Automatic Representative View Selection of a 3D Cultural Relic Using Depth Variation Entropy and Depth Distribution Entropy

Automatically selecting a set of representative views of a 3D virtual cultural relic is crucial for constructing wisdom museums. There is no consensus regarding the definition of a good view in computer graphics; the same is true of multiple views. View-based methods play an important role in the field of 3D shape retrieval and classification. However, it is still difficult to select views that not only conform to subjective human preferences but also have a good feature description. In this study, we define two novel measures based on information entropy, named depth variation entropy and depth distribution entropy. These measures were used to determine the amount of information about the depth swings and different depth quantities of each view. Firstly, a canonical pose 3D cultural relic was generated using principal component analysis. A set of depth maps obtained by orthographic cameras was then captured on the dense vertices of a geodesic unit-sphere by subdividing the regular unit-octahedron. Afterwards, the two measures were calculated separately on the depth maps gained from the vertices and the results on each one-eighth sphere form a group. The views with maximum entropy of depth variation and depth distribution were selected, and further scattered viewpoints were selected. Finally, the threshold word histogram derived from the vector quantization of salient local descriptors on the selected depth maps represented the 3D cultural relic. The viewpoints obtained by the proposed method coincided with an arbitrary pose of the 3D model. The latter eliminated the steps of manually adjusting the model’s pose and provided acceptable display views for people. In addition, it was verified on several datasets that the proposed method, which uses the Bag-of-Words mechanism and a deep convolution neural network, also has good performance regarding retrieval and classification when dealing with only four views.

Environmentally Driven Color Variation in the Pearl Oyster Pinctada margaritifera var. cumingii (Linnaeus, 1758) Is Associated With Differential Methylation of CpGs in Pigment- and Biomineralization-Related Genes

Today, it is common knowledge that environmental factors can change the color of many animals. Studies have shown that the molecular mechanisms underlying such modifications could involve epigenetic factors. Since 2013, the pearl oyster Pinctada margaritifera var. cumingii has become a biological model for questions on color expression and variation in Mollusca. A previous study reported color plasticity in response to water depth variation, specifically a general darkening of the nacre color at greater depth. However, the molecular mechanisms behind this plasticity are still unknown. In this paper, we investigate the possible implication of epigenetic factors controlling shell color variation through a depth variation experiment associated with a DNA methylation study performed at the whole genome level with a constant genetic background. Our results revealed six genes presenting differentially methylated CpGs in response to the environmental change, among which four are linked to pigmentation processes or regulations (GART, ABCC1, MAPKAP1, and GRL101), especially those leading to darker phenotypes. Interestingly, the genes perlucin and MGAT1, both involved in the biomineralization process (deposition of aragonite and calcite crystals), also showed differential methylation, suggesting that a possible difference in the physical/spatial organization of the crystals could cause darkening (iridescence or transparency modification of the biomineral). These findings are of great interest for the pearl production industry, since wholly black pearls and their opposite, the palest pearls, command a higher value on several markets. They also open the route of epigenetic improvement as a new means for pearl production improvement.

Hydrogeological, topographical and drain factors controlling water table depth variation and potential nitrate reduction in subsurface drained clayey till area

<p>Nutrient losses in agricultural areas have detrimental effects not only on the surface water quality but are also unfavorable for sustainable agriculture practices. In Denmark, there are currently nitrate regulations applied for ID15 catchments (15 square km scale), nevertheless, it is crucial to know how much nitrogen is retained in the root zone, saturated zone, riparian zone as N-retention varies widely within ID15 catchments. Currently, N-retention mapping does not incorporate N-retention in the root zone on ID15 scale. N-retention in the root zone of subsurface drained clayey areas is potentially influenced by the variation in water table depth. Therefore, we will evaluate the effect of shallow hydrogeology, topography and drain parameters on local (sub-field scale) water table depth variation using a case study in eastern Jutland, Denmark.</p><p>The aim of the study was to assess which hydrogeological variables, drain parameters and topographical variables control water table depth variation in the root zone. This analysis was aided by a groundwater flow model code (MODFLOW). For the following purpose, hydrological data (drain flow at the outlet and depth to the water table in piezometers) and geophysical data (subsurface electrical conductivity) were collected. The geophysical data was collected by two ground-based electromagnetic systems (DUALEM and tTEM). The electrical conductivities were directly translated into two zones of homogeneous hydraulic conductivities based on a threshold value. Hydraulic properties were varied for each zone. Areas with no geophysical data were simulated using Direct Sampling, a Multi Points Statistics method. We generated several flow models, which had a varying spatial distribution of hydraulic zones and varying hydraulic properties (input factors). Moreover, boundary conditions (lateral fluxes), topographical smoothing and drain parameters (drain conductance and drain depths) were some of the other input factors we considered in this work. Model boundary conditions data were obtained from the national hydrological model. The variation in input factors was related to variation in simulated water table depths and drain flow at the outlet using a one-at-a-time sensitivity analysis.</p><p>Drain flow fraction, depth to the water table and drain discharge are analyzed as the quantity of interest for both wet and dry periods. Drain fraction is calculated as the ratio of the area contributing to the drainage to the area contributing to the recharge within the same area. The results will discover crucial controlling components of water table depth with which variations in N-retention can be estimated between different fields. The emphasis is to discover the connection between hydrogeological, topographical, and drain variables, and water table depth. We will examine potential implications for evaluating drain fraction and potential nitrate reduction.</p>

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry. Among all errors, thermal error affects the machining accuracy considerably. Because of the significant impact of Industry 4.0 on machine tools, existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data. A thermal error modeling method is proposed based on bidirectional long short-term memory (BiLSTM) deep learning, which has good learning ability and a strong capability to handle a large group of dynamic data. A four-layer model framework that includes BiLSTM, a feedforward neural network, and the max pooling is constructed. An elaborately designed algorithm is proposed for better and faster model training. The window length of the input sequence is selected based on the phase space reconstruction of the time series. The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting. The average depth variation of the workpiece was reduced from approximately 50 µm to less than 2 µm after compensation. The reduction in maximum depth variation was more than 85%. The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

Pengaruh Kedalaman Sudu Mangkok terhadap Unjuk Kerja Turbin Kinetik

The purpose of this study was to determine the effect of depth variations of the bowl blade on the performance of kinetic turbines. The test has conducted experimentally on a laboratory scale. In this study three vertical shaft, kinetic turbines were used with blade depth variations of 2 cm, 3 cm, and 4 cm. Each turbine will be tested on different rotation variations and flowrate variations. Parameters such as turbine power and efficiency will be determined based on the results of measurements of water velocity, water level, and braking load. The results showed that the depth of the bowl blade affected the performance of the kinetic turbine. The highest kinetic turbine performance was obtained in the turbine with 4 cm blade depth variation, followed by the turbine with 3 cm blade depth variation and the lowest turbine performance was obtained at 2 cm blade depth variation. The maximum performance of the turbine is obtained at 4 cm blade depth variation at 80 rpm and 65 m3/h water discharge, where the power generated is 13.2 Watts and efficiency is 34.5%.