Real-world Problems Research Articles

Implementing Problem-Based Learning in Marketing Education: A Systematic Review and Analysis

The marketing industry is rapidly evolving, prompting educational programs to adapt and provide graduates with relevant skills and knowledge. This systematic literature review and bibliometric analysis (SLRBA) explores the incorporation of problem-based learning (PBL) in marketing education. PBL is a teaching approach that encourages students to solve real-world problems by integrating theory and practice, thereby improving their critical thinking and decision-making skills. Our review synthesizes data from 71 sources, detailing the definition of PBL, strategies for its implementation in marketing education, and the associated benefits and challenges. The findings show that PBL enhances student engagement, provides hands-on experience, and equips students for real-world marketing challenges. Effective strategies for PBL adoption include leveraging social media, serious games, E-learning, and feedback systems to facilitate active learning and student participation. However, PBL poses challenges, such as high resource requirements, student resistance, and concerns over information accuracy. This review underscores the importance of PBL in closing the gap between theoretical concepts and practical skills, helping marketing students better meet industry expectations. The paper concludes with suggestions for future research and best practices for integrating PBL in marketing education.

Solving a real-world package delivery routing problem using quantum annealers.

Research focused on the conjunction between quantum computing and routing problems has been very prolific in recent years. Most of the works revolve around classical problems such as the Traveling Salesman Problem or the Vehicle Routing Problem. The real-world applicability of these problems is dependent on the objectives and constraints considered. Anyway, it is undeniable that it is often difficult to translate complex requirements into these classical formulations. The main objective of this research is to present a solving scheme for dealing with realistic instances while maintaining all the characteristics and restrictions of the original real-world problem. Thus, a quantum-classical strategy has been developed, coined Q4RPD, that considers a set of real constraints such as a heterogeneous fleet of vehicles, priority deliveries, and capacities characterized by two values: weight and dimensions of the packages. Q4RPD resorts to the Leap Constrained Quadratic Model Hybrid Solver of D-Wave. To demonstrate the application of Q4RPD, an experimentation composed of six different instances has been conducted, aiming to serve as illustrative examples.

Research on Teaching Methods Based on Mathematical Modeling and Their Applications

Mathematical modeling serves as a crucial bridge between mathematical theory and real-world problems, playing an increasingly important role in education. This paper aims to explore the current state, applications, and future prospects of mathematical modeling teaching methods. Firstly, the article provides an overview of the basic concepts and theoretical foundations of mathematical modeling, introducing the classifications and characteristics of traditional teaching methods and modern teaching methods, including problem-based modeling teaching methods, project-based teaching methods, and computer-assisted modeling teaching methods. Secondly, by analyzing the effectiveness, technical support, and innovations in the application of mathematical modeling teaching methods, the paper reveals the importance of teaching effectiveness, technology application, and innovation. Finally, the paper looks ahead to the challenges faced by mathematical modeling teaching, future development trends, and policy recommendations, offering references for further improving the quality of mathematical modeling education and student capabilities. The research presented in this paper is significant for promoting the innovation and optimization of mathematical modeling education.

Resource allocation for Hyphantria cunea invasive management:a novel simulation-based optimization model

In this study, we propose an integrated mixed-integer programming (MIP) model for allocating a limited budget to control a destructive invasive insect endangering the forest vegetation, Hyphantria cunea (H. cunea). Our model seeks to minimize the number of infected and dead trees within a preset planning period, factoring in various infestation scenarios, migration behaviors, resource limitations, intervention timing, and monitoring capabilities. Our modeling framework is novel in the sense that it depicts the transmission process of H. cunea as an infectious compartmental model. Our optimization model is also meaningful because it innovatively bridges the spread dynamics of H. cunea and the optimal resource allocation by using the time-varying number of infected trees that can be accepted in testing and surveillance. Our numerical test data and parameter settings have been collected from large-scale field surveys on H. cunea in Jiangsu Province over the past three years. The scenarios-based method offers significant computational advantages in searching for the best alternatives to real-world size problems in a rational time. Furthermore, our test results specify that the proposed model can not only aid in controlling H. cunea, but also can be adopted as a potential tool for managing other invasive species in the future.

Application of improved Jellyfish search algorithm for 9-parameters cell extraction and GMPPT in PV systems

Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

Solving higher dimensional expensive black box global optimization problems using sparse directional search on surrogates

We present a new algorithm Directional Optimization Search with Surrogate (DOSS), for optimizing problems with box constraints and a computationally expensive black-box objective function. DOSS is a radial basis function (RBF) based method that mainly focuses on higher dimensional and computationally expensive objective functions that can be multimodal. DOSS introduces three new techniques not previously used in earlier RBF algorithms, including using a combination of the coordinates knowledge level, fewer initial sampling points, and the surrogate’s gradient information. Numerical results on a test set including 14 test problems with 36, 48, and 60 dimensions show that DOSS outperforms two recently published algorithms RBFOpt and TuRBO, and earlier RBF algorithms such as DYCORS. TuRBO is a Gaussian process based optimization algorithm, which outperformed earlier state-of-the-art methods. DOSS algorithm also has a good performance on real-world optimization problems, including robot pushing and rover trajectory planning problems. Almost sure convergence for the DOSS algorithm is also proven in this paper. An implementation of DOSS is available at https://doi.org/10.5281/zenodo.13731558.

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.

Integrating Local Wisdom with Project-Based Learning to Enhance 21st-Century Skills in the Society 5.0 Era

In the context of Society 5.0, education must equip students with essential 21st-century skills such as problem-solving, collaboration, and creativity. However, traditional teacher-centered methods often fail to develop these competencies effectively. To address this issue, we developed a Project-Based Learning Model integrated with local wisdom (PBLM-e) within the Merdeka Belajar framework. The PBLM-e model enhances critical thinking and problem-solving skills by embedding culturally relevant, real-world problems into the learning process. This study employed a mixed-method approach, combining Research & Development (R&D) and experimental design, with 116 junior high school students in Makassar, Indonesia. Pre- and post-tests assessed students’ problem-solving abilities across four key indicators: understanding the problem, devising a plan, executing the plan, and reflecting on the solution. Results showed significant improvements in all indicators, demonstrating the model’s effectiveness in fostering essential skills. The integration of local wisdom made learning more meaningful, connecting academic content with students’ cultural contexts. The PBLM-e model presents a practical approach to preparing students for the challenges of Society 5.0, and future research should explore its long-term impact and the potential for incorporating advanced technologies.

Mathematical Evaluation and Dynamic Transmissions of a Tumor Growth Model Using a Generalized Singular and Non-Local Kernel

Currently, fractional calculus plays a critical role in improving control techniques, analyzing disease transmission dynamics, and solving several other real-world problems. This research investigates the time-fractional tumor growth model using an innovative approach. The new modified fractional derivative operator employs a singular and non-local kernel, based on Atangana and Baleanu's concepts with the Caputo derivative. The tumor growth model used the newly modified fractional operator, which provided numerical simulation. With the introduction of this new operator, we provide significant analysis for the tumor growth epidemic model. We have proven the uniqueness and stability conditions of the model by utilizing Banach's fixed point theory and the Picard successive approximation method. Using the Laplace-Adomian decomposition method (LADM), we found the numerical solution to the Modified Atangana-Baleanu-Caputo derivative model. We have verified the convergence analysis of the suggested scheme. We ultimately utilize the suggested method to obtain numeric outcomes and simulations for the tumor growth model. The study investigates the effect of multiple biological variables on the transmission of tumor growth dynamics.

ChatGPT as Teacher Assistant for Physics Teaching

This study explores the integration of ChatGPT as a teaching assistant in physics education, emphasizing its potential to transform traditional pedagogical approaches. ChatGPT facilitates interactive and inquiry-based learning grounded in constructivist learning theory, allowing students to engage actively in experiments and better grasp abstract concepts through hands-on activities. The AI's adaptive dialogue systems promote socio-constructivist learning by encouraging social interaction and personalized feedback, which is essential for addressing individual learning gaps and enhancing student engagement. ChatGPT's ability to simulate real-world physics problems and provide immediate feedback fosters experiential learning, making complex concepts more accessible and promoting critical thinking skills. By offering tailored interventions and adapting to individual learning paces, ChatGPT supports a personalized educational experience that caters to the unique needs of each student. This adaptability is particularly beneficial in physics education, where students often struggle with abstract concepts and require immediate clarification to progress effectively. The paper concludes that the synthesis of generative AI and pedagogy has the potential to reshape science education, fostering deeper understanding and curiosity among students. By leveraging innovative methodologies and augmented data, ChatGPT enriches teacher-student interactions, creating a comprehensive educational experience that promotes a culture of curiosity and exploration, thereby nurturing future scientists and engineers. Ultimately, the integration of ChatGPT into physics education offers a valuable opportunity to enhance student engagement and understanding of scientific concepts through interactive and personalized support.

Omnicare: A Comprehensive ML/DL-Based Prediction System for Healthcare and Agriculture

Effective decision-making in healthcare and agriculture can be challenging due to the complexity and volume of data involved. Traditional methods often require extensive manual analysis and domain-specific expertise, which can be time-consuming and prone to error. The challenge is compounded by the need for real-time insights and accurate predictions to address critical issues such as disease diagnosis and crop management. Omnicare tackles these challenges by using advanced machine learning and deep learning technologies to facilitate predictions. With pretrained models, it delivers precise insights for medical and agricultural needs, eliminating the need for user-provided training data. This streamlined, user-friendly approach enhances decision-making and represents a significant advancement in applying AI to real-world problems

Optimization of vehicle conceptual design problems using an enhanced hunger games search algorithm

Abstract Electric vehicles have become a standard means of transportation in the last 10 years. This paper aims to formalize design optimization problems for electric vehicle components. It presents a tool conceptual design technique with a hunger games search optimizer that incorporates dynamic adversary-based learning and diversity leader (referred to as HGS-DOL-DIL) to overcome the local optimum trap and low convergence rate limitations of the Hunger Games search algorithm to improve the convergence rate. The performance of the proposed algorithms is studied on six widely used engineering design problems, complex constraints, and discrete variables. For the HGS-DOL-DIL practical feasibility analysis, a case study of shape optimization of an electric car suspension arm from the industry is carried out. Overall, the inclusion of the OL strategy has proven its superiority in solving real-world problems, especially in solving real-world problems such as shape optimization of an electric vehicle automobile suspension arm, showing that the algorithm improves the search space improves the solution quality, and reflects its potential to find global optimum solutions in a well-balanced exploration and exploitation phase.

AI-Enhanced Geomatics Engineering: Innovative Solutions and Applications Using ChatGPT, an Advanced AI Language Model

This study investigates the potential of the advanced artificial intelligence language model, ChatGPT, in innovative solutions and applications within Geomatics Engineering, under the title "AI-Enhanced Geomatics Engineering: Innovative Solutions and Applications Using ChatGPT". ChatGPT is renowned for its ability to generate human-like text and has been trained on extensive datasets. Geomatics Engineering encompasses various sub-disciplines such as geographic information systems (GIS), remote sensing, global positioning systems (GPS), geodesy, and geomatics engineering. The research analyzes how AI technology, exemplified by models like ChatGPT, can be leveraged in Geomatics Engineering projects to enhance project management, improve data analysis accuracy, increase process efficiency, and impact project outcomes. AI models like ChatGPT enable the analysis of large geospatial datasets and play a crucial role in understanding and interpreting complex datasets. Furthermore, these technologies are expected to save time and contribute to obtaining more accurate results in Geomatics Engineering applications. The study also demonstrates how the integration of AI into Geomatics Engineering disciplines can be achieved through case studies and practical applications. These case studies illustrate how AI-supported technologies can be applied to real-world problems and improve current practical applications within the industry. In conclusion, this research underscores how AI can play a progressive role in Geomatics Engineering and emphasizes the transformative impact future technological advancements can have in these disciplines. The integration and utilization of such technologies are of paramount importance for exploring potential applications in this field.

Open Competency Optimization: A Human-Inspired Optimizer for the Dynamic Vehicle-Routing Problem

The vehicle-routing problem (VRP) is a popular area of research. This popularity springs from its wide application in many real-world problems, such as logistics, network routing, E-commerce, and various other fields. The VRP is simple to formulate, but very difficult to solve and requires a great deal of time. In these cases, researchers use approximate solutions offered by metaheuristics. This work involved the design of a new metaheuristic called Open Competency Optimization (OCO), which was inspired by human behavior during the learning process and based on the competency approach. The aim is the construction of solutions that represent learners’ ideas in the context of an open problem. The candidate solutions in OCO evolve over three steps. Concerning the first step, each learner builds a path of learning (finding the solution to the problem) through self-learning, which depends on their abilities. In the second step, each learner responds positively to the best ideas in their group (the construction of each group is based on the competency of the learners or the neighbor principle). In the last step, the learners interact with the best one in the group and with the leader. For the sake of proving the relevance of the proposed algorithm, OCO was tested in dynamic vehicle-routing problems along with the Generalized Dynamic Benchmark Generator (GDBG).

Using digital mobile games to increase the support for nature conservation

AbstractDigital games are an increasingly dominant form of digital entertainment with billions of players globally. While most of these games have a commercial focus, fields like public health and education have seen a growth of “serious games,” which aim to solve real world problems. In the context of biodiversity conservation, mobile games have been controversial, with some raising concerns around the way digital channels risk replacing the very nature they portray, therefore deepening a “nature‐deficit disorder.” We ran a randomized controlled trial to evaluate the impact of the mobile game “Kākāpō Run” on pro‐environmental behaviors amongst a cohort of 200 participants in New Zealand. Kākāpō Run was developed by a UK conservation charity, and aims to increase the support for Kākāpō conservation, as well as to increase pro‐environmental behaviors linked to Kākāpō conservation. Study participants completed a 10‐minute questionnaire before spending 1 hour playing their assigned mobile game over seven days. This was monitored by asking participants to share screenshots of their app usage for the duration of the experiment. After this, all participants re‐took the questionnaire. We found a positive impact across some knowledge and attitudes questions, behavioral intentions linked to willingness to volunteer time and support policies aiming to remove invasive predators, as well as manage pet cats actively. However, we found no change in willingness to donate or actual donations. This research showcases both the potential of mobile games for conservation outreach and marketing, and the importance of rigorous impact evaluation. We call for conservationists engaged in designing and promoting mobile games to approach game design and evaluation in a more research‐centered way to help develop an evidence base around the intended and unintended consequences of game playing. This mainstreaming of gaming science across conservation will be critical to allow mobile games to realize their potential as a leading communication channel.

The Synopsis of Soft Computing.

The traditional method of problem-solving, known as hard computing, is limited in its ability to handle modern digital technology and real-world problems accurately. Soft computing, a newer paradigm, offers a more versatile approach by utilizing multi-valued logic and human knowledge to solve complex, nonlinear problems efficiently. Unlike hard computing, soft computing can handle imprecise data and uncertainty effectively. This methodology has been successfully applied across various sectors, including scientific, industrial, and medical fields, providing more accurate results. Soft computing is good for its contributions to revolutionizing problem-solving techniques, being tolerant of imprecision, uncertainty, and linguistic variables, and offering approximate solutions to intricate problems.

Neurointerventional surveys between 2000 and 2023: a systematic review

BackgroundSurveys are increasingly used in neurointervention to gauge physicians’ and patients’ attitudes, practice patterns, and ‘real-world’ treatment strategies, particularly in conditions for which few, or no evidence-based, recommendations exist. While...

Developing modelling competencies in pre-service mathematics teachers through reflective thinking skills

Background: Ghana’s commitment to quality education has been reflected in its goal of providing equal access to high-quality education, leading to the reforms of the New Common Core Mathematics Curriculum for Basic Schools. The study explored the integration of mathematical modelling using reflective thinking skills, which are not currently core competencies in Ghanaian basic school curriculum.Aim: The study examined the modelling proficiency of pre-service mathematics teachers by assessing their reflective thinking abilities in a modelling laboratory context.Setting: The study focussed on pre-service mathematics teachers from two education colleges in Ghana.Methods: Using purposive sampling to select participants, a quasi-experimental design with pre- and post-test interventions was employed. Data were analysed through content and inferential analysis, supplemented by interviews.Findings: The findings indicated that the comparison group lacked prior knowledge of modelling problems and struggled with comprehension tasks. In contrast, the experimental group successfully translated real-world problems into mathematical models.Conclusion: Providing pre-service mathematics teachers access to a modelling laboratory and modelling-eliciting activities was essential for developing future modellers. This approach would enhance their effectiveness in teaching foundational mathematics in Ghanaian education.Contribution: This study advocated for re-orienting the mathematics curriculum at both Basic schools and Colleges of Education in Ghana to include mathematical modelling and reflective thinking skills as core components.

Experimental and Theoretical Comparison and Analysis of Surface-Enhanced Raman Scattering Substrates with Different Morphologies

The following research paper concerns the analysis and characterisation of commercially available surface-enhanced Raman scattering (SERS) substrates. SERS has long been a potentially very powerful method with a great deal of interest around it; however, there are still many obstacles which do not allow SERS to be easily applied to real-world detection and analysis problems. As such, research around the various types of substrates is ongoing, in the hope of streamlining and improving the Raman enhancement mechanism. Scanning electron microscope images were obtained for each of the three substrates, and their features and scales were described. Enhanced Raman spectra for Rhodamine B were obtained for a range of concentrations using each of the three substrates, and, in addition, surface enhancement maps are presented. Enhancement factors were calculated for the 1358 cm−1 peak of Rhodamine B. Complementing the experimental work, theoretical FEM modelling in COMSOL Multiphysics was performed, with the resulting calculations yielding an enhancement prediction adequately accurate to the real substrates.

Multi-strategy fusion improved Northern Goshawk optimizer is used for engineering problems and UAV path planning

Addressing the imbalance between exploration and exploitation, slow convergence, local optima Traps, and low convergence precision in the Northern Goshawk Optimizer (NGO): Introducing a Multi-Strategy Integrated Northern Goshawk Optimizer (MINGO). In response to challenges faced by the Northern Goshawk Optimizer (NGO), including issues like the imbalance between exploration and exploitation, slow convergence, susceptibility to local optima, and low convergence precision, this paper introduces an enhanced variant known as the Multi-Strategy Integrated Northern Goshawk Optimizer (MINGO). The algorithm tackles the balance between exploration and exploitation by improving exploration strategies and development approaches. It incorporates Levy flight strategies to preserve population diversity and enhance convergence precision. Additionally, to avoid getting trapped in local optima, the algorithm introduces Cauchy mutation strategies, improving its capability to escape local optima during the search process. Finally, individuals with poor fitness are eliminated using the crossover strategy of the Differential Evolution algorithm to enhance the overall population quality. To assess the performance of MINGO, this paper conducts an analysis from four perspectives: population diversity, balance between exploration and exploitation, convergence behavior, and various strategy variants. Furthermore, MINGO undergoes testing on the CEC-2017 and CEC-2022 benchmark problems. The test results, along with the Wilcoxon rank-sum test results, demonstrate that MINGO outperforms NGO and other advanced optimization algorithms in terms of overall performance. Finally, the applicability and superiority of MINGO are further validated on six real-world engineering problems and a 3D Trajectory planning for UAVs.