Scaling Model Research Articles

Dynamic workload-aware DVFS for multicore systems using machine learning

With growing heterogeneity and complexity in applications, demand to design an energy-efficient and fast computing system in multi-core architecture has heightened. This paper presents a regression-based dynamic voltage frequency scaling model which studies and utilizes workload characteristics to obtain optimal voltage–frequency (v–f) settings. The proposed framework leverages the workload profile information together with power constraints to compute the best-suited voltage–frequency (v–f) settings to (a) maintain global power budget at chip-level, (b) maximize performance while enforcing power constraints at the per-core level. The presented algorithm works in conjunction with the workload characterizer and senses change in application requirements and apply the knowledge to select the next setting for the core. Our results when compared with two state-of-the-art algorithms MaxBIPS and TPEq achieve the average power reduction of 33% and 25% respectively across 32-core architecture for PARSEC benchmarks.

Radiometric Measurements of Atmospheric Attenuation Over a Tropical Location

AbstractThe information of atmospheric attenuation at Ka‐band is important during rain and clear air condition. In this paper, Ka‐band attenuation data obtained from radiometric measurements at Kolkata, India (22°034′E, 88°022′N) have been presented. The role of water vapor is examined in determining the behavior of atmospheric attenuation during and without rain condition at 22.24, 26.24, and 31.4 GHz frequencies. It has been observed that at lower rain rate (<5 mm/hr), attenuation at 22.24 GHz is the highest among three frequencies due to water vapor absorption at this frequency. Millimeter Wave Propagation (MPM) model has been used to observe the contribution of rain and water vapor in determining atmospheric attenuation for different rain rates. It has been noted that at 22.24 GHz, water vapor attenuation is comparable to the rain attenuation up to 5 mm/hr. The measured attenuation from radiometer has been compared with ITU‐R model‐generated data and both the attenuation data match reasonably well up to rain rate 20 mm/hr. The attenuation obtained from radiometric measurements shows saturation with increasing rain rate due to the darkening effect. The ITU‐R frequency scaling model has been used to compare the measured and calculated attenuation at 31.4 GHz. This study gives some useful information about the radiometric measurements of atmospheric attenuation at a tropical location that shows that not only rain rate but also water vapor is an important cause of attenuation at Ka‐band.

Possible biases in scaling-based estimates of glacier change: a case study in the Himalaya

Abstract. Approximate glacier models are routinely used to compute the future evolution of mountain glaciers under any given climate-change scenario. A majority of these models are based on statistical scaling relations between glacier volume, area, and/or length. In this paper, long-term predictions from scaling-based models are compared with those from a two-dimensional shallow-ice approximation (SIA) model. We derive expressions for climate sensitivity and response time of glaciers assuming a time-independent volume–area scaling. These expressions are validated using a scaling-model simulation of the response of 703 synthetic glaciers from the central Himalaya to a step change in climate. The same experiment repeated with the SIA model yields about 2 times larger climate sensitivity and response time than those predicted by the scaling model. In addition, the SIA model obtains area response time that is about 1.5 times larger than the corresponding volume response time, whereas scaling models implicitly assume the two response times to be equal to each other. These results indicate the possibility of a low bias in the scaling model estimates of the long-term loss of glacier area and volume. The SIA model outputs are used to obtain parameterisations, climate sensitivity, and response time of glaciers as functions of ablation rate near the terminus, mass-balance gradient, and mean thickness. Using a linear-response model based on these parameterisations, we find that the linear-response model outperforms the scaling model in reproducing the glacier response simulated by the SIA model. This linear-response model may be useful for predicting the evolution of mountain glaciers on a global scale.

From necessity to opportunity: Scaling bricolage across resource‐constrained environments

AbstractResearch summaryEnterprises in low‐resource contexts often rely on bricolage (i.e., making do by applying resources at hand to new problems). However, bricolage has traditionally been regarded as a way to temporarily get by, potentially constraining growth if continued over time. This has been explained by factors such as limited development of learning competencies. Surprisingly, we encountered a social organization appearing to use bricolage to scale extensively into a variety of locations. This puzzling observation prompted our research question: Can bricolage be scaled, and if so, how and why? We embarked on a process study of this organization, leading to a novel conceptual model of scaling bricolage: as a low‐cost replication process of heuristics, enabling fit with a diversity of local environments, as well as cross‐unit learning.Managerial summaryHow do organizations emerge, survive, and scale in resource‐scarce environments? Traditional scaling models tend to rely on considerable financial resources and companies often struggle to adjust to diverse contexts. In contrast, we identified and studied an organization in Sub‐Saharan Africa that we argue used simple rules to scale bricolage—making the best out of what is at hand—successfully in diverse low‐resource contexts. Our paper provides a novel conceptual model of scaling bricolage: a low‐cost replication process of heuristics, enabling fit with a diversity of local environments, as well as cross‐unit innovation and learning.

Measurement of muon-induced high-energy neutrons from rock in an underground Gd-doped water detector

We present a measurement of the rate of correlated neutron captures in the WATCHBOY detector, deployed at a depth of approximately 390 meters water equivalent (m.w.e.) in the Kimballton Underground Research Facility (KURF). WATCHBOY consists of a cylindrical 2 ton water target doped with 0.1% gadolinium, surrounded by a 40 ton undoped water hermetic shield. We present a comparison of our results with the expected rate of correlated neutron captures arising from high-energy neutrons incident on the outside of the WATCHBOY shield, predicted by a hybrid FLUKA/GEANT4-based simulation. The incident neutron energy distribution used in the simulation was measured by a fast neutron spectrometer, the 1.8-ton Multiplicity and Recoil Spectrometer (MARS) detector, at the same depth. We find that the measured detection rate of two correlated neutrons is consistent with that predicted by simulation. The result lends additional confidence in the detection technique used by MARS, and therefore in the MARS spectra as measured at three different depths. Confirmation of the fast neutron flux and spectrum is important as it helps validate the scaling models used to predict the fast neutron fluxes at different overburdens.

Adaptive Multidimensional Scaling: Brand Positioning Based on Decision Sets and Dissimilarity Judgments

Assessing market structure by deriving a brand positioning map and segmenting customers is essential for supporting brand-related marketing decisions. We propose adaptive multidimensional scaling (ADMDS) for simultaneously deriving a brand positioning map and market segments using customer data on cognitive decision sets and brand dissimilarities. In ADMDS, the judgment task is adapted to the individual customer where dissimilarity judgments are collected only for those brands within a customers’ awareness set. Thus, respondent fatigue and unfamiliarity with the brands are circumvented thereby improving the validity of the dissimilarity data obtained, as well as the multidimensional spatial structure derived from them. Estimation of the ADMDS model results in a spatial map in which the brands and derived segments of customers are jointly represented as points. The closer a brand is positioned to a segment’s ideal brand, the higher the probability that the brand is considered and chosen. An assumption underlying this model representation is that brands within a customers’ consideration set are relatively similar. In an experiment with 200 respondents and 4 product categories, this assumption is validated. We illustrate adaptive multidimensional scaling model on commercial data for 20 midsize car brands evaluated by 212 members of an on-line consumer panel. Potential applications of the method and future research opportunities are discussed.

Bioprosthetic aortic valve diameter and thickness are directly related to leaflet fluttering: Results from a combined experimental and computational modeling study

ObjectiveBioprosthetic heart valves (BHVs) are commonly used in surgical and percutaneous valve replacement. The durability of percutaneous valve replacement is unknown, but surgical valves have been shown to require reintervention after 10 to 15 years. Further, smaller-diameter surgical BHVs generally experience higher rates of prosthesis–patient mismatch, which leads to higher rates of failure. Bioprosthetic aortic valves can flutter in systole, and fluttering is associated with fatigue and failure in flexible structures. The determinants of flutter in BHVs have not been well characterized, despite their potential to influence durability. MethodsWe use an experimental pulse duplicator and a computational fluid-structure interaction model of this system to study the role of device geometry on BHV dynamics. The experimental system mimics physiological conditions, and the computational model enables precise control of leaflet biomechanics and flow conditions to isolate the effects of variations in BHV geometry on leaflet dynamics. ResultsBoth experimental and computational models demonstrate that smaller-diameter BHVs yield markedly higher leaflet fluttering frequencies across a range of conditions. The computational model also predicts that fluttering frequency is directly related to leaflet thickness. A scaling model is introduced that rationalizes these findings. ConclusionsWe systematically characterize the influence of BHV diameter and leaflet thickness on fluttering dynamics. Although this study does not determine how flutter influences device durability, increased flutter in smaller-diameter BHVs may explain how prosthesis–patient mismatch could induce BHV leaflet fatigue and failure. Ultimately, understanding the effects of device geometry on leaflet kinematics may lead to more durable valve replacements.

Granular scaling laws for helically driven dynamics.

Exploration of granular physics for three-dimensional geometries interacting with deformable media is crucial for further understanding of granular mechanics and vehicle-terrain dynamics. A modular screw propelled vehicle is, therefore, designed for testing the accuracy of a novel helical granular scaling law in predicting vehicle translational velocity and power. A dimensional analysis is performed on the vehicle and screw pontoons. Two additional pontoon pairs of increased size and mass are determined from dimensional scalars. The power and velocity of these larger pairs are predicted by the smaller pair using the scaling relationships. All three sets are subjected to ten trials of five angular velocities ranging from 13.7 to 75.0 revolutions per minute in a high interlock lunar regolith analog derived from mining tailings. Experimental agreement for prediction of power (3-9% error) and translational velocity (2-12% error) are observed. A similar set of geometries is subjected to multibody dynamics and discrete element method cosimulations of Earth and lunar gravity to verify a gravity-dependent subset of the scaling laws. These simulations show agreement (under 5% error for all sets) and support law validity for gravity between Earth and lunar magnitude. These results support further expansion of granular scaling models to enable prediction for vehicle-terrain dynamics for a variety of environments and geometries.

Regional sub-hourly extreme rainfall estimates in Sicily under a scale invariance framework

Design of urban drainage systems or flood risk assessment in small catchments often requires knowledge of very short-duration rainfall events (less than 1 h). Unfortunately, data for these events are often unavailable or too scarce for a reliable statistical inference. However, regularities in the temporal pattern exhibited by storm records, known as scaling properties of rainfall, could help in characterizing extreme storms at partially gauged sites better than the application of traditional statistical techniques. In this work, a scaling approach for estimating the distribution of sub-hourly extreme rainfall in Sicily (Italy) is presented based on data from high-resolution rain gauges with a short functioning period and from low-resolution rain gauges with longer samples. First, simple scaling assumption was tested for annual maxima rainfall (AMR) data from 10 min to 24 h duration, revealing that the simple scaling regime holds from 20 to 60 min for most of the stations. Then, scaling homogeneous regions were classified based on the values of the scaling exponent. In each region, this parameter was regionalized through power-law relationships with the median of 1 h AMR data. After that, regional Depth Duration Frequency (DDF) curves were developed by combining the scale-invariant framework with the generalized extreme value (GEV) probability distribution and used to estimate T-year sub-hourly extreme rainfalls at sites where only rainfall data for longer durations (≥ 1 h) were available. The regional GEV simple scaling model was validated against sub-hourly historical observations at ten rain gauges, generally yielding, in relation to the scaling exponent value, to similar or better sub-hourly estimates than empirical approach.

Comparison of Local, Regional, and Scaling Models for Rainfall Intensity-Duration-Frequency Analysis.

Intensity-duration-frequency (IDF) analyses of rainfall extremes provide critical information to mitigate, manage, and adapt to urban flooding. The accuracy and uncertainty of IDF analyses depend on the availability of historical rainfall records, which are more accessible at daily resolution and, quite often, are very sparse in developing countries. In this work, we quantify performances of different IDF models as a function of the number of available high-resolution (N τ ) and daily (N 24h) rain gauges. For this aim, we apply a cross-validation framework that is based on Monte Carlo bootstrapping experiments on records of 223 high-resolution gauges in central Arizona. We test five IDF models based on (two) local, (one) regional, and (two) scaling frequency analyses of annual rainfall maxima from 30-min to 24-h durations with the generalized extreme value (GEV) distribution. All models exhibit similar performances in simulating observed quantiles associated with return periods up to 30 years. When N τ >10, local and regional models have the best accuracy; bias correcting the GEV shape parameter for record length is recommended to estimate quantiles for large return periods. The uncertainty of all models, evaluated via Monte Carlo experiments, is very large when N τ ≤ 5; however, if N 24h ≥ 10 additional daily gauges are available, the uncertainty is greatly reduced and accuracy is increased by applying simple scaling models, which infer estimates on subdaily rainfall statistics from information at daily scale. For all models, performances depend on the ability to capture the elevation control on their parameters. Although our work is site specific, its results provide insights to conduct future IDF analyses, especially in regions with sparse data.

Cross-stream migration of droplets in a confined shear-thinning viscoelastic flow: Role of shear-thinning induced lift

Shear-thinning viscoelastic (STVE) flows exhibit intriguing phenomena owing to their complex rheology and the coupling of various forces involved. Here, we present an understanding of the cross-stream migration of droplets in a confined STVE flow and unravel the role of a shear-thinning induced lift force (FSM) in their dynamical behavior. We perform experiments with popular STVE liquids of different molecular weights and concentrations (c) for Reynolds numbers Re < 1 and Weissenberg numbers Wi = 0.01–7.4. Our results reveal larger droplets (of drop-to-channel ratio β ≥ 0.28) that follow their original streamlines, whereas smaller droplets (β ≤ 0.2) exhibit center ward migration and the migration rates depend upon the drop-to-medium viscosity (k) and elasticity (ξ) ratios. The lateral displacement of droplets is tracked using high-speed imaging that is used to estimate the relevant forces using suitable correlations. We find that the migration dynamics of droplets is underpinned by the non-inertial lift (FNIL), viscoelastic lift (FVM, FVD), and shear-thinning induced lift (FSM) forces. We provide experimental evidence of the proposed FSM and, from analytical scaling and empirical modeling, develop an expression for FSM ∼ ΔμΔγ̇D3.7/h1.7 (with R2 = 0.95) for an object at a distance h from the wall and with a drop in viscosity Δμ and strain rate Δγ̇ across its diameter D. Our study sheds light on the underlying dynamics on droplets in an STVE medium and opens up avenues for sorting and focusing of drops in an STVE medium at low Re.

Restoring Color Perception to the Blind: An Electrical Stimulation Strategy of Retina in Patients with End-stage Retinitis Pigmentosa

Bioelectronic retinal prostheses that stimulate the remaining inner retinal neurons, bypassing degenerated photoreceptors, have been demonstrated to restore some vision in patients blinded by retinitis pigmentosa (RP). These implants encode luminance of the visual scene into electrical stimulation, however, leaving out chromatic information. Yet color plays an important role in visual processing when it comes to recognizing objects and orienting to the environment, especially at low spatial resolution as generated by current retinal prostheses. In this study, we tested the feasibility of partially restoring color perception in blind RP patients, with the aim to provide chromatic information as an extra visual cue. Case series. Seven subjects blinded by advanced RP and monocularly fitted with an epiretinal prosthesis. Frequency-modulated electrical stimulation of retina was tested. Phosphene brightness was controlled by amplitude tuning, and color perception was acquired using the Red, Yellow, Green, and Blue (RYGB) hue and saturation scaling model. Brightness and color of the electrically elicited visual perception reported by the subjects. Within the tested parameter space, 5 of 7 subjects perceived chromatic colors along or nearby the blue-yellow axis in color space. Aggregate data obtained from 20 electrodes of the 5 subjects show that an increase of the stimulation frequency from 6 to 120 Hz shifted color perception toward blue/purple despite a significant inter-subject variation in the transition frequency. The correlation between frequency and blue-yellow perception exhibited a good level of consistency over time and spatially matched multi-color perception was possible with simultaneous stimulation of paired electrodes. No obvious correlation was found between blue sensations and array placement or status of visual impairment. These findings present a strategy for the generation and control of color perception along the blue-yellow axis in blind patients with RP by electrically stimulating the retina. It could transform the current prosthetic vision landscape by leading in a new direction beyond the efforts to improve the visual acuity. This study also offers new insights into the response of our visual system to electrical stimuli in the photoreceptor-less retina that warrant further mechanistic investigation.

A new approach to explore climate change impact on rainfall intensity–duration–frequency curves

Increasing the concentration of CO2 and other greenhouse gases in the atmosphere makes a large-scale change in atmospheric processes. Among the most dramatic effects of climate change, its impact on the type, amount, and intensity of rainfall is of particular importance. The goal of the present study was to provide a new method to investigate climate change impacts on rainfall intensity—duration—frequency (IDF) in the southwest of Iran. For this purpose, IDF curves were determined using recorded short-term rainfall from 15 stations. Then, the efficiency of the scale invariance theory was evaluated in determining IDFs by comparing IDFs resulting from observed data and IDFs estimated from the scaling model. Results demonstrated that two sets of IDFs had a good coincidence. In the next step, the data from three RCM models of the CORDEX project under two scenarios (RCP4.5 and RCP8.5) were used, and daily rainfall was downscaled by the musica package. Finally, IDFs were determined for two future periods including 2025–2049 as near future and 2075–2099 as far future. The result showed that rainfall intensity decreased in the north and central parts of the region.

Local Disorder-Induced Elevation of Intrinsic Anomalous Hall Conductance in an Electron-Doped Magnetic Weyl Semimetal.

Topological materials are expected to show distinct transport signatures owing to their unique band-inversion characteristic and band-crossing points. However, the intentional modulation of such topological responses through experimentally feasible means has yet to be explored in depth. Here, an unusual elevation of the anomalous Hall effect (AHE) is obtained in electron (Ni)-doped magnetic Weyl semimetals Co_{3-x}Ni_{x}Sn_{2}S_{2}, showing peak values in the anomalous Hall-conductivity, Hall-angle, and Hall-factor at a relatively low doping level of x=0.11. The separation of intrinsic and extrinsic contributions using the TYJ scaling model indicates that such a significant enhancement is dominated by the intrinsic mechanism of the electronic Berry curvature. Theoretical calculations reveal that compared with the Fermi-level shifting from electron filling, a usually overlooked effect of doping, that is, local disorder, imposes a striking effect on broadening of the bands and narrowing of the inverted gap, thus resulting in an elevation of the integrated Berry curvature. Our results not only realize an enhancement of the AHE in a magnetic Weyl semimetal, but also provide a practical design principle for modulating the bands and transport properties in topological materials by exploiting the local disorder effect from doping.

Frequency Scaling Model for the Prediction of Total Tropospheric Attenuation Time Series at EHF

A frequency scaling model aiming to predict total tropospheric attenuation time series at EHF is presented. One version of the model [total attenuation frequency scaling (TAFS)] is more accurate but, besides the reference attenuation time series at low frequency, it also requires additional ancillary inputs (e.g., radiometric data and information on the raindrop size distribution); a second version (S-TAFS) is simpler, though at the expenses of a slightly worse performance. Both TAFS and simplified TAFS (S-TAFS) first rely on isolating the attenuation induced by different atmospheric constituents, each of which is separately up-scaled to the target frequency. The two methods are tested against a full year of data collected at Ka -band and $Q$ -band at Politecnico di Milano, Milan, Italy, in the framework of the Alphasat Aldo Paraboni propagation experiment. Results indicate that both methods offer a very good accuracy in scaling the total attenuation from Ka- to $Q$ -bands, both in terms of first-order statistics and of time series. This corroborates the use of TAFS and S-TAFS to predict the total tropospheric attenuation at much higher frequency bands (e.g., the $W$ -band), for which no measurements are currently available, starting from the largely available Ka -band measurements.

Cutting mechanics of wood by beetle larval mandibles

Wood boring is a feature of several insect species and is a major cause of severe and irreparable damage to trees. Adult females typically deposit their eggs on the stem surface under bark scales. The emerging hatchlings live within wood during their larval phase, avoiding possible predation, whilst continually boring and tunneling through wood until pupation. A study of wood boring by insects offers unique insights into the bioengineering principles that drive evolutionary adaptations. We show that larval mandibles of the coffee wood stem borer beetle (Xylotrechus quadripes: Cerambycidae) have a highly sharp cusp edge to initiate fractures in Arabica wood and a suitable shape to generate small wood chips that are suitable for digestion. Cuticle hardness at the tip is significantly enhanced through zinc-enrichment. A hollow architecture significantly reduces bending stresses at the mandibular base without compromising the structural integrity. Finite element model of the mandible showed highest stresses in the tip region; these decreased to significantly lower values at the start of the hollow section. A scaling model based on a fracture mechanics framework shows the importance of the mandible shape in generating optimal chip sizes. These findings contain general principles in tool design and put in focus interactions of insects and their woody hosts.

Neural Network-Assisted Development of High-Entropy Alloy Catalysts: Decoupling Ligand and Coordination Effects

<h2>Summary</h2> High-entropy alloys (HEAs) recently emerged as promising catalysts due to their immense chemical space and tunability. However, the large chemical space presents challenges for comprehensive characterization due to experiments' trial-and-error nature. Here, we leverage neural network (NN) and density functional theory to simultaneously account for ligand effect (spatial arrangement of different elements) and coordination effect (different crystal facets and defects) for predicting the adsorption energy. The developed NN model demonstrates three advantages: (1) high accuracy, with a mean absolute error of 0.09 eV; (2) universality, with applicability to bimetallic catalysts; and (3) simplicity, with 36 NN parameters and its further simplification to a linear scaling model at a slight loss of accuracy. Using the trained NN model validated with experimental literature, we decouple the comparative extents of ligand and coordination effects. Our results endow high practical significance and provide important insights for rational design of HEA catalysts.

A simple and effective Measurement-Changes-Correction strategy for damage identification with aleatoric and epistemic model errors

The model errors are often inevitably encountered when a practical structure is analyzed by a mathematical baseline model, and such model errors would have undesired effects on damage identification. Aiming to alleviate the effect of the model errors, a new Measurement-Changes-Correction strategy that simply uses measurement changes to correct the measured data is developed in this article. To this end, the performance of the Measurement-Changes-Correction strategy under the aleatoric and epistemic model errors is systematically analyzed. The analysis is proceeded along with comparison to the conventional two-step strategy where the intact damage parameters are first updated from the measured data and then, damage identification is conducted upon the updated parameters. Thereafter, it is found that (1) the Measurement-Changes-Correction strategy performs as well as the two-step strategy under small aleatoric and epistemic model errors, (2) the Measurement-Changes-Correction strategy can still work under large scaling model parameter errors, but the two-step strategy may not, and (3) the Measurement-Changes-Correction strategy only requires correction of the measured data by the measurement changes so that the computation cost by the Measurement-Changes-Correction strategy is almost half of that by the two-step strategy. Numerical examples and International Association for Structural Control–American Society of Civil Engineers benchmark problems are studied to verify the performance of the proposed Measurement-Changes-Correction strategy.

Dynamic Scalability Model for Containerized Cloud Services

Cloud computing has become an important research area in large-scale computing systems and is being employed by many organizations in government, businesses, and industry. Schemes and appropriate models for dynamic resources provisioning in the cloud environment have been extensively studied. To date, the research literature is lacking schemes and models that offer dynamic scalability in which Quality of Service (QoS) and high performance are provided to customers with the usage of the least number of cloud resources, especially for containerized services hosted on the cloud. With dynamic scalability, cloud computing can offer on-demand, timely, and dynamically adjustable computing resources to services hosted on the cloud. This paper presents a dynamic scaling model based on queueing theory to scale containers virtual resources and satisfy the customer Service Level Agreements (SLA) while guarding costs of scaling very low. The aim is to improve the virtual computing resources utilization and satisfy SLA constraints in terms of CPU utilization, system response time, system drop rate, system number of tasks, and system throughput. Simulation results are provided using Java Modelling Tools simulation tool, which shows that our proposed model can determine under any offered workload the needed containers instances to satisfy the required QoS parameters.

Oblique impact of microspheres on the surface of quiescent liquid

Abstract