Social Distance in Interactions between Children with Autism and Robots

The use of non-industrial robots, called service robots, is increasing in the welfare fields to meet the demand for robot therapy among individuals with autism. The more simple communication structures and repetitive behaviors of robots, compared to humans, make it easier for children with autism to interpret communication and respond appropriately. Interacting with a robot allows for social distance to be designed and maintained depending on a person’s social interaction needs. To simulate natural social interactions, robots need to perform social distance in some way. In the context of interacting with autistic children, understanding their social response levels is crucial for the robot to implement decisions regarding the distance kept during the interaction. In this study, an experiment was conducted to examine the accuracy of a detection program and explore the correlations between the social responsiveness of children and social distance, wherein 15 autistic children interacted with a robot on a one-to-one basis for about 20 min. The results revealed that both programs implemented in the robot-assisted autism therapy were effective in detecting social distance in a natural HRI situation.

Freeze-Float System for High-throughput Measurement of Ice Nucleation

In this publication, we developed the high throughput screening implementation of freeze-float selection platform system we established in the previous publication. The goal of this publication is to expand the system to higher throughput and accuracy. In the following sections, we describe the steps for automated droplets generations, adaptation of freeze-float selection using controlled and uniform temperature cooling system, and semi-automated droplets detection program. We aimed to improve previously published system to add functional advantages, such as; a) increased efficiency of the screening with fewer manipulation steps; b) increased accuracy of measurement due to the increased sample size; c) increased uniformity of temperature distribution by incorporating the controlled-rate freezer. In the following sections, we describe the steps for automated droplets generations, freeze-float system adaptations using controlled and uniform temperature cooling system, and semi-automated droplets detection program.

MENINGKATKAN SINERGI ORANG TUA DAN GURU DALAM PROGRAM DETEKSI PERKEMBANGAN ANAK

Changes that occur in the child's growth and development phase are the result of the maturation process and the learning process. There needs to be a harmonious synergy between parents and teachers to optimize children's development, psychological and physical functions of children. There are children's problems, including: independence, difficulty adapting, temper tantrums, lack of discipline, indulgence, so a program is needed to provide solutions to these problems. This community service provides solutions through the Child Development Detection Program, Counseling and Parenting in 3 kindergarten school locations in Jember. The method used is a qualitative approach. The data were collected through observation, interviews and documentation, then analyzed descriptively. The results of the data indicate the achievement of the program. Community service is able to bridge the perceptions of parents and teachers so that synergies and effective communication are established in order to optimize children's development.

Research on Semi-Physical Simulation Testing of Inter-Satellite Laser Interference in the China Taiji Space Gravitational Wave Detection Program

To guarantee a smooth in-orbit space gravitational wave detection for the Taiji mission, a semi-physical simulation test of inter-satellite laser interference is carried out. The semi-physical simulation test consists of three aspects: the establishment of the inter-satellite laser link, interferometry of the inter-satellite ranging, and simulation of the space environment. With the designed specifications for the semi-physical simulation platform, the test results for the inter-satellite laser interference can be obtained. Based on the semi-physical simulation test, the risks of inter-satellite laser interference technology can be mitigated, laying a solid foundation for the successful detection of in-orbit gravitational waves.

An AI-Based Detection System for Mooring Line Failure

Safe and productive offshore operations are of utmost importance, with monitoring the integrity of mooring lines on floating offshore platforms being one of the key factors. The conventional method uses sensors installed on mooring components, which may fail over time and can be costly to replace. Alternative methods using dry and non-intrusive monitoring systems offer a lot of potentials to the industry. An alternative method that uses only Differential Global Positioning System (DGPS) data has been proposed by Sidarta et al. (2018, 2019), and it does not require any information on environmental conditions. This alternative method is based on monitoring shifts in the low-frequency periods and mean yaw angles as a function of vessel positions, mass and added mass. The method utilizes Artificial Intelligence, specifically Artificial Neural Network (ANN), for the detection of mooring line failure, which is a pattern recognition and classification problem. The ANN model learns to recognize and classify patterns of intact mooring lines and those of a broken line. One of the proposed models is a group identification model, in which the model identifies the mooring group that has a broken line. This paper shows that an ANN model can be quite robust and tolerant in dealing with conditions that are somewhat different from its training. As an example, an ANN model for detecting mooring line failure on a spread moored FPSO has been trained using MLTSIM hydrodynamic simulations with quasi static model of the mooring lines and risers to significantly reduce the computational time to generate the ANN training data. The trained ANN model can properly function when tested using fully coupled OrcaFlex hydrodynamic simulations with environmental conditions that are not included in the training. Moreover, although the ANN model has been trained using simulations with a completely removed line, the trained model can still function for a line broken at the bottom. This ANN model is an ANN-based status detection model, which is one of the key components in the ALANN (Anchor Lines monitoring using Artificial Neural Networks) System. The system also composes of an ANN-based system evaluation model, an algorithm-based status detection program and an event detection program. A series of fully coupled dynamic simulations have been used to test the ALANN System. Most of the simulations have a single mooring line failure that occurs randomly during simulation, and the failed line varies for different simulations. Each simulation lasts for six hours. The ALANN System uses a two-hour time window at a time and moves every 20 minutes. The tests demonstrate how each component of the ALANN System contributes to and improves the robustness of the overall solution.

Reverse transcription PCR to detect low density malaria infections

Background: Targeted malaria elimination strategies require highly sensitive tests to detect low density malaria infections (LDMI). Commonly used methods for malaria diagnosis such as light microscopy and antigen-based rapid diagnostic tests (RDTs) are not sensitive enough for reliable identification of infections with parasitaemia below 200 parasites per milliliter of blood. While targeted malaria elimination efforts on the Thailand-Myanmar border have successfully used high sample volume ultrasensitive quantitative PCR (uPCR) to determine malaria prevalence, the necessity for venous collection and processing of large quantities of patient blood limits the widespread tractability of this method. Methods: Here we evaluated a real-time reverse transcription PCR (RT-qPCR) method that reduces the required sample volume compared to uPCR. To do this, 304 samples collected from an active case detection program in Kayin state, Myanmar were compared using uPCR and RT-qPCR. Results: Plasmodium spp. RT-qPCR confirmed 18 of 21 uPCR Plasmodium falciparum positives, while P. falciparum specific RT-qPCR confirmed 17 of the 21 uPCR P. falciparum positives. Combining both RT-qPCR results increased the sensitivity to 100% and specificity was 95.1%. Conclusion: Malaria detection in areas of low transmission and LDMI can benefit from the increased sensitivity of ribosomal RNA detection by RT-PCR, especially where sample volume is limited. Isolation of high quality RNA also allows for downstream analysis of malaria transcripts.

Drop tower tests of Taiji-1 inertial sensor substitute

Taiji-1, which is the first technical verification satellite of China’s Space Gravitational Wave Detection Program, was successfully launched on August 31, 2019. The mission aimed to investigate the key technologies used in space gravitational wave detection. The inertial sensor, which was one of the main payloads, measured the residual acceleration of the satellite, and verified the drag-free control technology. Its performance was crucial to the success of the Taiji-1 mission. To ensure its performance in orbit, the inertial sensor was fully evaluated prior to launch. Owing to the gravitational acceleration on the ground, it is impossible to verify all the properties of the inertial sensor in a routine laboratory. A feasible method to conduct such tests is to use a drop tower. To guarantee the safety of the inertial sensor, a substitute was used with similar structure and circuit design. A total of 20 falls in three groups were completed, a set of research methods was established, and the importance of conducting simulations before the drop tests was verified. For the first time, the switch of different circuit gains in a drop tower test has been achieved and the National Microgravity Laboratory of China (NMLC) drop tower’s residual accelerations in three dimensions were measured. The results demonstrated that the microgravity level of the drop tower can reach about 58 μg0 in the fall direction and 13 μg0 along the horizontal axes.

Detecting Fake Covid 19 News

The fake news Detection program exists to help its users distinguish between useful information and baseless rumours. It helps one to verify it themselves. In the current coronavirus disease (COVID-19) pandemic, misinformation is particularly prevalent, leading to people believing false and potentially harmful statements and posts. The spread of panic and misunderstanding among the public can be reduced if fake news is detected quickly. This covid 19 fake news detection model is specifically built to identify fake news.