During the COVID-19 pandemic, surface disinfection using prevailing chemical disinfection methods had several limitations. Due to cost-inefficiency and the inability to disinfect shaded places, static UVC lamps cannot address these limitations properly. Moreover, the average market price of the prevailing UVC robots is huge, approximately 55,165 USD. In this research firstly, a requirement elicitation study was conducted using a semi-structured interview approach to reveal the requirements to develop a cost-effective UVC robot. Secondly, a semi-autonomous robot named UVC-PURGE was developed based on the revealed requirements. Thirdly, a two-phased evaluation study was undertaken to validate the effectiveness of UVC-PURGE to inactivate the SARS-CoV-2 virus and the capability of semi-autonomous navigation in the first phase and to evaluate the usability of the system through a hybrid approach of SUPR-Q forms and subjective evaluation of the user feedback in the second phase. Pre-treatment swab testing revealed the presence of both Gram-positive and Gram-Negative bacteria at 17 out of 20 test surfaces in the conducted tests. After the UVC irradiation of the robot, the microbial load was detected in only 2 (1D and 1H) out of 17 test surfaces with significant reductions (95.33% in 1D and 90.9% in 1H) of microbial load. Moreover, the usability evaluation yields an above-average SUPR-Q score of 81.91% with significant scores in all the criteria (usability, trust, loyalty, and appearance) and the number of positive themes from the subjective evaluation using thematic analysis is twice the number of negative themes. Additionally, compared with the prevailing UVC disinfection robots in the market, UVC-PURGE is cost-effective with a price of less than 800 USD. Moreover, small form factor along with the real time camera feedback in the developed system helps the user to navigate in congested places easily. The developed robot can be used in any indoor environment in this prevailing pandemic situation and it can also provide cost-effective disinfection in medical facilities against the long-term residual effect of COVID-19 in the post-pandemic era.INDEX TERMS COVID-19, UVC robot, medical robotics, infection control, disinfection methods.
Identifying requirements for an information system is an important task and conceptual modelling is the first step in this process. Conceptual modelling plays a critical role in the information system design process and usually involves domain experts and knowledge engineers who brainstorm together to identify the required knowledge to build an information system. The conceptual modelling process starts with the collection of necessary information from the domain experts by the knowledge engineers. Afterwards, the knowledge engineers use traditional model driven engineering techniques to design the system based on the collected information. Natural language based conceptual modelling frameworks or systems are used to help domain experts and knowledge engineers in eliciting requirements and building conceptual models from a natural language text. In this paper, we discuss the state of the art of some recent conceptual modelling frameworks that are based on natural language. We take a closer look at how these frameworks are built, in particular at the underlying motivation, architecture, types of natural language used (e.g., restricted vs unrestricted), types of the conceptual model generated, verification support of the requirements specifications as well as the conceptual models, and underlying knowledge representation formalism. We also discuss some future research opportunities that these frameworks offer.
A rapid multiple biomolecules based life detection protocol (MBLDP-R) from soil samples is proposed to embed in a scientific rover subsystem targeted for planetary analysis missions complying the guidelines of Science Mission of University Rover Challenge 2021 (URC 2021). The proposed protocol selects suitable biomolecules from a preliminary list through a requirement analysis driven filtration process emphasizing two factors: a) rules of URC 2021 and b) compatibility of the biomolecule test equipment to be embedded in a rover subsystem. To sort out the best test methods for finally selected biomolecules, a weighted qualitative test scoring methodology is applied. A rover subsystem that implements the protocol was built to perform onboard sample analysis. Evaluation results show that: 1) ) the proposed MBLDP-R protocol could effectively predict the classes with an average f1-score of 98.65% (macro) and 90.00% (micro) and the area under the Receiver Operating Characteristics (AUC-ROC) curve shows the sample categories to be correctly predicted 92% of the time (97% Extant, 88% Extinct and 92 % in case of NPL) and 2) the protocol is time-efficient with an average completion time of 17.60 minutes that demonstrates the rapid nature of the protocol to detect bio signatures in soil samples.
Human-assistance rovers have a broad prospect in the field of space robotics, as a significant number of organizations and researchers have been investing in the design and development of sophisticated rovers for planetary exploration. In order to promote research and development in the design of nextgeneration MARS rovers, an annual University Rover Challenge (URC) is hosted by the MARS Society in the United States. In this study, we highlight the design and development process of several novel subsystems of a human-assistance planetary exploration rover and their successive integration in the prototype named PHOENIX, which is a rover that participated in the URC 2021. First, a detailed requirement elicitation has been conducted, for designing a conceptual framework for a rover capable of planetary exploration. Secondly, the design and development process has been detailed for five basic subsystems (power, communication, primary-manipulator, chassis with drive, processing) and two mission-specific subsystems (scientific exploration and autonomous navigation), as well as their successive integration into the rover. Afterwards, a detailed evaluation study has been conducted in order to validate the performance of the developed system. Terrain traversability, autonomy in navigation, and sophisticated task execution capabilities have been evaluated individually within this study. Additionally, the capability of the rover in detecting bio-signatures from soil samples using a novel Multiple Bio-molecular Rapid Life Detection (MBLDP-R) protocol has also been evaluated. The developed scientific exploration subsystem is capable of detecting the presence of life from soil samples with a 92% success rate, and from rock samples with a success rate of 93.33%.
Social media have become an indispensable part of peoples’ daily lives. Research suggests that interactions on social media partly exhibit individuals’ personality, sentiment, and behavior. In this study, we examine the association between students’ mental health and psychological attributes derived from social media interactions and academic performance. We build a classification model where students’ psychological attributes and mental health issues will be predicted from their social media interactions. Then, students’ academic performance will be identified from their predicted psychological attributes and mental health issues in the previous level. Firstly, we select samples by using judgmental sampling technique and collect the textual content from students’ Facebook news feeds. Then, we derive feature vectors using MPNet (Masked and Permuted Pre-training for Language Understanding), which is one of the latest pre-trained sentence transformer models. Secondly, we find two different levels of correlations: (i) users’ social media usage and their psychological attributes and mental health status and (ii) users’ psychological attributes and mental health status and their academic performance. Thirdly, we build a two-level hybrid model to predict academic performance (i.e., Grade Point Average (GPA)) from students’ Facebook posts: (1) from Facebook posts to mental health and psychological attributes using a regression model (SM-MP model) and (2) from psychological and mental attributes to the academic performance using a classifier model (MP-AP model). Later, we conduct an evaluation study by using real-life samples to validate the performance of the model and compare the performance with Baseline Models (i.e., Linguistic Inquiry and Word Count (LIWC) and Empath). Our model shows a strong performance with a microaverage f-score of 0.94 and an AUC-ROC score of 0.95. Finally, we build an ensemble model by combining both the psychological attributes and the mental health models and find that our combined model outperforms the independent models.
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