Explore the vast range of titles available.

The world has suffered from the COVID-19 pandemic. While it is expected that societies will learn lessons from this experience, knowledge about how people responded to the pandemic in its early stages is very limited. With the aim of urgently providing policymakers with scientific evidence about how to better inform the public about fighting against COVID-19, this study made an initial attempt to assess how people responded to the COVID-19 outbreak during its early stages. Based on a life-oriented approach, this study collected data on a large set of behaviors and attitudes through a nationwide retrospective panel survey conducted in Japan at the end of March 2020, when the country had 1953 confirmed infection cases in total. Valid data were collected from 1052 residents from the whole of Japan, taking into account a balanced population distribution in terms of age, gender, and region. Respondents were asked to report changes in their daily activity-travel behavior, long-distance trips, and other life activities caused by the COVID-19 pandemic and associated factors (information reliability, risk perception, attitudes about policy-making and communications with the public, etc.). Results of both aggregate and modeling analyses (using a structural equation model and a data mining approach) indicate that poor communication with the public may have been closely related to the spread of COVID-19 in Japan, and that effective interventions should be made by focusing on interactions between target persons and close members of their social networks. It is also revealed that differentiated communications are necessary to encourage different types of behavioral changes. Risk communication should be better designed to encourage people to voluntarily modify their needs in life [L] and perform the needed activities [A] at places with sufficient spaces [S] and proper duration of time and at the proper timing [TING]. Such a LASTING approach may be crucial to enhance the effects of massive public involvement in mitigating the spread of COVID-19. The findings from this study are not only useful to tackle the current pandemic, but also have a long-term value for addressing future pandemics.

With its unique cultural contributions and rich cultural connotations, Chinese culture has become increasingly influential in the world, so how to spread Chinese culture has become the proposition of the times. With this in mind, the purpose of this study is to find the differences between self-shaping and other-shaping and find the best way to adapt and disseminate traditional Chinese culture. Based on both quantitative and qualitative research, this study uses film subtitles, audience reviews, and interview material as samples to analyze the domestic movie Nezha: Birth of the Devil and the foreign movie Mulan. The results show that filmmakers prefer a similar theme and also use many cultural symbols, but have a very different emphasis on movie adaptations. Moreover, both domestic and foreign audiences haven’t formed an inclusive attitude to appreciate foreign movies; therefore, corresponding strategies should be adopted to better suit the audience’s interest and innovate traditional culture. In this way, this study can provide a new perspective for the adaptation and dissemination of traditional Chinese culture.

The portrayal of characters in television shows has a significant impact on captivating audiences and shaping their overall viewing experience. Characters are essential elements that draw viewers into the narrative and make them emotionally invested in the story. Through their actions, emotions, and relationships, characters become relatable and allow the audience to connect with the show on a deeper level. Moreover, well-developed characters with compelling storylines can leave a lasting impression on viewers, leading to a more satisfying and memorable viewing experience. Thus, the portrayal of characters holds immense importance in ensuring the success and popularity of television shows.In this literature review, this paper delves into the ways in which industry multi-camera approaches contribute to the depiction and development of characters in the timeless sitcom I Love Lucy . Through qualitative analysis, this study explores how the implementation of multi-camera angles, live audience reactions, and real-time interactions impact the show’s comedic timing, expressions, and the chemistry between characters. By examining the strategic use of multicamera tactics in “I Love Lucy” including wide shots, reaction shots, and two-shots, this paper are able to observe the effectiveness of these techniques in capturing physical comedy, nuanced character interactions, and interpersonal dynamics. These tactics greatly enhance audience engagement, allowing viewers to fully immerse themselves in the world of the show. The findings of this research emphasize the significance of multi-camera approaches in character portrayal. By employing various camera angles and capturing live audience reactions, television creators and producers have the ability to create captivating characters that resonate with audiences. Additionally, these techniques foster strong audience connections, as viewers become more emotionally invested in the lives and stories of the characters. Ultimately, this literature review provides valuable insights for television creators and producers who aspire to create compelling characters and establish meaningful connections with their audiences. By understanding the impact of multi-camera approaches on character portrayal, industry professionals can employ these tactics to their advantage, resulting in more engaging and memorable television experiences for viewers.

The built environment can contribute to the spread of the novel coronavirus disease (COVID-19) by facilitating human mobility and social contacts between infected and uninfected individuals. However, mobility data capturing detailed interpersonal transmission at a large scale are not available. In this study, we aimed to objectively assess the influence of key built environment factors, which create spaces for activities—“inferred activity” rather than “actually observed activity”—on the spread of COVID-19 across townships in China at its initial stage through a random forest approach. Taking data for 2994 township-level administrative units, the spread is measured by two indicators: the ratio of cumulative infection cases (RCIC), and the coefficient of variation of infection cases (CVIC) that reflects the policy effect in the initial stage of the spread. Accordingly, we selected 19 explanatory variables covering built environment factors (urban facilities, land use, and transportation infrastructure), the level of nighttime activities, and the inter-city population flow (from Hubei Province). We investigated the spatial agglomerations based on an analysis of bivariate local indicators of spatial association between RCIC and CVIC. We found spatial agglomeration (or positive spatial autocorrelations) of RCIC and CVIC in about 20% of all townships under study. The density of convenience shops, supermarkets and shopping malls (DoCSS), and the inter-city population flow (from Hubei Province) are the two most important variables to explain RCIC, while the population flow is the most important factor in measuring policy effects (CVIC). When the DoCSS gets to 21/km 2 , the density of comprehensive hospitals to 0.7/km 2 , the density of road intersections to 72/km 2 , and the density of gyms and sports centers to 2/km 2 , their impacts on RCIC reach their maximum and remain constant with further increases in the density values. Stricter policy measures should be taken at townships with a density of colleges and universities higher than 0.5/km 2 or a density of comprehensive hospitals higher than 0.25/km 2 in order to effectively control the spread of COVID-19.

This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs.

Porous N-doped carbons hold good prospects for application in supercapacitor due to their low-cost, large surface area, good surface wettability, high electrical conductivity as well as extra pseudocapacitance. However, most synthetic methods required the tedious and multiple-step process with the assistance of hard/soft templates or the massive use of chemical reagents, and exogenous nitrogen sources, which made them difficult to realize industrial production and application. Here, we described a novel hierarchical porous N-doped carbons fabricated by a facile and sustainable approach via hydrothermal treatment and subsequent carbonization process by using renewable bamboo shoots as the starting material without any templates, additional chemical activation and nitrogen source. The obtained bamboo shoot-derived carbons possessed a large BET surface area (up to 972 m 2  g −1 ), hierarchically interconnected porous framework, rich and uniform nitrogen incorporation (3.0 at%). Benefiting from these unique features, the novel carbon-based electrode materials displayed a high capacitance of 412 F g −1 in KOH electrolyte and long cycling life stability. Thus, an advanced electrode material for high-performance supercapacitor was successfully assembled by a simple and scalable synthesis route with abundant renewable resources freely available in nature.

In RSSI-based indoor visible light positioning systems, when only RSSI is used for trilateral positioning, the receiver height needs to be known to calculate distance. Meanwhile, the positioning accuracy is greatly affected by multi-path effect interference, with the influence of the multi-path effect varying across different areas of the room. If only one single processing is used for positioning, the positioning error in the edge area will increase sharply. In order to address these problems, this paper proposes a new positioning scheme, which uses artificial intelligence algorithms for point classification. Firstly, height estimation is performed according to the received power data structure from different LEDs, which effectively extends the traditional RSSI trilateral positioning from 2D to 3D. The location points in the room are then divided into three categories: ordinary points, edge points and blind points, and corresponding models are used to process different types of points, respectively, to reduce the influence of the multi-path effect. Next, processed received power data are used in the trilateral positioning method for calculating the location point coordinates, and to reduce the room edge corner positioning error, so as to reduce the indoor average positioning error. Finally, a complete system is built in an experimental simulation to verify the effectiveness of the proposed schemes, which are shown to achieve centimeter-level positioning accuracy.

Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been many studies using deep learning to classify and detect algae, most of them have focused on the relatively simple task of algal classification. In addition, some existing algal detection studies not only use small datasets containing limited algal species, but also only prove that object detection algorithms can be applied to algal detection tasks. These studies cannot implement the real-time detection of algae and timely warning of algal blooms. Therefore, this paper proposes an efficient self-organized detection system for algae. Benefiting from this system, we propose an interactive method to generate the algal detection dataset containing 28,329 images, 562,512 bounding boxes and 54 genera. Then, based on this dataset, we not only explore and compare the performance of 10 different versions of state-of-the-art object detection algorithms for algal detection, but also tune the detection system we built to its optimum state. In practical application, the system not only has good algal detection results, but also can complete the scanning, photographing and detection of a 2 cm × 2 cm, 0.1 mL algal slide specimen within five minutes (the resolution is 0.25886 μm/pixel); such a task requires a well-trained algal expert to work continuously for more than three hours. The efficient algal self-organized detection system we built makes it possible to detect algae in real time. In the future, with the help of IoT, we can use various smart sensors, actuators and intelligent controllers to achieve real-time collection and wireless transmission of algal data, use the efficient algal self-organized detection system we built to implement real-time algal detection and upload the detection results to the cloud to realize timely warning of algal blooms.

With the rapid development of nanotechnology, layered double hydroxides (LDHs) have attracted considerable attention in the biomedical field due to their highly tunable composition and structure, superior biocompatibility, multifunctional bioactivity, and exceptional drug delivery performance. However, a focused and comprehensive review addressing the role of LDHs specifically in tissue regeneration has been lacking. This review aims to fill that gap by providing a systematic and in-depth overview of recent advances in the application of LDHs across various regenerative domains, including bone repair, cartilage reconstruction, angiogenesis, wound healing, and nerve regeneration. Beyond presenting emerging applications, the review places particular emphasis on elucidating the underlying mechanisms through which LDHs exert their therapeutic effects. Although LDHs demonstrate considerable promise in regenerative medicine, their clinical translation remains in its infancy. To address this, we not only provided our insights into the personalized problems that arise in the application of various tissues, but also focused on discussing and prospecting the common challenges in the clinical translation of LDHs. These challenges include optimizing synthesis techniques, enhancing biosafety and stability, improving drug-loading efficiency, designing multifunctional composite materials, and establishing pathways that facilitate the transition from laboratory research to clinical practice.

Highly social activities like leisure and tourism are considered to have positive effects on the elderly’s mental health. Taking Japan as a case study, this research aims to clarify how leisure and tourism contribute to the elderly’s quality of life (QOL) in the domains of leisure and intimacy by comparing populated and depopulated areas. Such research has strong implications for achieving Sustainable Development Goals (SDGs) through healthy aging, but relevant efforts are quite limited. In this study, leisure and tourism behaviors are captured by visit frequency, travel party and expenditure. Quality of life is measured by happiness and life satisfaction in different life domains, in line with the life-oriented approach. Data were collected in 2014 via a nationwide online survey in Japan. Applying a structural equation model (SEM) approach, it is found that leisure behavior contributes to maintaining the elderly’s QOL in leisure life and intimacy domains. Tourism behavior only contributes to QOL in populated areas. Leisure activities strongly enhance QOL in terms of intimacy and improve the neighborhood relationship of the elderly in depopulated areas and family life in populated areas. Enriching daily leisure activities for the elderly would improve their intimate relationships in depopulated areas.