Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction, Communication, and Distribution of Breast Cancer Risk

Prediction, Communication, and Distribution of Breast Cancer Risk Matthew Patrick Banegas Chair of the Supervisory Committee: Professor Beti Thompson Department of Health Services Hispanic women represent the only racial/ethnic group in the United States (US) for which breast cancer is the most frequently diagnosed cancer and leading cause of cancer deaths. Despite the availability of tools to assess breast cancer risk, relatively little is known about the performance of current breast cancer risk prediction models and distribution of breast cancer risk in Hispanic women. Furthermore, there is limited evidence on the utility of decision aids designed to help women at high risk for breast cancer make informed choices about chemoprevention. To address these gaps, the overall goal of the proposed research work was threefold: 1) evaluate breast cancer risk projections for Hispanic women; 2) assess the distribution of breast cancer risk among Hispanic women; and 3) assess the impact of a webbased, tailored decision aid for women at high risk for breast cancer. We used data from two nationally representative datasets with information on US Hispanic women, and a randomized trial of women at high risk of breast cancer. Statistical analyses included multivariate linear, logistic, and Cox regression techniques. Findings from this work contribute valuable information on the use of estimates of breast cancer risk in research, clinical practice and public health. First, the National Cancer Institute’s Breast Cancer Risk Assessment Tool (BCRAT), the most widely used breast cancer risk prediction model, underestimates risk of developing invasive breast cancer in US Hispanic women. Second, the risk of developing invasive breast among Hispanic women, based on the BCRAT, is significantly lower compared to non-Hispanic white women, though breast cancer risk also significantly differs between certain Hispanic subgroups. Third, personalized decision aids may be effective tools to provide useful information about women’s risk of developing specific types of breast cancer, as well as to facilitate informed medical decisions, reduce patients’ decisional conflict, and empower patients to choose a treatment strategy that best reflects their own values. Together, these results improve our understanding of the prediction and distribution of breast cancer risk among US Hispanic women, and help identify ways in which breast cancer risk can be communicated to help inform women about their treatment options.
Global Self-Labeled Distribution Analysis for Hyperspectral Band Selection

A global self-labeled distribution analysis (GSLDA) for hyperspectral image (HSI) band selection is proposed in this paper, which focuses on an unsupervised method to ascertain the band discrimination. In order to generate the band labels for further analysis, the concept of the local minimum spanning forest (LMSF) is introduced into the construction of the global self-labeled band partitions based on graph theory. Meanwhile, the novel scoring strategy of triple-density indexes is applied to analyze the labeled-band distribution for determining the selected band subset with clear discrimination. The feasibility of the proposed method is evaluated on real hyperspectral data and the experiment results show a competitive good performance, which demonstrates that the selected bands hold apparent global discrimination and robust noise immunity.
The Accurate Mathematic Model Based on 2×2-Matrix for Anisotropic Photonic Crystals with Metamaterial Layers

The mathematic model of wave behavior within photonic crystal with anisotropic metamaterial layers based on 2×2 translation matrix is presented for the first time. The numerical calculations and result comparison with well-known 4×4 matrix methods are given also. It is proved also that the layer change theorem is valid for an anisotropic photonic crystal.
Power Quality Improvement of Grid Connected DFIG using Fuzzy Controller

Paper presents fuzzy based sensorless control strategy for grid connected doubly fed induction generator. In this system doubly fed induction generator connected with pulse width modulated voltage source converter to the rotor and stator is connected to utility grid. Rotor side voltage source converter connected with uncontrolled diode rectifier through dc link and battery storage system. A fuzzy based controller is used to improve the performance of rotor side controller. Comparatively reduced cost can be achieved by proposed control technique as a single voltage source converter and an uncontrolled diode rectifier is used in place of conventional back to back converter. Simulation studies were performed to analyse the performance of the proposed control technique. The result shows that the proposed strategy enhances the system stability, power factor and also improves the power quality with change in system parameters such as wind sped and connected load.
Olefinic reagents tested for peptide derivatization with switchable properties: stable upon Collision Induced Dissociation and cleavable by In-Source Paternò-Büchi Reactions.

This contribution is part of our ongoing efforts to develop innovative cross-linking (XL) reagents and protocols for facilitated peptide mixture analysis and efficient assignment of cross-linked peptide products. In this report we combine in-source Paternò-Büchi (PB) photo-chemistry with a tandem mass spectrometry approach to selectively address the fragmentation of a tailor-made cross linking reagent. The PB photochemistry, so far exclusively used for the identification of unsaturation sites in lipids and in lipidomics, is now introduced to the field of chemical cross-linking. Based on trans-3-hexenedioic acid, an olefinic homo bifunctional amine reactive XL reagent was designed and synthesized for this proof-of-principle study. Condensation products of the olefinic reagent with a set of exemplary peptides are used to test the feasibility of the concept. Benzophenone is photochemically reacted in the nano-electrospray ion source and forms oxetane PB reaction products. Subsequent CID-MS triggered retro-PB reaction of the respective isobaric oxetane molecular ions and delivers reliably and predictably two sets of characteristic fragment ions of the cross-linker. Based on these signature ion sets, a straightforward identification of covalently interconnected peptides in complex digests is proposed. Furthermore, CID-MSn experiments of the retro-PB reaction products delivers peptide backbone characteristic fragment ions. Additionally, the olefinic XL reagents exhibit a pronounced robustness upon CID-activation, without previous UV-excitation. These experiments document that a complete backbone fragmentation is possible, while the linker-moiety remains intact. This feature renders the new olefinic linkers switchable between a stable, non-cleavable cross-linking mode and an in-source PB cleavable mode.
Spectral Singularities in Non-Hermitian Cylindrical Geometries

Spectral singularities are zero-width resonances associated to real-frequency poles in the scattering operator, which can occur in non-Hermitian structures characterized by suitable spatial distributions of gain and loss. Here, we study this phenomenon in connection with cylindrical structures, and address its exploitation for the tailoring and control of the scattering response.
Using Optical and Thermal Data for Tracking Snowmelt Processes in Alpine Area

Alpine catchments represent a fundamental reservoir of fresh water at midlatitude. Remote sensing offers the opportunity to estimate snow properties in the optical, thermal and microwave domains. In particular, the possibility to estimate snow density from remote sensing is relevant and still represents a great challenge for the remote sensing scientific community. Since changes of snow density and liquid water content occur continuously in the snowpack, spatial and temporal patterns of optical and thermal data can give information about snowmelt processes. The main goal of this study is to evaluate if snow thermal inertia can be an indicator of snowmelt processes and to evaluate its relationship with snow variables, with particular attention to snow density. This study is a first attempt in exploiting thermal inertia for monitoring snow dynamics, and it may open new perspectives for early detection of snowmelt processes and snow parameters from remote sensing observations.
Evaluation of Regression Algorithms and Features on the Energy Disaggregation Task

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.
Evader Cooperative Capture by Multiple Pursuers with Area-Minimization Policy

In response to the incidents of drones interfering with normal flights in recent years, we propose a strategy for the cooperative pursuit of single evader using multiple pursuers. First, in the case where the evader’s position is known exactly, the tracking strategy based on the area-minimization policy is provided for a group of pursuers tracking a single evader. Then, the tracking strategy considering the evader’s position uncertainties is proposed. After that, simulation results are provided to show the effectiveness of the proposed strategies. Finally, we point out the work that needs to be done in the future.
Performance Analysis of Discontinuous Pulse Width Modulation Schemes on PUC-5 Inverter

Considerable research is being carried out on the effect of the choice of PWM scheme on inverter performance. Discontinuous PWM schemes, initially developed by “Depenbrock” in 1977, has become one of the most promising modern PWM methods. This modulation scheme is also known as “Depenbrock PWM” scheme. In this paper the discontinuous modulation schemes are applied on the recently introduced five level Packed U-Cell (PUC-5) inverter. The THD index, common mode voltage (CMV) and utilization of DC link voltage are analyzed as they are important parameters of a typical inverter. To control theses parameters and output voltage, PWM is a best option for it. A comprehensive analysis of these modulation schemes are shown on the basis of the output THD and common mode voltage applied for PUC-5 inverter. A single phase prototype model is also presented which is used for validation of the concept.
Retrieval of Crop Biophysical Parameters Using C-Band: Preparing for the Radarsat-Constellation

In preparation for Canada’s launch of the RADARSAT-Constellation, this study examines the use of VV-VH intensities to estimate the Leaf Area Index (LAI) of corn. LAI is indicative of crop productivity. Two implementations of the Water Cloud Model performed equally well in estimating corn LAI over sites in Poland and Canada with correlation coefficients over 0.8 and Root Mean Square Errors and Mean Average Errors of 0.72-0.73 m2m-2 and 0.47-0.54 m2m-2, respectively. This research will continue to pull in data from other international sites. If results remain robust, a strong case can be made to use an integration of Sentinel-1 and RCM for operational crop condition monitoring.
An impact assessment of climate and landuse change on water resources in the Han river.

As climate changes and abnormal climates have drawn research interest recently, many countries utilize the GCM, which is based on SRES suggested by IPCC, to obtain more accurate forecast for future climate changes. Especially, many research attempts have been made to simulate localized geographical characteristics by using RCM with the high resolution data globally. To evaluate the impacts of climate and landuse change on water resources in the Han-river basin, we carried out the procedure consisting of the CA-Markov Chain, the Multi-Regression equation using two independent variables of temperature and rainfall, the downscaling technique based on the RegCM3 RCM, and SLURP. From the CA-Markov Chain, the future landuse change is forecasted and the future NDVI is predicted by the Multi-Regression equation. Also, RegCM3 RCM 50 sets were generated by the downscaling technique based on the RegCM3 RCM provided by KMA. With them, 90 year runoff scenarios whose period is from 2001 to 2090 are simulated for the Han-river basin by SLURP. Finally, the 90-year simulated monthly runoffs are compared with the historical monthly runoffs for each dam in the basin. At Paldang dam, the runoffs in September show higher increase than the ones in August which is due to the change of rainfall pattern in future. Additionally, after exploring the impact of the climate change on the structure of water circulation, we find that water management will become more difficult by the changes in the water circulation factors such as precipitation, evaporation, transpiration, and runoff in the Han-river basin.
Thyroid-stimulating hormone is associated with nonalcoholic steatohepatitis in patients with chronic hepatitis B

Abstract The relationship of thyroid function parameters with nonalcoholic steatohepatitis (NASH) in patients with chronic hepatitis B (CHB) remains unknown. Hence, we assessed the impact of thyroid function parameters on NASH in patients with CHB. Consecutive patients with CHB with concurrent nonalcoholic fatty liver disease (NAFLD) were recruited. Liver histology and baseline examinations were carried out in each patient. The associated risk factors for NASH were evaluated. A total of 361 patients with CHB with biopsy-proven NAFLD were included. There was a significant difference in the serum thyroid-stimulating hormone (TSH) level between patients with NASH and non-NASH (3.24 ± 2.00 vs 2.05 ± 1.35 mIU/L, P < .01). Moreover, the NASH prevalence in patients with euthyroidism was significantly higher than in the subclinical hypothyroidism (SCH) patients (P < .001). In multivariate analyses, higher serum concentration of TSH was significantly correlated with NASH (odds ratio [OR]: 1.69, 95% confidence interval [CI]: 1.24–2.31; P = .001). In particular, patients suffering from SCH had a higher risk of having NASH (OR: 4.28, 95% CI: 1.18–15.53; P = .027). Elevated serum TSH level was the independent predictive factor of incident NASH in patients with CHB. Whether the thyroid function parameters should be integrated into future diagnostic scores predicting advanced diseases requires further study.
Backstepping Control with Online Parameter Estimator for Converter System

A backstepping control with online parameter estimator for parallel converter system is proposed in this paper. The proposed method is designed under dq0 coordinate system, the circulating current of 0-component can be suppressed by using an embedded algorithm and the extra controller is omitted. An online inductance and resistance estimator adopting an optimal algorithm by solving the weighted least squares (WLS) problem to further improve the control performance. The simulation results verify that this strategy shows satisfactory performance for circulating currents suppressing and voltage control over the condition of inaccurate parameters.
Corn Biomass Estimation Using Sentinel-2 and VENµS Data Based on A Simple Light Use Efficiency Method

In this study, Sentinel-2 and VENµS remote sensing data were combined to estimate corn biomass based on a simple light use efficiency method. The RMSE between the measured dry aboveground biomass and estimated dry aboveground biomass is 112.36 g/m2. The estimates show good agreements with the measured biomass, indicating that the effective light use efficiency (ELUE) can be effectively estimated using maximal fAPAR throughout the growing season.
Synergistic Use of Satellite Active and Passive Microwave Observations to Estimate Typhoon Intensity

Typhoon (TC) is one of the most powerful and destructive natural disasters. The analysis and determination of TC intensity is of great importance for disaster prevention [1] - [3] . Satellite remote sensing has become an effective means of monitoring TCs based on its high temporal and spatial resolution and large coverage. It is possible to estimate TC intensity using these satellite measurements when direct measurements are not available [4] . Microwave observations from polar-orbiting satellites can play a crucial role in revealing convective organization and eyewall structure that would otherwise be obscured by cloud tops [5] . Passive microwave sensors, such as SSM/I, TRMM/TMI, have been used to estimate TC intensity [6] - [10] . Besides, scatterometer also has allowed for continuous observation of ocean surface vector winds. Thus, scatterometer is a potential alternative for monitoring TCs. However, scatterometer measured wind speed range 2-24m/s, which make it very difficult to directly obtain the intensity of TCs [11] .
Steroids in the treatment of nonarteritic anterior ischemic optic neuropathy

Abstract Background: Non-arteritic anterior ischemic optic neuropathy (NAION) is the common cause of acute and subacute optic neuropathy in adults over the age of 50. Steroid administration in NAION seems to be in practice and is advised frequently by neurologists. The controversy regarding steroid usage in NAION is far from settled, with strong opinions on both sides. Despite a large amount of articles on this topic, but the results have not always been consistent. To address this gap, we decided to conduct a meta-analysis of all available published studies in order to better understand the effectiveness of steroids in treating NAION. Objectives: To identify the effectiveness of steroids in treating NAION. Methods: We performed a meta-analysis using databases, including PUBMED EMBASE, and the Cochrane library, to find relevant studies. The weighted mean difference (WMD) was determined for BCVA in steroid and nonsteroid groups. Results: Eight studies were included and summarized in this analysis. The studies included 720 eyes (392 NAION eyes and 328 eyes of normal controls). Heterogeneity among these studies was low (I2 = 0%). Because of the presence of heterogeneity, we conducted a fixed effects model to assess the effect of steroids on visual acuity in patients with NAION. The meta-analysis clearly demonstrated that in NAION, steroids did not significantly improve visual acuity (WMD = −0.02 [95% CI: −0.10 to 0.06], Z = 0.40, P = .69). After sensitivity analysis via the leave-one-out method, WMD was not significantly changed. Conclusions: Our meta-analysis found that steroids do not significantly improve visual acuity in NAION. In view of their long list of side effects, attempts at reversing ischemia should not involve the use of steroids.
A novel molecular representation with BiGRU neural networks for learning atom

Molecular representations play critical roles in researching drug design and properties, and effective methods are beneficial to assisting in the calculation of molecules and solving related problem in drug discovery. In previous years, most of the traditional molecular representations are based on hand-crafted features and rely heavily on biological experimentations, which are often costly and time consuming. However, recent researches achieve promising results using machine learning on various domains. In this article, we present a novel method named Smi2Vec-BiGRU that is designed for learning atoms and solving the single- and multitask binary classification problems in the field of drug discovery, which are the basic and also key problems in this field. Specifically, our approach transforms the molecule data in the SMILES format into a set of sample vectors and then feeds them into the bidirectional gated recurrent unit neural networks for training, which learns low-dimensional vector representations for molecular drug. We conduct extensive experiments on several widely used benchmarks including Tox21, SIDER and ClinTox. The experimental results show that our approach can achieve state-of-the-art performance on these benchmarking datasets, demonstrating the feasibility and competitiveness of our proposed approach.
Internal Model Controller Design for Boost Converter by Stochastic Optimisation

As the power electronics devices found in many constant voltage applications, control of these devices is essential for our day to day life. Of them the Boost converter control is a challenging task as it exhibits non-minimum phase behaviour when operated in continuous conduction mode (CCM). This paper presents a comparative analysis of controller design for Boost Converter with optimised Internal Model Control. The Internal Model Control only has single design variable, i.e., filter time constant $(\lambda)$. The filter time constant is optimised with the help of the stochastic optimisation methods i.e., Genetic Algorithm and Particle Swarm Optimisation and performance of the both controllers are compared in terms of the transient performance parameters and Integral Square Error.
The Lived Experiences, Perceptions, and Considerations of Patients After Operable Lung Cancer Concerning Nonparticipation in a Randomized Clinical Rehabilitation Trial

The purpose of this study was to explore the lived experiences, perceptions, and considerations of individuals who declined participation in a randomized clinical trial involving exercise rehabilitation after surgery for lung cancer. An interpretive phenomenological approach was applied comprising interviews with 15 individuals who did not wish to participate in the trial. The findings shed light on a discrepancy between their freedom to act and make decisions and the limitations of having to act in a certain way. The participants found themselves in a gray area between a healthy life and a good life, as influenced by societal norms and taking responsibility for one’s own health and rehabilitation. When including patients in rehabilitation after lung cancer, having insight into the underlying narrative on values and the good life, priorities in daily life, social context and the norms embedded in people’s self-understanding is crucial.
Multi-Input Power Converter for Renewable Energy Sources using Active Current Sharing Schemes

In this paper, a multi-input power converter (MIPC) dedicated for renewable energy system (RES) using an active current sharing scheme is proposed. The designed power converter consists of three inputs from RES which are photovoltaic and fuel cells. It is known that the RES has a varying and intermittent characteristic in its output. Hence, the control scheme is required to stabilize the regulated output. This paper mainly focuses on designing an MIPC which employs an averaged current sharing mode as its active current sharing error signalling processing control strategy. A conventional control for the same MIPC converter using pulse width generator were also simulated for comparison purposes. The MIPC with three varying input voltage was design to produce 400 V DC. The simulation of both MIPC under different input voltage condition was carried out. The results show that the proposed scheme was able to regulate and provide the desired output voltage to a certain input voltage variation as opposed to the fixed control.