ABSTRACT
The Double Asteroid Redirection Test (DART) mission is NASA's first planetary defense mission to demonstrate the viability of kinetically impacting an asteroid and deflecting its trajectory. The DART spacecraft successfully launched on November 24, 2021 from the Vandenberg Space Force Base and successfully made impact on Dimorphos, the smaller asteroid in the Didymos system, on September 26, 2022. The DART spacecraft has one instrument called Didymos Reconnaissance and Asteroid Camera for Optical navigation (DRACO). DRACO is an imaging telescope that, in conjunction with the SMART Navigation algorithm, autonomously guided the DART spacecraft to the asteroid. Because DRACO is a mission critical and light sensitive instrument, the DRACO Door mechanism was designed as the protective cover. The door functions to shield DRACO from stray light during launch, to deploy in space once when commanded, and to stay 180 degrees open for the duration of the mission. The DRACO Door went through several iterations during the design phase with decisions on various components such as Frangibolts ®, torsion springs, hardstops, and latches. After fabrication and assembly, the door went through a rigorous environmental testing plan, which included deployment testing, vibration testing, and thermal vacuum testing. After successful qualification of the mechanism, the door was installed and integrated into the DART spacecraft. It should be noted that during the fabrication of the mechanism piece-parts, the COVID-19 pandemic began, and the effects of the pandemic were seen in the challenges faced during the DRACO door assembly and testing. Under the constraints of the pandemic, the DART spacecraft was successfully built, tested, and launched, and the DRACO door was successfully deployed on December 7, 2021. The door has continued to function as intended. This paper will discuss the design choices behind the door components, the environmental qualification test program, and the installation of the door onto the DART spacecraft. In addition, this paper will discuss the lessons learned and the challenges of fabricating and testing the flight hardware. © 2023 IEEE.
ABSTRACT
Purpose: In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective: In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. Method: We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. Results: We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion: By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.
ABSTRACT
The college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. Thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. The study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs' criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. The system's features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student's applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system's use case. © 2022 IEEE.
ABSTRACT
MNLTour is a virtual tour system for selected tourist spots situated within the city of Manila. It utilizes 360-degree images, 2D images, voice recordings, and virtual reality technology to offer an immersive user experience of the virtual environment. The virtual tour system was developed using the Unity3D software and was then integrated into web and mobile applications accessible through web browsers and android smartphones, respectively. MNLTour aims to promote the wonders of Manila city by showcasing some of its historical tourist spots that have been severely affected by the outbreak of the COVID-19 pandemic. The developed web and mobile applications were tested and later evaluated to assess the overall quality of the software in accordance with ISO 9126 standard. The evaluation statements primarily focus on the aspects of functionality, efficiency, usability, effectiveness, and user satisfaction in using the application. Descriptive statistics was used to analyze and summarize the data gathered from the evaluation respondents. The evaluation of the application in both platforms turned out to have admirable evaluation results;hence, it's safe to say that the developed software has an acceptable overall quality. © 2022 IEEE.
ABSTRACT
Nowadays, SPOCs (Small Private Online Courses) have been used as complementary methods to support classroom teaching. SPOCs are courses that apply the usage of MOOCs (Mas sive Open Online Courses), combining classroom with online education, making them an exciting alternative for contexts such as emergency remote teaching. Although SPOCs have been contin- uously proposed in the software engineering teaching area, it is crucial to assess their practical applicability via measuring the effectiveness of this resource in the teaching-learning process. In this context, this paper aims to present an experimental evaluation to investigate the applicability of a SPOC in a Verification, Validation, and Software Testing course taught during the period of emergency remote education during the COVID-19 pandemic in Brazil. Therefore, we conducted a controlled experiment comparing alternative teaching through the application of a SPOC with teach ing carried out via lectures. The comparison between the teaching methods is made by ana- lyzing the students' performance during the solving of practical activities and essay questions on the con tent covered. In addition, we used questionnaires to analyze students' motivation during the course. Study results indicate an improvement in both motivation and performance of students participating in SPOC, which corroborates its applicability to the software testing teaching area.
ABSTRACT
Ambient noise causes annoying difficulty for listeners, especially in online learning and work-from-home environments such as during the COVID-19 pandemic. The aim of this work was to employ the neural network to mitigate such ambient noise in the online environment. The software was designed, implemented, and tested on 4 types of noise. The algorithm used was a fully connected network. The results indicated that the standard fully connected network might not be an effective solution for a specific situation. Nonetheless, the processing time was very low, making it possible for real-time application on standalone devices. The implementation using leaky ReLu, creating leaky networks, offered slightly better results in English speeches, i.e. an average of 1.382 and 0.4389 in the PESQ and STOI, respectively. The Thai leaky networks, on another hand, exhibited an average of 3.111 and 0.7096 in PESQ and STOI, respectively. © 2022 IEEE.
ABSTRACT
This study aims to examine the suitability of the conceptual model regarding the effect of business strategy, user competence, organizational structure on the Effectiveness of Management Accounting Software (MAS), and how the influence between the variables studied during the Covid 19 Pandemic took place. Quantitative methods are used to test the suitability of the proposed model and to determine the predicted effect between the variables studied. The data were tested using Covarian Base Structural Equation Model (CB-SEM) with Lisrell 8.5 software. This study uses primary data collected through questionnaires to a population of 118 State-Owned Enterprises (SOEs) Management Accounting departments in Indonesia, with a selected sample size of 100 SOEs, which were selected using a simple random technique. The results of this study succeeded in confirming the conceptual model developed by the researcher and empirically proving the influence of business strategy on MAS Effectiveness, user competence on MAS Effectiveness and organizational structure on MAS Effectiveness in Indonesian SOEs companies. The effectiveness of MAS depends on the relevance of the needs of its users. The right business strategy, effectively provides relevant information for the company to design MAIS according to user needs, so that the company's operational activities run effectively and efficiently, the company is able to implement a Cost Reduction Strategy through production cost savings, production process accuracy, implementing product differentiation and low pricing strategy with a focus on customer needs during the Covid-19 pandemic. The results of this study contribute to producing strategic management accounting information to anticipate business continuity during and after the Covid 19 Pandemic and to help overcome the crisis due to the Covid 19 pandemic in the early stages of the Covid 19 Pandemic by optimizing business strategy, organizational structure, and company HR competencies. © 2022 IEEE.
ABSTRACT
LBPH (Local Binary Pattern Histogram) is a Facial recognition algorithm used to monitor a COVID infected person using a non-contact method of isolation check. The algorithm is programmed using Python software and the results are analysed using visual studio code. The program extracts feature from an input test image and compares it with the system database. The major goal would be to send the message if the person has violated the isolation norms. This algorithm captures the image of an isolated COVID patient when he/she breaks the isolation norms by opening the door and trying to escape from isolation. © 2022 IEEE.
ABSTRACT
The physics output of modern experimental HEP collaborations hinges not only on the quality of its software but also on the ability of the collaborators to make the best possible use of it. With the COVID-19 pandemic making in-person training impossible, the training paradigm at Belle II was shifted from periodic workshops towards guided self-study. To that end, the study material was rebuilt from scratch as a series of modular and hands-on lessons tightly integrated with the software documentation using Sphinx. Each lesson contains multiple exercises that are supplemented with hints and complete solutions. Rather than duplicating information, students are systematically taught to work with the technical reference documentation to find the important sections for themselves. Unit tests ensure that all examples work with different software versions, and feedback buttons make it easy to submit comments for improvements. © Published under licence by IOP Publishing Ltd.
ABSTRACT
Distribued Generations (DG) have economic, financial, and environmental benefits. DG reduces power losses in the distribution system but has a negative impact on the protection devices. In this article, the IEEE 33 bus system will be used and tested by adding up to three DG units using MATLAB/SIMULINK software. the optimization techniques that will be used are Grey Wolf Optimizer, Whale Optimization Algorithm, Genetic Algorithm, and Coronavirus Herd Immunity or COVID-19 optimization techniques to select the optimal site and size of the DG units based on the lowest pay-back period considering the voltage limits and power losses. The paper proposes a modified mutation operator for COVID-19 based on Gaussian and Cauchy mutations to have better performance and lower variance. The proposed algorithm is compared with the other optimization techniques. The proposed algorithm achieved better results, which proved to have competitive performance with state-of-the-art evolutionary algorithms. © 2022 IEEE.
ABSTRACT
Research on IT integration into teaching is an interdisciplinary field that has both educational (didactics) and informatics components. In particular, the situation with the Covid 19 pandemic has forced a push to address personal IT support for teachers in distance education. However, this runs into the problem of the lack of personal educational software, so that in practice the teacher has to adapt to existing technology and test how it can be used for teaching. In this context, the work of a university teacher requires the mass creation of educational content, its transfer between offline computers (laptop, classroom computers) and online environments (web, virtual learning environments, academic information systems, clouds, networks). Given the nature of university teaching, IT support solutions for self-study also face a challenge. However, no single technology covers such a broad scope, so there is a lack of universal solutions. The authors minimize this gap by programming universal software tailored to the needs of the teacher and by building a combined offline/online IT infrastructure on which to conduct the research. Collaborative research by an international team using the infrastructure is a solution to automate the creation of educational packages, including the multi-lingual support. The article clarifies the categories of barriers that the team had to overcome, either from a didactic or an informatics perspective. Here, a new paradigm using a specific data structure (called virtual knowledge) for the rapid reduction and concentration of educational content was proven to simulate virtually any teacher activity. Therefore, the goal of further research is to use the results and experiences to date to build a multilingual learning portal. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Web applications have experienced a widespread adaptation owing to the agile Service Oriented Architecture (SOA) reflecting the ever-changing software needs of users. Google Meet is one of the top video conferencing applications, especially in the post-COVID19 era. Security and privacy concerns are therefore critical. This paper presents an extensive digital forensic analysis of Google Meet running on multiple browsers and software platforms including Google Chrome, Mozilla Firefox, and Microsoft Edge browsers in Windows 10 and Linux. Artifacts, traces of potential evidence, are extracted from different locations on a client's desktop, including the memory and browser. These include meeting records, communication records, email addresses, profile pictures, history, downloads, bookmarks, cache, cookies, etc. We explore how different Random Access Memory (RAM) sizes of client devices impact the persistence and format of extracted memory artifacts. A memory artifact extraction tool is developed to automate the extraction of artifacts identified via unstructured string analysis. Google Meet forensic artifacts are critical in that they are potential digital evidence in relevant criminal investigations. Additionally, they highlight that user data can be extracted despite implementing multiple privacy and security mechanisms. © 2022 The Author(s)
ABSTRACT
Corona Virus (COVID-19) is a virus that is endemic almost all over the world, including Indonesia. COVID-19 was first confirmed by the World Health Organization (WHO) on December 31, 2019, in Wuhan City, Hubei Province, China, and then rapidly expanded outside of China. To suppress the Covid-19 case, medical volunteers are needed as the main actors in efforts to handle Covid-19 patients. This makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. This also makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. The use of hazmat clothes is one of the efforts to protect health workers when in contact with Covid-19 patients. Hazmat clothes are technically referred to as "encapsulated waterproof protective clothing” which is PPE that must be used for officers from the risk of contracting the Covid-19 virus through airborne droplets and contact with patients and patient body fluids. Although hazmat clothing is an important PPE for health workers to stay protected, the use of hazmat clothing for a long time often makes medical personnel feel uncomfortable when providing services. Based on the problems above, the researchers conducted a study on the heat pipe - thermoelectric hazmat suit cooling vest. This technology can absorb more heat than other methods by simply applying the principle of capillarity to the wicks on the pipe walls. schematic of testing a cooling vest on a hazmat suit. The loading on the thermoelectric is given through the DC - Power supply. The temperature data read by the sensor will be detected by the computer system using the NI 9123 and C-DAQ 9174 modules. The test results can be viewed using the NI LabView 2017 software. The temperature used in this experiment is the result of tests carried out for 30 min. Based on the tests that have been carried out, the heat pipe-based thermoelectric hazmat suit cooling vest has been able to reach the lowest thermoelectric temperature of 24,42 ∘C, which is distributed through heat pipes to body parts. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
More than two years after the start of the coronavirus disease (COVID-19) pandemic, the whole world continues to be impacted by this global crisis. Indonesians use the social media platform Twitter to share information and opinions about coronavirus disease (COVID-19) vaccination. This study was conducted to determine the views of Indonesians toward the government's COVID-19 vaccination program and to test the capability of several machine learning techniques to classify sentiments expressed on Twitter. The performance of four machine learning algorithms was tested: the Naïve Bayes, k-Nearest Neighbors (kNN), Decision Tree, and Support Vector Machine (SVM) algorithms. The findings show that the SVM algorithm exhibited the best performance in terms of accuracy (92%) compared to the Naïve Bayes, kNN, and Decision Tree algorithms. A grid search technique was also used to optimize performance based on parameter settings in the algorithm used. © 2022 IEEE.
ABSTRACT
The process of making changes to software after it has been delivered to the client is known as maintainability. Maintainability deals with new or changed client requirements. Service-oriented architecture (SOA) is a method for developing applications that helps services work on different environments. SOA works on patterns of distributed systems that help different applications communicate with each other using different protocols. To assess the maintainability of service-oriented architecture, different factors are required. Some of these factors are analyzability, changeability, stability, and testability. Modification is the process of upgrading the software functionality. After modification of service-oriented architecture, the module will go to the testing phase. The evaluation and verification of whether a software product or application performs as intended is known as testing. The testing phase is a combination of various stages, such as individual module testing and testing after collaborations between them. This testing stage is time-consuming in the maintenance process. The term "outlier" refers to a module in software systems that deviates significantly from the rest of the module. It represents the collection of data, variables, and methods. For instance, the program might have been coded mistakenly or an investigation might not have been run accurately. To detect the outlier module, test cases are needed. A methodology is proposed to reduce the predefined test cases. K-means clustering is the best approach to calculate the number of test cases, but the outlier is not automatically determined. In this paper, a hybrid clustering approach is applied to detect the outlier. This clustering method is used in software testing to count the number of comments in various software and in medical science to diagnose the disease of Covid patients. The experimental outcomes show that our strategy achieves better results.
ABSTRACT
The onset of the COVID-19 pandemic necessitated a change in the mode of delivery of most of our teaching programs. Within a few months, academics responded to the lack of contact with students by generating and using online content to deliver their modules. Even before this forced change, curricula had already undergone significant development. But whereas teaching methods had morphed over time, learning approaches and assessment strategy remained stagnant. The change to online teaching and assessment during the pandemic revealed that in many modules, assessments are still testing at low cognitive levels, rewarding recall instead of understanding. It also revealed that plagiarism and collusion in online assessments were rife, and the type of assessments offered created an enabling environment for this. Students seldom engage with the course material during the semester, except for assignments. Almost all learning occurs in the short period before the main exam session (or in recent years, with the possibility of deferring exams, the students can extend this period into the supplementary session). The change to continuous assessment placed an unusual strain on students accustomed to this particular learning style. They were now forced to engage with material throughout the semester. In this study the student network was considered. Counter intuitively, this has actually strengthened as a result of the pandemic, with students using a variety of communication platforms to engage with one another. As part of this work, the informal study groups and other partnerships that have arisen were investigated as a means to support the formal teaching program. A system based on peer-to-peer interaction was piloted in an undergraduate chemical engineering program, over two modules at the third- and fourth-year levels. The system awards points for various peer activities that usually occur in an informal way, which can be translated into bonus marks on assessments. In doing so, the system addressed a potentially contentious but powerful supposition, i.e. is there a way to exploit the knowledge sharing potential of plagiarism and collusion for a better purpose? Such systems have traditionally been used in businesses and large corporations to motivate and reward employees. The recent pilot has demonstrated that the system, as implemented within an undergraduate program and linked to assessments that test at higher cognitive levels, can improve student engagement and performance. © 2022 IEEE.
ABSTRACT
Automated sample-to-answer systems that promptly diagnose emerging infectious diseases, such as zoonotic diseases, are crucial to preventing the spread of infectious diseases and future global pandemics. However, automated, rapid, and sensitive diagnostic testing without professionals and sample capacity and type limitations remains unmet needs. Here, we developed an automated sample-to-answer diagnostic system for rapid and accurate detection of emerging infectious diseases from clinical specimens. This integrated system consists of a microfluidic platform for sample preparation and a bio-optical sensor for nucleic acid (NA) amplification/detection. The microfluidic platform concentrates pathogens and NAs in a large sample volume using adipic acid dihydrazide and a low-cost disposable chip. The bio-optical sensor allows label-free, isothermal one-step NA amplification/detection using a ball-lensed optical fiber-based silicon micro-ring resonator sensor. The system is integrated with software to automate testing and perform analysis rapidly and simply;it can distinguish infection status within 80 min. The detection limit of the system (0.96 × 101 PFU) is 10 times more sensitive than conventional methods (0.96 × 102 PFU). Furthermore, we validated the clinical utility of this automated system in various clinical specimens from emerging infectious diseases, including 20 plasma samples for Q fever and 13 (11 nasopharyngeal swabs and 2 saliva) samples for COVID-19. The system showed 100% sensitivity and specificity for detecting 33 samples of emerging infectious diseases, such as Q fever, other febrile diseases, COVID-19, human coronavirus OC43, influenza A, and respiratory syncytial virus A. Therefore, we envision that this automated sample-to-answer diagnostic system will show high potential for diagnosing emerging infectious diseases in various clinical applications. © 2023 Elsevier B.V.
ABSTRACT
The onset of the COVID-19 pandemic necessitated a change in the mode of delivery of most of our teaching programs. Within a few months, academics responded to the lack of contact with students by generating and using online content to deliver their modules. Even before this forced change, curricula had already undergone significant development. But whereas teaching methods had morphed over time, learning approaches and assessment strategy remained stagnant. The change to online teaching and assessment during the pandemic revealed that in many modules, assessments are still testing at low cognitive levels, rewarding recall instead of understanding. It also revealed that plagiarism and collusion in online assessments were rife, and the type of assessments offered created an enabling environment for this. Students seldom engage with the course material during the semester, except for assignments. Almost all learning occurs in the short period before the main exam session (or in recent years, with the possibility of deferring exams, the students can extend this period into the supplementary session). The change to continuous assessment placed an unusual strain on students accustomed to this particular learning style. They were now forced to engage with material throughout the semester. In this study the student network was considered. Counter intuitively, this has actually strengthened as a result of the pandemic, with students using a variety of communication platforms to engage with one another. As part of this work, the informal study groups and other partnerships that have arisen were investigated as a means to support the formal teaching program. A system based on peer-to-peer interaction was piloted in an undergraduate chemical engineering program, over two modules at the third- and fourth-year levels. The system awards points for various peer activities that usually occur in an informal way, which can be translated into bonus marks on assessments. In doing so, the system addressed a potentially contentious but powerful supposition, i.e. is there a way to exploit the knowledge sharing potential of plagiarism and collusion for a better purpose? Such systems have traditionally been used in businesses and large corporations to motivate and reward employees. The recent pilot has demonstrated that the system, as implemented within an undergraduate program and linked to assessments that test at higher cognitive levels, can improve student engagement and performance. © 2022 IEEE.
ABSTRACT
The experimental component is an essential method in Engineering education. Sometimes the availability of laboratories and components is compromised, and the COVID-19 pandemic worsened the situation. Resorting to an accurate simulation seems to help this process by allowing students to develop the work, program, test, and validate it. Moreover, it lowers the development time and cost of the prototyping stages of a robotics project. As a multidisciplinary area, robotics requires simulation environments with essential characteristics, such as dynamics, connection to hardware (embedded systems), and other applications. Thus, this paper presents the Simulation environment of SimTwo, emphasizing previous publications with models of sensors, actuators, and simulation scenes. The simulator can be used for free, and the source code is available to the community. Proposed scenes and examples can inspire the development of other simulation scenes to be used in electrical and mechanical Engineering projects. © 2022 IEEE.
ABSTRACT
The countermeasure for preventing COVID-19 should be further studied in order to make sure countries are prepared for the endemic phase. The biggest challenge of COVID-19 is its high infection rate and infection mortality rate. Robots offer a very good solution to this, hence, we developed a robot that can autonomously navigate a closed indoor room, sanitize it, and monitor social proximity practices. The quality of the hardware design, electronic system and software developments are conducted and experimental works to test the performance of the robot are performed. © 2022 IEEE.