Miracle-dz/UniFAR-FTUs huggingface dataset:
Occupational Pattern, Wage Rates and Earning Disparities in India: A Decomposition Analysis

Inequalities in livelihood among socio-economic groups are caused by disparities in job availability, type and sector of employment and earnings there from, and transcend the boundaries of current generation through their impact on capability formation. This paper explores issues of occupational distribution, wage rates, and total earnings in the Indian labour market over the last decade across social classes, regions, gender, and job types. The relative roles played by discrimination during entry and wage setting, and that by endowment in explaining the occupational and earning disparities have been examined through Decomposition technique. The findings suggest increasing disparities in recent times caused by both Discrimination and Endowment gaps.
Uncertainty in Seismic Capacity of Masonry Buildings

Seismic assessment of masonry structures is plagued by both inherent randomness and model uncertainty. The former is referred to as aleatory uncertainty, the latter as epistemic uncertainty because it depends on the knowledge level. Pioneering studies on reinforced concrete buildings have revealed a significant influence of modeling parameters on seismic vulnerability. However, confidence in mechanical properties of existing masonry buildings is much lower than in the case of reinforcing steel and concrete. This paper is aimed at assessing whether and how uncertainty propagates from material properties to seismic capacity of an entire masonry structure. A typical two-story unreinforced masonry building is analyzed. Based on previous statistical characterization of mechanical properties of existing masonry types, the following random variables have been considered in this study: unit weight, uniaxial compressive strength, shear strength at zero confining stress, Young’s modulus, shear modulus, and available ductility in shear. Probability density functions were implemented to generate a significant number of realizations and static pushover analysis of the case-study building was performed for each vector of realizations, load combination and lateral load pattern. Analysis results show a large dispersion in displacement capacity and lower dispersion in spectral acceleration capacity. This can directly affect decision-making because both design and retrofit solutions depend on seismic capacity predictions. Therefore, engineering judgment should always be used when assessing structural safety of existing masonry constructions against design earthquakes, based on a series of seismic analyses under uncertain parameters.
Comparison of IIR Filterbanks and FFT Filterbanks in Cochlear Implant Speech Processing Strategies

The first stage of all cochlear implant speech processing strategies is a filterbank which decomposes the speech signal into several frequency bands. Two types of filterbanks can be used to achieve this goal and mimic the spectral analysis performed within the cochlea. The first is based on the Fast Fourier Transform (FFT). The second type of filterbank is composed of Infinite Impulse Response (IIR) filters. This study aims to compare the performances of the two filterbanks by evaluating the outputs of acoustic models. Therefore, a standard strategy was implemented and tested with four different filterbanks. Butterworth IIR filters were used in the first and the FFT with 128, 256 and 512 points were used in the three other filterbanks. The evaluation of the performances was obtained by considering the word recognition obtained by an Automatic Speech Recognition (ASR) system developed within this work and the accuracy of the pitch coding. The results showed that the word recognition was higher when using the IIR filterbank or the 256-point FFT. Besides, increasing the number of FFT points gave more accurate pitch contours. However, the pitch coding performance was better with the IIR and the 512-FFT filterbanks.
Cell Proliferation and Granule Cell Dispersion in Human Hippocampal Sclerosis

Granule cell dispersion (GCD) is observed in approximately 40% of cases of hippocampal sclerosis (HS) in patients with epilepsy. Studies in animal models suggest that GCD may be a consequence of enhanced proliferation of granule cell precursors as a result of seizures. We quantified the number of cells in cycle in subfields of the hippocampus with immunohistochemistry for Mcm2 in 14 HS cases with or without severe GCD compared to 6 epilepsy patients without classical HS or GCD as well as 5 postmortem controls. Higher numbers of Mcm2-positive cells were seen in the region of the granule cell layer in patients with severe GCD, and immunolabeling with Geminin and Ki-67 confirmed a proportion were progressing through cycle. Double labeling with Mcm2 and GFAP confirmed the majority of these cycling cells were GFAP-negative and occasional cells stained colocalized with stem cell marker nestin. These findings support the view that GCD may be a phenomenon related to increased progenitor cell proliferation in patients with hippocampal damage and chronic epilepsy.
Insights into machine learning:Data clustering and classification algorithms for astrophysical experiments

Data analysis domain dealing with data exploration, clustering and classification is an important problem in many experiments of astrophysics, computer vision, bioinformatics etc. The field of machine learning is increasingly becoming popular for performing these tasks. In this thesis we deal with machine learning models based on unsupervised and supervised learning algorithms. In unsupervised learning category, we deal with Self-Organizing Map (SOM) with new kernel function. The data visualization/exploration and clustering capabilities of SOM are experimented with real world data set problems for finding groups in data (cluster discovery) and visualisation of these clusters. Next we discuss ensembling learning, a specialized technique within the supervised learning field. Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets. They grow multiple learner algorithms and combine them for getting better accuracy results. Generally decision trees learning algorithm is used as base classifiers to grow these ensembles. In this thesis we experiment with Random Forests (RF) and Back-Propagation Neural Networks (BPNN) as base classifiers for making ensembles. Random Forests is a recent development in tree based classifiers and quickly proven to be one of the most important algorithms in the machine learning literature. It has shown robust and improved results of classifications on standard data sets. We experiment the working of the ensembles of random forests on the standard data sets available in University of California Irvine (UCI) data base. We compare the original random forest algorithm with their ensemble counterparts and discuss the results. Finally we deal the problem of image data classification with both supervised (ensemble) learning and unsupervised learning. We apply the algorithms developed in the thesis for this task. These image data are taken from the MAGIC telescope experiment, which collects the images of particle rays coming from the outer universe. We apply the ensembles of RF, BPNN for making a supervised classification of images and compare the performance results. Then we discuss a SOM system, developed for making an automatic classification of images using the unsupervised techniques.
Using Current Data to Define New Approach in Age Related Macular Degeneration: Need to Accelerate Translational Research

Age related macular degeneration (AMD) is one of the major retinal degenerative disease of ageing whose complex genetic basis remains undeciphered. The involvement of various other factors like mitochondrial genes, cytoskeletal proteins and the role of epigenetics has been described in this review. Several population based AMD genetic studies have been carried out worldwide. Despite the increased publication of reports, clinical translation still eludes this davastating disease. We suggest models to address roadblocks in clinical translation hoping that these would be beneficial to drive AMD research towards innovative biomarkers and therapeutics Therefore, addressing the need large autopsy studies and combining it with efficient use of bioinformatic tools, statistical modeling and probing SNP-biomarker association are key to time bound resolution of this disease.
Extracting of Specific Knowledge through RBF-Fuzzy Networks and Genetic Algorithms

This paper proposes the application of Genetic Learning as a procedure for the optimal design and training of neuro-fuzzy systems. Once this learning procedure has been implemented, hybridization between Genetic Algorithms (GA) and the traditional local search technique is carried out to form a Hybrid Algorithm, in order to achieve the maximum possible efficiency in the search, and to be able to exceed the performance of traditional techniques. The proposed neuro-fuzzy model and learning methods have some restrictions in order to find coherent fuzzy rules of the problem to be solved. Effectiveness of the developed techniques when solving the problem of identification of functions will be assessed in comparison to that of habitual training techniques, with the help of many practical tests. Finally, conclusions will be drawn in terms of the test results. Key-Words: Fuzzy Logic, Neural Networks, Genetic Algorithms, Neuro-Fuzzy Models, Radial Basis Functions (RBF), Supervised Learning Algorithm.
Educational attainment, labour market conditions and the timing of births

This paper presents analyses of the effects of women’s education and the rate of aggregate unemployment on birth hazards using data from the 1958 and 1970 British cohort studies. The hazard of first birth was negatively associated with higher levels of education. Once controls for unobservables were included, there was no relationship between education and the hazard of second births. Lagged unemployment was found to be negatively related to first birth hazards but this was only statistically significant among the later cohort, while for higher order births there was evidence of a positive association with unemployment.
Melanocortin receptor 1 and black pigmentation in the Japanese ornamental carp (Cyprinus carpio var. Koi)

Colors and their patterns are fascinating phenotypes with great importance for fitness under natural conditions. For this reason and because pigmentation is associated with diseases, much research was devoted to study the genetics of pigmentation in animals. Considerable contribution to our understanding of color phenotypes was made by studies in domesticated animals that exhibit dazzling variation in color traits. Koi strains, the ornamental variants of the common carp, are a striking example for color variability that was selected by man during a very short period on an evolutionary timescale. Among several pigmentation genes, genetic variation in Melanocrtin receptor 1 was repeatedly associated with dark pigmentation phenotypes in numerous animals. In this study, we cloned Melanocrtin receptor 1 from the common carp. We found that alleles of the gene were not associated with the development of black color in Koi. However, the mRNA expression levels of the gene were higher during dark pigmentation development in larvae and in dark pigmented tissues of adult fish, suggesting that variation in the regulation of the gene is associated with black color in Koi. These regulatory differences are reflected in both the timing of the dark-pigmentation development and the different mode of inheritance of the two black patterns associated with them. Identifying the genetic basis of color and color patterns in Koi will promote the production of this valuable ornamental fish. Furthermore, given the rich variety of colors and patterns, Koi serves as a good model to unravel pigmentation genes and their phenotypic effects and by that to improve our understanding of the genetic basis of colors also in natural populations.
Photometric Observation and Numerical Simulation of Early Superhumps in BC Ursae Majoris during the 2003 Superoutburst

We carried out time-resolved photometric observations of an SU UMa-type dwarf nova, BC UMa, during its superoutburst in 2003 February. The period of early superhumps is 0.06258(13) d and the amplitude is 0.04 mag. After the early superhump phase, common superhumps with an amplitude of 0.3 mag and a period of 0.064466(16) d developed. The change rate of the superhump periods ( P PSH=PSH )( PSH :s uperhump period) was positive throughout the superoutburst. The superhump period excess (" � PSHP � 1 orb � 1 )i s 3% and wederived a mass ratio of 0.13. We have modeled early superhump light curves including irradiation effects of the accretion disk and secondary star. The observed early superhumps can be reproduced when two-armed spirals appear on the accretion disk. The longterm data taken from AAVSO, VSOLJ, and VSNET indicate that BC UMa shows normal outbursts and two types of superoutbursts (short-faint and long-bright). BC UMa is the first example to show such two types of superoutbursts. The supercycle of BC UMa is between 600 d and 1000 d. This suggests that BC UMa is an intermediate type of dwarf novae between WZ Sge and SU UMa.
Chaos communication performance analysis: Taking advantage of statistical theory

This paper seeks to outline and illustrate the statistical basis of performance modelling of chaos-based communication systems. It is argued that likelihood decoding of message bits and theoretical derivation of decoder error probability leads to exact results and enhanced engineering insights. Moreover, the exact results, replacing earlier Gaussian approximations, suggest ways to optimally design such systems. The main emphasis will be on coherent chaos shift-keying communication, both in its single- and multiple-user versions.
An Efficient Intrusion Detection System Based on Fuzzy Genetic Approaches

One of the solutions for the security of the systems and computer networks is the formation of intrusion detection systems. In the design of these systems, the techniques of artificial intelligence such as neural network, data mining techniques, expert systems, genetic algorithms and fuzzy systems are applied. The main aim is the design of the system that besides exact detection is with low error. The main aim of detection in these systems is their analyzer. It seems that if the input of analyzer of the systems is language variables and final detection is done as fuzzy inference, the results of the analysis get better and an exact detection is done. In addition to reduction of error, it can present good information in the form of fuzzy rules to the security expert of the system. Genetic algorithms by flexible and prevalent searching ability can be applied for optimized learning of fuzzy system rule base. The current paper aimed to evaluate, design and implement fuzzy genetic intrusion detection system. The experiments and the results showed the considerable effect of using the proposed architecture on the improvement of detection. 1. Introduction One of the solutions for the security of the systems and computer networks is the formation of intrusion detection systems. These systems are similar to computer buzzer and alarm suspect events and attacks in the environment. The aim of most of the security solutions as firewall encryption and authentication systems of security and avoiding intrusion is making the systems secure and avoiding intrusion. As system security is costly and is not provided completely, we need a system that instead of avoiding attack and event, detect the reason and report. Intrusion detection systems are applied. The prediction of the attacks and tracing the intruder at complex and higher levels is studied. There are different kinds of intrusion detection systems, and are different from different aspects. One of the differences of these systems is their analysis method on input data for detection. The method that system analyzer engine applies as the most important component of the system, the system detection power is determined. In misuse detection systems as a special type of these systems from analysis, definite models of attacks are presented to the system for detection. If the implementation is good in these systems, all the defined attacks in the system are detected as without error. But the existing problem is the increasing …
Complexity and Unwinding for Intransitive Noninterference

The paper considers several definitions of information flow security for intransitive policies from the point of view of the complexity of verifying whether a finite-state system is secure. The results are as follows. Checking (i) P-security (Goguen and Meseguer), (ii) IP-security (Haigh and Young), and (iii) TA-security (van der Meyden) are all in PTIME, while checking TO-security (van der Meyden) is undecidable, as is checking ITO-security (van der Meyden). The most important ingredients in the proofs of the PTIME upper bounds are new characterizations of the respective security notions, which also lead to new unwinding proof techniques that are shown to be sound and complete for these notions of security, and enable the algorithms to return simple counter-examples demonstrating insecurity. Our results for IP-security improve a previous doubly exponential bound of Hadj-Alouane et al.
Impaction grafting of the acetabulum with ceramic bone graft substitute mixed with femoral head allograft: High survivorship in 43 patients with a median follow-up of 7 years

Background and purpose Restoration of bone stock at revision hip surgery remains a challenge. Alternative graft materials with suitable mechanical properties for impaction grafting have been sought due to issues with infection, antigenicity, cost, and availability of allograft. We have previously presented good short-term results of the use of BoneSave, a biphasic porous ceramic bone graft substitute, consisting of sintered 80% tricalcium phosphate and 20% hydroxyapatite, in a 50:50 mix with femoral head allograft. We now present the medium-term results. Methods We conducted a retrospective review of a cohort of 43 consecutive patients undergoing impaction grafting of contained acetabular defects by multiple surgeons at a single center. 34 patients received uncemented acetabular components and 9 received cemented components. Patients were followed up radiographically and with the self-reported satisfaction scale (SAPS), Oxford hip score (OHS), and the Short-Form 12 (SF12) health survey. Kaplan-Meier survivorship analysis was performed with revision of the acetabular component, revision of any part of the construct, and reoperation as endpoints. Results The fate of all cases was known. Median follow-up of the surviving patients was 80 (69–106) months. 15 patients died during the follow-up period, 14 with their construct in situ. The survivorship of the grafted acetabulum and acetabular component was 94% (95% CI: 99–78) at 7 years. 1 patient had been revised for aseptic loosening of the acetabulum and 1 for deep infection. The mean OHS was 31 (SD 12), the mean SF12 physical-component score (PCS) was 38 (SD 13), the median SAPS was 83 (0–100), and the median SF12 mental-component score (MCS) was 55 (23–65). The graft material became incorporated in all 3 zones of the acetabulum in 23 out of 24 cases that had complete radiographic follow-up. Interpretation These medium-term results show that BoneSave is a reliable material for impaction grafting of the acetabulum when used in conjunction with femoral head allograft.
The navigational control of autonomous underwater vehicle (AUV) using color petri nets

This paper addresses the navigational control and execution of motion estimation in the intelligent AUV system using Color Petri Nets simulation. This process is on the bases of the real-time network cycle and pre-planned directions as well as real-time data collection to estimate, decide and control various commands within underwater landmarks and waypoints. The other feature of this approach is its simplicity in the detection and recognition of possible underwater objects within the navigation route of underwater operational activity. The technique makes it attractive from the computational standpoint and guarantees its application to real-time autonomous guidance and the navigation system.
Improvement of the tracking accuracy through the use of the support vector machine

This paper extend the work by Aslam [7] by adding the direction information of the moving objection. The goal is to track the moving object by using binary sensors whose observations indicates whether or not the object is moving toward or moving away form. Aslam's work mentioned the use of the moving direction of the object but did not give the specific detail on how to obtain the moving direction information. In this paper, we propose the technique to extract the moving direction of the observations of binary sensors. Here, the support vector machine(SVM) is employed to estimate the moving direction of the object since we observe that the optimal hyperplane resulting from the SVM is likely to be perpendicular to the moving direction. Because the same observation as proposed by Aslam is used in our approach, no additional communication between sensor nodes are needed. Our results show that, with this simple approach, the modest to high performance improvement can be achieved.
New Rust Disease of Korean Willow (Salix koreensis) Caused by Melampsora yezoensis, Unrecorded Pathogen in Korea

Abstract During the growing season of 2015, leaf specimens with yellow rust spots were collected from Salix koreensis Andersson, known as Korean willow, in riverine areas in Cheonan, Korea. The fungus on S. koreensis was identified as the rust species, Melampsora yezoensis, based on the morphology of urediniospores observed by light and scanning electron microscopy, and the molecular properties of the internal transcribed spacer rDNA region. Pathogenicity tests confirmed that the urediniospores are the causal agent of the rust symptoms on the leaves and young stems of S. koreensis. Here, we report a new rust disease of S. koreensis caused by the rust fungus, M. yezoensis, a previously unrecorded rust pathogen in Korea.
REQUIREMENTS FOR A FAILURE MODE TAXONOMY FOR USE IN CONCEPTUAL DESIGN

In this paper, we present the current state of a failure mode taxonomy, and discuss its potential to explore the failure mode space of aerospace applications. More fundamentally, we discuss what constitutes a good description of a failure that can be detected at the conceptual design stage. Elemental (physics-based) descriptions of failure modes are derived for several domains identified through analyses of historical accident and failure databases for NASA applications. The end product will offer a standard failure mode taxonomy that can be used in conjunction with a functional modeling approach to help detect potential operational failures early in conceptual design