Region-specific Parameters Research Articles

Region specific economic model for solid sorbent direct air capture using geothermal energy resources (S-DAC-GT)

Low temperature geothermal resources, ranging from 80° to 120 °C, may substantially lower both the cost and the CO2 emissions footprint of CO2 direct air capture (DAC) systems. This paper provides a model for determining a region-specific economic analysis for DAC with solid sorbent (S-DAC) using geothermal resources (S-DAC-GT).This paper provides a methodology and program for calculating estimated cost and carbon emissions for potential S-DAC facilities on a region-specific basis. The paper outlines the necessary region-specific characteristics required as parameters for the techno-economic model. The region-specific characteristics are then applied to an S-DAC energy and cost model based on existing literature to calculate the levelized cost per tonne of CO2 captured and stored. Further, the model provides a reasonable approximation of the carbon intensity of the S-DAC-GT system. These calculations allow selecting and prioritizing regions appropriate for potential S-DAC-GT facilities operating at a scale of ∼1 Mt CO2 captured and stored per year.This paper presents a novel approach that addresses several gaps in current DAC techno-economic analyses found in literature. First, the model is highly customizable, allowing the user to apply their own proprietary sorbent properties and region specific parameters to determine precise costs and carbon emissions for their hypothetical S-DAC-GT facility. Second, the overwhelming majority of DAC techno-economic analyses in literature assume away variability of ambient conditions, presuming stable temperature and humidity during operations. This paper directly integrates these large local ambient effects on sorbent performance and evaluates S-DAC-GT costs and carbon emissions based on specific and variable conditions dictated by the user. Third, the flexibility of the model allows the user to rapidly compare S-DAC-GT costs and carbon emissions in different user selected regions, with custom sorbent characteristics. The model provides visualizations of the sensitivity of S-DAC-GT costs and carbon emissions to different parameters and provides visualizations of the cost and carbon emissions due to variability in ambient conditions.Existing techno-economic analyses are either region agnostic or constrain their results to a specific region. The novelty of this paper and its model is the deeper technical and economic analysis of using geothermal energy as the thermal resource, coupled with customizability and visualizations that enable accelerated differentiation of S-DAC-GT costs and carbon intensity by region.

Regional biomechanical characterization of human ascending aortic aneurysms: Microstructure and biaxial mechanical response.

The ascending thoracic aortic aneurysm (ATAA) is a permanent dilatation of the vessel with a high risk of adverse events, and shows heterogeneous properties. To investigate regional differences in the biomechanical properties of ATAAs, tissue samples were collected from 10 patients with tricuspid aortic valve phenotype and specimens from minor, anterior, major, and posterior regions were subjected to multi-ratio planar biaxial extension tests and second-harmonic generation (SHG) imaging. Using the data, parameters of a microstructure-motivated constitutive model were obtained considering fiber dispersion. SHG imaging showed disruptions in the organization of the layers. Structural and material parameters did not differ significantly between regions. The non-symmetric fiber dispersion model proposed by Holzapfel etal. [25] was used to fit the data. The mean angle of collagen fibers was negatively correlated between minor and anterior regions, and the parameter associated with collagen fiber stiffness was positively correlated between minor and major regions. Furthermore, correlations were found between the stiffness of the ground matrix and the mean fiber angle, and between the parameter associated with the collagen fiber stiffness and the out-of-plane dispersion parameter in the posterior and minor regions, respectively. The experimental data collected in this study contribute to the biomechanical data available in the literature on human ATAAs. Region-specific parameters for the constitutive models are fundamental to improve the current risk stratification strategies, which are mainly based on aortic size. Such investigations can facilitate the development of more advanced finite element models capable of capturing the regional heterogeneity of pathological tissues. STATEMENT OF SIGNIFICANCE: Tissue samples of human ascending thoracic aortic aneurysms (ATAA) were collected. Samples from four regions underwent multi-ratio planar biaxial extension tests and second-harmonic generation imaging. Region-specific parameters of a microstructure-motivated model considering fiber dispersion were obtained. Structural and material parameters did not differ significantly between regions, however, the mean fiber angle was negatively correlated between minor and anterior regions, and the parameter associated with collagen fiber stiffness was positively correlated between minor and major regions. Furthermore, correlations were found between the stiffness of the ground matrix and the mean fiber angle, and between the parameter associated with the collagen fiber stiffness and the out-of-plane dispersion parameter in the posterior and minor regions, respectively. This study provides a unique set of mechanical and structural data, supporting the microstructural influence on the tissue response. It may facilitate the development of better finite element models capable of capturing the regional tissue heterogeneity.

Future heat extremes and impacts in a convection-permitting climate ensemble over Germany

Abstract. Heat extremes and associated impacts are considered the most pressing issue for German regional governments with respect to climate adaptation. We explore the potential of a unique high-resolution, convection-permitting (2.8 m), multi-GCM (global climate model) ensemble with COSMO-CLM (Consortium for Small-scale Modeling Climate Limited-area Modelling) regional simulations (1971–2100) over Germany regarding heat extremes and related impacts. We find a systematically reduced cold bias especially in summer in the convection-permitting simulations compared to the driving simulations with a grid size of 7 km and parametrized convection. The projected increase in temperature and its variance favors the development of longer and hotter heat waves, especially in late summer and early autumn. In a 2 ∘C (3 ∘C) warmer world, a 26 % (100 %) increase in the heat wave magnitude index is anticipated. Human heat stress (universal thermal climate index (UTCI) > 32 ∘C) and region-specific parameters tailored to climate adaptation revealed a dependency on the major landscapes, resulting in significantly higher heat exposure in flat regions such as the Rhine Valley, accompanied by the strongest absolute increase. A nonlinear, exponential increase is anticipated for parameters characterizing strong heat stress (UTCI > 32 ∘C, tropical nights, very hot days). Providing region-specific and tailored climate information, we demonstrate the potential of convection-permitting simulations to facilitate improved impact studies and narrow the gap between climate modeling and stakeholder requirements for climate adaptation.

The solubility and deposition flux of East Asian aerosol metals in the East China Sea: The effects of aeolian transport processes

Aerosol dissolvable metals are considered to be readily bioaccessible so that their input would influence the growth and composition of marine phytoplankton and affect elemental cycling globally. However, it is highly challenging to measure or estimate reliable deposition fluxes of aerosol dissolvable metals in the ocean partially due to the impacts of complicated processes involved in pre- and post-deposition of aerosols. We have collected lithogenic dust from major Chinese deserts and size-fractionated aerosols from the East China Sea (ECS) to study the variations of their dissolvable metals by using three operationally defined leaching protocols (ultrapure water, buffer, and Berger leaches). We have systematically investigated the changes of the distribution patterns of the metals to evaluate the potential impacts of the transport processes on the flux estimates of different elements. In addition to the extremely high solubilities observed for anthropogenic type elements, we found variations for solubilities of lithogenic type elements (Ti, Al, Fe) increase with increasing sizes by the three leaching treatments. Without knowing the size specific information (mass and solubility), our observations indicate that the deposition fluxes of lithogenic type elements would be significantly overestimated. Compared with the solubility of the desert dust, we found that all solubilities for lithogenic type elements in the largest aerosols were significantly enhanced. For example, the Fe solubilities increased up to 68, 6, and 3 folds for ultrapure water, buffer, and Berger treatments, respectively. Attributed to the difference of the impacts of the transport processes in different regions, the extent of the enhancement would be region specific. Comparing some other recent laboratory studies, we argue that the solubilities obtained by buffer and Berger leaches are more realistic to represent aerosol solubility in the ocean than ultrapure water leach. It would be essential to carry out similar field studies in other regions as this study to obtain region specific parameters of dissolvable aerosol metals to achieve better global modeling estimates on the fluxes of dissolvable aerosol metals in the ocean.

Evaluation of methane generation rate and energy recovery potential of municipal solid waste using anaerobic digestion and landfilling: A case study of Dhanbad, India.

In this study, two most common biological waste to energy conversion techniques have been analysed and compared on the basis of methane generation and energy recovery potential. The biogas generation potential has been estimated using anaerobic co-digestion experiment. The main substrate used for this study was food waste, while cow dung and anaerobic digester sludge were used as co-digestion substrates. Food waste was used because of its maximum composition (36% of total wastes) in the study area (Dhanbad city, India) with high biogas generation potential. Cow dung acted as a natural buffer, while anaerobic digester sludge was a source of active inoculum. Based on the maximum biogas yield of 402 mL g-1 VS and annual food waste generation rate in the study area, the energy recovery potential using anaerobic digestion was estimated to be 6.59 × 106 kWh year-1. Presently, the wastes in the mixed form are being dumped on a large abandoned land with an approximate area of 93 ha in the outskirts of Dhanbad city in an uncontrolled manner. The annual landfill gas generation from the existing landfill has been estimated using Landfill Gas Emissions Model (LandGEM) based on the waste characteristics, anticipated landfill life and other region-specific parameters of the present study area. The maximum electrical energy recovery potential of 44.62 × 105 kWh from landfill gas has been estimated for the year 2041. Although, the results are based on the waste generation and characteristics of Dhanbad city, the comparison methodology can be applied to other cities.

Temporal and regional variability of cumulative ecological risks of pesticides in Japanese river waters for 1990–2010

We quantitatively evaluated the cumulative ecological risks from multiple pesticides used in paddy fields in Japan. Moreover, we visualized the temporal and regional variability of those risks for 1990–2010. Considering the region-specific parameters of environmental conditions, region-specific predicted environmental concentrations were estimated at 350 river-flow monitoring sites in Japan. Then the multi-substance potentially affected fraction (msPAF) was calculated as a risk index of multiple pesticides by using the computation tool NIAES-CERAP. The median msPAF values for insecticides and herbicides decreased by 92.4% and 53.1%, respectively, from 1990 to 2010. This substantial reduction in ecological risk was attributed to the development of low-risk pesticides by manufacturers, the efforts of farmers in risk reduction, and tighter regulation by the Japanese government. In particular, the substantial reduction of the ecological risk from insecticides was largely due to the decrease in the use of organophosphorus insecticides.

Regional Differences in the Perception of Daylit Scenes across Europe Using Virtual Reality. Part I: Effects of Window Size

ABSTRACT The size of window openings is widely acknowledged as an important factor in our spatial perception. However, little is known about how the perception and preference of windows changes between countries, leaving a gap of knowledge regarding the applicability of research findings across latitudes. This article presents the outcomes of a study investigating regional differences in the perception of spaces with varying window size (small, medium, and large), space size (small and large), spatial context (working and social), and sky type (overcast and two types of clear sky). As the regional differences were the main studied factor, the study was performed in Norway, Switzerland, and Greece, representing northern, central, and southern European latitudes, respectively, and used virtual reality as a means to replicate the same experiment in different locations. In total, 406 participants evaluated eight spatial attributes using an 11-point Likert-type scale. Results indicated that regional differences could be observed in the participants’ responses, with significant differences in how pleasant and calm the space was perceived, found not only between participants in Greece and Norway in all the studied window sizes, but also between Greece and Switzerland for the medium and large windows, indicating that even small variations in latitude within Europe can affect the spatial perception. The findings of this study reveal that spaces with specific fenestration characteristics might not induce the same response across different latitudes in Europe, and thus, have important implications for daylighting and architectural design, which would motivate the use of region-specific parameters.

Calibration Of Regional Vulnerability Functions By Applying Earthquake Events Database

The paper describes the structure and content of the Information System database containing information on earthquake events, which is developed and supported within the framework of computer support for the EMERCOM of the Russian Federation. The database is assigned to provide analytical support for decision making in case of an emergency situation, including tools for mathematical simulation of hazardous excitation, the response of elements at risk to excitation and loss generation. The calibration procedure of the earthquake vulnerability functions for buildings and structures using the database with descriptions of events is presented. The calibrated functions of earthquake vulnerability for buildings of different types are applied to provide an acceptable accuracy of situational assessments for the case of a strong earthquake. The examples of earthquake damage estimations for the test site in Siberia showed that region-specific parameters in the vulnerability functions yield more reliable results to estimate possible damage and losses due to a large earthquake. For Irkutsk City, the estimates of the numbers of heavily damaged and completely collapsed buildings obtained when using different sets of parameters for vulnerability functions differ by 30%. Such difference in damage estimates can significantly affect the plans for rescue and recovery operations. The conclusion is made about the advantage of the calibrated functions application for near real-time damage and loss assessment due to strong earthquakes in order to ensure population safety and territory sustainable development.

Harnessing cross-region disparities to assess the impact of tourism on regional growth in Europe

ABSTRACT We use regional data across 27 European countries and look into the effect of tourist arrivals on regional growth during 2000–2017. The research is set in an augmented Solow model estimated via a rich and flexible empirical strategy, with different underlying assumptions about the impacts of the regressors and unobservables. A first demarcation line across our set of estimators is between the ‘pooled’ estimators (assuming common parameters across regions) and the ‘heterogeneous’ ones (allowing for region-specific parameters). An additional axis of differentiation lies in the way the impact of unobservables is accounted for via the estimation method: some methodologies hypothesize a common impact of the latter across space, while others make room for a possible differentiated effect over the regions. Among the pooled estimators, our preferred one reveals a positive and significant contribution of tourism to regional growth. Among the heterogeneous estimators, our favored one corroborates the previous result and uncovers vast regional disparities in terms of the effect of tourist arrivals on growth. A preliminary investigation suggests that regions endowed with a large share of highly educated population experienced the greatest growth-impact of tourist arrivals.

Broad-band ground-motion simulation of 2016 Amatrice earthquake, Central Italy

SUMMARYOn 24 August 2016 at 01:36 UTC a ML6.0 earthquake struck several villages in central Italy, among which Accumoli, Amatrice and Arquata del Tronto. The earthquake was recorded by about 350 seismic stations, causing 299 fatalities and damage with macroseismic intensities up to 11. The maximum acceleration was observed at Amatrice station (AMT) reaching 916 cm s–2 on E–W component, with epicentral distance of 15 km and Joyner and Boore distance to the fault surface (RJB) of less than a kilometre. Motivated by the high levels of observed ground motion and damage, we generate broad-band seismograms for engineering purposes by adopting a hybrid method. To infer the low frequency seismograms, we considered the kinematic slip model by Tinti et al . The high frequency seismograms were produced using a stochastic finite-fault model approach based on dynamic corner-frequency. Broad-band synthetic time-series were therefore obtained by merging the low and high frequency seismograms. Simulated hybrid ground motions were compared both with the observed ground motions and the ground-motion prediction equations (GMPEs), to explore their performance and to retrieve the region-specific parameters endorsed for the simulations. In the near-fault area we observed that hybrid simulations have a higher capability to detect near source effects and to reproduce the source complexity than the use of GMPEs. Indeed, the general good consistency found between synthetic and observed ground motion (both in the time and frequency domain), suggests that the use of regional-specific source scaling and attenuation parameters together with the source complexity in hybrid simulations improves ground motion estimations. To include the site effect in stochastic simulations at selected stations, we tested the use of amplification curves derived from HVRSs (horizontal-to-vertical response spectra) and from HVSRs (horizontal-to-vertical spectral ratios) rather than the use of generic curves according to NTC18 Italian seismic design code. We generally found a further reduction of residuals between observed and simulated both in terms of time histories and spectra.

Spatially varying age-period-cohort analysis with application to US mortality, 2002-2016.

Many public health databases index disease counts by age groups and calendar periods within geographic regions (e.g., states, districts, or counties). Issues around relative risk estimation in small areas are well-studied; however, estimating trend parameters that vary across geographic regions has received less attention. Additionally, small counts (e.g., $<10$) in most publicly accessible databases are censored, further complicating age-period-cohort (APC) analysis in small areas. Here, we present a novel APC model with left-censoring and spatially varying intercept and trends, estimated with correlations among contiguous geographic regions. Like traditional models, our model captures population-scale trends, but it can also be used to characterize geographic disparities in relative risk and age-adjusted trends over time. To specify the joint distribution of our three spatially varying parameters, we adapt the generalized multivariate conditional autoregressive prior, previously used for multivariate disease mapping. Specified in this manner, region-specific parameters are correlated spatially, and also to one another. Estimation is performed using the No-U-Turn Hamiltonian Monte Carlo sampler in Stan. We conduct a simulation study to assess the performance of the proposed model relative to the standard model, and conclude with an application to US state-level opioid overdose mortality in men and women aged 15-64 years.

Tomotherapy treatment site specific planning using statistical process control

BackgroundThis study investigated planned MLC distribution and treatment region specific plan parameters to recommend optimal delivery parameters based on statistical process techniques. MethodsA cohort of 28 head and neck, 19 pelvic and 23 brain pre-treatment plans were delivered on a helical tomotherapy system using 2.5 cm field width. Parameters such as gantry period, leaf open time (LOT), actual modulation factor, LOT sonogram, treatment duration and couch travel were investigated to derive optimal range for plans that passed acceptable delivery quality assurance. The results were compared against vendor recommendations and previous publications. ResultsNo correlation was observed between vendor recommended gantry period and percentage of minimum leaf open times. The range of gantry period (min–max) observed was 16–21 s for head and neck, 15–22 s for pelvis and 13–18 s for brain plans respectively. It was also noted that the highest percentage (average (X-) ± SD) of leaf open times for a minimum time of 100 ms was seen for brain plans (53.9 ± 9.2%) compared to its corresponding head and neck (34.5 ± 4.2%) and pelvic (32.0 ± 9.4%) plans respectively. ConclusionsWe have proposed that treatment site specific delivery parameters be used during planning that are based on the treatment centre and have detailed recommendations and limitations for the studied cohort. This may enable to improve efficiency of treatment deliveries by reducing inaccuracies in MLC distribution.

Anatomical Region-Specific In Vivo Wireless Communication Channel Characterization.

In vivo wireless body area networks and their associated technologies are shaping the future of healthcare by providing continuous health monitoring and noninvasive surgical capabilities, in addition to remote diagnostic and treatment of diseases. To fully exploit the potential of such devices, it is necessary to characterize the communication channel, which will help to build reliable and high-performance communication systems. This paper presents an in vivo wireless communication channel characterization for male torso both numerically and experimentally (on a human cadaver) considering various organs at 915MHz and 2.4 GHz. A statistical path loss (PL) model is introduced, and the anatomical region-specific parameters are provided. It is found that the mean PL in decibel scale exhibits a linear decaying characteristic rather than an exponential decaying profile inside the body, and the power decay rate is approximately twice at 2.4 GHz as compared to 915MHz. Moreover, the variance of shadowing increases significantly as the in vivo antenna is placed deeper inside the body since the main scatterers are present in the vicinity of the antenna. Multipath propagation characteristics are also investigated to facilitate proper waveform designs in the future wireless healthcare systems, and a root-mean-square delay spread of 2.76 ns is observed at 5 cm depth. Results show that the in vivo channel exhibit different characteristics than the classical communication channels, and location dependence is very critical for accurate, reliable, and energy-efficient link budget calculations.

A model for national freight flows, distribution centers, empty trucks and urban truck movements

ABSTRACTTrucks travel both short distances for local deliveries and long distances for transporting goods across the country. Often their travel behavior is tour-based, they run under tight schedules and under curfew on selected roads. Despite these differences from personal travel, in practice truck models largely follow person travel methods. To overcome this shortcoming, a two-layer truck model is developed for the Chicago Metropolitan Area. Long-distance trucks are driven by commodity flows, with distribution centers, rail yards, marine ports and airports being represented explicitly. Empty trucks are accounted for as well. For the short-distance truck model, a novel parameter estimation method makes use of limited data to derive region-specific parameters. The model is fully operational and validates reasonably well against traffic counts.

Towards a revised climatology of summertime dimethylsulfide concentrations and sea–air fluxes in the Southern Ocean

Environmental context The trace gas dimethylsulfide (DMS) is emitted from surface ocean waters to the overlying atmosphere, where it forms aerosols that promote cloud formation and influence Earth’s climate. We present an updated climatology of DMS emissions from the vast Southern Ocean, demonstrating how the inclusion of new data yields higher regional sources compared with previously derived values. Our work provides an important step towards better quantifying the oceanic emissions of an important climate-active gas. Abstract The Southern Ocean is a dominant source of the climate-active gas dimethylsulfide (DMS) to the atmosphere. Despite significant improvements in data coverage over the past decade, the most recent global DMS climatology does not include a growing number of high-resolution surface measurements in Southern Ocean waters. Here, we incorporate these high resolution data (~700000 measurements) into an updated Southern Ocean climatology of summertime DMS concentrations and sea–air fluxes. Owing to sparse monthly data coverage, we derive a single summertime climatology based on December through February means. DMS frequency distributions and oceanographic properties (mixed-layer depth and chlorophyll-a) show good general coherence across these months, providing justification for the use of summertime mean values. The revised climatology shows notable differences with the existing global climatology. In particular, we find increased DMS concentrations and sea–air fluxes south of the Polar Frontal zone (between ~60 and 70°S), and increased sea–air fluxes in mid-latitude waters (40–50°S). These changes are attributable to both the inclusion of new data and the use of region-specific parameters (e.g. data cut-off thresholds and interpolation radius) in our objective analysis. DMS concentrations in the Southern Ocean exhibit weak though statistically significant correlations with several oceanographic variables, including ice cover, mixed-layer depth and chlorophyll-a, but no apparent relationship with satellite-derived measures of phytoplankton photophysiology or taxonomic group abundance. Our analysis highlights the importance of using regional parameters in constructing climatological DMS fields, and identifies regions where additional observations are most needed.

Inter-Comparison and Evaluation of the Global LAI Product (LAI3g) and the Regional LAI Product (GGRS-LAI) over the Area of Kazakhstan

Long-term global datasets of the Leaf Area Index (LAI) are important for monitoring global vegetation dynamics and are an important input for Earth system models (ESM). The comparison of long-term datasets is based on two recently available datasets both derived from AVHRR (Advanced Very High Resolution Radiometer) time series. The LAI3g dataset is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) from AVHRR sensors and best-quality MODIS LAI data. The second long-term LAI dataset is based on the 8-km spatial resolution GIMMS-AVHRR data (Goettingen GIS &amp; Remote Sensing, GGRS dataset). The GGRS-LAI product uses a satellite-based LAI. This algorithm uses a three-dimensional physical radiative transfer model, which establishes the relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site-/region-specific parameters, including the vegetation architecture variables, such as leaf angle distribution, clumping index and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. As a main result of our study, we could summarize that the differences between both products are most pronounced at the start and the end of the growing season. During the spring and autumn months, the LAI difference maps showed a considerable difference of LAI GGRS and LAI3g. LAI3g is characterized by a considerably earlier start and a later finish to the growing season than LAI GGRS. Moreover, LAI3g showed LAI &gt; 0 during the winter months when any green vegetation is absent in all land covers of Kazakhstan. A direct cause for this could be a too high base level of the LAI3g during the leafless phase.

The Regional Snowfall Index

This paper describes a new snowfall index that quantifies the impact of snowstorms within six climate regions in the United States. The regional snowfall index (RSI) is based on the spatial extent of snowfall accumulation, the amount of snowfall, and the juxtaposition of these elements with population. Including population information provides a measure of the societal susceptibility for each region. The RSI is an evolution of the Northeast snowfall impact scale (NESIS), which NOAA's National Climatic Data Center began producing operationally in 2006. While NESIS was developed for storms that had a major impact in the Northeast, it includes all snowfall during the lifetime of a storm across the United States and as such can be thought of as a quasi-national index that is calibrated to Northeast snowstorms. By contrast, the RSI is a regional index calibrated to specific regions using only the snow that falls within that region. This paper describes the methodology used to compute the RSI, which requires region-specific parameters and thresholds, and its application within six climate regions in the eastern two-thirds of the nation. The process used to select the region-specific parameters and thresholds is explained. The new index has been calculated for over 580 snowstorms that occurred between 1900 and 2013 providing a century-scale historical perspective for these snowstorms. The RSI is computed for category 1 or greater storms in near–real time, usually a day after the storm has ended.

Relating structural and functional connectivity in MRI: a simple model for a complex brain.

Advances in magnetic resonance imaging (MRI) allow to gain critical insight into the structure of neural networks and their functional dynamics. To relate structural connectivity [as quantified by diffusion-weighted imaging (DWI) tractography] and functional connectivity [as obtained from functional MRI (fMRI)], increasing emphasis has been put on computational models of brain activity. In the present study, we use structural equation modeling (SEM) with structural connectivity to predict functional connectivity. The resulting model takes the simple form of a spatial simultaneous autoregressive model (sSAR), whose parameters can be estimated in a Bayesian framework. On synthetic data, results showed very good accuracy and reliability of the inference process. On real data, we found that the sSAR performed significantly better than two other reference models as well as than structural connectivity alone, but that the Bayesian procedure did not bring significant improvement in fit compared to two simpler approaches. Nonetheless, we also found that the values of the region-specific parameters inferred using Bayesian inference differed significantly across resting-state networks. These results demonstrate 1) that a simple abstract model is able to perform better that more complex models based on more realistic assumptions, 2) that the parameters of the sSAR can be estimated and can potentially be used as biomarkers, but also 3) that the sSAR, while being the best-performing model, is at best still a very crude model of the relationship between structure and function in MRI.

Ground motion prediction equation considering combined dataset of recorded and simulated ground motions

Himalayan region is one of the most active seismic regions in the world and many researchers have highlighted the possibility of great seismic event in the near future due to seismic gap. Seismic hazard analysis and microzonation of highly populated places in the region are mandatory in a regional scale. Region specific Ground Motion Predictive Equation (GMPE) is an important input in the seismic hazard analysis for macro- and micro-zonation studies. Few GMPEs developed in India are based on the recorded data and are applicable for a particular range of magnitudes and distances. This paper focuses on the development of a new GMPE for the Himalayan region considering both the recorded and simulated earthquakes of moment magnitude 5.3–8.7. The Finite Fault simulation model has been used for the ground motion simulation considering region specific seismotectonic parameters from the past earthquakes and source models. Simulated acceleration time histories and response spectra are compared with available records. In the absence of a large number of recorded data, simulations have been performed at unavailable locations by adopting Apparent Stations concept. Earthquakes recorded up to 2007 have been used for the development of new GMPE and earthquakes records after 2007 are used to validate new GMPE. Proposed GMPE matched very well with recorded data and also with other highly ranked GMPEs developed elsewhere and applicable for the region. Comparison of response spectra also have shown good agreement with recorded earthquake data. Quantitative analysis of residuals for the proposed GMPE and region specific GMPEs to predict Nepal–India 2011 earthquake of Mw of 5.7 records values shows that the proposed GMPE predicts Peak ground acceleration and spectral acceleration for entire distance and period range with lower percent residual when compared to exiting region specific GMPEs.

Age- and gender-related differences in cortical geometry and microstructure: Improved sensitivity by regional analysis

ObjectiveWhile the importance of cortical structure quantification is increasingly underscored by recent literature, conventional analysis techniques obscure potentially important regional variations in cortical structure. The objective of this study was to characterize the spatial variability in cortical geometry and microstructure at the distal radius and tibia using high resolution peripheral quantitative computed tomography (HR-pQCT). We show that spatially-resolved analysis is able to identify cortical sub-regions with increased sensitivity to the effects of gender and aging. MethodsHR-pQCT scans of 146 volunteers (92 female/54 male) spanning a wide range of ages (20–78years) were analyzed. For each subject, radius and tibia scans were obtained using a clinical HR-pQCT system. Measures describing geometry (cortical bone thickness (Ct.Th)), microstructure (porosity (Ct.Po), pore diameter (Ct.Po.Dm), and pore size heterogeneity (Ct.Po.Dm SD)), and cortical bone density were calculated from the image data. Biomechanical parameters describing load and stress distribution were calculated using linear finite element analysis. Cortical quadrants were defined based on anatomic axes to quantify regional parameter variation. Subjects were categorized by gender, and age, and menopausal status for analysis. ResultsSignificant regional variation was found in all geometric and microstructural parameters in both the radius and tibia. In general, the radius showed more pronounced and significant variations in all parameters as compared with the tibia. At both sites, Ct.Po displayed the greatest regional variations. Correlation coefficients for Ct.Po and Ct.Th with respect to load and stress distribution provided evidence of an association between regional cortical structure and biomechanics in the tibia. Comparing women to men, differences in Ct.Po were most pronounced in the anterior quadrant of the radius (36% lower in women (p<0.01)) and the posterior quadrant of the tibia (27% lower in women (p<0.01)). Comparing elderly to young women, differences in Ct.Po were most pronounced in the lateral quadrant of the radius (328% higher in elderly women (p<0.001)) and the anterior quadrant of the tibia (433% higher in elderly women (p<0.001)). Comparing elderly to young men, the most pronounced age differences were found in the anterior radius (205% higher in elderly men, (p<0.001)) and the anterior tibia (190% higher in elderly men (p<0.01)). All subregional Ct.Po differences provided greater sensitivity to gender and age effects than those based on the global means. ConclusionThese results show significant regional variation in all geometric and microarchitectural parameters studied in both the radius and tibia. Quantification of region-specific parameters provided increased sensitivity in the analysis of age- and gender-related differences, in many cases providing statistically significant differentiation of groups where conventional global analysis failed to detect differences. These results suggest that regional analysis may be important in studies of disease and therapeutic effects, particularly where microstructural parameters based on global analyses have thus far failed to identify a response in bone quality.