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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]

Three-dimensional variation in structural components or fiber alignments results in complex mechanical property distribution in tissues and biomaterials. In this paper, we use a physics-informed UNet-based neural network model (El-UNet) to discover the three-dimensional (3D) internal composition and space-dependent material properties of heterogeneous isotropic and transversely isotropic materials without a priori knowledge of the composition. We then show the capabilities of El-UNet by validating against data obtained from finite-element simulations of two soft tissues, namely, brain tissue and articular cartilage, under various loading conditions. We first simulated compressive loading of 3D brain tissue comprising of distinct white matter and gray matter mechanical properties undergoing small strains with isotropic linear elastic behavior, where El-UNet reached mean absolute relative errors under 1.5 % for elastic modulus and Poisson's ratio estimations across the 3D volume. We showed that the 3D solution achieved by El-UNet was superior to relative stiffness mapping by inverse of axial strain and two-dimensional plane stress/plane strain approximations. Additionally, we simulated a transversely isotropic articular cartilage with known fiber orientations undergoing compressive loading, and accurately estimated the spatial distribution of all five material parameters, with mean absolute relative errors under 5 %. Our work demonstrates the application of the computationally efficient physics-informed El-UNet in 3D elasticity imaging and provides methods for translation to experimental 3D characterization of soft tissues and other materials. The proposed El-UNet offers a powerful tool for both in vitro and ex vivo tissue analysis, with potential extensions to in vivo diagnostics. Elasticity imaging is a technique that reconstructs mechanical properties of tissue using deformation and force measurements. Given the complexity of this reconstruction, most existing methods have mostly focused on 2D problems. Our work is the first implementation of physics-informed UNets to reconstruct three-dimensional material parameter distributions for isotropic and transversely isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using synthetic data generated using finite element models of biological tissues with high bio-fidelity–the brain and articular cartilage. Our method can be implemented in elasticity imaging scenarios for in vitro and ex vivo mechanical characterization of biomaterials and biological tissues, with potential extensions to in vivo diagnostics. [Display omitted] [ABSTRACT FROM AUTHOR]

Driver behavior is one of the principal factors associated with road accidents. Much research to date focusing on Machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors. In this paper, the development of an android mobile application (Driver Alert) is described, with the aim of collecting data from mobile phone sensors data to identify certain patterns and understand drivers' behaviors. Additional information was recorded regarding weather and traffic information, using public API's to complement the data directly collected from the vehicle. Four machine learning models (K-Means, Algorithm Agglomerative Hierarchical, Random Forest and Support Vector Machines) were tested and compared to identify different driver profiles. A native mobile application named DriverAlert was developed to support collect data and make it available, through an online dashboard, to drivers and researchers. Due to the available tools and libraries, it possesses, Python language was used, as it is a powerful programming language for workloads in data science, machine learning, and scientific computing. [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]

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]

The technological progress in computer science (particularly, in machine learning) has contributed to the improvement of medical services, both in detecting and treating diseases. The large volumes of data, that are overwhelming for human experts (doctors, nurses), can easily be managed by automated systems, as long as we have the computational resources. Obviously, human experts are still essential in the process - we think of the use of computer science in medicine as a collaboration between medical staff and artificial intelligence. The usual types of data that can be processed by automated systems are text, sound, and image types. In this paper, we approach the diagnosis subject and focus on data consisting of sound. We created a heart murmur detection system - it analyzes recordings and tells the user whether the sound samples indicate a heart murmur or not, based on a trained machine learning model. One of the main advantages of our system is the fact that we ran a large number of experiments, with different configurations of denoising techniques and features taken into consideration. We were able to draw some interesting conclusions, for example, we found out which features are the most important for the classification and which features are not worth computing. Also, our work denotes a thorough understanding of sound processing. [ABSTRACT FROM AUTHOR]

A clinical internship is currently required by the American Board of Radiology prior to Radiology residency. The purpose of this investigation is to evaluate practicing radiologists' perspectives on the value of the internship and their recommendations for optimization. A five-minute online survey was distributed via email to practicing radiologist members of the American College of Radiology. A total of 566 completed responses (11.3% response rate) were received. Most respondents agreed that their internship was essential for improving non-radiology clinical knowledge (84%) and affirming their decision to become a radiologist (74%). Most respondents (59%) disagree that the one-year internship before residency should be eliminated. Most (53%) of the radiologists in an academic practice agreed that internship should be integrated into Radiology residency. If radiologists were to redesign the internship ("PreRad Internship"), a majority of the respondents would include training in other medical specialties (71%), working along technologists (55%) and informatics/AI/computer science (54%). While the greatest proportion (50%) of interventional radiologists reported a Surgery internship would be the most beneficial for their primary subspecialty (50%), diagnostic radiologists most commonly (27%) reported the PreRad Internship would be the most beneficial. The greatest proportions of Abdominal-, Breast-, and Neuroradiology-trained respondents reported a PreRad Internship would be the most beneficial internship for their primary field of subspecialty Radiology practice (32%, 36%, and 33%, respectively). The internship before Radiology residency offers some benefits but could be further optimized. There is support from practicing radiologists for a redesigned, more Radiology-specific PreRad Internship. • If radiologists were to redesign the internship ("PreRad Internship"), most would include training in other specialties and working with technologists. • Diagnostic radiologists most commonly reported a PreRad internship would be the most beneficial for their primary subspecialty. [ABSTRACT FROM AUTHOR]

We propose a strongly-constrained physics-informed neural network method with the derivative information to more suitably predict soliton dynamics of the nonlinear partial differential equation. We integrate more constrained physical information into the neural network, namely we add the derivative information of solutions for the nonlinear partial differential equation to the neural network structure, and introduce adaptive weight and flexible learning rate, to accelerate the convergence of the loss function of the network. As an example, we use this new method to predict a variety of soliton solutions of higher-order nonlinear Schrödinger equation. Moreover, we introduce the derivative of soliton solution to further reflect the stability of the data set. Results show that this new method can achieve the prediction in a wider solution area than the traditional method. Compared with results in previous literatures, the accuracy of the strongly-constrained physics-informed neural network method in predicting soliton dynamics is improved by 2-12 times. Therefore, this neural network is a forward-looking technology for scientific computing and automatic modeling. [ABSTRACT FROM AUTHOR]

Social networking platforms will be a tool for people to share their positive and negative emotions. Suicide ideation detection is one of the important fields in social network analysis. Exploiting the data published on social networking platforms will help to prevent suicide and reduce the rate of suicide in the world. The purpose of this study is to review the effects of using social networking platforms to identify and categorize suicidal ideation. In addition to presenting the most popular methods used in computer science to discern between suicide notes and non-suicide notes. [ABSTRACT FROM AUTHOR]

Recent developments in numerous scientific computing fields have been made possible by physics-informed neural networks (PINNs), a class of machine learning techniques that incorporate physical knowledge into neural networks. However, PINNs still encounter difficulties in simulating a kind of partial differential equations (PDEs), i.e. gradient flow equations of complex dynamic systems. Through analysis from the neural tangent kernel's (NTK) point of view, we find that directly training PINNs on the large temporal window may lead to failure. To tackle this problem, we utilize one of the gradient flow equations' significant physical properties, the energy dissipation law, to propose an adaptive energy-based sequential training method for PINNs. Specifically, the gradient of free energy is employed to determine the adaptive temporal steps of the training window, which also enhances the training efficiency. Numerical experiments are demonstrated among gradient flow equations including Cahn-Hilliard, heat conduction, and Allen-Cahn equations. Our method not only outperforms PINNs but also preserves the energy dissipation law, which shows the ability to solve complex long-time simulation problems of gradient flow systems. [ABSTRACT FROM AUTHOR]

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. The numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts. • Introduces method that strictly enforces continuity at time-window interfaces. • Solves time-dependent problems with C 0 , C 1 , and C 2 temporal continuity. • Introduces and solves a new benchmark problem with chaotic jerky dynamics. • Demonstrates superior accuracy over soft-constrained counterparts. [ABSTRACT FROM AUTHOR]

This work highlights an approach for incorporating realistic uncertainties into scientific computing workflows based on finite elements, focusing on prevalent applications in computational mechanics and design optimization. We leverage Matérn-type Gaussian random fields (GRFs) generated using the SPDE method to model aleatoric uncertainties, including environmental influences, variating material properties, and geometric ambiguities. Our focus lies on delivering practical GRF realizations that accurately capture imperfections and variations and understanding how they impact the predictions of computational models as well as the shape and topology of optimized designs. We describe a numerical algorithm based on solving a generalized SPDE to sample GRFs on arbitrary meshed domains. The algorithm leverages established techniques and integrates seamlessly with the open-source finite element library MFEM and associated scientific computing workflows, like those found in industrial and national laboratory settings. Our solver scales efficiently for large-scale problems and supports various domain types, including surfaces and embedded manifolds. We showcase its versatility through biomechanics and topology optimization applications, emphasizing the potential to influence these domains. The flexibility and efficiency of SPDE-based GRF generation empowers us to run large-scale optimization problems on 2D and 3D domains, including finding optimized designs on embedded surfaces, and to generate design features and topologies beyond the reach of conventional techniques. Moreover, these capabilities allow us to model and quantify geometric uncertainties on reconstructed submanifolds, such as the interpolated surfaces of cerebral aneurysms provided by postprocessing CT scans. In addition to offering benefits in these specific domains, the proposed techniques transcend specific applications and generalize to arbitrary forward and backward problems in uncertainty quantification involving finite elements. [Display omitted] [ABSTRACT FROM AUTHOR]

• Abstraction is at the intersection of opinions between components of CT. • Researchers predict that CSUnplugged will improve CT (hence abstraction too). • 11 activities were used, and the abstraction skill test was developed in the research. • CS unplugged significantly affected the students' abstraction skills. • The development of abstraction resulted from the progress of 11 abstracting steps. Since computational thinking is a high-level cognitive skill and its conceptual framework has not yet been clarified, the assessment of this skill is problematic. While there is no consensus in the literature about the components of computational thinking, the abstraction component is located at a point where opinions intersect. In addition, it is predicted that computer science unplugged activities will improve computational thinking, and there is limited research examining this effect. As a result, this research aims to demonstrate the alleged effects of computer science unplugged activities on abstraction skill which is considered the critical component of computational thinking. The study, which was planned with a mixed methods research design, lasted 8 weeks. Quantitative data were obtained from the abstraction skill test developed by the researchers, while qualitative data were obtained from observations and task- based interviews after each activity. Quantitative findings showed that computer science unplugged activities significantly improve abstraction skills and thus computational thinking. Qualitative findings not only revealed that developments in the six abstracting steps caused this significant difference but also identified abstracting steps where the effect of each activity was limited. Suggestions for organizing activities to improve these steps are given. Finally, suggestions are presented for the conceptual framework of computational thinking based on abstraction. [ABSTRACT FROM AUTHOR]

Time harmonic acoustic wave scattering problems modeled by the unbounded Helmholtz equations are widely used in medical and military fields. A fast and flexible solver for the Helmholtz equation is expected in engineering applications. Fortunately, data-driven neural network algorithms show extraordinary promise in scientific computing and artificial intelligence. We solve the Helmholtz equation by decomposing the equation into a coupling equation system that explicitly includes the real and imaginary parts of the solution. The real and imaginary parts are approximated by the different neural networks, respectively. The unbounded domain is reduced to a problem on a bounded domain via the Dirichlet-to-Neumann operator. Thus, a coupling physics-informed neural network (cPINN) is constructed. On the other hand, in exploring the convergence analysis for PINNs, we obtain the convergence results for cPINNs through the universal approximation and Sobolev space theory instead of using the neural tangent kernel theory. We prove that the cPINNs converge to the solution of the Helmholtz equation in H 0 1 space as the number of neurons approaches infinity. Finally, we propose some numerical results. [ABSTRACT FROM AUTHOR]

Opinion dynamics (OD) models simulate the evolution of personal opinions using computer science, social dynamics, physics, and complexity science tools on social networks. However, current opinion dynamics models mainly utilize agent-based models to simulate user interactions but still need to leverage large-scale data fully. Social networks typically involve a vast number of users and information, making it difficult for models to capture the complex interactions in real social networks. Additionally, many models overlook the specific mechanisms of user interactions, such as how network individuals influence and interact with one another, resulting in inaccuracies in modeling individual opinion evolution. This paper proposes a novel approach that integrates the probabilistic bounded confidence model (PBC) with deep learning, i.e., Neural Probabilistic Bounded Confidence (NPBC), to tackle these challenges. The NPBC model adopts an attention network, a long short-term memory (LSTM) network, and a feedforward neural network to approximate the evolution of individual opinions within networks. It satisfies the data approximation and the users' opinion update rule that conforms to the PBC model. This model enhances the understanding of opinion dynamics, helping to develop effective strategies for opinion guidance in social networks. Finally, the proposed NPBC model is evaluated on three synthetic and Twitter datasets. The results demonstrate that the proposed NPBC model performs better than the six baseline methods in predicting user opinions. Compared to baseline models, the model's accuracy (ACC) and comprehensive evaluation (F1) scores improve by up to 18% and 7%, respectively. • Combining deep learning and probabilistic bounded confidence model. • Using fully connected layers to map users and time to the same vector space. • The attention mechanism captures the internal correlations between users and time. • The long short-term memory network is used to capture the sequence features. [ABSTRACT FROM AUTHOR]

The auto-scaling feature is fundamental to serverless computing, and it automatically allows applications to scale as needed. Hence, this allows applications to be configured to adapt to current traffic and demands and acquire resources as necessary without the need to manage servers directly. Auto-scaling is an important principle in developing serverless applications that is considered and increasingly recognized by academia and industry. Despite the strong interest in auto-scaling in serverless computing in the scientific and industrial community, no clear, comprehensive, and systematic investigation has been conducted. As part of the study of automatic scaling in serverless computing, key strategies and approaches are investigated during the lifecycle of cloud applications. This research examines three key approaches to automatically scaling serverless computing applications in the taxonomy presented. These approaches include machine learning (ML)-based, frameworks-based, and models-based. Additionally, we provide an overview of key performance metrics essential to the auto-scaling process of cloud applications and discuss the requirements. It discusses key concepts and limitations of serverless computing approaches, challenges, future directions, and research opportunities. [ABSTRACT FROM AUTHOR]

The Singular Value Decomposition (SVD) of matrices is a widely used tool in scientific computing. In many applications of machine learning, data analysis, signal and image processing, the large datasets are structured into tensors, for which generalizations of SVD have already been introduced, for various types of tensor–tensor products. In this paper, we present innovative methods for approximating this generalization of SVD to tensors in the framework of the Einstein tensor product. These singular elements are called singular values and singular tensors respectively. The proposed method uses the tensor Lanczos bidiagonalization applied to the Einstein product. In most applications, as in the matrix case, the extremal singular values are of special interest. To enhance the approximation of the largest or the smallest singular triplets (singular values and left and right singular tensors), a restarted method based on Ritz augmentation is proposed. Numerical results are proposed to illustrate the effectiveness of the presented method. In addition, applications to video compression and facial recognition are presented. [ABSTRACT FROM AUTHOR]

Graph energies and spectral radii have application in molecular computing and computer sciences. In this paper, we concentrate on new findings about the Symmetric Division Deg, (SDD) energies, and (SDD) spectral radii of p splitting and p shadow graphs built on the foundation of any regular graph. In fact it becomes natural to ask how the SDD energies and spectral radii of newly formed graphs depend on the SDD energies and spectral radii of the given regular graph. [ABSTRACT FROM AUTHOR]

Multiscale is a hallmark feature of complex systems, presenting challenges for traditional numerical methods due to their reliance on local Taylor series constraints. Further, multiscale techniques often face limitations in identifying appropriate heuristic closures used to handle unresolved scales or processes within the system being modeled. In this study, we develop an efficient time-stepping strategy for multiscale problems. The proposed method's novelty lies in synergizing the hierarchical deep learning formalism with an adaptive step-size estimation process to approximate dynamical system flow maps across timescales efficiently. The model is purely data-driven and provides improved forecasting accuracy compared to fixed-step neural network time steppers. To demonstrate the effectiveness of the proposed scheme, we provide numerical simulations for several canonical nonlinear systems and source codes for their implementation. We believe the method has the potential to benefit multiscale analysis of complex systems and encourage further investigation in this area. • Identifies potential drawbacks in the current multiscale methods. • Introduces efficient time-stepping algorithm for multiscale problems using hierarchical deep learning. • Demonstrates the application of adaptive time-stepping on canonical ODEs and PDEs with noisy measurements. [ABSTRACT FROM AUTHOR]

• Creative learning in computer science is necessary and important to define. • The variable creative learning in computing needs to be operationalized and measured through universal metrics and scales. • The development of creative learning in computer science does not depend entirely on the curriculum but on how it is taught. The training of creative computer professionals is considered a strategic process in different countries because it is essential for the computerization of society. This paper proposes an approach to the study of creative computer science learning for the digital transformation of modern society. The aim of the research was to evaluate the creative learning of computer science during the training of computer science professionals. An instrument derived from previous research and metrics was applied to evaluate creative learning in the two groups of terminal years of the computer engineering course at the University of Matanzas with a mixed paradigm. The quantitative analysis diagnosed that more than sixty-five percent of the students are evaluated as poor and fair in both years. The qualitative analysis revealed the inadequacies of the teaching staff in the use of computer science teaching approaches, and the four stages of formation of creative learning in computer science in the degree course: pre-university stage, basic stage, professional stage and research stage. They also revealed three key factors in the formation of creative learning in computer science: the integration of subjects, the solution of real projects, and the mainly emotional support provided by teachers and other professionals to students during their integration into the computerization of organizations. [ABSTRACT FROM AUTHOR]

Many problems in scientific computing and engineering fields lead to the solution of generalized absolute value equations (GAVE). In this paper, by simultaneously splitting both coefficient matrices in the differential and non-differential parts of the GAVE, a modified Newton-based matrix splitting (MNMS) iteration method is proposed. The MNMS iteration method not only covers the well-known generalized Newton iteration method as well as the recent proposed modified Newton-based iteration method and the Newton-based matrix splitting iteration method for solving the GAVE, but also results in a series of relaxation versions that are very flexible in real applications. Convergence properties of the MNMS iteration method are studied in detail when the coefficient matrices are positive definite matrices and H + -matrices. Finally, three numerical examples are presented to show the feasibility and effectiveness of the proposed MNMS iteration method. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors [1,2] , can efficiently simulate the entire loading history of non-local gradient damage propagation. To achieve this goal, we first adopt a Temporal Convolutional Network (TCN) as the neural network of choice to capture the history-dependent evolution of the non-local strain in a coarsely meshed domain. The quality of the network predictions governs the computational performance of I-FENN, and therefore we perform an extended investigation aimed at enhancing them. We explore a data-driven vs. physics-informed TCN setup to arrive at an optimum network training, evaluating the network based on a coherent set of relevant performance metrics. We address the crucial issue of training a physics-informed network with input data that span vastly different length scales by proposing a systematic way of input normalization and output un-normalization. We then integrate the trained TCN within the nonlinear iterative FEM solver and apply I-FENN to simulate the damage propagation analysis. I-FENN is always applied in mesh idealizations different from the one used for the TCN training, showcasing the framework's ability to be used at progressively refined mesh resolutions. We illustrate several cases that I-FENN completes the simulation using either a modified or a full Newton–Raphson scheme, and we showcase its computational savings compared to both the classical monolithic and staggered FEM solvers. We underline that we satisfy very strict convergence criteria for every increment across the entire simulation, providing clear evidence of the robustness and accuracy of I-FENN. All the code and data used in this work will be made publicly available upon publication of the article. • Non-local damage propagation is modeled for the first time with a TCN-based I-FENN. • I-FENN is faster than monolithic and staggered FEM and savings scale with model size. • I-FENN's robustness is shown by satisfying very strict convergence criteria. • Input/output normalization methods are applied to enhance the network training. • I-FENN is always applied in different training and testing mesh idealizations. [ABSTRACT FROM AUTHOR]

Block Gram-Schmidt algorithms serve as essential kernels in many scientific computing applications, but for many commonly used variants, a rigorous treatment of their stability properties remains open. This work provides a comprehensive categorization of block Gram-Schmidt algorithms, particularly those used in Krylov subspace methods to build orthonormal bases one block vector at a time. Known stability results are assembled, and new results are summarized or conjectured for important communication-reducing variants. Additionally, new block versions of low-synchronization variants are derived, and their efficacy and stability are demonstrated for a wide range of challenging examples. Numerical examples are computed with a versatile Matlab package hosted at https://github.com/katlund/BlockStab , and scripts for reproducing all results in the paper are provided. Block Gram-Schmidt implementations in popular software packages are discussed, along with a number of open problems. An appendix containing all algorithms type-set in a uniform fashion is provided. [ABSTRACT FROM AUTHOR]

Solving large-scale linear equations is an important problem in signal processing, machine vision, financial mathematics, quantum physics, chemistry, and many other areas. Due to the curse of dimensionality, classic numerical treatments of such problems are difficult and inefficient, which are usually addressed by low rank approximation methods. Tensor train decomposition (TTD) is one of these methods that can reduce the execution cost theoretically. In this work, we present a TTD based batch alternating least squares (BALS) method, termed as TTD-BALS, for solving large-scale linear equations. TTD-BALS makes the computation efficient and stable, since it can convert the large-scale optimization problem into sequential optimization subproblems with smaller scale. We further uncover that all existing TTD-based methods together with our TTD-BALS are not universal and can be troubled by local minima, and therefore they are not suitable for general and arbitrary linear equations. Fortunately, based on our analysis, if some specific requirements we identified can be satisfied, TTD-BALS is able to provide a very good solution. In fact these requirements can be satisfied in a wide range of domains such as signal processing and scientific computing. Furthermore, we prove that the proposed method is able to reduce the computational complexity, and conduct numerical experiments to confirm its effectiveness. [ABSTRACT FROM AUTHOR]

RSA (Rivest-Shamir-Adleman) cryptosystem is the most popular asymmetric key cryptographic algorithm used in computer science and information security. Recently, an RSA-like cryptosystem was proposed using a novel product that arises from a cubic field connected to the cubic Pell equation. The relevant key equation is e d ≡ 1 mod (p 2 + p + 1) (q 2 + q + 1) with N = p q. This RSA variant is claimed to be robust against the Wiener's attack and hence the bit-size of the private key could be shorter, namely d < N 1 / 4. In this paper, we explore the further security analysis and investigate the potential small private exponent attack. We show that such RSA variant is particularly vulnerable to the lattice-based method. To be specific, we can carry out the lattice-based small private exponent attack if d < N 2 − 2 , which is less secure than the standard RSA. Furthermore, we conduct numerical experiments to verify the validity of the proposed attack. [ABSTRACT FROM AUTHOR]

Gauss quadrature rules associated with a nonnegative measure with support on (part of) the real axis find many applications in Scientific Computing. It is important to be able to estimate the quadrature error when replacing an integral by an ℓ -node Gauss quadrature rule in order to choose a suitable number of nodes. A classical approach to estimate this error is to evaluate the associated (2 ℓ + 1) -node Gauss–Kronrod rule. However, Gauss–Kronrod rules with 2 ℓ + 1 real nodes might not exist. The (2 ℓ + 1) -node generalized averaged Gauss formula associated with the ℓ -node Gauss rule described in Spalević (2007) [16] is guaranteed to exist and provides an attractive alternative to the (2 ℓ + 1) -node Gauss–Kronrod rule. This paper describes a new representation of generalized averaged Gauss formulas that is cheaper to evaluate than the available representation. [ABSTRACT FROM AUTHOR]

The article discusses the main aspects of the importance of developing a high-performance environment for scientific research in the context of digital transformation. Some historical facts are presented that testify to the role played by the Soviet scientists in the development of this sphere. An analysis of the experience of the FRC CSC RAS in the creation of a modern high-performance platform and its use for scientific research is given. [ABSTRACT FROM AUTHOR]

The numerical simulation of cardiac electrophysiology is a highly challenging problem in scientific computing. The Bidomain system is the most complete mathematical model of cardiac bioelectrical activity. It consists of an elliptic and a parabolic partial differential equation (PDE), of reaction–diffusion type, describing the spread of electrical excitation in the cardiac tissue. The two PDEs are coupled with a stiff system of ordinary differential equations (ODEs), representing ionic currents through the cardiac membrane. Developing efficient and scalable preconditioners for the linear systems arising from the discretization of such computationally challenging model is crucial in order to reduce the computational costs required by the numerical simulations of cardiac electrophysiology. In this work, focusing on the Bidomain system as a model problem, we have benchmarked two popular implementations of the Algebraic Multigrid (AMG) preconditioner embedded in the PETSc library and we have studied the performance on the calibration of specific parameters. We have conducted our analysis on modern HPC architectures, performing scalability tests on multi-core and multi-GPUs settings. The results have shown that, for our problem, although scalability is verified on CPUs, GPUs are the optimal choice, since they yield the best performance in terms of solution time. [ABSTRACT FROM AUTHOR]

There is continuing debate about which climate-policy instruments are most appropriate to reduce emissions. Undertaking a global survey among scientists who published on climate policy, we provide a systematic overview of (dis)agreements about six main types of policy instruments. The survey includes various fields across the social and natural sciences. The results show that, on average, all instruments are considered important, with direct regulation receiving the highest rating and adoption subsidies and cap-and-trade the lowest. The latter is surprising given the theoretical advantages and real-world success of the EU-ETS. Next, clustering scientific fields based on how important they consider the instruments, we determine five distinct groups, with (a) ecological economists and (b) mathematics/computer science being most dissimilar from other discipline clusters. We explain disagreement through assessing the relative importance assigned to policy criteria effectiveness, efficiency, equity and socio-political feasibility, as well as researchers' attitudes and background. Paying special attention to carbon pricing, motivated by its contested key role, we identify three respondent clusters, namely 'enthusiasts', 'undecided', and 'skeptics'. Examining various policy arguments, we find that agreeing that carbon pricing effectively limits energy/carbon rebound and has potential to be harmonized globally have the strongest association with giving importance to this policy. • We survey researchers from diverse fields to examine views on climate policies. • Direct regulation is on average rated as most important. • Environmental and ecological economists hold contrasting views on cap-and-trade. • Support for carbon pricing relates to expectations of curbing rebound and global harmonization. • Many other factors are assessed, such as policy criteria, climate worry and ideology. [ABSTRACT FROM AUTHOR]

Crossflow filtration with pore blocking is an unsteady system affected by operating conditions. We model pore blocking and solve the governing partial differential equations using the method of lines. The loss in permeability is neither spatially nor temporally uniform. Operating at constant flux results in a greater permeate of clean water but at a higher operating cost than operating at constant pressure. • Mathematical model of unsteady crossflow filtration with pore blocking. • Numerical method of lines solver for differential–algebraic equations. • Operating at constant pressure is more efficient than at constant flux. [ABSTRACT FROM AUTHOR]

• We present and analyze a NGAOR method for solving LCPs, in which one special case reduces to a NGSOR method. • We prove the convergence of the proposed methods when the system matrix is an - matrix (irreducible or strictly diagonally dominant matrix). • Experimental results highlight the effectiveness and efficiency of our proposed methods. The linear complementarity problems (LCPs) can be encountered in various scientific computing, management science, and operations research. In this study, we introduce and analyze a new generalized accelerated overrelaxation (NGAOR) method for solving LCPs, in which one special case reduces to a new generalized successive overrelaxation (NGSOR) method. Moreover, we prove the convergence of the proposed methods when the system matrix is an H -matrix (irreducible or strictly diagonally dominant matrix). Numerical results for several experiments are present to show the effectiveness and efficiency of the proposed methods. AMS classification: 65F10, 90C33 [ABSTRACT FROM AUTHOR]

Interface problems have long been a major focus of scientific computing, leading to the development of various numerical methods. Traditional mesh-based methods often employ time-consuming body-fitted meshes with standard discretization schemes or unfitted meshes with tailored schemes to achieve controllable accuracy and convergence rate. Along another line, mesh-free methods bypass mesh generation but lack robustness in terms of convergence and accuracy due to the low regularity of solutions. In this study, we propose a novel method for solving interface problems within the framework of the random feature method (RFM). This approach utilizes random feature functions in conjunction with a partition of unity as approximation functions, and solves a linear least-squares system to obtain the approximate solution. In the context of interface problems, two innovative and crucial components are incorporated into the RFM. Firstly, we utilize two sets of random feature functions on each side of the interface, allowing for the inclusion of low regularity or even discontinuous behaviors in the solution. Secondly, the construction of the loss function is based on the assessment of the partial differential equation, initial/boundary conditions, and the interface condition on collocation points. This approach ensures that these conditions are equally satisfied. Consequently, the challenges arising from geometric complexity primarily manifest in the generation of collocation points, a task amenable to standard methods. Importantly, the proposed method retains its meshfree characteristics and robustness when addressing problems featuring intricate geometries. We validate our method through a series of linear interface problems with increasingly complex geometries, including two-dimensional elliptic and three-dimensional Stokes interface problems, a three-dimensional elasticity interface problem, a moving interface problem with topological change, a dynamic interface problem with large deformation, and a linear fluid–solid interaction problem with complex geometry. Our findings show that despite the solution often being only continuous or even discontinuous, our method not only eliminates the need for mesh generation but also maintains high accuracy, akin to the spectral collocation method for smooth solutions. Remarkably, for the same accuracy requirement, our method requires two to three orders of magnitude fewer degrees of freedom than traditional methods, demonstrating its significant potential for solving interface problems with complex geometries and predetermined intricate evolution. [ABSTRACT FROM AUTHOR]

A review of current trends in scientific computing reveals a broad shift to open-source and higher-level programming languages such as Python and growing career opportunities over the next decade. Open-source modeling tools accelerate innovation in equation-based and data-driven applications. Significant resources have been deployed to develop data-driven tools (PyTorch, TensorFlow, Scikit-learn) from tech companies that rely on machine learning services to meet business needs while keeping the foundational tools open. Open-source equation-based tools such as Pyomo, CasADi, Gekko, and JuMP are also gaining momentum according to user community and development pace metrics. Integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to radically accelerate progress. However, long-term support mechanisms are still necessary to sustain the momentum and maintenance of critical foundational packages. • Review of open-source modeling software in scientific computing. • Critical evaluation of innovation with open-source modeling tools. • Perspective on the future of open-source tools in specialization and integration. [ABSTRACT FROM AUTHOR]

• Crack tracing algorithm can be combined with a variety of crack-bridge models. • Cracks are implicitly detected on a one-by-one basis. • Implementation using open-source scientific computing tools with a web application. The paper describes a probabilistic model capturing the multiple-cracking behavior of unidirectional brittle-matrix composites loaded in tension. The approach to modeling of composite fragmentation introduces two salient features that enhance both efficiency and flexibility compared to existing simulation methods: First, the algorithm identifies the emerging cracks one by one within a minimum number of load increments. Second, the crack-tracing algorithm is based on an abstract description of a crack bridge behavior. As a result, it is possible to combine the crack-tracing algorithm with a wide variety of crack bridge models, i.e. non-linear, deterministic or probabilistic. In this way, specific phenomena of bond behavior in different types of composites can be accounted for. The random nature of matrix cracking is reproduced using a random field simulation of the matrix strength. The model is verified by reproducing analytical results available for constant bond-slip law. The feasibility and robustness of the algorithm is demonstrated using an interactive web application that is directly executable from within a public github.com repository. [ABSTRACT FROM AUTHOR]

The complexity of graph isomorphism (GraphIso) is a famous problem in computer science. For graphs G and H , it asks whether they are the same up to a relabeling of vertices. In 1981, Lubiw proved that list restricted graph isomorphism (ListIso) is NP -complete: for each u ∈ V (G) , we are given a list L (u) ⊆ V (H) of possible images of u. After 35 years, we revive the study of this problem and consider which results for GraphIso can be modified to solve ListIso. We prove: 1) Under certain conditions, GI -completeness of a class of graphs implies NP -completeness of ListIso. 2) Several combinatorial algorithms for GraphIso can be modified to solve ListIso : for trees, planar graphs, interval graphs, circle graphs, permutation graphs, and bounded treewidth graphs. 3) ListIso is NP -complete for cubic colored graphs with sizes of color classes bounded by 8 with all lists of size at most 3. [ABSTRACT FROM AUTHOR]

For a function f : { − 1 , 1 } n → { − 1 , 1 } the relationship between the sum of its linear Fourier coefficients f ̂ (i) (defined by f ̂ (i) ≔ 1 2 n ∑ x ∈ { − 1 , 1 } n f (x) x i for i = 1 , 2 , ... , n and x = (x 1 , ... , x n)) and its degree d is a problem in theoretical computer science related to social choice. In that regard, in 2012, O'Donnell conjectured that ∑ i = 1 n f ̂ (i) ≤ d d − 1 ⌊ d − 1 2 ⌋ 2 1 − d. In 2020, Wang (Wang, 2020) proved that the conjecture is equivalent to a conjecture about cryptographic resilient Boolean functions and proved that the conjecture is true for every n , when d = 1 and d = n − 1. In this paper, we contribute theoretically to this problem by showing that if the conjecture is true for some specific relatively small value n 0 , which depends on d , then it is valid for any n ≥ n 0 . In particular, we show that the conjecture is correct for every n , when d = 2 and d = 3. However, despite these promising results, we present a counterexample when d = 4 , and so the conjecture is not true in general. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a fast-growing research area in computer science that aims to mimic cognitive processes through a number of techniques. Supervised machine learning, a subfield of AI, includes methods that can identify patterns in high-dimensional data using labeled 'ground truth' data and apply these learnt patterns to analyze, interpret, or make predictions on new datasets. Supervised machine learning has become a significant area of interest within the medical community. Radiology and neuroradiology in particular are especially well suited for application of machine learning due to the vast amount of data that is generated. One devastating disease for which neuroimaging plays a significant role in the clinical management is stroke. Within this context, AI techniques can play pivotal roles for image-based diagnosis and management of stroke. This overview focuses on the recent advances of artificial intelligence methods – particularly supervised machine learning and deep learning – with respect to workflow, image acquisition and reconstruction, and image interpretation in patients with acute stroke, while also discussing potential pitfalls and future applications. • Artificial Intelligence (AI) is a growing subfield of computer science that aims to mimic cognitive processes through a number of techniques. • Superficial AI, which is comprised of techniques that learn patterns with the use of labels of 'ground truth' data, have become a significant area of interest within the medical community. As images are, in essence, volumes of mineable data, radiology is a field of medicine particularly well suited to supervised AI techniques. • AI techniques could play pivotal roles in neuroradiology, particular in the diagnosis and management of time sensitive diseases such as stroke. • Several AI techniques, comprised of machine and deep learning methods, have shown incredible promise as an adjunct to a neuroradiologist's workflow, particularly within stroke imaging. • Although encouraging, there are data protection and medicolegal implications that must be considered for widespread clinical use of AI techniques. [ABSTRACT FROM AUTHOR]

We present a parallel data structure for the discretization of partial differential equations which is based on distributed point objects and which enables the flexible, transparent, and efficient realization of conforming, nonconforming, and mixed finite elements. This concept is realized for elliptic, parabolic and hyperbolic model problems, and sample applications are provided by a tutorial complementing a lecture on scientific computing. The corresponding open-source software is based on this parallel data structure, and it supports multilevel methods on nested meshes and 2D and 3D as well as in space–time. Here, we present generic results on porous media applications including multilevel preconditioning and multilevel Monte Carlo methods for uncertainty quantification. [ABSTRACT FROM AUTHOR]

deal.II is a state-of-the-art finite element library focused on generality, dimension-independent programming, parallelism, and extensibility. Herein, we outline its primary design considerations and its sophisticated features such as distributed meshes, h p -adaptivity, support for complex geometries, and matrix-free algorithms. But deal.II is more than just a software library: It is also a diverse and worldwide community of developers and users, as well as an educational platform. We therefore also discuss some of the technical and social challenges and lessons learned in running a large community software project over the course of two decades. [ABSTRACT FROM AUTHOR]

Kaskade 7 is a finite element toolbox for the solution of stationary or transient systems of partial differential equations, aimed at supporting application-oriented research in numerical analysis and scientific computing. The library is written in C++ and is based on the Dune interface. The code is independent of spatial dimension and works with different grid managers. An important feature is the mix-and-match approach to discretizing systems of PDEs with different ansatz and test spaces for all variables. We describe the mathematical concepts behind the library as well as its structure, illustrating its use at several examples on the way. [ABSTRACT FROM AUTHOR]

• Clinical target volume definition in radiotherapy is challenging. • The contribution of computational methods is discussed. • Goals are automation, consistency, and ultimately improvements. • Image segmentation algorithms can automate the process in parts. • Mathematical models may quantitatively describe tumor progression patterns. Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries. [ABSTRACT FROM AUTHOR]

• Neuroscience is rapidly adopting machine-learning in many forms. • Overfitting of parameters in machine-learning is well-known but also misunderstood. • Over-hyping can occur when analyses are optimized, and despite cross-validation. • Over-hyping threatens the integrity of the machine-learning neuroscience literature. • Alternative methods are provided that allow detection and prevention of over-hyping. Machine learning has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, and MEG data. With these powerful techniques comes the danger of overfitting of hyperparameters which can render results invalid. We refer to this problem as 'overhyping' and show that it is pernicious despite commonly used precautions. Overhyping occurs when analysis decisions are made after observing analysis outcomes and can produce results that are partially or even completely spurious. It is commonly assumed that cross-validation is an effective protection against overfitting or overhyping, but this is not actually true. In this article, we show that spurious results can be obtained on random data by modifying hyperparameters in seemingly innocuous ways, despite the use of cross-validation. We recommend a number of techniques for limiting overhyping, such as lock boxes, blind analyses, pre-registrations, and nested cross-validation. These techniques, are common in other fields that use machine learning, including computer science and physics. Adopting similar safeguards is critical for ensuring the robustness of machine-learning techniques in the neurosciences. [ABSTRACT FROM AUTHOR]

• Automatic assessment of L2 phonetic fluency in read-aloud speech without an ASR system. • High agreement between automatic and human assessments. • Longitudinal coherence between fluency scores and learning stage. Automatic second language (L2) speech fluency assessment has been one of the ultimate goals of several projects aiming at designing Computer-Assisted Pronunciation Training (CAPT) tools for L2 learners. Usually, three challenges must be tackled in order to solve the issues at stake: 1) Defining fluency from a threefold interdisciplinary perspective (acoustic and perceptual phonetics, computer science, L2 education); 2) Using a cost-effective algorithm; 3) Testing the procedure with actual learners' data. Despite rapid technical developments in the field of automatic speech processing, the tools which are actually available for learners are still scarce, and most of them rely on automatic speech recognition (ASR). Moreover, most research on the topic is focusing on English as the target L2. Therefore, in this article, we address the following research questions: (a) is it possible to use a non-ASR-based low-level signal segmentation algorithm to predict human expert assessment of phonetic fluency in beginner Japanese learners of French in a text-reading task during the first stages of their learning? (b) if the answer to (a) is positive, then what are the best predictors of phonetic fluency among a set of available measures (see below for more details)? (c) is it possible to use this algorithm to monitor the evolution of phonetic fluency (and of its associated predictors) in these learners in a longitudinal study? As a first step, a corpus of French sentences read aloud by 12 Japanese learners of different proficiency levels in French was used to design a prediction system. The read-aloud speech data was perceptually annotated by three human experts on four dimensions: overall speech fluency, speech rate, regularity of speech rate, speech fluidity (i.e. smoothness of transitions between phones). Inter-rater agreement and reliability were high for all dimensions, and the average human ratings were compared with the scores provided by our prediction system. The results show strong correlations between human and automatic scores of speech rate and regularity of speech rate, and a weak correlation for speech fluidity. Automatic scores were finally combined together through a multiple linear regression model in order to predict overall speech fluency. The best model led to a correlation coefficient of.92 between automatic and human ratings, with a root-mean-square error of.38. In the second step of this study, a corpus of identical sentences read aloud four times over two years by 12 Japanese learners of French (after 4, 7, 12, and 19 months of French courses in Japan) was fed to the automatic system. The results show regular progress in overall speech fluency, which fits with the regular progress the Japanese learners under scrutiny were expected to make through their academic program in French at their university in Japan every semester. Our study suggests a positive answer to our first and third research questions, with speech rate as the best predictor to answer our second research question. In a pedagogical perspective, it seems that such a simple algorithm could be integrated in a CAPT tool to monitor learners' progress in phonetic fluency in reading-aloud tasks. [ABSTRACT FROM AUTHOR]

Differential privacy is commonly used in the computer science literature as a mathematical definition of privacy for the purpose of quantifying and bounding privacy loss. It induces a preference order over the set of privacy-jeopardizing mechanisms which, in turn, adhere to some properties of this order. We show that a set of five such properties uniquely captures the ordinal implications of prioritizing the alternatives in agreement with differential privacy. The model can also be applied to evaluate the appropriateness of differential privacy in different settings. [ABSTRACT FROM AUTHOR]

• Survey the applications of information techniques on the built heritage documentation. • Consider techniques of computer graphics, photogrammetry, BIM, GIS and ontology. • Emphasize the application of BIM on built heritage (HBIM). • Discuss the integration of HBIM and other information techniques. • Demonstrate the potential to extend HBIM capabilities. Built heritage documentation involves the 3D modelling of the geometry (typically using 3D computer graphics, photogrammetry and laser scanning techniques) and information management of semantic knowledge (i.e., using Geographic Information System (GIS) and ontology tools). The recent developed Building Information Modelling (BIM) technique combines 3D modelling and information management. One of its modern application is heritage documentation and has generated a new concept of Historic/Heritage Building Information Modelling (HBIM). This paper summarises the applications of these information techniques on the built heritage documentation. We utilise Web of Science Collection to monitor the publications on built heritage documentation. We analyse the research trend in heritage modelling by comparing the attention paid by researchers before and during the 2010s. The results show that photogrammetry is always the most popular method in heritage modelling. More and more works in heritage modelling have begun to use laser scanning, computer science, GIS and especially BIM techniques. Ontologies and 3D computer graphics are traditional ways for heritage documentation. Moreover, we pay attention to the roles of BIM on heritage documentation and conduct a detailed discussion on how to extend the HBIM capabilities by integrating with other techniques. The integration provides possible enhanced functions in HBIM, including accurate parametric modelling from computer graphics, automatic semantic segmentation of 3D point cloud from reality-based modelling, spatial information management and analysis by GIS, and knowledge modelling by ontology. [ABSTRACT FROM AUTHOR]

At the time of this writing, searching Google Scholar for 'light-sheet microscopy' returns almost 8500 results; over three-quarters of which were published in the last 5 years alone. Searching for other advanced imaging methods in the last 5 years yields similar results: 'super-resolution microscopy' (>16 000), 'single-molecule imaging' (almost 10 000), SPIM (Single Plane Illumination Microscopy, 5000), and 'lattice light-sheet' (1300). The explosion of new imaging methods has also produced a dizzying menagerie of acronyms, with over 100 different species of 'light-sheet' alone, from SPIM to UM (Ultra microscopy) to SiMView (Simultaneous MultiView) to iSPIM (inclined SPIM, not to be confused with iSPIM, inverted SPIM). How then is the average biologist, without an advanced degree in physics, optics, or computer science supposed to make heads or tails of which method is best suited for their needs? Let us also not forget the plight of the optical physicist, who at best might need help with obtaining healthy samples and keeping them that way, or at worst may not realize the impact their newest technique could have for biologists. This review will not attempt to solve all these problems, but instead highlight some of the most recent, successful mergers between biology and advanced imaging technologies, as well as hopefully provide some guidance for anyone interested in journeying into the world of live-cell imaging. [ABSTRACT FROM AUTHOR]

Whilst the field of digital forensics is now well established, its research community can be considered relatively emerging in comparison to the associated areas of traditional forensic and computer sciences. As a result, this comment article takes a quick look at the demographics of digital forensics research over the last 20 years, with metadata from 6589 articles being extracted and analysed from Scopus in order to provide a brief insight into this field's research activity. [ABSTRACT FROM AUTHOR]

• Improved process understanding of continuous wet granulation process with deep learning technology. • Application of deep learning techniques for an innovative process monitoring. • Optimization of deep neural networks for pharmaceutical process monitoring. • Application of innovative Process Analytical Technology (PAT) equipment. • Real-world example of process monitoring of a continuous production line. Continuous Manufacturing (CM) of pharmaceutical drug products is a new approach within the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line for production of solid dosage forms was investigated. The line was composed of the subsequent continuous unit: operations feeding – twin-screw wet-granulation – fluid-bed drying – sieving and tableting. The formulation of a commercial entity was selected for this study. Several critical process parameters were evaluated in order to probe the process and to characterize the impact on quality attributes. Seven critical process parameters have been selected after a risk analysis: API and excipient mass flows of the two feeders, liquid feed rate and rotation speed of the extruder and rotation speed, temperature and airflow of the dryer. Eight quality attributes were controlled in real time by Process Analytical Technologies (PAT): API content after blender, after dryer, in tablet press feed frame and of tablet, LOD after dryer and PSD after dryer (three PSD parameters: x10 x50 x90). The process parameter values were changed during production in order to detect the impact on the quality of the final product. The deep learning techniques have been used in order to predict the quality attribute (output) with the process parameters (input). The use of deep learning reduces the noise and simplify the data interpretation for a better process understanding. After optimization, three hidden layers neural network were selected with 6 hidden neurons. The activation function ReLU (Rectified Linear Unit) and the ADAM optimizer were used with 2500 epochs (number of learning cycle). API contents, PSD values and LOD values were estimated with an error of calibration lower than 10%. The level of error allow an adequate process monitoring by DNN and we have proven that the main critical process parameters can be identified at a higher levelof process understanding. The synergy between PAT and process data science creates a superior monitoring framework of the continuous manufacturing line and increase the knowledge of this innovative production line and the products that it makes. [ABSTRACT FROM AUTHOR]