentation of cell organelles in microscopy images is an important step to extract several features, such as the morphology, density, size, shape and texture of these organelles. These quantitative analyses provide valuable information to classify and diagnose diseases, and to study biological processes.
Revisiting nucleosynthesis in globular clusters - The case of NGC 2808 and the role of He and K

Context. Motivated by recent reports concerning the observation of limited enrichment in He but excess K in stars of globular clusters, we revisit the H-burning conditions that lead to the chemical properties of multiple stellar populations in these systems. Aims. In particular, we are interested in correlations of He and K with other elements, such as O, Na, Al, Mg and Si, reported in stars of NGC 2808. Methods. We performed calculations of nucleosynthesis at constant temperature and density, exploring the temperature range of 25 to 200 × 10 6 K (25 to 200 MK), using a detailed nuclear reaction network and the most up-to-date nuclear reaction rates. Results. We find that Mg is the most sensitive “thermometer” of hydrostatic H-burning conditions, pointing to a temperature range of 70–80 MK for NGC 2808, while He is a lesser – but not negligible – constraint. Potassium can be produced at the levels reported for NGC 2808 at temperatures >180 MK and Si at T > 80 MK. However, in the former temperature range Al and Na are totally destroyed and no correlation can be obtained, in contrast to the reported observations. None of the putative polluter sources proposed so far seem to satisfy the ensemble of nucleosynthesis constraints.
Constrained simultaneous nonlinear quantile regression

Restricted quantile regressionintroduced by He (1997)Rearrangement quantile estimatorproposed by Chernozhukov et al. (2010)Multi-objective quantile estimatorsStepwise multiple quantile regressionsuggested by Wu & Liu (2009)Simultaneous multiple quantile regressiondeveloped by Bondell et al. (2010)
Brain tumours, brain metastases, and neuroinflammation: Insights from neuroimaging studies.

Brain tumors and brain metastases induce changes in brain tissue remodelling that lead to immunosuppression and trigger an inflammatory response within the tumor microenvironment. These immune and inflammatory changes can influence invasion and metastasis. Other neuroinflammatory and necrotic lesions may occur in patients with brain cancer or brain metastases as sequelae from treatment with radiotherapy. Glioblastoma (GBM) is the most aggressive primary malignant brain cancer in adults. Imaging methods such as positron-emission tomography (PET) and different magnetic resonance imaging (MRI) techniques are highly valuable for the diagnosis and therapeutic evaluation of GBM and other malignant brain tumors. However, differentiating between tumor tissue and inflamed brain tissue with imaging protocols remains a challenge. Here, we review recent advances in imaging methods that have helped to improve the specificity of primary tumor diagnosis versus evaluation of inflamed and necrotic brain lesions. We also comment on advances in differentiating metastasis from neuroinflammation processes. Recent advances include the radiosynthesis of 18F-FIMP, an L-type amino acid transporter 1 (LAT1)-specific PET probe that allows better differentiation between tumor tissue and inflammation compared to previous probes; and the combination different advanced imaging protocols with the inclusion of radiomics and machine learning algorithms.
Modeling and Simulation of Three-Point Bending Experiment of H-Beam and Teaching Application in Higher Vocational Colleges

In view of the problem of poor quality of Material Mechanics experiment teaching, this paper puts forward a method of using simulation technology to assist teaching. In order to solve students’ learning doubts about bending deformation of H-beam, the research team established a geometric model together with students. Based on the geometric model, a finite element model of three-point bending was established. Bending deformation behavior of beams under different pressures was simulated, and rich digital teaching resources such as Mises stress nephograms and animations were obtained. These nephograms and animations have effectively solved students’ learning doubts. The introduction of computational finite element technology into the teaching of Material Mechanics is an innovation of teaching methods. On the one hand, rich digital resources can clearly and intuitively display boring mechanical concepts. On the other hand, the application of simulation technology not only improves students’ interest in learning, arouses their enthusiasm in learning, but also expands their knowledge. The application of finite element technique in Material Mechanics should be further explored.