Miracle-dz/UniFAR-FTUs huggingface dataset:
88. JOINT PERCEPTION AFTER HIP OR KNEE REPLACEMENT1 SURGERY

Purpose: The goal of this study is to compare patients’ perception of their hip or knee joint following joint replacement surgery. Method: A total of 357 patients who underwent hip or knee joint surgery were included in the study. Of the patients who had knee replacement surgery, 46 had unicompartmental knee replacement (UKR) and 119 had total knee replacement (TKR). In the group of patients who had hip replacement surgery, 98 underwent hip resurfacing (SRA) and 97 had total hip replacements (THR). The perception patients had of their replaced articulation as well as functional outcome scores such as the WOMAC and SF-36 were measured at one year post-surgery and compared between the four groups. Results: Although global satisfaction and clinical outcome scores were excellent in all four groups, WOMAC scores at 1–2 year follow-up were significantly different between hip or knee replacement surgery (p 2 =0.951). Conclusion: Hip and knee joint replacement surgery are recognized as highly effective medical interventions in terms of cost/benefit ratio in current medical practice. It is remarkable to see that replacement of each of these articulations can yield vastly different results in terms of patient function, perception and satisfaction. From this study, it is clear that research in prosthetic development and surgical techniques should be focused on the interventions such as knee joint replacement, which are not yet capable of offering both a high level of function and satisfaction to patients.
XBPF: An Extensible Breast Cancer Prognosis Framework for Predicting Susceptibility, Recurrence and Survivability

: Breast cancer is the second most lethal type of cancer causing death of woman. As a thumb rule prevention is better than cure. Prevention is possible with life style changes and healthy habits. It is also important to have early detection of it to prevent death. Many researchers contributed towards early detection, prognosis and better treatment of breast cancer in the last two decades causing decline of mortality rate. However, the breast cancer problem is still alarming and needs further research in the area of betterment of detection and prediction besides methods for treating it. Breast cancer prognosis is the holistic approach that covers three important aspects of research including prediction of susceptibility, recurrence and survivability. In this paper we propose an Extensible Breast Cancer Prognosis Framework (XBPF) for breast cancer prognosis which includes susceptibility or risk assessment, recurrence or redevelopment of the cancer after resolution, and survivability. We proposed a representative feature subset selection (RFSS) algorithm that is used along with SVM to improve efficiency in prognosis. SEER dataset is used to have experiments. A prototype is built to demonstrate proof of the concept. Our empirical study revealed that the framework is useful in prognosis of breast cancer instead of focusing on a particular aspect like susceptibility, survivability and recurrence individually. SVM-RFSS has shown significant performance improvement over state of the art prognosis methods.
DWT based Adaptive Image Enhancement with Shrinkage Denoising

: Image enhancement is a pre-processing step to improve visual content or perception of image and provide better input for human or machine perception. In this paper, a new image enhancement algorithm that combines a multiscale image enhancement method with an adaptive enhancement is presented. The multiscale image enhancement method involves definition of a new contrast measure in terms of cumulative distribution function of image. It is implemented in discrete wavelet transform (DWT) domain. The adaptive enhancement is based on modification of image histogram and it provides a maximum contrast improvement. Since enhancement procedure may amplify noise, a noise suppression step is added. The new image enhancement algorithm has several advantages. First, the noise reduction step in this method can prevent noise amplification during enhancement and preserves original image information. Second, the wavelet based DWT can enhance image details in different scale representation of the image. Third, an adaptive enhancement procedure in this method can provide a maximum contrast improvement to image. Experimental results demonstrate that the advantage of proposed enhancement algorithm to achieve better visual quality for the enhanced images.
Linear Regressions of Predicting Rainfall over Kalay Region

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by /4.0) ABSTRACT Regression analysis is a statistical technique for investigating the relationship between variables. In this paper, rainfall and water level prediction models are discussed with the use of empirical statistical technique, Simple Linear Regression and analyzed the development of the predictive power of Linear Regression model to forecast the predicting rainfall and water level over Kalay in Sagaing Region for 10 years (2008-2017). The data of the monthly rainfall and water level used in this study were obtained from Meteorology and Hydrology Department of Kalay, Myanmar. In July 2015, Kalay was affected by the floods. So the rainfall and water level are predicted for next five years in this paper.
Statistical Analysis of C. difficile Infection in a Large Hospital in Genoa, Northern Italy: Questioning the Ward- Based Transmission

Clostridium difficile (CDI) infection is considered a ward-based nosocomial infection, due to contagious diffusion of organism among patients. The hypothesis of inter-human diffusion of C. difficile contradicts well ascertained rules of microbial ecology and host-pathogen interaction, and has been recently questioned by molecular studies. We aimed to question the same hypothesis through a statistical analysis, i.e., multilevel modelling and ecological approaches, and assessed CDI risk in San Martino Hospital of Genoa (2010 2015) according to wards and time of occurrence. Six representative CD strains were ribotyped. The evaluation of 514 CDI cases showed that the risk of disease and rate of incidence in wards were independent, while the frequency of cases and number of wards involved exhibited a positive relationship, excluding the typical epidemic pattern of contagious diffusion, i.e., many cases in few wards. The extra-binomial variability due to ward clustering was not significant, indicating homogeneity in the probability of CDI occurrence across all wards. Three hundred sixty-eight patients changed ward, without showing connection between the frequency of cases in new wards and incidence among new subjects. Thanks also to different CD ribotypes from a same ward, the study excludes CDI occurrence due to inter-human contagion in our institution.
Practical Approach towards Issue on Ergonomic Training With Respect to Productivity Improvement

Productivity is an important indicator of economic growth and social health. It plays a crucial role in describing business opportunities in the society and it can also be vital in identifying key factor that are attributed to how healthy (or unhealthy) the working population is. Human capital plays an important part in the growth of economy. At micro level, it also affects the productivity. The main concern is not the measurement of productivity in traditional way, but to understand the human factors involved in the progress of organizational productivity.
Variable Compression Ratio Diesel Engine Performance Analysis

-Variable Compression Ratio (V.C.R) engine test rig can be used to determine the effect of Compression Ratio (C.R) on the performance and emissions of the engine. The combustion phenomena, when provided with a pressure transducer. The performance frequency parameters like efficiencies, power adopted, and specific fuel consumption are determined. Further, combustion phenomenon is also observed through this work, we can find the optimum compression ratio for which the best performance is possible. In order to find out optimum compression ratio, experiments were carried out on a single cylinder four stroke variable compression ratio diesel engine. Tests were carried out at compression ratios of 16.5, 17.0, 17.5, 18.0 and 19.0 at different loads the performance characteristics of engine like Brake power (BP), Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC). Results show a significant improved performance at a compression ratio 19.0. The compression ratios lesser than 19.0 showed a drop in break thermal efficiency, rise in fuel consumption.
Recognition of Textual Entailment using Composition of Semantic Relation

: The Recognition of textual entailment is a generic approach to ﬁ nd whether a given two sentence infer the same meaning. There are various approaches for recognition of textual entailment but in this article we have chosen semantic based approach because it gives higher level of understanding when compared to the syntactic approaches or other approaches. Among various Semantic approaches extracting Semantic role label use the semantic parser to detect relations in the form of verb argument structure. But the semantic parser ﬁ nds only the basic relation. The observation that combining elementary semantic relations yields more relations is introduced in CSR algorithm[2]. In This work we have applied the CSR algorithm for recognizing the textual entailment and the accuracy is enhanced by pre-processing the T-H pair for Co-Reference resolution. The obtained relation is extended to RTE-4 challenge with the capability of Sense Disambiguation
A Survey on Various Image Fusion Techniuqes

— Fusion of images is an important concept and can be used in a wide variety of applications, especially in the enhancement of images taken from satellites. Although there are various techniques through which images can be fused. Image Fusion is an integration of more than two images so that the invisible or blurred part of the images is visible in the resultant image. Guided filtering is a technique of filtering images so that the fused image is an enhanced image. But the technique implemented for the image fusion is not efficient in terms of fused ratio and the technique is not feasible for all type of images. Here in this paper a complete survey of all the techniques implemented for the image fusion so that by analyzing their advantages and issues an efficient technique is implemented in future for image fusion.
Estrogenic and anxiolytic effects of the decoction of stem bark of anthotheca (Welw.)

Khaya anthotheca dryness in postmenopausal women and is also known to have anxiolytic properties. was designed to evaluate estrogen ovariectomized adult rats, as well as to evaluate its anxiolytic activities in the maze the vaginal epithelia and stimulated the acini differentiation test, the extract increased the percentage of number of entries (p <0.05; p <0.01) and the percentage of time spent (p < 0.05) into open arms. of number of entries (p < 0.05; p < 0. closed arms. The extract also induced an increase of total arms entries (p < 0.05; p < 0.01) and rearing (p < 0.05). Moreover, there was a decrease of defecation and grooming (p < 0.05; p < 0.01). These properties, likely due to the presence of some estrogen
An Approach for Face Recognition System Using Convolutional Neural Network and Extracted Geometric Features

: Faces contain a great deal of valuable information in automatic image processing to extract semantic content for effective analysis and pattern recognition. It requires efficient algorithms that are capable to extract relevant semantic information. Recognizing a person on a photograph tells a lot about the overall content of the picture. The principal aim of facial analysis is to extract valuable information from face images such as position in the image, facial characteristics, facial expressions, the person’s gender or identity. Now we consider the most important approaches to facial image analysis and present novel methods based on convolutional neural networks to detect, normalize, and recognize faces and facial features. In this paper the problems of automatic appearance-based facial analysis with machine learning techniques are described and common specific sub-problem like facial feature detection, analyzing geometric features and face recognition which are crucial parts of many applications in the context of indexation, surveillance, access-control or human machine-interaction are handled. To tackle these problems a new technique called Convolutional Neural Network (CNN) is used which is inspired by biological evidence found in the visual cortex of mammalian brains and can be applied to many different classification problems. The proposed system is a CNN-based method for automatic facial feature detection and extraction that employs a hierarchical procedure which first detects the eyes, the nose and the mouth and then refines the result by identifying 10 different facial feature points. The detection rate is 87% to 96% for different databases tolerating an error of 10% of the inter-ocular distance. Finally a novel face recognition system based on CNN architecture learning a non-linear mapping of the image space into a lower dimensional sub-space where the different classes are more easily separable. This approach produces better result for different facial databases in detection and recognition with classical face recognition approaches using PCA or LDA.Back propagation algorithm is used as learning algorithm for CNN which is a Multi-Layer Perceptrons. A Siamese CNN is used for face verification by receiving two face images and comparing to decide if they belong to the same person or not.
Smart Firewall Integrated Intelligent Transportation System for Security in Ubiquitous Computing

--In the current era, the size as well as the dimensions of traffic is increasing to a huge extend now days. The traffic system throughout the world is making use of technology for tracking, monitoring, congestion control and many other applications. The issue of congestion is very common in big cities where thousands of automobile systems are running on the roads. Vehicular ad hoc networks (VANET) and global positioning systems are widely used for security, integrity and overall performance of the vehicular networks in the big cities. As far as technology is there, the big issue comes about the security, cracking of security codes or jamming the network which is controlling the complete GPS or VANET. Ubiquitous computing is one of the recent technologies that is under phase of implementation under Internet of Things (IoT). In Ubiquitous computing, there is machine to machine communication and this is the main point addressed in this manuscript. In this paper, an effective model for the intelligence transportation system (ITS) is proposed addressing the aspects of security, integrity and performance towards VANET and GPS [3] are pointed out and solved using smart firewall based integration of wireless sensor nodes. In this scenario, each vehicle shall communicate with the local base station and these signals shall be monitored and logged by the satellite as in the wireless sensor networks. In this paper, the integration of Firewall Technology, VANET and GPS is proposed that makes the overall vehicular network infrastructure secured and in high performance.
Simulation and Analysis of conventional based Power System State Estimation using MATLAB

— As the power system grows larger and more complex, real time monitoring and control becomes vital in order to achieve a reliable operation of the power system. Especially, with world wide restructuring of the power industry, power system state estimation has got more importance as a real time monitoring tool. Because of the open access and the operation of the transmission networks, the patterns of power flow in deregulated power system have become less predictable compared to the integrated systems of the past. In order to achieve a more secure and economic operation of such a complicated system, utility operators are to be properly informed of the operating condition of the current power system. Hence real time monitoring of system is an extremely important part of the modern day Energy management system or energy control centers. In this paper, a new fundamental WLS based Static State Estimation technique namely Singular Value Decomposition is used which is far better than WLS based Normal Equation method. Furthermore, comparative analysis of both WLS based techniques has been taken out for various operating situations of the power system. For the verification of both the algorithms IEEE-14 bus test case system is utilized and results are carried out using m.file in MATLAB software.
Facial Recognition System: A Review

— Biometric Recognition has always been the chief aspect for identification and verification, facial recognition among these is an increased use due to its authenticity and mass identification properties. Facial Recognition involves particular choice of features where feature selection involves concluding upon unique ones for better classification and simultaneously provides enhanced discriminatory power. PCA [Principal Component Analysis], works on orthogonal projection basis for recognition with Eigen faces of decreased face space, Independent Component Analysis [ICA] searches for linear transformation. PCA+LDA[Linear Discriminant Analysis] is applied continuously to a smaller and smaller set of samples, better separating classes while the number of classes become small deep down the tree. Application of kernel subspace representations to face recognition, gives us better discrimination. Lesser face space corrupts the software. Obstacles such as illumination and expressional variances along with pose and profile problems prevent 100% accuracy of the system. Most algorithms discussed, solve these problems to a certain extent. The working of the different algorithms followed by their advantages and disadvantages that lead to concluding combination or individual methods best suited.
Safety and Efficacy of Infliximab in Primary Biliary Cirrhosis Associated with Rheumatoid Arthritis

Background: Primary biliary cirrhosis (PBC) is an autoimmune disease. Implication of TNF alpha in its pathogenesis has been suggested. Findings: We describe the case of a 56-year-old female patient with rheumatoid arthritis (RA) treated with antiTNF alpha agents (Infliximab). During this treatment, perturbation of liver tests was noted. Investigations concluded to PBC. Ursodesoxicholic acid was prescribed in association with infliximab. This therapy maintained liver enzymes within the normal range with good response of the RA. Conclusions: This case suggests that TNF alpha blockers may be a good therapeutic alternative when PBC is associated to Rheumatoid Arthritis.
Prognosis Modeling of Nitrous Oxide Emissions After Catalytic Reduction

The present research aims to assess the effect of increasing the thickness of a secondary catalyst layer for N2O emission reduction at a nitric acid plant in Devnya, Bulgaria upon the ambient air quality. A mathematical modeling is done for simulating the dispersion of N2O emissions from the plant into the ground atmospheric layer taking into account the specific topographic and meteorological conditions of the region. Separate graphic models are done illustrating the dispersion of N2O emissions at two main scenarios – at current thickness of the secondary catalyst layer of 60 mm and at future increase of the catalyst layer thickness to 90 mm. Modeling results indicate that under equivalent meteorological conditions the planned increase of the secondary catalyst layer thickness leads to 69 % reduction of the annual average N2O concentration in the atmosphere. Maximum N2O concentrations over specific periods of time (1 hour, 8 hours and 24 hours) are also reduced over 3 times within the outlines of the exposed areas. Research results provide prognosis on the impact of the increased thickness of the secondary catalyst layer as a measure for N2O emission reduction upon the ambient air quality of the source region. Prognosis modeling provides a tool for assessing the contribution of N2O emissions from nitric acid production to the overall greenhouse gas emissions in long-term future periods regarding the implementation of quantitative commitments under the Kyoto Protocol. Index Terms ambient air quality, greenhouse gas emissions, nitrous oxide, prognosis air dispersion modeling, secondary catalyst layer.
A Survey on Hadoop Distributed File System for Cloud Computing Using Rebalancing Algorithm

: The main objective of the paper is to Enhance distributed load rebalancing algorithm to cope with the load imbalance factor, movement cost, and algorithmic overhead. The load rebalance algorithm is compared against a centralized approach in a production system and the performance of the proposal implemented in the Hadoop distributed file system for cloud computing applications. The cloud applications process large amount of data to provide the desired results. Data volumes to be processed by cloud applications are growing much faster than computing power. This growth demands on new strategies for processing and analyzing the information. The paper explores the use of Hadoop Map Reduce framework to execute scientific workflows in the cloud. Cloud computing provides massive clusters for efficient large computation and data analysis. In such file systems, a file is partitioned into a number of file chunks allocated in distinct nodes so that Map Reduce tasks can perform in parallel over the nodes to make resource utilization effective and to improve the response time of the job. In large failure prone cloud environments files and nodes are dynamically created, replaced and added in the system due to which some of the nodes are over loaded while some others are under loaded. It leads to load imbalance in distributed file system. To overcome this load imbalance problem, a fully distributed Load rebalancing algorithm has been implemented, which is dynamic in nature does not consider the previous state or behavior of the system (global knowledge) and it only depends on the present behavior of the system and estimation of load, comparison of load, stability of different system, performance of system, interaction between the nodes, nature of load to be transferred, selection of nodes and network traffic. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature.