Miracle-dz/UniFAR-FTUs huggingface dataset:
Grief after Pandemic Loss: Factors Affecting Grief Experiences (the CO-LIVE Study)

Abstract It has been suggested that grief after losing a significant other person during the COVID-19 pandemic is more severe than before the pandemic. However, little is known about the factors associated with COVID-19-related grief. This study aims to examine whether grief among relatives of people who died during the first COVID-19 wave was associated with factors such as (in)sufficient opportunity to be with the dying person, relatives’ appreciation of how the person died, and “unfinished business” between the bereaved and the deceased. The study involved 200 Dutch relatives who had lost a person during the pandemic. Grief was measured with the Hogan Despair subscale. Data were analyzed using correlations and multivariable regression analysis. Our findings revealed that two-thirds of bereaved relatives reported that they had not had sufficient opportunity to be with the dying person in the final days. However, this experience was not significantly correlated with despair. A negative appreciation of the dying process and remaining unfulfilled wishes as part of “unfinished business” between the dying person and their relative were associated with higher levels of despair, particularly among partners. It is crucial to ensure that relatives can experience good end-of-life care for their dying loved one and be enabled to resolve family issues, to mitigate the impact of the pandemic.
The Effect of Emotional Intelligence and Job Stress on Nurse Commitment with Satisfaction as a Mediation Variable

This study aims to examine and analyze the effect of emotional intelligence and work stress on nurse commitment with satisfaction as a mediating variable at Andalas University Hospital, Padang. The population is all employees at Andalas University Hospital. The sampling method used is the census method. The research data was collected by distributing questionnaires to 75 respondents. Data processing and analysis was carried out using SmartPLS. The results showed that emotional intelligence, job stress and job satisfaction had a significant effect on nurse commitment, emotional intelligence and work stress had a significant effect on job satisfaction and job satisfaction served as a partial mediation between emotional intelligence and work stress on nurse commitment.
Filter design, fault estimation and reliable control for networked time-varying systems: a survey

ABSTRACT This paper is concerned with the overview of the recent progress in filter design, fault estimation and reliable control for networked time-varying systems (NTVSs). Firstly, the concepts of the NTVS, incomplete information, filtering, estimation and control have been introduced. Secondly, the incomplete information phenomena are considered include, but not limited to, communication delays, measurement losses, actuator/sensor faults, signal quantizations and randomly occurring uncertainties. Subsequently, the developments as well as some recent results on filter design, fault estimation and reliable control are reviewed for NTVSs subject to incomplete information in great detail. Finally, conclusions are given and some possible future research directions are pointed out concerning the filtering, estimation and control problems.
Investigating two-dimensional deformations of the head of the Central Route of the South–North Water Diversion Project in China with TerraSAR-X datasets

Abstract As a landmark in China’s hydraulic engineering history, the Central Route of the South–North Water Diversion Project (SNWD-CR) plays a major role in alleviating severe water shortages in Northern China. Existing InSAR monitoring of the SNWD-CR based on one-dimensional viewing geometry and Sentinel-1 imaging is insufficient for a comprehensive assessment of deformation causes. Moreover, the poor geocoding accuracy hampers the previous interpretation of the estimated deformations. Here we carry out comprehensive two-dimensional deformation analyses over the head of the SNWD-CR by integrating the multitemporal PS/DS InSAR and the stress–strain model, where the well-designed topography and geocoded error correction can help to identify the causes of deformation. We apply our strategy to ascending and descending TerraSAR-X meter-resolution datasets acquired from June 2020 to February 2021. Geocoding errors in both ascending and descending orbits are corrected, and the relationship between topography error and horizontal positioning error is discussed. The multi-data fusion results reveal insignificant deformations in the east–west direction except for a makeshift rigid-frame bridge, whereas the vertical deformation field shows two uplifts and a subsidence area in the narrow canal with a maximum rate of 23 mm/y, which are likely to be related to rainfall and water diversion pressure. Furthermore, the combination of the external DEM and topographic residual offers the chance to reconstruct the high-accuracy DEM.
The relationship between corporate governance and financial management practices

This research paper examines the relationship between corporate governance and financial management practices. The study adopts a comprehensive literature review and data analysis approach to investigate the impact of corporate governance mechanisms on financial management decisions and performance. By analyzing a wide range of empirical studies and financial data, this research aims to shed light on the significance of effective corporate governance in ensuring sound financial management practices within organizations. The findings provide valuable insights for both academics and practitioners interested in understanding and improving corporate governance and financial management practices.
Optimal Trading Under the American Perpetual Put Option for Geometric Brownian Motion and Mean-reverting Processes

Optimal trading under the american perpetual put option for Geometric Brownian Motion and Mean-Reverting Processes by Ines Larissa Siebigteroth The University of Wisconsin–Milwaukee, 2017 Under the Supervision of Professor Chao Zhu This thesis is focused on the perpetual American put option under the geometric Brownian motion and mean-reverting models. Two approaches, which have been applied before to the call option of a mean-reverting process, will be studied in details for the two models. The first approach amounts to solving the associated quasi-variational inequality for the optimal stopping problem. A verification theorem is proved to demonstrate that the solution to the quasi-variational inequality agrees with the value function. The second approach is based on detailed analyses of an auxiliary two-point stopping problem, which leads to an explicit expression for the value function. Both approaches identify an optimal execution rule for the two models.
Impacts of increasing greenhouse gas concentrations and deforestation on extreme rainfall events in the Amazon basin: A multi‐model ensemble‐based study

The main objective of this study was to evaluate the effects of increasing greenhouse gases (GHGs) and total deforestation of the Amazon on extreme rainfall events in the Amazon basin. In order to quantify these impacts, numerical experiments were performed with the Eta regional climate model forced from initial and boundary conditions from the BESM, HadGEM2‐ES, and MIROC5 earth system models. In the experiment related to the increase in GHGs, numerical simulations were performed according to the IPCC RCP8.5 scenario. The effect of deforestation was quantified via an experiment in which the forest in the Amazon basin was replaced by areas of degraded pasture in the Eta model. For the analyses of the changes in extreme rainfall events, the multi‐model ensemble technique was used. The results were evaluated in terms of anomalies relative to the sensitivity and control experiments. In the results, it was observed that in an RCP8.5‐type GHG emission scenario there is a statistically significant increase in the maximum number of consecutive days without rain, a reduction in the maximum number of consecutive rainy days, reduction in total annual precipitation, and reduction in maximum annual precipitation accumulated over one and 5 days, respectively. The results for the scenario with increased GHGs and large‐scale deforestation in the Amazon basin are similar to the RCP8.5 scenario, but the intensity of changes in climate indices is significantly greater. It was also verified that the changes in the climatic indices are strongly associated with alterations in the energy balances at the surface and, consequently, in the large‐scale circulation. In general, it can be highlighted that the climate in the Amazon region is strongly dependent on the presence of the forest.
A Low-Profile Light-Weight Ultra-Wideband Tightly Coupled Array for Sub-6 GHz Applications

This paper presents a low-profile light- weight ultra-wideband tightly coupled array for Sub-6 GHz applications. The antenna element consists of horizontal and vertical structures, which can be fed directly by the 50 Ω coaxial line, avoiding the expensive and complex baluns. The vertical structure allows the antenna element to achieve wide-angle scanning without using a complex and bulky superstrate layer. To further lower the antenna profile, a portion of the vertical structure is placed horizontally. Ultimately, an infinite array is designed with a 4.8:1 bandwidth (0.62- 3 GHz) and an active VSWR < 3.2 while scanning up to ±45° in the E- and H- planes. Its profile is only 0. 09λlow, where λlow is the wavelength at the lowest operating frequency. The proposed antenna has the merit of low- profile, light-weight, ultra-wide bandwidth, wide scanning range, and low cost.
MultiBUGS: Massively parallel MCMC for Bayesian hierarchical models

MultiBUGS (this https URL) is a new version of the general-purpose Bayesian modelling software BUGS that implements a simple, generic algorithm for parallelising Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian hierarchical models. The algorithm parallelises evaluation of the product-form likelihoods formed when a parameter has many children in the hierarchical model; and parallelises sampling of conditionally-independent sets of parameters. A simple heuristic algorithm is used to decide which approach to use for each parameter and to apportion computation across computational cores. This enables MultiBUGS to automatically parallelise the broad range of statistical models that can be fitted using BUGS-language software, making the dramatic speed-ups of modern multi-core computing accessible to applied statisticians, without requiring any experience of parallel programming. We demonstrate the use of MultiBUGS using a simple random effects logistic regression model, and on simulated data designed to mimic a hierarchical e-health linked-data study of methadone prescriptions including 425,112 observations and 20,426 random effects. Reliable posterior inference for the e-health model takes several hours in existing software, but MultiBUGS can perform inference in only 29 minutes using 48 computational cores.
Advanced Traffic Sign and Lane Discernment

Frameworks based on computer vision can be deployed for innumerable tasks assisting the vehicle driver, as such the ADAS has its roots emerging from such deep learning algorithms and CNN architectures. In this paper, a machine learning model has been deployed with the aim of assisting the human driver in analysing the road lanes and the approaching traffic signs in much efficient manner taking into account the adverse weather conditions. The work of analysing the road has been bifurcated into 2 separate tasks which have been carried out in parts. In the first part, the algorithm devised will detect the approaching traffic signs and in the second part, the algorithm would carry out the lane assisting task both of which would be done taking bad weather conditions in consideration. With each iteration of detecting the road signs and classifying the lanes, an additional algorithm would be implemented simultaneously to retrieve clear images removing the fog and other such adverse weather obstructions. Since it is not possible for human drivers to perform the operation required all at once as efficiently as machines, the algorithm devised would ensure the automation of the driving system giving the human driver a sense of relief.
CARP-YOLO: A Detection Framework for Recognising and Counting Fish Species in a Cluttered Environment

In the research area of object detection, the recognition of different fish species in a cluttered environment is a challenging task. In the live fish market, different fish species with variations in size, angle, and scale make it hard for the common people to recognize the species properly. Again, counting and sorting fish species is an important task in the fisheries industry. A dataset named JUDVLP-WBUAFS: Fishdb-Detection.v1 with 400 images is prepared by collecting images from the different live fish markets in West Bengal under unconstrained environments. Various augmentations like flip, rotation, blur, gaussian noise, hue saturation, and RGBshift have been applied to make the dataset more diversified and less prone to overfitting. A total of six fish species, Labeo catla, Labeo rohita, Cirrhinus mrigala, Labeo bata, Hypophthalmichthys molitrix, and Ctenopharyngodon idella, are considered in this study for the purposes of recognition and counting. Two popular object detection deep learning networks, YOLOv3 and YOLOv5, with different variants, have been applied to the original and augmented datasets individually. Using YOLOv5l and the YOLOv3-SPP network, the best mAP@0.5 of 0.764 is achieved on the original dataset. In the augmented dataset, the best mAP@0.5 of 0.84 is achieved using the YOLOv3-SPP network. The mAP@0.5 value on both datasets shows a promising result for the recognition of fish species in some extremely cluttered environments. This study is expected to help common people and the fishing industry in a variety of contexts.
Sustainable choices of plant-based (‘super’) foods: examining the consumption patterns of German consumers on avocados

This paper aims to better understand consumer awareness of the environmental impact of plant-based (‘super’-) foods, using avocados as an example. Since all food production impacts the environment, both meat-based and plant-based, there is scope for more sustainable food choices. Avocados have positive health properties while being considered critically for the potential negative environmental impact of their production. This study examines the avocado consumption behaviour of German consumers and the extent to which knowledge and dietary patterns are related to this. Data from 373 respondents from Germany were collected through an online consumer survey. Bivariate comparisons for avocado consumption and logistic regression analyses were performed to explore avocado consumption behaviour. The frequency of avocado consumption among respondents was moderate, averaging once per month. Avocado consumption was related to a flexitarian diet, older age and higher income, and urban dwellers. Knowledge of the environmental impacts of avocado cultivation had no influence. Respondents’ self-assessed knowledge about avocados was low. To help consumers in making conscious, sustainable choices for plant-based foods, it is necessary to provide accessible and comparable information on the environmental impact of food products. However, further life cycle assessments on avocado production are clearly needed in order to produce valid information material. A flexitarian diet with reduced consumption of animal foods is an important prerequisite for environmental sustainability. Drawing attention to environmentally friendly plant-based food choices for flexitarian consumers could further encourage them to become food innovators for a healthy planet by reducing climate impact, land use, and energy and water consumption.
Role of Clinical Insight at First Month in Predicting Relapse at the Year in First Episode of Psychosis (FEP) Patients

Introduction: Clinical insight constitutes a useful marker of the progress and outcome of the First Episode of Psychosis (FEP), and lack of insight has been associated with more severe psychopathology, treatment non-adherence, and rehospitalization/relapse. In this study, we aimed to further investigate the possible role of insight as a predictor of relapse, its relation to diagnosis, and other parameters of positive psychotic symptomatology (delusions, hallucinations, and suspiciousness). Methods: The Athens FEP study employed a prospective, longitudinal cohort design in which consecutive newly diagnosed patients with psychosis were interviewed and asked to voluntarily participate after completing informed consent. A total of 88/225 patients were examined at three different time points (baseline, month, and year). Their scores in the relevant items of the Positive and Negative Syndrome Scale (PANSS) were compared (G12 for insight, P1 for delusions, P3 for hallucinations, and P6 for suspiciousness), and they were further associated to diagnosis and the outcome at the end of the year (remission/relapse). Results: In total, 22/88 patients with relapse at the year had greater scores in G12 for both the month and the year, and this finding was corroborated after adjusting the statistical analysis for demographics, diagnosis, social environment, and depression via multiple logistic regression analysis. Moreover, delusions and suspiciousness were significantly higher in patients diagnosed with non-affective psychosis compared to those diagnosed with affective psychosis (p < 0.001) at the first month. Conclusions: Lack of insight at the first month may serve as a predictor of relapse at the year.
Backpropagating Constraints-Based Trajectory Tracking Control of a Quadrotor With Constrained Actuator Dynamics and Complex Unknowns

In this paper, a backpropagating constraints-based trajectory tracking control (BCTTC) scheme is addressed for trajectory tracking of a quadrotor with complex unknowns and cascade constraints arising from constrained actuator dynamics, including saturations and dead zones. The entire quadrotor system including actuator dynamics is decomposed into five cascade subsystems connected by intermediate saturated nonlinearities. By virtue of the cascade structure, backpropagating constraints (BCs) on intermediate signals are derived from constrained actuator dynamics suffering from nonreversible rotations and nonnegative squares of rotors, and decouple subsystems with saturated connections. Combining with sliding-mode errors, BC-based virtual controls are individually designed by addressing underactuation and cascade constraints. In order to remove smoothness requirements on intermediate controls, first-order filters are employed, and thereby contributing to backstepping-like subcontrollers synthesizing in a recursive manner. Moreover, universal adaptive compensators are exclusively devised to dominate intermediate tracking residuals and complex unknowns. Eventually, the closed-loop BCTTC system stability can be ensured by the Lyapunov synthesis, and trajectory tracking errors can be made arbitrarily small. Simulation studies demonstrate the effectiveness and superiority of the proposed BCTTC scheme for a quadrotor with complex constrains and unknowns.
INTELIGÊNCIA ARTIFICIAL E RECONHECIMENTO DE VOZ NA UNIDADE DE RESPOSTA AUDÍVEL (URA)

Atualmente, a Unidade de Resposta Audível (URA) é o principal meio de comunicação entre empresas e clientes. O cliente pode realizar ligações para prestadora de serviços através da discagem direta gratuita a fim de obter informações ou até mesmo realizar serviços de automações como por exemplo: fazer o informe de pagamento no atendimento eletrônico sem a necessidade do atendimento humano. Para melhorar a performance desse formato de atendimento será apresentada a forma como a Inteligência Artificial está sendo implementada no campo da Machine Learning e sendo um modelo de negócio inovador por meio do motor de reconhecimento de voz. Serão abordados os modelos estatísticos da rede Bayesiana e o modelo oculto de Markov (HMM), utilizados no motor de reconhecimento de voz, cuja finalidade é realizar o aprendizado de máquina fazendo o tratamento e a melhoria contínua dos problemas atuais encontrados nos atendimentos eletrônicos como regionalismo, pausa na fala, ruído e ligações com volume baixo. Além disso, também serão comentadas as soluções para os problemas apresentados como o Spoltec versão português do Linguistic Data Consortium (LDC) que é um consórcio aberto para microfone de várias regiões, essa técnica é utilizada dentro do próprio motor de reconhecimento de voz para transcrever áudios em texto e criar TAGs de relacionamento com elocuções extraídas da URA. A proposta aplicada por TAG é capaz de reproduzir resultados esperados e consistentes para aprendizado de máquina tanto para a melhoria da experiência do cliente na voz quanto nas análises de comportamento.
Compressive sensing unmixing algorithm for breast cancer detection

Traditional breast cancer imaging methods using microwave imaging (MWI) seek to recover the complex permittivity of the tissues at each voxel in the imaging region. This approach is suboptimal in that it does not directly consider the permittivity values that healthy and cancerous breast tissues typically have. The authors describe a novel unmixing algorithm for detecting breast cancer. In this approach, the breast tissue is separated into three components, low water content, high water content, and cancerous tissues, and the goal of the optimisation procedure is to recover the mixture proportions of each component. By utilising this approach in a hybrid digital breast tomosynthesis/MWI system, the unmixing reconstruction process can be posed as a sparse recovery problem such that compressive sensing techniques can be employed. A numerical analysis is performed, which demonstrates that cancerous lesions can be detected from their mixture proportion under the appropriate conditions.
Central exclusive quarkonia production in the forward region at LHCb

The LHCb detector and LHC running conditions are ideally suited to measure central exclusive production. Recent results of central exclusive production of J/Ψ, Ψ(2S) and double charmonium are presented. Results are consistent with theoretical expectations. Prospects for measurements of central exclusive production with a new detector installed for the next running period are discussed.