Computational Experiments Research Articles

Determining the aerodynamic performance of a high-speed unmanned marine wig craft

The study object of this paper is the aerodynamic processes during the movement of an unmanned aerial vehicle flying low over the underlying surface. The ground effect is known to enhance the aerodynamic performance of low-flying aircraft, particularly larger ones. However, this effect is most noticeable for large objects. Unmanned vehicles are typically characterized by their relatively compact geometry. This study explores the aerodynamic processes involved in the flight of a small-sized vehicle using the principle of dynamic support over a surface. The particular prototype of the unmanned aerial vehicle suggested by the authors has been examined herein. The aim of the study is to evaluate the aerodynamic forces affecting a small-sized unmanned high-speed vehicle that employs the dynamic principle of support over the surface (WIG craft) by using CFD modeling. In contrast to most available studies on the ground effect, a 3D problem statement was used in this work. Computational experiments have visualized the physical fields surrounding an aircraft in flight over the ground. This study determines how the distance from the surface affects the aerodynamic properties of a small-sized aircraft, as well as the height of the effective zone where ground effect influences the small-sized WIG craft within . It has been shown that approaching the surface leads to a shift in the center of pressure of the vehicle, which leads to a change in aerodynamic momentum. This phenomenon must be taken into account when designing a control system to provide a stable flight. The study's findings are directly relevant for the development of a type of unmanned vehicles that use the dynamic principle of support over the surface.

Heatmap Design for Probabilistic Driver Repositioning in Crowdsourced Delivery

This paper proposes the use of heatmaps as a control lever to manage the probabilistic repositioning of independent drivers in crowdsourced delivery platforms. The platform aims to maximize order fulfillment by dynamically matching drivers and orders and selecting heatmaps that trigger the probabilistic flow of unmatched drivers to balance driver supply and delivery requests across the service region. We develop a Markov decision process (MDP) model to sequentially select matching and heatmap decisions in which the repositioning behavior of drivers is captured by a multinomial logit discrete choice model. Because of the curse of dimensionality and the endogenous uncertainty of driver repositioning, the MDP model is solved using a rolling-horizon stochastic lookahead policy. This policy decomposes matching and heatmap decisions into two optimization problems: a two-stage stochastic programming upper bounding problem for matching decisions and a mixed-integer programming problem for heatmap decisions. We also propose a simple policy for efficiently solving large-scale problems. An extensive computational study on instances derived from the Chicago ride-hailing data set is conducted. Computational experiments demonstrate the value of heatmaps in improving order fulfillment beyond the level achieved by matching alone (up to 25%) and identify conditions that affect the benefit of using heatmaps to guide driver repositioning. Funding: The authors gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada through Discovery Grants [Grants RGPIN-2024-04881, RGPIN-2020-04498, and RGPIN-2019-06207] awarded to the first, second, and third authors, respectively. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0418 .

Identifying the evolution of through cracks in iron-reinforced hollow slabs under the influence of a standard fire temperature mode

The object of this study is the fire resistance of reinforced concrete hollow slabs at the onset of the limit state of loss of integrity. The problem of accurate modeling of the formation and development of cracks in concrete was investigated. The paper reports an analysis of the results of the stress-strain state of a reinforced concrete hollow slab during fire exposure for devising a method for evaluating the fire resistance of such structures upon the onset of the limit state of loss of integrity. According to EN 1992-1-2, the determination of fire resistance of structures is provided by calculation methods, however, there is no such procedure for reinforced concrete hollow slabs. Many scientific works offer refined methods for evaluating only the loss of load-bearing and heat-insulating capabilities, leaving aside the issue of loss of integrity. Thus, this can lead to a biased evaluation of reinforced concrete hollow floor slabs according to the criterion of the limit state of loss of integrity, which in turn can put under a threat to the fire safety of buildings, which threatens the life and health of people. According to the results of the calculation, a parameter has been determined, according to which the onset of the limit state of fire resistance, in particular, the loss of integrity, was established. Summarizing the damage distributions, it was assumed that in the case of reaching the critical plastic deformation of 2.5e-3 in concrete finite elements, they are excluded from the general set of finite elements. Thus, in the case of the formation of through cracks, the removal of finite elements is taken as a parameter to identify the onset of the limit state of loss of integrity. According to the results of the computational experiment, it was established that through cracks in a fragment of a reinforced concrete hollow slab are formed in 44 min. According to the results of the research, the method of evaluating the fire resistance of such structures based on the onset of the limit state of loss of integrity has been substantiated. Such a method could be applied during design, which provides an opportunity to determine the limit of fire resistance in reinforced concrete hollow slabs

Role of Dynamical Asymmetry on the Orientation of Block Copolymers in Shear Flow: Computer Simulation and Experiment

Role of Dynamical Asymmetry on the Orientation of Block Copolymers in Shear Flow: Computer Simulation and Experiment

An Improved Negotiation-Based Approach for Collecting and Sorting Operations in Waste Management and Recycling

This paper addresses the problem of optimal planning for collection, sorting, and recycling operations. The problem arises in industrial waste management, where distinct actors manage the collection and the sorting operations. In a weekly or monthly plan horizon, they usually interact to find a suitable schedule for servicing customers but with a not well-defined scheme. We proposal an improved negotiation-based approach using an auction mechanism for optimizing these operations. Two interdependent models are presented: one for waste collection by a logistics operator and the other for sorting operations at a recycling plant. These models are formulated as mixed-integer linear programs where costs associated with sorting and collection are to be minimized, respectively. We describe the negotiation-based approach involving an auction where the logistics operator bids for collection time slots, and the recycling plant selects the optimal bid based on the integration of sorting and collection costs. This approach aims to achieve an optimization of the entire waste management process. Computational experiments are presented.

Research artifacts for human-oriented experiments in software engineering: An ACM badges-driven structure proposal

Context:The Open Science (OS) movement promotes the value of making public the research artifacts (datasets, analysis scripts, guidelines, etc.) used during empirical studies. OS is widely known in areas such as Medicine or Biology, where the process of sharing research artifacts is subject to strict protocols. Unfortunately, in Software Engineering (SE), this process is carried out in a non-systematic way, resulting in incomplete or inaccurate material shared by researchers, which hinders the reproducibility and replicability of empirical studies. Nevertheless, in recent years, it seems that the Empirical Software Engineering (ESE) community is embracing some of the proposed OS initiatives, such as the one proposed by the Association for Computing Machinery (ACM), which provides a badge system to evaluate the quality of a research artifact. This badge system has been adopted by several SE conferences as a method of assessing research artifacts. Aims:Focusing on human-oriented experiments (HOEs) in SE, whose research artifacts are more complex than those for computational experiments, this work applies Design Science Research (DSR) with a twofold purpose: (i) review the current status of HOEs research artifacts publication through evaluation of this practice in the most relevant ESE journals , and (ii) propose a structured outline for HOEs research artifacts driven by the aforementioned ACM badging policy. Method:Regarding the first purpose, we carried out a survey to analyze the current status of the publication of research artifacts considering relevant peer review journals and the quality of 106 research artifacts published in these journals with respect to the ACM badging policy. For the second purpose, an iterative process was carried out to review the content of 106 research artifacts research and their concordance with ACM badges, obtaining a structured scheme for HOEs research artifacts that has been validated through a detailed review of 12 research artifacts obtained from some of those of ACM badges in relevant SE conferences. In addition, we validated the proposal in the research artifacts of 2 of our own experiments. Results:Our survey reveals issues such as the 39,70% of journal studies making completely accessible their research artifacts; most of the analyzed research artifacts are incomplete; the most common repositories used in the ESE community to share the research artifacts are GitHub, institutional repositories, and Zenodo. On the other hand, the validated and structured research artifact outline consists of a list of ordered sections containing a set of artifacts, which can be mandatory or not to achieve a particular ACM badge. For its internal validation, several improvement iterations on the first release of the outline have been carried out based on the conference guidelines, the ACM badging policy, and other relevant proposals. Conclusions:Although the ESE community is making great efforts in standardization, review, and digital publishing related to OS, the availability and completeness of research artifacts can be improved. Our proposal for the elaboration of structured research artifact outline meets the requirements of HOEs in SE. Nevertheless, further research is needed not only to improve and externally validate it but also to disseminate its use among the research community.

Ultra-broadband diffractive imaging with unknown probe spectrum

Strict requirement of a coherent spectrum in coherent diffractive imaging (CDI) architectures poses a significant obstacle to achieving efficient photon utilization across the full spectrum. To date, nearly all broadband computational imaging experiments have relied on accurate spectroscopic measurements, as broad spectra are incompatible with conventional CDI systems. This paper presents an advanced approach to broaden the scope of CDI to ultra-broadband illumination with unknown probe spectrum, effectively addresses the key challenges encountered by existing state-of-the-art broadband diffractive imaging frameworks. This advancement eliminates the necessity for prior knowledge of probe spectrum and relaxes constraints on non-dispersive samples, resulting in a significant extension in spectral bandwidth, achieving a nearly fourfold improvement in bandlimit compared to the existing benchmark. Our method not only monochromatizes a broadband diffraction pattern from unknown illumination spectrum, but also determines the compressive sampled profile of spectrum of the diffracted radiation. This superiority is experimentally validated using both CDI and ptychography techniques on an ultra-broadband supercontinuum with relative bandwidth exceeding 40%, revealing a significantly enhanced coherence and improved reconstruction with high fidelity under ultra-broadband illumination.

Counterfeit banknote identification based on outliers detection methods

Counterfeit banknotes in circulation in the economy are a long-standing and recurring problem. Technological improvements are applied to produce increasingly convincing counterfeit banknotes. In contrast, sophisticated strategies to identify counterfeit notes remain an up-to-date and fascinating research topic of great interest. The literature presents several techniques for detecting counterfeit banknotes. In this study, outlier detection methodologies are presented as adapted mechanisms to assist in the fight against counterfeit banknotes. This study addresses and tests multivariate outlier detection techniques' efficiency in identifying counterfeit banknotes. A comprehensive computational experiment proves the great adaptability of the methods to multivariate outliers in the identification of counterfeit banknotes. In particular, the Data-driven Cluster Analysis Method (DDCAM) technique presented promising and optimistic results through several quality and performance measurement metrics, suggesting a hopeful future for detecting counterfeit banknotes.

Redesigning a NSGA-II metaheuristic for the bi-objective Support Vector Machine with feature selection

The Support Vector Machine is a well-known technique used in supervised classification. Feature selection offers several benefits but also adds complexity to the problem. In this paper, we consider the soft margin SVM and given that two different objectives are considered simultaneously, obtaining the Pareto front , or at least a good approximation of it, gives the decision-maker a wide variety of solutions and has several advantages over having only one solution. The only metaheuristic that has been developed to give an approximation of such a front is a NSGA-II based technique. However, the design of such technique presents some limitations that are analyzed in this paper. We present a new metaheuristic that has been completely redesigned in order to overcome those drawbacks. We compare both techniques through an extensive computational experiment that demonstrates the superior efficiency of the new technique.

Trainable Reference Spikes Improve Temporal Information Processing of SNNs With Supervised Learning.

Spiking neural networks (SNNs) are the next-generation neural networks composed of biologically plausible neurons that communicate through trains of spikes. By modifying the plastic parameters of SNNs, including weights and time delays, SNNs can be trained to perform various AI tasks, although in general not at the same level of performance as typical artificial neural networks (ANNs). One possible solution to improve the performance of SNNs is to consider plastic parameters other than just weights and time delays drawn from the inherent complexity of the neural system of the brain, which may help SNNs improve their information processing ability and achieve brainlike functions. Here, we propose reference spikes as a new type of plastic parameters in a supervised learning scheme in SNNs. A neuron receives reference spikes through synapses providing reference information independent of input to help during learning, whose number of spikes and timings are trainable by error backpropagation. Theoretically, reference spikes improve the temporal information processing of SNNs by modulating the integration of incoming spikes at a detailed level. Through comparative computational experiments, we demonstrate using supervised learning that reference spikes improve the memory capacity of SNNs to map input spike patterns to target output spike patterns and increase classification accuracy on the MNIST, Fashion-MNIST, and SHD data sets, where both input and target output are temporally encoded. Our results demonstrate that applying reference spikes improves the performance of SNNs by enhancing their temporal information processing ability.

Joint optimization of truck-drone routing for last-mile deliveries in urban areas

This paper presents a joint routing optimization model to satisfy the on-demand customer requirements by minimising the transportation and time penalty costs of trucks and drones, considering the constraints on paths and delivery time of trucks and drones, the drone battery capacity limit, and the customer demands. The improved genetic algorithm is proposed to solve the optimization model followed by the computational experiments demonstrating the superiority of the improved genetic algorithm over the original genetic algorithm in computational efficiency, economy, and punctuality. Further, the comparative analysis demonstrates that the multi-drone joint delivery mode considering the customer demands performs better than the other delivery modes and that the delivery routes can be re-optimised promptly to satisfy the real-time customer demands. The sensitivity analysis on the drone design parameters provides theoretical insights into deploying the size and speed of drone platoons when promoting truck-drone joint delivery in applications.

Numerical Solution of the Cauchy Problem for the Helmholtz Equation Using Nesterov’s Accelerated Method

In this paper, the Cauchy problem for the Helmholtz equation, also known as the continuation problem, is considered. The continuation problem is reduced to a boundary inverse problem for a well-posed direct problem. A generalized solution to the direct problem is obtained and an estimate of its stability is given. The inverse problem is reduced to an optimization problem solved using the gradient method. The convergence of the Landweber method with respect to the functionals is compared with the convergence of the Nesterov method. The calculation of the gradient in discrete form, which is often used in the numerical solutions of the inverse problem, is described. The formulation of the conjugate problem in discrete form is presented. After calculating the gradient, an algorithm for solving the inverse problem using the Nesterov method is constructed. A computational experiment for the boundary inverse problem is carried out, and the results of the comparative analysis of the Landweber and Nesterov methods in a graphical form are presented.

Fitness landscapes of buffer allocation problem for production lines with unreliable machines

We study the structural properties of the buffer allocation problem from the fitness landscape perspective. We consider manufacturing flow lines with series–parallel network structure. The machines are supposed to be unreliable, their time to failure and repair time are exponentially distributed. Tentative solutions are evaluated by means of an approximate method based on the Markov models aggregation. We carry out computational experiments with local search and genetic algorithms in order to evaluate the fitness landscape properties of previously published instances and their modifications. It turns out that the so-called ‘massif central’ or ‘big valley’ structure of the fitness landscape is present but only partially: The fitness of local optima is negatively correlated with the distance to the best found solution, yet the set of local optima cannot be encompassed by a ball of relatively small size with respect to the size of solution space. Moreover, we show that in many problem instances, several clusters of local optima can be identified. The performance of genetic algorithms is discussed with respect to population clustering and the permanent usage of crossover is recommended.

Homodimerization of CB2 cannabinoid receptor triggered by a bivalent ligand enhances cellular signaling

G protein-coupled receptors (GPCRs) exist within a landscape of interconvertible conformational states and in dynamic equilibrium between monomers and higher-order oligomers, both influenced by ligand binding. Here, we show that a homobivalent ligand formed by equal chromenopyrazole moieties as pharmacophores, connected by 14 methylene units, can modulate the dynamics of the cannabinoid CB2 receptor (CB2R) homodimerization by simultaneously binding both protomers of the CB2R-CB2R homodimer. Computational and pharmacological experiments showed that one of the ligand pharmacophores binds to the orthosteric site of one protomer, and the other pharmacophore to a membrane-oriented pocket between transmembranes 1 and 7 of the partner protomer. This results in unique pharmacological properties, including increased potency in Gi-mediated signaling and enhanced recruitment of β-arrestin. Thus, by modulating dimerization dynamics, it may be possible to fine-tune CB2R activity, potentially leading to improved therapeutic outcomes.

A novel angiotensin I-converting enzyme inhibitory peptide APPLRP from Grifola frondosa ameliorated the Ang II-induced vascular modeling in zebrafish model by mediating smooth muscle cells

Grifola frondosa has garnered significant popularity as an edible mushroom attributable to its exceptional taste and nutritional benefits. This study isolated APPLRP, a potent ACE-inhibitory peptide, from the alcohol-soluble fraction of Grifola frondosa. The underlying mechanisms of APPLRP in antihypertension were explored through computational chemistry, cell experiments, and zebrafish model. Results demonstrated that APPLRP was an active competitive ACE inhibitor (IC50 = 29.93 μM) that could bind to the active pocket S2 and S1′ of ACE. APPLRP exhibited resistance to pepsin and pancreatin digestion. In vitro experiments revealed that APPLRP significantly attenuated Ang II-induced VSMCs proliferation and migration by down-regulating AT1R expression and inhibiting ERK1/2 and STAT3 phosphorylation. APPLRP intervention significantly ameliorated myocardial fibrosis, as evidenced by reductions in cardiac output, blood flow velocity, and cardiac collagen deposition levels in Ang II-induced hypertensive zebrafish model. Furthermore, APPLRP improved vascular remodeling in hypertensive zebrafish, indicated by increased vessel diameter and decreased vessel wall thickness. Notably, APPLRP treatment resulted in down-regulation of ACE and up-regulation of ACE2 expression in the vessels of hypertensive zebrafish. These findings indicated that APPLRP was a representative component of Grifola frondosa peptides, and its antihypertensive effects were associated with ACE inhibition and the improvement of VSMCs-mediated vascular remodeling.

FRRI: A novel algorithm for fuzzy-rough rule induction

Interpretability is the next frontier in machine learning research. In the search for white box models — as opposed to black box models, like random forests or neural networks — rule induction algorithms are a logical and promising option, since the rules can easily be understood by humans. Fuzzy and rough set theory have been successfully applied to this archetype, almost always separately. As both approaches offer different ways to deal with imprecise and uncertain information, often with the use of an indiscernibility relation, it is natural to combine them. The QuickRules [20] algorithm was a first attempt at using fuzzy rough set theory for rule induction. It is based on QuickReduct, a greedy algorithm for building decision superreducts. QuickRules already showed an improvement over other rule induction methods. However, to evaluate the full potential of a fuzzy rough rule induction algorithm, one needs to start from the foundations. Accordingly, the novel rule induction algorithm, Fuzzy Rough Rule Induction (FRRI), we introduce in this paper, uses an approach that has not yet been utilised in this setting. We provide background and explain the workings of our algorithm. Furthermore, we perform a computational experiment to evaluate the performance of our algorithm and compare it to other state-of-the-art rule induction approaches. We find that our algorithm is more accurate while creating small rulesets consisting of relatively short rules.

Parallel branch-and-price algorithms for the single machine total weighted tardiness scheduling problem with sequence-dependent setup times

Scheduling problems occur in a broad range of real-world application fields and have attracted a huge set of research articles. However, there is only little research on exact algorithms for scheduling problems, many of which are NP-hard in the strong sense. We investigate the problem on a single machine with a total weighted tardiness objective function and sequence-dependent setup times. First, we adopt a serial branch-and-price algorithm from the literature and present a modified branching strategy and a primal heuristic. Second, we use the potential of parallel computing architectures by presenting two parallel versions of the branch-and-price algorithm. Third, we conduct extensive computational experiments to show that our parallelization approaches provide substantial parallel speedups on well-known benchmark instances from the literature. We further observe that the parallel speedups achieved by our parallel algorithms are very robust among all tested instances.

A Method for Inferring Polymers Based on Linear Regression and Integer Programming.

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In this paper, we design a new method for inferring a polymer based on the framework. For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers. We also use linear regression as a building block of constructing a prediction function in the framework. The results of our computational experiments reveal a set of chemical properties on polymers to which a prediction function constructed with linear regression performs well. We also observe that the proposed method can infer polymers with up to 50 nonhydrogen atoms in a monomer form.

Reliability estimation for individual predictions in machine learning systems: A model reliability-based approach

The conventional aggregated performance measure (i.e., mean squared error) with respect to the whole dataset would not provide desired safety and quality assurance for each individual prediction made by a machine learning model in risk-sensitive regression problems. In this paper, we propose an informative indicator ℛx to quantify model reliability for individual prediction (MRIP) for the purpose of safeguarding the usage of machine learning (ML) models in mission-critical applications. Specifically, we define the reliability of a ML model with respect to its prediction on each individual input x as the probability of the observed difference between the prediction of ML model and the actual observation falling within a small interval when the input x varies within a small range subject to a preset distance constraint, namely ℛx=Py∗−ŷ∗≤εx∗∈Bx, where y∗ denotes the observed target value for the input x∗,ŷ∗ denotes the model prediction for the input x∗, and x∗ is an input in the neighborhood of x subject to the constraint Bx=x∗x∗−x≤δ. The developed MRIP indicator ℛx provides a direct, objective, quantitative, and general-purpose measure of “reliability” or the probability of success of the ML model for each individual prediction by fully exploiting the local information associated with the input x and ML model. Next, to mitigate the intensive computational effort involved in MRIP estimation, we develop a two-stage ML-based framework to directly learn the relationship between x and its MRIP ℛx, thus enabling to provide the reliability estimate ℛx for any unseen input instantly. Thirdly, we propose an information gain-based approach to help determine a threshold value pertaing to ℛx in support of decision makings on when to accept or abstain from counting on the ML model prediction. Comprehensive computational experiments and quantitative comparisons with existing methods on a broad range of real-world datasets reveal that the developed ML-based framework for MRIP estimation shows a robust performance in improving the reliability estimate of individual prediction, and the MRIP indicator ℛx thus provides an essential layer of safety net when adopting ML models in risk-sensitive environments.

Integrating one-to-many peer-to-peer ridesharing and public transit for morning commute on a mobility-as-a-service platform

Mobility-as-a-Service (MaaS) seamlessly integrates public transit with mobility-on-demand services to provide users with a one-stop tailored mobility service. Motivated by the practices of the SocialCar project, we study the integration of one-to-many peer-to-peer ridesharing and public transit in the morning commute setting. However, it becomes extremely difficult for the platform to optimise matching, routeing and scheduling for the users over a large network. For practical application, we develop a decision model for the integrated matching problem and propose an iterative distributed optimisation algorithm. The algorithm decomposes the original problem into parallel small subproblems within clusters and iteratively updates clustering as guided by theoretical results; furthermore, an incremental approach is adopted when updating clusters to reduce computational complexity. We conduct extensive computational experiments with real-world data, which demonstrate that our algorithm can quickly generate high-quality solutions for MaaS systems in practice while substantially reducing the users' vehicle miles travelled.