Scale Heterogeneity Research Articles

Effectiveness and safety of virtual reality rehabilitation after stroke: an overview of systematic reviews

Virtual reality (VR) is an innovative neurorehabilitation modality that has been variously examined in systematic reviews. We assessed VR effectiveness and safety after cerebral stroke. In this overview of systematic reviews, we searched eleven databases (Cochrane Database of Systematic Reviews, EMBASE, MEDLINE, SCOPUS, ISI Web of Science, CINAHL, PsycINFO, Pedro, Otseeker, Healthevidence.org, Epistemonikos) and grey literature from inception to January 17, 2023. Studies eligible for inclusion were systematic reviews published in English that included adult patients with a clinical diagnosis of stroke (acute to chronic phase) undergoing any kind of immersive, semi-immersive or non-immersive VR intervention with or without conventional therapy versus conventional therapy alone. The primary outcome was motor upper limb function and activity. The secondary outcomes were gait and balance, cognitive and mental function, limitation of activities, participation, and adverse events. We calculated the degree of overlap between reviews based on the corrected covered area (CCA). Methodological quality was assessed using the A MeaSurement Tool to Assess systematic Reviews (AMSTAR 2) and the Certainty of Evidence (CoE) using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Discordances between results were examined using a conceptual framework based on the Jadad algorithm. This overview is registered with PROSPERO, CRD42022329263. Of the 58 reviews included (n=345 unique primary studies), 42 (72.4%) had conducted meta-analysis. More than half of the reviews (58.6%) were published between 2020 and 2022 and many (77.6%) were judged critically low in quality by AMSTAR 2. Most reported the Fugl Meyer Assessment scale (FMA-UE) to measure upper limb function and activity. For the primary outcome, there was a moderate overlap of primary studies (CCA 9.0%) with discordant findings. Focusing on upper limb function (FMA-UE), VR with or without conventional therapy seems to be more effective than conventional therapy alone, with low to moderate CoE and probable to definite clinical relevance. For secondary outcomes there was uncertainty about the superiority or no difference between groups due to substantial heterogeneity of measurement scales (eg, methodological choices). A few reviews (n=6) reported the occurrence of mild adverse events. Current evidence suggests that multiple meta-analyses agreed on the superiority of VR with or without conventional therapy over conventional therapy on FME-UE for upper limb. Clinicians may consider embedding VR technologies into their practice as appropriate with patient's goals, abilities, and preferences. However, caution is needed given the poor methodological quality of reviews. Italian Ministry of Health.

Gut microbiome perturbation, antibiotic resistance, and Escherichia coli strain dynamics associated with international travel: a metagenomic analysis

Culture-based studies have shown that acquisition of extended-spectrum β-lactamase-producing Enterobacterales is common during international travel; however, little is known about the role of the gut microbiome before and during travel, nor about acquisition of other antimicrobial-resistant organisms. We aimed to identify (1) whether the gut microbiome provided colonisation resistance against antimicrobial-resistant organism acquisition, (2) the effect of travel and travel behaviours on the gut microbiome, and (3) the scale and global heterogeneity of antimicrobial-resistant organism acquisition. In this metagenomic analysis, participants were recruited at three US travel clinics (Boston, MA; New York, NY; and Salt Lake City, UT) before international travel. Participants had to travel internationally between Dec 8, 2017, and April 30, 2019, and have DNA extractions for stool samples both before and after travel for inclusion. Participants were excluded if they had at least one low coverage sample (<1 million read pairs). Stool samples were collected at home before and after travel, sent to a clinical microbiology laboratory to be screened for three target antimicrobial-resistant organisms (extended-spectrum β-lactamase-producing Enterobacterales, carbapenem-resistant Enterobacterales, and mcr-mediated colistin-resistant Enterobacterales), and underwent DNA extraction and shotgun metagenomic sequencing. We profiled metagenomes for taxonomic composition, antibiotic-resistant gene content, and characterised the Escherichia coli population at the strain level. We analysed pre-travel samples to identify the gut microbiome risk factors associated with acquisition of the three targeted antimicrobial resistant organisms. Pre-travel and post-travel samples were compared to identify microbiome and resistome perturbation and E coli strain acquisition associated with travel. A total of 368 individuals travelled between the required dates, and 296 had DNA extractions available for both before and after travel. 29 travellers were excluded as they had at least one low coverage sample, leaving a final group of 267 participants. We observed a perturbation of the gut microbiota, characterised by a significant depletion of microbial diversity and enrichment of the Enterobacteriaceae family. Metagenomic strain tracking confirmed that 67% of travellers acquired new strains of E coli during travel that were phylogenetically distinct from their pre-travel strains. We observed widespread enrichment of antibiotic-resistant genes in the gut, with a median 15% (95% CI 10-20, p<1 × 10-10) increase in burden (reads per kilobase per million reads). This increase included antibiotic-resistant genes previously classified as threats to public health, which were 56% (95% CI 36-91, p=2 × 10-11) higher in abundance after travel than before. Fluoroquinolone antibiotic-resistant genes were aquired by 97 (54%) of 181 travellers with no detected pre-travel carriage. Although we found that visiting friends or relatives, travel to south Asia, and eating uncooked vegetables were risk factors for acquisition of the three targeted antimicrobial resistant organisms, we did not observe an association between the pre-travel microbiome structure and travel-related antimicrobial-resistant organism acquisition. This work highlights a scale of E coli and antimicrobial-resistant organism acquisition by US travellers not apparent from previous culture-based studies, and suggests that strategies to control antimicrobial-resistant organisms addressing international traveller behaviour, rather than modulating the gut microbiome, could be worthwhile. US Centers for Disease Control and Prevention and National Institute of Allergy and Infectious Diseases.

Visitors’ willingness to pay for ecosystem conservation in Grenada

Understanding visitors’ willingness to pay (WTP) for natural ecosystems can assist policymakers in managing the environmental assets that form the foundation of nature-based tourism. To earn the highest economic return from limited budgetary resources, tourist-dependent destinations must identify sustainable sources of conservation funding and prioritize spending across ecosystems and initiatives. The purpose of this study is to assess the potential for visitor donations to serve as a source of conservation funding and investigate preferences and willingness to pay for multiple ecosystems concurrently, using data from a stated preference survey administered in Grenada, a small island state in the southern Caribbean that relies on nature-based tourism. Results from a discrete choice experiment and a contingent valuation exercise reveal a reluctance to donate to conservation initiatives by more than half of respondents. Visitors place the highest value on improvements to coral reefs, followed by rainforests, mangroves, and beaches. Average willingness to donate to a conservation trust fund ranged from US $8.00 to US $15.00, and from US $17.00 to US $52.00 for conservation plans targeting improvements to specific ecosystems. Few factors are found to be associated with willingness to pay. Scale heterogeneity may be a more significant source of variation than preference heterogeneity.

Effect of microstructure and microtexture evolution of oxide layer over J82B steel on mechanical descaling properties

The oxide layer covering a high-carbon steel wire rod must be efficiently removed to form ‘a well-adhered yet easy-to-remove oxide scale’ during the mechanical descaling process. In this study, the relationship between the microstructural characteristics of the oxides and the efficiency of mechanical descaling was investigated. An electron backscatter diffraction analysis was conducted prior to bending and microscratch testing to clarify the microstructural discrepancies, namely, the phase distribution and texture orientation of the oxide constituents within the oxide scale system over the steel wire rod. The oxide scale system exhibits a double-scale layered structure, primarily consisting of FeO and Fe3O4. The low-angle and low-Σ coincident site lattice (CSL) boundaries in the Fe3O4 grains inhibit crack propagation without diffusion interactions, supporting oxide spallation in the large flake state during descaling. However, if the Fe3O4 grains were distributed along the boundary between the oxide scale and the iron substrate and had a high orientation consistency with the α-Fe grains, the grown oxide scale spalled into fine fragments, and a considerable amount of oxide scale residue firmly adhered to the descaled steel specimen. Stress relief and ion vacancy diffusion mechanisms govern the growth of Fe3O4 grains near the surface and at the interface of the matrix. These results provide guidelines for adjusting the oxide scale heterogeneity of high-carbon steels to improve their descaling performance.

An empirical analysis of the influence of human capital on enterprise innovation performance: A focus on the nature of enterprise ownership and enterprise scale

Purpose: This research examines how the heterogeneity of enterprise ownership nature and scale influences the relationship between human capital and innovation performance.&#x0D; Design/Methodology/Approach: This study makes use of data from the list of companies on China's expanding enterprise market from 2009-2020 through regression analysis using the panel data least squares approach.&#x0D; Findings: Higher levels of human capital and staff training have a positive impact on innovation performance but employee salaries and welfare levels have a negative impact on innovation performance. The effect of human capital stock and employee training on the performance of innovation in companies is more pronounced in state-owned enterprises or small companies. The nature of ownership or enterprise size has no significant effect on employee wage and welfare levels or the innovation performance of enterprises.&#x0D; Conclusion: The study's findings suggest that the relationship between human capital and innovation performance is complex and closely correlated with the type of ownership and size of the business.&#x0D; Research Limitations: More factors of firm heterogeneity such as firm age, industry and region were not verified. &#x0D; Practical Implications: It provides a new way for enterprises to improve innovation performance.&#x0D; Contribution to Literature: The purpose of this paper is to broaden the research viewpoint on how human capital investments affect the performance of enterprise innovation from two perspectives: the nature of enterprise ownership and the size of the enterprise.

China’s digital infrastructure: Networks, systems, standards

In the People’s Republic of China, the development of information infrastructures has been a cardinal component of the national modernization project for more than four decades. While most discussions of digital infrastructure in the country focus on the ‘Chinese internet,' framed by architectures of control like the Great Firewall and governmental initiatives like Internet Plus, this special issue contends that China’s digital infrastructure extends from the physical cables laid under urban streets to the ideological capture of surveillance systems, and from the smart home devices domesticated by elderly citizens to the QR codes plastered on everyday life surfaces. Through their interdisciplinary and innovative studies, this issue’s contributors push discussions of China’s digital infrastructure beyond the reduction to authoritarian control and the triumphal rhetorics of governmental imaginaries. Given the breadth of analytical scale and the variety of research methods featured in this collection, the nine contributions to this special issue are organized in three clusters, each centered around one of three key terms from infrastructure studies: networks, systems, and standards. By accounting for heterogeneous scales and relationships through which China’s digital infrastructure emerges, consolidates, and falls apart, these articles produce original knowledge about complex sociotechnical processes and develop productive concepts for future scholarship.

Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation.

Comprehensive semantic segmentation on renal pathological images is challenging due to the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times larger than that of the peritubular capillaries, making it impractical to segment both objects on the same patch, at the same scale. To handle this scaling issue, prior studies have typically trained multiple segmentation networks in order to match the optimal pixel resolution of heterogeneous tissue types. This multi-network solution is resource-intensive and fails to model the spatial relationship between tissue types. In this article, we propose the Omni-Seg network, a scale-aware dynamic neural network that achieves multi-object (six tissue types) and multi-scale (5× to 40× scale) pathological image segmentation via a single neural network. The contribution of this article is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining. By learning from 150,000 human pathological image patches from six tissue types at three different resolutions, our approach achieved superior segmentation performance according to human visual assessment and evaluation of image-omics (i.e., spatial transcriptomics).

Simulations of the effect of smectite‐to‐illite transition in shales on permeability and overpressures using a stochastic approach, a Norwegian margin case study

AbstractThe smectite‐illite transition in shales due to subsidence, temperature changes and diagenesis influences many processes in a sedimentary basin that can contribute to overpressure build up like reducing the shale permeability. The smectite‐rich layers can form sealing barriers to fluid flows that will influence pore pressure prognosis for drilling campaigns, contribute to sealing caprocks for possible CO2 storage and to sealing of plugging and abandonment wells. In this work, we have included the diagenetic smectite‐illite transition into a three‐dimensional pressure simulation model to simulate its effect on pressure build‐up due to reduced shale permeabilities over geological time scale. We have also tested effect of thermal history and potassium concentration on the process of smectite‐illite transition and the associated smectite‐illite correction on permeability. A new smectite‐illite correction has been introduced, to mimic how shale permeability will vary dependent on the smectite‐illite transition. Stochastic Monte Carlo simulations have been carried out to test the sensitivity of the new correction parameters. Finally, a 3D Monte Carlo pore pressure simulation with 1000 drawings has been carried out on a case study covering Skarv Field, and Dønna Terrace offshore Mid‐Norway. The simulated mean overpressures are in range with observed overpressures from exploration wells in the area for the Cretaceous sandy Lysing Formation and for the two Cretaceous Intra Lange Formation sandstones. The simulated smectite content versus depth is in line with published XRD dataset from wells. The corresponding modelled present‐day permeabilities for the shales including the smectite‐illite transition are two magnitudes higher than measured permeabilities on small samples in the laboratory using transient decay method. The measured permeabilities are in the range of 2.66·10−18 to 3.94·10−22 m2 (2695 to 0.39 nD) for the North Sea database and represent the end members for shales‐permeabilities with the lowest values, since the small samples are selected with no or minor natural fractures. This work shows that by upscaling shale permeabilities from mm‐scale to km scale, natural fractures and sedimentary heterogeneities will increase the shale permeabilities with a factor of two and that by including permeability correction controlled by the smectite fraction, pressure ramp can be simulated due to diagenesis effect in shales.

Theoretical investigation of nonergodic solute dispersion in natural porous formations characterized by persistent and antipersistent power-law log-conductivity correlations

Tracer dispersion within log-conductivity fields represented by power-law semivariograms is investigated by an analytical first-order Lagrangian approach that, in treating subsurface flow and transport, resorts to the superposition principle of an infinite double hierarchy of mutually independent scales of heterogeneity. The results of the investigation, which are corroborated by a preliminary field validation, and also interpreted in terms of probabilistic risk analysis, say that transport anomaly is intrinsically associated with evolving-scale heterogeneous porous formations, regardless of their semivariogram scaling exponent b. In contrast with what was found by previous studies that dealt with this subject in a Lagrangian framework, it is demonstrated that: (1) the magnitude of nonergodic dispersion is nonmonotonically related to b; (2) consistently assuming a characteristic advective-heterogeneity length-scale leads to a universal (and quadratic) dependence of the dimensionless macrodispersion coefficients on the dimensionless time. Additionally, it is demonstrated that, in the presence of fractal heterogeneity, and unlike what happens for short-range correlations, diffusion acts as an antagonist mechanism in terms of Fickian dispersion achievement. Finally, the reinterpretation of antipersistent and persistent correlations as a double hierarchy of oscillatory nonperiodic and periodic fields, respectively, besides allowing for a technical explanation of all the detected trends, envisions a possible alternative methodology for their numerical generation.

Evapotranspiration controls across spatial scales of heterogeneity

AbstractIn semi‐arid regions where irrigation causes thermal surface heterogeneities, evapotranspiration is controlled not only by the land surface but also by the interaction between the surface and the atmospheric boundary layer. The spatial scale of heterogeneity impacts the processes that drive the diurnal variability of evapotranspiration. In this study, we combine data with a conceptual, coupled land–atmosphere model to study the drivers of evapotranspiration across spatial scales of heterogeneity for the Land Interactions with the Atmosphere in the Iberian Semi‐arid Environment (LIAISE) field campaign. We use a latent heat tendency equation as a diagnostic tool to quantify the contributions of various surface‐ and boundary‐layer driven processes on surface latent heat flux. We define the following spatial scales based on surface characteristics: regional, landscape, and local. We find that, at the regional and landscape scales (1 km), the feedback mechanisms between the surface fluxes and the resulting boundary‐layer dynamics enhance the daily latent heat flux by 64% to 77%. Conversely, at the local scale (100 m), surface‐driven processes are most important for governing evapotranspiration. At the local scale, we find that the energy stored at the surface enhances evaporation in the late afternoon, which could explain the observed time lag between net radiation and latent heat flux. Furthermore, the combined effects of entrainment at the boundary‐layer top and advection of heat and moisture enhance daily latent heat by 27% at the regional scale to 43% at the local scale. This analysis also has implications for the land‐surface modelling community. It suggests that properly reproducing evapotranspiration to capture the relevant processes depends on capacity of the model to resolve the interaction of scales in surface heterogeneous conditions.

On the Control of Soil Heterogeneity, Péclet Number and Spatially Variable Diffusion Over Unsaturated Transport

AbstractPhysical properties of soils are ubiquitously heterogeneous. This spatial variability has a profound, yet still partially understood, impact on conservative transport. Moreover, molecular diffusion is often a disregarded process that can have an important counter‐intuitive effect on transport: diffusion can prevent non‐Fickian tailing by mobilizing mass otherwise trapped in low velocity zones. Here, we focus on macroscopically homogeneous soils presenting small scale heterogeneity, as described by the Miller‐Miller theory. We then analyze the dynamic control of soil heterogeneity, advection and diffusion on conservative transport. We focus especially on the importance of diffusion and of its tortuosity‐dependent spatial variability on the overall transport. Our results confirm previous finding that relatively high Péclet number systems are highly sensitive to the degree of heterogeneity, which promotes non‐Fickian transport. Also, diffusion appears to have a profound impact on transport, depending on both the degree of heterogeneity and the Péclet number. For a high Péclet number and a very heterogeneous system, diffusion leads to the counter‐intuitive decrease of non‐Fickian spreading described previously. This is not observed for a low Péclet number due to the impact of the spatial variability in the diffusion coefficient, which appears to be a significant controlling factor of transport by promoting or preventing the accumulation of mass in low velocity zones. Globally, this work (a) highlights the complex, synergistic effect of soil heterogeneity, advective fluxes and diffusion on transport and (b), alerts on potential upscaling challenges when the spatial variability of such key processes cannot be properly described.

Enhanced Mixing and Reaction in Converging Flows: Theory and Pore‐Scale Imaging

AbstractMixing fronts at the interface of opposing flows are compressed at a constant rate. The resulting exponential stretching of fluid elements leads to enhanced chemical gradients and biogeochemical processes. This process is similar as what occurs in the pore space of 3D chaotic flows. However, it is so far not known how such fluid compression controls the amplitude of mixing and reaction rates in porous media. Here we derive analytical predictions for the mixing width, the maximum reaction rate and the reaction intensity in compressed mixing fronts as a function of the Péclet and Damköhler numbers. We developed an experimental setup providing pore scale measurements of mixing and reaction rates in mixing fronts at the interface of converging flows. The theory accurately predicts the scaling of mixing and reaction with the Péclet number both in porous micromodels and simple Hele‐Shaw cells. Additionally, we found that the presence of pore scale heterogeneities in the porous micromodels enhances reaction rates by a factor of 4 compared to the Hele‐Shaw cells. Using numerical simulations of pore scale velocity fields, we attributed this phenomenon to the enhancement of pore‐scale compression due to the presence of grains in accelerating flows. These findings provide new insights into the dynamics of mixing‐induced reactions in porous media.

Chasing the fitness optimum: temporal variation in the genetic and environmental expression of life-history traits for a perennial plant.

The ability of plants to track shifting fitness optima is crucial within the context of global change, where increasing environmental extremes may have dramatic consequences for life history, fitness, and ultimately population persistence. However, tracking changing conditions relies on the relationship between genetic and environmental variance, where selection may favour plasticity, the evolution of genetic differences, or both depending on the spatial and temporal scale of environmental heterogeneity. Over three years, we compared the genetic and environmental components of phenological and life-history variation in a common environment for the spring perennial Geum triflorum. Populations were sourced from alvar habitats that exhibit extreme but predictable annual flood-desiccation cycles and prairie habitats that exhibit similar but less predictable variation in water availability. Heritability was generally higher for early life-history (emergence probability) relative to later life-history traits (total seed mass), indicating that traits associated with establishment are under stronger genetic control relative to later life-history fitness expressions, where plasticity may play a larger role. This pattern was particularly notable in seeds sourced from environmentally extreme but predictable alvar habitats relative to less predictable prairie environments. Fitness landscapes based on seed source origin, largely characterized by varying water availability and flower production, described selection as the degree of maladaptation of seed source environment relative to the prairie common garden environment. Plants from alvar populations were consistently closer to the fitness optimum across all years. Annually, the breadth of the fitness optimum expanded primarily along a moisture gradient, with inclusion of more populations onto the expanding optimum. These results highlight the importance of temporally and spatially varying selection in life-history evolution, indicating plasticity may become a primary mechanism needed to track fitness for later life-history events within perennial systems.

Land scale heterogeneity, collective action and soil erosion control: evidence from the Loess Plateau of China

This paper investigates the effect of land scale heterogeneity on participation by farmers in collective action and the level of erosion control, and its mechanism of action, using survey data from 45 villages and 1,149 households in a typical erosion area of the Yellow River basin. The results show that there is a non-linear, U-shaped relationship between land size heterogeneity and farmers’ participation in collective action and the level of erosion control, respectively, and that collective action plays a mediating role in the U-shaped relationship between land size heterogeneity and level of erosion control. This implies that maintaining a homogeneous distribution of land among households within the village collectivity and a large disparity in land allocation both promote participation in collective action and thus improve the level of erosion control. Using an endogenous regression transformation model to deal with endogeneity and robustness tests, the findings remain robust. In the mechanism analysis section, variables for the level of trust and co-ordination of interests are introduced and a non-linear, U-shaped relationship is found between land size heterogeneity through the effect of both on collective action. This suggests that the allocation of land resources as described above can co-ordinate the level of trust and the direction of interests among farmers, leading to conditions for co-operation and thus facilitating farmers’ participation in collective action.

Improvement of research methodology mesostructures of atmospheric fronts on the basis of ground­based microwave radiometry

The paper considers the some results of experimental studies of the mesoscale structure of atmospheric fronts using a ground-based microwave water vapor radiometer. A methodology for studying the mesoscale structure of atmospheric fronts is described, which includes two stages: the stage of syn- optic (frontological) analysis and the stage of mesoscale analysis based on measurements of atmospheric moisture and cloud liquid water content using a microwave water vapor radiometer. The application of the methodology is demonstrated by the example of the analysis of the results of observation of the case of the passage of warm and cold fronts through the Leningrad region on September 9—14, 2020. Based on the analysis of the results of previous studies, the features of changes in the characteristics of moisture content during the passage of atmospheric fronts are generalized. The growth of the atmospheric water vapor and cloud liquid water content is delayed relative to the appearance of harbingers of a warm cloud front of the upper tier. The growth of moisture content characteristics has a wave-like character, which can be explained by the manifestation of the strip mesostructure of the front. Estimates of the characteristic scales of spatial heterogeneities in the structure of atmospheric fronts for the area under consideration differ from similar characteristics obtained by P. Hobbs for the northeastern coast of North America. In the course of mesoscale analysis, the type of atmospheric front crossing the observation point can be clarified. The results obtained determine the prospects for improving the methods of diagnostics and current forecasting of dangerous weather phenomena during the passage of atmospheric fronts using ground-based microwave radiometers

Emission-concerned coordinated dispatching of electrified autonomous mobility-on-demand system and power system incorporating heterogeneous spatiotemporal scales

Electrified autonomous mobility-on-demand (E-AMoD) systems will play a crucial role in future cities, which couple the carbon emissions generated by the transportation system and the power system. This paper proposes a joint dispatching framework to facilitate city decarbonization by coordinating the fleet operator and power system operator. Given the substantial differences in spatial and temporal dispatching scales between the two systems, this framework effectively integrates micro vehicle-level service dispatching with macro fleet-level charging scheduling. Firstly, a novel bipartite matching model efficiently solves the short-term passenger serving and rebalancing dispatching at the vehicle level. The service dispatching results are then aggregated as spatial-temporal transition flows across different areas and time intervals. Incorporating these mobility constraints, the long-term charging-discharging scheduling at the fleet level is captured as an expanded network flow model and integrated with the power system dispatching model, which aims at renewable energy utilization and carbon emission mitigation. Finally, the fleet-level scheduling results are disaggregated to the serving-rebalancing-charging sequences of each vehicle, for practical implementation. To validate the proposed framework, numerical experiments are conducted on a large-scale real-world case. Multiple charging schedule scenarios, including heuristic charging, smart charging, and smart charging-discharging, are investigated under both centralized and distributed supplied power systems. (1) The framework proves to be an effective solution for addressing the differences in dispatching spatiotemporal scales, calculation efficiencies, and privacy concerns between power and traffic systems. (2) Through power-traffic coordination, the E-AMoD fleet demonstrates its ability to alleviate 8% of average renewable energy curtailment and reduce nearly 15% of the total emissions in a power system installed with renewable distributed generators. (3) Furthermore, the extent of emission mitigation effects is influenced by critical factors such as fleet size, renewable energy profile, and power system capacity. Overall, the E-AMoD system show great potential as mobile storage devices to provide spatial-temporal flexibility to the power system and contribute to city decarbonization.

Aggregating social benefits of endangered species protection: the case of the Cook Inlet beluga whale

ABSTRACT The Cook Inlet beluga whale (CIBW) is an endangered whale found in waters off the state of Alaska that is protected under the U.S. Endangered Species Act. The federal recovery plan estimates recovery costs will total $73 million (in 2013 dollars). In this study, we use data from a stated preference discrete choice experiment (CE) study to estimate the aggregate benefits of recovering the CIBW and generally for improving its conservation status and reducing extinction risk. Estimated CE models account and test for utility scale heterogeneity, attribute non-attendance, self-selection bias, and demographic effects. Aggregation methods that differ in adjustments made to the mean household welfare and/or to the number of population units are compared by assessing their impact on the resulting aggregate (population-level) welfare estimates. Alaska households are willing to pay between $34 million (95% CI of [$25 million, $44 million]) and $103 million ([$74 million, $131 million]) for full recovery of the CIBW, depending upon the model and aggregation assumptions. While some of the state-level recovery value estimates are below the total cost of combined federal and state recovery actions, accounting for welfare benefits beyond the state of Alaska justifies recovery actions by the benefit–cost criterion.

Spatial heterogeneity and impact scales of driving factors of precipitation changes in the Beijing-Tianjin-Hebei region, China

Changes in land surface properties during urbanization have a significant impact on variations in precipitation. Little research has been carried out on spatial heterogeneity and influence strength of the driving factors of precipitation changes at different urbanization scales. Using a trend analysis and multi-scale geographically weighted regression, this study analysed the spatial heterogeneity and impact scale of driving factors of precipitation changes in 156 urban units in the Beijing-Tianjin-Hebei urban agglomeration region (Jing-Jin-Ji). In summer, RAD (radiation), RHU (relative humidity), WIN (wind speed), and POP (urban population density) were found to act on a small regional scale, AOD (aerosol optical depth) on a medium regional scale, and NDVI (normalized difference vegetation index), NLI (night time light intensity), UHI (urban heat island intensity), and AREA (urban area size) on a global scale. In winter, AREA and WIN acted on a medium regional scale, UHI on a large regional scale, and AOD, NDVI and NLI on a global scale. Across the whole year, NDVI and AREA had a medium regional impact and NLI a large regional one. Variations in natural factors, such as RAD and RHU, had a great influence on the spatial heterogeneity of precipitation changes, whereas human factors, such as NLI and UHI, had a small influence. In summer, AOD mainly affected Tangshan and Qinhuangdao in the northeast and Cangzhou in the southeast of the Jing-Jin-Ji. RHU and AREA primarily affected the cities of Handan and Xingtai. In winter, NLI, AREA, WIN, and UHI had significant effects in the cities of Handan and Xingtai, with AREA being the most important factor. In the Shijiazhuang-Hengshui area, RAD and NLI played a significant role; in the Beijing-Zhangjiakou-Chengde area, the most important factor affecting precipitation changes was the variation in POP. These results provide a scientific basis for flood disaster risk management in the Jing-Jin-Ji and the establishment of differentiated climate policies in different cities.

Fog computing network security based on resources management

Fog computing paradigm is designed as an extension of cloud computing due to the need for a supporting platform that is capable of providing the requirements of the Internet of Things (IoT). However, due to its features, fog obviously confronts numerous security and privacy risks, such as huge scale geolocation, heterogeneity, and mobility. Indeed, there are many problems resulting from security violations and breaches. Thus, to exceed these problems, we propose an efficient access control system, ameliorated with appropriate monitoring function and risk estimation to detect abnormal user’s behavior and then deactivating illegitimate anomaly actions. Indeed, based on the risk value, we compute the trust level that will then be made into an access certificate, which would be provided to the user. This security certificate is used to authenticate and authorize users in case of re-connection in another time, without repeating the whole access control process from the beginning. Moreover, a comprehensive resource management mechanism is proposed to ameliorate the system performance and so to maintain low latency. Our aim is to further enhance data security, privacy and resource management for IoT users. To demonstrate the efficiency, feasibility, and security of our proposed scheme, we perform an extensive simulation using Network Security Simulator (Nessi2).

Recognition of rock materials after high-temperature deterioration based on SEM images via deep learning

Accurately identifying the high-temperature history experienced by rocks is essential for understanding their behaviour and predicting properties. However, current approaches are limited by the heterogeneity of rocks, test scale and costs. Here, we proposed an economical, efficient and accurate approach to identifying the rocks after high-temperature deterioration via deep learning. This deep learning-based method exhibited superior abilities in distinguishing the heat-treated rock. Using a scanning electron microscopy (SEM) image covering a size of 14.6 μm × 14.6 μm, the high-temperature deterioration history of rocks can be recognized with an accuracy of 80.2%. Features such as cracks, rock patterns, and cleavage steps in SEM images would further improve the recognition accuracy. For example, SEM images with higher fractal box dimensions show a higher recognization accuracy, especially for temperatures under 600 °C. Besides, using the deep Taylor decomposition algorithm, the high-temperature deterioration regions of the rocks in the microscale were successfully located, extracted, and characterized for the first time. This study highlights the vast potential of the deep learning-based approach in damage deterioration identification of rock after high temperature, which significantly extends the application of deep learning in underground projects.