Geometric Similarity Measurement Method for Micro Scene Generalization

Geometric similarity plays an important role in geographic information retrieval, map matching, and data updating. Many approaches have been developed to calculate the similarity between simple features. However, complex group objects are common in map and spatial database systems. With a micro scene that contains different types of geographic features, calculating similarity is difficult. In addition, few studies have paid attention to the changes in a scene’s geometric similarity in the process of generalization. In this study, we developed a method for measuring the geometric similarity of micro scene generalization based on shape, direction, and position. We calculated shape similarity using the hybrid feature description, and we constructed a direction Voronoi diagram and a position graph to measure the direction similarity and position similarity. The experiments involved similarity calculation and quality evaluation to verify the usability and effectiveness of the proposed method. The experiments showed that this approach can be used to effectively measure the geometric similarity between micro scenes. Moreover, the proposed method accounts for the relationships amongst the geometrical shape, direction, and position of micro scenes during cartographic generalization. The simplification operation leads to obvious changes in position similarity, whereas delete and merge operations lead to changes in direction and position similarity. In the process of generalization, the river + islands scene changed mainly in shape and position, the similarity change in river + lakes occurred due to the direction and location, and the direction similarity of rivers + buildings and roads + buildings changed little.

Data Journalist : One Data Indonesia (SDI) Weak of Data

ABSTRACT The government built Satu Data Indonesia (SDI) as part of implementing the principle of open government. Through SDI, all data from the government from in Indonesia including data from government agencies can be easily accessed by visiting the data.go.id website. This situation should be a good ecosystem to develop data journalism in Indonesia, because the government provides abundant data. This study aims to see the implementation of SDI from the perspective of data journalists. Data journalist is a profession that is directly related to SDI, they are tasked with managing data to be presented to the public in a simple and easy-to-understand manner for the general public. By interviewing data journalists from Katadata and Lokadata (two online media who declare themselves as data journalism), this qualitative research is expected to provide constructive input on the implementation of One Data Indonesia. In general, the existence of SDI is appreciated by data journalists. But data journalists from Katadata and Lokadata do not use SDI as part of their news-seeking activities. They see SDI has basic problems, the lack of data availability, data relevance problems, problems with easy data access, no data updates (updates) to data reliability. In addition, problems with data updating and data reliability, on the other hand, not all regions in Indonesia are connected to SDI. As a result, data journalists do not use Satu Data Indonesia as an instrument to make news. They prefer to dig up data through data mining on the internet and collect data gradually in their data bank for processing at a later date. Keywords: One Data Indonesia, Open Government, Online Media, Data Journalism

Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules’ characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank’s resources will increase substantially in the coming years.

A Visual Feedback Tool for Quantitative Pressure Monitoring in Lower-Limb Prosthetic Sockets

Obtaining a good socket fit is an iterative process dependent on the skill and experience of the prosthetist creating it and requires individualisation based on the size and shape. There is no standard measurement system used to aid prosthetic socket creation despite the severe impacts on physical health and quality of life if one is ill fitting. Pressure sensors embedded in a prosthetic socket were used to collect data at the socket–residuum interface. To choose an interpolation method, the sensor array was simplified to a 2D grid with a border for extrapolation and tested using previously collected walking test pressure data. Four multivariable interpolation methods were evaluated to create a colour map of the pressure data. Radial basis function interpolation was chosen, as it produced a clear image with a graduated interpolation between data points, and was used to create a colour map across the surface of a 3D prosthetic socket model. For the model to be accessible to clinical audiences, a desktop application was created using PyQt to view the model. The application allowed for connection to the sensors via Bluetooth, with the pressure data updating on the 3D model in real time. Clinician feedback on the application showed the potential for a clinical product; however, further development informed by feedback from rehabilitation clinicians and prosthesis users is required.

Visual Biofeedback Tool for Quantitative Pressure Monitoring in Lower-Limb Prosthetic Sockets

Obtaining a good socket fit is an iterative process dependent on the skill and experience of the prosthetist creating it, and requiring individualisation based on the size and shape. There is no standard measurement system used to aid prosthetic socket creation, despite the severe impacts on physical health and quality of life if one is ill-fitting. Pressure sensors embedded in a prosthetic socket were used to collect data at the socket-residuum interface. To choose an interpolation method, a 2D grid was used, with previously collected walking test pressure data, to simplify the sensor array with a border for extrapolation. Four multivariable interpolation methods were evaluated to create a colour map of the pressure data. Radial Basis Function interpolation was chosen as it produced a clear image with a graduated interpolation between data points and was used to create a colour map across the surface of a 3D prosthetic socket model. For the model to be accessible to clinical audiences, a desktop application was created using PyQt to view the model. The created application allowed for connection to the sensors via Bluetooth, with the pressure data updating the colour map on the 3D model in real-time. The created application shows the potential for a clinical product, however further development informed by feedback from rehabilitation clinicians and prosthesis users is required

Museum Genomics

Natural history collections are invaluable repositories of biological information that provide an unrivaled record of Earth's biodiversity. Museum genomics—genomics research using traditional museum and cryogenic collections and the infrastructure supporting these investigations—has particularly enhanced research in ecology and evolutionary biology, the study of extinct organisms, and the impact of anthropogenic activity on biodiversity. However, leveraging genomics in biological collections has exposed challenges, such as digitizing, integrating, and sharing collections data; updating practices to ensure broadly optimal data extraction from existing and new collections; and modernizing collections practices, infrastructure, and policies to ensure fair, sustainable, and genomically manifold uses of museum collections by increasingly diverse stakeholders. Museum genomics collections are poised to address these challenges and, with increasingly sensitive genomics approaches, will catalyze a future era of reproducibility, innovation, and insight made possible through integrating museum and genome sciences. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

DAMPAK IMPLEMENTASI PEMBELAJARAN DARING PADA MASA PANDEMI COVID-19

At current, the Covid-19 pandemic is a global health threat with confirmed cases and high mortality. WHO declared this outbreak a global pandemic because the transmission of this virus was very fast and most countries in the world also exposed to it. Recent data updating the number of confirmed patients worldwide is 23,491,520 positive cases. Indonesia is one of the countries that has been exposed to this virus the beginning of March until now on 25 August 2020, there were 157,859 confirmed cases spread across 34 provinces and 415 districts / cities. The purpose of this study was to identify the impact the teachers, students and parents felt during the Covid 19 pandemic during the implementation of online learning. This study used a method descriptive analysis content. This method is used to describe some information about the impact of the Covid-19 pandemic on implementing online learning. The results of this study are the impact on teacher performance, that is, the loss of teacher motivation in teaching and there are teachers who do not understand use of electronic devices, the impact on the learning process is that students are not familiar with the distance learning process,Impact on parents as student guardians is issue between students and teachers and economic conditions can affect the learning process of the students. In the midst of this pandemic, parents are very influential in children's development, such as trying to create a supportive environment to motivate children in learning with these different situations, and the government is expected to participate in embracing the complaints and needs of children in learning, for example internet quotas. Hopefully with joint efforts, the pandemic will end quickly and we can return to building a pleasant situation better than before.

A Systematic Review on COVID 19 Treatment and Management

There is a new world health crisis threatening the public with spread of COVID-19 (Coronavirus Disease-2019). The virus outbreak has been declared a public health emergency of international concern by World Health Organization (WHO). The pandemic of coronavirus disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents an unprecedented challenge to identify effective drugs for prevention and treatment. We here summarize the current clinical characteristics data to guide potential COVID-19 about Prevention, Diagnosis, Treatments and Prevention of COVID-19. In this review, we extracted data from various Research Report, WHO guidelines and other articles. It is important to aware the readers that new data updating nearly every hour regarding clinical characteristics, diagnosis, treatment strategies, and outcomes COVID-19. All over the world the disease has caused varying degrees of illness. Patient shows various symptoms usually fever, cough, sore throat, breathlessness, fatigue, and malaise among others. The disease is being rehabilitate through general treatment, symptomatic treatment, by using antiviral drugs, oxygen therapy and by the immune system. It is necessary to recognize the potential cases as soon as possible and isolate the suspected people from the confirmed cases of COVID-19, to avert the potential transmission of infection to other patients and health care staff.

An Open Source Low-Cost Device Coupled with an Adaptative Time-Lag Time-Series Linear Forecasting Modeling for Apple Trentino (Italy) Precision Irrigation

Precision irrigation represents those strategies aiming to feed the plant needs following the soil’s spatial and temporal characteristics. Such a differential irrigation requires a different approach and equipment with regard to conventional irrigation to reduce the environmental impact and the resources use while maximizing the production and thus profitability. This study described the development of an open source soil moisture LoRa (long-range) device and analysis of the data collected and updated directly in the field (i.e., weather station and ground sensor). The work produced adaptive supervised predictive models to optimize the management of agricultural precision irrigation practices and for an effective calibration of other agronomic interventions. These approaches are defined as adaptive because they self-learn with the acquisition of new data, updating the on-the-go model over time. The location chosen for the experimental setup is a cultivated area in the municipality of Tenna (Trentino, Alto Adige region, Italy), and the experiment was conducted on two different apple varieties during summer 2019. The adaptative partial least squares time-lag time-series modeling, in operative field conditions, was a posteriori applied in the consortium for 78 days during the dry season, producing total savings of 255 mm of irrigated water and 44,000 kW of electricity, equal to 10.82%.