Miracle-dz/UniFAR-FTUs huggingface dataset:
Switching angles generation for selective harmonic elimination by using artificial neural networks and quasi-newton algorithm

A hybrid method based on artificial neural networks (ANNs) and Quasi-Newton algorithm is proposed to generate the switching angles for selective harmonic elimination (SHE), which makes a compromise among the memory consumption, executing efficiency and the solution precision. Unlike the other ANNs based methods which use ANNs to directly give the final switching angles, this hybrid method just uses ANNs to give the initial values, which lowers the precision requirement on training the ANNs, so, the number of the neurons can be reduced and less on-chip memories are required. Then, the Quasi-Newton algorithm is used to solve the exact switching angles from the initial values given by the ANNs, which guarantees the solving efficiency and the solution precision. The case of 11-level staircase modulated converter is studied by using the single-layer back-propagation (BP) neural networks. The trained neural networks have only 9 neurons in the hidden layer and the output initial values can meet the convergent requirement of the Quasi-Newton algorithm in the full range of modulation index. The total executing time is about 70ms on a STM32F407 microcontroller, as the executed code is automatically generated by MATLAB, the executing time could be further reduced if the code is manual optimized. Experiment results are also shown to verify the correctness of the switching angles generated by the proposed hybrid method.
A method for utilizing diploid/dominance in genetic search

A method is proposed for implementing diploid/dominance in genetic search. Both diploid strings, in a single gene example are evaluated to produce two intermediate phenotypes which then determine dominance. Experimental results indicate that changing global optima are accurately identified and followed. The method is problem independent and can be readily extended to a polyploid chromosome structure. <<ETX>>
Factors Affecting the Introduction of New Vaccines to Poor Nations: A Comparative Study of the Haemophilus influenzae Type B and Hepatitis B Vaccines

Background A major effort to introduce new vaccines into poor nations of the world was initiated in recent years with the help of the GAVI alliance. The first vaccines introduced have been the Haemophilus influenzae type B (Hib) and the hepatitis B (Hep B) vaccines. The introduction of these vaccines during the first phase of GAVI's operations demonstrated considerable variability. We set out to study the factors affecting the introduction of these vaccines. The African Region (AFRO), where new vaccines were introduced to a substantial number of countries during the first phase of GAVI's funding, was selected for this study. Methodology/Principal Findings GAVI-eligible AFRO countries with a population of 0.5 million or more were included in the study. Countries were analyzed and compared for new vaccine introduction, healthcare indicators, financial indicators related to healthcare and country-level Governance Indicators, using One Way ANOVA, correlation analysis and Qualitative Comparative Analysis (QCA). Introduction of new vaccines into AFRO nations was associated primarily with high country-level Governance Indicator scores. The use of individual Governance Indicator scores, as well as a combined Governance Indicator score we developed, demonstrated similar results. Conclusions/Significance Our study results indicate that good country-level governance is an imperative pre-requisite for the successful early introduction of new vaccines into poor African nations. Enhanced support measures may be required to effectively introduce new vaccines to countries with low governance scores. The combined governance score we developed may thus constitute a useful tool for helping philanthropic organizations make decisions regarding the type of support needed by different countries to achieve success.
Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma

We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs) when the number of observations is much larger than the number of coefficients ($n \gg p \gg 1$). In this regime, optimization algorithms can immensely benefit from approximate second order information. We propose an alternative way of constructing the curvature information by formulating it as an estimation problem and applying a Stein-type lemma, which allows further improvements through sub-sampling and eigenvalue thresholding. Our algorithm enjoys fast convergence rates, resembling that of second order methods, with modest per-iteration cost. We provide its convergence analysis for the general case where the rows of the design matrix are samples from a sub-gaussian distribution. We show that the convergence has two phases, a quadratic phase followed by a linear phase. Finally, we empirically demonstrate that our algorithm achieves the highest performance compared to various algorithms on several datasets.
A game-based mobile-learning platform – description and evaluation

This paper presents a platform for mobile educatio n l games, named "Quest for Knowledge. " The platform allows for creation of interactive educati onal games that include a multimedia content (video , audio, documents, maps) and playing of games outside class room . Students access the games using personal com puters and mobile terminals (phones, PDAs). The usual game scenarios include team-based solving of specific t asks and missions. The games are remotely monitored and mana ged by the teacher who interacts with teams, gives hint and guidance which lead to the final result. The concep t of development and playing of mobile educational games has been developed under the project eMAPPS, 6 EU Frame work Programme and tested in groups of more than 20 0 students and teachers in Slovenia and other Europea n countries. The evaluation of games played in Slov enia has indicated a very strong acceptance of the concept, es ecially by elementary-school children.
Optimization metaheuristics for minimizing variance in a real-world statistical application

Non-technical energy losses mostly arise from illegal use of energy. Energy distribution companies need to estimate the sources of these losses in order to take actions for reducing them. In this work we formulate a stratified sampling procedure as a non-linear restricted optimization problem, in which the variance of overall energy loss due to the fraudulent activities is minimized. Solving this problem analytically is difficult and therefore we customize two metaheuristics, Genetic Algorithm and Simulated Annealing, for finding practical solutions for the problem. Numerical experiments and comparison to a proportional allocation scheme are presented.
Characterization of Renal Angiotensin-Converting Enzyme 2 in Diabetic Nephropathy

Abstract—ACE2, initially cloned from a human heart, is a recently described homologue of angiotensin-converting enzyme (ACE) but contains only a single enzymatic site that catalyzes the cleavage of angiotensin I to angiotensin 1–9 [Ang(1–9)] and is not inhibited by classic ACE inhibitors. It also converts angiotensin II to Ang(1–7). Although the role of ACE2 in the regulation of the renin-angiotensin system is not known, the renin-angiotensin system has been implicated in the pathogenesis of diabetic complications and in particular in diabetic nephropathy. Therefore, the aim of this study was to assess the possible involvement of this new enzyme in the kidney from diabetic Sprague-Dawley rats to compare and contrast it to ACE. ACE2 and ACE gene and protein expression were measured in the kidney after 24 weeks of streptozocin diabetes. ACE2 and ACE mRNA levels were decreased in diabetic renal tubules by ≈50% and were not influenced by ACE inhibitor treatment with ramipril. By immunostaining, both ACE2 and ACE protein were localized predominantly to renal tubules. In the diabetic kidney, there was reduced ACE2 protein expression that was prevented by ACE inhibitor therapy. The identification of ACE2 in the kidney, its modulation in diabetes, and the recent description that this enzyme plays a biological role in the generation and degradation of various angiotensin peptides provides a rationale to further explore the role of this enzyme in various pathophysiological states including diabetic complications.
Divergence in Dialogue

One of the best known claims about human communication is that people's behaviour and language use converge during conversation. It has been proposed that these patterns can be explained by automatic, cross-person priming. A key test case is structural priming: does exposure to one syntactic structure, in production or comprehension, make reuse of that structure (by the same or another speaker) more likely? It has been claimed that syntactic repetition caused by structural priming is ubiquitous in conversation. However, previous work has not tested for general syntactic repetition effects in ordinary conversation independently of lexical repetition. Here we analyse patterns of syntactic repetition in two large corpora of unscripted everyday conversations. Our results show that when lexical repetition is taken into account there is no general tendency for people to repeat their own syntactic constructions. More importantly, people repeat each other's syntactic constructions less than would be expected by chance; i.e., people systematically diverge from one another in their use of syntactic constructions. We conclude that in ordinary conversation the structural priming effects described in the literature are overwhelmed by the need to actively engage with our conversational partners and respond productively to what they say.
Computational design of Candida boidinii xylose reductase for altered cofactor specificity

In this study we introduce a computationally‐driven enzyme redesign workflow for altering cofactor specificity from NADPH to NADH. By compiling and comparing data from previous studies involving cofactor switching mutations, we show that their effect cannot be explained as straightforward changes in volume, hydrophobicity, charge, or BLOSUM62 scores of the residues populating the cofactor binding site. Instead, we find that the use of a detailed cofactor binding energy approximation is needed to adequately capture the relative affinity towards different cofactors. The implicit solvation models Generalized Born with molecular volume integration and Generalized Born with simple switching were integrated in the iterative protein redesign and optimization (IPRO) framework to drive the redesign of Candida boidinii xylose reductase (CbXR) to function using the non‐native cofactor NADH. We identified 10 variants, out of the 8,000 possible combinations of mutations, that improve the computationally assessed binding affinity for NADH by introducing mutations in the CbXR binding pocket. Experimental testing revealed that seven out of ten possessed significant xylose reductase activity utilizing NADH, with the best experimental design (CbXR‐GGD) being 27‐fold more active on NADH. The NADPH‐dependent activity for eight out of ten predicted designs was either completely abolished or significantly diminished by at least 90%, yielding a greater than 104‐fold change in specificity to NADH (CbXR‐REG). The remaining two variants (CbXR‐RTT and CBXR‐EQR) had dual cofactor specificity for both nicotinamide cofactors.
Growth, innovation, scaling, and the pace of life in cities

Humanity has just crossed a major landmark in its history with the majority of people now living in cities. Cities have long been known to be society's predominant engine of innovation and wealth creation, yet they are also its main source of crime, pollution, and disease. The inexorable trend toward urbanization worldwide presents an urgent challenge for developing a predictive, quantitative theory of urban organization and sustainable development. Here we present empirical evidence indicating that the processes relating urbanization to economic development and knowledge creation are very general, being shared by all cities belonging to the same urban system and sustained across different nations and times. Many diverse properties of cities from patent production and personal income to electrical cable length are shown to be power law functions of population size with scaling exponents, β, that fall into distinct universality classes. Quantities reflecting wealth creation and innovation have β ≈1.2 >1 (increasing returns), whereas those accounting for infrastructure display β ≈0.8 <1 (economies of scale). We predict that the pace of social life in the city increases with population size, in quantitative agreement with data, and we discuss how cities are similar to, and differ from, biological organisms, for which β<1. Finally, we explore possible consequences of these scaling relations by deriving growth equations, which quantify the dramatic difference between growth fueled by innovation versus that driven by economies of scale. This difference suggests that, as population grows, major innovation cycles must be generated at a continually accelerating rate to sustain growth and avoid stagnation or collapse.
Collaborative synchronous video annotation via the watch-and-comment paradigm

Earlier work has described the WaCTool, which allows a user to perform multimodal annotations (text, digital ink and audio comments) and edit operations on a linear video, producing an interactive video as a result. The fact that the prototype had been extended to allow users to use an integrated chat and share resources in a peer-to-peer-based environment lead users to identify an opportunity to share digital ink notes. The work reported in this paper aims at evaluating a prototype designed to allow the synchronous collaborative editing comments on top of video frames selected by users for annotation. We discuss our aim in allowing this new form of collaboration and present the results of evaluation using four complementary methods: think aloud, heuristics, cognitive walkthrough and questionnaires.
Coronary reserve of high- and low-flow regions in the dog heart left ventricle.

BACKGROUND Left ventricular myocardial blood flow is spatially heterogeneous. The hypothesis we tested was whether myocardial areas with a steady-state flow <0.5 times mean flow are underperfused and areas with flow > 1.5 times mean flow are overperfused. METHODS AND RESULTS In anesthetized beagle dogs (n=10), the relationship between local blood flow versus S-adenosylhomocysteine (SAH) concentration, a measure of the free intracellular adenosine concentration, and lactate, a measure of the myocardial NADH/NAD+ ratio, were determined under control conditions and after coronary constriction. Control local myocardial blood flow was 0.99+/-0.46 mL x min(-1) x g(-1), with a coefficient of variation of 0.36+/-0.12 (n=256 per heart; sample wet mass, 125+/-30 mg). Tissue concentrations of SAH (3.4+/-2.5 nmol/g) and lactate (1.88+/-0.80 micromol/g) were not elevated in low-flow samples. However, after coronary artery constriction, poststenotic blood flow decreased from 1.00+/-0.27 to 0.49+/-0.22 mL x min(-1) x g(-1) (P<0.04), with significant correlation between local SAH and flow (r=-0.59) and lactate and flow (r = -0.50). Although nearly all samples from control high-flow regions showed increased SAH concentrations if relative flow after stenosis was <1.0, control low-flow samples frequently displayed low SAH concentrations. The percent reduction in flow determined the changes in the local SAH and lactate concentration, independent of the local control blood flow. CONCLUSIONS When the coronary inflow is unrestricted, the oxygen supply to control low-flow regions meets metabolic demand. Flow to control high-flow regions reflects a higher local demand rather than overperfusion. Thus, blood flow heterogeneity most likely reflects differences in aerobic metabolism.
Does Bad News Go Away Faster?

We study the relationship between content and temporal dynamics of information on Twitter, focusing on the persistence of information. We compare two extreme temporal patterns in the decay rate of URLs embedded in tweets, defining a prediction task to distinguish between URLs that fade rapidly following their peak of popularity and those that fade more slowly. Our experiments show a strong association between the content and the temporal dynamics of information: given unigram features extracted from corresponding HTML webpages, a linear SVM classifier can predict the temporal pattern of URLs with high accuracy. We further explore the content of URLs in the two temporal classes using various textual analysis techniques (via LIWC and trend detection). We find that the rapidly-fading information contains significantly more words related to negative emotion, actions, and more complicated cognitive processes, whereas the persistent information contains more words related to positive emotion, leisure, and lifestyle.
Remote control for pervasive services

In this paper, we present the REACHeS architecture for controlling pervasive services through physical user interfaces, using a mobile terminal and icons placed in the environment. An icon advertises a service that can be started by touching the icon with a mobile terminal. This service activation configures the mobile terminal as a remote control for the service. We have implemented this architecture and designed an icon set. The physical user interface is based on RFID technology: the terminals are equipped with RFID readers and RFID tags are placed under the icons. We present the first prototype applications and the first usability tests that we have carried out.
Detachment of Solids and Nitrifiers in Integrated, Fixed‐Film Activated Sludge Systems

Despite the importance of detachment to biofilm processes, detachment phenomena are not well understood. In this study, researchers investigated biofilm detachment from free‐floating biofilm carriers that were established in an integrated, fixed‐film activated sludge (IFAS) installation in Mississauga, Ontario. A method for assessing detachment from biofilm carrier systems was devised, evaluated, and refined during this study. In the absence of substrate, superficial air velocity significantly affected the 24‐hour detachment rates of total suspended solids from the carriers. Short‐term growth conditions did not appear to significantly affect the rate of detachment of solids and nitrifiers. The measured solids‐detachment rates were found to be described by a second order function of biofilm attached growth total solids with a detachment coefficient of 0.006 ± 0.0008 (g/m · d) −1.
Cash Management and Payment Choices: A Simulation Model with International Comparisons

Despite various payment innovations, today, cash is still heavily used to pay for low-value purchases. This paper develops a simulation model to test whether standard implications of the theory on cash management and payment choices can explain the use of payment instruments by transaction size. In particular, using diary survey data from Canada, France, Germany and the Netherlands, we test the assumption that cash is still the most efficient payment instrument, and the idea that people hold cash for precautionary reasons when facing uncertainty about their future purchases. The results of the simulations show that these two factors are significant determinants of the high shares of low-value cash payments in Canada, France and Germany. Yet, they are not so crucial in the Netherlands, which exhibits a significant share of low-value card transactions. We discuss how the differences in payment markets across countries may explain the performance of the model.
Competition Policy and the Incentive to Innovate: The Dynamic Effects of Microsoft v. Commission

Microsoft v. Commission indicates a shift in competition policy at the expense of protections for intellectual property. The case applies "essential facilities" arguments to Microsoft's server operating system and "tying" arguments to its Windows Media Player. The dynamic effects of Microsoft v. Commission pose a substantial risk to the incentive to innovate in several ways. First, mandatory licensing and unbundling of the elements of an invention erode intellectual property rights. Second, the targeting of multinational corporations by the European Union creates barriers to international trade whose impacts extend across the global economy. Third, the interpretation of "abuse of a dominant position" focuses on market outcomes rather than on anticompetitive conduct, thus penalizing successful innovators and rewarding their competitors. Competition policy based on Microsoft v. Commission diminishes the incentive to innovate.
Epidemiology of tropical cyclones: the dynamics of disaster, disease, and development.

Tropical cyclones—variously defined as hurricanes, typhoons, and cyclones—regularly impact human populations and periodically produce devastating weather-related natural disasters. The epidemiology of tropical cyclones is fundamentally determined by the physical forces of massive cyclonic systems intersecting with patterns of human behavior. The destructive forces of cyclonic winds, inundating rains, and storm surge are frequently accompanied by floods, tornadoes, and landslides (1, 2). Human factors include land use and settlement patterns, building design and construction, forecasting and warning systems, risk perception, evacuation, and sheltering. Preparedness and mitigation strategies for minimizing harm include family disaster planning, stocking of hurricane supplies, protection of home sites, timely response to public warnings, and alertness to poststorm hazards. Public health consequences associated with tropical cyclones include storm-related mortality, injury, infectious disease, psychosocial effects, displacement and homelessness, damage to the health-care infrastructure, disruption of public health services, transformation of ecosystems, social dislocation, loss of jobs and livelihood, and economic crisis. These outcomes disproportionately befall developing nations, and human factors strongly influence the observed disparities (3).
Adaptive Firefly Algorithm: Parameter Analysis and its Application

As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm — adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem — protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0. 4Å and 1. 5Å from the native constrains with noise free and 10% Gaussian white noise.