Miracle-dz/UniFAR-FTUs huggingface dataset:
Claustrophobic fear and the magnetic resonance imaging procedure

The study was designed to assess the characteristics of fear induced by the Magnetic Resonance Imaging Procedure (MRI). The responses of two groups are compared: 1) patients who are completely encapsulated within the MRI machine, and 2) patients who are scanned only below the hip and are therefore not enclosed within the MRI machine. Fear is a multifaceted phenomenon composed of three components: 1) cognitive, 2) behavioral, and 3) physiological. Within this study three the components were assessed to provide a more detailed description of MRI-related claustrophobic fear. Knowing the specific factors which influence patient anxiety will help determine future treatment of MRI-related distress. Since many patients endure the scan while experiencing moderate to severe anxiety the extent to which anxiety influences the diagnostic value of the examination (i.e. clarity of scan image) was evaluated. There was a significant correlation between Beck Anxiety Inventory scores, a measure of subjective distress and physiological symptoms accompanying stress, and the level of motion artifact in the scan picture. Thus, anxiety contributes to motion artifact which degrades the MRI scan image. The poorer quality scan may reduce the diagnostic value of the scan making it necessary for several repetitions which increase hospital time, expense, and patient discomfort. Regression analyses were used to assess the contribution of several variables (e.g. anxiety sensitivity, fears of suffocation and restriction, fearful thoughts about the experience) to provide a predictive profile of those patients who may experience the most distress undergoing the procedure. Two variables were predictive of anxiety during the MRI scan scores on the Claustrophobia Questionnaire and endorsement of fearful thoughts about the procedure. Patients who were more likely to experience distress in the scan scored higher on these measures indicating their primary concerns were about the claustrophobic aspects of MRI situation and the thoughts they have about undergoing the experience. The thoughts most highly correlated were those related to suffocation and restriction. Eleven out of eighty eighty patients experienced a panic attack during the scan, of which three resulted in scan
Enhanced Light Extraction in AlInGaN UV Light-Emitting Diodes by an Embedded AlN/AlGaN Distributed Bragg Reflector

A novel kind of AlInGaN ultraviolet (UV) light-emitting diode (LED) with an embedded AlN/Al0.3Ga0. 7N distributed Bragg reflector (DBR) is proposed to enhance light extraction efficiency (LEE). The simulation technique we adopt to calculate the LEE of LEDs is based on the theory of spontaneous emission in a layered medium, the well-known mode-matching technique and the scattering matrix approach. The AlN/Al0.3Ga0. 7N DBR was intentionally designed to have peak reflectivity at the LED emission wavelength and the optical properties of the DBR were simulated by using the transfer matrix method. A high LEE of 45.7% at 370 nm wavelength was predicted for a proposed AlInGaN UV LED consisting of 24 periods of the AlN/Al0.3Ga0. 7N DBR, which is 1.5 times of that of the conventional AlInGaN UV LED. The investigation shows that the AlN/Al0.3Ga0. 7N DBR grown on GaN templates with sapphire as a substrate by MOCVD can enhance the LEE effectively and would be very promising for the fabrication of high performance GaN-based UV LEDs.
A Dynamic User Optimal Assignment Problem of Link Variables Based on the Cell Transmission Model

This paper develops a method for estimating the link travel time by using the cell transmission model (CTM) and formulates a variational inequality problem of dynamic user optimal (DUO) based on the link variables.In our DUO formulation,the variables are towards the destinations,which,at each interval,can provide the link inflow rates,the link outflow rates,the link flows and actual travel times,consequently providing complete inducing information for travelers.To get the solutions,we employ a modified projection method for the problem based on link variables.Our DUO formulation is a good combination of the road traffic flow model and the network traffic assignment problem.Its practicability and advantages are confirmed by numerical simulation.
Students’ perceptions on learning, motivation, and performance through project-based learning: undergraduate students’ case

Project-based learning is an approach that is able to facilitate authentic learning. Authentic learning as an experiential process that prepares future educators can help the development of necessary teaching skills. Assessment of project-based learning is required to evaluate how it successfully promotes students’ learning. A survey was conducted to assess students’ perspectives on the implementation of project-based learning. Undergraduate students who enrolled in a mathematics learning strategy course at a faculty of teaching training at a university participated in the survey. Student samples responded to the questionnaire based on their video production assignment project experiences. The collected research data were then analyzed descriptively. Data analysis results suggested that project-based learning positively impacted students’ learning, motivation, and performance. As a result, future research and learning can investigate and implement project-based learning in greater depth.
Equivalent Reduced DC Network Models With Nonlinear Load Functions: A Data-Driven Approach

Nonconvex AC power flow models feature a heavy computational burden and lack optimality guarantees to be considered in industry expansion and operational planning activities. Additionally, the consideration of full network models, in general, leads to undesirable levels of computational burden, even under standard DC approximations. Notwithstanding, current reduction methods fail to accurately reproduce actual system behavior for a wide range of operating points. In this context, we propose a novel data-driven network reduction method to generate equivalent reduced DC power flow models with nonlinear load functions. Our method aims at providing a high-quality representation of the internal system by minimizing the mismatch between the response of the equivalent DC model and the complete AC power flow (or real measurements) under multiple operating-point data. Thus, the approach can be interpreted as a physics-informed machine-learning method as it trains (or estimates) some parameters of the reduced DC power-flow model to fit pre-calculated data. The method determines: 1) a reduced external equivalent network model (topology and parameters), 2) the optimal allocation of external power injections to the boundary buses, and 3) the coefficients determining artificial nonlinear load functions of the operating-point data. Out-of-sample evaluation tests corroborate the performance of our model against modified Ward reduction benchmark models.
Multiobjective shape optimization of latticed shells for elastic stiffness and uniform member lengths

A multiobjective optimization approach is presented for shape design of latticed shells. The objective functions are the strain energy under static loads and the variance of member lengths. Numerical results are shown for the latticed shells with three types; namely, triangular grid, quadrilateral grid, and hexagonal grid. The topology of each grid is fixed, and the locations of control points or the nodal coordinates are considered as design variables. The constraint approach is used for multiobjective optimization. Optimization results show that the triangular-grid shell with uniform member lengths turn out to be a cylindrical surface with equilateral triangles. The optimal shapes for quadrilateral grids are highly dependent on the initial solutions, and there exists a kind of bifurcation for the set of local optimal solutions in the objective function space. Finally, various shapes with uniform member lengths are found for the latticed shells with hexagonal grids.
Adapting to Twitter: The Entanglement of Journalistic Values and Online Personas

ABSTRACT As more journalists rely on social media platforms like Twitter to gather information and report the news, scholars have identified an increasing tension between traditional journalistic values and reporters’ construction of online personas. Moreover, networked journalists build these mediated personas—following platforms’ affordances—to better engage with fellow journalists and algorithmic audiences. Drawing on data from 15 in-depth interviews with Kosovar Albanian political journalists and editors, this article offers a unique perspective by examining the negotiation process between professional standards and journalists’ online personas on Twitter. Via their online personas, influenced by the audience and the platforms’ affordances and logic, journalists and editors have given rise to new role conceptions and standards in local political journalism. The results show that political journalists and editors in Kosovo view Twitter as a platform where they can display both professional and personal layers of their identity. Furthermore, they feel unrestricted by journalistic standards, taking on roles outside the traditional scope of journalism: as good citizens, they express opinions on political issues; as activists, they advocate for personally meaningful causes; but they also see themselves as mordant critics of political figures.
History and Future of HER2-Targeted Therapy for Advanced Gastric Cancer

Human epidermal growth factor receptor 2 (HER2) is a receptor tyrosine kinase that belongs to the human epidermal growth factor receptor family. It is overexpressed/amplified in approximately 20% of gastric or gastroesophageal junction cancers. HER2 is being developed as a therapeutic target in a variety of cancers, and several agents have been shown to be effective in breast cancer. The development of HER2-targeted therapy for gastric cancer successfully began with trastuzumab. However, while effective in breast cancer, the successive anti-HER2 agents lapatinib, T-DM1, and pertuzumab failed to demonstrate benefits regarding survival in gastric cancer compared with the existing standard therapies. Intrinsic differences lie between gastric and breast cancer in terms of HER2-positive tumor biology, which may make development difficult. Recently, a novel anti-HER2 agent, trastuzumab deruxtecan, was introduced, and the development of HER2-positive gastric cancer agents has been moving to the next stage. This review summarizes the current HER2-targeted therapy for gastric or gastroesophageal cancer in chronological order and describes the promising future of HER2-targeted therapy.
Study on Combinations of Remote Sensing and Cotton Model to Retrieve Initial Inputs and Parameters

A remote sensing and cotton inversion model has been established on the basis of assimilation theory,assimilating LAI,and taking three optimization methods(SCE,SA,DE),to combine of remote sensing and COSIM cotton model which is relatively developed well.The inversion model can retrieve initial inputs and parameters needed by cotton model.Therefore,the study can resolve the problems of lack of initial inputs when crop model is applied from spot to region.Inversed parameters are sowing date,planting population,amount of nitrogen and irrigation.Simulation test showed that the inversion model established in this paper was correct at great extent.In addition,we have tried to use the established remote sensing-COSIM cotton inversion model with SCE scheme to inverse parameters and predict cotton yield in 11 counties in north part of Xinjiang province.Generally speaking,the results from inversion model including yields and sowing dates,sowing density,nitrogen application amount,and irrigation amount in the application areas are largely consistent with the estimation of real situation.Quantitative comparison by observed and inversed values at two points shows that application results of remote sensing-COSIM cotton inversion model on the regional scale are better.Yield estimated errors of the two points are-5.8% and-5.1%,respectively,which are lower for large area yield prediction.If COSIM model can improve its simulation veracity,all kinds of errors of results will be much lower.
An Improved Continued Fraction Method to Simplify TransferFunction Model

In this article, a new continued fraction method to simplify the single I/O transfer fractional model is presented, which can be obtained using division algorithm dividing. The method differs from the three continued fractions methods in form, it has the advantages of fast abasing exponent speed, and can maintain the basic characteristics of original system (including stability and un - stability) in the process of simplification.
Association Between Infants Anthropometric Outcomes With Maternal AHEI-P and DII Scores

The present study sought to examine the association between an infant’s anthropometric outcomes with maternal Dietary Inflammatory Index (DII) and Alternate Healthy Eating Index for Pregnancy (AHEI-P) scores during the third trimester of pregnancy. This prospective cohort study was applying 130 pregnant women, at the pregnancy training center in west Tehran, Iran (November 2020 to July 2021). The maternal dietary intake, and body mass index (BMI), and social economic level were evaluated. The data about birth weight, birth height, head circumference, and, gestational age at birth were extracted from each child’s health records. The ultimate sample included 122 (93.8%) pairs of women/newborn children. The participants, mean age was 28.13 ± 4.66 years with gestational age between 28 to 40 weeks and the mean of BMI was 24.62 ± 3.51. Our outcomes, after adjustment for confounding factors, suggested that those newborn infants in the highest quartile of maternal DII score had a significantly lower weight (p < 0.001) and height (p = 0.05), in comparison to those in the lowest quartile, but not head circumference (p = 0.18). Moreover, after adjustment for confounding factors, results suggested that those newborn infants in the First quartile of maternal AHEI-P score had a significantly lower weight (p = 0.018) and, in comparison to those in the higher quartile. It appears that newborn infants with lower maternal DII and higher AHEI-P scores may have a better anthropometric outcome. Further longitudinal and in-depth qualitative and quantitative studies, with a longer-term follow-up, is warranted to support the integrity of our outcomes.
Mystery and Magic: Perceptions of Social Work within an Acute Hospital Setting

Health and social care integration has been promoted by successive governments as the answer to meeting increased need within limited budgets. However, there has been little research in the UK regarding Social Workers working in integrated health teams. This study explores how social work is perceived by a range of professionals working within three different models of integration in the same acute hospital in the South-East of England. The research team (all practicing social workers) used a mixed method approach - combining questionnaires and focus groups – to explore similarities and differences in the perception of the social work role held by professionals working within contexts which had different types or models of integration. Thematic analysis of the qualitative data identified five key themes. These included social workers: providing ‘checks and balances’ to health professionals, ‘linking and bridging’ between the hospital, the community and patients, holding ‘mysterious knowledge’, having ‘strong interpersonal skills’ and providing a ‘function in team dynamics’’. We conclude by outlining recommendations for practice and further research, the limitations of our study and our approach to reflexivity as research practitioners.
Deep Reinforcement Learning-Based Spatiotemporal Decision of Utility-Scale Highway Portable Energy Storage Systems

Mobile charging is an efficient solution to meet peak charging demand on highways. In this article we propose a deep reinforcement learning (DRL)-based approach to maximize the revenue of a utility-scale highway portable energy storage system (PESS) for on-demand electric vehicle charging. We consider a PESS that consists of an electric truck, battery, and charging stations on highways. Actions include the selection of stations and routes, the selection of charging and discharging, and the corresponding charging/discharging power selection. The first two are discrete, while the last is continuous. To deal with the hybrid action space and time-varying state space of PESS, we design an action space, observation space, and reward function according to its characteristics; in addition, we develop a state-of-the-art DRL model for online decision-making considering the uncertainty in the real-time market price of electricity. Through numerical simulations with real-world electricity price data in California, we demonstrate the effectiveness of the proposed method.
Decentralized Task Assignment for Mobile Crowdsensing With Multi-Agent Deep Reinforcement Learning

Task assignment is a fundamental research problem in mobile crowdsensing (MCS) since it directly determines an MCS system’s practicality and economic value. Due to the complex dynamics of tasks and workers, task assignment problems are usually NP-hard, and approximation-based methods are preferred to impractical optimal methods. In the literature, a graph neural network-based deep reinforcement learning (GDRL) method is proposed in Xu and Song (2022) to solve routing problems in MCS and shows high performance and time efficiency. However, GDRL, as a centralized method, has to cope with the limitation in scalability and the challenge of privacy protection. In this article, we propose a multi-agent deep reinforcement learning-based method named communication-QMIX-based multi-agent DRL (CQDRL) to solve a task assignment problem in a decentralized fashion. The CQDRL method not only inherits the merits of GDRL over handcrafted heuristic and metaheuristic methods but also exploits computation potentials in mobile devices and protects workers’ privacy with a decentralized decision-making scheme. Our extensive experiments show that the CQDRL method can achieve significantly better performance than other traditional methods and performs fairly close to the centralized GDRL method.
Projected gradient methods for Non-negative Matrix Factorization based relevance feedback algorithm in medical image retrieval

Under ideal condition,multi-cyclostationary signals,which are overlapped in the time-frequency domain,can preserve the distinct characteristic without disturbance in cyclic-frequency domain.Unfortunately,in practical application where getting the infinite data length is impossible,the estimation of the cyclic correlation function of the noise and the signal component is not zero due to the limited data length.Accordingly,the performance of cyclostationary method may be negatively affected.In this paper,the quantitative relation between the cyclic correlation function of the noise,the interesting signal,the interference signal and its available data length is deduced,and some conclusions useful for the practical signal processing of cyclostationary signals are obtained,which are further verified by several simulations.
Local gravity-capillary flows near the intersection of a free surface with a vertical wall

Local solutions near the intersection of a free surface with a vertical wall are constructed numerically. Both gravity and surface tension are included in the dynamic boundary condition. It was shown that the solutions are characterized by the angle γ between the free surface and the wall at the separation point. There is a solution for each value of π/2<γ<π. As γ→π/2 and γ→π, the solutions reduce to the pure gravity solutions of Vanden-Broeck & Tuck [1].
Spectral residual method of saliency detection based on the two-dimensional fractional Fourier transform domain

As one of classic methods of frequency domain based saliency detection, Spectral residual (SR) method has shown several advantages. However, it usually produces higher saliency values at object edges instead of generating maps that uniformly cover the whole object, which results from failing to exploit all the spatial frequency content of the original image. The Two-Dimensional Fractional Fourier transform (2D-FRFT) is a generalized form of the traditional Fourier Transform (FT) which can abstract more meaningful information of the image under certain conditions. Based on this property, we propose a new method which detects the salient region based on 2D-FRFT domain. Moreover, we also use Hough transform detection and a band-pass filter to refine the saliency map. We conduct experiments on a common used dataset: MSRA. The proposed method is compared with several other saliency detection methods and shown to achieve superior result.
An Improvised Modulation and Control Approach for Dual Active Bridge DC–DC Converter System

Aimed at the control and modulation of dual active bridge bidirectional dc (DAB-BIDC) converter, an unconventional approach is applied to explore its effect in symmetrical wave-based isolated dc–dc converters. The existing control schemes of DAB-BIDC converter with unified or optimal phase shift modulation have the constraints like complex control schemes, light load efficiency, current stress, and reactive power. As 50 Hz grid-connected converter uses a sinusoidal pulse width modulation to minimize the requirement for passive filters and improves the power quality. Similarly, this work investigates the same analogy for the high-frequency (HF) symmetrical wave converters to get effective modulation and control scheme for the DAB-BIDC converter. Furthermore, the proposed sinusoidal pulsewidth-phase shift modulation scheme has been implemented by the optimally derived control parameters using Lagrange’s Multiplier Method to minimize the reactive power flow and current stress reduction at the HF link. Therefore, the DAB-BIDC converter with the proposed modulation and control approach can be used for EV charging and solid-state transformers applications. Finally, the proposed work is validated using MATLAB simulation and experimental setup having turn ratio 36/12 V, fundamental frequency 5-kHz, and switching frequency 100-kHz for the experimental validation using OPAL-RT.