In the Future Metaverse, What Kind of UGC do Users Need?

With the COVID-19 pandemic, people's real-life interactions diminished, and the game-based metaverse platforms such as Minecraft and Roblox are on the rise. The main users of these platforms are teenagers, they generate content in a virtual environment, which can significantly increase the activity of the platform. However, the experience of User-Generated Content in the metaverse is not very good. So what kind of support do users need to improve the efficiency of generating content in the metaverse? To investigate teenage users' preferences and expectations of it, this paper interviewed 72 teenagers aged 12-22 who are familiar with the metaverse game, and distilled 4 suggestions that can help promote metaverse users to generate content. © 2023 IEEE.

Multi-Attentional U-Net for Medical Image Segmentation

Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results. © 2022 IEEE.

An ultrasensitive electrochemiluminescence immunosensor for SARS-CoV-2 nucleocapsid protein detection based on signal amplification strategy of DMSN@QDs

Based on signal amplification strategy of dendritic mesoporous silica nanospheres loaded with CdSe/ZnS quantum dots (DMSN@QDs), an ultrasensitive electrochemiluminescence (ECL) immunosensor with magnetic separation was constructed for the detection of SARS-CoV-2 nucleocapsid protein (NP). DMSN, a mesoporous material with abundant radial pores, large specific surface area and high porosity, can increase the loading capacity of QDs and hinder their aggregation as the nanocarrier. DMSN@QDs with good ECL efficiency were used as signal labels to construct a sandwich immunosensor. The designed ECL immunosensor displayed a good linear relationship for NP concentrations ranging from 0.005 ng mL(-1) to 50 ng mL(-1), with a limit of detection of 3.33 pg mL(-1). The ECL immunosensor was successfully applied to detect NP in human serum samples with satisfactory recovery. This strategy provided a new method for detecting NP and expanded the application field of DMSN.

Comparing the Effects of Traditional Media and Social Media Use on General Trust in China During the COVID-19 Pandemic

Although previous studies have indicated a generally positive association between social media use and general trust, the differential impacts of traditional media and social media on general trust and their underlying mechanisms have yet to be fully elaborated. Drawing on the three-dimensional definition of social capital (i.e., personal networks, social norms, and interpersonal trust) as its theoretical framework, this study comparatively examines the impacts of traditional media and social media on general trust, focusing on the mediating role of social capital. Analyses of survey data from China (N = 1,519) during the first stage of COVID-19 demonstrate that social media has a positive relationship with general trust. In addition, social media has two opposing indirect effects on general trust through increased interpersonal trust and personal networks. In contrast, traditional media use has no positive relationship with general trust, either directly or indirectly, although it has a positive relationship with social norms. The differential consequences of using traditional media and social media on general trust are discussed from the perspective of social capital.

Gaussian Process-aided Transfer Learning for Probabilistic Load Forecasting against Anomalous Events

A probabilistic load forecasting method that can deal with sudden load pattern changes caused by abnormal events such as COVID-19 is proposed in this paper. The deep residual network (ResNet) is first applied to extract the load pattern for the normal period from historical data. When an abnormal event occurs, a Gaussian Process (GP) with a composite kernel is utilized to adapt to the changes on load pattern by estimating the forecasting residual of the ResNet. The designed kernel enables the proposed method to adapt rapidly to changes in the load pattern and effectively quantify the uncertainties caused by the abnormal event using a few training samples. Comparative tests with state-of-the-art point and probabilistic forecasting methods demonstrate the effectiveness of the proposed method. IEEE

Discovering spatiotemporal flow patterns: where the origin–destination map meets empirical orthogonal function decomposition

Flows are usually represented as vector lines from origins to destinations and can reflect the movements of individuals or groups in space and time. Revealing and analyzing the spatiotemporal flow patterns are conducive to understanding information underlying movements. This paper proposes a new method called the OD–EOF (Origin–Destination–Empirical Orthogonal Function) to discover important spatiotemporal flow patterns on the premise of maintaining the pairwise connections between origins and destinations. We first construct a spatiotemporal flow matrix that contains connection information between origins and destinations and temporal flow information by adding a temporal dimension to the OD map. Then, we decompose the spatiotemporal flow matrix into spatial modes and corresponding time coefficients by EOF decomposition. The decomposition results depict the prominent spatial distribution of and temporal variation in flows, with most of the spatiotemporal characteristics highly concentrated into the first few spatial modes. The method is evaluated by five synthetic datasets and a user study and subsequently applied to analyze the impact of the COVID-19 pandemic on the spatiotemporal patterns of human mobility in China during the Spring Festival travel rush in 2020 and 2021. The results show the prominent spatiotemporal patterns of human mobility during these periods under the influence of the COVID-19 pandemic outbreak and the normalization of pandemic prevention and control. © 2023 Cartography and Geographic Information Society.

Stimuli-responsive hydrogels for cancer immunotherapy

Immunotherapy plays an important role in cancer treatment by activating or suppressing the immune system. However, there are still a series of challenges to overcome regarding the delivery vehicles of immunotherapeutic agents and their effective activation at tumor sites. Meanwhile, owing to their well-hydrated environment and capability of immobilizing biological cargos, hydrogels in combination with immunotherapies provide a chance to enhance the antitumor immune response with reduced side effects. In addition, stimuli-responsiveness has been also widely applied to optimize the pharmacokinetics with an improved therapeutic outcome. In this review, we discuss the opportunities for the combination of immunotherapy and stimuli-responsive hydrogels, such as light, temperature, ultrasound and magnetically responsive hydrogels, for effective cancer treatment. Finally, we explore the potential of stimuli-responsive hydrogels as vaccine implants against cancer and Covid-19.

Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic's impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered;at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government's strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term. © 2022 by the authors.

Integration of a hybrid scan approach and in-house high-resolution MS2 spectral database for charactering the multicomponents of Xuebijing Injection

Xuebijing (XBJ) Injection is a reputable patent Chinese medicine widely used to cure sepsis, among the Chinese ″Three Medicines and Three Prescriptions″ solution to fight against COVID-19. We were aimed to achieve the comprehensive multicomponent characterization from the single drugs to traditional Chinese medicine (TCM) formula, by integrating powerful data acquisition and the in-house MS2 spectral database searching. By ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS), a hybrid scan approach (HDMSE-HDDDA) was developed, while the HDMSE data for five component drugs and 56 reference compounds were acquired and processed to establish an in-house MS2 spectral database of XBJ. Good resolution of the XBJ components was accomplished on a Zorbax Eclipse Plus C18 column within 24 min, while a fit-for-purpose HDMSE-HDDDA approach was elaborated in two ionization modes for enhanced MS2 data acquisition. XBJ MS2 spectral library was thus established on the UNIFITM platform involving rich structure-related information for the chemicals from five component drugs. We could identify or tentatively characterize 294 components from XBJ, involving 81 flavonoids, 51 terpenoids, 42 phthalides, 40 organic acids, 13 phenylpropanoids, seven phenanthrenequinones, six alkaloids, and 54 others. In contrast to the application of conventional MS1 library, this newly established strategy could demonstrate superiority in the accuracy of identification results and the characterization of isomers, due to the more restricted filtering/matching criteria. Conclusively, the integration of the HDMSE-HDDDA hybrid scan approach and the in-house MS2 spectral database can favor the efficient and more reliable multicomponent characterization from single drugs to the TCM formula. © 2022 The Author(s)

The impact of the media on tourism development and income inequality

This paper examines whether the relationship between tourism development and income inequality is sensitive to the media environment. Using panel data from 88 countries for the period 1996 to 2020, we find that countries with uncensored media environments experience lower income inequality as the tourism industry develops. We also find that a favourable media environment enhances tourism development. Further analysis shows that asymmetries in a hostile media environment;namely, media biasedness, media corruption, and harassment of journalists, inhibit tourism development, particularly in emerging countries. This paper calls for strong support for press freedom to develop the tourism industry as countries emerge from the adverse effects of the COVID-19 pandemic.

Effects of Transitioning From a Virtual to In-Person Lifestyle on Depression, Anxiety, and Loneliness in Adolescents With Psychiatric History

Objectives: The COVID-19 global pandemic has caused children and adolescents to face substantial challenges in their daily lives, but as the pandemic restrictions are lifted, children and adolescents have varying opinions about returning to a prepandemic lifestyle. This study investigates the impact of the transition out of a virtual lifestyle to an in-person lifestyle on the mental health of children and adolescents with psychiatric conditions. Method(s): A retrospective review of medical records was conducted on individuals who were treated in an inpatient or outpatient setting at the UCLA Department of Child and Adolescent Psychiatry following the pandemic. Subjects completed a questionnaire that collected both subjective and objective data on mental health, social and family functioning, academics, daily time spent, and more. The Patient Health Questionnaire-9 (PHQ-9), General Anxiety Disorder-7 (GAD-7), and UCLA Loneliness Scale scores were used to measure depression, anxiety, and loneliness, respectively. Result(s): A total of 93 subjects under the age of 21 years were enrolled in the study (Mage= 14.2;49 females, 24 males;20 transgender/nonbinary). Using the subject's subjective response regarding the transition from a virtual to in-person lifestyle, subjects were categorized by having positive, negative, and ambivalent feedback. Each feedback group was then compared using a single-factor ANOVA, which found that mental health scores for all 3 groups in all 3 measures had significant differences. There was a significant decline in depression scores (PHQ-9) with a decline in positivity toward the transition (p <.05). Subjects in the feedback groups experienced the same trend in symptoms of anxiety (GAD-7) (p <.05) and Loneliness Scale scores (p <.05). Conclusion(s): Online learning and returning to an in-person lifestyle has made significant impacts on the overall mental health of pediatric psychiatric patients. These results show that there are many different experiences within this transition, and that subjective data is a good indicator of mental health scores. Further research can be done to investigate the specific trends or implications of various diagnoses on the transition from a virtual to in-person lifestyle. ADOL, AD, DDD Copyright © 2022

Impact of COVID-19 Pandemic on Engineering Education: Case Study with the Online Laboratory NCSLab

The coronavirus disease 2019 (COVID-19) has changed education, which stimulated the adoption of online teaching and learning. Therefore, the research on the impact of COVID-19 on online laboratories, which can be used for online teaching and learning assessment, is crucial to enlighten the effects of COVID-19 on engineering education. Using the online laboratory Networked Control System Laboratory (NCSLab) as a case study, the research in this paper is aimed to fill the gap of COVID-19 on education by exploring the visitors and users of this online laboratory. To illustrate the impact of the COVID-19 pandemic, a special 100-day time slot in 2020 within the COVID-19 pandemic is selected, and data are collected from a web analytics tool and records in the NCSLab database. For comparison, corresponding data in 2018 and 2019 are also collected within a specific 100-day time slot. The results show that there is an increase in both the numbers of visitors and users to NCSLab, and an increase in experiment configurations during the COVID-19 pandemic compared with the previous two years in 2018 and 2019, which indicates that more learners are seeking alternatives for remote learning. The evaluation results show that the online laboratory is helpful to the comprehension of course content and achieves intended outcomes, while the use of online laboratories as a replacement for traditional laboratories is controversial. As the effects of the COVID-19 pandemic may continue for years globally, the research in this paper could provide insights for future research and development of online laboratories and also for other online education platforms.

Is mass rapid transit applicable for deep integration of freight-passenger transport? A multi-perspective analysis from urban China

Integrating goods movement into public/mass rapid transit (MRT) is an emerging initiative to improve urban freight transport services and sustainability. This paper explores new prospects to achieve extensive non-road city logistics based on a deep freight-on-rail-transit (FoRT) strategy. To begin, the strengths, weaknesses, opportunities, and threats (SWOT) of deep FoRT are analyzed in view of the development status quo of urban China. Next, a multi-criteria assessment model driven by real-world data and 11 quantified metrics is proposed to judge the suitability for developing the MRT-based integrated logistics system (MILS) in 16 Chinese cities. Finally, critical factors influencing MILS project adoption are explicated, and the possible supportive policies are discussed from aspects of planning, regulation, funding, marketization, and innovations. Results show that the alignment with national development goals, rich social-environmental benefits, and stakeholder interest are the primary drivers of deep FoRT strategy, whereas poor planning and decision-making, governance and management deficiencies, and high investment could be the main hurdles. Priority of MILS project adoption in the selected cities is divided into four tiers, where Shanghai, Beijing, and Shenzhen are recognized as the three best candidates. Strong and coordinated policies are needed to integrate the strategy into urban planning.