Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Odometry/Inertial Integration for Enhanced Land Vehicle Navigation in GNSS Denied Environment

Self-driving vehicles have attained a huge research interest as it offers new opportunities for more secure and convenient transportation. Vision is considered one of the significant inputs to self-driving systems in terms of obstacle avoidance and navigation purposes. Recently, Consumer Portable Devices (CPDs) such as smartphones and tablets are continuously equipped with enhanced sensors and cameras that can be used in the navigation or obstacle avoidance applications.This paper presents an approach for enhancing the land vehicle navigation in GNSS denied environment by integrating the Visual Odometry (VO), On-Board Diagnostics information, and inertial sensors. The visual odometry is collected through a CPD camera (smartphone) which is mounted on the vehicle dashboard where the vehicle change of heading is estimated. On the other hand, the vehicle forward velocity information is acquired through On-Board Diagnostics II (OBD-II). In this paper, the proposed approach is a Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) loosely coupled integration scheme where the VO heading change and the forward OBD-II velocity are used to aid the low-cost INS during GNSS signal outage to limit its drift through Extended Kalman Filter (EKF). The employed visual odometry estimates the orientation change between successive frames which requires a feature extraction step to extract the features in every frame followed by primary matching to match the features and estimate the rotation between frames. Experimental road test is implemented, and the results show significant position accuracy enhancement using the proposed approach compared with typical Dead Reckoning (DR) solution where the average position Root Mean Square Error (RMSE) of the DR is around 14 meters for 60 s GNSS signal outage while the average position RMSE for the proposed navigation approach is 4.40 meters during the same GNSS signal outage.
Effect of Added Monosodium Glutamate on Characteristics of Polyamide Dope Solutions

Formic acid (FA) solutions prepared with various concentrations of polyamide 66 (PA 66) and monosodium glutamate (MSG) were evaluated in terms of properties, such as density, viscosity, and cloud point. The influence on density was insignificant, whereas the viscosity was strongly affected by the amount of PA 66 and MSG additive. The solutions were further evaluated by casting them in a flat film form and determining the demixing time in a humid atmosphere. The considered cases at lower polymer concentrations at various MSG amounts, indicated that the demixing time increased with increase in polymer concentration. The time for demixing, however, decreased for a given higher amount of polymer when the amount of additive was increased in the dope solution. Membranes were prepared at various coagulant bath temperatures. The tensile strength and degree of adsorption (DOA) of these membranes were found. The tensile strength was higher when the membranes were prepared at higher temperature. The DOA, on the other hand, was higher for the membranes formed at lower temperature.
Semi-Supervised Land Cover Classification Using Pi-SAR2 Observation Data

The National Institute of Information and Communications Technology (NICT) developed and is operating the airborne X-band polarimetric and cross-track interferometric synthetic aperture radar (SAR): Pi-SAR2. Deep learning is one of the machine learning techniques that has recently been actively used in various fields, including remote sensing. However, it is difficult to prepare sufficient annotated training data for supervised learning. This paper proposes a semi-supervised deep learning method for land cover classification using Pi-SAR2 polarimetric data and reports the results of an experimental investigation.
Parametric Modeling of UWB Antennas Using Convolutional Neural Networks

A convolutional neural network (CNN) model is proposed for parametric modeling of ultra-wideband (UWB) antennas. Given the image of an antenna, CNN fast predicts its electromagnetic (EM) responses and speeds up the design process. The validity and efficiency of the proposed model is confirmed by a parametric modeling example of a UWB band-notched antenna. The results also demonstrate that this model has potential use in mapping the relationship between the antenna structure and corresponding EM responses.
Gaussian Process Bandits for Online Influence Maximization

We study the online influence maximization problem, which aims to select the seed set that achieves the maximum influence spread on a social network while learning the unknown diffusion parameters simultaneously. As the underlying diffusion parameters are unknown, a learner will interact with the network and optimize its strategy using the influence feedback in an online manner. Most previous works model the influence parameters based on a predefined linear function using node/edge features and minimize the estimation error in learning. However, the correlations between individuals are ignored in estimation, and the manually drafted linear assumption leads to restrictions. In this paper, we propose to utilize the Gaussian Process to estimate the underlying diffusion parameters as well as learn latent node factors using historical statistics. This novel online learning method circumvents the restrictions of predefined linear assumption and leverages the ignored edge correlations for estimation. The online learning of latent node factors utilizes the network assortativity and avoids manually drafted node/edge features. Further, we adopt the bandit framework to devise an online influence maximization algorithm, which is a natural combination of Gaussian Process estimation and upper confidence bound algorithm. Experiments conducted on public datasets reveal that the proposed method achieves superior online influence spread compared with state-of-the-art baselines and is robust w.r. t hyperparameters.
Achievable Rate Analysis of Hybrid Massive MIMO Uplink with Imperfect Phase Shifters

In a multiuser massive multiple-input multipleoutput (MIMO) system, hybrid analog-and-digital structure is widely applied due to the high cost of deploying a large number of radio-frequency (RF) chains to drive the large antenna array. Phase shifter network is a common way of accomplishing the analog component. However, phase shifters impaired by hardware constrains can seriously degrade the performance of the system. This paper investigates the influence of imperfect phase shifters on the uplink achievable rate of the system with fullyconnected and sub-connected architectures. We derive a tractable expression for the uplink achievable sum rate. The proposed studies show that massive antennas are able to compensate for the performance degradation caused by imperfect phase shifters in the hybrid massive MIMO system. Our analytical results are verified by extensive simulations.
Baseline and Monitoring Methods for Seagrass Meadows

Seagrass meadows are one of the most vulnerable habitats of coastal or estuarine waters in Australia. Their continued existence near centres of human population is in peril, but all levels of government are now under pressure to protect these valuable habitats. The first step is to measure the health and abundance of the seagrasses. To help managers to do this, I describe some of the methods used successfully in Australia and overseas. Aerial surveys and remote sensing are discussed as the method for gross scale inventory gathering. Permanent transects can detect changes at a much smaller scale. Biomass, density and productivity measurements all indicate the “health” of seagrass meadows. These methods are described with emphasis on the need for statistical rigour and regard for temporal and spatial variability. If managers and researchers use the same or similar methods throughout the coastal and estuarine seagrass habitats, they can compare results and use baseline studies and findings from other areas, where relevant.
An Open Set Domain Adaptation Network Based on Adversarial Learning for Remote Sensing Image Scene Classification

Remote sensing image scene classification refers to assigning specific semantic labels for remote sensing images. Due to the lack of labeled remote sensing images, domain adaptation is applied to remote sensing image scene classification. However, recent proposed methods mainly focus on the closed set scenario. In this paper, we explore the open set scenario and introduce an open set domain adaptation network (OSDANet) for remote sensing image scene classification. Inspired by the idea of Generative Adversarial Network (GAN), we design a feature generator as well as a classifier which are learnt in an adversarial way. The purpose of the classifier is to find a boundary between the source and the target samples, while the feature generator attempts to force target samples away from the boundary. Especially, for the target samples, the feature generator will determine whether to align them with source samples or reject them as unknown target samples. The experimental results have indicated the effectiveness of the proposed method.
Simple and Fast Method to Optimize Nanotextured Interfaces of Thin-Film Silicon Solar Cells

A simple and fast method was developed to determine the optimal surface texture of thin-film silicon solar cells. The optical wave propagation was studied for microcrystalline thin-film silicon solar cells with integrated line and triangular gratings. The developed method based on rigorous coupled wave analysis provides a good agreement with experimental data. The short circuit current is enhanced by 60% up to 20–21 mA/cm2 for grating periods of 500–700 nm and grating heights of 300–500 nm. The method facilitates an analysis of nanotextured solar cells which is 20 times faster than conventional approaches like finite difference and finite integral simulations.
COVID-19 ads as political weapon

In view of the emergence of mobility restrictions and social isolation imposed by the coronavirus or COVID-19 pandemic, digital media, especially social networks, become a breeding ground for fake news, political attacks and large-scale misinformation. The impacts of this 'infodemic' can take even greater proportions when using sponsored content on social networks, such as Facebook ads. Using the Facebook ad library we collected more than 391K facebook ads from 90 different countries. Choosing ads from Brazil as the focus of research, we found ads with political attacks, requests for donations, doctors prescribing vitamin D as a weapon to fight coronavirus, among other contents with evidence of misinformation.
SOLUBILIZATION OF POLYCYCLIC AROMATIC HYDROCARBONS IN MICELLAR NONIONIC SURFACTANT SOLUTIONS

Experimental data are presented on the enhanced apparent solubilities of naphthalene, phenanthrene, and pyrene resulting from solubilization in aqueous solutions of four commercial, nonionic surfactants: an alkyl polyoxyethylene (POE) type, two octylphenol POE types, and a nonylphenol POE type. Apparent solubilities of the polycyclic aromatic hydrocarbon (PAH) compounds in surfactant solutions were determined by radiolabeled techniques. Solubilization of each PAH compound commenced at the surfactant critical micelle concentration and was proportional to the concentration of surfactant in micelle form. The partitioning of organic compounds between surfactant micelles and aqueous solution is characterized by a mole fraction micelle-phase/aqueous-phase partition coefficient, K{sub m}. Values of log K{sub m} for PAH compounds in surfactant solutions of this study range from 4.57 to 6.53. Log K{sub m} appears to be a linear function of log K{sub ow} for a given surfactant solution. A knowledge of partitioning in aqueous surfactant systems is a prerequisite to understanding mechanisms affecting the behavior of hydrophobic organic compounds in soil-water systems in which surfactants play a role in contaminant remediation or facilitated transport.
Prevalence of Hospital Acquired Infection and Antibiotic Trends in ICU in Southern India

Hospital acquired infection (HAI) is a key factor in determining clinical outcomes among patients admitted in intensive care units. Critically ill patients in intensive care unit (ICU) are 5 -10 times more likely to acquire nosocomial infections than those in general wards. [1,2] Studies on nosocomial infections in ICUs found that respiratory tract infections, blood stream infections, urinary tract infections and soft tissue infections are the common nosocomial infections in ICUs. [3,4,5] Samples of urine, blood, pus, sputum and endotracheal tube (ETT) secretions when evaluated had Gram negative isolates followed by Staphylococcus aureus, Coagulase negative Staphylococcus species and Enterococcus International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 11 (2020) Journal homepage: http://www.ijcmas. com
SMU Data Science Review SMU

Machine reading comprehension and question answering are topics of considerable focus in the field of Natural Language Processing (NLP). In recent years, language models like Bidirectional Encoder Representations from Transformers (BERT) [3] have been very successful in language related tasks like question answering. The difficulty of the question answering task lies in developing accurate representations of language and being able to produce answers for questions. In this study, the focus is to investigate how to train and fine tune a BERT model to improve its performance on BioASQ, a challenge on large scale biomedical question answering. Our most accurate BERT model achieved an F1 score of 76.44 on BioASQ, indicating successful performance in biomedical question answering.
Local Administration of Low-Dose Simvastatin-Conjugated Gelatin Hydrogel for Tendon-Bone Healing in Ligament

Anterior cruciate ligament (ACL) reconstruction with the hamstring tendon graft takes a long time, as the tendon graft needs to heal at the site of the bone–tendon integration in the created bone tunnels. Several reports have shown the therapeutic effects of simvastatin on bone formation with neovascularization. The aim of this study was to test the hypothesis that enhanced angiogenesis and osteogenesis by locally applied simvastatin promotes tendon–bone healing after ACL reconstruction. Rabbits received ACL reconstruction with hamstring tendon graft and were implanted with either simvastatin-conjugated gelatin hydrogel or gelatin hydrogel alone in their bone tunnels, and then bone regeneration and neovascularization at tendon–bone interface and biomechanical properties were assessed. Histological analysis at week 2 demonstrated that tendon–bone healing was signiﬁcantly greater with angiogenesis and osteogenesis in the simvastatin-treated group than in the control group. Computed tomography at weeks 2 and 4 showed a signiﬁcantly smaller tibial bone tunnel in the simvastatin-treated group. Biomechanical testing at week 2 demonstrated a signiﬁcant increase in ultimate failure load in the simvastatin-treated group. This study suggested that local administration of low-dose simvastatin-conjugated gelatin hydrogel promotes the tendon–bone healing via its effect on both angiogenesis and osteogenesis at an early phase in a rabbit Bevacizumab totally inhibited the beneﬁcial effects of simvastatin on tendon–bone healing. The results demonstrated that the positive effect of simvastatin on tendon–bone healing was associated with increased angiogenesis and osteogenesis, whereas an inhibition of VEGF did decrease not only angiogenesis but also osteogenesis in promoted healing at the tendon–bone junction. These ﬁndings suggest that angiogenesis plays a more important role in tendon–bone healing than osteogenesis.
Competencies and Orientation of Entrepreneurs on Dynamic Capabilities towards Organizational Performance by Considering External Environment: Development of a Conceptual Model

Entrepreneurship is a process of creating a new business to produce and market new, innovative or existing product. Small business is a bridge for entrepreneurs to perform. Even though entrepreneurial competencies and entrepreneurial orientation have been considered separately on small business performance, little research attention has been given to the theoretical mix of entrepreneurial competencies and entrepreneurial orientation in a single research work. Very few studies have been considered dynamic capabilities as a mediator and external environment as a moderator to facilitate the relationships between entrepreneurship and
DOI: 10

Interstitial nitrides and carbides of early transition metals in groups IV-VI exhibit platinum-like electronic structures, which make them promising candidates to replace noble metals in various catalytic reactions. Herein, we present the preparation and characterization of nano-sized transition metal nitries and carbides of groups IV-VI (Ti, V, Nb, Ta, Cr, Mo, and W) using mesoporous graphitic carbon nitride (mpg-C3N4), which not only provides confined spaces for restricting primary particle size but also acts as a chemical source of nitrogen and carbon. We studied the reactivity of the metals with the template under N2 flow at 1023 K while keeping the weight ratio of metal to template constant at unity. The produced nanoparticles were characterized by powder X-ray diffraction, CHN elemental analysis, nitrogen sorption, X-ray photoelectron spectroscopy, and transmission electron microscopy. The results show that Ti, V, Nb, Ta, and Cr form nitride phases with face centered cubic structure, whereas Mo and W forme carbides with hexagonal structures. The tendency to form nitride or carbide obeys the free formation energy of the transition metal nitrides and carbides. This method offers the potential to prepare the desired size, shape and phase of transition
Renewable hydrogen: carbon formation on Ni and Ru catalysts during ethanol steam-reforming

Biomass is probably the only realistic green and sustainable carbonaceous alternative to fossil fuels. By degradation and fermentation, it can be converted into bioethanol, which is a chemical with a range of possible applications. In this study, the catalytic steam-reforming of ethanol for the production of hydrogen is investigated, along with quantitative and qualitative determinations of carbon formation on the catalysts by TPO and TEM experiments. A Ru/MgAl2O4 catalyst, a Ni/MgAl2O4 catalyst as well as Ag- and K-promoted Ni/MgAl2O4 catalysts were studied. The operating temperature was between 673 and 873 K, and a 25 vol% ethanol–water mixture was employed. Deactivation of the catalysts by carbon formation is the main obstacle for industrial use of this process. Carbon formation was found to be highly affected by the operating temperature and the choice of catalyst. The effect of Ag addition was a rapid deactivation of the catalyst due to an enhanced gum carbon formation on the Ni crystals. Contrary to this, the effect of K addition was a prolonged resistance against carbon formation and therefore against deactivation. The Ru catalyst operates better than all the Ni catalysts, especially at lower temperatures.
Model Predicted Distribution of PM2.5 Exposure-Related Health Effects from Marcellus Shale Gas Development in Pennsylvania, 2005-2017

BackgroundDevelopment of Marcellus shale gas in recent years has turned into a public concern due to air quality changes and associated health impacts caused by emissions from shale gas development. Controlling standards and regulations were devised to provide health protection from exposure to such emissions, but health impact incidences have been reported in the areas where shale gas activities were prevailing. This study estimates the changes in expected health impacts associated to fine particulate matter (PM2.5) emissions from shale gas development in Pennsylvania between 2005 and 2017.MethodsThe change in incidence rates is estimated using a Gaussian plume model to simulate PM2.5 concentrations over time and distance and a health risk model to calculate the relative risk of exposure associated with different concentration levels.ResultsSimulation results indicate that PM2.5 emissions from shale gas development could increase the incidence of health outcomes at specific locations over the studied timespan and in case of asthma, this increase could be as high as 20,411 persons over 13-years.ConclusionThis study identifies the areas in Pennsylvania where more health risk assessment could be prioritized. An effective health-governing regulation should account for level of development activities as well as spatial characteristics (i.e., climate and population) of their vicinity to successfully provide health protection from air pollutants caused by shale gas development.
Optimal Auditing under Learning and Sympathy: A Game Theory Approach

In this paper, a game-theoretic framework is proposed to address the problem of fraud deterrence and detection taking into consideration fundamental principles and threats that may affect the performance of an audit process. Based on evidence from social psychology and on the outcomes of statistical sampling theory on audit quality, a mathematical model based on game theory is proposed providing reasonable assurance that the financial statements as a whole are free from material misstatements. It is proven that the auditing/fraud detection game between two new engagement parties has a unique mixed strategy equilibrium, between an experienced auditor and a client has a unique pure strategy equilibrium, whereas in the long-run the game converges to a pure strategy equilibrium that is governed by sympathy. Furthermore, a closed form solution to the optimal auditor’s replacement problem is extracted. The validity of the proposed scheme is tested on empirical data and modeling results comply with the International Federation Accountants Code of Ethics that requires the key audit partner to be rotated after a predefined period.
Heat Utilization of Incinerator Chimneys as Mini Power Generator Based on Thermoelectric

The thermoelectric generator (TEG) has long been used to produce electrical energy. When a temperature difference occurs between two semi-conductor materials on TEG, this thermoelectric element will produce a voltage difference and flow an electric current known as the 'Seebeck effect'. This research was conducted to determine the potential of electrical energy from the TEG module as an alternative energy source by utilizing chimney heat from an incinerator for household non-organic waste. Tests are carried out with a variation of 1, 2 and 3 m chimney height with solid waste treatment without compaction and compaction. The test results show that with 12 TEG modules arranged in series electricity can produce an output voltage with a maximum temperature difference of each ΔV 3.31 Volt ; ΔT 15.0 °C (1 m), ΔV 3.92 Volt; ΔT 17.8 °C (2 m), ΔV 4.84 Volt ; ΔT 22.0 °C (3 m) while the compaction of each value ΔV 6.34 Volt ; ΔT 29.2 °C (1 m), ΔV 7.69 Volt ; ΔT 35.2 °C (2 m), ΔV 9.09 Volt ; ΔT 41.5 °C (3 m). The potential power that can be produced is as big as 3.22 W (1 m); 4.56 W (2 m); 6.88 W (3 m) while the compaction of waste is equal to each value 11.80 W (1 m); 17.36 W (2 m); 24.25 W (3 m). The addition of chimney height variation resulted in an increase in the hot side temperature (Th), cold side (Tc) and temperature difference (ΔT) so that heat energy and power energy increased.