Miracle-dz/UniFAR-FTUs huggingface dataset:
Periodontal Pathogens and Clinical Periodontal Status of School Children: A Cross-Sectional Study

Although gingivitis affects dentate people in all ages it reaches high prevalence levels in children and adolescents. Purpose: This cross-sectional study compared the frequency of target bacterial species and its relation to periodontal status in children. Methods: 254 systemically healthy children, between 6 and 12 years of age, with mixed dentition, having a healthy periodontium or gingivitis were selected. Whole-mouth dichotomous plaque and gingival indices were evaluated and microbial samples were collected from tongue dorsum, first molars, right maxillary and left mandibular incisors. Results: P. gingivalis was the most frequent pathogen in the sulci of periodontally healthy children; T. forsythia and A. actinomycetemcomitans were the less detected species in tongue samples. P. gingivalis was the most frequent pathogen in both teeth and tongue samples among gingivitis children. C. rectus was more frequent in the sulci of healthy children while frequency of P. gingivalis was higher in gingivitis. Conclusions: It can be concluded that P. gingivalis was highly frequent and that C. rectus was more frequent in heathy children. At this range of age clinical status was not always directly related to the presence of the searched pathogens.
Visual Tree Convolutional Neural Network in Image Classification

In image classification, Convolutional Neural Net-work(CNN) models have achieved high performance with the rapid development in deep learning. However, some categories in the image datasets are more difficult to distinguished than others. Improving the classification accuracy on these confused categories is benefit to the overall performance. In this paper, we build a Confusion Visual Tree(CVT) based on the confused semantic level information to identify the confused categories. With the information provided by the CVT, we can lead the CNN training procedure to pay more attention on these confused categories. Therefore, we propose Visual Tree Convolutional Neural Networks(VT-CNN) based on the original deep CNN embedded with our CVT. We evaluate our VT-CNN model on the benchmark datasets CIFAR-10 and CIFAR-100. In our experiments, we build up 3 different VT-CNN models and they obtain improvement over their based CNN models by 1.36%, 0.89% and 0.64%, respectively.
Measurement and analysis of land and water bottom vibrations induced by underwater blasting

In order to investigate the characteristics of the water bottom and land vibration induced by underwater blasting, a water bottom vibrometer was developed and applied to a field measurement test to get actual vibration signals on land and water bottom. The characteristics of the vibration signals were studied in the time domain and frequency domain separately, and the influence of water medium on the land vibrations was specially analyzed by using the frequency order wavelet packets algorithm. It shows that the water bottom vibrometer could collect vibration signals effectively with stable performance. The signal analysis shows that in the same horizontal epic-distance of the same underwater blasting, the water bottom vibration has larger amplitude, higher main frequency and wider frequency band than the land vibration. The land vibration mainly consists of two components that in low frequency caused by direct blasting shock and high frequency caused by water oscillation impact. The water impact vibration components extracted from the land vibration by wavelet packets decomposition algorithm distribute more widely in time domain with earlier peak time than the blasting vibration components, and overlap staggered with each other.
C18 Sep-Pak extractable dissolved organic copper related to hydrochemistry in the North-West Mediterranean

Abstract Extractable organic copper using C18 Sep-Pak cartridges was investigated in seawater after laboratory experiment showed that the (C18 Sep-Pak) cartridges were reliable, in open and coastal waters with normal levels of dissolved organic carbon, for the separation of a specific fraction of organo-copper complexes. Given that the Sep-Pak cartridges retain the hydrophobic fraction of the dissolved organic matter, this extraction technique was applied for studying the characteristics of this particular hydrophobic dissolved organic copper fraction (hDOCu) in the north-western Mediterranean waters. Surface distribution of hDOC is influenced by organic matter input from the river Rhone and its estuary as well as the physical processes affecting the primary productivity such as coastal upwelling. By correlating hDOCu concentrations with total dissolved copper and other hydrochemical data such as salinity and dissolved organic carbon, it was possible to examine the behaviour of hDOCu in the water masses of different sources and ages. Marine organic matter has been shown to have high complexing capacity. Productive superficial and intermediate waters as well as deep waters showed relatively high and comparable complexing capacity indicating that old organic matter may have strong complexing sites.
Physical Therapists' Pursuit of an Academic Doctoral Degree: Strategies for Success

Introduction. Physical therapy programs are challenged to meet the expectations of a blend of academic and clinical doctorally prepared faculty. The purpose of this qualitative phenomenological study is to reveal the specific knowledge and behaviors of physical therapists pursuing an academic doctoral degree that will lead to a successful journey. Methods. An inductive content approach guided data collection and data analysis allowing in-depth exploration of this phenomenon from the participant's perspective. A purposive sampling method was used involving physical therapists with varied experiences with respect to time to completion, program, and institution type in their pursuit of an academic doctoral degree. Results. The doctoral journey is described as facilitated by the individual's ability to (theme 1) create a “good fit” between themselves and the program that they select and (theme 2) use effective strategies to sustain pursuit to completion. Discussion and Conclusion. The lived experience of these participants is consistent with the metaphor of doctoral education being represented as a quest to “become a PhD” rather than a journey to “get a PhD. ” Recommendations emerging from the themes were developed to positively influence the number of physical therapists (PTs) who reach completion, the time to completion, and the quality of the experience. Increasing the number of PhD PTs will help combat the growing physical therapy faculty shortage, as well as increase the potential for the production of physical therapy research.
Interactive comment on “Human amplified changes in precipitation-runoff patterns in large river basins of the Midwestern United States” by

This paper analyses observed changes in streamflow patters in four large basins of the Midwestern Unites States, and investigate their association with changes in climate (precipitation and evapotranspiration) and changes in land-use and land-cover, specifically the increasing cultivation of soybean and corn enhanced by artificial drainage. By analysing 79 years (1935 2013) of precipitation, streamflow, artificial drainage, and cultivation data, the authors provide a comprehensive statistical time series analysis to identify breakpoint years that could show relations between changes in magnitude and trends of the variables. Also, through the application of a simple water budget, the changes in basin storage are associated to changes in climate or in land-cover affecting streamflow response. The study concludes that artificial drainage as part of large
Concept for network intrusion detection system based on recurrent neural network classifier

This paper presents the effects of problem based learning project on a high-school student in Technology school “Electronic systems” associated with Technical University Sofia. The problem is creating an intrusion detection system for Apache HTTP Server with duration 6 months. The intrusion detection system is based on a recurrent neural network classifier namely long-short term memory units.
Hematological values and total protein of Brasileiro de Hipismo and Breton mares during pregnancy

Erythrocyte and leukocyte parameters and total protein (TP) of Brasileiro de Hipismo (BH) and Breton mares were studied during pregnancy. A total of 175 healthy mares, varying from 3 to 13 years old were used. Among the animals, 89 were BH mares (14 non pregnant and 75 pregnant) and 86 Breton mares (11 non pregnant and 75 pregnant). The mares were divided into four groups for each breed: control group (non pregnant mares). group 1 (first period of pregnancy, 25-110 days, n=25), group 2 (second period of pregnancy, 111-210 days, n=25) and group 3 (third period of pregnancy, 211-340 days, n=25). The values for erythrocyte, leukocyte were compared with the obtained total protein. Red blood cells count (RBC), packed cell volume (PCV) and haemoglobin concentration (Hb) increased in the second period of pregnancy and decreased in the last period in both the BH and Breton breed, furthermore these same variables were found to significantly higher in the BH breed for non pregnant and pregnant mares during all three gestational periods. The numbers of total leucocytes and segmented neutrophils increased after the second period in both breeds studied.
The use of interactive wireless keypads for interprofessional learning experiences by undergraduate emergency health students

The purpose of the study was to examine the use of interactive wireless keypads for interprofessional learning experiences. This study used a cross-sectional methodology in the form of a questionnaire to elicit responses about learning experiences using wireless keypads from a convenience sample of first year undergraduate emergency health students over one university semester in 2007. The findings in this study showed that students felt educational technology was helpful and important in their learning, with wireless keypad use providing high satisfaction. Class interaction using wireless keypads has assisted students to better appreciate and understand other health care disciplines within an inter-professional education setting. Integration of educational technology such as wireless keypads are considered important elements in undergraduate health care students learning approaches. Students have appreciated the alternative teaching and learning approach that wireless keypads have offered, thus improving engagement, interactivity, and uniquely, providing a broader understanding of other allied health care professions.
Sustaining and scaling up research-informed professional development for mathematics teachers

Education research journals regularly report on small-scale studies that have been successful in changing mathematics teachers’ classroom practices. But it is rare to find professional development programs for teachers of mathematics that have been scaled up and sustained over time. This article reports on a research-informed, large-scale professional development project initiated by a state education system in Australia that now involves a large number of schools and teachers. The project developed a cluster model for bringing together primary and secondary school teachers to build their curriculum knowledge, confidence, and enthusiasm for teaching mathematics. The study aimed to identify factors that contribute to the sustainability and scaling up of this initiative. Drawing mainly on interviews with 61 participants – teachers, school curriculum leaders, principals, and regional education leaders – the study identified factors related to professional development content, collective action, and leadership at the school and regional levels. The findings contribute to the literature on mathematics education reform and identify implications for supporting teachers and schools in this enterprise.
Shading Fraction based Global Maximum Power Prediction for Photovoltaic Energy Conversion Systems

Maximum power point tracking is the key to optimum utilisation of photovoltaic energy conversion systems. With increasing photovoltaic system installations in dense areas, the panels become prone to partial shading. In the event of partial shading, it becomes all the more important to operate the system at global maximum power point, failing which the system loses high amount of power. If the PV system does not incorporate shade-aware design: long operations under partial shading lead to potential system failure. This work identifies several possible output characteristics of PV panel arrays under different shading conditions along with a method of identifying the global maximum power point in each case. Using this method, given shading fraction on a PV array, many global MPPT algorithms can be developed and implemented. The understanding of panel output characteristics also facilitates the prediction of partial shading and the shade pattern, which prevents hotspot formation and system degradation.
Discussion on the Teaching of Applied Multivariate Statistical Analysis Course in Local Undergraduate College

Applied multivariate statistical analysis is a branch of mathematical statistics and a professional core curriculum for the students majoring in the applied statistics. The method of multivariate statistical analysis is playing an increasingly wide range of applications in the realm of natural sciences and social sciences. It is of great significance to study how to carry out the teaching of the course of multivariate statistical analysis. Basing on the teaching practice of the author, this paper explores the teaching methods of multivariate statistical analysis and puts forward some useful teaching methods in local undergraduate college.
RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation

Semantic image segmentation, which assigns labels in pixel level, plays a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning. However, one central problem of these methods is that deep convolutional neural network gives little consideration to the correlation among pixels. To handle this issue, in this paper, we propose a novel deep neural network named RelationNet, which utilizes CNN and RNN to aggregate context information. Besides, a spatial correlation loss is applied to train RelationNet to align features of spatial pixels belonging to same category. Importantly, since it is expensive to obtain pixel-wise annotations, we exploit a new training method to combine the coarsely and finely labeled data. Experiments show the detailed improvements of each proposal. Experimental results demonstrate the effectiveness of our proposed method to the problem of semantic image segmentation, which obtains state-of-the-art performance on the Cityscapes benchmark and Pascal Context dataset.
Studies on Ameliorative Effects of Morin, Rutin, Quercetin and Vitamin-E against the Doxorubicin-induced Cardiomyopathy

The present study was used to evaluate the effects of naturally occurring antioxidants vitamin-E, morin, rutin and quercetin on the doxorubicin (DOX)-induced cardiomyopathy in a rabbit model. The authors evaluated the ameliorative effects flavonoids and vitamin-E against DOX administration. Thirty New Zealand white rabbits aged between 5-6 months and averaging 2.5-3.0 kg in weight were divided into 5 groups of 6 in each were treated with vitamin E (50 IU/kg body weight) and flavonoids morin, rutin and quercetin (20mg/kg body weight) for four weeks and two doses of doxorubicin (10mg/kg body weight) at the end of 28 days. The flavonoids affected the levels of serum enzymes SGOT, SGPT, ALP, minerals Sodium, Potassium and Phosphorus, oxidative markers catalase (CAT), lipid peroxidation, glutathione-stransferase (GST), reduced glutathione (GSH) both in whole erythrocytes and tissues of liver, heart and kidneys. The study concludes that the flavonoids have a protective role in the abatement of doxorubicininduced cardiomyopathy by regulating the oxidative stress.
Analytic expression for the aerosol mass efficiencies for polydispersed accumulation mode

Aerosol mass efficiencies for extinction, scattering, and absorption are important parameters to understand aerosol optical properties. Although the mass efficiency is functions of the refractive index and particle size distribution, due to the complexity of the efficiency, mass efficiency parameters are usually regarded as a size independent and assumed to depend mainly on the chemical composition of aerosols. In this study, we calculated the mass efficiencies of polydispersed aerosols based on different aerosol types. An analytical approach to the approximated formula of the mass efficiency of each chemical species was developed and evaluated by fitting the results to those of the Mie theory that calculated the optical properties of chemical species based on the refractive index and size. We used the lognormal size distributions and external mixture approximations that represent the polydispersity of aerosol particles. Size ranges of 0.5–2.5 µm in the geometric mean diameter were considered for five different chemical species. The parameters of fitting curves were generalized for polydispersed aerosols as functions of the geometric mean diameter and the geometric standard deviation. The results of the newly developed analytic approach showed a good agreement with those of the Mie theory. The proposed approach provides an effective means to estimate the mass extinction efficiency of polydispersed multi-component aerosols.
Design of a Pipelined and Parameterized VLIW Processor : ρ-VEX v 2 . 0

In this paper, we describe an improved design of a VLIW processor that aims to bridge the gap between application-specific and general-purpose processors. Our discussion focuses on the new design and implementation of the reconfigurable and parameterized VLIW processor called ρ-VEX by introducing pipelining and forwarding logic. Our processor is parameterized in the issue-width, the type and number of functional units, the size of the multi-ported register files, the size of its memories, and in the presence of forwarding logic. It can execute custom assembly operations or run profile-driven compiler optimized code. Our experimental results encompass resource utilization of our designs (including register file optimizations) and execution times on a Virtex-6 FPGA. The execution times are compared to its multi-cycle predecessor and the MicroBlaze for 6 selected benchmarks. The results show that our new design runs 6.64 times faster than the multi-cycle design, and that forwarding provides performance gains between 1.32 and 2.93 over the (unforwarded) implementation. Finally, our initial results demonstrate that our forwarded 4-issue ρ-VEX processor running at 150 MHz, performs between 1.04 and 2.72 times faster than the speed-optimized MicroBlaze processor running at 200 MHz.
THE EARNING LOSSES AFTER MIGRATION IN BRAZIL 1

The effects of migration on wages in the host region depend mostly on the way the abilities distribution of migrants can be compared to the abilities distribution of non-migrant population. A stylized fact of this literature is that migrants are not a random sample from the source region. In this sense, using panel data of the RAIS-Migra from 1995 and 2002, the aim of this paper is to estimate the impacts of migration and assimilation on wage differentials. Specifically, the data allow tracking workers in the labor market among different Brazilian states along the time, with information about wages before and after migration. As a result, we can use the fixed effects method in order to control the self-selection bias. First, we analyses the effects of migration on wages. Second, we examine the economic adjustment of migrants, estimating the effect of assimilation on wages. Then, the main contribution of this study is to evaluate the wage differentials of migrants in the host region after the control of non-observable characteristics. In case of migration in Brazil, São Paulo is the state that absorbs the most part of migrants. Additionally, the flow of workers and the wage differentials among São Paulo and the other Brazilian states as well are important. In this sense, the analysis will focus on the labor market of this state. The most important results attest the presence of omitted bias in OLS regressions as a consequence of migrants’ self-selection. Seemingly, migrants have wage gains relative to non-migrants in the OLS regressions. However, this wage advantage expires when we include the control of nonobservable abilities. The negative coefficient obtained from fixed effects wage regressions shows the wage losses to the worker after migration to São Paulo. Most part of these losses are a result of the higher cost of living in such state. In the assimilation analysis, the evidences show that the period of residence in São Paulo is an important variable to determine the migrant earnings after the inclusion of individual fixed effects. There is a wage convergence after 1.4 years, but the returns growth occurs at decreasing rates in 3 years at most. At least, the gains from out-migration are positive, even after the inclusion of fixed effects. This can be understood since the cost of living in São Paulo is an important factor in the out-migration event. This paper is organized as follows. The next section presents the database used and the summary statistics. The econometric model of the migration impact on wages is presented in section 3. Following, the results of return estimated considering observable and non-observable characteristics, and the assimilation process are presented in section 4. Finally, we present the concluding remarks in section 5.
A Bioeconomic Analysis of the Duration of Conservation Contracts

Conservation and restoration of native vegetation is often a gradual process which may require many years to transform an ecosystem from one vegetative state to a target ecosystem. This process is stochastic, with some changes potentially irreversible. In contrast, contracts with landholders to undertake conservation measures on their property are typically for less than ten years and often make no contingencies for re-contracting at the end of the contract period. The risk to land holders and conservation agencies of contracts not being renewed and the consequent potential loss of previous investment means including covenants in conservation contracts may be attractive to both parties. A model is developed to empirically examine the optimal dynamic conservation contract and the possible role of covenants in the costs and benefits of contracts.
A Deep Graphical Model for Layered Knowledge Transfer

Deep architectures can now be well trained on massive labeled data. However, there exist many application scenarios where labeled data are sparse or absent. Domain adaptation and multi-task transfer learning provide attractive options when related labeled data or tasks are abundant from different domains. In this paper, a new graphical modeling approach to multi-layer factorization based domain adaptation is explored to address the scenarios that insufficient labeled data are available for supervised learning. A deep convolutional factorization based transfer learning (DCFTL) algorithm is proposed to facilitate layer-wise transfer learning between domains. Completely based on graphical model representation, the proposed framework can seamlessly merge inference and learning, and has clear interpretability of conditional independence. The empirical performances on image classification tasks in both supervised and semi-supervised adaptation settings illustrate the effectiveness and generalization of the proposed deep layered knowledge transfer framework.