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General matrix multiplication (gemm) is a fundamental kernel in scientific computing and current frameworks for deep learning. Modern realisations of gemm are mostly written in C, on top of a small, highly tuned micro-kernel that is usually encoded in assembly. The high performance realisation of gemm in linear algebra libraries in general include a single micro-kernel per architecture, usually implemented by an expert. In this paper, we explore a couple of paths to automatically generate gemm micro-kernels, either using C++ templates with vector intrinsics or high-level Python scripts that directly produce assembly code. Both solutions can integrate high performance software techniques, such as loop unrolling and software pipelining, accommodate any data type, and easily generate micro-kernels of any requested dimension. The performance of this solution is tested on three ARM-based cores and compared with state-of-the-art libraries for these processors: BLIS, OpenBLAS and ArmPL. The experimental results show that the auto-generation approach is highly competitive, mainly due to the possibility of adapting the micro-kernel to the problem dimensions. [ABSTRACT FROM AUTHOR]

With the continuous progress of technology, the subject of life science plays an increasingly important role, among which the application of artificial intelligence in the medical field has attracted more and more attention. Bell facial palsy, a neurological ailment characterized by facial muscle weakness or paralysis, exerts a profound impact on patients' facial expressions and masticatory abilities, thereby inflicting considerable distress upon their overall quality of life and mental well-being. In this study, we designed a facial attribute recognition model specifically for individuals with Bell's facial palsy. The model utilizes an enhanced SSD network and scientific computing to perform a graded assessment of the patients' condition. By replacing the VGG network with a more efficient backbone, we improved the model's accuracy and significantly reduced its computational burden. The results show that the improved SSD network has an average precision of 87.9% in the classification of light, middle and severe facial palsy, and effectively performs the classification of patients with facial palsy, where scientific calculations also increase the precision of the classification. This is also one of the most significant contributions of this article, which provides intelligent means and objective data for future research on intelligent diagnosis and treatment as well as progressive rehabilitation. [ABSTRACT FROM AUTHOR]

In scientific computing, neural networks have been widely used to solve partial differential equations (PDEs). In this paper, we propose a novel RBF-assisted hybrid neural network for approximating solutions to PDEs. Inspired by the tendency of physics-informed neural networks (PINNs) to become local approximations after training, the proposed method utilizes a radial basis function (RBF) to provide the normalization and localization properties to the input data. The objective of this strategy is to assist the network in solving PDEs more effectively. During the RBF-assisted processing part, the method selects the center points and collocation points separately to effectively manage data size and computational complexity. Subsequently, the RBF processed data are put into the network for predicting the solutions to PDEs. Finally, a series of experiments are conducted to evaluate the novel method. The numerical results confirm that the proposed method can accelerate the convergence speed of the loss function and improve predictive accuracy. [ABSTRACT FROM AUTHOR]

Hier wird die erfolgreiche kristallisationsinduzierte dynamische Racematspaltung (CIDR) eines α‐lithiierten Phosphinoborans mithilfe des leicht zugänglichen und kostengünstigen chiralen Liganden (R,R)‐TMCDA beschrieben. Ausgehend von dem wichtigen P‐prochiralen Baustein Dimethylphenylphosphinoboran konnten Phosphinoborane in Ausbeuten von bis zu 80 % und Enantiomerenverhältnissen von bis zu 98 : 2 durch Kristallisation des lithiierten Zwischenprodukts erhalten werden. NMR‐basierte Deuterium‐Markierungsexperimente zeigen, dass die Epimerisierung in Lösung auf dem intermolekularen Protonentransfer zwischen dem nicht lithiierten Phosphinoboran und dem entsprechenden lithiierten Zwischenprodukt beruht, sodass die Anwesenheit überschüssigen Eduktes in einem optimierten Lösungsmittelgemisch der Hauptfaktor für eine erfolgreiche enantioselektive Synthese ist. Quantenchemische Berechnungen mit verschiedenen auf Festkörperstrukturen basierenden Modellsystemen bestätigen diese experimentellen Ergebnisse. Durch den Einblick in den Epimerisierungsmechanismus wurden wesentliche Prinzipien für die CIDR von lithiierten Phosphinoboranen aufgeklärt, die auf andere wichtige P‐stereogene Verbindungen und einfache chirale Amine erweitert werden könnten. [ABSTRACT FROM AUTHOR]

With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be reliably simulated. Such simulations can however be very time-intensive, requiring millions of CPU hours to perform. One solution is multi-fidelity emulation, which uses data of different fidelities to train an efficient predictive model which emulates the expensive simulator. For complex scientific problems and with careful elicitation from scientists, such multi-fidelity data may often be linked by a directed acyclic graph (DAG) representing its scientific model dependencies. We thus propose a new Graphical Multi-fidelity Gaussian Process (GMGP) model, which embeds this DAG structure (capturing scientific dependencies) within a Gaussian process framework. We show that the GMGP has desirable modeling traits via two Markov properties, and admits a scalable algorithm for recursive computation of the posterior mean and variance along at each depth level of the DAG. We also present a novel experimental design methodology over the DAG given an experimental budget, and propose a nonlinear extension of the GMGP via deep Gaussian processes. The advantages of the GMGP are then demonstrated via a suite of numerical experiments and an application to emulation of heavy-ion collisions, which can be used to study the conditions of matter in the Universe shortly after the Big Bang. The proposed model has broader uses in data fusion applications with graphical structure, which we further discuss. [ABSTRACT FROM AUTHOR]

Metaphors that compare the computer to a human brain are common in computer science and can be traced back to a fertile period of research that unfolded after the Second World War. To conceptualize the emerging "intelligent" properties of computing machines, researchers of the era created a series of virtual objects that served as interpretive devices for representing the immaterial functions of the computer. This paper analyses the use of the terms "artificial" and "virtual" in scientific papers, textbooks, and popular articles of the time, and examines how, together, they shaped models in computer science used to conceptualize computer processes. [ABSTRACT FROM AUTHOR]

Solving tensor systems is a common task in scientific computing and artificial intelligence. In this paper, we propose a tensor randomized average Kaczmarz method with adaptive parameters that exponentially converges to the unique least Frobenius norm solution of a given consistent tensor system under the t-product structure. In order to accelerate convergence, a tensor average Kaczmarz method based on stochastic heavy ball momentum technique (tAKSHBM) is proposed. The tAKSHBM method utilizes iterative information to update parameters instead of relying on prior information, addressing the problem in the adaptive learning of parameters. Additionally, the tAKSHBM method based on Fourier transform is proposed, which can be effectively implemented in a distributed environment. It is proven that the iteration sequences generated by all the proposed methods are convergent for given consistent tensor systems. Finally, we conduct experiments on both synthetic data and practical applications to support our theoretical results and demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The relationship between philosophy and science has always been complementary. Today, while science moves increasingly fast and philosophy shows some problems in catching up with it, it is not always possible to ignore such relationships, especially in some disciplines such as philosophy of mind, cognitive science, and neuroscience. However, the methodological procedures used to analyze these data are based on principles and assumptions that require a profound dialogue between philosophy and science. Following these ideas, this work aims to raise the problems that a classical connectionist theory can cause and problematize them in a cognitive framework, considering both philosophy and cognitive sciences but also the disciplines that are near to them, such as AI, computer sciences, and linguistics. For this reason, we embarked on an analysis of both the computational and theoretical problems that connectionism currently has. The second aim of this work is to advocate for collaboration between neuroscience and philosophy of mind because the promotion of deeper multidisciplinarity seems necessary in order to solve connectionism's problems. In fact, we believe that the problems that we detected can be solved by a thorough investigation at both a theoretical and an empirical level, and they do not represent an impasse but rather a starting point from which connectionism should learn and be updated while keeping its original and profoundly convincing core. [ABSTRACT FROM AUTHOR]

Matrices with low-rank structure are ubiquitous in scientific computing. Choosing an appropriate rank is a key step in many computational algorithms that exploit low-rank structure. However, estimating the rank has been done largely in an ad-hoc fashion in large-scale settings. In this work we develop a randomized algorithm for estimating the numerical rank of a (numerically low-rank) matrix. The algorithm is based on sketching the matrix with random matrices from both left and right; the key fact is that with high probability, the sketches preserve the orders of magnitude of the leading singular values. We prove a result on the accuracy of the sketched singular values and show that gaps in the spectrum are detected. For an m × n (m ≥ n) matrix of numerical rank r , the algorithm runs with complexity O (m n log ⁡ n + r 3) , or less for structured matrices. The steps in the algorithm are required as a part of many low-rank algorithms, so the additional work required to estimate the rank can be even smaller in practice. Numerical experiments illustrate the speed and robustness of our rank estimator. [ABSTRACT FROM AUTHOR]

Digital computers were invented in the 1940s. They are sophisticated and versatile machines whose functioning is grounded in elaborate theory. Advances in theory and the availability of computers helped computer science to develop as an academic discipline, and university departments for the same started coming up in the 1960s. Computer science covers all phenomenon related to computers and consists primarily of man-made laws governing building, programming, and using computers. Computational thinking is a way of thinking influenced by computers and computer science. There are two schools of thought on computational thinking. The first school sees computational thinking as the use of computers to explore the world, while the other sees computational thinking as the application of concepts from computer science to solve real-world problems. Scholars typically agree that computational thinking has four essential components, viz., abstraction, decomposition, algorithm design, and generalization. Computational thinking is often feted by computer scientists as a useful skill that can be used by anybody anywhere. However, it is necessary to find out ways for successfully using computational thinking in domains other than computer science before it can be declared a universal skill. [ABSTRACT FROM AUTHOR]

Purpose: The present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale. Design/methodology/approach: The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface. Findings: The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool. Originality/value: Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections. [ABSTRACT FROM AUTHOR]

As a representative of the fourth-generation light sources, the High Energy Photon Source (HEPS) in Beijing, China, utilizes a multi-bend achromat lattice to obtain an approximately 100 times emittance reduction compared with thirdgeneration light sources. New technologies bring new challenges to operate the storage ring. In order to meet the beam commissioning requirements of HEPS, a new framework for the development of high-level applications (HLAs) has been created. The key part of the new framework is a dual-layer physical module to facilitate the seamless fusion of physical simulation models with the real machine, allowing for fast switching between different simulation models to accommodate the various simulation scenarios. As a framework designed for development of physical applications, all variables are based on physical quantities. This allows physicists to analytically assess measurement parameters and optimize machine parameters in a more intuitive manner. To enhance both extensibility and adaptability, a modular design strategy is utilized, partitioning the entire framework into discrete modules in alignment with the requirements of HLA development. This strategy not only facilitates the independent development of each module but also minimizes inter-module coupling, thereby simplifying the maintenance and expansion of the entire framework. To simplify the development complexity, the design of the new framework is implemented using Python and is called Python-based Accelerator Physics Application Set (Pyapas). Taking advantage of Python's flexibility and robust library support, we are able to develop and iterate quickly, while also allowing for seamless integration with other scientific computing applications. HLAs for both the HEPS linac and booster have been successfully developed. During the beam commissioning process at the linac, Pyapas's ease of use and reliability have significantly reduced the time required for the beam commissioning operators. As a development framework for HLA designed for the new-generation light sources, Pyapas has the versatility to be employed with HEPS, as well as with other comparable light sources, due to its adaptability. [ABSTRACT FROM AUTHOR]

The book "Philosophy of Computer Science: An Introduction to the Issues and the Literature" by William J. Rapaport is a comprehensive monograph that analyzes the core issues defining the philosophy of computer science. It covers topics such as the nature of computer science, the definition of algorithms, the Church-Turing Thesis, the ontology of computer programs, and the relationship between computer science, ethics, and artificial intelligence. The book is intended for specialists in the field but can also be used as an introductory textbook for undergraduate teaching, requiring no background in philosophy or computer science. [Extracted from the article]

This paper introduces the Julia programming language as a dynamic, cost-effective, and efficient framework for implementing structural analysis packages. To achieve this, the finite element method was implemented for plane frames addressing the elastic instability problem through the Finite Element Method (FEM). Julia is a language open source, multiplatform, high-level and high-performance for technical and scientific computing, its compiler allows you to achieve speeds comparable to languages such as C and FORTRAN, but with more productive development dynamics due to its programming flexibility. Benchmarks between Julia and MATLAB are employed to discuss the processing costs, the programming techniques and paradigms used for computational performance. The results demonstrate that Julia performed the same analysis as the language used for comparison in 88.40% of the time, in addition to the fact that in loops comparisons case it reached 41.7% of the time for iteration, confirming its significant potential as a development tool of computational packages for structural analysis and scientific computing in general. [ABSTRACT FROM AUTHOR]

Deep neural networks are extensively used in text mining and Natural Language Processing is to enable computers to understand, analyze, and generate natural language data, such as text or speech, but semantic resources, such as taxonomies and ontologies, are not fully included in deep learning. In this paper, we use Deep Convolutional Neural Network (Deep CNN) to classify research papers using the Computer Science Ontology, an ontology of research areas in the field of computer science. It takes as input the abstract and keywords of a particular research paper and returns the relevant research topic. To evaluate our ontology, we used a gold standard dataset that includes research articles. To further improve text classification results, we propose to design a Deep CNN model. We then used ontology matching to reduce the classes and get better results. Experimental results show that the proposed approach outperforms the one with the highest precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches. [ABSTRACT FROM AUTHOR]

This paper analyzes the tradeoffs between performance and resilience against cyber attacks of applications organized as workflows. The static nature of current workflows is a major benefit to attackers. To combat this advantage, a promising new approach inspired to Moving Target Defense (MTDs) was developed to increase a workflow's robustness to cyber attacks. This approach is based on dynamic reconfigurations of workflow tasks to reduce an attacker's probability of succeeding in completing the reconnaissance phase before launching an attack. Dynamic reconfigurations increase the resilience of a workflow against cyber attacks but increase its execution time due to the overhead of reconfigurations. As a part of this paper, we developed metrics that capture the impact of reconfigurations on a workflow's execution time and resilience against cyber attacks. The paper also presents recursive algorithms for computing the execution time and the reconnaissance function of a workflow. Our analysis relied on extensive trace-driven simulations of workflows from five different traces from the Workflow Trace Archive (WTA) and we used 6000 workflows from three different domains: scientific computing, engineering, and industrial. Our analysis of the results showed that there is a significant difference at the 95% confidence level due to reconfiguration on the resilience of workflows and demonstrated a consistent behavior across all five trace domains. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new class of micro-electromechanical oscillators. Some investigations based on Melnikov's approach are applied for identifying some chaotic possibilities. We demonstrate also some specialized modules for investigating the dynamics of these oscillators. This will be included as an integral part of a planned much more general Web-based application for scientific computing. It turns out that the theoretical apparatus for studying the circuit implementation (design, fabricating, etc.) of the considered differential model for large parameter values is extremely complex and requires a serious investigation. This is the reason to offer this model to the attention of specialists working in this scientific direction. Some open problems related to the use of existing computer algebraic systems for the study of this class of oscillators for large values of n , m and N are also posed. In general, the entire article is subordinated to this frank conversation with the readers with the sole purpose being the professional upgrading of the specialized modules provided for this purpose in subsequent licensed versions of CAS. [ABSTRACT FROM AUTHOR]

The article discusses the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite mission, which is a joint effort between the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA). The mission aims to improve weather forecasts and climate projections by studying cloud, aerosol, and radiation processes. EarthCARE will use novel instruments to retrieve more accurate properties of clouds, aerosols, and precipitation, and provide in-orbit verification of their impact on radiative fluxes. The article also pays tribute to Dr. Tobias Wehr, the mission scientist who passed away in 2023 and played a crucial role in the development of EarthCARE. [Extracted from the article]

We demonstrate an order of magnitude reduction in the sensitivity to optical crosstalk for neighboring trapped-ion qubits during simultaneous single-qubit gates driven with individual addressing beams. Gates are implemented via two-photon Raman transitions, where crosstalk is mitigated by offsetting the drive frequencies for each qubit to avoid first-order crosstalk effects from inter-beam two-photon resonance. The technique is simple to implement, and we find that phase-dependent crosstalk due to optical interference is reduced on the most impacted neighbor from a maximal fractional rotation error of 0.185 (4) without crosstalk mitigation to ≤ 0.006 with the mitigation strategy. Furthermore, we characterize first-order crosstalk in the two-qubit gate and avoid the resulting rotation errors for the arbitrary-axis Mølmer–Sørensen gate via a phase-agnostic composite gate. Finally, we demonstrate holistic system performance by constructing a composite CNOT gate using the improved single-qubit gates and phase-agnostic two-qubit gate. This work is done on the Quantum Scientific Computing Open User Testbed; however, our methods are widely applicable for individual addressing Raman gates and impose no significant overhead, enabling immediate improvement for quantum processors that incorporate this technique. [ABSTRACT FROM AUTHOR]

The graphics processing units (GPUs) are becoming a compelling acceleration strategy for geoscience numerical model due to their powerful computing performance. In this study, AMD's heterogeneous compute interface for portability (HIP) was implemented to port the GPU acceleration version of the Piecewise Parabolic Method (PPM) solver (GPU-HADVPPM) from the NVIDIA GPUs to China' s domestically GPU-like accelerators as GPU-HADVPPM4HIP, and further introduced the multi-level hybrid parallelism scheme to improve the total computational performance of the HIP version of CAMx (CAMx-HIP) model on the China' s domestically heterogeneous cluster. The experimental results show that the acceleration effect of GPU-HADVPPM on the different GPU accelerator is more obvious when the computing scale is larger and the maximum speedup of GPU-HADVPPM on the domestic GPU-like accelerator is 28.9 times. The hybrid parallelism with a message passing interface (MPI) and HIP enables achieve up to 17.2 times speedup when configure 32 CPU cores and GPU-like accelerators on the domestic heterogeneous cluster. And the OpenMP technology is introduced to further reduce the computation time of CAMx-HIP model by 1.9 times. More importantly, by comparing the simulation results of GPU-HADVPPM on NVIDIA GPUs and domestic GPU-like accelerators, it is found that the simulation results of GPU-HADVPPM on domestic GPU-like accelerators have less difference than the NVIDIA GPUs, and the reason for this difference may be related to the fact that the NVIDIA GPU sacrifices part of the accuracy for improved computing performance. All in all, the domestic GPU-like accelerators are more accuracy for scientific computing in the field of geoscience numerical models. Furthermore, we also exhibit that the data transfer efficiency between CPU and GPU has an important impact on heterogeneous computing, and point out that optimizing the data transfer efficiency between CPU and GPU is one of the important directions to improve the computing efficiency of geoscience numerical models in heterogeneous clusters in the future. [ABSTRACT FROM AUTHOR]

Reproducibility of results is a cornerstone of the scientific method. Scientific computing encounters two challenges when aiming for this goal. Firstly, reproducibility should not depend on details of the runtime environment, such as the compiler version or computing environment, so results are verifiable by third-parties. Secondly, different versions of software code executed in the same runtime environment should produce consistent numerical results for physical quantities. In this manuscript, we test the feasibility of reproducing scientific results obtained using the IllinoisGRMHD code that is part of an open-source community software for simulation in relativistic astrophysics, the Einstein Toolkit. We verify that numerical results of simulating a single isolated neutron star with IllinoisGRMHD can be reproduced, and compare them to results reported by the code authors in 2015. We use two different supercomputers: Expanse at SDSC, and Stampede2 at TACC. By compiling the source code archived along with the paper on both Expanse and Stampede2, we find that IllinoisGRMHD reproduces results published in its announcement paper up to errors comparable to round-off level changes in initial data parameters. We also verify that a current version of IllinoisGRMHD reproduces these results once we account for bug fixes which have occurred since the original publication. [ABSTRACT FROM AUTHOR]

The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is a satellite mission implemented by the European Space Agency (ESA) in cooperation with the Japan Aerospace Exploration Agency (JAXA) to measure vertical profiles of aerosols, clouds, and precipitation properties together with radiative fluxes and derived heating rates. The data will be used in particular to evaluate the representation of clouds, aerosols, precipitation, and associated radiative fluxes in weather forecasting and climate models. The satellite embarks four instruments: the ATmospheric LIDar (ATLID), the Cloud Profiling Radar (CPR), the Multi-Spectral Imager (MSI), and the Broadband Radiometer (BBR). The science data acquired by the four satellite instruments are processed on ground. Calibrated instrument data – level 1 data products – and retrieved geophysical data products – level 2 data products – are produced in the ESA and JAXA ground segments. This paper provides an overview of the data processing chains of ESA and JAXA and explains the instrument level 1 data products and main aspects of the calibration algorithms. Furthermore, an overview of the level 2 data products, with references to the respective dedicated papers, is provided. [ABSTRACT FROM AUTHOR]

Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with posterior error estimates to assess the quality of the output. To meet this need, this paper proposes two diagnostics: a leave-one-out error estimator for randomized low-rank approximations and a jackknife resampling method to estimate the variance of the output of a randomized matrix computation. Both of these diagnostics are rapid to compute for randomized low-rank approximation algorithms such as the randomized SVD and randomized Nystr\"om approximation, and they provide useful information that can be used to assess the quality of the computed output and guide algorithmic parameter choices. [ABSTRACT FROM AUTHOR]

The main purpose of scientific research is to discover fundamental principles and solve practical problems. Although tremendous progress has been made on both fronts, the lack of effective tools and efficient organizational structure still stands as the main bottleneck for scientific progress. The rapid development of artificial intelligence (AI) offers a new possibility. In recent years, deep learning has had an impressive performance, both in helping to solve fundamental scientific problems and in improving the effectiveness of scientific research tools. A new set of infrastructure is emerging, leading us to a new paradigm, the "Android paradigm", for doing scientific research. [ABSTRACT FROM AUTHOR]

Matrix–matrix multiplication is of singular importance in linear algebra operations with a multitude of applications in scientific and engineering computing. Data structures for storing matrix elements are designed to minimize overhead information as well as to optimize the operation count. In this study, we utilize the notion of the compact diagonal storage method (CDM), which builds upon the previously developed diagonal storage—an orientation-independent uniform scheme to store the nonzero elements of a range of matrices. This study exploits both these storage schemes and presents efficient GPU-accelerated parallel implementations of matrix multiplication when the input matrices are banded and/or structured sparse. We exploit the data layouts in the diagonal storage schemes to expose a substantial amount of fine-grained parallelism and effectively utilize the GPU shared memory to improve the locality of data access for numerical calculations. Results from an extensive set of numerical experiments with the aforementioned types of matrices demonstrate orders-of-magnitude speedups compared with the sequential performance. [ABSTRACT FROM AUTHOR]

Simulating solute transport in heterogeneous porous media poses computational challenges due to the high-resolution meshing required for traditional solvers. To overcome these challenges, this study explores a mesh-free method based on deep learning to accelerate solute transport simulation. We employ Physics-informed Neural Networks (PiNN) with a periodic activation function to solve solute transport problems in both homogeneous and heterogeneous porous media governed by the advection-dispersion equation. Unlike traditional neural networks that rely on large training datasets, PiNNs use strong-form mathematical models to constrain the network in the training phase and simultaneously solve for multiple dependent or independent field variables, such as pressure and solute concentration fields. To demonstrate the effectiveness of using PiNNs with a periodic activation function to resolve solute transport in porous media, we construct PiNNs using two activation functions, sin and tanh, for seven case studies, including 1D and 2D scenarios. The accuracy of the PiNNs' predictions is then evaluated using absolute point error and mean square error metrics and compared to the ground truth solutions obtained analytically or numerically. Our results demonstrate that the PiNN with sin activation function, compared to tanh activation function, is up to two orders of magnitude more accurate and up to two times faster to train, especially in heterogeneous porous media. Moreover, PiNN's simultaneous predictions of pressure and concentration fields can reduce computational expenses in terms of inference time by three orders of magnitude compared to FEM simulations for two-dimensional cases. [ABSTRACT FROM AUTHOR]

This study uses a national longitudinal sample of 612 Latinx STEM bachelor's degree recipients across 171 higher education institutions to examine the STEM undergraduate experiences and institutional contexts that predict their values toward conducting research that will have a meaningful impact on underserved communities. Results show that several background and precollege characteristics, particular STEM majors, learning about science and technology in society, and institutional characteristics predict this empowerment outcome. While attending a Hispanic-Serving Institution (HSI) did not have a direct effect on the outcome, it was found to reduce the negative effect of majoring in math/statistics and computer science/tech. Implications for our current understanding of Latinx STEM success and the role of HSIs and PWIs are provided. [ABSTRACT FROM AUTHOR]

High-fidelity computer simulations of childbirth remain prohibitively expensive and time consuming, making them impractical for guiding decision-making during obstetric emergencies. Cheap computer simulations that preserve the accuracy of high-fidelity models can be developed using surrogate modeling. Two common approaches to surrogate modeling are physics-based reduced order modeling (ROM) and machine learning (ML), with the latter gaining popularity as the scientific computing community seeks to leverage advances from other, mostly non-physics-based, computational strategies. Although ROM and ML have been compared for various problems, to our knowledge, such a comparison for simulations of vaginal deformations is currently missing. This study provides a baseline numerical comparison between methods from these two fundamentally different approaches. Since there are many methods falling into each modeling approach, to provide a fair and natural comparison, we select a basic model from each category, with each allowing (i) a straightforward implementation in commercial software packages, and (ii) use by practitioners with limited experience in the field. As a benchmark for the numerical comparison of the ROM and ML approaches, we use the finite element (FE) modeling of the ex vivo deformations of rat vaginal tissue subjected to inflation testing to study the effect of a pre-imposed tear. From the ROM strategies, we consider a simplified Galerkin ROM (G-ROM) that is based on the linearization of the underlying nonlinear equations. From the ML strategies, we select a feed-forward neural network to create mappings from constitutive model parameters and luminal pressure values to either the FE displacement history (in which case we denote the resulting model ML) or the proper orthogonal decomposition (POD) coefficients of the displacement history (in which case we denote the resulting model POD-ML). The numerical investigation of G-ROM, ML, and POD-ML takes place in the reconstructive regime. The numerical results show that the G-ROM outperforms the ML model in terms of offline central processing unit (CPU) time for model training, online CPU time required to generate approximations, and relative error with respect to the FE models. The G-ROM achieves superior error performance to the best ML model with 11 POD basis functions. With higher-dimensional POD bases, the G-ROM achieves a relative error 3 orders of magnitude lower than that of the best ML model with an online CPU time still on the same order of magnitude as the best ML model. The POD-ML model improves on the speed performance of the ML, having online CPU times comparable to those of the G-ROM given the same size of POD bases. However, the POD-ML model does not improve on the error performance of the ML and is still outperformed by the G-ROM for POD bases of size greater than 11. This baseline numerical investigation serves as a starting point for future computer simulations that consider state-of-the-art G-ROM and ML strategies, and the in vivo geometry, boundary conditions, and material properties of the human vagina, as well as their changes during labor. [ABSTRACT FROM AUTHOR]

General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from researchers in graph analyzing, scientific computing, and deep learning. Many optimization techniques have been developed for different applications and computing architectures over the past decades. The objective of this article is to provide a structured and comprehensive overview of the researches on SpGEMM. Existing researches have been grouped into different categories based on target architectures and design choices. Covered topics include typical applications, compression formats, general formulations, key problems and techniques, architecture-oriented optimizations, and programming models. The rationales of different algorithms are analyzed and summarized. This survey sufficiently reveals the latest progress of SpGEMM research to 2021. Moreover, a thorough performance comparison of existing implementations is presented. Based on our findings, we highlight future research directions, which encourage better design and implementations in later studies. [ABSTRACT FROM AUTHOR]

Python has become an essential programming language for scientific computing and data analysis and processing. Various multiple-point statistics (MPS) algorithms are used to characterize complex heterogeneous structures and phenomena in earth sciences. However, there is currently no Python library that integrates mainstream MPS methods for simulation and computation in geosciences. Aiming to establish a stable MPS tool, we developed an open-source Python library of commonly used MPS methods, named pyMPSLib. pyMPSLib consists of ENESIM, SNESIM, and DS algorithms and provides a flexible and convenient API interface. To ensure the maintainability of pyMPSLib, the Python objects and toolkits of MPS algorithms are defined and implemented. To improve the compatibility and extensibility of the presented library, uniform coding standard is adopted in pyMPSLib. We performed the parameter sensitivity analysis under multiple configurations to validate the performance of the library. This open-source library also provides optional tools to quantitatively evaluate the realizations of the integrated MPS methods. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to commemorate the late scholar Loet Leydesdorff for his great academic contribution on the basis of data from Web of Science. In the span of more than 40 years, he had 526 publications, with the years 2004-2021 being the most productive (394 publications). His international collaborations spread widely across 36 countries, with Germany, the USA, the UK, China, Russia, and South Korea being the most significant. His most frequent collaboration partners included Lutz Bornmann (Germany), Staša Milojević (USA), Caroline Wagner (USA), Henry Etzkowitz (USA), Jonathan Adams (UK), Ronald Rousseau (Belgium), and Ping Zhou (China). With a broad and deep knowledge background, Leydesdorff's research extended across multiple disciplines and fields, but he was most active in library and information science and computer science. He made profound contributions to the study of bibliometrics, innovation systems (the Triple Helix model), and communications. Leydesdorff had a remarkable and extensive citation impact, with citations in 221 WoS subject categories from 120 countries. His publications in 1996, 1998, 2000, 2005, 2006, and 2012 are highly cited, and those on university-industry-government relations (the Triple Helix model) are the most cited. [ABSTRACT FROM AUTHOR]

We present the Julia package Manifolds.jl, providing a fast and easy-to-use library of Riemannian manifolds and Lie groups. This package enables working with data defined on a Riemannian manifold, such as the circle, the sphere, symmetric positive definite matrices, or one of the models for hyperbolic spaces. We introduce a common interface, available in ManifoldsBase.jl, with which new manifolds, applications, and algorithms can be implemented. We demonstrate the utility of Manifolds.jl using Bézier splines, an optimization task on manifolds, and principal component analysis on nonlinear data. In a benchmark, Manifolds.jl outperforms all comparable packages for low-dimensional manifolds in speed; over Python and Matlab packages, the improvement is often several orders of magnitude, while over C/C++ packages, the improvement is two-fold. For high-dimensional manifolds, it outperforms all packages except for Tensorflow-Riemopt, which is specifically tailored for high-dimensional manifolds. [ABSTRACT FROM AUTHOR]

Usable security involves designing security measures that accommodate users' needs and behaviors. Balancing usability and security poses challenges: the more secure the systems, the less usable they will be. On the contrary, more usable systems will be less secure. Numerous studies have addressed this balance. These studies, spanning psychology and computer science/engineering, contribute diverse perspectives, necessitating a systematic review to understand strategies and findings in this area. This systematic literature review examined articles on usable security from 2005 to 2022. A total of 55 research studies were selected after evaluation. The studies have been broadly categorized into four main clusters, each addressing different aspects: (1) usability of authentication methods, (2) helping security developers improve usability, (3) design strategies for influencing user security behavior, and (4) formal models for usable security evaluation. Based on this review, we report that the field's current state reveals a certain immaturity, with studies tending toward system comparisons rather than establishing robust design guidelines based on a thorough analysis of user behavior. A common theoretical and methodological background is one of the main areas for improvement in this area of research. Moreover, the absence of requirements for Usable security in almost all development contexts greatly discourages implementing good practices since the earlier stages of development. [ABSTRACT FROM AUTHOR]

Numerical simulation is an efficient tool for evaluation and prediction of material properties and behavior in many industrial domains such as the development of novel materials and medicines. For numerical studies of complex processes or systems with high fidelity, various data processing tools, modeling and simulation programs are typically involved, desiring an integrated platform that can effectively manage the collaboration of such software resources and the execution of the underlying simulation workflow for efficiency purpose. Such a platform could be practically built with a scientific computing workflow engine that focuses on the automatic scheduling and execution of a batch of interrelated computing tasks. In this work, the main procedures on construction of a specialized integrated simulation platform for material research based on a general purpose scientific computing workflow engine named HSWAP is introduced in detail, and its application to molecule screening process of energetic materials is demonstrated. Due to the flexibility and the extensibility of the platform, the work could be handily extended to the screening of other materials such as protein to find optimized protein structures or high entropy alloys to find the best configuration of component contents, as well as other application scenarios such as geometry optimizations of complex structures. [ABSTRACT FROM AUTHOR]

Python is becoming increasingly popular in scientific computing. The package MPI for Python (mpi4py) allows writing efficient parallel programs that scale across multiple nodes. However, it does not support non-contiguous data via slices, which is a well-known feature of NumPy. In this work, we therefore evaluate several methods to support the direct transfer of non-contiguous arrays in mpi4py. This significantly simplifies the code, while the performance basically stays the same. In a PingPong-, Stencil- and Lattice-Boltzmann-Benchmark, we compare the common manual copying, a NumPy-Copy design and a design that is based on MPI derived datatypes. In one case, the MPI derived datatype design could achieve a speedup of 15% in a Stencil-Benchmark on four compute nodes. Our designs are superior to naive manual copies, but for maximum performance manual copies with pre-allocated buffers or MPI persistent communication will be a better choice. [ABSTRACT FROM AUTHOR]

Sparse tensor algorithms are becoming widespread, particularly in the domains of deep learning, graph and data analytics, and scientific computing. Current high-performance broad-domain architectures, such as GPUs, often suffer memory system inefficiencies by moving too much data or moving it too far through the memory hierarchy. To increase performance and efficiency, proposed domain-specific accelerators tailor their architectures to the data needs of a narrow application domain, but as a result cannot be applied to a wide range of algorithms or applications that contain a mix of sparse and dense algorithms. This article proposes Symphony, a hybrid programmable/specialized architecture that focuses on the orchestration of data throughout the memory hierarchy to simultaneously reduce the movement of unnecessary data and data movement distances. Key elements of the Symphony architecture include (1) specialized reconfigurable units aimed not only at roofline floating-point computations but also at supporting data orchestration features, such as address generation, data filtering, and sparse metadata processing; and (2) distribution of computation resources (both programmable and specialized) throughout the on-chip memory hierarchy. We demonstrate that Symphony can match non-programmable ASIC performance on sparse tensor algebra and provide 31× improved runtime and 44× improved energy over a comparably provisioned GPU for these applications. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of learning a regression function without assuming its functional form. This problem is referred to as symbolic regression. An expression tree is typically used to represent a solution function, which is determined by assigning operators and operands to the nodes. Cozad and Sahinidis propose a nonconvex mixed-integer nonlinear program (MINLP), in which binary variables are used to assign operators and nonlinear expressions are used to propagate data values through nonlinear operators, such as square, square root, and exponential. We extend this formulation by adding new cuts that improve the solution of this challenging MINLP. We also propose a heuristic that iteratively builds an expression tree by solving a restricted MINLP. We perform computational experiments and compare our approach with a mixed-integer program–based method and a neural network–based method from the literature. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the Applied Mathematics activity within the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research [Grant DE-AC02-06CH11357]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0050) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0050). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Recent experiments show that the diffusivities of C8 and C10 n‐alkanes in ZIF‐8 are higher than those of C7 and C9, respectively. We investigated this unusual 'odd‐even' effect by simulating the adsorption of C7‐C14 n‐alkanes in ZIF‐8 using hybrid Monte Carlo molecular simulations. The resultant adsorption isotherms, guest‐host energies, isosteric heats, and chain length distributions are analyzed for trends among the n‐alkanes. ZIF‐8 cages filled with n‐alkanes are characterized using a combination of image processing and data science to quantify the differences in packing that occur with chain length. Results indicate that packing changes drastically from C7 to C8 and from C9 to C10, which is consistent with the diffusion trends previously reported in experiments. [ABSTRACT FROM AUTHOR]

The Hutchinson method can be used to estimate the trace of a function of a matrix A, that is, tr(f(A))$\mathrm{tr}(f(A))$, stochastically. With the variance of the Hutchinson estimator decreasing as O(1/N)$\mathcal {O}(1/\sqrt {N})$, techniques to reduce the variance of this estimator become necessary due to the high accuracy required by some calculations in scientific computing. In lattice QCD, where A is the Dirac operator D stemming from a discretization of the Dirac equation, the accuracy of some calculations involving tr(f(A))$\mathrm{tr}(f(A))$ renders them practically unfeasible if one uses the Hutchinson estimator to approximate the trace. One such variance reduction technique, known as multigrid multilevel Monte Carlo, has been introduced recently to deal with tr(D−1)$\mathrm{tr}(D^{-1})$ for the Laplace 2D, Gauge Laplacian, and Schwinger problems. We use it here in the context of lattice QCD, and moreover, we analyze its impact when dealing with tr(D−1P∼H)$\mathrm{tr}(D^{-1} \widetilde{P}^{H})$, where P∼$\widetilde{P}$ is the matrix that emerges when a displacement in one of the four spacetime dimensions of the lattice is introduced. This latter trace is of importance when computing generalized parton distributions in lattice QCD. [ABSTRACT FROM AUTHOR]

The race for the most efficient, accurate, and universal algorithm in scientific computing drives innovation. At the same time, this healthy competition is only beneficial if the research output is actually comparable to prior results. Fairly comparing algorithms can be a complex endeavor, as the implementation, configuration, compute environment, and test problems need to be well‐defined. Due to the increase in computer‐based experiments, new infrastructure for facilitating the exchange and comparison of new algorithms is also needed. To this end, we propose a benchmark framework as a set of generic specifications for comparing implementations of algorithms using test cases native to a community. Its value lies in its ability to fairly compare and validate existing methods for new applications, as well as compare newly developed methods with existing ones. As a prototype for a more general framework, we have begun building a benchmark tool for the model order reduction (MOR) community. The data basis of the tool is the collection of the Model Order Reduction Wiki (MORWiki). The wiki features three main categories: benchmarks, methods, and software. An editorial board curates submissions and patrols edited entries. Data sets for linear and parametric‐linear models are already well represented in the existing collection. Data sets for non‐linear or procedural models, for which only evaluation data, or codes/algorithmic descriptions, rather than equations, are available, are being added and extended. Properties and interesting characteristics used for benchmark selection and later assessments are recorded in the model metadata. Our tool, the Model Order Reduction Benchmarker (MORB), is under active development for linear time‐invariant systems and solvers. An ontology (MORBO) and knowledge graph are being developed in parallel. They catalog benchmark problem sets and their metadata and will also be integrated into the Mathematical Research Data Initiative (MaRDI) Portal to help improve the findability of such data sets. MORB faces a number of technical and field‐specific challenges, and we hope to recruit community input and feedback while presenting some initial results. [ABSTRACT FROM AUTHOR]

The accuracy of a classification is fundamental to its interpretation, use and ultimately decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the true accuracy. Mis-estimation of classification accuracy metrics and associated mis-interpretations are often due to variations in prevalence and the use of an imperfect reference standard. The fundamental issues underlying the problems associated with variations in prevalence and reference standard quality are revisited here for binary classifications with particular attention focused on the use of the Matthews correlation coefficient (MCC). A key attribute claimed of the MCC is that a high value can only be attained when the classification performed well on both classes in a binary classification. However, it is shown here that the apparent magnitude of a set of popular accuracy metrics used in fields such as computer science medicine and environmental science (Recall, Precision, Specificity, Negative Predictive Value, J, F1, likelihood ratios and MCC) and one key attribute (prevalence) were all influenced greatly by variations in prevalence and use of an imperfect reference standard. Simulations using realistic values for data quality in applications such as remote sensing showed each metric varied over the range of possible prevalence and at differing levels of reference standard quality. The direction and magnitude of accuracy metric mis-estimation were a function of prevalence and the size and nature of the imperfections in the reference standard. It was evident that the apparent MCC could be substantially under- or over-estimated. Additionally, a high apparent MCC arose from an unquestionably poor classification. As with some other metrics of accuracy, the utility of the MCC may be overstated and apparent values need to be interpreted with caution. Apparent accuracy and prevalence values can be mis-leading and calls for the issues to be recognised and addressed should be heeded. [ABSTRACT FROM AUTHOR]

Organic molecules have been extensively utilized in various applications including materials science, chemical, and biomedical fields. Traditionally, the design of organic molecules is achieved through experimental approaches, guided by conceptual insights, intuition, and experience. Although these experimental approaches have been successfully utilized to unveil various high‐performance materials, these methods show serious limitations due to vast design space and the ever‐increasing demand for organic molecules (new materials). Artificial intelligence with computer science is used by modern researchers to design materials with better performance and for predicting the properties of new materials. Herein, benzofuran‐based building blocks are used as a standard molecule to search for new building blocks. Similarity analysis is performed to screen/search potential candidates for photodetectors based on the chemical structural similarity. Extended‐connectivity fingerprints (ECFPs) are used for the similarity analysis. The virtual libraries of unique monomers are enumerated. The breaking retro‐synthetically interesting chemical substructures (BRICS) method is also used to design building blocks by automatically decomposing and combining monomers in enumerated libraries. Moreover, this work offers a potential way to identify new monomers for photodetectors cost‐effectively and rapidly. [ABSTRACT FROM AUTHOR]

In recent years, the advancement of deep learning has led to the utilization of related technologies to enhance the efficiency and accuracy of scientific computing. Physics-Informed Neural Networks (PINNs) are a type of deep learning method applied to scientific computing, widely used to solve various partial differential equations (PDEs), demonstrating tremendous potential. This study improved upon original PINNs and applied them to forward and inverse problems in the nonlinear science field. In addition to incorporating the constraints of PDEs, the improved PINNs added constraints on gradient information, which further enhanced the physical constraints. Moreover, an adaptive learning method was used to update the weight coefficients of the loss function and dynamically adjust the weight proportion of each constraint term. In the experiment, the improved PINNs were used to numerically simulate localized waves and two-dimensional lid-driven cavity flow described by partial differential equations. Meanwhile, we critically evaluate the accuracy of the prediction results. Furthermore, the improved PINNs were utilized to solve the inverse problems of nonlinear PDEs, where the results showed that even with noisy data, the unknown parameters could be discovered satisfactorily. The study results indicated that the improved PINNs were significantly superior to original PINNs, with shorter training time, increased accuracy in prediction results, and greater potential for application. [ABSTRACT FROM AUTHOR]

We present a higher-order convergent numerical solver for active polar hydrodynamics in three-dimensional domains of arbitrary shape, along with a scalable open-source software implementation for shared- and distributed-memory parallel computers. This enables the computational study of the nonlinear dynamics of out-of-equilibrium materials from first principles. We numerically solve the nonlinear active Ericksen–Leslie hydrodynamic equations of three-dimensional (3D) active nematics using both a meshfree and a hybrid particle-mesh method in either the Eulerian or Lagrangian frame of reference. The solver is validated against a newly derived analytical solution in 3D and implemented using the OpenFPM software library for scalable scientific computing. We then apply the presented method to studying the transition of 3D active polar fluids to spatiotemporal chaos, the emergence of coherent angular motion in a 3D annulus, and chiral vortices in symmetric and asymmetric 3D shapes resembling dividing cells. Overall, this provides a robust and efficient open-source simulation framework for 3D active matter with verified numerical convergence and scalability on parallel computers. [ABSTRACT FROM AUTHOR]

The objective of this article is to identify the scientific production in the use of information and communication technologies (ICT) in the contribution and promotion of nature tourism. Its methodological development was built from a work of scientific bibliometric analysis under an exploratory-quantitative character, allowing the integration and application of statistical processes expressed from the technological tools used such as VosViewer, SciMat, and Bibliometrix of R. The indicators used emphasized academic activities (publications, dispersion, collaboration) and impact at the level of citation, co-citation, and impact factor, The scientific production extracted for the search period between 2011 and the first semester of 2020 was 356 documents. As a result, the analysis and graphs of the evolution of publications, geographical origin, and types of documents were obtained, as well as the themes and trends associated with technologies and the analysis of journals, authors, citations, and influential keywords, the large number of tourism proposals implemented in the world on natural environments and supported with digital technologies for their development, This reality is reflected in the documents generated in the last five years, where 212 articles in 129 journals have reflected this evolution. To conclude with a quantitative analysis of each of the categories studied and the examination of the inclusion of ICT in nature tourism, based on the publications in the Scopus database. The areas in which the publications are located are focused on environmental, social, agricultural, ICT, and biological sciences, where the context of the publications is based on the new tourism alternatives that seek the protection of natural resources, the low impact on the environment, the monitoring of sites and tourists and the control of the spaces in which this economic activity is carried out, supporting many countries, especially those of the emerging economies. A total of 216 journals were identified. [ABSTRACT FROM AUTHOR]

Complex systems, as interwoven miscellaneous interacting entities that emerge and evolve through self-organization in a myriad of spiraling contexts, exhibit subtleties on global scale besides steering the way to understand complexity which has been under evolutionary processes with unfolding cumulative nature wherein order is viewed as the unifying framework. Indicating the striking feature of non-separability in components, a complex system cannot be understood in terms of the individual isolated constituents' properties per se, it can rather be comprehended as a way to multilevel approach systems behavior with systems whose emergent behavior and pattern transcend the characteristics of ubiquitous units composing the system itself. This observation specifies a change of scientific paradigm, presenting that a reductionist perspective does not by any means imply a constructionist view; and in that vein, complex systems science, associated with multiscale problems, is regarded as ascendancy of emergence over reductionism and level of mechanistic insight evolving into complex system. While evolvability being related to the species and humans owing their existence to their ancestors' capability with regards to adapting, emerging and evolving besides the relation between complexity of models, designs, visualization and optimality, a horizon that can take into account the subtleties making their own means of solutions applicable is to be entailed by complexity. Such views attach their germane importance to the future science of complexity which may probably be best regarded as a minimal history congruent with observable variations, namely the most parallelizable or symmetric process which can turn random inputs into regular outputs. Interestingly enough, chaos and nonlinear systems come into this picture as cousins of complexity which with tons of its components are involved in a hectic interaction with one another in a nonlinear fashion amongst the other related systems and fields. Relation, in mathematics, is a way of connecting two or more things, which is to say numbers, sets or other mathematical objects, and it is a relation that describes the way the things are interrelated to facilitate making sense of complex mathematical systems. Accordingly, mathematical modeling and scientific computing are proven principal tools toward the solution of problems arising in complex systems' exploration with sound, stimulating and innovative aspects attributed to data science as a tailored-made discipline to enable making sense out of voluminous (-big) data. Regarding the computation of the complexity of any mathematical model, conducting the analyses over the run time is related to the sort of data determined and employed along with the methods. This enables the possibility of examining the data applied in the study, which is dependent on the capacity of the computer at work. Besides these, varying capacities of the computers have impact on the results; nevertheless, the application of the method on the code step by step must be taken into consideration. In this sense, the definition of complexity evaluated over different data lends a broader applicability range with more realism and convenience since the process is dependent on concrete mathematical foundations. All of these indicate that the methods need to be investigated based on their mathematical foundation together with the methods. In that way, it can become foreseeable what level of complexity will emerge for any data desired to be employed. With relation to fractals, fractal theory and analysis are geared toward assessing the fractal characteristics of data, several methods being at stake to assign fractal dimensions to the datasets, and within that perspective, fractal analysis provides expansion of knowledge regarding the functions and structures of complex systems while acting as a potential means to evaluate the novel areas of research and to capture the roughness of objects, their nonlinearity, randomness, and so on. The idea of fractional-order integration and differentiation as well as the inverse relationship between them lends fractional calculus applications in various fields spanning across science, medicine and engineering, amongst the others. The approach of fractional calculus, within mathematics-informed frameworks employed to enable reliable comprehension into complex processes which encompass an array of temporal and spatial scales notably provides the novel applicable models through fractional-order calculus to optimization methods. Computational science and modeling, notwithstanding, are oriented toward the simulation and investigation of complex systems through the use of computers by making use of domains ranging from mathematics to physics as well as computer science. A computational model consisting of numerous variables that characterize the system under consideration allows the performing of many simulated experiments via computerized means. Furthermore, Artificial Intelligence (AI) techniques whether combined or not with fractal, fractional analysis as well as mathematical models have enabled various applications including the prediction of mechanisms ranging extensively from living organisms to other interactions across incredible spectra besides providing solutions to real-world complex problems both on local and global scale. While enabling model accuracy maximization, AI can also ensure the minimization of functions such as computational burden. Relatedly, level of complexity, often employed in computer science for decision-making and problem-solving processes, aims to evaluate the difficulty of algorithms, and by so doing, it helps to determine the number of required resources and time for task completion. Computational (-algorithmic) complexity, referring to the measure of the amount of computing resources (memory and storage) which a specific algorithm consumes when it is run, essentially signifies the complexity of an algorithm, yielding an approximate sense of the volume of computing resources and seeking to prove the input data with different values and sizes. Computational complexity, with search algorithms and solution landscapes, eventually points toward reductions vis à vis universality to explore varying degrees of problems with different ranges of predictability. Taken together, this line of sophisticated and computer-assisted proof approach can fulfill the requirements of accuracy, interpretability, predictability and reliance on mathematical sciences with the assistance of AI and machine learning being at the plinth of and at the intersection with different domains among many other related points in line with the concurrent technical analyses, computing processes, computational foundations and mathematical modeling. Consequently, as distinctive from the other ones, our special issue series provides a novel direction for stimulating, refreshing and innovative interdisciplinary, multidisciplinary and transdisciplinary understanding and research in model-based, data-driven modes to be able to obtain feasible accurate solutions, designed simulations, optimization processes, among many more. Hence, we address the theoretical reflections on how all these processes are modeled, merging all together the advanced methods, mathematical analyses, computational technologies, quantum means elaborating and exhibiting the implications of applicable approaches in real-world systems and other related domains. [ABSTRACT FROM AUTHOR]

The Residue Neutrosophic Set (RNS) is a new idea in image additional pixel level. Our idea is to make an analysis based on the additional pixel amount. In recent decades, computer vision has revolutionized image analysis by researchers. Image segmentation is a more investigated topic in the science of computer vision. Neutrosophic is a sophisticated mathematical idea to solve a myriad of challenges. The objective is to invent a neutrosophic technique to execute image thresholding. In the article, the residue methodology was applied, which denotes the residual values of neutrosophic membership intensities. This article will explore a novel idea for image thresholding termed RNS. There will be three types of RNS techniques: minimum, average, and maximum. The concepts of existing thresholding techniques in neutrosophic solvation are considered in this proposal. This article adopts novel methodologies to provide an integrated visionary path segmentation methodology. Furthermore, the proposed technique reaches a better average accuracy score. [ABSTRACT FROM AUTHOR]

Numerical simulation is an efficient tool for evaluation and prediction of material properties and behavior in many industrial domains such as the development of novel materials and medicines. For numerical studies of complex processes or systems with high fidelity, various data processing tools, modeling and simulation programs are typically involved, desiring an integrated platform that can effectively manage the collaboration of such software resources and the execution of the underlying simulation workflow for efficiency purpose. Such a platform could be practically built with a scientific computing workflow engine that focuses on the automatic scheduling and execution of a batch of interrelated computing tasks. In this work, the main procedures on construction of a specialized integrated simulation platform for material research based on a general purpose scientific computing workflow engine named HSWAP is introduced in detail, and its application to molecule screening process of energetic materials is demonstrated. Due to the flexibility and the extensibility of the platform, the work could be handily extended to the screening of other materials such as protein to find optimized protein structures or high entropy alloys to find the best configuration of component contents, as well as other application scenarios such as geometry optimizations of complex structures. [ABSTRACT FROM AUTHOR]