Miracle-dz/UniFAR-FTUs huggingface dataset:
CAN WE STILL USE CLASSIC SEISMIC HAZARD ANALYSIS FOR STRONG AND DEEP VRANCEA EARTHQUAKES?

Seismic hazard is quantified by three parameters: level of severity, and spatial and temporal measurements. Thus, the purpose of a seismic hazard assessment is to determine these three parameters from instrumental, historical and geological observations. The characterization of earthquake ground motion for engineering applications generally involves the use of empirical models referred to as groundmotion prediction equations (GMPEs). In classic seismic hazard analysis, this GMPE describes a relationship between a ground motion parameter Y (PGA, PGV, MMI) in site, earthquake magnitude M, source-site distance R, and uncertainty or residual δ. Nothing on epicenter ground acceleration (PGA), velocity (PGV), MMI intensity. Selection of a ground motion for engineering design and other considerations requires a clear understanding of seismic hazard among stakeholders, seismologists and engineers in particular. The authors are coming with data in connection to strong and deep Vrancea earthquakes, the large uncertainties of peak ground accelerations (PGA) recorded in epicenter and far of epicenter in extra-Carpathian area after last three strong earthquakes (August 30,1986; MW = 7.1; h = 131.4 km; May 30,1990; MW = = 6.9; h = 90.9 km; May 31,1990; MW = 6.4; h = 86.9 km). There are many PGA records in Romanian and Republic of Moldavia seismic stations. Many of them are larger than in epicenter Vrâncioaia seismic station. Consequently, classic seismic hazard analysis is not applicable at all to strong and deep Vrancea earthquakes.
Biochemical and cytogenetic effects of Imazethapyr on Cicer arietinum L.

Weeds are a serious obstacle to the production of chickpea. Imazethapyr (IM) herbicide is used to control weeds in most of the pulses, including chickpea. Mitotic abnormalities, chromosomal behavior, and protein content in chickpea (Cicer arietinum L.), due to IM treatment, were studied. The chickpea seeds (variety JG-11) were germinated in sterilized Petri dishes, 9 cm in diameter, on Whatman filter paper moistened with 10 ml of either Hoagland nutrient solution (control) or five concentrations of IM (0.5, 1, 2.5, 5, and 10 ppm). From the cytologic point of view, observations demonstrated that the mitotic frequency in root meristematic cells diminished, and that abnormality frequency increased parallel to the increase in concentrations of IM. The herbicide was highly mito-inhibitory and induced chromosomal irregularities, such as stickiness, lagging, scattering, and chromosome bridges. The endosperm and root-shoot axis’ protein content decreased with increasing of the herbicide concentration in all the treatments. It can be argued that IM produces undesirable side effects during mitosis in chickpea’s somatic cells and biochemical parameters.
Identifying parameter regions for multistationarity

Mathematical modelling has become an established tool for studying the dynamics of biological systems. Current applications range from building models that reproduce quantitative data to identifying systems with predefined qualitative features, such as switching behaviour, bistability or oscillations. Mathematically, the latter question amounts to identifying parameter values associated with a given qualitative feature. We introduce a procedure to partition the parameter space of a parameterized system of ordinary differential equations into regions for which the system has a unique or multiple equilibria. The procedure is based on the computation of the Brouwer degree, and it creates a multivariate polynomial with parameter depending coefficients. The signs of the coefficients determine parameter regions with and without multistationarity. A particular strength of the procedure is the avoidance of numerical analysis and parameter sampling. The procedure consists of a number of steps. Each of these steps might be addressed algorithmically using various computer programs and available software, or manually. We demonstrate our procedure on several models of gene transcription and cell signalling, and show that in many cases we obtain a complete partitioning of the parameter space with respect to multistationarity.
Nonlinear weighted median filters in dyadic decomposition of images

There are many techniques of loss image compression based on dyadic decomposition of images. In these decompositions always linear filters are used. In this paper it is proposed to use nonlinear weighted median filters in the place of linear ones and it is shown how median filters may be used in dyadic decomposition. This technique has been compared with the non-standard wavelet decomposition, in particular with the Haar wavelet filtering. The experiments made show that in the loss image compression case using median filters gave better visual quality of the reconstructed images.
2DLDA-based texture recognition in the aspect of objective image quality assessment

The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition. It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression. Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method. The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database-Brodatz Textures. Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy.
Research Project Proposal: Deep Image Denoising

The goal of image restoration is to recover the original, clean image, starting from a corrupted image. Depending on the type of corruption, image restoration tasks can be divided into deblurring, super-resolution, denoising, inpainting, text removal and many others. In particular, image denoising has the goal of estimating the clean image from its observed version corrupted by noise. Modern image denoising lies at the intersection of signal processing, computer science and machine learning. Indeed, recent technological and methodological advances in deep learning have allowed the employment of convolutional neural networks (CNNs) for image restoration purposes. The obvious application for image denoising is to provide to the user a pleasant and clear image by removing as much noise as possible, without losing details. From a technical point of view, a denoised image is an essential prerequisite for more high-level computer vision tasks and complex deep learning pipelines (e.g. autonomous driving). Moreover, denoiser can be plugged in as a modular part of model-based optimization methods to solve other restoration problems. The current state of the art methods have achieved great denoising performance and some of them can produce an estimate reasonably fast. A very large variety of deep learning denoisers were proposed in the literature during the last years suggesting different architectures, approaches, noise models, and even brand new CNN units. In our project we will experiment with new modules or architectures rather than focusing on minor variations and fine tuning of existing models.
International Symposium on Ultrasonics-2015, 22-24 January 2015, Department of Physics, Rashtrasant Tukdoji Maharaj Nagpur University, Nagpur, Maharashtra, India

The focus of paper is a comparative study of the performance (amplification and excitation characteristics) of polaron induced and acoustical phonon induced parametric amplifiers. Based on hydrodynamic model and coupled mode theory, an analytical investigation of both kinds of parametric interaction has been made. Explicit expressions for the gain coefficient and threshold pump field for the onset of the acoustic phonon driven and polaron driven parametric instabilities are derived. We have numerically investigated the dependence of the threshold field on external parameters for a typical polar semiconductor (InSb) at 77 K, irradiated with m μ 6 . 10 CO2 laser. Amplification characteristics are also explored through the estimation of parametric gain. Parametric gain in case of polaron induced parametric interaction is found to be enhanced by a factor of 10 as compared to that of acoustical phonon induced parametric interaction, whereas a close look at threshold pump amplitudes required to incite above said processes reveals that lower pump field is required to achieve polaron induced parametric amplification. External magnetic field and carrier concentration emerged as a control parameter to reduce threshold and improve parametric gain. These results could be important for the clearer understanding and improvement of the performance of parametric amplifiers. It is hoped that a low cost amplifier by using n-InSbCO2 system can be fabricated using the outcome of the present work.
Role of calcium in increasing tolerance of Hyacinth bean to salinity

Article history: Received on: 21/09/2013 Revised on: 8/10/2013 Accepted on: 15/10/2013 Available online: 30/10/2013 In many crop species, supplemental Ca2+ alleviates the inhibition of growth in plants subjected to NaCl stress. This work assesses the ameliorating effect of CaCl2 on NaCl-stressed seedlings of Hyacinth bean. Ten day old seedlings were stressed either solely with NaCl (100-500 mM NaCl) or with NaCl (100-500 mM NaCl) + CaCl2 (10 mM) and compared with control and CaCl2 (10 mM) treated seedlings. Seedlings were harvested at time intervals of 24, 48 and 72 h after stress for analysis. NaCl-stressed seedlings showed reduced growth as indicated by growth index and relative water content (RWC) while a comparatively less decline in these parameters was seen in seedlings stressed with supplemental CaCl2. An enhancement in levels of H2O2, MDA, GSH, ASC, TSS and photosynthetic pigments noted in stressed seedling supplemented with CaCl2 was evident of its role in ameliorating salinity stress. Supplementation was also found to increase the activity of metabolic enzyme AMY paving the way for partial amelioration of stress caused by salinity.
Characterization of extracellular polymeric substance producing isolates from wastewaters and their antibacterial prospective

Bacteria have the ability of biofilm formation, in which the cells attach to each other within a self-produced matrix of extracellular polymeric substance (EPS). The aim of the present research work was to isolate EPS-producing bacteria from wastewater. Total 21 bacterial isolates were screened for EPS production based on mucoid and slimy colonies. Out of 21 isolates, nine efficient isolates were selected for the production of EPS. These efficient bacterial strains were also checked for their antimicrobial potential against Salmonella sp. , Escherichia coli, and Klebsiella sp. The isolates ASA3, H2E7, H2F8, and ASB4 inhibited the growth of Salmonella sp. , E. coli, and Klebsiella sp, while isolate ASB5, H2C6, and H2E9 only showed inhibitory effects against Salmonella sp. The maximum concentration of EPS (i.e., 17.2 g/l) was produced by strain ASB4 within 3 days of incubation.
Treatment of Textile Wastewater by Combining Biological Processes and Advanced Oxidation

Treatment of textile wastewater is challenging because the water contains toxic compounds that have low biodegradability. Dyes, detergents, surfactants, biocides and more are used to improve the textile process and to make the clothes resistant to physical, chemical and biological agents. New technologies have been developed in the last decades and in particular Advanced Oxidation Processes (AOPs) have shown considerable potential for treatment of industrial effluents. These processes however are expensive and full scale applications are still scarce. In addition, the complex oxidation chemistry transforms the pollutants into a very large number of degradation intermediates which may be even more toxic than the original compounds. This thesis presents a novel treatment approach where two AOPs, photo-Fenton oxidation and ozonation, are used after an anaerobic biofilm process for treatment of textile wastewater, azo dyes degradation and removal of toxicity. The biological treatment cleaves the azo bonds of the dyes and consumes the biodegradable compounds whereas the following advanced oxidation degrades the aromatic amines and other by-products that are recalcitrant to biological degradation. The approach that includes photo-Fenton oxidation resulted in higher reduction of chemical oxygen demand (COD) than that with ozonation when treating real textile wastewater. The latter however resulted in higher reduction of toxicity towards the bacteria Vibrio fischeri and the shrimp Artemia salina. Mutagenic effects were detected in the untreated and biologically treated effluent, but not after photo-Fenton oxidation and ozonation. Environmental impact and costs of the two treatment strategies, at the operating conditions used in this study, are high compared with the full-scale biological process used in the Netherlands, where the wastewater is actually treated. Substitution of artificial light with sunlight and upscaling would result in great improvements in terms of electricity requirements and ozone consumption hence would bring down the environmental impact to values that are comparable to the biological process, suggesting that full scale implementation can be achieved. Further research should look into combining this treatment approach with technologies that allow water and salt recovery and reuse, to make the textile industry more sustainable.
A Novel Mutation in the Promoter Region of Avian Uncoupling Protein3 Associated with Feed Efficiency and Body Composition Traits in Broiler Chicken.

Master degree of Animal Breeding, Tarbiat Modares University, Tehran, Iran Association Professor at Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran Iran PhD student of Animal Breeding and Genetic in Tarbiat Modares University, Tehran, Iran *Corresponding author’s E-mail: Masoudia@modares.ac. ir; ORCID: 0000-0002-3935-0476 Received: 14 Jan. 2020 Accepted: 26 Feb. 2020 ABSTRACT
Enumeration of pyramids of one-dimensional pieces of arbitrary fixed integer length

We consider pyramids made of one-dimensional pieces of fixed integer length a and which may have pairwise overlaps of integer length from 1 to a. We prove that the number of pyramids of size m, i.e. consisting of m pieces, equals (am-1,m-1) for each a >= 2. This generalises a well known result for a = 2. A bijective correspondence between so-called right (or left) pyramids and a-ary trees is pointed out, and it is shown that asymptotically the average width of pyramids is proportional to the square root of the size.
Literature Survey on Various Classification Algorithms in Machine Learning

: Machine learning is one of the fastest emerging technologies in the field of computing. It enables computers to lean without being explicitly programmed. In this paper, we present and discuss a various classification algorithm that can be used to predict Box-Office performance based on twitter messages. These algorithms can be applied to solve other problems as well, including-classifying ham or spam messages from the email dataset, classifying positive or negative reviews and many more. The focus of this paper will be mainly on Naïve Bayes, SVM, k-NN, Neural Network and Random Forest.
The Multiple Mini Interview for admission to nursing – male perspectives

This article was migrated. The article was marked as recommended. Aims: The aim of this study was to gain the perspectives of men undergoing recruitment to a nursing degree programme by the process of multiple mini interviews (MMIs). Background: MMIs are used increasingly to select undergraduate students for degree courses, particularly in the healthcare sciences but the impact of MMIs on initiatives to increase gender diversity in these professions is unknown. Design: The study employed a qualitative research approach using a thematic framework of the MMI process. Methods: The study took place between January 2018 - April 2018 and a total of eight students attended for focus groups. Results: Respondents viewed the MMI process as stressful, and also reported that some of the stations created more stress than others, as they were conscious of the gender issues within some of the scenarios. Despite this they also reported the MMI to be a satisfactory selection tool. Conclusion: Participants found the use of MMIs to comprise a valid selection process which, while imperfect and female-dominated, did not unduly disadvantage male candidates. Further research involving multiple nursing schools as well as medical schools is needed to further evaluate the impact of the MMI as a selection tool on male applicants.
Factors of Weft Knitted Fabrics Related to the Bursting Strength

In this study the different parameters of the weft knitted fabrics like stitch length, yarn count, course and wales per unit length, GSM and porosity are considered to know their influence on the bursting strength. As, bursting strength is important for weft knitted fabrics due to its dimensional properties, shrinkage percentage is also drawn in attention. Key part of this study is to eliminate the less important parameters which have no significant relation to the bursting strength of the weft knitted fabrics. This study supports that, the bursting strength of the fabric is dependent on stitch density, stitch length and shrinkage percentage of the weft knitted fabrics. It also supports that it depends on all of these three parameters more than that of their separate impact. This study will help in producing fabrics with higher strength with respect to these parameters.
Survival Predictors of People Living with HIV/ AIDS in Wamena, Papua

Background: Quality of life (QoL) is an important component in the evaluation of the well-being of people living with HIV and AIDS (PLWHA), especially with the appreciable rise in longevity of PLWHA. The analysis of the quality of life is essential for the healthcare policies and services directed toward PLWHA, since this indicator values the perception of people about their own life and health. However, little is known about the determinant of QoL in PLWHA in Papua. The aim of this study was to analyze survival predictors of people living with HIV/ AIDS in Wamena, Papua, Indonesia. Subjects and Method: This was a retrospective cohort study conducted in Wamena Hospital, Papua, from December 2017 to February 2018. A cohort of 304 PLWHA was selected for this study from the medical record at Wamena Hospital. The dependent variable was survival. The independent variables were adherence to ART, nutritional status, age, and coinfection. The data were collected from medical record and questionnaire. The data were analyzed by Cox regression model. Results: The risk of dying of PLWHA if adherent to take the ART was lower than not adherent to take the ART (HR=0.45; 95% CI= 0.06 to 0.33; p= 0.002). The risk of dying of PLWHA with poor nutritional status was higher than good nutritional status (HR= 12.78; 95% CI= 6.81 to 23.98; p< 0.001). The risk of dying of PLWHA aged ≥ 35 years was slightly higher than <35 years (HR= 1.47; 95% CI= 1.00 to 2.17; p= 0.050). The risk of dying of PLWHA with coinfected was higher than without coinfected (HR= 1.59; 95% CI= 1.15 to 2.23; p= 0.006). Conclusion: The risk of dying of PLWHA decreases if adherent to take the ART, but increases with poor nutritional status, age ≥ 35 years, and coinfection.
Reasoning about Java programs in higher order logic using PVS and Isabelle

This thesis describes the first steps of a project aimed at formal verification of Java programs. The work presented here is part of a larger project called LOOP, for Logic of Object Oriented Programming. A semantics for Java is described in type theory and it is shown how this semantics forms the basis for program verification. The verifications are done with the use of interactive theorem provers,namely PVS and Isabelle. Both theorem provers are described in some detail, resulting in a comparison of the strong and weak points of both systems. A Hoare logic is discussed, which is especially tailored to reasoning about Java. The LOOP project resulted in the construction of the so-called LOOP compiler, which takes Java classes as input and returns PVS or Isabelle theories, capturing the semantics of the Java program, as output. This thesis also briefly describes a specification language for Java, called JML (Java modeling language). This language can be used to specify Java classes.Finally, two non-trivial case studies are described in this thesis, verifying properties of standard Java library classes. The first case study verifies a class invariant over class Vector, the second case study verifies the functional behaviour of the class AbstractCollection
Context-Dependent Kernels for Object Classification

Kernels are functions designed in order to capture resemblance between data and they are used in a wide range of machine learning techniques, including support vector machines (SVMs). In their standard version, commonly used kernels such as the Gaussian one show reasonably good performance in many classification and recognition tasks in computer vision, bioinformatics, and text processing. In the particular task of object recognition, the main deficiency of standard kernels such as the convolution one resides in the lack in capturing the right geometric structure of objects while also being invariant. We focus in this paper on object recognition using a new type of kernel referred to as "context dependent. ” Objects, seen as constellations of interest points, are matched by minimizing an energy function mixing 1) a fidelity term which measures the quality of feature matching, 2) a neighborhood criterion which captures the object geometry, and 3) a regularization term. We will show that the fixed point of this energy is a context-dependent kernel which is also positive definite. Experiments conducted on object recognition show that when plugging our kernel into SVMs, we clearly outperform SVMs with context-free kernels.