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This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

Delay and disruption claims involve a complex process that often result in disputes, unnecessary expenses and time loss on construction projects. This study aims to review and synthesize the contributions of previous research undertaken in this area and propose future directions for improving the process of delay and disruption claims.

This study adopted a holistic systematic review of literature following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A total of 230 articles were shortlisted related to delay and disruption claims in construction using Scopus and Web of Science databases.

Six research themes were identified and critically reviewed including delay analysis, disruption analysis, claim management, contract administration, dispute resolution and delay and disruption information and records. The systematic review showed that there is a dearth of research on managing the wide-ranging information required for delay and disruption claims, ensuring the transparency and uniformity in delay and disruption claims’ information and adopting an end-user’s centred research approach for resolving the problems in the process of delay and disruption claims.

Complexities in delay and disruption claims are real-world problems faced by industry practitioners. The findings will help the research community and industry practitioners to prioritize their energies toward information management of delay and disruption claims.

This study contributes to the body of knowledge in delay and disruption claims by identifying the need for conducting more research on its information requirements and management. Subsequently, it provides an insight on the use of modern technologies such as drones, building information modeling, radio frequency identifiers, blockchain, Bigdata and machine learning, as tools for more structured and efficient attainment of required information in a transparent and consistent manner. It also recommends greater use of design science research approach for delay and disruption claims. This will help to ensure delay and disruption claims are the least complex and less dispute-prone process.

This study explores how problem-based learning (PBL) programs can address Sustainable Development Goals (SDGs) via the higher education (HE) curriculum, teaching materials and relevant assessments, supporting learning at scale for HE institutions.

Employing SDGs and their indicators as the coding framework, our two-phase study evaluates the curriculum and teaching materials of seven PBL programs at a leading higher education institution (HEI). The first phase involved a content analysis to assess the degree of sustainability integration in 156 relevant courses. The second phase applied a semi-automated mapping protocol to analyze learning and teaching materials in 120 relevant courses.

The school aligns with 17 SDGs (100%), covering 94 indicators (55.62%). On average, each program within the school addresses over ten of these goals and incorporates more than 24 associated indicators. However, the study reveals an imbalance in the incorporation of SDGs, with some goals not yet deeply and comprehensively embedded in the curriculum. While there is a substantial focus on sustainability theories, the practical implications of SDGs in emerging countries, particularly through case studies and assessments, require significant enhancement.

Mapping SDGs allows HEIs to identify strengths and gaps in SDG integration, thereby improving the PBL approach to enhance student work readiness in sustainability-focused careers.

Through the lens of transformative learning theory, this study provides evidence of SDG integration into PBL curricula. It highlights a mapping methodology that enables HEIs to evaluate their sustainability readiness in curriculum, teaching materials and relevant assessments.

This study investigates a novel educational strategy in science, technology, engineering and mathematics (STEM) teaching that integrates the engineering design process (EDP) as a framework. The strategy aims to help teachers explain STEM concepts in a simplified way. We employed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to enable a systematic review that evaluated the effectiveness of this approach in improving both teaching and learning in STEM subjects.

In order to fulfill the objectives of the review, key data were extracted from each of the 400 articles that were reviewed from three databases: Scopus, ProQuest Central and EBSCO. Two types of analysis were conducted, namely descriptive analysis and literature classification.

This systematic review analyzed 44 articles on the EDP, focusing on 18 detailed studies mainly from ProQuest, SCOPUS and EBSCO. It revealed a limited focus on gender’s impact on EDP and a trend toward interdisciplinary use and integrated research approaches. The study underscores the need for further exploration of demographic influences and preparation programs in EDP across various disciplines, aiming to inform future research and educational policies.

The study’s value lies in its comprehensive assessment of engineering design (ED) research over the past decade, serving as a key reference point. It highlights progress in the field, consolidates findings and provides insights into the field’s evolution, guiding future research directions in ED.

This chapter contributes to the scholarship of teaching and learning in extracurricular enterprise and entrepreneurship education. It draws on research from two annual ‘Business Challenge Weeks’ (BCW) held at Oxford Brookes University in 2021 and 2022, in which teams of postgraduate students from three faculties worked on external client projects, supported by an academic mentor. It presents and discusses findings derived from a survey and interviews conducted after the second of these years. The chapter takes a transdisciplinary perspective, after Budwig and Alexander (2020), Piaget (1972) and Klein et al. (2001) and explores the relationship between this and the enterprise and entrepreneurship development pipeline set out by QAA (2018). It analyses the experiences of the three main participating groups engaged in the challenge weeks – students, external clients and academic mentors – and explores the organising challenges inherent in multiparty pedagogical initiatives. The chapter contributes to knowledge in this area by revealing and reflecting on the motivations and expectations of the three participant groups, the roles they played during the week and the outcomes they reported. It also expands understanding of transdisciplinary enterprise pedagogy.

In today’s world, empowering individuals, promoting social cohesion and advancing economic development all hinge on access to high-quality education, prioritizing diversity, inclusion and equality. Rethinking current educational strategies using cyber-physical learning assets is necessary to accommodate the learning inclusivity and equity and escalating demands of a globalized world. There is a pressing demand for evidence to support the efficacy of collaborative learning in transforming curriculum and fostering learner inclusion. However, it is recognized as a pedagogical technique within the quality education domain. This study aims to address this knowledge gap by investigating how hybridized cybergogy paradigms facilitate collaborative learning, focusing on diversity, equity and inclusion, to improve educational quality in higher education.

This study used a qualitative approach with an exploratory design guided by an interpretive philosophical perspective. The data was gathered from 60 prospective teachers from the public sector university of Sindh, Pakistan. Semi-structured interviews were conducted with participants. They were then analyzed using theme analysis to understand their views on the potential of hybridized cybergogy paradigms for collaborative learning to improve the quality of education provided at institutions.

The study results confirm that learners benefit from increased access to learning resources, improved critical thinking and problem-solving skills and a more diverse and inclusive classroom working together in a collaborative hybridized cybergogy setting. By fostering SDG 4 (Quality Education) and the 21st-century skills necessary for global marketplace engagement and competing in progressive environments, this creative method equips learners with the capabilities to face modern global challenges.

The study offers valuable practical suggestions to stakeholders in higher education, including faculty, policymakers and teacher education programs, for integrating hybridized cybergogy and collaborative learning to align curricula with sustainable development goals. Additionally, it bridges a significant gap in the existing literature, which will aid future researchers interested in exploring this area.

This study stands out as it explores an underexamined area while providing novel educational insights.

The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.

The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.

(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.

The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.

The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

Project managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study focuses on resource-constrained project scheduling in multi-project environments. The research simplifies the problem by adopting a single-project perspective using gain coefficients.

It employs uncertainty theory and multi-objective programming to construct a model. The optimal solution is identified using Matlab, while LINGO determines satisfactory alternatives. By combining these methods and considering actual construction project situations, a compromise solution closely approximating the optimal one is derived.

The study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.

The research highlights three main contributions: effective resource utilization, project prioritization and conflict management, and addressing uncertainty. It offers decision support for project managers to balance resource allocation, resolve conflicts, and adapt to changing project demands.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.