Miracle-dz/UniFAR-FTUs huggingface dataset:
Conforming to interface structured adaptive mesh refinement technique for modeling moving boundary problems

This thesis introduces a new computational framework relying on a non-iterative mesh generation algorithm for modeling moving boundary transient diffusion problems. In particular, we focus on simulating the activation-diffusion-controlled pitting corrosion phenomenon, which is a specific class of such problems. In order to locally adapt the FE mesh to the evolving domain morphology, we employ the Conforming to Interface Structured Adaptive Mesh Refinement (CISAMR) technique. CISAMR combines customized versions of the h-adaptivity, r-adaptivity, and sub-triangulation algorithms to transform a structured grid into a conforming mesh. One of the key advantages of this technique for modeling problems with evolving geometries is that only the elements located along the moving boundary must be modified to regenerate a conforming mesh at each time step. This feature not only facilitates the remeshing process but also reduces the error associated with projecting the solution to the nodes of the new mesh. A detailed discussion is provided on different aspect of the CISAMR implementation for modeling moving boundary problems, including the nodal projection procedure and evaluating the moving interface velocity. After presenting a detailed convergence study, we verify the accuracy of this method for modeling pitting corrosion problems by comparing results with analytical solutions and phase field simulations. Additional example problems are also presented
A SYSTEM’S VIEW OF E-LEARNING SUCCESS MODEL AND ITS A SYSTEM’S VIEW OF E-LEARNING SUCCESS MODEL AND ITS IMPLICATIONS TO E-LEARNING EMPIRICAL RESEARCH IMPLICATIONS TO E-LEARNING EMPIRICAL RESEARCH

: A stream of empirical research over the past decade that identiﬁed predictors of e-learning success suggests that there are several critical success factors (CSFs) that must be managed effectively to fully realize promise for e-learning. A problem with empirical distance learning research comes with modeling methods. Especially, there are two approaches of modeling, with or without mediating variables. This paper argues that the simple cause-effect relationship modelling approach in some cases may lead to misleading and false conclusions. As a basis of theoretical foundation to justify our approach, we introduce a system’s view of e-learning success model. Then, we present two examples from previous published papers that may mislead the effect of a CSF on the outcomes of e-learning systems. We conclude that the simple cause-effect relationship model approach in some cases may lead to misleading and false conclusions. Future e-learning empirical research should avoid the simple complex cause-effect relationship model.
Predicting Collaborations in Co-authorship Network

In this paper, we study the problem of predicting collaborations in co-authorship network. We formulated our task in terms of link prediction problem on weighted co-authorship network, in which authors play the role of nodes, and weighted edges connecting two authors are formed by storing either a number or quality metric of research papers co-authored by these authors. Our task is then formulated as regression machine learning model based on network features constructed using network embedding. We evaluate our edge embeddings on large AMiner co-authorship network for (un)weighted node2vec network embeddings and also on the dataset containing temporal information on National Research University Higher School of Economics (HSE) over twenty five years of research articles indexed in Russian Science Citation Index and Scopus for predicting the quality of future research publications measures in terms of quartiles corresponding to published journals indexed in Scopus. We showed that our model of network edge representation has better performance for stated regression task on both, AMiner and HSE co-authorship networks.
Particle Filtering Approach To Fault Detection and Isolation for GPS Integrity Monitoring

Reliability of a navigation system is one of great importance for navigation purposes. Therefore, an integrity monitoring system is an inseparable part of aviation navigation system. Failures or faults due to malfunctions in the systems should be detected and repaired to keep the integrity of the system intact. In this paper, we present a method based on particle filtering for detecting failures which affect only subsets of system measurement. In addition to a main particle filter, which processes all the measurements to give the optimal state estimate, a bank of auxiliary particle filters is also used, which process subsets of the measurements to provide the state estimates which serve as failure detection references. After the statistical property of the state estimate of the main particle filter and those of the auxiliaries is derived, failure detection is undertaken by checking the consistency between the state estimate of the main particle filter and those of the auxiliaries by means of the likelihood ratio (LR) test. The effectiveness of the proposed approach is illustrated in a problem of GPS (Global Positioning System) autonomous integrity monitoring system.
Networked Multimedia for Communication and Collaboration.

Networked multimedia is described as a tool for collaborative learning, focusing on learning through discussion. It is proposed that multimedia may be used to structure and enhance communication, thereby promoting learning. The background and application of collaborative learning is outlined. Several dimensions of media which influence the evolution of its use are examined. Two examples of multimedia discussion tools are used as illustration: The Multimedia Forum Kiosk (MFK) and SpeakEasy, a discussion tool for World Wide Web (WWW). MFK is an interface for discussion that makes use of multimedia for socially relevant representations. MFK has two intended effects: to allow the user to internalize and learn from the community knowledge base, and to augment the knowledge base by synthesizing new ideas. SpeakEasy, currently an experimental discussion tool for WWW, builds upon the ideas of MFK. Because it is accessible via network, it is a different-times, different-places type of communication medium. As participants make assertions, questions, and responses to one another about various issues, a structured, graphical representation emerges making conceptualization of the discussion possible. Five figures illustrate the applications. (Contains 32 references.) (MAS) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. **********************************************************************
Implementasi Algoritma AES (Advanced Encryption Standard) 256 Bit Dan Kompresi Menggunakan Algoritma Huffman Pada Aplikasi Voice Recorder

Abstrak -Keamanan data dengan cara enkripsi tidak hanya dapat dilakukan pada file berbentuk teks dan gambar saja. Tetapi, dapat dilakukan pada file suara yaitu dengan melakukan voice recorder pada aplikasi enkripsi yang dibuat. Agar voice dapat dilakukan enkripsi, maka voice tersebut harus diubah menjadi data digital yang akan dikenali oleh komputer. Dengan melakukan enkripsi hasil file biasanya akan semakin besar dibandingkan file sebelum di enkripsi, hal tersebut karena ada penambahan chiper pada setiap data yang dienkripsi. Hasil file yang besar tersebut dapat berpengaruh terhadap media penyimpanan (storage). Maka pada penelitian ini akan mengimplementasikan algoritma AES (Advanced Encryption Standard) 256 bit untuk mengenkripsi dan mendekripsi file voice recorder dan Algoritma Huffman Coding sebagai kompresi file untuk memperkecil hasil file enkripsi agar memori yang dipakai tidak terlalu besar. Hasil yang didapat adalah perbedaan dari ukuran file enkripsi dan kompresi dibandingkan dengan menggunakan enkripsi saja sebesar 40%. Sedangkan waktu proses enkripsi lebih lama dibandingkan dengan dekripsi, rata-rata waktu dari 20 sampel pengujian pada proses enkripsi membutuhkan 18 detik, pada proses dekripsi 5 detik.
Effect of Directed Versus Spontaneous Pushing During the Second Stage of Labor on Birth Outcome among Primiparas

Background: Although spontaneous and directed pushing techniques adopted during labor have been studied during the past several decades, controversy still surrounds the results regarding obstetric variables. Aim: The aim of this study was to compare the effect of directed pushing versus spontaneous pushing during the second stage of labor on birth outcome. Setting & Subject: The study was conducted in Maternity and Childhood Hospital at Zagazig University Hospitals. It comprised a purposive sample of 200 parturient women in their second stage of labor. The study subjects were divided into equal groups of 100 "spontaneous and directed pushing groups". Tools: three tools were used for data collection (a structured interview sheet, physical assessment sheet, visual analogue scale, modified fatigue symptom checklist and birth outcome sheet). Results: revealed that spontaneous pushing had a better and positive effect on maternal vital signs. The duration of pushing was lower in the spontaneous pushing group than directed pushing. Most of the women in the spontaneous pushing group had significantly a lower pain index after 1 hour from full cervical dilatation and perceived less fatigue within 1-4 hours of childbirth (p=0.000). They also had significantly lower rates of episiotomy, perineal lacerations, postpartum hemorrhage and cesarean section. Their newborn had the highest Apgar score at the first and fifth minutes and none of them had fetal complications. Conclusion: It can be concluded that improved fetal status, neonatal and maternal outcomes have been identified when women were allowed to bear-down in response to their own natural urges rather than in response to commands for repeated, sustained pushes using Valsalva maneuver. Recommendations: It is recommended that; the utilization of the spontaneous pushing technique must be encouraged, with randomized clinical trial to provide further confirmation of the study findings.
Modeling of photovoltaic modules in Simulink and Simscape packages of Matlab software

The paper presents the possibilities of modeling the functioning of photovoltaic modules in MATLAB engineering software packages. In SIMULINK package one implemented a single-mode mathematical model of the photovoltaic module for determining current-voltage characteristics. The model was verified for manufacturer-supplied data and actual measurement results made with the I-V 400 meter. The new capabilities for physical modeling of photovoltaic modules are also available in the SIMSCAPE package. Simulation results obtained from the SIMSCAPE model were also compared with the results of selected measurements under the specified radiation and temperature conditions.
Adaptive data acquisition for communication networks

ADAPTIVE DATA ACQUISITION FOR COMMUNICATION NETWORKS by Behzad Ahmadi In an increasing number of communication systems, such as sensor networks or local area networks within medical, financial or military institutions, nodes communicate information sources (e.g., video, audio) over multiple hops. Moreover, nodes have, or can acquire, correlated information sources from the environment, e.g., from data bases or from measurements. Among the new design problems raised by the outlined scenarios, two key issues are addressed in this dissertation: 1) How to preserve the consistency of sensitive information across multiple hops; 2) How to incorporate the design of actuation in the form of data acquisition and network probing in the optimization of the communication network. These aspects are investigated by using information-theoretic (source and channel coding) models, obtaining fundamental insights that have been corroborated by various illustrative examples. To address point 1), the problem of cascade source coding with side information is investigated. The motivating observation is that, in this class of problems, the estimate of the source obtained at the decoder cannot be generally reproduced at the encoder if it depends directly on the side information. In some applications, such as the one mentioned above, this lack of consistency may be undesirable, and a so called Common Reconstruction (CR) requirement, whereby one imposes that the encoder be able to agree on the decoder’s estimate, may be instead in order. The rate-distortion region is here derived for some special cases of the cascade source coding problem and of the related Heegard-Berger (HB) problem under the CR constraint. As for point 2), the work is motivated by the fact that, in order to enable, or to facilitate, the exchange of information, nodes of a communication network routinely take various types of actions, such as data acquisition or network probing. For instance, sensor nodes schedule the operation of their sensing devices to measure given physical quantities of interest, and wireless nodes probe the state of the channel via training. The problem of optimal data acquisition is studied for a cascade source coding problem, a distributed source coding problem and a two-way source coding problem assuming that the side information sequences can be controlled via the selection of cost-constrained actions. It is shown that a joint design of the description of the source and of the control signals used to guide the selection of the actions at downstream nodes is generally necessary for an efficient use of the available communication links. Instead, the problem of optimal channel probing is studied for a broadcast channel and a point-to-point link in which the decoder is interested in estimating not only the message, but also the state sequence. Finally, the problem of embedding information on the actions is studied for both the source and the channel coding set-ups described above. ADAPTIVE DATA ACQUISITION FOR COMMUNICATION NETWORKS
Time-Varying and Asymmetric Relationship between Energy Use and Macroeconomic Activity

Bu calismada, enerji kullanimi, cikti, sermaye stoku ve istihdam arasindaki dinamik etkilesimler ABD verileri kullanilarak analiz edilmistir. Bu calismadan elde edilen bulgular, bu etkilesimlerin zamanla degistigini ve bir degiskenin baska bir degiskende gerceklesen buyuk ve kucuk soklara verdigi tepkilerin asimetrik oldugunu gostermektedir. Benzer sekilde, bir degiskene vuran pozitif ve negatif soklarin diger degiskenler uzerindeki etkileri birbirinden farklidir. Daha ozel olarak bu calismada, makroekonomik degiskenlerin negatif enerji soklarina pozitif soklara gore daha fazla tepki verdigi bulgusuna ulasilmistir. Ayrica, calisma sonuclarina gore, kucuk enerji soklarinin buyuk soklara gore makroekonomik degiskenler uzerindeki etkileri daha buyuktur. Bunlara ek olarak, uretim faktorlerine yonelik soklar enerji kullanimini azaltmaktadir. Bu bulgu, enerji ve diger uretim faktorlerinin tamamlayici olmak yerine ikame olduguna isaret etmektedir. Bu calismadan elde edilen bulgularin cok acik politika sonuclari bulunmaktadir. Calisma sonuclarina gore, enerji tasarrufunu destekleyen politika uygulamalari ekonomik buyumeye engel olmamaktadir. Ayrica, daha verimli ve enerji tasarrufu saglayan sermaye ve uretim teknolojilerini tesvik eden politikalar, enerji kullanimini azaltmaya yardimci olabilmektedir.
A Fast Logic Mapping Algorithm for Multiple-Type-Defect Tolerance in Reconfigurable Nano-Crossbar Arrays

Unlike conventional CMOS circuits, nano-crossbar arrays have considerably high defect rates. Multiple-type defects randomly occur both on crosspoint switches and wires that substantially complicates the design phase of the circuits with an elimination of systematic design choices. In order to overcome this problem, a logic mapping methodology is presented in this paper. A fast heuristic algorithm using pre-mapping logic morphing, defect oriented adaptive sorting, matching with Hadamard multiplication, and backtracking is introduced. The proposed algorithm covers both crosspoint defects including stuck-open and stuck-closed types and wire defects including bridging and broken types. Effects of stuck-closed defects, mostly disregarded in the literature, are studied in depth. In simulations, an industrial benchmark suit is used for obtaining runtime and success rate values of the proposed algorithm in comparison with those of the existing algorithms in the literature. A relative accuracy evaluation is also given in comparison with exact mapping techniques. Finally, the steps of the algorithm that are based on pre-mapping and heuristic matching techniques, are separately justified with experimental results.
Resolving neuronal population code and coordination with gradient boosted trees

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem of neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction system, we present evidence how gradient boosted trees, a non-linear and supervised machine learning tool, learns the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. As an example, we show how the method reveals a temporally shifted coordination in a thalamo-cortical circuit during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine learning tools thus open new avenues for benchmarking model-based characterization of spike trains.
Conception and control of a WTGS system using a novel control technique based on MPC-DPC-MVF with a switch optimization process

The work proposed through this paper presents a novel technique applied for the control of a wind turbine generation system (WTGS) prototype. A model predictive control combined with a direct power control technique (MPC-DPC) based on SVM control and a novel switching cycle sequence pattern for thermal switch losses reduction have been adopted. The novel developed direct power control (DPC) scheme based on a multivariable filter (MVF) topology for power estimation process has been illustrated. The control of the converter system is adopted through a predictive control strategy to achieve a complete and an instantaneous control over the active and the reactive powers flowing from and to the converter. An experimental implementation has been achieved for the validation of the system using both real-time simulation mode and a full control system with hardware synchronization mode. A switches losses modelling was achieved to evaluate the performances of the proposed control technique.
new/s/leak – A Tool for Visual Exploration of Large Text Document Collections in the Journalistic Doman

Journalists strive for newsworthy stories for the public. To find those stories they need to explore and read documents from large collections such as the Kissinger Cables. This is very time consuming, since the the text document collections are too large to read them alone – even in a team. Interactive text visualization can support journalists in this endeavor. Several tools exists, but interviews with our collaboration journalists revealed their various drawbacks. Therefore, we develop and present a prototype of our novel system new/s/leak, which combines natural language processing and visualization adapted specifically to the journalists’ needs.
Design and Analysis of Hydrostatic Transmission System

This study develops a hydraulic circuit to drive a conveying system dealing with heavy and delicate loads. Various safety circuits have been added in order to ensure stable working at high pressure and precise controlling. Here we have shown the calculation procedure based on an arbitrarily selected load. Also the circuit design and calculations of various components used is depicted along with the system simulation. The results show that the system is stable and efficient enough to transmit heavy loads by functioning of the circuit. By this information, one can be able to design their own hydrostatic circuits for various heavy loading conditions.
The optimization of activity-travel sequences by means of reinforcement learning

Given a sequence of activities and transport modes, this paper evaluates the use of a Reinforcement Machine Learning technique. The technique simulates time and location allocation for a given set of sequences and enables the prediction of a more complete and consistent activity pattern. A computer code has been established to automate the process and has been validated on empirical data.
The influence of corporate commitment educators on the efficiency of educational institutions

The aim of this study is to determine the level of teachers' corporate commitment. For this purpose, a scale was applied to 47 teachers in 12 general schools. The scale is a 5-point Likert-type scale consisting of 28 items. The reliability and validity studies of the scale were conducted. As a result of factor analysis, it was found that this scale was a one-dimensional scale. The scale consists of 4 factors. These factors are named as subscales as following: dedication to the school, dedication to the teaching profession and dedication to the working group. The study concludes that the effective efficiency of institutions depends on the adaptation of the objectives and values of the school and its profession by teachers.
Forecasting Stock Index Based On BP Neural Network Algorithm

BP neural network to forecast non-linear system, especially the financial data system, has a very strong capacity. A system model based on BP algorithm for forecast stock index was studied and shanghai complex index was forecasted in this paper. This paper has collected the index data of shanghai stock market (238 working days) during the period of 2017-01-08 to 2017-12-28. This paper was applying back propagation network to forecast shanghai complex index. The simulation result was shown that the BP algorithm was effective and feasible in stock index short-term forecast, and can achieve high accuracy.
OPACs, Users, and Readers’ Advisory: Exploring the Implication of User-Generated Content for Readers’ Advisory in Canadian Public Libraries

An analysis of user-generated content (UGC) of 22 adult fiction titles in 43 Canadian public libraries that use BiblioCommons, SirsiDynix, and Encore was conducted to examine the contribution of UGC on readers’ advisory services. Findings indicate that UGC provides insight into the affect, subject, and protagonists of a work. Nous avons procede a une analyse de contenus generes par les utilisateurs sur 22 titres de fiction pour adultes dans 43 bibliotheques publiques canadiennes utilisant BiblioCommons, SirsiDynix et Encore, afin d’examiner la contribution du contenu genere par les utilisateurs aux services d’avis aux lecteurs. Les resultats indiquent que les contenus generes par les utilisateurs donnent un apercu sur le sujet, les protagonistes et les affects d'une oeuvre.