Miracle-dz/UniFAR-FTUs huggingface dataset:
Building age profile and figure-ground image: defining the urban development pattern of Balikpapan City

As one of Indonesian city that emerged from the industrial sector, Balikpapan grew and was inhabited by transmigrates from various cultures. The development and growth patterns of the Balikpapan urban area was influenced by the culture of the residents, due to the absence of empire influence or colonialism. This research was conducted to identify the oldest area as a starting point of the urban development patterns of Balikpapan. The building-age profile method was used to identify the oldest building groups or blocks, which belong to the city’s heritage zone. In this analysis, a series of figure-ground images in several periods were collected to indicate the building age. Through the process of overlaying solid-void maps, the pattern of space-built interaction was generated as the patterns of urban development. Results shown that urban development, which was determined through overlay technique, was organic pattern, which was formed by several connected irregular shapes. The conservation of the urban pattern indicates the effort to achieve urban identity, as one of the sustainable urban form indicator.
Classification of Power Quality Events - An Inclusive Review

Power Quality is major issue that has led to large amount of research by the industry and academic experts. The increased uses of the electronics in electrical utilities have created a need of monitoring the power quality and provide the mitigation to enhance the performance of the electrical equipment. With advancement in technologies and availability of various online monitoring tools, it is very important to assess and review these technologies. The increased rate of research in this field is proven to be beneficial to the consumers. Customers are now able to request for better quality of service and making the Power Quality issue more important. This paper provide an inclusive review of various application of signal processing on power quality events, analysis of artificial intelligent techniques to classify the signals and optimization techniques for decision making. The paper also explores the upcoming opportunities for power quality classification and further improvement in terms of higher performance of the electrical equipment.
High heat removal technique for a lithium neutron generation target used for an accelerator-driven BNCT system

Accelerator-driven neutron sources have been developed worldwide for boron neutron capture therapy (BNCT) instead of nuclear reactors. Nagoya University is currently developing a compact DC accelerator driven BNCT system using a sealed lithium (Li) target. The target receives a high heat load by a proton beam, and the heat then requires efficient removal. This study developed a high-efficiency heat removal technique that uses V-shaped staggered ribs on the heat removal side of the flow channels. We performed experiments using an electron beam to confirm the technique performance. The results showed double the heat removal performance compared to the smooth surface channel. The value expected to sufficiently remove 6.6 MW/m2 of heat load provided by the proton beam at the Nagoya University neutron source for BNCT.
Automated image analysis for quantification of materials microstructure evolution

In this work, an automated image analysis procedure for the quantification of microstructure evolution during creep is proposed for evaluating scanning electron microscopy micrographs of a single crystal Ni-based superalloy before and after creep at 950 °C and 350 MPa. scanning electron microscopy-micrographs of γ/γ′ microstructures are transformed into binary images. Image analysis, which involves pixel by pixel classification and feature extraction, is then combined with a supervised machine learning algorithm to improve the binarization and the quality of the results. The binarization of the gray scale images is not always straight forward, especially when the difference in gray levels between the γ-channels and the γ′-phase is small. To optimize feature extraction, we utilized a series of bilateral filters as well as a machine learning algorithm, known as the gradient boosting method, that was used for training and classifying the micrograph pixels. After testing the two methods, the gradient boosting method was identified as the most effective. Subsequently, a Python routine was written and implemented for the automated quantification of the γ′ area fraction and the γ channel width. Our machine learning method is documented and the results of the automatic procedure are discussed based on results which we previously reported in the literature.
DCSNet: A Surface Defect Classification and Segmentation Model by One-Class Learning

Researches in surface defect classification and segmentation technology have been seen significant progress in recent years. However, there are few works on One-Class learning in this direction by a single model. In previous researches, some problems remain unsolved in the surface defect detection methods, e.g. the training needs a large number of samples and these models cannot classify and locate the surface defect accurately, etc. The main contribution in this work is that we summarize the overall ideas of previous research in network design and propose a multi-task model which could be trained only using a few of positive samples. Meanwhile, the experiments on AITEX detection datasets[1] which get 84.4% DR, 4.4% FAR and 34.2% MIOU, and conduct an ablation experiment in real industrial product dataset to validate the effect of different backbones on DCSNet. It’s worth mentioning that DCSNet provides a solution to the task of surface defect classification and segmentation based on One-Class learning. The code will be open source in ext-link-type="uri" xmlns:xlink="http://www.w3. org/1999/xlink" xlink:href="https://agit.ai/wyxxx/zhengtu">https://agit.ai/wyxxx/zhengtu.
Mask Recognition Method Based on Graph Convolutional Network

The rapid spread of COVID-19 worldwide has exacerbated the health crisis and affected our daily lives. Medical researchers have shown that wearing masks in public places is essential to reduce the spread of COVID-19 infection. The increase in the accuracy of the identification of masks in public places can effectively prevent the further spread of the epidemic. In this paper, we propose a mask recognition network based on graph convolutional network, D-GCN. The network adopts the method of combining convolutional neural network and graph convolutional neural network. First, Dense Net 101 is used to extract features of the real-time image to be tested, and then GCN processes the training label information to form a directed graph through the word embedding vector, it forms a new classifier based on the label connection between the training pictures, and processes the extracted feature vectors. Finally the classification result is output and completing the recognition. The experiment is carried out on the mask dataset and MAFA dataset, and the recognition accuracy is significantly improved compared with previous methods.
WSN Topology Control algorithm for High Speed Railway Monitoring

In order to solve the safety problems caused by the continuous improvement of train speed and the continuous growth of railway scale, it is inevitable to apply wireless sensor network (WSN) to train operation environment monitoring. In order to prolong the survival time of wireless monitoring network, the game theory is used to construct a distributed topology model. In order to solve the problem of network lifetime shortening caused by uneven energy consumption of nodes, the Theil index is introduced to improve the energy consumption balance of nodes, and a topology control algorithm for energy consumption balance (EBTCA) is proposed. Experiments show that the algorithm has good results in terms of network lifetime and energy balance.
Customer Satisfaction Research based on Customer Service Dialogue Corpus

Recently, consumers are increasingly inclined to contact customer service for help when they encounter problems and have high demands for remote support. A high-quality customer service connects the company with its customers and establishes a positive image. Applying customer satisfaction metrics to measure the quality and efficiency of customer service is widely used, yet most of the existing customer service evaluation systems rely on manual processes, which are clearly unsustainable and costly. We introduce an ERNIE-based customer satisfaction analysis model that automatically analyses the text of customer service dialogues and scores them from four perspectives (i.e., product, service, process and overall) without human involvement. Furthermore, we construct a corpus containing around 1500 entries of dialogues texts transcribed from customer service consultation and scale it up to 9 times in the training phase. Results show that our model performs better compared to the baseline model and demonstrates a good generalization ability as well.
Coherent radiation of active Josephson traveling-wave antennas

The dynamics of large superwavelength open systems which are free-standing multi-wire lines with a large number of Josephson junctions, DC bias batteries and other lumped elements is analyzed using direct numerical simulation. Such systems represent the simplest version of the active Josephson antennas proposed in our earlier studies and show promise as terahertz and subterahertz radiation sources. We have studied dependences of the radiation characteristics on the antenna geometry, number of junctions, DC bias current and the lumped elements position and parameters. Detailed simulation results reveal a multitude of phase transitions between the dynamical states, which differ in the number of Josephson junctions synchronized by the excited waves of current and, hence, in the radiation power emitted into open space also as in radiation patterns. It is shown that, in the absence of noise, the Josephson antennas can emit completely coherent radiation.
Fourier Transform Infra-Red spectrophotometry observation to find appropriate wavelength for non-invasive blood glucose level measurement optical device

The appropriate wavelength is essential for non-invasive blood glucose level measurement optical device. We conducted this experimental observation to find the usable wavelength candidate for non-invasive blood glucose level measurement optical device in 1000 nm to 2500 nm range. We run this observation in Prodia Bogor and Food Processing and Crops Technology Lab, Faculty of Agricultural Engineering, IPB University, July 2019. We obtained fasting, 15 mnt postprandial, and 30 mnt postprandial blood sample from 10 randomly selected consenting non blinded healthy adult subjects between 18 to 60 years old. We measured spectrophotometric absorbance for each blood sample. We did the measurement using BUCHI NIR FLEX 500. We compared the result to blood glucose level by standard gold measurement. We also compared the Pearson correlation, and the standard deviation of all samples to then-existing wavelength source LED provided by Thorlabs. We found that the highest absorbance and standard deviation wavelength is at 1939 nm. Wavelength LEDs candidates that represent measuring blood glucose levels is 1200, 1300, 1450, 1750, and 1950 nm. We did not find a severe adverse effect from each participant. Researchers should confirm the trial results with in vivo human observation.
Elemental Abundances of Prominence Material inside ICMEs

A small number of interplanetary coronal mass ejections (ICMEs) have been identified that contain measurable contributions from prominence plasma. In situ measurements from during these events are marked by the presence of unusually low-charge states of C, O, and Fe, representing ionization equilibrium formation temperatures of ∼104–105 K, consistent with prominence material observed at the Sun. We present a thorough analysis of the elemental abundances of a wide variety of heavy ions, measured by Advanced Composition Explorer/SWICS, in prominence material observed in the solar wind. We find that prominence material observed in situ tends to be more enriched in heavy ions than the surrounding ICME plasma and the fast and slow solar wind. We also find that the material is on average moderately enhanced in low first ionization potential elements compared to photospheric abundances, with values that lie between fast and slow solar wind. In rare instances, where in situ prominence material is observed to have clear, persistent, low-charge states over longer periods of time, it exhibits elemental abundances that are photospheric in nature. However, in most prominence events we see indications that the associated material contains a mixture of prominence and adjacent ICME plasma. The anomalous behavior of the elemental and ionic composition in ICMEs with and without prominence material can be used to study physical processes that occur during CME initiation and release.
Research on the origin of solid super-slip and friction based on Raman spectroscopy

It is estimated that about 1 / 3 ~ 1 / 2 of the world’s energy consumption is caused by friction and wear. About 80% of the parts of general machinery and equipment are scrapped due to wear. In China, the annual loss caused by friction is more than 4 trillion. Therefore, to explore the origin of friction and realize solid super sliding has become a key point of people’s attention. But the basic scientific principle and mechanism of friction origin are difficult to explore, so how to give a reasonable method to solve this problem is particularly important. This paper focuses on the use of Raman spectroscopy to observe the solid super slip phenomenon and phonon dissipation, through Raman spectroscopy to determine the orientation of molecules on the friction interface, detect the related properties of two-dimensional materials, as well as crystal defects and their apparent morphology to explore the origin of friction, and to reduce the energy loss caused by friction.
Molecular Detection of the Gene (Glutathione S-Transferase Zeta Class-Like) Responsible for Stress Tolerance and Studying Its Genetic Expression in a Number of Snake Melon Cultivars

The experiment was conducted in the winter agricultural season 2020 in the greenhouses of the Al-Fares Company in Al-Zubair district, with the aim of investigating one of the important genes responsible for tolerating Armenian cucumber for stress conditions, which is the gene (glutathione S-transferase zeta class-like) in 21 Armenian cucumber cultivars. Isolation of RNA from leaves after 100 days of cultivation at the saline level 5 dSm−1, as well as a control treatment of 1 dSm−1and studying their gene expression using RT-qPCR (Real Time-quantitative Polymerase Chain Reaction) technique. The results showed the excelled of the cultivars under the influence of salt stress, Egypt, Babylon, Mosul (Carmelis), Kirkuk, Diyala and Karbala, as they gave the highest expression of their gene expression (25.12, 21.87, 19.04, 21.87, 19.04 and 20.40), respectively, so they can be considered salt-tolerant cultivars. This is because the expression values for the gene (glutathione S-transferase zeta class-like) were high compared to the rest of the cultivars. As for the cultivars whose sensitivity to salinity was confirmed by the experiment, they are Iranian (Ghani), Italian, Baghdad and Amara. These cultivars gave the lowest gene expression values (2.73, 4.46, 4.14 and 3.49), respectively.
Tidal Heating Did Not Dry out Io and Europa

The Galilean satellites exhibit a clear trend in composition from the rocky Io, close to Jupiter, to the icy and distant Callisto. Proposed causes of this trend can be roughly divided by when the trend developed—either as a result of the material accreted, as a byproduct of the accretion process, or due to the subsequent diverging evolution of the moons. While the first two options have been heavily favored in the existing literature and were therefore studied previously, in this work, we directly address the last of these possibilities. To do so, we determine the range of plausible tidal heating experienced by these moons and how efficiently that energy could be converted into mass loss. We find that while the total tidal energy does exceed the energy required to lose an ice shell, the loss process would have to be highly energy efficient. Examining a range of loss processes, we find that only in extreme cases could enough mass be lost from Europa and no cases where enough mass could be lost from Io. We conclude that this compositional gradient must have been in place by the end of accretion.
Infrared Small Target Detection Based on Multi-Scale Local Contrast Network

To mitigate the challenge of the lack of the intrinsic features of the small target which possesses only few pixels, we designed a multi-scale local contrast network which combines features extracted from different scales and layers for single-frame infrared small target detection in this paper. We extract the multi-scale contrasts features in the same layer by multi-scale local contrast module and fuse the cross-layer contrast features by local contrast fusion module. Moreover, we embed these modules at each stage as a depth-wise nonlinear layer into an end-to-end convolutional network to predict the small target. We conduct extensive experiments and comparison with other SOTA methods to verify the performance of our method. The results show that our method outperforms many of its competitors, and when taking computation cost into consideration, it will be more outstanding.
High-frequency pulse train generation in dispersion-decreasing fibre: using experimental data for the metrology of longitudinally nonuniform fibre

This paper describes experiments concerned with pulse train generation from initially modulated cw light in an anomalous dispersion decreasing fibre. We have obtained stable contrast trains of subpicosecond pulses with a repetition rate in the range 100 – 300 GHz and demonstrated generation of an optical comb spectrum with a –20-dB width of up to 80 nm. A method has been proposed for reconstructing an unknown dispersion profile of a longitudinally nonuniform fibre by comparing experimental data and numerical simulation results.
Retraction Retraction: Research on Multimodal Emotion Analysis Algorithm Based on Deep Learning (J. Phys.: Conf. Ser. 1802 032054)

In recent years, more and more people are keen to express their feelings and opinions in the form of pictures and texts on social media at the same time, which makes the multimodal data with pictures and texts as the main content growing. Compared with monomodal data, multimodal data contains more information and can reveal the real feelings of users. The analysis of the emotion of these massive multimodal data is helpful to better understand people's attitudes and viewpoints and has a wide range of application scenarios. In order to solve the problem of information redundancy in multimodal emotion classification tasks, based on the tensor fusion scheme, a multimodal emotion analysis method based on attention neural network is proposed. This method constructs a text feature extraction model and an image feature extraction model based on attention neural network, which highlights the key areas of image emotional information and the words containing emotional information, which makes the expression of single-modal features more concise and accurate. The tensor product of each mode is regarded as the joint feature expression of multimodal data, and the redundant information of joint features is eliminated by principal component analysis, and then the emotion category of multimodal data is obtained by support vector machine. The proposed model is evaluated on two real Twitter image data sets. The experimental results show that, compared with other emotion classification models, this method has a great improvement in classification accuracy, recall rate, F1 index and accuracy.
Numerical simulation studies of the flow field and pollutant dispersion in the street canyon under different thermal stratifications

This paper simulates the flow and dispersion in the street canyon under different thermal stratifications using three-dimensional (3-D) Computational Fluid Dynamics (CFD) methodology, and compares the simulated results with the wind-tunnel experiment data reported by Uehara et al. Monin-Obukhov length (L) is used to describe the atmospheric flow under different thermal stratifications. The results show that the effect of thermal stratification on the flow structure and pollutant dispersion within the street canyon is significant. The magnitude of velocity in the middle part of the street canyon is relatively small under different conditions. The intensity of vortex is significantly influenced by thermal stratification. The maximum value of k/U 700 2 appears near the windward corner of the building under different conditions. The high-concentration region appears in the upwind part of the canyon. The normalized concentration on the leeward side is greater than those of windward side within the street canyon under different conditions.
Synthesis, Characterization and Performance Evaluation of Pure Silica MCM-41 for Effective Removal of Dibenzothiophene from Petroleum Distillate

The presence of sulfur-containing compound (e.g. Dibenzothiophene, DBT) in petroleum distillates such as diesel is of great concern due to the environmental problems it poses on the environment. In this study, synthesis and performance evaluation of MCM-41 adsorbent for enhanced removal of DBT from petroleum distillates in batch adsorption mode is reported. MCM-41, employed in this study for adsorptive desulphurization, was successfully synthesized with Cetyltrimethylammonium bromide (C19H42BrN) and fumed silica (SiO2). The gel was stirred for 30 min at room temperature and allowed to age at varied durations ( 24 h, 48 h & 72 h) before hydrothermal synthesis and then calcined at 550 C. Different analytical techniques were used to characterize the synthesized adsorbents, such as; Fourier transform infrared (FTIR) to check the surface nature, N2 physi-sorption at 77 K to examine the textural property; Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM) equipped with Energy Dispersive X-ray (EDX) to check the surface morphology/elemental composition; and X-ray diffraction (XRD) to check the crystallinity of the adsorbent. The adsorptive desulphurization experiments were carried out using a model diesel prepared by dissolving 0.1 g of dibenzothiophene in 100 mL of hexane. The desulfurized model diesel was analyzed using Gas Chromatography/Mass Spectrometer (GC/MS). The adsorption performance of each adsorbent was evaluated in a batch mode. The results showed that, aging time has significant impact on the crystallinity and textural properties of MCM-41. The results show that crystallinity increased with decreasing aging period with smaller surface area as compared to adsorbent whose gel was aged for more hours. Furthermore, the textural properties seem to improve as the aging time increased. MCM-41, whose gel was aged for 72 h showed the highest adsorption capacity of 36.2 mg/g with highest DBT removal of 96.5 %. The information and results presented in this study could therefore provide a platform for further studies in this field.