Miracle-dz/UniFAR-FTUs huggingface dataset:
Discrete-Jet Film Cooling: A Comparison of Computational Results With Experiments

Large scale computational analyses have been conducted and results compared with experiments to understand coolant jet and crossflow interaction in discrete–jet film cooling. Detailed three–dimensional elliptic Navier–Stokes solutions, with high order turbulence modeling, are presented for film cooling using a new model enabling simultaneous solution of fully coupled flow in plenum, film–hole, and cross–stream regions. Computations are carried out for the following range of film cooling parameters typically found in gas turbine airfoil applications: single row of jets with a film–hole length–to–diameter ratio of 1.75 and 3.5; blowing ratio from 0.5 up to 2; coolant–to–crossflow density ratio of 2; streamwise injection angle of 35 degrees; and pitch–to–diameter ratio of 3. Comparison of computational solutions with experimental data are in good agreement Moreover, the current results complement experiments and support previous interpretations of measured data and flow visualization. The results also explain important aspects of film cooling, such as the development of complex flow within the film–hole in addition to the well known counterrotating vortex structure in the cross–stream.Copyright © 1993 by ASME
The mediation role of knowledge management between academic management and business education

Abstract The goal of this study is to examine knowledge management’s function as a mediator between academic management and business education. Knowledge management evolved in the last decades as an important component of the organizational management that focuses on intangible resources. In the knowledge economy, knowledge became a strategic resource and it needs a different type of management because it has different economic characteristics by comparison with tangible resources which are represented by physical objects. Knowledge management becomes dominant in the knowledge-intensive organizations, like universities, research centers, consulting companies, and those organizations where knowledge density and knowledge processes intensity determine the organizational performance. From this perspective, it is interesting to analyze the role played by knowledge management in mediating the complex correlation between academic management and business education. Academic management is considered an independent variable and business education the outcome of the whole management of the teaching and learning processes. Knowledge management is the mediator of the whole process. The present research is based on qualitative and quantitative analyses. Qualitative investigation consist in a critical analysis of the literature, and quantitative research is a survey based on a questionnaire addressed to students and professors involved in business education from two Romanian universities. Statistical processing was done with SPSS version 26.0 and the macro PROCESS version 3.5. The findings back up the basic hypothesis and support the research model.
Applying Machine Learning to Carotid Sonographic Features for Recurrent Stroke in Patients With Acute Stroke

Background Although carotid sonographic features have been used as predictors of recurrent stroke, few large-scale studies have explored the use of machine learning analysis of carotid sonographic features for the prediction of recurrent stroke. Methods We retrospectively collected electronic medical records of enrolled patients from the data warehouse of China Medical University Hospital, a tertiary medical center in central Taiwan, from January 2012 to November 2018. We included patients who underwent a documented carotid ultrasound within 30 days of experiencing an acute first stroke during the study period. We classified these participants into two groups: those with non-recurrent stroke (those who has not been diagnosed with acute stroke again during the study period) and those with recurrent stoke (those who has been diagnosed with acute stroke during the study period). A total of 1,235 carotid sonographic parameters were analyzed. Data on the patients' demographic characteristics and comorbidities were also collected. Python 3.7 was used as the programming language, and the scikit-learn toolkit was used to complete the derivation and verification of the machine learning methods. Results In total, 2,411 patients were enrolled in this study, of whom 1,896 and 515 had non-recurrent and recurrent stroke, respectively. After extraction, 43 features of carotid sonography (36 carotid sonographic parameters and seven transcranial color Doppler sonographic parameter) were analyzed. For predicting recurrent stroke, CatBoost achieved the highest area under the curve (0.844, CIs 95% 0.824–0.868), followed by the Light Gradient Boosting Machine (0.832, CIs 95% 0.813–0.851), random forest (0.819, CIs 95% 0.802–0.846), support-vector machine (0.759, CIs 95% 0.739–0.781), logistic regression (0.781, CIs 95% 0.764–0.800), and decision tree (0.735, CIs 95% 0.717–0.755) models. Conclusion When using the CatBoost model, the top three features for predicting recurrent stroke were determined to be the use of anticoagulation medications, the use of NSAID medications, and the resistive index of the left subclavian artery. The CatBoost model demonstrated efficiency and achieved optimal performance in the predictive classification of non-recurrent and recurrent stroke.
The Determinants of Length of Homeless Shelter Stays: Evidence-Based Regression Analyses

Objective: To identify determinants that contribute to the length of homeless shelter stay. Methods: We utilized a unique dataset from the Homeless Management Information Systems from Boston, Massachusetts, United States, which contains 44,197 shelter stays for 17,070 adults between Jan. 2014 and May 2018. Results: Our statistical analyses and regression model analyses show that factors that contribute to the length of a homeless shelter stay include being female, senior, disability, being Hispanic, or being Asian or Black African. A significant fraction of homeless shelter stays (76%) are experienced by individuals with at least one of three disabilities: physical disability, mental health issues, or substance use disorder. Recidivism also contributes to longer homeless shelter stays. Conclusion: The results suggest possible program and policy implications. Several factors that contribute to longer homeless shelter stay, such as gender, age, disability, race, and ethnicity, may have funding implications. Age may point to the need for early interventions. Disability is developmental and may benefit from treatment and intervention. Finally, we find that length of stay and recidivism are not independent, and may form a vicious cycle that requires additional investigation.
Maternal Choline Supplementation and High-Fat Feeding Interact to Influence DNA Methylation in Offspring in a Time-Specific Manner

Maternal methyl donor supplementation during pregnancy has demonstrated lasting influence on offspring DNA methylation. However, it is unknown whether an adverse postnatal environment, such as high-fat (HF) feeding, overrides the influence of prenatal methyl donor supplementation on offspring epigenome. In this study, we examined whether maternal supplementation of choline (CS), a methyl donor, interacts with prenatal and postnatal HF feeding to alter global and site-specific DNA methylation in offspring. We fed wild-type C57BL/6J mouse dams a HF diet with or without CS throughout gestation. After weaning, the offspring were exposed to HF feeding for 6 weeks resembling a continued obesogenic environment. Our results suggest that maternal CS under the HF condition (HFCS) increased global DNA methylation and DNA methyltransferase 1 (Dnmt1) expression in both fetal liver and brain. However, during the postnatal period, HFCS offspring demonstrated lower global DNA methylation and Dnmt1 expression was unaltered in both the liver and visceral adipose tissue. Site-specific DNA methylation analysis during both fetal and postnatal periods demonstrated that HFCS offspring had higher methylation of CpGs in the promoter of Srebf1, a key mediator of de novo lipogenesis. In conclusion, the influence of maternal CS on offspring DNA methylation is specific to HF feeding status during prenatal and postnatal periods. Without continued CS during the postnatal period, global DNA methylation enhanced by prenatal CS in the offspring was overridden by postnatal HF feeding.
The Role of RAB GTPases and Its Potential in Predicting Immunotherapy Response and Prognosis in Colorectal Cancer

Background: Colorectal cancer (CRC) is the third most common cancer worldwide, in which aberrant activation of the RAS signaling pathway appears frequently. RAB proteins (RABs) are the largest Ras small GTPases superfamily that regulates intracellular membrane trafficking pathways. The dysregulation of RABs have been found in various diseases including cancers. Compared with other members of Ras families, the roles of RABs in colorectal cancer are less well understood. Methods: We analyzed the differential expression and clinicopathological association of RABs in CRC using RNA sequencing and genotyping datasets from TCGA samples. Moreover, the biological function of RAB17 and RAB34 were investigated in CRC cell lines and patient samples. Results: Of the 62 RABs we analyzed in CRC, seven (RAB10, RAB11A, RAB15, RAB17, RAB19, RAB20, and RAB25) were significantly upregulated, while six (RAB6B, RAB9B, RAB12, RAB23, RAB31, and RAB34) were significantly downregulated in tumor tissues as compared to normal. We found that the upregulated-RABs, which were highly expressed in metabolic activated CRC subtype (CMS3), are associated with cell cycle related pathways enrichment and positively correlated with the mismatch repair (MMR) genes in CRC, implying their role in regulating cell metabolism and tumor growth. While, high expression of the downregulated-RABs were significantly associated with poor prognostic CRC mesenchymal subtypes (CMS4), immune checkpoint genes, and tumor infiltrating immune cells, indicating their role in predicting prognosis and immunotherapy efficacy. Interestingly, though RAB34 mRNA is downregulated in CRC, its high expression is significantly associated with poor prognosis. In vitro experiments showed that RAB17 overexpression can promote cell proliferation via cell cycle regulation. While, RAB34 overexpression can promote cell migration and invasion and is associated with PD-L1/PD-L2 expression increase in CRC cells. Conclusions: Our study showed that RABs may play important roles in regulating cell cycle and immune-related pathways, therefore might be potential biomarkers in predicting prognosis and immunotherapy response in CRC.
Effects of Low-Calorie Nutrition Claim on Consumption of Packaged Food in China: An Application of the Model of Consumer Behavior

More and more packaged products in China have been labeled as low-calorie products since the official implementation of nutrition claims in 2007. But little was known about the impact of such claims on the Chinese consumption of low-calorie food on the background of increasing rates of obesity among the Chinese population. This study sought to fill the gap by applying a consumer behavior model to a nationally representative online survey by means of structural equation modeling. The findings revealed that nutrition claims significantly affect the consumption of low-calorie products. Specifically, marketing stimulus on low-calorie products first affected consumer psychology, then consumer decision-making, and finally consumer responses. Despite the significant role of consumer psychology and decision-making in consumption, consumers were susceptible to the influence of targeted marketing strategies for foods with a low-calorie claim. It is recommended that appropriate use of low-calorie nutrition claims by manufacturers and choices of low-calorie food by consumers according to their own needs should be encouraged.
A Survey on Assertion-based Hardware Verification

Hardware verification of modern electronic systems has been identified as a major bottleneck due to the increasing complexity and time-to-market constraints. One of the major objectives in hardware verification is to drastically reduce the validation and debug time without sacrificing the design quality. Assertion-based verification is a promising avenue for efficient hardware validation and debug. In this article, we provide a comprehensive survey of recent progress in assertion-based hardware verification. Specifically, we outline how to define assertions using temporal logic to specify expected behaviors in different abstraction levels. Next, we describe state-of-the art approaches for automated generation of assertions. We also discuss test generation techniques for activating assertions to ensure that the generated assertions are valid. Finally, we present both pre-silicon and post-silicon assertion-based validation approaches that utilize simulation, formal methods as well as hybrid techniques. We conclude with a discussion on utilizing assertions for verifying both functional and non-functional requirements.
An Effective Approach for Rumor Detection of Arabic Tweets Using eXtreme Gradient Boosting Method

Twitter is currently one of the most popular microblogging platforms allowing people to post short messages, news, thoughts, and so on. The Twitter user community is growing very fast. It has an average of 328 million active accounts today, making it one of the most common media for getting information during any influential or important event. Because it is freely used by the public, some credibility checking is required, especially when it comes to events of high importance. Automatic rumor detection in Arabic tweets is a challenging task due to the changes in the structural and morphological nature of the Arabic language, which makes the detection of rumors more difficult than in other languages. In this article, we proposed an effective approach for rumor detection of Arabic tweets using an eXtreme gradient boosting (XGBoost) classifier. We conducted a set of experiments on a public dataset that contained a large number of rumor and non-rumor tweets. The model uses a comprehensive set of features, including content-based, user-based, and topic-based features, allowing one to look at credibility from different angles. The experimental results demonstrated that the proposed XGBoost-based approach achieves 97.18% accuracy on 60% of the dataset as a training set, which is the highest accuracy rate compared with the other methods used in recent related work.
A Survey of Techniques for Fulfilling the Time-Bound Requirements of Time-Sensitive IoT Applications

This article surveys existing techniques for meeting the time-bound requirements of time-sensitive applications in the Internet of Things (IoT). To provide the foundation for identifying and classifying relevant techniques, we present three sample time-sensitive IoT applications and their time-bound requirements, describe the main computation and network resources in IoT that can be used to process such applications, and identify the main challenges in meeting their time-bound requirements. Based on these, the article presents a comprehensive literature review of existing techniques and tools that can help meet application-specific time-bound requirements in IoT. The article also includes a gap analysis in existing research outcomes and proposes research directions for bridging the remaining research gaps in supporting time-sensitive IoT applications.
Characterizing ontogeny of quantity discrimination in zebrafish

A sense of non-symbolic numerical magnitudes is widespread in the animal kingdom and has been documented in adult zebrafish. Here, we investigated the ontogeny of this ability using a group size preference (GSP) task in juvenile zebrafish. Fish showed GSP from 21 days post-fertilization and reliably chose the larger group when presented with discriminations of between 1 versus 3, 2 versus 5 and 2 versus 3 conspecifics but not 2 versus 4 conspecifics. When the ratio between the number of conspecifics in each group was maintained at 1 : 2, fish could discriminate between 1 versus 2 individuals and 3 versus 6, but again, not when given a choice between 2 versus 4 individuals. These findings are in agreement with studies in other species, suggesting the systems involved in quantity representation do not operate separately from other cognitive mechanisms. Rather they suggest quantity processing in fishes may be the result of an interplay between attentional, cognitive and memory-related mechanisms as in humans and other animals. Our results emphasize the potential of the use of zebrafish to explore the genetic and neural processes underlying the ontogeny and function of number cognition.
Chicory for milk production

The 1997–98 season has seen a marked increase in the amount of chicory planted on dairy farms in New Zealand. However, no New Zealand data are available for milk production from dairy cows grazing chicory. As part of a larger trial examining the effect of summer forage crops on milk production, Grasslands Puna chicory was compared with Barkant turnips. In January and March 1998, grazing trials were conducted at No.1 Dairy, Dairying Research Corporation using 60 lactating twin cows. In January, chicory and turnips were break-fed at a constant allowance (4 kg DM/cow/ day) to supplement pasture offered at 3 allowances (15, 27.5 and 40 kg DM/cow/day). In March, these crops were fed at 3 levels (0, 4 and 8 kg DM/cow/ day) to supplement pasture offered at a constant allowance of 25 kg DM/cow/day. Chicory and turnips gave similar milksolids (MS) responses of 40–41 g MS/kg DM offered in January. Milksolids responses in March were higher for turnips, 34 g MS/kg DM vs 32 g MS/kg DM from chicory. In January and March the size of the milksolids response declined with increasing allowance. This could be attributed to an increasing amount of substitution at higher allowances. Chicory produced between 8.8 and 13.8 t DM/ha from 4 October to 10 March, offering a high quality summer-autumn forage crop and improved milk production when supplemented with pasture.
Space Vector Modulation Method-based Common Mode Voltage Reduction for Active Impedance-Source T-Type Inverter

In recent years, many pulse width modulation techniques have been explored for three-level impedance-source inverters. Among them, a space vector modulation (SVM) technique using upper/lower shoot-through (UST/LST) insertion provides high voltage gain and satisfactory output voltage quality. This paper further introduces a new SVM control method to reduce the magnitude of common-mode voltage (CMV) without affecting the output voltage quality and voltage gain. With this approach, only small vectors with low magnitudes of CMV are adopted to synthesize an output voltage vector. The UST and LST states are also inserted to these small vectors to boost the DC-link voltage in high voltage gain and high modulation index. The comparison of CMVs between this strategy and other schemes is presented to demonstrate the effectiveness of the proposed method. The simulation and experiments are conducted to verify the accuracy of the theory.
HGposeGUCN: A Lightweight Network for 3D Hand Pose Estimation from a Single RGB Image

3D hand pose estimation from a single RGB image is a challenging task which takes only one RGB image as input and predicts all the hand joints’ 3D positions as output. Some previous works use two or more stages, while some other adopt some kind of model based methods to observe 3D joints positions. These previous works got relatively precise predictions, while they are always short of model weight or limited by the chosen model. In this paper, we propose a lightweight but efficient network named HGposeGUCN-Net for this problem. In our proposed HGposeGUCN-Net, we use a lightweight 2-stack hourglass network to obtain the image feature maps and 21 hand joints heatmaps first. The feature maps and initial 2D positions are fed into a Res2d module to observe the residual 2D coordination which are adopted to update the initial 2D positions got from heatmaps and bring the network more generalization ability. Finally, the refined 2D coordinates are sent into our Hand Graph U-net which can converse the 2D coordinates to 3D. The whole network can be trained end-to-end and on the published STB and RHD datasets, experiments shows that the proposed net structure has better performance.
Day-ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning

To effectively deal with the challenge of optimal dispatch caused by uncertainties such as renewable energy and load in active distribution network, a day-ahead optimal dispatching strategy for active distribution network based on improved deep reinforcement learning is proposed. First, the day-ahead optimal dispatch problem for the active distribution network is modeled as a multistage stochastic programming model. Multistage models attempt to capture the dynamics of unfolding uncertainties over time and adjust decisions dynamically and adaptively. Second, with the help of deep neural network to approximate the power flow model of the active distribution network in a data-driven manner, an improved deep reinforcement learning algorithm is proposed to solve the corresponding multistage stochastic programming problem. The proposed improved deep reinforcement learning algorithm effectively solves the problem that trial-and-error information acquisition methods are difficult to apply to high-cost power systems. Finally, the effectiveness of the proposed day-ahead optimization dispatch strategy for active distribution network based on improved deep reinforcement learning is verified by a modified IEEE33 case.
Circuit-Level Exploration of Ternary Logic Using Memristors and MOSFETs

In recent decades, the performance of binary computers has escalated through transistor scaling. However, due to the impotent forecasts of transistor scaling, ternary systems are regaining attention. Among many ternary device candidates, a passive device called memristor that is based on resistance switching is considered a good candidate when integrated with MOSFETs. Therefore, in this paper, we design various terna-ry logic based on memristors and MOSFETs from primitive logic to sequential logic and perform a thorough diagnosis for circuit design. We highlight design issues that should be resolved (e.g., signal distortion and high static current) and present practical solutions such as “Strength Design. ” Then, we report a proper design methodology of sequential circuits considering the spike phenomena of memristor-based gates. We present 16 novel ternary logic cells and circuitry, including the design of the first balanced ternary full-adder (TFA) and memristor-based ternary pulsed-latch (MTPL). By our TFA, we emphasize that it is possible to design the most practical ternary circuits using memristors and MOSFETs. Our TFA uses 97 transistors and 87 memristors, which is the most reasonable TFA design that has the highest potential to be implemented in the near future. Besides, the proposed MTPL uses 16 transistors and 10 memristors, and it occupies only 72.7% of the silicon area, compared to the master-slave ternary flip-flop.