Miracle-dz/UniFAR-FTUs huggingface dataset:
A STUDY ON PLANNING AND PLATFORM FOR INTERACTIVE EXHIBITION OF SCIENTIFIC CULTURAL HERITAGE

Abstract. This study has developed an experiential exhibition system of scientific cultural heritage through interactive projection mapping. A bronze mirror with fine linear design was selected as an exhibition subject considering the intuitiveness and responsiveness of research and development results. First, three-dimensional (3D) scanning was performed to construct a digital database of the original forms, and computer graphics-based 3D modeling was also performed to build exhibition materials and 3D printed objects. Furthermore, objective scenarios were created based on a traditional science experience, understanding the present bronze mirror, and processes of restoring the corroded original bronze mirror through modern conservation science. The technique selected to implement the scientific content of bronze mirror as media art in this study is interactive projection mapping. Moreover, a large 3D printing of bronze mirror was installed at the center of the exhibition space to allow users and all viewers to concurrently engage in scientific cultural heritage content. Simultaneous projection mapping on the front and rear of the printed bronze mirror model enhanced 3D effects and the immersion of the content. To construct an interactive space connecting the virtual and reality, ore, mold, bronze mirror replicas, 3D printing outputs, and cultural products used in the process of producing bronze mirrors were exhibited to make the exhibit more intuitive for visitors. This interactive projection mapping-based scientific and cultural exhibition of the bronze mirror is an experiential exhibition system which combines traditional science, modern conservation science, and future exhibition technology.
Novel Low-RCS Circularly Polarized Antenna Arrays via Frequency-Selective Absorber

New electromagnetic (EM) structures are demonstrated to realize low-radar-cross section (RCS) antennas by making effective use of frequency-selective absorber (FSA). According to the well-known reciprocity principle, the two-layered FSA can be considered as an actual receiving antenna and then transformed to a circularly polarized (CP) antenna. At the radiation state, a truncated patch resonator on the bottom layer is fed by a coaxial probe so as to produce CP wave, and it further excites the slots on the upper layer, resulting in its radiation toward free space, and when the antenna at the stealth state, the detective incident wave can be effectively absorbed outside the radiation band, thus achieving RCS reduction. The design strategies are then explained and verified with the aid of the corresponding equivalent circuit models. Two examples of <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> antenna arrays were designed to validate the flexibilities of the proposed design method. Finally, the <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> antenna array was fabricated and measured, and reasonable agreement is achieved.
Multi-Aspect Embedding for Attribute-Aware Trajectories

Motivated by the proliferation of trajectory data produced by advanced GPS-enabled devices, trajectory is gaining in complexity and beginning to embroil additional attributes beyond simply the coordinates. As a consequence, this creates the potential to define the similarity between two attribute-aware trajectories. However, most existing trajectory similarity approaches focus only on location based proximities and fail to capture the semantic similarities encompassed by these additional asymmetric attributes (aspects) of trajectories. In this paper, we propose multi-aspect embedding for attribute-aware trajectories (MAEAT), a representation learning approach for trajectories that simultaneously models the similarities according to their multiple aspects. MAEAT is built upon a sentence embedding algorithm and directly learns whole trajectory embedding via predicting the context aspect tokens when given a trajectory. Two kinds of token generation methods are proposed to extract multiple aspects from the raw trajectories, and a regularization is devised to control the importance among aspects. Extensive experiments on the benchmark and real-world datasets show the effectiveness and efficiency of the proposed MAEAT compared to the state-of-the-art and baseline methods. The results of MAEAT can well support representative downstream trajectory mining and management tasks, and the algorithm outperforms other compared methods in execution time by at least two orders of magnitude.
A Switching Controller for a Class of MIMO Bilinear Systems With Time Delay

In this paper, we propose a state-dependent switching controller for multiple-input multiple-output (MIMO) bilinear systems with constant delays in both the state and the input. The control input is assumed to be restricted to take only a finite number of values. The stability analysis of the closed loop is based on a Lyapunov–Krasovskii functional, and the design is reduced to solve a system of linear matrix inequalities. The controller can be designed by considering (state) delay-dependent or delay-independent conditions.
A Bioinspired Lightweight Wrist for High-DoF Robotic Prosthetic Arms

The prosthetic arms generally face several challenges: most critically, they are not lightweight to the users and do not provide full degrees of freedom (DoF) for the arms. In order to address this problem, we propose a novel bioinspired lightweight and high-DoF wrist for prosthetic arms. The proposed robotic wrist is lightweight, albeit more dexterous by simplifying the human wrist structure into a two-row ellipsoid joint and implementing novel ligament routing. The two-row ellipsoid structure effectively reduces the weight of the full-DoF wrist within sufficient ranges of motion required for its mobility. A novel routing-by-ligament tunnel allows for the employment of stiffer ligaments in a compact structure, thereby securing the full DoF of the wrist without compromising its load capacity. We conducted a musculoskeletal simulation to show that the bioinspired wrist helps to increase the energy efficiency of the entire upper limb. Our experimental results demonstrated that the proposed wrist greatly reduces the weight of the entire prosthetic arm while improving its range of motion, tensile stiffness, and the control performance.
Superpixel-Driven Optimized Wishart Network for Fast PolSAR Image Classification Using Global ${k}$ -Means Algorithm

Limitation of optical remote sensing technology gave rise to synthetic aperture radar (SAR) imaging. SAR is a microwave imaging technique, which promises to have a long-range propagation characteristic allowing imaging under harsh weather conditions or in hostile lighting situation. This has opened up a domain of classification using polarimetric SAR (PolSAR) images. In this article, we propose a fast PolSAR image classification algorithm, which uses not only pixel-based feature but also spatial features around each pixel. This is achieved by introducing superpixel-driven optimized Wishart network. The first improvement suggested in this article is to take advantage of a fast global $k$ -means algorithm for obtaining optimal cluster centers within each class. It uses real-valued vector representation of PolSAR coherency matrix along with fast matrix inverse and determinant algorithms to reduce computational overhead. Our method then exploits the information of neighboring pixels by forming a superpixel so that even a noisy pixel may not be assigned a wrong class label. The proposed network uses dual-branch architecture to efficiently combine pixel and superpixel features. We concluded that our proposed method has better efficiency in terms of classification accuracy and computational overhead compared with other deep learning-based methods available in the literature.
Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration

This paper proposes a machine learning–based multifidelity modeling (MFM) and information-theoretic Bayesian optimization approach where the associated models can have complex discrepancies among each other. Advantages of MFM-based optimization over a single-fidelity surrogate, specifically under complex constraints, are discussed with benchmark optimization problems involving noisy data. The MFM framework, based on modeling of the varied fidelity information sources via Gaussian processes, is augmented with information-theoretic active learning strategies that involve sequential selection of optimal points in a multiscale architecture. This framework is demonstrated to exhibit improved efficiency on practical engineering problems like high-dimensional design optimization of compressor rotor via implementing its multiscale architecture and calibration of expensive microstructure prediction model. From the perspective of the machine learning–assisted design of multiphysics systems, advantages of the proposed framework have been presented with respect to accelerating the search of optimal design conditions under budget constraints.
FootFAST screening tool for overuse injury risk: Psychometric properties of its three visual assessments of function.

s screened Full-text articles assessed for eligibility (n = 251) Studies included in qualitative synthesis (n = 11) 1 . 4 In cl u d ed 1 . 3 E li g ib il it y 1 . 2 S cr ee n in g 1 . 1 Id en ti fi ca ti o n Records excluded (n = 97) Full-text articles excluded based on the inclusion/exclusion criteria (n = 240) Records excluded (n = 38)
Automatic Defect Detection for Web Offset Printing Based on Machine Vision

In the printing industry, defect detection is of crucial importance for ensuring the quality of printed matter. However, rarely has research been conducted for web offset printing. In this paper, we propose an automatic defect detection method for web offset printing, which consists of determining first row of captured images, image registration and defect detection. Determining the first row of captured images is a particular problem of web offset printing, which has not been studied before. To solve this problem, a fast computational algorithm based on image projection is given, which can convert 2D image searching into 1D feature matching. For image registration, a shape context descriptor is constructed by considering the shape concave-convex feature, which can effectively reduce the dimension of features compared with the traditional image registration method. To tolerate the position difference and brightness deviation between the detected image and the reference image, a modified image subtraction is proposed for defect detection. The experimental results demonstrate the effectiveness of the proposed method.
Roles of Persons with Severe Mental Illness in the Community: Past, Present, and Future

patient, which can become their only role and produce a feeling of loss of identity, of having no meaningful role at all [1 ,2]. The goal of psychiatric rehabilitation is to improve clients' skills and environmental supports in order to enable them to fulfill social roles [3,4]. Vaccaro, Pitts, & Wallace [5] assert that treatment and rehabilitation cannot be efficient if they are not connected with clients' goals and desired roles. Everyone should be able to feel that he occupies a position in society [6]. For Bigelow and his
DESIGN OF A POLARIZATION INSENSITIVE WIDEBAND ABSORBER USING GRAPHENE BASED METASURFACE

In this paper, we demonstrate the design of a polarization-independent wideband absorber of light that consists of a perforated graphene sheet on top of a lossless dielectric spacer placed on a metallic reflector. The single layer absorber is duly designed based on impedance matching concept. The simulated results indicate that the structure produces 0.98 THz broad absorption from 1.80 THz to 2.72 THz with absorptivity larger than 90% at the normal incidence. The electromagnetic (EM) field distributions and the plots of surface power loss density have been illustrated to explain the absorption mechanism of the structure. The variation of chemical potential from 0.8 to 1.2 eV keeps 90% absorption bandwidth as much as 1 THz band. The polarization-insensitive feature and the properties under oblique incidence are also investigated. Finally, the interference theory is used to analyze and interpret the broadband absorption mechanism.
Additional Capacity Value From Synergy of Variable Renewable Energy and Energy Storage

Current capacity markets often consider capacity credits from each resource independently, irrespective of the portfolio of resources, potentially overvaluing or undervaluing the capacity contribution of variable renewable energy (VRE) and energy storage (ES) in the grid. We propose a method for calculating the standalone and integrated capacity value of an added VRE resource with existing ES resources. The difference between the integrated and standalone value is the portfolio effect. This is the additional capacity value gained by the synergy of VRE and the existing fleet. Using chronological dispatch simulations and two different reliability metrics to estimate firm capacity, we demonstrate on a small test system that the portfolio effect can be substantial.
Low-Rank Tensor Models for Improved Multidimensional MRI: Application to Dynamic Cardiac $T_1$ Mapping

Multidimensional, multicontrast magnetic resonance imaging (MRI) has become increasingly available for comprehensive and time-efficient evaluation of various pathologies, providing large amounts of data and offering new opportunities for improved image reconstructions. Recently, a cardiac phase-resolved myocardial <inline-formula><tex-math notation="LaTeX">$T_1$</tex-math></inline-formula> mapping method has been introduced to provide dynamic information on tissue viability. Improved spatio-temporal resolution in clinically acceptable scan times is highly desirable but requires high acceleration factors. Tensors are well-suited to describe interdimensional hidden structures in such multi-dimensional datasets. In this study, we sought to utilize and compare different tensor decomposition methods, without the use of auxiliary navigator data. We explored multiple processing approaches in order to enable high-resolution cardiac phase-resolved myocardial <inline-formula><tex-math notation="LaTeX">$T_1$</tex-math></inline-formula> mapping. Eight different low-rank tensor approximation and processing approaches were evaluated using quantitative analysis of accuracy and precision in <inline-formula><tex-math notation="LaTeX">$T_1$</tex-math></inline-formula> maps acquired in six healthy volunteers. All methods provided comparable <inline-formula><tex-math notation="LaTeX">$T_1$</tex-math></inline-formula> values. However, the precision was significantly improved using local processing, as well as a direct tensor rank approximation. Low-rank tensor approximation approaches are well-suited to enable dynamic <inline-formula><tex-math notation="LaTeX">$T_1$</tex-math></inline-formula> mapping at high spatio-temporal resolutions.
Digital Network as a Learning Tool for Health Sciences Students

An online learning module for health sciences students with various educational backgrounds was implemented at the University of Tromsø (UiT). The purpose of this article is to examine how participation in a joint, web-based course can be a didactic tool that promotes motivation and contributes to interactions among health sciences students. The study is based upon findings from focus group interviews with students who participated in a joint online course, as well as on recordings of activity in online discussions.
School nursing documentation: Knowledge, attitude, and barriers to using standardized nursing languages and current practices

The independent, complex role of a school nurse requires accurate documentation of assessments, interventions, and outcomes. Consistent documentation by all school nurses is crucial to study the impact of nursing interventions on student’s health and success in school. While standardized nursing languages are available, the actual use of these languages by school nurses is in the infancy stages of implementation. This national survey of school nurses reveals diverse practices in school nursing documentation. Using Everett Rogers’ (2003) Diffusion of Innovation (DOI) theory, a web-based survey allowed respondents to identify their knowledge and attitude towards the use of standardized languages, including NANDA International (NANDA-I), Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC). Respondents also rated barriers to adopting the use of NANDA-I, NIC, and NOC (NNN). The results of this survey serve as a foundation for moving the practice of school nursing towards consistent documentation. Ultimately, the implementation of NNN will allow school nurses to document more consistently, base practice decisions on evidence, and improve the health and academic success of students in schools. Abstract Approved: ____________________________________ Thesis Supervisor ____________________________________ Title and Department ____________________________________ DateApproved: ____________________________________ Thesis Supervisor ____________________________________ Title and Department ____________________________________ Date SCHOOL NURSING DOCUMENTATION: KNOWLEDGE, ATTITUDE, AND BARRIERS TO USING STANDARDIZED NURSING LANGUAGES AND CURRENT PRACTICES by Sharon Kay Guthrie Yearous A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Nursing in the Graduate College of The University of Iowa
An Intelligent Artificial Neural Network Modeling of a Magnetorheological Elastomer Isolator

Recently, magnetorheological elastomer (MRE) has been paid increasingly attention for vibration mitigation devices with the benefits of low power cost, fail safe performances, and fast responses. To make full use of the striking advantages of MRE device, a highly precise model should be developed to predict its dynamic performances. In the work, an MRE isolator in shear–squeeze mixed mode is developed and tested under dynamic loadings. The nonlinear performances in various displacement amplitude and currents are shown. An artificial neural network model with a back-propagation algorithm is proposed to characterize the nonlinear hysteresis of MRE isolator for its implementation in vibration control applications. This model utilized the displacement, velocity, and applied current as inputs and output force as output. The results show that the proposed model has high modeling accuracy and can well portray the complicated behaviors of MRE isolator with different excitations, which shows a fundamental basis for structural vibration control.
Detecting Maneuvering Target Accurately Based on a Two-Phase Approach From Remote Sensing Imagery

Maneuvering target detection in satellite images is difficult due to their small sizes, blurred appearances under various illuminations and shadows, and occlusion by trees and buildings. Recently, a fully convolutional regression network (FCRN) was proposed and achieved by the state-of-the-art performance in the Munich vehicle database. However, such a one-phase approach often makes mistakes at difficult places because of its swift glance and rejecting any second check. In this letter, a new object spatial density building net (SDBN) was designed, and a two-phase detection approach was proposed. It used the first SDBN to generate candidate regions and the second SDBN to proceed with a meticulous check on the object categories. Experiments on four maneuvering target databases, the Munich vehicle database, the Open Vehicle Database of San-Francisco (OVDS), the Overhead Imagery Research Data Set (OIRDS), and the Open Aircraft Database (OAD) show that the proposed method outperforms FCRN by an obvious margin. In addition, the accurate geometrical parameters (positions, orientations, and lengths) of all the objects were computed based on the spatial density maps, and the published experimental result of FCRN in OIRDS was pointed out and the corrected result was given. All source codes and databases are available at http://www.github. com/cxy177/SDBN.
The Nearby Very Low Mass Triple Star LHS 1070

The component masses and kinematic parameters of the triple star LHS 1070 are redetermined by using the three-body model. It is shown that the discrepancy between new observations and the two-body fitting results is due to the three-body effects, which justifies the necessity of using the three-body model. Numerical integration shows that the three-body system remains integrated and its hierarchical configuration remains unchanged since the formation of the components. This implies that the triple star may not be formed by a three-body encounter. Also, the possibility that the most massive component is itself a tightly bounded binary is excluded based on the redetermined dynamical mass. The improved dynamical masses of the three components are greatly helpful to constrain at the lower end of the stellar empirical mass–luminosity relation.
High-Resolution Temperature Distribution Measurement of GIL Spacer Based on OFDR and Ultraweak FBGs

The temperature distribution on the gas-insulated line (GIL) spacer affects surface charge accumulation. However, the spacer temperature distribution is hard to measure because the conventional electric sensors are large dimensions, hard to multiplex, and sensitive to electromagnetic interference (EMI). In this article, a distributed temperature measurement system of GIL spacer based on the optical frequency domain reflectometry (OFDR) was developed to solve the problems. In this system, ultraweak fiber Bragg grating (FBG) was adopted to replace single-mode fiber due to its higher signal-to-noise ratio. The demodulation method was described for compensating the nonlinear frequency tuning errors induced by the unstable tunable laser. In addition, the relationship between the spacer temperature and the wavelength shift was determined in the calibration experiment. Moreover, the temperature distribution on a 126-kV GIL spacer was measured by the proposed OFDR system. The data processing method for 3-D surface temperature on cone-type spacer was also introduced. High-resolution temperature distribution on the spacer surface was successfully obtained using OFDR system, which is unable to accomplish with the conventional electric temperature sensor.