Miracle-dz/UniFAR-FTUs huggingface dataset:
Using text analysis to assess the mental health impacts of COVID-19 on rural healthcare providers

The pandemic increased the stress levels of frontline healthcare providers in every country across the globe, and continues to do so. Health care providers in the rural regions of the U.S. were more vulnerable to the impact of COVID-19 due to the lack of resources and the population they serve. A qualitative study of 25 rural healthcare providers in a western U.S. state was conducted during 2020. In this report, Linguistic Inquiry and Word Count (LIWC) was used to compare the use of emotion and reward words in 12 providers interviewed before the onset of COVID-19 on March 11, 2020, to 13 interviews conducted after COVID-19 onset. Results indicated that providers used fewer positive emotion words after COVID-19 onset compared to before COVID. Negative emotion words also decreased. Words related to the rewarding aspects of their work increased after COVID-19, but differed among the pre- and post-COVID groups. Positive emotion words was more strongly related to rewards in the post-COVID group. Providers reported concerns about access to care for those unable to be treated for the increased depression, anxiety and suicide ideation related to the pandemic. The rural health care system needs policies directed at improving patient access to care and greater funding.
Manufacturability Evaluation and Generation of Re-Design Suggestions for Machined Parts

Design-for-manufacturability (DFM) is a current research area due to the intense economic competition, requiring industries to produce high quality products at lower cost and in a shorter time. The DFM concept requires the product designers to consider the manufacturing issues of a product along with the geometrical and design aspects. Many of the reported DFM systems do not yield quantitative metrics or indices on the manufacturability of the part features. A provision for manufacturability indices and/or metrics in DFM systems is essential as it would provide a quantitative understanding of the relative easy and/or difficulties of manufacturing the features and their alternatives. This paper presents a fuzzy set-based manufacturability evaluation system that can provide quantitative indices for prismatic parts that will be machined using 3-axis machining centres. This manufacturability evaluation methodology incorporates design and machining issues, and supports the concept of concurrent engineering. The methodology uses an analytic hierarchy process method to evaluate the intricacy, accessibility, orientation, dimensional tolerances and surface finish specifications of a part, to yield quantitative geometrical and technological manufacturability indices that can be used to evaluate and compare designs. Re-design suggestions can be offered to the designers based on these evaluations.
A Fuzzy Time-Driven Activity-Based Costing Model in an Uncertain Manufacturing Environment

Due to substantial information required to complete the conventional ABC model, its popularity faded away while the Time-Driven Activity-Based Costing (TDABC) is introduced. TDABC formulates cost equations on the basis of time, called a "time equation". When all consumed resources are converted into a unit of time, difficulties on large information handling are minimized. However, TDABC is not flawless. Under uncertain circumstances, TDABC is incapable of accounting for any variation that might occur, leading to insufficient information to make the right decision. The fuzzy sets theory is widely known as a logical approach that deals with reasoning to manage uncertainty. In manufacturing cost analysis, uncertainty is mainly found in annual budget distributed to each support and operating departments. The objective of this study is to propose a new framework of a fuzzy-TDABC. Using a fuzzification technique, the uncertain parameters are transformed into fuzzy sets before being bundled together by time equations. The sets are then defuzzified to a real value which is deemed to be the most accountable representative of product cost. It is expected that this model will provide more reliable and complete information for managerial and strategic planning.
Two-Stage Hybrid Approach of Deep Learning Networks for Interstitial Lung Disease Classification

High-resolution computed tomography (HRCT) images in interstitial lung disease (ILD) screening can help improve healthcare quality. However, most of the earlier ILD classification work involves time-consuming manual identification of the region of interest (ROI) from the lung HRCT image before applying the deep learning classification algorithm. This paper has developed a two-stage hybrid approach of deep learning networks for ILD classification. A conditional generative adversarial network (c-GAN) has segmented the lung part from the HRCT images at the first stage. The c-GAN with multiscale feature extraction module has been used for accurate lung segmentation from the HRCT images with lung abnormalities. At the second stage, a pretrained ResNet50 has been used to extract the features from the segmented lung image for classification into six ILD classes using the support vector machine classifier. The proposed two-stage algorithm takes a whole HRCT as input eliminating the need for extracting the ROI and classifies the given HRCT image into an ILD class. The performance of the proposed two-stage deep learning network-based ILD classifier has improved considerably due to the stage-wise improvement of deep learning algorithm performance.
A submatrix spatial coherence approach to minimum variance beamforming combined with sign coherence factor for coherent plane wave compounding

BACKGROUND: The coherent plane wave compounding (CPWC) is a promising technique to enhance the imaging quality while maintaining the high frame rate in the plane wave ultrasound imaging. Recently, the spatial-coherence-based method has been specially designed to process echo matrix required by the minimum variance (MV) method. OBJECTIVE: In this paper, a novel beamforming method that integrates the submatrix-spatial-coherence-based MV with the sign coherence factor (SCF) is proposed to further improve the imaging quality. METHOD: The submatrix smoothing technique is modified to smooth and de-correlate signals of the receiving array dimension. Then, the SCF is used to modify the input vector of the beamformer, which can reduce side lobe noises with almost no increase in the amount of calculation. Simulation, phantom, in vivo, and sound velocity error experiments have been performed to verify the superiority of the proposed beamformer. RESULTS: The imaging results show that the proposed approach performs better in the imaging resolution and contrast compared to the traditional CPWC method. CONCLUSION: The robustness of the proposed method is enhanced, and the over-suppression phenomenon can be alleviated, which is a phenomenon that occurs in the original spatial-coherence and SCF methods.
A Study of Data Augmentation for ASR Robustness in Low Bit Rate Contact Center Recordings Including Packet Losses

Client conversations in contact centers are nowadays routinely recorded for a number of reasons—in many cases, just because it is required by current legislation. However, even if not required, conversations between customers and agents can be a valuable source of information about clients or future clients, call center agents, markets trends, etc. Analyzing these recordings provides an excellent opportunity to be aware about the business and its possibilities. The current state of the art in Automatic Speech Recognition (ASR) allows this information to be effectively extracted and used. However, conversations are usually stored in highly compressed ways to save space and typically contain packet losses that produce short interruptions in the speech signal due to the common use of Voice-over-IP (VoIP) in these systems. These effects, and especially the last one, have a negative impact on ASR performance. This article presents an extensive study on the importance of these effects on modern ASR systems and the effectiveness of using several techniques of data augmentation to increase their robustness. In addition, ITU-T G.711, a well-known Packet Loss Concealment (PLC) method is applied in combination with data augmentation techniques to analyze ASR performance improvement on signals affected by packet losses.
The Swedish key action 'The Healthy Building' - research results achieved during the first three years period 1998-2000

The first stage of The Swedish Healthy Building program has advanced the understanding of a number of hitherto unexplained building related health problems. Important insights have been achieved into the mechanisms and effects of biological agents indoors. New methodologies for assessing microbial emissions have been developed. The mechanisms of interaction between pollutant gases, building materials and room air are now better understood especially the mechanisms behind sorption and sink effects. In the indoor environment a large number of risk factors for adverse health effects are suspected. Some of these risk factors originate in misconduct in the design, construction and maintenance of buildings. Especially damp or humid buildings present increased risks but other factors also play a role.
Circuit clustering for cluster-based FPGAs using novel multiobjective genetic algorithms

Circuit clustering is one of the most crucial steps in a post-synthesis FPGA CAD flow. It attempts to efficiently fit synthesised logic functions into FPGA logic clusters. On a FPGA, different clusterings result in different circuit mappings, which affect FPGA utilisation, routability and timing, and therefore impact the circuit performance. This research proposes the use of a Multi Objective Genetic Algorithm (MOGA) as a methodology to solve the cluster-based FPGA circuit clustering problem. Four alternative approaches based on MOGA methods are proposed in this research: RVPack is inspired by the stochastic feature that exists in Evolutionary Algorithms (EAs). GGAPack, GGAPack2, DBPack and HYPack, T-HYPack (Timing-driven HYPack) are then proposed and developed, which are fully customised MOGA-based circuit clustering methods. GGAPack clusters a circuit using a top-down perspective, and DBPack uses a new bottom-up perspective. HYPack combines GGAPack and HYPack -- a hybrid method. According to experimental results, a few conclusions are drawn: It is possible to improve the performance of the greedy algorithm based circuit clustering methods by incorporating randomness. The performance of MOGA based top-down clustering is poor; however, using MOGA to cluster a circuit from a bottom-up perspective can produce better solutions. T-HYPack clustered circuit has the best timing performance compared with state-of-the-art methods. The experimental results also reflect a wider potential for using GAs to solve FPGA circuit mapping problems.
MR brain tissue classification based on the spatial information enhanced Gaussian mixture model

BACKGROUND: Classifying T1-weighted Magnetic Resonance brain scans into cerebrospinal fluid, gray matter and white matter is one of the most critical tasks in neurodegenerative disease analysis. Since manual delineation is a labor-intensive and time-consuming process, automated methods have been widely adopted for this purpose. One group of commonly used method by biomedical researchers are based on Gaussian mixture model. The main drawbacks of this model include complex computational cost and parameter selection with the presence of imaging defects such as intensity inhomogeneity and noise. OBJECTIVE: To alleviate these aspects, an improved Gaussian mixture model-based method is proposed in this work. METHODS: Standard mixture model was used to formulate individual voxel intensity. A set of spatial weightings were created to represent local tissue characteristics. The emphasis of this method is its “lite” and robust implementation mode highlighted by a dedicated entropy term. The Expectation-Maximization algorithm was then iteratively executed to estimate model parameters. The Maximum a Posteriori criterion was employed to determine for each voxel if it belongs to a certain tissue. RESULTS: The proposed method was validated on both simulated and real MR scans. The averaged Dice coefficient of segmented brain tissues on each dataset ranged between [66.41, 87.42] for cerebrospinal fluid, [80.57, 85.35] for gray matter, and [83.17, 85.63] for white matter. CONCLUSIONS: Experiments illustrated the effectiveness and reliability in tissue classification against imaging defects compared with manually constructed reference standard.
Control, Automation and Instrumentation An integrated process monitoring approach combining dynamic independent component analysis and local outlier factor

In this paper a novel process monitoring scheme for reducing the type І and type ІІ error rates in the monitoring phase is proposed. First, the proposed approach uses an augmented data matrix to implement the process dynamic. Then, we apply independent component analysis (ICA) transformation to the augmented data matrix, and eliminate the outliers using the local outlier factor (LOF) algorithm. Finally, the control limit based on the LOF value of the cleaned data are obtained. In the monitoring phase, if the LOF value of each sample exceeds the control limit, fault has occurred; otherwise, data is normal. The proposed method is applied to fault detection in both a simple multivariate dynamic process and the Tennessee Eastman process. In both processes, type І and type ІІ error rates are witnessed to reduce by considering the process dynamic and performing the LOF algorithm. Results clearly indicate better performance of the proposed scheme compared to the alternative methods.
Distribution network supports for transmission system reactive power management

To mitigate high voltages in transmission systems with low demands, traditional solutions often consider the installation of reactive power compensators. The deployment and tuning of numbers of VAr compensators at various locations may not be cost-effective. This thesis presents an alternative method that utilises existing parallel transformers in distribution networks to provide reactive power supports for transmission systems under low demands. The operation of parallel transformers in small different tap positions, i.e. with staggered taps, can provide a means of absorbing reactive power. The aggregated reactive power absorption from many pairs of parallel transformers could be sufficient to provide voltage support to the upstream transmission network.Network capability studies have been carried out to investigate the reactive power absorption capability through the use of tap stagger. The studies are based on a real UK High Voltage distribution network, and the tap staggering technique has been applied to primary substation transformers. The results confirm that the tap staggering method has the potential to increase the reactive power demand drawn from the transmission grid.This thesis also presents an optimal control method for tap stagger to minimise the introduced network loss as well as the number of tap switching operations involved. A genetic algorithm (GA) based procedure has been developed to solve the optimisation problem. The GA method has been compared with two alternative solution approaches, i.e. the rule-based control scheme and the branch-and-bound algorithm. The results indicate that the GA method is superior to the other two approaches.The economic and technical impacts of the tap staggering technique on the transmission system has been studied. In the economic analysis, the associated costs of applying the tap staggering method have been investigated from the perspective of transmission system operator. The IEEE Reliability Test System has been used to carry out the studies, and the results have been compared with the installation of shunt reactors. In the technical studies, the dynamic impacts of tap staggering or reactor switching on transmission system voltages have been analysed. From the results, the tap staggering technique has more economic advantages than reactors and can reduce voltage damping as well as overshoots during the transient states.
Prismatic gravity compensator for variable payloads

Gravity compensators are beneficial for lifting systems as they are passive and can also ensure low energy consumption which is translated into low actuation force/torque for users and actuators. This paper presents a prismatic gravity compensator for variable payloads between 0.8 kg and 8.7 kg over a stroke of 800 mm which can be used for cranes, gantry and other lifting systems. The impact of friction is also studied and a strategy is proposed to still achieve balancing for various payloads.
ARIMA vs. MACHINE LEARNING IN TERMS OF EQUITY MARKET FORECASTING

Through this paper we aimed to develop a comparison between ARIMA, Prophet, KNN and Neural Networks in terms of stock prices forecasting. After reviewing the literature, we noticed that there is a plethora of studies that address this problem of forecasting, but very few have made comparisons that include ARIMA, machine learning, but also the Prophet forecasting model developed by Facebook, which brought interesting results for certain data series. Based on methodologies validated by other authors, we compared these models in our paper and we sought to obtain promising results regarding performance evaluation. The comparison was made in-sample, the training period being 01/01/2010 – 31/07/2021, but also out-of-sample (01/08/2021 – 31/10/2021). The study was performed for Societe Generale’s stock, using daily observations. Statistical loss functions such as RMSE, MPE, MAPE, MAE, and ME were used for comparison. The results indicated an outperformance of Neural Networks, both in-sample and out-of-sample, this model being on the 1st place according to the aggregated score. It is also noteworthy that the ARIMA model was in second place in-sample, ahead of KNN, but for out of sample these two algorithms changed their positions. On the other hand, the Prophet algorithm performed the weakest, both in-sample and out of sample. Also, we must underlie that all four algorithms had a clear tendency to overestimate the price of Societe Generale, according to the results of the statistical loss functions ME and MPE. Finally, it should be noted that the results were consistent with what other authors found out, especially for the out-of-sample period, where the machine learning models performed best.
43) Consideration of the Influence of Independent Factors on Inspection Results and Reliability Updating of Marine Structures

A method for reliability updating of a group of similar structures is presented. The sources for variation among inspection results of structural details are divided into the effect of the particular structure a detail is imbedded in, the effect of the detail type, and the effect of a modeling error term. By using the proposed method, a deeper understanding of the causes of the differences among inspection results is possible, and as a consequence, a better inspection planning can be made. Furthermore, since data of several structures and details are investigated at the same time, faster convergence rates to the true values are obtained than if just one individual structure is considered, making it possible for an owner or operator to keep the reliability of his fleet above a specified minimum level more cost effectively.
High-k dielectrics based field plate edge termination engineering in 4H-SiC Schottky diode

ABSTRACT This paper develops a deep insight into the behaviour of high-k dielectric-based field plate on Ni/4H-SiC Schottky diode. It tries to explain the mechanism by which high-k materials outperform silicon dioxide, when used under the field plate. Phenomena like modulation of field enhancement factor, reshaping of equipotential contours and expansion of depletion region while maintaining fixed depletion ratio (length/width = 2.3) helps to understand the electrical behaviour of high-k dielectric-based field plate. High-k materials relaxed the equipotential contours under the field plate edge which resulted in electric field reduction up to 88% and significant drop from 6.6 to 2.2 in field enhancement factor at device edges. The study considers the field plate of different dielectrics (SiO2, Si3N4, Al203, HfO2) and in each case, analytically explores the optimisation of field plate parameters (overlap length and dielectric thickness, dielectric constant). All the investigations have been done using numerical simulations on calibrated setup.
Estimating Human Weight From a Single Image

Body weight, as one of the biometric traits, has been studied in both the forensic and medical domains. However, estimating weight directly from 2-D images is particularly challenging since visual inspection is rather sensitive to the distance between the subject and camera, even for frontal view images. In this case, the widely used body mass index (BMI), which is associated with body height and weight, can be employed as a measure of weight to indicate health conditions. Previous works on the estimation of BMI have predominantly focused on using multiple 2-D images, 3-D images, or facial images; however, these cues are not always available. To address this issue, we explore the feasibility of obtaining BMI from a single 2-D body image with the dual-branch regression framework proposed in this work. More specifically, the framework comprises an anthropometric feature computation branch and a deep learning-based feature extraction branch. One aggregation layer maps all the features to an estimated BMI value. In addition, a new public 2-D image-to-BMI dataset, which contains 4189 images (1477 males and 2712 females) from approximately 3000 subjects with attributes including gender, age, height, and weight, was collected and released to facilitate the study. Extensive experiments confirm that the proposed framework combining anthropometric features and deep features outperforms the single-type feature approaches to BMI estimation in most cases.
The Role of Seasonality and the ENSO Mode in Central and East Pacific ENSO Growth and Evolution

A cyclostationary Linear Inverse Model (CSLIM) is used to investigate the seasonal growth of tropical Pacific El Niño / Southern Oscillation (ENSO) events with canonical, Central Pacific (CP), or Eastern Pacific (EP) sea surface temperature (SST) characteristics. Analysis shows that all types of ENSO events experience maximum growth toward final states occurring in November and December. ENSO events with EP characteristics also experience growth into May and June, but CP events do not. A single dominant “ENSO-mode”, growing from an equatorial heat content anomaly into a characteristic ENSO-type SST pattern in about 9 months (consistent with the delayed / recharge oscillator model of ENSO), is essential for the predictable development of all ENSO events. Notably, its seasonality is responsible for the late calendar year maximum in ENSO amplification. However, this ENSO-mode alone does not capture the observed growth and evolution of diverse ENSO events, which additionally involve the seasonal evolution of other non-orthogonal Floquet modes. EP event growth occurs when the ENSO-mode is initially “covered up” in combination with other Floquet modes. The ENSO-mode’s slow seasonal evolution allows it to emerge while the other modes rapidly evolve and / or decay, leading to strongly amplifying and more predictable EP events. CP events develop when the initial state has a substantial contribution from Floquet modes with meridional mode-like SST structures. Thus, while nearly all ENSO events involve the seasonally-varying ENSO-mode dynamics, the diversity and predictability of ENSO events cannot be understood without identifying contributions from the remaining Floquet modes.