Computation, Volume 10, Issue 6 (June 2022) – 25 articles

An aging society increases the demand for emergency services, such as EMS. The more often EMS is needed by patients, the more medical staff are needed. During the COVID-19 pandemic, the lack of medical staff became a critical issue. This research aims to combine the allocation of trained volunteers to substitute for medical staff and solve the EMS relocation problem. The objective of the proposed research is to (1) minimize the costs of the system and (2) maximize the number of people covered by the EMS within a predefined time. A multiobjective variable neighborhood strategy adaptive search (M-VaNSAS) has been developed to solve the problem. From the computational results, it can be seen that the proposed method obtained a better solution than that of current practice and the genetic algorithm by 32.06% and 13.43%, respectively. Full article

The purpose of speech enhancement is to improve the quality of speech signals degraded by noise, reverberation, or other artifacts that can affect the intelligibility, automatic recognition, or other attributes involved in speech technologies and telecommunications, among others. In such applications, it is essential to provide methods to enhance the signals to allow the understanding of the messages or adequate processing of the speech. For this purpose, during the past few decades, several techniques have been proposed and implemented for the abundance of possible conditions and applications. Recently, those methods based on deep learning seem to outperform previous proposals even on real-time processing. Among the new explorations found in the literature, the hybrid approaches have been presented as a possibility to extend the capacity of individual methods, and therefore increase their capacity for the applications. In this paper, we evaluate a hybrid approach that combines both deep learning and wavelet transformation. The extensive experimentation performed to select the proper wavelets and the training of neural networks allowed us to assess whether the hybrid approach is of benefit or not for the speech enhancement task under several types and levels of noise, providing relevant information for future implementations. Full article

Cracks in structural components may ultimately lead to failure of the structure if not identified sufficiently early. This paper presents a crack-identification method based on time-domain. Captured time-domain data are processed into central difference approximation of displacement of each node (point) in the structure. Abnormally high central difference approximation of displacement of a node relative to those of its neighborhood points indicates a crack at that point. A suite of simulation experiments and numerical calculations was conducted to find out whether the proposed identification method could accurately identify the location of a crack in a cantilever beam under moving load compared to the location found by an exact solution method, and the outcomes indicated that it was as able as the analytical method. The proposed method is an FEA analysis, an approach familiar to virtually every engineer. Therefore, the relative amount of time and effort spent on developing the proposed method for a specific application is much less than those spent on developing an analytical method. The saved time and effort should enable more engineering personnel to perform routine checks on structural elements of their interest more simply and frequently. Full article

Several applications require to estimate the power production of photovoltaic (PV) systems under partial shading conditions. For example, dynamic reconfiguration of the array connections is needed to maximize the power production under partial shading conditions, which requires estimating the power generated by the PV array in several possible configurations. Therefore, a fast and accurate modeling technique is needed to perform those calculations in practical times and with low estimation errors. To address those kinds of problems, this paper proposes a modeling approach based on the double-diode model to provide high accuracy at low voltage and low irradiance conditions, which are important for partial-shading analysis. Moreover, the proposed modeling technique is designed to be implemented in parallel processing devices; thus, the calculation time is much shorter in comparison with classical serial solutions. The proposed model is tested in terms of accuracy and speed, obtaining satisfactory results. Finally, the applicability of the parallel model in reconfiguration applications is demonstrated using an application example. Full article

Symmetry in nature is a result of biological self-organization, driven by evolutionary processes. Detected by the visual systems of various species, from invertebrates to primates, symmetry determines survival relevant choice behaviors and supports adaptive function by reducing stimulus uncertainty. Symmetry also provides a major structural key to bio-inspired artificial vision and shape or movement simulations. In this psychophysical study, local variations in color covering the whole spectrum of visible wavelengths are compared to local variations in luminance contrast across an axis of geometrically perfect vertical mirror symmetry. The chromatic variations are found to delay response time to shape symmetry to a significantly larger extent than achromatic variations. This effect depends on the degree of variability, i.e., stimulus complexity. In both cases, we observe linear increase in response time as a function of local color variations across the vertical axis of symmetry. These results are directly explained by the difference in computational complexity between the two major (magnocellular vs. parvocellular) visual pathways involved in filtering the contrast (luminance vs. luminance and color) of the shapes. It is concluded that color variability across an axis of symmetry proves detrimental to the rapid detection of symmetry, and, presumably, other structural shape regularities. The results have implications for vision-inspired artificial intelligence and robotics exploiting functional principles of human vision for gesture and movement detection, or geometric shape simulation for recognition systems, where symmetry is often a critical property. Full article

Swarm robotics finds inspiration in nature to model behaviors, such as the use of pheromone principles. Pheromones provide an indirect and decentralized communication scheme that have shown positive experimental results. Real implementations of pheromones have suffered from slow sensors and have been limited to controlled environments. This paper presents a novel technology to implement real pheromones for swarm robotics in outdoor environments by using magnetized ferrofluids. A ferrofluid solution, with its deposition and magnetization system, is detailed. The proposed substance does not possess harmful materials for the environment and can be safely handled by humans. Validation demonstrates that the substance represents successfully pheromone characteristics of locality, diffusion and evaporation on several surfaces in outdoor conditions. Additionally, the experiments show an improvement over the chemical representation of pheromones by using magnetic substances and existing magnetometer sensor technologies, which provide better response rates and recovery periods than MOX chemical sensors. The present work represents a step toward swarm robotics experimentation in uncontrolled outdoor environments. In addition, the presented pheromone technology may be use by the broad area of swarm robotics for robot exploration and navigation. Full article

The fractional differential equations involving different types of fractional derivatives are currently used in many fields of science and engineering. Therefore, the first purpose of this study is to investigate the qualitative properties including the stability, asymptotic stability, as well as Mittag–Leffler stability of solutions of fractional differential equations with the new generalized Hattaf fractional derivative, which encompasses the popular forms of fractional derivatives with non-singular kernels. These qualitative properties are obtained by constructing a suitable Lyapunov function. Furthermore, the second aim is to develop a new numerical method in order to approximate the solutions of such types of equations. The developed method recovers the classical Euler numerical scheme for ordinary differential equations. Finally, the obtained analytical and numerical results are applied to a biological nonlinear system arising from epidemiology. Full article

Pattern recognition is a multidisciplinary area that received more scientific attraction during this period of rapid technological innovation. Today, many real issues and scenarios require pattern recognition to aid in the faster resolution of complicated problems, particularly those that cannot be solved using traditional human heuristics. One common problem in pattern recognition is dealing with multidimensional data, which is prominent in studies involving spectral data such as ultraviolet-visible (UV/Vis), infrared (IR), and Raman spectroscopy data. UV/Vis, IR, and Raman spectroscopy are well-known spectroscopic methods that are used to determine the atomic or molecular structure of a sample in various fields. Typically, pattern recognition consists of two components: exploratory data analysis and classification method. Exploratory data analysis is an approach that involves detecting anomalies in data, extracting essential variables, and revealing the data’s underlying structure. On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. This article discusses the fundamental assumptions, benefits, and limitations of some well-known pattern recognition algorithms including Principal Component Analysis (PCA), Kernel PCA, Successive Projection Algorithm (SPA), Genetic Algorithm (GA), Partial Least Square Regression (PLS-R), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Partial Least Square-Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN). The use of UV/Vis, IR, and Raman spectroscopy for disease classification is also highlighted. To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods. Full article

The strong and functional couplings among ecological, economic, social, and technological processes explain the complexification of human-made systems, and phenomena such as globalization, climate change, the increased urbanization and inequality of human societies, the power of information, and the COVID-19 syndemic. Among complexification’s features are non-decomposability, asynchronous behavior, components with many degrees of freedom, increased likelihood of catastrophic events, irreversibility, nonlinear phase spaces with immense combinatorial sizes, and the impossibility of long-term, detailed prediction. Sustainability for complex systems implies enough efficiency to explore and exploit their dynamic phase spaces and enough flexibility to coevolve with their environments. This, in turn, means solving intractable nonlinear semi-structured dynamic multi-objective optimization problems, with conflicting, incommensurable, non-cooperative objectives and purposes, under dynamic uncertainty, restricted access to materials, energy, and information, and a given time horizon. Given the high-stakes; the need for effective, efficient, diverse solutions; their local and global, and present and future effects; and their unforeseen short-, medium-, and long-term impacts; achieving sustainable complex systems implies the need for Sustainability-designed Universal Intelligent Agents (SUIAs). The proposed philosophical and technological SUIAs will be heuristic devices for harnessing the strong functional coupling between human, artificial, and nonhuman biological intelligence in a non-zero-sum game to achieve sustainability. Full article

This paper presents a finite-element-based computational model to evaluate the thermal behaviour of composite slabs with a steel deck submitted to standard fire exposure. This computational model is used to estimate the temperatures in the slab components that contribute to the fire resistance according to the load-bearing criterion defined in the standards. The numerical results are validated with experimental results, and a parametric study of the effect of the thickness of the concrete on the temperatures of the slab components is presented. Composite slabs with normal or lightweight concrete and different steel deck geometries (trapezoidal and re-entrant) were considered in the simulations. In addition, the numerical temperatures are compared with those obtained using the simplified method provided by the standards. The results of the simulations show that the temperatures predicted by the simplified method led, in most cases, to an unsafe design of the composite slab. Based on the numerical results, a new analytical method, alternative to the simplified method, is defined in order to accurately determine the temperatures at the slab components and, thus, the bending resistance of the composite slabs under fire conditions. Full article

Numerical methods, including machine-learning methods, are now actively used in applications related to elastic guided wave propagation phenomena. The method proposed in this study for material-properties characterization is based on an algorithm of the clustering of multivariate data series obtained as a result of the application of the matrix pencil method to the experimental data. In the developed technique, multi-objective optimization is employed to improve the accuracy of the identification of particular parameters. At the first stage, the computationally efficient method based on the calculation of the Fourier transform of Green’s matrix is employed iteratively and the obtained solution is used for filter construction with decreasing bandwidths providing nearly noise-free classified data (with mode separation). The filter provides data separation between all guided waves in a natural way, which is needed at the second stage, where a more laborious method based on the minimization of the slowness residuals is applied to the data. The method might be further employed for material properties identification in plates with thin coatings/interlayers, multi-layered anisotropic laminates, etc. Full article

I present two novel thread-safe in-place streaming schemes for the lattice Boltzmann method (LBM) on graphics processing units (GPUs), termed Esoteric Pull and Esoteric Push, that result in the LBM only requiring one copy of the density distribution functions (DDFs) instead of two, greatly reducing memory demand. These build upon the idea of the existing Esoteric Twist scheme, to stream half of the DDFs at the end of one stream-collide kernel and the remaining half at the beginning of the next, and offer the same beneficial properties over the AA-Pattern scheme—reduced memory bandwidth due to implicit bounce-back boundaries and the possibility of swapping pointers between even and odd time steps. However, the streaming directions are chosen in a way that allows the algorithm to be implemented in about one tenth the amount of code, as two simple loops, and is compatible with all velocity sets and suitable for automatic code-generation. The performance of the new streaming schemes is slightly increased over Esoteric Twist due to better memory coalescence. Benchmarks across a large variety of GPUs and CPUs show that for most dedicated GPUs, performance differs only insignificantly from the One-Step Pull scheme; however, for integrated GPUs and CPUs, performance is significantly improved. The two proposed algorithms greatly facilitate modifying existing code to in-place streaming, even with extensions already in place, such as demonstrated here for the Free Surface LBM implementation FluidX3D. Their simplicity, together with their ideal performance characteristics, may enable more widespread adoption of in-place streaming across LBM GPU codes. Full article

The Analogue Ensemble (AnEn) method enables the reconstruction of meteorological observations or deterministic predictions for a certain variable and station by using data from the same station or from other nearby stations. However, depending on the dimension and granularity of the historical datasets used for the reconstruction, this method may be computationally very demanding even if parallelization is used. In this work, the classical AnEn method is modified so that analogues are determined using K-means clustering. The proposed combined approach allows the use of several predictors in a dependent or independent way. As a result of the flexibility and adaptability of this new approach, it is necessary to define several parameters and algorithmic options. The effects of the critical parameters and main options were tested on a large dataset from real-world meteorological stations. The results show that adequate monitoring and tuning of the new method allows for a considerable improvement of the computational performance of the reconstruction task while keeping the accuracy of the results. Compared to the classical AnEn method, the proposed variant is at least 15-times faster when processing is serial. Both approaches benefit from parallel processing, with the K-means variant also being always faster than the classic method under that execution regime (albeit its performance advantage diminishes as more CPU threads are used). Full article

Unmanned aerial vehicles (UAVs) are becoming essential tools for surveying and monitoring forest ecosystems. However, most forests are found on steep slopes, where capturing individual tree characteristics might be compromised by the difference in ground sampling distance (GSD) between slopes. Thus, we tested the performance of treetop detection using two algorithms on canopy height models (CHMs) obtained with a commercial UAV (Mavic 2 Pro) using the terrain awareness function (TAF). The area surveyed was on a steep slope covered predominantly by fir (Abies mariesii) trees, where the UAV was flown following (TAF) and not following the terrain (NTAF). Results showed that when the TAF was used, fir trees were clearly delimited, with lower branches clearly visible in the orthomosaic, regardless of the slope position. As a result, the dense point clouds (DPCs) were denser and more homogenously distributed along the slope when using TAF than when using NTAF. Two algorithms were applied for treetop detection: (connected components), and (morphological operators). (connected components) showed a 5% improvement in treetop detection accuracy when using TAF (86.55%), in comparison to NTAF (81.55%), at the minimum matching error of 1 m. In contrast, when using (morphological operators), treetop detection accuracy reached 76.23% when using TAF and 62.06% when using NTAF. Thus, for treetop detection alone, NTAF can be sufficient when using sophisticated algorithms. However, NTAF showed a higher number of repeated points, leading to an overestimation of detected treetop. Full article

Dual active bridge converters (DAB) are used to interconnect photovoltaic (PV) generators with AC and DC buses or isolated loads. However, a controller is needed to provide a stable and efficient operation of the DAB converter when the PV generator must be interconnected with a DC bus, which is particularly important in microinverter applications. Therefore, this paper proposes the design of a cascade controller for a PV system based on a DAB converter. The converter is controlled using a peak current control and an adaptive PI voltage control; thus the methodology to design the cascade controller is developed in two steps; first, the PV system formed by a PV generator, a DAB converter, and an inverter or load is introduced, including the description of the leakage current; as a second step, the model of the PV system to design the cascade controller is presented. Then, a relationship between the phase shift factor and the peak current of the leakage inductor is derived, which is used to design the peak current controller to ensure the DAB converter operation at the most efficient operating condition. On the other hand, an adaptive PI controller for the PV voltage is designed to ensure the reference tracking provided by a maximum power point (MPP) algorithm. The effectiveness of the proposed cascade controller is demonstrated through realistic examples simulated in PSIM. The power and control circuits implemented in PSIM are presented to encourage the use of the proposed solution. The simulation results confirm the correct operation of the control system, which mitigates the oscillatory perturbation produced by an inverter connected to the PV system, and also ensures the maximum power extraction from the PV panel by following the MPP reference. Full article

This paper presents the generalized information system theory, which is enlarged into pure quantum systems using wave probability functions. The novelty of this approach is based on analogies with electrical circuits and quantum physics. Information power was chosen as the relevant parameter, which guarantees the balance of both components—information flow and information content. Next, the principles of quantum resonance between individual information components, which can lead to emergent behavior, are analyzed. For such a system, adding more and more probabilistic information elements can lead to better convergence of the whole to the resulting trajectory due to phase parameters. The paper also offers an original interpretation of information “source–recipient” or “resource–demand” models, including not yet implemented “unused resources” and “unmet demands”. Finally, possible applications of these principles are shown in several examples from the quantum gyrator to the hypothetical possibility of explaining some properties of the consciousness. Full article

This work concerns the optimal control of a passive particle in viscous flows. This is relevant since, while there are many studies on optimal control in inviscid flows, there is little to no work in this context for viscous flows, and viscosity cannot always be neglected. Furthermore, in many tasks, there is a need to reduce the energy spent; thus, energy-optimal solutions to problems are important. The aim of this work is to investigate how to optimally move a passive particle advected by a Lamb–Oseen (viscous) vortex between two given points in space in a given time interval while minimising the energy spent on this process. We take a control acting only on the radial component of the motion, and, by using the Pontryagin’s Maximum Principle, we find an explicit time-dependent extremal. We also analyse how the energy cost changes with the viscosity of the flow.The problem is transformed into a parameter search problem with two parameters related to the radial and angular coordinates of the initial point. The energy cost of the process increases with viscosity as long as the passive particle maintains the number of full turns it makes around the vortex. However, the energy cost increases if the increase in viscosity forces the particle to make fewer full revolutions around the vortex. Full article

COVID-19 has become the largest pandemic in recent history to sweep the world. This study is devoted to developing and investigating three models of the COVID-19 epidemic process based on statistical machine learning and the evaluation of the results of their forecasting. The models developed are based on Random Forest, K-Nearest Neighbors, and Gradient Boosting methods. The models were studied for the adequacy and accuracy of predictive incidence for 3, 7, 10, 14, 21, and 30 days. The study used data on new cases of COVID-19 in Germany, Japan, South Korea, and Ukraine. These countries are selected because they have different dynamics of the COVID-19 epidemic process, and their governments have applied various control measures to contain the pandemic. The simulation results showed sufficient accuracy for practical use in the K-Nearest Neighbors and Gradient Boosting models. Public health agencies can use the models and their predictions to address various pandemic containment challenges. Such challenges are investigated depending on the duration of the constructed forecast. Full article

Long-term pavement performance (LTPP) was used to investigate factors contributing to pavement skid resistance. The random effect model, with a Poisson distribution, was employed to analyze the relationship between various variables and pavement friction as a response, while accounting for the repetitive nature of panel-data observations. The results highlight a significant improvement in the model fit compared with the standard Poisson model. In this study, all pairwise interaction terms, instead of the additive impacts of various predictors, were considered. The results of this study highlight that the impacts of various predictors on pavement friction are not additive, but multiplicative. For instance, it was found that the impacts of pavement age, average annual temperature, number of lanes and annual Equivalent Single Axle Load (ESAL) on the pavement friction vary based on pavement type or on whether the pavement type is concrete or asphalt. The findings provide important information regarding the maintenance of pavement by paying the foremost attention to the pavement types for adjusting friction. This is one of the earliest studies that takes complex relations across various predictors and pavement frictions into consideration. A discussion regarding the implications of the findings is provided in the context of this study. Full article

Innumerable industries now use multi-agent systems (MASs) in various contexts, including healthcare, security, and commercial deployments. It is challenging to select reliable business protocols for critically important safety-related systems (e.g., in healthcare). The verification and validation of business applications is increasingly explored concerning multi-agent systems’ group social commitments. This study explains a novel extended reduction verification method to model-check business applications’ critical specification rules using action restricted computation tree logic (ARCTL). In particular, we aim to conduct the verification process for the CTL^GC logic using a reduction algorithm and show its effectiveness to handle MASs with huge models, thus, showing its importance and applicability in large real-world applications. To do so, we need to transform the CTL^GC model to an ARCTL model and the CTL^GC formulas into ARCTL formulas. Thus, the developed method was verified with the model-checker new symbolic model verifier (NuSMV), and it demonstrated effectiveness in the safety-critical specification rule support provision. The proposed method can verify up to 2.43462 × 10¹⁴ states MASs, which shows its effectiveness when applied to real-world applications. Full article

We investigate the thermomechanical behavior of 3D printing of metals in the laser-based powder bed fusion (L-PBF) process, also known as selective laser melting (SLM). Heat transport away from the printed object is a limiting factor. We construct a one-dimensional thermoviscoelastic continuum model for the case where a thin fin is being printed at a constant velocity. We use a coordinate frame that moves with the printing laser, and apply an Eulerian perspective to the moving solid. We consider a steady state similar to those used in the analysis of production processes in the process industry, in the field of research known as axially moving materials. By a dimensional analysis, we obtain the nondimensional parameters that govern the fundamental physics of the modeled process. We then obtain a parametric analytical solution, and as an example, illustrate it using material parameters for 316L steel. The nondimensional parameterization has applications in real-time control of the L-PBF process. The novelty of the model is in the use of an approach based on the theory of axially moving materials, which yields a new perspective on modeling of the 3D printing process. Furthermore, the analytical solution is easy to implement, and allows fast exploration of the parameter space. Full article

The paper deals with computation techniques applied in preprocessing of gravity data, which are based on time series analysis by using mathematical and statistical smoothing techniques such as moving average, moving median, cumulative and moving average, etc. The main aim of gravity data preprocessing is to avoid abrupt errors caused by a sudden movement of the subsoil due to human or natural activities or systematic instrumental influences and so provide relevant gravity values, which are then subjected to further processing. The new approach of the described research involves the preprocessing phase in gravity data analysis to identify and avoid gross errors, which could influence the size of unknown parameters estimated by the least square method in the processing phase. Full article

This paper presents an Artificial Neural Network (ANN)-based approach for predicting tunnel stability that is both dependable and accurate. Numerical solutions to the instability of unlined horseshoe tunnels in cohesive-frictional soils are established, primarily by employing numerical upper bound (UB) and lower bound (LB) finite element limit analysis (FELA). The training dataset for an ANN model is made up of these numerical solutions. Four dimensionless parameters are required in the parametric analyses, namely the dimensionless overburden factor γD/c′, the cover-depth ratio C/D, the width-depth ratio B/D, and the soil friction angle ϕ. The influence of these dimensionless parameters on the stability factor is explored and illustrated in terms of a design chart. Moreover, the failure mechanisms of a shallow horseshoe tunnel in cohesive-frictional soil that is influenced by the four dimensionless parameters are also provided. Therefore, the current stability solution, based on FELA and ANN models, is presented in this paper, allowing for the efficient and accurate establishment and evaluation of an optimum surcharge loading of shallow horseshoe tunnels in practice. Full article

This paper presents an updated and broad review of swarm robotics research papers regarding software, hardware, simulators and applications. The evolution from its concept to its real-life implementation is presented. Swarm robotics analysis is focused on four aspects: conceptualization, simulators, real-life robotics for swarm use, and applications. For simulators and robots, a detailed comparison between existing resources is made. A summary of the most used swarm robotics applications and behaviors is included. Full article

Frangible bullets have a unique property that disintegrates into fragments upon hitting a hard target or obstacle. This peculiar ability to become fragments after impact is called frangibility. In this study, frangibility testing was carried out theoretically via modeling using the explicit dynamics method with ANSYS Autodyn solver integrated by ANSYS Workbench software. This paper aims to analyze frangibility through two main factors: material properties and projectile design. The results show the scattering and remaining bullet fragments after impact. According to the modeling results, the frangibility factor values are 9.34 and 10.79, respectively. Based on the frangibility factor, errors based on the frangibility factor by comparing the experimental results and simulations for AMMO 1 and AMMO 2 are 10.5% and 1.09%. Based on simulation results, the AMMO 2 design bullet scattering pattern shows several scattering particles more than the AMMO 1 design, with the furthest distance scattering AMMO 1 and AMMO 2 bullets being 1.01 m and 2658 m. Full article