Complex Real-world Problems Research Articles

Utilising H5P Simulations to Enhance Social Work Education

Higher education is tasked with the challenge of producing graduate-ready professionals. Thus, alternative learning and assessment activities are needed to provide students with real-life complex experiences, particularly in fields like social work. This quasi-experimental design study explored the effectiveness of H5P virtual simulation to teach assessment and direct practice skills to social work students (n = 80). Mixed-methods data based on the pre/post outcomes of skill development were analysed using descriptive and bi-variate analysis as well as thematic analysis for qualitative data. A Wilcoxon signed-rank test reveals significant outcomes in assessment and practice skills between pre- and post-measures. The qualitative findings include three key themes: (1) realism, (2) engagement, and (3) practice skills. This study provides evidence and highlights the importance of using virtual simulation to help students develop skills to manage complex real-world problems.

A Review of the Homogenized Lattice Boltzmann Method for Particulate Flow Simulations: From Fundamentals to Applications

The homogenized lattice Boltzmann method (HLBM) has emerged as a flexible computational framework for studying particulate flows, providing a monolithic approach to modeling pure fluid flows and flows through porous media, including moving solid and porous particles, within a unified framework. This paper presents a thorough review of HLBM, elucidating its underlying principles and highlighting its diverse applications to particle-laden flows in various fields as reported in literature. These include studies leading to new fundamental knowledge on the settling of single arbitrarily shaped particles as well as application-oriented research on wall-flow filters, hindered settling, and evaluation of the damage potential during particle transport. Among the strengths of HLBM are its monolithic approach, which allows seamless simulation of different fluid-solid interactions, and its ability to handle arbitrary particle shapes, including irregular and concave geometries, while resolving surface interactions to capture local forces. In addition, its parallel scheme based on the lattice Boltzmann method (LBM) results in high computational efficiency, making it suitable for large-scale simulations, even though LBM requires small time steps. Important future development needs are identified, including the addition of a lubrication force correction model, performance enhancements, such as support for hybrid parallelization and GPU, and the extension of compatible contact models to accommodate concave shapes. These advances promise expanded capabilities for HLBM and broader applicability for solving complex real-world problems.

Stability and computational analysis of Influenza-A epidemic model through double time delay

Delay factors demonstration has a significant role in controlling a strain of infectious disease instead of a pharmaceutical strategy. According to the World Health Organization (WHO), 3–5 million cases are reported annually and approximately 290,000 to 650,000 respiratory deaths annually. So, in the present study, we develop a delayed mathematical model based on delay differential equations (DDEs) for the influenza epidemic using a deterministic approach by introducing double delay parameters. The four distinct sub-populations are considered susceptible, exposed, infected, and recovered. For the rigorous analysis, the fundamental properties of the model like positivity, boundedness, existence, and uniqueness, were studied. The influenza-free equilibrium (IFE) and influenza-existing equilibrium (IEE), are the two nonnegative equilibrium points that the model demonstrates. Both locally and globally, the asymptotic stability of the equilibrium points of the model is established and shown under specific situations of reproduction number. Additionally, investigated the model's parameter sensitivity and determined the relative sensitivity of each parameter. Both standard and nonstandard methods—such as Euler, Runge-Kutta, and nonstandard finite difference with a delayed sense—are presented to make computational analysis support a dynamical analysis and the best visualization of results. The stability of the non-standard finite difference scheme is thoroughly analyzed around the steady states of the model. Additionally, the results show that the nonstandard finite difference approximation is an efficient, cost-effective method, independent of time step size, to solve such highly nonlinear and complex real-world problems.

Integrating Learning and Serious Game Design: The Development and Application of the Learning and Game Integration (LGI) Framework using the ‘Blackout’ Serious Game

Serious games are increasingly recognised as effective educational tools capable of simulating complex scenarios that traditional methods cannot replicate. This paper introduces the Learning and Game Integration (LGI) Framework, designed to seamlessly integrate educational content with interactive game design. Utilising the “Blackout” serious game, which simulates real world complex problem, this study demonstrates how the LGI Framework can be utilised as learning intervention to enhance learning by teaching Design Thinking to university students. The framework consists of four stages: Educational Content Integration, Theoretical Underpinning, Case Study Selection, and Game Design. The “Blackout” game enables students to engage with various stakeholder perspectives, deeply analyse problems, and develop innovative solutions under crisis conditions. Preliminary feedback from its implementation in an undergraduate business course (N=753) indicates increased student engagement and confidence in applying Design Thinking concepts. This paper contributes to the field of business education by presenting an innovative approach to teaching innovative problem-solving skills through serious games, emphasising the need for holistic educational frameworks that bridge theoretical learning with real-world application.

Analysis of efficient discretization technique for nonlinear integral equations of Hammerstein type

Purpose This study focuses on investigating the numerical solution of second-kind nonlinear Volterra–Fredholm–Hammerstein integral equations (NVFHIEs) by discretization technique. The purpose of this paper is to develop an efficient and accurate method for solving NVFHIEs, which are crucial for modeling systems with memory and cumulative effects, integrating past and present influences with nonlinear interactions. They are widely applied in control theory, population dynamics and physics. These equations are essential for solving complex real-world problems. Design/methodology/approach Demonstrating the solution’s existence and uniqueness in the equation is accomplished by using the Picard iterative method as a key technique. Using the trapezoidal discretization method is the chosen approach for numerically approximating the solution, yielding a nonlinear system of algebraic equations. The trapezoidal method (TM) exhibits quadratic convergence to the solution, supported by the application of a discrete Grönwall inequality. A novel Grönwall inequality is introduced to demonstrate the convergence of the considered method. This approach enables a detailed analysis of the equation’s behavior and facilitates the development of a robust solution method. Findings The numerical results conclusively show that the proposed method is highly efficacious in solving NVFHIEs, significantly reducing computational effort. Numerical examples and comparisons underscore the method’s practicality, effectiveness and reliability, confirming its outstanding performance compared to the referenced method. Originality/value Unlike existing approaches that rely on a combination of methods to tackle different aspects of the complex problems, especially nonlinear integral equations, the current approach presents a significant single-method solution, providing a comprehensive approach to solving the entire problem. Furthermore, the present work introduces the first numerical approaches for the considered integral equation, which has not been previously explored in the existing literature. To the best of the authors’ knowledge, the work is the first to address this equation, providing a foundational contribution for future research and applications. This innovative strategy not only simplifies the computational process but also offers a more comprehensive understanding of the problem’s dynamics.

Interval pairwise comparisons in the presence of infeasibilities: Numerical experiments

Pairwise comparisons constitute a fundamental part of many multiple-criteria decision-making methods designed to solve complex real-world problems. One of the pervasive features associated with the complexity of any problem is uncertainty. Experts are rarely able to consistently and accurately evaluate a set of alternatives under consideration due to time pressure, cognitive bias, the intricacy or intangible essence of the problem, a lack of requisite knowledge or experience, or other reasons. Interval pairwise comparisons (IPCs) allow for this uncertainty in a natural way; however, the problem of inconsistency (or infeasibility) may arise. That is, a set of interval comparisons may not allow experts to find a solution in the form of a priority vector. The aim of this paper is to provide a comparison of existing priority deriving methods for inconsistent (infeasible) IPCs via numerical examples and simulations. Our results indicate that the Interval Stretching Method is the best with respect to preserving original preferences. In addition, the question of the uniqueness of the solution is investigated for selected methods, with the Fuzzy Preference Programming method being the best in providing a unique solution. Since the majority of examined methods provide mostly non-unique solutions, modifying these methods in order to provide unique solutions might be a research direction worth considering in the future.

A novel hybrid genetic algorithm and Nelder-Mead approach and it’s application for parameter estimation

Background Traditional optimization methods often struggle to balance global exploration and local refinement, particularly in complex real-world problems. To address this challenge, we introduce a novel hybrid optimization strategy that integrates the Nelder-Mead (NM) technique and the Genetic Algorithm (GA), named the Genetic and Nelder-Mead Algorithm (GANMA). This hybrid approach aims to enhance performance across various benchmark functions and parameter estimation tasks. Methods GANMA combines the global search capabilities of GA with the local refinement strength of NM. It is first tested on 15 benchmark functions commonly used to evaluate optimization strategies. The effectiveness of GANMA is also demonstrated through its application to parameter estimation problems, showcasing its practical utility in real-world scenarios. Results GANMA outperforms traditional optimization methods in terms of robustness, convergence speed, and solution quality. The hybrid algorithm excels across different function landscapes, including those with high dimensionality and multimodality, which are often encountered in real-world optimization issues. Additionally, GANMA improves model accuracy and interpretability in parameter estimation tasks, enhancing both model fitting and prediction. Conclusions GANMA proves to be a flexible and powerful optimization method suitable for both benchmark optimization and real-world parameter estimation challenges. Its capability to efficiently explore parameter spaces and refine solutions makes it a promising tool for scientific, engineering, and economic applications. GANMA offers a valuable solution for improving model performance and effectively handling complex optimization problems.

An enhanced Moth-Flame optimizer with quality enhancement and directional crossover: optimizing classic engineering problems

As a popular meta-heuristic algorithm, the Moth-Flame Optimization (MFO) algorithm has garnered significant interest owing to its high flexibility and straightforward implementation. However, when addressing engineering constraint problems with specific parameters, MFO also exhibits limitations such as fast convergence and a tendency to converge to local optima. In order to address these challenges, this paper introduces an enhanced version of the MFO, EQDXMFO. EQDXMFO integrates a Quality Enhancement (EQ) strategy and a Directional Crossover (DX) mechanism, fortifying the algorithm’s search dynamics. Specifically, the DX mechanism is designed to augment the population’s diversity, enhancing the algorithm’s exploratory potential. Concurrently, the EQ strategy is employed to elevate the solution quality, which in turn refines the convergence precision of the algorithm. To verify the effectiveness of EQDXMFO, experiments are carried out on the test set of the IEEE CEC2017. A total of 5 classical algorithms, five excellent MFO variants, and seven state-of-the-art algorithms are selected for comparison, which confirm the significant advantages of EQDXMFO. Next, EQDXMFO is applied to five complex engineering constraint problems, demonstrating that EQDXMFO can optimize realistic problems. The comprehensive analysis shows that EQDXMFO has strong optimization capabilities and provides methods for research on other complex real-world problems.

Enhancing Complex Problem-Solving Skills through STEM-Based Spatial Geometry E-Modules

Learning spatial geometry is still abstract thereby it is boring and less meaningful. As a result, learning outcomes in terms of skills and cognitive aspects become less optimal. Currently, students are no longer rely on textbooks as a learning resource. E-module is digitally designed learning resources that support independent learning. The e-module would be integrated with STEM approach. STEM learning activities are very important and need to be familiarized in learning, because the activities help prospective teachers in building the concepts learned, as well as to practice solving complex real-world problems. The method was research and development using the ADDIE model. The research subjects were 78 students who were prospective mathematics teachers of Mathematics Education Study Program, Universitas Tidar. Data were collected by using observation sheets, interview guides, questionnaires, and complex problem-solving tests. There are three main results from the study; first, STEM-based spatial geometry E-module is declared to be strongly valid according to expert validators. Second, based on student responses, this e-module is practical and the third, based on the results of complex problem-solving skills test, this STEM-based spatial geometry e-module is effective.

ASPECT: Answer Set rePresentation as vEctor graphiCs in laTex

Abstract Logic programming is a declarative programming paradigm that finds extensive use in the field of Artificial Intelligence (AI). As a result, it has become a valuable tool used in university courses for teaching students AI techniques. Besides Prolog language, the more recent Answer Set Programming (ASP) language turns out to be a powerful tool for developing advanced applications due to the expressiveness of the language and the availability of efficient solving systems. Unfortunately, the output of ASP solvers can be difficult to interpret, since it is a set of atoms, often long and verbose. This is most true in the case of students learning the language or in the case of experts developing applications for complex real-world problems. For these reasons, the ability to produce, when possible, a graphical representation of the solver output becomes useful to ensure easier interpretation of the results. In this paper we present ASPECT, a sub-language of ASP in which the user can directly define, in an intuitive and declarative way, a graphical representation of the answer set. The ASPECT atoms can be converted into the popular LaTeX markup language to produce vector graphics. The documents produced by ASPECT are easy to embed in documents such as scientific articles, course handouts and presentations. Also, the development of user-friendly interfaces is critical for wider use of similar technologies in the industrial sector as well. Moreover, ASPECT is also extended to deal with temporal information, and provide graphical animations of answer sets that enclose the temporal dimension, such as in planning problems. Finally, we advocate the use of ASPECT to create complex and animated presentations starting from a declarative specification.

Improving derivative-free optimization algorithms through an adaptive sampling procedure

Black-box optimization plays a pivotal role in addressing complex real-world problems where the underlying mathematical model is unknown or expensive to evaluate. In this context, this work presents a method to enhance the performance of derivative-free optimization algorithms by integrating an adaptive sampling process. The proposed methodology aims to overcome the limitations of traditional methods by intelligently guiding the search towards promising regions of the search space. To achieve this, we utilize machine learning models, which effectively substitute first principles models. Furthermore, we employ the error maximization approach to steer the exploration towards areas where the surrogate model deviates significantly from the true model. Moreover, we involve a heuristic method, an adaptive sampling procedure, that repeats calls to a widely-used derivative-free optimization algorithm, SNOBFIT, allowing for the creation of new and improved surrogate models. To evaluate the efficiency of the proposed method, we conduct a comparative analysis across a benchmark set of 776 continuous problems. Our findings indicate that our approach successfully solved 93% of the problems. Notably, for larger problems, our method outperformed the standard SNOBFIT algorithm by achieving a 19% increase in problem-solving rate, and when, we introduced an additional termination criterion to enhance computational efficiency, the proposed method achieved a 31% time reduction compared to SNOBFIT.

Self-tuning multi-layer optimization algorithm (STML): An innovative parameter-less approach

Computational intelligence (CI)-based methods offer a practical approach to overcoming the significant challenges posed by analytical and enumeration optimization methods when dealing with complex real-world problems. However, a notable drawback of these algorithms is the need for time-consuming and computationally demanding fine-tuning procedures to achieve optimal performance. This paper proposes a novel parameterless auto-tuning meta-heuristic architecture called the self-tuning multi-layer (STML). The fundamental concept behind this architecture involves a multi-layer structure where the inner layer optimizes the main problem. In contrast, the outer layer utilizes information obtained during the search to fine-tune the performance of the inner layer. This feature eliminates manual fine-tuning, as it can autonomously handle this task. A series of mathematical and benchmark problems were employed to demonstrate the computational prowess of the STML. The results indicate its superiority over other meta-heuristic algorithms. Additionally, the STML showcases robustness, as evidenced by the numerical proximity of results obtained from different independent runs on these benchmark problems.

A Deep Learning Approach for Intrusion Detection Systems –A Review

Throughout the past decade, the researchers have developed many a number of intrusion detection systems. Some of these were developed to work on host based and some were network-based intrusion detection systems. The presented systems are combination of HIDS/NIDS and signature/anomaly based, or hybrid systems. The intrusion detection system is an intelligence system known as computational intelligence. The main goal of computational intelligence is to provide solutions to complex real-world problems. An effective IDS must use more than standard mathematical techniques and conventional analysis methods combined with soft computing techniques to synergistically create a more robust IDS. In this paper, we review three important areas of research that have significant implication for proposed framework. First, we review existing shallow learning intrusion detection systems both in machine and deep learning. In the second section, we review existing hybrid intrusion detection systems. Finally, we provide some findings and analysis of the literature studies.

Study of [formula omitted] dimensional fractional order non-linear Benney equation using an analytical technique

The area related to fractional order partial differential equations (FOPDEs) has also attracted attention. Therefore, we focuss our attention to study nonlinear Benney equations of using power law type Caputo derivative of fractional order. To compute a series-type solution for the problem under consideration, we combine the Adomian approach with the Laplace transform. Our findings are supported by graphical representations and numerical interpretations, illustrating the applicability and insights gained from this approach in solving complex real-world problems.

Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems

Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems

Universal machine learning approach to volcanic eruption forecasting using seismic features

Introduction: Volcano seismology has successfully predicted several eruptions and includes many reliable methods that have been adopted extensively by volcanic observatories; however, there are several problems that still lack solutions. Meanwhile, the overwhelming success of data-driven models to solve predictive complex real-world problems positions them as an effective addition to the monitoring systems deployed in volcanological observatories.Methods: By applying signal processing techniques on seismic records, we extracted four different seismic features, which usually change their trend when the system is approaching an eruptive episode. We built a temporal matrix with these parameters then defined a label for each temporal moment according to the real state of the volcanic activity (Unrest, Pre-Eruptive, Eruptive). To solve the remaining problem developing early warning systems that are transferable between volcanoes, we applied our methodology to databases associated with different volcanic systems, including data from both explosive and effusive episodes, recorded at several volcanic scenarios with open and closed conduits: Mt. Etna, Bezymianny, Volcán de Colima, Mount St. Helens and Augustine.Results and Discussion: This work proposes the use of Neural Networks to classify the volcanic state of alert based on the behaviour of these features, providing a probability of having an eruption. This approach offers a Machine Learning tool for probabilistic short-term volcanic eruption forecasting, transferable to different volcanic systems. This innovative method classifies the state of volcanic hazard in near real-time and estimates a probability of the occurrence of an eruption, resulting in a period from at least hours to several days to forecast an eruption.

Nonlinear optimization of optical camera multiparameter via triple integrated Gradient-based optimizer algorithm

Metaheuristic techniques are widely used to solve various complex real-world problems. Camera calibration is a critical task in machine vision, which aims to determine the optical camera parameters through 2D image features and corresponding 3D spatial features. To overcome the limitations of traditional methods, such as weak local optima avoidance and low calibration accuracy, a triple integrated Gradient-based optimizer (TIGBO_CC) is proposed for nonlinear optimization of optical camera parameters. Several improvements are made to the original Gradient-based optimizer (GBO). First, in the exploration phase, a fitness-distance balance is adopted to select the most contributing candidate to guide the update of the solution. Second, a random search technique is adopted in the exploitation phase. The switching between the original mode and the additional mode follows the probability. Finally, a neighborhood local search is also introduced. Small-step fine search is performed near the optimal solution to improve the quality of the final solution. TIGBO_CC is evaluated on 12 classical functions, the CEC2020 test suite, and the CEC2022 test suite. The experiments include sensitivity analysis, effectiveness analysis, scalability analysis, function value, convergence curve, box plot, Friedman test, Wilcoxon test, etc. Furthermore, a nonlinear camera calibration model is established. Taking the mean reprojection error as the objective function, TIGBO_CC solves the optimal optical camera parameters by minimizing this function. In different scenarios, the calibration method based on TIGBO_CC has high precision and strong reproducibility. Comprehensive analysis shows that TIGBO_CC has excellent optimization ability and practicality.

Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization

In this paper, we innovatively propose the Arctic Puffin Optimization (APO), a metaheuristic optimization algorithm inspired by the survival and predation behaviors of the Arctic puffin. The APO consists of an aerial flight (exploration) and an underwater foraging (exploitation) phase. In the exploration phase, the Levy flight and velocity factor mechanisms are introduced to enhance the algorithm's ability to jump out of local optima and improve the convergence speed. In the exploitation phase, strategies such as the synergy and adaptive change factors are used to ensure that the algorithm can effectively utilize the current best solution and guide the search direction. In addition, the dynamic transition between the exploration and development phases is realized through the behavioral conversion factor, which effectively balances global search and local development. In order to verify the advancement and applicability of the APO algorithm, it is compared with nine advanced optimization algorithms. In the three test sets of CEC2017, CEC2019, and CEC2022, the APO algorithm outperforms the other compared algorithms in 72%, 70%, and 75% of the cases, respectively. Meanwhile, the Wilcoxon signed-rank test results and Friedman rank-mean statistically prove the superiority of the APO algorithm. Furthermore, on thirteen real-world engineering problems, APO outperforms the other compared algorithms in 85% of the test cases, demonstrating its potential in solving complex real-world optimization problems. In summary, APO proves its practical value and advantages in solving various complex optimization problems by its excellent performance.

A novel community development algorithm and its application to optimize main steam temperature of supercritical units

This paper summarizes the community where people live development laws and proposes a novel meta-heuristic called community development algorithm (CDA). To enhance the optimization process, CDA innovatively designed two unique attraction effects based on the degree of people’s direct contribution to society and abstracted four novel inter-particle learning ways based on people’s four social activities: Marriage, Entrepreneurship, Working, and University. Theoretically, it is demonstrated that CDA can satisfactorily balance the exploration and the exploitation stages, and necessary and sufficient conditions for CDA convergence are derived. The 59 functions of Congress on Evolutionary Computation 2014 and 2017 are introduced as the benchmark. Under the pressure of other 32 well-known meta-heuristics, statistical results, including averages, standard deviations, rank-sum tests, and sign-rank tests, as well as graphical results, including convergence curves and box plots, confirm that CDA is competent for solving numerical optimization problems with efficiency and reliability. Significantly, the quantitative analysis demonstrated that CDA’s average ranking is ahead of the emperor penguin optimizer by 73.5% while ahead of the fully informed search algorithm by 62.1%, respectively. Additionally, the scalability and fastness of CDA are validated by scalability and wall clock time analysis, and the classical application of Congress on Evolutionary Computation 2011 demonstrates the outstanding accuracy and stability of CDA. Finally, CDA is utilized to optimize the controller parameter in the main steam temperature control system of the supercritical unit. The control response curves show that CDA generally provides better parameters than compared algorithms, demonstrating the merits and potential to solve complex real-world engineering problems.

Identifying multidisciplinary problems from scientific publications based on a text generation method

Abstract Purpose A text generation based multidisciplinary problem identification method is proposed, which does not rely on a large amount of data annotation. Design/methodology/approach The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique; second, it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model; third, it extracts problem phrases from generated titles according to regular expression rules; fourth, it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm; finally, it identifies multidisciplinary problems based on the disciplinary labels of papers. Findings Experiments in the “Carbon Peaking and Carbon Neutrality” field show that the proposed method can effectively identify multidisciplinary research problems. The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field. Research limitations It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness. Practical implications Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments, fund valuable multidisciplinary problems for research management authorities, and borrow ideas from other disciplines for researchers. Originality/value This approach proposes a novel multidisciplinary problem identification method based on text generation, which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.