A well-known result by Graham in Euclidean Ramsey Theory states that, for every positive real number $A$, every coloring of the plane with finite number of colors contains a monochromatic triangle of area $A$. We consider canonical versions of this result. We show that every $3$-coloring of the plane integer lattice contains either a rainbow triangle of area $1/2$ or a monochromatic rectangle of any given area whose sides are parallell to the axes. We also show that, under natural conditions, there are numbers $A$ and $B$ such that every coloring of the plane integer lattice contains either a monochromatic rectangle of area $A$ or a rainbow trapezoid of area $B$. As usual, only vertex colors are considered: e.g., a monochromatic rectangle is a set of four points in the lattice which a) are the vertices of a rectangle and b) are assigned the same color.
Raphaëlle Delpech, Henri Panjo, Alexis Costalat
et al.
Abstract Objectives We studied the association between GPs’ characteristics and the places they practise, in terms of the supply and demand for primary care and of the registration of new patients for ongoing care at the office or for house calls. Study design An exhaustive simulated patient survey enabled us to determine the GPs practising in the Paris metropolitan region who were accepting new patients for registration for continuing care at their office and/or for house calls. Methods We studied the associations between the characteristics of GPs who were accepting new patient registrations and those describing their office location. Results In 2017–2018, we contacted 8171 physicians (87.6% of the GPs in the region), 49.70% were willing to register a new patient for office visits and 18.7% for house calls. In both situations (office and visit), doctors who most frequently agreed to register new patients were men in solo practices, who had no secretary and did not practise alternative medicine. GPs in areas with low levels of deprivation and relatively few individuals with costly chronic diseases agreed more frequently than those elsewhere to register new patients. No characteristic describing the supply of primary care was associated with agreement to register new patients. Conclusions The difficulties of finding a GP in the most deprived areas and with the most people with chronic diseases suggest the need to develop policies facilitating the settlement of new doctors in such areas.
The increasing global urbanization, particularly in coastal regions, coupled with the risks of climate change and land subsidence, underscores the need to monitor coastal urban development for sustainability. This study focused on the coastal metropolitan regions of Poland's Tri-City and Indonesia's Semarang, employing GIS, remote sensing (RS), and cloud computing. By integrating nighttime light (NTL) and the Built-Up Land Features Extraction Index (BLFEI) through Google Earth Engine (GEE) and Object-Based Image Analysis (OBIA), the study aimed to gain insights into urban development trends. The methodology encompassed image collection, analysis, and classification over three decades (1992, 2007, 2022). Despite efforts to enhance accuracy through built-up masking in subsequent years, the methodology achieved an overall accuracy of 95% for the 2022 maps, while maps in 1992 and 2007 fell short (overall accuracy ranging from 0.81 to 0.90) in comparison. The analysis revealed a gradual expansion of built-up areas in both regions, with Gdynia and Gdańsk emerging as primary drivers in the Tri-City metropolitan region and Semarang as the primary driver in the Semarang metropolitan region. Notably, the Semarang metropolitan region exhibited an increase in waterbody areas, attributed to coastal flooding and land subsidence challenges.
Transitioning to a “green” economy will require many industries to change their activities, raising concerns about the elimination of occupations and the need for significant retraining of the workforce. These concerns have increased resistance to a green transition from some sectors of society. Yet if skills embodied in current economic tasks can be reapplied to activities that facilitate a green transition, the retraining challenge might be lessened. Using a new taxonomy of sustainable economic activities – those that can contribute to climate change mitigation or adaptation – we estimate the number of US, German, and Canadian workers already employed in industries that are equipped to undertake sustainable economic activities. While the fraction of potential green workers varies considerably across metropolitan areas, in each country over one-third-of workers could conceivably contribute to a green economic transition by applying their existing skills to new activities. This represents >47 million workers in the US. Thus, a transition to a green economy may require more that firms reconfigure their workforces than individual workers reconfigure their skill sets.
Simon J. Fifer, Karen Winkler, Meredith Cummins
et al.
ABSTRACT Background Long‐acting somatostatin analogues (LA SSAs) are commonly used as first‐line treatment in well‐differentiated low and intermediate grade 1–2 gastroenteropancreatic neuroendocrine tumours (GEP‐NETs). As a long‐term treatment option for NETs, treatment preferences should be taken into consideration. The objective of this study is to evaluate the relative importance of different LA SSA treatment features among patients with NETs, clinicians and nurses in Australia. Methods A discrete choice experiment (DCE) method was used to analyse treatment preferences for NETs, embedded in an online survey. Participants could choose between three hypothetical treatment options: ‘deep subcutaneous injectable treatment’, ‘deep intramuscular injectable treatment’, ‘oral treatment’; and an opt‐out. Each option was described by seven attributes: ‘Progression free survival’ (PFS), ‘symptom control for diarrhoea and flushing’, ‘risk of gastrointestinal (GI) side effects’, ‘frequency of administration’, ‘treatment administration,’ ‘treatment delivery,’ and ‘availability of patient support’. A Mixed Multinomial Logit model (MMNL) was used for analysis. Results A total of 54 patients with NETs, 27 clinicians, and 9 nurses completed the DCE (of 33, 26 and 20 planned respectively). The statistical model showed an overall preference for oral administration. ‘PFS’ was the most important attribute, followed by ‘symptom control for diarrhoea and flushing’ and ‘risk of GI side effects’. ‘PFS’ and ‘symptom control for diarrhoea and flushing’ were also found to be more important to healthcare professionals (HCPs, i.e., clinicians and nurses) than patients with NETs, while the attributes ‘symptom control for diarrhoea and flushing’ and ‘risk of GI side effects’ were found to be more important in non‐metropolitan areas (vs. metropolitan areas). Conclusions Analysis showed how patients with NETs and HCPs prioritise treatment attributes for NETs differently. Because the duration of use of LA SSA for the treatment of NETs is often long, these differences highlight the importance of involving the patient when making treatment decisions.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
AbstractBased on limited theoretical applicability of metropolitan area concept under China’s circumstances and the planning practice in Greater Shanghai Metropolitan Area, this article proposes a Chinese understanding on the concept of “Metropolitan Area”, which extends its core connotation from a commuting area to a multi-functional area. It further proposes to utilize the preparation of spatial plans as means to promote high-quality development of metropolitan areas and to achieve coordinated regional development. Metropolitan area planning takes spatial planning as its basic attribute, and simultaneously embodies the contents of economic and social arrangements. It could become a binding contract based on consensus among member cities. In terms of technical innovations in planning practice, this article firstly suggests using a “problem-based, goal-targeted, action-driven, space-oriented and implementation-guided” approach to form a framework of “objective (indicator)-strategy-action-mechanism” to realize collaborative goals. Secondly, the spatial plan should focus on “bottom line factors” as well as “collaborative factors”, establishing a technical framework for regional spatial organization from multiple dimensions such as ecological pattern, city function, spatial structure, and conduction mechanism which drive transformations in regional governance modes.
Atikah Fitria Muharromah, Thaddeus M. Carvajal, Maria Angenica F. Regilme
et al.
Abstract Background The adaptive divergence of Aedes aegypti populations to heterogeneous environments can be a driving force behind the recent expansion of their habitat distribution and outbreaks of dengue disease in urbanized areas. In this study, we investigated the population genomics of Ae. aegypti at a regional scale in Metropolitan Manila, Philippines. Methods We used the Pool-Seq double digestion restriction-site association DNA sequencing (ddRAD-Seq) approach to generate a high number of single nucleotide polymorphisms (SNPs), with the aim to determine local adaptation and compare the population structure with 11 microsatellite markers. A total of 217 Ae. aegypti individuals from seven female and seven male populations collected from Metropolitan Manila were used in the assays. Results We detected 65,473 SNPs across the populations, of which 76 were non-neutral SNPs. Of these non-neutral SNPs, the multivariate regression test associated 50 with eight landscape variables (e.g. open space, forest, etc.) and 29 with five climate variables (e.g. air temperature, humidity, etc.) (P-value range 0.005–0.045) in female and male populations separately. Male and female populations exhibited contrasting spatial divergence, with males exhibiting greater divergence than females, most likely reflecting the different dispersal abilities of male and female mosquitoes. In the comparative analysis of the same Ae. aegypti individuals, the pairwise F ST values of 11 microsatellite markers were lower than those of the neutral SNPs, indicating that the neutral SNPs generated via pool ddRAD-Seq were more sensitive in terms of detecting genetic differences between populations at fine-spatial scales. Conclusions Overall, our study demonstrates the utility of pool ddRAD-Seq for examining genetic differences in Ae. aegypti populations in areas at fine-spatial scales that could inform vector control programs such as Wolbachia-infected mosquito mass-release programs. This in turn would provide information on mosquito population dispersal patterns and the potential barriers to mosquito movement within and around the release area. In addition, the potential of environmental adaptability observed in Ae. aegypti could help population control efforts. Graphical Abstract
Frank Ngeni, Boniphace Kutela, Tumlumbe Juliana Chengula
et al.
Bike-sharing systems have grown in popularity in metropolitan areas, providing a handy and environmentally friendly transportation choice for commuters and visitors alike. As demand for bike-sharing programs grows, efficient capacity planning becomes critical to ensuring good user experience and system sustainability in terms of demand. The random forest model was used in this study to predict bike-sharing station demand and is considered a strong ensemble learning approach that can successfully capture complicated nonlinear correlations and interactions between input variables. This study employed data from the Smart Location Database (SLD) to test the model accuracy in estimating station demand and used a form of explainable artificial intelligence (XAI) function to further understand machine learning (ML) prediction outcomes owing to the blackbox tendencies of ML models. Vehicle Miles of Travel (VMT) and Greenhouse Gas (GHG) emissions were the most important features in predicting docking station demand individually but not holistically based on the datasets. The percentage of zero-car households, gross residential density, road network density, aggregate frequency of transit service, and gross activity density were found to have a moderate influence on the prediction model. Further, there may be a better prediction model generating sensible results for every type of explanatory variable, but their contributions are minimum to the prediction outcome. By measuring each feature's contribution to demand prediction in feature engineering, bike-sharing operators can acquire a better understanding of the bike-sharing station capacity and forecast future demands during planning. At the same time, ML models will need further assessment before a holistic conclusion.
Monitoring wildfires has become increasingly critical due to the sharp rise in wildfire incidents in recent years. Optical satellites like Sentinel-2 and Landsat are extensively utilized for mapping burned areas. However, the effectiveness of optical sensors is compromised by clouds and smoke, which obstruct the detection of burned areas. Thus, satellites equipped with Synthetic Aperture Radar (SAR), such as dual-polarization Sentinel-1 and quad-polarization RADARSAT-1/-2 C-band SAR, which can penetrate clouds and smoke, are investigated for mapping burned areas. However, there is limited research on using compact polarisation (compact-pol) C-band RADARSAT Constellation Mission (RCM) SAR data for this purpose. This study aims to investigate the capacity of compact polarisation RCM data for burned area mapping through deep learning. Compact-pol m-chi decomposition and Compact-pol Radar Vegetation Index (CpRVI) are derived from the RCM Multi-look Complex product. A deep-learning-based processing pipeline incorporating ConvNet-based and Transformer-based models is applied for burned area mapping, with three different input settings: using only log-ratio dual-polarization intensity images images, using only compact-pol decomposition plus CpRVI, and using all three data sources. The results demonstrate that compact-pol m-chi decomposition and CpRVI images significantly complement log-ratio images for burned area mapping. The best-performing Transformer-based model, UNETR, trained with log-ratio, m-chi decomposition, and CpRVI data, achieved an F1 Score of 0.718 and an IoU Score of 0.565, showing a notable improvement compared to the same model trained using only log-ratio images.
Starting from AJ Bu's recent article that computed explicit expressions for the GENERATING functions of sums of powers of areas under Dyck and Motzkin paths, we deduce from them explicit expressions for the actual sequences. This enables taking the limits of the scaled moments and confirming, in an entirely elementary way, that they tend to those of the area under Brownian Excursion (up to any specified moment).
The formula for the area of a rhumb polygon, a polygon whose edges are rhumb lines on an ellipsoid of revolution, is derived and a method is given for computing the area accurately. This paper also points out that standard methods for computing rhumb lines give inaccurate results for nearly east- or west-going lines; this problem is remedied by the systematic use of divided differences.
Avalanche disaster is a major natural disaster that seriously threatens the national infrastructure and personnel's life safety. For a long time, the research of avalanche disaster prediction in the world is insufficient, there are only some basic models and basic conditions of occurrence, and there is no long series and wide range of avalanche disaster prediction products. Based on 7 different bands and different types of multi-source remote sensing data,this study combined with existing avalanche occurrence models, field investigation and statistical data to analyze the causes of avalanche. The U-net convolutional neural network and threshold analysis were used to extract the distribution of long time series avalanch-prone areas in two study areas, Heiluogou in Sichuan Province and along the Zangpo River in Palong, Tibet Autonomous Region. In addition, the relationship between earthquake magnitude and spatial distribution and avalanche occurrence is also analyzed in this study. This study will also continue to build a prior knowledge base of avalanche occurrence conditions, improve the prediction accuracy of the two methods, and produce products in long time series interannual avalanch-prone areas in southwest China, including Sichuan Province, Yunnan Province, and Tibet Autonomous Region. The resulting products will provide high-precision avalanche prediction and safety assurance for engineering construction and mountaineering activities in Southwest China.
Demand for reliable statistics at a local area (small area) level has greatly increased in recent years. Traditional area-specific estimators based on probability samples are not adequate because of small sample size or even zero sample size in a local area. As a result, methods based on models linking the areas are widely used. World Bank focused on estimating poverty measures, in particular poverty incidence and poverty gap called FGT measures, using a simulated census method, called ELL, based on a one-fold nested error model for a suitable transformation of the welfare variable. Modified ELL methods leading to significant gain in efficiency over ELL also have been proposed under the one-fold model. An advantage of ELL and modified ELL methods is that distributional assumptions on the random effects in the model are not needed. In this paper, we extend ELL and modified ELL to two-fold nested error models to estimate poverty indicators for areas (say a state) and subareas (say counties within a state). Our simulation results indicate that the modified ELL estimators lead to large efficiency gains over ELL at the area level and subarea level. Further, modified ELL method retaining both area and subarea estimated effects in the model (called MELL2) performs significantly better in terms of mean squared error (MSE) for sampled subareas than the modified ELL retaining only estimated area effect in the model (called MELL1).
Matteo Straccamore, Vittorio Loreto, Pietro Gravino
Cities and metropolitan areas are major drivers of creativity and innovation in all possible sectors: scientific, technological, social, artistic, etc. The critical concentration and proximity of diverse mindsets and opportunities, supported by efficient infrastructures, enable new technologies and ideas to emerge, thrive, and trigger further innovation. Though this pattern seems well established, geography's role in the emergence and diffusion of new technologies still needs to be clarified. An additional important question concerns the identification of the innovation pathways of metropolitan areas. Here, we explore the factors that influence the spread of technology among metropolitan areas worldwide and how geography and political borders impact this process. Our evidence suggests that political geography has been highly important for the diffusion of innovation till around two decades ago, slowly declining afterwards in favour of a more global innovation ecosystem. Further, the visualisation of the evolution of countries and metropolitan areas in a 2d space of competitiveness and diversification reveals the existence of two main innovation pathways, discriminating between different strategies towards progress. Our work provides insights for policymakers seeking to promote economic growth and technological advancement through tailored investments in prioritarian innovation areas.
This letter presents a self-contained system for robust deployment of autonomous aerial vehicles in environments without access to global navigation systems and with limited lighting conditions. The proposed system, application-tailored for documentation in dark areas of large historical monuments, uses a unique and reliable aerial platform with a multi-modal lightweight sensory setup to acquire data in human-restricted areas with adverse lighting conditions, especially in areas that are high above the ground. The introduced localization method relies on an easy-to-obtain 3-D point cloud of a historical building, while it copes with a lack of visible light by fusing active laser-based sensors. The approach does not rely on any external localization, or on a preset motion-capture system. This enables fast deployment in the interiors of investigated structures while being computationally undemanding enough to process data online, onboard an MAV equipped with ordinary processing resources. The reliability of the system is analyzed, is quantitatively evaluated on a set of aerial trajectories performed inside a real-world church, and is deployed onto the aerial platform in the position control feedback loop to demonstrate the reliability of the system in the safety-critical application of historical monuments documentation.
Slope failures and landslides cause economic damage and deaths worldwide. These losses can be minimized by integrating different methodologies, instruments, and data monitoring to predict future landslides. In the constantly growing metropolitan area of Cluj-Napoca, Romania, changes in land cover, land use, and build-up areas are an issue. The unprecedented urban sprawl pushed the city limits from the Somes River to hilly terrain prone to landslides and erosion. This study focuses on a landslide-prone area where a previous slope failure caused significant economic losses. It combines topo-geodetic measurements, UAV monitoring of surface displacement, GIS spatial analysis, ground-penetrating radar investigations, and geotechnical assessment. Two years of data show that the slope is undergoing surface erosion, with soil displacements of a few centimeters. Geodetic monitoring of the retaining wall’s control points indicates a small rotation. Coupled with georadar profile imaging showing changes in soil and rock layers with an uplift trend, it was deduced that the area suffers from a global instability. The findings provide valuable information about the dynamics of landslides and erosion for forecasting future movements and developing preventative strategies based on a new methodology that combines affordable and prevalent instrumentation and techniques.
Japanese encephalitis (JE) is a mosquito-borne zoonotic neurological disease caused by Japanese encephalitis virus (JEV). JEV is the most common cause of viral encephalitis in Southeast Asia and the Western Pacific Rim. The fatality rate due to JE can reach 25% and up to 50% of the patients who survive develop permanent neurological damage. The annual incidence of human JE markedly increased in the Republic of Korea (ROK) in 2010 but the reason for the reemergence of JE in the ROK has not been established. This study aimed to assess the extent of exposure of domestic dogs to JEV in the Seoul metropolitan area, one of the most populated areas in the world. This cross-sectional study serologically investigated the spread of JEV in the dog population. Using serum neutralization test, we examined 3597 serum samples collected from pet, shelter, stray, and farmed dogs in the Seoul metropolitan area from 2006 to 2012. JEV-neutralizing antibodies found in shelter, stray, and farmed dogs sampled in approximately 2010 demonstrated increased JEV circulation in the dog population during the 2010 resurgence of JE reported among humans in the ROK. Five out of 1102 pet dogs (0.5%) were found to be neutralizing antibodies against JEV and 24 of 719 shelter dogs (3.1%) were positive with a peak of 13.0% in 2010. In addition, 25 of 690 stray dogs (3.6%) were positive with a peak of 9.7% in 2009, a year before the 2010 resurgence of JE. Furthermore, 39 of 1086 farmed dogs (3.6%) were seropositive with a peak of 9.2% in 2009. We therefore suggest that monitoring dog populations for seroconversion or seropositive dogs within JEV-active endemic areas may be useful for identifying risk areas for JE outbreaks and that JEV activity in dogs can be an indicator as the harbingers of JEV in humans.
Ševčík Michal, Chaloupková Markéta, Zourková Ilona
et al.
The implementation of smart projects can contribute to solving the current development problems of municipalities and cities of varied sizes. Although the concept of smart development is a vague term in the literature, in practice it refers to projects based on the use of modern technologies, to improve the quality of life considering economic, social, and environmental dimensions. However, not all local governments in the Czech Republic implement smart projects, even though the smart city concept is currently receiving considerable attention from national governments and from the European Union. This paper characterizes the perception of barriers to the implementation of smart projects from the perspective of fifteen representatives and officials of local governments located in the Brno Metropolitan Area in the Czech Republic. The research was conducted using semi-structured interviews with these fifteen territorial actors. It was found that the barriers to the implementation of smart projects are related to internal factors in the municipalities, such as the lack of interest of municipal leaders and officials or potential technical complications accompanying the implementation of projects. However, external factors such as the Czech government’s vague grasp of the smart cities concept or cyber threats also play a role. Perceived barriers were categorized according to their type and schematized.
This work studies the distribution of warehouses and distribution centers (W&DCs) in California and analyzes their potential relationships with disadvantaged communities (DACs). Through aggregated spatial analyses and econometric modeling, the research compares the concentration of W&DCs in five metropolitan planning organizations (MPOs) in California. The analyses show that the weighted geometric centers of W&DCs have shifted slightly toward city central areas in all five MPOs in the last few years, contrasting to the logistics sprawl trends evidenced in previous research. In the Bay Area and Southern California, W&DCs are more prevalent in areas with higher pollution burden, according to the CalEnviroScreen (CS) score. In Southern California, the study analyzes disaggregate industrial real estate data of 49,697 property transactions (properties sold) between 1989 and 2018. On average, the size of the facilities transacted have decreased, especially for those closer to the urban center. These results are confirmed using parametric and non-parametric data analyses. During recent years, smaller and closer (to the urban core) facilities represent the largest share in the transactions, consistent with the trends in e-commerce and its associated distribution requirements. Moreover, the data show a disproportionate sitting of facilities in areas where DACs reside. The paper ends with a discussion of policy and planning recommendations.
Transportation engineering, Transportation and communications