Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated chargers for EV's and PHEV's: examples and new solutions

The battery is an important component in an electric vehicle (EV) or a plug-in hybrid electric vehicle (PHEV) and it should be charged from the grid in a cost efficient, preferably fast and definitely safe way. The charger could be an on board or an off board charger. For an on board charger it is possible to use available hardware of the traction system, mainly the inverter and the electric motor, in the charger circuit. This is called an integrated charger. In this paper, different examples of integrated chargers are reviewed and explained. Additionally, other possible solutions of integrated chargers are described.
Novel Framework for Spectral Clustering using Topological Node Features(TNF)

Spectral clustering has gained importance in recent years due to its ability to cluster complex data as it requires only pairwise similarity among data points with its ease of implementation. The central point in spectral clustering is the process of capturing pair-wise similarity. In the literature, many research techniques have been proposed for effective construction of affinity matrix with suitable pair- wise similarity. In this paper a general framework for capturing pairwise affinity using local features such as density, proximity and structural similarity is been proposed. Topological Node Features are exploited to define the notion of density and local structure. These local features are incorporated into the construction of the affinity matrix. Experimental results, on widely used datasets such as synthetic shape datasets, UCI real datasets and MNIST handwritten datasets show that the proposed framework outperforms standard spectral clustering methods.
Research on product quality evaluation based on big data analysis

In order to evaluate product quality from nonnumerical data, we propose the product quality evaluation model based on big data analysis including data collecting, data preprocessing, quality feature extraction, vector quantization and quality classification. Quality feature word extension algorithm, reviews quantization algorithm and machine learning algorithm are applied. We finally obtain the qualified rate(88.94%) and 7 features that most concerned by consumers through the analysis of 184,967 effective product reviews of wooden toys. In the end, we compare the SVM machine learning algorithm with decision tree and naive bayes, and discuss the credibility of the results. Our research on product quality evaluation extends the application of big data analysis, and also presents a new method to evaluate product quality in the field of manufacture.
A New Line-Based Orthogonal Iteration Pose Estimation Algorithm

The Orthogonal Iteration (OI) algorithm for pose estimation from point correspondences is global convergent, efficient and robust, which make this algorithm quite attractive. In this paper, we extend the OI algorithm to 2D-3D line correspondences and present an iterative algorithm referred to as LBOI (Line-Based Orthogonal Iterative Algorithm). We first derive a new form of fundamental equations and get an error function which has actual geometry meaning. We then divide the error function into two parts and minimize both of them to estimate the best pose parameters iteratively. The iteration process of our method is similar as that of the OI algorithm. However, the rotation matrix is computed twice in each iteration in our method. For different groups of line correspondences, we present different methods for getting an initial guess. Synthetic experiment data show the proposed algorithm is insensitive to initialization, efficient and accurate. Keywords-Orthogonal Iteration; line correspondences; pose estimation; iterative algorithm;
Translating content-based authorizations for XML documents

Access control policies of XML documents are often specified based on user roles and data content of the documents. Content-based authorization is crucial for providing fine-grained access control to data in XML document. Since authorization rules (authorizations, for short) use path expressions of XPath for locating data in documents, authorization definition is related to structure of the document. However, the structure of XML documents tends to change by various reasons such as application extension and information exchange between organizations. Therefore, authorizations must be revised whenever they become incompatible with a new structure of the document. As far as we know, no previous work has discussed the problem of transforming content-based authorizations for XML documents by using schema mapping information. We define classes for schema and document transformations that allow transforming authorizations without access to source and target XML documents. We propose an algorithm that computes authorizations of role-based access control (RBAC) model for a target DTD instance from given RBAC authorizations of a source DTD instance and schema mapping information under the specified classes of schema and document transformations while preserving the authorization policy of the source DTD instance.
On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms

We introduce a version of the cavity method for diluted mean-field spin models that allows the computation of thermodynamic quantities similar to the Franz–Parisi quenched potential in sparse random graph models. This method is developed in the particular case of partially decimated random constraint satisfaction problems. This allows us to develop a theoretical understanding of a class of algorithms for solving constraint satisfaction problems, in which elementary degrees of freedom are sequentially assigned according to the results of a message passing procedure (belief propagation). We confront this theoretical analysis with the results of extensive numerical simulations.
Enhanced Gas Recovery by CO2 Injection and Sequestration: Effects of Temperature, Vertical and Horizontal Orientations on Dispersion Coefficient

Enhanced gas recovery (EGR) by CO2 injection and sequestration is receiving increased attention within the research community. This is as a result of its potential to be an avenue for the simultaneous additional recovery of natural gas from the reservoir and provide a safe CO2 sequestration site. However, the major problem with this technology lies in the excessive mixing of the injected CO2 and the nascent natural gas (CH4) during the displacement process. This excessive mixing is the reason why the technology has not been widely patronised, given that the recovered CH4 will be heavily contaminated with the injected CO2 thereby making it "lacking" as sales gas after recovery. This hinders the market value of the recovered CH4 and eventually renders it not viable economically. Hence, highlighting the factors responsible for the mixing could provide technical solution to minimise the mixing phenomenon during EGR. This research focuses on the temperature effects and the orientation of the injection pattern of the technique. An experimental core flooding simulation was carried out at a temperature of 50°C and a pressure of 1300 psig and varying injection rates of 0.2 - 0.5 ml/min on Grey Berea sandstone core sample with the sample situated in both vertical and horizontal orientations. It was observed that at higher temperature (50°C) suitable for many gas reservoirs, the disperstion coefficient increased significantly compared to our earlier work (Abba, Abbas, & Nasr, 2017) at 40°C by a factor of 2.3. This trend was due to the increased energy of the gas molecules at the observed conditions, thereby increasing their mobilities. Conversely, the dispersion coefficient also increased significantly by a factor of 3.4 in the horizontal orientation at lower injection rates compared to the vertical core flooding with the concentration profiles showing significant capillary tailing effects at higher flowrates. This signified the effect of gravity in the horizontal orientation was more pronounced at lower injection rates during the injection of CO2 and this will have tremendous effect on the flow behavior of supercritical CO2 during the gas-gas displacement process.
Rendering UML Activity Diagrams as Human-Readable Text

We describe a modification of the Petri Net Linear Form notation to support the rendering of Unified Modeling Language (UML) Activity Diagrams as human-readable text. This new notation, called the Activity Diagram Linear Form, allows UML Activity Diagrams to be expressed in an alternate form with the superior accessibility, compatibility, and simplicity of use of a plain text representation. For some applications, these benefits greatly outweigh the aesthetical and pedagogical advantages of a visual representation.
Sheet erosion assessment in Măhăceni tableland using the U.S.L.E. model

Throughout Măhăceni Tableland, actual erosion processes on slopes, show a strong dynamics and they do realise interrelations and self connections, with a high degree of degradation for some specific areas. Surface erosion is quite active and it carries out some damage potential. The present study shows a way for evaluating the proclivity of steep areas, to contemporary slope erosional processes, using the U.S.L.E. model. The purpose is to realise the sheet erosion map for Măhăceni Tableland.
Efficient FFT pruning algorithm for non-contiguous OFDM systems

This paper presents the study of an efficient trade-off between memory requirements and performance for the implementation of FFT pruning algorithm. FFT pruning algorithm is utilized in NC-OFDM systems to simplify the FFT algorithm complexity in presence of subcarrier sparseness. State-of-the-art implementations offer good performance with the drawback of high resources utilization, i.e. data memory for storage of the configuration matrix. In this work we introduce the partial pruning algorithm as an efficient way to implement FFT pruning, obtaining a balanced trade-off between performance and resources allocation. Cycle accurate simulation results showed that even in presence of low-medium input sparseness levels the proposed algorithm can reduce the computation time of at least a 20% factor, when compared to traditional FFT algorithms and, at the same time, decreases the memory utilization up to a 20% factor over state of the art pruning algorithms.
Model-based imaging of cardiac electrical excitation in humans

Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling ECG mapping and three-dimensional (3-D) + time anatomical data, the electrical excitation sequence can be imaged completely noninvasively in the human heart. In this paper, a bidomain theory-based surface heart model AT imaging approach was applied to single-beat data of atrial and ventricular depolarization in two patients with structurally normal hearts. In both patients, the AT map was reconstructed from sinus and paced rhythm data. Pacing sites were the apex of the right ventricle and the coronary sinus (CS) ostium. For CS pacing, the reconstructed AT pattern on the endocardium of the right atrium was compared with the CARTO map in both patients. The localization errors of the origins of the initial endocardial breakthroughs were determined to be 6 and 12 mm. The sites of early activation and the areas with late activation were estimated with sufficient accuracy. The reconstructed sinus rhythm sequence was in good qualitative agreement with the pattern previously published for the isolated Langendorff-perfused human heart.
Fabrication of nanoscale zero-mode waveguides using microlithography for single molecule sensing

We present a novel approach to the fabrication of zero-mode waveguides (ZMWs) using inexpensive processing techniques. Our method is capable of rapid fabrication of circular nanoapertures with diameters ranging from 70 nm to 2 μm, allowing us to perform a detailed characterization of the dependence of the fluorescence emission on the waveguide diameter. We also validated the use of the fabricated ZMWs by detecting single molecule binding events with a signal-to-noise ratio of ten.
Apical Sealing Ability of Mineral Trioxide Aggregate, Intermediate Restorative Material and Calcium Enriched Mixture Cement: A Bacterial Leakage Study

Introduction: This in vitro study compared the apical sealing ability of three common root end filling materials namely mineral trioxide aggregate (MTA), intermediate restorative material (IRM) and calcium-enriched mixture (CEM) cement using a bacterial leakage model. Methods and Materials: The study was conducted on 83 single-rooted human teeth. Tooth crowns were cut and root canals were prepared using the step-back technique. Apical 3 mm of the roots were cut and a three-mm-deep cavity was prepared using an ultrasonic instrument. The samples were divided into three groups (n=25) according to the root-end filling material including MTA, IRM and CEM cement. The roots were inserted into cut-end microtubes. After sterilization with ethylene oxide, microtubes were placed in sterile vials containing 10 mL of Brain Heart Infusion (BHI) broth and incubated at 37°C and 0.1 mL of Enterococcus faecalis suspension compatible with 0.5 McFarland standard (1.5×108 cell/ ml), which was refreshed daily. This procedure was continued for 70 days. The data were analyzed using the chi-square, Kruskal-Wallis and log rank tests. The level of significance was set at 0.05. Results: No significant difference was found in bacterial microleakage among three groups; MTA showed slightly (but not significantly) less microleakage than IRM and CEM. However, the difference in the mean time of microleakage was significant among the groups (P<0.04) and in MTA samples leakage occurred in a longer time than CEM (P<0.012). Conclusion: The three tested root end filling materials had equal sealing efficacy for preventing bacterial leakage.
A demand-driven approach for efficient interprocedural data flow analysis

Despite the increasing importance of data flow analysis, today's applications in compiler optimizations and software tools still rely on traditional exhaustive analysis algorithms. Exhaustively computing data flow information, especially if interprocedural analysis is involved, is known to be costly. This dissertation develops and experimentally evaluates a new approach to interprocedural data flow analysis that is demand-driven rather than exhaustive. Demand-driven analysis reduces the time and space overhead of exhaustive algorithms by limiting the analysis effort to the collection of information that is actually needed. A general framework is developed for deriving demand-driven interprocedural analysis algorithms from a standard algebraic description of the data flow problem. This framework models a demand for data flow information as a set of queries. A system of query propagation rules is derived that formally describes the resolution of a query. The framework includes a generic demand-driven algorithm that determines the response to a query by a polynomially bounded number of applications of these rules. Experimental results are presented that demonstrate the benefits of the demand-driven approach in practice. Experimentation with two analysis problems, namely reaching definitions and copy constant propagation, shows that demand-driven analysis performs well in practice and reduces the time and space requirements when compared with exhaustive analysis. Additional experimentation evaluates the demand-driven approach when used in a specific software engineering application. The experiments show that demand-driven analysis, if used in the context of data flow integration testing, is significantly faster than exhaustive analysis and even outperforms an improved version of the exhaustive analysis that is based on incremental updates. While experimentation demonstrates that demand-driven analysis can achieve considerable improvements over traditional exhaustive algorithms, the analysis may still include redundant computations. To eliminate these remaining redundancies, the technique of congruence partitioning is developed. Congruence partitioning is performed to optimize the performance of data flow analysis in a preparatory phase prior to the actual solution computation. Congruence partitioning prevents redundant computations by directly manipulating and reducing the solution equation system. A general framework for congruence partitioning is presented that can be used to optimize the performance of either exhaustive or demand-driven analysis algorithms.
State-Based Control of Fuzzy Discrete-Event Systems

To effectively represent possibility arising from states and dynamics of a system, fuzzy discrete-event systems (DESs) as a generalization of conventional DESs have been introduced recently. Supervisory-control theory based on event feedback has been well established for such systems. Noting that the system state description, from the viewpoint of specification, seems more convenient, we investigate the state-based control of fuzzy DESs in this paper. An approach to finding all fuzzy states that are reachable by controlling the system is presented first. After introducing the notion of controllability for fuzzy states, a necessary and sufficient condition for a set of fuzzy states to be controllable is then provided. It was also found that event- and state-based controls are not equivalent, and the relationship between them was further discussed. Finally, we examine the possibility of driving a fuzzy DES under control from a given initial state to a prescribed set of fuzzy states and then keeping it there indefinitely
Peer counting and sampling in overlay networks: random walk methods

In this article we address the problem of counting the number of peers in a peer-to-peer system, and more generally of aggregating statistics of individual peers over the whole system. This functionality is useful in many applications, but hard to achieve when each node has only a limited, local knowledge of the whole system. We propose two generic techniques to solve this problem. The Random Tour method is based on the return time of a continuous time random walk to the node originating the query. The Sample and Collide method is based on counting the number of random samples gathered until a target number of redundant samples are obtained. It is inspired by the "birthday paradox" technique of [6], upon which it improves by achieving a target variance with fewer samples. The latter method relies on a sampling sub-routine which returns randomly chosen peers. Such a sampling algorithm is of independent interest. It can be used, for instance, for neighbour selection by new nodes joining the system. We use a continuous time random walk to obtain such samples. We analyse the complexity and accuracy of the two methods. We illustrate in particular how expansion properties of the overlay affect their performance.
Survivability modeling and analysis for a power-aware wireless ad hoc network

In this paper we propose a Markov model to describe the node behavior for a power-aware wireless ad hoc network, where two energy consumption (high and low) levels are assumed. Since the energy consumption level reflects all the states on the node behavior, we extend the existing DoS attack model in the literature by introducing energy consumption levels in each state. Based on the analytical results on the steady-state probability, we approximately evaluate the quantitative network survivability under three modeling scenarios; Poisson, binomial and negative binomial ones. In numerical examples, we derive the network survivability and investigate the impact on the energy consumption level in terms of the quantitative survivability analysis. It is shown that the network survivability strongly depends on the energy consumption level.
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics

The study of microorganisms that pervade each and every part of this planet has encountered many challenges through time such as the discovery of unknown organisms and the understanding of how they interact with their environment. The aim of this review is to take the reader along the timeline and major milestones that led us to modern metagenomics. This new and thriving area is likely to be an important contributor to solve different problems. The transition from classical microbiology to modern metagenomics studies has required the development of new branches of knowledge and specialization. Here, we will review how the availability of high-throughput sequencing technologies has transformed microbiology and bioinformatics and how to tackle the inherent computational challenges that arise from the DNA sequencing revolution. New computational methods are constantly developed to collect, process, and extract useful biological information from a variety of samples and complex datasets, but metagenomics needs the integration of several of these computational methods. Despite the level of specialization needed in bioinformatics, it is important that life-scientists have a good understanding of it for a correct experimental design, which allows them to reveal the information in a metagenome.
Pemekaran Wilayah dan Otonomi Daerah Pasca Reformasi di Indonesia: Konsep, Fakta Empiris dan Rekomendasi ke Depan

Basically, the regional expansion is a form of regional autonomy and is one of the things that need to be considered because of the presence of regional expansion is expected to further maximize equitable regional development and regional development. In the spirit of regional autonomy was also the emerging paradigm of regional expansion to speed up the implementation of development, ease of public service to the community, as well as the acceleration of social welfare. In the reform era, the space for the area for the proposed establishment of a New Autonomous Region opened wide by the regional expansion policy based on Law no. 22, 1999. With such a policy, the policy of regional expansion is now more dominated by the political process rather than technocratic process.
Analysis of frequency diversity in MC-CDMA systems under correlated fading channels

MC-CDMA can obtain a good frequency diversity effect through spreading and despreading operation in frequency domain. In this paper, the impact of channel parameters to inherent frequency diversity is analyzed at first. Then, three spreading methods to achieve the frequency diversity are investigated. Not only frequency diversity but also multiple-user interference (MUI) is taken into account in our analysis. The achieved frequency diversity gains with different spreading methods are also affected by the combining schemes at the receivers when there are multiple users in the systems.
A Class of Alternating Group Iterative Method for Diffusion Equations

In this paper, a high order implicit scheme for solving diffusion equations is presented, based on which a class of alternating group explicit iterative method(AGI) is derived. Also the convergence analysis and the stability analysis are given. In the end, the results of numerical experiment are presented, which shows the AGI method is convergent and suitable for parallel computation. Mathematics Subject Classification: 65M06, 74S20