Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Upon-Diagnosis Blood Eosinophilic Count with Frequency and Severity of Annual Exacerbation in Chronic Obstructive Pulmonary Disease: A Prospective Longitudinal Analysis

Introduction There is a controversy regarding the relationship between blood eosinophil count and COPD exacerbation. We aimed to determine whether peripheral eosinophils upon COPD diagnosis could affect the frequency and severity of annual acute exacerbation of COPD (AECOPD). Methods This prospective study was conducted on 973 newly diagnosed COPD patients who were under 1-year follow-up in a pulmonology center in Iran. The Cox proportional model, polynomial regression, and receiver operator characteristic curves were conducted to evaluate the impact of the eosinophil levels on AECOPD. A linear regression model was conducted to evaluate the continuous association of eosinophilic count with AECOPDs. Results Patients with eosinophil >200 cells/microliter were higher pack-year smokers with more pulmonary hypertension prevalence compared to COPD patients with <200 cells/microliter. There was a positive correlation between the eosinophilic count and the frequency of AECOPDs. Eosinophil >900 cells/microliter and eosinophil >600 cells/microliter had a sensitivity of 71.1% and 64.3%, respectively, in predicting the occurrence of more than one AECOPD. Eosinophilic count cutoff of 800 cells/microliter had the highest Youden index with sensitivity and specificity of 80.2% and 76.6%, respectively, for incident AECOPD in newly diagnosed patients. Using a linear model, increasing 180 cells/microliter in serum eosinophils was associated with further exacerbation. Evaluating gender, BMI, smoking pack-year, FEV1/FVC, CAT score, GOLD score, pulmonary hypertension, annual influenza, pneumococcal vaccinations, leukocytosis, and blood eosinophils, only blood eosinophils (hazard ratio (HR) = 1.44; 95% confidence interval = 1.33–2.15; p value = 0.03) and GOLD score (HR = 1.19; 95% CI = 1.30–1.52; p value = 0.03) were found as independent risk factors of AECOPD >3 episodes/year. Requirement for ICU admission, invasive ventilation, and mortality rate due to AECOPDs was similar between eosinophilic and noneosinophilic groups. Conclusion Eosinophilia upon COPD diagnosis is a factor of recurrent AECOPDs. To reduce the risk of AECOPDs and the burden of disease, clinicians may consider inhaler corticosteroids and domiciliary oxygen with a lower threshold for eosinophilic-COPD patients regardless of their clinical status.
Ataxia-telangiectasia: una revisión desde la etiopatogenia al manejo actual con descripción de casos reportados en Perú

La Ataxia-Telangiectasia (AT) es una rara enfermedad de herencia autosómica recesiva y de afección multisistémica, caracterizada por ataxia progresiva, inmunodeficiencia variable con infecciones recurrentes, riesgo incrementado de neoplasias con o sin telangiectasias óculo-cutáneas. La AT es causada por variantes patogénicas bialélicas en el gen ATM. Su diagnóstico se basa en la sospecha de un cuadro clínico compatible, niveles elevados de alfafetoproteína, atrofia cerebelosa y estudios genéticos. No existe tratamiento curativo de AT y su manejo se basa en medidas de soporte y prevención de complicaciones y asesoramiento genético. En esta revisión, actualizamos la epidemiología, manifestaciones clínicas, diagnóstico y tratamiento de AT incluyendo una búsqueda de casos publicados en el Perú.
A NEW NEURAL NETWORK BASED POWER SYSTEM HARMONICS ANALYSIS ALGORITHM WITH HIGH ACCURACY

A triangle base function based new artificial neural network (ANN) algorithm was presented by which the frequencies, amplitudes and phases of fundamental and various orders of harmonics could be obtained once only. The convergence theorem of the presented ANN algorithm was proposed and proved, and the simulation cases of harmonic analysis by use of the presented algorithm were given. The simulation results showed that the presented harmonic measuring method was very accurate and the calculation amount was small, so it could be applied to power system harmonic measurement.
Analyzing Tensor Power Method Dynamics: Applications to Learning Overcomplete Latent Variable Models

In this paper we provide new guarantees for unsupervised learning of overcomplete latent variable models, where the number of hidden components exceeds the dimensionality of the observed data. In particular, we consider multi-view mixture models and spherical Gaussian mixtures with random mean vectors. Given the third order moment tensor, we learn the parameters using tensor power iterations. We prove that our algorithm can learn the model parameters even when the number of hidden components $k$ is significantly larger than the dimension $d$ up to $k = o(d^{1.5})$, and the signal-to-noise ratio we require is significantly lower than previous results. We present a novel analysis of the dynamics of tensor power iterations, and an efficient characterization of the basin of attraction of the desired local optima.
Survival function estimation with non parametric adaptive refined descriptive sampling algorithm: A case study

ABSTRACT This paper proposes non parametric adaptive refined descriptive sampling algorithm (NARDS) to estimate the survival function in lifetime models using Kaplan–Meier and Fleming–Harrington estimators. Simulations were performed using real data to generate inputs. The obtained results prove that NARDS works well on non parametric data.
The Relationship Of Body Shape Satisfaction With Personal Intimacy And Sexual Satisfaction Among Married Females

The purpose of the current study was to investigate the association between body shape, intimacy and sexual satisfaction in the life of married females. It has been hypothesized that there is a significant relationship of body shape, intimacy and sexual satisfaction of married females. Also with respect to the duration of marriage, impact of body shape, intimacy and also sexual satisfaction has been analyzed. Sample of 293 married females, with age range of 25-28(M=1.96, SD=.1714) years was chosen through convenient purposive sampling technique. Scales used are Body Shape Satisfactions Scale, Personal Assessment of Intimacy in relationship scale (PAIR) and Sexual Satisfaction Scale. Correlation is used through statistical analysis on data through Statistical Package for the Social Sciences (SPSS -25). The findings of the current research revealed that there is significant weak negative correlation of body shape satisfaction with sexual satisfaction in married females (r=-.20 p=0.01); body shape satisfaction with personal intimacy (r=-.232 p=0.01) and a positive moderate correlation of sexual satisfaction with personal intimacy (r=.694 p=0.01). The results also show that there is no significant difference between body shape satisfaction and years of marriage of married females however, there is a significant correlation between years of marriage and sexual satisfaction, also shows the significant relationship of Body shape, intimacy and sexual satisfaction may result in dysfunctional and dissatisfying sexual experiences and the study suggests that incorporating body image intervention into therapy for sexual problems may be useful.
Target Classification for 3D-ISAR Using CNNs

In maritime surveillance, inverse synthetic aperture radar (ISAR) is a technique for imaging non-cooperative targets, with classification typically performed by the radar operator. By automating the target classification process, the operator workload will be reduced significantly and the classification accuracy can be improved. Traditional classification approaches use geometric features extracted from images of known targets to form a training dataset that is later used to classify an unknown target. While these approaches work reasonably well, deep learning based techniques have recently demonstrated significant improvements over conventional processing schemes in many areas of radar. The classification of traditional 2D-ISAR imagery is difficult due to the motion of the sea causing a wide range of imagery. The 3D-ISAR technique was developed as an alternative representation with the target represented by a three-dimensional point cloud. In this article, we investigate how 3D-ISAR can be used for the classification of maritime targets. The proposed scheme makes use of features extracted from the 3D-ISAR generated point cloud of the target from different perspectives (i.e. side, top and front views) to form three point density images. These are then fed into a convolutional neural network to classify the targets.
The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines

Underground mining operations present critical safety hazards due to limited visibility and blind areas, which can lead to collisions between mobile machines and vehicles or persons, causing accidents and fatalities. This paper aims to survey the existing literature on anti-collision systems based on computer vision for pedestrian detection in underground mines, categorize them based on the types of sensors used, and evaluate their effectiveness in deep underground environments. A systematic review of the literature was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify relevant research work on anti-collision systems for underground mining. The selected studies were analyzed and categorized based on the types of sensors used and their advantages and limitations in deep underground environments. This study provides an overview of the anti-collision systems used in underground mining, including cameras and lidar sensors, and their effectiveness in detecting pedestrians in deep underground environments. Anti-collision systems based on computer vision are effective in reducing accidents and fatalities in underground mining operations. However, their performance is influenced by factors, such as lighting conditions, sensor placement, and sensor range. The findings of this study have significant implications for the mining industry and could help improve safety in underground mining operations. This review and analysis of existing anti-collision systems can guide mining companies in selecting the most suitable system for their specific needs, ultimately reducing the risk of accidents and fatalities.
Modeling and Solution for Assignment Problem

In this paper, the mixed-integer linear programming (MILP) of minimax assignment is formed, and a solution called Operations on Matrix is presented and compared with the solutions of exhaustion and MILP. Theoretical analyses and numerical tests show that the operations on matrix are efficient well-implied enumeration for both minimax and global-minimum assignment problems. Keywords—Assignment problem, method of exhaustion, mixed-integer linear programming (MILP), operations on matrix. I. INTRODUCTION HE global-minimum assignment problem is described, see, e.g. (1), by language as: There are n people and n tasks. Each person completes and only completes a task. The payment in each person for a task is given. The problem is, which person completes which task such that the total payment is minimum? Let ij x be the 2 n 0-1 decision variables, where 1 ij x = represents person i for task j , and 0 ij x = , otherwise. The
Operator-based robust nonlinear control and its realization for a multi-tank process by using a distributed control system

In this paper, a robust nonlinear control design method using an operator-based robust right coprime factorization approach and its realization based on a distributed control system (DCS) device are considered for a multi-tank process. In detail, for the multi-tank process, consisting of a water-level process and a water-flow process, theoretical models are developed according to the Bernoulli theorem. Based on the obtained models, a robust nonlinear feedback control design is presented by using robust right coprime factorization for the multi-tank process. Further, from a large-scale industrial application viewpoint, the realization of the designed operator-based robust right coprime factorization controllers is considered by using a DCS device. Because there are some nonlinear functions in the designed controllers which cannot be realized straightforwardly in the DCS device such that the designed controllers need to be realized approximately. That is, there exist some parasitic terms for the approximated realization of the controllers in the real system. As a result, the parasitic terms and processes’ unknown uncertainties should be considered simultaneously. In this paper, a robust condition is derived to guarantee robust stability of the nonlinear feedback control system with the parasitic terms and the uncertainties. Moreover, tracking controller design problem for the multi-tank process is discussed. Finally, by using a DCS device (CENTUM CS3000), experimental results are given to confirm the effectiveness of the proposed design scheme.
Deep Sigma Point Processes-Assisted Chance-Constrained Power System Transient Stability Preventive Control

This paper proposes a deep sigma point processes (DSPP)-assisted chance-constrained power system transient stability preventive control method to deal with uncertain renewable energy and loads-induced stability risk. The traditional transient stability-constrained preventive control is reformulated as a chance-constrained optimization problem. To deal with the computational bottleneck of the time-domain simulation-based probabilistic transient stability assessment, the DSPP is developed. DSPP is a parametric Bayesian approach that allows us to predict system transient stability with high computational efficiency while accurately quantifying the confidence intervals of the predictions that can be used to inform system instability risk. To this end, with a given preset confidence probability, we embed DSPP into the primal dual interior point method to help solve the chance-constrained preventive control problem, where the corresponding Jacobian and Hessian matrices are derived. Comparison results with other existing methods show that the proposed method can significantly speed up preventive control while maintaining high accuracy and convergence.
Polycystic Ovary Syndrome: Pathophysiology and Controversies in Diagnosis

Polycystic ovary syndrome (PCOS) is a complex and heterogeneous disorder that commonly affects women in the reproductive age group. The disorder has features that propose a blend of functional reproductive disorders, such as anovulation and hyperandrogenism, and metabolic disorders, such as hyperglycemia, hypertension, and obesity in women. Until today, the three implemented groups of criteria for the diagnosis of PCOS are from the National Institutes of Health (NIH) in the 1990s, Rotterdam 2003, and the Androgen Excess Polycystic Ovary Syndrome 2009 criteria. Currently, the most widely utilized criteria are the 2003 Rotterdam criteria, which validate the diagnosis of PCOS with the incidence of two out of the three criteria: hyperandrogenism (clinical and/or biochemical), irregular cycles, and polycystic ovary morphology. Currently, the anti-Müllerian hormone in serum is introduced as a substitute for the follicular count and is controversially emerging as an official polycystic ovarian morphology/PCOS marker. In adolescents, the two crucial factors for PCOS diagnosis are hyperandrogenism and irregular cycles. Recently, artificial intelligence, specifically machine learning, is being introduced as a promising diagnostic and predictive tool for PCOS with minimal to zero error that would help in clinical decisions regarding early management and treatment. Throughout this review, we focused on the pathophysiology, clinical features, and diagnostic challenges in females with PCOS.
Prediction of the Future State of Pedestrians While Jaywalking Under Non-Lane-Based Heterogeneous Traffic Conditions

This study proposes a novel framework to predict jaywalkers' future state in non-lane-based heterogeneous traffic conditions by combining the effects of the surrounding dynamics with jaywalkers' poses. Different variables, such as the pedestrian pose, walking speed, location in the road environment, count and direction of approaching traffic, speed and type of closest approaching vehicle, and so forth, are used as input variables. The dataset for this study consists of 47,588 samples gathered by analyzing 1753 jaywalkers under non-lane-based heterogeneous traffic situations. Keypoint detection on the pedestrian body is made using MediaPipe. YOLOv4 and DeepSORT are used to detect and track road users to get trajectory data. Training and testing datasets are prepared for different prediction horizons to test the proposed models’ applicability for roads of varying design speeds. Four machine learning models based on ensemble techniques, namely random forest (RF), adaptive boosting (AdaBoost), gradient boosting, and extreme gradient boosting, are trained and tested for different prediction horizons from 0.5 to 4 s. Up to the prediction horizon of 1 s, all models performed equally well with Area under the ROC curve (AUC) values above 0.95. At higher prediction horizons, the RF is found to outperform the other models. All models, except AdaBoost, maintained an AUC value of greater than 0.9 when predicting future states up to a maximum of 2.5 s. The proposed model performs well for both short-term and long-term predictions by combining the effect of surrounding dynamics with pedestrian stance and speed. The outcomes can be utilized to assist infrastructure-to-vehicle connectivity in empowering vehicles to navigate through jaywalkers safely, enhancing pedestrian safety.
Smartphone-Based Psychotherapeutic Interventions in Blended Care of Cancer Survivors: Nested Randomized Clinical Trial

Background Cancer is related to not only physical but also mental suffering. Notably, body image disturbances are highly relevant to cancer-related changes often persisting beyond recovery from cancer. Scalable and low-barrier interventions that can be blended with face-to-face psychotherapy for cancer survivors are highly warranted. Objective The aim of the study is to investigate whether smartphone-based bodily interventions are more effective to improve the mood of patients with cancer than smartphone-based fairy tale interventions (control intervention). Methods We recruited patients with cancer in 2 Swiss hospitals and conducted daily, fully automated smartphone-based interventions 6 times a week for 5 consecutive weeks, blended with weekly face-to-face group body psychotherapy. We applied 2 types of smartphone-based interventions using a within-subject design, randomly assigning patients daily to either bodily interventions or fairy tales. Each intervention type was presented 3 times a week. For this secondary analysis, 3-level mixed models were estimated with mood assessed by the 3 Multidimensional Mood Questionnaire subscales for good-bad mood, wakefulness, and calmness as key indicators. In addition, the effects on experience of presence, vitality, and burden assessed with visual analog scales were investigated. Results Based on the data from s=732 interventions performed by 36 participants, good-bad mood improved (β=.27; 95% CI 0.062-0.483), and participants became calmer (β=.98; 95% CI 0.740-1.211) following smartphone-based interventions. Wakefulness did not significantly change from pre- to postsmartphone–based intervention (β=.17; 95% CI –0.081 to 0.412). This was true for both intervention types. There was no interaction effect of intervention type with change in good-bad mood (β=–.01; 95% CI –0.439 to 0.417), calmness (β=.22; 95% CI –0.228 to 0.728), or wakefulness (β=.14; 95% CI –0.354 to 0.644). Experience of presence (β=.34; 95% CI 0.271-0.417) and vitality (β=.35; 95% CI 0.268-0.426) increased from pre- to postsmartphone–based intervention, while experience of burden decreased (β=–0.40; 95% CI –0.481 to 0.311). Again, these effects were present for both intervention types. There were no significant interaction effects of intervention type with pre- to postintervention changes in experience of presence (β=.14; 95% CI –0.104 to 0.384), experience of vitality (β=.06; 95% CI –0.152 to 0.265), and experience of burden (β=–.16; 95% CI –0.358 to 0.017). Conclusions Our results suggest that both smartphone-based audio-guided bodily interventions and fairy tales have the potential to improve the mood of cancer survivors. Trial Registration ClinicalTrials.gov NCT03707548; https://clinicaltrials.gov/study/NCT03707548 International Registered Report Identifier (IRRID) RR2-10.1186/s40359-019-0357-1
Studies of Rate of Convergence for Approximations of American Type Options

This master thesis presents three different approximation lattice methods, namely binomial sum model, trinomial sum model and skeleton model, for pricing of American options. The underlying stock price process is assumed to be a geometric Gaussian random walk. First we study reward functions for American options and impose certain conditions on simulating price processes to insure the existence of corresponding reward functions. The conditions un- der which the reward functions for approximating price process converge to the corresponding limiting rewards are also presented. Also backward recur- rence algorithm, for reward functions is discussed. The convergence conditions of these three approximation models are tested. Then numerical tests based on the methods are implemented in MATLAB and some comparisons are made.
The complete chloroplast genome sequence of Camellia atrothea (Ericales: Theaceae)

Abstract Camellia atrothea H.T. Chang, H.S. Wang & B.H. Chen 1983 is a native Camellia species in China, which has significant economic and breeding values. Here, we assembled and analyzed the complete chloroplast genome sequence of C. atrothea based on the whole genome sequencing data from Illumina NovaSeq6000 platform. Results show that the complete chloroplast genome of C. atrothea is 157,099 bp in length, and comprises a large single-copy (LSC, 86,643 bp) region, a small single-copy (SSC, 18,276 bp) region, and a pair of inverted repeats (IRs, 26,090 bp) region. Annotation of the C. atrothea chloroplast genome predicts a total of 133 genes, including 88 protein-coding genes, 8 ribosomal RNA genes, and 37 transfer RNA genes. The overall GC content accounts for 37.30% of the genome, which is clearly consistent with that of other previously published Camellia species from section Thea. Phylogenetic analyses of the whole chloroplast genome sequences of 14 Camellia species using Actinidia chinensis as outgroup reveals that C. atrothea has a close relationship with C. leptophylla.
Clifford Algebras and the Classical Groups

The Clifford algebras of real quadratic forms and their complexifications are studied here in detail, and those parts which are immediately relevant to theoretical physics are seen in the proper broad context. Central to the work is the classification of the conjugation and reversion anti-involutions that arise naturally in the theory. It is of interest that all the classical groups play essential roles in this classification. Other features include detailed sections on conformal groups, the eight-dimensional non-associative Cayley algebra, its automorphism group, the exceptional Lie group G2, and the triality automorphism of Spin 8. The book is designed to be suitable for the last year of an undergraduate course or the first year of a postgraduate course.