Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization

This paper presents a microgrid (MG) energy management strategy by considering renewable energy and battery storage systems. Renewable energy, including wind power generation and solar power generation, is integrated into the distribution network, for which is formulated the optimal dispatch model of mixed-power generation by considering the charging/discharging scheduling of battery storage systems. The MG system has an electrical link for power exchange between the MG and the utility during different hours of the day. Based on the time-of-use (TOU) and all technical constraints, an enhanced bee colony optimization (EBCO) is proposed to solve the daily economic dispatch of MG systems. In the EBCO procedure, the self-adaption repulsion factor is embedded in the bee swarm of the BCO in order to improve the behavior patterns of each bee swarm and increase its search efficiency and accuracy in high dimensions. Different modifications in moving patterns of EBCO are proposed to search the feasible space more effectively. EBCO is used for economic energy management of grid-connected and stand-alone scenarios, and the results are compared to those in previous algorithms. In either grid-connected or stand-alone scenarios, an optimal MG scheduling dispatch is achieved using micro-turbines, renewable energy and battery storage systems. Results show that the proposed method is feasible, robust and more effective than many previously-developed algorithms.
Mass distribution and kinematics of the barred galaxies NGC 3992 and NGC 7479

We present morphological and kinematical models for the intermediate-type barred galaxies NGC 3992 and NGC 7479. Two-dimensional models of the observed NIR surface brightness distributions with three components (bulge, bar, disk) are used for the construction of corresponding models of the stellar mass distribution. The resulting potentials are implemented numerically in a code for the computation of collisionless closed orbits for Hα clouds. These gas clouds serve as test particles in the potential and are used for the examination of the phase space and the construction of artificial gas rotation curves. The artificial rotation curves are confronted with observed Hα rotation curves. The model parameters of bulge, disk, and bar are adjusted by iteratively computing and evaluating a large number of different models, until an optimal fit to the observations was obtained. The effects of changing single parameter values and possible error sources are discussed. Special emphasis is put on the role of bar asymmetries in the case of NGC 7479 and the bar pattern speed in both objects. Final models are presented which reproduce the overall morphological structure (bulge, bar, disk) of NGC 3992 and NGC 7479 as well as the Hα rotation curves with most of their details in a consistent manner. The parameters needed for an optimal representation of the Hα rotation curves and those derived morphologically are in very good agreement. It is shown that our modelling technique is able to explain the Hα velocity fields of both galaxies especially in the regions dominated by the bar component.
DR-Marker: A Novel Diminishing-Reality-Based AR Registration

This paper proposes a novel diminishing-reality-based AR registration, called DR-marker. It is used in our proposed filmmaking system ARFMS. Although it is based on marker-based registration method, the user will not see the special marker in the real environment via diminishing reality technology. Users can apply the DR-marker just like real scene element, because it takes the advantages of marker-based and marker less-based registration approaches. After marker detection, projecting model is built to replace the marker. Then the projecting model can be tracked like a CG object. The marker may be occluded by scene elements. The virtual-to-real occlusion ensures that the DR-marker looks like the real scene elements without wrong spatial relationship. And DR-marker approach is based on a simple setup. This paper implements DR-marker in real-time. It demonstrates DR-marker is a stable and low-computation AR registration method. DR-marker can be effective used in the ARFMS. The user can perform with the CG character and stage property just like real ones.
BenchKit, a Tool for Massive Concurrent Benchmarking

Benchmarking numerous programs in a reasonable time requires the use of several (potentially multicore) computers. We experimented such a situation in the context of the MCC (Model Checking Contest @ Petri net) where we had to operate more than 52000 runs for the 2013 edition. This paper presents BenchKit, a tool to operate programs on sets of potentially parallel machines and to gather monitoring information like CPU or memory usage. It also samples such data over the execution time. BenchKit has been elaborated in the context of the MCC and will be used for the 2014 edition.
Empirical Case Studies in Attribute Noise Detection

The quality of data is an important issue in any domain-specific data mining and knowledge discovery initiative. The validity of solutions produced by data-driven algorithms can be diminished if the data being analyzed are of low quality. The quality of data is often realized in terms of data noise present in the given dataset and can include noisy attributes or labeling errors. Hence, tools for improving the quality of data are important to the data mining analyst. We present a comprehensive empirical investigation of our new and innovative technique for ranking attributes in a given dataset from most to least noisy. Upon identifying the noisy attributes, specific treatments can be applied depending on how the data are to be used. In a classification setting, for example, if the class label is determined to contain the most noise, processes to cleanse this important attribute may be undertaken. Independent variables or predictors that have a low correlation to the class attribute and appear noisy may be eliminated from the analysis. Several case studies using both real-world and synthetic datasets are presented in this study. The noise detection performance is evaluated by injecting noise into multiple attributes at different noise levels. The empirical results demonstrate conclusively that our technique provides a very accurate and useful ranking of noisy attributes in a given dataset.
Effectiveness of Parent–Child Number Board Game Playing in Promoting Chinese Kindergarteners’ Numeracy Skills and Mathematics Interest

ABSTRACT Research Findings: In Study 1, we observed 32 Chinese kindergarteners playing a number board game with their caregivers in dyads. Number board game playing provided important opportunities for kindergarteners and their caregivers to talk about an array of number concepts, but their numeracy-related exchanges rarely went beyond counting. In Study 2, 88 Chinese kindergarteners and their parents were randomly assigned to 1 of 4 groups, namely, the game with parent training group, the game without parent training group, the exercise book group, and the control group. After a 4-week intervention, kindergarteners in the 3 treatment groups who began as relatively unskilled in numeracy showed comparable improvement in 3 numeracy tasks, and the effect sizes were often larger in the game with parent training group than in the game without parent training group. Moreover, kindergarteners in the 2 game groups tended to show relatively heightened mathematics interest. Practice or Policy: Findings of these 2 studies suggest that number board game playing has the potential benefit of promoting kindergarteners’ numeracy competence and mathematics interest. Training of parents might even optimize kindergarten children’s gains from parent–child number board game playing.
Union status of young men in Britain: a decade of change

Previous empirical studies of individual union status in Britain have been cross-sectional. In contrast, we use longitudinal data from the National Child Development Study, to estimate the determinants of male trade union membership over the period 1981-1991. As suggested by union theories, we find that it is important to control for unobserved individual heterogeneity, and our preferred model allows for correlation of individual heterogeneity with observable variables. Our estimates reveal that the observed decline in very large workplaces, and the contraction of the public sector, explain about one third of the predicted decline in union membership over the period. Copyright © 2000 John Wiley & Sons, Ltd.
Image-guided streamline placement on curvilinear grid surfaces

The success of using a streamline technique for visualizing a vector field usually depends largely on the choice of adequate seed points. G. Turk and D. Banks (1996) developed an elegant technique for automatically placing seed points to achieve a uniform distribution of streamlines on a 2D vector field. Their method uses an energy function calculated from the low-pass filtered streamline image to guide the optimization process of the streamline distribution. This paper proposes a new technique for creating evenly distributed streamlines on 3D parametric surfaces found in curvilinear grids. We make use of Turk and Banks's 2D algorithm by first mapping the vectors on a 3D surface into the computational space of the curvilinear grid. To take into the consideration the mapping distortion caused by the uneven grid density in a curvilinear grid, a new energy function is designed and used for guiding the placement of streamlines in the computational space with desired local densities.
Unsupervised Learning of a Finite Dirichlet Mixture Model

This paper presents an unsupervised algorithm for learning a finite Dirichlet mixture model. The proposed approach for estimating the parameters of a Dirichlet mixture is based on the Maximum Likelihood (ML) and Fisher scoring methods. Experimental results are presented for the following applications: summarizing of image databases for efficient retrieval, and human skin color modeling and its application to skin detection in multimedia databases.
Top Quark Chromomagnetic Dipole Moment in the Littlest Higgs Model with T-Parity

The littlest Higgs model with T-parity, which is called LHT model, predicts the existence of the new particles, such as heavy top quarks, heavy gauge bosons, and mirror fermions. We calculate the one-loop contributions of these new particles to the top quark chromomagnetic dipole moment (CMDM) ΔK. We find that the contribution of the LHT model is one order of magnitude smaller than the standard model prediction value.
The Use of Lisp in Semantic Web Applications

The Semantic Web adds a layer of logic and metadata to the current World Wide Web. By utilizing traditional Artifical Intelligence (AI) and Knowledge Representation (KR) techniques for both the construction of new documents and linking of existing ones, the Semantic Web facilitates machineto-machine (or ”agent-to-agent”) communication. Lisp’s proven reliability and flexibility in AI and KR make it ideal for constructing intelligent Semantic Web applications. In this paper, we survey the current use of Lisp on the Semantic Web, and suggest some potential uses of it in the future. We conclude the paper with descriptions of Lisp in selected Semantic Web projects that demonstrate its strength and usability. General Terms Semantic Web, AI, Lisp, Web Services, Reasoning, Planning
Teaching Agile Collaboration Skills in the Classroom

Agile methodologies like Scrum or Extreme Programming have come a long way over the last fifteen years. Recent quantitative studies show that many companies have successfully adopted agile methodologies. It was found that in agile software development, experience leads to collaboration. It could also be shown that successful professional agile teams tend to use more collaboration practices. In 2013, the new Computer Science studies at the University of Applied Sciences were started. For this, a new curriculum was developed. This paper presents and discusses the lectures, labs and educational software projects in the programming and software engineering modules. It is discussed how agile collaboration and collaboration practices can be taught in the classroom. For this, the setup and observations of an agile student project are presented and different online collaboration tools are discussed. It is argued that software engineering education benefits significantly from embracing the modern collaboration tools the Internet has made available.
The Development of a Portable SPR Bioanalyzer for Sensitive Detection of Escherichia coli O157:H7

The purpose of this study was to develop a portable surface plasmon resonance (SPR) bioanalyzer for the sensitive detection of Escherichia coli O157:H7 in comparison with an enzyme-linked immunosorbent assay (ELISA). The experimental setup mainly consisted of an integrated biosensor and a homemade microfluidic cell with a three-way solenoid valve. In order to detect Escherichia coli O157:H7 using the SPR immunoassay, 3-mercaptopropionic acid (3-MPA) was chemisorbed onto a gold surface via covalent bond for the immobilization of biological species. 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) and N-hydroxysuccinimide (NHS) were used as crosslinker reagents to enable the reaction between 3-MPA and Escherichia coli O157:H7 antibodies by covalent –CO–NH– amide bonding. The experimental results were obtained from the Escherichia coli O157:H7 positive samples prepared by 10-, 20-, 40-, 80-, and 160-fold dilution respectively, which show that a good linear relationship with the correlation coefficient R of 0.982 existed between the response units from the portable SPR bioanalyzer and the concentration of Escherichia coli O157:H7 positive samples. Moreover, the theoretical detection limit of 1.87 × 103 cfu/mL was calculated from the positive control samples. Compared with the Escherichia coli O157:H7 ELISA kit, the sensitivity of this portable SPR bioanalyzer is four orders of magnitude higher than the ELISA kit. The results demonstrate that the portable SPR bioanalyzer could provide an alternative method for the quantitative and sensitive determination of Escherichia coli O157:H7 in field.
Lipoprotein Lipase, Tissue Expression and Effects on Genes Related to Fatty Acid Synthesis in Goat Mammary Epithelial Cells

Lipoprotein lipase (LPL) serves as a central factor in hydrolysis of triacylglycerol and uptake of free fatty acids from the plasma. However, there are limited data concerning the action of LPL on the regulation of milk fat synthesis in goat mammary gland. In this investigation, we describe the cloning and sequencing of the LPL gene from Xinong Saanen dairy goat mammary gland, along with a study of its phylogenetic relationships. Sequence analysis showed that goat LPL shares similarities with other species including sheep, bovine, human and mouse. LPL mRNA expression in various tissues determined by RT-qPCR revealed the highest expression in white adipose tissue, with lower expression in heart, lung, spleen, rumen, small intestine, mammary gland, and kidney. Expression was almost undetectable in liver and muscle. The expression profiles of LPL gene in mammary gland at early, peak, mid, late lactation, and the dry period were also measured. Compared with the dry period, LPL mRNA expression was markedly greater at early lactation. However, compared with early lactation, the expression was lower at peak lactation and mid lactation. Despite those differences, LPL mRNA expression was still greater at peak, mid, and late lactation compared with the dry period. Using goat mammary epithelial cells (GMEC), the in vitro knockdown of LPL via shRNA or with Orlistat resulted in a similar degree of down-regulation of LPL (respectively). Furthermore, knockdown of LPL was associated with reduced mRNA expression of SREBF1, FASN, LIPE and PPARG but greater expression of FFAR3. There was no effect on ACACA expression. Orlistat decreased expression of LIPE, FASN, ACACA, and PPARG, and increased FFAR3 and SREBF1 expression. The pattern of LPL expression was similar to the changes in milk fat percentage in lactating goats. Taken together, results suggest that LPL may play a crucial role in fatty acid synthesis.
Problem-Solving Support in a Constraint- based Tutor for UML Class Diagrams

We present COLLECT-UML, a constraint-based tutoring system that teaches object-oriented analysis and design using Unified Modelling Language (UML), a popular object-oriented modelling technology. Constraint-Based Modelling (CBM) has been used successfully in several tutoring systems, which have proven to be effective in evaluations performed in real classrooms. In this paper, we present problem-solving support available in COLLECT-UML. The system observes students’ actions and adapts to their knowledge and learning abilities. We describe the system’s architecture and functionality. The effectiveness of the system has been evaluated in two studies with students taking ITS and software engineering courses. Objective data shows that students’ performance increases significantly while interacting with the system, and that they do learn the domain concepts. The students have enjoyed the system’s adaptivity and found it a valuable asset to their learning.
Overcoming the Gap between Design at Electronic System Level (ESL) and Implementation for Networked Electronics

Transition to model based design of systems at electronic sys-tem level (ESL) has greatly reduced the complexity by raising the level of abstraction. It resulted in improvements in quality of design, reduction of development time, and reduction of number of iterations in design. A new emerging problem is the widening of the gap between design at ESL and implementation. In this paper a method is presented to overcome this gap. For the design of a inexpensive high precision positioning system based on GPS, additional sensors, and differential signals, functional requirements are mapped into a MLDesigner 1 model containing functional model, environmental model, and use cases. Annotations on the model are automatically translated in an architectural model with channels, resources, events, and memory from a developed standardized library. After design iterations on architecture from a selected sub-model, an implementation framework for hardware and software code is generated and I/O speciﬁc parameters are added. The code can be used on FPGAs and embedded processors.
Statistical analysis of SHAPE-directed RNA secondary structure modeling.

The ability to predict RNA secondary structure is fundamental for understanding and manipulating RNA function. The information obtained from selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) experiments greatly improves the accuracy of RNA secondary structure prediction. Recently, Das and colleagues [Kladwang, W. , et al. (2011) Biochemistry 50, 8049-8056] proposed a "bootstrapping" approach for estimating the variance and helix-by-helix confidence levels of predicted secondary structures based on resampling (randomizing and summing) the measured SHAPE data. We show that the specific resampling approach described by Kladwang et al. introduces systematic errors and underestimates confidence in secondary structure prediction using SHAPE data. Instead, a leave-data-out jackknife approach better estimates the influence of a given experimental data set on SHAPE-directed secondary structure modeling. Even when 35% of the data were left out in the jackknife approach, the confidence levels of SHAPE-directed secondary structure prediction were significantly higher than those calculated by Das and colleagues using bootstrapping. Helix confidence levels were thus underestimated in the recent study, and the resampling approach implemented by Kladwang et al. is not an appropriate metric for evaluating SHAPE-directed secondary structure modeling.
Multiple Mini-Interviews (MMI) and Semistructured Interviews for the Selection of Family Medicine Residents: A Comparative Analysis

Background. Family Medicine Residency Program at the Aga Khan University has applicants for the residency position in excess of the positions offered resulting in formulation of certain selection criteria. The objective of this study was to compare MMI versus semistructured interviews for assessing noncognitive domains in the selection of residents. The secondary objectives were to determine perceptions of the interviewers and candidates for the acceptability and feasibility of MMI as a selection tool. Methods. The candidates underwent semistructured interviews along with MMI and identical attributes were tested in both. The attributes tested were safe doctor, communication skills, professionalism, problem solving, team approach, ethical issues, reasons for selecting family medicine, and commitment to the program. Descriptive statistics were calculated and comparison between ratings for MMI and interview was performed by Wilcoxon sign rank test. Results. Total number of candidates was 14. On comparison between interview and MMI, the scores were not statistically different for all attributes except ethics (mean interview scores: 3.04, mean MMI scores: 2.5, and P value 0.046). Conclusion. The study showed no difference between MMI and semistructured interviews. However, it needs to be replicated in order to determine the predictive validity and feasibility of MMI over time.
ntelligent News Filtering

As the size of the Internet increases, the amount of data available to users has dramatically risen, resulting in an information overload for users. This work involved the creation of an intelligent information news filtering system named INFOS (Intelligent News Filtering Organizational System) to reduce the user's search burden by automatically eliminating Usenet news articles predicted to be irrelevant. These predictions are learned automatically by adapting an internal user model that is based upon features taken from articles and collaborative features derived from other users. The features are manipulated through keyword-based techniques and knowledge-based techniques to perform the actual filtering. Knowledge-based systems have the advantage of analyzing input text in detail, but at the cost of computational complexity and the difficulty of scaling up to large domains. In contrast, statistical and keyword approaches scale up readily but result in a shallower understanding of the input. A hybrid system integrating both approaches improves accuracy over keyword approaches, supports domain knowledge, and retains scalability. The system would be enhanced by more robust word disambiguation.