Miracle-dz/UniFAR-FTUs huggingface dataset:
FEATURES BASED ON NEIGHBORHOOD PIXELS DENSITY - A STUDY AND COMPARISON

In optical character recognition applications, the feature extraction method(s) used to recognize document images play an important role. The features are the properties of the pattern that can be statistical, structural and/or transforms or series expansion. The structural features are difficult to compute particularly from hand-printed images. The structure of the strokes present inside the hand-printed images can be estimated using statistical means. In this paper three features have been purposed, those are based on the distribution of B/W pixels on the neighborhood of a pixel in an image. We name these features as Spiral Neighbor Density, Layer Pixel Density and Ray Density. The recognition performance of these features has been compared with two more features Neighborhood Pixels Weight and Total Distances in Four Directions already studied in our work. We have used more than 20000 Devanagari handwritten character images for conducting experiments. The experiments are conducted with two classifiers i.e. PNN and k-NN.
Hydrodynamics and Thermodynamics of Ice Accretion through Impact of Supercooled Large Droplets: Experiments, Simulations and Theory

Icing of solid surfaces is an ever-present problem for many engineering applications. In particular ice accretion due to the impact and freezing of supercooled water drops is rich in various physical processes and of relevance for aviation, road traffic, shipping, wind turbines, and high-voltage power lines and insulators. It is initiated by the impact of water drops being in a thermodynamic meta-stable state, followed by nucleation of the impacting drops and ending up with solidification of the liquid, potentially influenced by the impact surface. All of these processes have been separately the focus of extensive experimental, theoretical and numerical studies for many decades. However, for the first time the present work attempts to consolidate the understanding of all involved mechanisms, and to examine them in the context of icing of surfaces. Following the aforementioned subdivision, the subprocesses of ice accretion are separately examined using experimental, theoretical and numerical approaches. Non-isothermal impact of both supercooled water drops and water drops initially at room temperature onto surfaces below the freezing point of water is studied experimentally and numerically, and the heat transfer during drop impact is theoretically modeled. Nucleation during drop impact is experimentally studied and theoretically modeled. The freezing process of supercooled water drops with wall contact is investigated, employing a novel experimental approach, and the influence of the wall is theoretically modeled. Finally, the mutual influence between fluid flow during drop impact and freezing of the impinging liquid is experimentally examined and theoretically modeled. Due to its comprehensive nature and the application of new experimental approaches, the present work constitutes a fundamental contribution to a better understanding of the processes taking place during ice accretion by supercooled water drops. It provides theoretical models which allow the prediction of heat transfer during non-isothermal drop impact, the quantitative analysis of experiments aimed at nucleation during drop impact, the prediction of the characteristic solidification velocity for the case of water freezing with wall contact, and the prediction of the residual ice layer thickness after impact of an individual supercooled water drop.
Parametric Estimation of Quality Adjusted Lifetime (QAL) Distribution for two General Illness-Death Models

In this work, we consider the parametric estimation of quality adjusted lifetime (QAL) distribution for two general illness-death models, namely, competing illness-death model and reversible illness-death model. The first model is progressive and involves multiple states, while the second one incorporates possibility of recovery. The main focus of this paper is to derive the theoretical distribution of QAL for the two illness-death models, under some parametric models for the sojourn time distributions in different states. The QAL distribution is then estimated by replacing the parameters in its theoretical expression by the corresponding estimates obtained by using standard survival analysis techniques. The performance of the estimator is investigated through a simulation study. AMS (2000) Subject Classification : 62N02
Efficient Resource Allocation Utilizing Q-Learning in Multiple UA Communications

In recent years, unmanned aircraft systems (UASs) have garnered significant attention, and the demand for communication utilizing unmanned aircrafts (UAs) has increased. However, the limited available frequency for UA communication, despite the increasing utilization, poses a problem. Moreover, for the practical application of UA communication, the changes and differences in the propagation environment and communication demand of each UA communication must be considered because of the variety of supposed services, and the considerable interference caused by obstacles and reflected waves, due to high UA mobility. In this research, we construct a UA communication management system at the UAS operating frequency, with reference to the physical channel configuration of the LTE and LTE Sidelink, utilizing Q-Learning. In addition, we determine the effectiveness of the proposed method by evaluating various propagation-environment scenarios and communication demands.
Promoting sustainable mobility in communities with citizen participation: approaches, perspectives and results of a Living Lab in Germany

The present contribution deals with a practical insight into the design, implementation, and evaluation of different participation formats (on-site, direct mail, online) to participate in a living lab. A total sample of 290 citizens was recruited to promote sustainable mobility (i.e. walking and cycling) and improve urban space quality. Results further address the influence of participation methodology on participants' evaluation, willingness to participate and reported satisfaction with the participation used as well as predictors for participation satisfaction. Although the sample was not representative, the results suggest that citizen participation contributed to a more sustainable mobility awareness and a higher acceptance of the urban transformation.
Simplified Emulation of Active Load Modulation for a Millimeter-Wave GaN MMIC Doherty Power Amplifier Design

Based on large-signal simulations, this article proposes a simplified method to emulate the active load modulation between the carrier and peaking transistors in a Doherty power amplifier (DPA) design. In contrast to the conventional load–pull method, the proposed method contributes to emulating the active load modulation between the load-modulated transistors with considering their nonlinear interaction. It offers the advantages of predicting the real-world performance of the load-modulated transistors and providing the optimum load-modulation trajectories to be synthesized in a DPA design. To verify the proposed method, a two-way millimeter-wave (mmWave) gallium nitride (GaN) microwave monolithic integrated circuit (MMIC) DPA is designed from 25.5 to 27 GHz. The measured results show that the realized MMIC DPA achieves power-added efficiency (PAE) higher than 30% at saturation, 27% at 6-dB back-off, and 21.5% at 8-dB back-off, with a saturated output power higher than 31.4 dBm across the band. When tested using a 400-MHz modulated signal with digital predistortion (DPD), the MMIC DPA shows adjacent channel power ratio (ACPR) levels better than −40 dBc over the band.
The COVID-19 Crisis, National Innovation Systems, and World Development

In this think-piece, I reflect on what lessons innovation scholars and innovation policymakers in the developing world can draw from the COVID-19 crisis. While it has confirmed the fundamental importance of science and technology in coping with a major global challenge, it has also shown its limitations and the importance of institutions and organisational capabilities. While the crisis has demonstrated the necessity to build stronger national innovation systems (NSI) in the South, it has also simultaneously shown the need to go beyond national governance and move in the direction of a global innovation system. The COVID-19 crisis has reinforced new trends in technology and in global competition that challenge innovation system theory and innovation policy. The crisis stimulated the application and development of artificial intelligence and accelerated the concentration of intellectual capital in a handful of tech giants located in the US and China. While the volume of trade in digital services kept growing, there was a dramatic fall in the volume of global value chain trade in tangibles. These developments intensified the China-US rivalry and undermined transnational collaborations in science and technology. Countries in the South aiming at building stronger national innovation systems need to do so under new circumstances, where artificial intelligence is emerging as a strategic technology, where intellectual monopolies harvest data worldwide, where great powers are engaged in technological rivalry, and where linking up with global value chains for tangibles has become less of an option. One implication is that the issue of scale has become more critical than before; groupings of small and medium-sized countries need to integrate economically and politically in order to develop crucial digital capabilities and competitiveness. Such moves in the direction of forming transnational innovation systems are consonant with strategies to cope with global challenges.
Trade Infrastructure and Export Competitiveness in the East African Community

The purpose of this paper is to examine the influence of trade infrastructure components: port infrastructure, telephone usage and electricity on the export competitiveness of firms in the East African Community (EAC). The study adopted the structural gravity model and the Poisson Pseudo Maximum Likelihood (PPML), a nonlinear estimation method that was applied in STATA on balanced panel data for the period of 2007 to 2018. Data was obtained from World Bank International Trade Centre (ITC) and World Bank development indicators. Results show that telephone usage, electricity and port infrastructure are positive and significant predictors of export competitiveness in East African Community partner states. The results of this study show that electricity usage, telephone usage and port infrastructure are important contributors to improving export competitiveness in the EAC. There is a need to examine the intricate nature of the EAC economy in order to further this study’s findings. The EAC partner states need to embrace deep integration by removing the behind-the-border trade barriers, in addition to other trade restrictions, to create a common economic space among member states. This will further shrink the delivery time and the tracking and tracing of exports hence improving the competitiveness of EAC exports within the region and outside. Also, common and harmonized economic policies and regulations can be implemented through mutual recognition agreements where countries agree to recognize one another’s conformity assessments.
Topics in quantum measurement of many-body systems

In quantum physics, measurement exhibits fundamentally different behaviour to the classical case, having direct effect on the observed system. As a result, the very act of observation in quantum systems plays a non-trivial role, and can be used as a method of controlling the dynamics of the system. With the rise of quantum technologies, understanding and exploiting these phenomena offers a great boon. Here, we investigate a selection of the possibilities offered by quantum measurement for characterising and manipulating many-body systems, with particular focus on ultracold atomic gases. We first study how microscopic quantum structures can be used to indirectly probe larger systems. After deriving a general result, we consider the specific case of impurity atoms immersed in a quantum gas, and demonstrate that this enables density-related properties of the gas to be inferred from measurements of solely the impurity internal state. Following this, we explore how the backaction from measurement can be used for control of quantum systems. Utilising the quantum Zeno effect that results from persistent measurement of a quantum state, we show how fully-quantum many-body light-matter interactions enable the engineering of atomic states and dynamics through measurement of the light, leading to an abundance of interesting phenomena, including genuinely multipartite entangled states, long-range correlated tunnelling, and the quantum simulation of long-range and correlated interactions. The resulting structure imparted by the light on the matter can also be used to detect and measure atomic entanglement. Finally, we generalise quantum Zeno dynamics to the regime where the measurement timestep is finite, and demonstrate that the resulting system evolution can exhibit correlated processes.
DISCRIMINANT SCORE (D SCORE) A MODEL TO PREDICT LUNG CANCER BASED ON THE EXPRESSION STUDY OF PANEL OF GENES AND MICRORNAS IN THE PLASMA.

Background: Inspite of that there is no effective method for screening of highly susceptible groups such as chronic heavy smokers. The present study depends on expression of panel of genes and microRNAs that are involved or the result of lung cancer formation in blood samples taken from cancer and non-cancer lung cases to design a discrimination score that can exclude or predict lung cancer. Objective: Is to design a discrimination score (D score) to predict lung cancer in blood samples based of expression study of a panel of genes and microRNAs. Patients and Method: A case control study on expression of P53, KRAS, c-MYC, and Her-2/neu genes, microRNAs 21, 34a, 92, and 98 in blood samples taken from patients positive and negative for lung cancer. Results: The discrimination score (D score) was obtained per a
Network Modularity as a Foundation for Neural Reuse

The neural reuse framework developed primarily by Michael Anderson proposes that brain regions are involved in multiple and diverse cognitive tasks and that brain regions flexibly and dynamically interact in different combinations to carry out cognitive functioning. We argue that the evidence cited by Anderson and others falls short of supporting the fundamental principles of neural reuse. We map out this problem and provide solutions by drawing on recent advances in network neuroscience, and we argue that methods employed in network neuroscience provide the means to fully engage in a research program operating under the principles of neural reuse.
Estimation of edge density in noisy networks

While it is common practice in applied network analysis to report various standard network summary statistics, these numbers are rarely accompanied by some quantification of uncertainty. Yet any error inherent in the measurements underlying the construction of the network, or in the network construction procedure itself, necessarily must propagate to any summary statistics reported. Here we study the problem of estimating the density of edges in a noisy network, as a canonical prototype of the more general problem of estimating density of arbitrary subgraphs. Under a simple model of network error, we show that consistent estimation of such densities is impossible when the rates of error are unknown and only a single network is observed. We then develop method-of-moment estimators of network edge density and error rates for the case where a minimal number of network replicates are available. These estimators are shown to be asymptotically normal as the number of vertices increases to infinity. We also provide the confidence intervals for quantifying the uncertainty in these estimates based on the asymptotic normality. We illustrate the use of our estimators in the context of gene coexpression networks.
Exploring the stability and residual response versus scan angle effects in SNPP VIIRS sensor data record reflectance products using deep convective clouds

Abstract. The reflective solar bands (RSB) of the Visible and Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership satellite are calibrated using solar diffuser and lunar observations. To evaluate the performance of the VIIRS RSB calibration, the deep convective clouds (DCC) are utilized as invariant targets to assess the stability of NASA’s VIIRS sensor data record (SDR) reflectance product for 10 moderate resolution bands (M-bands, M1 to M5 and M7 to M11) and three imagery resolution bands (I-bands, I1 to I3). All the frames along with each scan are divided into six aggregation zones for the scan angle. An empirical bidirectional reflectance distribution function (BRDF) correction is performed to SDR reflectances over DCC to reduce the scattering anisotropy in view and solar angles. The BRDF-corrected reflectances are used to assess the stability and to explore the residual RVS effects for selected bands. Results of this study show that after more than 6 years of on-orbit operation, the use of the prelaunch-based RVS still meets the radiometric requirement for the VIIRS RSB. Examination of the small but noticeable RVS residuals indicates they occur in a few shortest wavelength bands based on a time-dependent quadratic fit of the reflectance trends obtained at all scan angle zones. As the mission continues, continuing monitoring of on-orbit RVS stability and its effects on VIIRS SDR reflectance products are necessary with the DCC technique in the future VIIRS calibrations to ensure the quality of the RSB SDR products.
LDA Extension via Oblique Projection

The classical linear discriminant analysis (LDA) was previously modified by orthogonal projection into null space LDA (N_LDA) and direct LDA (D_LDA) for solving small sample size (SSS) problem. In this paper, the author proposes an extension of LDA by oblique projection, wherein N_LDA and D_LDA are included as special cases, to reduce discriminative information loss resulted from single N_LDA or D_LDA. The effectiveness of the proposed algorithm is tested by image forensics and face recognition.
Process tracking and monitoring based on discrete jumping model

ABSTRACT The jumping model has been used as an effective tool in tracking and detecting changes for continuous statistics in various applications. In this article, we extend the current jumping model from the continuous case to the discrete case to track and monitor the changes in attribute data. In this method, the jumping model–based posterior distribution of the process mean is constructed with attribute data and prior knowledge of the process. The posterior distribution consists of several components that account for the weights of the process to be “in-control” or “out-of-control. ” Using the component representing the in-control weight as the monitoring index, a jumping model–based control chart is developed to monitor the attribute data process. The proposed chart is further extended to cover different out-of-control modes. The performance of the jumping model–based chart is investigated and compared to conventional control charts through numerical studies and a real-world data set. The results demonstrate the effectiveness of the proposed chart.
The musicscape in hotel businesses: Evidence from online reviews

Musicscape is an important topic in tourism and hospitality contexts. However, there are few studies on this subject in the accommodation sector. The study aims to determine the effect of ‘musicscape dimensions’ on customer behavior. To achieve this aim, 2357 online reviews of 28 hotels in Antalya, Turkey were analyzed by content analysis. The framework proposed by Oakes was used in the study. The research findings provide hotel managers with practical advice on the effective use of musicscape in tourism and hospitality settings. In addition, it was concluded that customers evaluated the loudness and fast tempo of music negatively, and that the harmony of music broadcasts and their environment offered by a hotel is positively perceived by customers.
Supersymmetry Theory in Warped Extra Dimension

We study supersymmetry theory in higher dimensions. In five dimensional anti-de Sitter space, we construct the most general supersymmetric matter coupling in the rigid and gauged cases. We use the component and warped N = 1 superspace formalism. By comparing their results, we find several interesting issues related to boundary effects. For instance, Gibbons-Hawking-York terms are necessary on the AdS5 boundary. We find the warped space version of spontaneous superpotential generation and reinterpret it as a tuning of a surface-localized Fayet-Iliopoulos term at the component level, or equivalently as a tuning of a boundary-localized superpotential in superspace. On the other hand, in the bulk, we find the N = 2 Fayet-Iliopoulos term must be fixed. We study boundary problems systematically from the variational principle. We find proper boundary conditions and consistent constraints so that all 8 supercharges are preserved on the boundary. The bulk action is then truly invariant under both supersymmetries; and super-multiplets on the boundary have close relations with the ii
Adaptive Neural Backstepping Control Design for A Class of Nonsmooth Nonlinear Systems

This paper investigates the problems of backstepping control for a class of nonsmooth nonlinear systems. With the help of set-valued functions (or maps) and set-valued derivatives, a new Lyapunov criterion is developed for nonsmooth systems. The concept of semi-globally uniformly ultimately bounded (SGUUB) stability that is widely used for smooth nonlinear systems in lower triangular form is, for the first time, applied to the nonsmooth case, and such a concept provides a theory foundation for the subsequent backstepping control design. Then, two types of continuous controllers are designed based on the backstepping technique and adaptive neural network (NN) algorithm. In the light of Cellina approximate selection theorem and smooth approximation theorem for Lipschitz functions, the system under investigation is first transformed into an equivalent model. Then, the first type of controller can be efficiently designed by using the traditional backstepping technique. On the other hand, when the unknown uncertainty is taken into account in the equivalent systems, another type of controller is designed based on adaptive NN control strategy. It is proved that in both cases, all the closed-loop signals are SGUUB by using our proposed controllers. Finally, a numerical example with two types of controllers is supplied to show the availability of the provided control schemes.
Nonparametric model selection in hazard regression

We propose a novel model selection method for a nonparametric extension of the Cox proportional hazard model, in the framework of smoothing splines ANOVA models. The method automates the model building and model selection process simultaneously by imposing a penalty on the norms instead of squared norms. It is a natural extension of the LASSO to the situation where component selection is of interest. We further propose an efficient algorithm based on a reformulation of the penalized likelihood. Adaptive choice of the smoothing parameter is discussed. Both simulations and real examples suggest that our proposal is very powerful for model selection and component estimation in survival analysis. MSC : 62N02, 62G08.
PERICARDIOCENTESIS VS SUBXYPHOID PERICARDIOTOMY - COMPARISON OF EFFICACY AND SAFETY

Background :-Comparison of safety and effectiveness of pericardiocentesis versus subxyphoid pericardiotomy in patients with large pericardial effusion (PE) defined as more than 2 cm fluids behind the posterior wall of left ventricle measured by echocardiography. Methods :-A retrospective analysis was performed on consecutive patients with the diagnosis of large