Computer Output Research Articles

Exploring the impacts of high-speed rail on technology-intensive manufacturing: the case of the Yangtze River Delta region, 2007–2016

High-speed rail (HSR) may influence economic activities that rely heavily on innovation by facilitating skilled labour, face-to-face interactions, and knowledge spillovers. This study explores how HSR development affects the spatial distribution of technology-intensive manufacturing (TIM) in the Yangtze River Delta (YRD), China. Using a panel dataset including 24 cities for the period 2007–2016 and employing the output of communications equipment, computers, and other electronic equipment (CCOE) as a proxy for TIM’s economic productivity at the city level, we apply the staggered difference-in-differences (DID) and spatial Durbin model (SDM) to measure the impacts of HSR’s initial opening and connectivity on CCOE development and capture the spatial spillover effects of HSR connectivity. Our findings indicate that the initial opening of HSR and HSR connectivity are negatively associated with CCOE productivity in both DID and SDM. Additionally, the reduction of CCOE is more pronounced in cities with larger populations and higher levels of economy. Moreover, HSR has a more significant effect on CCOE than other manufacturing sectors. However, the spillover effects remain insignificant, indicating HSR’s limited impact on CCOE development in adjacent cities within the YRD.

First principles insight into the study of the structural, stability, and optoelectronic properties of alkali-based single halide perovskite ZSnCl3 (Z = Na/K) materials for photovoltaic applications.

Metal halide perovskites are crystalline materials with a sharp increase in popularity and rapidly becoming a major contender for optoelectronic device applications. In this work, we provide the optoelectronic features of a possible novel candidate, ZSnCl3 (Z = Na/K) Sn-based on a detailed numerical simulation. The output of the current computations is compared to the results that are currently available, and a respectable agreement is noted. The studied compounds were cubic in nature and structurally stabe. The mechanical properties reflectthe mechanical stability and ductility of the proposed materials. The Sn-based single perovskite compounds proposed in this study are mechanically stable and ductile. The narrow direct band gap for NaSnCl3 and KSnCl3 are 1.36 eV and 1.47 eV, respectively, using the HSE06 hybrid function with the Boltztrp2 integrated in Quantum ESPRESSO (QE) software. The effective use of these compounds in perovskite solar cells and other optoelectronic applications was confirmed by optical absorption spectral measurements conducted in the photon energy range of 0-20 eV.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing.

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

DNN acceleration in vehicle edge computing with mobility-awareness: A synergistic vehicle–edge and edge–edge framework

In recent years, vehicular networks have seen a proliferation of applications and services such as image tagging, lane detection, and speech recognition. Many of these applications rely on Deep Neural Networks (DNNs) and demand low-latency computation. To meet these requirements, Vehicular Edge Computing (VEC) has been introduced to augment the abundant computation capacity of vehicular networks to complement limited computation resources on vehicles. Nevertheless, offloading DNN tasks to MEC (Multi-access Edge Computing) servers effectively and efficiently remains a challenging topic due to the dynamic nature of vehicular mobility and varying loads on the servers. In this paper, we propose a novel and efficient distributed DNN Partitioning And Offloading (DPAO), leveraging the mobility of vehicles and the synergy between vehicle–edge and edge–edge computing. We exploit the variations in both computation time and output data size across different layers of DNN to make optimized decisions for accelerating DNN computations while reducing the transmission time of intermediate data. In the meantime, we dynamically partition and offload tasks between MEC servers based on their load differences. We have conducted extensive simulations and testbed experiments to demonstrate the effectiveness of DPAO. The evaluation results show that, compared to offloaded all tasks to MEC server, DPAO reduces the latency of DNN tasks by 2.4x. DPAO with queue reservation can further reduce the task average completion time by 10%.

A numerical computational technique for solving controllability of impulsive Hilfer fractional integro-differential equation with order [formula omitted]

The primary focus of this paper is the concept of controllability criteria for the impulsive Hilfer fractional integro-differential equation (IHFrIDE) of order 0≤ϖ≤1 and 1<ϑ<2 in Hilbert space. We use the iterative process and the strongly cosine family to obtain a given system outcome. We use the degree method to demonstrate the existence of a solution for a given dynamical system. We obtained uniqueness results from Gronwall’s inequality and also addressed the controllability criteria for our given problem. The outputs of numerical computations demonstrate the efficiency of our present method.

Wave-Function Network Description and Kolmogorov Complexity of Quantum Many-Body Systems

Programmable quantum devices are now able to probe wave functions at unprecedented levels. This is based on the ability to project the many-body state of atom and qubit arrays onto a measurement basis which produces snapshots of the system wave function. Extracting and processing information from such observations remains, however, an open quest. One often resorts to analyzing low-order correlation functions—that is, discarding most of the available information content. Here, we introduce wave-function networks—a mathematical framework to describe wave-function snapshots based on network theory. For many-body systems, these networks can become scale-free—a mathematical structure that has found tremendous success and applications in a broad set of fields, ranging from biology to epidemics to Internet science. We demonstrate the potential of applying these techniques to quantum science by introducing protocols to extract the Kolmogorov complexity corresponding to the output of a quantum simulator and implementing tools for fully scalable cross-platform certification based on similarity tests between networks. We demonstrate the emergence of scale-free networks analyzing experimental data obtained with a Rydberg quantum simulator manipulating up to 100 atoms. Our approach illustrates how, upon crossing a phase transition, the simulator complexity decreases while correlation length increases—a direct signature of buildup of universal behavior in data space. Comparing experiments with numerical simulations, we achieve cross-certification at the wave-function level up to timescales of 4 μs with a confidence level of 90% and determine experimental calibration intervals with unprecedented accuracy. Our framework is generically applicable to the output of quantum computers and simulators with access to the system wave function and requires probing accuracy and repetition rates accessible to most currently available platforms. Published by the American Physical Society 2024

Integrating computational models and machine learning for corrugated absorber plate solar collector thermal predictions

This research provides a computational analysis, development, and empirical testing of a Corrugated Absorber Plate Solar Collector (CAPSC) with fins inserted Thermal Energy Storage (TES) system outlet temperature and thermal efficiency, which is then predicted employing machine learning techniques. For computational analysis, the governing equations of the CAPSC device have been generated and integrated into Computational Fluid Dynamics (CFD) software. Several experimental tests were conducted in both summer and winter seasons during the year, with varying mass flow rates ranging from 0.014 to 0.064 kg/s. The maximum average temperature difference among computational and experimental CAPSC outlet temperatures has been 1.72 °C at 0.044 kg/s. However, to enhance the thermal efficiency forecast of the CAPSC with fins inserted TES system, machine learning techniques such as Multiple Linear Regression (MLR), Random Forest (RF), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) have been used. A substantial empirical dataset of 288 samples was utilized to analyze the machine-learning approaches. The observed statistical error and regression values in all techniques indicate the adequacy of the approaches presented. When compared to MLR, KNN, and RF techniques, ANN, followed by XGBoost, provides the most accurate forecast. Notably, XGBoost demonstrated superior accuracy due to its ability to handle complex non-linear relationships and interactions within the dataset. Furthermore, to improve the thermal efficiency forecast of the CAPSC with fins inserted TES system by integrating the computational model with the machine learning techniques by utilizing computational CAPSC output temperature as one of the inputs. This CFD with machine learning integration technique considerably enhanced the thermal performance forecast accuracy.

Water resources management considering groundwater instability affected by climate change scenarios

In the present study, using GMS and MODFLOW code, a model of Khorramabad aquifer (Iran) was developed under unsteady conditions with considering the climate change (as a study innovation) in scenarios of RCP26 (optimistic) and RCP85 (pessimistic) based on results of CNRM-CM5 global climate model (Centre National de Recherches Me'te'orologiques—phase 5 of the Coupled Model). Using surface runoff data and computational level output from GMS, an integrated model was created in WEAP. According to scenarios of RCP26 and RCP85 for periods (2040–2069) and (2070–2099), the climate change effects on surface water and groundwater were evaluated. Results showed that water level calculated by GMS was obtained with high accuracy in calibration period (RMSR = 1.42 m, MSR = 0.85 m) and in verification period (MAE = 0.88, r = 0.98 and NSE = 0.97), which was in agreement with observed water levels of wells in region. The water level in RCP26 (2040–2069 and 2070–2099) compared to observed water level, on average, decreased by 0.154 m and 1.175 m, respectively, and in RCP85 for the same period, a decrease of 0.621 m and 0.753 m of calculated water level was observed compared to baseline. Then, allocation of surface water and groundwater resources to consumption sectors (agricultural, urban and industrial sectors) was analyzed using WEAP. Results showed that the lowest deficit of water resources in all sectors occurred in RCP26 (2040–2069) with a value of 7% and the highest deficit occurred in RCP85 (2070–2099) with a value of 180% compared to baseline.

Abstract 2327: MADVAR: An algorithm that improves the relevance of computational biology output, while reducing compute time and space requirements

Abstract High-throughput methods implemented in biology research produce a continuously growing array of data input that are used to produce data output with an increasing abundance of features. While growth in the volume and diversity of data input can be highly valuable for studying biological systems, it presents the challenge of managing enormous quantities of features, many of which are not relevant to the specific research question being asked. This excess data input burdens storage and computation of downstream clustering and machine learning tasks. A common approach used to manage this data input relies on filters applied to the features by their variance across the sample set, while applying random cutoffs. Our proprietary algorithm (“MADVAR”) enables the prioritization of variable features from high-throughput continuous data, by automatically finding an optimal cutoff for the distribution of the data. Based on the right-skew nature of biological data distribution, MADVAR finds and excludes the "0 variance peak" using the median of the distributions and the median absolute deviation (MAD). MADVAR enables a faster analysis with a reduced memory requirement, and dramatically improves clustering results with minimal loss of relevant features. Citation Format: Gilad Silberberg, Michael Ritchie. MADVAR: An algorithm that improves the relevance of computational biology output, while reducing compute time and space requirements [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2327.

From digital to quantum epidemiology: The Quantum Data Lake concept for big data related to viral infectious diseases

The development of quantum epidemiology represents the next anticipated phase in epidemiology transformation, driven by the emergence of new quantum technologies. Epidemiology is currently transitioning into the digital era and undergoing a paradigm shift from a data-driven to a value-driven strategy. Epidemiology data are characterized by uncertainty, multidimensionality, and disconnection, thereby correlating with the preferential quantum approach for data exposition, value creation, and modeling. Examples of such complex epidemiology data include the data on DNA viruses with associated symptoms and diseases. The Quantum Data Lake concept is proposed and consists of several layers and quantum tools, including Robson semantic triples, Quantum Universal Exchange Language, Hyperbolic Dirac Net, &amp;ldquo;quantum ribosome&amp;rdquo; structure, quantum random access memory, teleportation, Quantum Query Language, non-Hermitian gates, and tensor networks (e.g., matrix product state, projected entangled pair state, and multiscale entanglement renormalization ansatz [MERA]), alongside PT-symmetry properties. PT-symmetry can serve as an intuitive modeling tool, and PT-symmetry breaking can detect the hidden shift in the information that is permanently updated in the Data Lake. The computational output is presented as PT-symmetry gain/loss equilibrium breaking in the form of a complex number, i.e., two possible variants of epidemic modeling. For MERA, non-Hermiticity with spontaneous PT-symmetry breaking can theoretically appear as a violation of the entanglement monotonicity and local entanglement gain, leading to a non-reversible character of the coarse-graining transformation. The duality of PT-symmetry equilibrium breaking can be compared to, for example, the estimation of the best and worst scenarios simultaneously, or the gain of entanglement can display a significant correlation between some studied parameters embedded into the data. The fundamental difference between digital and quantum epidemiology is the implementation of quantum logic and reliance on a quantum theory.

Biobank-scale inference of multi-individual identity by descent and gene conversion

We present a method for efficiently identifying clusters of identical-by-descent haplotypes in biobank-scale sequence data. Our multi-individual approach enables much more computationally efficient inference of identity by descent (IBD) than approaches that infer pairwise IBD segments and provides locus-specific IBD clusters rather than IBD segments. Our method's computation time, memory requirements, and output size scale linearly with the number of individuals in the dataset. We also present a method for using multi-individual IBD to detect alleles changed by gene conversion. Application of our methods to the autosomal sequence data for 125,361 White British individuals in the UK Biobank detects more than 9 million converted alleles. This is 2,900 times more alleles changed by gene conversion than were detected in a previous analysis of familial data. We estimate that more than 250,000 sequenced probands and a much larger number of additional genomes from multi-generational family members would be required to find a similar number of alleles changed by gene conversion using a family-based approach. Our IBD clustering method is implemented in the open-source ibd-cluster software package.

Photonic delay reservoir computer based on ring resonator for reconfigurable microwave waveform generator.

To achieve an autonomously controlled reconfigurable microwave waveform generator, this study proposes and demonstrates a self-adjusting synthesis method based on a photonic delay reservoir computer with ring resonator. The proposed design exploits the ring resonator to configure the reservoir, facilitating a nonlinear transformation and providing delay space. A theoretical analysis is conducted to explain how this configuration addresses the challenges of microwave waveform generation. Considering the generalization performance of waveform generation, the simulations demonstrate the system's capability to produce six distinct representative waveforms, all exhibiting a highly impressive root mean square error (RMSE) of less than 1%. To further optimize the system's flexibility and accuracy, we explore the application of various artificial intelligence algorithms at the reservoir computer's output layer. Furthermore, our investigation delves deeply into the complexities of system performance, specifically exploring the influence of reservoir neurons and micro-ring resonator parameters on calculation performance. We also delve into the scalability of reservoirs, considering both parallel and cascaded arrangements.

Computational Fluid Dynamic Assessment of Patients with Congenital Heart Disease from 3D Rotational Angiography.

For congenital heart disease patients, multiple imaging modalities are needed to discern anatomy and functional information such as differential blood flow. During cardiac catheterization, 3D rotational angiography (3DRA) can provide CTA-like images, enabling anatomical information and intraprocedural guidance. We seek to establish whether unique aspects of this technique can also generate quantitative functional blood flow information. We propose that systematic integration of 3DRA imaging, catheter hemodynamic information, and computational fluid dynamics (CFD), can provide quantitative information regarding blood flow dynamics and energetics, without additional imaging or procedures. We report a single center retrospective feasibility study comprising four patients with 3DRA imaging and a complete set of hemodynamic data. 3DRA was processed and segmented to reconstruct vascular regions of interest (ROI), and a computational grid for CFD modeling of blood flow through the ROI was generated. Blood flow was simulated by integrating catheter hemodynamic data to devise boundary conditions at vascular ROI inlets and outlets. The 3DRA-based workflow successfully generated key computational outputs commonly used for cardiovascular applications, including flow patterns, distribution fractions, wall shear stress. Computational outputs obtained were as detailed and resolved as those obtained from more commonly used CT or MR angiography. Accuracy was confirmed by comparing computed flow distributions with measurements for 2 cases, showing less than 2.0% error from the measured data. Systematic integration of catheter hemodynamic information, 3DRA imaging, and CFD modeling, provides an effective and feasible alternative to obtain important quantitative blood flow information and visualization, without additional imaging.

Quantification of the crack propagation and global failure mechanism of single- and multi-ring masonry arch bridges

This paper presents the development of a novel crack-based damage quantification method to assess the damage accumulation in masonry arch bridges subjected to externally applied loading. Initially, a damage index was proposed, considering the initiation and extension of tensile and sliding cracks. Then, the proposed damage indexing was adopted into a two-dimensional discrete element code of analysis to evaluate single and multi-ring masonry arch bridges. The numerical model was validated by comparing the computational outputs against the experimental results obtained from the literature. Following the validation, masonry arch bridge models were developed, featuring the four most representative bond configurations in arch barrels. Also, different bond properties were assigned to investigate their impact on the global failure characteristics of single- and multi-ring masonry arch bridges. From the analyses of results of the multi-ring masonry arch bridges investigated in this study, it was shown that the rapid growth of tensile cracks in radial joints and the occurrence of shear slippage in circumferential joints between adjacent rings were observed to be early signs for multi-ring bridges before the ultimate load reached. By analysing the correlation between the normalised damage index with the normalised load, damage accumulation in different masonry arch bridges were identified. This study offers a novel perspective on quantitively evaluating the damage accumulation and failure mechanism of masonry arch bridges and the findings may provide valuable insights into the assessment and management of existing masonry arch bridges.

STUDY MAGNETIC ELECTRON SCATTERING FORM FACTOR OF 24Mg ISOTOPE

The magnetic form factor of 24Mg isotope under inelastic electron scattering was subjected to computational scrutiny via the utilization of the Oxbash code. The endeavor involved elucidating energy levels intrinsic to this nucleus through the application of the shell model, wherein the model space encompassed zbm, psd, spsdpf, and sd configurations. In the context of the sd model, the investigation harnessed two pioneering USD-type Hamiltonians, denoted as USDC and USDI, in conjunction with tailored amendments to these interaction potentials, referred to as USDCm and USDIm. This study culminated in comprehensively juxtaposing all computational outputs with empirical data sources, including information extracted from the National Nuclear Data Center (NNDC) repository. Significantly, this systematic examination underscored a notable congruence between the derived computational outcomes and the empirical observations. This alignment became conspicuously pronounced subsequent to judicious adjustments made to the g-factors, specifying values of = 1.060 and = -0.060. The profound unity between the findings of this study and experimental data manifests as compelling evidence, substantiating the efficacy and precision of the employed interaction models. It implies a reliable capacity of these models in the precise computation of magnetic form factors M1, M2, and M3

A new experimental design to study speech adaptations in spontaneous human-computer conversations

Interest is growing for how human interlocutors make phonetic adaptations during spontaneous conversations. Given the increasing popularity of AI chatbots, research also needs to account for adaptations in human–computer interactions, an area under-investigated presumably due to methodological challenges in generating controlled conversational responses. Most studies involve scripted computer output, which may obstruct the dynamicity and the oral–aural medium of a natural conversation. To circumvent these constraints, we present a new experimental design that generates unscripted audio computer responses in human–computer conversations during a collaborative game played on Zoom. This design is unique in several aspects. First, the game (Escape Room) requires discussions on placing pictures (depicting target words/sounds) in specific locations, where misperceptions of target words between interlocutors may cause confusions, thus motivating natural adaptations. Second, to enable real-time computer responses, we adopt the wizard-of-oz paradigm typically used in the field of human–computer interaction, where a human confederate inputs text responses behind-the-scenes. Third, a programmable text-to-speech synthesizer converts the text input to audio output. The design demonstrated in this presentation opens the door to new analyses, tracking the dynamicity of speech adjustments over time. Moreover, it is generalizable to studying speech adaptations across interlocutor backgrounds.

Co-creation and critical factors for the development of an efficient public e-tourism system

This study identifies the factors that guide the adoption of a public e-tourism system resulting in value co-creation in the UAE. Integrating and comparing factors drawn from the third version of the Technology Acceptance Model (TAM3), the Technology-Task-Fit (TTF) theory, and push-to-use, an Analytic Hierarch Process (AHP) model was implemented with data collected using a structured questionnaire from purposively selected UAE e-tourism experts (N = 15) and analyzed using Microsoft Excel. The findings revealed that usefulness, convenience of use, and push-to-use were the most critical aspects for achieving an efficient public e-tourism system that allows for value co-creation in that order of ranking. The findings also suggest that computer self-efficiency is the most critical factor in effectively establishing an e-tourism system followed by government push-to-use. In conclusion, the findings demonstrate that usefulness and ease-of-use backed by computer self-efficiency, result demonstrability, and output quality are vital for the efficient adoption of a public e-tourism system resulting in value co-creation in the UAE.

Decisionalizing the problem of reliance on expert and machine evidence

Abstract This article analyzes and discusses the problem of reliance on expert and machine evidence, including Artificial Intelligence output, from a decision-analytic point of view. Machine evidence is broadly understood here as the result of computational approaches, with or without a human-in-the-loop, applied to the analysis and the assessment of the probative value of forensic traces such as fingermarks. We treat reliance as a personal decision for the factfinder; specifically, we define it as a function of the congruence between expert output in a given case and ground truth, combined with the decision-maker’s preferences among accurate and inaccurate decision outcomes. The originality of this analysis lies in its divergence from mainstream approaches that rely on standard, aggregate performance metrics for expert and AI systems, such as aggregate accuracy rates, as the defining criteria for reliance. Using fingermark analysis as an example, we show that our decision-theoretic criterion for the reliance on expert and machine output has a dual advantage. On the one hand, it focuses on what is really at stake in reliance on such output and, on the other hand, it has the ability to assist the decision-maker with the fundamentally personal problem of deciding to rely. In essence, our account represents a model- and coherence-based analysis of the practical questions and justificatory burden encountered by anyone required to deal with computational output in forensic science contexts. Our account provides a normative decision structure that is a reference point against which intuitive viewpoints regarding reliance can be compared, which complements standard and essentially data-centered assessment criteria. We argue that these considerations, although primarily a theoretical contribution, are fundamental to the discourses on how to use algorithmic output in areas such as fingerprint analysis.

Explainable hierarchical clustering for patient subtyping and risk prediction.

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK's National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.

Characterizing the Atmospheric Dynamics of HD 209458b-like Hot Jupiters Using AI-driven Image Recognition/Categorization

In order to understand the results of recent observations of exoplanets, models have become increasingly complex. Unfortunately, this increases both the computational cost and output size of said models. We intend to explore if AI image recognition can alleviate this burden. We used DYNAMICO to run a series of HD 209458-like models with different orbital radii. Training data for a number of features of interest was selected from the initial outputs of these models. This was used to train a pair of multi-categorization convolutional neural networks (CNNs), which we applied to our outer-atmosphere-equilibrated models. The features detected by our CNNs revealed that our models fall into two regimes: models with shorter orbital radii exhibit significant global mixing that shapes the dynamics of the entire atmosphere, whereas models with longer orbital-radii exhibit negligible mixing except at mid-pressures. Here the initial nondetection of any trained features revealed a surprise: a nightside hot spot. Analysis suggests that this occurs when rotational influence is sufficiently weak that divergent flows from the dayside to the nightside dominate over rotational-driven transport, such as the equatorial jet. We suggest that image classification may play an important role in future, computational, atmospheric studies. However special care must be paid to the data feed into the model, from the color map, to training the CNN on features with enough breadth and complexity that the CNN can learn to detect them. However, by using preliminary studies and prior models, this should be more than achievable for future exascale calculations, allowing for a significant reduction in future workloads and computational resources.