Miracle-dz/UniFAR-FTUs huggingface dataset:
Offering Outworld Experiences to In-Patients With Dementia Through Virtual Reality: Mixed Methods Study

Background Research has suggested that institutionalization can increase the behavioral and psychological symptoms of dementia. To date, recent studies have reported a growing number of successful deployments of virtual reality for people with dementia to alleviate behavioral and psychological symptoms of dementia and improve quality of life. However, virtual reality has yet to be rigorously evaluated, since the findings are still in their infancy, with nonstatistically significant and inconclusive results. Objective Unlike prior works, to overcome limitations in the current literature, our virtual reality system was co-designed with people with dementia and experts in dementia care and was evaluated with a larger population of patients with mild to severe cases of dementia. Methods Working with 44 patients with dementia and 51 medical experts, we co-designed a virtual reality system to enhance the symptom management of in-patients with dementia residing in long-term care. We evaluated the system with 16 medical experts and 20 people with dementia. Results This paper explains the screening process and analysis we used to identify which environments patients would like to receive as an intervention. We also present the system’s evaluation results by discussing their impact in depth. According to our findings, virtual reality contributes significantly to the reduction of behavioral and psychological symptoms of dementia, especially for aggressive, agitated, anxious, apathetic, depressive, and fearful behaviors. Conclusions Ultimately, we hope that the results from this study will offer insight into how virtual reality technology can be designed, deployed, and used in dementia care.
Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification

Gene expression data are usually known for having a large number of features. Usually, some of these features are irrelevant and redundant. However, in some cases, all features, despite being numerous, show high importance and contribute to the data analysis. In a similar fashion, gene expression data sometimes have limited instances with a high rate of imbalance among the classes. This can limit the exposure of a classification model to instances of different categories, thereby influencing the performance of the model. In this study, we proposed a cancer detection approach that utilized data preprocessing techniques such as oversampling, feature selection, and classification models. The study used SVMSMOTE for the oversampling of the six examined datasets. Further, we examined different techniques for feature selection using dimension reduction methods and classifier-based feature ranking and selection. We trained six machine learning algorithms, using repeated 5-fold cross-validation on different microarray datasets. The performance of the algorithms differed based on the data and feature reduction technique used.
Antipenetration Performance of Multilayer Protective Structure by the Coupled SPH-FEM Numerical Method

Multilayer composite structures have significant advantages in antipenetration protection. Problems such as element distortion are more likely to occur when the FEM is used to simulate the heterogeneous composite structure against penetration with large deformation. A coupled smoothed particle hydrodynamic (SPH)-FEM is proposed to simulate the antipenetration performance of multilayer protective structures under the penetration of high-speed hemispherical-nosed projectiles. The large deformation and broken areas are calculated by the SPH method, which overcomes the problem of element distortion in FEM. In other areas, the FEM is used to improve the calculation efficiency. The results indicated that simulating multilayer plates using the coupled SPH-FEM can achieve the ballistic limit and deformation that agree with the experiment. Moreover, the deformation of single-layer, in-contact double-layer, spaced double-layer, and sandwich target plates with a core layer of water was studied and discussed. The influence of the faceplate and core layer on the penetration resistance performance was discussed in this paper by applying LSDYNA to establish the model of 3D SPH-FEM and calculate the dynamic process. In addition, the relationship between initial-residual velocity, deformation, and damage failure behavior was obtained.
Limits of equisymmetric 1 -complex dimensional families of Riemann surfaces

We describe the limit surfaces of some equisymmetric 1-complex dimensional families of Riemann surfaces in the boundary of the Deligne-Mumford compactification of moduli space. We provide a description of such nodal Riemann surfaces in terms of the group of automorphisms defining the family. We apply our method to some known examples.
An Inverse Finite Element Analysis and A Parametric Study of Small Punch Tests

An Inverse Finite Element Analysis and a Parametric Study of Small Punch Tests. (December 2011) Zhenzhen Xu, B.S, Central South University, China Chair of Advisory Committee: Dr. Xin-Lin Gao Small punch test (SPT) has been widely used to evaluate in-service materials in nuclear fusion facilities. Early use of SPTs is largely based on empirical relations or curve fitting from experimental data, while recent applications of SPTs take advantage of finite element methods. In this study, an improved inverse finite element analysis procedure is proposed to obtain constitutive relations from load-displacement curves recorded in SPTs. In addition, a parametric study is performed to evaluate the effects of SPT parameters including friction coefficient, punch head diameter, sample thickness, specimen scale and boundary conditions. The proposed inverse finite element (FE) method improves the accuracy of existing inverse FE methods, and the current parametric study provides a basis for the standardization of SPT procedures in the future.
Is Liquidity Risk Priced in Artificial Intelligence ETFs?

We examine if liquidity risk is priced in ETF equity returns and ETF equity premiums. We also show if these relationships hold in extreme market situations, which includes pre-and post-Covid periods. We find that liquidity risk is an important determinant of ETF equity valuation, and equity premiums are sensitive to liquidity levels. The equity premium tends to increase at the higher levels of bid-ask spread in the pre and post-Covid periods indicating that the information is not fully available to the public or to investors. Our results are robust across different sub-groups categorized based on the characteristics (age and size) of the ETFs. Our results have implications for asset pricing and price discovery and show that investors hold ETFs with high equity premiums even when the liquidity risk is high. A positive liquidity premium exists in Artificial Intelligence ETF markets and has implications for price discovery.
Non-invasive depth-resolved imaging through scattering layers via speckle correlations and parallax

Various approaches for imaging through scattering layers have been proposed, but very few of them provide depth-resolved images. Here, we propose and experimentally demonstrate an approach for non-invasive depth-resolved imaging of objects hidden behind a scattering medium based on speckle correlations and parallax. The relative intensities of the objects' autocorrelations and the positions of the cross-correlations, both change from different apertures. The reconstruction of objects is achieved by subtraction of the autocorrelations and the conventional Fienup-type iterative phase-retrieval algorithm, and the objects' relative position is retrieved from the relative position of the cross-correlation to the central; moreover, the depth information of objects is decoded by the displacement of the cross-correlations through different apertures. This multi-aperture technique allows sharp imaging of objects with full actual depth information at once.
The Impact of Digital Menus on Customer Behavioral Intentions in Casual Dining Restaurants: Mediating Role of Customer Satisfaction

Purpose: Despite the tremendous importance of innovative technology in restaurant sector, review of literature has presented finite researches into this side, particularly in Egyptian restaurant sector. Limited research studied the antecedent of customer satisfaction toward digital menus. This research intends to examine the role of digital menus towards customer’s behavioral intentions, as conspicuous by customers' evaluation of perceived usefulness, interactivity, media enjoyment and consumption visions and behavioral intentions towards digital menus image through their satisfaction in the Egyptian restaurant sector. Design/methodology/approach : A number of 389 survey directed to customers usually visit casual restaurants applied digital menus to affirm recently proposed model to enhance Egyptian casual restaurants. Structural equation modeling has been utilized for hypotheses testing. Findings: The paper conclusion clarifies that perceived usefulness, interactivity, media enjoyment and consumption visions are the major
The Importance of Analysis in Applied Behavior Analysis

To be a behavior analyst literally means to analyze behavior, and to analyze behavior means to conduct an experimental analysis, also called a functional analysis. This article explores the question of whether or to what degree experimental or functional analysis occurs in applied behavior analytic research and practice. It addresses the secondary question of what kind of training should be required for practitioners, including whether they should even be trained to carry out experimental analysis. The article then suggests 7 levels of experimental analysis that practitioners can carry out to better understand the behaviors not only of their clients but of all the people in the client’s life, including parents, other family members, the behavior analyst him- or herself, and other stakeholders. The article concludes that, excluding practical, logistical, or ethical hurdles, and in the absence of an army of basic or applied researchers, applied behavior analytic practitioners should be able to carry out analysis of any behavior in any setting, and such skills should be required by the Behavior Analysis Certification Board.
A Forward-Backward Splitting Method with Component-wise Lazy Evaluation for Online Structured Convex Optimization

We consider large-scale optimization problems whose objective function is expressed as the sum of convex functions. Such problems appear in various fields, such as statistics and machine learning. One of the solution methods for the problems is the online gradient method, which exploits gradients of a few functions in the objective function at each iteration. In this paper, we focus on sparsity of two vectors: a solution and a gradient of each function in the objective function. To get a sparse solution, we usually add the L1 regularization term to the objective function. Then, since the L1 regularization term consists of all decision variables, the usual online gradient methods update all variables even if each gradient is sparse. To reduce the computations of all variables at each iteration, we may use a lazy evaluation, which only updates the variables corresponding to the nonzero components of the gradient by ignoring the L1 regularization terms, and evaluates the ignored ones later. Since the lazy evaluation does not exploit the information of the terms at all iterations, the online algorithm with the lazy evaluation might converge slowly. In this paper, we propose a forward-backward splitting type method that exploits the L1 terms corresponding to the nonzero components of the gradient at each iteration.We show that its regret bound is O( √ T ), where T is the number of functions in the objective function. Moreover, we report numerical experiments for the proposed method, which show the proposed method is promising.
Reweighted Price Relative Tracking System for Automatic Portfolio Optimization

In this paper, we propose a novel reweighted price relative tracking (RPRT) system for automatic portfolio optimization (APO). In the price prediction stage, it automatically assigns separate weights to the price relative predictions according to each asset’s performance, and these weights will also be automatically updated. In the portfolio optimizing stage, a novel tracking system with a generalized increasing factor is proposed to maximize the future wealth of next period. Besides, an efficient algorithm is designed to solve the portfolio optimization objective, which is applicable to large-scale and time-limited situations. Extensive experiments on six benchmark datasets from real financial markets with diverse assets and different time spans are conducted. RPRT outperforms other state-of-the-art systems in cumulative wealth, mean excess return, annual percentage yield, and some typical risk metrics. Moreover, it can withstand considerable transaction costs and runs fast. It indicates that RPRT is an effective and efficient APO system.
Development of a Numerical Model to Analyze the Condition of Prestressed Concrete Cylinder Pipe (PCCP)

Prestressed Concrete Cylinder Pipe (PCCP) is a large-diameter and high-pressure conduit for drinking water and wastewater transmission. Due to its large diameter, high pressure, and mode of breakdown, PCCP failures usually have catastrophic consequences. To mitigate failures, it is very important to assess the condition of the pipe and take proactive measures, such as repair, rehabilitation, or replacement. There are many challenges in assessing the condition of PCCP. PCCP has a complex structure with several layers of materials (e.g. mortar coating, prestressing wire, steel cylinder, and concrete core) working together under loading. This means that there are many factors that can cause pipe failure, and that failure mechanisms are complicated. Data collection could be difficult, and existing data are often unavailable or unreliable. Considerable research has been conducted by scholars and engineers in developing models to evaluate the condition of PCCP. There are mainly two types of models: statistical models, and numerical models using finite element method. Statistical models consider only a few factors, such as pipe age and failure rate, to predict the failure of PCCP. However, the failure of PCCP can be caused by many other factors including pipe material, and loading conditions. Models only considering a few factors are not robust enough for reliable results. The current numerical models assume that all broken wires are centrally distributed in the same location and broken wires have no prestress, thus all broken wires are completely removed from the model. These assumptions could be overly conservative when actual broken wires are distributed in different locations along the pipeline and broken wires have remaining prestress due to the bond between the wire and mortar coating. Therefore, a more comprehensive numerical model is needed to have a better understanding of the condition of PCCP. In this research, an extensive literature and practice review was conducted on PCCP failures to understand the critical factors that affect pipe condition. The available technologies commonly used to detect pipe defects were reviewed in order to better understand the accuracy and uncertainties of the collected data. Existing models were reviewed to better understand their limitations and to advance the research on condition analysis of PCCP using numerical models. Based on these comprehensive reviews, this dissertation proposed a numerical model to analyze the condition of PCCP for its long-term performance management. Detailed structural components such as concrete cores, prestressing wires, steel cylinder, and mortar coating were modelled. The interactions between different layers of pipe components were considered. An
Wear Behavior Performance of Polymeric Matrix Composites Using Taguchi Experiments

This research estimates the effect of independent factors like filler (3%, 6%, 9%, 11% weight fraction), normal load (5N, 10N, 15N), and time sliding (5,7 , 9 minutes) on wear behavior of unsaturated polyester resin reinforced with jute fiber and waste eggshell and, rice husk powder composites by utilizing a statistical approach. The specimens polymeric composite prepared from resin unsaturated polyester filled with (4% weight fraction) jute fiber, and (3%, 6%, 9%, 11% weight fraction) eggshell, and rice husk by utilizing (hand lay-up) molding. Dry sliding wear experiments were carried utilizing a standard (pin on disc test setup) following a well designed empirical schedule that depends on Taguchi’s experimental design L9 (MINITAB 16) with the determined (S/N) ratio and analysis of different (ANOVA) optimal factors to minimize the wear rate. Results exhibit that the presence the of 11% weight fraction eggshell and rice husk powder with 4% weight fraction jute fiber improves the wear resistance of unsaturated polyester composite materials significantly. The filler content was observed to be the major significant factor influencing the wear rate followed by time sliding and normal load. From the results of the S/N ratio, the optimization of wear parameters is obtained at specimens A3, A6, A7, and A10 as to minimize wear rate.
The management of sudden sensorineural hearing loss during COVID-19 pandemic.

Introduction: The COVID-19 pandemic constituted a significant challenge for healthcare systems. Epidemiological restrictions led to deferral of healthcare and influenced the variety of symptoms reported by patients suffering from Sudden Sensorineural Hearing Loss. Aim: The aim of the study was to compare the duration of treatment implementation and symptoms reported by patients suffering from SSNHL before and during the COVID-19 pandemic. Material and methods: Patients admitted to the Military Institute of Medicine - National Research Institute for SSNHL treatment were included in a survey conducted between 01.10. 2021-31.12. 2021. Questions on symptoms reported, chronic conditions, timeframe between first symptoms and implementation of pharmacotherapy, and hyperbaric oxygen therapy were included in the survey. The results were compared with data obtained by the Team between 01.2017-12.2019. Results: 34 patients (19 females, 15 males) of the average age of 46.3 who participated in the survey indicated a significantly delayed implementation of pharmacotherapy and hyperbaric oxygen therapy in comparison to population from before the pandemic (12.08 vs. 5.41 [days]; 17.5 vs. 8.29 [days]). The survey also showed differences in the number of symptoms reported. Discussion: The differences in time of implementation of SSNHL treatment could have been related to pandemic restrictions which contributed to longer queueing for health benefits and fear of contact with health service. Differences in symptomatology point towards SARS-CoV-2 as a pathogenetic factor of SSNHL. Conclusions: The COVID-19 pandemic significantly influenced the treatment of Sudden Sensorineural Hearing Loss. The study demonstrated a diverse symptomatology of SSNHL before and during the pandemic.
Extensional rheometry through hyperbolic contraction

To understand fluid rheology, properties such as consistency and viscosity are determined in both shear and extensional flow. Many commonly-used materials and formulations exhibit complex rheological properties and in order to determine the behaviour as well as predict and govern their properties, effective and reliable characterisation of these fluids is important. Rheological properties are used as quality control parameters, since they have an impact at all stages of material processing, across multiple disciplines, from formulation development, stability during processing to final product performance and stability. Furthermore, rheological properties can vary depending upon external conditions, and can be correlated with the product microstructure. Various techniques are available for characterising viscoelastic liquids in shear, but extensional rheological properties are experimentally more difficult to determine and thus not many techniques are available commercially. In this work, a measuring technique for extensional rheology based on hyperbolic contraction flow is developed and evaluated through numerical and experimental studies. The evaluation was performed using numerical simulations through a hybrid finite element/finite volume scheme to evaluate the behaviour of the flow through a hyperbolic contraction nozzle. Several configurations, differing in contraction angle, were considered, and contraction-expansion configurations were also evaluated. The hyperbolical shape was found to give an almost constant strain rate throughout the measuring regime in contrast to the other configurations, and the influence of shear from the wall was minimised. The use of a corrected pressure drop, called the excess pressure drop, as a measure of extensional properties has been evaluated and proved to strongly depend on extensional viscosity. By changing the configuration to a contraction-expansion configuration, the shear contribution to the excess pressure drop could be neglected. This was also verified experimentally. The numerical results were compared with experimental measurements of the pressure drop using a set of model test fluids with various rheological properties, a Newtonian, a Boger and a shear-thinning fluid. By matching the shear viscosity of the model fluids, it was possible to address the influence of elasticity and shear thinning. Both numerical and experimental results demonstrated increasing excess pressure drops over the Newtonian reference line with increasing deformation rates for both the hyperbolic contraction configuration and the contraction-expansion configuration. This opposed to previously reported results on the sharp 4:1:4 configurations, which show pressure drops under the Newtonian reference line. Finally, the hyperbolic contraction device was utilised to develop edible model fluids differing in rheological properties with the aim of determining the rheological influence on swallowing. Fluid elasticity was overall found to be beneficial for safe and easy swallowing.
The Shortest Path Isolation Index: A New Measure for Individual-level Residential Segregation

The study of segregation is essential for understanding how place influences life outcomes. Traditional segregation indices rely heavily on the use of areal units for calculation, which risks introducing both measurement and interpretation error. Using individual-level data avoids many of the problems facing traditional area-level indices. However, few segregation indices currently exist that are capable of utilizing such data. Given that our understanding is only as good as our measurement, it is imperative that our measures accurately reflect our perceptions of segregation. Utilizing the recent release of the complete 1940 Census count data, this article details a new individual-level segregation measure—the shortest path isolation (SPI) index. The SPI index captures the degree of racial isolation experienced by an individual, regarding both distance and interpersonal contact. With West Philadelphia as a sample study area, this article highlights the benefits of the SPI index for studying segregation at the individual level.