Size Scaling Research Articles

Nuclear engineering and design nuclear heat supply system for a small district heating reactor

Small Modular Reactors (SMRs) are currently considered to be a potential solution for decarbonizing the district heating sector. The LUT Heat Experimental Reactor (LUTHER) is a concept for a small modular nuclear heating plant that is being designed to meet the demands of Nordic district heating networks while also incorporating high safety standards. This paper presents an extension of the work pursued by LUT University by proposing a reactor module that allows for easy scaling of unit sizes ranging from 2 MW to 120 MW. The pressure tube assembly geometry, which has been developed specifically for the LUTHER reactor module, was analyzed by modeling two significantly different-sized variants that utilize this unique structure. The modular design of LUTHER enables complete factory-assembly and the use of standard road transport for unit sizes up to 120 MW. This design prioritizes high inherent safety, targeting for siting near population centers. The proposed heating reactor concept offers a viable means of decarbonizing the district heating sector by replacing existing combustion-based production with emissions-free nuclear heat.

Cell size induced bias of current density in hypertrophic cardiomyocytes

ABSTRACT Alterations in ion channel expression and function known as “electrical remodeling” contribute to the development of hypertrophy and to the emergence of arrhythmias and sudden cardiac death. However, comparing current density values – an electrophysiological parameter commonly utilized to assess ion channel function – between normal and hypertrophied cells may be flawed when current amplitude does not scale with cell size. Even more, common routines to study equally sized cells or to discard measurements when large currents do not allow proper voltage-clamp control may introduce a selection bias and thereby confound direct comparison. To test a possible dependence of current density on cell size and shape, we employed whole-cell patch-clamp recording of voltage-gated sodium and calcium currents in Langendorff-isolated ventricular cardiomyocytes and Purkinje myocytes, as well as in cardiomyocytes derived from trans-aortic constriction operated mice. Here, we describe a distinct inverse relationship between voltage-gated sodium and calcium current densities and cell capacitance both in normal and hypertrophied cells. This inverse relationship was well fit by an exponential function and may be due to physiological adaptations that do not scale proportionally with cell size or may be explained by a selection bias. Our study emphasizes the need to consider cell size bias when comparing current densities in cardiomyocytes of different sizes, particularly in hypertrophic cells. Conventional comparisons based solely on mean current density may be inadequate for groups with unequal cell size or non-proportional current amplitude and cell size scaling.

Crossover in densities of confined particles with finite range of interaction

We study a one-dimensional classical system of N particles confined within a harmonic trap. Interactions among these particles are dictated by a pairwise potential V(x), where x is the separation between two particles. Each particle can interact with a maximum of d neighbouring particles on either side (left or right), if available. By adjusting the parameter d, the system can be made nearest neighbour (d=1) to all-to-all (d=N−1) interacting. As suggested by prior studies, the equilibrium density profile of these particles is expected to undergo shape variations as d is changed. In this paper, we investigate this crossover by tuning the parameter f(=d/N) from 1 to 0 in the large N limit for two distinct choices of interaction potentials, V(x)=−|x| and V(x)=−log⁡(|x|) which correspond to 1d one-component plasma and the log-gas model, respectively. For both models, the system size scaling of the density profile for fixed f turns out to be the same as in their respective all-to-all case. However, the scaling function exhibits diverse shapes as f varies. We explicitly compute the average density profile for any f∈(0,1] in the 1d plasma model, while for the log-gas model, we provide approximate calculations for large (close to 1) and small (close to 0) f. Additionally, we present simulation results to numerically demonstrate the crossover and compare these findings with our theoretical results.

Size Scaling of Condensates in Multicomponent Phase Separation.

Constant proportionalities between cells and their intracellular organelles have been widely observed in various types of cells, known as intracellular size scaling. However, the mechanism underlying the size scaling and its modulation by environmental factors in multicomponent systems remain poorly understood. Here, we study the size scaling of membrane-less condensates using microdroplet-encapsulated minimalistic condensates formed by droplet microfluidics and mean-field theory. We demonstrate that the size scaling of condensates is an inherent characteristic of liquid-liquid phase separation. This concept is supported by experiments showing the occurrence of size scaling phenomena in various condensate systems and a generic lever rule acquired from mean-field theory. Moreover, it is found that the condensate-to-microdroplet scaling ratio can be affected by the solute and salt concentrations, with good agreement between experiments and predictions by theory. Notably, we identify a noise buffering mechanism whereby condensates composed of large macromolecules effectively maintain constant volumes and counteract concentration fluctuations of small molecules. This mechanism is achieved through the dynamic rearrangement of small molecules in and out of membrane-free interfaces. Our work provides crucial insights into understanding mechanistic principles that govern the size of cells and intracellular organelles as well as associated biological functions.

The dynamical bulk boundary correspondence and dynamical quantum phase transitions in the Benalcazar–Bernevig–Hughes model

In this article we demonstrate that dynamical quantum phase transitions (DQPTs) occur for an exemplary higher order topological insulator, the Benalcazar–Bernevig–Hughes model, following quenches across a topological phase boundary. A dynamical bulk boundary correspondence is also seen both in the eigenvalues of the Loschmidt overlap matrix and the boundary return rate. The latter is found from a finite size scaling analysis for which the relative simplicity of the model is crucial. Contrary to the usual two dimensional case the DQPTs in this model show up as cusps in the return rate, as for a one dimensional model, rather than as cusps in its derivative as would be typical for a two dimensional model. We explain the origin of this behaviour.

Ex-ante LCA of magnet recycling: Progressing towards sustainable industrial-scale technology

To alleviate the pressure on the rare earth supply chain, new technologies are under development for recovering, recycling and remanufacturing NdFeB magnets. In this study, the anticipated environmental performance of large-scale recycling is investigated and compared to the production of primary magnets. To do so, this ex-ante life cycle assessment combines input from measurements of pilot processes, expert technology forecasts, thermodynamic modeling, and equipment data from manufacturers. We examined the effect of four technology developments: process changes, size scaling, internal recycling, and optimization.The results show that at pilot scale, recovered NdFeB powders have lower impacts than primary powders for almost all impact categories. This demonstrates that the recovery of NdFeB alloys is environmentally beneficial. Magnets from anticipated large-scale recycling have over 80% lower impacts than primary magnets in most of the impact categories analyzed. All four investigated types of technology development contributed to this improved performance. The final configuration was validated by comparison with an industrial reference and theoretical optimum configuration. Four magnet manufacturing routes (sintering, extrusion, metal injection molding, bonding) have distinct environmental profiles, but all can progress to similarly low levels of impact. The choice among routes should be primarily based on the functional requirements.

Enhancing Carrier Mobility in Monolayer MoS2 Transistors with Process-Induced Strain.

Two-dimensional electronic materials are a promising candidate for beyond-silicon electronics due to their favorable size scaling of electronic performance. However, a major challenge is the heterogeneous integration of 2D materials with CMOS processes while maintaining their excellent properties. In particular, there is a knowledge gap in how thin film deposition and processes interact with 2D materials to alter their strain and doping, both of which have a drastic impact on device properties. In this study, we demonstrate how to utilize process-induced strain, a common technique extensively applied in the semiconductor industry, to enhance the carrier mobility in 2D material transistors. We systematically varied the tensile strain in monolayer MoS2 transistors by iteratively depositing thin layers of high-stress MgOx stressor. At each thickness, we combined Raman spectroscopy and transport measurements to unravel and correlate the changes in strain and doping within each transistor with their performance. The transistors displayed uniform strain distributions across their channels for tensile strains of up to 0.48 ± 0.05%, at 150 nm of stressor thickness. At higher thicknesses, mechanical instability occurred, leading to nonuniform strains. The transport characteristics systematically varied with strain, with enhancement in electron mobility at a rate of 130 ± 40% per % strain and enhancement of the channel saturation current density of 52 ± 20%. This work showcases how established CMOS technologies can be leveraged to tailor the transport in 2D transistors, accelerating the integration of 2D electronics into a future computing infrastructure.

REAL TIME FACE ATTENDANCE SYSTEM USING DEEP LEARNING

One's face is a physical depiction of who they are. As a result, we have suggested a facial recognition- based automatic student attendance system. The usage of face recognition technology in everyday life is particularly beneficial for security control systems. Face recognition is used by the FBI (Federal Bureau of Investigation) for criminal investigations as well as the airport security system to identify criminals. In the first step of our suggested method, video framing, the camera is activated using a user-friendly interface. The Viola-Jones method is used to identify and segment the face ROI from the video frame. If necessary, image size scaling is done at the pre-processing step to prevent information loss. After using the median filter to reduce noise, color pictures are transformed into grayscale versions. Then, to improve the contrast of the pictures, contrast-limited adaptive histogram equalization (CLAHE) is applied. Principal component analysis (PCA) and improved local binary pattern (LBP) are used appropriately in the face identification stage to extract the characteristics from facial pictures. By minimizing the lighting impact and boosting identification rates, the improved local binary pattern outperforms the original local binary pattern in our suggested method. The features that were retrieved from the training pictures and the features that were extracted from the test images are then compared. The face pictures are then identified and categorized using the upgraded LBP, PCA, and algorithm that produced the best results. The acknowledged student's attendance will then be recorded and saved in the excel file. A warning will be sent if a student signs in more than once. The student who is not enrolled will also be allowed to register right away. When two photographs per person are trained, the average recognition accuracy is 100% for high-quality images, 94.12% for low-quality images, and 95.76% for the face database.

Scaling photonic integrated circuits with InP technology: A perspective

The number of photonic components integrated into the same circuit is approaching one million, but so far, this has been without the large-scale integration of active components: lasers, amplifiers, and high-speed modulators. Emerging applications in communication, sensing, and computing sectors will benefit from the functionality gained with high-density active–passive integration. Indium phosphide offers the richest possible combinations of active components, but in the past decade, their pace of integration scaling has not kept up with passive components realized in silicon. In this work, we offer a perspective for functional scaling of photonic integrated circuits with actives and passives on InP platforms, in the axes of component miniaturization, areal optimization, and wafer size scaling.

Regulation of Network Condensation Based on Fitness Ordered Access Strategy

The condensation in complex networks disclose the underlying mechanism of the monopoly in socioeconomic system, which can help us to design and access the anti-monopoly policy based on the study on network condensation. Inspired by the consideration, We introduce a set of rearrangement mechanism into the fitness model to regulate the order of nodes with different fitness to enter the network, and study the influence of this regulation strategy on network condensation. By extensive Monte Carlo simulations and finite size scaling analysis, we obtain the critical rearrangement index under a typical fitness distribution, establish the relationship between the index and the condensation intensity, and finally construct the condensation phase diagram. These results show that there exists an interval of the critical rearrangement index, outside which the condensation of the fitness model will be effectively suppressed. We carry out a theoretical analysis on some key results to understand their underlying origin, and discuss their instructive significance on the anti-monopoly market management.

Magnetic switching driven by spin–orbit torque in topological-insulator-based (Bi0.5Sb0.5)2Te3/Ta/CoFe/Cu/CoFe/IrMn heterostructure

The giant spin–orbit torque (SOT) generated by topological surface states in topological insulators (TIs) provides an energy-efficient writing method for magnetic memory. In this study, we demonstrate a topological insulator/spin valve (TI/SV) device that operates at room temperature. An ultrathin, high-quality TI (Bi0.5Sb0.5)2Te3 (BST) thin film is epitaxially grown as a functional layer on a (0001)-Al2O3 substrate via molecular beam epitaxy in ultrahigh vacuum. Subsequently, Ta/CoFe/Cu/CoFe/IrMn layers are grown on BST/Al2O3 thin films using magnetron sputtering to form TI/SV devices via a subsequent standard lithography process. The resulting TI/SV devices exhibit a giant magnetoresistance of up to ∼1.1% at room temperature. Additionally, a low switching current density of approximately 1.25 × 105 A cm−2 is achieved, which implies high potential for further reducing the energy consumption of SOT-based devices. The SOT conversion efficiency and charge-spin conversion efficiency of the TI layer are approximately 4.74 × 10−6 Oe A−1 cm2 and 1.33, respectively, as extracted from the SOT-induced shift of the magnetic switching field. Moreover, the switching current density reduces steadily with the device size scaling down. This study can facilitate the realization of energy-efficient magnetic memory devices in the future.

Digital holographic content manipulation for wide-angle holographic near-eye displays

In recent years, the development of holographic near-eye displays (HNED) has surpassed the progress of digital hologram recording systems, especially in terms of wide-angle viewing capabilities. Thus, there is capture-display parameters incompatibility, which makes it impossible to reconstruct recorded objects in wide-angle display. This paper presents a complete imaging chain extending the available content for wide-angle HNED of pupil and non-pupil configuration with narrow-angle digital holograms of real objects. To this end, a new framework based on the phase-space approach is proposed that includes a set of affine transformations required to account for all differences in capture-display cases. The developed method allows free manipulation of the geometry of reconstructed objects, including axial and lateral positioning and size scaling. At the same time, it has a low computational effort. The presented work is supported with non-paraxial formulas developed using the phase-space approach, enabling accurate tracing of the holographic signal, its reconstruction, and measuring appearing deformations. The applicability of the proposed hologram manipulation method is proven with experimental results of digital hologram reconstruction in wide-angle HNED.

SeqGPT: An Out-of-the-Box Large Language Model for Open Domain Sequence Understanding

Large language models (LLMs) have shown impressive abilities for open-domain NLP tasks. However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format. Their performances on NLU tasks are highly related to prompts or demonstrations and are shown to be poor at performing several representative NLU tasks, such as event extraction and entity typing. To this end, we present SeqGPT, a bilingual (i.e., English and Chinese) open-source autoregressive model specially enhanced for open-domain natural language understanding. We express all NLU tasks with two atomic tasks, which define fixed instructions to restrict the input and output format but still ``open'' for arbitrarily varied label sets. The model is first instruction-tuned with extremely fine-grained labeled data synthesized by ChatGPT and then further fine-tuned by 233 different atomic tasks from 152 datasets across various domains. The experimental results show that SeqGPT has decent classification and extraction ability, and is capable of performing language understanding tasks on unseen domains. We also conduct empirical studies on the scaling of data and model size as well as on the transfer across tasks. Our models are accessible at https://github.com/Alibaba-NLP/SeqGPT.

Precise and scalable self-organization in mammalian pseudo-embryos.

Gene expression is inherently noisy, posing a challenge to understanding how precise and reproducible patterns of gene expression emerge in mammals. Here we investigate this phenomenon using gastruloids, a three-dimensional in vitro model for early mammalian development. Our study reveals intrinsic reproducibility in the self-organization of gastruloids, encompassing growth dynamics and gene expression patterns. We observe a remarkable degree of control over gene expression along the main body axis, with pattern boundaries positioned with single-cell precision. Furthermore, as gastruloids grow, both their physical proportions and gene expression patterns scale proportionally with system size. Notably, these properties emerge spontaneously in self-organizing cell aggregates, distinct from many in vivo systems constrained by fixed boundary conditions. Our findings shed light on the intricacies of developmental precision, reproducibility and size scaling within a mammalian system, suggesting that these phenomena might constitute fundamental features of multicellularity.

Universal scaling between wave speed and size enables nanoscale high-performance reservoir computing based on propagating spin-waves

Physical implementation of neuromorphic computing using spintronics technology has attracted recent attention for the future energy-efficient AI at nanoscales. Reservoir computing (RC) is promising for realizing the neuromorphic computing device. By memorizing past input information and its nonlinear transformation, RC can handle sequential data and perform time-series forecasting and speech recognition. However, the current performance of spintronics RC is poor due to the lack of understanding of its mechanism. Here we demonstrate that nanoscale physical RC using propagating spin waves can achieve high computational power comparable with other state-of-art systems. We develop the theory with response functions to understand the mechanism of high performance. The theory clarifies that wave-based RC generates Volterra series of the input through delayed and nonlinear responses. The delay originates from wave propagation. We find that the scaling of system sizes with the propagation speed of spin waves plays a crucial role in achieving high performance.

Efficient DEM modeling of solid flavor particle mixing in a rotary drum

An efficient Discrete Element Method (DEM) modeling methodology for mixing of solid flavor particles in a rotating drum is presented. Machine learning and optimization algorithms are combined with geometric scaling and particle size distribution trimming to produce several orders of magnitude reduction in computational expense relative to the traditional approach of modeling at the physical system scale and performing purely simulation-based model calibration.The validity of the methodology is evaluated by comparing DEM model results for the evolution of the mixture uniformity with a laboratory scale drum and good agreement is observed. The limitations of the approach with respect to the amount of data required and the degree of geometric scaling and particle size trimming that is possible are also discussed.The approach developed could serve as an example for future simulation studies on fine particles in various processes that are relevant to the food, ingredient and pharmaceutical industry.

Unpacking urban scaling and socio-spatial inequalities in mobility: Evidence from England

Prior research on the scaling of city size and inequality has a primary focus on economic factors such as income. Limited research has addressed socio-spatial disparities in mobility, involving physical activities and social interactions among individuals and population groups. Utilising mobile phone app data, this study measured inequalities using multiple mobility-related indicators (i.e. the number of activity points, the radius of gyration, self-containment, and social interaction indices) and related to population size by scaling models. In England’s context, these indicators unfolding mobility patterns and social issues display different scaling regimes, varying from sublinear to super-linear. It was observed that larger cities are associated with greater social interactions, particularly among socioeconomically advantaged groups; however, they also exhibit exacerbated self-segregation. Due to the radiation effect of big cities, the performances (e.g. travel radius) of small surrounding towns deviate from the predicted values of scaling models. Within cities, the evenness of indicators is independent of population size and produces distinct spatial patterns. The findings expand upon previous research and provide a more nuanced understanding of the complex relationship between city size, urban inequality, and human mobility.

Study on stress in trench structures during silicon IGBTs process-oxidation

In silicon insulated gate bipolar transistors, the trench gate structure is used to achieve smaller cell size and lower ON resistance, and thereby reduces energy loss. However, the thermal process can cause large stress near the trench and sometimes degrades device performance. This study proposed a three-dimensional model of a silicon chip with trench structures to analyze the stress distribution induced by thermal process around the trench, the scribe line, and the bottom surface of the chip. The calculated stress is in good agreement with measurement by Raman spectroscopy. The mesa top has much higher stress than the scribe line and the bottom surface. The stress depends on oxide thickness and the size scaling may reduce the stress.

On size-independent sample complexity of ReLU networks

We study the sample complexity of learning ReLU neural networks from the point of view of generalization. Given norm constraints on the weight matrices, a common approach is to estimate the Rademacher complexity of the associated function class. Previously [9] obtained a bound independent of the network size (scaling with a product of Frobenius norms) except for a factor of the square-root depth. We give a refinement which often has no explicit depth-dependence at all.

On the magnetization of the 120° order of the spin-1/2 triangular lattice Heisenberg model: a DMRG revisited.

We revisit the issue about the magnetization of the 120° order in the spin-1/2 triangular lattice Heisenberg model with density matrix renormalization group (DMRG). The accurate determination of the magnetization of this model is challenging for numerical methods and its value exhibits substantial disparities across various methods. We perform a large-scale DMRG calculation of this model by employing bond dimension as large asD=24000and by studying the system with width as large asLy=12. With careful extrapolation with truncation error and suitable finite size scaling, we give a conservative estimation of the magnetization asM0=0.208(8). The ground state energy per site we obtain isEg=-0.5503(8). Our results provide valuable benchmark values for the development of new methods in the future.