Computational Solution Research Articles

Visualizing the human olfactory projection and ancillary structures in a 3D reconstruction

Visualizing in 3D the histological microanatomy of the human olfactory projection from the olfactory mucosa in the nasal cavity to the olfactory bulbs in the cranial cavity necessitates a workflow for handling a great many sections. Here, we assembled a 3D reconstruction of a 7.45 cm3en-bloc specimen extracted from an embalmed human cadaver. A series of 10 µm coronal sections was stained with quadruple fluorescence histology and scanned in four channels. A trained anatomist manually segmented six structures of interest in a subset of the sections to generate the ground truth. Six convolutional neural networks were then trained for automatic segmentation of these structures in 1234 sections. A high-performance computing solution was engineered to register the sections based on the fluorescence signal and segmented structures. The resulting 3D visualization offers several novel didactic opportunities of interactive exploration and virtual manipulation. By extrapolating manual counts of OSNs in a subset of sections to the calculated volume of the envelope of the entire olfactory epithelium, we computed a total of ~2.7 million OSNs in the specimen. Such empirically derived information helps assess the extent to which the organizational principles of the human olfactory projection may differ from those in mice.

A Bilevel Approach to the Facility Location Problem with Customer Preferences Under a Mill Pricing Policy

This paper addresses the facility location problem under a mill pricing policy, integrating customers’ behavior through the concept of preferences. The problem is modeled as a bilevel optimization problem, where the existence of ties in customers’ preferences can lead to an ill-posed bilevel problem due to the possible existence of multiple optima to the lower-level problem. As the commonly employed optimistic and pessimistic strategies are inadequate for this problem, a specific approach is proposed bearing in mind the customers’ rational behavior. In this work, we propose a novel formulation of the problem as a bilevel model in which each customer faces a lexicographic biobjective problem in which the preference is maximized and the total cost of accessing the selected facility is minimized. This allows for a more accurate representation of customer preferences and the resulting decisions regarding facility location and pricing. To address the complexities of this model, we apply duality theory to the lower-level problems and, ultimately, reformulate the bilevel problem as a single-level mixed-integer optimization problem. This reformulation incorporates big-M constants, for which we provide valid bounds to ensure computational tractability and solution quality. The computational study conducted allows us to assess, on the one hand, the effectiveness of the proposed reformulation to address the bilevel model and, on the other hand, the impact of the length of the customer preference lists and fixed opening cost for facilities on the computational time and the optimal solution.

Machine Learning-Based Optimization Models for Defining Storage Rules in Maritime Container Yards

This paper proposes an integrated approach to define the best consignment strategy for storing containers in an export yard of a maritime terminal. The storage strategy identifies the rules for grouping homogeneous containers, which are defined simultaneously with the assignment of each group of containers to the available blocks (bay-locations) in the yard. Unlike recent literature, this study focuses specifically on weight classes and their respective limits when establishing the consignment strategy. Another novel aspect of this work is the integration of a data-driven algorithm and operations research. The integrated approach is based on unsupervised learning and optimization models and allows us to solve large instances within a few seconds. Results obtained by spectral clustering are treated as input datasets for the optimization models. Two different formulations are described and compared: the main difference lies in how containers are assigned to bay-locations, shifting from a time-consuming individual container assignment to the assignment of groups of containers, which offers significant advantages in computational efficiency. Experimental tests are organized into three campaigns to evaluate the following: (i) The computational time and solution quality (i.e., space utilization) of the proposed models; (ii) The performance of these models against a benchmark model; (iii) The practical effectiveness of the proposed solution approach.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

Heat transfer enhancement in magnetohydrodynamic hybrid nanofluids over a Bi-directional extending sheet with slip and convective conditions

The hybrid nanofluids can enhance cooling systems in microelectronics by enhancing heat dissipation from processes which makes them vital for maintaining optimal performance. They are also beneficial in solar systems where efficient heat transfer is essential for exploiting the energy detention. Thus, this study has studied the three-dimensional flow of a water-based hybrid nanofluid comprising of Fe3O4 and CuO nanoparticles on a bi-directional extending sheet. The bi-directional extending sheet is subjected to thermal convective and velocity slip conditions. In addition, the effects of heat source, magnetic field, thermal radiation, viscous dissipation, and Joule heating are employed. The partial differential equations (PDEs) that represent the mathematical model are subsequently converted to ordinary differential equations (ODEs) by applying the appropriate similarity variables. The shooting technique is incorporated to determine the computational solution of the converted ODEs. The effectiveness of the current study is confirmed by published results, which also validate the current outcomes. Based on the results, it is concluded that CuO and Fe3O4 solid nanoparticle volume fractions improved the thermal distribution while decreasing the velocity distributions along x and y-axes. The thermal distribution is improved by the thermal heat source, thermal radiation, thermal Biot number, and thermal Eckert numbers. CuO-water nanofluid has a higher velocity panel than CuO-Fe3O4/water hybrid nanofluid. Conversely, the CuO-Fe3O4/water hybrid nanofluid has a higher thermal profile than the CuO-water nanofluid. CuO-water nanofluid flow has higher surface drags than CuO-Fe3O4/water hybrid nanofluid flow. CuO-water nanofluid has a lower heat transfer rate than CuO-Fe3O4/water hybrid nanofluid.

Urban Canopy Parameters’ Computation and Evaluation in an Indian Context Using Multi-Platform Remote Sensing Data

Urban Canopy Parameters (UCPs) are crucial for urban microclimate modeling; however, the scarce availability of precise UCP data in developing regions limits their application for urban climates. This study investigated the use of multi-platform remote sensing data viz. very high-resolution satellite (VHRS) optical stereo and Unmanned Aerial Vehicle (UAV) datasets for the computation of UCPs in high-density urban scenarios in India, with varied development characteristics. The results demonstrated high accuracy in terms of building height and footprint extraction from both datasets, key inputs for UCP computation. However, UCPs from UAV data have displayed relatively high accuracy for building footprints (86%), building height (RMSE ~ 0.05 m), and land use/land cover classification (90%). Performance evaluation of computed UCPs against a 3D reference geodatabase showed high prediction accuracy for most UCPs, with overall biases, mean absolute error, and root-mean-square error values significantly better than 1 m, with strong correlation (0.8–0.9). It was concluded that VHRS optical stereo and UAV datasets offer a secure, reliable, and accurate solution for UCP computation in urban areas, particularly in developing regions. These findings have significant implications for urban climate research and the sustainable development of rapidly urbanizing areas facing resource and policy constraints.

Hyperdimensional computing: a framework for stochastic computation and symbolic AI

Hyperdimensional Computing (HDC), also known as Vector Symbolic Architectures (VSA), is a neuro-inspired computing framework that exploits high-dimensional random vector spaces. HDC uses extremely parallelizable arithmetic to provide computational solutions that balance accuracy, efficiency and robustness. The majority of current HDC research focuses on the learning capabilities of these high-dimensional spaces. However, a tangential research direction investigates the properties of these high-dimensional spaces more generally as a probabilistic model for computation. In this manuscript, we provide an approachable, yet thorough, survey of the components of HDC. To highlight the dual use of HDC, we provide an in-depth analysis of two vastly different applications. The first uses HDC in a learning setting to classify graphs. Graphs are among the most important forms of information representation, and graph learning in IoT and sensor networks introduces challenges because of the limited compute capabilities. Compared to the state-of-the-art Graph Neural Networks, our proposed method achieves comparable accuracy, while training and inference times are on average 14.6× and 2.0× faster, respectively. Secondly, we analyse a dynamic hash table that uses a novel hypervector type called circular-hypervectors to map requests to a dynamic set of resources. The proposed hyperdimensional hashing method has the efficiency to be deployed in large systems. Moreover, our approach remains unaffected by a realistic level of memory errors which causes significant mismatches for existing methods.

Quarta: quantum supervised and unsupervised learning for binary classification in domain-incremental learning

Quantum machine learning recently gained prominence due to the promise of quantum computers in solving machine learning problems that are intractable on a classical computer. Nevertheless, several studies on problems which remain challenging for classical computing algorithms are emerging. One of these is classifying continuously incoming data instances in incremental fashion, which is studied in this paper through a hybrid computational solution that combines classical and quantum techniques. Hybrid approaches represents one of the current ways for the use of quantum computation in practical applications. In this paper, we show how typical issues of domain-incremental learning can be equally addressed with the properties of quantum mechanics, until to offer often better results. We propose the framework QUARTA to combine algorithms of quantum supervised learning, that is, variational quantum circuits, and techniques used in quantum unsupervised learning, that is, distance estimation. We aim at keeping the classification capabilities, which have learned on previously processed data instances, preserved as much as possible, and then acquiring new knowledge on new data instances. Experiments are performed on real-world datasets with quantum simulators.

A deep reinforcement learning approach towards distributed Function as a Service (FaaS) based edge application orchestration in cloud-edge continuum

Serverless computing has emerged as a new cloud computing model which in contrast to IoT offers unlimited and scalable access to resources. This paradigm improves resource utilization, cost, scalability and resource management specifically in terms of irregular incoming traffic. While cloud computing has been known as a reliable computing and storage solution to host IoT applications, it is not suitable for bandwidth limited, real time and secure applications. Therefore, shifting the resources of the cloud-edge continuum towards the edge can mitigate these limitations. In serverless architecture, applications implemented as Function as a Service (FaaS), include a set of chained event-driven microservices which have to be assigned to available instances. IoT microservices orchestration is still a challenging issue in serverless computing architecture due to IoT dynamic, heterogeneous and large-scale environment with limited resources. The integration of FaaS and distributed Deep Reinforcement Learning (DRL) can transform serverless computing by improving microservice execution effectiveness and optimizing real-time application orchestration. This combination improves scalability and adaptability across the edge-cloud continuum. In this paper, we present a novel Deep Reinforcement Learning (DRL) based microservice orchestration approach for the serverless edge-cloud continuum to minimize resource utilization and delay. This approach, unlike existing methods, is distributed and requires a minimum subset of realistic data in each interval to find optimal compositions in the proposed edge serverless architecture and is thus suitable for IoT environment. Experiments conducted using a number of real-world scenarios demonstrate improvement of the number of successfully composed applications by 18%, respectively, compared to state-of-the art methods including Load Balance, Shortest Path algorithms.

Impact of Brownian motion and thermophoresis in magnetohydrodynamic dissipative: Radiative flow of chemically reactive nanoliquid thin films on an unsteady expandable sheet in a composite media

AbstractThis study comprehensively examines magnetohydrodynamic heat transport characteristics within a thin nanofluid film on a stretchable sheet embedded in a composite medium. By considering factors such as the unsteady nature of sheet velocity, Brownian motion, thermophoresis, thermally radiative heat, irregular heat generation/sink, chemical reactions, and dissipation due to viscous fluid, the research provides valuable insights into the variations in fluid velocity, temperature, and nanoparticles concentration. The computational solution utilizes the efficient numerical method that enables accurate predictions of system behavior under varying conditions. Notable findings include the influence of Schmidt numbers on nanoparticle concentration distribution, the opposing impact of thermophoresis parameter values, and the influence of Brownian motion and heat source/sink on temperature profiles in thin nanofluid film. Also, nanoliquid film thickness is reduced by enhancing the porous parameter values and Hartmann number values. The nanoliquid film becomes thinner when the space‐dependent heat source/sink parameter is considered compared to the temperature‐dependent heat source/sink coefficient. In space‐dependent and temperature‐dependent cases, the increase in these parameters leads to a decrease in the temperature gradient. Furthermore, it is observed that higher thermophoresis values correspond to reduced nanoparticle concentration gradient profiles. Also, enhancement in the chemical reaction values leads to an expansion in the solutal boundary region surrounding nanoparticles, and as a consequence, the concentration gradient of nanoparticles is enhanced. This research has significant potential for optimizing heat performance and advancing innovation in industrial and engineering processes.

Flux reconstruction approach for long-time calculations of the linearized Euler equation

The Linearized Euler Equations (LEE) are one of the fundamental governing equations in acoustics research. Due to the relatively small magnitude of acoustic disturbances compared to the physical quantities of the background flow, ensuring highly precise solutions without dissipation and dispersion over time is essential. The Flux Reconstruction (FR) method, recognized as a high-order numerical method with flexible construction capabilities, has been extensively utilized in recent years for solving partial differential equations numerically. This paper introduces an offsetting numerical flux for one-dimensional LEE. Subsequently, it develops a flux reconstruction scheme with energy-conserving characteristics, referred to as the ECFR scheme, aimed at achieving high-precision numerical solutions for prolonged computations. The energy-conserving feature of the scheme is substantiated through theoretical derivation and further corroborated by numerical validation. Moreover, the impact of Mach numbers and the parameter of the offsetting flux on the scheme's performance is discussed. The stability of the scheme is also analyzed, with the limit of the CFL number being provided. The novel ECFR scheme facilitates obtaining high-precision acoustic numerical solutions, thereby providing strong support for the study of complex engineering problems such as aircraft, submarine, and automobile noise.

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the Allen–Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400 °C at an average rate of 30 °Cmin, traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal’s microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. These relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

Cloud Computing Security Issues, Challenges, and Solutions

Cloud computing has revolutionized the way agencies manage and deliver their IT offerings by means of imparting scalable, bendy, and fee-effective solutions. However, with these good sized blessings come complicated protection demanding situations. The allotted nature of cloud environments, coupled with multi-tenant infrastructures, offers new risks that require sturdy and adaptive security frameworks. This paper goals to provide an in-intensity exploration of the primary security problems in cloud computing, the underlying challenges companies face in securing cloud services, and the solutions designed to mitigate these dangers

A reinforcement learning approach to solving very-short term train rescheduling problem for a single-track rail corridor

Railway operations are regularly affected by incidents such as disturbances and disruptions, which cause temporary operational restrictions to the trains in the network. Compared to real-time disturbances and disruptions, sometimes these incidents can be known at a short notice, e.g., 24–48 h beforehand, which is known as the Very-Short-Term-Planning in British rail operations. This paper presents a novel reinforcement learning based approach for rescheduling train services for in a single-track corridor with bi-directional traffic. As an important subfield of machine learning, reinforcement learning offers an alternate strategy for tackling the NP-hard train (re)scheduling problems and shows its advantages in balancing computational efficiency and solution quality. We propose a Q-learning approach with a tiered rewarding strategy and lightweight train representation in state vectors, which enables more efficient learning and knowledge sharing among homogeneous trains. Compared with an existing reinforcement learning approach, our proposed method can find better quality solutions due to its unique representation of state vectors and a novel tiered rewarding/punishing mechanism, overcoming certain disadvantages in existing approaches. Knowledge reusability is another advantage of the proposed approach, as prior knowledge obtained from training one instance can significantly enhance the performance of another, potentially more challenging, instance on the same corridor with substantially reduced computational time and effort on algorithm development. We also discuss the potential applications of the knowledge reusability feature inherent in reinforcement learning algorithms, which we believe will benefit the entire industry in addressing NP-hard problems through data-driven technologies.

An Efficient Tour Construction Heuristic for Generating the Candidate Set of the Traveling Salesman Problem with Large Sizes

In this paper, we address the challenge of creating candidate sets for large-scale Traveling Salesman Problem (TSP) instances, where choosing a subset of edges is crucial for efficiency. Traditional methods for improving tours, such as local searches and heuristics, depend greatly on the quality of these candidate sets but often struggle in large-scale situations due to insufficient edge coverage or high time complexity. We present a new heuristic based on fuzzy clustering, designed to produce high-quality candidate sets with nearly linear time complexity. Thoroughly tested on benchmark instances, including VLSI and Euclidean types with up to 316,000 nodes, our method consistently outperforms traditional and current leading techniques for large TSPs. Our heuristic’s tours encompass nearly all edges of optimal or best-known solutions, and its candidate sets are significantly smaller than those produced with the POPMUSIC heuristic. This results in faster execution of subsequent improvement methods, such as Helsgaun’s Lin–Kernighan heuristic and evolutionary algorithms. This substantial enhancement in computation time and solution quality establishes our method as a promising approach for effectively solving large-scale TSP instances.

Irrotational Flow over Ogee Spillway Crest: New Solution Method and Flow Geometry Analysis

A spillway is a hydraulic structure of major importance in dam safety, and its current analysis usually involves a hybrid approach combining CFD modeling with experimental research, either using well-known WES design charts or conducting new model experiments in the laboratory. Flow over spillway crests involves fluid accelerations, making irrotationality an adequate simplification of the Navier–Stokes (NS) equations. However, an efficient tool using this method is currently lacking for spillway flow, particularly for ogee spillway flow. This work focuses on this aspect of the problem, and a new method for computing irrotational flow solutions over ogee spillways is proposed by developing flow net computational solutions. The proposed method entails a new iterative procedure in the complex potential plane where free surface pressures are exactly set to zero, contrary to other methods, and an automatic determination of the critical point, the unknown energy head, and the free surface profile. The model generates solutions efficiently in only a few seconds on a personal workstation, permitting a fast estimate of spillway flow operation, and is thus an effective complement to experimental and NS-CFD modeling. The solutions produced are compared with observations of a high operational head equal to five times the design head of the ogee crest, resulting in reasonable agreement. The application of the new model to investigate the limitations of analytical equations used in spillway flow, like Jaeger’s theory, establishes limits for its use by relating its curvature parameter to the spillway chute slope.

Beyond Time-Homogeneity for Continuous-Time Multistate Markov Models

–Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov processes describe data that are observed irregularly over time, as is often the case in longitudinal medical data, for example. Assuming that a continuous-time Markov process is time-homogeneous, a closed-form likelihood function can be derived from the Kolmogorov forward equations—a system of differential equations with a well-known matrix-exponential solution. Unfortunately, however, the forward equations do not admit an analytical solution for continuous-time, time- inhomogeneous Markov processes, and so researchers and practitioners often make the simplifying assumption that the process is piecewise time-homogeneous. In this article, we provide intuitions and illustrations of the potential biases for parameter estimation that may ensue in the more realistic scenario that the piecewise-homogeneous assumption is violated, and we advocate for a solution for likelihood computation in a truly time-inhomogeneous fashion. Particular focus is afforded to the context of multistate Markov models that allow for state label misclassifications, which applies more broadly to hidden Markov models (HMMs), and Bayesian computations bypass the necessity for computationally demanding numerical gradient approximations for obtaining maximum likelihood estimates (MLEs). Supplemental materials are available online.

Adjustable robust optimization for integrated fleet sizing and vehicle routing problem with simultaneous delivery and pickup in closed-loop supply chain management

ABSTRACT The fleet sizing problem is a critical challenge in closed-loop supply chains because of the additional requirements of managing product returns. To tackle this challenge, this study addresses the integration of fleet sizing with vehicle routing problem in closed-loop supply chain management, considering simultaneous delivery and pickup under demand uncertainty. A mixed-integer linear programming model is proposed to minimize vehicle acquisition and shipment costs, and a multi-stage adjustable robust optimization formulation is employed to handle uncertainty. Prior studies have neglected to address this important problem due to a lack of efficient solving methods. Hence, three exact, heuristic, and metaheuristic algorithms are developed to solve the problem. The numerical experiments demonstrate that the proposed methods outperform the existing Hyper-box based Branch-and-Partition method, improving computational efficiency and solution quality. This study also helps practitioners make better fleet sizing decisions under uncertainty.

An improved solution of the probability density evolution equation for analyzing stochastic structural responses with adaptive time steps and initial conditions

The probability density evolution method (PDEM) offers an innovative approach for analyzing the stochastic vibration responses of structures. Given the complexities inherent in real-world challenges, the probability density evolution equations (PDEE) often require numerical methods for effective resolution. Consequently, improving computational efficiency and solution accuracy is of paramount importance. Currently, the impulse function discretization method (IFDM) faces several challenges, including limited differentiability in the spatial direction for initial value conditions. Furthermore, the probability density function produced by this method frequently deviates significantly from the true value, undermining the accuracy of the PDEE. Additionally, when addressing vibration responses characterized by rapid evolutionary velocities, the use of a globally fixed uniform time step size (FUTSS) results in a marked decrease in computational efficiency. To tackle these challenges, this study introduces an improved probability density evolution method (IPDEM), which integrates the Gaussian kernel density estimation discretization method (GKDEDM) and an adaptive non-uniform time step size (ANUTSS). The advantages of the IPDEM are illustrated through two numerical simulation examples. The findings indicate that the GKDEDM not only enhances spatial differentiability but also provides a more accurate estimation of the probability density at the initial moment, thereby improving the accuracy of the PDEE. Furthermore, the ANUTSS significantly reduces the number of iterations required while preserving precision, leading to a substantial optimization of computation time.

Editorial: Inaugural Issue of the International Journal of Bridge Engineering, Management and Research (IJBEMR)

It is my pleasure to share with you the inaugural issue of the newly established International Journal of Bridge Engineering, Management and Research. The International Journal of Bridge Engineering, Management and Research (BER) is a fully open access online-only journal. It serves as a forum for the publication of scientific and technical papers related to all aspects of bridge engineering and management, including structural, seismic, hydraulic and geotechnical risk analysis, structural health monitoring, static and dynamic assessments, structural retrofitting, and resiliency enhancement. The journal also publishes research papers on tunnels focusing on the areas mentioned above. Technical research papers and case studies dealing with innovative technological or computational solutions to bridges and tunnels, as well as experimental methods and novel design and theoretical analysis procedures are strong candidates for publication in the journal. This inaugural issue features five high quality papers in bridge engineering. Through this issue, I am also inviting you to register with the journal and submit your research manuscripts to the journal. We are committed to expedited and thorough peer review and publication of your research.