Telepresence Robots and Controlling Techniques in Healthcare System

In this era of post-COVID-19,humans are psychologically restricted to interact less with other humans.According to the world health organization(WHO),there are many scenarios where human interactions cause severe multiplication of viruses from human to human and spread worldwide.Most healthcare systems shifted to isolation during the pandemic and a very restricted work environment.Investigations were done to overcome the remedy,and the researcher developed different techniques and recommended solutions.Telepresence robot was the solution achieved by all industries to continue their operations but with almost zero physical interaction with other humans.It played a vital role in this perspective to help humans to perform daily routine tasks.Healthcare workers can use telepresence robots to interact with patients who visit the healthcare center for initial diagnosis for better healthcare system performance without direct interaction.The presented paper aims to compare different telepresence robots and their different controlling techniques to perform the needful in the respective scenario of healthcare environments.This paper comprehensively analyzes and reviews the applications of presented techniques to control different telepresence robots.However,our feature-wise analysis also points to specific technical,appropriate,and ethical challenges that remain to be solved.The proposed investigation summarizes the need for further multifaceted research on the design and impact of a telepresence robot for healthcare centers,building on new perceptions during the COVID-19 pandemic.

Analysis of LDPC Code in Hybrid Communication Systems

Free-space optical(FSO)communication is of supreme importance for designing next-generation networks.Over the past decades,the radio frequency(RF)spectrum has been the main topic of interest for wireless technology.The RF spectrum is becoming denser and more employed,making its availability tough for additional channels.Optical communication,exploited for messages or indications in historical times,is now becoming famous and useful in combination with error-correcting codes(ECC)to mitigate the effects of fading caused by atmospheric turbulence.A free-space communication system(FSCS)in which the hybrid technology is based on FSO and RF.FSCS is a capable solution to overcome the downsides of current schemes and enhance the overall link reliability and availability.The proposed FSCS with regular low-density parity-check(LDPC)for coding techniques is deliberated and evaluated in terms of signal-to-noise ratio(SNR)in this paper.The extrinsic information transfer(EXIT)methodology is an incredible technique employed to investigate the sum-product decoding algorithm of LDPC codes and optimize the EXIT chart by applying curve fitting.In this research work,we also analyze the behavior of the EXIT chart of regular/irregular LDPC for the FSCS.We also investigate the error performance of LDPC code for the proposed FSCS.

Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary

Social media provide digitally interactional technologies to facilitate information sharing and exchanging individuals.Precisely,in case of disasters,a massive corpus is placed on platforms such as Twitter.Eyewitness accounts can benefit humanitarian organizations and agencies,but identifying the eyewitness Tweets related to the disaster from millions of Tweets is difficult.Different approaches have been developed to address this kind of problem.The recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic rules.However,these approaches suffer from limitations as they are dataset-dependent and not scalable.In this paper,we proposed a method to identify eyewitnesses from Twitter.To experiment,we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the eyewitness.Considering each feature,a dictionary of words was created with the Word Dictionary Maker algorithm,which is the crucial contribution of this research.This algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words dictionary.Further,keyword matching for each feature in tweets is performed.If a feature exists in a tweet,it is termed as 1;otherwise,0.Similarly,for 13 features,we created a file that reflects features in each tweet.To classify the tweets based on features,Naïve Bayes,Random Forest,and Neural Network were utilized.The approach was implemented on different disasters like earthquakes,floods,hurricanes,and Forest fires.The results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure values.At the same time,the proposed approach gained a 0.88 value of F-measure.The results were comparable as the proposed approach is not dataset-dependent.Therefore,it can be used for the identification of eyewitness accounts.

Sustainable Learning of Computer Programming Languages Using Mind Mapping

In the current era of information technology,students need to learn modern programming languages efficiently.The art of teaching/learning program-ming requires many logical and conceptual skills.So it’s a challenging task for the instructors/learners to teach/learn these programming languages effectively and efficiently.Mind mapping is a useful visual tool for establishing ideas and connecting them to solve problems.This research proposed an effective way to teach programming languages through visual tools.This experimental study uses a mind mapping tool to teach two programming environments:Text-based Programming and Blocks-based Programming.We performed the experiments with one hundred and sixty undergraduate students of two public sector universities in the Asia Pacific region.Four different instructional approaches,including block-based language(BBL),text-based languages(TBL),mind map with text-based language(MMTBL)and mind mapping with block-based(MMBBL)are used for this purpose.The results show that instructional approaches using a mind mapping tool to help students solve given tasks in their critical thinking are more effective than other instructional techniques.

Corporate Social Responsibility and Brand Community Engagement:Dual Mediating Roleof Brand and Community Identifications in the Chinese Decor Industry

Despite a plethora of studies on how corporate social responsibility(CSR)generates favorable consumer outcomes,the existing literature provides limited insights about how CSR may affect inter-consumer connection and brand community engagement.Enhancing consumer engagement in the brand community is one of the key marketing objectives for strengthening the brand-consumer relationship.This study aims to explore the role of corporate social responsibility in enhancing brand community engagement and examines the dual mediating role of brand identification and community identification.Quantitative research was conducted and an adapted questionnaire was used.Survey data were collected from 405 Chinese consumers,and structural equational modeling was used to test the hypothesis.Results demonstrated that CSR motivates consumers to engage with the brand community.Further,brand identification and community identification perform the role of partial mediators.