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Continuous global warming and frequent extreme high temperatures keep the urban climate health risk increasing, seriously threatening residents’ emotional health. Therefore, analysis on spatial distribution of the health risk that the urban heat island (UHI) effect imposes on emotional health as well as basic research on the characteristics of vulnerable populations need to be conducted. This study, with Tianjin city as the case, analyzed data from Landsat remote-sensing images, meteorological stations, and digital maps, explored the influence of summer UHI effect on distress (a typical negative emotion factor) and its spatiotemporal evolution, and conducted difference analysis on the age groups, genders, family state, and distress levels of vulnerable populations. The results show: (1) During the period of 1992–2020, the level and area of UHI influence on residents’ distress drastically increased–influence level elevated from level 2–4 to level 4–7, and highlevel influence areas were concentrated in six districts of central Tianjin. (2) Influence of the UHI effect on distress varied in different age groups–generally dropping with fluctuations as residents got older, especially residents aged 50–59. (3) Men experienced a W-shaped pattern in distress and were more irritable and unsteady emotionally; while women were more sensitive to distress in the beginning, but they became more placid as temperature got higher. (4) Studies on family status show that couples living together showed sound heat resistance in the face of heat stress, while middle-aged and elderly people living alone or with children were relatively weak in adjusting to high ambient temperature.

The emotional health of urban residents has been seriously threatened by frequent and normalized heat waves. This study constructed the VI-level assessment standard for emotional health risk using data from satellite images, meteorological sites, questionnaire surveys, and statistical yearbooks to assess the effect of high temperatures on negative emotions in Hangzhou. The results showed that the morphological changes of urban high-temperature areas were aggregated from a cross-shape to a large patch shape, then dispersed into cracked patch shapes. Additionally, the health risk of daytime negative emotions peaked at the VI-level from 1984 to 2020, and the influence level of the typical period risk increased by 1–2 levels compared with the daytime. Additionally, driven by urban spatial structure policies, the risk pattern of emotional health expanded outward from a single center into multiple centers. The emotional health risk level rose and then descended in urban centers, and the innovation industries drove the variation tendency of hot spots. Furthermore, high educational background, employment, and couples living together were critical variables that could alleviate the emotional health risk to the middle-aged and elderly population. This study aimed to optimize the urban spatial structure and alleviate residents’ emotional health hazards for healthy urban planning.

Background: We explored the spatio-temporal characteristics of urban heat island (UHI) effect on cardiovascular diseases (CVDs). Methods: The land surface temperatures (LST) were retrieved from four Landsat remote-sensing images’ data, the temperature data from 95 meteorological stations, and analysis data on CVDs mortality. Based on these data, landscape pattern indexes were used to analyze the pattern-process-function and the mechanism. Results: During 1984–2017, the effects of UHI on CVDs increased, thereby increased the mortality by 28.8%. The affected areas gradually expand from the central area of the city and undergo three evolution stages; the highly affected areas are mainly distributed in central and southern regions, and patches increase in number. The areas and ratio of high-level patches also show an upward tendency, increasing dominance in the overall landscape. Patches of the overall landscape become more complicated in shape, whereas those of high-level ones become less complicated. Concentration degree of the overall landscape decreases gradually with the types of landscapes patches increasing, reaching a rather even space distribution. Conclusion: Increased temperatures exacerbated by UHI lead to increased CVD mortality. As cities expand, the effects of UHI on CVDs increase in terms of both intensity and areas, with the overall landscape in uneven distribution, high-level affected areas in point distribution, and low-level ones in large-area concentration.

Background: Urban heat island (UHI) is being intensified with the progress of urbanization. Meanwhile, respiratory diseases caused by high temperature become common. This study explores the influences of UHI on respiratory diseases (J00-J99) and the evolutionary characteristics of the spatial pattern of such influences. Methods: The pattern–process–function and the influencing mechanism of UHI on respiratory diseases were evaluated through landscape pattern indexes from 1992 to 2018 in Tianjin, China. The basis was on data from Landsat TM/OLI/TIRS remote-sensing images, meteorological stations, and mortality of respiratory diseases. Results: The fluctuating influence of UHI on the respiratory diseases in Tianjin has increased from 1992 to 2018, showing a significant phase-based characteristic. During 2011-2018, the influence has soared greatly, and mortality risk has increased by 101%, and the influenced area has reached 349 km². Furthermore, the regional space clustered, and the influenced patches are in irregular shape, and the highly influenced area is enlarged. Moreover, the indexes of the landscape level of the influenced areas all decrease. The patches at all levels are fragmented and distributed discontinuously. Spatially, the influenced areas gradually extend from the urban center to the suburbs. Conclusion: UHI causes a higher mortality of respiratory diseases because it increases daily average air temperature in summer. With respect to landscape pattern, the influenced areas at low level is highly interconnected and relatively concentrated, whereas the influenced area at high level is distributed in clusters. In general, the influenced area is fragmented and discontinuously distributed in urban center.

Against the backdrop of urban sustainable development around the world, how to coordinate both economic growth and ecological benefits in urban space becomes an important problem. Therefore, this study simulated and predicted the spatiotemporal changes in urban economy and ecosystem service value (E.S.V.) equivalent ratio under the current policies by 2030, and analysed how adjusting planning policies influences economy and ecology. This process was based on the future land use simulation (F.L.U.S.) model of coupled neural network, and on methods assessing the spatial changes in ecosystem services and land economy. This study aims to analyse urban land economy and E.S.V., and assess how China's land spatial planning guides and promotes high-quality urban economic development. Results show that artificial intelligence (A.I.) simulation can forecast the results of spatial planning policies of national lands, to make policy-making more forward-looking. The guidance of planning policies on urban expansion accelerates the increase in economic value of urban residential and commercial lands, thereby promoting the economic growth. However, adjusted planning policies may lead to ecological destruction. So, this study provides model verifications and path guidance to realise coordinated sustainable development between economy and ecology, serving as an important reference to formulating proper policies for urban development.

Green space is one of the main measures to alleviate urban heat islands (UHI). The transformation mechanism of daytime and nighttime scale sensitivity of vegetation coverage to reduce the UHI effect in a daily cycle has been unclear. As a result, we propose a scale sensitivity measurement algorithm to study the spatial and temporal response relationship between UHI and green coverage. Based on the scale theory of landscape ecology and the method of geostatistical analysis, we adopted ArcGIS, MATLAB, SPSS, and other data processing software as well as a large amount of measured and high-resolution satellite imagery data of Beijing and Tianjin to quantitatively study their spatial scale sensitivity and daily variation features of urban green spaces to reduce summer UHI. The results show that first, the green coverage rate and the UHI intensity experience positive and negative correlations during the daytime, and negative correlations at night. When the correlation coefficient is significant, there is a linear relationship between the UHI intensity and the core green rate. Second, the reduction of the UHI by green spaces displays spatial and temporal change scale sensitivity characteristics. The radius scale of daytime sensitivity is 15m, and the radius scale of nighttime sensitivity is 60m. The study's conclusion enriches the theoretical parameters of landscape ecological scales and patterns, and provides spatial and temporal scales for systematic planning of green space to reduce UHI.

Urban water bodies play an important role in reducing summertime urban heat island (UHI) effects. Previous studies focused mainly on the impact of water bodies of large areas, and there is no analysis of the efficacy and scale effect of how small and medium-sized water bodies reduce the UHI effects. Hence, these studies could not provide theoretical support for the scientific planning and design of urban water bodies. This study aims to confirm, within different scale ranges, the efficacy of a water body in reducing the summertime UHI effects. We propose a scale sensitivity method to investigate the temporal and spatial relationship between urban water bodies and UHI. Based on the scale theory and geostatistical analysis method in landscape ecology, this study used the platforms of 3S, MATLAB, and SPSS to analyze the distance-decay law of water bodies in reducing summertime UHI effects, as well as the scale response at different water surface ratios. The results show that the influence of water surfaces on UHIs gradually decreases with increasing distance, and the temperature rises by 0.78 °C for every 100 m away from the water body. During daytime, there is a scaled sensitivity of how much water surfaces reduce the summertime UHI effects. The most sensitive radius from the water was found at the core water surface ratio of 200 m. A reduction of UHI intensity by 2.3 °C was observed for every 10% increase of the average core water surface ratio. This study provides a theoretical reference to the control of heat islands for the planning and design of urban water bodies.

With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities.

COVID-19 is a new respiratory illness caused by SARS-CoV-2, and has constituted a global public health emergency. Cat is susceptible to SARS-CoV-2. However, the prevalence of SARS-CoV-2 in cats remains largely unknown. Here, we investigated the infection of SARS-CoV-2 in cats during COVID-19 outbreak in Wuhan by serological detection methods. A cohort of serum samples were collected from cats in Wuhan, including 102 sampled after COVID-19 outbreak, and 39 prior to the outbreak. Fifteen sera collected after the outbreak were positive for the receptor binding domain (RBD) of SARS-CoV-2 by indirect enzyme linked immunosorbent assay (ELISA). Among them, 11 had SARS-CoV-2 neutralizing antibodies with a titer ranging from 1/20 to 1/1080. No serological cross-reactivity was detected between SARS-CoV-2 and type I or II feline infectious peritonitis virus (FIPV). In addition, we continuously monitored serum antibody dynamics of two positive cats every 10 days over 130 days. Their serum antibodies reached the peak at 10 days after first sampling, and declined to the limit of detection within 110 days. Our data demonstrated that SARS-CoV-2 has infected cats in Wuhan during the outbreak and described serum antibody dynamics in cats, providing an important reference for clinical treatment and prevention of COVID-19.

Duck enteritis virus (DEV), duck tembusu virus (DTMUV), and highly pathogenic avian influenza virus (HPAIV) H5N1 are the most important viral pathogens in ducks, as they cause significant economic losses in the duck industry. Development of a novel vaccine simultaneously effective against these three viruses is the most economical method for reducing losses. In the present study, by utilizing a clustered regularly interspaced short palindromic repeats (CRISPR)/associated 9 (Cas9)-mediated gene editing strategy, we efficiently generated DEV recombinants (C-KCE-HA/PrM-E) that simultaneously encode the hemagglutinin (HA) gene of HPAIV H5N1 and pre-membrane proteins (PrM), as well as the envelope glycoprotein (E) gene of DTMUV, and its potential as a trivalent vaccine was also evaluated. Ducks immunized with C-KCE-HA/PrM-E enhanced both humoral and cell-mediated immune responses to H5N1 and DTMUV. Importantly, a single-dose of C-KCE-HA/PrM-E conferred solid protection against virulent H5N1, DTMUV, and DEV challenges. In conclusion, these results demonstrated for the first time that the CRISPR/Cas9 system can be applied for modification of the DEV genome rapidly and efficiently, and that recombinant C-KCE-HA/PrM-E can serve as a potential candidate trivalent vaccine to prevent H5N1, DTMUV, and DEV infections in ducks.