Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasi-dynamic subgrid-scale kinetic energy equation model for large-eddy simulation of compressible flows

Abstract A quasi-dynamic subgrid-scale (SGS) kinetic energy one-equation eddy-viscosity model is introduced in this paper for large-eddy simulation (LES) of compressible flows. With the additional SGS kinetic energy equation, the SGS kinetic energy can be predicted properly. Then, using the dual constraints of SGS kinetic energy and the SGS kinetic energy flux, the eddy-viscosity model can be determined exactly. Taking a similar scheme as the expansion of the SGS stress, other unclosed quantities in the equations to be solved could be well modelled separately. Therefore, with the advance of the equations, all the model coefficients can be determined dynamically. Differing from the classic dynamic procedure, the new methodology needs no test filtering, and thus it could also be called a quasi-dynamic procedure. Using direct numerical simulation of compressible turbulent channel flow, the $a$ $priori$ test shows that the key modelled quantities of the suggested model display high correlations with the real values. In LES of compressible turbulent channel flows of the Mach number being $1.5$ and 3.0, the proposed model can precisely predict some important quantities, including the mean velocity, Reynolds stress and turbulent flux, and it can also supply more abundant turbulent structures. For the compressible flat-plate boundary layer, the new model can correctly predict the transition process, mean velocity and turbulence intensities in the turbulent region. The results show that the proposed model has the advantage of scale adaptivity. Finally, the new model is applied to LES of turbulent mixing in spherical converging Richtmyer–Meshkov instability, and the accurate results show that the new model has a good ability for LES of complex fluids.
A Visualized Knowledge Map of Enterprise Social Capital in the Context of Incubation Networks

Purpose – This study aims to provide a systematic knowledge map for researchers who work in the field of enterprise social capital. Additionally, the aim is to help them quickly understand the trending research topics, hot spots and evolutionary trends from the context of incubation networks. Design/methodology/approach – The authors searched for 874 articles on corporate social capital from the Web of Science database source journals from 1986 to 2021. Then, CITESPACE and VOSviewer software are used to extract the fields of the literature and perform data visualization. Findings – The results show that the number of corporate social capital research papers under the incubation network increases year by year. At present, the research hotspots include social capital, corporate social responsibility, corporate performance, innovation, corporate management and governance, network and strategy. Research will be paid more attention to corporate social responsibility, social capital promotion of new enterprises under incubator network, and social capital accumulation of family enterprises. Incubation network provides an integrated platform for new enterprises to obtain external social capital, and strengthens the social capital of enterprises through cooperative relations. Originality/value – This paper adopts bibliometrics method to mine data and draws knowledge map to systematically reveal research progress and trend.
Impact of similarity measures on clustering mixed data

In many domains, we face heterogeneous data with both numeric and categorical attributes. Clustering such data is challenging because the notion of similarity is not well defined due to the multiple data types. Existing clustering algorithms for these data are mainly based on two strategies: the homogenization one where all attributes are converted to a single type and the mixed one where similarity measures for the different data types are combined to define a similarity measure for heterogeneous data. We propose a framework in which we evaluate and compare several clustering algorithms using these two strategies on many real-world data sets. Then, motivated by the importance of similarity in clustering and the diversity of similarity measures for each data type, we proposed as a second study, to evaluate how their choice affects the performance of clustering algorithms using the mixed strategy. Our results suggest that the mixed strategy is preferable to the homogenization one since it uses adapted similarity measures for the different data types. Furthermore, the choice of similarity measures is very important for most of used mixed methods and an optimal choice may lead to great improvements compared to classically used similarity measures.
Personalized Federated Learning on Non-IID Data via Group-based Meta-learning

Personalized federated learning (PFL) has emerged as a paradigm to provide a personalized model that can fit the local data distribution of each client. One natural choice for PFL is to leverage the fast adaptation capability of meta-learning, where it first obtains a single global model, and each client achieves a personalized model by fine-tuning the global one with its local data. However, existing meta-learning-based approaches implicitly assume that the data distribution among different clients is similar, which may not be applicable due to the property of data heterogeneity in federated learning. In this work, we propose a Group-based Federated Meta-Learning framework, called G-FML, which adaptively divides the clients into groups based on the similarity of their data distribution, and the personalized models are obtained with meta-learning within each group. In particular, we develop a simple yet effective grouping mechanism to adaptively partition the clients into multiple groups. Our mechanism ensures that each group is formed by the clients with similar data distribution such that the group-wise meta-model can achieve “personalization” at large. By doing so, our framework can be generalized to a highly heterogeneous environment. We evaluate the effectiveness of our proposed G-FML framework on three heterogeneous benchmarking datasets. The experimental results show that our framework improves the model accuracy by up to 13.15% relative to the state-of-the-art federated meta-learning.
The Impact of Perceived Customer Participation on Employees' Emotional Labor: The Cross-Level Moderating Effect of Customer Orientation

Based on the job demand-resource theory, it is proposed that employees perceived customer participation as a job demand increases employees' emotional labor, which in turn affects the service output - customer satisfaction. The moderating role of employees' customer orientation as a job resource between perceived customer participation and employees' emotional labor is also considered. A quantitative study of 354 data was conducted to reveal the relationship between them. The results show that perceived customer participation positively affects both deep acting and surface acting. Perceived customer participation positively affects customer satisfaction through the mediation of deep acting and negatively affects customer satisfaction through the mediation of surface acting. Customer orientation has a positive relationship with deep acting and a negative relationship with surface acting, and negatively moderates the positive relationship of perceived customer participation with surface acting.
Data Leakage Mitigation of User-Defined Functions on Secure Personal Data Management Systems

Personal Data Management Systems (PDMSs) arrive at a rapid pace providing individuals with appropriate tools to collect, manage and share their personal data. At the same time, the emergence of Trusted Execution Environments (TEEs) opens new perspectives in solving the critical and conflicting challenge of securing users’ data while enabling a rich ecosystem of data-driven applications. In this paper, we propose a PDMS architecture leveraging TEEs as a basis for security. Unlike existing solutions, our architecture allows for data processing extensiveness through the integration of any user-defined functions, albeit untrusted by the data owner. In this context, we focus on aggregate computations of large sets of database objects and provide a first study to mitigate the very large potential data leakage. We introduce the necessary security building blocks and show that an upper bound on data leakage can be guaranteed to the PDMS user. We then propose practical evaluation strategies ensuring that the potential data leakage remains minimal with a reasonable performance overhead. Finally, we validate our proposal with an Intel SGX-based PDMS implementation on real data sets.
Development of an e-learning teaching and learning platform using game theory - Applications of Sports Games and Real-life Scenarios

Using digital educational technologies in face-to-face classes and online learning can enhance students’ active engagement, interaction and motivation in the aspects of teaching and learning in mathematics. Based on four indicators of the mathematical thinking processes, namely specializing, generalizing, conjecturing and convincing, we build a zero-sum game theory based e-learning platform suitable for analyzing different sports and real-life scenarios. Different types of game strategy solvers are embedded in the online platform. We then discuss significant features of this platform that can enhance higher levels of analysis, synthesis and usage so users can have a better understanding of “what the teacher is teaching” and “what the student is learning”. Through the transition from “knowing-what” to “knowing-why” knowledge in the mathematical thinking process, conceptual and procedural knowledge iterative approaches are used in the e-learning activities to encourage broader math interests in students and strengthen students’ problem solving skills. Results of this study using pre-test and post-test measurements have shown that the users become more familiar with the game theory tools.
Exploring the Non-Visual Acquisition of Targets on Touch Phones and Tablets

Abstract. Touch-based mobile phones and tablets have started to be shipped with built-in accessibility features, fostering the inclusion of disabled people. Particularly, blind people can use these devices resorting to screen reading software commonly along with a painless exploration approach. However, for a large part of the population, a first approach to these devices is still challenging and a future reigned by touch surfaces presents as daunting. The success with such gadgets varies and is highly dependent on the user’s abilities. To ease a blind person’s experience with mobile devices a better understanding of touch interface demands is required. In this paper, we present a study with 41 blind people where they performed low-level target acquisition tasks with different devices. Results revealed that differences in device size and the simple addition of a physical border are likely to deeply affect user performance. These differ-ences should be further explored to foster inclusion.
A Thpok-directed transcriptional circuitry promotes Bcl6 and Maf to orchestrate T follicular helper differentiation.

The generation of high-affinity neutralizing antibodies, the objective of most vaccine strategies, occurs in B cells within germinal centers (GC) and requires rate-limiting ‘help’ from follicular helper CD4 + T (Tfh) cells. Although Tfh differentiation is an attribute of MHC II-restricted CD4 + T cells, the transcription factors driving Tfh differentiation, notably Bcl6, are not restricted to CD4 + T cells. Here we identified a requirement for the CD4 + -specific transcription factor Thpok during Tfh cell differentiation, GC formation and antibody maturation. Thpok promoted Bcl6 expression and bound to a Thpok-responsive region in the first intron of Bcl6. Thpok also promoted the expression of Bcl6-independent genes, including the transcription factor Maf, which cooperated with Bcl6 to mediate the impact of Thpok on Tfh cell differentiation. Our findings identify a transcriptional program that links the CD4 + lineage with Tfh differentiation, a limiting factor for efficient B cell responses, and suggest avenues to optimize vaccine generation. drives of regulators Bcl6 and Maf to of + into T follicular helper (Tfh) cells, thus linking the CD4 + lineage to Tfh cell differentiation. encodes Thpok) revealed that Thpok was required in mature CD4 + T cells for Tfh differentiation and provision of help to B cells in multiple in vivo experimental models of immune responses. Thpok promoted the expression of Bcl6 as well as that of Bcl6-independent genes essential for B cell help, of which one, the transcription factor Maf, cooperated with Bcl6 to mediate the impact of Thpok on Tfh cell differentiation in vivo. Our findings reveal a role for Thpok upstream of Bcl6 in Tfh cells, and connect the genetic program that determines the CD4 + T cell lineage with Tfh differentiation.
NCCTG Phase I Trial N057K of Everolimus (RAD001) and Temozolomide in Combination with Radiation Therapy in Newly Diagnosed Glioblastoma Multiforme Patients 1

Background— The mammalian target of rapamycin (mTOR) functions within the PI3K/Akt signaling pathway as a critical modulator of cell survival. On the basis of promising pre-clinical data, the safety and tolerability of therapy with the mTOR inhibitor RAD001 in combination with radiation (RT) and temozolomide (TMZ) was evaluated in this Phase I study. Methods— All patients received weekly oral RAD001 in combination with standard chemoradiotherapy, followed by RAD001 in combination with standard adjuvant temozolomide. RAD001 was dose escalated in cohorts of 6 patients. DLTs were defined during RAD001 combination therapy with TMZ/RT. Results— Eighteen patients were enrolled with a median follow-up of 8.4 months. Combined therapy was well tolerated at all dose levels with 1 patient on each dose level experiencing a DLT: Grade 3 fatigue, Grade 4 hematologic toxicity, and Grade 4 liver dysfunction. Throughout therapy, there were no Grade 5 events, 3 patients experienced Grade 4 toxicities and 6 patients had Grade 3 toxicities attributable to treatment. On the basis of these results, the recommended Phase II dose currently being tested is RAD001 70 mg/week in combination with standard chemo-radiotherapy. FDG PET scans also were obtained at
Analysis of High-Frequency Dispersion Characteristics of Capacitance and Loss Factor of Aramid Paper Impregnated with Various Dielectric Liquids

This paper presents research results of the loss factor of high voltage insulation samples made of aramid paper impregnated with various types of insulating oil. The analysis was carried out in the high frequency domain in the range of 50 Hz to 1 MHz. The experiments were carried out for three impregnation types: mineral oil, synthetic ester and natural ester. The influence of temperature in the range of 20 °C to 100 °C, which is typical when using this type of insulation in power transformers, was taken into account. In addition, the process of influence of initial aging of aramid paper was simulated by heat soaking the samples before their impregnation at a temperature of 250 °C in multiple time intervals. Based on the analyses of dispersion characteristics of insulation sample loss factors, conclusions and recommendations for further diagnostics of aramid-oil insulations using the method described were delineated.
Exact capacity of downlink NOMA system

Non‐orthogonal multiple access (NOMA) is a key technology beyond 5G/6G wireless and mobile communication systems. Simply because of the high efficiency of resource utilization, the system can meet the ever‐increasing requirements of higher data rates and ultra‐reliability. Keeping that in mind, we seek in this paper to derive simple closed‐form expressions to compute the maximum achievable user rate and formulate the probability density function of the SNR and SINR in the system. In addition to that, we determine the outage probability of the system in a generalized and explicit form. All of that is in terms of the user's deterministic propagation channel parameters, such as power allocation factor, noise level, position, and pathloss coefficient. Simulation results validate the accuracy of the obtained formulae.
About Cryptographic Properties of the Qalqan Encryption Algorithm

Today data confidentiality and privacy are ensured by various encryption algorithms (public key and secret key algorithms). There are no any universal encryption algorithms and different encryption algorithms are implemented to solve different security tasks. Many states have national standards of encryption but Kazakhstan hasn’t it today. This paper presents an approach to the development of the Qalqan block symmetric encryption algorithm, taking into account complex cryptographic and operational requirements, as well as the cryptographic properties of the linear and nonlinear blocks of the algorithm that affect its cryptographic strength.
Method Detection Audit Data Anomalies on Basis Restricted Cauchy Machine

The paper presents a method for the anomalies detection in waste-free production audit data based on the neural network model of Gauss-Bernoulli bidirectional restricted Cauchy machine (BRCM). The purpose of the work is to increase the efficiency of audit data analysis of waste-free production on the basis of the neural network model of anomalies detection without the use of the marked data that simplifies audit. To achieve this goal, the following tasks have been set and solved: offered model of generalized multiple transformations of audit data in the form of a two-layer neural network. Cauchy offered neural network model of Gauss-Bernoulli bidirectional restricted Cauchy machine possesses a heteroassociative memory; works real data; has no restrictions for memory capacity; provide high accuracy of anomalies detection; uses Cauchy's distribution that increases the speed of convergence of a method of parametrical identification. To increase the speed of Gauss-Bernoulli parametric identification of a bidirectional restricted Cauchy machine, a parametric identification method was developed to be implemented on a GPU using CUDA technology. The offered method allows increasing training speed by approximately proportional to the product of numbers of neurons in the hidden layer and power of a training set. The made experiments confirmed the operability of the developed software and allow to recommend it for use in practice in a subsystem of the automated analysis of DSS of audit for detection of anomalies.
Honeypot Security Efficiency versus Deception Solution (short paper)

Deception technology has appeared on the market of information security systems relatively recently. However, some experts still consider Security Deception to be just a more advanced “honeypot. ” In this article, we will try to highlight both the similarities and fundamental differences between these two solutions. In the first part, we will tell you about honeypot, how this technology developed and what are its advantages and disadvantages. And in the second part, we will dwell on the principles of operation of platforms for creating a distributed infrastructure of false targets (DDP)
Resistance to juvenile hormone and an insect growth regulator in Drosophila is associated with an altered cytosolic juvenile hormone-binding protein

The Met mutant of Drosophila melanogaster is highly resistant to juvenile hormone Im (JH III) or its chemical analog, methoprene, an insect growth regulator. Five major mechanisms of insecticide resistance were examined in Met and susceptible Met+ flies. These two strains showed only minor differences when penetration, excretion, tissue sequestration, or metabolism of [3H]JH m was measured. In contrast, when we examined JH III binding by a cytosolic binding protein from a JH target tissue, Met strains had a 10-fold lower binding affmity than did Met+ strains. Studies using deficiency-bearing chromosomes provide strong evidence that the Met locus controls the binding protein characteristics and may encode the protein. These studies indicate that resistance in Met flies results from reduced binding affinity of a cytosolic binding protein for JH III.
A Numerical Study on the Impact of Soil Freezing on the Continental-Scale Seasonal Cycle

The thermal and hydrological impacts of soil freezing on the climate system are studied using an atmospheric general circulation model. The difference between runs with and without soil freezing, i.e., including and excluding the latent heat of fusion and the impermeability of frozen soil, shows that the inclusion of soil freezing leads to higher surface temperature in middle and high latitudes over land in summer. This leads to larger water vapor flux and precipitation in Southeast Asia. The higher temperature over land in summer results from lower evaporation caused by lower surface soil moisture. The low surface soil moisture is caused by additional runoff of snowmelt in spring due to the impermeability of frozen soil and low soil liquid water due to underlying frozen ground. Precipitation is lower in the middle of the Eurasian and North American Continents due to the lower evaporation. The sensitivity of temperature change to soil moisture change is high in regions where potential evaporation is high. In winter, deep soil temperature is higher with soil freezing in frozen ground regions due to the latent heat of freezing, but the surface temperature difference is governed more by atmospheric dynamical responses. The signs of the impacts are found to be the same in one-dimensional experiments with various hydrological schemes and parameters.
A Personalized Prediction Model for Outcomes after Allogeneic Hematopoietic Cell Transplant in Patients with Myelodysplastic Syndromes

Allogeneic hematopoietic stem cell transplantation (HCT) remains the only potentially curative option for myelodysplastic syndromes (MDS). Mortality after HCT is high, with deaths related to relapse or transplant-related complications. Thus, identifying patients who may or may not benefit from HCT is clinically important. We identified 1514 patients with MDS enrolled in the Center for International Blood and Marrow Transplant Research Registry and had their peripheral blood samples sequenced for the presence of 129 commonly mutated genes in myeloid malignancies. A random survival forest algorithm was used to build the model, and the accuracy of the proposed model was assessed by concordance index. The median age of the entire cohort was 59 years. The most commonly mutated genes were ASXL1(20%), TP53 (19%), DNMT3A (15%), and TET2 (12%). The algorithm identified the following variables prior to HCT that impacted overall survival: age, TP53 mutations, absolute neutrophils count, cytogenetics per International Prognostic Scoring System–Revised, Karnofsky performance status, conditioning regimen, donor age, WBC count, hemoglobin, diagnosis of therapy-related MDS, peripheral blast percentage, mutations in RAS pathway, JAK2 mutation, number of mutations/sample, ZRSR2, and CUX1 mutations. Different variables impacted the risk of relapse post-transplant. The new model can provide survival probability at different time points that are specific (personalized) for a given patient based on the clinical and mutational variables that are listed above. The outcomes’ probability at different time points may aid physicians and patients in their decision regarding HCT.