Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of microRNA-363-3p as an essential regulator of chondrocyte apoptosis in osteoarthritis by targeting NRF1 through the p53-signaling pathway

Osteoarthritis (OA) is a degenerative joint disease that affects the physical, and mental health of middle-aged and elderly people. The aims of the present study were to determine the biological function and molecular mechanisms of miR-363-3p in chondrocyte apoptosis. Exploration of the molecular mechanisms of OA may be helpful in the understand of the causes, and facilitating the prevention and treatment of OA. In the present study, the expression of nuclear respiratory factor1 (NRF1) was downregulated in the articular cartilage of OA rats in vivo and lipopolysaccharide (LPS)-treated chondrocytes in vitro. MicroRNAs (miRNA) are regulators of gene expression in the progression of OA. TargetScan software was used to predict that NRF1 was a potential target for miRNA (miR)-363, and this was confirmed in subsequent experiments. The expression of miR-363-3p was negatively correlated with the expression of NRF1, and its expression was significantly upregulated in OA model rats and in LPS-induced chondrocytes compared with the expression in the respective controls. In addition, the overexpression of miR-363-3p increased the levels of interleukin (IL)-1β, IL-6 and tumor necrosis factor-α in vivo, and was demonstrated to promote chondrocyte injury and apoptosis by Safranin O staining and TUNEL. Moreover, the inhibition of miR-363-3p expression increased the expression of NRF1 and protected chondrocytes from apoptosis in vitro and in vivo, whereas the overexpression of miR-363-3p downregulated NRF1 expression and promoted LPS-induced chondrocyte apoptosis through the p53 pathway in vitro. The results of this study suggested that miR-363-3p-mediated inhibition of NRF1may be associated with chondrocyte apoptosis in OA.
Hepatitis B subviral envelope particles use the COPII machinery for intracellular transport via selective exploitation of Sec24A and Sec23B

Hepatitis B virus (HBV) is a leading cause of liver disease. Its success as a human pathogen is related to the immense production of subviral envelope particles (SVPs) contributing to viral persistence by interfering with immune functions. To explore cellular pathways involved in SVP formation and egress, we investigated host–pathogen interactions. Yeast‐based proteomics revealed Sec24A, a component of the coat protein complex II (COPII), as an interaction partner of the HBV envelope S domain. To understand how HBV co‐opts COPII as a proviral machinery, we studied roles of key Sec proteins in HBV‐expressing liver cells. Silencing of Sar1, Sec23, and Sec24, which promote COPII assembly concomitant with cargo loading, strongly diminished endoplasmic reticulum (ER) envelope export and SVP secretion. By analysing Sec paralog specificities, we unexpectedly found that the HBV envelope is a selective interaction partner of Sec24A and Sec23B whose functions could not be substituted by their related isoforms. In support, we found that HBV replication upregulated Sec24A and Sec23B transcription. Furthermore, HBV encountered the Sec24A/Sec23B complex via an interaction that involved the N‐terminal half of Sec24A and a di‐arginine motif of its S domain, mirroring a novel ER export code. Accordingly, an interference with the COPII/HBV cross‐talk might display a tool to effectively inhibit SVP release.
Identification of Potential Biomarkers of Radiation Exposure in Blood Cells by Capillary Electrophoresis Time-of-Flight Mass Spectrometry

Biodosimetry is a useful method for estimating personal exposure doses to ionizing radiation. Studies have identified metabolites in non-cellular biofluids that can be used as markers in biodosimetry. Levels of metabolites in blood cells may reflect health status or environmental stresses differentially. Here, we report changes in the levels of murine blood cell metabolites following exposure to X-rays in vivo. Levels of blood cell metabolites were measured by capillary electrophoresis time-of-flight mass spectrometry. The levels of 100 metabolites were altered substantially following exposure. We identified 2-aminobutyric acid, 2′-deoxycytidine, and choline as potentially useful markers of radiation exposure and established a potential prediction panel of the exposure dose using stepwise regression. Levels of blood cell metabolites may be useful biomarkers in estimating exposure doses during unexpected radiation incidents.
Changes of Quality of Minimally-Processed Pineapple (Ananas comosus, var. ‘Queen Victoria’) during Cold Storage: Fungi in the Leading Role

Minimally-processed pineapple stored under refrigerated conditions is highly perishable. We aimed to characterize the evolution of physicochemical, sensory and microbiological quality during cold storage. Pineapple batches were sampled from several locations in Reunion Island and then minimally processed. In the processing step, the variability of firmness and counts of yeasts and molds were observed. Moreover, correlations between the sampling season and pH and b* color component, as well as between fungal population and b* parameter were observed. During storage, the visual aspect of pineapple cuts changed to brown and shiny, whereas olfactive descriptors shifted from fruity descriptors and fresh to fermented, alcoholic and milky. The values for pH, TA and TSS did not significantly vary according to storage time. A decrease in firmness and C* color parameter was observed. Yeast and mold counts were significantly higher after 7 days of storage. The diversity in yeasts and molds was mainly dependent on the considered batches observed from PCR-DGGE profiles. Fungal species were isolated from spoiled pineapple cuts. The implication of Penicilllium citrtrinum, Talaromyces amestolkiae, Rhodotorula mucilaginosa, Saccharomyces cerevisiae, and Meyerozyma caribbica in the spoilage of minimally-processed pineapple cuts was further demonstrated.
LSTM: An Image Classification Model Based on Fashion-MNIST Dataset

The application of Neural Network (NN) in image classification has received much attention in recent years. While most previous works focus on the application of Convolutional Neural Network (CNN), this research aims to develop and optimize a Recurrent Neural Network (RNN) model to finish the classification task on FashionMNIST dataset. The model is expected to be both relatively accurate and time-saving. I develop the model with Long-Short Term Memory (LSTM) technique to reduce the risk of gradient vanishing that traditional RNN faces. I use fine tuning and cross validation to optimize the model, and I also test Heuristic Pattern Reduction method and Network Pruning method. The model can achieve a high accuracy at more than 89% with acceptable time consumption. Compared with other models, this result is relatively good, but there is still room for improvement. PyTorch framework is used for experimentation.
Pitfalls of ORION-Based Simulation

ORION is the standard tool for modeling interconnect area and power within the architecture community. Despite its prevalence, a quick survey of academic work shows multiple implementations with energy and area estimates which greatly differ from ORION estimates. In this paper we perform an in-depth analysis of ORION’s area and power modeling in order to locate potential pitfalls and sources of discrepancy. This analysis is performed on a component-wise basis, focusing primarily on the router datapath components of input buffers and crossbar switches. Overall, we observe that due to its generality and chosen circuit implementations, ORION overstates the following parameters: buffer area (5x), buffer power (1.4x), crossbar area (30x). By systematically overestimating the achievable area and power of on-chip routers, ORION overstates system-level interconnect power and the relative gain obtainable from improvements in router microarchitecture.
Textual Description for Mathematical Equations

Reading of mathematical expression or equation in the document images is very challenging due to the large variability of mathematical symbols and expressions. In this paper, we pose reading of mathematical equation as a task of the generation of the textual description which interprets the internal meaning of this equation. Inspired by the natural image captioning problem in computer vision, we present a mathematical equation description ( MED ) model, a novel end-to-end trainable deep neural network based approach that learns to generate a textual description for reading mathematical equation images. Our MED model consists of a convolution neural network as an encoder that extracts features of input mathematical equation images and a recurrent neural network with attention mechanism which generates description related to the input mathematical equation images. Due to the unavailability of mathematical equation image data sets with their textual descriptions, we generate two data sets for experimental purpose. To validate the effectiveness of our MED model, we conduct a real-world experiment to see whether the students are able to write equations by only reading or listening their textual descriptions or not. Experiments conclude that the students are able to write most of the equations correctly by reading their textual descriptions only.
A Hierarchical Feature Fusion-based Method for Defect Recognition with a Small Sample

As one of the breakthroughs in modern manufacturing, deep learning (DL) performs large-scale network architectures and achieves some outstanding performances in vision-based defect recognition. However, most of these large-scale networks require a large sample for training, and a small sample might cause the networks overfitting and collapse. Since the defect often occurs with a low probability, it is costly to collect large-scale samples. To overcome this problem, a hierarchical feature fusion-based method is introduced for defect recognition with a small sample. The proposed method divides a pretrained VGG16 network into different blocks, and learns the hierarchical features from the low- and high- level blocks. The results are better than the other methods. This result manifests the proposed method suits problem, and the defect recognition could be deployed earlier with the proposed method.
A Spatio-Spectral Hybrid Convolutional Architecture for Hyperspectral Document Authentication

Hyperspectral Document Image (HSDI) analysis allows for efficient and accurate differentiation of inks with visually similar color but unique spectral response, which is a crucial step in authentication of documents. Various HSDI based ink discrimination methods are available in the current literature, however, more accurate and robust methods are required to empower document authentication. Contrary to the former ink mismatch detection methods based on spectral features only, we present a novel method based on deep learning that exploits the spectral correlation as well as the spatial context to enhance ink mismatch detection. Spectral responses of the target pixel and its neighboring pixels are organized in an image format and fed to a Convolutional Neural Network (CNN) for classification. The proposed method achieves the highest accuracy among the other ink mismatch detection methods on the UWA Writing Ink Hyperspectral Images database (WIHSI), which demonstrates the effectiveness of deep learning models employing spatio-spectral hybrid features for document authentication. Detailed experimental analysis for selection of appropriate CNN architecture, spatio-spectral data format and training ratio is presented along with a comparison with the previous methods on this subject.
A Revised KDD Procedure for the Modeling of Continuous Production in Powder Processing

In this paper, a revised Knowledge Discovery in Databases (KDD) procedure is proposed, which is designed especially for data mining in powder processing and other types of continuous production. The revised KDD procedure includes data preprocessing, feature engineering, machine learning and model evaluation. The proposed methods are implemented and evaluated using a dataset from a fluidized bed opposed jet mill. The evaluation results show that the machine learning model can accurately predict the product quality in this scenario and capture the internal relations between processing parameters and product quality.
Automatic Synthetic Document Image Generation using Generative Adversarial Networks: Application in Mobile-Captured Document Analysis

In this paper, we propose a method using Generative Adversarial Networks for automatically synthesizing document images that are similar to real printed documents captured by mobile phone's camera in unconstrained environment. We focus on the simulation of image defects for unconstrained mobile image acquisition procedure (non-uniform illumination, defocusing, optical and mechanical deformations, vibrations, noise in electronic components,.). Our approach is proven to be low-cost as it only requires a collection of real document images without any annotation. Experimental results show the effectiveness of our approach to improve OCR (Optical Character Recognition) recognition rate in a mobile-captured document images framework. Although in this paper, we focus on modern printed document images, our proposed approach could be extended to another type of documents, including historical one.
A Reactive GRASP Heuristic Algorithm for Vehicle Routing Problem with Release Date and Due Date Incurring Inventory Holding Cost and Tardiness Cost

In this paper, vehicle routing problems with release dates and due dates (VRPRDD) are considered. Each customer order will incur an inventory holding cost if the order is held back at the depot after its release date and a tardiness cost if the delivery happens after its due date. The mathematical model is proposed and solved using Reactive GRASP heuristics. The results from the heuristics are compared with that of the mathematical model solved by CPLEX. Results of various data sets are used to analyze the effect of cost and due date flexibility on the routes generated. The proposed heuristic method outperforms the MILP solver of CPLEX in terms of quality of solution and the computation time taken. We study the managerial implications and insights subject to flexibility in due dates, and cost structure.
Wafer Map Defect Recognition Based on Deep Transfer Learning

Due to the complexity and dynamics of the semiconductor manufacturing processes, wafer maps will present various defect patterns caused by various process faults. Identification of wafer map defect patterns can help operators in finding out root-causes of abnormal processes, and then ensures that the manufacturing process is restored to the normal state as soon as possible. This paper proposes a wafer map defect recognition (WMDR) model based on integration of deep transfer learning. Our model reduces the training time and improves feature learning performance of DenseNet. In addition, the recognition algorithm based on transfer learning can solve the problem of class imbalance in the WMDR task.
The Assessment of Internal Service Quality Perception of System Administrators - Case of Services Provided by Data Centre Hosting to Local Bank in South Africa

This study assesses the perception of service quality provided by data hosting companies to system administrators at one of South Africa's five major banks in Johannesburg in terms of the difference between the expected quality level and the actual service quality received to suggest recommendations aimed at resolving the issues that would be exposed. The SERVQUAL model was adopted in the survey design to classify key service quality dimensions. With a 100% response rate, an analysis of the gap score was conducted on the data collected. The findings showed that such an exercise has never been conducted at the company over the past five years, and it indicated that the overall quality of service is at its lowest as perceived by system administrators which leads to a constantly decreasing level of customer satisfaction. From the evaluation of all the quality dimensions, none of them met customers satisfaction criteria, with all the dimensions presenting negative gap scores. The study recommends that data hosting centers should implement data center service quality (DCSQ) to successfully meet the quality service expectations of system administrators.
An Approach for Severity Prediction of Autism Using Machine Learning

Effective early diagnosis of autism can have a significant impact on its intervention and treatment. In this paper, an approach is proposed for comprehensively considering genetic factors and environmental factors to predict the severity of autism. According to the Childhood Autism Rating Scale (CARS), a sample set was collected from the autism clinic and a predictive model based on a stacked sparse autoencoder combined with a softmax classifier was constructed. We compared the proposed model with decision trees and support vector machines. Experiments show that the proposed model has a highest accuracy in predicting the severity of autism. Our method can help patients predict their condition and assist doctors in accurate diagnosis.
What are the Sentiments About the Autonomous Delivery Robots?

The technology for autonomous driving is developing rapidly. One application of autonomous driving is the Autonomous Delivery Robots (ADR). The ADR has gained a lot of attention in both the academia and industry. However, most of the extant studies about the ADR focus on the technical side. There is a lack of research on the users' perspective about this new technology. This study applies topic modeling and sentiment analyses to summarize the comments in Weibo about the ADR. The results show that there are both positive and negative sentiments regarding the ADR. The contribution of this study is that the results can help researchers understand the users' concerns about ADR. The negative sentiments can provide guidance to the ADR developers to improve the design of the robots and to allay the users' concerns.
Fast Text/non-Text Image Classification with Knowledge Distillation

How to efficiently judge whether a natural image contains texts or not is an important problem. Since text detection and recognition algorithms are usually time-consuming, and it is unnecessary to run them on images that do not contain any texts. In this paper, we investigate this problem from two perspectives: the speed and the accuracy. First, to achieve high speed for efficient filtering large number of images especially on CPU, we propose using small and shallow convolutional neural network, where the features from different layers are adaptively pooled into certain sizes to overcome difficulties caused by multiple scales and various locations. Although this can achieve high speed but its accuracy is not satisfactory due to limited capacity of small network. Therefore, our second contribution is using the knowledge distillation to improve the accuracy of the small network, by constructing a larger and deeper neural network as teacher network to instruct the learning process of the small network. With the above two strategies, we can achieve both high speed and high accuracy for filtering scene text images. Experimental results on a benchmark dataset have shown the effectiveness of our method: the teacher network yields state-of-the-art performance, and the distilled small network achieves high performance while maintaining high speed which is 176 times faster on CPU and 3.8 times faster on GPU than a compared benchmark method.
Which Is the Priority for the Public while Adopting Energy-saving Facilities? An Analysis of Association between Acceptance and Attitudes toward Using Energy-saving Facilities

This paper aims to explore the consistency between conservation cognition and behavior toward adopting energy-saving facilities by establishing a canonical correlation model between acceptance and attitudes toward using energy-saving facilities with conducting a questionnaire based on the ABC (Affect, Behavior, and Cognition) model. The results show that the proposed model has a good model fitness and there is a high canonical correlation coefficient between acceptance and attitudes while people using energy-saving facilities. Besides, for the acceptance of energy-saving facilities, affect factor is the priority for the public. According to the results, we suggest that affect appeal should be given priority while promoting energy conservation. That is while planning an energy-saving system, designing an energy-saving facility, developing a set of energy-saving education courses, and promoting energy-saving activities, considering affect factor will be more popular.
Performance Gap Between Valid and Invalid Patents in Six Technology Fields

In past studies, it has often been assumed that valid patents are related to the quality of patents. However, whether the quality of valid patents is higher than that of invalid patents is yet to be verified. This study uses three indicators, namely the citations per patent, science linkage, and patent breadth, to explore whether a performance gap exists between valid and invalid patents. First, valid and invalid patents from the whole United States Patent and Trademark Office data are analyzed. Then, we further examine whether a performance gap exists between valid and invalid patents in six technical fields. The results indicate that a performance gap exists between valid and invalid patents. The detailed reasons for this gap should be studied in the future.
Are We Ready for the Agenda 2030 for Sustainable Development?

Are we as educators of future engineers ready for the United Nations Agenda 2030 for sustainable development? We make a comparative study of the Master's programme in Industrial Engineering and Management at two Swedish Universities. Our conclusion is that we as educators and programme managers are not yet in the right process for Agenda 2030 and the necessary transition into a sustainable future.
Cyber Physical Production Systems: A Review of Design and Implementation Approaches

Cyber Physical System (CPS) is a very crucial and promising technology in Industry 4.0 context. The application of CPS in the production and manufacturing environment gave rise to the term Cyber Physical Production Systems (CPPSs). CPPSs hold great potential to make production systems become intelligent, resilient and self-adaptive by utilizing the cyber world to realize the distributed collaboration in the physical world. There is growing interest in CPPSs, yet there is a scarcity of review to document the current status of CPPSs. This review aims to classify the current research activities within CPPSs field with a special focus on design and implementation approaches in view of industrial engineering and to analyze research gaps based on the literature review. Findings of this review can be used as the basis for future research in CPPSs and related topics.
A Mathematical Programming Model for the Green Mixed Fleet Vehicle Routing Problem with Realistic Energy Consumption and Partial Recharges

A green mixed fleet vehicle routing with realistic energy consumption and partial recharges problem (GMFVRP-REC-PR) is addressed in this paper. This problem involves a fixed number of electric vehicles and internal combustion vehicles to serve a set of customers. The realistic energy consumption which depends on several variables is utilized to calculate the electricity consumption of an electric vehicle and fuel consumption of an internal combustion vehicle. Partial recharging policy is included into the problem to represent the real life scenario. The objective of this problem is to minimize the total travelled distance and the total emission produced by internal combustion vehicles. This is a new variant of problem which is developed from a mixed fleet of electric and internal combustion vehicles, full recharging policy, and operational cost minimization. A mixed integer programming model is then developed to address this problem and commercial software is utilized to solve the model.