Stratified Ranked Set Sampling With Missing Observations for Estimating the Difference

The authors develop the estimation of the difference of means of a pair of variables X and Y when we deal with missing observations. A seminal paper in this line is due to Bouza and Prabhu-Ajgaonkar when the sample and the subsamples are selected using simple random sampling. In this this chapter, the authors consider the use of ranked set-sampling for estimating the difference when we deal with a stratified population. The sample error is deduced. Numerical comparisons with the classic stratified model are developed using simulated and real data.

Characteristics of Adverse Events in Bee Venom Therapy Reported in South Korea: A Survey Study

This study was aimed at investigating Korean patients’ experience with bee venom therapy (BVT) and providing evidence to enhance BVT safety. Thus, an anonymous online survey was conducted between August 22 and 28, 2018. Five hundred respondents who underwent pharmacopuncture (PA) within one year were surveyed (sample error: 95 ± 4.38%). Of these, 32 respondents were excluded and 468 were evaluated. Of the 468, 61 reported experiencing adverse events after PA. The adverse event rate was higher in the BV-PA(Bee venom-Pharmacopuncture) group than in the non-A group; however, intergroup differences were insignificant. There were no significant differences in mild symptom intensity between the BV-PA and non-BV-PA groups (p = 0.572). However, there was a significant intergroup difference in severe symptom intensity (p < 0.001). Additionally, the BV-PA and non-BV-PA groups did not significantly differ in their level of satisfaction either overall or in terms of effectiveness and safety (p = 0.414, p = 0.339, and p = 0.675, respectively). Furthermore, the BV-PA and non-BV-PA groups did not differ regarding intent to re-treat (p = 0.722). Severe adverse events such as anaphylactic shock were not reported; however, BVT practitioners should be cautious when applying it.

Accurate and Hybrid Regularization - Robust Regression Model in Handling Multicollinearity and Outlier Using 8SC for Big Data

Regressions have been continuously received great attention. However, there are still open issues in regression, and two of the issues is regression with multicollinearity and outlier. Regularization (Ridge, Lasso, and Elastic Net) techniques implement a means to control regression coefficients. The methods can decrease the variance and reduce our sample error for tackle multicollinearity. In robust regression, it is a form of regression method designed to overcome outliers. Robust regression is an important method for analyzing data that are infected with outliers. The data have been interacted on the second order interaction. The data contained 435 different independent interaction variables. The primary focus of this paper is to analyze and compare the impact of three different variable selection techniques regularization regression algorithms for the data seaweed drying. After that, it will be analyzed through robust regression (Tukey Bi-Square, Hampel, and Huber). As the result, the Lasso-Hampel was better than others with the MAE (4.09641), RMSE (5.275992), MAPE (7.9962), SSE (182491.2), R-square (0.6514791), and R-square Adjusted (0.649279). The method of Lasso-Hampel is able to be relied on investigation of the accuracy in big data obtained from regularization and robust regression.

Towards understanding the power of quantum kernels in the NISQ era

A key problem in the field of quantum computing is understanding whether quantum machine learning (QML) models implemented on noisy intermediate-scale quantum (NISQ) machines can achieve quantum advantages. Recently, Huang et al. [Nat Commun 12, 2631] partially answered this question by the lens of quantum kernel learning. Namely, they exhibited that quantum kernels can learn specific datasets with lower generalization error over the optimal classical kernel methods. However, most of their results are established on the ideal setting and ignore the caveats of near-term quantum machines. To this end, a crucial open question is: does the power of quantum kernels still hold under the NISQ setting? In this study, we fill this knowledge gap by exploiting the power of quantum kernels when the quantum system noise and sample error are considered. Concretely, we first prove that the advantage of quantum kernels is vanished for large size of datasets, few number of measurements, and large system noise. With the aim of preserving the superiority of quantum kernels in the NISQ era, we further devise an effective method via indefinite kernel learning. Numerical simulations accord with our theoretical results. Our work provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.

Retrieval and Calculation of Vertical Aerosol Mass Fluxes by a Coherent Doppler Lidar and a Sun Photometer

The direct and indirect radiation forcing of aerosol particles deeply affect the energy budget and the atmospheric chemical and physical processes. To retrieve the vertical aerosol mass fluxes and to investigate the vertical transport process of aerosol by a coherent Doppler lidar (CDL), a practical method for instrumental calibration and aerosol optical properties retrieval based on CDL and sun photometer synchronization observations has been developed. A conversion of aerosol optical properties to aerosol microphysical properties is achieved by applying a well-developed algorithm. Furthermore, combining the vertical velocity measured simultaneously with a CDL, we use the eddy covariance (EC) method to retrieve the vertical turbulent aerosol mass fluxes by a CDL and sun photometer with a spatial resolution of 15 m and a temporal resolution of 1 s throughout the planetary boundary layer (PBL). In this paper, we present a measurement case of 24-h continuous fluxes observations and analyze the diurnal variation of the vertical velocity, the aerosol backscatter coefficient at 1550 nm, the mean aerosol mass concentration, and the vertical aerosol mass fluxes on 13 April 2020. Finally, the main relative errors in aerosol mass flux retrieval, including sample error σF,S, aerosol optical properties retrieval error σF,R, and error introduced from aerosol microphysical properties retrieval algorithm σF,I, are evaluated. The sample error σF,S is the dominating error which increases with height except during 12:00–13:12 LST. The aerosol optical properties retrieval error σF,R is 21% and the error introduced from the aerosol microphysical properties retrieval algorithm σF,I is less than 50%.

Leisure, work and simultaneous study in distance and virtual students (Ocio, trabajo y estudio simultáneo de estudiantes en modalidad a distancia y virtual)

The studies, analysis, reflections and positions regarding the leisure-work relationship is not new and is current, although sometimes it is not tangible and visible at first glance, even more so when the young population in Colombia takes advantage of distance and virtual education to be able to work and generate income and thus pay for a study; Thus, it was sought to analyze the incidence of working and studying simultaneously in the leisure time and spaces of students in distance and virtual mode of the Minuto de Dios-Uniminuto University Corporation in Bogotá-Colombia. Methodologically, the study was executed from a non-experimental, cross-sectional-descriptive design, an on-line questionnaire-type information collection instrument was used with categories: serious leisure, casual leisure, digital leisure and satisfaction with leisure time. A representative sample of 486 students was used with a standard deviation of .5, a confidence level of 98% and an acceptable sample error limit of .05 for a population size of 12096 students. The incidence of students working and studying simultaneously in distance and virtual modes is reflected in a recurrence that tends to be medium to low in the development of activities of serious leisure, casual leisure and digital leisure; However, satisfaction with leisure time is high, with which it can be inferred that leisure manages to fulfill a restorative function in terms of well-being. Resumen: Los estudios, análisis, reflexiones y posturas frente a la relación ocio-trabajo no es nueva y está vigente, aunque en ocasiones no sea tangible y visible a primera vista, más aún cuando la población joven en Colombia aprovecha la educación a distancia y virtual para poder trabajar y generar ingresos económicos y así solventar un estudio, es así que se buscó analizar la incidencia de trabajo y estudio simultáneo en los tiempos y espacios de ocio de estudiantes en modalidad a distancia y virtual de la Corporación Universitaria Minuto de Dios-Uniminuto en Bogotá-Colombia. Metodológicamente, el estudio fue abordado desde un diseño no experimental, de tipo transversal-descriptivo, se utilizó un instrumento de recolección de información tipo cuestionario on-line a partir de las categorías de ocio serio, ocio casual, ocio digital y satisfacción con los tiempos de ocio. Se trabajó con una muestra representativa de 486 estudiantes teniendo en cuenta una desviación estándar de .5, un nivel de confianza del 98% y un límite aceptable de error muestral de .05 para un tamaño de población de 12096 estudiantes. La incidencia de trabajo y estudio simultáneo de los estudiantes en modalidad a distancia y virtual, se refleja en una recurrencia que tiende a ser media y baja en el desarrollo de actividades de ocio serio, ocio casual y ocio digital; no obstante, la satisfacción con los tiempos de Ocio, es alta, con lo que se puede inferir que, el ocio logra cumplir una función restaurativa en términos de bienestar.

Education, caste and occupational differentials in fertility in India

This study analyses the impact of four prominent factors (education, caste, occupation and development) on fertility in India. The study has also analysed the spatial variations of TFR in India. This spatial analysis also helps in identifying the impact of fertility on Development in India. Present study estimates fertility from census that covers all the reproductive age-women, and it does not allow any chance of sample error; this method has also adopted by UNFPA. Logistic regression analysis used to understand the impact of education, caste and occupation on fertility. Co-relation matrix and regression analysis used to determine the relation between TFR, IMR and Development. In India, West Bengal (2.41) state has the least fertility rate while Jammu and Kashmir (4.70) has highest one. Southern states have lower fertility along with Eastern states while North-east and central states have recorded high fertility. Women who have passed above secondary school of education give a prominent impact on fertility reduction in comparison to illiterate, primary and middle school passed. Marginal workers have very high fertility compare to main workers due to their feeling of insecurity in earnings and they increase number of child counted as gross family income.

Diagnostic Assessment of Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, and is characterized by fat accumulation at levels exceeding 5% in hepatocytes due to insulin resistance. The disease spectrum ranges from simple nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH)/NASH-related fibrosis or cirrhosis defined by histological findings. Unlike simple NAFL, NASH/NASH-related fibrosis or cirrhosis increases the risk of liver-related morbidity or mortality. Therefore, accurate diagnosis of NASH/NASH-related fibrosis or cirrhosis is needed for management of patients with these diseases. Currently, liver biopsy is the only way to confirm the presence of NASH in an individual with features of NAFLD, but this has some limitations, including sample error, interpretation error, and the invasiveness of the procedure. Therefore, there have been a number of attempts to develop noninvasive methods for differential diagnosis of NASH/NASH-related fibrosis or cirrhosis easily and quickly. Here, we review the assessments for diagnosing NAFLD and the methods for differential diagnosis of NASH/NASH-related fibrosis or cirrhosis.

A Novel Convex Clustering Method for High-Dimensional Data Using Semiproximal ADMM

Clustering is an important ingredient of unsupervised learning; classical clustering methods include K-means clustering and hierarchical clustering. These methods may suffer from instability because of their tendency prone to sink into the local optimal solutions of the nonconvex optimization model. In this paper, we propose a new convex clustering method for high-dimensional data based on the sparse group lasso penalty, which can simultaneously group observations and eliminate noninformative features. In this method, the number of clusters can be learned from the data instead of being given in advance as a parameter. We theoretically prove that the proposed method has desirable statistical properties, including a finite sample error bound and feature screening consistency. Furthermore, the semiproximal alternating direction method of multipliers is designed to solve the sparse group lasso convex clustering model, and its convergence analysis is established without any conditions. Finally, the effectiveness of the proposed method is thoroughly demonstrated through simulated experiments and real applications.