Computational Solution Research Articles

Emerging Microorganisms and Infectious Diseases: One Health Approach for Health Shared Vision.

Emerging infectious diseases (EIDs) are newly emerging and reemerging infectious diseases. The National Institute of Allergy and Infectious Diseases identifies the following as emerging infectious diseases: SARS, MERS, COVID-19, influenza, fungal diseases, plague, schistosomiasis, smallpox, tick-borne diseases, and West Nile fever. The factors that should be taken into consideration are the genetic adaptation of microbial agents and the characteristics of the human host or environment. The new approach to identifying new possible pathogens will have to go through the One Health approach and omics integration data, which are capable of identifying high-priority microorganisms in a short period of time. New bioinformatics technologies enable global integration and sharing of surveillance data for rapid public health decision-making to detect and prevent epidemics and pandemics, ensuring timely response and effective prevention measures. Machine learning tools are being more frequently utilized in the realm of infectious diseases to predict sepsis in patients, diagnose infectious diseases early, and forecast the effectiveness of treatment or the appropriate choice of antibiotic regimen based on clinical data. We will discuss emerging microorganisms, omics techniques applied to infectious diseases, new computational solutions to evaluate biomarkers, and innovative tools that are useful for integrating omics data and electronic medical records data for the clinical management of emerging infectious diseases.

Optimizing green splits in high‐dimensional traffic signal control with trust region Bayesian optimization

AbstractCentralized traffic signal control has long been a challenging, high‐dimensional optimization problem. This study establishes a simulation‐based optimization framework and develops a novel optimization algorithm based on trust region Bayesian optimization (TuRBO), which can efficiently obtain an approximate optimal solution to the high‐dimensional traffic signal control problem. Local Gaussian process (GP), trust region, and Thompson sampling are employed in the TuRBO and contribute considerably to performance in terms of computational speed, solution quality, and scalability. Empirical studies are carried out using data from Mudanjiang and Chengdu, China. The performance of TuRBO is compared with that of Bayesian optimization (BO), genetic algorithm and random sampling. The results show that TuRBO converges the fastest because of its ability to balance exploration and exploitation through the trust region and Thompson sampling. Meanwhile, because TuRBO enables more efficient exploitation through the local GP, the solution quality of TuRBO outperforms others significantly. The average waiting time achieved by TuRBO was 2.84% lower than that achieved by BO. Finally, the method has been successfully extended to a large network with 233‐dimensional spaces and 122 signalized intersections, demonstrating that the developed methodology can deal with high‐dimensional traffic signal control effectively for real case applications.

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations.

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.

Decoding Retinoblastoma: Unveiling Gene Networks and Potential Targets through In Silico Analysis

Background Retinoblastoma is an aggressive cancer whose majority of patients are infants and children below the age of five. Approximately 80% of the total patients of retinoblastoma reside in low-to-middle-income countries like India. Lack of public and medical awareness and the absence of significant and regular clinical trials to test and authenticate new potential treatments impede the process of treating retinoblastoma. Attempts have been made to establish an effective way to diagnose retinoblastoma early so that it can be controlled in time, but so far, no significant success has been documented on that front. Moreover, recent strategies include computational and informatics solutions to identify potential targets at a genetic level to alter the expression of defective proteins in human subjects. Aim The main aim of the current study is to unravel the potential targets of Retinoblastoma, an aggressive pediatric cancer, utilizing an in silico network biology approach. Methods In the present study, we have utilized the gene network analysis approach to identify hub genes that affect the expression in the human system. We developed the Protein – Protein Interaction network utilizing 158 genes extracted from the NCBI OMIM database and identified 15 key genes, which were then subjected to metascape analysis to identify pathways and processes that affect and prioritize genes based on their significance scores. We were able to identify the following target genes: RBBP4, TFDP1, and RBBP7. Result RBBP4, TFDP1, and RBBP7 were identified as the most novel target genes against retinoblastoma after gene network and enrichment analysis. Conclusion Our in-silico network analysis unveiled the intricate mechanisms behind the progression of retinoblastoma by dissecting 158 associated genes in humans. Thus, this work not only illuminates the underlying dynamics of the disease but also offers a promising avenue for intervention.

Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons

Abstract. The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions, which necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic-wake-meandering-based mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension of a study that addressed constructing a diurnal cycle evolution based on experimental data (Quon, 2024). Here, this inflow is used to validate the turbine structural and wake-meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from the coupled Simulator fOr Wind Farm Applications (SOWFA)–OpenFAST tool. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data are available. To this end, we compared the load results from FAST.Farm and SOWFA–OpenFAST to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10 min statistics of strain gauge measurements during 3.5 h of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake-steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.

Heuristic approaches for non-exhaustive pattern-based change detection in dynamic networks

Dynamic networks are ubiquitous in many domains for modelling evolving graph-structured data and detecting changes allows us to understand the dynamic of the domain represented. A category of computational solutions is represented by the pattern-based change detectors (PBCDs), which are non-parametric unsupervised change detection methods based on observed changes in sets of frequent patterns over time. Patterns have the ability to depict the structural information of the sub-graphs, becoming a useful tool in the interpretation of the changes. Existing PBCDs often rely on exhaustive mining, which corresponds to the worst-case exponential time complexity, making this category of algorithms inefficient in practice. In fact, in such a case, the pattern mining process is even more time-consuming and inefficient due to the combinatorial explosion of the sub-graph pattern space caused by the inherent complexity of the graph structure. Non-exhaustive search strategies can represent a possible approach to this problem, also because not all the possible frequent patterns contribute to changes in the time-evolving data. In this paper, we investigate the viability of different heuristic approaches which prevent the complete exploration of the search space, by returning a concise set of sub-graph patterns (compared to the exhaustive case). The heuristics differ on the criterion used to select representative patterns. The results obtained on real-world and synthetic dynamic networks show that these solutions are effective, when mining patterns, and even more accurate when detecting changes.

AN INNOVATIVE IT SOLUTIONS AGENCY (hub of technology innovative solutions)

ABSTRACT : An innovative IT solution company that provides a variety of services, such as software development, cloud computing, cybersecurity, data analytics, and artificial intelligence, is introduced in the proposal. In order to provide unique solutions for challenging business problems, the agency plans to incorporate cutting-edge technologies like blockchain, IoT, and machine intelligence. Delivering individualised solutions through in-depth market research, agile development, strategic alliances, talent development, and all-encompassing support and training will be the agency’s primary focus. A competitive edge, better decision-making, higher levels of security, and sustained growth are among the anticipated results. By offering clients significant value and long-term growth, the agency hopes to become a leader in the IT services sector. Businesses and organisations in the modern digital age rely more and more on cutting-edge IT solutions to boost productivity, improve customer experiences, and keep a competitive edge. The goal of this project is to create a cutting-edge IT solution company that will provide services and solutions specifically designed to fulfil the demands of different industries. Service diversification entails offering a wide range of IT services, such as data analytics, cloud computing, cybersecurity, software development, and artificial intelligence (AI) solutions. Innovation Focus: To develop custom solutions that tackle particular business difficulties, take advantage of cutting-edge technologies like blockchain, the Internet of Things (IoT), and machine learning. A customer-focused strategy: Adopt a client-first approach to guarantee individualised solutions via thorough needs analysis and ongoing involvement. Guarantee of Quality: Put into practice strict quality control procedures. KEYWORDS – STEM education, interdisciplinary learning, project-based instruction, active learning, innovation

PackMASNet: An information integration approach for quality inspection in industry 5.0

Industry 5.0 (I5.0) is increasingly governed by Operation Technology and Information Technology. It hosts Deep Learning (DL) solutions as a premier tool for the informatization of Quality Inspection (QI) of Mass Produced Products (MPP) and Mass Customized Products (MCP) at the on-premise edge devices. Besides, these ever-evolving MCPs in I5.0 require newer DL models to informatize customized quality parameters, mandating significant storage and computing resources, that is challenging to achieve at resource-constraint edge platform. This work proposes PackMASNet, a continual learning-based information integration scheme that sustainably incorporates the QI information of multiple MCPs in a single Deep Neural Network (DNN) under the following constraints: (i) resource-limited on-premise edge devices, (ii) no access to prior product data, and (iii) negligible deterioration of quality information of frequently manufactured MPPs. A lightweight Cosine-similarity threshold mechanism is incorporated to leverage the benefits of knowledge transfer (cloning) while avoiding its adverse impact in multi-varied MCPs. The scheme proposes DNN performance, storage, computation, data, and time efficient solutions for industries implementing the edge computing paradigm on I5.0. The efficacy of the proposed scheme is validated for the predictive QI of real-world use case in injection molding. The scheme preserves the frequently used information of MPP by 99.07%, saves computation cost by 132 times, reduces the DNN training time by 20.45%, and improves overall performance by 43.60% over the state-of-the-art schemes.

Long solution times or low solution quality: On trade-offs in choosing a power flow formulation for the Optimal Power Shutoff problem

The Optimal Power Shutoff (OPS) problem is an optimization problem that makes power line de-energization decisions in order to reduce the risk of igniting a wildfire, while minimizing the load shed of customers. This problem, with DC linear power flow equations, has been used in many studies in recent years. However, using linear approximations for power flow when making decisions on the network topology is known to cause challenges with AC feasibility of the resulting network, as studied in the related contexts of optimal transmission switching or grid restoration planning. This paper explores the accuracy of the DC OPS formulation and the ability to recover an AC-feasible power flow solution after de-energization decisions are made. We also extend the OPS problem to include variants with the AC, Second-Order-Cone, and Network-Flow power flow equations, and compare them to the DC approximation with respect to solution quality and time. The results highlight that the DC approximation overestimates the amount of load that can be served, leading to poor de-energization decisions. The AC and SOC-based formulations are better, but prohibitively slow to solve for even modestly sized networks thus demonstrating the need for new solution methods with better trade-offs between computational time and solution quality.

A numerical approach for solving nonlinear fractional Klein–Gordon equation with applications in quantum mechanics

Abstract In this paper, we propose a numerical approach for solving the nonlinear fractional Klein–Gordon equation (FKGE), a model of significant importance in simulating nonlinear waves in quantum mechanics. Our method combines the Bernoulli wavelet collocation scheme with a functional integration matrix to obtain approximate solutions for the proposed model. Initially, we transform the main problem into a system of algebraic equations, which we solve using the Newton–Raphson method to extract the unknown coefficients and achieve the desired approximate solution. To theoretically validate our method, we conduct a comprehensive convergence analysis, demonstrating its uniform convergence. We perform numerical experiments on various examples with different parameters, presenting the results through tables and figures. Our findings indicate that employing more terms in the utilized techniques enhances accuracy. Furthermore, we compare our approach with existing methods from the literature, showcasing its performance in terms of computational cost, convergence rate, and solution accuracy. These examples illustrate how our techniques yield better approximate solutions for the nonlinear model at a low computational cost, as evidenced by the calculated CPU time and absolute error. Additionally, our method consistently provides better accuracy than other methods from the literature, suggesting its potential for solving more complex problems in physics and other scientific disciplines.

Multilevel domain decomposition-based architectures for physics-informed neural networks

Physics-informed neural networks (PINNs) are a powerful approach for solving problems involving differential equations, yet they often struggle to solve problems with high frequency and/or multi-scale solutions. Finite basis physics-informed neural networks (FBPINNs) improve the performance of PINNs in this regime by combining them with an overlapping domain decomposition approach. In this work, FBPINNs are extended by adding multiple levels of domain decompositions to their solution ansatz, inspired by classical multilevel Schwarz domain decomposition methods (DDMs). Analogous to typical tests for classical DDMs, we assess how the accuracy of PINNs, FBPINNs and multilevel FBPINNs scale with respect to computational effort and solution complexity by carrying out strong and weak scaling tests. Our numerical results show that the proposed multilevel FBPINNs consistently and significantly outperform PINNs across a range of problems with high frequency and multi-scale solutions. Furthermore, as expected in classical DDMs, we show that multilevel FBPINNs improve the accuracy of FBPINNs when using large numbers of subdomains by aiding global communication between subdomains.

The Effect of Viscous Dissipation and Joule Heating on Poiseuille Flow of Hyperbolic Tangent Fluid

In this paper, we showed the computer solutions for the Flow of Poiseuille of (MHD) hyperbolic tangent impossible-to-compress fluid Amid a pair of parallel slabs that are sloped at an angle utilizing a uniformly porous medium. Heat transfer attempts and slip boundary conditions are taken into account. In the energy equation, radiation, joule heating, and viscous dissipation are also accounted for. It is presented in a cartesian coordinate system that supplies the model of the governing equations of the hyperbolic tangent fluid flow. The equations of velocity and Temperature are solved numerically by using "ND solver built". The effects of different factors such that Hartmann number, Darcy number, slip parameter, Grashof number, Brinkman number, radiation parameter and others on velocity and temperature profiles are explored and presented in the Mathematica program. It is noticed that the profiles of velocity and Temperature are an increasing function of Darcy number, slip parameter, Grashof number, inclination angle, pressure gradient, Brinkman number, radiation parameter, and index-power parameter while they are a decreasing function of Hartmann and Weissenberg numbers. The research intends to improve our understanding of the thermal behavior of hyperbolic tangent fluids in practical applications by studying the combined effects of viscous dissipation and Joule heating on the Poiseuille flow. The potential ramifications of this phenomenon extend to multiple domains, encompassing engineering, materials science, and fluid dynamics.

Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices.

The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary ("Normal"/"Pathologic") and multiclass ("Normal", "CAD" (coronary artery disease), "MVP" (mitral valve prolapse), and "Benign" (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet/CinC 2016 dataset are used to train the binary and multiclass classifiers, respectively. Each PCG signal is pre-processed, with spike removal, denoising, filtering, and normalization; afterward, it is divided into 5 s frames with a 1 s shift. Subsequently, a feature set is extracted from each frame to train and test the binary and multiclass classifiers. Concerning the binary classification, the trained classifiers yielded accuracies ranging from 92.4 to 98.7% on the test set, with memory occupations from 92.7 kB to 11.1 MB. Regarding the multiclass classification, the trained classifiers achieved accuracies spanning from 95.3 to 98.6% on the test set, occupying a memory portion from 233 kB to 14.1 MB. The NNs trained and tested in this work offer the best trade-off between performance and memory occupation, whereas the trained k-NN models obtained the best performance at the cost of large memory occupation (up to 14.1 MB). The classifiers' performance slightly depends on the signal quality, since a denoising step is performed during pre-processing. To this end, the signal-to-noise ratio (SNR) was acquired before and after the denoising, indicating an improvement between 15 and 30 dB. The trained and tested models occupy relatively little memory, enabling their implementation in resource-limited systems.

GPU parallel processing to enable extensive criticality analysis in state estimation

SummaryPower system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far.

The Terminator: An Integration of Inner and Outer Approximations for Solving Wasserstein Distributionally Robust Chance Constrained Programs via Variable Fixing

We present a novel approach aimed at enhancing the efficacy of solving both regular and distributionally robust chance constrained programs using an empirical reference distribution. In general, these programs can be reformulated as mixed-integer programs (MIPs) by introducing binary variables for each scenario, indicating whether a scenario should be satisfied. Whereas existing methods have focused predominantly on either inner or outer approximations, this paper bridges this gap by studying a scheme that effectively combines these approximations via variable fixing. By checking the restricted outer approximations and comparing them with the inner approximations, we derive optimality cuts that can notably reduce the number of binary variables by effectively setting them to either one or zero. We conduct a theoretical analysis of variable fixing techniques, deriving an asymptotic closed-form expression. This expression quantifies the proportion of binary variables that should be optimally fixed to zero. Our empirical results showcase the advantages of our approach in terms of both computational efficiency and solution quality. Notably, we solve all the tested instances from literature to optimality, signifying the robustness and effectiveness of our proposed approach. History: Accepted by Andrea Lodi/Design & Analysis of Algorithms — Discrete. Funding: This work was supported by Office of Naval Research [N00014-24-1-2066]; Division of Civil, Mechanical and Manufacturing Innovation [2246414]. 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.2023.0299 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0299 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Medoid splits for efficient random forests in metric spaces

An adaptation of the random forest algorithm for Fréchet regression is revisited, addressing the challenge of regression with random objects in metric spaces. To overcome the limitations of previous approaches, a new splitting rule is introduced, substituting the computationally expensive Fréchet means with a medoid-based approach. The asymptotic equivalence of this method to Fréchet mean-based procedures is demonstrated, along with the consistency of the associated regression estimator. This approach provides a sound theoretical framework and a more efficient computational solution to Fréchet regression, broadening its application to non-standard data types and complex use cases.

ACGND: towards lower complexity and fast solution for dynamic tensor inversion

Dynamic Tensor Inversion (DTI) is an emerging issue in recent research, prevalent in artificial intelligence development frameworks such as TensorFlow and PyTorch. Traditional numerical methods suffer significant lagging error when addressing this issue. To address this, Zeroing-type Neural Dynamics (ZND) and Gradient-type Neural Dynamics (GND) are employed to tackle the DTI. However, these two methods exhibit inherent limitations in the resolution process, i.e. high computational complexity and low solution accuracy, respectively. Motivated by this technology gap, this paper proposes an Adaptive Coefficient Gradient Neural Dynamics (ACGND) for dynamically solving the DTI with an efficient and precise manner. Through a series of simulation experiments and validations in engineering applications, the ACGND demonstrates advantages in resolving DTI. The ACGND enhances computational efficiency by circumventing matrix inversion, thereby reducing computational complexity. Moreover, its incorporation of adaptive coefficients and activation functions enables real-time adjustments of the computational solution, facilitating rapid convergence to theoretical solutions and adaptation to non-statinary scenarios. Code is available at https://github.com/Maia2333/ACGND-Code-Implementation.

Inverse kinematics solution and control method of 6-degree-of-freedom manipulator based on deep reinforcement learning

The advent of Industry 4.0 has significantly promoted the field of intelligent manufacturing, which is facilitated by the development of new technologies are emerging. Robot technology and robot intelligence methods have rapidly developed and been widely applied. Manipulators are widely used in industry, and their control is a crucial research topic. The inverse kinematics solution of manipulators is an important part of manipulator control, which calculates the joint angles required for the end effector to reach a desired position and posture. Traditional inverse kinematics solution algorithms often face the problem of insufficient generalization, and iterative methods have challenges such as large computation and long solution time. This paper proposes a reinforcement learning-based inverse kinematics solution algorithm, called the MAPPO-IK algorithm. The algorithm trains the manipulator agent using the MAPPO algorithm and calculates the difference between the end effector state of the manipulator and the target posture in real-time by designing a reward mechanism, while considering Gaussian distance and cosine distance. Through experimental comparative analysis, the feasibility, computational efficiency, and superiority of this reinforcement learning algorithm are verified. Compared with traditional inverse kinematics solution algorithms, this method has good generalization and supports real-time computation, and the obtained result is a unique solution. Reinforcement learning algorithms have better adaptability to complex environments and can handle different sudden situations in different environments. This algorithm also has the advantages of path planning, intelligent obstacle avoidance, and other advantages in dynamically processing complex environmental scenes.

MS-GIFT: Multi-Sided Geometry-Independent Field ApproximaTion Approach for Isogeometric Analysis

The Geometry-Independent Field approximaTion (GIFT) technique, an extension of isogeometric analysis (IGA), allows for separate spaces to parameterize the computational domain and approximate solution field. Based on the GIFT approach, this paper proposes a novel IGA methodology that incorporates toric surface patches for multi-sided geometry representation, while utilizing B-spline or truncated hierarchical B-spline (THB-spline) basis for analysis. By creating an appropriate bijection between the parametric domains of distinct bases for modeling and approximation, our method ensures smoothness within the computational domain and combines the compact support of B-splines or the local refinement potential of THB-splines, resulting in more efficient and precise solutions. To enhance the quality of parameterization and consequently boost the accuracy of downstream analysis, we suggest optimizing the composite toric parameterization. Numerical examples validate the effectiveness and superiority of our suggested approach.

Assembly Theory of Binary Messages

Using assembly theory, we investigate the assembly pathways of binary strings (bitstrings) of length N formed by joining bits present in the assembly pool and the bitstrings that entered the pool as a result of previous joining operations. We show that the bitstring assembly index is bounded from below by the shortest addition chain for N, and we conjecture about the form of the upper bound. We define the degree of causation for the minimum assembly index and show that, for certain N values, it has regularities that can be used to determine the length of the shortest addition chain for N. We show that a bitstring with the smallest assembly index for N can be assembled via a binary program of a length equal to this index if the length of this bitstring is expressible as a product of Fibonacci numbers. Knowing that the problem of determining the assembly index is at least NP-complete, we conjecture that this problem is NP-complete, while the problem of creating the bitstring so that it would have a predetermined largest assembly index is NP-hard. The proof of this conjecture would imply P ≠ NP since every computable problem and every computable solution can be encoded as a finite bitstring. The lower bound on the bitstring assembly index implies a creative path and an optimization path of the evolution of information, where only the latter is available to Turing machines (artificial intelligence). Furthermore, the upper bound hints at the role of dissipative structures and collective, in particular human, intelligence in this evolution.