Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of Flexible Patterns by Structural Simplification

In the field of computational design of meta materials, complex patterns of meso-structures have seen increased interest because of the ability to locally control their flexibility through adjustment of the meso-structure parameters. Such structures come with a number of advantages like, despite the simplicity of their fabrication, their ability to nestle to sophisticated free-form surfaces. However, the simulation of such complex structures still comes with a high computational cost. We propose an approach to reduce this computational cost by abstracting the meso-structures and encoding the properties of their elastic deformation behavior into a different set of material parameters. We can thus obtain an approximation of the deformed pattern by simulating a simplified version of the pattern using the computed material parameters.
Measuring the Effect Size of Coefficient of Determination and Predictive Relevance of Exogenous Latent Variables on Endogenous Latent Variables through PLS-SEM

In this paper the researchers investigated the effect size f2 of each independent variables was determined after the calculations relating to R2 value, p-values, t-values and bootstrap confidence intervals by using PLSSEM (Partial least square-structural equation modelling). Effect size f2 is calculated in order to determine the individual constructs power and impact on endogenous latent variable, attachment. A relative measure of predictive relevance q2 effect size is also computed. To fulfil the aim of the study, a structured quantitative research survey has been conducted with 320 sample size. The results emerged from the research survey shows that the effect size of the service quality constructs on overall satisfaction of the consumers and level of attachment of consumers towards gym is small to medium. The q2 effect size of the impact of service quality constructs on overall satisfaction level of customers and levels of attachment of customers towards gym is medium to large. At the end we observed a International Journal of Pure and Applied Mathematics Volume 119 No. 18 2018, 39-48 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl. eu/hub/ Special Issue http://www.acadpubl. eu/hub/
DESIGN AND MODELING OF A RESONANT INDUCTIVELY COUPLED WIRELESS POWER TRANSFER SYSTEM FOR MICRO AERIAL VEHICLES by

This thesis discusses the design, modeling, and experimental validation of an inductively coupled wireless power transfer (WPT) system to power a micro aerial vehicle (MAV) without an onboard power source. MAVs are limited in utility by flight times ranging from 5 to 30 minutes. Using WPT for MAVs, in general, extends flight time and can eliminate the need for batteries. In this paper, a resonant inductive power transfer system (RIPT), consisting of a transmit (Tx) coil on a fixed surface and a receive (Rx) coil attached to the MAV, is presented, and a circuit is described. The RIPT system design is modeled to determine a suitable geometry for the coils, and the model validated experimentally. It is found that for the MAV used in this work, a suitable geometry of coils is a 19cm diameter planar spiral Tx coil made with 14 AWG copper wire, seven turns, and 5cm pitch paired with an Rx coil made of 16-20AWG wire, 13cm–20cm diameter, 1mm pitch, and one to two turns. A demonstration of an MAV being powered 11cm above the Tx coil with the WPT system in a laboratory setting is presented. The MAV consumes approximately 12 Watts. The overall power efficiency of the RIPT system from RF power source output to MAV motors is approximately 32%. This thesis is dedicated to my wife, Emily, for her encouragement and support through the course of this work.
In Silico Pharmacophore Study and Structural Optimization of Nafamostat Yield Potentially Novel Transmembrane Protease Serine 2 (TMPRSS2) Inhibitors Which Block the Entry of SARS-CoV-2 Virus into Human Cells

The past 6 months since December 2019 were marked by the COVID-19 pandemic caused from the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the urgent state worldwide, many efforts have been directed on repurposing approved drugs to facilitate the discovery of effective therapies. In this work, I employ molecular docking (in silico) as an approach to study the intermolecular interactions between Nafamostat mesylate – an approved anticoagulant drug, and transmembrane serine protease 2 (TMPRSS2) which is crucial for coronaviruses to enter host cells. Furthermore, structural optimization of Nafamostat is performed using pharmacophoric approach which indicates some small molecules as potentially effective TMPRSS2 inhibitors and pharmaceutical candidates for COVID-19 pandemic.
Edge Computing and Distributed Intelligence

The convergence of Internet of Things (IoT), Artificial Intelligence (AI), 5G, Big Data and cloud computing technologies is creating an emerging edge to cloud computing model to overcome the challenges of traditional cloud computing in supporting the upcoming Internet of (smart and autonomous) things. Edge computing complements cloud computing by offering local computational facilities to enable processing of extremely large amount of data originated from IoT devices and providing intelligent decisions close to the things. As edge devices are geographically dispersed and may be resource constraint, many challenges exist in deploying and executing intelligent applications over these heterogeneous resources. This chapter will address these challenges through 3 sections to discuss: 1) what is edge computing and the resource management approaches in edge environments, especially application deployment mechanisms in the IoT-Edge-Cloud continuum; 2) the benefits and challenges of implementing distributed intelligence and data analytics in the edge environments with exemplary use cases; 3) the distributed learning architecture and algorithms for edge computing, with a special focus on distributed deep learning based approaches.
Differential Responses to Personalized Learning Recommendations Revealed by Event-Related Analysis

Educators are increasingly embracing personalization in online and blended learning programs as a means of focusing students’ investment of time and energy into learning plans that are best tailored to their individual needs. When personalized learning tools are deployed into structured learning environments like schools, however, educators and students must consider program provided recommendations alongside potentially immutable factors like set daily schedules, mandated curricula, and student needs in other content areas. These on-the-ground factors make researching the impacts of personalized learning challenging because they are difficult to measure directly, especially for digital programs deployed at scale. Inspired by a widely influential methodology in brain imaging, we tackled this challenge by employing an event-related approach that emphasizes changes in student behavior that are time-locked to changes in program provided usage recommendations. Our analysis reveals that while student usage time can often be quite far from the amount recommended, students nevertheless respond to changes in program recommendations by adjusting usage in a corresponding manner. We further extend this general approach to demonstrate that students more often stayed on track toward their end of year goals following a week where they met or exceeded their program provided recommendation. Through these examples, we demonstrate the value of an event-related approach towards understanding how personalized paths can positively influence student learning.
Document Clustering and Labeling for Research Trend Extraction and Evolution Mapping

In this study, a method is being proposed to extract research trends and their temporal evolution, throughout discrete time periods. For this purpose, a document embedding method is developed, adapting contextualized word embedding techniques. The method utilizes published academic documents as knowledge units, then clusters them into groups, each representing a series of related fields of research. Various labeling techniques are also explored, including source title popularity, author keyword popularity, term popularity, term importance, and Wikipedia-based automated labeling to evaluate the quality of clusters and explore their explainability. A case study is conducted on Artificial Intelligence (AI) related publications, putting the method to test and observe the evolution of AI within the studied periods. In this study, we show that utilization of neural embeddings in conjunction with paragraph-term weights would provide simple, yet reliable paragraph embeddings, that can be used for clustering of the textual data. Additionally, we show that cluster centroids can be used for cluster tagging, labeling, and inter-connecting for topic evolution study.
Spatial distribution of soil mechanical strength in a controlled traffic farming system as determined by cone index and geostatistical techniques.

Controlled traffic farming (CTF) is a mechanisation system in which all load-bearing wheels are confined to the least possible area of permanent traffic lanes and where crops are grown in permanent, non-trafficked beds. In well-designed systems, the area affected by traffic represents less than 15% of the total field cropped area. The extent and distribution of soil compaction at locations laterally outboard of the permanent traffic lanes may explain the performance of the crop on the rows located either side of the wheeling. This compaction is due to lateral displacement of soil caused by repetitive wheeling, the effect of soil-tyre interaction and the soil conditions (strength) at the time of traffic. The impact of compaction on crop rows adjacent to permanent traffic lanes is also dependent on the seasonal effect of weather, because of changes in soil water availability. This work was conducted to model the spatial distribution of soil mechanical strength under increasing number of tractor passes to simulate the soil conditions that may be encountered in CTF systems at locations near-permanent traffic lanes. The study was conducted on a Typic Argiudoll (26% clay, 72% silt, 2% sand) with four traffic intensities (0, 6, 12 and 18 passes) using a 120 HP tractor (overall mass: 6.3 Mg). Traffic treatments were applied to experimental plots using a completely randomized block design with three replications per treatment. The spatial distribution of soil strength within wheeled and non-wheeled zones was determined using a cone penetrometer (depth range: 0–300 mm) and geostatistical techniques. In all treatments, cone index showed a quadratic response with depth, which explained between 67% and 88% of the variation in soil strength. The number of tractor passes had no effect on the range of spatial dependence of residuals. No differences were observed in the proportion of grid cells where penetration resistance was greater than 2 MPa (considered to be the soil strength limit for root growth of most arable crops) between-traffic treatments, or wheeled and non-wheeled zones, respectively. The overall mean proportion (± 95% confidence interval) of grid cells (4.9 ± 4.5%) suggested that this measure has a relatively high variability and therefore may not be a reliable parameter to be used in the design of future experimental work.
Harbingers of Collaboration? The Role of Early-Class Behaviors in Predicting Collaborative Problem Solving

Collaborative problem solving behaviors are diﬃcult to identify and foster due to their amorphous and dynamic nature. In this paper, we investigate the value of considering early class period behaviors, based on small group development theory, for building predictive machine learning models of collaborative behaviors during problem solving. Over 12 weeks, 20 small groups of undergraduate students solved problems facilitated by a digital joint problem space tool on tablet computers, in the 50-minute discussion component of an engineering course. We annotated 16,270 video clips of groups for collaborative behaviors including task relatedness, talk content, peer interaction, teaching assistant interaction, and tablet usage. We engineered two subsets of features from tablet log ﬁle data: onset features (early collaborative problem solving behavior characteristics calculated from the ﬁrst ten minutes of the class) and concurrent features (more general collaborative behaviors from the whole class period). We compared accuracy between the onset, concurrent, and onset + concurrent features in machine learning models. Re-sults exhibited a U-shaped pattern of accuracy over class time, and showed that onset features alone could not be used to eﬀectively model groups’ collaborative behaviors over the entire class time. Furthermore, analysis did not show support for signiﬁcant gain in accuracy when onset features were combined with concurrent features. Finally, we discuss implications for studying collaborative learning and development of software to facilitate collaboration.
Formulation and characterization of vegetable oil blends rich in omega-3 fatty acids.

Seven blends of vegetable oils were formulated from flaxseed, olive and canola oils differing in their content of n–6 to n–3 and n–9 to n–6 to n–3 fatty acids (FAs) ratio. FA composition, oil classes, triacylglycerol fractions and identity characteristics of the original three oils and their seven blends were determined. Results showed marked differences in the FAs composition between the three oils used for preparing the seven oil blends. The prepared blends had n–3 to n– 6 FAs ratio which varied from 1.2: 1 to 1: 9.1 with the highest ratio of n–3 to n– 6 FAs in flaxseed – canola oil blends. Increasing the level of canola oil in canola - olive oil blends increased from the linolenic acid and lowered from linoleic acid. Meanwhile increasing the proportion of olive oil in the vegetable oil blends was associated with a decrease in iodine value, linolenic acid, n–3 to n–6 and saturated to unsaturated FAs ratio. The opposite trend was noticed with increasing flaxseed and/or canola oil in such blend. Slight differences were observed in the specific gravity, refractive index and saponification values. Among the formulated blends, triacylglycerols were the major fractions of three oils and their seven blends. Also, the triacylglycerols of flaxseed and canola oil blends were fractionated into 9 groups rich in unsaturated triacylglycerols.
Biodegradation And Mushroom Cultivation Studies On Peanut Shells And Corn Stalks By Pleurotus Ostreatus Under Solid State Fermentation Conditions

Peanut shells (PS) and corn stalks (CS) are abundant agro-industrial wastes th t are mainly disposed of by burning or soil incorporation. The biodegradation of PS and CS in various substrate combinations (SCs) was studied under solid state fermentation with Pleurotus ostreatus. A two factorial experiment in a completely randomized design (CRD) was used i n this study. The abilities of the selected SCs to support fruit body yield was also investi gat d as a one factorial experiment in a CRD. Analysis of variance (ANOVA) was used t o de ermine significant differences at 5% level of significance. The results from the biodegrada tion studies showed that increasing the CS proportion between 50-100% in substrate combinations increased the los s of organic matter (LOM), degradation of lignin and hemicellulose, C/N ratio, and the concentrations of minerals like Mg, Mn, Zn, and Pb. It also decreased the degradation of cellulose and mineral concentrations of Ca, Cu, Fe, K, and P. Mushroom cultivation on selecte d substrate combinations revealed that substrates supplemented with 5% rye (R) ha d higher biological efficiencies and lower C/N ratio than non supplemented substrates. The increased degradation of PS by the addition of CS suggests that the combination of the two substrates could be a useful means for biodegrading PS to obtain bio-products of interest. Also, the highest biological efficiencies of 144.09 and 128.09% obtained with substrate combinations 70PS:5R:25CS and 45PS:5R:50CS, respectively, indicates the possible commercial us e of the two substrates for profitable oyster mushroom production in the mushroom industry.
An Exploration of Failure Prediction and Failure Detection in a Cloud Based Environment

Abstract In this paper, we investigated the various methods towards Failure Detection (FD) and Failure Prediction (FP) in a cloud-based environment. A failure in cloud is said to take place when there is a difference between the service provided to the customers and the service that was initially intended to be provided. In other words, the services by the system do not satisfy the constraints of the customer. In today's world, cloud is used extensively and failures like this must be avoided. Basically, in cloud environments there are three different types of failures, Virtual Machine (VM), software and hardware. For preventing failures, it is important to accurately predict or detect them in cloud and then adopt a suitable strategy to get rid of it. FD is a method that is used to identify the exact location of an already present failure in the system before it can cause any damage. FP is the process of accurately auguring whether a failure will occur or not at a particular location. We came across that by using approaches like Bayesian Interface, Recurrent Neural Network, and Big Data model one can effectively predict failures in cloud environment. And by using methods like Volterra series, deep learning one can detect failures. While predicting or detecting failures a lot of parameters must be put in consideration like-resource efficiency, resiliency and service recovery performance. All the papers have been evaluated carefully and have been presented in a useful manner.
Impact of Industrial Processing on Hungarian Agro- Export Trade -Gravity Model Approach-

In this thesis, a gravity model approach was used in order to analyze the main factors affecting the trade flow of Hungarian agro-commodity and food export to its twenty most important partner countries. The thesis created three individual product categories; differentiated according to the level of processing: raw material, firstly processed products and secondarily processed products. The empirical model was applied using panel datasets for each category for the period 2005-2012 (from the first entire year after EU accession until the latest observed year). The gravity estimates implied the importance of economy size effects, distance, exchange rate and a set of dummy variables as determinants of Hungary’s agro-export trade. Furthermore, the study analyzed one-way fixed effects in the model, which accounted for heterogeneity in time. With this specification, the individual effects of time periods were evaluated on the created categories and revealed that the secondarily processed products were the most resistant against trade shocks in the international market environment. In order to calculate unbiased and consistent estimates, the datasets were econometrically tested for choosing the most appropriate regression procedure. Groupwise heteroskedasticity, contemporaneous correlation and first-order autocorrelation were found in the panel datasets, which were controlled by Panel-Corrected Standard Error estimates, where the parameters were estimated by Prais-Winsten regression. The datasets were analyzed by the same estimation procedure for comparing the results and evaluating the effect of industrial processing. The basic gravity estimates (effect of economic sizes and economic distance) of the differentiated product categories were found statistically significant with their expected signs. The GDP increase of the host countries stimulated the market for twice as much as the Hungarian GDP increase. The estimates showed that the structure of raw material export and secondary processed product export were more closely resembled than the firstly processed products. These results are important for trade policy formulation to promote processed Hungarian food products to the World market to strengthen the food processing sector by providing jobs and establishing economic improvement in the rural region.
VideoPlus

This paper describes an approach to capturing the appearance and structure of immersive environments based on the video imagery obtained with an omnidirectional camera system. The scheme proceeds by recovering the 3D positions of a set of point and line features in the world from image correspondences in a small set of key frames in the image sequence. Once the locations of these features have been recovered the position of the camera during every frame in the sequence can be determined by using these recovered features as fiducials and estimating camera pose based on the location of corresponding image features in each frame. The end result of the procedure is an omnidirectional video sequence where every frame is augmented with its pose with respect to an absolute reference frame and a 3D model of the environment composed of point and line features in the scene. By augmenting the video clip with pose information we provide the viewer with the ability to navigate the image sequence in new and interesting ways. More specifically the user can use the pose information to travel through the video sequence with a trajectory different from the one taken by the original camera operator. This freedom presents the end user with an opportunity to immerse themselves within a remote environment and to control what they see.
Assistance to support the Self-Maintenance between Models and Documents in the Development and Use Phase of PSS

To cope with these challenges, different discipline-specific (e.g. CAD, Simulation, etc.) and interdisciplinary (e.g. structural and behavioral systems models based on SysML) are used. During the development of an aPS and a CPPS, various disciplines collaborate and have to exchange these models. If these models are not or cannot be exchanged; instead, exported documents are often used as information and knowledge carriers [1].
Global Contribution Robots Secure Multichannel Routing Protocol in MANET

: Know a day’s achieving a most reliable security in the field of the MANET is emerging as a new way and new approach to achieve better Quality of service and making the system more Robust, More Secure but achieving reliability is one of the challenging task which further opens a new way to carry out more research in the field of MANET. Here in this paper we are introducing GCRSMR (Global Contribution Robots Secure Multichannel Routing Protocol) protocol which is designed by using various approaches that includes GC-Algorithm, Robust Source Routing Mechanism and the concept of Multicast routing scheme to make reliable robust secure communication in MANET.
INFLUENCE OF CROSS FLOW ON THE PROPAGATION AND STEERING OF ACOUSTIC FIELD

The present study numerically estimates the influence of cross flow on the propagation and steering of acoustic waves. A linear phased array is simulated to steer acoustic waves in a desired direction by employing delay law. The steering capabilities of the linear array is critically evaluated under the influence of cross flow with various velocity profiles typically encountered in practical situations. Here, we have solved linearized Euler equations using 6th order compact scheme for evaluating spatial derivative along with five stage optimized Runge-Kutta scheme for temporal integration. Used scheme offers high resolution and displays neutrally stable, physical dispersion relation preserving nature which guarantees accurate estimation of sound field. We observed that the acoustic steering with flow does not follow the simple vector addition law. The propagation of acoustic pressure field under the influence of cross flow is non-linear. The influence of different velocity profiles (shear) on the non-linearity of acoustic wave propagation is also quantified. These results are useful in practical applications where acoustic waves are steered under the influence of cross flow.