Miracle-dz/UniFAR-FTUs huggingface dataset:
GPU based multi-scale depth map calculation for 3D reconstruction

When using multi-view stereo (MVS) method to calculate depth map for reconstructing dense 3D model, the model precision is often influenced by low-texture areas. To solve this problem, in this paper, we present an efficient multi-scale depth map based MVS method. First, the coarsest scale depth map is calculated which can easily capture the rich texture information in low-texture areas. Then we upsample this depth map to higher scale, and four forward-backward reprojections are used to reduce the error of depth difference between the last scale and the current scale. In addition, a GPU and CPU based cooperative optimization architecture is built to optimize and accelerate depth map computation in Patch Match (PM) stage. Experimental results show that, the proposed method can reconstruct quite accurate and dense point clouds compared to state-of-the-art methods.
Mediating effect of motivation on employees’ performance in a private hospital, Indonesia

Quality human resources in a hospital is very important in producing excellent services, both medical personnel those are paramedics and non-medical personnel who work together in one unit that must be developed and maintained well in order to be able to produce excellent service and compete in welcoming the era of globalization. Increased incomes experienced by some people in Indonesia create new lifestyles that are far from health so that greater community vulnerability to disease and this is a new challenge for hospitals to improve services in the health sector. The research seeks to explain important factors that affect employee performance in a private hospital. The novelty of the research is to look for and compare the direct and indirect effects between factors that affect performance, especially the role of motivation as a mediating variable. The research uses 100 samples taken from employees of one private hospital in Indonesia. The result finds out that there is a significant influence between work environment, competence, and motivation on performance both directly and indirectly.
Enhanced Activity of Immobilized or Chemically Modified Enzymes

Re-engineering enzymes with high activities in the given environments different from the physiological one has been constantly pursued for application of enzymatic catalysis in industrial biocatalytic processes, pharmaceutical industry, biosensing, etc. Re-engineering enzyme catalysts by chemical approaches, including immobilization and chemical modification, represents a simple but effective route. The unusual phenomenon that immobilized or chemically modified enzymes display higher activities than native enzymes has been observed in both single- and multiple-enzyme systems. Recent achievements in enhancing enzymatic activities in both single-and multiple-enzyme systems by chemical approaches are summarized in this review. We propose that these enhanced enzymatic activities can be attributed to the well-designed specific interactions between immobilization carriers (or chemical modifiers) and enzymes, substrates, or reaction media. In addition to this mechanism, which is applicable for both single- and m.
Cyclopropenone (c-C3H2O) as a Tracer of the Nonequilibrium Chemistry Mediated by Galactic Cosmic Rays in Interstellar Ices

While gas-phase astrochemical reaction networks nicely replicate the abundance of hydrogen-deficient organics like linear cyanopolyynes, pathways to complex organic molecules (COMs)—organic molecules with six or more atoms—have not been completely understood, with gas-phase models often significantly underestimating fractional abundances of the astronomically observed organics by orders of magnitude. Here, by exploiting cyclopropenone (c-C3H2O) as a tracer, laboratory experiments on the processing of an ice mixture of acetylene(C2H2) and carbon monoxide (CO) by energetic electrons coupled with astrochemical model simulations expose a previously poorly explored reaction class leading to COMs via galactic cosmic-ray-mediated nonequilibrium chemistry. These processes occur within interstellar ices at ultralow temperatures, but not through traditional radical–radical pathways on grain surfaces in the warm-up phase of the ices as hypothesized for the last decades, but more likely through barrierless excited state reactions during the irradiation.
Induction of gene expression and chitinolytic activities of putative chitinase gene bacteria Serratia plymuthica UBCF_13

The most dominant disease that causes low production of chili in Indonesia is anthracnose. Anthracnose disease is caused by the pathogenic fungi called Colletotrichum sp. The control of anthracnose disease can be done by using bio fungicides which can be developed from the class of enzymes that degrade pathogenic fungal cell walls. These enzymes are known as hydrolase enzymes, one of which is Chitinase. Chitinase is an enzyme that catalyzes the breakdown of chitin polymer compounds in β-1,4 glycosidic bonds. Chitin is a polymer compound that makes up the fungal cell wall. Chitinolytic bacteria can produce chitinase enzymes. S. plymuthica bacteria produce chitinase enzyme, one of which is putative chitinase [Chi_Put]. The purpose of this study was to express the Chi_Put gene from S. plymuthica UBCF_13 in pGEM-T Easy vector using Escherichia coli BL21 and to test the chitinolytic activity of Chi_Put in degrading chitin in-vitro. Chi_Put gene expression was induced by IPTG and in combination with Mg2+ and Cu2+ metal ions. The highest gene expression was marked by the thickest protein band on the SDS-PAGE visualization with a molecular weight of 47 kDa, namely in the treatment of expression induction with IPTG plus Mg2+ metal ion. The chitinolytic activity of Chi_Put UBCF_13 was tested using solid LB media containing 3% colloidal chitin. The longest clear zone diameter on the last day of observation was the expression induction treatment with Mg2+ with a length of 2.90 cm.
Construction of Scene Tibetan Dataset Based on GAN

For the research of Tibetan scene text detection and recognition, it is time-consuming and laborious to collect and annotate natural scene data labels manually. Therefore, artificial synthetic data is of great significance for promoting relevant work. This paper focuses on the study of replacing other languages in the scene with Tibetan, while maintaining the style of the original text. We decompose the problem into three sub-networks: text style transfer network, background inpainting network and fusion network. Firstly, the text in the foreground image is replaced by the text style transfer network to generate the foreground image. Then the background inpainting network erases the original text in the style image, and uses the surrounding information around the text to fill the text area to generate the background image. Finally, the generated foreground image and background image are used to generate the target image by the fusion network. We experimented with conversions from English to Tibetan and English to English to verify the generalization and robustness of the network. Experimental results show that its accuracy (SSIM, PSNR) on some datasets (SVT, ICDAR 2013) has been improved to some extent.
Research on Tibetan-Chinese neural network machine translation with few samples

Machine translation is an important task in natural language processing, and the study of Tibetan-Chinese neural machine translation is of profound significance in promoting Tibetan-Chinese scientific and cultural exchanges and the development of education and culture. In this paper, we investigate the performance of these techniques and methods on Tibetan-Chinese NMT with few samples by using deactivated word lists, data augmentation (back translation), pre-training models (ELMO), and attention mechanisms for the techniques and methods widely used in NMT, using seq2seq and Transformer models as the baseline, and finally, the BLEU value of Tibetan-Chinese NMT is increased from the initial 5.53 to 19.03.
Resilience: A new concepts in dealing with hydro-meteorological disaster and it’s application at the provincial level in Indonesia

Resilience is more capable of dealing with hydro-meteorological disasters compared to the risk. Resilience is a positive concept that puts forward adaptation efforts so that more in line with sustainable development goals. This study aims to introduce the resilience concept in dealing with hydro-meteorological disasters and apply it at the provincial level in Indonesia. The methods used are: 1) measuring the climate risk index (CRI), 2) measuring the adaptation capacity index (ACI), and 3) classifying the provinces based on CRI and ACI. Grouping CRI and ACI are used to define provinces which have the potential to bounce back better (low CRI - high ACI), bounce back (high CRI – high ACI), recover but worse than before (low CRI -low ACI), and collapse (high CRI – low ACI). The study results indicate that throughout 2017 there are 10 provinces that South Sumatra, Central Java, and West Nusa Tenggara. In contrast, 3 provinces with the most have the potential to bounce back better, 7 provinces bounce back, 7 provinces recover but worse than before, and 10 provinces collapse. In general, 3 provinces with the lowest resilience are resilient levels are Riau, Bangka Belitung, and Riau Islands.
Implementation of ANN Trained Voltage Control Scheme for Grid Islanded DG System

Distributed generation plays a significant role in power generation, but the standalone system has some limitations like excess power generation and sudden increment in load. Grid interconnected DG system mitigates all this type of problems but some different questions arise in this interconnection. How to synchronize the DG system with the grid? If any change in voltage or frequency they lead to disconnect the grid from DG system. But due to sudden loose of grid supply, the phase angle is changed in filter terminal voltage and leads same change in load voltage because when the grid is connected the load receives power from both DG and grid. In grid-connected system current controller is generally used, to maintain the constant current in load side, so in islanding conditions, the voltage profile will get damage and power factor is also dropped. This paper proposes a strategy of two controllers for both grid-connected and intentional islanding modes. PI controller based constant current regulator for grid-connected mode, while ANN based VC controller for intentional islanding mode. These two controllers are operated according to changes occurred at Point of Common Coupling (PCC).
Realization of shock accelerometer sequence-to-sequence calibration based on deep learning

The sensitivity parameters or mathematical model of sensor are obtained through traditional shock accelerometer under calibration test, and then the measurement signal is restored through compensation and correction. As the shock signal with complex components is used in a harsh environment, it is very difficult to accurately restore the measured signals. In the meantime, the deconvolution process of signal recovery is an inverse problem in mathematical physics, which has ubiquitously ill-posed problems, thereby bringing great challenges to the accuracy and precision of the solution. Thus, we propose a depth calibration network based LSTM, which can be used to learn the mapping relationship between the calibrated sensor signal and the standard signal. The measured signal can be restored through a data-driven sequence-to-sequence calibration network, which is trained and verified through the exclusive open-source data set of shock signals. The test results proved the superior performance of the network in shock signal calibration.
Weighted SRGAN and Reconstruction Loss Analysis for Accurate Image Super Resolution

Super resolution (SR) image generation is to generate a high-resolution image from a given low-resolution image, which can be used in numerous vision tasks, such as small object detection and specific image processing. Generative adversarial network for super resolution (SRGAN) is the mainstream framework in SR task, which utilizes two reconstruction losses, including MSE loss and VGG loss. However, the influence of these losses on model learning is not examined. In this paper, the importance of MSE and VGG losses is analyzed. The loss function of traditional SRGAN is improved, and the weights of MSE and VGG losses are set manually. The weights range from 0 to 1, and the sampling interval is 0.1. Furthermore, a learnable parameter to dynamically adjust the two weights is proposed. Experiments on the datasets, including Set5, Set14, BAS100, and Urban100, show that our method is capable of generating much better images than SRGAN, with higher values of PSNR and SSIM. It is found that the MSE loss makes a greater contribution to learning the discriminative model and the VGG loss plays a supplementary role. Our WSRGAN can apply to most SRGAN-based methods to improve their accuracy.
An Improved MPCC Method Based on the Predictive Delay Error Analysis for PMSM Speed Control System

Taking permanent magnet synchronous motor (PMSM) as the research object, the model predictive current control strategy (MPCC) was studied. In order to solve the shortcoming that the MPCC strategy depends on the motor prediction model, reduce the prediction error caused by MPCC and enhance the prediction accuracy of the system, an improved MPCC model was constructed by a two-step prediction method. At the same time, compared with the system based on Field-Oriented Control (FOC) and MPCC control and the system based on improved MPCC control, the proposed control strategy can make the PMSM run stably and efficiently, and reduce the torque ripple and the THD value of the three-phase stator resistance current. The simulation results show that the motor system controlled by the method can run stably, and then the correctness and effectiveness of the strategy are verified.
Application of principal component analysis in evaluation of epidemic situation policy implementation

In order to study the impact of various policies issued by the government on epidemic prevention and control, we first considered them from three aspects: the degree of leniency of policies, the degree of stringency of policy implementation and the degree of self-consciousness of individuals, broken down into 10 indicators, designed a questionnaire to collect relevant data. Using Principal Component Analysis (PCA), 4 principal components were extracted from 10 indexes, and the scores and total scores of each principal component were obtained. Finally, the corresponding suggestions are given.
Electric current distribution in thin Ti-Cu base coatings

The electrical resistivity of thin layer (film, coating) about 5 microns’ thick has been measured by the classical four-point probes method. The layer includes titanium-copper alloy grains up to 5 microns in size. Unexpectedly we encountered a physical phenomenon that spoils the measurements: the fluctuation of measurement results, i.e. the error of measurement may exceed 1000%. Under certain conditions such crystals may exert some long-range influence leading to tenfold fluctuations of results of measuring the electrical resistivity, despite of the fact that these crystals are evenly distributed and do not create large-size clusters It was found that the relative scatter of measurement results decreases along with the increase of the distance between the electrodes (probes) due to averaging of currents, however, the error decreasing occurs inversely proportional to the logarithm of the distance between the probes. That is why the scatter remains significant at distances between the applied probes thousands times bigger than crystal grains. It decreases much slower than it occurs in case of one dimensional long strip where the error of measurement is simply inversely proportional to the distance between the probes. It creates huge difficulties for measurement of surface resistance. To overcome these difficulties, the method for the statistical proceeding of the non-uniform results of multiple measurements of electrical resistivity in two-dimensional systems has been proposed, which enable to extract information about average surface resistance even from such confusing set of measurements. By help of such statistic the four probe method can be potentially used in industry, for example, for controlling the quality of metal film coatings, used for heating by electrical current for de-icing of aircrafts or wind turbines. The Paper type: Research paper
Effect of different banana pseudostem parts on their starch yield, morphology and thermal properties

Since banana tree cannot produce fruit twice, there is an abundance of pseudostem left as abandoned crop waste and this leads to environmental pollution. This research was conducted to evaluate the effect of different parts of banana pseudostem on the yield, morphology and thermal properties of the starch. The pseudostem was divided into three parts, which are the outer part of pseudostem (OPS), inner part of pseudostem (IPS) and the whole part of pseudostem (WPS). The starch yield from different parts was evaluated. The morphological and thermal properties of each starch were determined by scanning electron microscopy and thermoanalytical technique, respectively. The result shows that yield of starch obtained were 0.08 % higher in OPS compared to IPS. The yield of starch obtained range from 0.12-0.20 %. The iodine test had confirmed the presence of starch obtain from the pseudostem. All the starch showed irregular and spherical shape based on morphology observation. The gelatinization temperature of starch was between 71.15°C-76.50°C. The gelatinization temperature of starch obtained from IPS is 3.6 % higher compared to OPS and WPS. The results from this finding show that the starch obtained from pseudostem could be a potential source of starch that can be commercialized, and thus increase the sources of starch in the food industry.
A Characterization of the Circumstellar Gas of WD 1124−293 Using Cloudy

Between 30% and 50% of white dwarfs (WDs) show heavy elements in their atmospheres. This pollution is thought to arise from the accretion of planetesimals perturbed by outer planet(s) to within the WD’s tidal disruption radius. A small fraction of these WDs show either emission or absorption from circumstellar (C-S) gas. The abundances of metals in the photospheres of WDs with C-S gas are mostly similar to the bulk composition of the Earth. The C-S component arises from gas produced through collisions and/or the sublimation of disintegrating planetesimals. High-resolution spectroscopic observations of WD 1124−293 reveal photospheric and C-S absorption of Ca in multiple transitions. Here, we present high signal-to-noise ratio spectra, an updated WD atmosphere analysis, and a self-consistent model of its C-S gas. We constrain the abundances of Ca, Mg, and Fe in the photosphere of WD 1124−293, and find agreement with the abundances of these three species in the C-S gas. We find the location of the C-S gas is ∼100 white dwarf radii, the C-S and photospheric compositions are thus far consistent, the gas is not isothermal, and the amount of C-S Ca has not changed in two decades. We also demonstrate how to use Cloudy to model C-S gas viewed in absorption around polluted WDs. Modeling the abundances of gas around polluted WDs with Cloudy provides a new method to measure the composition of exoplanetesimals and will allow a direct comparison to the composition of rocky bodies in the solar system.
A novel hybrid feature extraction method using LTP, TFCM, and GLCM

Image classification and feature extraction have been studied extensively and used efficiently in several applications. This paper suggests a novel method by combining three main methods for texture feature extraction. The proposed method is based on Local Ternary Pattern (LTP), Texture Feature Coding Method (TFCM), and Gray Level Cooccurrence Matrix (GLCM). We have entitled our method as GCLTP which is stand for Gray Coding Local Ternary Pattern. The combination of LTP, TFCM, and GLCM is assigned a unique value used to extract the features of an image. GCLTP is tested using images are taken from the Brodatz database. A set of 22 features were extracted from images. GCLTP is experimentally accomplished a high accuracy in classification by using the most known classifiers.
State-space Representation of Matérn and Damped Simple Harmonic Oscillator Gaussian Processes

Gaussian processes (GPs) are used widely in the analysis of astronomical time series. GPs with rational spectral densities have state-space representations which allow (n) evaluation of the likelihood. We calculate analytic state space representations for the damped simple harmonic oscillator and the Matérn 1/2, 3/2 and 5/2 processes.
Improved collaborative filtering recommendation algorithm based on user attributes and K-means clustering algorithm

Aiming at the problem of poor performance of collaborative filtering algorithm on data sets with large sparsity, this paper proposes an improved collaborative filtering recommendation algorithm which integrates user attributes and K-means clustering. When considering user similarity, the weight of user attributes is introduced to reduce the impact of data sparsity on similarity calculation. Meanwhile, the characteristics of user’s age, gender and occupation are concerned. At the same time, combined with K-means clustering, the algorithm can further improve the accuracy of the recommendation model.
The Influence of Surface Ground Material on the Outdoor Thermal Value

The provision of open space in the building for educational purposes such as university is mandatory to ensure the balance between the buildings and the environment. Besides, the availability of open space in construction projects, particularly architecture, is also listed as one of the requirements that must be included when applying for a building permit. What is more, besides its functional factor, open space planning is expected to enhance campus aesthetics. The strategy in choosing the proper material for covering open areas in the tropical climate country is still challenging. It holds a significant contribution to reducing the effect of urban heat island phenomena. This article discusses the impact of changing surface ground materials on the site radiation of an open space called “lapangan lilin,” located in Atma Jaya University, Yogyakarta, Indonesia. The research incorporated computer simulation methods, ENVI-met, to compare several types of materials and their effects on the environment. The result shows that of the four types of materials that being compared (paving, soil, asphalt, and grass), the soil material gives the best outdoor thermal value impact compared to other materials.