Computational Tests Research Articles

Production scheduling with multi-robot task allocation in a real industry 4.0 setting

The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations. However, research often oversimplifies the Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot Flexible Job Shop (MRFJS) scheduling problem with limited buffers. It involves non-identical parallel machines and robots with varying capabilities overseeing material handling under blocking conditions. The case study is based on a real Industry 4.0 scenario, where the layout restricts each robotic arm’s access, requiring strategic buffer placement for part transfers. A Mixed-Integer Programming (MILP) model aims to minimize makespan, followed by a new Genetic Algorithm (GA) using Roy and Sussman’s Alternative Graph. Computational tests on various scales and real data from a manufacturing plant demonstrate the GA’s efficacy in solving complex scheduling problems in real-world production settings. Based on the data, the Proposed Genetic Algorithm (PGA), with an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared to the Basic Genetic Algorithm (BGA), with an average ARD of 0.38%. This percentage indicates that the PGA significantly outperforms in solving complex scheduling problems.

Evaluation of radiality constraints for power distribution networks

This work carried out an empirical study with the objective of determining the most efficient strategy in guaranteeing the radiality of the distribution network during the reconfiguration. The reconfiguration problem in a distribution network aims to reduce technical losses by changing the paths that energy takes through the network until it reaches consumers. In this problem, a mandatory constraint is to ensure that the network operates radially. However, the literature does not present a consensus on the best way to guarantee this restriction. Computational experiments were carried out using the two main methodologies in the literature: limiting the number of input edges; and search with removal of cycles in each partial solution. A third and new strategy was proposed as an alternative to the literature using the concept of minimal cuts in graphs. Computational tests showed that the strategy of limiting the number of input edges was the most efficient in ensuring radiality. We also point out a drawback in the applicability of this strategy to networks with high penetrations of distributed generation (DG). For this reason, our experiments also analyze the other strategies that are suitable for DG power flows. For this case, we show that the methodology proposed in this paper provides a better way to tackle modern networks arising from applications of smart grids.

Integrating sequence-dependent setup times and blocking in hybrid flow shop scheduling to minimize total tardiness

This study addresses the minimization of total tardiness in a hybrid flow shop scheduling problem with sequence-dependent setup times and blocking constraints. Each production stage includes multiple machines, and there are no buffers between the stages. The setup time required to process a job depends on the previously processed job. Two mixed-integer linear programming models are developed to formulate the problem. Moreover, an iterative local search algorithm and hybrid genetic algorithms are proposed to have quality solutions with minimal computational efforts. Several computational tests are conducted to tune the heuristic parameters for better performance. Computational experiments are carried out to evaluate the performance of solution methodologies in terms of quality and time. The results indicate that while mixed-integer programming models can solve small-size problem instances, they are not capable of solving large-sized instances. However, the proposed heuristic algorithms find quality solutions for all instances in a very short time.

Apache Spark based distributed clustering for big data analytic with application to 3D road network

The vast amount of data stored nowadays has turned big data analytics into a very promising research field. Clustering is an essential step in data analysis, widely used for classification, collecting statistics, and acquiring insights in specific domains of knowledge. However, the most of existing algorithms based on Lloyd-Forgy’s method, have an enormously huge average-case complexity while clustering data sets with a large number of features, which may be superpolynomial time (NP-hard) and are severely constrained in terms of speed, productivity, and adaptability. Aiming to improve Lloyd-Forgy’s clustering performance, K-means++ algorithms, a variety of algorithm-level optimizations which is not been well-studied, is discussed along with very promising gaussian mixture model (GMM) and soft clustering based Fuzzy C-means (FCM). Further, for fast and distributed data processing and to leverage the benefits of big data platforms, such as Apache Spark, Spark-based clustering methods are applied on three-dimensional (3D) road network data set which is collected from UCI repository. However, Spark-based clustering research is still in infancy. The distributed computation tests are conducted by allocating two core processors and one databricks unit (DBU) with 15 GB memory and measuring execution times, as well as root mean square error (RMSE), mean absolute error (MAE), clustering accuracy, and silhouette values. The results are promising and provide new research directions in the field of spark-based clustering on big data.

An explicit peridynamics model for hydraulic fracturing in porous media

PurposeThe purpose of this paper is to systematically investigate the hydraulic fracture branching phenomena in porous media under different loading conditions and the stepwise phenomenon. The effect of the pore pressure in hydraulic fracturing branching is studied, and more evidence for the stepwise phenomenon with the peridynamics approach is provided.Design/methodology/approachA fully coupled fluid-filled explicit peridynamics model is developed to simulate the complex evolution of crack branching and stepwise phenomena in saturated porous media. Based on the peridynamics theory, an explicit time integration scheme is used to solve the coupled equation system including rock deformation, fluid flow and fracture propagation. Using the proposed model, a series of peridynamic computational tests are performed to examine two common kinds of phenomena observed in hydraulic fracturing: the crack branching phenomenon and the stepwise phenomenon.FindingsFor crack branching phenomenon, the results obtained indicate that sufficient loading is required in order to initiate the crack branching process. Compared with the stress applied on crack surfaces condition, crack branching is more easily induced with the stress applied on boundaries. In addition, for the fluid-driven crack (stress applied on crack surfaces), the existence of pore pressure will depress the growth and branching of the crack. For stepwise phenomena, the results obtained indicate that the peridynamics is a promising tool to study the stepwise phenomenon. The stepwise phenomenon is more distinct under mechanical loading conditions due to the solid behaviour. A sudden jump or crack extension will happen when enough energy is accumulated in the hydraulic fracturing system.Research limitations/implicationsIn this study, the explicit method is used, which means it is conditionally stable, and the critical time step needs to be decided. The reason to use the explicit method is for the study purpose; the explicit method is faster and has no need for matrix inversions.Originality/valueThis study helps to understand the effect of the pore pressure in hydraulic fracturing branching and provides more evidence for the stepwise phenomenon with peridynamics.

DEVELOPMENT OF AUGMENTED REALITY-BASED MATHEMATICS LEARNING MEDIA TO FACILITATE STUDENTS' MATHEMATICAL COMPUTATIONAL THINKING SKILLS

The development of digital technology has positively impacted the world of education, especially in creating more interactive and innovative learning media. The integration of Augmented Reality (AR) in learning is one of the innovations that can provide new learning experiences for students by presenting visualizations of abstract concepts in real life. This research aims to develop AR-based mathematics learning media to facilitate students' mathematical computational thinking skills. The research method used is Research and Development (R&D) with the ADDIE development model (Analysis, Design, Development, Implementation, Evaluation). The research subjects involved 16 Insan Cendekia Sheikh Yusuf Gowa High School students. The research instruments used included product validation sheets, learner and teacher response questionnaires, and mathematical computational thinking ability tests. Analysis of research data focused on analyzing the validity, practicality, and effectiveness of Augmented Reality-based mathematics learning media. The results showed that the Augmented Reality-based mathematics learning media developed had met the criteria of validity, practicality, and effectiveness.

Differentiated E-Module Based on Problem-Based Learning: Natural Science Subject

Finding out whether PBL-based differentiated e-modules are feasible, practical, and effective are the three main objectives of this project. This study uses the ADDIE paradigm (Analysis, Design, Develop, Implement, and Evaluate) in conjunction with the Research and Development (R&D) technique. Thirty seventh-grade earth and solar system students served as the study's subjects. In addition to phases of individual trials, small group trials, and field trials, this study included validation tests conducted by professionals in media, instructional design, and materials. The study's findings revealed: In the Very Feasible category, Material Experts scored 85.09, while Learning Design Experts scored 92.87. Extremely feasible Experts in Media Design scored 89.44 in the category of Very Feasible. Field trials scored 87.29 in the Very Feasible category, small group trials scored 87.30, and individual trials scored 87.27 in the Very Feasible category. At a significance threshold of α = 0.05, the computation yielded tcount = 4.256 and ttable = 1.67, indicating that tcount > ttable. These findings indicate that, at a significance level of 5%, there is a substantial difference between the learning outcomes of the students in the experimental and control classes, with H0 being rejected and Ha being accepted. According to the efficacy test's computation, According to the data, students who get instruction using PBL-based differentiated E-modules achieve better learning outcomes than those who receive printed books (81.25% > 73.33%). Therefore, it can be said that the Science on Earth and Solar System class VII PBL-based differentiated E-modules at SMP N 43 Medan are superior to using printed books.

Algorithm for Searching the Optimal Regulator Supply Mode in the Process of Manufacturing Polymer Products

Introduction. The high demand for polymer products creates the need for constant mo­dernization of the technological aspects of their production, increasing the efficiency of which is impossible without a model description and solving problems of optimization of its main technological stages. The current needs for manufacturing the products with a specified structure and properties make the issue of creating tools for solving optimization problems very relevant. One of the tools for controlling the product molecular weight is using the fractional mode to supply a regulator, the composition and dosage of which are often selected empirically.Aim of the Study. The aim of the study is to develop methods and algorithms to determine the mode for multipoint supplying a regulator in the continuous-running manufacturing of polymer products with specified molecular characteristics.Materials and Methods. To choose the optimal regulator supply mode, there is used a heuristic method represented by a genetic optimization algorithm. This algorithm is based on the mechanism for creating a population of potential solutions that are subjected to the operations of crossing, mutation and selection imitating the processes of inheritance and evolution in nature. To assess the molecular characteristics of the copolymerization pro­duct, there is applied a kinetic modeling approach based on the use of molecular weight distribution moments. For a mathematical description of continuous-running production, there are used recurrent relations characterizing the transfer of the reaction mass between ideally mixed reactors.Results. According to the conditions for organizing continuous-running manufacturing, it is possible to add a regulator at the beginning of the process in the third and sixth poly­merizers along the battery. In order to determine the regulator supply mode, the optimization criterion was developed in the form of a functional reflecting the absolute difference between the calculated and specified values of the number-average and mass-average molecular weights. The software implementation of the developed method and optimization algorithm, and the computational tests carried out made it possible to identify a number of solutions, each of which contributes to manufacturing a product with specified molecular characteristics. Visualization of some resulting solutions demonstrates different molecular weight dynamics throughout the process.Discussion and Conclusion. Through using the developed method and algorithm, there has been solved the problem of identifying the three-point molecular weight control regime for the continuous-running process of producing styrene-butadiene copolymer. The choice of a genetic algorithm for the study and optimization of complex multifactorial physicochemical systems is justified by the fact that it allows searching for one or more system parameters in both a discrete and continuous set of variables and contributes to finding a global optimum due to the random nature in the search for solutions. The variety of solutions obtained for the problem makes it possible to control the process of polymer synthesis in the case of constant monitoring of the physicochemical characteristics of the product.

Dynamic Panel Threshold Spatial Durbin Model with an Application to the Relationship between Financial Development and Green Growth

Abstract This paper studies computation, estimation, inference and specification testing in dynamic panel threshold spatial Durbin (DPTSD) model with multiple thresholds. We first develop a new Markov chain Monte Carlo (MCMC) based algorithm to jointly estimate the threshold parameters and simultaneously construct the confidence intervals for the parameters, after suggesting a within-group spatial two-stage least squares estimator. We then construct test statistics for threshold effect and the number of thresholds. Monte Carlo experiments indicate that the proposed estimator and tests have desired performance in finite samples. We finally apply the DPTSD model to investigate the relationship between financial development and green growth, and find that the empirical results based on the DPTSD model are quite different from these based on the dynamic panel threshold model.

The Influence of Music Producing and Creativity on Computational Thinking in Primary School Children

Computational thinking (CT) can be developed in a multitude of ways. Well-known examples are plugged-in and unplugged applications that focus primarily on the (combined) usage of visual, textual, or tangible modalities. Less obvious are applications where CT development can be established via an auditory modality, to which the importance of creativity is attributed. When reasoning from such a different contextual perspective, it is interesting to investigate whether the self-creation of electronic music influences CT development and what added value can be attributed to creativity. Therefore, a mixed-methods study was conducted on primary school pupils aged 10 to 13 using FL-Studio Mobile© music-producing software. Quantitative data were obtained using a pre-test–post-test assessment administered via a validated Computational Thinking Test (CTt). Qualitative data were obtained by conducting interviews to ascertain identifiable effects on CT sub-characteristics to determine the influence of creativity and creative thinking and children’s perceptions in this regard. Our results indicate that applying music-making software has measurable effects on CT development, particularly with respect to invoking and using auditory modalities. Notable findings were identified on the CT sub-characteristics “loops”, “conditionals”, “functions”, “nesting”, and “CT tasks required”. Our study shows that producing music using technology stimulates creativity, which seems to be an important parameter for CT development. Differences between girls and boys were observable. Further research on the interaction between CT and creativity, combining different modalities, is recommended.

Analytical Information on the Results of Teacher Certification of the Department for Education Quality Development of the Ministry of Education and Science of the Kyrgyz Republic.

This article is dedicated to voluntarily based certification of school teachers from seven oblasts (Chui, Naryn, Talas, Issyk-Kul, Jalal-Abad, Osh, Batken) of the Kyrgyz Republic and from Bishkek city. We would like to remind you that the first step in this certification process involves registering a digital portfolio of teachers in accordance with the certification, which is then used to assign a qualification category on the Department for Development of Education Quality's electronic portal. Next, the documents were examined by highly qualified experts. Upon completion of all the above work, computer testing of all candidates was conducted on the appointed date. Upon completion of the test, the test results were instantly displayed on the screen, and a protocol on the test results was handed to each candidate. General statistics were then transferred to the relevant department of the Ministry of Education and Science for further policy implementation. The main objective of this event is to stimulate teachers in professional growth, motivate teachers in continuous professional growth, improve the quality and results of teachers’ teaching activities, and develop a system of methodological support and mentoring.

The Impact of the Interactive Floor Device and Aerobic Training on Executive Functions in Children.

Considering the importance of physical activity on the development of cognitive functions in children, the aim of this study was to assess the effects of a ten-week training program using the Interactive Floor device (© Funtronic), i.e., a kinesthetic educational game, and aerobic activity training on executive functions in 9-year-old children. Given current knowledge of the advantages of gamification and on-task switching, stronger improvement was expected for the Interactive Floor device than aerobic exercise activities. Sixty-four children (29 boys/35 girls) were randomly assigned to the Interactive Floor (n = 22), Aerobic Training (n = 22), or Control groups (n = 20). The participants had their cognitive abilities assessed twice (pre- and post-intervention) using computer tests from the Vienna Test System (VTS) and subtests from the Wechsler Intelligence Scale for Children® Fifth Edition (WISC®-V). From VTS, the Stroop Test was used to measure inhibition and attentional control, while the Corsi Block test assessed visuospatial short-term working memory. To assess auditory working memory, the Digit Span subtest from the WISC®-V was applied. Additionally, fluid intelligence was estimated using Raven's Progressive Matrices. Repeated-measures mixed ANOVA and post hoc tests with Bonferroni correction for multiple comparisons showed that all intervention program groups improved in terms of intelligence and non-verbal abstract reasoning. The second significant finding in this study was that especially children from the Interactive Floor group developed their executive functions, i.e., inhibition and attentional control as well as their spatial short-term memory capacity. The results suggest that a combination of both physical exercise and cognitive games in the Interactive Floor group resulted in greater improvement in cognitive abilities in children than aerobic exercise or physical education lessons. It seems that a multidisciplinary approach combining physical and cognitive stimulation effectively promotes child development. Future programs aiming to improve cognitive skills in children should consider incorporating interactive and engaging activities that stimulate both the body and the mind.

DEFECT CHARACTERIZATION OF METALLIC MATERIALS USING SELF-PARAMETERIZED DENSITY-BASED CLUSTERING AND COMPUTATIONAL INTELLIGENCE TECHNIQUES

Rapid growth of manufacturing industries is propelled by transformative technologies such as machine intelligence, autonomous computing and non-destructive testing (NDT). During the manufacturing of wrought products, there is no guarantee that the final product is 100-% flawless. Thus, all final products are subjected to quality checking to identify and eliminate defective products. In industries, most of internal defects are identified using NDT techniques, which fail to precisely characterize the defects. In this paper a novel algorithm, called Self-Parameterized Density-Based Clustering (SPDBC), is proposed for defect characterization. The proposed clustering method uses spatial parameters to identify the size and position of defects by filtering out the noise and other data that correspond to the non-defect area. Using these filtered data, computational intelligence techniques are employed to predict the defect type. SPDBC achieved Jaccard indices of 97.02 % and 98.78 % for identifying the defect size and position, respectively. Gradient boosting regression trees (GBRT) achieved a maximum accuracy of 97.44 % in predicting the defect type. As a result, the proposed approach can assist NDT experts in various sectors to differentiate between problem severities faster and replace defective parts before any major breakdown occurs.

Courier assignment in meal delivery via integer programming: A case study in Rome

We present an optimization model for assigning orders to couriers developed for an Italian meal delivery firm focusing on Rome. The firm focuses on top-end restaurants and customers and pursues high Quality of Service through careful management of delays. Our model reflects that in the firm’s business, the majority of orders are placed in advance. This took us to design a sequential decision process implementing a rolling horizon approach where we do not try to anticipate future demands. We, therefore, iterate the solution of a fully deterministic optimization problem, the Offline Couriers Assignment Problem (ocap), where we assume full knowledge of the orders and aim at minimizing delays and rejections. We solve ocap through integer linear programming and in particular by a “flow-like” formulation on a suitable network whose size is kept as small as possible. We validate both the quality of this formulation and the sequential decision process through some computational tests on real instances collected on the ground. We make these instances available to the scientific community.

Multi-step optimization with operational scenarios for hull form and propulsor design for pod-driven cruise ships.

With the increasing demand for reducing CO2 emissions by the International Maritime Organization (IMO), controlling a ship's energy consumption at the design stage is crucial for proposing a 'greener' design. Some efforts have been made to consider the Energy Efficiency Design Index (EEDI) and the Energy Efficiency Operational Index (EEOI); however, the latter remains highly complex and contentious. In this study, a multistep optimization analysis method was developed to integrate EEDI and EEOI evaluations during the design stage to meet low emission requirements. Additionally, an Efficient Operational Scenario Analysis Method (EOSAM) was established to consider operational energy efficiency. This method was applied to optimize the hull and propulsor design of a pod-driven cruise ship. By accounting for operating conditions in both calm water and wave scenarios, and utilizing high-fidelity computational fluid dynamics simulations and model test validation, the delivered power was reduced by up to 7.1% compared to the original design, and the EEOI decreased by an average of 16.7%.

A Linguistic–Sensorimotor Model of the Basic‐Level Advantage in Category Verification

AbstractPeople are generally more accurate at categorizing objects at the basic level (e.g., dog) than at more general, superordinate categories (e.g., animal). Recent research has suggested that this basic‐level advantage emerges from the linguistic‐distributional and sensorimotor relationship between a category concept and object concept, but the proposed mechanisms have not been subject to a formal computational test. In this paper, we present a computational model of category verification that allows linguistic distributional information and sensorimotor experience to interact in a grounded implementation of a full‐size adult conceptual system. In simulations across multiple datasets, we demonstrate that the model performs the task of category verification at a level comparable to human participants, and—critically—that its operation naturally gives rise to the basic‐level‐advantage phenomenon. That is, concepts are easier to categorize when there is a high degree of overlap in sensorimotor experience and/or linguistic distributional knowledge between category and member concepts, and the basic‐level advantage emerges as an overall behavioral artifact of this linguistic and sensorimotor overlap. Findings support the linguistic–sensorimotor preparation account of the basic‐level advantage and, more broadly, linguistic–sensorimotor theories of the conceptual system.

(Invited) BIG-MAP - Going BIG to MAP an Accelerated Path to Sustainable Battery Materials Discovery

The large-scale European research initiative, the Battery Interface Genome – Materials Acceleration Platform (BIG-MAP), set forth to reinvent the ways we invent batteries in Europe by developing a distributed framework for accelerated autonomous discovery of sustainable battery materials and interfaces. A central element in this process is the development of a closed-loop MAP infrastructure, where multiple and geographically distributed laboratories or tenants can work jointly using autonomous workflows to co-optimize materials properties. Here, we show an example using the Fast INtention-Agnostic LEarning Server (FINALES) framework [1] to orchestrate a two-pronged optimization task, where both optimization tasks vary the composition of a battery electrolyte composed of ethylene carbonate (EC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF6). However, one targets the optimization of ionic conductivity, while the other aims to maximize the end-of-life (EOL) of coin cells [2]. A second critical component is the “BIG”, where establishing spatio-temporal structure-property relations of the dynamic processes at solid-liquid interfaces is key to developing more efficient and durable batteries. Fundamental and performance-limiting interfacial processes like forming the Solid-Electrolyte Interphase (SEI) span numerous time- and length scales, and despite decades of research, the fundamental understanding of the structure-property relations remains elusive. Ab initio molecular dynamics (AIMD) generally provides sufficient accuracy to describe chemical reactions and the making and breaking of chemical bonds at these interfaces [3]. Still, the cost is prohibitively high to reach sufficiently long time- and length scales to ensure proper statistical sampling, and machine learning (ML) potentials offer a potential solution to this challenge [4]. Still, training ML-based potentials capable of handling activated processes in organic or aqueous electrolytes remains a fundamental challenge since the potential must capture both intra- and intermolecular interactions in the electrolyte and during chemical reactions at the interface [5]. Finally, we present new approaches using foundation models [6] and new transition state training sets [7] for chemical reaction networks and machine/deep learning models to predict the spatio-temporal evolution of electrochemical interphases [8]. We also discuss the development of active learning methods to accelerate the segmentation of microstructures in non-destructive 3D imaging techniques, such as X-ray nano-holo-tomography, and enable the visualization of battery electrodes. Finally, we discuss how such models trained on multi-sourced and multi-fidelity data from multiscale computer simulations, operando characterization, high-throughput synthesis, and testing to provide uncertainty-aware and explainable ML for early prediction of patterns from chemical spectra (Figure 1) [9]. Vogler, Stein et al., Mater, 2023, 6, 2647-2665 Vogler, Krarup et al., 2024; doi: zenodo.org/records/10987727 Yang, Bhowmik, Vegge, Hansen, Chem. Sci., 2023, 14, 3913 Bhowmik, Castelli, Garcia-Lastra, Jørgensen, Winther, Vegge, Energy Storage Mater., 2019, 21, 446 Bhowmik, Vegge, et al., Adv. Energy Mater, 2021, 2102698 Batatia et al., arXiv, 2024, doi: 10.48550/arXiv.2401.00096 Schreiner, Bhowmik, Vegge, Winther, Mach. Learn. Sci. Tech., 2022, 3, 045022 Schreiner, Bhowmik, Vegge, Winther, Sci. Data, 2022, 9, 779 Rieger, Wilson, Vegge, Flores, Digital Discovery, 2023,2, 1957-1968 Figure 1

Study on noise reduction and structural optimization of ventilated bio-metamaterial plates for acoustic applications

This study focuses on the noise reduction performance and structural optimization of ventilated metamaterial plates designed for bio-acoustic applications, where effective sound attenuation and ventilation are both crucial. Traditional soundproofing materials, which rely on mass and thickness, are inadequate for bio-acoustic environments that require lightweight and compact solutions. In contrast, bio-acoustic metamaterials use resonance effects to attenuate sound while maintaining necessary airflow selectively. This research evaluates multiple metamaterial plate configurations through computational simulations and experimental testing, examining their performance in terms of Sound Transmission Loss (STL), airflow rates, and von Mises stress. The results reveal that Plate Configuration 1 offers the highest STL at 39.14 dB but at the cost of lower airflow efficiency (0.69 m3/s) and increased structural stress (24.83 MPa). Plate Configuration 2 achieves the best airflow efficiency (0.82 m3/s) but with lower noise reduction (STL of 35.42 dB). Plate Configuration 3 provides a balanced performance, with moderate noise attenuation (STL of 37.89 dB), good airflow (0.75 m3/s), and structural stability (von Mises stress of 22.12 MPa). The study concludes that bio-acoustic metamaterials can be effectively optimized for different bio-acoustic applications by carefully tuning their geometry, making them suitable for eco-acoustics, wildlife monitoring, and medical devices where noise control and airflow are critical.

Optimized determination of saturation numbers in fullerene graphs using tabu search

The saturation number is a structural invariant related to matchings. Let G be a graph with edge set E ( G ) . M ⊆ E ( G ) is a maximal matching if it is not a subset of some other matching in graph G . The smallest possible number of edges in a maximal matching of G is called the saturation number. Determining the saturation number of a graph is an NP-hard problem. The aim of this paper is to study the Tabu Search (TS) algorithm for solving this problem. The proposed algorithm can be implemented on general graphs. However, in our computational tests, TS found a saturation number for fullerene graphs, and we compared the obtained results with the methods presented in other studies.

BIODEGRADABLE COMPOSITES OF SUGARCANE BAGASSE AND VEGETAL POLYURETHANE FOR BIOMEDICAL APPLICATIONS

Due to the environmental problems caused by polymers, it is desirable to use biodegradable biopolymers such as vegetable polyurethane and sugar cane bagasse fibers. Therefore, the work aimed at the development of biodegradable biocomposites of sugarcane bagasse fibers for application in orthoses and evaluated their viability through mechanical, chemical, biodegradation and computational simulation tests. It was possible to obtain PU composites with sugarcane bagasse, which showed good interaction through the analysis of scanning electron microscopy images. It was observed that the addition of sugar cane bagasse fibers to the PU increased impact resistance, Young's modulus, there was a decrease in elongation and hardness and that the addition of fibers maintained the maximum tension value. The water absorption test showed that the fibers increased water absorption and biodegradation compared to polyurethane, which is advantageous for the orthosis, as it causes less accumulation of water between the patient's skin and the orthosis and reduces problems of infections and wounds. The computational simulation showed that it would be possible to make an orthosis with the PU composite with sugarcane bagasse and that for that it would be necessary to optimize the design of the orthosis. The use of PU composite with sugarcane bagasse in the medical field is promising, as it is a non-toxic material, from a renewable source and that uses agro-industrial waste with low added value, also presenting the advantage of providing better comfort to the patient