Miracle-dz/UniFAR-FTUs huggingface dataset:
National disparities in colorectal cancer screening in patients with comorbid conditions: an analysis of the Behavioral Risk Factor Surveillance System

Abstract Context Each year, approximately 53,200 people die in the U.S. from colorectal cancer (CRC), indicating a need to increase screening efforts. Some studies have suggested mammography use is higher in patients with comorbid conditions, a reflection of increased follow up. Another study found that patients with obesity were less likely to be screened for CRC than nonobese patients. However, no study has assessed the impact of multiple comorbidities on CRC screening. Objectives To analyze CRC screening rates in patients with comorbidities compared with healthy patients, and to assess whether the number of comorbid diagnoses impacted screening rates. Methods A cross sectional analysis of patients who received CRC screening was performed using the 2018 and 2019 Behavioral Risk Factor Surveillance System (BRFSS). Respondents were classified as having had CRC screening if they answered “yes” to either of the following: “Have you ever had a blood stool test? ” or “Have you ever had a sigmoidoscopy/colonoscopy? ” Respondents younger than age 50 were excluded. A weighted multivariate logistic regression model was constructed to determine adjusted risk ratios (ARR). Confidence intervals (CI) were reported at 95%. Results We identified 279,784 respondents who met inclusion criteria. Of those, 79.7% (sample n=222,879; population N=46,304,360) of respondents had received CRC screening. Patients with diabetes, hypertension, skin cancer, chronic obstructive pulmonary disease (COPD), arthritis, depression, and chronic kidney disease were significantly more likely to be screened than those without comorbidities. There was no statistically significant difference in screening rates between patients with and without cardiovascular disease. Compared with patients with zero comorbidities, those with one were significantly more likely to receive screening (ARR, 1.11; CI, 1.09–1.12) as were those with two to four (ARR, 1.2; CI, 1.18–1.22). Patients with five or more comorbidities were significantly less likely to be screened than those with two to four (ARR, 1.12; CI, 1.1–1.14). Conclusions Patients with one or more comorbidities were more likely to be screened than those without comorbidities, but those with five or more conditions were less likely to be screened than patients with two to four conditions. This indicates that physicians may be more fatigued and less likely to recommend CRC screening to patients with many comorbidities compared with patients diagnosed with only a few conditions. The results of this study add to the literature by identifying an interaction between the number of comorbidities and likelihood of being screened for CRC.
Update and clinical management of anti-DNA auto-antibodies

Abstract Anti-deoxyribonucleic acid (DNA) antibodies in the clinical laboratory are intimately linked to the diagnosis and monitoring of systemic lupus erythematosus (SLE); however, the characteristics of the analytical methods and the properties of the antibodies themselves are heterogeneous. To review the definition and properties of anti-double-stranded anti-DNA (anti-dsDNA) antibodies, the adequacy of analytical methods, and the clinical requirements for this biomarker. Through PubMed we searched the existing literature with the terms anti-dsDNA, editorial, review, guideline, meta-analysis and SLE. The last search, anti-dsDNA and SLE restricted to the last two years. Information was expanded through related articles and those published in official state bodies related to anti-dsDNA and SLE. Clinical laboratory methods for anti-dsDNA analysis and their characteristics are analyze. The clinical utility of anti-dsDNA in its diagnostic, clinical association and follow-up aspects of SLE is reviewed. There is wide variability in analytical methods and deficits in standardization persist. They are part of the current SLE classification criteria and are used as markers in the follow-up of the disease. Their diagnostic usefulness improves when they are determined in antinuclear antibody (ANA)-positive patients. In follow-up, quantification is of interest, preferably with the same analytical method (given the deficits in standardization).
Compact smile-like mode converter antenna with high power capacity level

ABSTRACT This paper investigates the design, simulation, fabrication, and measurement of a compact mode converter antenna (MCA). The smile-like MCA (SMCA), at 12 GHz frequency, is designed. Due to the increase in the antenna cross-section, the probability of the electrical breakdown is dramatically reduced. Electrical breakdown may occur at very high power outputs of the electromagnetic sources. The power capacity level of the SMCA is 648 MW. Also, the length of the antenna is 0.47 λ. Simulation results indicate 99% mode conversion efficiencies. The return loss is −22 dB, the gain is more than 19 dB, and the sidelobe level is less than −37 dB. Also, the bandwidth is 200 MHz, for both E and H planes. The maximum main lobe deviation from the bore-sight is less than 0.2°. The simulation results are in good agreement with the measurement results.
Virtual identity crisis: The phenomenology of Lockean selfhood in the “Age of Disruption”

ABSTRACT From the end of the seventeenth century to now well into the 21st, John Locke’s theory of personal identity has been foundational in the field of philosophy and psychology. Here we suggest that there are two fundamental threads intertwined in Lockean identity, the flux of perception-thought-action (i.e. continuity of consciousness) and memory. Using Maurice Merleau-Ponty, Paul Ricoeur, and Bernard Steigler as guides we will see that these threads constitute a phenomenological self (l’ésprit), a lived experience of our identity that is not only perhaps the most essential component of our humanity, but also the most threatened in today’s ongoing, commercial convergence of the real and the virtual in the “Age of Disruption”.
Utilizing Quantitative in Situ FTIR Spectroscopy To Identify Well-Coordinated Pt Atoms as the Active Site for CO Oxidation on Al2O3-Supported Pt Catalysts

Relationships between geometric structures of active metallic sites and areal rates of reaction (structure sensitivity) are extensively studied for supported metal catalysts. For CO oxidation on irreducible oxide-supported Pt catalysts, there still exists a discrepancy regarding structure sensitivity. Theoretical and single-crystal analyses suggest the CO oxidation reaction rate should be highly structure sensitive, whereas measurements on supported Pt catalysts show only minimal structure sensitivity. Here, we used quantitative in situ diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) to investigate the influence of CO oxidation reaction conditions on the fraction of well-coordinated (WC) and under-coordinated (UC) Pt active sites on a series of four α-Al2O3-supported Pt catalysts with average Pt sizes ranging from ∼1.4 to 19 nm. Pt nanoparticle surfaces were observed to restructure under CO oxidation reaction conditions, increasing the fraction of UC Pt sites. Reconstruction rendered .
Architecting an Enterprise Data Lake, A Covid19 Case Study

Data is increasing at an enormous rate every day. Traditionally data has resided in silosacross any organization,so it’s difficult to have a complete picture for data driven business decision making. Data lake addresses the problem of rate of increase of data by providing “schema on read”, better integration and cheaper storage. It also solves the data silos problemby providing a central platform for a variety of data housing needs. However, implementing a data lake becomes challenging as the implementation needs to address the additional needs like metadata management, data discovery, data governance, data lifecycle management, security and centralized access controls mechanisms. This paper intends to provide a comprehensive architecture of data lake to address these challenges. We have also conducted and documented our experiments with publicly available datasets about COVID19 to validate the design and applicability of the proposed architecture for business analytics purposes.
Objective Gender and Age Recognition from Speech Sentences

In this work, an automatic gender and age recognizer from speech is investigated. The relevant features to gender recognition are selected from the first four formant frequencies and twelve MFCCs and feed the SVM classifier. While the relevant features to age has been used with k-NN classifier for the age recognizer model, using MATLAB as a simulation tool. A special selection of robust features is used in this work to improve the results of the gender and age classifiers based on the frequency range that the feature represents. The gender and age classification algorithms are evaluated using 114 (clean and noisy) speech samples uttered in Kurdish language. The model of two classes (adult males and adult females) gender recognition, reached 96% recognition accuracy. While for three categories classification (adult males, adult females, and children), the model achieved 94% recognition accuracy. For the age recognition model, seven groups according to their ages are categorized. The model performance after selecting the relevant features to age achieved 75.3%. For further improvement a de-noising technique is used with the noisy speech signals, followed by selecting the proper features that are affected by the de-noising process and result in 81.44% recognition accuracy.
Modeling and Forecasting Dynamic Factors of Pricing in E-commerce

The rapid development of information technologies contribute to the search for various methods to increase the e-commerce company's profits. Big Data technologies are conducive to developing varieties of personalized pricing strategies, in particular dynamic pricing meaning a process of setting and developing prices for goods and services, where prices change synchronously for all consumers depending on demand under current market conditions. The implementation of dynamic pricing approaches is directly related to the use of appropriate software capable of performing data mining. The important tasks are to study the successful experience of powerful e-commerce platforms in relation to the tools that they used for analysis, and to expand the toolkit of methods to support personalized pricing strategies with methods working with Big Data. The purpose of this research is to study the experience of modeling dynamic pricing and the proposal to involve the PLS regression both to confirm the importance of dynamic factors and to predict prices in the implementation of this strategy of personalized pricing in e-commerce. The approaches to pricing in e-commerce are studied in the research. The features of different types of personalized pricing (dynamic, customized, transactional) are identified too. The approaches to modeling of dynamic pricing are explored. Applying PLS regression for predicting the impact of dynamic factors on price is proposed. The study places special emphasis on identifying the dynamic factors that have become increasingly important in recent years. Results of modeling confirm that linear regression is not able to identify hidden predictors among a large number of collinear factors (dynamic factors are often hidden and collinear) whereas PLS regression does. The proposed approach is implemented by using data of the vacation rental e-platform Airbnb.
Brassinosteroid (BR) and arbuscular mycorrhizal (AM) fungi alleviate salinity in wheat

ABSTRACT This study was conducted to assess the impacts of brassinosteroide (BR), arbuscular mycorrhizal (AM) fungi, Glomus mosseae and their interactions on salt stress tolerance in Triticum aestivum L. After foliar spraying of mycorrhizal and non-mycorrhizal plants by 5 µM epibrassinolide, they were subjected to 0 and 150 mM sodium chloride (NaCl) for 2 weeks. The experiment was conducted in a randomized complete block design, replicated 4 times. Our results showed a probable potential of BR and/or AM fungi in improving salt tolerance of plants. Total phenol and proline content increased in BR and/ or AM treatments. AM fungi promoted plant growth, including leaf area, shoot and root dry weights, and lengths under saline condition. Moreover, BR improved growth parameters except root dry weights and lengths. This study indicated that BR and/or AM fungi may contribute to improve salt tolerance of the plant.
Efficient Inter-Device Task Scheduling Schemes for Multi-Device Co-Processing of Data-Parallel Kernels on Heterogeneous Systems

Heterogeneous systems consisting of multiple multi-core CPUs and many-core accelerators have recently come into wide use, and more and more parallel applications are developed in such a heterogeneous system. To fully utilize multiple compute devices to cooperatively and concurrently execute data-parallel kernels on heterogeneous systems, a feedback-based dynamic and elastic task scheduling scheme is proposed, which can provide a better load balance, a greater device utilization, and a lower scheduling overhead by flexibly and dynamically adjusting the workload between devices during execution. The proposed method is more suitable for data-parallel kernels whose computation and data are uniformly distributed, but is less suitable for data-parallel kernels whose computation and data are non-uniformly distributed. Thus, an asynchronous-based dynamic and elastic task scheduling scheme is proposed, which can avoid device underutilization, load imbalance across devices, and frequent kernel launches, inter-device data transfers and inter-device synchronizations by dynamically adjusting the chunk size according to the performance change during runtime. A series of experiments are conducted with 8 representative parallel applications on a hybrid CPU-GPU-MIC system, the results show that the proposed two inter-device task scheduling schemes can achieve the efficient CPU-GPU-MIC co-processing of different parallel applications by effectively partitioning work across devices.
Property Prediction of Medical Magnesium Alloy based on Machine Learning

In the research on the performance of magnesium alloy materials as medical implant materials, which is generally measured by plenty of mechanical experiments and corrosion resistance experiments, and this process is time-consuming and inefficient. Machine learning algorithms can train the model based on existing sample data and apply the trained model to predict the data. This paper regards mass fraction of alloying elements, casting or deformation process, heat treatment process and temperature of alloy performance test as features and considers ultimate tensile strength, yield strength, elongation and corrosion rate as labels. It utilizes machine learning to establish least absolute shrinkage and selection operator regression, random forest regression and support vector regression prediction models. The mean absolute error, the mean square error and the coefficient of determination three model evaluation indicators compare the prediction effects of the three regression models. After comparative analysis, the coefficient of determination of the random forest regression model is between 0.8 and 0.9, owning the best prediction effect. The random forest regression model can predict the mechanical properties and corrosion resistance of medical magnesium alloy materials more accurately.
Electrophoretic Deposition of CuIn1–xGaxSe2 Thin Films Using Solvothermal Synthesized Nanoparticles for Solar Cell Application

CuIn1–xGaxSe2 (CIGS) thin films are successfully prepared by convenient electrophoretic deposition, using colloidal nanoparticles. This is the first report which focuses on the electrophoretic deposition (EPD) of presynthesized CIGS nanoparticles directly from their colloid for solar cell application. In this research, CIGS nanoparticles are first synthesized via solvothermal process and then dispersed in a media containing a mixture of ethanol as the solvent and triethylenetetramine as the additive to be used for the film deposition via the electrophoretic method. By simple adjustment of the electrophoretic parameters, including applied voltage, pH, deposition time, and composition of nanoparticles, CIGS thin films with controlled thickness and optoelectronic properties can be fabricated. The highest photovoltaic efficiency of 5.57% is obtained in the CuIn0.75Ga0. 25Se2 sample. It is believed that this fabrication approach may open up a new gate to reduce the production cost of a highly demanding CIGS abs.
On approximation with the continuous polynomial cubic splines

Algorithms for the calculation of the continuous cubic polynomial splines of the maximum defect are considered. The behavior of constants and the Lebesgue functions for cubic polynomial splines in the case of non-uniform condensing grids are discussed. The algorithm is proposed for constructing an irregular grid consistent. This grid provides the minimum error when the left, the right, or the middle cubic polynomial splines.
Triple-Voltage-Vector Model-Free Predictive Current Control for Four-Switch Three-Phase Inverter-Fed SPMSM Based on Discrete-Space-Vector Modulation

The four-switch three-phase (FSTP) inverters are known for their cost-effective advantages and minimal switching losses. However, such inverter topology’s progress is lagging due to control constraints and requirements, including voltage vector limitations and parameter perturbations. To overcome the issue, this paper proposes a triple-voltage-vector model-free predictive current control (TVV-MFPCC) for FSTP inverter-fed surface permanent magnet synchronous motor (SPMSM) drives. The proposed TVV-MFPCC uses the principle of discrete-space-vector modulation (DSVM) to increase the voltage vector selections. Three primary voltage vectors, either the same or distinct, are linearly combined to yield the synthesized voltage vectors. A redundant voltage vector reduction scheme is also introduced to lessen calculations by optimally reducing the candidate voltage vectors to sixteen equivalent hybrid switching modes. To improve prediction accuracy, the TVV-MFPCC performs three different current readings and three current difference calculations in each sampling period. Experiments using a TMS320F28379D microcontroller are conducted to compare the performance of the proposed TVV-MFPCC against conventional MFPCC (C-MFPCC) and validate the scheme.
Trident: A Hybrid Correlation-Collision GPU Cache Timing Attack for AES Key Recovery

Given the parallel processing capabilities of Graphics Processing Units (GPUs), many applications are exploiting GPUs and cryptographic systems have also begun to leverage GPUs to accelerate encryption/decryption. Recent work has identified how microarchitectural side-channel attacks can be carried out on AES (Advanced Encryption Standard) by exploiting the SIMT characteristics and memory coalescing of GPUs. In this work, we first show that previously proposed correlation-based side-channel attacks are not feasible on modern GPUs that support narrower data-cache accesses via a sectored-cache microarchitecture–resulting in memory accesses from different levels of the memory hierarchy. In comparison, we identify how negative timing correlation can occur in modern GPUs when data is fetched from different levels of the cache hierarchy. We then propose Trident - a hybrid cache-collision timing attack on GPUs that can fully recover all AES key bytes on modern GPUs. Cache collisions in GPUs present challenges due to the large number of threads and the number of samples required. To address these challenges, Trident consists of three different components - negative timing correlation, cache-collision attack, and chosen plaintext attack. We leverage the negative timing correlation to recover earlier key bytes of AES while exploiting cache-collision attacks for the latter AES key bytes. To enable GPU cache collision attacks, we exploit memory coalescing to control the number of memory accesses through chosen-plaintext attacks to significantly reduce the number of timing samples needed. Our proposed Trident attack results in over 10× reduction in the number of samples needed to recover the key bytes compared with prior work, while still being successful in full AES key recovery in modern GPUs. We also propose TridentShield - a latency-based countermeasure to the Trident attack that minimizes throughput degradation in GPUs.
Determination of the Pharmaceutical Treatment-Dosage for Cancer Patients Using Non-Linear Optimal Control Techniques

Cancer remains one of the most important diseases and causes of death. In this study, a non-linear mathematical model of tumor growth with immune response, under the effects of chemotherapeutic treatment is studied. Two cost-efficient optimal control approaches are presented based on direct collocation and state dependent Riccati equation methods in order to optimize the pharmaceutical treatment-dosage to the patients. Finally, the numerical results from each method are presented, providing an overall better regimen, when compared to similar previous studies, by successfully eradicating the tumor and minimizing the side-effects of chemotherapy.
eSail: a body tracking interactive game for elderly and patients with locomotor system problems

This paper presents a computer game with automatic body detection and tracking based on the computer camera acquisition for arms and legs rehabilitation. However, with the correct application of different functions and game mechanics, this entertainment can be directed for the good of patients with motor problems, or for elderly people. This research is aimed to study the effectiveness and advantages of computer game platforms in comparison with conventional physical exercise. The experiment involved 13 people. The age of patients ranged from 22-32 (including people with locomotor system problems) to 60-70 years. For each of them the study was divided into two stages: in the first one, the subject was offered to perform a series of physical exercises aimed at warming up the upper and lower body parts. In the second stage, which was held the next day, the subjects performed the same set of exercises, but already in the form of a computer game. After each stage, a pre-prepared survey was conducted which characterized the emotional reaction of each of the subjects to the difference in approaches to rehabilitative physical exercises. The difference in the effectiveness of the game over standard exercises was evaluated by the means of the effectiveness of numerical indicators (table of scores) and the analysis of the therapist. The conducted tests show that at the stage of laboratory testing the average time to pass the current build of the game platform is 512.8 seconds (8.7 minutes), excluding the time to realize the task. The total time including all aspects of the test is 762.6 seconds (12.71 minutes). At the stage of tests with the target audience: the average passing time was 1115.9 seconds (18.5 minutes), the total time was 1809 seconds (30.15 minutes). Evaluating the emotional state and level of motivation shows that player is more interested when hear about an interactive game than the usual physical exercises in front of the mirror. According to the obtained data, it can be concluded that an ordinary set of physical exercises for rehabilitation is more effective if presented as a computer game. The patients have a higher involvement in the process, as well as motivation to improve their results.