Miracle-dz/UniFAR-FTUs huggingface dataset:
Toxicity of Alkyl Sulfate Surfactant Detergent (AS) to Larvae of Vannamei Shrimp (Litopenaeus vannamei) From Marine Waters, District of Lingga Timur, Lingga Regency

The coastal area of East Lingga Subdistrict, Lingga Regency has been designated by the Lingga Regency government as the center for the development of shrimp pond cultivation. This study aims to determine the level of toxicity of the surfactant alkyl sulfate (AS) detergent to post larvae (PL) of Vannamei shrimp (Litopenaeus vannamei). An acute test was performed on PL10 samples exposed to AS media for 96 hours. Then the Median Lethal Concentration (LC50) value was calculated for 4 times (24 hours, 48 hours, 72 hours, and 96 hours). Furthermore, the PL-15 sample was AS in the sub chronic test, then the sample was exposed to AS media for 24 hours, then the growth rate and changes in the gill organ structure of the PL samples were observed. From the test results, it is known that the LC50 values in the AS test for the 24th, 48th, 72th, and 96th times of the shrimp larvae samples obtained LS50 values were 33.6 mg/l, 29.4 mg/l, respectively. 24.3 mg/l, and 22.8 mg/l. Subsequently, an increase in AS concentration by 17.11 mg/l in the rearing medium resulted in a decrease in the growth rate of post larvae. The acute and sub chronic testing period showed changes in the behavior of post-larvae samples. The histopathological observations found damage to the structure of the sample gill organs and hepatopancreas. Symptoms of damage to the structure of the gills began to appear at AS concentrations of 25.58 mg/l at time 96 and 72 with an LC50 value of 34.99 mg/l. Furthermore, it was also seen at an AS concentration of 9.78 mg/l at time 24. The results of this study found that AS toxicity to Vannamei larvae increased with increasing concentration and exposure time.
Development of a self-driving car prototype for educational and research purposes

Self-driving car (SDC) is one of the most challenging topics in artificial intelligence. However, current off-the-shelf SDC platforms are expensive and difficult to modify, which prevents more researchers from working towards the improvement of self-driving technologies. This paper presents the development of a SDC prototype with low-cost sensors and an open-source software platform for educational and academic research purposes. The goal of such a SDC prototype is to serve as a testbench, which allows students and researchers to easily test new algorithms of SDC. Localization is one of the most basic and important problems in SDC. To achieve high performance using low-cost sensors, a localization method based on Lidar-INS fusion was investigated under the framework of Kalman filtering. Autonomous driving experiments were conducted on roads of a university campus environment and the results are used to show the performance.
A Survey on Recent Advances in Image Captioning

Image captioning, an interdisciplinary research field of computer vision and natural language processing, has attracted extensive attention. Image captioning aims to produce reasonable and accurate natural language sentences to describe images. It requires the captioning model to recognize objects and describe their relationships accurately. Intuitively, it is difficult for a machine to have the general image understanding ability like human beings. However, deep learning provides the basis for intelligent exploration. In this review, we will focus on recent advanced deep methods for image captioning. We classify existing methods into different categories and discuss these categories respectively; meanwhile, we discuss the related datasets and evaluation metrics. We also prospect the future research directions.
Novel accelerated Stochastic Progressive Photon Mapping rendering with neural network

Recently, deep learning-based approaches have led to dramatic improvements for Monte Carlo rendering at the low sampling rate. Most of these approaches are aimed at path tracing. However, they are not suitable for photon mapping. In this paper, we develop a novel accelerate stochastic progressive photon mapping approaches with neural network. First, our framework utilizes the particle-based rendering and focuses on photon density estimation. We train a neural network to predict a kernel function to aggregate photon contributions at shading point. Then we construct a estimation images with the prediction network. During experiments, we could find that there are spike pixels and noises in estimation images sometimes. So we present the improved denoising network to post-process the estimation images. Finally, we can obtain the high-quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared with previous photon mapping methods. Besides, our denoising network can reduce most multi-scale noises on both low-frequency and high-frequency areas while preserving more illumination details, especially caustics, compared with other state-of-the-art learning-based denoising methods.
Expanding the technology acceptance model with the inclusion of trust and mobility to assess e-wallet user behavior: Evidence from OVO consumers in Indonesia

In view of the government’s demonetizing decision to make Indonesia a cashless society, this study aimed to discover the important factors influencing consumers’ attitudes and intentions in using the e-wallet payment system. The authors wanted to find out whether trust, mobility, easiness of use, and usefulness attract customers and encourage them for usage. The quantitative approach in this study was due to the practicality of interpretation and analysis of the data. The primary data was collected by conducting the survey through questionnaires that were sent to the respondents. To test the proposed research model, the authors collected data using a questionnaire survey from 286 OVO consumers in Jakarta. The research findings reveal that mobility is the weakest factor, and perceived usefulness is the strongest factor affecting the users’ attitudes and intention towards using the e-wallet payment system.
Innovative trend analysis of annual maximum precipitation in Gowa regency

Floods and drought are two events that can have a negative impact on human survival. In addition, the impact of these two events can also affect agriculture, fisheries, tourism, housing, transportation and others. Trend analysis is an analysis that can be used to identify extreme rainfall events such as floods and drought. The results of the trend analysis can be useful for water resource planning and management. Therefore, the aim of this study is to obtain an overview of the annual maximum precipitation trend in Gowa Regency. The study uses the daily precipitation data from the Sungguminasa and Bonto Sallang Stations of Gowa Regency for 31 years (1988 – 2018). The data was obtained from the Water Resources, Human Settlements, Spatial Planning and Development Office of South Sulawesi Province. The method used is the Mann-Kendall test, Theil-Sen approach, and innovative trend analysis. The results of a lag-one serial correlation test found that all stations are serially independent. Based on the MK method that both stations show negative trend, but significant only at Sungguminasa station at the 95% confidence level. The results of the ITA method show that all stations are significant negative trends at the 95% level. One of advantages of the ITA method is the ability to detect the significant hidden trends in time series.
Network Subgraph Pattern Mining

Mining local connection patterns in large-scale networks is of great significance for understanding the structure and function of biological networks and online social networks. In practical applications, there are many problems and challenges, such as unknown network structure, large network scale, large number of subgraphs, and large operation of subgraph pattern analysis, which make it very difficult to analyze the local connection patterns of large-scale network graph accurately and quickly. In order to solve this problem, this paper designs a method of crawler and sampling to obtain the topology of the unknown network. At the same time, the deviation introduced in the process of data acquisition is modeled and analyzed, which can be compensated and corrected. A sampling unbiased estimation method suitable for large-scale static graph and high-speed dynamic flow graph is designed. The research results of this paper can be used to accurately and quickly mine and estimate the eigenvalues of local connection patterns, and provide important technical means for computer network traffic monitoring, online social network analysis and biomolecular network information mining.
Predicting Construction Cost Using Regression Techniques For Residential Building

Building prices fluctuate greatly each year. Price fluctuations can be on the prices of building materials, human resources, and other cost requirements used in construction. This fluctuation makes it difficult to estimate how much it will cost to build a house in the next year. There are several studies that have discussed budget plan predictions, but few estimate the planned budget for housing development, especially in Indonesia, because it is not yet known how to predict the cost of housing construction in the future, from here it is necessary to conduct research to obtain a formula in predicting development costs housing. The method is calculating the cost modeling using the regression method, the regression method used is Linear Regression. linear regression studies the linear relationship between the dependent variable Y and a single independent variable X. As a result, the formula obtained for the prediction of the cost of housing construction is y = 1,169,813.19 + (334,729,758) X where y is cost is predicted while X is what year with r2 is 0,10.
Single Information Extraction Algorithm of Mechanical Equipment Usage Information Recording Based on Deep Learning

The compressor work order record document records the compressor fault information and the corresponding solution. This paper attempts to use natural language processing technology to analyze the compressor work order record document and automatically identify the equipment entity and fault description information. Firstly, the equipment information and fault information are separated from the work order record document, and the equipment entity data set and fault description data set are constructed. Then, based on the BERT preprocessing model, the sequence labeling model is fine-tuned, and the automatic identification models of compressor equipment name and fault description are constructed respectively. The experimental results on the equipment entity data set and fault description data set show that the automatic identification F1 value of the above model for compressor equipment entity and fault description reaches 95.05% and 74.44% respectively, which exceeds 9.71% and 16.85% of the BiLSTM+CRF model commonly used in the industry, which verifies the effectiveness of the method.
The Occurrence and Distribution of Microplastic in Sediment of the Coastal Waters of Bengkalis Island Riau Province

The presence of microplastics, which was defined as small pieces of plastic less than 5 mm in size, in the aquatic environment has become a global concern these days. Once in the aquatic environment, microplastics might float in the water column or sink to the bottom sediment, depending on the particle density. This study was conducted in the coastal waters of Bengkalis Island in early 2020 with the aim to determine the occurrence and distribution of microplastics in sediment from six stations with suspected different sources of pollutant input. Sediment samples were taken using the PVC pipe at the depth of 0-10 cm, brought back to laboratory, observed the types and calculated their abundance. The results of the study showed that the highest abundance of microplastics was found at station 6 (83.33 and 26.67 particles/kg for fiber and film) and the lowest abundance was at stations 2 (30.00 particles/kg for fiber) and station 3 (10.00 particles/kg for films). The abundance of fiber type between stations was significantly different (p<0.05), but not for film type (p>0.05). The abundance of microplastics based on colour in each type showed that the highest abundance was in blue colour for fiber and white colour for film type. The abundance of microplastics in the northern part was found to be significantly (p<0.05) higher than in the southern part of Bengkalis Island which was assumed to be due to differences in oceanographic characteristics in both areas such as differences in the type and size of the sediment substrate.
Blind Quality Index for Tone-mapped HDR Images and Multi-Exposure Fused Images

Tone mapping operators (TMOs) and multi-exposure fusion algorithms (MEFs) are employed to visualize the informative contents of high dynamic range (HDR) images on the standard dynamic range (SDR) devices, but also lead to visual quality degradation. Thus, it is urgent to evaluate the perceptual quality of tone-mapped images (TMIs) and multi-exposure fusion images (MEFIs). This paper proposes a blind quality index for TMIs/MEFIs by capturing exposure, colorfulness and structure characteristic features. The connection between feature space and associated subjective ratings is established via a regression model. The proposed method is tested on the public database and compared to the state-of-the-art blind image quality assessment (IQA) methods. The experimental results show that the proposed method achieves superior performance.
Chirality and its spontaneous symmetry breaking in two liquid crystal systems

Chirality, or handedness, is a key concept spanning all fields of natural science, from biology to mathematics. Chiral structures can arise from achiral building blocks that lack a handedness if their assembly is unstable to chiral deformations, a phenomenon called spontaneous symmetry breaking. We theoretically study the role of chirality in two systems composed of liquid crystals dissolved or suspended in water, and our results match those obtained experimentally by our collaborators. In the first system, we study achiral liquid crystals whose Frank twist modulus is much lower than their splay and bend Frank moduli and which are confined in capillaries. Under homeotropic anchoring, their ground state configuration undergoes spontaneous chiral symmetry breaking when the twist modulus decreases enough relative to the splay and bend moduli. Under degenerate planar anchoring, a small twist-to-saddle-splay ratio of elastic moduli leads to degenerate twisted configurations even though an undeformed configuration is possible. Measuring the twist profile of an experimental system produces a value for the saddle-splay constant, which has been difficult to achieve previously. Under either boundary condition, domain walls and point defects, whose topological charges depend on chirality, separate domains with different degenerate configurations, and certain ones are energetically preferred over others. In the second system, we study filamentous viruses acting as colloidal liquid crystals under the influence of depletion, which promotes condensation of the viruses into 2D colloidal monolayers. These membranes have tunable chirality and show a rich array of emergent behaviors, including a transition from a circular shape to a striking starfish shape upon changing the chirality of constituent viruses, partial coalescence via domain walls through which the viruses twist by 180 degrees, and phase-separated rafts of a particular size when two virus species with different lengths and opposite chiralities are used. We formulate a simple theory combining Frank elasticity and depletion that shows how entropy and chirality drive the formation and behavior of these diverse structures. Our work may facilitate the design of chiral sensors and reconfigurable materials and suggests that chirality contributes not only biochemically but also physically to the behavior of lipid rafts in biomembranes. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Physics & Astronomy First Advisor Tom C. Lubensky
Physics-informed generative neural network: an application to troposphere temperature prediction

The troposphere is one of the atmospheric layers where most weather phenomena occur. Temperature variations in the troposphere, especially at 500 hPa, a typical level of the middle troposphere, are significant indicators of future weather changes. Numerical weather prediction is effective for temperature prediction, but its computational complexity hinders a timely response. This paper proposes a novel temperature prediction approach in framework of physics-informed deep learning. The new model, called PGnet, builds upon a generative neural network with a mask matrix. The mask is designed to distinguish the low-quality predicted regions generated by the first physical stage. The generative neural network takes the mask as prior for the second-stage refined predictions. A mask-loss and a jump pattern strategy are developed to train the generative neural network without accumulating errors during making time-series predictions. Experiments on ERA5 demonstrate that PGnet can generate more refined temperature predictions than the state-of-the-art.
Artificial intelligence based Parameter Optimization Technique for selection of Optimal Real-Time Scheduling Algorithm

In Modern days, real time system plays an important role in our modern and digital society. The success of any real time application is totally depending upon the selection of optimal scheduling algorithm. In real time application every task should have the nature of deadlines and time when they arrived, on the basis of these parameters we observe the response time of different scheduling algorithm then we select the optimal algorithm for a particular application. So in this paper our aim is to reduce the complexity of real time system researcher for selection of scheduling algorithm for a particular application. This artificial intelligence based approach is an extent the state of any real time system in the area of scheduling. This approach works in any uni-processor system. Introduction
The use of iot technology based on the forward chaining method to monitor the feasibility of rice field

This study is aimed to implement a system based on the forward chaining algorithm to provide real-time information on irrigation and soil feasibility. There are three aspects studied, namely, water level, soil moisture, and temperature. The system developed uses an ultrasonic sensor as a water level detector, a soil moisture sensor as a soil moisture detector, and a DS18B20 sensor for measuring temperature. This system uses the ISP8266 module NodeMCU microcontroller as a Wi-Fi module and uses a solar cell to utilize the sun as an environmentally friendly power supply. Two tests were carried out, including laboratory testing and field testing. Laboratory testing shows that the system works with an accuracy of 57%. Tests were carried out on the three aspects studied. Meanwhile, field testing shows that some of the rice fields in Cekik Hamlet are not yet suitable for processing based on land feasibility. There are 8 paddy fields with an average area of 25 square meters, and they are named rice fields B, C, D, E, F, G, H, and I. The test results showed that the lands suitable for work are rice field B and rice field E. The feasibility value is measured based on water availability above 3 cm, wet soil moisture with a value of 300 or below 300, and a temperature below 25 degrees Celsius. Validation was obtained from farmers, the head of rice field irrigation (Prajuru Subak), and agricultural extension workers. It can be concluded that the system developed has been able to provide information on irrigation or water availability and the feasibility of cultivating the land in real-time.
Fostering experimental competences of prospective physics teachers

Physics teachers are often faced with the challenge of having to set up difficult experiments, or they have to consciously manipulate parameters in order to be able to demonstrate a phenomenon convincingly. Comprehensive laboratory courses are standard procedure in any study program for prospective physics teachers. However, many students, even after completing standard laboratory courses, show difficulties in standard experimental situations, such as measuring an electric current. We report on a new seminar concept for students in physics teacher study programs. This concept is based on the current state of research in physics education, on the teachers’ professional competences, and on the modelling of experimental competence, justifying its embedding in teacher training. We present first results of a pilot study carried out to evaluate the seminar.
Application of Neural Network and Computer in Intelligent Robot

Autonomous mobile robot is widely used in security patrol, freight, autopilot, and family life and other fields, improve the application effect is one of the most important factor is to be able to accurate navigation path, so how to design a highly efficient, intelligent autonomous mobile robot navigation path algorithm has been a popular research topic in the field. This paper mainly studies the application of the combination of neural network and computer in the field of intelligent robot. The Pninnet model is used to simulate the 3D simulation environment built in this paper, and the path navigation method of the autonomous mobile robot based on the end-to-end model is realized, and an algorithm based on ray detection is proposed to achieve obstacle avoidance. Through several groups of comparative experiments (such as comparison with Pilotnet model, etc.) and analysis, it is proved that the algorithm in this paper improves the efficiency of robot navigation research and the robot developed has a better performance in navigation in unknown or familiar environments.
Human CO2 generation rates in small enclosures for different test cases

The present study deals with experimental measurements of CO2 generation rates, due to the human occupation of a full-scale experimental mock-up simulating the astronaut crew quarters aboard the International Space Station. The estimation of CO2 generation rates follows different methods as described in the literature. A single test subject in four different testing cases is considered, one at rest representing the baseline case and the other three cases at varying levels of physical activity or at rest but with a fixed breathing frequency requested from the human subject. The study results indicate that imposing a fixed breathing rate even while at rest increases the generation rate unpredictably. Following literature metabolic rate estimations, the latter two cases are equivalent to the subject being engaged in light or medium physical activities. The results are used to form recommendations for studies measuring human CO2 generation rates.