Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface Dynamics Associated with Redox Processes on TiO2 Nanoparticles

TiO 2 anatase nanoparticles have shown interesting properties as ultraviolet (UV) light photocatalysts for water and air remediation as well as solar water splitting. The surface of the nanoparticle plays a vital role in controlling the catalytic reactivity. For example, reactant adsorption, breaking/forming of chemical bonds, and product desorption all take place on the surface. In situ observations of such catalysts at the atomic level is required to follow structural transformations under near-reaction conditions thus elucidating the reaction and deactivation mechanisms. An optical fiber based in situ illumination system has been built and installed on an aberration-corrected environmental transmission electron microscope (TEM), allowing the sample to be illuminated with a broadband light source (200-800 nm in wavelength) while exposing to heat and gases such as water vapor. Negative spherical aberration imaging (NCSI) was used to enhance the contrast from oxygen and titanium columns to allow observation of structural dynamics induced by the light and water [1]. Anatase nanoparticles were hydrothermally synthesized. An aberration-corrected FEI Titan 80-300 was employed at 300 kV to image the particles with a single-electron-detection K2 camera operated in the counted mode. In our initial work, we used the electron beam to simulate the effect of light illumination. Moderate and controlled electron dose may be used to simulate the light illumination effect [2
Strategic Information Systems Planning (SISP) Practices in Health Care Sectors of Rwanda

Strategic Information Systems Planning (SISP) is an area of great value in information technology as concerns and interests to Information Systems. SISP has consistently been ranked the top issue for IS executive since 1970â€™s in developed and non-developed countries.Studies show that Strategic Information Systems Planning (SISP)can help the manager to design information systems, develop IS strategy and distribute resources for IS strategy. However, in Rwanda ,the use of SISP in healthcare sectors Â is still limited and under-researched. The key goal of this research is to find out the positive impactÂ and the gain for putting into practice the SISP to Developing countries, particularly Rwanda Health sector. The research is aimed at provingthe limitations of the existing Information Systems within the healthcare sector of Rwanda.Furthermore, Hybrid SWOT approach will be used to give solutions to the challenges faced in the implementation of SSIP for health sectors in Rwanda.
PV Based Grid System for Power Quality Enhancement using Instantaneous P-Q Theory

The SPV interfaced grid system with battery storage unit, bidirectional VSI interconnected shunt active filter, an adaptive Proportional Integral (PI) controller is employed for improve current and voltage profile enhancement. The p-q theory is the common employed technique to extract the basic fundamental current harmonic components from affected or polluted power supply by means of diode rectifier or non linear load. The basic concepts of reactive power (IRP) theory includes it’s a time domain model and it can be applicable for both 3P3W and 3P4W. Initially, the control technique called instantaneous real and reactive power theory or p-q theory has been developed for three phase three wire system and then later it was extend to three phase four wire system. This system is valid under steady state as well as dynamic state. The bidirectional VSI based shunt APF is controlled by the PI based instantaneous reactive power theory. The proposed PI Controller interfaced shunt Active Power Filter is used to maintain DC-Link voltage.SPV system is interfaced with incremental conductance MPPT algorithm. This SAPF control scheme has to provide the following benefits like current harmonic mitigation as well as reactive power compensation
Characterisation of fault plane and coseismic slip for the May 2, 2020, Mw 6.6 Cretan Passage earthquake from tide-gauge tsunami data and moment tensor solutions

Abstract. We present a source solution for the tsunami generated by the Mw 6.6 earthquake that occurred on May 2, 2020, about 807thinsp;km offshore south of Crete, in the Cretan Passage, on the shallow portion of the Hellenic Arc Subduction Zone (HASZ). The tide-gauges recorded this local tsunami on the southern coast of Crete island and Kasos island. We used these tsunami observations to constrain the geometry and orientation of the causative fault, the rupture mechanism and the slip amount. We first modelled an ensemble of synthetic tsunami waveforms at the tide-gauge locations, produced for a range of earthquake parameter values as constrained by some of the available moment tensor solutions. We allow for both a splay and a back-thrust fault, corresponding to the two nodal planes of the moment tensor solution. We then measured the misfit between the synthetic and the observed marigrams for each source parameter set. Our results identify the shallow steeply-dipping back-thrust fault as the one producing the lowest misfit to the tsunami data. However, a rupture on a lower angle fault, possibly a splay fault, with a sinistral component due to the oblique convergence on this segment of the HASZ, cannot be completely ruled out. This earthquake reminds us that the uncertainty regarding potential earthquake mechanisms at a specific location remains quite significant. In this case, for example, it is not possible to anticipate if the next event will be one occurring on the subduction interface, on a splay fault, or on a back-thrust which seems the most likely for the event under investigation. This circumstance bears important consequences because back-thrust and splay faults might enhance the tsunamigenic potential with respect to the subduction interface due to their steeper dip. Then, these results are relevant for tsunami forecasting both in the framework of the long-term hazard assessment and of the early warning systems.
Distributed Counterfactual Modeling Approach for Investigating Hospital-Associated Racial Disparities in COVID-19 Mortality

Several studies have found that black patients are more likely than white patients to test positive for or be hospitalized with COVID-19, but many of these same studies have found no difference in in-hospital mortality. These studies may have underestimated racial differences due to reliance on data from a single hospital system, as adequate control of patient-level characteristics requires aggregation of highly granular data from several institutions. Further, one factor thought to contribute to disparities in health outcomes by race is site of care. Several differences between black and white patient populations, such as access to care and referral patterns among clinicians, can lead to patients of different races largely attending different hospitals. We sought to develop a method that could study the potential association between attending hospital and racial disparity in mortality for COVID-19 patients without requiring patient-level data sharing among collaborating institutions. We propose a novel application of a distributed algorithm for generalized linear mixed modeling (GLMM) to perform counterfactual modeling and investigate the role of hospital in differences in COVID-19 mortality by race. Our counterfactual modeling approach uses simulation to randomly assign black patients to hospitals in the same distribution as those attended by white patients, quantifying the difference between observed mortality rates and simulated mortality risk following random hospital assignment. To illustrate our method, we perform a proof-of-concept analysis using data from four hospitals within the OneFlorida Clinical Research Consortium. Our approach can be used by investigators from several institutions to study the impact of admitting hospital on COVID-19 mortality, a critical step in addressing systemic racism in modern healthcare.
Evolution of COVID-19 mortality over time: results from the Swiss hospital surveillance system (CH-SUR)

Background: When comparing the periods of time during and after the first wave of the ongoing SARS-CoV-2/COVID-19 pandemic in Europe, the associated COVID-19 mortality seems to have decreased substantially. Various factors could explain this trend, including changes in demographic characteristics of infected persons, and the improvement of case management. To date, no study has been performed to investigate the evolution of COVID-19 in-hospital mortality in Switzerland, while also accounting for risk factors. Methods: We investigated the trends in COVID-19 related mortality (in-hospital and in-intermediate/intensive-care) over time in Switzerland, from February 2020 to May 2021, comparing in particular the first and the second wave. We used data from the COVID-19 Hospital-based Surveillance (CH-SUR) database. We performed survival analyses adjusting for well-known risk factors of COVID-19 mortality (age, sex and comorbidities) and accounting for competing risk. Results: Our analysis included 16,030 episodes recorded in CH-SUR, with 2,320 reported deaths due to COVID-19 (13.0% of included episodes). We found that overall in-hospital mortality was lower during the second wave of COVID-19 compared to the first wave (HR 0.71, 95% CI 0.69 - 0.72, p-value < 0.001), a decrease apparently not explained by changes in demographic characteristics of patients. In contrast, mortality in intermediate and intensive care significantly increased in the second wave compared to the first wave (HR 1.48, 95% CI 1.42 - 1.55, p-value < 0.001), with significant changes in the course of hospitalisation between the first and the second wave. Conclusion: We found that, in Switzerland, COVID-19 mortality decreased among hospitalised persons, whereas it increased among patients admitted to intermediate or intensive care, when comparing the second wave to the first wave. We put our findings in perspective with changes over time in case management, treatment strategy, hospital burden and non-pharmaceutical interventions. Further analyses of the potential effect of virus variants and of vaccination on mortality would be crucial to have a complete overview of COVID-19 mortality trends throughout the different phases of the pandemic.
Strategic Utilization of Choice-Board Technology: A Conceptual Framework

asking such basic business questions as: (1) In what business is the firm engaged in? (2) Am I selling PCs or am I selling business solutions that use computer software and hardware? (3) Am I selling apparel or am I trying to make my customers feel good about themselves in the context of dresses? The differentiation is achieved in the context of some particular customer segment. Customers. A strategic conception in the context of choice-boards leads to questions such as: (1) Who are my customers? (2) How sophisticated are
Assessing the quality of sources in Wikidata across languages: a hybrid approach

Wikidata is one of the most important sources of structured data on the web, built by a worldwide community of volunteers. As a secondary source, its contents must be backed by credible references; this is particularly important, as Wikidata explicitly encourages editors to add claims for which there is no broad consensus, as long as they are corroborated by references. Nevertheless, despite this essential link between content and references, Wikidata's ability to systematically assess and assure the quality of its references remains limited. To this end, we carry out a mixed-methods study to determine the relevance, ease of access, and authoritativeness of Wikidata references, at scale and in different languages, using online crowdsourcing, descriptive statistics, and machine learning. Building on previous work of ours, we run a series of microtasks experiments to evaluate a large corpus of references, sampled from Wikidata triples with labels in several languages. We use a consolidated, curated version of the crowdsourced assessments to train several machine learning models to scale up the analysis to the whole of Wikidata. The findings help us ascertain the quality of references in Wikidata and identify common challenges in defining and capturing the quality of user-generated multilingual structured data on the web. We also discuss ongoing editorial practices, which could encourage the use of higher-quality references in a more immediate way. All data and code used in the study are available on GitHub for feedback and further improvement and deployment by the research community.
R2D: Learning Shadow Removal to Enhance Fine-Context Shadow Detection

Current shadow detection methods perform poorly when detecting shadow regions that are small, unclear or have blurry edges. To tackle this problem, we propose a new method called Restore to Detect (R2D), where we show that when a deep neural network is trained for restoration (shadow removal), it learns meaningful features to delineate the shadow masks as well. To make use of this complementary nature of shadow detection and removal tasks, we train an auxiliary network for shadow removal and propose a complementary feature learning block (CFL) to learn and fuse meaningful features from shadow removal network to the shadow detection network. For the detection network in R2D, we propose a Fine Context-aware Shadow Detection Network (FCSD-Net) where we constraint the receptive ﬁeld size and focus on low-level features to learn ﬁne context features better. Experimental results on three public shadow detection datasets (ISTD, SBU and UCF) show that our proposed method R2D improves the shadow detection performance while being able to detect ﬁne context better compared to the other recent methods.
The Optimization of the Constant Flow Parallel Micropump Using RBF Neural Network

The objective of this work is to optimize the performance of a constant flow parallel mechanical displacement micropump, which has parallel pump chambers and incorporates passive check valves. The critical task is to minimize the pressure pulse caused by regurgitation, which negatively impacts the constant flow rate, during the reciprocating motion when the left and right pumps interchange their role of aspiration and transfusion. Previous works attempt to solve this issue via the mechanical design of passive check valves. In this work, the novel concept of overlap time is proposed, and the issue is solved from the aspect of control theory by implementing a RBF neural network trained by both unsupervised and supervised learning. The experimental results indicate that the pressure pulse is optimized in the range of 0.15 – 0.25 MPa, which is a significant improvement compared to the maximum pump working pressure of 40 MPa.
Investigating the Relationship Between World Development Indicators and the Occurrence of Disease Outbreaks in the 21st Century: A Case Study

The timely identification of socio-economic sectors vulnerable to a disease outbreak presents an important challenge to the civic authorities and healthcare workers interested in outbreak mitigation measures. This problem was traditionally solved by studying the aberrances in small-scale healthcare data. In this paper, we leverage data driven models to determine the relationship between the trends of World Development Indicators and occurrence of disease ∗All authors contributed equally to this research. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. KDD ’21, August 14–18, 2021, ACM, New York, NY, USA, 4 pages © 2021 Association for Computing Machinery. ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn outbreaks using worldwide historical data from 2000-2019, and treat it as a classic supervised classification problem. CART based feature selection was employed in an unorthodox fashion to determine the covariates getting affected by the disease outbreak, thus giving the most vulnerable sectors. The result involves a comprehensive analysis of different classification algorithms and is indicative of the relationship between the disease outbreak occurrence and the magnitudes of various development indicators.
Long-range hopping and indexation assumption in 1D topological insulators

In this paper, we show that the introduction of long-range hoppings in 1D topological insulator models implies that different possibilities of site indexation must be considered when determining the bulk topological invariants in order to avoid the existence of hidden symmetries. The particular case of the extended SSH chain is addressed as an example where such behavior occurs. In this model, the introduction of long-range hopping terms breaks the bipartite property and a band inversion occurs in the band structure as the relative values of the hopping terms change, signaling a crossover between hopping parameter regions of “influence” of different chiral symmetries. Furthermore, edge states become a linear combination of edge-like states with different localization lengths and reflect the gradual transition between these different chiral symmetries.
Radion dynamics in multibrane Randall-Sundrum model

The radion in the Randall-Sundrum model is stabilized by the back reaction of a bulk scalar field with its VEV depending on the fifth dimensional coordinate. We studied the radion dynamics in an extended scenario, where intermediate branes exist between the UV and IR branes. Our analysis proves that the formalism of EFT delivers the same equations of motion as the linearized Einstein equation with all junction conditions satisfied. The relic of 5d diffeomorphism is broken after including the Goldberger-Wise stabilization and a unique radion field is conjectured as legitimate in the RS metric perturbation.
Impacts of the image alignment over frequency for VLBI Global Observing System

Aims. The VLBI Global Observing System, which is the next generation of geodetic VLBI and is called VGOS, observes simultaneously at four frequency bands in the range 3.0–10.7 GHz (expected to be extended to 14 GHz). Because source structure changes with frequency, we aim to study the source position estimates from the observations of this new VLBI system. Methods. Based on an ideal point source model, simulations are made to determine the relation between the source positions as determined by VGOS observations and the locations of the radio emission at the four bands. Results. We obtained the source positions as determined by VGOS observations as a function of the source positions at the four frequency bands for both group and phase delays. The results reveal that if the location of the radio emission at one band is offset with respect to that at the other bands, the position estimate can be shifted to the opposite direction and even by more than three times that offset. Conclusions. The VGOS source positions will be very variable with time and very imprecise in the sense of relating to the locations of the radio emission at the four bands, if the effects of source structure are not modeled. The image alignment over frequency is essential in order to model the effects of source structure in VGOS observations, which is the only way to mitigate these strong frequency-dependent impacts on VGOS source positions.
Penrose limits in non-Abelian 
T
 dual of Klebanov-Tseytlin background

In this paper we consider the Klebanov-Tseytlin background and its non-Abelian T-dual geometry along a suitably chosen SU(2) subgroup of isometries. We analyse the Penrose limits along various null geodesics of both the geometries. We observe that, the KlebanovTseytlin geometry does not admit any pp-wave solutions. However, the T-dual background gives rise to pp-wave solution upon taking the Penrose limit along some appropriate null geodesic. We comment on the possible gauge theory dual for our pp-wave background.
From None to Severe: Predicting Severity in Movie Scripts

In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Substance consumption, and Frightening scenes. The problem is handled using a siamese network-based multitask framework which concurrently improves the interpretability of the predictions. The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions. The proposed dataset and source code are publicly available at our GitHub repository.
Human Recognition based on Retinal Bifurcations and Modified Correlation Function

Nowadays high security is an important issue for most of the secure places and recent advances increase the needs of high-security systems. Therefore, needs to high security for controlling and permitting the allowable people to enter the high secure places, increases and extends the use of conventional recognition methods. Therefore, a novel identification method using retinal images is proposed in this paper. For this purpose, new mathematical functions are applied on corners and bifurcations. To evaluate the proposed method we use 40 retinal images from the DRIVE database, 20 normal retinal image from STARE database and 140 normal retinal images from local collected database and the accuracy rate is 99.34 percent.
Latexify Math: Mathematical Formula Markup Revision to Assist Collaborative Editing in Math Q&A Sites

Collaborative editing questions and answers plays an important role in quality control of Mathematics StackExchange which is a math Q&A Site. Our study of post edits in Mathematics Stack Exchange shows that there is a large number of math-related edits about latexifying formulas, revising LaTeX and converting the blurred math formula screenshots to LaTeX sequence. Despite its importance, manually editing one math-related post especially those with complex mathematical formulas is time-consuming and error-prone even for experienced users. To assist post owners and editors to do this editing, we have developed an edit-assistance tool, MathLatexEdit for formula latexification, LaTeX revision and screenshot transcription. We formulate this formula editing task as a translation problem, in which an original post is translated to a revised post. MathLatexEdit implements a deep learning based approach including two encoder-decoder models for textual and visual LaTeX edit recommendation with math-specific inference. The two models are trained on large-scale historical original-edited post pairs and synthesized screenshot-formula pairs. Our evaluation of MathLatexEdit not only demonstrates the accuracy of our model, but also the usefulness of MathLatexEdit in editing real-world posts which are accepted in Mathematics Stack Exchange.
Human Initiated Grasp Space Exploration Algorithm for an Underactuated Robot Gripper Using Variational Autoencoder

Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents an efficient procedure for exploring the grasp space of a multifingered adaptive gripper for generating reliable grasps given a known object pose. This procedure relies on a limited dataset of manually specified expert grasps, and use a mixed analytic and data-driven approach based on the use of a grasp quality metric and variational autoencoders. The performances of this method are assessed by generating grasps in simulation for three different objects. On this grasp planning task, this method reaches a grasp success rate of 99.91% on 7000 trials.