Spatial Simulation Research Articles

Is spectral pixel-to-pixel variation a reliable indicator of grassland biodiversity? A systematic assessment of the spectral variation hypothesis using spatial simulation experiments

Covering 30%–40% of the terrestrial surface, grasslands are important hosts of biodiversity, crucial for nutrient cycles and carbon sequestration. However, these ecosystems face a pressing threat in the form of biodiversity loss, which can disrupt their functioning and resilience. Addressing this challenge requires effective monitoring of biodiversity changes on large scales. Remote sensing emerges as a valuable tool in this endeavour, enabling the assessment of grassland biodiversity through the analysis of vegetation patterns, species composition, and ecosystem health over extensive areas.According to the spectral variation hypothesis (SVH), the link between pixel-to-pixel spectral variation and species diversity in remote sensing images can be used to retrieve plant diversity based on spectral data. Nevertheless, the transferability of the proposed relation across ecosystem types, seasons and spatial resolutions remains unclear. The absence of comprehensive data has hindered systematic assessments of the SVH so far, which would ideally incorporate coherent sets of diversity estimates from remote sensing data and in-situ plant diversity measurements.With this study, we present a combined approach that brings together trait data from field measurements, simulations of spatial species distributions and radiative transfer models for a systematic and in-depth analysis of the SVH in temperate grasslands. Based on simulated grassland communities with different diversity levels, we assessed the spectral-to-species diversity relationship across (1) three temperate grassland types, (2) three seasons and, (3) five spatial resolutions (from 10 m to 0.2 m pixel size). We used the mean Euclidean distance and Rao’s Q as measures for spectral diversity and different biodiversity indices to describe the species and trait diversity of the simulated grassland communities.Based on 45000 simulated grassland communities in five different spatial resolutions, we found that the spectral-to-species diversity relationship is not stable across grassland types and seasons, despite the spectral diversity metric used. Correlations with spectral diversity were inconsistent for the different applied diversity indices and no single index outperformed the others. Spectral diversity was mainly driven by the spatial resolution (i.e. pixel size) of the image and not by species richness (SR) or functional trait diversity (FD) per se. Our results further underline that the link between SR and FD is not always prominent in plant communities and the basic assumption of the SVH is fulfilled only under certain conditions. Consequently, we argue that FD, which is an important driver of the spectral signature of a plant community, is not inevitably linked to the number of present species in an image. We conclude that the interplay of SR and FD is crucial for the expression of the spectral-to-species diversity relationship. This study clearly underlines the context-dependency of the SVH and we point out that, although of promising value for distinct ecosystems, it is not universally applicable in grasslands.

Multipath diffusion process and spatial accumulation simulation of Cd in lead-zinc mining areas

This study combined process simulation and actual measurement to construct a multipath diffusion and spatial accumulation model of Cd in a typical lead-zinc mining area through accuracy and root mean square error(RMSE) analysis. The results indicated that (1) the diffusion of Cd was in a quadratic inverse proportional relationship with the distance from the pollution source within watershed. The average annual atmospheric Cd sedimentation in study area was 0.71 * 10−6 g and the contribution of runoff diffusion to Cd exceeded 80%. (2) With the increase in the concentration range of Cd content (k) carried by unit runoff sediment, the model accuracy and RMSE showed decreasing trends. However, when the lower and upper limits of k were 10% and 90%, the model accuracy reached 75%. (3) Two sub-watersheds with same dominant wind direction but different runoff directions were selected to verify the model accuracy, indicating that the model construction method can precisely simulate the spatial accumulation of Cd in similar mining areas. The results provide a scientific basis for the prevention of heavy metal diffusion in lead-zinc mines. Future research should focus on the migration pathways of heavy metals through vertical infiltration caused by rainfall to further optimise the model structure and accuracy.

A novel and dynamic land use/cover change research framework based on an improved PLUS model and a fuzzy multiobjective programming model

Spatial reconstruction and scenario simulation of historical processes and future trends of land use/cover change (LUCC) can help to reveal the historical background of land conversion and the spatial distribution of future land. Moreover, there is a close relationship between the spatiotemporal dynamics of land use/cover and changes in different ecosystem services (ESs). Using this relationship to simulate future land use scenarios is important. In this study, an LUCC dynamic analysis framework (LSTM-PLUS-FMOP) was constructed based on a deep learning time series forecasting model (LSTM), a parallelized urban land use simulation (PLUS) model and a fuzzy multiobjective programming (FMOP) model. The PLUS model was used to analyze the driving mechanism of land expansion and explore the land conversion pattern. In addition, three land conversion scenarios were established: natural land expansion (NLE), economic development priority (EDP) and regional sustainable development (RSD). The FMOP model and the relationship between LUCC and ES were used to perform a spatial simulation of land conversion. The uncertainty parameters in the model were treated by intuitionistic fuzzy numbers (IFSs). This study applied the constructed framework to the Yellow River Basin of Shaanxi Province (YRB-SX). The results showed that (1) from 2000 to 2020, the cropland area of the YRB-SX continuously decreased by 12.67 × 104 ha, while the built-up area continuously increased by 28.25 × 104 ha. The net reduction in woodland and grassland area was 13.90 × 104 ha. (2) The relative error range of land prediction using the LSTM model was 0.0003– 0.0042. This model had better accuracy than the Markov chain prediction model. (3) The cropland area decreased by 0.26% (NLE), 0.85% (EDP) and 1.68% (RSD) under the three scenarios. The built-up area increased by 25.01%, 32.76% and 14.72%, respectively. The RSD scenario followed the principles of ecological protection and spatial constraints, which mitigated the degradation of the ecosystem to some extent. This coupled simulation framework will help to obtain land allocation schemes that meet the requirements of ecological protection and provide solutions for rational land management.

A three-class carbon pool system for normalizing carbon mapping and accounting in coastal areas

Climate change is a key issue receiving increasing global attention. Coastal areas play an important role in mitigating climate change because of enormous potentials for carbon sequestration. Simulating and evaluating the carbon storage, increment, and value of coastal areas is of considerable importance in achieving carbon neutrality. In this study, the South Coast of Hangzhou Bay in China was selected as the study area. A three-class carbon pool system was established based on vegetation and soil and considering the coastline as the boundary for comprehensively revealing the carbon sink in coastal areas and accurately distinguishing the roles of different carbon pools. Then, the maps of carbon storage and increment were generated by integrating the field and remote sensing data through “from point to area” spatial simulations. An accounting list was developed for systematically summarizing, precisely quantifying, and visually displaying the carbon storage, increment, and value following the three-class carbon pools. Results indicated that the carbon sink showed a gradient change from sea to land. Salt marshes had low storage but high increment, whereas areas above the coastline had the opposite characteristics. The total carbon storage of vegetation and soil were 1417.94 Gg and 3359.07 Gg, respectively, and their carbon increments during 2011–2022 were 309.17 Gg/yr and 29.46 Gg/yr, respectively. The carbon storage per area of vegetation and soil were 27.66 Mg/ha and 53.09 Mg/ha, respectively, and their carbon increments during 2011–2022 were 6.03 Mg/(ha yr) and 0.47 Mg/(ha yr), respectively. As the main body of blue carbon, salt marshes have achieved distinctly higher increment than the adjacent areas above the coastline. The carbon increment per area of soil was lower than 1/5 of vegetation in salt marshes. Mudflats had low carbon increment but large storage because of the extensive area. Alien species had higher increment in terms of vegetation but similar one from the perspective of soil compared with native species, denoting the low decrease of soil carbon sink when eliminating alien species. Considering the hazard of alien species to the health of coastal wetlands, this indicates the feasibility and necessity to develop the high-quality carbon sink in the study area. The carbon sink value based on the carbon stored in the soil summed to 1.68 million yuan, and were increased by 33%, 7%, 48%, and 45% in scenarios of baseline scenario, economic development, ecological protection, and comprehensive optimization, respectively. The present study validated the high carbon increment and immense increase potential and provided an applicable system to normalize carbon mapping and accounting in coastal areas.

Development of a coarse-grained molecular dynamics model for poly(dimethyl-co-diphenyl)siloxane.

Polydimethylsiloxane is an important polymeric material with a wide range of applications. However, environmental effects like low temperature can induce crystallization in this material with resulting changes in its structural and dynamic properties. The incorporation of phenyl-siloxane components, e.g., as in a poly(dimethyl-co-diphenyl)siloxane random copolymer, is known to suppress such crystallization. Molecular dynamics (MD) simulations can be a powerful tool to understand such effects in atomistic detail. Unfortunately, all-atomistic molecular dynamics (AAMD) is limited in both spatial dimensions and simulation times it can probe. To overcome such constraints and to extend to more useful length- and time-scales, we systematically develop a coarse-grained molecular dynamics (CGMD) model for the poly(dimethyl-co-diphenyl)siloxane system with bonded and non-bonded interactions determined from all-atomistic simulations by the iterative Boltzmann inversion (IBI) method. Additionally, we propose a lever rule that can be useful to generate non-bonded potentials for such systems without reference to the all-atomistic ground truth. Our model captures the structural and dynamic properties of the copolymer material with quantitative accuracy and is useful to study long-time dynamics of highly-entangled systems, sequence-dependent properties, phase behaviour, etc.

Spatial characterization and simulation of new defects in corroded pipeline based on In-Line Inspections

Spatial characterization and simulation of new defects in corroded pipeline based on In-Line Inspections

Genome-wide identification, characterization, and evolutionary analysis of the barley TALE gene family and its expression profiles in response to exogenous hormones.

Three-amino-loop-extension (TALE) family belongs to the homeobox gene superfamily and occurs widely in plants, playing a crucial role in regulating their growth and development. Currently, genome-wide analysis of the TALE family has been completed in many plants. However, the systematic identification and hormone response analysis of the TALE gene family in barley are still lacking. In this study, 21 TALE candidate genes were identified in barley, which can be divided into KNOX and BELL subfamilies. Barley TALE members in the same subfamily of the phylogenetic tree have analogically conserved motifs and gene structures, and segmental duplications are largely responsible for the expansion of the HvTALE family. Analysis of TALE orthologous and homologous gene pairs indicated that the HvTALE family has mainly undergone purifying selective pressure. Through spatial structure simulation, HvKNOX5-HvKNOX6 and HvKNOX5-HvBELL11 complexes are all formed through hydrogen bonding sites on both the KNOX2 and homeodomain (HD) domains of HvKNOX5, which may be essential for protein interactions among the HvTALE family members. Expression pattern analyses reveal the potential involvement of most HvTALE genes in responses to exogenous hormones. These results will lay the foundation for regulation and function analyses of the barley TALE gene family in plant growth and development by hormone regulation.

A multiscale spatial modeling framework for the germinal center response.

The germinal center response or reaction (GCR) is a hallmark event of adaptive humoral immunity. Unfolding in the B cell follicles of the secondary lymphoid organs, a GC culminates in the production of high-affinity antibody-secreting plasma cells along with memory B cells. By interacting with follicular dendritic cells (FDC) and T follicular helper (Tfh) cells, GC B cells exhibit complex spatiotemporal dynamics. Driving the B cell dynamics are the intracellular signal transduction and gene regulatory network that responds to cell surface signaling molecules, cytokines, and chemokines. As our knowledge of the GC continues to expand in depth and in scope, mathematical modeling has become an important tool to help disentangle the intricacy of the GCR and inform novel mechanistic and clinical insights. While the GC has been modeled at different granularities, a multiscale spatial simulation framework - integrating molecular, cellular, and tissue-level responses - is still rare. Here, we report our recent progress toward this end with a hybrid stochastic GC framework developed on the Cellular Potts Model-based CompuCell3D platform. Tellurium is used to simulate the B cell intracellular molecular network comprising NF-κB, FOXO1, MYC, AP4, CXCR4, and BLIMP1 that responds to B cell receptor (BCR) and CD40-mediated signaling. The molecular outputs of the network drive the spatiotemporal behaviors of B cells, including cyclic migration between the dark zone (DZ) and light zone (LZ) via chemotaxis; clonal proliferative bursts, somatic hypermutation, and DNA damage-induced apoptosis in the DZ; and positive selection, apoptosis via a death timer, and emergence of plasma cells in the LZ. Our simulations are able to recapitulate key molecular, cellular, and morphological GC events, including B cell population growth, affinity maturation, and clonal dominance. This novel modeling framework provides an open-source, customizable, and multiscale virtual GC simulation platform that enables qualitative and quantitative in silico investigations of a range of mechanistic and applied research questions on the adaptive humoral immune response in the future.

Interactive Visual Analysis of Spatial Sensitivities.

Sensitivity analyses of simulation ensembles determine how simulation parameters influence the simulation's outcome. Commonly, one global numerical sensitivity value is computed per simulation parameter. However, when considering 3D spatial simulations, the analysis of localized sensitivities in different spatial regions is of importance in many applications. For analyzing the spatial variation of parameter sensitivity, one needs to compute a spatial sensitivity scalar field per simulation parameter. Given n simulation parameters, we obtain multi-field data consisting of n scalar fields when considering all simulation parameters. We propose an interactive visual analytics solution to analyze the multi-field sensitivity data. It supports the investigation of how strongly and in what way individual parameters influence the simulation outcome, in which spatial regions this is happening, and what the interplay of the simulation parameters is. Its central component is an overview visualization of all sensitivity fields that avoids 3D occlusions by linearizing the data using an adapted scheme of data-driven space-filling curves. The spatial sensitivity values are visualized in a combination of a Horizon Graph and a line chart. We validate our approach by applying it to synthetic and real-world ensemble data.

Simulation Study on Rain-Flood Regulation in Urban “Gray-Green-Blue” Spaces Based on System Dynamics: A Case Study of the Guitang River Basin in Changsha

Urban rainstorms and flood disasters are the most common and severe environmental problems worldwide. Many factors influence rain-flood control simulation, forming a complex network system of interconnected and mutually constraining elements. In terms of spatial scale selection, existing research on rain-flood disaster risk largely relies on a single-scale infrastructure index system and has not yet focused on urban “gray-green-blue” spatial scale simulations for rain-flood storage. Regarding research methodology, applying system dynamics methods to the simulation of rain-flood storage and disaster prevention planning in watershed cities is still in its initial stages. System dynamics models can simulate the feedback interactions among various sub-elements in the coupled mega-system, fully addressing complex issues within the system structure that involve multiple variables, non-linear relationships, and numerous feedback loops, thereby compensating for the inadequacies of traditional linear models in the collaborative management of rain-flood risks. Taking the Changsha Guitang River Basin as an example, this paper constructs a system dynamics model covering four dimensions: natural environment, socio-economics, internal structure, and policy development. It aims to derive the optimal planning scheme for gray-green-blue spatial coordination in rain-flood storage by weighing four different development scenarios. The simulation results show: (1) Simply changing the surface substrates without considering rainwater discharge and the plan that emphasizes the construction of municipal drainage facilities will see the capacity gap for rain-flood storage-space construction continue to widen by 2035. This indicates that the plans mentioned above will struggle to bear the socio-economic losses cities face during rain-flood disasters. (2) The plan of combining gray and green infrastructures sees the rain-flood storage construction capacity turn from negative to positive from 2024, rising to 52.259 billion yuan by 2035. This reflects that the plan can significantly reduce the rainwater volume in the later stages of low-impact development infrastructure construction, mitigate rain-flood disaster risks, and reduce government investment in rain-flood disaster risk management, making it a relatively excellent long-term rain-flood storage space planning option. (3) The rain-flood regulation space planning scheme, under the combined effect of the urban “gray-green-blue” network system, sees the capacity for rain-flood storage construction turn positive a year earlier than the previous plan, reaching 54.232 billion yuan by 2035. This indicates that the scheme can not only effectively respond to extreme flood and rainstorm disasters but also maintain ecological environment benefits and mitigate the socio-economic losses caused by disasters, making it the optimal choice for future government disaster management planning. The research results provide a theoretical framework and practical insights for territorial spatial planning, rain-flood control management, and resilient city construction in watershed areas.

Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020

The pursuit of higher-resolution and more reliable spatial distribution simulation results for air pollutants is important to human health and environmental safety. However, the lack of high-resolution remote sensing retrieval parameters for gaseous pollutants (sulfur dioxide and ozone) limits the simulation effect to a 1 km resolution. To address this issue, we sequentially generated and optimized the spatial distributions of near-surface PM2.5, SO2, and ozone at a 1 km resolution in China through two approaches. First, we employed spatial sampling, random ID, and parameter convolution methods to jointly optimize a tree-based machine-learning gradient-boosting framework, LightGBM, and improve the performance of spatial air pollutant simulations. Second, we simulated PM2.5, used the simulated PM2.5 result to simulate SO2, and then used the simulated SO2 to simulate ozone. We improved the stability of 1 km-resolution SO2 and ozone products through the proposed sequence of multiple-pollutant simulations. The cross-validation (CV) of the random sample yielded an R2 of 0.90 and an RMSE of 9.62 µg∙m−3 for PM2.5, an R2 of 0.92 and an RMSE of 3.9 µg∙m−3 for SO2, and an R2 of 0.94 and an RMSE of 5.9 µg∙m−3 for ozone, which are values better than those in previous related studies. In addition, we tested the reliability of PM2.5, SO2, and ozone products in China through spatial distribution reliability analysis and parameter importance reliability analysis. The PM2.5, SO2, and ozone simulation models and multiple-air-pollutant (MuAP) products generated by the two optimization methods proposed in this study are of great value for long-term, large-scale, and regional-scale air pollution monitoring and predictions, as well as population health assessments.

Explore the spatial pattern of carbon emissions in urban functional zones: a case study of Pudong, Shanghai, China.

It is crucial for the development of carbon reduction strategies to accurately examine the spatial distribution of carbon emissions. Limited by data availability and lack of industry segmentation, previous studies attempting to model spatial carbon emissions still suffer from significant uncertainty. Taking Pudong New Area as an example, with the help of multi-source data, this paper proposed a research framework for the amount calculation and spatial distribution simulation of its CO2 emissions at the scale of urban functional zones (UFZs). The methods used in this study were based on mapping relations among the locations of geographic entities and data of multiple sources, using the coefficient method recommended by the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed that the emission intensity of industrial zones and transport zones was much higher than that of other UFZs. In addition, Moran's I test indicated that there was a positive spatial autocorrelation in high emission zones, especially located in industrial zones. The spatial analysis of CO2 emissions at the UFZ scale deepened the consideration of spatial heterogeneity, which could contribute to the management of low carbon city and the optimal implementation of energy allocation.

Simulation of the Spatial Flow of Wind Erosion Prevention Services in Arid Inland River Basins: A Case Study of Shiyang River Basin, NW China

Wind erosion is a key global environmental problem. As an important protective measure to provide services to the ecosystems in wind-eroded areas, the wind erosion prevention service is of great significance to the management of wind and sand hazards and ecological environment restoration in the wind-eroded areas and the neighboring areas. Taking the Shiyang River basin as the study area, the quality of supplies for wind erosion prevention services was estimated using the RWEQ model for the years 2005, 2010, 2015, and 2020; the trajectories of air masses at wind speeds higher than the sand-causing wind speeds were simulated based on the forward trajectory module of the HYSPLIT model for a 24 h period; the spatial simulation of the flow of wind erosion prevention services on a daily scale with Minqin Station as the sand source was carried out; and the beneficiary areas of wind erosion prevention services were identified. Based on the RWEQ model, the spatial patterns of potential wind erosion, actual wind erosion, and wind and sand stabilization services were obtained, and the supply areas were divided. From 2005 to 2020, the wind erosion prevention service flow in the Shiyang River basin was distributed along a northwest–southeast direction, with a radial decrease from the center to the periphery, and with an extremely strong extraterritorial effect. The amount of wind erosion in the basin has a variable downward tendency over time and a spatial distribution pattern of high in the north and low in the south. The area of higher sand fixation is distributed in the eastern oasis area and desert junction zone. The HYSPLIT model was used to simulate the transport paths of wind and sand within 24 h during 2005–2020, the transmission paths of the wind erosion prevention service flow were obtained to be 59–134, and the flows were 2.55 × 104–3.85 × 106 t, displaying a changing trend of first decreasing, then increasing, and then decreasing. Gansu Province, Ningxia Hui Autonomous Region, and Inner Mongolia Autonomous Region are the most important areas benefiting from the wind erosion prevention service flow in the Shiyang River basin. The wind erosion prevention service flows in the basin benefit 47 cities in 9 provinces.

Non-modal behavior in the linear regime of high-speed boundary layer flows: Flow–thermodynamic interactions

The flow–thermodynamic interactions in the transient linear regime of high-speed boundary layers starting from non-modal initial conditions are studied using direct numerical simulation. These simulations are performed at different Mach numbers: M∈[3,6]. The perturbation velocity field is decomposed into solenoidal and dilatational components using the Helmholtz decomposition. It is shown that at high speeds, random pressure perturbations evolve to their asymptotic state in three distinct stages. In stage 1, pressure–dilatation engenders rapid transfer from internal to kinetic energy leading to a balance between the two forms. Pressure–dilatation maintains this balance throughout stage 2 with harmonic exchange of energy between the two forms. During this stage, the stable modes decay and the unstable modes establish ascendancy. Stage 3 behavior is dominated almost exclusively by the most unstable mode. Both internal and kinetic energies grow at the rate predicted by linear stability analysis. At this stage, pressure–dilatation is small and production dominates the flow evolution. This behavior is also observed in narrow-band perturbation evolution. Spatial boundary layer simulations are also performed to examine the non-parallel effects on the observed behavior. It is seen that the role of pressure–dilatation essentially remains the same as observed in the parallel flow case.

Architectural and Urban Spatial Digital Simulations

Architectural and Urban Spatial Digital Simulations

Artificial Intelligence and Street Space Optimization in Green Cities: New Evidence from China

In the context of the green economy and sustainable urban development, the rapid expansion of urban construction has given rise to pressing public health concerns, notably environmental pollution and the increased prevalence of chronic illnesses linked to swift urbanization. These urban health issues are escalating, prompting significant attention to the concept of creating “healthy cities”. Meanwhile, the planning and design of urban street space have a far-reaching impact on urban residents’ quality of life and health. Urban planners are facing challenges and need to follow the principle of a green economy while meeting the needs of residents for public activities and adapting to motor vehicle traffic. This study explores the optimization of urban street space to promote the harmonious coexistence between people and cars. This study actively explores the relationship between health, urban environment, and social background, focusing on promoting the harmonious coexistence between people and vehicles, especially the optimization goal of sharing urban streets. The study’s main goal is to design a road that can meet the needs of citizens’ public activities and accommodate motor vehicles, which conforms to the principle of a green economy. To achieve this, geographic information system (GIS) technology and a genetic algorithm (GA) are employed to optimize shared urban street spaces. Among them, GIS tools are used for spatial simulation to evaluate the effect of different shared street space configurations. The urban shared street space is gradually optimized through GA’s selection, crossover, and mutation operations. Simulation experiments are conducted to determine the relationship between street space utilization and the elements of a healthy city, ultimately striving to identify the optimal design parameters for shared street spaces. The research results reveal that the urban street space is optimized from the three aspects of shared allocation of facilities resources, replacement of land use functions, and mixed layout of facilities, and the utilization rate of urban streets is finally ensured to reach 53.43%, fully assuming the essential functions of urban streets. This innovative approach bridges the gap between urban development and public health, offering valuable insights for sustainable urban space planning and enhanced living environments within the framework of the green economy.

Spatial cooperative simulation of land use-population-economy in the Greater Bay Area, China

Fast urbanization brings great challenges to sustainable development goals, such as excessive exploitation and population explosion. Classical cellular automata (CA) have been widely used to independently simulate the change of spatial features, i.e. land use, population, economic production, etc. However, most CA models rely on historical data as static driving factors to simulate future scenarios while ignoring the inter-wined influences among multiple features in the development process. To address this issue, this study proposes a spatial cooperative simulation (SCS) approach to simulate the land use, population, and economy changes. The SCS approach starts with a separate CA model to obtain the initial scenes of each feature. Then, the simulation results of each other two features are used as dynamically updated driving factors, rather than the static historical data, to capture the inter-wined influence of multiple features during the development process. This step is iteratively performed until the changes of each feature converge and the final simulation results will be reported. The simulation experiment in Greater Bay Area demonstrates that the SCS approach can well capture the simultaneous development process and outperforms baseline approaches. The SCS approach is capable of forecasting future development scenarios and facilitates spatial planning and infrastructure synergies.

Spatial simulation of autologous cell defection for cancer treatment.

Cancer cells are highly cooperative in a nepotistic way and evolutionarily dynamic. Present cancer treatments often overlook these aspects, inducing the selection of resistant cancer cells and the corresponding relapse. As an alternative method of cancer elimination, autologous cell defection (ACD) was suggested by which modified cancer cells parasitically reliant on other cancer cells are implemented to the cancer cluster. Specifically, modified cancer cells should not produce costly growth factors that promote the growth of other cancer cells while receiving the benefit of exposure to such growth factors. Analytical models and rudimentary experiments up to date provide the medical feasibility of this method. In this study, I built comprehensive spatial simulation models by embracing the effects of the multiple growth factors, the Warburg effect, mutations and immunity. The simulation results based on planar spatial structures indicate that implementation of the defective modified tumours may replace the existing cancer cluster and defective cells would later collapse by themselves. Furthermore, I built a mathematical model that compares the fitness of the cells adjacent to the hypertumour-cancer interface. I also calculated whether anticancer drugs that reduce the effects of the growth factors promote or demote the utility of ACD under diverse fitness functions. The computational examination implies that anticancer drugs may impede the therapeutic effect of ACD when there is a strong concavity in the fitness function. The analysis results could work as a general guidance for effective ACD that may expand the paradigm of cancer treatment.

Refractive extended depth-of-focus lens design based on periodic power profile for presbyopia correction.

Limitations of existing diffractive multifocal designs for presbyopia correction include discrete foci and photic phenomena such as halos and glare. This study aimed to explore a methodology for developing refractive extended depth-of-focus (EDoF) lenses based on a periodic power profile. The proposed design technique employed an optical power profile that periodically alternated between far, intermediate and near distances across the pupil radius. To evaluate the lens designs, optical bench testing was conducted. The impact on visual performance was assessed using a spatial light modulator-based adaptive optics vision simulator in human subjects. Additionally, the effects of pupil size change and lens decentration on retinal image quality were examined. A comparative performance analysis was carried out against a typical diffractive trifocal design and a monofocal lens. The proposed design method was found to be effective in uniformly distributing light energy across all object distances within the desired depth of focus (DoF). While trade-offs between overall image quality and DoF still exist, the EDoF lens design, when tested in human subjects, provided a continuous DoF spanning over 2.25 D. The results also revealed that the EDoF design had a slightly higher dependence on changes in pupil size and lens decentration than the diffractive trifocal design. The proposed design method showed significant potential as an approach for developing refractive EDoF ophthalmic lenses. These lenses offer a continuous DoF but are slightly more susceptible to variations in pupil size and decentration compared with the diffractive trifocal design.

GWR-assisted integrated estimator of finite population total under two-phase sampling: a model-assisted approach

In survey sampling, auxiliary information is used to precisely estimate the finite population parameters. There are several approaches available in the literature that provide a practical method for incorporating auxiliary information during the estimation stage. In order to effectively utilize the auxiliary information, a geographically weighted regression (GWR) model-assisted integrated estimator of finite population total under a two-phase sampling design has been proposed in this article. Spatial simulation studies have been conducted to empirically assess the statistical properties of the proposed estimator. In the presence of spatial non-stationarity, empirical findings reveal that the proposed estimator outperforms all existing estimators such as two-phase HT, ratio, and regression estimators, demonstrating the importance of spatial information in survey sampling.