Speech Recognition Using Intelligent Piezoresistive Sensor Based on Polystyrene Sphere Microstructures

Rapid advances in wearable sensing technology have demonstrated unprecedented opportunities for artificial intelligence. In comparison with the traditional hand-held electrolarynx, a wearable and intelligent artificial throat with sound-sensing ability is a more comfortable and versatile method to assist disabled people with communication. Herein, a piezoresistive sensor with a novel configuration is demonstrated, which consists of polystyrene (PS) spheres as microstructures sandwiched between silver nanowires and reduced graphene oxide layers. In fact, changes in the device's conducting patterns are obtained by spay-coating the various weight ratios and sizes of the PS microspheres, which is a fast and convenient way to establish microstructures for improving sensitivity. The wearable artificial throat device also exhibits high sensitivity, fast response time, and ultralow intensity level detection. Moreover, the device's excellent mechanical-electrical performance allows it to detect subtle throat vibrations that can be converted into controllable sounds. In this case, an intelligent artificial throat is achieved by combining a deep learning algorithm with a highly flexible piezoresistive sensor to successfully recognize five different words (help, sick, patient, doctor, and COVID) with an accuracy exceeding 96%. Herein, new opportunities in voice control as well as other human-machine interface applications are opened.

Investigation of Ultraviolet-C light-emitting diode for airborne disinfection in air duct

Given the current coronavirus (COVID-19) situation around the world, we may have to face a long-term battle with coronavirus. It is necessary to prepare and stay resilient with some other techniques to improve air quality in buildings, especially in clinics and hospitals. In this paper, we have developed Ultraviolet-C (UVC) light-emitting diode (LED) modules which can be implemented in air ducts in heating, ventilation, and air conditioning system for airborne disinfection. An LED module is designed with LED panels as the basic unit so that it is easy to scale up to accommodate for air ducts with different sizes. Both experiments and simulations are carried out to study its disinfection performance. The results show that more than 76% and 85% of the pathogen can be inactivated within 60 and 90 min, respectively, in a meeting room with a volume of 107 m3 by using one LED module. Simulations for two LED modules show that the disinfection efficacy is more than two times compared to that of one LED module. In addition to the pathogen used in the experiments, the disinfection performance of the LED module for inactivation of SARS-CoV-2 virus based on the literature is investigated numerically. It shows that more than 99.70% of pathogens receive UV dose larger than 4.47 J/m2, leading to an almost 89.10% disinfection rate for SARS-CoV-2 virus within one hour using the two LED modules in the same meeting room. © 2023 Author(s).

A Short and Highly Efficient Method for Producing Meltblown Nano Yarn with a Helical Structure and High Conductivity

The COVID-19 outbreak has led to the overproduction of meltblown fabrics commonly used in personal protective equipment such as face mask. Moreover, the yield ofconventional fabrication methods for meltblown fabrics have poor mechanical properties and lack accessional value and functional applicability. In this study, a short and highly efficient process was employed to produce polypropylene/polypyrrole (PPy) meltblown nanoyarn (PPMNY). The mechanical properties were improved by utilizing a helical structure, and the conductivity was enabled using a combination of PPy nanoparticles. The breaking force of the proposed PPMNY was as high as 10.1cN/tex at 9T/10 cm, nearly 3.3 times more than PPMNY without the helical structure. The breaking force of the proposed PPMNY was unaffected by the washing process, and the frictional properties and snarling information were similarly maintained by the helical structure. Additionally, the optimal conductivity of the proposed PPMNY reached 0.044S·m-1. Therefore, the novel methods investigated in this study can improve the properties of meltblown fabrics to yield a highly efficient and low-cost technique to produce conductive PPMNY. This concept can be extended for solving the problems of the single two-dimensional structure with poor mechanical properties and application on Smart Wearable with preferable conductivity. © Textile Bioengineering and Informatics Symposium Proceedings 2022 - 15th Textile Bioengineering and Informatics Symposium, TBIS 2022.

An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection

Coronavirus 2019 has brought severe challenges to social stability and public health worldwide. One effective way of curbing the epidemic is to require people to wear masks in public places and monitor their mask-wearing states by suitable automatic detectors. However, existing models struggle to simultaneously achieve the requirements of both high precision and real-time performance. To solve this problem, we propose an improved lightweight face mask detector based on YOLOv5, which can achieve an excellent balance of precision and speed. Firstly, a novel backbone ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention mechanism is proposed as the backbone. Afterward, an efficient path aggression network BiFPN is applied as the feature fusion neck. Furthermore, the localization loss is replaced with alpha-CIoU in model training phase to obtain higher-quality anchors. Some valuable strategies such as data augmentation, adaptive image scaling, and anchor cluster operation are also utilized. Experimental results on AIZOO face mask dataset show the superiority of the proposed model. Compared with the original YOLOv5, the proposed model increases the inference speed by 28.3% while still improving the precision by 0.58%. It achieves the best mean average precision of 95.2% compared with other seven existing models, which is 4.4% higher than the baseline.

China macroeconomic report 2021: China's macroeconomy marches towards normalisation

In the year 2021, China's economy continues to recover and moves towards policy normalisation. This report identifies features indicating the beginning of China's macroeconomic normalisation, the internal and external pressure it faces, and the supporting policies. Due to the economic recovery and the base effect, China's real GDP growth rate is projected to reach above 8.0% in 2021, and the quarterly growth rate will drop from 18.3% in Q1 to 5.0% in Q4, showing a declining trend. Based on qualitative assessments and statistical forecasts, this report puts forward some policy suggestions.

Applying Information Technology for Cross Border Disaster Risk Reduction Through Public Private Partnership Amidst COVID-19

The crisis of humankind, the COVID-19 pandemic, brought the threats but the opportunities toward collaboration on disaster risk reduction (DRR) through public private partnership (PPP) over sustainable development goals (SDGs) at global landscape. Under the direct impact to the social-cultural, political and economic interests, we are in time for change. In the past decade, the concept of PPP has been widely applied to facilitate supply chain resilience after the 2011 the Great East Japan Earthquakes and Tsunami on business continuity planning (BCP). The highlight in 2019 is to implement PPP on climate extremes capacity building programme “Plant Back Better (PBB)” initiatives for Livelihood Continuity Planning (LCP) through the smart technology and climate-smart agriculture across the border for international collaborations. How to look out each other in distance will be the future challenge amidst COVID-19. Consolidating 10 years of implementation findings, this paper developed a cross-border PPP approach on utilizing smart technology for better governance on emergency preparedness and building a stronger partnership engagement on real-time basis. On the digital transformation era, this paper illustrated how we came across the barrier of language, cultural difference and border-control to deliver the DRR mission through PPP amidst the COVID-19 crisis on a common operational picture for emergency preparedness, response and recovery. © 2021, IFIP International Federation for Information Processing.

Application of Disaster Management Information for the COVID-19 Pandemic

Since end of 2019, because of the worldwide spread of COVID-19, the whole world has been struggling for solutions to mitigate adverse impacts and worsening situations, which demonstrate very dynamic characteristics requiring high integration among government agencies to make decisions and deliver operation. Within countries around the world, most of disaster management agencies are the primary institutions working with health agencies to cope with health emergencies. To comparing disaster management with public health management, there are lots of common grounds like procedures to deal with emergency and allocations of resources. However, the differences between the two fields offer an opportunity for mutual leaning on best practices of emergency management. For example, in case of disaster event, for better response and control, commanding officials usually dispatch several teams and concentrate resources as much as possible to effectively respond to emergency. According to reports on containing COVID-19 spread, whenever any decisions should consider social distancing first that limits operational space because of safety reason. This paper explores possible collaboration between disaster management with public health management and discusses direction about how to apply information of disaster risk management for public health events. More focal topics concentrate on data processing of population distribution and information integration. © 2021, IFIP International Federation for Information Processing.

SIMULATION OF AN ANNULAR LIQUID JET WITH A COAXIAL SUPERSONIC GAS JET IN A MEDICAL INHALER

Medical inhalers have been used for the treatment of a wide range of respiratory diseases including COVID-19. In this study, we investigate the annular liquid jet breakup with a coaxial supersonic gas jet using large eddy simulations. This contributes to a further understanding and improvement of medical inhaler designs. The liquid is sucked in by the low pressure as a result of the high velocity gas jet and breaks up due to the interaction with the gas jet. This type of spray nozzle configuration is commonly used in medical inhalers. Two different gas nozzle diameters are studied. The simulated liquid structure is compared with preliminary, qualitative experimental results. The gas jet pressure and radial velocity of the liquid are found to be coupled and the interaction between them plays an important role in formation of the liquid structure. The effect of gas nozzle diameter on flow rate, mean radius of the liquid, and mean radial velocity, as well as its oscillation behavior has been investigated. The power development of dominate frequencies of averaged radial liquid velocity along the flow direction is shown. The growth of the instabilities can be observed from these results.

The Construction and Effect of the MOOC-based One Plus Three Teaching Model During the COVID-19 Pandemic

"Undisruptive learning with disruptive classes" is the principle for Chinese higher education institutions to handle the challenge they encounter during the COVID-19 pandemic. The purpose of this study is to promote online teaching across three scenarios: online teaching for students within one college, online teaching for students across different colleges, and online training for primary and secondary school teachers. Guided by the "Internet+" concept, we design the MOOC-Based One Plus Three teaching model. Moreover, we apply the model in designing and developing online courses on the Chinese Universities MOOC platform. Empirical results have shown that the model effectively solves the needs of large-scale emergency teaching during the pandemic across online teaching for students within one college, online leaching for students across different colleges, and online training for primary and secondary school teachers. This model supports the rapid implementation of online teaching in more than 180 colleges and over more than 20 courses across the country with 58,107 preservice teachers, in-service teachers, related business employees enrolled. This highly feasibly model embodies the Internet thinking and consists of various teaching, methods, and thus maximize the effectiveness of online teaching. This model especially supports the online teaching of the Instructional Technology program at universities (e.g., Hubei University of Education) which are severely affected by the pandemic. Therefore, the model has a positive promotion and application value.

Research on Support Services of MOOC-Based Online Teacher Professional Development During the COVID-19 Pandemic

During the COVID-19 Pandemic, the Ministry of Education of the People's Republic of China has noted that elementary and secondary schools in various regions should ensure "Undisrupted Learning with Disrupted Classes". Therefore, it is important to carry out online teacher professional development (PD) to ensure the quality of basic education. We adopt the literature research method, theoretical deduction method, investigation research method and evaluation research method. Guided by the fundamental principles of online teaching and the "Internet +" thinking, we follow the general online teacher PD process, which is building a community, defining PD issues, providing PD resources, organizing PD activities, generating PD outcomes, and evaluating the outcomes. The Chinese Universities MOOCs Platform is deployed to provide PD resources, organize and manage PD activities. Then we design and apply the MOOC-based online teacher PD support model. Empirical results have shown that this model effectively meets the online PD needs, and corrects the misconceptions of online teaching of first-line elementary and secondary school teachers during the pandemic. Moreover, this model can promote teachers' engagement in online PD. At the same time, we foster a batch of typical online teacher PD cases, which contributes to the online teaching practice to achieve "Undisrupted Learning with Disrupted Classes".

Pandemic Response and Crisis Informatics: An Imperative for Public Health Messaging

The focus of this research is to examine the usage patterns exhibited by users of online search engines in the midst of COVID-19. We aim to understand how the queries are structured and their timing on the various platforms that citizens are using to check the availability of Personal Protective Equipment (PPE) since the outbreak of the COVID-19 public health crisis. Understanding and analyzing peak volume for information platforms is critical, especially for public health policy, with a mind toward crisis informatics. In this study, we collect all the data of users querying data from Face Mask Map (FMM), a real-time application which displays the inventory status for all stores selling PPE. This data is from the point at which the public health crisis became widely known to the time at which PPE availability saturated the market. As COVID-19 continues to proliferate and affect people around the globe, official organizations such as Department of Health and World Health Organization (WHO) utilize Web or Social Media (Facebook or Twitter) to announce up-to-date news, e.g. daily confirmed cases or in order to update policy regarding resource management. We then correlate the significant announcements from public health officials, specifically published by Ministry of Health and Welfare (MoHW) in Taiwan, that are concerning usage and distribution of PPE. We find that the temporal dynamics of aggregated users behavior are consistent with the events. For the practitioner of disaster management, it is critical to be able to identify when the public will consistently react to public health announcements for the purpose of ensuring proper supply distribution and avoid misallocation. It is our hope that the study can help to build an effective online disaster preparedness information system, in the consideration of computing and public psychology, to better respond to disaster with a greater corpus of data. © 2020 IEEE.

Mechanism of Tanreqing Capsule on treatment of coronavirus disease 2019 based on network pharmacology

Objective: To explore the network regulation mechanism of Tanreqing Capsule (TRQC) on the treatment of coronavirus disease 2019 (COVID-19). Methods: Potential targets of the 19 major constituents in TRQC were predicted by the Swiss Target Prediction server and TCMSP database. Gene ontology (GO) function enrichment and pathway analysis of the targets were analyzed by Omicsbean analytic system and String 10 database. Finally, Cytoscape 3.6.1 software was used to construct the network pharmacology map. Results: A total of 19 compounds affected 68 pathways such as IL-17 signaling pathway, T cell receptor signaling pathway, arachidonic acid metabolism, cAMP signaling pathway, PI3K-Akt signaling pathway, influenza A, etc, by acting on 163 related targets, which associated with anti-inflammation, immune regulation, antipyresis, eliminating phlegm, relieving cough and asthma, analgesia, antibacterial and antiviral, and sedation. The network of "compound-target-pathway-pharmacological action-efficacy" was also constructed. Conclusion: The major constituents in TRQC, including scutellarin, baicalin, oroxylin-7-O-glucuronide, chrysin-7-O-glucuronide, forsythin, forsythiaside E, forsythiaside D, chlorogenic acid, caffeic acid, isochlorogenic acid A, ursodeoxycholic acid and chenodeoxycholic acid, may interfere with efficacy-related biological processes associated with antipyresis, eliminating phlegm and removing toxin by acting on key protein like TNF, EGFR, NOS3, PTGS2, IL2, GABBR1, MAPK14, ADRB2, REN, VCAM1, ACHE, PTPRC, etc.