The Relationship of Money Ethics on Tax Evasion with Intrinsic Religiosity, Extrinsic Religiosity, and Materialism as Moderating Variables (Case on Private Taxpayers Listed in Kpp Pratama Ternate)

This study aims to find out the influence of money ethics on tax evasion with intrinsic religiosity, extrinsic religiosity, and materialism as moderating variables. The sample is selected by using convinience sampling method with sample size of a 100 respondents as primary data. This study uses simple regression and moderated regression analysis for hypothesis testing. The result of this study shows that money ethics has an effect on tax evasion, intrinsic religiosity moderarates the relationship between money ethics and tax evasion. Extrinsic religiosity does not moderate the relationship bertween money ethics and tax evasion. Materialism moderates the relationship between money ethics and tax evasion

Propagation of near-inertial oscillations through a geostrophic flow

The method of multiple time scales is used to obtain an approximate description of the linear propagation of near-inertial oscillations (NIOs) through a three-dimensional geostrophic flow. This ‘NIO equation’ uses a complex field, M(x, y, z, t), related to the demodulated horizontal velocity by Mz = exp (if0t)(u + iv), where f0 is the inertial frequency. The three processes of wave dispersion, advection by geostrophic velocity and refraction (geostrophic vorticity slightly shifts the local inertial frequency) are all included in the formulation. The NIO equation has an energy conservation law, so that there is no transfer of energy between NIOs and the geostrophic flow in the approximation scheme. As an application, the NIO equation is used to examine propagation of waves through a field of smaller scale, geostrophic eddies. The spatially local ζ/2 frequency shift, identified by earlier WKB calculations (ζ is the vertical vorticity of the geostrophic eddies), is not expressed directly in the wave field: the large-scale NIO samples regions of both positive and negative ζ so that there is cancellation. Instead, the ζ/2 frequency shift is rectified to produce an average dispersive effect. The calculation predicts that an NIO with infinite horizontal scale has a frequency shift −Kf0m2/N2 where K is average kinetic energy density of the geostrophic eddies, m the vertical wavenumber of the NIO, f0 the inertial frequency and N the buoyancy frequency. Because of the dependence of the frequency shift on m2, there is an effective vertical dispersion, whose strength is proportional to the eddy kinetic energy. This process greatly increases the vertical propagation rate of synoptic scale NIOs

Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources

Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset

Soil Moisture Conditions and Crop Water Use

Soil moisture during the growing season is essential to obtain optimal yield. Optimal yield is affected by the availability of moisture as stored water in the soil profile or timely recharge during rain events. Soil texture, tillage practices, residue cover, drainage and weed control can play a significant role in soil moisture availability. Generally, the no-till system is the most effective practice in conserving soil moisture among other tillage systems, especially during dry periods in rain-fed agricultural areas

Short Corn and Variable Growth—2013

“Knee high by the fourth of July!” Some of us grew up with that old saying which either foreshadowed disaster or forecast bumper yields. If corn was knee high by the fourth, most thought it would mature before the first fall frost. That was good news! As we look back, the statement perhaps was valid back when the first target corn planting date was mid-May