Model Library Research Articles

Strategy for HBIM implementation using high-resolution 3D architectural documentation based on laser scanning and photogrammetry of the José de Alencar theatre

Cultural heritage (CH) relevant importance to society leads to the need for historical, architectural, historical, and structural documentation. Such documents are necessary for the effective conservation and restoration of heritage buildings. Digitalization and documentation have been challenging and multidisciplinary topics for cultural heritage (CH) involving new technologies for information capture. 3D architectural documentation of existing heritage buildings is possible due to digital survey technologies joined with BIM methodology. The José de Alencar Theatre was surveyed with an integrated approach using a laser scanner and photogrammetry. BIM methodology based on digital scanning and image-based data was applied to generate a 3D model and extensive BIM library. The results indicate that a reliable BIM model of the José de Alencar theatre can be done, generating useful documentation and information on the historical construction based on the laser scanner and photogrammetry data with architectural geometry well-detailed.

Using PharmaPy with Jupyter Notebook to teach digital design in pharmaceutical manufacturing.

The use of digital tools in pharmaceutical manufacturing has gained traction over the past two decades. Whether supporting regulatory filings or attempting to modernize manufacturing processes to adopt new and quickly evolving Industry 4.0 standards, engineers entering the workforce must exhibit proficiency in modeling, simulation, optimization, data processing, and other digital analysis techniques. In this work, a course that addresses digital tools in pharmaceutical manufacturing for chemical engineers was adjusted to utilize a new tool, PharmaPy, instead of traditional chemical engineering simulation tools. Jupyter Notebook was utilized as an instructional and interactive environment to teach students to use PharmaPy, a new, open-source pharmaceutical manufacturing process simulator. Students were then surveyed to see if PharmaPy was able to meet the learning objectives of the course. During the semester, PharmaPy's model library was used to simulate both individual unit operations as well as multiunit pharmaceutical processes. Through the initial survey results, students indicated that: (i) through Jupyter Notebook, learning Python and PharmaPy was approachable from varied coding experience backgrounds and (ii) PharmaPy strengthened their understanding of pharmaceutical manufacturing through active pharmaceutical ingredient process design and development.

SFQEDtoolkit: A high-performance library for the accurate modeling of strong-field QED processes in PIC and Monte Carlo codes

Strong-field quantum electrodynamics (SFQED) processes are central in determining the dynamics of particles and plasmas in extreme electromagnetic fields such as those present in the vicinity of compact astrophysical objects or generated with ultraintense lasers. SFQEDtoolkit is an open source library designed to allow users for a straightforward implementation of SFQED processes in existing particle-in-cell (PIC) and Monte Carlo codes. Through advanced function approximation techniques, high-energy photon emission and electron-positron pair creation probability rates and energy distributions are calculated within the locally-constant-field approximation (LCFA) as well as with more advanced models [Phys. Rev. A 99, 022125 (2019)]. SFQEDtoolkit is designed to provide users with high-performance and high-accuracy, and neat examples showing its usage are provided. In the near future, SFQEDtoolkit will be enriched to model the angular distribution of the generated particles, i.e., beyond the commonly employed collinear emission approximation, as well as to model spin and polarization dependent SFQED processes. Notably, the generality and flexibility of the presented function approximation approach makes it suitable to be employed in other areas of physics, chemistry and computer science. Program summaryProgram Title: SFQEDtoolkit (version 1.0)CPC Library link to program files:https://doi.org/10.17632/j7mhj3cg3m.1Developer's repository link:https://github.com/QuantumPlasma/SFQEDtoolkitLicensing provisions: GPLv3Programming language: C++, FortranNature of problem: The accurate simulation of strong-field QED processes in the interaction of particles or plasmas with strong electromagnetic fields is critical for present and future experiments with ultraintense lasers as well as to unveil the dynamics around compact astrophysical objects. State-of-the-art PIC and Monte Carlo codes implement strong-field QED processes by means of lookup tables, where the approximated function is evaluated on a fixed set of points. This typically results in numerical artifacts such as stairlike structures in the approximated function, and in a reduced efficiency when large size lookup tables are necessary. An easily implementable open-source accurate and efficient simulation tool is required to address problems at the high-intensity frontier of plasma physics and astrophysics.Solution method: Strong-field QED process rates and particle distributions are calculated by leveraging advanced approximation methods based on variable and function transformation, asymptotic expansions as well as Chebyshev polynomial expansions. This provides a numerically-stable and efficient approach that allows us to overcome all limitations of existing methods based on standard lookup tables.

Vehicle Counting And Traffic Congestion Detection Using YOLOv3

The purpose of this project is to utilize Python modules and a sophisticated deep learning algorithm to identify, categorize, track, and tally moving vehicles from highway CCTV footage. Additionally, the system predicts traffic congestion by analyzing the number of vehicles in consecutive video frames. When congestion is detected, the application automatically notifies the traffic police who receive a message on their mobile, prompting them to address the existing traffic jam in the area. The project's core involves a vision-based vehicle detection and counting system that relies on the YOLOv3 model and openCv-python library to achieve its objectives.

Hyperparameter optimization method based on dynamic Bayesian with sliding balance mechanism in neural network for cloud computing

Hyperparameter optimization (HPO) of deep neural networks plays an important role of performance and efficiency of detection networks. Especially for cloud computing, automatic HPO can greatly reduce the network deployment cost by taking advantage of the computing power. Benefiting from its global-optimal search ability and simple requirements, Bayesian optimization has become the mainstream optimization method in recent years. However, in a non-ideal environment, Bayesian method still suffers from the following shortcomings: (1) when search resource is limited, it can only achieve inferior suboptimal results; (2) the acquisition mechanism cannot effectively balance the exploration of parameter space and the exploitation of historical data in different search stages. In this paper, we focused on the limited resources and big data provided by the cloud computing platform, took the anchor boxes of target detection networks as the research object, employed search resource as a restraint condition, and designed a dynamic Bayesian HPO method based on sliding balance mechanism. The dynamism of our method is mainly reflected in two aspects: (1) A dynamic evaluation model is proposed which uses the cross-validation mechanism to evaluate the surrogate model library and select the best model in real time; (2) A sliding balance mechanism is designed based on resource constraints to seek a balance between exploration and exploitation. We firstly augment the recommended samples of probability of improvement acquisition function by using k-nearest neighbor method, then introduce Hausdorff distance to measure the exploration value and match sampling strategy with resource utilization, which makes it slide smoothly with resource consumption to establish a dynamic balance of exploration to exploitation. The provided experiments show that our method can quickly and stably obtain better results under the same resource constraints compared with mature methods like BOHB.Graphical

Design reuse method of corn picking device based on case-based reasoning

In order to shorten the design cycle of corn snapping mechanism, a case reuse design method of snapping devices based on user requirements was proposed. A matter-element model is used to build a case matter-element database and parametric model library together to form a case database; the case attributes are divided, the retrieval scope is narrowed through the matching of core parameters, and the similarity of matching parameters and performance evaluation parameters is calculated by using analytic hierarchy process and deviation maximization method to realize the retrieval of similar cases. The transformation relationship between the design requirements and the main driving parameters of the parametric model is established by using the rule association method to realize the case’s modification. The engineering discrete element method is used to simulate the reuse case, and an improved method is proposed according to the simulation results. The improved device is verified by simulation and field experiments. The results show that the operating performance of the improved snapping device is improved, and the feasibility and effectiveness of the design reuse method are verified, which can provide technical reference for the intelligent design of agricultural machinery and equipment.

Forest Fire Spread Simulation and Fire Extinguishing Visualization Research

There are three main types of forest fires: surface fires, tree crown fires, and underground fires. The frequency of surface fires and tree crown fires accounts for more than 90% of the overall frequency of forest fires. In order to construct an immersive three-dimensional visualization simulation of forest fires, various forest fire ignition methods, forest fire spread, and fire extinguishing simulation exercises are studied. This paper proposes a lightweight forest fire spread method based on cellular automata applied to the virtual 3D world. By building a plant model library using cells to express different plants, and by building a 3D geometric model of plants to truly capture the combustion process of a single plant, we can further simulate forest-scale fire propagation and analyze the factors that affect forest fire spread. In addition, based on the constructed immersive forest scene, this study explored various forms of fire extinguishing methods in the virtual environment, mainly liquid flame retardants such as water guns, helicopter-dropped flame retardants, or simulated rainfall. Therefore, the forest fire occurrence, spread, and fire extinguishing process can be visualized after the interactive simulation is designed and implemented. Finally, this study greatly enhanced the immersion and realism of the 3D forest fire scene by simulating the changes in plant materials during the spread of a forest fire.

Multi-Core ARM-Based Hardware-Accelerated Computation for Spiking Neural Networks

Distributed edge computing platforms are of great significance for the implementation of brain-like computing research. Due to the limited power consumption and real-time requirements, hardware acceleration of computing units is a challenging task. Taking advantage of both scalable hardware framework and lower cost, this paper designs a multi-core distributed computing platform for spiking neural networks. Particularly, a shared memory partition structure is utilized to participate in hardware acceleration. Through the spike-queue-based synaptic mapping mechanism, each parallel computing unit deals with efficient point-to-point connections. In addition, this platform provides a basic community unit (BCU) that encapsulates a standard neuron model library and rich peripheral interfaces. With the support of GUI, users can quickly build large-scale systems. The experimental results show that a single BCU can accommodate more than 10,000 neurons updated in real time at a power consumption of 273.6mW. The extended BCU group is able to perform network dynamic simulations in the basal ganglia-thalamus plausible biological network composed of H-H neurons as well as MNIST dataset classification in the LIF network. The outstanding flexibility and real-time performance of the proposed hardware architecture provide great potential for embedded applications of neural computation.

Configurable Intelligent Design Based on Hierarchical Imitation Models

The deterministic AI system under review is an alternative to neural-network-based machine learning. In its application fields, which are science, technology, engineering, and business, the implementation of rule-based AI systems leads to benefits such as accuracy and correctness of design, and personalization of the process itself and the results. An algorithmic AI suite is based on design and logical imitation models alone, without creating and/or using Big Data and knowledge bases. Excessive complexity of configuration and high design resource capacity, which are inherent in deterministic systems, are balanced by a special methodology. A hierarchical modeling approach gives a quasi-dynamic network effect, symmetric to the analogous effect in neural networks. System performance is improved by deterministic reference training capable of modifying imitation models in online interaction with users. Such training, which serves as an alternative to neural machine learning, can be implemented by means of experimental partially empirical algorithms and system–user dialogues to build reference model libraries (portfolios). Partially empirical algorithms based on experimental design methods and system user dialogues are used to create reference model libraries (portfolios) that form a deterministic training system, which can be an alternative to neural machine learning. Estimated resources can be saved by using modified optimization techniques and by controlling the computational complexity of the algorithms. Since the proposed system in the considered layout has no analogues, and the relevant research and practical knowledge are extremely limited, special methods are required to implement this project. A gradual, phased implementation process involves the step-by-step formation of sets of algorithms with verification tests at each stage. Each test is performed using an iteration method, and each test includes test, tweak, and modification cycles. Final testing should lead to the development of an AI algorithm package, including related methodological and working papers.

GEOMETRIC MODELLING OF HETEROGENEOUS LATTICE STRUCTURES THROUGH FUNCTION REPRESENTATION WITH LATTICEQUERY

AbstractLattice structures are lightweight and possess other unique mechanical and physical properties. Additive manufacturing techniques are often used to produce these structures. Additive manufacturing provides manufacturing freedom that significantly surpasses the one provided by conventional subtractive manufacturing. However, a gap exists between the manufacturing freedom and the geometric modelling freedom in additive manufacturing: it can be extremely challenging to model the designed part because of its high geometric complexity, such as heterogeneous lattice structures. While several tools on the market allow geometric modelling of such structures available on the market, the customization of lattice parameters can still be significantly improved. Moreover, no open-source tools exist to address this issue or to model lattice structures in general. This work presents a novel open-source library for the geometric modelling of lattice structures with customized parameters. The parameter customization is enabled with the function representation approach.

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library.

Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies. Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values. We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation. A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.

SMILES SSPs: a library of semi-empirical MILES stellar population models with variable [α/Fe] abundances

ABSTRACT We present a new library of semi-empirical stellar population models that are based on the empirical MILES and semi-empirical sMILES stellar libraries. The models span a large range of age and metallicity, in addition to an [α/Fe] coverage from −0.2 to +0.6 dex, at MILES resolution (FWHM = $2.5\,$ Å) and wavelength coverage ($3540.5-7409.6\,$ Å). These models are aimed at exploring abundance ratios in the integrated light from stellar populations in star clusters and galaxies. Our approach is to build SSPs from semi-empirical stars at particular [α/Fe] values, thus producing new SSPs at a range of [α/Fe] values from sub-solar to super-solar. We compare these new SSPs with previously published and well-used models and find similar abundance pattern predictions, but with some differences in age indicators. We illustrate a potential application of our new SSPs, by fitting them to the high signal-to-noise data of stacked SDSS galaxy spectra. Age, metallicity, and [α/Fe] trends were measured for galaxy stacks with different stellar velocity dispersions and show systematic changes, in agreement with previous analyses of subsets of those data. These new SSPs are made publicly available.

Experimental Models for Rare Melanoma Research—The Niche That Needs to Be Addressed

Melanoma, the tumor arising from the malignant transformation of pigment-producing cells—the melanocytes—represents one of the most severe cancer types. Despite their rarity compared to cutaneous melanoma, the extracutaneous subtypes such as uveal melanoma (UM), acral lentiginous melanoma (ALM), and mucosal melanoma (MM) stand out due to their increased aggressiveness and mortality rate, demanding continuous research to elucidate their specific pathological features and develop efficient therapies. Driven by the emerging progresses made in the preclinical modeling of melanoma, the current paper covers the most relevant in vitro, in vivo, and in ovo systems, providing a deeper understanding of these rare melanoma subtypes. However, the preclinical models for UM, ALM, and MM that were developed so far remain scarce, and none of them is able to completely simulate the complexity that is characteristic to these melanomas; thus, a continuous expansion of the existing library of experimental models is pivotal for driving advancements in this research field. An overview of the applicability of precision medicine in the management of rare melanoma subtypes is also provided.

To Close is Impossible to Leave it: Where Should be Put a Comma?

The article is a response to the article by V. K. Stepanov “A great reduction in quantity of libraries: particular management errors or a general perspective for the field (analysis of the experience of the Moscow region)”, published in the previous issue of Bibliosphere. Three main theoretical and methodological contradictions that reduce the reliability of the results obtained by the author and the validity of the recommendations proposed by him for regional librarianship are identified and analyzed.The first of them concerns the departure from the principle of historicism, as well as the complexity of the consideration of the phenomenon under study, and as a result the social nature of librarianship is presented incorrectly by the author of the article. It is noted that the basic social function determining the vector of domestic library construction was the function of educating a Soviet person, his ideological, cultural, scientific and technical enlightenment through the guidance of book reading in a specially selected library fund. Intermediary functions were associated with the shortcomings of book publishing in the field of leisure literature and played a secondary role. Thus, automation and computerization of book publishing and bookselling affect librarianship only indirectly, since the material nature of book publishing does not affect the cultural and educational functions of libraries.The second contradiction is related to the correctness of the analysis and interpretation of statistical data reflecting the process of library development in the Moscow region. The analysis of statistics shows that the negative dynamics in regional library construction is caused by the process of reduction of settlements, its natural decline and aging of the population. The increase in the number of users of municipal libraries indicates that the reduction in the number of libraries does not lead to a decrease in the level of library services of the population.The third contradiction concerns following the principle of scientific objectivity of research. The author noted only negative trends, leaving regional library construction programs aimed at the development of model libraries outside the scope of his analysis.

A conceptual digital twin framework for city logistics

Urban logistics is one of the key elements of urban mobility planning. The use of real-time information systems in logistics operations generates an enormous amount of data, nowadays used mainly for the purpose of monitoring and control of large flows of goods. At the same time, urban planners, business stakeholders, and city administrators are in need of adaptive, data-driven decision support solutions to address today's urban logistics problems. Recently, digital twins have received a lot of attention to support advanced experimentation, simulation and decision-making for on-demand logistics operations. Questions still remain on how to realize these for urban logistics management in a mixed public-private stakeholder context. We argue that this lack of a specific framework for city logistics with a model library for data mergers, linking physical and virtual data exchange, can compromise the timely adoption of digital twin technology. We contribute to filling this gap by presenting a systematic review of the literature, proposing a conceptual framework for digital twin applications in urban logistics, and providing use case scenarios for their demonstration. Together, these should advance the technical implementation of digital twins in a sustainable city logistics context.

MachineTFBS: Motif-based method to predict transcription factor binding sites with first-best models from machine learning library

This study aims to model the high-affinity transcription factor (TF) binding sites (TFBSs) in Saccharomyces cerevisiae promoters using high-throughput experimental TF binding data to train, test, and validate models. We introduce MachineTFBS, a machine learning based TFBS prediction tool consisting of a library of machine learning models generated by treating each TF's protein binding microarray (PBM) dataset as an individual optimization problem and selectively generating the first-best performing models. Since the modeling quality of machine learning methods varies for different TFs, we tested Random Forest, eXtreme Gradient Boosting, and up to 5-depth Deep Learning models. We extracted 274 high-resolution PBM datasets of 159 S. cerevisiae TFs from the UniProbe database. We designed an algorithm for the greedy selection of the first-best models and feature combinations from five feature extraction functions for each PBM data. The optimal subset of the functions was identified using a feature elimination algorithm. The feature elimination algorithm advances in successive cycles for each PBM data until the elimination of any feature(s) improves model performance or the feature set size is reduced to 1; in turn, reports the optimal subset. MachineTFBS predicts TFBSs with an average Matthews Correlation Coefficient score of 0.873.

Relative contributions of different neural sources to the EEG

Dogma dictates that the EEG signal is generated by postsynaptic currents (PSCs) because there are an enormous number of synapses in the brain, and PSCs have relatively long durations. However, PSCs are not the only potential source of electric fields in the brain. Action potentials, afterpolarizations, and presynaptic activity can also generate electric fields. Experimentally it is exceedingly difficult to delineate the contributions of different sources because they are casually linked. However, using computational modeling, we can interrogate the relative contributions of different neural elements to the EEG. We used a library of neuron models with morphologically realistic axonal arbors to quantify the relative contributions of PSCs, action potentials, and presynaptic activity to the EEG signal. Consistent with prior assertions, PSCs were the largest contributor to the EEG, but action potentials and afterpolarizations can also make appreciable contributions. For a population of neurons generating simultaneous PSCs and action potentials, we found that the action potentials accounted for up to 20% of the source strength while PSCs accounted for the other 80% and presynaptic activity negligibly contributed. Additionally, L5 PCs generated the largest PSC and action potential signals indicating that they the dominant EEG signal generator. Further, action potentials and afterpolarizations were sufficient to generate physiological oscillations, indicating that they are valid source contributors to the EEG. The EEG emerges from a combination of multiple different source, and, while PSCs are the largest contributor, other sources are non-negligible and should be included in modeling, analysis and interpretation of the EEG.

Fast advancing layer method for viscous mesh generation

An efficient Advancing Layer Method (ALM) is presented to create semi-structured prisms on viscous walls, in which a procedure that checks possible front intersections is essential to its efficiency. This paper develops various novel schemes to improve the algorithm’s efficiency precisely while not sacrificing its robustness and the resulting mesh quality. First, it employs a set of new techniques, and data structures are developed to improve the efficiency of the front-check procedure. Then, within each octant, a new filter is developed to reduce the intersection computations in the searching process. In addition, data structures are well designed to store the contiguously accessed data in each computing-intensive loop in a contiguous space for a potentially better cache hit ratio. We built a geometry model library formed by examples of industrial complexity to demonstrate the practicability of the algorithm. All the efforts mentioned above enable us to reduce the percentage of computing time taken by intersection check to an acceptable level (approximately 26%), which make it no longer be the most time-consuming part.

Off-design performance of micro-scale solar Brayton cycle

A novel methodology to design a micro-scale, solar-only, air-breathing, open Brayton cycle and assess its on- and off-design performance. The methodology is applied to generate and assess six thermodynamic layouts over a range of solar irradiation levels. All plants have the same on-design requirements to create a baseline to compare their off-design performance. PyCycle, a thermodynamic cycle modeling library to model jet engine performance, is revised to transform the jet engine performance modeling to solar thermal plant performance modeling and used to create a volumetric receiver component. A response surface surrogate model of the receiver is created for design optimization to maximize the component-level efficiency. The compressor and turbine maps are scaled for the balance of the plant. Off-design efficiency, mass flow rate, operation range, turbomachinery maps, and maximum power output are presented. Since the methodology can be adapted to all plant sizes, the results are normalized to on-design condition. The outcome of this study demonstrates the impact of the thermodynamic configuration on off-design performance and provides a methodology to design plants that are more robust across a range of solar irradiation levels and can be operated in a more flexible manner. Compared to single shaft configuration, solar radiation operation range is improved by 5%, with 6% less mass flow, and operates more efficiently than the benchmark case over 85% of the operating regime.

Electric Load Modeling for Managing Electric Energy Consumption Through the PDCA Cycle in the Library Building of Andalas University

The need for electrical energy in the public building sector is quite dominant, reaching 33-40% of the total energy consumption of all sectors. To control electrical energy in buildings effectively, an Energy Management System (EMS) is needed. EMS stage consists of Plan-Do-Check-Act (PDCA). The study conducted in this research is on how to use the PDCA cycle method to model the consumption of electrical energy in campus buildings in a sustainable manner. Modeling of energy-efficient electrical loads is formed in several ways: modeling by replacing T8 fluorescent lamps and CFLs with LED, modeling by replacing non-inverter AC with inverter AC, modeling by the number of library visitors, modeling by improving the power factor, and finally, modeling with a combination of the four previous models. Modeling using LEDs results in savings of 1,464 kWh per month; inverter AC modeling saves 1742 kWh per month; modeling by adjusting the number of visitors saves 857 kWh per month; and modeling by improving the power factor saves 56 kWh per month. This electrical load modeling can save up to 8 kW of power outside of the library's operating hours. Meanwhile, during operational hours, the modeling library can save up to 30 kW of electric power