Miracle-dz/UniFAR-FTUs huggingface dataset:
Archives of Nursing Practice and Care

Objectives: Evaluation of the quality of life for patient with total hip arthroplasty surgery (THA) and whether patients with poor function before THA have the ability to recover less than those with less preoperative disability. Subjects and methods: A prospective study evaluated the preoperative and 3-month postoperative health-related quality of life score of 41 patients with THA due to hip osteoarthritis (OA) and femoral head necrosis (FHN) in Hanoi Medical University Hospital from February 201 to November 2017. The study divided into 2 function groups following the median preoperative Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) physical function scores. Results: Average age is 53.78± 8.95. Male/female ratio is approximately 3: 1. 87.8% patients with femoral head necrosis situation. 100% patients had good health related to quality of life. However, low function group improves more signifi cantly than high function group. In addition, the proportion of normal number patients after 3 months of THA between low and high function group was 20.38%, and 23.53%, respectively for WOMAC physical functioning. Conclusion: Total hip arthroplasty surgery signifi cantly improves patient health and well-being. THA surgery helps patients improve signifi cant quality of life regardless the level of physical functioning before surgery. Research Article The outcomes of postoperative total hip arthroplasty following Western Ontario McMaster Universities Osteoarthritis Index (WOMAC): A prospective study Tran Trung Dung*, Pham Trung Hieu and Nguyen Thi Thuy Dung Hanoi Medical University, Hanoi, Vietnam Received: 12 January, 2018 Accepted: 13 February, 2018 Published: 14 February, 2018 *Corresponding authors: Tran Trung Dung, Associate Professor, MD, PhD, Hanoi Medical University, No. 1, Ton That Tung Street, Hanoi, Vietnam, Tel. : +84-24-238523798; Fax: +86-2438523798; E-mail:
Sales & operations planning: case study on the engineer-to -order production model in the entertainment industry

Goal: This article characterizes the application of the Sales and Operations Planning (S&OP) process in an audiovisual content production industry, whose production model is Engineer-To-Order (ETO), adding important empirical knowledge for S&OP in this production strategy within a unique context. Design / Methodology / Approach: The methodology adopted is based on an exploratory case study. The research adopts a well-known framework to characterize the process observed within the company. Results: The findings characterize the S&OP process based on the main components of the context, inputs, structure and process and outcomes / results, demonstrating that, even with the particularities of the industry, S&OP can be a key process to manage the business complexity and to achieve the objectives by the company. Limitations of the investigation: This study was based on the observations made in only one company, which is a research limitation towards the generalizability of the findings. Thus, for a deeper understanding of the theme, future research is necessary, expanding the observations to other companies. Practical implications: Practitioners can take stock of the S&OP characterization and the lessons learned herein to help to support their S&OP initiatives, especially regarding ETO contexts. Originality / Value: This article sought to reduce the research-practice gap in the literature concerning the need for a complete characterization of the S&OP process from empirical researches and the understanding of S&OP application in the ETO production model.
DISCRIMINATION OF VARIETAL GROUPS AND HYBRIDS OF COFFEA CANEPHORA SPECIES USING MULTIVARIATE ANALYSIS

 ABSTRACT: Coffee growing is one of the most important agricultural activities in the world market. Coffea canephora is one of the most commercially relevant species, which can be divided into the varietal groups Conilon and Robusta. These varietal groups have complementary agronomic interests. Therefore, hybrids are obtained through crosses between these groups. Given the difficulty in differentiating the two varietal groups of genotypes in the field, the correct discrimination is essential for the definition of crosses in breeding programs. In this context, the present work aimed to apply a discriminant analysis (DA) to define functions to differentiate between varietal groups and hybrids of C. canephora, and identify the most relevant phenotypic traits in these functions. Data from 165 genotypes from the Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural e do Centro Agronómico Tropical de Investigación y Enseñanza were used to measure different plant traits. It was applied the quadratic DA with the best performance for genotype discrimination, with an average apparent error rate of 0.0364. Cercosporiose incidence, rust incidence, vegetative vigor, plant height and diameter of the canopy projection were the most important traits in the varietal groups' discrimination.
What drives a textile firm to adopt an eco-label?

This paper investigates the factors that influence a textile firm’s decision to adopt an eco-label. Logit regression is conducted using data from 128 textile firms listed on the Pakistan Stock Exchange. Results reveal that environmental performance, profitability, market share and firm size have significant positive effect on the adoption of eco-labels whereas tax burden has a significant negative effect. Furthermore, an exporting textile firm is more likely to adopt an eco-label than the others. Likewise, a yarn manufacturing firm is more likely to take on an eco-label relative to a fabric or textile composite manufacturing firm.
Linearization of a Coupled tank MIMO system and its validation using MATLAB

The coupled tank set-up is an important element used in process control industries with an objective of controlling levels of liquid in the tank. The interacting coupled tank level control system has been described in this paper is multivariable in nature. The dynamic model of such a system being non-linear requires linearization such that the conventional controllers such as PID controller can be designed. The linearization is carried out using a Taylor series expansion. The extracted linear model along with PI controller is further used to simulate the system performance in Matlab and the validation of the same is carried out through the experimentation on the physical set-up.
Communication and Security Trade-Offs for Battery-Powered Devices: A Case Study on Wearable Medical Sensor Systems

In order to reduce the workload of hospital staff and to provide better services to hospitalized patients, attempts are made to integrate patient monitoring systems directly into hospital networks. Monitoring systems must respond to more and more technological challenges. They are ideally portable and wireless, to free the patient from the hospital bed. At the same time, to enable better patient follow-up, a large amount of information needs to be transmitted and processed in real time. Challenges in the design of such systems include energy-efficient processing and communication, and guaranteeing the security of the measured data. This paper describes a wearable sensor system, integrated into a hospital network, that supports high data rates generated by multiple sensors. With a strongly motivated focus on end-to-end security, we explore trade-offs with respect to security schemes and implementations, and wireless network protocols. The results show that the energy efficiency of the resulting system is comparable to existing systems that support far less sensor data and that compromise on end-to-end security by offloading security operations to a delegation server. To our knowledge, this is the first work that explores the impact of the security scheme and the wireless network protocol on the energy consumption of a wearable device, while providing true end-to-end security.
Design of Wideband Sub-6 GHz 5G MIMO Antenna with Isolation Enhancement Using an MTM-Inspired Resonators

This paper proposes a low-profile wideband multiple-input-multiple-output (MIMO) antenna operated in the frequency of Sub-6 GHz 5G new radio applications. The ENG metamaterial (MTM) structures were used in isolation enhancement. The isolation of the proposed flag-shaped MIMO antenna with MTM achieved high isolation at frequency ranges of 3 - 6 GHz. The lowest transmission coefficient of -40.58 dB at 3.7 GHz. The diversity performances were investigated by the transmission coefficient (isolation), envelope correlation coefficient was less than 0.025 dB, and diversity gain was more than 9.95 dB. Furthermore, the maximum gain was 3.28 dBi at 3.1 GHz. The radiation patterns were a quasi-omnidirectional, the proposed flag-shaped MIMO antenna is suitable for Sub-6Ghz 5G new radio applications.
Audio Based Violent Scene Detection Using Extreme Learning Machine Algorithm

In, this paper we proposed the work which addresses to a methodology for Audio Based Violent Scene Detection by practicing distinct features and classifier using two different datasets. Classifier Extreme Learning Machine (ELM) for Violent Scene Detection (VSD) using Temporal and Frequency domain audio features is demonstrated. As the violent scene detection in videos has realistic importance in a number of applications, such as sensible surveillance, video retrieval, Internet filtering, film rating, toddler protection towards violent behavior and so on. Hence, in digitalize form needs a Violence Detection system. For this study we use, two types of datasets Standardized Media Eval VSD-2014 and other is Customized dataset for the same ELM classifier. After successful training and testing, 85.7% accuracy is achieved by ELM for VSD-2014 dataset and 88.89% for Customized dataset respectively.
Experimental Understanding of the Impact of Channel Percolation on Low Frequency Noise Using Transient Enhanced Diffusion of Channel Dopants

This paper investigates the impact of channel percolation on low frequency noise (LFN). Using transient enhanced diffusion (TED) of channel dopants, the degree of channel percolation was changed. It was demonstrated that LFN in low drain current region was degraded by enhanced channel percolation induced by TED of channel dopants.
FEM - Circuit Model of a BLDC Motor to Study Phase Advance and Dwell Control

This paper presents a Computer-Aided-Design (CAD) of a Brushless DC motor (BLDC) by coupling a Finite Element Method (FEM) based electromagnetic field model with the electric circuit equations of a DC-DC chopper, using COMSOL Multiphysics professional software. The transistors are simulated by means of controlled switches, while free wheel diodes are modeled using COMSOL library. The field-circuit model is used to analyze the real phase current variation at different rotor speeds. Phase advance and dwell control, two common methods mentioned in literature for enhancing speed-torque BLDC characteristic when maximum voltage is imposed, are modeled, and presented in the paper. Compared to other models based on Simulink, phase advance and dwell control are simulated considering motor magnetic saturation and influence of saliency.
Impact of K-NN imputation Technique on Performance of Deep Learning based DFL Algorithm

Deep Learning based device free localization (DFL) technique is gaining momentum due to the contribution of deep model in target location scenario. The performance of the model is not only depending on parameters used but also on the dataset used. If the dataset is an incomplete one the performance of the model is affected unless a suitable preprocessing technique that handles the missing data is chosen. Our work proposes a novel K-NN imputation technique during preprocessing of the data for handling missing data problem and assessing the performance of deep learning based DFL model in target location estimation. The performance of the deep learning model has improved further with K-NN imputation technique. The performance measures used for assessing the impact of K-NN imputation based pre-processing are average accuracy, categorical accuracy and precision.
Open Innovation in SMEs: A Review of Trends

This paper aims to explore the trends of open innovation in SMEs and to suggest the future research. Open innovation is to use inflow and outflow knowledge for expanding the market that is successful in large companies and multinational companies. In recent years, the studies of open innovation in SMEs are getting more attention. This review pursues the direction over almost 20 years based on Scopus database. European countries were examined to outstandingly publish articles the most, followed by Asian countries. 38 articles were selected for content analysis. Firstly, this study found open innovation collaboration with the government is a good choice for SMEs. Secondly, although using open innovation has a positive effect on financial performance, negative results can be found under some conditions. Lastly, absorptive capacity is one of the important factors to be realized for open innovation implementation. The higher capacity absorbs, the higher performance appears.
Improved QPSO with Selected Random Mean for Electromagnetic Problems

A new QPSO (Quantum-behaved Particle Swarm Optimization) with selected random mean is proposed in this paper for solving electromagnetic optimization problems. The selection procedure is a probabilistic mechanism indirectly based on the fitness measure of the solutions, solutions which are candidates for the QPSO mean term. The selection is controlled through a locality search parameter which is adapted during the optimization process to improve the overall performance of the algorithm. To test the performances two benchmark electromagnetic functions are considered, namely TEAM22 and Loney’s solenoid
Influence of Viscosity on Radial Diffusion of Fluids in Paper Substrates

The current increasing interest in portable biosensing platforms led to their development on flexible porous substrates, such as filter paper. As these devices rely on fluid transport under capillary action, without the use of an external force, studies of the diffusion process can provide useful data for optimizing their working capabilities. We present here the results obtained by experimentally studying the radial diffusion of various fluids. The diffusion in filter paper has been recorded for six fluids with different viscosity. For each of them, the displacement of the fluid front has been measured along eight radial directions and then we investigated the possibility of describing this diffusion process using Gillespie’s model.
A Process Model for Microservices Design and Identification

Microservices have been established as an architectural style for developing modern applications. A major challenge in theory and praxis is to identify a adequate granularity of microservices. In order to achieve a consistent and easily understandable process, this paper presents a process model for the design and identification of microservices. This process model should support the development of microservices and the selection of suitable identification approaches. The process model is deductively derived on the basis of existing identification approaches from literature. This serves to classify the approaches into the phases of the presented process model but also to identify research gaps and thus new approaches. The process model is evaluated first by integrating existing identification approaches into the model. Second, a case study shows that the process model allows for multiple perspectives on microservice architecture and can lead to different architecture alternatives. As a result, microservice architecture decisions can be better justified, compared and derived by software architects in a standardized way. We also show that the process model can be integrated into existing software development processes.
A Novel Learning Early-Warning Model Based on Knowledge Points and Question Types

Learning early-warning is of great importance to many educational domains, such as adaptive learning and personalized teaching, and has drawn numerous research attention in recent decades. In order to solve the problem of large prediction granularity in previous study. In this study, we seek to construct two novel features, including knowledge points and question types, and predict students' performance based on the two types of information. According to the predicted results, we divide the early-warning into 3 levels, and provide different levels of guidance and reminders for different warning levels of students. We did experiments based on the two types data of 141 students in Peking University. The result shows that our method has been significantly improved compared with Linear regression, RF and Adaboost. The experiment shows that the model's predicted grades and the real data Pearson correlation coefficient is 0.890568, and the accuracy of predicting warning levels is 85.81%.
Quality Assurance for Automated Commit Message Generation

Many automated commit message generation (CMG) approaches have been proposed for facilitating the understanding of software changes. They are shown to be promising and can generate commit messages that are semantically relevant to the reference messages for a number of commits. However, a large proportion (over 50%) of semantically irrelevant commit messages are also generated simultaneously. Such messages may mislead developers, require additional efforts of developers to confirm and filter out, and hinder the application of existing CMG approaches in practice. For tackling this problem, prior work mainly focuses on proposing new methods to improve the generation accuracy. However, another promising way for bridging the gap between CMG approaches and the practice has not been well investigated, which is: can we automatically assure the semantic relevance of the generated messages? To that end, in this work, we propose an automated Quality A ssurance framework for commit message generation (QAcom). QAcom can assure the quality of generated commit messages by automatically filtering out the semantically-irrelevant generated messages and preserving the semantically-relevant ones as many as possible. In particular, QAcom consists of a Collaborative-Filtering-based (CF) component and a Retrieval-based (RE) component. Given a commit message generated by a CMG approach, QAcom estimates whether this generated message is semantically relevant to its ground truth, which is unknown when estimating, based on both the collaborative filtering algorithm and the similarity between this commit and historical commits. We evaluate the effectiveness of QAcom by "plugging" it in three state-of-the-art CMG approaches. Experimental results on three public datasets show that QAcom can effectively filter out semantically-irrelevant generated messages and preserve semantically-relevant ones.
StuckInSpace: Exploring the Difference Between Two Different Mediums of Play in a Multi-Modal Virtual Reality Game

With the rising popularity of Virtual Reality (VR), there is also a rising interest in co-located multiplayer experiences, as people want to play VR games together with their friends. As having multiple VR headsets is out of reach to the average consumer, we need to look into different possible ways of including multiple people in this play space. We have created a multi-modal co-located multiplayer VR game, Stuck in Space, that introduces a second player in two ways - one with a PC (the baseline that a lot of current games do), as well as a tracked Phone that can be used as a ‘window into the virtual world’. We have conducted a user study (n = 24) where we explore the difference in immersion and co-presence between the two versions using two questionnaires (IPQ and NMMoSP), as well as a thematic analysis of the subsequent interview data, from which 5 themes emerged. Surprisingly, we found no significant difference in co-presence or immersion based on the quantitative data. However, the qualitative analysis helps reveal one of the main reasons why that is - maintaining a mental model of the real world while also being in the virtual world makes it harder for the person wearing the headset to immerse themselves and feel co-present. From these themes and sub-themes we theorize that each of the two versions has positives and negatives that cancel each other out in the quantitative data, and for there to be a difference we would need to accentuate or change certain elements of the game. The results show that introducing a second player through a Phone is not detrimental in terms of co-presence and immersion and that it is a viable way of doing so, although certain design considerations would have to be taken into account to minimize the negatives.
A Study of the Electrical and Mechanical Reliability Properties of Suspended Graphene NEMS Devices for ESD Protection Applications

Recent studies have found that a novel and unique above- I C graphene- based nano-electro- mechanical system (gNEMS) transient switch can be a potential ESD protection. This new ESD switch device is designed by using a suspended graphene membrane structure. In this work, we present experimental investigations and physical insights into the reliability issue and failure mechanism of this graphene ESD device by transient transmission line pulse (TLP) measurement. The mechanical bending evolution and fracture of the graphene membrane via electrostatic actuation assessed with the transient analysis in ESD time scales.
DPSRGAN: Dilation Patch Super-Resolution Generative Adversarial Networks

Single Image Super-Resolution (SISR) has proven itself as a highly challenging and ill-posed problem. Multiple methods have been applied to this problem in the past, with varying degrees of success. Recently, methods using deep learning such as Generative Adversarial Networks (GAN) and Variational Auto-Encoders (VAE) in particular have proven to be extremely effective. However, most of the present methods either create a blurry output, lacking fine details, or use extremely heavy models to achieve better results. We introduce a novel, lightweight GAN architecture for 4× super-resolution of images, which builds on previous methods, showing high quality of features both qualitatively and quantitatively. To achieve this, we use dilated convolutions in our generator architecture, a Markovian discriminator, a modified loss function and a training process more typical of a conditional GAN (cGAN). For testing our results qualitatively, we use Mean Opinion Score (MOS). The obtained MOS show the effectiveness of our model at generating visually superior images. Our code is available at https://www.github. com/kushalchordiya216/Super-Resolution.
Irony Detection in Non-English Tweets

Sentiment analysis is the interpretation and classification of emotions conveyed by text data. While there have been many attempts to classify the sentiment of a given text, there have been few models that can do the same when provided with non-English data exhibiting sarcasm or irony. This paper aims to compare various techniques of sarcasm detection and decide which method works the best for datasets of different sizes and types. The models have been tested on datasets of three different non-English languages - Arabic, French and a Hindi-English code-mix. None of the presented models are language-specific and can be run on data of any language. A comparison between a sub-word model, the usage of Term Frequency–Inverse Document Frequency (TF-IDF) and neural networks, a Long Short-Term Memory (LSTM) model and machine learning techniques such as Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, Naive Bayes (NB), Support Vector Machine (SVM) Linear, SVM radial basis function (RBF), SVM Sigmoid has been performed. The output for each language and model has been evaluated based on their F1-score, accuracy, precision, and recall.