Simulation Architecture Research Articles

Virtual simulators in the automated control system subsystem for training operators of metal-cutting machines

The article describes the proposed method of automated construction of virtual simulators for the subsystem of personnel training of the automated control systems of production. Its distinctive feature is the work with the customer based on the provision of functional videos from production, as well as the formalization of the design and development process in the form of structures for the process of creating software and visual modules taking into account the specifics of the development of virtual reality simulators. An ontological model has been developed for the construction of virtual simulators for training machine tool operators in the automated control systems of production, which uses the identified basic concepts and relationships between them in the subject area of work of machine tool operators. Its implementation is based on the Protege software environment. The following diagrams are proposed: functional and contextual IDEF0 of the functioning of virtual simulators for training in working on universal metal-cutting machines: milling and turning. The architecture of a virtual simulator for use in the subsystem of training personnel of automated control systems of production, as well as a structural diagram of the formation of practice-oriented skills in working on metal-cutting machines using virtual simulators are described.

The Role of Cache Memory In Enhancing Microprocessor Performance in PT. Srikandi Sinergi Sakti

Cache memory in microprocessors has an important role in improving computer system performance by reducing data access time. This research aims to test the hypothesis that increasing the size and level of cache memory can significantly improve microprocessor performance. The research methodology involves a literature study on the concept of cache memory and experimental simulations using computer architecture simulators, such as Gem5, to model scenarios with varying cache sizes and levels. In these simulations, performance parameters such as memory access latency, throughput, and Instructions Per Cycle (IPC) were measured and analyzed. The results show that increasing cache size and level generally contributes towards improving microprocessor performance by reducing data access time. Further statistical analysis supports the hypothesis that there is a positive correlation between cache size and level and system efficiency. These findings provide useful insights in future microprocessor architecture design and memory system optimization.

Cognitive Modeling for Effective Emergency Response: An Agent-Based Simulation Architecture

Cognitive Modeling for Effective Emergency Response: An Agent-Based Simulation Architecture

Analysis of Random Distortions in the Elements of the Basic Cell for an Analog-Digital Pipelined Converter

This paper presents an analysis of random distortions in the elements of the basic cell for an analog-digital pipelined converter. Pipelined converters are popular for their high sampling rates and resolution ranges. Each basic cell has a few-bit digital-analog converter connected to its output with a digital-to-analog converter to compare the input signal. This study aims to analyze the effects of random variations in the converters' elements. The proposed system is designed, with experiments conducted to observe the effects of distortions. The results of simulated architecture and distortion tests are presented, and the reached conclusions are presented in the fourth section. The basic cell used to build the converter is described in Equation 1, which describes the cell's operation. Six cells cascade-connected were used to construct the converter, with the input of the first block being the input and its output connected to the input of the second station. The true output of the converter is constructed by each of the output bits of each cell, with the bit of the first station being the most significant bit until reaching the output bit in the last cell.

Enhancing architectural space layout design by pretraining deep reinforcement learning agents

Abstract Space layout design is a fundamental yet complex problem in architecture. This task’s inherent complexity, arising from the need to balance numerous geometric configurations and topological relations while adhering to specific constraints, poses significant challenges. Recent advancements in deep reinforcement learning have shown promise in addressing similar planning problems, suggesting its potential utility for innovative space layout solutions. However, a critical limitation of deep reinforcement learning is its struggle with generalizing learned strategies to unseen scenarios. In the context of architectural design, this limitation could prevent deep reinforcement learning from being a scalable design method. Pretraining has emerged as a transformative strategy within the field of artificial intelligence, especially in the realm of foundational models, to enhance the generalization capabilities of learning algorithms. While pretraining is being the central focus of this paper, our approach diverges from conventional pretraining methods that focus on pixel-level design of layouts as is in diffusion based model. Instead, we propose an architectural simulation of space layout design that could embody the multifaceted essence of architectural design. To this end, we have developed a space layout simulator called SpaceLayoutGym that serves dual purposes: first, as an environment for the reinforcement learning agent to interact with and learn the intricacies of design, and second, as a tool for generating a dataset of design scenarios and their corresponding design solutions for model pretraining. We then used imitation learning to pretrain the agent on the generated training design scenarios. This process is being followed by a fine-tuning phase by using proximal policy optimization algorithm on new design scenarios. Our results demonstrate that pretraining can enhance the generalization capabilities of deep reinforcement learning in space layout design, paving the way for more adaptable and scalable artificial intelligence-aided architectural design.

A powerful framework for differential co-expression analysis of general risk factors.

Differential co-expression analysis (DCA) aims to identify genes in a pathway whose shared expression depends on a risk factor. While DCA provides insights into the biological activity of diseases, existing methods are limited to categorical risk factors and/or suffer from bias due to batch and variance-specific effects. We propose a new framework, Kernel-based Differential Co-expression Analysis (KDCA), that harnesses correlation patterns between genes in a pathway to detect differential co-expression arising from general (i.e., continuous, discrete, or categorical) risk factors. Using various simulated pathway architectures, we find that KDCA accounts for common sources of bias to control the type I error rate while substantially increasing the power compared to the standard eigengene approach. We then applied KDCA to The Cancer Genome Atlas thyroid data set and found several differentially co-expressed pathways by age of diagnosis and BRAF mutation status that were undetected by the eigengene method. Collectively, our results demonstrate that KDCA is a powerful testing framework that expands DCA applications in expression studies.

Integrated simulation architecture and key technologies for ship air defense and anti-missile defense

Integrated simulation architecture and key technologies for ship air defense and anti-missile defense

Architectural simulation from point clouds: Between precision and historical validity

Computer simulation of the incidence of natural light in heritage buildings has entailed a very fruitful field of experimentation within Digital Art History in recent years, although little has been discussed about its validity. This article seeks to demonstrate that although the accuracy of the results decreases when simulating the incidence of natural light of a heritage interior on a point cloud compared to a CAD, the margin of error can be accepted in historical studies. To carry out this test, the church of the Dominican convent of San Jacinto in Seville (Spain) has been used as a model. This interior has been analyzed with RELux and DIALux starting from a CAD and photogrammetry without simplifying it or cleaning it. With these examples, it can be concluded that these tools become useful for the study of historical buildings, despite their limitations.

Application and Prospect of Artificial Intelligence Technology in Low-Carbon Cities—From the Perspective of Urban Planning Content and Process

In the era of digital transformation, artificial intelligence (AI) technology—one of the swiftest growing emerging technologies—when integrated with urban planning, can introduce innovative approaches for low-carbon city development and foster the attainment of dual carbon objectives: carbon neutrality and peak carbon emissions. Current research predominantly investigates the influence and alterations of emerging technologies on urban elements, yet it overlooks a comprehensive examination of the applicable procedures of these technologies and their potential synergy with urban planning. Consequently, this study employs a systematic literature review to delve into the application of AI in sectors such as architecture, transportation, land use, and green space development. It categorizes the specific impact processes into monitoring, identification, simulation, and prediction. By offering an exhaustive analysis of urban planning’s content and methodology, this paper elucidates the role of AI technology in the creation of low-carbon cities. The study found that: (1) Due to the varying degrees of application and integration with professional technologies in different fields, the current research focuses more on architecture, land use, and transportation. (2) Combining the four steps of urban planning, artificial intelligence can be divided into monitoring, recognition, simulation, and prediction types, each with its own characteristics. (3) Overall, AI technology is mainly applied in the identification and simulation of architecture, transportation, and land use. (4) There is still room for improvement in the application of AI technology in waste emissions and other algorithms.

Accuracy of Genomic Predictions Cross Populations with Different Linkage Disequilibrium Patterns.

There is a considerable global population of beef cattle, with numerous small-scale groups. Establishing separate reference groups for each breed in breeding practices is challenging, severely limiting the genome selection (GS) application. Combining data from multiple populations becomes particularly attractive and practical for small-scale populations, offering increased reference population size, operational ease, and data sharing. To evaluate potential for Chinese indigenous cattle, we evaluated the influence of combining multiple populations on genomic prediction reliability for 10 breeds using simulated data. Within-breed evaluations consistently yielded the highest accuracies across various simulated genetic architectures. Genomic selection accuracy was lower in Group B populations referencing a Group A population (n = 400), but significantly higher in Group A populations with the addition of a small Group B (n = 200). However, accuracy remained low when using the Group A reference group (n = 400) to predict Group B. Incorporating a few Group B individuals (n = 200) into the reference group resulted in relatively high accuracy (~60% of Group A predictions). Accuracy increased with the growing number of individuals from Group B joining the reference group. Our results suggested that multi-breed genomic selection was feasible for Chinese indigenous cattle populations with genetic relationships. This study's results also offer valuable insights into genome selection of multipopulations.

Proactive–reactive microservice architecture global scaling

We develop a novel approach for run-time global adaptation of microservice applications, based on synthesis of architecture-level reconfigurations. More precisely, we devise an algorithm for proactive–reactive automatic scaling that reaches a target system’s Maximum Computational Load by performing optimal deployment orchestrations. We evaluate our approach by developing a platform for the modeling and simulation of microservice architectures, and we use such a platform to compare local/global and reactive/proactive scaling. Empirical benchmarks, obtained through our platform, show that proactive global scaling consistently outperforms the reactive approach, but the best performances can be obtained by our original approach for mixing proactivity and reactivity. In particular, our approach surpasses the state-of-the-art when both performance and resource consumption are considered.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Coiled Coil Peptide Tiles (CCPTs): Expanding the Peptide Building Block Design with Multivalent Peptide Macrocycles.

Owing to their synthetic accessibility and protein-mimetic features, peptides represent an attractive biomolecular building block for the fabrication of artificial biomimetic materials with emergent properties and functions. Here, we expand the peptide building block design space through unveiling the design, synthesis, and characterization of novel, multivalent peptide macrocycles (96mers), termed coiled coil peptide tiles (CCPTs). CCPTs comprise multiple orthogonal coiled coil peptide domains that are separated by flexible linkers. The constraints, imposed by cyclization, confer CCPTs with the ability to direct programmable, multidirectional interactions between coiled coil-forming "edge" domains of CCPTs and their free peptide binding partners. These fully synthetic constructs are assembled using a convergent synthetic strategy via a combination of native chemical ligation and Sortase A-mediated cyclization. Circular dichroism (CD) studies reveal the increased helical stability associated with cyclization and subsequent coiled coil formation along the CCPT edges. Size-exclusion chromatography (SEC), analytical high-performance liquid chromatography (HPLC), and fluorescence quenching assays provide a comprehensive biophysical characterization of various assembled CCPT complexes and confirm the orthogonal colocalization between coiled coil domains within CCPTs and their designed on-target free peptide partners. Lastly, we employ molecular dynamics (MD) simulations, which provide molecular-level insights into experimental results, as a supporting method for understanding the structural dynamics of CCPTs and their complexes. MD analysis of the simulated CCPT architectures reveals the rigidification and expansion of CCPTs upon complexation, i.e., coiled coil formation with their designed binding partners, and provides insights for guiding the designs of future generations of CCPTs. The addition of CCPTs into the repertoire of coiled coil-based building blocks has the potential for expanding the coiled coil assembly landscape by unlocking new topologies having designable intermolecular interfaces.

Buildings as Batteries

Aggregators are increasingly using portfolios of various end-user resources such as local renewable generation, batteries, EVs, and demand-side management to participate in the electricity markets as virtual power plants. Whilst the smart control of energy within buildings is well established for cost optimization, the prospect of an aggregator collaborating with the energy managers of buildings to trade electricity in the wholesale markets is under researched. This paper uses a state-of-the-art architectural simulator to model the thermal inertia of a typical high-rise building in London and represent the thermodynamics of its hourly occupancy and weather responses. The building is simulated as a virtual battery by manipulating the HVAC settings. Price arbitrage on the electricity wholesale market and contracting with the local distribution network for flexible reserve services are both feasible and profitable. It is concluded that aggregators can apparently use buildings as additional assets within their trading portfolios. JEL Classification: L85 and L94.

Early-stage design support for ice-shell architecture with integrated snow drift simulation tool

The data on snow distribution on roofs can aid in optimizing the weatherability of ice-shell architecture. However, there is currently a lack of technology for predicting snow drift in early-stage design. The study proposes a snow drift simulation method for ice shells, utilizing Butterfly to call the OpenFOAM solver. A snow drift simulation tool based on this method was developed and validated through three case studies. The validation results provide suggestions for mesh accuracy and time steps for typical ice-shell architecture simulation with the tool. Simulation results from the tool are used to analyze the impact of ice-shell architectural specific features (texture, plane, orientation) on weatherability, focusing on the coupling optimization of snow and radiation for typical ice shells. The study demonstrates the tool's effectiveness, simplifying the simulation process and extending its applicability to other cold-region buildings. The feature analysis indicates that the ribbed texture more effectively blocks solar radiation through snow distribution.

Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing

Abstract. We provide a detailed description of the ice-sheet layer age tracer Englacial Layer Simulation Architecture (ELSA) – a model that uses a straightforward method to simulate the englacial stratification of large ice sheets – as an alternative to Eulerian or Lagrangian tracer schemes. ELSA's vertical axis is time, and individual layers of accumulation are modeled explicitly and are isochronal. ELSA is not a standalone ice-sheet model but requires unidirectional coupling to another model providing ice physics and dynamics (the “host model”). Via ELSA's layer tracing, the host model’s output can be evaluated throughout the interior using ice core or radiostratigraphy data. We describe the stability and resolution dependence of this coupled modeling system using simulations of the last glacial cycle of the Greenland ice sheet using one specific host model. Key questions concern ELSA's design to maximize usability, which includes making it computationally efficient enough for ensemble runs, as well as exploring the requirements for offline forcing of ELSA with output from a range of existing ice-sheet models. ELSA is an open source and collaborative project, and this work provides the foundation for a well-documented, flexible, and easily adaptable model code to effectively force ELSA with (any) existing full ice-sheet model via a clear interface.

A hierarchical Bilayered scaffold for periodontal complex structure regeneration.

The periodontal tissue comprises alveolar bone, cementum, and periodontal ligament (PDL), forming a highly hierarchical architecture. Although current therapies could regenerate the hard tissue well, the simultaneous reconstruction of hard and soft tissue remains a great clinical challenge with the major difficulty in highly orientated PDL regeneration. Using the unidirectional freeze-casting method and biomimetic mineralization technique, we construct a hierarchical bilayer scaffold with the aligned chitosan scaffold with ZIF-8 resembling PDL, and intrafibrillarly mineralized collagen resembling alveolar bone. The hierarchical bilayer scaffold exhibits different geomorphic clues and chemical microenvironments to realize a perfect simulation of the natural periodontal hierarchical architecture. The aligned scaffold with ZIF-8 could induce the fibrogenic differentiation of bone mesenchymal stromal cells (BMSCs), and the mineralized scaffold could induce osteogenic differentiation of BMSCs. The hierarchical bilayer scaffold could simulate periodontal complex tissue, exhibiting great promise for synchronized multi-tissue regeneration of periodontal tissue.

Hippocampal storage and recall of neocortical "What"-"Where" representations.

A key question for understanding the function of the hippocampus in memory is how information is recalled from the hippocampus to the neocortex. This was investigated in a neuronal network model of the hippocampal system in which "What" and "Where" neuronal firing rate vectors were applied to separate neocortical modules, which then activated entorhinal cortex "What" and "Where" modules, then the dentate gyrus, then CA3, then CA1, then the entorhinal cortex, and then the backprojections to the neocortex. A rate model showed that the whole system could be trained to recall "Where" in the neocortex from "What" applied as a retrieval cue to the neocortex, and could in principle be trained up towards the theoretical capacity determined largely by the number of synapses onto any one neuron divided by the sparseness of the representation. The trained synaptic weights were then imported into an integrate-and-fire simulation of the same architecture, which showed that the time from presenting a retrieval cue to a neocortex module to recall the whole memory in the neocortex is approximately 100 ms. This is sufficiently fast for the backprojection synapses to be trained onto the still active neocortical neurons during storage of the episodic memory, and this is needed for recall to operate correctly to the neocortex. These simulations also showed that the long loop neocortex-hippocampus-neocortex that operates continuously in time may contribute to complete recall in the neocortex; but that this positive feedback long loop makes the whole dynamical system inherently liable to a pathological increase in neuronal activity. Important factors that contributed to stability included increased inhibition in CA3 and CA1 to keep the firing rates low; and temporal adaptation of the neuronal firing and of active synapses, which are proposed to make an important contribution to stabilizing runaway excitation in cortical circuits in the brain.

Delineation of the impact on temporal behaviors of off-axis photoemission in an ultrafast electron microscope

Efforts to push the spatiotemporal imaging-resolution limits of femtosecond laser-driven ultrafast electron microscopes (UEMs) to the combined angstrom–fs range will benefit from stable sources capable of generating high bunch charges. Recent demonstrations of unconventional off-axis photoemitting geometries are promising, but connections to the observed onset of structural dynamics are yet to be established. Here we use the in-situ photoexcitation of coherent phonons to quantify the relative time-of-flight (r-TOF) of photoelectron packets generated from the Ni Wehnelt aperture and from a Ta cathode set-back from the aperture plane. We further support the UEM experiments with particle-tracing simulations of the precise electron-gun architecture and photoemitting geometries. In this way, we measure discernible shifts in electron-packet TOF of tens of picoseconds for the two photoemitting surfaces. These shifts arise from the impact that the Wehnelt-aperture off-axis orientation has on the electron-momentum distribution, which modifies both the collection efficiency and the temporal-packet distribution relative to on-axis emission. Future needs are identified; we expect this and other developments in UEM electron-gun configuration to expand the range of material phenomena that can be directly imaged on scales commensurate with fundamental structural dynamics.

A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods.

The internal arrangement of a muscle's fibers with respect to its mechanical line of action (muscle architecture) is a major determinant of muscle function. Muscle architecture can be quantified using diffusion tensor magnetic resonance imaging-based tractography, which propagates streamlines from a set of seed points by integrating vectors that represent the direction of greatest water diffusion (and by inference, the local fiber orientation). Previous work has demonstrated that tractography outcomes are sensitive to the method for defining seed points, but this sensitivity has not been fully examined. To do so, we developed a realistic simulated muscle architecture and implemented four novel methods for tract seeding: seeding along the muscle-aponeurosis boundary with an updated procedure for rounding seed points prior to lookup in the muscle boundary mask and diffusion tensor matrix (APO-3); voxel-based seeding throughout the muscle volume at a user-specified spatial frequency (VXL-1); voxel-based seeding throughout the muscle volume at a variable spatial frequency (VXL-2), and seeding near external and internal muscle boundaries (VXL-3). We then implemented these methods in an example human dataset. The updated aponeurosis seeding procedures allow more accurate and robust tract propagation from seed points. The voxel-based seeding methods had quantification outcomes that closely matched the updated aponeurosis seeding method. Further, the voxel-based methods can accelerate the overall workflow and may be beneficial in high throughput analysis of multi-muscle datasets. Continued evaluation of these methods in a wider range of muscle architectures is warranted.

Influence of Material Properties on Ruggedness Evaluation of Package Architectures for SiC Power Devices

Button shear tests at different temperatures between different mold compounds and Cu-leadframes have been performed to evaluate the adhesion of mold compounds to the inner surfaces of SiC-power devices. The results at different temperatures show the behavior of different material and layer stack combinations under storage at room temperatures as well as under operating conditions with elevated temperatures, where adhesion and thus the hermeticity of the device against moisture is significantly lowered. It has been shown that different thermomechanical properties of the mold compounds as well as the usage of different adhesion promoter materials have a significant effect on the adhesion properties of the mold compound to the leadframe surface of the SiC power devices, which have direct impact on the ruggedness and lifetime stability. Furthermore, these results have been correlated to thermomechanical simulations of the package architecture of SiC power devices under stress. Different wire bond architectures have been evaluated in simulations with and without die top delamination taken into account, showing that EMC delamination may play a major role in the lifetime stability of SiC power devices under thermomechanical stress like simulated in TCT- , IOL- and PTC-testing.