Miracle-dz/UniFAR-FTUs huggingface dataset:
A new type of full scale photovoltaic DC power generation system and its control strategy of modular multilevel converter

This new full scale photovoltaic DC power generation system was proposed to improve the efficiency of the new energy power system. The system consists of two parts: the series DC-DC converter part which realizes the maximum power output of the PV system and the establishment of the high voltage DC bus, and the MMC which realizes the high voltage and high power inverter. In this paper, we paid close attention on the operation principle of modular multilevel converter. Much work had been done to establish the switching state space model based on MMC and AC small-signal model. We put forward a dual-loop three-phase access strategy to grid based on carrier phase-shifted sinusoidal pulse width modulation technique to realize the significant degradation decoupling of modular multilevel converter model, in which MMC outer loop is subjected to the voltage while inner loop is subjected to the current. In this paper, an MMC simulation system with 4kV direct current bus and 5 level voltage is modeled to test the feasibility. And the stability and rapid response of the simulation model is outstanding.
Don't only Feel Read: Using Scene text to understand advertisements

We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks.
Keys to Soldier and Winged Adult Termites (Isoptera) of Florida

Illustrated identification keys are presented for soldiers and winged adults of the following 17 termite species known from Florida: Calcaritermes nearcticus Snyder, Neotermes castaneus (Burmeister), N. jouteli (Banks), N. luykxi Nickle and Collins, Kalotermes approximatus Snyder, Incisitermes milleri (Emerson), I. minor (Hagen), I. schwarzi (Banks), I. snyderi (Light), Cryptotermes brevis (Walker), and C. cavifrons Banks, Family Kalotermitidae; Coptotermes formosanus Shiraki, Reticulitermes flavipes (Kollar), R. hageni Banks, R. virginicus (Banks), and Prorhinotermes simplex (Hagen), Family Rhinotermitidae; and Amitermes floridensis Scheffrahn, Mangold, & Su, Family Termitidae.
The universally conserved GTPase HflX is an RNA helicase that restores heat-damaged Escherichia coli ribosomes

The ribosome-associated GTPase HflX acts as an antiassociation factor upon binding to the 50S ribosomal subunit during heat stress in Escherichia coli. Although HflX is recognized as a guanosine triphosphatase, several studies have shown that the N-terminal domain 1 of HflX is capable of hydrolyzing adenosine triphosphate (ATP), but the functional role of its adenosine triphosphatase (ATPase) activity remains unknown. We demonstrate that E. coli HflX possesses ATP-dependent RNA helicase activity and is capable of unwinding large subunit ribosomal RNA. A cryo–electron microscopy structure of the 50S–HflX complex in the presence of nonhydrolyzable analogues of ATP and guanosine triphosphate hints at a mode of action for the RNA helicase and suggests the linker helical domain may have a determinant role in RNA unwinding. Heat stress results in inactivation of the ribosome, and we show that HflX can restore heat-damaged ribosomes and improve cell survival.
Phase classification of chronic myeloid leukemia using convolution neural networks

In the disseminate state of human health, there are over 100 types of different cancers in which Chronic myeloid leukemia (CML) is a commonly identified disease. Identification of CML phases such as chronic phase, accelerated phase, and blast crisis phase plays an important role in determining what kind of treatment the patient should receive. In recent times, the machine learning field has taken a spectacular twist with the acclivity of the Artificial Neural Networks (ANNs). These computational models which are biologically inspired are able to give a satisfactory performance in comparison with previous forms of artificial intelligence among various machine learning tasks. From the variants of ANN architecture, Convolutional Neural Network (CNN) is considered as one of the massive computational paradigm. Currently, CNN is used in various applications like image and video recognition, natural language processing and recommendation systems. In this paper, we propose a method to build an image classifier which uses the concept of convolution neural network in classifying the different phases of CML, which can help the doctor correctly identify the present condition of the patient so that appropriate treatment can be given.
A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry

Taking financial ratios as independent variables, this study used the framework of a neural network applied to hospitality firm bankruptcy, comparing the results to those of a logit model. Based on the empirical results of the two methodologies, the neural network obtained a higher accuracy rate than the logit model in an in-sample test. However, when tested with a holdout sample for verification, both models achieved a 100% accuracy rate. The study found that ‘total liabilities to total assets’ was a significant variable based on the results of both the t-test and logit analysis. Since hospitality firms are known for being highly leveraged, the conclusion can be drawn that extensive debt financing, when not accompanied by the competitive market value of equity, could play a pivotal role in forcing firms to file for bankruptcy.
Parasitic infections and resource economy of Danish Iron Age settlement through ancient DNA sequencing

In this study, we screen archaeological soil samples by microscopy and analyse the samples by next generation sequencing to obtain results with parasites at species level and untargeted findings of plant and animal DNA. Three separate sediment layers of an ancient man-made pond in Hoby, Denmark, ranging from 100 BC to 200 AD, were analysed by microscopy for presence of intestinal worm eggs and DNA analysis were performed to identify intestinal worms and dietary components. Ancient DNA of parasites, domestic animals and edible plants revealed a change in use of the pond over time reflecting the household practice in the adjacent Iron Age settlement. The most abundant parasite found belonged to the Ascaris genus, which was not possible to type at species level. For all sediment layers the presence of eggs of the human whipworm Trichuris trichiura and the beef tapeworm Taenia saginata suggests continuous disposal of human faeces in the pond. Moreover, the continuous findings of T. saginata further imply beef consumption and may suggest that cattle were living in the immediate surrounding of the site throughout the period. Findings of additional host-specific parasites suggest fluctuating presence of other domestic animals over time: Trichuris suis (pig), Parascaris univalens (horse), Taenia hydatigena (dog and sheep). Likewise, alternating occurrence of aDNA of edible plants may suggest changes in agricultural practices. Moreover, the composition of aDNA of parasites, plants and vertebrates suggests a significant change in the use of the ancient pond over a period of three centuries.
The potential for snow to supply human water demand in the present and future

Runoff from snowmelt is regarded as a vital water source for people and ecosystems throughout the Northern Hemisphere (NH). Numerous studies point to the threat global warming poses to the timing and magnitude of snow accumulation and melt. But analyses focused on snow supply do not show where changes to snowmelt runoff are likely to present the most pressing adaptation challenges, given sub-annual patterns of human water consumption and water availability from rainfall. We identify the NH basins where present spring and summer snowmelt has the greatest potential to supply the human water demand that would otherwise be unmet by instantaneous rainfall runoff. Using a multi-model ensemble of climate change projections, we find that these basins—which together have a present population of ∼2 billion people—are exposed to a 67% risk of decreased snow supply this coming century. Further, in the multi-model mean, 68 basins (with a present population of >300 million people) transition from having sufficient rainfall runoff to meet all present human water demand to having insufficient rainfall runoff. However, internal climate variability creates irreducible uncertainty in the projected future trends in snow resource potential, with about 90% of snow-sensitive basins showing potential for either increases or decreases over the near-term decades. Our results emphasize the importance of snow for fulfilling human water demand in many NH basins, and highlight the need to account for the full range of internal climate variability in developing robust climate risk management decisions.
Analyzing rich qualitative data to study pencil-puzzle-based assignments in CS1 and CS2

Pencil puzzles (puzzles such as sudoku and many others that are designed to be solved by humans, promoting computational thinking) provide a natural context for CS1/2 assignments. In a prior work we analyzed Likert-scaled student responses and assignment/course grades to show that not only are such assignments effective but are also largely independent of gender and prior computing experience. This paper focuses on open-ended student comments, both to see if they provide additional insights about the assignments and student perceptions not apparent from the Likert-scaled responses, and to see if these comments are consistent with the results from the prior work. We surveyed over 1000 students who had used pencil-puzzle-based assignments and invited them to make open-ended comments in their survey responses. We used grounded theory to develop codes for the large volume of student survey comments, as well as for semi-structured interviews with the instructors and focus groups with student TAs. Statistical analysis of the coded comments identified several interesting relationships, such as students being appreciative of their learning even when they perceived the assignments as difficult, which were not available from the Likert-scaled data. The analysis also confirmed that these assignments are largely gender- and experience-neutral. We conclude by discussing how these results and the coding process lead to improvements in assignment development and inform future research directions.
G-Gene

The large availability of touch-sensitive screens fostered the research in gesture recognition. The Machine Learning community focused mainly on accuracy and robustness to noise, creating classifiers that precisely recognize gestures after their performance. Instead, the User Interface Engineering community developed compositional gesture descriptions that model gestures and their sub-parts. They are suitable for building guidance systems, but they lack a robust and accurate recognition support. In this paper, we establish a compromise between the accuracy and the provided information introducing G-Gene, a method for transforming compositional stroke gesture definitions into profile Hidden Markov Models (HMMs), able to provide both a good accuracy and information on gesture sub-parts. It supports online recognition without using any global feature, and it updates the information while receiving the input stream, with an accuracy useful for prototyping the interaction. We evaluated the approach in a user interface development task, showing that it requires less time and effort for creating guidance systems with respect to common gesture classification approaches.
ASSOCIATIONS BETWEEN FUNCTIONAL MILESTONES AND PSYCHIATRIC ADMISSIONS IN AN URBAN AREA : UTILITY OF A CLUSTER-ANALYTICAL APPROACH

ASSOCIATIONS BETWEEN FUNCTIONAL MILESTONES AND PSYCHIATRIC ADMISSIONS IN AN URBAN AREA: UTILITY OF A CLUSTER-ANALYTICAL APPROACH Cristiana Montemagni, Tiziana Frieri, Vincenzo Villari & Paola Rocca Department of Neuroscience, University of Turin, Struttura Semplice di Coordinamento a Valenza Dipartimentale (SSD), Department of Neuroscience and Mental Health A.O. U. Città della Salute e della Scienza di Torino, Torino, Italy Department of Neuroscience and Mental Health, SC Psychiatry SPDC, A.O. U. Città della Salute e della Scienza, Turin, Italy
Non-Equilibrium Phase Transition in an Atomistic Glassformer: the Connection to Thermodynamics

Tackling the low-temperature fate of supercooled liquids is challenging due to the immense timescales involved, which prevent equilibration and lead to the operational glass transition. Relating glassy behaviour to an underlying, thermodynamic phase transition is a long-standing open question in condensed matter physics. Like experiments, computer simulations are limited by the small time window over which a liquid can be equilibrated. Here we address the challenge of low temperature equilibration using trajectory sampling in a system undergoing a nonequilibrium phase transition. This transition occurs in trajectory space between the normal supercooled liquid and a glassy state rich in low-energy geometric motifs. Our results indicate that this transition might become accessible in equilibrium configurational space at a temperature close to the so-called Kauzmann temperature, and provide a possible route to unify dynamical and thermodynamical theories of the glass transition.
Design and simulation of a forcible buck-boost controlling solar powered DC motor

This research paper deals with the design and simulation of facile and robust Buck-Boost controlling solar powered DC motor control system. Photovoltaic (PV) array were used as DC power source and Buck-Boost type Switching Mode Power Supply (SMPS) was used to track the power output of the motor and optimize the output of the motor at the rated speed and power. Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers were used to regulate the DC-DC converter and control any oddity of voltage and current. Feedback comparator was used to regulate constant SMPS output. Consecutive tests were performed using MATLAB Simulink for control techniques by varying irradiance and temperature of photovoltaic (PV) array. It also showed the performance of Buck-Boost converter, DC Motor, PI controller and PID controller.
Monochromatic textures' features extraction using extended GLCM approach for classification of autonomous cleaning robot work area

One of commonly used methods for scanning the work area around mobile robot is to use machine vision. This paper's focus is on extracting features from monochrome natural textures for the purpose of texture classification using extended Gray Level Coincidence Matrix (GLCM) approach. Main idea of this approach is to slice original image into smaller parts, calculate four well-known Haralick's Features for each part separately and then use one of commonly used statistical measures to obtain series of features for task of classification. Simulations using texture base derived from popular Amsterdam Library of Textures (ALOT) database were performed. Evaluation of classification performance with this extended method for different number of slices was performed using Re-substitution Loss, F-measure and Cross-validation loss of calculated classifiers as quality criteria. In general, obtained results show that it is possible to improve classification quality by introducing this extended approach.
Classification of wear phenomena by specific ultrasonic emission detection for prognostic purposes

Tribological effects (e.g., friction) often define the functionality of typical mechanical elements and mechanical engineering structures. If friction processes does not work well due to bad tribological conditions, sliding surfaces may be destroyed and the components functionality may be reduced up to a complete loss of functionality. The definition of this damage level depends on the particular application and the related tolerable level of deterioration. Hence, this is an individual characteristic that has to be quantized and quantified beforehand, so that the related knowledge can be used for automated supervision, for example in the context of condition-based maintenance concepts etc. In the tribological context the surface of the considered individual component is usually evaluated by visual inspection, which is time-consuming and a subjective measure. Furthermore, material displacements, inner cracks,. might not be detected by visual inspection. Therefore, an automated and continuous monitoring of safety relevant structures affected by wear effects may be useful to improve SHMor CM-related goals. Destructive testing concepts reveal the level of deterioration of a component at a discrete point of time. Though, the progression of fatigue is hardly reconstructable and the process cannot be continued with the same component. This is only possible with non-destructive methods, which observe/measure those signals that indicate the fatigue progression. Hence, the destructive testing provides the reference damage level at discrete operating times while the nondestructive testing fills the gap between those discrete information with the damage progression in between. This contribution deals with the problem of detecting and monitoring signals indicating tribological effects with a non-destructive concept. Therefore a new sensor technology is applied and first considerations about the related data filtering technique are considered. The main idea is to monitor ultrasonic emission properties of the tribo-system. For first experiments using this technique a test rig for wear examination with variable lubrication, and normal force has been developed. This tribological system is equipped with several sensors, amongst others several piezoelectric materials. The transducers are used as ultrasonic sensors, measuring the structure-borne noise. The goal is to connect the characteristic signals unambiguously to their unique sources, e.g., abrasive wear and surface fatigue. This contribution details the possibility and application of structure-borne noise measurements, and shows preparative results for determination of deterioration and for distinguishing different wear-related effects.
Microarray breast cancer data classification using machine learning methods

In this study We used microarray breast cancer data for classification of the patients using machine learning methods. First, 8 different machine learning algorithms are applied to the data, without applying any feature selection methods. Then two different feature selection methods are applied. The results of the classifications are compared with each other and with the results of the first case. The methods applied are SVM, KNN, MLP, Decision Trees, Random Forest, Logistic Regression, Adaboost and Gradient Boosting Machines. After applying the two different feature selection methods with the best 50 features are applied, SVM gave the best results. MLP is applied using different number of layers and neurons to examine the effect of the number of layers and neurons on the classification accuracy. It is determined that the increase in the number of layers sometimes decreased, sometimes didn't change the accuracy.
Finding Original Image Of A Sub Image Using CNNs

Convolututional Neural Networks have achieved state of the art in image classification, object detection and other image related tasks. In this paper I present another use of CNNs i.e. if given a set of images and then giving a single test image the network identifies that the test image is part of which image from the images given before. This is a task somehow similar to measuring image similarity and can be done using a simple CNN. Doing this task manually by looping can be quite a time consuming problem and won't be a generalizable solution. The task is quite similar to doing object detection but for that lots training data should be given or in the case of sliding window it takes lot of time and my algorithm can work with much fewer examples, is totally unsupervised and works much efficiently. Also, I explain that how unsupervised algorithm like K-Means or supervised algorithm like K-NN are not good enough to perform this task. The basic idea is that image encodings are collected for each image from a CNN, when a test image comes it is replaced by a part of original image, the encoding is generated using the same network, the frobenius norm is calculated and if it comes under a tolerance level then the test image is said to be the part of the original image.
A review of community detection algorithms in signed social networks

Now a days social network analysis has gained much attention. A community in social networks indicates that nodes within the group are densely connected and connections between groups are sparse. These social networks can be modeled as signed social networks that contain both positive and negative links or relations. In the social networks community mining or detection is the task of grouping the nodes together on the basis of their linked pattern. In the literature, various community detection algorithms have been proposed. This paper represents an overview of community detection algorithms in the signed social networks along with their classification. Then after we have a comparative study of the community detection algorithms.
Exploratory Analysis of the Microbiological Potential for Efficient Utilization of Fiber Between Lantang and Duroc Pigs

There is growing interest in the use of unconventional feed ingredients containing higher dietary fiber for pig production due to increasing prices of cereal grains and the potential health benefits of dietary fiber on host animals. This study aimed to gain insight into the community-wide microbiome population between the Chinese native Lantang pigs and the commercial Duroc pigs to uncover the microbiological mechanisms for the degradation capacity of fiber in pigs. Utilizing the metagenomics approach, we compared the phylogeny and functional capacity of the fecal microbiome from approximately 150-day-old female Lantang and Duroc pigs fed a similar diet. The structure of the fecal microbial community from the two pig breeds was different at the genus level; the number of genes associated with fiber degradation was higher in Lantang pigs. Further analysis and prediction of their functions from the fecal microbiomes of the two pig breeds revealed that the degradation capacities of fiber, branched chain fatty acids, and oligosaccharides were higher in Lantang pigs. The ability of lignocellulose bonding modules and the transport capacities of xylose, L-arabinose, ribose and methyl galactose were also higher in Lantang pigs. Similarly, the metabolic capacities of xylose, ribose, and fucose and the potential effectiveness of the tricarboxylic acid cycle (TCA) and gene abundance in the hydrogen sink pathway were higher in the fecal microbiome from Lantang pigs. Lantang pigs have a higher capacity to utilize dietary fiber than Duroc pigs, and the differences in the capability to utilize dietary fiber between the indigenous and commercial pigs could be differences in the composition and biological function of the gut microbiota.