The Effect of Implementing Self-Assessment System on Tax Compliance for Small-Medium Enterprises (SMEs), Lembang, West Bandung District

Small-Medium Enterprises (SMEs) play a vital role in economic development of Indonesia. To support its economic development, the government is obliged to generate revenue. In order to generate more revenue, Indonesian government implemented self- assessment system in compliance with tax obligations. Most of the SMEs are compliant while some are not that aware of it because of lack of socialization. The aim of this study is to comprehend the effect of implementing self-assessment system on tax compliance. This study focused on SMEs in Lembang, West Java, Indonesia. It was exploratory research based on data were gathered from West Java Regency Office. Questionnaires were distributed to 70 (based on Slovin formula) respondents from the total population of 244. The result showed that the correlation of implementation of self-assessment system on tax compliance is weak, thus, the findings showed that implementation of self-assessment system has no significant influence on tax compliance. Implementation of self-assessment system has no significant influence on tax compliance. There are several reasons for this, such as, most of the people around the district of Lembang are not yet fully aware about its implementation. Also, it is not well socialized by the tax authorities from the Revenue Offices. It lacks personnel to do the socialization. The stated reasons were affirmed during the interview with the Head of SMEs located in West Bandung Regency. Implementation of the self-assessment System is worth to grab by SMEs in the district of Lembang, Bandung, Indonesia. It is highly recommended to socialize again the implementation and assign personnel that can extend time and effort to socialization

Reduced Order Modeling for Parameterized Time-Dependent PDEs using Spatially and Memory Aware Deep Learning

We present a novel reduced order model (ROM) approach for parameterized time-dependent PDEs based on modern learning. The ROM is suitable for multi-query problems and is nonintrusive. It is divided into two distinct stages: A nonlinear dimensionality reduction stage that handles the spatially distributed degrees of freedom based on convolutional autoencoders, and a parameterized time-stepping stage based on memory aware neural networks (NNs), specifically causal convolutional and long short-term memory NNs. Strategies to ensure generalization and stability are discussed. The methodology is tested on the heat equation, advection equation, and the incompressible Navier-Stokes equations, to show the variety of problems the ROM can handle

Reference blindness: the influence of references on trust in Wikipedia

In this study we show the influence of references on trust in information. We changed the contents of reference lists of Wikipedia articles in such a way that the new references were no longer in any sense related to the topic of the article. Furthermore, the length of the reference list was varied. College students were asked to evaluate the credibility of these articles. Only 6 out of 23 students noticed the manipulation of the references; 9 out of 23 students noticed the variations in length. These numbers are remarkably low, as 17 students indicated they considered references an important indicator of credibility. The findings suggest a highly heuristic manner of credibility evaluation. Systematic evaluation behavior was also observed in the experiment, but only of participants with low trust in Wikipedia in general

Sub-micrometer distribution of Fe oxides and organic matter in Podzol horizons

The spatial distribution of soil constituents at the micrometer scale is of great importance to understand processes controlling the formation of micro-aggregates and the stabilization of organic carbon. Here, the spatial distribution of organic and mineral constituents in Podzol horizons is studied by concerted measurements of (i) the content of various forms of Fe, Al, Si and C determined by selective extraction in the fine earth fraction of soil (f < 2 mm); (ii) the elemental composition of the clay fraction (f < 2 um) with lateral resolution using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and with surface selectivity using X-ray photoelectron spectroscopy (XPS); (iii) the specific surface area (SSA) of fine earth and clay fractions by krypton physisorption. The SSA of the fine earth in illuvial horizons is predominantly due to finely divided Fe oxides, including goethite, characterized by an equivalent particle size of about 10 mu m. Kaolinite platelets of about 2 gm size account for a large volume proportion in the clay fraction but have a minor contribution to SSA. Fe oxides and organic matter (OM) are intimately associated. Heterogeneity at the um scale is created by local variations in the relative amounts of kaolinite and Fe-OM associations. These two kinds of physical entities are in random mixture. Moreover, variation of C/Fe atomic ratios reveals sub-mu m scale heterogeneity. The latter is due to variation in the relative proportion of organic compounds and Fe oxides, indicating that aggregation of nanoparticles, and not only mere adsorption or pore filling, plays a role in these associations. In this regard, our results highlight that OM associated with Fe protects Fe oxides against physical displacement and that part of this associated OM is oxidizable by NaOCl treatment. These findings demonstrate that the concept of OM stabilization through association with Fe must be revisited when considering the sub-mu m scale level because fine Fe oxide particles can be easily dispersed during oxidation of associated carbon. Combination of physical fractionation and microanalysis (e.g. SEM-EDS, vibrational spectroscopy) offer promising perspectives to clarify the relationship between chemical composition and sub-inn scale architecture, and to better understand soil processes

A Parametric Study of Radiative Dipole Body Array Coil for 7 Tesla MRI

In this contribution we present numerical and experimental results of a parametric quantitative study of radiative dipole antennas in a phased array configuration for efficient body magnetic resonance imaging at 7T via parallel transmission. For magnetic resonance imaging (MRI) at ultrahigh fields (7T and higher) dipole antennas are commonly used in phased arrays, particularly for body imaging targets. This study reveals the effects of dipole positioning in the array (elevation of dipoles above the subject and inter-dipole spacing) on their mutual coupling, $B_1^{+}$ per $P_{acc}$ and $B_1^{+}$ per maximum local SAR efficiencies as well as the RF-shimming capability. The numerical and experimental results are obtained and compared for a homogeneous phantom as well as for a real human models confirmed by in-vivo experiments

Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications

In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, the Markov Chain Generative Adversarial Neural Network (MCGAN), to alleviate the computational costs associated with solving the Bayesian inference problem. GANs pose a very suitable framework to aid in the solution of Bayesian inference problems, as they are designed to generate samples from complicated high-dimensional distributions. By training a GAN to sample from a low-dimensional latent space and then embedding it in a Markov Chain Monte Carlo method, we can highly efficiently sample from the posterior, by replacing both the high-dimensional prior and the expensive forward map. This comes at the cost of a potentially expensive offline stage in which training data must be simulated or gathered and the GAN has to be trained. We prove that the proposed methodology converges to the true posterior in the Wasserstein-1 distance and that sampling from the latent space is equivalent to sampling in the high-dimensional space in a weak sense. The method is showcased in two test cases where we perform both state and parameter estimation simultaneously and it is compared with two conventional approaches, polynomial chaos expansion and ensemble Kalman filter, and a deep learning-based approach, deep Bayesian inversion. The method is shown to be more accurate than alternative approaches while also being computationally faster, in multiple test cases, including the important engineering setting of detecting leaks in pipelines

Understanding Root Uptake of Nutrients, Toxic and Polluting Elements in Hydroponic Culture

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