Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Messaging on Expectations and Understanding of Long COVID: An Online Randomised Trial

Objectives: We examined whether providing different types of information about Long COVID would affect expectations about the illness. Design: A 2 (Illness description: Long COVID vs ongoing COVID-19 recovery) x 2 (Illness uncertainty: uncertainty emphasised vs uncertainty not emphasised) x 2 (Efficacy of support: enhanced support vs basic support) between-subjects randomised online experimental study. Setting: The online platform Prolific, collected in October 2021. Participants: A representative sample of 1110 members of the public in the UK. Interventions: Participants were presented with a scenario describing a positive COVID-19 test result and then presented with one of eight scenarios describing a Long COVID diagnosis. Primary and Secondary Outcome Measures: Various outcome measures relating to illness expectations were captured including: symptom severity, symptom duration, quality of life, personal control, treatment control and illness coherence. Results: We ran a series of 2 x 2 x 2 ANOVAs on the outcome variables. We found a main effect of illness description: individuals reported longer symptom duration and less illness coherence when the illness was described as Long COVID (compared to ongoing COVID-19 recovery). There was a main effect of illness uncertainty: when uncertainty was emphasised, participants reported longer expected symptom duration, less treatment control, and less illness coherence than when uncertainty was not emphasised. There was also a main effect of efficacy of support: participants reported higher personal control and higher treatment control when support was enhanced (compared to basic support). We also found an interaction between illness description and efficacy of support: when support was enhanced, participants reported less illness coherence for Long COVID (compared to ongoing COVID-19 recovery). Conclusions: Communications around Long COVID should not emphasise symptom uncertainty and should provide people with information on how they can facilitate their recovery and where they can access additional support. The findings also suggest that use of the term ongoing COVID-19 recovery, where possible, may reduce negative expectations associated with the illness.
On the Use of Speaker Information for Automatic Speech Recognition in Speaker-imbalanced Corpora

We address the effective use of speaker information for automatic speech recognition (ASR) in a speaker-imbalanced dataset. Recently, joint speaker and speech recognition has been investigated in end-to-end (E2E) systems. However, speaker information as the output of speaker recognition (SRE) is not explicitly used for ASR in these systems. Inspired by speaker embedding for ASR, we propose a direct connection of SRE to the ASR decoder. The E2E architecture allows for backpropagating the ASR loss to the SRE decoder, resulting in joint optimisation. The architecture is beneficial for speaker-sparse datasets such as meetings and low-resource language settings, in which speaker clustering is conducted to compensate minor speakers. We also make a systematic comparison of our proposed method with other methods, including multi-task learning (MTL), adversarial learning (AL), and speaker attribute augmentation (SAug). It is shown that the use of speaker cluster information improves both ASR and SRE, and the proposed method outperforms other methods. It reduces errors of the baseline model by 3.35% and 8.23% for ASR and SRE, respectively.
Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals’ daily activities, providing information that is reflective of an individual’s health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual’s health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual’s health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.
Symmetry-Based Brain Abnormality Detection Using Machine Learning

Medical image processing, which includes many applications such as magnetic resonance image (MRI) processing, is one of the most significant fields of computer-aided diagnostic (CAD) systems. the detection and identification of abnormalities in the magnetic resonance imaging of the brain is one of the important applications that uses magnetic resonance imaging and digital image processing techniques. In this study, we present a method that relies on the symmetry and similarity between the two lobes of the brain to determine if there are any abnormalities in the brain because tumours cause deformations in the shape of one of the lobes, which affects this symmetry. The proposed approach overcomes the challenge arising from different shapes of brain images of different people, which poses an obstacle to some approaches that rely on comparing one person’s brain image with other people's brain images. In the proposed method the image of the brain is divided into two parts, one for the left lobe and the other for the right lobe. Some measures are extracted from the features of the image of each lobe separately and the distance between the corresponding metrics are calculated. These distances are used as the independent variables of the classification algorithm which determines the class to which the brain belongs. Metrics extracted from various features, such as colour and texture, were studied, discussed and used in the classification process. The proposed algorithm was applied to 366 images from standard datasets and four classifiers were tested namely Naïve Bayes (NB), random forest (RF), logistic regression (LR), and support vector machine (SVM). The obtained results from these classifiers have been discussed thoroughly and it was found that the best results were obtained from RF classifiers where the accuracy was 98.2%. Finally, The results obtained and the limitations were discussed and benchmarked with state-of-the-art approaches.
2D-FC-ADMM reconstruction algorithm for quantitative optoacoustic tomography in a highly scattering medium: simulation study

This study introduces a novel and powerful algorithm for optoacoustic tomography (OAT) in heterogeneous optical scattering media surrounded by optical fibers and ultrasonic transducers in a ring arrangement. The proposed algorithm uses the extended formulation of full-waveform inversion (FWI) based on the alternating direction method of multipliers (ADMM) and fluence compensation (FC). The speed of sound (SOS) map is required in this algorithm as prior information to perform classification and find different regions in the SOS map with equal optical absorption coefficients. The SOS map can be extracted by another FWI procedure applied to the acoustic data generated and recorded by transducers. The fluence map was calculated by Zemax inside the medium at near-infrared (NIR) optical wavelength. Finally, the proposed algorithm iteratively updates the optical absorption coefficients until the data computed with the estimated coefficients match the recorded photoacoustic signals at ultrasonic transducers. This approach shows extremely better results than classical time-reversal algorithms.
NOVEL FLEXIBLE EDDY CURRENT ARRAY SENSOR SYSTEM FOR PROXIMITY SENSING

Novel proximity eddy current sensor array and its testing system are developed to realize the real-time inspection of the small gaps between large curved surfaces. The flexible sensor probe, manufactured by the flexible printed circuit board (FPCB) process, is designed to consist of a forked coils array with a long flat cable by the investigations to the design, process and substrate materials for the eddy current array. The time division multiplexing (TDM) technology is introduced for the eddy current array to scan and sample all the sensor coils. Some reforms are implemented to the conventional oscillator to solve the problem of the coils having a low Q-factor and improve the performance of the sensor system. Experimental results show that the measurement accuracy of the sensor system can reach±0.5% for a 2 mm full scale and the sensor system is suitable for online gap measurements.
Modeling uncertainties of 
tt¯W±
 multilepton signatures

In light of recent discrepancies between the modeling of $t\bar{t} W^\pm$ signatures and measurements reported by the Large Hadron Collider (LHC) experimental collaborations, we investigate in detail theoretical uncertainties for multi-lepton signatures. We compare results from the state-of-the-art full off-shell calculation and its Narrow Width Approximation to results obtained from the on-shell $t\bar{t} W^\pm$ calculation, with approximate spin-correlations in top-quark and $W$ decays, matched to parton showers. In the former case double-, single-, and non-resonant contributions together with interference effects are taken into account, while the latter two cases are only based on the double resonant top-quark contributions. The comparison is performed for the LHC at $\sqrt{s} = 13$ TeV for which we study separately the multi-lepton signatures as predicted from the dominant NLO contributions at the perturbative orders $\mathcal{O}(\alpha_s^3\alpha^6)$ and $\mathcal{O}(\alpha_s\alpha^8)$. Furthermore, we combine both contributions and propose a simple way to approximately incorporate the full off-shell effects in the NLO computation of on-shell $pp\to t\bar{t} W^\pm$ matched to parton showers.
Defect Passivation Using Trichloromelamine for Highly Efficient and Stable Perovskite Solar Cells

Nonradiative recombination losses caused by defects in the perovskite layer seriously affects the efficiency and stability of perovskite solar cells (PSCs). Hence, defect passivation is an effective way to improve the performance of PSCs. In this work, trichloromelamine (TCM) was used as a defects passivator by adding it into the perovskite precursor solution. The experimental results show that the power conversion efficiency (PCE) of PSC increased from 18.87 to 20.15% after the addition of TCM. What’s more, the environmental stability of PSCs was also improved. The working mechanism of TCM was thoroughly investigated, which can be ascribed to the interaction between the –NH– group and uncoordinated lead ions in the perovskite. This work provides a promising strategy for achieving highly efficient and stable PSCs.
Intelligent Reflecting Surface Assisted mmWave Communication Using Mixed Timescale Channel State Information

A key challenge for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication is that the signals at mmWave band are highly susceptible to blockage. To address this challenge, we introduce intelligent reflecting surface (IRS) to increase coverage area and improve communication performance. This paper considers a joint design of hybrid precoders at the base station and the passive precoder at the IRS to maximize the average spectral efficiency in an IRS-assisted mmWave MIMO system by exploiting the mixed timescale channel state information (CSI). Specifically, the hybrid precoders are designed according to the instantaneous CSI of the overall channel, while the IRS reflection coefficient matrix is optimized using the statistical CSI of all links. However, such a design problem is challenging to solve due to the non-convexity and the mixed timescale. This work proposes efficient algorithms to design jointly the hybrid precoders and the IRS reflection coefficient matrix where the update of the IRS reflection coefficient matrix is independent of the hybrid precoders. Simulation results demonstrate the effectiveness of the proposed algorithms. More interestingly, the results also show that adding low-cost reflector elements at the IRS can reduce the number of required high-cost radio frequency chains.
Design and Development of a High-Performance Quadrotor Control Architecture Based on Feedback Linearization

The purpose of this thesis is to outline the development of a high-performance quadrotor control system for an AscTec Hummingbird quadrotor using direct motor speed control within a Vicon motion capture system environment. A Ground Control Station (GCS) acts as a user interface for selecting flight patterns and displaying sensor values. An on-board Intel Edison embedded Linux computer acts as the quadrotor’s controller. The Vicon system measures the quadrotor’s position and orientation, while the Hummingbird’s stock AscTec Autopilot board provides inertial measurements and receives motor speed commands. Based on the flight pattern set by the GCS, smooth and differentiable trajectories are generated. A control program was written for the Edison to obtain measurements, receive flight pattern commands, perform state estimation, calculate control laws, send motor speed commands to the Autopilot board, and log values. The program was written as a multithreaded C++ program for increased performance.
Disconnected Pancreatic Duct Syndrome: Pancreatitis of the Disconnected Pancreas and Its Role in the Development of Diabetes Mellitus

INTRODUCTION: Disconnected pancreatic duct syndrome (DPDS) is a recognized complication of necrotizing pancreatitis (NP). Manifestations include recurrent peripancreatic fluid collections (R-PFC) and pancreatocutaneous fistulae (PC-Fistulae). Pancreatitis of the disconnected pancreatic segment (DPDS-P) and its relationship to new-onset diabetes after pancreatitis (NODAP) are not well characterized. METHODS: We performed a retrospective cohort study of consecutive patients with NP admitted to University of California, San Francisco from January 2011 to June 2019. A diagnosis of a disconnected pancreatic duct (PD) was confirmed using computed tomography and magnetic resonance cholangiopancreatography/endoscopic retrograde cholangiopancreatography. DPDS was defined as a disconnected PD presenting with R-PFC, PC-Fistulae, or DPDS-P. The primary outcome was NODAP, defined as diabetes mellitus (DM) occurring >3 months after NP. Cox proportional hazards regression was used to evaluate the relationship between DPDS and NODAP. RESULTS: Of 171 patients with NP in this study, the mean clinical follow-up was 46 ± 18 months and the imaging follow-up was 38 ± 20 months. Twenty-seven patients (16%) developed DPDS-P at a median of 28 months. New-onset DM occurred in 54 of the 148 patients (36%), with 22% developing DM within 3 months of NP and 14% developing NODAP at a median of 31 months after AP. DPDS-P was associated with NODAP when compared with non-DPDS patients (adjusted hazard ratio 5.63 95% confidence interval: 1.69–18.74, P = 0.005) while R-PFCs and PC-Fistulae were not. DISCUSSION: DPDS and NODAP occurred in 28% and 14% of the patients, respectively. Pancreatitis of the disconnected pancreas occurred in 16% of the patients and was associated with higher rates of NODAP when compared with patients with other manifestations of DPDS and patients without DPDS.
Overlapping transcriptional programs promote survival and axonal regeneration of injured retinal ganglion cells

Injured neurons in the adult mammalian central nervous system often die and seldom regenerate axons. To uncover transcriptional pathways that could ameliorate these disappointing responses we analyzed three interventions that increase survival and regeneration of mouse retinal ganglion cells (RGCs) following optic nerve crush (ONC) injury, albeit not to a clinically useful extent. We assessed gene expression in each of 46 RGC types by single cell transcriptomics following ONC and treatment. We also compared RGCs that regenerated to those that survived but did not regenerate. Each intervention enhanced survival of most RGC types, but type-independent axon regeneration required manipulation of multiple pathways. Distinct computational methods converged on separate sets of genes selectively expressed by RGCs likely to be dying, surviving, or regenerating. Overexpression of genes associated with the regeneration program enhanced axon regeneration in vivo, indicating that mechanistic analysis can be used to identify novel methods for promoting regeneration of injured neurons.
Mesenchymal Stem Cells for Treatment of Delayed-Healing Tympanic Membrane Perforations Using Hyaluronate-based Laminas as a Delivery System: An Animal Model With Histopathologic Study

Hypothesis: Bone marrow derived-mesenchymal stem cells (BM-MSCs) improve the healing of chronic tympanic membrane perforations (cTMPs) in an animal model. Background: cTMPs generate significant morbidity and reduced quality of life, usually requiring surgical assistance. With growing interest in alternative therapies, we sought to evaluate the effect of BM-MSC-therapy on the healing of cTMPs. Methods: Sixty cTMPs were established in C57Bl/6 mice and randomized into four groups: hyaluronate scaffold as graft plus BM-MSCs (n = 19 ears), scaffold plus cell culture media (n = 16), scaffold plus phosphate-buffered saline (PBS, n = 12), and no intervention (n = 13). Hyaluronate scaffolds with or without BM-MSCs were applied on 8-week perforated eardrums. After a blinded assessment of perforation sizes at baseline and 2 weeks after treatment, mean perforation reduction rates (%) were compared. Histology characterization was then performed. Results: Mean perforation size reduction rates were significantly higher for cTMPs that received scaffolds plus BM-MSCs (Student's t test, p = 0.0207, 12.3% [95% CI: 7.8–16.7]) and scaffolds plus cell culture media (p = 0.0477, 11.3% [95% CI: 4.4–18.2]) when compared with no intervention (4.2% [95% CI: 1.2–7.2]). This was not observed when treating eardrums with scaffolds plus PBS (7.3% [95% CI: 2.7–11.9]). On histology, BM-MSC-treated eardrums demonstrated restoration of the trilaminar configuration and reduced inflammatory changes, while other groups developed tissue architecture disorganization and hypercellular infiltrates surrounding the perforation site. Conclusions: BM-MSCs and cell culture media equivalently increased cTMP healing rates. Cell-based therapy conferred a restoration of the trilaminar configuration of the eardrum with relatively compact and organized fibrous layers.
Strengthening laboratory capacity for HIV vaccine clinical trials and epidemiological studies in Eastern and Southern Africa

Background: Conducting successful HIV vaccine clinical trials in resource-limited settings is hampered by lack of adequate laboratory capacity at trial sites, poor infrastructure, lack of well-trained technical personnel, and inadequate laboratory quality management Systems. We describe our approach to establishing sustainable laboratory capacity for clinical trials in Africa. Methods: IAVI identified 9 CRCs where a capacity building program that supports immunology and clinical testing was established. Information from the 9 CRCs was collected retrospectively and compiled in Microsoft excel for descriptive statistics. Mapping was done in Quantum Geographic information system. Results: Newly built and refurbished laboratories have been equipped with the required testing laboratory equipment. All CRC laboratories (n=10, 100%) received Good Clinical Laboratory Practice (GCLP) accreditation between 2004 and 2016, and accreditation maintained annually. A total of 89 audits were done between 2005-2019. KAVI and KEMRI had the highest number of audits (n=11, 12.4%). IAVI successfully trained a total of 1811 individual, of which (n=1130, 62.7%) trained on GCLP, (n=330, 18.3%) Quality Management Systems, (n=311, 17,2%) laboratory techniques and (n=32,1.8%) between 2004 and 2021. All the 13 Assays were registered in either College of American pathologist (CAP) or Royal college of pathologists of Australasia (RCPA) for Proficiency testing. Conclusion: The establishment of GCLP accredited laboratories and well-trained personnel has created centers of excellence and it has enabled them to attract independent competitive research funding. The GCLP accreditation and standardized testing procedures ensured reliable and accurate data, especially important for multi-country and multi-center studies.
DAGmap: Multi-Drone SLAM via a DAG-Based Distributed Ledger

Simultaneous localization and mapping (SLAM) in unmanned vehicles, such as drones, has great usability potential in versatile applications. When operating SLAM in multi-drone scenarios, collecting and sharing the map data and deriving converged maps are major issues (regarded as the bottleneck of the system). This paper presents a novel approach that utilizes the concepts of distributed ledger technology (DLT) for enabling the online map convergence of multiple drones without a centralized station. As DLT allows each agent to secure a collective database of valid transactions, DLT-powered SLAM can let each drone secure global 3D map data and utilize these data for navigation. However, block-based DLT—a so called blockchain—may not fit well to the multi-drone SLAM due to the restricted data structure, discrete consensus, and high power consumption. Thus, we designed a multi-drone SLAM system that constructs a DAG-based map database and sifts the noisy 3D points based on the DLT philosophy, named DAGmap. Considering the differences between currency transactions and data constructions, we designed a new strategy for data organization, validation, and a consensus framework under the philosophy of DAG-based DLT. We carried out a numerical analysis of the proposed system with an off-the-shelf camera and drones.
A fault line selection method of small current grounding system based on wavelet de-noising and improved RBF neural network

The paper proposed a fault line selection method of small current grounding system based on wavelet de-noising and improved RBF neural network.Fault information matrix is obtained after normalization processing for maximum of absolute value of de-noised zero-sequence current,and the matrix is used as input of RBF neural network.Active value of input layer of the RBF neural network is calculated,and when the active value is in preset range,hidden layer and output layer of the RBF neural network are disconnected automatically,and neurons of the hidden layer split.After parameters such as weight,variance and central value of the RBF neural network are adjusted,the hidden layer and the output layer are reconnected and training result is output.Test sets are input into the trained RBF neural network to get fault line selection result.The example analysis result shows that the method cannot be influenced by fault phase angle and grounding resistance with accurate and reliable fault line selection.