Miracle-dz/UniFAR-FTUs huggingface dataset:
Frailty in Patients with Parkinson’s Disease: Associations with Disability and Timed Up and Go

Introduction: Aim of this study was identify the prevalence of frailty in patients with idiopathic Parkinson’s disease (PD), to describe the relationship between severity of the disease and frailty, and to evaluate if timed up and go (TUG) is an eligible test for determination of frailty in idiopathic PD patients. Methods: We conducted a cross-sectional study which included 66 patients, aged 60 and over in a tertiary hospital. Frailty was assessed by the Fried Frailty Index (FFI). Severity of the idiopathic PD was detected by the Hoehn and Yahr (H&Y) scale. Mobility was measured by the TUG test. Demographic characteristics and comprehensive geriatric assessments were evaluated. Descriptive statistics and logistic regression were used in analyses. Receiver operating characteristic (ROC) curves were used to identify the discriminative effect of TUG test on frailty. Results: The numbers of frail, prefrail, and robust subjects were 34 (51.5%), 24 (36.4%), and 8 (12.1%), respectively. Dependency in instrumental activities of daily living (IADL) was significantly associated with frailty (Odds ratio (OR): 36.00, Confidence interval (CI): 8.43–153.80). Multivariate logistic regression analysis results yielded, depression (OR: 10.37, CI: 2.82–38.12) and higher levodopa doses (OR: 6.28, CI: 1.77–22.24) were independently associated with frailty. TUG test performance was strongly associated with frailty with high sensitivity (0.806) and specificity (0.826) (Area under the curve (AUC): 0.831). Conclusions: Frailty is highly prevalent in idiopathic PD and is strongly associated with disabilities as well as specific risk factors of the disease. The TUG may be a reliable test for prediction of frailty in patients with idiopathic PD.
Heuristics Techniques for Scheduling Problems with Reducing Waiting Time Variance

In real computational world, scheduling is a decision making process. This is nothing but a systematic schedule through which a large numbers of tasks are assigned to the processors. Due to the resource limitation, creation of such schedule is a real challenge. This creates the interest of developing a qualitative scheduler for the processors. These processors are either single or parallel. One of the criteria for improving the efficiency of scheduler is waiting time variance (WTV). Minimizing the WTV of a task is a NP-hard problem. Achieving the quality of service (QoS) in a single or parallel processor by minimizing the WTV is a problem of task scheduling. To enhance the performance of a single or parallel processor, it is required to develop a stable and none overlap scheduler by minimizing WTV. An automated scheduler's performance is always measured by the attributes of QoS. One of the attributes of QoS is ‘Timeliness’. First, this chapter presents the importance of heuristics with five heuristic-based solutions. Then applies these heuristics on 1kWTV minimization problem and three heuristics with a unique task distribution mechanism on Qm|prec|WTV minimization problem. The experimental result shows the performance of heuristic in the form of graph for consonant problems.
From Digital Disruption to Business Model Scalability

: This article discusses the terms disruption, digital disruption, business models and business model scalability. It illustrates how managers should be using these terms for the benefit of their business by developing business models capable of achieving exponentially increasing returns to scale as a response to digital disruption. A series of case studies illustrate that besides frequent existing messages in the business literature relating to the importance of creating agile businesses, both in growing and declining economies, as well as hard to copy value propositions or value propositions that take a long time to replicate, business model scalability can be cornered into four dimensions. In many corporate restructuring exercises and Mergers and Acquisitions there is a tendency to look for synergies in the form of cost reductions, lean workflows and market segments. However, this state of mind will seldom lead to business model scalability capable of competing with digital disruption(s).
Differentiation of chronic mass-forming pancreatitis from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography

Purpose Both chronic mass-forming pancreatitis (CMFP) and pancreatic ductal adenocarcinoma (PDAC) are focal pancreatic lesions and share very similar clinical symptoms and imaging performance. There is great clinical value in preoperative differentiation of those two lesions. The purpose of this study was to investigate the value of computed tomography (CT) features in discriminating CMFP from PDAC. Patients and methods Forty-seven patients with pathologically confirmed PDAC and 21 patients with CMFP were included in this study. Demographic and CT features, including tumor location, size, margin, pancreatic or bile duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, and tumor contrast enhancement, were retrospectively analyzed and compared. Multivariate logistic regression analyses were adopted to identify relevant CT imaging features to discriminate CMFP from PDAC. Results There were significant differences between CMFP and PDAC with respect to main pancreatic duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, and tumor contrast enhancement. Delayed contrast enhancement (>70.5 Hounsfield units) showed high sensitivity and specificity of 84.2% and 84.7%. The areas under the curve (AUCs) of the predicting models based on qualitative and quantitative variables were 0.770 (95% CI: 0.660–0.880) and 0.943 (95% CI: 0.888–0.999), respectively. When all significant variables were used in combination to build a predicting model, the AUC was 0.969 (95% CI: 0.930–1.000) with 84.2% sensitivity and 94.7% specificity. Conclusion Main pancreatic duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, tumor size, and tumor contrast enhancement were shown to be useful CT imaging features in discriminating CMFP from PDAC.
Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data

Feature selection, or dimensionality reduction, has become a standard step in reducing large-scale neural datasets into usable signals for brain-machine interface and neurofeedback decoders. Current techniques in fMRI data reduce the number of voxels (features) by performing statistics on individual voxels or using traditional techniques that utilize linear combinations of features (e.g., principal component analysis (PCA)). However, these methods often do not account for the cross-correlations found across voxels and do not sufficiently reduce the feature space to support efficient real-time feedback. To overcome these limitations, we propose using factor analysis on fMRI data. This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs). Here, we demonstrate these methods in both NHP and human data. In NHP subjects (n=2), we reduced the number of features to an average of 26.86% and 14.86% of the total feature space to build our multinomial classifier. In one NHP subject, the average accuracy of classifying eight target locations over 64 sessions was 62.43% (+/-6.19%) compared to a PCA-based classifier with 60.26% (+/-6.02%). In healthy fMRI subjects, we reduced the feature space to an average of 0.33% of the initial space. Group average (n=5) accuracy of FA-based category classification was 74.33% (+/- 4.91%) compared to a PCA-based classifier with 68.42% (+/- 4.79%). FA-based classifiers can maintain the performance fidelity observed with PCA-based decoders. Importantly, FA-based methods allow researchers to address specific hypotheses about how underlying neural activity relates to behavior.
Constant Envelope Multiplexing Scheme for Three Equal Power Signals

This paper presents a mathematical design of a CEM (Constant Envelope Multiplexing) scheme for three equal power signals to achieve any target CEM power efficiency. The proposed scheme assumes that the chip pulses have one or two sample magnitude values. The design method and the results can be applied to a new satellite navigation system such as KPS (Korea Positioning System.)
Attribute-Based Keyword Search over Hierarchical Data in Cloud Computing

Searchable encryption (SE) has been a promising technology which allows users to perform search queries over encrypted data. However, the most of existing SE schemes cannot deal with the shared records that have hierarchical structures. In this paper, we devise a basic cryptographic primitive called as attribute-based keyword search over hierarchical data (ABKS-HD) scheme by using the ciphertext-policy attribute-based encryption (CP-ABE) technique, but this basic scheme cannot satisfy all the desirable requirements of cloud systems. The facts that the single keyword search will yield many irrelevant search results and the revoked users can access the unauthorized data with the old or outdated secret keys make this basic scheme not scale well in practice. To this end, we also propose two improved schemes (ABKS-HD-I, ABKS-HD-II) for the sake of supporting multi-keyword search and user revocation, respectively. In contrast with the state-of-the-art attribute-based keyword search (ABKS) schemes, the computation overhead of our schemes almost linearly increases with the number of users’ attributes rather than the number of attributes in systems. Formal security analysis proves that our schemes are secure against both chosen-plaintext attack (CPA) and chosen-keyword attack (CKA) in the random oracle model. Furthermore, empirical study using a real-world dataset shows that our schemes are feasible and efficient in practical applications.
Metrics for Systems Thinking in the Human Dimension

Abstract : The purpose of this memorandum is to provide documentation of research by the University of Virginia in collaboration with TRADOC Analysis Center, Monterey (TRAC-MTRY). This research is focused on methods of analyzing a large corpus of journal length, or longer, documents to quickly characterize the corpus and to find documents most associated with a target set of text. The projects central purpose is to identifying Systems Thinking within a large corpus of documents and measure the extent to which systems thinking is occurring within the domain specified by the breadth of documents included in the corpus. However, our method of document analysis may be useful in assisting analysts in conducting research literature reviews of large amounts of documents by targeting the most specific documents related to the research the analyst is attempting to conduct. Our method may also have other areas of ell as several other applications. The UVA final report, which is included as an attachment, shows the body of the work conducted.
Can Classification and Regression Tree Analysis Help Identify Clinically Meaningful Risk Groups for Hip Fracture Prediction in Older American Men (The MrOS Cohort Study)?

Although the WHO fracture risk algorithm (FRAX) is used to predict fracture, the utility of some simple machine‐learning methods, such as classification and regression trees (CARTs) should be evaluated to determine their efficacy in fracture prediction. Follow‐up time for the hip fracture analyses of 5977 community‐dwelling American men aged ≥65 years old was truncated to 10 years. There were 172 (2.9%) men who had an incident nontraumatic hip fracture. The CARTs were developed using hip BMD and common clinical risk factors as follows: model 1 = using classification with continuous variables of age, total hip BMD, and femoral neck BMD, or together with common clinical risk factors; and model 2 = using classification with continuous variables of age, total hip BMD, femoral neck BMD, FRAX score, osteoporosis by T‐score at the hip, and common clinical risk factors. The predictive performance of risk models derived from CARTs was compared with the basic classification of FRAX at 3% (basic model). From model 1, discriminators selected by CART were total hip BMD, age, and femoral neck BMD; no other clinical risk factors were selected. From model 2, discriminators selected by CART were FRAX score, femoral neck BMD, and age. Compared with the basic model using only a high‐risk group by FRAX ≥3%, no significantly improved predictive performance was demonstrated by model 1 or model 2 as identified by CART with the area under the receiver‐operating characteristic curve for each model of 0.714 (95% CI, 0.676 to 0.751) or 0.726 (95% CI, 0.690 to 0.762) versus 0.703 (95% CI, 0.667 to 0.740), respectively. The improved overall net reclassification improvement index was 0.02 (95% CI, –0.04 to 0.08) and 0.05 (95% CI, –0.01 to 0.10), respectively. Although a FRAX category is a good clinical indicator for hip fracture risk, a simple classification by age and BMD may provide an alternative way to estimate a clinical risk level of 3.0%. © 2019 The Authors. JBMR Plus is published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.
Development of machine learning method of titanium alloy properties identification in additive technologies

Based on the experimentally established data on the parameters of microstructure, elemental and fractional composition of titanium alloy powders, four classes of their conformity (a material with excellent properties, optimal properties, possible defects in the material and defective material) as source raw materials for the additive technologies are identified. The basic characteristics of the material, which determine its belonging to a certain class, are established. Training and test samples based on 20 features that characterize each of the four classes of titanium alloy powders for the implementation of machine learning procedures were built. The developed method for identification of the class of material, based on the use of the second-order Kolmogorov-Gabor polynomial and the Random Forest algorithm, is described. An experimental comparison of the developed method work results with existing methods: Random Forest, Logistic Regression, and Support Vectors Machines based on the accuracy of their work in the training and application modes was made. The visualization of the results of all the investigated methods was given. The developed supervised learning method allows constructing models for processing a large number of each input vector characteristics. In this case, the Random Forest algorithm provides satisfactory generalization properties while retaining the advantages of an additional increase of the accuracy based on the Kolmogorov-Gabor polynomial. The main advantages of the developed method, in particular, regarding the additional increase of the accuracy of the classification task solution, are experimentally determined. The developed method allows increasing the modeling accuracy by 34.38, 33.34 and 3.13 % compared with the methods: Support Vectors Machine, Logistic Regression, and Random Forest respectively. The obtained results allow one to considerably reduce financial and time expenses during the manufacture of products by additive technologies methods. The use of artificial intelligence tools can reduce the complexity and energy efficiency of experiments to determine the optimum characteristics of powder materials.
Frequency Domain Based Analysis and Design of Norm-Optimal Iterative Learning Control

Frequency Domain Based Analysis and Design of Norm-Optimal Iterative Learning Control by Xinyi Ge Co-Chairs: Jeffrey L. Stein and Tulga Ersal In this thesis, novel frequency domain based analysis and design methods on NormOptimal Iterative Learning Control (NO-ILC) are developed for Single-Input-SingleOutput (SISO) Linear Time Invariant (LTI) systems. Modeling errors in general degrade the convergence performance of NO-ILC and hence ensuring Robust Monotonic Convergence (RMC) against model uncertainties is important. Although the robustness of NO-ILC has been studied in the literature, determining the allowable range of modeling errors for a given NO-ILC design is still an open research question. To fill this gap, a frequency domain analysis with a multiplicity formulation of model uncertainty is developed in this work to quantify and visualize the allowable modeling errors. Compared with the traditional formulation, the proposed new uncertainty formulation provides a less conservative representation of the allowable model uncertainty range by taking additional phase information into account and thus allows for a more complete evaluation of the robustness of NO-ILC. The analysis also clarifies how the RMC region changes as a function of NO-ILC weighting terms and therefore leads to several frequency domain design tools to achieve RMC for given model un-
Modeling observed and unobserved heterogeneity in choice experiments

Fast progress has recently been made in heterogeneity modeling in the choice experiment models. Especially scale heterogeneity has been given new explicit role and interpretation. This paper estimates scale heterogeneity extended stated preference models with special emphasis on including both observed and unobserved heterogeneity. The results show that the scale heterogeneity augmented models are not generally enough to consider both preference and scale heterogeneity. Instead the preference heterogeneity needs to be explicitly modeled also in the Generalized Multinomial Logit model.
Optimization of Mammary Tissue Displacement in Ultrasound Elastography

The displacement of mammary tissues in static ultrasound elastography is often contaminated by the speckle noise deteriorating its quality. Several techniques have been developed in this context, in order to treat the noise present in images of breast tissue displacement, the progress of research work in noise processing is always questioned, especially that must be taken into account the trade-off between noise reduction and preservation of breast tissue texture. In this paper, a new strategy has been proposed to reduce speckle noise. The proposed method not only filters the image against noise, but also preserves the details and contours of the tissue texture. The approach developed is based on the coupling between the image reconstructions by filtered back projection (RPF) with an adaptive filter. The proposed model proposed has been validated on an in-vivo database comprising 20 images of the breast tissues displacement. Qualitative and quantitative improvements were noted. By comparing the proposed method with the wavelet technique, we show that it is more efficient in terms of calculating the standard deviation between the pixels (SD), it is better in terms of calculation of the Contrast / Noise ratio (CNR). And is much faster than the wavelet technique. The results of the proposed model are encouraging, and the chosen method is ready to be used in the improvement of images of mammary tissue displacements in ultrasound elastography.
Clinical evaluation of smartphone-based fluorescence imaging for guidance and monitoring of ALA PDT

India has one of the highest rates of oral cancer incidence in the world, with an estimated 80,000 new cases per year, accounting for 30% of reported cancers. In rural areas, a lack of adequate medical infrastructure contributes to unchecked disease progression and dismal mortality rates. PDT emerges as a potential modality which can be implemented in resource limited settings, while photosensitizer fluorescence can be leveraged for treatment guidance. Here, as part of an ongoing clinical study evaluating low-cost technology for ALA PDT treatment, we evaluated the capability of a simple smartphone-based device for imaging ALA-induced PpIX fluorescence. The imaging device itself consists of an annulus of 405nm LEDs for PpIX excitation with emission filter in the center mounted over the phone camera. 18 subjects having <2 cm diameter (mean size; ~1.38 cm2) lesions with micro-invasive (≤5 mm. depth) moderately/well-differentiated squamous cell carcinoma were administered 60 mg/kg ALA in oral solution and imaged before and after delivery of 100 J/cm2 total light dose to the lesion surface. We will present comparative analysis of pre-and post-treatment fluorescence, white light, and ultrasound images. In general, PpIX fluorescence images obtained prior to therapeutic light delivery are able to resolve lesion margins while dramatic photobleaching in post-treatment images confirms the irradiated zone. Overall this approach is able to generate sufficient fluorescence contrast for treatment guidance and monitoring photobleaching while the use of a smartphone-based device provides a low-cost, widely available platform with potential for telemedicine integration.
The Impact of Mathematics Learning Model Implementation Based on APOS Theory (APOS Model) (A Case Study on Integral Calculus Learning)

This article aims to describe the impact of the implementation of Mathematics Learning Model based on APOS Theory (APOS Model), a Case Study on Integral Calculus Learning. APOS Model has been developed by using Plomp Design consisting of three phases, namely: 1) preliminary research, 2) prototyping phase, and 3) assessment phase. The model construction used Joyce and Weil model consisting of five components, namely: Syntax, Reaction System, Social System, Support System and Impact. The syntax of APOS Model consists of phases: Orientation, Practicum, Group Discussion, Class Discussion, Exercise and Evaluation. The impact of APOS Model implementation was investigated on assessment phase (effectiveness test) to the prototype of developed APOS Model. Data were collected by using instruments such as test sheet and questionnaire to observe the instructional impact and the nurturant impact. The collected data were analyzed by using descriptive statistic and inferential statistic. The analyzed data showed that the implementation of APOS Model is effective to increase the learning outcomes, increasing the activity and increasing the student’s motivation. This model is also effective to train the student’s ability on group cooperation and on communication in front of the class and having the positive responses of students. APOS Model can be one of the right choices on implementing Curriculum 13 which student centered.
Calibrating Dead Reckoning with Deep Reinforcement Learning

In general, as the moving distance increases, positioning accuracy degradation due to cumulative error is pointed out as a major problem in DR (Dead Reckoning). The distance-based positioning system has the advantage of high positioning accuracy depending on the sensor used. However, in environments where the use of infrastructure is restricted, such as security facilities or disaster environments, its utilization is limited, and Line of Sight (LOS) between devices must be guaranteed. To compensate for those limitations, we propose a framework for resetting the initial point of an object in DR. In order to reduce the position error that increases in proportion to the accumulated distance of DR, a method of resetting the starting point of DR to the position derived by distance-based trilateration was adopted. However, the process of resetting every moment when the trilateration-based position is derived can increase the system overhead and cause a positioning delay. Therefore, we derived the optimal starting point reset position and frequency by using Deep Reinforcement Learning (DRL). Through simulation, we verified that the proposed system improves the positioning accuracy compared to conventional DR through low resource consumption.