Characteristics and factors influencing the expansion of urban construction land in China.

As a new product of rapid urbanization, the sprawl of urban construction land can objectively reflect urban land use efficiency, which is of great significance to China's new urban construction. This study aimed to summarize the expansion patterns and utilization efficiency of urban construction land in China from the perspectives of the status, speed and trends of expansion, and to uncover the key factors that lead to the differential distribution of the expansion of construction land. It can also provide land management experience for other countries with rapid expansion of construction land. The results show the following. (1) The expansion of China's construction land presents a "point-line-plane" pattern of evolution, forming changing stages of point-like aggregation, linear series and planar spread. (2) China's construction land shows the characteristics of disorderly spread, a low utilization rate and low output efficiency. The speed of expansion presents clear characteristics of being high in the east and low in the west, mostly concentrated in the Yangtze River Delta, Pearl River Delta and the Beijing-Tianjin-Hebei urban agglomeration. Shanghai, Beijing, Shenzhen and Guangzhou have the highest intensity of construction land use. In Shandong Peninsula and eastern coastal areas, the intensity of the construction land use is generally high. In Xinjiang and Xizang, the intensity of construction land use is relatively low. (3) The urban economic level, population size, industrial structure, foreign investment and land policies have significant effects on the spatial distribution of the expansion of construction land.

The Effects of the Built Environment on the Mental Health of Older Adults: A Case Study in Hangzhou, China.

Background and Objectives: This study examines the association between the built environment and mental health among older adults by exploring the potential pathways through the perceived built environment, social interaction, and environmental pollution. Research Design and Methods: Research data were collected from 1 405 older adults aged 60 years and older in 30 residential neighborhoods of Hangzhou, China, in 2022. The participants' evaluation of the perceived built environment, social interaction, and environmental pollution were assessed. Mental health was measured by the 12-item Short-Form Health Survey. The built environment was measured through an evaluation of density, diversity, design, destination accessibility, distance to transit, and the age-friendly environment. Structural equation models were established based on 300-m, 500-m, and 800-m buffers from the entrance of residential neighborhoods. The propensity score method was employed for sensitivity analysis. Results: With regard to older adult's mental health, there were significant direct effects of the accessibility of health services, shopping services, and bus stops. A robust mediating effect of perceived built environment was displayed on the relationship between land use mix and mental health of older adults. Social interaction exhibited robust mediating effects between the age-friendly environment, the number of bus stops, and mental health of older adults. Discussion and Implications: This study provides empirical experience for related research on the built environment and mental health as well as policy implications for urban planners focusing on community planning.

Mapping COVID-19's potential infection risk based on land use characteristics: A case study of commercial activities in two Egyptian cities.

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity's spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

Spatial congruency or discrepancy? Exploring the spatiotemporal dynamics of built-up expansion patterns and flood risk.

Most coastal cities have been experiencing unprecedented urbanization-induced flood risk, climatic events, and haphazard anthropogenic activities, jeopardizing residents' lives and building environments. Despite mounting flood-related studies, analyzing the correlation between the spatiotemporal dynamics of Built-up Expansion patterns (BE) and flood risk remains unknown and holds divergent perspectives. In this context, the coastal city of Alexandria, Egypt, characterized by multiple urban patterns and experiencing heavy rainfall annually, was selected as a testbed. Our method defined the spatiotemporal rates of BE from 1995 to 2023, quantified flood risk spatially, and finally investigated the correlation between BE and flood risk through spatial and statistical analysis. Our results show the built-up area occupied 30.32 % of the total city area till 2023, and the infilling pattern dominated the BE growth by 45.21 % of the total built-up area, followed by leapfrogging and edge expansion by 33.25 % and 21.55 %, respectively. The unplanned-infilling pattern is predominantly highly correlated with the flood-vulnerable peaks (correlation coefficient (rk) = 0.975, p-value < 0.05) and lowers dramatically towards planned-infilling regions with flood protections. Meanwhile, a spatial mismatch exists between high-risk peaks and leapfrogging and edge expansion (rk = 0.118 and 0.662, respectively, with a p-value < 0.01), indicating that controlling the built-up amount is inadequate for mitigating flood risk. Porosity-based urban configuration and spatial distribution of built-up patches in harmony with nature-based solutions are recommended for shaping flood-resilient and effective urban planning.

Machine learning based classification of yogurt aroma types with flavoromics.

Traditional sensory evaluation, relying on human assessors, is vulnerable to subjective error and lacks automation. Nonetheless, the complexity of human sensation makes it challenging to develop a computational method in place of human sensory evaluation. To tackle this challenge, this study constructed logistic regression classification models that could predict yogurt aroma types based on aroma-active compound concentrations with high classification accuracy (AUC ROC > 0.8). Furthermore, indicator compounds discovered from feature importance analysis of classification models led to the derivation of classification criteria of yogurt aroma types. Through constructing and analyzing machine learning models on yogurt aroma types, this study provides an automated pipeline to monitor sensory properties of yogurts.

Assessing the effectiveness of nature-based solutions-strengthened urban planning mechanisms in forming flood-resilient cities.

Cities have experienced rapid urbanization-induced harsh climatic events, especially flooding, inevitably resulting in negative and irreversible consequences for urban resilience and endangering residents' lives. Numerous studies have analyzed the effects of anthropogenic practices (land use changes and urbanization) on flood forecasting. However, non-structural mitigation's effectiveness, like Nature-Based Solutions (NBS), has yet to receive adequate attention, particularly in the Middle East and North Africa (MENA) region, which have become increasingly significant and indispensable for operationalizing cities efficiently. Therefore, our study investigated the predictive influence of incorporating one of the most common NBS strategies called low-impact development tools (LID) (such as rain gardens, bio-retention cells, green roofs, infiltration trenches, permeable pavement, and vegetative swale) during the urban planning of Alexandria, Egypt, which experiences the harshest rainfall annually and includes various urban patterns. City characteristics-dependent 14 LID scenarios were simulated with recurrence intervals ranging from 2 to 100 years using the LID Treatment Train Tool (LID TTT), depending on calibrated data from 2015 to 2020, by the Nash-Sutcliffe efficiency index and deterministic coefficient, and root-mean-square error with values of 0.97, 0.91, and 0.31, respectively. Our findings confirmed the significant effectiveness of combined LID tools on total flood runoff volume reduction by 73.7%, revealing that different urban patterns can be used in flood-prone cities, provided LID tools are considered in city planning besides grey infrastructure to achieve optimal mitigation. These results, which combined multiple disciplines and were not explicitly mentioned in similar studies in developing countries, may assist municipalities' policymakers in planning flood-resistant, sustainable cities.

Changes in Key Aroma Compounds and Esterase Activity of Monascus-Fermented Cheese across a 30-Day Ripening Period.

Monascus-fermented cheese (MC) is a new type of mold-ripened cheese that combines a traditional Chinese fermentation fungus, Monascus purpureus M1, with Western cheese fermentation techniques. In this study, the compositions of the volatile aroma compounds in MC were analyzed during a 30-day ripening period using SPME-Arrow and GC-O-MS. The activity of esterase in MC, which is a key enzyme catalyzing esterification reaction, was determined and compared with the control group (CC). Next, sensory analysis was conducted via quantitative descriptive analysis followed by Pearson correlation analysis between esterase activity and the key flavor compounds. A total of 76 compounds were detected. Thirty-three of these compounds could be smelled at the sniffing port and were identified as the key aroma compounds. The esterase activity in MC was found to be 1.24~1.33 times that of the CC. Moreover, the key odor features of ripened MC were alcohol and fruity flavors, considerably deviating from the sour and cheesy features found for the ripened CC. Furthermore, correlation analysis showed that esterase activity was strongly correlated (|r|> 0.75, p < 0.05) with various acids such as pentanoic and nonanoic acids and several aromatic esters, namely, octanoic acid ethyl ester and decanoic acid ethyl ester, revealing the key role that esterases play in developing the typical aroma of ripened MC.

Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation.

Emotional responses are significant for understanding public perceptions of urban green space (UGS) and can be used to inform proposals for optimal urban design strategies to enhance public emotional health in the times of COVID-19. However, most empirical studies fail to consider emotion-oriented landscape assessments under dynamic perspectives despite the fact that individually observed sceneries alter with angle. To close this gap, a real-time sentimental-based landscape assessment framework is developed, integrating facial expression recognition with semantic segmentation of changing landscapes. Furthermore, a case study using panoramic videos converted from Google Street View images to simulate changing scenes was used to test the viability of this framework, resulting in five million big data points. The result of this study shows that through the collaboration of deep learning algorithms, finer visual variables were classified, subtle emotional responses were tracked, and better regression results for valence and arousal were obtained. Among all the predictors, the proportion of grass was the most significant predictor for emotional perception. The proposed framework is adaptable and human-centric, and it enables the instantaneous emotional perception of the built environment by the general public as a feedback survey tool to aid urban planners in creating UGS that promote emotional well-being.

Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen.

Countries worldwide are reopening their businesses despite the continuing COVID-19 crisis and the emergence of new variants. In this context, knowing whether the reopening of businesses at various locations exposes higher risk to the public is essential. Whether urban density correlates with the potential infection risk as concluded by previous studies of the COVID-19 pandemic remains unknown. In this study, taking the Macau Peninsula as a testbed, we first identified business locations for daily activities according to the latest point of interest (POI) data and generated the potential risk surface for COVID-19 infection. Then, using the cellular phone network and urban footprint data, we further analyzed the spatial relationship between COVID-19 potential risk and urban density of population and morphology through visual analytics. Results show that while some degree of spatial congruency exists between medium-risk peaks and urban density hotspots, apparent spatial mismatch exists for high-risk peaks, indicating that the traditional planning control based on urban density is inadequate for mitigating public health risks. POI-based spatial layout and configuration better reflecting business services and associated human activities are recommended in future planning and policy-making for more resilient cities in the post-pandemic era.

Role of urban planning characteristics in forming pandemic resilient cities - Case study of Covid-19 impacts on European cities within England, Germany and Italy.

In recent decades, the world has witnessed a variety of emerging infectious diseases, some of which developed to pandemic world threatening outbreaks, the ongoing COVID-19 is known to be taking the lead in claiming lives around the globe and thus, urging people to trail its increasing figures. Therefore, this research aims to emphasize the role of urban planning in containing such outbreaks through running a series of analytical and statistical studies on European cities, worst inflicted region, to analyze the main urban features they share and that may be propagating the disease spread according to their population size, density, form, intracity connectivity and intercity connectivity. This study, as far as we know of, is the first practice to evaluate both the individual and combined impacts of these factors on recorded rates of infections. According to the context of this research, it is concluded that the diversity found in urban features are, to a large degree, related to cities being more vulnerable than others. Intracity connectivity through public transport is found to be the possible prime factor of this study, and is followed by population size, density, and intercity connectivity. Urban morphology seems to also contribute to such outbreak, with both radial and grid cities being associated to higher infections rates as to linear cities. Henceforth, setting priorities in post-pandemic urban planning schemes is essential for planning resilient cities that are capable to thrive and maintain functionality with lowest possible infections amid else possible diseases that are to follow in severity.

Development of a duplex SYBR Greenâ based real-time PCR assay for detection of porcine epidemic diarrhea virus and porcine bocavirus3/4/5.

The duplex real-time PCR assay based on SYBR Green І was developed for detection of porcine epidemic diarrhea virus (PEDV) and porcine bocavirus (PBoV) 3/4/5 genotypes simultaneously. Two pairs of specific primers were designed targeting the N gene sequence of PEDV and VP1 gene sequence of PBoV3/4/5. PEDV and PBoV3/4/5 could be distinguished by their different melting temperatures (Tm) in one sample. The Tm value of PEDV was 83.5 °C, and the Tm value of PBoV3/4/5 was 78.5 °C, while other swine pathogens showed no specific melting peaks. The detection limits of this assay were 10 copies/µL for both PEDV and PBoV3/4/5. A total of sixty-three intestinal tissue samples were collected from piglets suffering from diarrhea, and the viral nucleic acids detected and identified by the real-time PCR assay and conventional PCR assay. The duplex real-time PCR detection results showed that the prevalence of PEDV and PBoV3/4/5 was 85.7% and 46%, respectively, and the co-infection rate of the two viruses was 28.6%. These results indicated that this duplex real-time PCR assay was a sensitive, specific and reproducible method for differentiating PEDV and PBoV3/4/5 or their co-infection.

Development of a TB green II-based duplex real-time fluorescence quantitative PCR assay for the simultaneous detection of porcine circovirus 2 and 3.

Porcine circovirus 3 (PCV3), as a newly emerged circovirus, is widely distributed in pig populations worldwide. Co-infection of PCV2 and PCV3 has been reported frequently in clinical samples. In the present study, a TB Green II-based duplex real-time polymerase chain reaction (qPCR) was developed to rapidly and differentially detect PCV2 and PCV3. The assay specifically detected PCV2 and PCV3, with no fluorescence signals being detected for other non-targeted pig pathogens. The duplex qPCR showed a high degree of linearity (R2 > 0.998), and its limits of detection were 10 and 78 copies/µL for PCV2 and PCV3, respectively. The duplex qPCR could detect and differentiate PCV2 (melting peaks at 85.5 °C) and PCV3 (melting peaks at 82.5 °C), and showed high repeatability and reproducibility, with intra- and inter-assay coefficients of variation of less than 2.0%. Fifty-six tissue samples from 18 pig farms were used to evaluate the duplex qPCR method. The results revealed infection rates of 66.07% (37/56) and 39.28% (22/56) for PCV2 and PCV3, respectively. The PCV2 + PCV3 co-infection rate was 39.28% (22/56). The developed method could be used as an efficient molecular biology tool for epidemiological investigations of PCV2 and PCV3.

Analysis of genetic variation of porcine circovirus type 2 within pig populations in central China.

In order to investigate the genetic diversity of porcine circovirus type 2 (PCV2), 284 clinical tissue samples were collected from different pig farms in central China from 2015 to 2017. A total of 162 tissue samples (162/284, 57.04%) were positive for PCV2 by PCR, and subsequently, the complete genome of 36 of these PCV2 samples was cloned and sequenced. The sequencing results showed that 37 complete PCV2 sequences were obtained from 36 PCV2-positive clinical samples. These PCV2 strains were relatively conserved and extremely homologous to the representative classical PCV2 strains. Of these, 20 PCV2 strains belonged to genotype PCV2d, 14 belonged to PCV2b, and three others belonged to PCV2a. Coinfection with PCV2b and PCV2d was identified in one sample (DF-2). These results show that PCV2d may be gradually replacing PCV2b as the predominant PCV2 genotype in central China, and that other genotypes also exist in individual regions. The results of this study will aid in our understanding of the molecular epidemiology of PCV2.

Development of a SYBR green I-based duplex real-time fluorescence quantitative PCR assay for the simultaneous detection of porcine epidemic diarrhea virus and porcine circovirus 3.

The development of a rapid, specific, and sensitive SYBR Green I-based duplex real-time quantitative PCR assay is described for the simultaneous detection of porcine epidemic diarrhea virus (PEDV) and porcine circovirus type 3 (PCV3). The assay specifically detected PEDV and PCV3, with no fluorescence detected for other non-targeted pig pathogens. The assay showed a good linear relationship, and the limits of detection for this assay were 34.6 copies/µL and 61.2 copies/µL for PEDV and PCV3, respectively. The assay exhibited high repeatability and reproducibility, with intra-assay and inter-assay variation coefficients less than 2.0%. A clinical evaluation using intestinal tissue and fecal samples from piglets suffering from diarrhea at different pig farms in China revealed that the singular infection rates of PEDV and PCV3 were 43.94% (29/66) and 16.67% (11/66), respectively, while the co-infection rate of PCV3 with PEDV was 27.27% (18/66). The results indicate this assay is a rapid and reliable diagnostic tool for PEDV and PCV3 monitoring and surveillance in the field, and provides technical support for the quantitative detection of clinical samples infected or co-infected with PEDV and PCV3.

Detection and phylogenetic analysis of porcine circovirus type 3 in central China.

Porcine circovirus type 3 (PCV3) is the pathogen responsible for a new infectious disease that was first reported in 2016 in the United States. To further investigate the epidemic profile and genetic diversity of the virus, one hundred and seventy clinical samples (110 tissue samples and 60 serum samples) were collected from 41 different pig farms in 14 cities in central China, and a SYBR Green I-based quantitative real-time PCR method was developed to detect PCV3. The partial cap genes of four field strains from four different farms were sequenced and analysed. The results showed the detection limit was 2.19 × 101 genome copies/µl. Fifty-three of 170 samples were detected as positive for PCV3, giving a PCV3-positive rate of 31.18%, with 48.78% (20/41) of pig farms harbouring PCV3, which varied from 20% to 42.86% between 2013 and 2017. PCV3 could be detected in samples from pigs with different clinical presentations, and the PCV3-positive rates varied for these different clinical presentations. The partial capsid genes of four PCV3 strains (designated YZ, LY-03, NY and SP) shared 96.3%-99.4% nucleotide identity with those available in GenBank. Phylogenetic analysis based on the capsid gene of 32 PCV3 strains showed that the four PCV3 strains in this study were clustered with the China/GD2016 and South Korea Ku-1606 strains. The results of this study will aid our understanding of the molecular epidemiology of PCV3.