Static Layers Research Articles

Web-based visualization of static and dynamic HD road traffic map layers

Web-based visualization of static and dynamic HD road traffic map layers

Taylor vortices in yield stress fluids with static wall layers on the outer cylinder

Taylor vortices in yield stress fluids with static wall layers on the outer cylinder

Feasibility study on cooling scheme design of the concrete shielding for swimming pool-type low-temperature heating reactor

Elevating the operational parameters of Swimming Pool-type Low-Temperature Heating Reactors to augment heating capacity raises concerns about heightened water temperatures within the pool. Elevated water temperatures pose a significant challenge to the integrity and durability of the concrete shielding, necessitating innovative solutions for effective thermal insulation. Existing approaches, such as insulation coatings and static insulation water layers, have shown limitations in maintaining prolonged cooling efficiency under reactor operating conditions. In response to this challenge, our research introduced a novel approach: the implementation of a flowing water layer within the concrete structure. A specialized insulation water layer is integrated within the reactor to efficiently isolate high-temperature pool water from the surrounding concrete shielding. A comprehensive experimental setup is established to assess the insulation effectiveness, yielding insights into the water layer’s insulation performance. Results demonstrate a significant radial temperature difference of up to 14.31 °C between the cold and hot walls of the water layer within the experimental parameters. Investigation into critical experimental variables indicates that inlet positioning minimally affects insulation efficiency. This study not only verifies the viability of the insulation water layer approach but also furnishes crucial experimental data for future research endeavors about the cooling scheme of the Swimming Pool-type Low-Temperature Heating Reactor.

Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning.

This dataset presents global soil organic carbon stocks in mangrove forests at 30 m resolution, predicted for 2020. We used spatiotemporal ensemble machine learning to produce predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. This was done by using training data points of both SOC (%) and BD in mangroves from a global dataset and from recently published studies, and globally consistent predictive covariate layers. A total of 10,331 soil samples were validated to have SOC (%) measurements and were used for predictive soil mapping. We used time-series remote sensing data specific to time periods when the training data were sampled, as well as long-term (static) layers to train an ensemble of machine learning model. Ensemble models were used to improve performance, robustness and unbiasedness as opposed to just using one learner. In addition, we performed spatial cross-validation by using spatial blocking of training data points to assess model performance. We predicted SOC stocks for the 2020 time period and applied them to a 2020 mangrove extent map, presenting both mean predictions and prediction intervals to represent the uncertainty around our predictions. Predictions are available for download under CC-BY license from 10.5281/zenodo.7729491 and also as Cloud-Optimized GeoTIFFs (global mosaics).

Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies.

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Assessing hydrological and water quality responses to dynamic landuse change at watershed scale in Mississippi

The hydrology and water quality modeling in a watershed are affected by land use land cover (LULC) input. This study differs from numerous LULC change studies by introducing multi-year LULC input in a single simulation of Soil and Water Assessment Tool (SWAT) model. The proposed approach highlighted the outperformance of the model with dynamic LULC input (DM) over static LULC input (SM) based on the magnitude and direction of the hydrological responses. The difference between DM and SM outputs allowed for studying effects of historical LULC change. Additionally, agricultural management operation inputs enabled more realistic simulation of runoff, sediments, total nitrogen (TN), and total phosphorus (TP). The SM used static landuse data layers for 2009, and DM used landuse data layers for 2009, 2015, and 2018 to represent changes in LULC distribution over time. The expansion of agricultural land (0.9%) and forest cover (0.5%), as well as the reduction of grassland, water, and barren areas (1.4%), were the significant LULC changes from 2009 to 2018. Even though the expansion of forest cover was identified from 2009 to 2015, a declining trend was observed from 2015 to 2018. The agricultural land cover increased consistently from 2009 to 2018. The expansion of agricultural land increased average annual surface runoff, sediment yield, TN, and TP loads by 1.3%, 5.4%, 5.8%, and 5.9% respectively at watershed scale as determined by DM model simulation results. At sub-watershed scale, agricultural land expansion increased runoff, sediment, TN, and TP loads by up to 5%, 16%, 15%, and 15% respectively whereas, the expansion of forest cover resulted in reduction in same parameters by up to 5%, 15%, 23%, and 26% respectively. In general, the study determined that the integration of dynamic LULC and agricultural operations in SWAT allows a more accurate representation of agricultural watersheds for hydrological and water quality analysis.

Love of Variety Based Latency Analysis for High Definition Map Updating: Age of Information and Distributional Robust Perspectives

High definition (HD) map is a key technology that enables autonomous driving, which has the characteristic of frequent updates and low delay requirements. In order to minimize the HD map updating latency, this paper jointly considers the generation and transmission processes of HD map. The generation process of HD map relies heavily on the data captured by different types of sensors, which is modeled by the federated analytics process. Also, in order to quantify the diversity of utilized sensors, the concept of love of variety is introduced in this paper. Then, considering HD map is composed of dynamic and static layers that have different latency requirements during the transmission process, this paper proposes a method to allocate edge server capacity to each HD map layer such that the overall information staleness can be minimized. Firstly, the deterministic edge capacity case is discussed and the solution is derived by obtaining the Karush—Kuhn—Tucker conditions. Then, considering the practice that an edge server provides services to multiple attached devices simultaneously, contentions among these devices make available capacity for the autonomous vehicle variational. Therefore, an uncertain edge capacity case is discussed as well, where the uncertainty is described by Wasserstein metrics and the problem is reformulated into distributional robust chance constrained optimization problem. And for the low latency purpose, we utilize an inner approximation to reduce the complexity of the original problem and find a suboptimal solution. Finally, simulation results demonstrate the effectiveness of our proposed methods that achieves the lowest latency for HD map updating.

Trend Analysis of Hydro-Climatological Factors Using a Bayesian Ensemble Algorithm with Reasoning from Dynamic and Static Variables

This study examines the variations in groundwater levels from the perspectives of the dynamic layers soil moisture (SM), normalized difference vegetation index (VI), temperature (TE), and rainfall (RA), along with static layers lithology and geomorphology. Using a Bayesian Ensemble Algorithm, the trend changes are examined at 385 sites in Kerala for the years 1996 to 2016 and for the months January, April, August, and November. An inference in terms of area under the probability curve for positive, zero, and negative trend was used to deduce the changes. Positive or negative changes were noticed at 19, 32, 26, and 18 locations, in that order. These well sites will be the subject of additional dynamic and static layer investigation. According to the study, additional similar trends were seen in SM during January and April, in TE during August, and in TE and VI during November. According to the monthly order, the matching percentages were 63.2%, 59.4%, 76.9%, and 66.7%. An innovative index named SMVITERA that uses dynamic layers has been created using the aforementioned variables. The average proportion of groundwater levels that follow index trends is greater. The findings of the study can assist agronomists, hydrologists, environmentalists, and industrialists in decision making for groundwater resources.

An optimized and efficient android malware detection framework for future sustainable computing

Android-based smart devices cater to services in almost every aspect of our lives like personal, professional, social, banking, business, etc. However, people with increasingly dependent on the smartphone, malicious attacks against these gadgets have increased exponentially. To achieve a secured smart environment for future sustainable computing, android-based smart devices must provide more resilient and attack-resistant commitments, so that the android malware detector, once trained on a dataset, can continue to identify new malware without retraining. However, most of the existing malware detection approach suffers from a high false-positive rate and low detection rate of new malware variants. Motivated by the aforementioned challenges. In this paper, an Optimized and efficient Ensemble Learning-based Android Malware Detection framework, called “OEL-AMD” is proposed. This framework employs statistical feature engineering to eliminate non-informative features as well as encode statistical characteristics, and Binary Grey Wolf Optimization (BGWO)-based meta-heuristic feature selection is used to prepare optimal feature sets for static and dynamic layers. Subsequently, different base learners are trained using hyper-parameters tuning to boost the inductive reasoning capability of the ensemble model for classification, and an aggregated performance is computed. The obtained highest classification accuracy is 96.95% for binary and 83.49% for the category classification using static and dynamic layer features, respectively reveals the promising performance. Further, the obtained results are compared with the existing methods, and statistical significance is evaluated. The statistical test verified the significance of the obtained results.

Primary cementing of vertical wells: displacement and dispersion effects in narrow eccentric annuli

Laminar miscible displacement flows in narrow, vertical, eccentric annuli show a wide range of interesting physical phenomena, e.g. static layers and channels, dispersive spikes/fronts and various instabilities. These are of relevance to the primary cementing of wells, where gas leakage and greenhouse gas emissions can result from ineffective cementing. The current popular way to model this process is via a Hele-Shaw approach, reducing the Navier–Stokes equations via scaling arguments and then averaging across the annular gap: the two-dimensional (2-D) gap averaged (2DGA) model. This leads to a reduced model that is computationally efficient and represents some important features of cementing flows, but is also deficient in capturing the effects of dispersion. While three-dimensional (3-D) simulations are able to capture dispersion and most other physical phenomena, they suffer from excessive computational times due to the extreme aspect ratios of cementing geometries and non-Newtonian properties of the fluids. Here we extend the 2DGA approach by modelling the high Péclet number limit of miscible duct displacement flows, to take into account the leading-order effects of dispersion. Our modified dispersive 2DGA model is well able to approximate dispersive effects that are observed by gap-averaging the results of 3-D computations, including geometric effects of eccentricity and instabilities. This represents a marked improvement over the 2DGA approach, while keeping the computational advantages of a 2-D model.

Programmed Shape-Morphing Material Using Single-Layer 4D Printing System.

The single-layer 4D printing technology that can be controllable in response to external stimuli is a tremendous challenge in many areas, including smart materials, robotics, and drug delivery systems. The single-layer 4D printing technique was enabled by light-focusing, which results in the difference of mechanical properties such as the coefficient of thermal expansion or Young’s modulus between focused and unfocused regions. However, 4D printing to the desired shape using single-layered material is challenging. In this paper, we demonstrate the programmed shape morphing by patterning both the static and shape-morphing layers using a single-layer 4D printing system. A shape-morphing layer is formulated by short-time (<3 s) illumination in UV light. Then a static layer is formulated by longer-time (>3 s) illumination in UV light. We expect this technique to lead to the development of micro-scale soft robots.

Static and dynamic water structures at interfaces: A case study with focus on Pt(111).

An accurate atomistic treatment of aqueous solid-liquid interfaces necessitates the explicit description of interfacial water ideally via ab initio molecular dynamics simulations. Many applications, however, still rely on static interfacial water models, e.g., for the computation of (electro)chemical reaction barriers and focus on a single, prototypical structure. In this work, we systematically study the relation between density functional theory-derived static and dynamic interfacial water models with specific focus on the water-Pt(111) interface. We first introduce a general construction protocol for static 2D water layers on any substrate, which we apply to the low indexsurfaces of Pt. Subsequently, we compare these with structures from a broad selection of reference works based on the Smooth Overlap of Atomic Positions descriptor. The analysis reveals some structural overlap between static and dynamic water ensembles; however, static structures tend to overemphasize the in-plane hydrogen bonding network. This feature is especially pronounced for the widely used low-temperature hexagonal ice-like structure. In addition, a complex relation between structure, work function, and adsorption energy is observed, which suggests that the concentration on single, static water models might introduce systematic biases that are likely reduced by averaging over consistently created structural ensembles, as introduced here.

Revealing Size Dependent Structural Transitions in Supported Gold Nanoparticles in Hydrogen at Atmospheric Pressure.

The enhancement of the catalytic activity of gold nanoparticles with their decreasing size is often attributed to the increasing proportion of low-coordinated surface sites. This correlation is based on the paradigmatic picture of working gold nanoparticles as perfect crystal forms having complete and static outer surface layers whatever their size. This picture is incomplete as catalysts can dynamically change their structure according to the reaction conditions and as such changes can be eventually size-dependent. In this work, using aberration-corrected environmental electron microscopy, size-dependent crystal structure and morphological evolution in gold nanoparticles exposed to hydrogen at atmospheric pressure, with loss of the face-centered cubic crystal structure of gold for particle size below 4nm, are revealed for the first time. Theoretical calculations highlight the role of mobile gold atoms in the observed symmetry changes and particle reshaping in the critical size regime. An unprecedented stable surface molecular structure of hydrogenated gold decorating a highly distorted core is identified. By combining atomic scale in situ observations and modeling of nanoparticle structure under relevant reaction conditions, this work provides a fundamental understanding of the size-dependent reactivity of gold nanoparticles with a precise picture of their surface at working conditions.

The Convolutional Neural Networks Training With Channel-Selectivity for Human Activity Recognition Based on Sensors.

Recently, the state-of-the-art performance in various sensor based human activity recognition (HAR) tasks have been acquired by deep learning, which can extract automatically features from raw data. In order to obtain the best accuracy, many static layers have been always used to train deep neural networks, and their weight connectivity in network remains unchanged. Pursuing the best accuracy in mobile platforms with a very limited computational budget at millions of FLOPs is impractical. In this paper, we make use of shallow convolutional neural networks (CNNs) with channel-selectivity for the use of HAR. As we have known, it is for the first time to adopt channel-selectivity CNN for sensor based HAR tasks. We perform extensive experiments on 5 public benchmark HAR datasets consisting of UCI-HAR dataset, OPPORTUNITY dataset, UniMib-SHAR dataset, WISDM dataset, and PAMAP2 dataset. As a result, the channel-selectivity can achieve lower test errors than static layers. The existing performance of deep HAR can be further improved by the CNN with channel-selectivity without any extra cost.

An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization

In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS). In contrast to adding or replacing a certain module of existing digital signal processing (DSP), we exploit the similarity between a communication system and a neural network (NN). By mapping a communication system to an NN, in which the equalization modules correspond to the convolutional layers and other modules can been regarded as static layers, the optimal transceiver-joint equalization coefficients can be obtained. In particular, the DSP structure of the communication system is not changed. Extensive numerical simulations are performed to validate the performance of the proposed method. For a 65 GBaud 16QAM signal, it can achieve a 0.76 dB gain when the number of WSSs is 16 with a -6 dB bandwidth of 73 GHz.

Analyzing Impacts of Interfacial Instabilities on the Sweeping Power of Newtonian Fluids to Immiscibly Displace Power-Law Materials

Injection of Newtonian fluids to displace pseudoplastic and dilatant fluids, governed by the power-law viscosity relationship, is common in many industrial processes. In these applications, changing the viscosity of the displaced fluid through velocity alteration can regulate interfacial instabilities, displacement efficiency, the thickness of the static wall layer, and the injected fluid’s tendency to move toward particular parts of the channel. The dynamic behavior of the fluid–fluid interface in the case of immiscibility is highly complicated and complex. In this study, a code was developed that utilizes a multi-component model of the lattice Boltzmann method to decrease the computational cost and accurately model these problems. Accordingly, a 2D inclined channel, filled with a stagnant incompressible Newtonian fluid in the initial section followed by a power-law material, was modeled for numerous scenarios. In conclusion, the results indicate that reducing the power-law index can regulate interfacial instabilities leading to dynamic deformation of static wall layers at the top and the bottom of the channel. However, it does not guarantee a reduction in the thickness of these layers, which is crucial to improve displacement efficiency. The impacts of the compatibility factor and power-law index variations on the filling pattern and finger structure were intensively evaluated.

Stress response function from Voronoi tessellation of static granular layers

A new technique to obtain the stress response function of static granular layers from the geometric properties of the corresponding Voronoi tessellations is presented. We measured the vertex displacements of the Voronoi polygons due to the application of a vertical force located on the surface of the layer. A protocol to obtain the stress response function is proposed by establishing a gauge between the geometric measurements and the contact forces, which allows to calculate the stress components indirectly. We show that the Voronoi tessellation response function—VTRF—exhibits the elastic characteristics of additivity, reversibility and linearity as the stress response function. Thus, we applied this protocol to calculate the stress response profiles of two different preparations and to compare them with the results obtained from microscopic measurements, that is, the stress response calculated using contact forces.

MoveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in r

Abstract Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two‐dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an r package, which provides tools to animate movement trajectories, overlaying simultaneous uni‐ or multi‐temporal raster imagery or vector data. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame‐wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates.

Evaluation of Dynamic Insulation Systems for Residential Buildings in Barcelona, Spain

Abstract This paper evaluates the potential energy cost savings when high R-value static insulation layers as well as dynamic insulation materials (DIMs) are applied to residential housing located in Barcelona Spain. The analysis considers three dwelling prototypes to characterize the existing housing stock in Barcelona including detached attached and apartments. In addition three vintages for each housing prototype are defined: before 1979 when building envelope insulation took effect in Spain between 1980 and 2006 and after 2006 when the building envelope insulation code became more restrictive. Using a modified 3R2C network model to determine thermal loads the performance of both static and dynamic insulation systems is evaluated when applied to exterior wall for various housing prototypes in Barcelona. The dynamic insulation R-value is selected based on a 2-step control strategy. The analysis results indicate that DIMs with the largest R-value step (i.e. difference between the high and the low R-values) achieve the highest savings in source energy reaching up to 19% reduction in source heating and cooling energy for the entire housing stock of Barcelona. The annual energy savings achieved by DIMs are valued to be 181 M€/year for the entire existing housing stock in Barcelona. In addition electrical peak demand reduction associated with retrofitting exterior walls for the existing Barcelona housing stock can result in future avoidance of building new power plants and can provide additional 144 M€ and 162 M€ for respectively static and dynamic insulation systems. Considering the current energy mix applying dynamic wall insulation systems for Barcelona existing housing stock could reduce annual CO2 emissions by more than 300 000 tons or 6.80% of the total carbon dioxide currently emitted to heat and cool homes.

Enhancing synchrony in multiplex network due to rewiring frequency

Most of the previous studies on synchrony in multiplex networks have been investigated using different types of intralayer network architectures which are either static or temporal. Effect of a temporal layer on intralayer synchrony in a multilayered network still remains elusive. In this paper, we discuss intralayer synchrony in a multiplex network consisting of static and temporal layers and how a temporal layer influences other static layers to enhance synchrony simultaneously. We analytically derive local stability conditions for intralayer synchrony based on the master stability function approach. The analytically derived results are illustrated by numerical simulations on up to five-layers multiplex networks with the paradigmatic Lorenz system as the node dynamics in each individual layer.