Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement in Properties of Wool fibers pretreated with chitosan and nano chitosan and dyed with saffron natural dye.

Wool fibers pretreated with chitosan and nano chitosan and dyed with saffron red and yellow mixture as natural dye by using microwave heating method. The effect of chitosan and nano chitosan concentrations on color strength (K/S) was measured. The results indicated that, wool fibers pretreated with chitosan and nano chitosan recorded higher color strength than the untreated fibers. Fastness properties and the color yield of the dye on wool fibers were evaluated. The results indicated that color fastness to rubbing, washing and perspiration of all dyed wool fibers are excellent to good. The morphologies structure of the untreated and pretreated wool fibers were examined by scanning electron microscopy (SEM). The untreated wool fibers have a rough surface. The pretreated wool fibers were swelling compared to the untreated fibers. The diameter of the fibers increased and has smooth and even surfaces. The changes in the surface morphology due to the effect of pretreatment with chitosan and nanochitosan.The antimicrobial activity with some species of bacteria and fungi were tested. The results indicated that the pretreated fibers exhibit higher reduction percent than the untreated fibers.
The relative effectiveness of line drawing algorithms at depicting 3D shape

OF THE DISSERTATION The relative effectiveness of line drawing algorithms at depicting 3D shape by Kevin Sanik Dissertation Director: Manish Singh Line drawings offer a way to depict 3D shape using a “minimal” representation. They can abstract away other features like shading, texture, color, and others while still yielding a vivid sense of shape. Attempts to have computers automatically generate line drawings vary based on the “definitions” used to determine line placement. However, the resulting drawings are inconsistent in quality. In order to improve these methods, we use perceptual studies to understand when these methods are effective and when they are not. In the first study, we compare the accuracy of 3D shape percepts from line definitions, artist drawings, and shaded images. We find that line drawings can yield percepts almost as accurate as shaded images, but that no line definition is universally superior to others. We also find some evidence that the quality of these line definitions depends on the geometric context of the line. In the second set of studies, we examine the factors that determine the relevant context of two line definitions: apparent ridges and suggestive contours. We found that the effectiveness of an apparent ridge was largely dependent on the maximal curvature along apparent ridge, while suggestive contours are affected by multiple geometric variables. The final chapter studies these geometric variables in greater detail, and uses them
Investigation of connection between deep learning and probabilistic graphical models

The field of machine learning (ML) has benefitted greatly from its relationship with the field of classical statistics. In support of that continued expansion, the following proposes an alternative perspective at the link between these fields. The link focuses on probabilistic graphical models in the context of reinforcement learning. Viewing certain algorithms as reinforcement learning gives one an ability to map ML concepts to statistics problems. Training a multi-layer nonlinear perceptron algorithm is equivalent to structure learning problems in probabilistic graphical models (PGMs). The technique of boosting weak rules into an ensemble is weighted sampling. Finally regularizing neural networks using the dropout technique is conditioning on certain observations in PGMs. Thesis Supervisor: Devavrat Shah Title: Professor of Electrical Engineering and Computer Science
Efficient Route Recommendation System Based On Keyword Using Candidate Route Generation And Travel Route Exploration

Social media like Facebook and Flicker are most popular in these days, so that sharing of images and visited records by users can be easy. The system aim to make an easy trip planning based upon travel history of users records. Users have their own views, requirements and preferences for their planning trips. So that here introducing text description as keywords instead of limited queries. Also different set of travel route recommendations are needed. Large amount of recorded data mining and ranked preferences are the main expanded version of already existing system to get an automatic system.Therefore, proposing an Efficient Travel Route Recommendation System Based On Keyword that uses data from users trip and travelling records. So in this system for the classification of places of interests uses a keyword extraction module, for accurate and matching with query keywords. An algorithm called route reconstruction is developed to make route of candidate preferences. To get the results, the system used Skyline routing concepts. And for the verification of genuinity of rated locations here introducing NER method.
Dinâmica do amônio e nitrato em solos consorciados com plantios de Paricá (Schizolobium Amazonicum) em Aurora do Pará, Pará

Com o objetivo de obter conhecimento da dinâmica do nitrogenio em solo sob as formas de N-mineral: amonio e nitrato de 3 diferentes areas cultivadas com o parica, parica x freijo (PF), parica x mogno (PM), parica x curaua x freijo (PCF) e floresta secundaria, foi avaliado as taxas liquidas de mineralizacao e nitrificacao, estoques de carbono e nitrogenio, pH, urease, respiracao basal e parâmetros fisicos (densidade e porosidade total). O estudo foi realizado no nordeste paraense no municipio de Aurora do Para, na fazenda da empresa Tramontina, S.A, nos meses de outubro e novembro de 2007, representando o periodo seco, e janeiro e marco de 2008 representando o periodo chuvoso. Nas duas areas cultivadas da fazenda e na floresta secundaria as amostras para as analises quimica e fisica foram retiradas nas profundidades de 0-10, 10-20 e 20-30 cm. Para as taxas liquidas de mineralizacao os maiores valores ocorreram na estacao seca, este resultado foi observado para todas as areas de cultivo. Para as taxas liquidas de nitrificacao os maiores valores ocorreram no mes de outubro de 2007 nas areas de capoeira e parica x curaua x freijo e no mes de marco nas areas de Parica x mogno (PM) e parica x freijo (PF). Os valores para as taxas liquidas de nitrificacao mostram que ocorreu interacao significativa entre tratamento e estacao. Os valores do estoque de carbono para todas as areas na estacao seca variaram de 11-17 Mg/ha e na estacao chuvosa de 15-25 Mg/ha. Os valores para o estoque de nitrogenio variaram de 0,78-1,80 Mg/ha na estacao seca e de 0,76-1,47 Mg/ha na estacao chuvosa. A relacao C/N para todos os sistemas ficou por volta de 15/1 indicando um rapido processo de decomposicao da materia orgânica nesses solos.
Gaussian process optimization with simulation failures

We address the optimization of a computer model, where each simulation either fails or returns a valid output performance. We suggest a joint Gaussian process model for classification of the inputs (computation failure or success) and for regression of the performance function. We discuss the maximum likelihood estimation of the covariance parameters, with a stochastic approximation of the gradient. We then extend the celebrated expected improvement criterion to our setting of joint classification and regression, thus obtaining a global optimization algorithm. We prove the convergence of this algorithm. We also study its practical performances, on simulated data, and on a real computer model in the context of automotive fan design.
Mesoscale Precipitation Climate Prediction for Brazilian South Region by Artificial Neural Networks

Numerical weather and climate use sophisticated mathematical models. These models are employed to simulate the atmospheric dynamics to perform a medium-range forecasting and climate prediction. Such an approach allows to estimate all meteorological variables for a future time period: wind fields, air temperature, pressure, moisture, and precipitation field. Precipitation is one of the most difficult fields for prediction. The latter statement is verified due to high variability in space and time. However, precipitation is a key issue in many activities of society. An alternative approach for climate prediction to the precipitation field is to employ the Artificial Neural Network (ANN). Such technique has a reduced computational cost in comparison with time integration of the partial differential equations. One challenge to employ an ANN is to determine the topology or configuration of a neural network. Here, a supervised ANN is designed to perform the precipitation prediction looking at two different periods: monthly and seasonal precipitation. The method is applied to the Southern region of Brazil. The definition of the neural network topology is addressed as an optimization problem. The best configuration is computed by minimizing a cost function. The optimization problem is solved by a new meta-heuristic: Multi-Particle Collision Algorithm (MPCA). In addition, a technique based on rough set theory is used to reduce the data space dimension. The predicted precipitation is evaluated by comparison with measured data. The prediction is also evaluated using full and reduced input data for a neural predictive model.
Realization of High-Efficiency Dual-Active-Bridge Converter With Reconfigurable Multilevel Modulation Scheme

Efficiency optimization of dual-active-bridge (DAB) converters involves the minimization of both conduction and switching losses. However, it is challenging to achieve these two objectives simultaneously due to their conflicting nature, i.e., circulating current is required to realize zero-voltage switching (ZVS) but the presence of an excessive amount of the circulating current is the main cause of conduction loss and low efficiency. This dilemma has been common to conventional DAB converter topologies and modulation schemes which cannot provide sufficient degrees of freedom (DoFs) to the design of their operation modes. This paper is an illustration of the merits of utilizing reconfigurability of the operation mode as a new DoF in designing the operation of DAB converters to minimize both conduction and switching losses. The proposed DAB converter is designed to switch between two operation modes. For 25%–100% of the rated output power, a new modulation scheme based on the four-level ac voltages is proposed to achieve zero circulating current, zero backflow power, and full-range ZVS. Below 25% of the rated output power, the new modulation scheme transitions smoothly to the enhanced dual-phase-shift (EDPS) modulation scheme that continues to achieve zero backflow power and full-range ZVS while the incurred circulating current is moderated by operating the ac voltages at half-amplitude. The experimental results obtained from a 2-kW prototype show that, in comparison to conventional modulation schemes, the proposed modulation scheme is capable of maintaining the highest efficiency over the full output power range and greatly improves the reliability of switches due to full-range ZVS operation.
An Efficient K-Means Algorithm and its Benchmarking against Other Algorithms

K-Means is a widely used partition based clustering algorithm famous for its simplicity and speed. It organizes input dataset into predefined number of clusters. K-Means has a major limitation -- the number of clusters, K, need to be pre-specified as an input. Pre-specifying K in the K-Means algorithm sometimes becomes difficult in absence of thorough domain knowledge, or for a new and unknown dataset. This limitation of advance specification of cluster number can lead to “forced” clustering of data and proper classification does not emerge. In this paper, a new algorithm based on the K-Means is developed. This algorithm has advance features of intelligent data analysis and automatic generation of appropriate number of clusters. The clusters generated by the new algorithm are compared against results obtained with the original K-Means and various other famous clustering algorithms. This comparative analysis is done using sets of real data.
Image Retrieval in Mobiles using Signature based Approach

Since camera based handheld devices are widely used in today's world, and we also tend to click pictures and store it. Hence there is a need for a system that could process the pictures clicked from a hand-held device and retrieve back similar images from a central image database along with the information tagged with it. Mobile phones have very limited display size and limited number of control keys, so most of these systems encounter serious difficulties for both presenting the query image and also showing the retrieval results. In this paper, we describe a way in which a captured image can be searched in the web using content based retrieval system.
Molecular dynamics simulations of ion separation in nano-channel water flows using an electric field

ABSTRACT Removal of undesired substances from water is a field of investigation recently focused at the nanoscale. Towards this direction, molecular dynamics simulations are conducted in this paper to investigate unwanted ion removal in nanochannel flows. The simulation method incorporates a Poiseuille-like water/ion flow system at the nanoscale where an electric field, of various magnitudes in the range of E = 0.25–1.5 V/Å, is applied perpendicular to the flow, leading anions and cations close to the wall regions, similar to the Capacitive De-Ionization method. The time needed for ions to reach equilibrium, i.e. to flow in the region near the walls while pure water flows in the channel interior, is t = 1.3 ns when E = 1.5 V/Å and t = 4.0 ns when E = 0.25 V/Å, showing a dependency on the value of the electric field. Calculations on density, velocity, and temperature values report on fluid properties to be used in the proposed desalination configuration and could act as a basis to guide novel technological applications and extend to higher scales.
The Relationship between Self-Esteem and Social Network Loneliness: A Study of Trainee School Counsellors

In this study, the relationship was investigated between self-esteem and loneliness in social networks among students in a guidance and psychological counselling teaching department. The study was conducted during the 2017-2018 academic year with 312 trainee school counsellors from Turkey. In terms of data collection, the Social Network Loneliness Scale, and the Self-esteem Scale were employed, and a statistical analysis of the data was conducted. We found a negative relationship between self-esteem and loneliness as experienced in social networks, although neither differs according to sex, age and class level. It was also found that those who use the Internet for communication purposes have high levels of loneliness and self-esteem in social networks. While self-esteem levels among users of the Internet are high, those who use it to read about or watch the news have high levels of loneliness. No relationship was found between self-esteem and social network loneliness levels and among those who use the Internet for playing games. Regular sporting habits were found to have a positive effect on self-esteem, but no effect on the level of loneliness in social networks.
Big Data Analysis: Apache Spark Perspective

the boom in the technology has resulted in emergence of new concepts and challenges. Big data is one of those spoke about terms today. Big data is becoming a synonym for competitive advantages in business rivalries. Despite enormous benefits, big data accompanies some serious challenges and when it comes to analyzing of big data, it requires some serious thought. This study explores Big Data terminology and its analysis concepts using sample from Twitter data with the help of one of the most industry trusted real time processing and fault tolerant tool called Apache Storm.
Sensing Margin Enhancement Technique Utilizing Boosted Reference Voltage for Low-Voltage and High-Density DRAM

In the case of dynamic random access memory (DRAM) using a voltage latched sense amplifier, various offset voltage cancellation techniques have been studied to secure the sensing margin. However, as coupling noise and process variations increase with technology scaling, it is impossible to obtain a sufficient sensing margin only by using an offset voltage cancellation technique. In addition, offset voltage cancellation techniques have several problems, such as area overhead and sensing speed slowdown at a low supply voltage. In this paper, we propose a sense amplifier that maximizes the sensing margin by boosting the reference bitline voltage during the charge sharing operation and adopting the offset voltage cancellation technique. The sensing voltage difference of the proposed sense amplifier increases by 50% or more than that of the conventional offset cancellation (OC) sense amplifier at the low supply voltage of 0.9 V, which can improve not only the sensing speed by 2 ns but also the sensing yield by 11.8%. In addition, the proposed sense amplifier achieves a stable sensing yield with a larger cell array height and thus can compensate the area overhead of 44% caused by the OC technique by decreasing the overall cell array height.
The Effects of Humic Acid Application on Soil and Foliar Iron Oxide Nanoparticle Sprays on Some Quantitative and Qualitative Traits of Snapdragon (Variety Montego Rose Bicolor)

Snapdragon is an herbaceous annual plant with large diversities in color and shape and a long flowering period; it is a resistant and semi-resistant plant from scrophulariaceae family. It is used as either cut flower or garden plant. The use of fertilizers with no side effects is necessary for the nature. Humic acid has been named amongst the nature-friendly organic fertilizers. In the present study, the effects of on-soil humic acid application in four concentrations (0mg/l, 50mg/l, 100mg/l and 200mg/l) and foliar iron oxide nanoparticles’ foliar sprays in four levels (0mg/l, 150mg/l, 300mg/l and 450mg/l) have been investigated on the quantitative and qualitative traits of snapdragon, Montego Rose Bicolor Variety. To do so, a factorial experiment within the format of complete randomized block design was carried out in three replications. The measured variables were plant height, number of florets, the emergence time of the first florets, wet and dry weights of the aerial organs and root, electrolyte leakage, total phenol, amount of soluble sugar, chlorophyll content and anthocyanin amount. Based on the results obtained from variance analysis, the mutual effect of humic acid and iron oxide nanoparticles was significant increase in the percentages of such traits as the length of the flower cluster, flower persistence, wet root weight, dry root weight, dry weight of the aerial organs, chlorophyll a, chlorophyll b, total chlorophyll, sugar content and electrolyte leakage in a 1% level. The effect of iron oxide nanoparticles alone was also significant increase in a 1% level in such traits as the wet weight of the aerial organs, total phenol content and anthocyanin amount but the plant height, number of ancillary branches, number of nodes, number of open florets, number of closed florets, the total number of florets and the time of the second series of flowering were not influenced by any of the applied treatments. The maximum amounts of the dry weight of the aerial organs (0.032g), the amount of chlorophyll b (16.91mg/g.wet weight) and sugar content (0.76mg/g.wet weight) were observed in Fe300Hu50 treatment; the maximum amounts of phenol (2.88mg/g.wet weight) and flower persistence (95.66 days) were observed in Fe150Hu0 treatment and the highest rates of total chlorophyll (30.78mg/g.wet weight), length of the flower cluster (5.9cm) and the lowest electrolyte leakage (21.23%) were seen in Fe300Hu100, Fe450Hu50 and Fe450Hu100 treatments.
Algorithms for Trust-Region Subproblems with Linear Inequality Constraints

MELANIE L. BAIN GRATTON: Algorithms for Trust-Region Subproblems with Linear Inequality Constraints. (Under the direction of Jon W. Tolle.) In the trust-region framework for optimizing a general nonlinear function subject to nonlinear inequality constraints, sequential quadratic programming (SQP) techniques generate subproblems in which a quadratic function must be minimized over a spherical region subject to linear inequality constraints. An interior-point algorithm proposed by Kearsley approximately solves these subproblems when the objective functions are large-scale and convex. Kearsley’s algorithm handles the inequality constraints with a classical log-barrier function, minimizing quadratic models of the log-barrier function for fixed values of the barrier parameter subject to the trust-region constraint. Kearsley recommends the LSTRS algorithm of Rojas et al. for minimizing these models. For the convex case, we prove convergence of Kearsley’s algorithm and suggest alternatives to the LSTRS algorithm. These alternatives include the new annulus algorithm of Griffin et al., which blends the conjugate gradient and sequential subspace minimization methods to yield promising numerical results. For the nonconvex case, we propose and test a new interior-point algorithm that incorporates the annulus algorithm into an SQP framework with trust regions.
A hybrid approach combining CUR matrix decomposition and weighted kernel sparse representation for plant leaf recognition

Plant species recognition is one of the important and difficult research areas. Many existing plant recognition and classification methods cannot meet the requirements of the automatic plant recognition system, due to the irregular, complex, and diverse nature of plant leaves. In this paper, a plant recognition approach is proposed by combining CUR decomposition and weighted kernel sparse representation (WKSR). The proposed method is different from traditional plant leaf classification methods. Instead of establishing a classification model by extracting the color, shape, and texture classifying features, the proposed method directly reduces the dimensionality of image and recognizes the test samples based on WKSR coefficients. In order to reduce the recognition time the proposed method uses class specific dictionary learning for sparse modeling. By using the comparison analysis, the proposed method is verified on four plant leaf datasets namely, Flavia leaf dataset, Swedish leaf dataset, Original leaf images and Leafsnap dataset with existing plant classification methods such as Random Forest, Support Vector Machine and K-Nearest Neighbors. The accuracy of the proposed method is 98% which is the best classification rate.