Miracle-dz/UniFAR-FTUs huggingface dataset:
A Theoretic Revelation of Strategic Positioning, Value Co-Creation and Innovation Performance

In recent years, with the extensive use of 4G and wireless network technology, the development of mobile Internet industry has shown explosive growth, which leads to rapid development of intelligent mobile phone industry. The coming of a new era, not only changes people's way of life, study and work, but also gradually changes the mode of operation and the strategic layout of an enterprise. This paper takes Xiaomi Company as a research object, basing on Michael Porter's competitive advantage theory, product strategy model, and value co-creation theory, to analyze the characteristics of creating value while innovating of Xiaomi Company. Grounded Theory is used in this paper to explore the relations between strategic positioning, value co-creation and innovation performance.
SYNTHESIS AND CHARACTERIZATION OF Ag@TiO2 CORE-SHELL NANOPARTICLES AND STUDY OF ITS ANTIBACTERIAL ACTIVITY

Core-shell type Ag@TiO2 nano particles were prepared by one pot simultaneous reduction of AgNO3 and hydrolysis of Ti(IV) isopropoxide. They were characterized by absorption, XRD, FTIR, TGA, DSC and HR-TEM techniques. XRD patterns show the presence of anatase form of TiO2 and the noble metal (Ag). High resolution transmission electron microscopic measurements revealed that their size is below 50 nm. The antibacterial properties of Ag@TiO2 core-shell nanoparticles against Escherichia coli (E.coli) and Staphylococcus aureus (S.aureus) were examined by the agar diffusion method . As a result E.coli and S.aureus were shown to be substantially inhibited by Ag@TiO2 core-shell nanoparticles. These result demonstrated that TiO2 supported on the surface of Ag NPs without aggregation was proved to be a good novel antibacterial activity.
Fixed Frequency Sliding Mode Control of Power Converters for Improved Dynamic Response in DC Micro-Grids

The rapid decrease in conventional energy resources and their harmful impact on the environment has brought the attention of the researchers towards the use of renewable energy technologies. The renewable energy systems are connected to Direct Current (DC) micro-grids via power electronic converters where the load conditions are unknown and network parameters are uncertain. These conditions call for the use of robust control techniques such as Sliding Mode Control (SMC) in order to regulate the grid voltage. However, SMC has a drawback of operating the power converter at variable switching frequency which results in degrading the power quality. This paper introduces a fixed frequency sliding mode controller that does not suffer from this predicament. A novel double integral type switching manifold is proposed to achieve voltage regulation of a DC micro-grid, in the presence of unknown load demands and un-modeled dynamics of the network. Rigorous mathematical analysis is carried out for the stability of the closed loop system and the technique is experimentally validated on position of a DC micro-grid using a specially designed test rig. For benchmarking purposes, a conventional Proportional Integral (PI) controller is also implemented. An improvement of 2.5% in rise time, 6.7% in settling time and reduction of voltage dip by 31.7% during load transaction is achieved as compared to the PI controller. The experiment confirms the hypothesis that fixed frequency SMC shows better performance than its counterpart in the phase of introduced disturbances.
High‐precision distance measurements and volume‐conserving segmentation of objects near and below the resolution limit in three‐dimensional confocal fluorescence microscopy

This study presents a method for high‐precision distance measurements and for the volume‐conserving segmentation of fluorescent objects with a size of the order of the microscopic observation volume. The segmentation was performed via a model‐based approach, using an algorithm that was calibrated by the microscopic point spread function. Its performance was evaluated for three different fluorochromes using model images and fluorescent microspheres as test targets. The fundamental limits which the microscopic imaging process imposes on the accuracy of volume and distance measurements were evaluated in detail. A method for the calibration of the axial stepwidth of a confocal microscope is presented. The results suggest that in biological applications, 3D distances and radii of objects in cell nuclei can be determined with an accuracy of ≤ 60 nm. Using objects of different spectral signature, 3D distance measurements substantially below the lateral half width of the confocal point spread function are feasible. This is shown both theoretically and experimentally.
Network inference and community detection, based on covariance matrices, correlations, and test statistics from arbitrary distributions

ABSTRACT In this article we propose methodology for inference of binary-valued adjacency matrices from various measures of the strength of association between pairs of network nodes, or more generally pairs of variables. This strength of association can be quantified by sample covariance and correlation matrices, and more generally by test-statistics and hypothesis test p-values from arbitrary distributions. Community detection methods such as block modeling typically require binary-valued adjacency matrices as a starting point. Hence, a main motivation for the methodology we propose is to obtain binary-valued adjacency matrices from such pairwise measures of strength of association between variables. The proposed methodology is applicable to large high-dimensional data sets and is based on computationally efficient algorithms. We illustrate its utility in a range of contexts and data sets.
Ecological Alertness of Cypriot Businesses

Participation in environmentally friendly or green initiatives for ensuring that all processes, products, and manufacturing activities adequately address current environmental apprehensions has become mandatory in today’s global operating environment by any organization. The ability of a business to meet the needs of the present world without compromising the sustainability and the well being of the future generations to meet their own needs is a social and ethical challenge. A lot of debate is being done over the last decades about the impact of consuming and wasting products on the environment. With the constant increase of consumers’ awareness on the matter, businesses and governments are asked to provide solutions to these questions. This growing trend concerning “green businesses” forces companies to develop and implement strategies to minimize environmental impact that their products and services have. The focus of this paper is to investigate the responsiveness of Cypriot businesses in the global call for ecologically friendly initiatives along with their impact on their performance, on their business strategy and on their consumers. The findings show that companies in Cyprus are pursuing various environmental initiatives involving mainly their recycling habits and issues, certification and energy preservation.
Panorama: A Data System for Unbounded Vocabulary Querying over Video

Deep convolutional neural networks (CNNs) achieve state-of-the-art accuracy for many computer vision tasks. But using them for video monitoring applications incurs high computational cost and inference latency. Thus, recent works have studied how to improve system efficiency. But they largely focus on small "closed world" prediction vocabularies even though many applications in surveillance security, traffic analytics, etc. have an ever-growing set of target entities. We call this the "unbounded vocabulary" issue, and it is a key bottleneck for emerging video monitoring applications. We present the first data system for tacking this issue for video querying, Panorama. Our design philosophy is to build a unified and domain-agnostic system that lets application users generalize to unbounded vocabularies in an out-of-the-box manner without tedious manual re-training. To this end, we synthesize and innovate upon an array of techniques from the ML, vision, databases, and multimedia systems literature to devise a new system architecture. We also present techniques to ensure Panorama has high inference efficiency. Experiments with multiple real-world datasets show that Panorama can achieve between 2x to 20x higher efficiency than baseline approaches on in-vocabulary queries, while still yielding comparable accuracy and also generalizing well to unbounded vocabularies.
Co-Endemicity of Plasmodium falciparum and HIV-Infections in Treated Patients is Uncorrelated in Benin City, Nigeria

The Human immunodeficiency virus (HIV) and malaria are two of the world’s most formidable pathogens. Coinfection has been shown to amplify the effects of both diseases with HIV infection enhancing the severity of malaria. Previous work in our laboratory has shown that individuals infected with malaria and HIV who are taking anti-retrovirals have the Plasmodium parasite in their bloodstream suggesting that the lack of anti-malarials in their drug regimen resulted in Plasmodium infection. In this study, we set out to determine the status of Plasmodium infection in a cohort of patients taking both anti-malarial and anti-retroviral drugs. Blood samples were collected from patients of the Edo district of Nigeria in Benin City co-infected with Plasmodium and HIV. We have found that 31 out of the 317 (9.78%) HIV patients on HAART and ACT had Plasmodium in their blood based on microscopic counts. Surprisingly, using the polymerase chain reaction (PCR), the prevalence was at 25.6% for Plasmodium. In addition, we have identified by PCR that Plasmodium falciparum is the only species infecting these patients. Furthermore, no significant relationship was found to exist between CD4+ T-cell counts and malarial infections (CD4 count<200 cells/µL (7.20%)) nor was the malaria parasite density significantly associated with CD4 count<200 cells/µL (P=0.595) in this study population in Benin City, Nigeria. These results suggest that other factors are involved in this complex interaction.
Factors Associated with an Individual's Satisfaction with Computer Aided Group Meetings: An Empirical Study

The use of group support systems (GSS) to improve the outcome of, and satisfaction with the meeting process continues to be an area of research interest. This empirical study looks at several individual characteristics that are believed to have an influence on the individual's satisfaction with the process of using a GSS as the medium for conducting the meeting. The factors selected were locus of control, group cohesion, computer selfefficacy, and computer avoidance. Participants were informed about the purpose of the study, given a practice session to introduce them to the technology, guided through the working portion of the meeting, and then asked to complete a post-session questionnaire. The results were analyzed with partial least squares (PLS) and all hypotheses were found to be significant at least at a . 1 level showing that locus of control, group cohesion, and computer avoidance directly influence user satisfaction with the meeting process. Computer avoidance was the only factor that had a negative relationship. The effect of computer self-efficacy was mediated by the computer avoidance factor. By better understanding individual characteristics that influence a person's satisfaction with the use of GSS, managers and facilitators may be able to ultimately improve participation, which in turn may improve the desired work product of the meeting.
Relativistic kinetic gases as direct sources of gravity

We propose a new model for the description of a gravitating multi particle system, viewed as a kinetic gas. The properties of the, colliding or non-colliding, particles are encoded into a so called one-particle distribution function, which is a density on the space of allowed particle positions and velocities, i.e. on the tangent bundle of the spacetime manifold. We argue that an appropriate theory of gravity, describing the gravitational field generated by a kinetic gas, must also be modeled on the tangent bundle. The most natural mathematical framework for this task is Finsler spacetime geometry. Following this line of argumentation, we construct a coupling between the kinetic gas and a recently proposed Finsler geometric extension of general relativity. Additionally, we explicitly show how the coordinate invariance of the action of the kinetic gas leads to a novel formulation of conservation of the energy-momentum distribution of the gas on the tangent bundle.
Indoor Air Quality in the United Arab Emirates

Air quality was measured inside 628 United Arab Emirates (UAE) personal residences. Weekly average concentrations of carbon monoxide (CO), formaldehyde (HCHO), hydrogen sulfide (H2S), nitrogen dioxide (NO2), sulfur dioxide (SO2), and three size fractions of particulate matter (PM2.5, PMc, and PM10) were determined in each home. In a subset of the homes, measurements of outdoor air quality, ultrafine PM concentrations, and elemental PM concentrations were also made. Questionnaires were administered to obtain information on housing demographics and lifestyle habits. Air measurements were performed using simple and cost effective passive samplers. The 90th percentiles of indoor CO, HCHO, H2S, NO2, and SO2 were 1.55 ppm, 0.05 ppm, 0.12 ppm, 0.01 ppm, and 0.05 ppm, respectively. Median indoor PM2.5, PMc, and PM10, concentrations were 5.73 μg/m3, 29.4 μg/m3, and 35.2 μg/m3, respectively. The median indoor concentration of ultrafine PM was 3.62 × 1010 particles/m3. Indoor/outdoor ratios for PM were 0.44, 0.41, and 0.38 for ultrafine PM, PM2.5, and PM10, respectively. These values fall within the range of other indoor air studies findings conducted in developing countries. Air conditioning, smoking, and attached kitchens were significantly correlated with indoor levels of carbon monoxide. In addition, indoor concentrations of PM2.5 and PM10 were significantly correlated with vehicles parked within five meters of the home, central air conditioning, and having attached kitchens. This is the first robust indoor air quality data set developed for the UAE. This study demonstrates that screening level tools are a good initial step for assessing air quality when logistical issues (distance, language, cultural, training) and intrusion into personal lives need to be minimized.
ASPECT: A spectra clustering tool for exploration of large spectral surveys

We present the novel, semi-automated clustering tool ASPECT for analysing voluminous archives of spectra. The heart of the program is a neural network in form of Kohonen's self-organizing map. The resulting map is designed as an icon map suitable for the inspection by eye. The visual analysis is supported by the option to blend in individual object properties such as redshift, apparent magnitude, or signal-to-noise ratio. In addition, the package provides several tools for the selection of special spectral types, e.g. local difference maps which reflect the deviations of all spectra from one given input spectrum (real or artificial). ASPECT is able to produce a two-dimensional topological map of a huge number of spectra. The software package enables the user to browse and navigate through a huge data pool and helps him to gain an insight into underlying relationships between the spectra and other physical properties and to get the big picture of the entire data set. We demonstrate the capability of ASPECT by clustering the entire data pool of 0.6 million spectra from the Data Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results regarding quality and completeness we track objects from existing catalogues of quasars and carbon stars, respectively, and connect the SDSS spectra with morphological information from the GalaxyZoo project.
SIMULATION AND EXPERIMENTAL VALIDATION OF FLOW-CURRENT CHARACTERISTIC OF A SAMPLE HYDRAULIC SERVO VALVE

This paper presents a novel model to simulate the ow-current characteristic curve of an electro-hydraulic servo valve in steady state condition. This characteristic curve has three major zones: dead zone, linear zone and saturation zone. By using the presented approach, we can simulate the behavior of all types of valves including under lapped, critical center (zero lapped) and over lapped valves. A hydraulic tester has been designed and constructed for validation of the results. It can test the performance of ow-current and some other properties of the valve. Comparison of experimental and simulated curves describes that the model has an acceptable accuracy in determining the four important valve characteristics: ow gain, saturation ow, saturation current and dead zone. As a result, the model can be used for other studies such as geometrical sensitivity analysis (the ultimate objective of this modeling) or design optimization. Since this model is presented in a computer based algorithm, it can be easily used to observe and measure the e ects of change in the parameters of interest (particularly geometrical parameters of the valve) on the ow-current curve.
Characteristics of PM2.5 at a High-Altitude Remote Site in the Southeastern Margin of the Tibetan Plateau in Premonsoon Season

The Tibetan Plateau (TP) is one of the world’s most sensitive areas for climate change. Previous studies have revealed that air pollutants emitted from South and Southeast Asia can be transported to and have a negative impact on the TP. However, the majority of the investigators have focused on the pollutant transport processes from South Asian regions (i.e., India and Bangladesh) and parts of Southeast Asia, while the regions adjacent to the southeast fringe of the TP (i.e., Burma and the Sino-Burmese border) have been neglected. Here, fine particulate matter (PM2.5) samples were collected during the period 11 March to 13 May 2018 at Gaomeigu, a high-altitude remote site in the southeastern margin of the TP. Characteristics, sources of PM2.5, and the potential source regions for different chemical components were investigated. During the sampling time, PM2.5 mass loadings ranged from 3.79 to 54.57 µg m−3, with an arithmetic mean concentration of 20.99 ± 9.80 µg m−3. In general, major peaks of organic carbon (OC) and elemental carbon (EC) always coincided with high loadings of K+ and NO3−, which implies that common combustion sources caused these species’ concentrations to covary, while the daily variations of crustal elements showed different trends with the other chemical compositions, suggesting different source regions for crustal materials. Five source factors were identified as possible aerosol sources for PM2.5 by positive matrix factorization (PMF). They are the mining industry (5.3%), characterized by heavy metal elements; secondary formation (18.8%), described by the high concentrations of NH4+ and SO42−; traffic-related emissions (26.7%), dominated by carbonaceous species (especially soot-EC) and some metal elements; fugitive dust (15.2%), represented by crustal elements (Ti, Fe, and Mn), Ca2+, and Mg2+; and biomass burning (34.0%), which is typified by high concentrations of K+, NO3−, char-EC, primary OC, and secondary OC. The concentration-weighted trajectory (CWT) analysis results showed that the northeast part of Burma is the potential source region for high concentrations of EC and NO3− due to biomass burning emissions, while the tourism industry surrounding Gaomeigu gave strong grid cell values of SO42− as well as moderate values of EC and NO3−. Moreover, the mining industry in the southwest direction of Gaomeigu has important impacts on the zinc concentrations.
UDC 330.35:330.322 FINANCIAL DEVELOPMENT, MACROECONOMIC VOLATILITY, AND ECONOMIC GROWTH

The main objective of this study is to empirically evaluate the impact of financial development upon macroeconomic volatility, economic growth and upon the relationship between macroeconomic volatility and economic growth.The panel dataset used in this study contains observations for 78 countries worldwide during the period 1960-1995. Most of the data sources used are standard and widely used in comparable empirical studies. There are four different relations that are estimated with the panel dataset: -the importance of a better-quality financial system for overall (average) economic growth (1); -the effect of (aggregate) growth volatility upon mean growth rate (2); the extent to which a more developed financial sector manages to dampen aggregate shocks, and thereby reduce growth volatility (3), and; -the power of financial intermediaries and stock markets to affect (change) the relationship between mean growth and volatility (4). The paper is organized as follows: the first section is an introduction to the research topic, while the second section gives an overview of some theoretical and empirical findings in four different, though interrelated, strands of the literature. Section three presents the dataset used and the empirical strategy pursued. The main findings are presented in section four. Finally, section five concludes.
Mathematical methods to locate touch points using laser optic modules

An interactive whiteboards are used in a variety of settings, including in classrooms, in corporate board rooms, in training rooms, in broadcasting studios, and others. Interactive whiteboard implementation is heavily dependent on touchscreen technologies. Existing technologies are suffering from high manufacturing cost, low resolution, screen size scalability when the display screen size is over 100 inches. In this paper, we introduce a new interactive whiteboard system that uses laser modules. The operational principal of the system is similar to that of the camera-based optic system. However, our system positions touches more accurately by using laser emitter and detector, and its operation is simpler due to the absence of image processing. We derived several expressions based on trigonometry calculation to operate the whiteboard including locating laser sensors, screen positioning, calibration, distortion detection, and point locating. With the simplest form of expressions, the operating hardware has little burden to consume software time. Keywords—calibration, laser sensor, touchscreen, interactive whiteboard.
Deep Learning Techniques for Credit Scoring

 Abstract — Credit scoring is a crucial phase in the risk management process of financial organizations and commercial banks. Deep Learning algorithms are the powerful techniques with significant improvements in classification performances in many applications of machine learning fields such as image processing and speech recognition. The key benefit of Deep Learning models is the ability to analyze and learn massive amounts of data. In this paper, we apply two deep learning architectures for credit scoring problem: 1) Sequential Deep Neural Network; 2) Convolutional Neural Network. Our experiments of three different datasets show that all the neural network performance better than traditional approaches.
Pricing of Complements and Network Effects

We discuss the case of a monopolist of a base good in the presence of complementary goods provided either by it or by other firms. We assess and calibrate the extent of the influence on the profits from the base good that is created by the existence of complementary goods, i.e., the extent of the network effect. We establish an equivalence between a model of a base and a complementary good and a reduced-form model of the base good in which network effects are assumed in the consumers’ utility functions as a surrogate for the presence of complementary goods produced by others. We also assess and calibrate the influence on profits of the intensity of network effects and quality improvements in both goods. We evaluate the incentive that a monopolist of the base good has to improve its quality rather than that of complementary goods. Finally, based on our results, we discuss an explanation of the fact that Microsoft Office has a significantly higher price than Microsoft Windows although both products have comparable market shares.