Miracle-dz/UniFAR-FTUs huggingface dataset:
Heart Failure Prediction Using Machine Learning Algorithm

According to WHO around 17.9 million Cardiovascular diseases (CVDs) are the principal effect of mortality everywhere, asserting the lifeline of an estimated 17.9 million humans every year, it is a guesstimated 32 % of worldwide. With the rising population, heart disease is becoming increasingly difficult to identify and treat at an early stage. With the help of machine learning (ML) advancement in the healthcare sector, we can build models which can predict heart diseases. The outcome of this paper is to create a model that will successfully predict whether a person has cardiovascular disease or not, based on these 11 clinical features. We have used the UCI heart failure prediction dataset for this research work. We have used five different machine learning (ML) algorithms in this paper Gradient Boosting(GB), Random Forest(RF), K-nearest neighbors (KNN), Logistic Regression(LR) and Support Vector Machine(SVM) has been used for the development of the model. After a comparison of all the above machine learning techniques found that SVM is the best suitable technique with 94.56 % efficiency in predicting whether or not a human has a cardiovascular ailment or not. In the implementation portion of the research, there is also a comparison explanation of all the algorithms. This effective prediction algorithm will also assist doctors in minimizing the amount of heart disease-related fatalities.
Increasing Transfer Rate of Sunlight Communication System Using 2×2 Switchable CCR Array

The transfer rate of a sunlight communication system is improved in this study. The transmitter end includes a 2×2 array of switchable corner cube retroreflectors (CCRs) to increase the transfer rate by at least four times. The fabrication of each CCR involves 3D printing and the making of a PDLC (polymer dispersed liquid crystal) switchable module. The images captured by a high-speed camera at the receiving end are analyzed to recover the data. The transmission distance is currently 72 m, a significant improvement compared to our earlier work (45 m). The transfer rate increases from 7 bits/s to 32 bits/s.
Robot-assisted Psycho-education to Enhance Alzheimer’s Caregiver Health

Approximately 6.5 million older adults are living with Alzheimer’s disease and related dementia (ADRD) in the United States. In 2021, more than 11 million Americans provided care for people with ADRD as informal caregivers. Dementia caregiving is exhausting and time-intensive which can lead to increased risk for poor mental and physical health. To address caregiver burden, we developed a robot-assisted psycho-educational program using a humanoid social robot for dementia caregivers. The intervention had 3 aims: to help caregivers prepare themselves for their roles in their care-giving journey, to teach caregivers techniques to mitigate stress and challenges of caregiving, and to prevent poor mental and physical health outcomes. A usability study was conducted with 9 caregivers to evaluate user experience and satisfaction with our program. Participants reported a positive user experience with the program and believed it would be useful to other dementia caregivers. The results demonstrated the feasibility of our robot-assisted psycho-educational intervention to support and benefit caregivers. Several programmatic limitations were identified which, once addressed, will allow for a wider application of the program among dementia caregivers.
Cluster Affiliation Model for Local Community Detection

Most community detection studies utilize the entire network to mine all communities. However, in practice, the whole network is often expensive to get or even unavailable. Besides, users may only concern the community containing the node they are interested in. To this end, local community detection emerges. Existing approaches based on Non-negative Matrix Factorization need to set parameters to extract subgraphs around the given node and detect overlapping communities rather than detecting one community for a given node. Therefore, in this paper, a novel parameter-free method, called LCAM (Cluster Affiliation Model for Local community detection), is proposed. It fits the edges in the network to obtain the affiliation of nodes and uses affiliation of nodes to gradually expand outward from the starting node to get the local community, instead of extracting a subgraph from networks before detecting local communities. Four real-world and five synthetic networks are adopted to test LCAM and experimental results demonstrate that LCAM yields better community structure than comparison algorithms.
COBLOC: A hierarchical method for blocking network data

A method is described for finding blockmodels of a given network using an agglomerative hierarchical clustering technique with optional backtrack capabilities. The clustering criterion is an index of the goodness of fit of the blockmodel that would be produced by a given clustering. This index is computed for each clustering considered. This method is compared on a priori grounds with other methods and comparative results are reported for its application to the Western Electric Bank Wiring Room data. Extensions combining this method with other clustering methods are discussed.
A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics

Although the atmospheric sciences community has been studying the effects of atmospheric stability and surface roughness on the planetary boundary layer for some time, their effects on wind turbine dynamics have not been well studied. In this study, we performed numerical experiments to explore some of the effects of atmospheric stability and surface roughness on wind turbine dynamics. We used large-eddy simulation to create atmospheric winds and compute the wind turbine flows, and we modeled the wind turbines as revolving and flexible actuator lines coupled to a wind turbine structural and system dynamic model. We examined the structural moments about the wind turbine blade, low-speed shaft, and nacelle; power production; and wake evolution when large 5-MW turbines are subjected to winds generated from low- and high-surface roughness levels representative of offshore and onshore conditions, respectively, and also neutral and unstable atmospheric conditions. In addition, we placed a second turbine 7 rotor.
Finite-Time Control Design for Multibody High-Speed Train Dynamic Models

This paper considers the finite-time output tracking control for a multibody high-speed train (HST) dynamic model. A four-car train unit model with aerodynamic resistance force is selected as a benchmark model, in which the speeds of the first and third cars are regarded as output signals, and only the second and third cars have input signals. The nonlinear system can be decompose into a zero dynamics subsystem and a control dynamics subsystem using the feedback linearization method. Finite-time controllers are designed to achieve the finite-time convergence of the output tracking error. Finally, a numerical example is given for illustration.
Citrus reticulata peel extract: an Anti-obesity therapy for Adolescents

The potential benefits of the water extract of Citrus reticulate peel were assessed on obesity and fat reduction in adolescent. Clinical trials were done on double-blind, placebo- controlled study, 40 obese adolescent subjects/each group randomized from both sex with age vary between 12 and 18 years old. Group A participants received an 800 mg of dry extract daily and group B received placebo. Both groups received three meals (2000 kcal/day) throughout the study. The primary outcome in group A was reduction in Body mass index (BMI), Body fat percentage (BF %) and in waist circumference (WC). Secondary outcomes were significant reduction in BMI, BF% and WC, as well as better lipid profile criteria. Statistically high significant reductions in BMI by 5.74 kg/m 2 (P<0.001), WC by 11.33 cm (P <0.001), body fat by 4.24 % (P = 0.006), total cholesterol (TC) by 35.56 mg/dl (P = 0.008) and triglyceride (TG) by 24.66 mg/dl (P <0.001) were observed in the group A compared to the group B as study conclusion. Many of the beneficial effects in management of obesity attributed to the presence of polyphenols and flavonoids constituents of the extract.
Food Insecurity and Depressive Symptoms Among Adolescents: Does Federal Nutrition Assistance Act as a Buffer?

ABSTRACT: Objective: Food insecurity has been associated with a variety of adverse outcomes in adolescents. However, further research is needed to elucidate whether adolescent self-report food insecurity contributes to depressive symptoms. This study examined the (1) association between food insecurity and depressive symptoms and (2) moderating role of federal nutrition assistance in a nonclinical sample of adolescents. Method: Adolescents aged 12 to 17 years (N = 141) and one parent/guardian completed self-report questionnaires as part of 2 larger studies examining adolescent stress and eating behaviors. Adolescents (M age = 13.79 years, SD = 1.60, 53.9% female) completed measures of food insecurity and depressive symptoms. Results: A bootstrapped linear regression model demonstrated that adolescent food insecurity was significantly, positively associated with depressive symptoms, 95% confidence interval (CI) (0.19–1.64), after controlling for biological sex. Among dyads eligible for federal nutrition assistance (N = 64), the interaction between adolescent food insecurity and National School Lunch Program (NSLP) participation did not reach significance, ΔF(1, 59) = 1.06, 95% CI (−3.80 to 9.29). The interaction between adolescent food insecurity and Supplemental Nutrition Assistance Program (SNAP) participation trended toward significance, ΔF(1, 59) = 2.88, 95% CI (−5.73 to 0.47). Conclusion: Adolescents with food insecurity may be at a greater risk for experiencing elevated depressive symptoms. Whereas NSLP participation did not act as a buffer, SNAP participation may attenuate the association between food insecurity and depressive symptoms. Larger longitudinal studies are needed to develop a greater understanding of factors that may alter the relationship between adolescent food insecurity and depressive symptoms.
Virtual reality with distractors to overcome public speaking anxiety in university students

The ability to communicate effectively is a crucial aspect of education. For college students, learning how to speak in public is essential for their academic and professional future. However, many students report fear of speaking in public, the so-called Public Speaking Anxiety (PSA). This study aims to implement a training program using Virtual Reality (VR) with distractors to reduce the college students’ anxiety. Anxiety was measured with two methods: electrodermal activity and self-report. We also analyze gender differences. There were an experimental and a control group. Both groups had to deliver the same speech twice: pre-test (before training); and post-test (after the training program) while participants’ electrodermal activity was measured. Only the experimental group was trained with VR. Students also completed the Public Speaking Anxiety Scale and a survey to examine their experience. The results showed that the VR training reduced the anxiety levels significantly in the experimental group, but there were no significant differences in the control group. The data also revealed a higher level of anxiety in male than in female students. Finally, participants reported a positive impression of the VR training. These results showed the effectiveness of Virtual Reality software with distractors to reduce public speaking anxiety.
Temperature dependence of the samarium magnetic form factor in SmCo5

Results of polarized neutron experiments on SmCo5 at 4.2 K and 300 K are reported. To overcome the problem of the huge Sm absorption the measurements were made at short wavelengths, but on a single crystal containing natural samarium. The Co moments are close to those found in YCo5. The Sm form factor is very different at 4.2 K and 300 K, giving direct evidence for exchange and crystal field mixing of excited multiplets into the ground multiplet. The Sm moment is smaller at 300 K than at 4.2 K and its orbital character is less pronounced. The cross‐over is calculated to occur at 350 K above which temperature the spin contribution exceeds the orbital one. The compound is ferromagnetic below the cross‐over and ferrimagnetic above. The exchange and crystal field parameters deduced from our results are in agreement with previous determinations [1].
Battling Arrow’s Paradox to Discover Robust Water Management Alternatives

AbstractThis study demonstrates how Arrow’s Impossibility Theorem, a theory of social choice, is of direct concern when formulating water-resources systems planning problems. Traditional strategies for solving multiobjective water resources problems typically aggregate multiple performance measures into single composite objectives (e.g., a priori preference weighting or grouping-like measures by category). Arrow’s Impossibility Theorem, commonly referred to as Arrow’s Paradox, implies that a subset of performance concerns will inadvertently dictate the properties of the optimized design alternative in unpredictable ways when using aggregated objectives. This study shows how many-objective planning can aid in battling Arrow’s Paradox. Many-objective planning explicitly disaggregates measures of performance while supporting the discovery of planning tradeoffs, using tools such as multiobjective evolutionary algorithms (MOEAs). An urban water portfolio planning case study for the Lower Rio Grande Valley, Tex.
Quantum Simulation of Interacting Spin Models with Trapped Ions

Title of dissertation: QUANTUM SIMULATION OF INTERACTING SPIN MODELS WITH TRAPPED IONS Kazi Rajibul Islam, Doctor of Philosophy, 2012 Dissertation directed by: Professor Christopher Monroe Joint Quantum Institute, University of Maryland Department of Physics and National Institute of Standards and Technology The quantum simulation of complex many body systems holds promise for understanding the origin of emergent properties of strongly correlated systems, such as high-Tc superconductors and spin liquids. Cold atomic systems provide an almost ideal platform for quantum simulation due to their excellent quantum coherence, initialization and readout properties, and their ability to support several forms of interactions. In this thesis, I present experiments on the quantum simulation of long range Ising models in the presence of transverse magnetic fields with a chain of up to sixteen ultracold 171Yb ions trapped in a linear radiofrequency Paul trap. Two hyperfine levels in each of the 171Yb ions serve as the spin-1/2 systems. We detect the spin states of the individual ions by observing state-dependent fluorescence with single site resolution, and can directly measure any possible spin correlation function. The spin-spin interactions are engineered by applying dipole forces from precisely tuned lasers whose beatnotes induce stimulated Raman transitions that couple virtually to collective phonon modes of the ion motion. The Ising couplings are controlled, both in sign and strength with respect to the effective transverse field, and adiabatically manipulated to study various aspects of this spin model, such as the emergence of a quantum phase transition in the ground state and spin frustration due to competing antiferromagnetic interactions. Spin frustration often gives rise to a massive degeneracy in the ground state, which can lead to entanglement in the spin system. We detect and characterize this frustration induced entanglement in a system of three spins, demonstrating the first direct experimental connection between frustration and entanglement. With larger numbers of spins we also vary the range of the antiferromagnetic couplings through appropriate laser tunings and observe that longer range interactions reduce the excitation energy and thereby frustrate the ground state order. This system can potentially be scaled up to study a wide range of fully connected spin networks with a few dozens of spins, where the underlying theory becomes intractable on a classical computer. Quantum Simulation of Interacting Spin Models with Trapped Ions
Quantum dots: using the known as well as exploring the unknown

Super-resolution microscopy, the imaging of features below the Abbe diffraction limit, has been achieved by a number of methods in recent years. Each of these methods relies on breaking one of the assumptions made in the derivation of the diffraction limit. While uniform spatial illumination, linearity and time independence have been the most common cornerstones of the Abbe limit broken in super-resolution modalities, breaking the ‘classicality of light’ assumption as a pathway to achieve super-resolution has not been shown. Here we demonstrate a method that utilizes the antibunching characteristic of light emitted by Quantum Dots (QDs), a purely quantum feature of light, to obtain imaging beyond the diffraction limit. Measuring such high order correlations in the emission of a single QD necessitates stability at saturation conditions while avoiding damage and enhanced blinking. This ability was facilitated through new understandings that arisen from exploring the QD ‘blinking’ phenomena. We summarize here two studies that contributed to our current understanding of QD stability.
POWER OF THE NEURAL NETWORK LINEARITY TEST

Recently, a new linearity test for time series was introduced based on concepts from the theory of neural networks. Lee et al. have already studied the power properties of this test and they are further investigated here. They are compared by simulation with those of a Lagrange multiplier (LM) type test that we derive from the same single‐hidden‐layer neural network model. The auxiliary regression of our LM type test is a simple cubic ‘dual’ of the Volterra expansion of the original series, and the power of the test appears superior overall to that of the other test.
A Darboux-type Theory of Integrability for Discrete Dynamical Systems

There is a method for searching first integrals for polynomial ordinary differential equations (usually called Darboux method) based on the knowledge of several of their invariant algebraic hypersurfaces. We extend this method to discrete dynamical systems, providing a way of searching invariants for them and we give several examples of application.
Size-controlled short nanobells: Growth and formation mechanism

Very short nanofibers containing only a few nanobells have been produced from nitrogen-doped carbon nanotubes by two methods, H2 plasma treatment, followed by grinding and oxidation with acidified potassium permanganate. The length of these short nanofibers can be controlled to give a wide range of sizes. The shortest nanofibers are composed of only a single nanobell with dimensions of ∼15 nm in diameter and length. High-resolution transmission electron microscope images reveal that their structures are closed at one end and open at the other. The formation mechanism of these short nanofibers and individual nanobells is discussed along with their potential for interesting nanotube-end physics and chemistry.
Hartree-Fock studies of surface properties of BaTiO 3

We study several surface properties of ${\mathrm{BaTiO}}_{3}$: namely, the surface charge, surface energy, and surface dynamical charges, within an ab initio Hartree-Fock scheme. We use both genuinely isolated slabs and periodically repeated slabs, with different terminations. We report these properties as functions of the thickness and termination of the slabs. The ability to deal with genuinely isolated slabs is a virtue of the localized-basis implementation that we adopt. In particular, the slab surface charge provides a value for the spontaneous polarization in excellent agreement with the calculation of the latter as a Berry phase, and with experiment as well. On the other hand, we find that when periodically repeated slabs are used, the interactions among slabs and the fictitious field imposed by the periodic boundary conditions can significantly affect the accuracy of the calculations, even at quite large vacuum separations.
Objective Cross-Section Analyses by Hermite Polynomial Interpolation on Isentropic Surfaces

Abstract An objective cross-section analysis scheme based upon Hermite Polynomial interpolation on surfaces of constant potential temperature is shown to define frontal-scale horizontal and vertical gradients of potential temperature and geostrophic wind using synoptic upper air sounding observations as input data. The objective routine was also found applicable to analysis of the thermal properties of oceanic frontal zones from bathythermograph sounding data. Calculations of potential vorticity, vertical wind shear, and Richardson number suggest the usefulness of the objective scheme for real-time specification of regions of stratospheric-tropospheric mass exchange and probable locations of jet-stream, frontal-zone-related, clear-air turbulence.
Surface critical behaviour and local operators with boundary-induced critical profiles

The authors present a simple argument showing that the surface energy density of a semi-infinite d-dimensional spin system has, in general, a leading thermal singularity of the same form, mod T-Tc mod 2- alpha , as the bulk free energy. At bulk criticality energy-energy correlations decay as r-2d parallel to the surface. These results hold for both free and fixed boundary spins, i.e. the 'ordinary' and 'extraordinary' transitions. The extraordinary critical behaviour of the magnetisation is the same as that of the energy density. They also confirm and generalise these results in two dimensions with an independent approach based on conformal invariance.
The outer atmospheres of the ‘hybrid’ bright giants: the chromospheres of α TrA (K4 II), ι AUR (K3 II), γ Aql (K3 II) and θ Her (K1 II)

Detailed models of the chromospheres of the «hybrid» bright giants α TrA (K4 II), ι Aur (K3 II), γ Aql (K3 II) and θ Her (K1 II) are presented. The models run from the base of the chromosphere and into the transition region. The chromospheric models are based on radiative transfer calculations for a range of atoms and emission lines, including : Mg II h and k; Al II] λ2669 A; C II λ1335 A; C II] λ2325 A; Si II λλ1808. 0, 1816.9 and 1817.5 A. The transition region models are made using standard emission measure techniques. The presence of steep turbulent velocity gradients leads to profiles of optically thin chromospheric emission lines that are strongly dependent on the distribution of emitting material with temperature
Nonparametric Mean Estimation with Missing Data

Abstract Missing data occur in most applied statistical analysis. The need to estimate the conditional or unconditional mean of a variable when some of its observations are missing is very frequent. In this article we study the effect of missing observations on the response variable in the estimation of a multivariate regression function. This effect is also considered in the estimation of the marginal mean. Following the research of Chu and Cheng [Chu, C. K. , Cheng, P. E. (1995). Nonparametric regression estimation with missing data. J. Statist. Planning Inference 48:85–99] for the univariate case, we propose three non-parametric estimators of the regression function based on the Multivariate Local Linear Smoother [see Ruppert, D. , Wand, M. P. (1994). Multivariate locally weighted least squares regression. Ann. Statist. 22(3):1346–1370]. The first consists of using only complete observations; the other two use simple or multiple imputation techniques respectively to complete the sample. The behavior in function of the Asymptotic Mean Squared Error (AMSE) is studied for the estimators. A method for obtaining optimal estimated bandwidth matrices based on the Bootstrap resampling mechanism is proposed.