Search For Solutions Research Articles

Non-destructive testing by eddy current sensor for crack orientation detection using HHO optimizer by differential probe

In this research, the design of non-destructive eddy current evaluation systems was improved using Harris Hawks optimization (HHO). To determine the orientation of cracks from eddy current data, a numerical approach was developed based finit elements methode FEM and on an iterative inversion technique that minimizes a non-linear least squares objective function that evaluates the overall difference between forecasts and observationsin axisymmetric model. In this paper the HHO optimization and steepest descent methods were compared and studied in order to minimize the objective function. To measure the precision of the estimated crack orientation, the lower bounds of the HHO optimization on crack orientation were obtained. This work explores the concept of an HHO algorithm that searches for the optimal solution within a large solution space for a specific crack orientation problem.The algorithm starts its search at any point in the solution space and looks for better solutions by searching in the "local" area surrounding the start point. The algorithm ends when it finds what it believes to be the best solution or when predetermined stopping criteria are met.

Cytotoxicity and Biological Activities of Geopopolis Extract from the Stingless bee (Melipona scutellaris) in Isolates of Staphylococcus aureus.

Geopropolis resins are produced by stingless bees (Meliponinae), developed from the collection of resinous materials, waxes and exudates, from the flora of the region where stingless bees are present, in addition to the addition of clay or earth in its composition. Several biological activities are attributed to Ethanol Extracts of Geopropolis (EEGP). The bioactive properties are associated with the complex chemical composition that the samples have. This work aims to evaluate the biological activities of the EEGP, in order to contribute with a natural therapeutic alternative, to face infections, mainly those caused by resistant strains of Staphylococcus aureus. The EEGP MIC tests showed antibacterial activity against two strains of S. aureus, both at concentrations of 550 μg/mL. The MBC performed with the inhibition values showed that the EEGP has bacteriostatic activity in both strains. Biofilm inhibition rates exhibited an average value greater than 65 % at the highest concentration. The EEGP antioxidant potential test showed good antioxidant activity (IC50) of 11.05±1.55 μg/mL. In the cytotoxicity test against HaCat cells, after 24 hours, EEGP induced cell viability at the three tested concentrations (550 μg/mL: 81.68±3.79 %; 1100 μg/mL: 67.10±3.76 %; 2200 μg/mL: 67.40±1.86 %). In view of the above, the safe use of EEGP from the brazilian northeast could be proven by the cytotoxicity test, and its use as an antioxidant and antibacterial agent has proven to be effective, as an alternative in combating oxidative stress and microorganisms such as S. aureus, which, through the spread and ongoing evolution of drug resistance, generates an active search for effective solutions.

Optimal selection of manufacturing enterprise financialization based on simulated annealing investment portfolio theory

The global market competition is becoming increasingly fierce, and manufacturing enterprises need to invest and expand. However, the traditional financial optimization of manufacturing enterprises has faced problems such as low efficiency and inaccurate search for the optimal solution, which has made manufacturing enterprises likely to face financial risks. The investment portfolio can enable enterprises to obtain the maximum profit on a certain risk level, or reduce their investment risk on a certain return level as far as possible. If the combinatorial optimization is realized, it can be applied to the optimal selection of manufacturing enterprises’ financialization. This article analyzed the respective characteristics of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithms, and analyzed the combination of GA and SA algorithms to solve the optimal investment portfolio through GA-SA algorithm, thereby helping manufacturing enterprises to make the optimal choice for financialization. The experimental results of this article indicated that the GA-SA algorithm solved the problem of GA algorithm easily falling into local optima, SA algorithm’s initial temperature and generation mechanism, and improved the efficiency of finding the optimal solution. Meanwhile, the experimental results showed that the average optimal solutions of Genetic Algorithm, Simulated Annealing algorithm, and GA-SA algorithms for 36 stock portfolios in Enterprise 1 were 69, 69, and 107, respectively. The average optimal solutions of the three algorithms for 36 stock portfolios in Enterprise 2 were 73, 90, and 112, respectively. This proves that the number of optimal solutions searched by GA-SA algorithm is higher than that of GA and SA algorithm, and also proves that it is effective to use GA-SA algorithm to optimize investment portfolio and help manufacturing enterprises to make optimal financial choices.

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance.

The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.

Performance of Aubergine Rootstocks against Verticillium dahliae Isolates in Southeastern Spain

Aubergine (Solanum melongena L.) (Solanaceae) is a widespread crop in the Mediterranean basin. Verticillium dahliae is one of the main soil-borne pathogens affecting the aubergine crop. Its control has traditionally been achieved by soil fumigation with chemical disinfectants. Restrictions on the use of chemical fumigants have led to the search for solutions in genetic resistance using rootstocks. In southeastern Spain, aubergines are grafted for the control of V. dahliae. Two Solanum torvum rootstocks (Hugo F1 and Torpedo) and a Solanum melongena hybrid (Javah F1) were tested against five isolates of V. dahliae obtained from grafted (A1 and A2) and ungrafted (Vd8, Vd17 and Vd66) aubergines compared with the susceptible cultivar Larne F1 under controlled conditions. Isolates from grafted plants infected all three rootstocks, with differences observed in the percentage of plants with symptoms and in the disease symptom severity. Three strains isolated from the ungrafted aubergines (Vd8, Vd17 and Vd66) infected Javah F1 rootstock. The Hugo F1 and Torpedo rootstocks showed a high level of resistance to V. dahliae, while Javah F1 was susceptible to the pathogen. The Hugo F1 and Torpedo rootstocks are suitable for mitigating the effects of Verticillium wilt in Mediterranean aubergine crops. Understanding the nature of the resistance from S. torvum could enhance the benefits of grafting or facilitate the introduction of resistance into commercial cultivars.

Mathematical Modeling of COVID-19 with Chronic Patients and Sensitivity Analysis

Human health is constantly threatened by the appearance and resurgence of several diseases, as shown by recent epidemics. COVID-19 was one of the epidemics that left its mark on the world in terms of economic and human damages. In the search for solution to this pandemic, the scientific community is involved in all its diversity. Mathematicians are taking part in the fight through mathematical modeling in various approaches. Ordinary derivative compartmental modeling approache is one of the techniques widely used in epidemiological modeling. This paper presents a mathematical contribution to fight against COVID-19 using a compartmental SQEICRS model. This model takes into account five stages. In particular, the role of chronic diseases on the dynamique of COVID-19, is focused. A mathematical analysis of the model has been carried out, and shows that the model is well-posed in the biological and mathematical sense. Aspects such as existence, equilibrium points and their stability, the basic reproduction number R0and sensitivity anlysis have been discussed. Sensitivity analysis allowed us to identify the parameters which contribute to the spread of the disease, including the chronicity rate due to chronic diseases. The direction of disease propagation was also determined according to <I>R</I><sub>0</sub>. Finally, the numerical results with Matlab are in conformity with theoretical results.

The Untapped Potential of Comparative Biology in Aging Research: Insights From the Extraordinary-Long-Lived Naked Mole-Rat.

The search for solutions to the vagaries of aging has, historically, been akin to searching at night in the bright light under street lamps by utilizing the few preexisting and well-established animal model systems. Throughout my career as a comparative biologist, I have ventured into the darkness across 4 continents and studied over 150 different animal species, many of which have evolved remarkable adaptations to survive on the harsh and rugged fitness landscape that exists outside of the laboratory setting. In this Fellows Forum, I will discuss the main focus of my research for the last 25 years and dig deeply into the biology of the preternaturally long-lived naked mole-rat that makes it an ideal model system for the characterization of successful strategies to combat aging.

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

Theoretical substantiation of system for organising preventive treatment of asphalt concrete road pavements

Introduction. Over the past 20 years, the fleet of vehicles in the Republic of Belarus has increased significantly, and, consequently, the intensity of traffic on roads with a proportional increase in wear and tear of their surfaces. The need to protect asphalt concrete pavements from premature destruction dictates the search for new technological solutions. To effectively protect asphalt concrete pavements from the complex effects of water and traffic loads in the autumn-winter and spring-winter periods, the author has developed and implemented one of the options for protecting asphalt concrete pavements of a highway – treating them with a hydrophobic preventive Protekt- 01composition.Materials and methods. The research used theoretical and practical results from the implementation of the developed hydrophobic prophylactic composition.Results. The article presents an overview of the state of the problem of preventing destruction of asphalt concrete pavements abroad and in the Republic of Belarus, an analysis of the measures taken to implement the developed hydrophobic preventive Protekt-01composition at transport infrastructure facilities, and also, based on the data obtained from production testing a ‘System for organizing preventive treatment’ has been proposed, which will be aimed at preventing the beginning and future destruction of the asphalt concrete pavement of highways and will be based on monitoring the technical condition of the control object, planning, organizing, implementing and managing preventive and repair measures using navigation equipment and software that makes it possible to optimize the balance of costs and road surface defects.Originality. Thus, based on the theoretical substantiation and the results of practical implementation of the developed composition, the following are proposed:- method for ensuring the operation of a control system for liquid road-building materials which enables to eliminate overconsumption of the distributed substance;- ‘System for organizing preventive treatment’, which enables to increase the service life of asphalt concrete pavement by 1.2–1.5 times.

Periodic solutions in reversible symmetric second order systems with multiple distributed delays

In this paper, we study a class of nonlinear differential equations with multiple distributed delays. We reduced the problem of search for periodic solutions of the distributed delay differential equations to that of finding zeros of an operator on some Banach space. Utilizing the equivariant degree theory, we obtain some sufficient conditions to guarantee the existence of multiplicity of periodic solutions. Also, we give some information about the symmetry of the periodic solutions. At last, we give an example to illustrate our result.

Methods of Traffic Distribution in a Heterogeneous High-Density Internet of Things Network

The paper presents a model and method for distributing traffic in a heterogeneous Internet of Things (IoT) network built on the basis of complex communication channels consisting of several subchannels using various signal transmission technologies, for example, radio, optical and acoustic. The developed methods for distributing traffic across subchannels of a heterogeneous IoT network make it possible to solve the problem of increasing network efficiency through the use of heterogeneous resources. The result of the study is methods of traffic distribution in the form of optimization problems for various solution search strategies. The results obtained can be used to build heterogeneous IoT networks and networks of robotic systems.

HOG-CNN based evaluation of cement integrity using 2D dispersion curves from an experimental through tubing logging setup

In oil wells, adequate cementation is essential to guarantee that the well has support for the casing and is able to isolate the well from groundwater. Operations known as Plugging & Abandonment (P&A) must carry out inspections in the cement layer of the well to ensure that the bond quality of the cement with the formation and the hydraulic isolation is adequate before well sealing. The interpretation of data logs during the decommissioning processes is made exclusively for a petrophysicists team, which makes the process susceptible to human errors. Furthermore, it requires a lot of analysis time, increasing the search for alternative solutions that help reduce errors in the interpretation of data logs. So far, the use of machine learning has proven to be a strong candidate in this search. This paper deals with frequency domain images (dispersion curves and Cepstrum) that are acquired from acoustic pressure signals obtained in the time domain and along the vertical axis of an experimental setup. These images are used to train machine learning models to test and compare the accuracies obtained. An adjustment of the hyperparameters was carried out to find those that best describe the simulated experimental characteristics. Furthermore, the convolution neural network (CNN) model was combined with the oriented gradient histogram technique (HOG) to verify its efficiency by analyzing experimental data. Two different databases were used in this work, one with 130 samples relating to the average results of the four hydrophones used in the experiment and one with 520 samples relating to all results. The results found demonstrated that when using the dispersion curves as input with the CNN the accuracy hits 100%. Other combinations of models were also tested hitting similar results. This article contributes to the current research scenario as it presents an innovative combination of machine learning techniques applied to the oil and gas field (HOG-CNN) with the proposal to compare different types of databases, such as using Cepstrum instead of dispersion curves and image processing.

Методика управленческого учета затрат и калькулирования себестоимости неамортизируемой организационно-технологической оснастки производства продукции промышленного предприятия

The article is devoted to the search for scientific and practical solutions aimed at improving the quality of the information and instrumental space of management cost accounting and calculating the cost of not amortised organizational and technological equipment of industrial enterprises. In the course of the conducted research on a number of economic entities of the Nizhny Novgorod region, methods of observation, collection, systematization, classification and reflection in information registers of information relevant to the studied subject area were used; complex managerial, practice-oriented and statistical types of analysis, scientific interpretation of accumulated data using calculations and graphical formalization; the existing theoretical and methodological approaches used in the subject area under study are considered; a methodology for managerial cost accounting and calculating the cost of OTP using analytical accounting registers formed directly in the context of this type of product is proposed. The integrated use of the proposed methodology will improve the quality of awareness of decision makers on production cost management; create institutional conditions for its application in the field of manufacturing products for their own technological needs.

STRATEGIC MANAGEMENT AND SUSTAINABLE ECONOMIC DEVELOPMENT

The article is devoted to the search for conceptual solutions to the problem of ensuring sustainable economic growth in the new unstable economic conditions. This topic remains extremely relevant in the modern world, where various challenges are faced, such as climate change, resource depletion, population growth, increased social inequality and other problems. The authors explore the evolution of the concept of sustainable development, highlighting various essential aspects of understanding the category of sustainability. In different time periods, climate change and environmental challenges, social justice and efficiency, global cooperation and risk management, digitalization and technological innovation have consistently been at the center of the problem of sustainable development. As a result, strategies to reduce the negative impact on the environment and adapt to climate change have been replaced by strategies of green technologies, which, in turn, have been replaced by strategies of international cooperation and coordination of actions by all members of society to solve global problems of our time. The authors prove the importance of considering not only environmental, but also social aspects when developing sustainable development strategies to ensure social justice and improve the quality of life of all segments of society. In conclusion, the authors conclude that risk management related to economic instability, environmental changes and other factors requires new strategies for sustainable development, which in turn is impossible without effective public management, public involvement, and informed decision-making.

The concept of an intelligent mobile application with elements of augmented reality and PLM software for value stream mapping

AbstractGrowing demands for variability, reducing losses and increasing production efficiency bring difficult tasks for business management. In order to cope with increasingly demanding tasks, a good knowledge of effective management methods is required, as well as orientation in a wide range of application programs. One of the approaches dealing with reducing losses and streamlining selected processes is the introduction of the Lean philosophy. This philosophy is firmly embedded in the concept of Industry 4.0 and its principles are proving to be highly effective. In the presented contribution, we will focus on the possibilities of reducing losses by implementing selected Lean principles in conjunction with selected program resources and elements of mixed reality. The goal is to create an intelligent mobile application that, based on a simulation model, would enable an in-depth analysis of selected processes from the aspect of material value stream mapping analysis. The model created in this way could serve as a relevant basis for top management in the search for solutions to optimize processes. In the article, we evaluate selected Lean methods, create a workplace simulation model using the PLM simulation tool Tecnomatix Plant Simulation, and the obtained data are presented in an intelligent mobile application based on augmented reality. After the online update, the created application conveys all required relevant information to managers and simplifies their decision-making process.

Recent advances in employing the Laplace homotopy analysis method to nonlinear fractional models for evolution equations and heat-typed problems

This study stems from the growing need for effective mathematical tools to tackle nonlinear fractional evolution equations, which find wide applications in physics and engineering. Traditional methods often struggle to provide exact closed-form solutions for such equations, leading to the development of novel approaches. In this context, the combination of the homotopy analysis method with the Laplace transform presents a promising avenue for obtaining exact solutions to these complex equations. By exploring this hybrid approach, the study aims to address the existing challenges in solving nonlinear fractional models and provide a reliable method for obtaining closed-form solutions. In this way, this study focuses on rendering the coupling between the homotopy analysis approach and the Laplace transform in the search for exact closed-form solutions for the class of fractional evolution and heat-typed equations. In fact, this class of equations features some interesting nonlinear Caputo fractional partial differential equations that model dissimilar physical processes in physics and engineering. A generalized recurrent scheme is derived, which is applicable to all the members of the governing class, and further analyzed the application of this very scheme on some test fractional initial-value problems of much concern. Eventually, the method has been found to be advantageous, with various advantages, including convergence to available exact solutions. Thus, of the advantage of the current study is innovative combination of mathematical methods, the applicability of these methods to a special class of fractional equations, the development of a generalized scheme, the successful solution of prototype problems, and the practical advantages of the method.

The physiological toll of arrests: An examination of arrest history on midlife allostatic load

PurposeTo understand how allostatic load – cumulative physiologic burden of stress – varies by amount and timing of arrests stratified by race/ethnicity and by sex. MethodsUsing The National Longitudinal Study of Adolescent to Adult Health, we calculated descriptive statistics and mean differences in bio-marker measured allostatic load by arrest history stratified by race/ethnicity and sex. ResultsOne-third of participants experienced at least one arrest, and most experienced arrests only as adults. Allostatic load scores were higher for those that had ever experienced an arrest compared to never (mean difference: 0.58 (0.33, 0.84)). Similar results held for men and women and across race/ethnicity, but Black non-Hispanic individuals had higher allostatic load at all levels compared to other individuals. ConclusionsExperiencing both any arrest and multiple arrests were associated with higher allostatic load. The stress of arrests may contribute to physiological maladaptations and poor health. The public health and law enforcement fields must recognize the detrimental consequences of arrests on physiological stress and search for non-carceral solutions.

Identifikasi Kerusakan Pada Landing Gear Pesawat Cessna C208B

One type of aircraft commonly used for flight training, patrol, and transportation in remote areas is the Cessna. It should be understood that each tool and component in the aircraft has its own level of importance and can experience failures in carrying out its functions. These failures often become a problem for users due to a limited understanding of the aircraft engine field. Issues arising from the aircraft's landing gear sometimes involve minor problems that do Does not require a high level of expertise. To solve this, it may be possible for someone with knowledge of landing gear to address the issue. By using the Case-Based Reasoning (CBR) method for fault identification, a technician's expertise in aircraft landing gear can be applied and integrated. The search for solutions or fault identification Can be obtained promptly In the testing results of the Fault System for Cessna C208B Aircraft Landing Gear using the CBR method, the system's calculations were consistent with the manual. However, the application still has some shortcomings in the system, such as the preprocessing stage, which has not been able to search for the root words in a question sentence.

A new adaptive Kriging-based optimization (AKBO) framework for constrained optimization problems: A case study on shared autonomous electric vehicle system design

Adaptive Kriging-based optimization (AKBO) efficiently performs optimization by employing adaptive sampling methods that sequentially update Kriging model. To solve constrained optimization problems, existing adaptive sampling methods add samples based on probability of feasibility (PoF) representing the probability that all constraints are satisfied. However, these methods have the limitation that samples are added inefficiently when the PoF is inaccurate. Therefore, this study proposes a new AKBO framework that is robust to instability of PoF in adaptive sampling. To this end, a near constraint boundary region that is expected to be close to the constraint boundary is defined based on a probability measure. Then, a near constraint boundary search (NCBS) algorithm, which explores the sample point that minimizes the objective function within the near constraint boundary region, is developed for global search, whereas a near optimum search (NOS) algorithm, which searches for better solutions that may exist near the current optimum candidate, is developed for local search. To alleviate the distortions of the Kriging model that may occur due to the highly nonlinear response, a truncated constraint function (TCF) method that limits the extremely violated constraint values used in the design of experiment (DoE) of the Kriging model is also proposed. This study verifies the performance of the proposed AKBO framework through the shared autonomous electric vehicle (SAEV) system design optimization problem. Comparison of optimum designs derived from various optimization methods shows that the proposed AKBO framework derives the feasible global optimum with high accuracy and efficiency.

Development of a solution search method using artificial intelligence

The object of the study is decision support systems. The problem of increasing decision-making efficiency in conditions of uncertainty and a set of different parameters was solved using a bio-inspired algorithm. The subject of the study is the decision-making process in management problems using the heron flock algorithm, the improved genetic algorithm and evolving artificial neural networks. A solution search method using the improved heron flock algorithm is proposed. The study is based on the heron flock algorithm to find a solution regarding the object state. Evolving artificial neural networks are used to train the heron flock algorithm, and an advanced genetic algorithm is used to select the best individuals of the heron flock. The method has the following sequence of actions: – input of initial data; – setting agents on the search plane; – numbering heron agents in the flock; – setting the initial velocity of heron agents; – waiting strategy for heron agents; – aggressive strategy; – checking the discriminatory condition; – selection of the best individuals from the heron flock; – ranking and sorting the obtained solutions; – training heron knowledge bases; – determining the amount of necessary computing resources of the intelligent decision support system. The originality of the proposed method consists in setting heron agents taking into account the uncertainty of the initial data, the noise degree of data about the analysis object state. The method makes it possible to reduce the time for decision-making at the level of 22–26 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems