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Optimization techniques, specially evolutionary algorithms, have been widely used for solving various scientific and engineering optimization problems because of their flexibility and simplicity. In this paper, a novel metaheuristic optimization method, namely human behavior-based optimization (HBBO), is presented. Despite many of the optimization algorithms that use nature as the principal source of inspiration, HBBO uses the human behavior as the main source of inspiration. In this paper, first some human behaviors that are needed to understand the algorithm are discussed and after that it is shown that how it can be used for solving the practical optimization problems. HBBO is capable of solving many types of optimization problems such as high-dimensional multimodal functions, which have multiple local minima, and unimodal functions. In order to demonstrate the performance of HBBO, the proposed algorithm has been tested on a set of well-known benchmark functions and compared with other optimization algorithms. The results have been shown that this algorithm outperforms other optimization algorithms in terms of algorithm reliability, result accuracy and convergence speed.

PurposeInterorganizational collaborations are crucial for delivering high-quality, integrated healthcare services. To maximize the benefits of these collaborative networks, effective governance structures and mechanisms must be in place. While previous studies have extensively examined organizational-level factors, such as partner capabilities and backgrounds, this study focuses on network-level factors, including collaboration structures and tie characteristics that shape effective network governance.Design/methodology/approachA systematic literature review (SLR) was conducted to identify and synthesize the key network-level factors influencing governance structures and mechanisms in healthcare networks.FindingsThe review identified 22 critical factors, categorized into three primary groups that impact network governance. These findings offer a robust foundation for developing context-sensitive governance models tailored to healthcare systems.Practical implicationsThis study provides valuable insights for healthcare practitioners, policymakers and researchers by highlighting key factors that can improve interorganizational collaboration within healthcare systems. The findings contribute to both theory and practice, with the potential to enhance healthcare service delivery and patient outcomes.Originality/valueThis study is the first to systematically identify and categorize the network-level factors that influence governance structures and mechanisms in healthcare networks, providing a comprehensive and novel contribution to the field.

The shift of focus from a cognitive to social view has drawn the attention of second language assessment researchers to delve into interactional competence as an intriguing topic in paired oral tests. To fill the gap on how interactional patterns compare across L1 and L2 interactions this study was specifically set to explore and compare 45 paired interactions of L1 English speakers, L1 Persian speakers, and L2 English learners. While the findings indicated that the collaborative pattern was the most common pattern in the three groups less than half of the interactions in total were collaborative. Furthermore, not all the interactional patterns were found in all the groups. The frequency and prominence of the common patterns also varied. The findings could be explained by cultural norms of speech (e.g., in taking turns) and interlocutor effects (e.g., dominating the conversation or adopting a passive role). The findings imply challenges for fair assessment when using paired tasks as assessment tools. Furthermore, the findings call for rubric development and rater training programs to include description and discussion of interactional patterns to help raters improve their understanding and rating of paired interactions.

The presence of silica in iron ore concentrate can have significant negative impacts on the efficiency and quality of steel production. As such, providing engineers with early and reliable information about the purity of iron ore concentrate is crucial for smooth mining operations. This paper reports on the development of a long short-term memory (LSTM) network and an ensemble data interpolation technique to enhance quality prediction in the froth flotation process of an iron ore mine. Our results demonstrate the ability of our model to accurately predict the silica content of iron ore concentrate on a minute-by-minute basis, as well as the ability to forecast hours in advance.

The traditional approach to presenting idioms relies mainly on teachers’ or materials writers’ judgement, one-by-one and quite incidentally; and the existing teaching materials and references for idioms are mostly intuition-based. However, a more recent approach to better teaching and learning idioms is to present them under categories of their common themes and topics. Corpus linguistics can be of much contribution through helping the design and development of more authentic and systematic materials using comprehensive corpora which are typically the best representatives of the target language. In this connection, the present study aimed at searching for the thematic index of 1506 idioms under 81 categories at the end of the Oxford Dictionary of Idioms in the largest freely available corpus, i.e. the Corpus of Contemporary American English (COCA), composed of more than 520 million words. To this end, we used a manuscript in PHP written by a professional computer programmer especially for this purpose. The findings yielded a list of idioms sorted based on their frequencies under their theme-based categories. To focus on the more frequently-used idioms of various themes in real contexts, materials designers, teachers, and learners of English can benefit from this idiom list in textbooks and classroom activities.

High-variety engineer-to-order (ETO) manufacturers often rely on quantity-based planning logic, in which planned output is weakly connected to finite machine and labor capacity. This disconnect can create workload peaks, congested queues, and delivery unreliability. Workload Control (WLC) offers a capacity-aware planning logic for ETO environments, but its implementation depends on informational conditions that many firms do not initially possess, including order traceability, production time data, capacity visibility, and data-quality control. Although WLC research has demonstrated its potential through analytical and simulation-based studies, empirical and longitudinal evidence on how firms build these preconditions remains limited. This paper investigates how WLC informational readiness is progressively developed in practice. Based on an action-learning-informed longitudinal case study in the shaft department of a European manufacturer of customized complex electrical machines, the study identifies four cumulative readiness stages: diagnosing the planning problem, establishing order traceability and performance visibility, building capacity visibility, and addressing the data-quality layer. The findings show how technical data infrastructure and organizational routines jointly support the transition from quantity-based planning toward capacity-aware planning. The paper contributes a practice-grounded process model of WLC informational readiness by shifting attention from the design of WLC mechanisms to the informational and organizational conditions required before such mechanisms can operate reliably. For practice, it offers a case-derived staged roadmap for manufacturers that cannot move directly from quantity-based planning to full WLC implementation.

Farmers require diverse and complex information to make agronomical decisions about crop management including intervention tasks. Generally, this information is gathered by farmers traversing their fields or glasshouses which is often a time consuming and potentially expensive process. In recent years, robotic platforms have gained significant traction due to advances in artificial intelligence. However, these platforms are usually tied to one setting (such as arable farmland), or algorithms are designed for a single platform. This creates a significant gap between available technology and farmer requirements. We propose a novel field agnostic monitoring technique that is able to operate on two different robots, in arable farmland or a glasshouse (horticultural setting). Instance segmentation forms the backbone of this approach from which object location and class, object area, and yield information can be obtained. In arable farmland, our segmentation network is able to estimate crop and weed at a species level and in a glasshouse we are able to estimate the sweet pepper and their ripeness. For yield information, we introduce a novel matching criterion that removes the pixel-wise constraints of previous versions. This approach is able to accurately estimate the number of fruit (sweet pepper) in a glasshouse with a normalized absolute error of 4.7% and an R 2 of 0.901 with the visual ground truth. When applied to cluttered arable farmland scenes it improves on the prior approach by 50%. Finally, a qualitative analysis shows the validity of this agnostic monitoring algorithm by supplying decision enabling information to the farmer such as the impact of a low level weeding intervention scheme.

In this paper, the V-shape development model approach for designing an automotive floor panel made by laminated structure is investigated to attain the best trade-off between the system and subsystem level requirements while improving the local and global performance of the vehicle. For this purpose, the bending and torsional stiffness of the body structure, as well as mass, strength, and vibration attenuation of the floor panel, are considered as design objectives at the system and subsystem levels. A multi-objective discrete optimization of a laminated configuration is performed using the Taguchi-based grey relational analysis. Material grades and thicknesses of the sandwich panel face sheets and thickness of the viscoelastic core with five levels are taken as discrete design variables. Moreover, the contribution ratios of each design factor on the performance characteristics are determined using the analysis of variance. By observing the results of the proposed approach, it is revealed that with an appropriate combination of layers, an optimum sandwich structure can be designed to fulfill all objectives simultaneously. Compared to the initial model, static strength and vibration attenuation are improved by 47.18% and 17.14%, respectively, while the global level characteristics, i.e., overall body structure stiffness, are properly preserved. Furthermore, the floor panel mass is decreased by 59.69%, which reduces the total mass of the structure by 1.75%.

Oral assessment is a challenging issue as various factors may play a role in language learners’ production. Task repetition and task type are among factors that may influence oral performance. Previous studies have produced contradictory results about the effect of these two factors. Furthermore, such studies have only focused on one of these factors. Thus, it is yet unclear how these two factors may function in tandem to affect oral performance. The present study was, therefore, aimed at filling this gap. Thirty-two EFL learners carried out four tasks made by combining task repetition and task type: Monologue with exact repetition, dialogue with exact repetition, monologue with procedural repetition, and dialogue with procedural repetition. Four raters evaluated the participants’ speaking performance using the Common European Framework of Reference speaking scale, including descriptors of range, accuracy, fluency, interaction, and coherence. A two-way repeated measures ANOVA was conducted to compare the participants’ performances across the four tasks. Results indicated no significant interaction effect for task repetition and task type on test takers’ performance after applying the Bonferroni adjustment. Additionally, the test takers received significantly higher scores on range and fluency when tasks involved exact repetition as opposed to procedural repetition. The findings further revealed that participants achieved significantly higher scores on accuracy in dialogue compared to monologue tasks. The findings hold implications for language teachers on how to use task types and task repetition in improving language learners’ oral production.

The use of asphaltic concrete cores for sealing embankments and rockfill dams is very important. The self-healing properties of bitumen, simple construction in cold and rainy conditions compared to clay cores, good flexibility and connection with embankment materials are the essential characteristics of asphaltic concrete. The main concern regarding the use of asphaltic concrete cores in Iran is mainly the performance of these dams under seismic loads. The evaluations of the performance of these types of dams in other countries show that asphaltic concrete cores perform satisfactorily in the static state, but in earthquake conditions, the situation may be different. In this paper, the static and seismic behavior of the Meijaran dam in Iran, Mazandaran, is evaluated for three core angles of 90°, 60° and 45°. This evaluation was conducted at the end of the impounding stage and after applying seismic loads using FLAC 2D software and Mohr–Coulomb consitutive models. The results were matched with the ICOLD recommendation to use angled cores in dams with asphaltic cores and showed that the dam performs better with angled cores. Finally, for the Meijaran dam, the results from the dynamic analysis are compared with the results from the centrifuge test.