Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Left Ventricular Rotational Deformation by Non-Conventional Echocardiography Before and After Transcatheter Closure of Large Secundum Atrial Septal Defects in Children By

Introduction: Atrial septal defects (ASD) are a common congenital heart disease which causes increased volume overload of the right side of the heart that leads to impairment of LV performance. LV torsion is a three dimensional (3D) performance, so it cannot be assessed accurately by the 2D speckle tracking method. Aim of the work: The purpose of this study was to evaluate left ventricular (LV) rotational & torsional deformation in large ASD patients undergoing transcatheter ASD device closure using 4D speckle tracking Imaging (STI). Methods: All patients who underwent transcatheter device closure of large ostium secundum ASD were included in the study. We assessed LV torsion in large ostium secundum ASD patients pre and post device closure by using 4D speckle tracking
IoT System for Sleep Quality Monitoring using Ballistocardiography Sensor

Sleep is very important for people to preserve their physical and mental health. The development of the ballistocardiography (BCG) sensor enables the possibility of day-to-day and portable monitoring at home. The goal of this study is to develop an IoT sleep quality monitoring system using BCG sensors, microcontrollers and cloud servers. The BCG sensor produces ECG data from the physical activity of the patient. The data is sent to the sensor and is read by the microcontroller. The sensor data is collected and pre-processed in the microcontroller. The microcontroller then transmits the data obtained from the BCG sensor to the cloud server for further analysis, i.e. to assess the sleep quality. The assessment of data transmission efficiency and resource consumption is carried out in this paper. The findings of the evaluation show that the proposed method achieves higher efficiency, lower response time and decreases memory usage by up to 77% compared to the conventional method.
Numerical Investigation of a time-dependent magnetic actuation technique for tagging biomolecules with magnetic nanoparticles in a microfluidic system

The magnetic body forces that act on mono-dispersed magnetic nanoparticle (MNP) tagged biomolecules in a microfluidic system can be efficiently used in various applications that involve separation and detection including DNA and protein analysis, bio-defense, drug delivery, and pharmaceutical development. However, for microfluidic devices to perform such operations for point-of-care analysis, it is often necessary to tag biomolecule of interest with MNPs on-chip. Tagging various fluid contents continuously in the micro-scale is often difficult due to slow diffusion process where length scale of mixing is large. Therefore, enormous time is needed for the biomolecules to be thoroughly mixed and combined with MNPs. In this work, we report an FEM model to demonstrate a novel method of tagging biomolecules with MNPs on-chip using timedependent magnetic field, produced due to the electrodes embedded in the device substrate beneath the microchannel. A time-dependent magnetic body forces produces oscillation in MNPs causing agitation in the surrounding fluid that otherwise follow laminar profile and overall speeds up the reaction kinetics of the tagging process. This strategy is easy to implement and can be integrated on a lab-on-a-chip system. The model was employed to quantify the effect of convection, diffusion, reaction and magnetic field on the tagging performance. Overall, the developed COMSOL model demonstrates that time-dependent magnetic actuation is an efficient tool to mix or tag MNPs with biomolecules in situ for the development of efficient point-ofcare microfluidic systems.
Tourists’ preferences for nature: do they change after their experience?

Nature based tourism has gained an important position in contributing to the dual goals of nature conservation and generating an income stream from the natural environment. To be more effective in providing services which facilitate the achievement of these goals, stakeholders need to identify tourists’ preferences and how they value their nature and wildlife experiences. Tourism, as an experiential good has the potential to impact tourists’ awareness, appreciation and actions in relation to the specific wildlife they encounter and the environment in general. Therefore, user preferences and valuations of nature and wildlife may change as a result of the experience. Exploring the valuation of environmental goods by analysing changes in respondents’ willingness to pay (WTP) before and after experiencing the good is, however, an area that has been given little attention in the literature. This research attempts to show how exposure to the environment is an influencing factor in the value formation of tourists as consumers. This study employs two choice experiments using the same sample of tourists before and after taking part in a nature and wildlife tour in Sri Lanka. They included alternative scenarios of identified trip attributes in relation to wildlife and services provided. A multinomial logit model is used to investigate the changes in user preferences, before and after the experience. The results indicate that tourists’ preferences for nature, wildlife and related services significantly improve after experiencing the tour. The paper also draws attention to future policies that could be designed to improve the quality of tourists’ experiences. 1 Preliminary draft-please do not quote without authors’ permission.
Priority-based Routing Framework for Image Transmission in Visual Sensor Networks: Experimental Analysis

A Visual Sensor Network (VSN) is a specialized Wireless Sensor Network (WSN) equipped with cameras. Its primary function is to capture images, videos and send them to power rich sink nodes for processing. As image data is much larger than scalar data sensed by a typical WSN, applications of VSN require much bigger amount of data to be transferred to the sink. Due to constraints of WSN such as low energy, limited CPU power and scarce memory, transmission of large amount of data becomes challenging. On the other hand, some VSN applications require critical image features sooner than the entire image to take action. In this paper, we provide the details of experiments done using our proposed Priority-based Routing Framework for Image Transmission (PRoFIT). PRoFIT is designed to deliver critical image features at high priority to the sink node for early processing. Peak signal-to-noise ratio (PSNR) analyses show that PRoFIT improves VSN application response time as compared to priority-less routing. This paper also contains the design of our VSN testbed. Multiple indoor and outdoor experiments were performed to validate the framework. This framework also improves the energy efficiency of the network. The results show that the PRoFIT is 40% efficient in terms of energy consumption.
Optimization of breast tomosynthesis

Breast tomosynthesis (BT) is an X-ray imaging technique recently introduced as an alternative or complement to standard digital mammography (DM) in breast imaging and breast cancer screening. In BT, a set of projection images is acquired over a limited angular range and reconstructed into a volume of slice images. The method includes many possible combinations of acquisition parameters that have to be optimized for best possible clinical performance and outcome. The visibility of breast cancer lesions is important in this context. Compared to DM images, the reconstructed BT volume provides additional information on depth, reducing the superposition of breast tissue, which may hide true lesions or appear as false positive findings. Thus, the BT volume also contains information about the distribution of dense tissue within the breast, which is of interest when estimating the radiation dose from DM and BT exposure. In this thesis, a simulation procedure was developed for the optimization of image acquisition and estimation of individual glandular dose in BT. The procedure was shown to be useful in generating BT images with realistic sharpness, though with higher image noise and contrast than experimentally acquired images (Paper I). The procedure was used to investigate the influence of angular range, distribution of projection images, and dose distribution on simulated microcalcifications in reconstructed BT volumes. Image acquisitions with very high central dose yielded significantly lower visibility than acquisitions with more uniform dose distributions, and the depth resolution increased with wider angular range (Paper II). A method for localizing dense tissue from reconstructed BT volumes was verified using the simulation procedure (Paper III). A prototype software program was used for automatic and objective estimation of breast density in BT, with similar performance as DM (Paper IV). Using software breast phantoms recreated from reconstructed BT volumes, the glandular dose could be estimated with good overall accuracy for breast phantoms with different amounts and distributions of dense tissue (Paper V). The developed simulation procedure has been a useful tool for optimizing acquisition parameters and estimating glandular dose in BT. The procedure could potentially be developed for further evaluation of the imaging chain and estimation of individual glandular dose in human cases.
Application of SWAT model to Assess Land Use and Climate Changes Impacts on Hydrology of Nam Rom River Basin in Vietnam

Land use/land cover (LULC) and climate changes are two main factors directly affecting hydrologic conditions. However, very few studies in Vietnam have investigated changes in hydrological process under the impact of climate and land use changes on a basin scale. The objective of this study is to assess the individual and combined impacts of land use and climate changes on hydrological processes for the Nam Rom river basin, Northwestern Viet Nam using Remote Sensing (RS) and Soil and Water Assessment Tools (SWAT) model. SWAT model was used for hydrological process simulation. Results indicated that SWAT proved to be a powerful tool in simulating the impacts of land use and climate change on catchment hydrology. The change in historical land use between 1992 and 2015 strongly contributed to increasing hydrological processes (ET, percolation, ground water, and water yield), whereas, climate change led to significant decrease of all hydrological components. The combination of land use and climate changes significantly reduced surface runoff (-16.9%), ground water (-5.7%), water yield (-9.2%), and sediment load (-4.9%). Overall climatic changes had more significant effect on hydrological components than land use changes in the Nam Rom river basin during the 1992–2015. Under impacts of projected land use and climate change scenarios in 2030 on hydrological process of the upper Nam Rom river basin indicate that ET and surface flow are more sensitive to the changes in land use and climate in the future. In conclusion, the findings of this study will basic knowledge of the effects of climate and land-use changes on the hydrology for future development of integrated land use and water management practices in Nam Rom river basin.
FLAIR: Accelerating Reads with Consistency-Aware Network Routing

We present FLAIR, a novel approach for accelerating read operations in leader-based consensus protocols. FLAIR leverages the capabilities of the new generation of programmable switches to serve reads from follower replicas without compromising consistency. The core of the new approach is a packet-processing pipeline that can track client requests and system replies, identify consistent replicas, and at line speed, forward read requests to replicas that can serve the read without sacrificing linearizability. An additional benefit of FLAIR is that it facilitates devising novel consistency-aware load balancing techniques. Following the new approach, we designed FlairKV, a key-value store atop Raft. FlairKV implements the processing pipeline using the P4 programming language. We evaluate the benefits of the proposed approach and compare it to previous approaches using a cluster with a Barefoot Tofino switch. Our evaluation indicates that, compared to state-of-the-art alternatives, the proposed approach can bring significant performance gains: up to 42% higher throughput and 35-97% lower latency for most workloads.
Stemming Text-based Web Page Classification using Machine Learning Algorithms: A Comparison

The research aim is to determine the effect of word-stemming in web pages classification using different machine learning classifiers, namely Naive Bayes (NB), k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Multilayer Perceptron (MP). Each classifiers' performance is evaluated in term of accuracy and processing time. This research uses BBC dataset that has five predefined categories. The result demonstrates that classifiers' performance is better without word stemming, whereby all classifiers show higher classification accuracy, with the highest accuracy produced by NB and SVM at 97% for F1 score, while NB takes shorter training time than SVM. With word stemming, the effect on training and classification time is negligible, except on Multilayer Perceptron in which word stemming has effectively reduced the training time.
Pd–Ce/ZIF-8 Nanocomposite for Catalytic Extraction of Sinomenine from Sinomenium acutum

Sinomenine is a naturally occurring alkaloid and commonly used as one of the bioactive drug components in rheumatoid arthritis (RA) treatment in the clinic. Varying supported palladium-based catalysts have been synthesized and examined as heterogeneous catalysts for catalytic extraction of sinomenine from Sinomenium acutum. Among various examined supported catalysts, Pd–Ce/ZIF-8 (zeolitic imidazolate framework-8) demonstrates promising catalytic activity in the extraction reaction with an improved yield of 2.15% under optimized conditions. The catalyst composite can be recovered by centrifuging, and reused. A total of three catalyst recycling processes were performed with constant activity. The catalyst Pd–Ce/ZIF-8 has a particle size range of 2–12 nm and a total Pd–Ce loading amount of 5.1 wt% (ZIF-8).
Socio-Economic Impact of Industrialisation and Mining on the Local Population: A Case Study of Nalco Industrial Area, Koraput

Price stability is one of the key objectives of monetary policy. To achieve such mandate, policy makers need to be forward-looking. This calls for good inflation forecasting ability for the monetary authorities. The inflation forecast is used to guide policy discussions in determining the appropriate policy stance to be adopted. In the case of an inflation targeting regime, the inflation forecast can alert policymakers to take policy response should the forecast indicate that the inflation outlook may deviate for the target. Hence, accurately forecasting the inflation is crucial for the monetary authorities.
7.5: Coarse Grain Simulations of Membranes

During the late 1960s at the dawn of Levinthal’s paradox, understanding the mechanism of protein folding became a critical area of study. This was likely the primary driving force for the coarse grained method in order to capture the phenomenon of the entire protein folding process. It was Levitt and Warshel who were the first people to successfully accomplish this during their work in the early 1970s. Their coarse graining method, published in 1975, modeled the small, bovine pancreatic trypsin inhibitor protein using pseudoatoms at the alpha carbon positions; the model reduced the degrees of freedom to one which described rotation along the central pseudobond for the three consecutive alpha carbon atoms; the force field used was described by the Lennard Jones potential; and Brownian Dynamics was used as the sampling scheme. The pioneering work substantiated but fell short of describing the packing and pair-wise interactions of side chains that are integral driving forces for obtaining specific folded structures. Just a year later, Levitt proposed an improved model accounting for side chain orientation and since then the method of coarse graining has only continued to progress in accuracy and expand to other biomolecules like nucleic acids, carbohydrates, water and lipids. The development of this inaugural coarse grained model has led to the many advancements in the method that exist in the current state of the art as it has become a crucial computational modeling tool for simulating meso-scale biological phenomena.2,6,10
Data Based Reconstruction of Duplex Networks\ast

It has been recognized that many complex dynamical systems in the real world require a description in terms of multiplex networks, where a set of common, mutually connected nodes belong to distinct network layers and play a different role in each layer. In spite of recent progress toward data based inference of single-layer networks, to reconstruct complex systems with a multiplex structure remains largely open. In this paper, we articulate a mean-field based maximum likelihood estimation framework to address this problem. In a concrete manner, we reconstruct a class of prototypical duplex network systems hosting two categories of spreading dynamics, and we show that the structures of both layers can be simultaneously reconstructed from time series data. In addition to validating the framework using empirical and synthetic duplex networks, we carry out a detailed analysis to elucidate the impacts of network and dynamics parameters on the reconstruction accuracy and the robustness.
New Trends in Microbial Epidemiology: Can An Old Dog Learn New Tricks?

In historical terms, almost 50 years ago, all the knowledge coming from clinical microbiology was provided by classical methods, mainly based on phenotypic observations and culture-dependent methods. With the advent of DNA fragment analysis, PCR-based methods and Sanger sequencing, the dawn of the Era of Molecular Diagnosis began. Significant improvements were achieved, namely improved resolution concerning the identification of etiological agents was possible in a reduced timeframe between sample collection and results. To overcome some of the difficulties presented by molecular diagnosis, almost 20 years ago, massive parallel sequencing technology was introduced into epidemiological studies. Nowadays, is possible to sequence the whole genome of a given microorganism (including information on antibiotic resistance, virulence factors, and evolutionary relationships) or to assess the complete diversity of microorganisms present in a given individual or in environmental samples (metagenomics). The present review covers the major outbreaks registered worldwide in the past 12 years and highlights the use of new technologies in source identification, outbreak detection, routes of transmission determination, pathogen evolution inference and mechanisms of pathogen adaptation to drug therapies.
Comparative Anti-malarial and Toxicological Properties of the Stem Bark ‎Extracts of Nauclea latifolia and Terminalia glaucescens Against ‎Plasmodium berghei-infected Mice

Article info: Received: 22 Jul 2019 Accepted: 12 Nov 2019 Online Published: 10 Jan 2020 Research Article: Comparative Anti-malarial and Toxicological Properties of the Stem Bark Extracts of Nauclea latifolia and Terminalia glaucescens Against Plasmodium berghei-infected Mice * Corresponding author: Eustace Bonghan Berinyuy, MSc. Address: Department of Biochemistry, Faculty of Medicine and Biomedical Sciences, University of Yaounde I, Yaounde, Cameroon. E-mail: eusbong1@gmail.com
Mosquitoes and frog-biting midges (Diptera: Culicidae and Corethrellidae) attracted to traps with natural frog calls and synthesized sounds at Iriomote Island, Ryukyu Archipelago, Japan

The attractiveness of frog sounds to biting Uranotaenia mosquitoes and Corethrella midges was determined using natural and synthesized sounds. The traps with each species’ call of eight frog species or a series of them, were set in a forest of Iriomote Island. Out of 17 mosquito species collected, Uranotaenia macfarlanei Edwards was the predominant species. Most Ur. macfarlanei females were collected by characteristic calls which had dominant frequency of about 1,000 Hz. We synthesized 23 sounds which varied in frequency spectrum (original burst, sinusoidal, and noise), wave form, repeated rhythm, and loudness of sounds. The burst sound was synthesized by editing the original scream of male Fejervarya sakishimensis Matsui et al., which had two dominant frequencies of 1,038 Hz and 2,312 Hz. The sinusoidal sound had a dominant frequency of 1,000 Hz, while the noise sound had no dominant frequency. Uranotaenia macfarlanei females were collected with the original burst and sinusoidal sounds more than noise sound. They were also well attracted when loudness was higher than about 75 dB SPL. The original burst sound also attracted Corethrella nippon Miyagi more than sinusoidal and noise sounds. Prediction model indicated that low loudness sounds still attracted C. nippon compared to Ur. macfarlanei.
Facial Emotion Recognition using Neighborhood Features

We present a new method for human facial emotions recognition. For this purpose, initially, we detect faces in the images by using the famous cascade classifiers. Subsequently, we then extract a localized regional descriptor (LRD) which represents the features of a face based on regional appearance encoding. The LRD formulates and models various spatial regional patterns based on the relationships between local areas themselves instead of considering only raw and unprocessed intensity features of an image. To classify facial emotions into various classes of facial emotions, we train a multiclass support vector machine (M-SVM) classifier which recognizes these emotions during the testing stage. Our proposed method takes into account robust features and is independent of gender and facial skin color for emotion recognition. Moreover, our method is illumination and orientation invariant. We assessed our method on two benchmark datasets and compared it with four reference methods. Our proposed method outperformed them considering both the datasets.
Three Essays on Debt

Three Essays on Debt Lijun Wang This dissertation contains three essays on debts of different forms that make contributions to the areas of international macroeconomics and spatial economics. In particular, the first two essays study sovereign debts. They examine sovereign default behaviors together with interactions between sovereign defaults and countries’ costs of borrowing. The third essay looks at bank loans. It explores the possibility of understanding economic agglomeration through distance-related financial frictions firms face when borrowing from banks.
Effect of nitrogen-doping on the surface chemistry and corrosion stability of TiO 2 films

TiO2 and N-doped TiO2 films were grown on AISI 316 stainless steel substrates and on Si (100) by metallorganic chemical vapor deposition (MOCVD) at 400 C and 500 C. X-ray photoelectron spectroscopy, scanning electron microscopy, and contact angle techniques were used to characterize de films. The corrosion behavior was assessed by monitoring the open circuit potential, electrochemical impedance spectroscopy and potentiodynamic polarization tests in 3.5 wt% NaCl solution at room temperature. The results show that 6.18 at% of nitrogen was introduced in the films grown at 400 C and 8.23 at% at 500 C, and that besides TiO2, nitrogen phases were identified. All the films are hydrophilic and the contact angles varied from 48 to 72. The films presented good homogeneity, low porosity and rounded grains in the range of 40–90 nm. The RMS roughness varied between 5.5 and 18.5 nm. Titanium dioxide films grown at 400 C showed better corrosion resistance than those grown at 500 C due to its compact morphology. Nitrogen-doping was not efficient to protect the substrate from corrosion. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).