Scaling Rules Research Articles

Scaling patterns of cortical folding and thickness in early human brain development in comparison with primates.

Across mammalia, brain morphology follows specific scaling patterns. Bigger bodies have bigger brains, with surface area outpacing volume growth, resulting in increased foldedness. We have recently studied scaling rules of cortical thickness, both local and global, finding that the cortical thickness difference between thick gyri and thin sulci also increases with brain size and foldedness. Here, we investigate early brain development in humans, using subjects from the Developing Human Connectome Project, scanned shortly after pre-term or full-term birth, yielding magnetic resonance images of the brain from 29 to 43 postmenstrual weeks. While the global cortical thickness does not change significantly during this development period, its distribution does, with sulci thinning, while gyri thickening. By comparing our results with our recent work on humans and 11 non-human primate species, we also compare the trajectories of primate evolution with human development, noticing that the 2 trends are distinct for volume, surface area, cortical thickness, and gyrification index. Finally, we introduce the global shape index as a proxy for gyrification index; while correlating very strongly with gyrification index, it offers the advantage of being calculated only from local quantities without generating a convex hull or alpha surface.

Scaling Rule, Energy Distribution and Energy Level Analysis for Morse Oscillator by Virtue of Hermann-Feynman Theorem

Scaling Rule, Energy Distribution and Energy Level Analysis for Morse Oscillator by Virtue of Hermann-Feynman Theorem

Impact of Network Topology on Neural Synchrony in a Model of the Subthalamic Nucleus-Globus Pallidus Circuit.

Synchronous neural oscillations within the beta frequency range are observed across the parkinsonian basal ganglia network, including within the subthalamic nucleus (STN) - globus pallidus (GPe) subcircuit. The emergence of pathological synchrony in Parkinson's disease is often attributed to changes in neural properties or connection strength, and less often to the network topology, i.e. the structural arrangement of connections between neurons. This study investigates the relationship between network structure and neural synchrony in a model of the STN-GPe circuit comprised of conductance-based spiking neurons. Changes in net synaptic input were controlled for through a synaptic scaling rule, which facilitated separation of the effects of network structure from net synaptic input. Five topologies were examined as structures for the STN-GPe circuit: Watts-Strogatz, preferential attachment, spatial, stochastic block, k-regular random. Beta band synchrony generally increased as the number of connections increased, however the exact relationship was topology specific. Varying the wiring pattern while maintaining a constant number of connections caused network synchrony to be enhanced or suppressed, demonstrating the ability of purely structural changes to alter synchrony. This relationship was well-captured by the algebraic connectivity of the network, the second smallest eigenvalue of the network's Laplacian matrix. The structure-synchrony relationship was further investigated in a network model designed to emulate the action selection role of the STN-GPe circuit. It was found that increasing the number of connections and/or the overlap of action selection channels could lead to a rapid transition to synchrony, which was also predicted by the algebraic connectivity.

Multifractal structure of cholinergic innervation in the tunica albuginea of rat testis. Comparison with different models simulating nerve networks

The complexity of the distribution of cholinergic innervation of the albuginea suggests that its structural pattern may exhibit multiple scaling rules rather than a single global scale. Therefore, the study of nerve fibers could be approached through multifractal analysis. The cholinergic fiber plexuses of the rat testicular albuginea, visualized by a histochemical technique for the detection of acetylcholine sterases, will be analyzed for the possible detection of their multifractal structure, calculating several types of multifractal spectra, namely multifractal spectrum f(α) versus α, and the generalized dimension DQ versus Q. In the present study, the results will be compared with the multifractal spectra of plexus models consisting of the elaboration of interconnected line lattices generated, either employing a binary matrix or using a tessellation matrix, and according to various realizations of randomly distributed points in a two-dimensional space. The results of this study suggest that the cholinergic nerve plexuses of the testicular albuginea of the rat show a multifractal behavior, and the multifractality of the real data is greater than that of the simulated networks. However, Voronoi tessellations models show a more remarkable similarity of the multifractal scale with the real data in comparison with those generated by a binary matrix.

Desalination Fuel Cell Stacks: Scaling up the Co-Production of Electricity and Clean Water

The world faces a rising demand for potable water and electricity, while a lack of clean water and use of polluting electricity sources are major hazards1. Nowadays reverse osmosis (RO) is widely used for sea and brackish water desalination2, where RO consumes ~3-4 kWh/m3 for seawater desalination3. A new class of water treatment technologies is emerging that is distinguished from the classical methods by utilizing chemical energy to power both water treatment and electricity generation simultaneously from a single electrochemical cell. When using the hydrogen/oxygen redox couple, such a cell is termed a desalination fuel cell (DFC) which was introduced by our group in 20204.A DFC utilizes a fuel cell anode and cathode to catalyze the chemical-to-electrical energy conversion, as well as a cation and anion exchange membrane to desalinate the feedwater flowing through the cell. A device with a single feed channel (figure a) was able to produce up to 10 kWh/m3 while desalinating water with sea-water level salinity4. In order to for this nascent technology to become practical, scale-up strategies need to be proposed and demonstrated.In this work we show results from the first scaled DFC, where we utilize scaling rules associated with electrodialysis by increasing the number of membrane pairs to allow either two or three feed channels (Figure b). We find the three feed channel device was associated with high voltage loss in the ohmic region and lower limiting current (figure c), but the salt concentration behaved linearly as a function of the current density as expected (figure d). The main voltage losses are clearly emanated from the cathode and the anode sides as the membranes potential loss was proven to be insignificant5. We showed that implementing higher acid concentration in the catholyte and higher base concentration in the anolyte channels can significantly improve performance of the stack. Figure (e) shows results using three different anolyte and catholyte solutions, with highest open circuit voltage (OCV) and improved polarization performance for 0.5M HClO4 and 0.5M NaOH in the catholyte and the anolyte, respectively. We also investigated the feed flow rate impact on DFC polarization performance and salt removal. Overall, we show successful implementation of a scaled-up DFC.

Quantum transport in novel self-similar structure based on graphene

A new self-similar graphene structure with different construction parameters is created to investigate the scalability of transmission coefficient. The transfer matrix formalism is used to calculate transmission spectra for generations of the self-similar structure. Two cases are analyzed: In the first case, the barriers were created by substrates, which induce a gap in the graphene. In the second case, the barriers were created by electric fields that can produce a displacement of the Dirac cones. We find that both cases show self-similarity patterns in their transmission spectra, which can be demonstrated through analytical equations called scaling rules, those rules connecting the generations of the structure. It results when the height of the barriers (V 0) is scaled or not, it gives different scaling rules, which shows that V 0 can be a revealing factor to find alternatives to scaling the transmission coefficient. Scaling rules can be useful because one can determine the transmission coefficient of generation i + 1 only by knowing a generation i.

Extrapolation of classical rheometry of plant protein pastes to extrusion conditions

This study explores alternative methods to measure the rheology of pastes based on soy and pea proteins, which are commonly used as starting material in extrusion for the preparation of meat analogs. Currently, it is still a challenge to obtain consistent rheological data on such materials for steady shear at conditions that are relevant for the extrusion of such systems. Hence, possibilities are explored for the extrapolation of results obtained with classical rheometers operating at temperatures below boiling point, to conditions that are relevant for the extrusion of such systems. The above was combined, with findings from off-line and in-line capillary rheometers, which can provide steady shear measurements above boiling point and at hydration levels as used in extrusion.We have analysed the consistency of our new results with previous oscillatory shear measurements, performed at conditions similar to those of meat analog extrusion. For validation of the extrapolation we have analysed whether the new rheology data follow similar scaling rules. As classical rheometers can handle only moderate torques, the measurements are limited to hydration levels higher than those used in extrusion (75–85%). Despite this limitation, strain sweeps with classical rheometers show that in this regime the elastic modulus also scales with Tg/T, the ratio of glass transition, and actual temperature. Yet, its dependency on Tg/T appears differently from measurements performed at extrusion-like conditions. Moreover, our current results indicate a strong thixotropic nature of plant protein pastes at temperatures below boiling point. Consequently, flow curves are only stable if performed from high to low shear rates. Nevertheless, our data can be mapped to a single master curve following the Herschel-Bulkley model, with parameters also following a scaling rule dependent on Tg/T.This Tg/T scaling is used to construct master curves with data from the off-line and in-line capillary rheometers. The obtained shift factors for the flow curves are in agreement with old literature data obtained for SPI, but they deviate significantly from our steady shear measurements with classical rheometers. Based on the results of our current and previous study we have a critical, general discussion on the possibility of extrapolation of classical rheometry of plant protein pastes to process conditions used for production of meat analogs.

Estimating the magnitude and sensitivity of energy fluxes for stickleback hosts and Schistocephalus solidus parasites using the metabolic theory of ecology.

Parasites are ubiquitous, yet their effects on hosts are difficult to quantify and generalize across ecosystems. One promising metric of parasitic impact uses the metabolic theory of ecology (MTE) to calculate energy flux, an estimate of energy lost to parasites. We investigated the feasibility of using metabolic scaling rules to compare the energetic burden of parasitism among individuals. Specifically, we found substantial sensitivity of energy flux estimates to input parameters used in the MTE equation when using available data from a model host-parasite system (Gasterosteus aculeatus and Schistocephalus solidus). Using literature values, size data from parasitized wild fish, and a respirometry experiment, we estimate that a single S. solidus tapeworm may extract up to 32% of its stickleback host's baseline metabolic energy requirement, and that parasites in multiple infections may collectively extract up to 46%. The amount of energy siphoned from stickleback to tapeworms is large but did not instigate an increase in respiration rate in the current study. This emphasizes the importance of future work focusing on how parasites influence ecosystem energetics. The approach of using the MTE to calculate energy flux provides great promise as a quantitative foundation for such estimates and provides a more concrete metric of parasite impact on hosts than parasite abundance alone.

Expanded Multiplexing on Sensor-Constrained Microfluidic Partitioning Systems.

Microfluidics can split samples into thousands or millions of partitions, such as droplets or nanowells. Partitions capture analytes according to a Poisson distribution, and in diagnostics, the analyte concentration is commonly inferred with a closed-form solution via maximum likelihood estimation (MLE). Here, we present a new scalable approach to multiplexing analytes. We generalize MLE with microfluidic partitioning and extend our previously developed Sparse Poisson Recovery (SPoRe) inference algorithm. We also present the first in vitro demonstration of SPoRe with droplet digital PCR (ddPCR) toward infection diagnostics. Digital PCR is intrinsically highly sensitive, and SPoRe helps expand its multiplexing capacity by circumventing its channel limitations. We broadly amplify bacteria with 16S ddPCR and assign barcodes to nine pathogen genera by using five nonspecific probes. Given our two-channel ddPCR system, we measured two probes at a time in multiple groups of droplets. Although individual droplets are ambiguous in their bacterial contents, we recover the concentrations of bacteria in the sample from the pooled data. We achieve stable quantification down to approximately 200 total copies of the 16S gene per sample, enabling a suite of clinical applications given a robust upstream microbial DNA extraction procedure. We develop a new theory that generalizes the application of this framework to many realistic sensing modalities, and we prove scaling rules for system design to achieve further expanded multiplexing. The core principles demonstrated here could impact many biosensing applications with microfluidic partitioning.

Asperity shape in flexoelectric/triboelectric contacts

Triboelectric and flexoelectric phenomena have seen significant recent interest for energy harvesting. However, the underlying science responsible for triboelectricity has yet to be completely understood, both the fundamentals and an understanding of how the energy output depends upon the shape of the interacting surface. Here, we investigate the role of the contacting shapes (asperities) in triboelectricity. We demonstrate that their shape and size is very important, obtaining qualitative agreement with experimental results. Further, we discuss how the impact of the shape depends on material, geometric, gradient elasticity and electronic transport details. We provide scaling rules which can be exploited to better design energy harvesting devices based upon either triboelectricity or flexoelectricity.

Comparison of sub-grid drag laws for modeling fluidized beds with the coarse grain DEM–CFD approach

Fluidized particulate systems can be well described by coupling the discrete element method (DEM) with computational fluid dynamics (CFD). However, the simulations are computationally very demanding. The computational demand is drastically reduced by applying the coarse grain (CG) approach, where several particles are summarized into larger grains. Scaling rules are applied to the dominant forces to obtain precise solutions. However, with growing grain size, an adequate representation of the interaction forces and, thus, representation of sub-grid effects such as bubble and cluster formation in the fluidized particulate system becomes challenging. As a result, particle drag can be overestimated, leading to an increase in average particle height. In this work, limitations of the system-to-grain ratio are identified but also a dependency on system width. To address this issue, sub-grid drag models are often applied to increase the accuracy of simulations. Nonetheless, the sub-grid models tend to have an ad hoc fitting, and thorough testing of the system configurations is often missing. Here, five different sub-grid drag models are compared and tested on fluidized bed systems with different Geldart group particles, fluidization velocity, and system-to-grain diameter ratios.

A comparative study of noise performance for InP HEMT

A comparative study to determine the scaling rules of noise model parameters for InP HEMT devices between 90 nm and 70 nm process is proposed in this paper. The expressions of noise model parameters in terms of four noise parameters are also derived. Both gate length and gate width scalable rules of the gate and drain noise model parameters (P and R) are discussed between 90 nm and 70 nm devices. Good agreement is obtained based on the comparison of measured and modeled noise parameters up to 45 GHz.

Strain regularisation using a non-local method in Coupled Eulerian-Lagrangian analyses

Conventional finite element analyses generally suffer from mesh dependency when considering softening effects. Non-local strain regularisation techniques have been developed to address this issue, which are generally complex to implement. In view of this, this paper introduces an more efficient procedure for implementation of a non-local method in Abaqus for Coupled Eulerian-Lagrangian (CEL) large deformation finite element undrained analyses. Results from a series of biaxial compression simulations demonstrate that the non-local method with a strain-softening Tresca model in CEL avoids mesh dependency. Owing to the stationary Eulerian element and the built-in mapping algorithm in CEL, high computational efficiency is achieved, adding no more than 12% to the computational cost. Guidance is provided on selection of internal length scales, element sizes and the search radius to ensure efficient non-local calculations, and it is shown that a softening scaling rule can also be used to allow use of practical mesh densities for some boundary value problems. Simulations of a number of classical geotechnical problems demonstrate the type of boundary value problems where the non-local method can effectively mitigate the mesh dependency, whilst also highlighting that the method fails to control the strain localisation that develops at soil-structure interfaces due to geometrical issues.

Empirical model for bandgaps of armchair graphene nanoribbons

Based on an empirical approach, we present a relation for the bandgaps of armchair graphene nanoribbons (aGNRs) as a function of their widths and number of armchair chains. Experimental bandgaps of a number of atomically well-defined aGNRs are studied here. It is found that experimentally reported bandgaps are underestimated or overestimated by existing theoretical models. Experimental data reveal that the bandgaps of N = 3p and N = 3p + 1 families of aGNR are directly related to the width and number of armchair chains. An empirical model for calculating the bandgaps of aGNRs is devised based on theoretical and experimental observations. Calculated bandgaps offer significant corrections for armchair aGNRs compared with previous tight-binding and density functional theory studies, and the amount of correction is represented by a semi-empirical correction term. The proposed relation indicates that not only the ribbon width but also the number of armchair chains plays a crucial role in determining the values and scaling rule for the bandgaps. Compared with previous models, our estimated bandgaps from the proposed empirical model can track qualitative and quantitative patterns of the experimental bandgaps very precisely, thus helping predict the bandgap of an aGNR for potential electronic and optoelectronic applications. The proposed empirical relation also gives insight into the correlation between the physical structure and electronic properties of a GNR. Furthermore, this model can be taken as a guide for devising a similar empirical model for calculating the bandgaps of other types of GNRs.

Massive MIMO Receiver Design with Channel State Information

In order to achieve an astounding data transfer rate, 5G Technology’s is essential obligation, this is the primal reason while the world considers it as an pivotal active research topic, Amongst numerous techniques available to attain the transfer rate, opted Massive Multiple-Input Multiple-Output (MIMO) system receivers with respect to CSI assuming the mathematic computations and complex equations would be deduced in a primordial passion, is examined in this study. For the uplink, we show the possible transfer rate for both receivers with perfect and imperfect channel state information (CSI), considering that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers. Channel estimation is carried out by transmitting pilot symbols when there is insufficient information about the channel state to establish its properties. Additionally, the influence of channel state information on the power scaling rule is illustrated, and a numerical analysis for combiner is derived. In this study, we compare the competence of receivers under ideal and imperfect CSI. In case of perfect CSI, Value of data rate is 84 bits/sec/Hz for ZF receiver and 45 bits/sec/Hz. In case of imperfect CSI, Value of data rate is 60 bits/sec/Hz for ZF receiver and 37 bits/sec/Hz with number of antennas being 500 and number of pilot bits as 10.

A theoretical framework for upscaling species distribution models

AbstractSpecies distribution models (SDM) have become one of the most popular predictive tools in ecology. With the advent of new computation and remote sensing technology, high‐resolution environmental data sets are becoming more and more common predictors in these modelling efforts. Understanding how scaling affects their outputs is therefore fundamental to understand their applicability.Here, we develop a theoretical basis to understand the consequences of aggregating occurrence and environmental data at different resolutions. We provide a theoretical framework, along with numerical simulations and a real‐world case study, to show how these scaling rules influence predictive outputs.We show that the properties of the environment–occurrence relationships change when the data are aggregated: the mean probability of occurrence and species prevalence increases, the optimal environmental values shift and classification rates increase at coarser resolutions up to a certain level. Furthermore, and contrary to the widespread expectation that high‐resolution data would produce better predictions, we show here that model performance may increase using coarser resolution data sets rather than the inverse. Finally, we also show that model performance depends not only on the environment–occurrence relationship but also on the interaction between this and the geography and distribution of the available environment.This theoretical framework helps understanding previously incoherent results regarding SDM upscaling and model performance, and illustrates how theoretical and empirical results can provide important feedbacks to advance in understanding scaling issues in macroecology. The interaction between the shape of the environment–occurrence relationship and the rates of change of the environment is fundamental to understand the effects of upscaling in model performance, and may explain why some models are more difficult to transfer to different regions. Most importantly, we argue that there are conceptual choices related to scaling and SDM fitting that require expert knowledge and further explorations between theory and practice in macroecology.

Taguchi Method for p-MOS Threshold Voltage Optimization with a Gate Length of 22nm

This paper describes the virtual design of a 22nm gate length p-type metal oxide semiconductor, PMOS. Silvaco, TCAD tools were used to fabricate the device design and to characterize the device’s electrical properties. Fixed field scaling rules are applied to obtain the transistor’s electrical parameters set by ITRS 2013. In order to take the challenges that arise in the fabrication of nano-sized transistors and enhance their performance, advanced and novel technologies are applied. Using the statistical modelling of L9 Taguchi methodology, the development process is primarily focused on the tool's edge voltage. Four parameters have been divided into three distinct steps in order to conduct nine different experiments. The final confirmation result indicates that VTH is closer to the nominal value -0.206V following optimization techniques. This matches the ITRS 2013 requirements for high performance. This paper examines the design of a p-MOS double gate containing a layer of graphene as it is known to have a high mobility value.

Crackling noise microscopy

Crackling noise is a scale-invariant phenomenon found in various driven nonlinear dynamical material systems as a response to external stimuli such as force or external fields. Jerky material movements in the form of avalanches can span many orders of magnitude in size and follow universal scaling rules described by power laws. The concept was originally studied as Barkhausen noise in magnetic materials and now is used in diverse fields from earthquake research and building materials monitoring to fundamental research involving phase transitions and neural networks. Here, we demonstrate a method for nanoscale crackling noise measurements based on AFM nanoindentation, where the AFM probe can be used to study the crackling of individual nanoscale features, a technique we call crackling noise microscopy. The method is successfully applied to investigate the crackling of individual topological defects, i.e. ferroelectric domain walls. We show that critical exponents for avalanches are altered at these nanoscale features, leading to a suppression of mixed-criticality, which is otherwise present in domains. The presented concept opens the possibility of investigating the crackling of individual nanoscale features in a wide range of material systems.

Application of Allometric Scaling and Salisbury Rule for the Prediction of Antimalarial Drugs for First-in-Pediatric Dose Selection.

In pediatric drug development, the selection of first-in-pediatric dose is of immense importance. Generally, the pharmacokinetic information and a safe and efficacious dose of a drug in adults are already known and this information can then be used to select first-in-pediatric dose.The objective of this study was to predict the pediatric dose of antimalarial drugsand compare the predicted dose with the recommended dose. In this study, two simple methods to project a first-in-pediatric dose to initiate a clinical trial for antimalarial drugs were evaluated. These two methods were Salisbury Rule and allometric scaling. The predicted doses of antimalarial drugs by the two methods were compared with the observed doses recommended by the World Health Organization (WHO) or the US Food and Drug Administration (FDA). In this study, 15 antimalarial drugs with 88 observations (different body weight groups) were evaluated. From allometric scaling, all 88 observations were within 0.5-1.5-fold and 0.7-1.3-fold prediction error. From Salisbury Rule, all 88 observations were within 0.5-1.5-fold and 86 observations were within 0.7-1.3-fold prediction error. The proposed methods are simple and quite accurate in their predictive power. These methods can be developed on a spreadsheet or a calculator in a very short period of time and are applicable to first-in-pediatric clinical trials or even in a clinical setting.

CFD-response surface methodology to optimize the effective thermal conductivity and homogeneity in tray dryer

Tray dryers are usually designed with simplistic scaling rules that do not account for all the transport phenomena associated with drying. The use of computational fluid dynamics coupled with response surface methodology can be a powerful tool to evaluate how different tray dryer design parameters affect the drying process. In this work, two tray dryers, one with a lateral air inlet and another with a bottom air inlet, were parameterized for the position of the air inlet, the dryer length, and the distance between the trays. A central composite design was chosen to determine the sample points, and the average turbulence viscosity and effective thermal conductivity as well as the homogeneity index were calculated. With these values, a response surface curve was constructed. The effective thermal conductivity and its homogeneity index were improved (80 % and 11 %, respectively) with an increased distance between trays and an air inlet located in the middle of the inlet face in the best scenario. In addition, the reductions in effective thermal conductivity outcomes were minimal due to the scale-up process in terms of the dryer length.