Miracle-dz/UniFAR-FTUs huggingface dataset:
Import Demand Elasticities for South Africa: A Cointegration Analysis

Abstract This study acknowledges the lack of foreign exchange as the main obstacle to faster economic growth in South Africa. It is noted that many studies have only focused on exports and export expansion alone as a means to eradicate this economic dilemma. This study considers the import component of the balance of payments, because together with exports the demand for imports clearly determines the behavior of the current account of the balance of payments as a whole. The focus of this paper is on the estimation and interpretation of results of the aggregate and disaggregated import demand elasticities for South Africa. The import demand model is estimated by a means of cointegration analysis. The major finding is that the import elasticity with respect to income is highly significant than the price elasticity of demand for imports. Given this result, concluding remarks highlight some critical policy implications and areas for further research.
Preliminary investigation of serum ferritin level and its reference interval in apparent healthy children population in Provincial Children’s Hospital

Abstract Objectives Serum ferritin (SF) is a biomarker of physiological iron stores. Reference intervals for ferritin about children are the subject of some controversy. Methods SF was assayed on Beckman analyzer. All results were retrieved from the electronic medical record (3,344 samples). Student’s t test and one-way ANOVA test were applied to compare two groups, with respect to continuous and discrete variables. Results The reference range of SF on reference population was 8.40–95.98 µg/L. Among the male, there was no significant difference in the average level between the 0–1 years old group and the 1–3 years old group, but there were significant differences between the other groups and the previous group. In terms of women, there was no significant difference in the average level of ferritin between the 1–3 years old group and 9–17 years old group and the previous group, but there were significant differences in the average levels of ferritin in other groups. Conclusions We established the reference intervals of ferritin of different age groups and gender groups. Our results have practical significance for the formulation of clinical reference range, which would be helpful in interpreting laboratory data and clinical decision-making.
Exploring the impact of customer experience on customer loyalty in e-commerce

Abstract In recent years, with the remarkable growth of the global digital market, specialists took a new direction of focus in the field of marketing, considering customer experience an important differentiator in today’s digital landscape. In an online setting, in contrast to traditional marketing, the dimensions of customer experience include informativeness, entertainment, social presence and sensory, elements that create customer value. The experience is an increasingly significant component in defining the success of an online company, becoming a source of competitive advantage for any e-business. Yet, to accomplish these favorable results, a business needs to focus on customer loyalty, a concept that is understood from a relational perspective as the consumer’s preferences to sustain a stable relationship with the brand, fact that involves consistent visits of the site, and in consequence, repeat purchases. The research presented in this paper is based on the literature review. Subsequently, a conceptual model was proposed as the intersection of the online customer experience dimensions with the concept of customer loyalty, which, in turn, includes consumer’s attitudes and behaviors toward an online business. The purpose of this study is twofold: (1) to offer an outline of opportunities to use customer experience for increasing customer loyalty and (2) to provide managerial implications and guides future research opportunities to empirically measure the impact of online customer experience components on customer loyalty.
Dealing with uncertainty in predicting the flows of visitors in Romanian museums

Abstract Understanding museum visitors’ behavioral patterns may help in predicting their number of visits. The aim of the research is to identify the trend models associated with the number of visitors, in 2012-2019, in 2 national museums in the top of the most visited museums in Romania: the National Museum of Natural History “Grigore Antipa” in Bucharest and the National Museum “Peles” in Sinaia and forecast the number of visitors to these objects in 2020 and 2021. Subsequently the same methods were used to analyze the total number of visitors to museums in Romania in 2012-2019 and to forecast the number of visitors for the years 2020 and 2021. The research method used was that of coefficients of variation, which aimed to determine the forecasts for the number of visitors through three types of models: linear, quadratic and exponential. However, Covid-19 pandemic dramatically changed the forecasts of visitor flows in the museums and the managers of these cultural heritage institutions had to cope with this situation.
Diverse Mechanisms of Protective Anti-Pneumococcal Antibodies

The gram-positive bacterium Streptococcus pneumoniae is a leading cause of pneumonia, otitis media, septicemia, and meningitis in children and adults. Current prevention and treatment efforts are primarily pneumococcal conjugate vaccines that target the bacterial capsule polysaccharide, as well as antibiotics for pathogen clearance. While these methods have been enormously effective at disease prevention and treatment, there has been an emergence of non-vaccine serotypes, termed serotype replacement, and increasing antibiotic resistance among these serotypes. To combat S. pneumoniae, the immune system must deploy an arsenal of antimicrobial functions. However, S. pneumoniae has evolved a repertoire of evasion techniques and is able to modulate the host immune system. Antibodies are a key component of pneumococcal immunity, targeting both the capsule polysaccharide and protein antigens on the surface of the bacterium. These antibodies have been shown to play a variety of roles including increasing opsonophagocytic activity, enzymatic and toxin neutralization, reducing bacterial adherence, and altering bacterial gene expression. In this review, we describe targets of anti-pneumococcal antibodies and describe antibody functions and effectiveness against S. pneumoniae.
Experimental Lead Paint Poisoning in Nonhuman Primates

Lead‐containing paints were administered orally to 27 rhesus monkeys for periods of 18–667 days. Lead acetate was fed to nine monkeys of three different species for 9–156 days. Excretion of one week's dose of lead in six primates ranged from 35 to 94%. The animals incurred moderate to extreme elevations of lead in blood, most lost weight, or had depressed weight gains, and developed Burtonian lines, some died suddenly and unexpectedly, and many terminated in a moribund state with profound anemia. Only one neonate had obvious signs of lead encephalopathy. The monkeys' ages, dose and source of lead, and possibly other factors, affected their response to lead.
Nrg1/ErbB signaling‐mediated regulation of fibrosis after myocardial infarction

Appropriate fibrotic tissue formation after myocardial infarction (MI) is crucial to the maintenance of the heart's structure. M2‐like macrophages play a vital role in post‐MI fibrosis by activating cardiac fibroblasts. Because the mechanism by which post‐MI cardiac fibrosis is regulated is not fully understood, we investigated, in vitro and in vivo, the cellular and molecular mechanisms of post‐MI fibrotic tissue formation, especially those related to the regulation of cellular senescence and apoptosis. CD206+F4/80+CD11b+ M2‐like macrophages collected from mouse hearts on post‐MI day 7 showed increased expression of neuregulin 1 (Nrg1). Nrg1 receptor epidermal growth factor receptors ErbB2 and ErbB4 were expressed on cardiac fibroblasts in the infarct area. M2‐like macrophage‐derived Nrg1 suppressed both hydrogen peroxide‐induced senescence and apoptosis of fibroblasts, whereas blockade of ErbB function significantly accelerated both processes. M2‐like macrophage‐derived Nrg1/ErbB/PI3K/Akt signaling, shown to be related to anti‐senescence, was activated in damaged cardiac fibroblasts. Interestingly, systemic blockade of ErbB function in MI model mice enhanced senescence and apoptosis of cardiac fibroblasts and exacerbated inflammation. Further, increased accumulation of M2‐like macrophages resulted in excessive post‐MI progression of fibrosis in mice hearts. The molecular mechanism underlying the regulation of fibrotic tissue formation in the infarcted myocardium was shown in part to be attenuation of apoptosis and senescence of cardiac fibroblasts by the activation of Nrg1/ErbB/PI3K/Akt signaling. M2‐like macrophage‐mediated regulation of Nrg1/ErbB signaling has a substantial effect on fibrotic tissue formation in the infarcted adult mouse heart and is critical for suppressing the progression of senescence and apoptosis of cardiac fibroblasts.
Experimental Psychology and Psychopathology

The training and testing procedures of a chimpanzee (Pan) in a computerized language-training situation will be reviewed. Particular attention will be given experimentation which indicates the subject's (i) rudimentary reading and sentence-completion skills, (H) object-naming skills, (Hi) color-naming skills, (iv) ability to use sentences and parts of sentences in new combinations in response to novel contexts of a problematic nature, and (v) ability to engage in a continuing exchange of linguistically correct communications tantamount to conversations with a human experimenter. Problems of defining language will be considered for the purpose of evaluating the degree to which it might be concluded that at least this one chimpanzee subject has mastered skills of the same class employed by humans in the use of language. Accepted: June 30, 1974.
Hierarchical decoupling of electromagnetic and haemodynamic cortical networks

| Whole-brain neural communication is typically estimated from statistical associations among electromagnetic or haemodynamic time-series. The relationship between functional network architectures recovered from these two types of neural activity remains unknown. Here we map electromagnetic networks (measured using magnetoencephalography; MEG) to haemodynamic networks (measured using functional magnetic resonance imaging; fMRI). We ﬁnd that the relationship between the two modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with close correspondence in unimodal cortex and poor correspondence in transmodal cortex. Comparison with the BigBrain histological atlas reveals that electromagnetic-haemodynamic coupling is driven by laminar differentiation and neuron density, suggesting that the mapping between the two modalities can be explained by cytoarchitectural variation. Importantly, haemodynamic connectivity cannot be explained by electromagnetic activity in a single frequency band, but rather arises from the mixing of multiple neurophysiological rhythms. Correspondence between the two is largely driven by slower rhythms, particularly the beta (15-29 Hz) frequency band. Collectively, these ﬁndings demonstrate highly organized but only partly overlapping patterns of connectivity in MEG and fMRI functional networks, opening fundamentally new avenues for studying the relationship between cortical micro-architecture and multi-modal connectivity patterns.
Retrieving 4D Signal in Complex Media Using the Full Waveform Inversion Paradigm

Various 4D processing techniques are based on 1D approximation and their applicability is limited in the case of dipping reservoirs. In this paper we present a synthetic case study with dips up to 40° and superimposed reservoirs where data have been generated using full waveform modelling. New processing, based on full waveform inversion technique has been applied to the data in order to extract the 4D signal. Our results show that even in a complex environment seismic monitoring can be considered.
The Atmospheric Ozone Monitoring System by using Internet of Things Technology for Nanosatellites (3U CubeSat)

This paper presents the system for monitoring the condition of the earth's ozone layer using Internet of Things (IoT) technology. The proposed system deploys a 3U CubeSat satellite to measure ultraviolet intensity to compare the intensity value of ultraviolet radiation at each terrestrial base station through the web application. The measured data is computed and classified by cloud computing into five levels of the regional ozone layer's quality: excellent, good, average, fair, and poor. These data can also be gathered as statistics and retrieved on a daily, monthly and annual basis. Consequently, by this information, campaigning for people to reduce the destructive behaviours of the ozone layer in each area would be incredibly advantageous. In addition, each base station can be built for less than $150 using commonly available electronics and sensors, allowing the proposed system to be implemented and deployed globally. As a result, this project would be of great benefit to humanity to have information on the health of the ozone layer in each region around the world. This paper could be better inform monitoring strategies to reduce the greenhouse effect that brings unfamiliar and unpredictable impacts of climate change to the world.
A Modified SWB Hexagonal fractal Spatial diversity antenna with high isolation using meander line approach

This manuscript introduces a novel miniaturized super wideband (SWB) hexagonal fractal spatial diversity 2-element multiple-input-multiple-output (MIMO) antenna for broadband applications, as a compact wideband antenna is required for military radio and space communication devices. The designed antenna includes two hexagonal (1st modified Koch iteration) Koch fractal radiators with two linear tapered feedlines (TLTF) and a step tapered slotted ground plane (STSGP) that provides the broad impedance characteristic with miniaturized size in the operating band of 1.78-30 GHz. Moreover, the STSGP is also accountable for enabling the isolation (S12/21) better than 10 dB. To further meet the isolation higher than 22 dB, a meander line is protruded from the ground plane while maintaining a 6 mm edge-to-edge distance between two antenna elements. Typically, the MIMO antenna with high isolation is beneficial for high-speed data transmission. The experimental outcomes represent that the proposed MIMO antenna is with the advantages of reasonably good diversity gain (nearly 10 dB), stable radiation patterns, high peak gain (almost 6.6 dBi), and moderate radiation efficiency (around 85%) across -10 dB impedance bandwidth. The total active reflection coefficient (TARC) and envelop correlation coefficient (ECC) are less than -25 dB and 0.1 respectively. Due to all these attributes, the proposed 2-element MIMO antenna array can be a potential candidate for military radio, spectrum sensing for cognitive radio, and space communication applications.
Meta Reinforcement Learning for Robust and Adaptable Robotic Assembly Tasks

Robots have achieved great success in assembly tasks in known and structured environments. However, assembly tasks in unknown and unstructured environments are still an open problem due to the limited adaptability and robustness of conventional methods and algorithms. Reinforcement learning represents a potential solution to this problem as it can learn any task through interaction with the environment without the need for prior knowledge or information, that's why it can be adaptable to new tasks. However, reinforcement learning requires a lot of interaction data with the environment to properly learn a given task, and changes in the task may require learning again from scratch, that's why it is sample inefficient. Meta reinforcement learning can be both adaptable and data efficient during test time when facing a new task, as it learns a fast adaptation procedure using prior knowledge gained through training on various tasks. In this paper, we show how meta reinforcement learning can be used to successfully preform peg-in-hole assembly tasks in unknown environments, with high uncertainty in the hole position and poor performance sensors, within small number of interaction trajectories with the environment.
The Correlate of Emotion and Gender Classification Using EEG Signals

Many current applications depend on human-computer interaction systems for service delivery. These applications need to know the personal features of users, such as their gender, to improve the user experience. Notably, therefore, it is possible to categorize gender based on speech and facial characteristics. Additionally, instead of considering body part features, gender prediction based on physiological measurements such as electroencephalogram (EEG) signals provides a strong and reliable method for an automatic gender classification system. EEG signals record brain activity from the outer scalp, which means they are contaminated by external and internal noise. Therefore, designing an effective gender detection mechanism based on EEG data is challenging because gender-related EEG features must be captured accurately. In this paper, we investigated the effect of emotions on a gender prediction system using EEG data in negative and positive emotional states. We proposed a model that extracts the power spectral density features of EEG signals in emotional states and predicts gender using three classifiers: decision tree, random forest, and multilayer perceptron. Furthermore, we studied the effect of omitting a single electrode and multiple electrodes in the EEG data on the proposed system. The experimental results demonstrate the effectiveness of using emotional state EEG data to identify gender. Furthermore, the random forest classifier achieved the lowest percentage error of 7% based on negative emotion EEG signal. Finally, the results revealed that the brain’s frontal lobe has a high level of success in enabling differentiation between males and females.
Retinal Vessel Segmentation Based on Multidirection Filter

In order to segment retinal vessels with complex morphology and structure, a retinal blood vessel segmentation method based on multi-direction filter is proposed. First, the image is enhanced by morphological top hat transformation and image histogram equalization. Then multi-directional Cake filtering is performed, and the filtered results are fused. Finally, the adaptive vector field divergence method is used to extract the threshold to obtain the final result. We demonstrate this using the task of segmenting blood vessels in fundus images of two standard datasets, DRIVE and STARE. The experimental results show that the proposed method adapts to the complex and changeable characteristics of retinal blood vessel scale information, and it can handle the junction of complex blood vessels with higher sensitivity.
Highly Squinted Airborne SAR Imaging Based on Multi-Core DSP System

Highly squinted airborne synthetic aperture radar (SAR) with high flexibility and wide observation range has capability of obtaining the target region in repeatedly. In this paper, a multi-core digital signal processor (DSP) imaging processing system built on the polar format algorithm (PFA) is proposed, which is able to deal with the highly squinted airborne SAR data in a satisfied processing speed. First, according to the principle of chirp scaling (PCS), range resampling is employed for SAR echo signal, which improves the processing efficiency significantly. Then, high-precision sinc-based interpolation is exploited to realize the azimuth resampling. Finally, autofocus technique is implemented for further compensating the azimuth phase error so as to obtain the well-focused SAR imagery. Experimental results utilizing the measured highly squinted airborne SAR data indicate that the formulated DSP system is capable of generating 2K*2K imagery within 0.671s, which promotes the further radar imaging in real-time processing.
Semi-supervised Deep Learning of Vessel Segmentation in Coronary Angiography

Vessel segmentation and stenosis classification in coronary angiography have guiding significance for doctors in the diagnosis of coronary artery disease. We used a total of 120 coronary angiography images collected by ourselves and labeled 70 images, which include 50 images for supervised training, 10 images for verification and 10 images for testing. For the vessel segmentation task, we used a traditional threshold segmentation method such as the Otsu method, a supervised deep learning method such as the UNet network. In order to make full use of the other 50 unlabeled images, we propose a semi-supervised deep learning method based on the semi-supervised adversarial network. The segmentation results of the semi-supervised deep learning method are better than the results of the supervised deep learning method, whose segmentation results are better than the results of the traditional method.
SDF-SLAM: A Deep Learning based Highly Accurate SLAM using Monocular Camera aiming at Indoor Map Reconstruction with Semantic and Depth Fusion

Simultaneous localization and mapping (SLAM) is considered as a key technique in augmented reality (AR), robotics and unmanned driving. In the field of SLAM, solutions based on monocular sensors have gradually become important due to their ability to recognize more environmental information with simple structures and low costs. Feature-based ORB-SLAM is popular in many applications, but it has many limitations in complex indoor scenes. Firstly, camera pose estimation based on monocular images is greatly affected by the environment; secondly, monocular images lack scale information and cannot be used to obtain image depth information; thirdly, monocular based SLAM builds a fused map of feature points that lacks semantic information, which is incomprehensible for machine. To solve the aforementioned issues, this paper proposes an SDF-SLAM model based on deep learning, which can perform camera pose estimation in a wider indoor environment and can also perform depth estimation and semantic segmentation on monocular images to obtain an understandable three-dimensional map. SDF-SLAM is tested and verified using a CPU platform and two sets of indoor scenes. The results show that the average accuracy of the predicted point cloud coordinates reaches 90%, and the average accuracy of the semantic labels reaches 67%. Moreover, compared with the state-of-the-art SLAM frameworks, such as ORB-SLAM, LSD-SLAM, and CNN-SLAM, the absolute error of the camera trajectory on indoor data with more feature points is reduced from 0.436 m, 0.495 m, and 0.243 m to 0.037 m, respectively. On indoor data with fewer feature points, they decrease from 1.826 m, 1.206 m, and 0.264 m to 0.124 m, respectively.