Sampling plans using extended EWMA statistic with and without auxiliary information

In this paper, we propose an Acceptance Sampling Plan (ASP) using the statistic suggested by Naveed et al. (2018) under the condition of known and unknown population standard deviation (SD) for the presence of with and without Auxiliary Information (AI). It is presumed that the study variable of quality trait and AI follow the bivariate normal distribution. The plan parameters of the recommended plan are discussed for all four cases under the constraint that specified producer and consumer risks are gratified. The suggested plan is compared in terms of sample size (SS) with numerous existing plans and showed that the presented plan has a smaller SS for any value of AQL, LQL. Various tables of plan parameters have been erected using various combinations of smoothing constants for industrial use. For the workable purpose, the industrial example has also been examined. At last, concluding remarks are discussed.

Investigating students' emotional stability as a predictor of stress management while engaging in flexible online learning during COVID-19

Due to the COVID-19 pandemic, restrictions and guidelines are being enacted for the safety of the people, and generally, it has caused difficulties in the daily activities among students. Hence, this quantitative non-experimental study aimed to determine if emotional stability is a predictor of stress management among Senior High School students of the University of San Carlos in Cebu City, Philippines in the school year 2020-2021, who engage in a flexible online modality of learning. Adapted Likert-type survey tools were utilized to gather data online from 80 Grade 12 students who were selected through stratified non-proportionate random sampling. Results using linear regression analysis revealed a significant R and R2 value of .28 and .08(adjusted R2 was .07) respectively, which means highly significant F (1, 78) = 6.79, MSresidual = .31, p < .05 using a 95% level of confidence with a .39 standard error of the estimate from the population standard deviation of the residuals. This means that the independent variable emotional stability (ES) significantly and statistically affects the dependent variable stress management (SM). The modeled equation SM = 1.71 + .50 (ES) predicts that the higher the level of emotional stability, the higher the level of stress management. This implies that emotionally stable students are better at managing their stress. These results encouraged collaboration among the academic and administrative support services in every institution and stronger social support at home. This time, parental involvement plays a vital role in the holistic development and formation of every student.

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.

Sensorless Posture Detection of Reluctance Spherical Motor Based on Mutual Inductance Voltage

In this paper, a sensorless rotor posture detection method based on the mutual inductance voltage of the stator coil is proposed to simplify the position detection element of a reluctance spherical motor. Firstly, the numerical relationship between the stator/rotor pole misalignment angle and the mutual inductance voltage of the stator coil is analyzed, which is used as the basis for judging the spatial position of the rotor. Secondly, an experimental platform is designed to verify the consistency between the calculated value and the experimental value of the mutual inductance voltage and to determine the appropriate excitation signal. Thirdly, based on the real-time voltages generated by the stator coil mutual inductance, an intelligent algorithm is used to invert the 3-DoF (degree-of-freedom) position angle of the spherical rotor combined with the motor structure constraints. The experimental results show that the detection method has a good on-line detection effect, and the population standard deviation is within 1.8° Therefore, the developed technique can be used for replacing the position detection method with sensors.

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

&lt;p&gt;Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, new tools are needed for automatically and regularly identifying changes in the time series. Thanks to regular acquisitions across most of the world by the ESA Sentinel-1 satellites constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geohazards such as landslides or volcanic eruptions.&lt;/p&gt;

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a simple statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.

PATTERNS OF INCREASING STUDENT MOTIVATION AND LEARNING INTEREST THROUGH LECTURER COMPETENCIES AT UNISKA KEDIRI

Kediri Islamic University (UNISKA) Kediri Is a Private Higher Education Institution organized by a Foundation that is composed based on overall science and technology or following Islamic guidance. The campus is a formal educational institution that has the responsibility to continue to educate students by organizing lecture activities as a realization of the vision and mission and goals of education. The method used in this study is classroom action research that is a systematic study of improving the implementation of educational practices by lecturers by taking actions in learning based on their reflection on the results of those actions. This Classroom Action Research uses a quantitative design to collect and analyze data in the form of numbers using the Population Standard Deviation Formula. The data in this study used a questionnaire distributed via Google Forms to the Population Amount. The purpose of this research is to find out the lecturers' competence in Islamic religious subjects towards student learning interest, to find out the increase in student motivation towards learning interest in Islamic religious subjects, to find out positive responses to student questions about lecture material that can link science and Islam in lectures on motivation and learning interest of students of the Faculty of Engineering UNISKA Kediri in First Semesters of the academic year 2019/2020. Based on the results of a questionnaire with a population of 156 students and 122 respondents, which were tested at less than 75% shows a score of three, which means there needs to be an improvement in improving the competence of religious lecturers and building student motivation and interest in learning.

A retrospective study of causes for mortality of Thoroughbred horses in Caracas, Venezuela (2008-2012)

ABSTRACT: The aim of this study was to describe the causes of death in Thoroughbred horses at the Racetrack “La Rinconada”, in Caracas (Venezuela) during 2008-2012. This study was conducted in a cohort of all Thoroughbred horses that died or were subjected to euthanasia. Data was collected retrospectively. Only horses for which a full necropsy report was available were included in the study. The carcass and all internal organs of each horse were examined and representative samples of tissues with abnormalities were collected in 10% neutral buffered formalin and processed for histopathological examination. Samples were collected for bacteriological or virological examination when indicated in gross examination. A descriptive statistical analysis was performed. A total of 532 Thoroughbred horses were examined post-mortem. Of these horses, 44% were females and 56% males. In general musculoskeletal injuries and dilaceration occurred specifically at higher frequency. The total year and horses dead for descriptive statistical analysis was to mean 106.4 and median 125; Standard Deviation: 47.82573; variation (Standard Deviation): 2287.3, population (Standard Deviation): 42.77663 and variance (Standard Deviation): 18229.84. In conclusion we identify and describe the causes of death in Thoroughbred Race Horses in Caracas, Venezuela. In order of importance the causes of death were principally skeletal muscle injuries resulting in fractures euthanasia, abdominal crisis were mostly bowel twists and gastric rupture andrespiratory pathologies such as the pneumonia, pleuritis presented a significant number of cases of pulmonary hemorrhage induced by exercise. Finally the multisystem pathologies were presented in low cases.