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The epidemic outbreaks of the last two decades have led governments to rely more on computational tools for establishing protection policies. Computational approaches to modeling epidemics traditionally rely on compartmental models, network models, or agent-based models (ABMs); however, each approach has its limitations, ranging from reduced realism to lack of tractability. Furthermore, the recent literature emphasizes the importance of points of interest (POIs) as sources of population mixing and potential outbreak hotspots. In response, this study proposes a novel urban spatial ABM validated using our augmented SICARQD epidemic model. To replicate daily activities more accurately, the urban area is divided into a matrix of points of interest (POIs) with agents that have unique paths that only permit infectious transmission within POIs. Our results provide a qualitative assessment of how urban characteristics and individual mobility patterns impact the infected population during an outbreak. That is, we study how population density, the total number of POIs (where the population concentrates), the average number of POIs visited by an agent, the maximum travel distance from the home location, and the quarantine ratio impact the dynamics of an outbreak. Our ABM simulation framework offers a valuable tool for investigating and controlling infectious disease outbreaks in urban environments with direct applicability to global policy makers. Full article

The aim of the study was to collect data about the prevalence and risk factors of apical periodontitis in a population of endodontically treated patients. The study group included 151 patients (52 males, 99 females; mean age 48.36 ± 15.708 yrs.) with 391 endodontically treated teeth (mean follow-up of 5.25 ± 1.759 yrs.). According to the initial tooth diagnosis, root-filled teeth were divided into Group A, root-filled teeth treated for pulpitis or for the purpose of prosthetic pulpectomies (vital pulp group), and Group B, root-filled teeth with non-vital pulp (necrotic pulp). Clinical and radiographic evaluation of the root and its periapical area were performed to establish the success/failure of endodontic therapy, the quality of the root canal fillings (length, density, taper), and coronal restoration. The presence of recurrent caries, periodontal pathology, or endo-periodontal lesions were also recorded. Univariate and multivariate analyses were used to determine the risk factors for apical periodontitis and calculate their odds ratios (ORs). For the root-filled vital pulp tooth group, the highest risks for apical periodontitis are associated with inadequate homogeneity (OR 30.938), periodontitis (OR 9.226), and over-filling (OR 8.800). For the root-filled non-vital pulp tooth group, the highest risks are associated with periodontitis (OR 4.235) and age over 60 yrs. (OR 4.875). For the necrotic pulp tooth group, multivariate analysis identified an age > 60 yrs., filled molars, intracanal posts, poor coronal restoration quality, under-filling, and periodontitis as significant combined risk factors. Inadequate root canal filling and periodontitis in both groups were risk factors associated with most cases of apical periodontitis. Other risk factors include age > 60 yrs., poor coronal restoration quality, and the presence of intracanal posts in root-filled teeth with necrotic pulp. Full article

A narrow genetic basis limits further the improvement of modern Gossypium hirsutum cultivar. The abundant genetic diversity of wild species provides available resources to solve this dilemma. In the present study, a chromosome segment substitution line (CSSL) population including 553 individuals was established using G. darwinii accession 5-7 as the donor parent and G. hirsutum cultivar CCRI35 as the recipient parent. After constructing a high-density genetic map with the BC₁ population, the genotype and phenotype of the CSSL population were investigated. A total of 235 QTLs, including 104 QTLs for fiber-related traits and 132 QTLs for seed-related traits, were identified from four environments. Among these QTLs, twenty-seven QTLs were identified in two or more environments, and twenty-five QTL clusters consisted of 114 QTLs. Moreover, we identified three candidate genes for three stable QTLs, including GH_A01G1096 (ARF5) and GH_A10G0141 (PDF2) for lint percentage, and GH_D01G0047 (KCS4) for seed index or oil content. These results pave way for understanding the molecular regulatory mechanism of fiber and seed development and would provide valuable information for marker-assisted genetic improvement in cotton. Full article

Dairy products, a major source of calcium, demonstrate a number of beneficial effects, not only protecting against the development of osteoporosis (OP) but also suppressing the onset of type-2 diabetes (T2DM) and improving bone mineral density (BMD). Dairy consumption is closely linked to lactose tolerance. One of the genetic factors predisposing individuals to lactose intolerance is rs4988235 polymorphism of the MCM6 gene. The aim of this reported study was to analyse the relationship between the rs4988235 variant of the MCM6 gene and bone mineral density and the risk of type-2 diabetes in women after menopause. Methods: The study was conducted among 607 female patients in the postmenopausal period in whom bone densitometry and vitamin-D3 levels were assayed and genotyping of the rs4988235 polymorphism of MCM6 gene was performed. The obtained results were analysed for the presence of T2DM, obesity surrogates, medical data, and past medical history. Results: The distribution of genotype frequencies was consistent with the Hardy–Weinberg equilibrium (p > 0.050). Postmenopausal women with the GG homozygote of rs4988235 polymorphism consumed significantly less calcium (dairy), which was probably related to the observed lactose intolerance. The GG homozygote of women with rs4988235 polymorphism was significantly more likely to have T2DM relative to the A allele carriers (p = 0.023). GG homozygotes had significantly lower femoral–vertebral mineral density despite the significantly more frequent supplementation with calcium preparations (p = 0.010), vitamin D (p = 0.01), and anti-osteoporotic drugs (p = 0.040). The obtained results indicate a stronger loss of femoral-neck mineral density with age in the GG homozygotes relative to the A allele carriers (p = 0.038). Conclusions: In the population of women after menopause, the carriage of the G allele of rs4988235 polymorphism of the MCM6 gene, i.e., among the patients with lactose intolerance, significantly increased the risk of developing T2DM and the loss of BMD. Full article

This article explores the intricate relationship between urban density and social sustainability by examining the impacts of both physical and perceived density on key social sustainability criteria. Physical density is defined by measurable attributes such as building height, spacing, and population. Perceived density, on the other hand, is shaped by residents’ subjective experiences influenced by visual, functional, social, and spatial factors. This study analyzes data from the literature using the thematic analysis method. It reveals that social sustainability can be negatively affected by increased density, depending on how residents perceive high density. Key factors such as building design, green spaces, availability of amenities, and street layout are crucial in shaping residents’ perceptions of density. These perceptions, in turn, affect inclusivity, community engagement, quality of life, cultural diversity, and social cohesion within urban areas. The research highlights the importance of integrating green spaces, public areas, and community amenities to mitigate negative perceptions of density and enhance overall quality of life. The findings suggest that achieving social sustainability requires a nuanced understanding of both physical and perceived density. This paper provides a comprehensive framework for understanding these complex relationships and offers valuable insights for creating socially sustainable urban environments. Full article

Over the last two decades, the United Arab Emirates (UAE) has experienced significant urban growth, prompting the Dubai Roads and Transport Authority (RTA) to advocate for sustainable transport solutions. This led to the implementation of the Light Rail Transit (LRT) to address urban mobility, environmental sustainability, and energy efficiency. Dubai has strategically prioritized infrastructure and transportation network expansion to support its rapid development. This paper aims to examine the critical role of the LRT system, particularly the metro and tramway, in steering Dubai towards sustainability. Metro and tramway systems offer crucial high-capacity public transport, enhance connectivity, stimulate economic growth, and contribute to a sustainable environment. The study assesses the transformative impact of the Dubai Metro on urban development, focusing on key stations like Jabal Ali, Al-Barsha First, and Business Bay. Using qualitative research methods, including GIS, spatial maps, interviews, case studies, and land use investigations, the research analyzes population density, connectivity, accessibility, and urban land use patterns around these stations. Results indicate a positive impact of the Dubai Metro on both commercial and residential land use, improved connectivity, and enhanced accessibility, reinforcing its role in cultivating a sustainable urban environment. Full article

Dockless bike-sharing mobility brings considerable benefits to building low-carbon transportation. However, the operators often rush to seize the market and regulate the services without a good knowledge of this new mobility option, which results in unreasonable layout and management of shared bicycles. Therefore, it is meaningful to explore the relationship between the built environment and bike-sharing ridership. This study proposes a novel framework integrated with the extreme gradient boosting tree model to evaluate the impacts and threshold effects of the built environment on the origin–destination bike-sharing ridership. The results show that most built environment features have strong nonlinear effects on the bike-sharing ridership. The bus density, the industrial ratio, the local population density, and the subway density are the key explanatory variables impacting the bike-sharing ridership. The threshold effects of the built environment are explored based on partial dependence plots, which could improve the bike-sharing system and provide policy implications for green travel and sustainable transportation. Full article

In recent years, China’s urbanization has accelerated, significantly impacting ecosystems and the carbon balance due to changes in urban land use. The spatial patterns of CO₂ emissions from urban land are essential for devising strategies to mitigate emissions, particularly in predicting future spatial distributions that guide urban development. Based on socioeconomic grid data, such as nighttime lights and the population, this study proposes a spatial prediction method for CO₂ emissions from urban land using a Long Short-Term Memory (LSTM) model with added fully connected layers. Additionally, the geographical detector method was applied to identify the factors driving the increase in CO₂ emissions due to urban land expansion. The results show that socioeconomic grid data can effectively predict the spatial distribution of CO₂ emissions. In the Yangtze River Economic Belt (YREB), emissions from urban land are projected to rise by 116.23% from 2020 to 2030. The analysis of driving factors indicates that economic development and population density significantly influence the increase in CO₂ emissions due to urban land expansion. In downstream cities, CO₂ emissions are influenced by both population density and economic development, whereas in midstream and upstream city clusters, they are primarily driven by economic development. Furthermore, technology investment can mitigate CO₂ emissions from upstream city clusters. In conclusion, this study provides a scientific basis for developing CO₂ mitigation strategies for urban land within the YREB. Full article

Candidate genes related to first pod height (FPH) traits in faba bean plants are crucial for mechanised breeding. However, reports on quantitative trait locus (QTL) mapping related to the FPH of faba bean are few, thus limiting the high-quality development of the faba bean industry to a certain extent. The identification and screening of candidate genes related to FPH is extremely urgent for the advancement of mechanised breeding for faba bean. In this study, a high-density genetic linkage map was constructed using genotyping-by-sequencing (GBS) of an F₃ population and QTLs (genes) related to FPH were identified. The genetic linkage map contained seven linkage groups with 3012 SNP markers with an overall length of 4089.13 centimorgan (cM) and an average marker density of 1.36 cM. Thirty-eight QTLs for the first pod node (FPN) and FPH were identified (19 each for FPN and FPH). The 19 QTLs associated with FPN were located on chromosomes 1L, 1S, 2, 3, 4, 5, and 6; the 19 QTLs associated with FPH were located on chromosomes 1L, 1S, 2, 3, 5, and 6. There was a co-localisation interval of qFPN6-1 and qFPH6-1 on chromosome 6. By annotating the QTL qFPH6-1 interval, 36 genes that may be related to FPH were identified, these genes are related to plant growth and development. The results provide a basis for the precise location of QTLs related to FPH and could accelerate the breeding of faba bean varieties adapted to mechanised harvesting. Full article

Green spaces play a critical role in enhancing the urban environment, improving livability, and providing essential ecosystem services. A city should have at least 25% green space from an environmental and health point of view. However, quantitative estimation is required to assess the extent and pattern of green space changes for proper urban management. The present study aimed to identify and track the changes in urban green spaces within the Dhaka South City Corporation (DSCC) of Bangladesh over a 30-year period (i.e., 1991–2021). Geospatial techniques were utilized to analyze green space dynamics using Landsat 4–5 TM satellite images from 1991, 2001, and 2011 and Landsat 8 images from 2021. Supervised image classification techniques and Normalized Difference Vegetation Index (NDVI) analysis were performed to assess the urban green space dynamics in DSCC. The results of our study revealed a significant 36.5% reduction in vegetation cover in the DSCC area over the study period. In 1991, the green area coverage in DSCC was 46%, indicating a relatively healthy environment. By 2001, this coverage had declined sharply to 21.3%, further decreasing to 19.7% in 2011, and reaching a low of just 9.5% in 2021. The classified maps generated in the study were validated through field observations and Google Earth images. The outcomes of our study will be helpful for policymakers and city planners in developing and applying appropriate policies and plans to preserve and improve urban green spaces in DSCC in Bangladesh and other Asian megacities with high population density. Full article

Computational fluid dynamics (CFD) modeling has emerged as a valuable tool for investigating complex processes like microencapsulation. This paper aims to validate the ability of CFD simulations to predict particle size distribution in a polymer microencapsulation process. The CFD modeling approach employed a Eulerian multiphase framework, incorporating a discrete population balance model to track the evolution of the droplet population. A realizable k-ε turbulence model and a multiple reference frame strategy were utilized to capture the system’s flow dynamics. The results reveal that while the CFD simulations align well with experimental data at higher agitation speeds (>10,000 rpm), discrepancies arise at lower speeds (<7500 rpm), indicating a challenge in accurately capturing turbulent viscous regimes. Despite these challenges, the CFD model demonstrates robust predictive capabilities for droplet formation and distribution in microencapsulation processes, validated by error margins within the acceptable limits. The validated model can be used as a reliable tool to guide experimental efforts and optimize process parameters, contributing to an enhanced understanding and control of microencapsulation processes. Full article

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state–response model, and social–economic–natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses. Full article

Population-dense urban areas often concentrate high commercial and industrial activity and intricate transportation systems. In crowded cities, extreme events can be even more damaging due to the high population they affect and the social inequalities that are likely to emerge. Extreme heat is a climate hazard that has been linked to high morbidity and mortality, especially in cities with high population densities. The way extreme heat events are felt in the population varies depending on a variety of factors, such as age, employment status, living conditions, air-conditioning, housing conditions, habits, behaviors and other socio-demographic parameters. In this article, we quantify and locate vulnerabilities of populations to extreme heat in order to formulate policy and practice recommendations that will make communities resilient and will shape the transition to a more sustainable future. This work contributes towards the achievement of Sustainable Development Goal 11—Sustainable Cities and Communities—by developing the tools to make cities and settlements resilient and sustainable. To this end, we analyze socioeconomic data at the NUTS3 level for the national case study of Greece and at the census tract level for the local case study of the city of Athens. The target variable for this study is defined as the average daily mortality during heatwaves per 100,000 individuals, and a methodology is developed for constructing this variable based on socioeconomic data available in public databases. The independent variables were selected based on their contribution to socioeconomic vulnerability; they include the percentage of elderly individuals, retirees, unemployed persons, renters, those living alone, those residing in smaller houses, those living in older houses and immigrants from developing countries. An ensemble gradient boosted tree model was employed for this study to obtain feature importance metrics that was used to construct a composite index of socioeconomic heat vulnerability. The socioeconomic heat vulnerability index (SHVI) was calculated for each prefecture in Greece and for each census tract in the city of Athens, Greece. The unique feature of this SHVI is that it can be applied to any geographical resolution using the same methodology and produces a result that is not only quantifiable, but also facilitates a comparison between vulnerability scores across different regions. This application aimed to map the SHVI of both prefecture and city, to examine the significance of scale, to identify vulnerability hotspots, and rank the most vulnerable areas, which are prioritized by authorities for interventions that protect public health. Full article

The Henan Yellow River Basin is an ecological support belt for the entire basin. It holds a significant position in high-quality development and ecological conservation within the Yellow River Basin. However, due to improper development activities, such as urban expansion and deforestation of farmland, certain areas of the region have encountered a series of ecological issues, posing significant challenges to ecosystem services. The scientific foundation for the sustainable development of the ecological environment in the Henan Yellow River Basin is established by research on the evolution characteristics and driving factors of ecosystem service functions. This study focuses on the Henan Yellow River Basin, by introducing remote sensing data and biomass data, assessing the spatiotemporal variations in ecosystem service by the InVEST model—including carbon stock, water yield, and soil conservation—from 2000 to 2020. It analyzes the ecosystem service functions of different land use types. It employs the Geodetector to identify the dominant driving factors behind the changes in these functions based on the improved InVEST model evaluated results. The findings reveal that from 2000 to 2020, total carbon stock increased by 1.86%, carbon stock per unit area rose by 1.81%, and the spatial distribution remained largely stable. The high-value regions were clustered in the west and part of the north, primarily consisting of forest land. Carbon stock capacity in other regions, mainly farmland and construction land, was poor, with forest land having the strongest carbon sequestration capacity, followed by grassland. Total water yield decreased by 20.08%, and water yield per unit area decreased by 20.03%, with a spatial distribution closely following the trend of precipitation distribution. The high-value regions were clustered in the south, primarily in forest land and farmland. The total amount of soil conservation decreased by 19.96%, and soil conservation per unit area decreased by 19.93%, with spatial distribution patterns similar to those of carbon stock and water yield. The high-value regions were concentrated in the southwestern and northern forested regions, while soil conservation capacity in areas primarily consisting of farmland and construction land was weaker. The divergence of carbon stock was most influenced by population density, water yield by precipitation, and soil conservation by slope. In conclusion, during the study period, while carbon storage increased, the significant decline in water yield and soil conservation highlighted critical issues in the ecosystem service functions of the region. These findings indicate the need for targeted conservation measures and sustainable development strategies to address the decline in ecosystem services and mitigate adverse environmental impacts, ensuring the long-term health of the region’s ecosystems. This study offers an in-depth understanding of the differentiation of ecosystem service functions and their driving factors, enabling precise assessment of regional ecosystem services, and providing a theoretical foundation for formulating effective regional ecological conservation policies. Full article

Public transport improves mobility and well-being for the rapidly aging population. However, few planning interventions have addressed the urban–rural disparity in bus usage among older adults. Using data from Zhongshan, China, this study adopts the eXtreme Gradient Boosting (XGBoost) model to examine urban–rural differences in the nonlinear relationship between built environment and daily bus usage among elderly adults. The results indicate nonlinearities across all built environment variables and stronger effects of the built environment in rural areas. Distance to transit contributes the most in urban neighborhoods but least in rural ones. Furthermore, dwelling unit density and green space accessibility play the biggest roles in the rural context. Additionally, the most effective ranges of intersection density, land use mixture, and CBD accessibility are greater in rural areas. The findings facilitate fine-grained and diversified planning interventions to facilitate bus usage among older adults in both urban and rural areas. Full article