Spatial Parameters Research Articles

A combined approach to geostatistical models and spatial information systems to evaluate the role of spatial parameters in the prevalence of cancer in Iran: a case study: bladder cancer

A combined approach to geostatistical models and spatial information systems to evaluate the role of spatial parameters in the prevalence of cancer in Iran: a case study: bladder cancer

A direct comparison of a next generation hyperspectral camera to state-of-the-art

Hyperspectral camera technology is advancing rapidly, and this paper seeks to compare a state-of-the-art industrial dual-camera setup to a single-camera system employing the latest chip technology (IMX990 from Sony). The hyperspectral cameras are compared over both the Visual and Short-Wave Infrared range (400–1700 nm) of the electromagnet spectrum. The spectral range and resolution, as well as spatial parameters and spectroscopic information are quantified with comparable optics, electronics, and test targets. Generally, enhanced spectral detail and reduced noise were observed for the single-camera compared to its peers. Thus, the IMX990 shows promising performance for the new generation of hyperspectral cameras directly relevant to industrial applications, such as detection, documentation, and sorting.

The passive optimization mechanism of winter thermal performance in commercial complex based on coupled multi-spatial parameters

In cold regions, public buildings often suffer from suboptimal thermal performance due to cold air intrusion. This study explores the synergistic impact of multiple spatial parameters on winter thermal environments to discover the passive optimization potential in commercial complexes. Airflow analysis covers the entire connected space from the entrance to the large atrium, integrating the collective effects of all critical spatial parameters. Based on 4500 Computational Fluid Dynamics (CFD) simulation, the study establishes how these parameters influence wind resistance and thermal comfort. Key findings reveal that the “Atrium-plan Ratio,” “Transitional Space-width,” and “Atrium-height,” are most impactful on wind resistance, contributing over 53 % collectively. For thermal comfort, “Atrium-volume,” “Atrium-section Form,” and “Atrium-height,” are most significant, with a combined weight exceeding 57 %. As wind speed increases, the effect of single parameters gradually diminishes, highlighting the synergistic effect of spatial combinations. A case study demonstrates an 80.5 % improvement in thermal performance score following optimization, underscoring the potential for low-carbon, high-efficiency design in commercial complexes.

Intelligent generation method of infection risk map and management system in hospital waiting room for respiratory infectious diseases

Respiratory infectious diseases, such as seasonal influenza, pose significant risks to public health safety through airborne transmission. This study aims to explore methods for controlling airborne infection risk in waiting rooms, a hotspot for cross-infection. A database of 990 samples was established by integrating the results of computational fluid dynamics and social force model simulations into the Wells-Riley equation. We introduced two indicators, Pr (Particle Removal Efficiency) and Hr (High-risk Area Ratio), to evaluate the cleanliness performance of waiting room spaces. Additionally, the Sci (Space Cleanliness Index) was proposed as a criterion for evaluating architectural designs and fine-tuning fresh air system operational parameters. We also unveiled the mapping relationship between spatial morphology parameters and ventilation design parameters in relation to the cleanliness performance of waiting rooms. An infection-risk prediction proxy model was established based on machine learning methods, suitable for rapid prediction of spatial infection risks in waiting rooms and optimization of fresh air system operational parameters. The findings can be applied to the selection of hospital design schemes and the optimization of ventilation operational control strategies, aiding in strengthening infectious disease control and reducing the public health safety risks of cross-infection from respiratory infections during peak seasons in hospitals.

Near-Ground wind field characteristics of tracking photovoltaic systems based on field measurements

The study conducts field measurements at a tracking photovoltaic power station in Zhongwei City, Ningxia Hui Autonomous Region, investigating near-ground incoming wind characteristics, spatial coherence, and their impact on the flow field of the tracking photovoltaic system. Actual measurements indicate a low dispersion degree in the roughness index of this area, with an average roughness index of α = 0.1228, which is consistent with the characteristics of open land and desert areas. The incoming wind speed profile largely complies with European wind load specifications, with turbulence intensity measured at different heights exceeding other wind load specifications, primarily due to increased sand movement contributing to turbulence in desert areas. Additionally, this study proposes calculation formulas for measured turbulence intensity, integration scale, and gust factor, offering valuable insights into the incoming wind field characteristics in this region. Furthermore, the investigation into spatial coherence and decay parameters revealed the superior performance of the Krenk model in high frequency bands and over long distances. Lastly, the tracking photovoltaic array induces noticeable shading effects, altering flow field characteristics significantly, with a diminished impact on wind flow velocity as the tilt angle of the photovoltaic panel decreases. These studies serve as a valuable reference for gaining a deeper understanding of wind field characteristics, wind load calculation, and the optimal design of tracking photovoltaic systems.

Detector configuration optimization method considering response time for spatial source parameter estimation in complex urban wind environment

In source parameter estimation, improper arrangement of detectors may lead to ineffective concentration measurement or even failure in leakage detection, which would hold back emergency response efforts. However, current methods for optimizing detector configuration have inherent flaws and thus cannot be ideally applied in real-world scenarios. To address their limitations, this paper develops the method for optimizing detector configuration by considering response time. A novel cost function is proposed to balance response time and estimation accuracy based on specific demands. The optimum configuration is then obtained by genetic algorithm iteration. The wind tunnel test of an urban environment with buildings of varying heights is conducted for the research purpose. The superiority of the proposed method is demonstrated by comparing spatial source parameter estimations using optimum, uniform, and random configurations respectively. The results show that our method avoids placing detectors at locations with low concentrations, i.e., places where readings are invalid. It enables more detectors to obtain readings quickly, generating more accurate estimations in a shorter time. Only with the optimum configuration do detectors show readings for all 15 sources, with each unknown source having the highest number of detectors with readings. In contrast, the uniform configuration results in no readings for 4 sources and the random configuration results in no readings for 11 sources when leaking happens. The proposed method optimizes the detector configuration in three-dimensional space with complex flows, making it more feasible in practical applications.

Research on the Effects of Spatial Forms in Residential Blocks on Road Traffic Noise Distribution in Typical City of China

Integrating the block system into construction is the current trend in the development of residential areas in China. Road traffic noise is the major noise source in residential blocks, and its relationship with spatial forms of blocks remains unclear. In this study, 852 block models (258 block road network models and 594 models with buildings inside) were established and simulated with SoundPLAN 8.2 software, in order to reveal the impact of spatial forms (road network morphology, neighborhood scale, and architectural texture) on the road traffic noise distribution in residential blocks. Meantime, a prediction model based on spatial morphology parameters is proposed. It was found that (1) Without considering the impact of buildings, both the road network morphology and neighborhood scale parameters have significant effects on the distribution of road traffic noise, but road network morphology has a larger effect than neighborhood scale. (2) In the presence of buildings within the block, architectural texture parameters have effects on the distribution of road traffic noise, but to a lesser extent than road network morphology and neighborhood scale parameters. (3) This research employs principal component analysis to reduce the dimensionality of urban spatial form parameters. Subsequently, a model was developed to predict overall noise exposure levels in residential areas, which was validated by example. This model can be used as a tool for rapid prediction and diagnosis of the block acoustic environment. These findings offer insights for the planning and design of residential blocks from the perspective of optimizing the acoustic environment.

Creating High-Resolution Precipitation and Extreme Precipitation Indices Datasets by Downscaling and Improving on the ERA5 Reanalysis Data over Greece

The aim of this study was to construct a high-resolution (1 km × 1 km) database of precipitation, number of wet days, and number of times precipitation exceeded 10 mm and 20 mm over Greece on a monthly and on an annual basis. In order to achieve this, the ERA5 reanalysis dataset was downscaled using regression kriging with histogram-based gradient boosting regression trees. The independent variables used are spatial parameters derived from a high-resolution digital elevation model and a selection of ERA5 reanalysis data, while as the dependent variable in the training stages, we used 97 precipitation gauges from the Hellenic National Meteorological Service for the period 1980–2010. These stations were also used for validation purposes using a leave-one-out cross-validation methodology. The results of the study showed that the algorithm is able to achieve better R2 and RMSE over the standalone ERA5 dataset over the Greek region. Additionally, the largest improvements were noticed in the wet days and in the precipitation over 10 and 20 mm, where the ERA5 reanalysis dataset overestimates the number of wet days and underestimates precipitation over 10 and 20 mm, while geographically, the ERA5 dataset performs the worst in the island regions of Greece. This indicates that the ERA5 dataset does not simulate the precipitation intensity accurately over the Greek region, and using our methodology, we were able to increase the accuracy and the resolution. Our approach delivers higher-resolution data, which are able to more accurately depict precipitation in the Greek region and are needed for comprehensive climate change hazard identification and analysis.

Multi-parameterised surface texture characterisation for ultra-precision machined surfaces

Abstract In surface metrology, the multi-parameterised characterisation of surface texture measurement is beneficial not only for surface quality evaluation but also for manufacturing process inspection. To bridge this gap for ultra-precision machined surfaces, a white light interferometer was firstly employed for measuring surface texture generated by orthogonal ultra-precision machining experiments. Next, surface texture was filtered by the zero-order Gaussian regression filter to the limited scale bandwidth. Then, twenty-one surface texture parameters were calculated based on seventy-five S-L surfaces according to the ISO 25178-2. Finally, the outlier effect of surface measurement was investigated by the 95%-99% rule and the Spearman correlation coefficient matrix was proposed to determine their statistical correlation. The results revealed that most of the height parameters (Sp, Sv, Sz, Ssk, and Sku), several function and related parameters (Vmp, Vvv, Spk, and Svk), and the spatial parameter (Str) and hybrid parameter (Sdr) presented a strong sensitivity to the outlier effect. The height parameters (Sa, Sq, Sp, Sv, and Sz), the function and related parameters (Vmp, Vmc, Vvv, Vvc, Spk, Svk, and Sk), and the spatial parameters (Sdq and Sdr) showed a strong correlation to each other, while the miscellaneous parameter Std had a weak correlation to the other parameters. This study provides a systematic multi-parameterised surface texture characterisation for ultra-precision machined surfaces to promote the advancement of nanotechnology and nanometrology.

On the surface roughness properties of fly ash-based geopolymer mortars with teff straw ash from the image analysis viewpoint

Fly ash (FA)-based geopolymer mortar is being considered for sustainability since it enjoys peculiar properties such as high mechanical properties and environmental benefits. However, the high curing temperatures of FA-based geopolymer mortar play a complex role in its outstanding mechanical behaviour thereby requiring other precursors, such as teff straw ash (TSA). In this study, we develop surface roughness properties of FA-based geopolymer mortars with TSA, where the ambient temperature is used for curing. The surface roughness characteristics of the FA-based geopolymer mortars with TSA exploit the surface roughness simulations from Gwyddion and the approach is demonstrated here in the contexts of spatial, hybrid and amplitude roughness parameters. The results show that some roughness parameters, including Ra and Rq values for FA-based geopolymer mortars with TSA, exhibit significant decreasing trends as the TSA contents increase. However, in comparison with the CA specimens (ambient temperature curing) and CE specimens (elevated temperature curing), the trends among the majority of the roughness parameters for FA-based geopolymer mortars with TSA turn out not to be very good probably due to changes in the internal structures of the mortars. Moreover, without the kurtosis values (Rku) of less than 3, it is easy to demonstrate that all the profiles of the investigated specimens reflect sharp valleys and peaks. The findings of surface roughness properties allow one to grasp the roughness parameters of FA-based geopolymer mortars with TSA. The approach for generating surface roughness properties of FA-based geopolymer mortars with TSA offers significant quantitative and qualitative information required for the bonding of mortar layers.

Tracking the movement of quartz sand particles with neural networks

Tracking the movement of particles is of paramount significance for studying the impact of particle breakage on the macroscopic mechanical behaviour of granular materials and remains a persistent challenge to date. This paper presents a novel particle tracking method that integrates Pointon and PointNetLK networks, enabling an accurate tracking of both intact and broken Leighton Buzzard sand (LBS) particles. Initially, morphological information of LBS particles was extracted from X-ray micro-tomography (CT) data collected from a miniature triaxial test. Various image processing techniques were applied to the raw CT images to achieve a realistic three-dimensional (3D) reconstruction. Subsequently, particle point cloud data was processed through sampling, Gaussian noise injection, and grouping for training and testing the Poynton and PointNetLK networks. Next, the correspondences among particles across different scans were established by PointConv, and the transformation matrix between two mutually matched particles was predicted using PointNetLK. Finally, an examination of the changes in the spatial distribution and morphological parameters of both tracked and untracked particles throughout the shearing process was conducted and followed by an analysis of particle kinematics.

Using spatial capture‐recapture models to estimate spotted hyaena (Crocuta crocuta) population density and assess the influence of sex‐specific covariates on space use and detection probability

AbstractDespite being resident or reintroduced to multiple South African protected areas, spotted hyaena (Crocuta crocuta) have been subject to limited population monitoring and are not managed in the same intensive way as other large carnivores. Considering the species' significant influence on ecosystem processes, robust estimates of population density are necessary to inform conservation management. In this study, we ran a single‐season camera trap survey in the Main Camp section of Addo Elephant National Park (Addo), South Africa, and used spatial capture‐recapture (SCR) models to estimate spotted hyaena population density. Spotted hyaena density was estimated to be 11.13 (± SE 1.96) individuals/100 km2, with an estimated abundance of 37 (± SE 6.44) individuals. Using our database of sexed spotted hyaena individuals, we found no significant difference for sex‐specific SCR parameters (baseline encounter probability and spatial scale parameter), suggesting the incorporation of sex‐specific information may not be as necessary for spotted hyaena as it is for territorial felids and other species with more pronounced differences in range size. Spotted hyaena density in Addo was comparable to previous SCR studies and towards the higher end of known densities. Our estimate provides an important baseline for monitoring reintroduction success in Addo, and we advocate for increased SCR monitoring of spotted hyaena in South African protected areas to inform a metapopulation approach to the species' management.

Assessing muscle quality as a key predictor to differentiate fallers from non-fallers in older adults.

Falling is an important public health issue because of its prevalence and severe consequences. Evaluating muscle performance is important when assessing fall risk. The study aimed to identify factors [namely muscle capacity (strength, quality, and power) and spatio-temporal gait attributes] that best discriminate between fallers and non-fallers in older adults. The hypothesis is that muscle quality, defined as the ratio of muscle strength to muscle mass, is the best predictor of fall risk. 184 patients were included, 81% (n = 150) were women and the mean age was 73.6 ± 6.83years. We compared body composition, mean grip strength, spatio-temporal parameters, and muscle capacity of fallers and non-fallers. Muscle quality was calculated as the ratio of maximum strength to fat-free mass. Mean handgrip and power were also controlled by fat-free mass. We performed univariate analysis, logistic regression, and ROC curves. The falling patients had lower muscle quality, muscle mass-controlled power, and mean weighted handgrip than the non-faller. Results showing that lower muscle quality increases fall risk (effect size = 0.891). Logistic regression confirmed muscle quality as a significant predictor (p < .001, OR = 0.82, CI [0.74; 0.89]). ROC curves demonstrated muscle quality as the most predictive factor of falling (AUC = 0.794). This retrospective study showed that muscle quality is the best predictor of fall risk, above spatial and temporal gait parameters. Our results underscore muscle quality as a clinically meaningful assessment and may be a useful complement to other assessments for fall prevention in the aging population.

Effects of the intermittent theta burst stimulation on gait, balance and lower limbs motor function in stroke: study protocol for a double-blind randomised controlled trial with multimodal neuroimaging assessments

IntroductionApproximately, 50% of stroke survivors experience impaired walking ability 6 months after conventional rehabilitation and standard care. However, compared with upper limb motor function, research on lower limbs rehabilitation through...

Morphology and internal structure of small-scale washovers formed in the coastal zone of the semi-enclosed tideless basin, Gulf of Gdańsk, Baltic Sea

This study explores the morphological features and internal structure of small-scale washovers along the south-eastern Baltic Sea coast, providing insights into these most widespread yet often neglected deposits in the recent research of geomorphological and sedimentary record of storm surges. A 15-year-long record of morphological changes of the coast was acquired from regional orthophotos to analyse their geometry and spatial characteristics. Sedimentological analyses comprising a description of deposits, grain size and shape analyses, and Ground Penetrating Radar profiling were undertaken to investigate the internal structure of washovers. The formation of washovers appeared to be correlated with the average winter NAO (North Atlantic Oscillation) index. The study revealed different scaling relationships of selected spatial parameters in two coastal settings and extended previously hypothesized relationships of length-area, area-volume, and length-volume. The internal structure of washovers is defined by low-angle planar cross-stratification and horizontal stratification, both disrupted by small troughs. Grain-size data indicated extremely short transport of dune and beach sediments, lately deposited in the form of washovers. The study proves that in all domains, the geomorphological characteristics of washovers are scalable, despite the different coastal settings, and even small-scale washovers fit the existing development models.

Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently.

The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where three male monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: each movement's direction corresponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show that this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, and also argue that not all parameters of movement are defined by different trajectories of population activity.

Large-scale flood modeling and forecasting with FloodCast

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptive flood modeling and forecasting framework that can perform at large scales, namely FloodCast. The framework comprises two main modules: multi-satellite observation and hydrodynamic modeling. In the multi-satellite observation module, a real-time unsupervised change detection method and a rainfall processing and analysis tool are proposed to harness the full potential of multi-satellite observations in large-scale flood prediction. In the hydrodynamic modeling module, a geometry-adaptive physics-informed neural solver (GeoPINS) is introduced, benefiting from the absence of a requirement for training data in physics-informed neural networks (PINNs) and featuring a fast, accurate, and resolution-invariant architecture with Fourier neural operators. To adapt to complex river geometries, we reformulate PINNs in a geometry-adaptive space. GeoPINS demonstrates impressive performance on popular partial differential equations across regular and irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence GeoPINS model to handle long-term temporal series and extensive spatial domains in large-scale flood modeling. This model employs sequence-to-sequence learning and hard-encoding of boundary conditions. Next, we establish a benchmark dataset in the 2022 Pakistan flood using a widely accepted finite difference numerical solution to assess various flood simulation methods. Finally, we validate the model in three dimensions - flood inundation range, depth, and transferability of spatiotemporal downscaling - utilizing SAR-based flood data, traditional hydrodynamic benchmarks, and concurrent optical remote sensing images. Traditional hydrodynamics and sequence-to-sequence GeoPINS exhibit exceptional agreement during high water levels, while comparative assessments with SAR-based flood depth data show that sequence-to-sequence GeoPINS outperforms traditional hydrodynamics, with smaller simulation errors. The experimental results for the 2022 Pakistan flood demonstrate that the proposed method enables high-precision, large-scale flood modeling with an average MAPE of 14.93 % and an average Mean Absolute Error (MAE) of 0.0610 m for 14-day water depth simulations while facilitating real-time flood hazard forecasting using reliable precipitation data.

SSN2: The next generation of spatial stream network modeling in R.

The SSN2 R package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN R package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the R-spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & Gómez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN R package but leverages modern R-spatial tools like sf (Pebesma, 2018). SSN2 also provides many useful features that were not available in the SSN R package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other R generic functions.

Genistein-3′-sodium sulfonate promotes brain functional rehabilitation in ischemic stroke rats by regulating astrocytes polarization through NF-κB signaling pathway

The activation and polarization of astrocytes are involved in neuroinflammation and brain functional rehabilitation after ischemic stroke. Our previous studies display the neuroprotective effect of genistein-3′-sodium sulfonate (GSS) in the acute phase of cerebral ischemia-reperfusion injury (CI/RI). This study aimed to investigate the brain function improvement of GSS during the recovery period after CI/RI in rats and to explore the potential mechanism from the perspective of astrocyte activation and polarization. The transient middle cerebral artery occlusion (tMCAO) rats were treated with GSS (1 mg/kg) continuously for 28 days. The behavior tests were measured to assess neurological function. The mRNA and protein expression in affected cerebral cortex were detected on day 29 after tMCAO. Our results demonstrated that GSS treatment significantly improved the spatial and temporal gait parameters in the Catwalk gait test, prolonged the time on the stick and increased the rotation speed in the rotarod test, and decreased the time to find the hidden platform and increased the time in the target quadrant in the Morris water maze test. In addition, GFAP, GBP2, C3, IL-1β protein expressions and Nos2A mRNA level were decreased, while Nrf2, BDNF, IL-10 protein expressions and Sphk1 and Nef2l2 mRNA levels increased after GSS treatment. Interestingly, GSS presented a strong binding affinity to TLR4 and suppressed the activation of NF-κB signaling. In conclusion, GSS can promote brain function recovery by inhibiting astrocyte activation and polarization to A1 phenotype, and enhancing astrocyte polarization to A2 phenotype via inactivating TLR4/NF-κB signaling, which provide a candidate compound for clinical rehabilitation therapy in the recovery period after ischemic stroke.

Single-belt vs. split-belt treadmill symmetry training: is there a perfect choice for gait rehabilitation post-stroke?

Post-stroke gait asymmetry leads to inefficient gait and a higher fall risk, often causing limited home and community ambulation. Two types of treadmills are typically used for training focused on symmetry: split-belt and single belt treadmills, but there is no consensus on which treadmill is superior to improve gait symmetry in individuals with stroke. To comprehensively determine which intervention is superior, we considered multiple spatial and temporal gait parameters (step length, stride time, swing time, and stance time) and their symmetries. Ten individuals with stroke underwent a single session of split-belt treadmill training and single belt treadmill training on separate days. The changes in step length, stride time, swing time, stance time and their respective symmetries were compared to investigate which training improves both spatiotemporal gait parameters and symmetries immediately after the intervention and after 5min of rest. Both types of treadmill training immediately increased gait velocity (0.08m/s faster) and shorter step length (4.15cm longer). However, split-belt treadmill training was more effective at improving step length symmetry (improved by 27.3%) without sacrificing gait velocity or step length. However, this step length symmetry effect diminished after a 5-min rest period. Split-belt treadmill training may have some advantages over single belt treadmill training, when targeting step length symmetry. Future research should focus on comparing the long-term effects of these two types of training and examining the duration of the observed effects to provide clinically applicable information.