Miracle-dz/UniFAR-FTUs huggingface dataset:
Mine gearbox fault diagnosis based on multiwavelets and maximum correlated kurtosis deconvolution

As the mine gearbox working conditions are poor, the fault signal is weak and usually drowned in background noise when gearbox occurring fault. The fault feature is very difficult to extract. Aiming at solving this problem, this paper proposed a mine gearbox fault feature extraction method which combines multiwavelets decomposition with maximum correlated kurtosis deconvolution (MCKD). The component of multiwavelets decomposing was processed by MCKD method, MCKD suppress the noise in the signal and enhance the weak impact feature of fault signal, the envelope of its deconvolution signal was calculated, then the fault could be judged by analyzing the prominent frequency component of envelope spectrum. Thus, the experiment analysis and engineering application verify the effectiveness of the proposed method.
ROLE OF VITAMIN D IN PREVENTION OF ACUTE EXACERBATION OF BRONCHIAL ASTHMA IN ADULTS

Objective: To determine role of vitamin D in prevention of acute exacerbation of bronchial asthma in adults when added to standard treatment. Methodology: The study included 80 patients of either gender with age ranging from 16 to 46 years. Bronchial asthma diagnosed for at least 1 year and vitamin D levels below 30 ng/ml, no history of smoking, intake of vitamin D 6 months before trial or other chronic diseases were included in the study. They were randomly divided into two groups by using computer generated random number table, group A and group B and were given combination of standard treatment of bronchial asthma and vitamin D vs standard treatment only respectively. Results: 25(62.5%) were male in group A while 21(52.5%) were male in group B. Mean age of study population was 29.56 ±8.06 years. Vitamin D was found more effective in prevention of exacerbations of bronchial asthma (P value 0.003). Conclusion: Vitamin D was efficacious in bronchial asthma in terms of prevention in number of exacerbations when added to standard treatment.
Molecular phylogeny and plant growth promoting traits of endophytic bacteria isolated from roots of seagrass Cymodocea serrulata

In this study, phylogeny and growth promoting attributes of bacterial endophytes isolated from root tissue of seagrass were investigated. Sequence analysis of 16S rDNA among the isolates displayed the presence of bacterial members affiliated to six different genera: Vibrio, Photobacterium, Bacillus, Aerococcus, Saccharomonospora and Kocuria. Most of the isolates shared high level 16S rDNA sequence similarity with bacteria previously reported from marine water column, crustaceans, corals and terrestrial rhizosphere. Plant growth promoting (PGP) attributes including ability to fix nitrogen, solubilize phosphate and produce ammonia, acetoin and indolic compound were assessed in all the isolates. Genetically diverse, endophytic isolates with different PGP abilities showed close relatedness with bacterial inhabitants of marine ecosystem and likely to have ecological relevance. It implies the need of further studies to validate the relationship between the isolated endophytes and seagrass for sustenance of marine ecology.
PEDAGOGICAL COMPETENCE OF BIOLOGY TEACHERS IN SECONDARY SCHOOL

The present study aims at investigating biology teachers on pedagogical competence in secondary schools in Jember regency. Pedagogical competence as of the teachers’ competence has been mandated by Government Regulation of Indonesia Number 19 Year 2005 Article 28 which states that teacher must possess academic qualification and competence as the learning agent, healthy both physically and mentally, and be able to achieve national education goal. This study is qualitative which employs questionnaire to collect the teachers‘ profiles as the data of the research. The questionnaire consists of 20 items which covers aspects of 1) Preparation (3 indicators items); 2) Instructional Planning (3 indicators of items); 3) Instructional Implementation (4 indicators items); 4) the Use of Instructional Media (3 indicator items); and 5) Conducting Instructional Evaluation (2 indicator items). The study reveals that the teachers’ perception on pedagogical competence is fair (the average score is 3.86). Article visualizations:
Search for correlations between the optical and radio polarization of AGNs II: VLBA polarization data at 12+15+22+24+43 GHz

Previous research showed most BL Lac objects and some quasars have aligned VLBI-core and optical polarizations, although some of the AGNs also showed no obvious relationship between VLBI-core and optical polarization angles. This may indicate that some AGNs have co-spatial regions of optical and radio emission, while others do not. Another possibility is that some of the VLBI cores had Faraday rotations of several tens of thousand of rad/m^2, which were not properly fit using the three-frequency data due to n\pi ambiguities in the observed polarization angles, leading to incorrect subtraction of the effects of the core Faraday rotation, and so incorrect intrinsic radio polarization angles \chi_0. We obtained additional 12+15+22+24+43GHz plus optical observations for 8 of 40 AGNs previously considered. Our results indicate that, although some VLBI radio cores have comparatively high rotation measures, this alone cannot explain the misalignments found between the radio core and optical VLBI polarization angles, \Delta\chi = |\chi_opt - \chi_0|. Comparison between \Delta\chi and (i) the orientation of \chi_0 relative to the jet direction, (ii) the degree of polarization of the core, (iii) a depolarization factor, (iv) the core rotation measures and (v) the core magnetic fields 1pc from the jet base do not yield evidence for any correlations between these properties. There is some evidence that the maximum observed \Delta\chi tends to decrease as the core-region magnetic field increases, suggesting that misalignments in \Delta\chi could be associated in part with relatively low core magnetic fields. Thus, although the overall distribution of \Delta\chi for all 40 sources in our sample does show a significant peak at \Delta\chi\sim0, it remains unclear what distinguishes these AGN cores from those showing appreciable misalignment between optical and VLBI-core polarization position angles.
The Optimum Dataset method – examples of the application

Data reduction is a procedure to decrease the dataset in order to make their analysis more effective and easier. Reduction of the dataset is an issue that requires proper planning, so after reduction it meets all the user’s expectations. Evidently, it is better if the result is an optimal solution in terms of adopted criteria. Within reduction methods, which provide the optimal solution there is the Optimum Dataset method (OptD) proposed by Blaszczak-Bąk (2016). The paper presents the application of this method for different datasets from LiDAR and the possibility of using the method for various purposes of the study. The following reduced datasets were presented: (a) measurement of Sielska street in Olsztyn (Airbrone Laser Scanning data – ALS data), (b) measurement of the bas-relief that is on the building in Gdansk (Terrestrial Laser Scanning data – TLS data), (c) dataset from Biebrza river measurment (TLS data).
TITLE A New Approach to Research on Public School District Performance : The " Employment Relations " Model . EQW

A new model of public school district performance concerns the "employment relations" (ER) system within a school district--the policies, practices, and actions that govern relations between a district and its teachers. The model posits that the performance of public school districts is propelled by the quality of ER. Productive ER, indicated by low levels of administrator-teacher conflict and positive work attitudes held by teachers, has been posited to facilitate high levels of school district performance. Archival data on 180 public school districts in Pennsylvania, cot.pined with survey data from 10,308 teachers from these districts, were used to test the model. Results show that productive ER in school districts is positively associated with the test pass rate and negatively associated with the dropout rate. These findings result after controlling for student socioeconomic status, per-student instructional expenditures, and the adequacy of school facilities. The study has managerial implications for school administrators because it focuses attention on the degree to which administrators effectively manage ER within schools. In conjunction with reform programs fozasing on curricula and school funding, training administrators to manage ER could be a relatively inexpensive component of an overall scheme for improving public education. (Contains 59 references.) (YLB) ******************************A**************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** EQW kik WORKING PAPERS A New Approach to Research on Public School District Performance: The "Employment Relations" Model
Advancing Surface Feature Encoding and Matching for More Accurate 3D Biometric Recognition

Accurate and efficient feature descriptors are crucial for the success of many pattern recognition tasks including human identification. Existing studies have shown that features extracted from 3D depth images are more reliable than those from 2D intensity images because intensity images are generally noisy and sensitive to illumination variation, which is challenging for many real-world applications like biometrics. Recently introduced 3D feature descriptors like Binary Shape and Surface Code have been shown improved effectiveness for 3D palm recognition. However, both methods lack theoretical support for the construction of the feature templates, which limits their matching accuracy and efficiency. In this paper, we further advance the Surface Code method and introduce the Efficient Surface Code, which describes whether a point tends to be concave or convex using only one bit per pixel. Our investigation also reveals that the discriminative abilities of the convex and concave regions are not necessarily equal. For example, line patterns on human palms and finger knuckles are expected to reveal more discriminative information than non-line regions. Therefore, we also propose a weighted similarity method in conjunction with the Efficient Surface Code instead of the traditional Hamming distance adopted in both Binary Shape and Surface Code. Comparative experimental results on both 3D palmprint and 3D finger knuckle databases illustrate superior performance to the aforementioned state-of-the-art methods, which validates our theoretical arguments.
Surfactant-Assisted Synthesis of a Mesoporous Form of Zirconium Phosphate with Acidic Properties

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A Quasi-Three-Dimensional Magnetic Equivalent Circuit Model of a Double-Sided Axial Flux Permanent Magnet Machine Considering Local Saturation

This paper presents a quasi-three-dimensional (3-D) nonlinear magnetic equivalent circuit (MEC) model for double-sided axial flux permanent magnet machines (AFPMMs) with fractional slot concentrated windings. The proposed model can take into account not only the local magnetic saturation effect but also the 3-D flux distribution. A coupled variable permeance element is proposed in airgap modeling that captures both axial and circumferential airgap flux densities. With the help of the coupled variable permeance element, the transient magnetic field is considered by the time varying connection of the airgap and the rotor. The proposed model can be used to predict the performance of AFPMMs, including the 3-D distribution of no-load and armature airgap flux densities, no-load back electromotive force, and electromagnetic torque. In order to verify the reliability of the MEC model, two AFPMM prototypes are taken as examples, and the accuracy is verified by comparing the calculated parameters of the MEC model with the finite element analysis and experimental results. Finally, the effect of local saturation on the performances of AFPMMs is investigated.
LucID: A Multimedia Educational Tool for Identification and Diagnostics

LucID is a multimedia expert system designed specifically to help users make a correct identification of a biological specimen or to correctly diagnose a particular problem. The program comes in two parts, a builder used in the creation of keys, and a player that enables the user to identify specimens or to diagnose a problem using the key. A major feature of LucID is its ease of use. This and other features of LucID are described and a number of examples are provided of how LucID is being used for identification and diagnosis, and to access information relevant to the item that is keyed-out. The role LucID is playing in recent decision support and training systems is outlined. Future possibilities for LucID are explored, including the role it can play in distance education, in training users to make accurate observations, and in teaching the logical processes involved in creating keys.
Face Alignment Based on Two-Stage Localization

The traditional cascade regression model could easily drop into local optimum when the initial localization is far from real shape.This paper proposesa face alignment scheme based ontwo-stage localization. In theface detection stage, the multi-task CNN is adopted to get the information oflandmarks and poseas the initial localization for second stage. In the second stage, the cascade regression based on random forest is implemented for face alignment, and the localization from first stage is used as the initial coarse localization for regression model to solve the problem of initialization sensitivity. The scheme can achieve excellent performance even in the case of large pose variations and occlusion. Compared with the traditional method, experiments show that our algorithm can get state-of-art resultsin real-time on benchmark of 300W dataset.
Multi neural network model for product recognition and labelling

Deep Convolutional Networks have demonstrated very good performance on classifying the ImageNet dataset. This work introduces a deep learning model with a unique architecture that performs classification on a custom dataset built from scratch. We report an architecture that consists of a network of classifiers, which incorporates the state of the art feedforward network architecture. Our system is built by placing a master classifier that forwards the image to 3 of the 10 classifiers that in turn perform the classification. We have trained the system for 131 different classes comprising of 1920 images of commonly found grocery products and commodities. On testing we received a top-1 error rate of 38.62 percentage, top-3 error rate of 25.36 percentage and a top-5 error rate of 20.28 percentage. Each of the 10 classifiers have been trained independently on a subset of the 131 classes. The master classifier is trained on the combined dataset. We use Transfer Learning and retrain the Inception v3 model for each classifier.
An Improved Stacked Auto-Encoder for Network Traffic Flow Classification

Network flow classification plays a very important role in various network applications and is a fundamental task in network flow control. However, the innovations in the multi-source network application and the elastic network architecture with the network flows of high volume, velocity, variety, and veracity pose unprecedented challenges on accurate network flow classification. In this article, an improved stacked auto-encoder is proposed to learn the complex relationships over the multi-source network flows by stacking several basic Bayesian auto-encoders. Specifically, to model the uncertainty contained in the network flows, the Bayesian auto-encoder is trained on the objects using the unsupervised learning strategy. Furthermore, the stacked auto-encoder is trained by the back-propagation algorithm using the supervised learning strategy to capture the complex relationships over the network flows. Finally, to assess the performance of the improved model, extensive experiments are conducted on two synthetic datasets based on the representative network flow datasets, that is, MAWI and DARPA 99. The results demonstrate that the improved stacked auto-encoder outperforms the traditional one in terms of classification accuracy.
The Effect of Government Consortia on R&D Productivity of Firms A case study of robot technology in Japan

Summary: This paper aims at evaluating the government-supported R&D consortia in Japan. These programs have already been the object of numerous studies. We provide further empirical evidences by focusing on the case of the robot technology (RT). Despite RT is one of the priorities in the government policy and one of the most promising field of development, it has been surprisingly relatively ignored by economic studies on R&D consortia. By comparison to previous studies, our paper has the following characteristics. First, whereas most of papers are using the number of patents as indicator for the outcomes of the programs, we are using indicators of quality of patents like claims or number of citations. It allows us to provide an estimation of quality adjusted research productivity. Second, we investigate indirectly the impact of the evolution of the design and the organization of the government programs in RT. Third we conduct a comparison between government-led collaboration and privately organized R&D collaboration. Our results are as follows. First, we find that participation by firms to government programs has a positive impact on the quality of their patents. Second, if we divide the sample period into two sub-periods, 1991-1997 and 1998-2004, we find there was no impact during the first sub-period, yet there is a positive impact during the second sub-period. Our interpretation is that this is the consequence of the changing orientation and structure of government programs in this field. Third, if we compare between government-led collaborative patents and private led collaborative patents, we find the significant effect of the former and the non significant effect of the latter.
Simple and low cost incremental conductance maximum power point tracking using buck-boost converter

Cost of the photovoltaic (PV) systems and their peripherals has been always an issue when considering them for various applications. Using an inexpensive maximum power point tracking (MPPT) system is a simple but efficient solution to reduce the cost of the PV systems and increase the public acceptance. This paper presents the simulation and hardware implementation of incremental conductance algorithm using buck-boost converter and PIC18F4520 controller. Design and simulation of the proposed system are presented using matlab and simulink tools. The proposed system is also tested on KC85T PV module for constant and changing weather conditions. Experimental results indicated the capability and functionality of the proposed system on tracking maximum power point with the voltage ripple ratio = 0.006, which is near to the ideal mathematically calculated assessment. The proposed MPPT system helps to reduce the complexity and cost of the PV systems and also ensures the largest operating region of the PV module.
An evaluation of ozone dry deposition in global scale chemistry climate models

Abstract. Dry deposition to the Earth's surface is an important process from both an atmospheric and biospheric perspective. Dry deposition controls the atmospheric abundance of many compounds as well as their input to vegetative surfaces, thus linking the atmosphere and biosphere. In many atmospheric and Earth system models it is represented using "resistance in series" schemes developed in the 1980s. These methods have remained relatively unchanged since their development and do not take into account more recent understanding of the underlying processes that have been gained through field and laboratory based studies. In this study we compare dry deposition of ozone across 15 models which contributed to the TF HTAP model intercomparison to identify where differences occur. We compare modelled dry deposition of ozone to measurements made at a variety of locations in Europe and North America, noting differences of up to a factor of two but no clear systematic bias over the sites examined. We identify a number of measures that are needed to provide a more critical evaluation of dry deposition fluxes and advance model development.
Analysis of Reflectarray Antenna Elements Under Arbitrary Incident Angles and Polarizations Using Generalized Boundary Conditions

A novel analysis method for reflectarray antenna elements is proposed in this letter, which is derived from generalized boundary conditions. First, the effective surface susceptibilities of the patch layer are extracted from two sets of simulations of given incident angles and polarizations. Then, the effective surface impedances of reflectarray antenna elements are analytically computed for arbitrary incident angles and polarizations, from which the reflection coefficients can be readily obtained. The detailed analysis procedure is presented, and its effectiveness is validated through several representative examples of reflectarray antenna elements.
Filtered OFDM with Index Modulation

In this paper, a novel multicarrier modulation scheme called Filtered Orthogonal Frequency Division Multiplexing with Index Modulation has been proposed for operation over frequency-selective and rapidly time-varying wireless fading channels. The filtering helps restricting the Out-of-Band Emission and indexing helps in reducing Transmitter Power. The performance of the proposed modulation scheme is compared with traditional multicarrier and index modulated multicarrier schemes to show that the proposed method is much better in terms of Out-of-Band power leakage. Indexing apparently reduces transmit data rate of the system. This paper also shows how to circumvent this apparent reduction in spectral efficiency without degrading other system parameters.