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Quantum machine learning (QML) algorithms have demonstrated the power of quantum computing for solving complex problems and big data in certain tasks. In this study, we explore the capabilities of QML for the classification of real-world biological large datasets including ten different cancer types based on gene expression values. By comparing the classification results obtained from the quantum algorithm with those from classical approaches, we disclose that the QML algorithm overall achieves comparable and reliable results. Moreover, we identify novel biomarkers that can contribute to the understanding of cancer biology. Some of these biomarkers are consistent with DNA promoter methylation. Our findings highlight the potential of QML in cancer classification and biomarker discovery, paving the way for future advancements in other disease research and clinical applications.Article highlights: QML could be implemented on real-world datasets to classify cancer types and identify biomarkers. QSVM outperformed some classical models in classification of ten cancer datasets. The novel biomarkers were found using quantum machine learning approach. Findings demonstrate the potential of QML in medical research and biomarker discovery. [ABSTRACT FROM AUTHOR]

Abstract Quantum machine learning (QML) algorithms have demonstrated the power of quantum computing for solving complex problems and big data in certain tasks. In this study, we explore the capabilities of QML for the classification of real-world biological large datasets including ten different cancer types based on gene expression values. By comparing the classification results obtained from the quantum algorithm with those from classical approaches, we disclose that the QML algorithm overall achieves comparable and reliable results. Moreover, we identify novel biomarkers that can contribute to the understanding of cancer biology. Some of these biomarkers are consistent with DNA promoter methylation. Our findings highlight the potential of QML in cancer classification and biomarker discovery, paving the way for future advancements in other disease research and clinical applications.

Technology and the availability of information have shifted the focus of researchers of engineering education towards experiential learning, a three-dimensional learning approach for students' engagement and development. The MIT report on The Global State of the Art in engineering education also shows the importance of experiential learning as a pedagogical tool for engineering education. Many researchers have discussed how this pedagogical tool is used to develop various skills like leadership, decision-making, teamwork etc. However, how experiential learning is mapped with learning outcomes and strategy for their assessment is yet not developed and discussed in depth. This research paper discusses the learning of students while working on the project undertaken under Mission Amrit Sarovar- Jal Dharohar Sanrakshan-2022 as part of the Azadi Ka Amrit Mahotsav initiative by the Government of India to rejuvenate ponds, advocating sustainability, and enhancement of analytical skills. The paper also discusses how effectively various learning aspects and understanding of all participating students would be mapped with required skill sets. The survey instrument has captured the detailed response to do an analysis of learning and later on, that has been mapped with required skill sets. Moreover, activities were designed with a perspective to provide experiential learning for students to engage with relevant officials, enhancing their communication and presentation skills. This also facilitates interdisciplinary collaboration in addressing environmental challenges and conserving valuable natural resources. The findings provide a direction for field-academic scenarios with practical knowledge and foster students' ability to contribute to real-world problems. [ABSTRACT FROM AUTHOR]

The computational techniques are a set of novel problem-solving methodologies that have attracted wider attention for their excellent performance. The handling strategies of real-world problems are artificial neural networks (ANN), evolutionary computing (EC), and many more. An estimated fifty thousand to ninety thousand new leishmaniasis cases occur annually, with only 25% to 45% reported to the World Health Organization (WHO). It remains one of the top parasitic diseases with outbreak and mortality potential. In 2020, more than ninety percent of new cases reported to World Health Organization (WHO) occurred in ten countries: Brazil, China, Ethiopia, Eritrea, India, Kenya, Somalia, South Sudan, Sudan, and Yemen. The transmission of visceral leishmaniasis is studied dynamically and numerically. The study included positivity, boundedness, equilibria, reproduction number, and local stability of the model in the dynamical analysis. Some detailed methods like Runge Kutta and Euler depend on time steps and violate the physical relevance of the disease. They produce negative and unbounded results, so in disease dynamics, such developments have no biological significance; in other words, these results are meaningless. But the implicit nonstandard finite difference method does not depend on time step, positive, bounded, dynamic and consistent. All the computational techniques and their results were compared using computer simulations. [ABSTRACT FROM AUTHOR]

In this paper, the performance of the Differential Evolution algorithm is evaluated when solving real-world problems. A Set of 13 engineering optimisation problems was selected from the fields of mechanics and industry to illustrate the usability of the Differential Evolution algorithm. Twelve variants of the standard Differential Evolution with various settings of the control parameters are compared with 19 state-of-the-art adaptive variants of this algorithm. The results are analysed statistically to achieve significant differences. Three variants of adaptive Differential Evolution provided better results compared to other algorithms. Some adaptive variants of Differential Evolution perform significantly worse than the original Differential Evolution with the fixed setting of the control parameters.

This paper deals a new numerical scheme to solve fractional differential equation (FDE) involving Caputo fractional derivative (CFD) of variable order β∈]0,1]. Based on a few examples and application models, the main objective is to show that FDE works more effectively than ordinary differential equations (ODEs). The proposed scheme is fractional Ralston's cubic method (RCM). The convergence analysis and stability analysis of the scheme is proved. The numerical scheme has been found without considering linearisation, perturbations, or any such assumptions. Finally, the efficiency of the proposed scheme will justify by solving a few examples of linear and non-linear FDEs with one application of FDE, world population growth (WPG) model of variable order β∈]0,1]. Also, the comparison of fractional RCM scheme has been shown with the existing fractional Euler method (EM) and fractional improved Euler method (IEM).

Teaching programming in an efficient and effective method has been the most difficult subject in the world of computing for the last couple of decades. Aside from a lack of laboratory equipment, the most prevalent issues are the lengthy time it takes to understand a problem, devising algorithms, writing coding, syntax, and semantic complexity of the programming language. Because of the wide range of students' backgrounds, misconceptions about the course, traditional classroom teaching methodology, and the limited allotted time available to cover the course, it is extremely difficult for a teacher to go beyond the fundamental concepts that impede the development of students' problem-solving abilities. The major goal of this study is to assist students to become better programmers by comprehending code and implementing applications for real-world problems. The study was conducted to uncover specific issues among students who lacked the necessary knowledge and skill sets for computer programming courses for computer science third-year students in the 2022 academic year. A sample of 23 students were chosen using randomized sampling technique for the investigations. Students’primary data were acquired through observation, questionnaires, and readily available data. According to the primary data collected, the researchers discovered that rough teacher-student relationships, disorganized laboratories and a lack of resources, the complicated nature of the course, and a lack of motivation in the teaching-learning process are factors that affect students' performance in understanding and solving real-world problems. The major issues were identified as lack of motivation to learn, persistence, inadequate laboratory facilities, lack of attractive teaching methodology, outdated teaching materials, lack of awareness of the benefit of the course, students' myths about the course difficulty, lack of practical guide materials, lab-assistant level of competence, student bad habit on computer usage, etc. Actions had been taken to solve the aforementioned problems through following well-organized and updated course materials, enabling students to develop a habit of teaching themselves, and establishing well-equipped laboratory facilities for practical demonstrations. The researchers believe that having applied the suggested action will bring a remarkable improvement to the knowledge among students. The researchers proposed different actions to be implementedfor both students and teachers. The recommended action was executed and actively followed up by the researchers. Finally, the researcher discovered that more than half of the students who were underperform get a better performance level and the students’self-confidence was improved, their problem-solving capabilities were enhanced, and they became more excited to engage in project work. [ABSTRACT FROM AUTHOR]

Nature-inspired meta-heuristics have demonstrated superior efficiency in the solution of complicated nonlinear optimization problems than conventional techniques. In this article, an enhanced moth flame optimization (EMFO) is designed using the mutualism phase from the symbiotic organism search (SOS) algorithm. The suggested approach is examined on 36 classical benchmark functions taken from literature. The outputs of EMFO are compared with the latest meta-heuristic algorithms and variants of the MFO algorithm. The comparison results indicate that our proposed method is competitive from the compared methods. Also, the Friedman rank test is used to evaluate the new algorithm's efficiency, and it is found that the rank of EMFO is superior. Finally, EMFO is being applied to solve seven real-world problems, and the outcomes of the proposed algorithm were found to be satisfactory. [ABSTRACT FROM AUTHOR]

A growing interest has been observed among K-12 school educators to incorporate maker pedagogy into science, technology, engineering, and mathematics (STEM) education to engage students in the design and making process. Both cognitive engagement and emotional engagement of students can be promoted through satisfying the psychological need of relatedness that concerns a sense of connection and belonging. How to support relatedness would influence the effective development of students' cognitive competencies, namely creativity and critical thinking, and non-cognitive characteristics, namely interest and identity. Therefore, the present study investigated how two relatedness support strategies—real-world problems (RWP) and mentoring influence the development of student's STEM-related cognitive competencies and non-cognitive characteristics in STEM marker activities. We implemented a 7-week intervention study with three classes of Grade 9 students (aged 13–15 years) in Hong Kong (n = 95). Three intervention conditions were designed in the experiment, comprising textbook problem (TBP), RWP, and RWP with mentoring (RWPM). Our analysis showed that (i) the differences in creativity among the three groups were non-significant, (ii) the RWP and RWPM groups showed stronger critical thinking than the TBP group, and (iii) the RWPM group exhibited stronger STEM interest and identity than the other two groups. This study revealed the effectiveness of adopting RWP strategy in developing secondary students' perceived cognitive competencies (e.g., creativity and critical thinking) and the feasibility of employing a mentoring mechanism for cultivating learners' perceived non-cognitive characteristics (e.g., STEM identity and interest). Hence, we also offered practical suggestions for teachers. [ABSTRACT FROM AUTHOR]

A growing interest has been observed among K-12 school educators to incorporate maker pedagogy into science, technology, engineering, and mathematics (STEM) education to engage students in the design and making process. Both cognitive engagement and emotional engagement of students can be promoted through satisfying the psychological need of relatedness that concerns a sense of connection and belonging. How to support relatedness would influence the effective development of students’ cognitive competencies, namely creativity and critical thinking, and non-cognitive characteristics, namely interest and identity. Therefore, the present study investigated how two relatedness support strategies—real-world problems (RWP) and mentoring influence the development of student’s STEM-related cognitive competencies and non-cognitive characteristics in STEM marker activities. We implemented a 7-week intervention study with three classes of Grade 9 students (aged 13–15 years) in Hong Kong (n = 95). Three intervention conditions were designed in the experiment, comprising textbook problem (TBP), RWP, and RWP with mentoring (RWPM). Our analysis showed that (i) the differences in creativity among the three groups were non-significant, (ii) the RWP and RWPM groups showed stronger critical thinking than the TBP group, and (iii) the RWPM group exhibited stronger STEM interest and identity than the other two groups. This study revealed the effectiveness of adopting RWP strategy in developing secondary students’ perceived cognitive competencies (e.g., creativity and critical thinking) and the feasibility of employing a mentoring mechanism for cultivating learners’ perceived non-cognitive characteristics (e.g., STEM identity and interest). Hence, we also offered practical suggestions for teachers.

This paper introduces a new metaheuristic technique known as the Greater Cane Rat Algorithm (GCRA) for addressing optimization problems. The optimization process of GCRA is inspired by the intelligent foraging behaviors of greater cane rats during and off mating season. Being highly nocturnal, they are intelligible enough to leave trails as they forage through reeds and grass. Such trails would subsequently lead to food and water sources and shelter. The exploration phase is achieved when they leave the different shelters scattered around their territory to forage and leave trails. It is presumed that the alpha male maintains knowledge about these routes, and as a result, other rats modify their location according to this information. Also, the males are aware of the breeding season and separate themselves from the group. The assumption is that once the group is separated during this season, the foraging activities are concentrated within areas of abundant food sources, which aids the exploitation. Hence, the smart foraging paths and behaviors during the mating season are mathematically represented to realize the design of the GCR algorithm and carry out the optimization tasks. The performance of GCRA is tested using twenty-two classical benchmark functions, ten CEC 2020 complex functions, and the CEC 2011 real-world continuous benchmark problems. To further test the performance of the proposed algorithm, six classic problems in the engineering domain were used. Furthermore, a thorough analysis of computational and convergence results is presented to shed light on the efficacy and stability levels of GCRA. The statistical significance of the results is compared with ten state-of-the-art algorithms using Friedman's and Wilcoxon's signed rank tests. These findings show that GCRA produced optimal or nearly optimal solutions and evaded the trap of local minima, distinguishing it from the rival optimization algorithms employed to tackle similar problems. The GCRA optimizer source code is publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/165241-greater-cane-rat-algorithm-gcra., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Bat algorithm (BA) has the advantage of fast convergence, but there is still room for improvement in accuracy and stability of solution. An efficient and robust fusion bat algorithm (ERFBA) is proposed to overcome these defects. In the population reconstruction, an effective diversity population (EDP) is reconstructed by designing a multi-strategy opposition-based learning with disturbance. In the exploration, an adaptive constraint step whale optimization algorithm is presented to obtain the promising regions with fewer blind spots by exploring EDP. In the exploitation, we design a new BA local search strategy by novel combination between dynamic regulation and Cauchy mutation to get accurate and stable solution. Numerous experiments show that ERFBA has remarkable advantages in accuracy and stability for many high dimension, unimodal and multimodal problems. Moreover, the proposed algorithm is further tested and applied in areas of intelligent data analysis and intelligent design. The results show that the overall performance of the proposed ERFBA is better than other existing algorithms. [ABSTRACT FROM AUTHOR]

The topic of this contribution is aimed at lower and upper secondary school mathematics teaching as well as at university training of teachers of mathematics. Joint use of computer algebra (CAS) and dynamic geometry software (DGS) or even an incorporation of CAS into DGS brings new possibilities to the teaching of mathematics, such as experimentation, the modelling of real-world situations or the deriving and proving of conjectures. We will deal with all these issues and how they can be realized with the free dynamic mathematics software GeoGebra, mainly in terms of the use of its tools of automated theorem proving. Particular examples of possible ways of utilizing the methods and tools of automated theorem proving will be presented with the aim of illustrating that dynamic geometry enriched with algebraic features can contribute to the effective fulfilment of the requirements for educational proof, from a decision on the correctness of a geometric property to the modelling of real-world phenomena. [ABSTRACT FROM AUTHOR]

Staff assignment is a compelling exercise that affects most companies and organizations in the service industries. Here, we introduce a new real-world staff assignment problem that was reported to us by a Swiss provider of commercial employee scheduling software. The problem consists of assigning employees to work shifts subject to a large variety of critical and noncritical requests, including employees’ personal preferences. Each request has a target value, and deviations from the target value are associated with integer acceptance levels. These acceptance levels reflect the relative severity of possible deviations, e.g., for the request of an employee to have at least two weekends off, having one weekend off is preferable to having no weekend off and thus receives a higher acceptance level. The objective is to minimize the total number of deviations in lexicographical order of the acceptance levels. Staff assignment approaches from the literature are not applicable to this problem. We provide a binary linear programming formulation and propose a matheuristic for large-scale instances. The matheuristic employs effective strategies to determine the subproblems and focuses on finding good feasible solutions to the subproblems rather than proving their optimality. Our computational analysis based on real-world data shows that the matheuristic scales well and outperforms commercial employee scheduling software. [ABSTRACT FROM AUTHOR]

The study aimed to find students’ ideas in solving oscillation harmonic motion, spesifically frequence of mass-spring system. The study conducted on 36 students in one of Malang state high school. Multiple choice with reason were used as instrument. The result show that most of students were more expert on solving texbook problem than real-world problem. Most of students’ argument on real world problem were not relevant. The dificculty was not caused by students’ blank slate but by recalling failed. Penelitian ini bertujuan untuk mengetahui pemikiran-pemikiran siswa dalam menyelesaikan persoalan terkait gerak osilasi, khususnya pada topik frekuensi sistem pegas-massa. Penelitian dilakukan di salah satu SMA Negeri di Malang dengan subjek penelitian sebanyak 36 siswa. Penelitian dilakukan dengan memberikan soal-soal pilihan ganda beralasan. Hasil penelitian menunjukkan bahwa siswa lebih mahir dalam menyelesaikan soal textbook dibandingkan soal-soal real-world. Argumen yang dipaparkan siswa pada soal-soal real-world kebanyakan tidak relevan dengan persoalan yang diberikan. Kesulitan siswa ini bukan karena siswa tidak memiliki pengetahuan terkait dengan persoalan, namun karena gagal memanggilnya.

While the number of college degrees conferred in the U.S. has increased for years, the number of college degrees conferred in computer and information systems in the U.S. decreased by 27% between 2005 and 2010. Research indicated that the decline was caused by student fear of computer and information technology jobs being outsourced overseas, the dot.com failure, the slow-down economy, and student weak knowledge of computer and information technology. This paper provides a hands-on immersive learning project of real-world problems and solutions and discusses how the project can engage students in immersing into the society, identifying the real-world problems, and generating solutions with computer information technology. Learning through the real-world hands-on activities, students would realize that computer information technology is the foundation for solving business problems in the Internet Age. More students would be excited about the computer information systems programs and be interested in majoring or minoring in computer information systems. [ABSTRACT FROM AUTHOR]

The symbiotic organisms search (SOS) algorithm was introduced by considering the relationships among the creatures in a natural ecosystem. Despite the superior efficiency of SOS, it has been observed that fixing benefit factors of mutualism phase at 1 or 2; the algorithm obstructs itself from an extensive and diverse search of the search region. Moreover, alteration of random dimensions in the parasitism phase increases the computational burden of the algorithm. Considering these limitations, a modified SOS algorithm, namely nwSOS, has been proposed in this study to solve higher dimensional optimization problems. In the suggested nwSOS, the benefit factors are calculated by a non-linear approach. The mutual vector is modified, and weights of both benefit factors are utilized to effectively explore and exploit the search region. Moreover, the parasitism phase is tailored to lessen the computational overhead. The modified method is then used to evaluate twenty basic benchmark functions using 100 and 500 dimensions. Results are compared with six state-of-the-art algorithms and with SOS and its five modified variants. Four designing issues from both unconstrained and constrained classifications are solved utilizing nwSOS. Complexity analysis, statistical analysis, and convergence analysis are executed to measure the algorithm's effectiveness from different aspects. Moreover, the proposed algorithm has been used for segmenting COVID-19 chest X-ray images with the help of multi-level thresholding approach using different thresholds. All the results confirmed the enhancement of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The authors developed a system that automatically creates regular aircraft flight schedules and minimizes the total number of aircraft required. Since the target problems are real-world problems having constraints that are intertwined in a complex manner, the authors used a genetic algorithm to achieve the optimization. In this paper, the authors describe the problem setup and genetic algorithm configuration and verify the algorithm's effectiveness through experiments using real data. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(12): 83–92, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10036 [ABSTRACT FROM AUTHOR]

Differential evolution and its variants have already proven their worth in the field of evolutionary optimization techniques. This study further enhances the success history-based adaptive differential evolution (SHADE) by hybridizing it with a modified Whale optimization algorithm (WOA). In the new algorithm, the two algorithms, SHADE and modified WOA, carry out the search process independently and share information like the global best solution and whole population and thus guides both the algorithms to explore and exploit new promising areas in the search space. It also reduces the chance of being trapped in local optima and stagnation. The proposed algorithm (SHADE–WOA) is tested, evaluating CEC 2017 functions using dimensions 30, 50, and 100. The results are compared with modified DE algorithms, namely SaDE, SHADE, LSHADE, LSHADE-SPACMA, and LSHADE-cnEpSin, also with modified WOA algorithms, namely ACWOA, AWOA, IWOA, HIWOA, and MCSWOA. The new algorithm's efficiency in solving real-world problems is examined by solving two unconstrained and four constrained engineering design problems. The performance is verified statistically using non-parametric statistical tests like Friedman's test and Wilcoxon's test. Analysis of numerical results, convergence analysis, diversity analysis, and statistical analysis ensures the enhanced performance of the proposed SHADE–WOA. • Introduced hybrid SHADE–WOA algorithm using SHADE and modified WOA. • A new phase "co-operative hunting strategy" is used on WOA. • A new parameter α is introduced in WOA for balancing exploration and exploitation. • Population information is shared among the algorithms to guide the search processes. • Compared with latest DE and WOA variants using CEC 2017 function set. [ABSTRACT FROM AUTHOR]