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Organizations are affected top-down by the overarching societies and bottom-up by foundational face-to-face encounters: societies provide norms, values, laws, institutions, beliefs, markets, political structures, and knowledge bases. What happens within organizations is done by people interacting with other people, arguing, discussing, convincing each other when preparing and making decisions. Organizations operate within social environments that leave their – however indirect – imprint on what is going on within organizations. This article argues that organizational sociology can benefit from an integrated theoretical framework that accounts for the embeddedness of organizations within the micro- and macro-levels of social order. The argument is developed in two main points: First, this article introduces the multilevel framework provided by Niklas Luhmann’s systems theory to demonstrate how organizations are shaped by the functionally differentiated macro-structure of society. Organizations follow and reproduce the operational logics of societal domains such as the political system, the economy, science, law, religion, etc. Second, this paper demonstrates how organizations are shaped by micro-level dynamics of face-to-face interactions. Face-to-face encounters form a social reality of its own kind that restricts and resists the formalization of organizational processes. Here, this article draws on Erving Goffman’s and Randall Collins’ work on interaction rituals, emotions, and solidarity, which is inspired by Durkheimian micro-sociology. At the end, this article brings together all the elements into one general account of organizations within the context of their macro- and micro-structural social environments. This account can yield a deeper and more sociological understanding of organizational behavior.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

This chapter examines the Netherlands’ challenges in safeguarding its low-lying coastline against rising sea levels and the consequences of coastal defense strategies on marine life, particularly in relation to SDG14. Sea-level rise necessitates increased soft coastal defense strategies, affecting seafloor areas and marine biodiversity through sand extraction and sand nourishments. The use of hard structures for coastal defense contributes to the loss of natural coastal habitats, raising biodiversity concerns. The chapter explores the potential benefits of artificial hard surfaces as marine habitats, emphasising the need for careful design to prevent ecological problems caused by invasive species. Strategies for enhancing biodiversity on human-made hard substrate structures, including material variations, hole drilling, and adaptations, are discussed. The ecological impact of marine sand extraction is examined, detailing its effects on benthic fauna, sediment characteristics, primary production, and fish and shrimp populations. Solutions proposed include improved design for mining areas, ecosystem-based rules for extraction sites, and ecologically enriched extraction areas. The ecosystem effects of marine sand nourishments are also analysed, considering the impact on habitat suitability for various species. The chemical effects of anaerobic sediment and recovery challenges are addressed. Mitigation measures, such as strategic nourishment location and timing, adherence to local morphology, and technical solutions, are suggested. The chapter underscores the importance of education in Nature-based Solutions and announces the launch of a new BSc programme in Marine Sciences at Wageningen University & Research, integrating social and ecological knowledge to address challenges in seas, oceans, and coastal regions and support SDG14 goals.

This paper posits the need for English language arts (ELA) teachers to foster students’ use of languaging about their relations with ecosystems and peers, leading to their engaging in collective action to critique and transform status-quo systems impacting the climate crisis.

This paper reviews the current theory of languaging theory and research that focuses on the use of languaging to enact relations with ecosystems and others and voice emotions for transforming communities and reducing emissions contributing to climate change.

This review of languaging theory/research leads to identifying examples of teachers having students critique the use of languaging constituting status quo energy and community/transportation systems, respond to examples of characters using languaging in literary texts, using languaging in discussing or writing about the need to address climate change, critiquing languaging in media promoting consumption, using media to interact with audiences and using languaging through engaging in role-play activities.

This focus on languaging in ELA classrooms is a unique perspective application of languaging theory, leading students to engage in collective, communal action to address the climate crisis.

This paper seeks to understand how a new elite, known as the cork aristocracy, emerged in the Bordeaux wine field, France, between 1850 and 1929 as wine merchants replaced aristocrats. Classic class and status perspectives, and their distinctive social closure dynamics, are mobilized to illuminate the individual and organizational transformations that affected elite wineries grouped in an emerging classification of the Bordeaux best wines. We build on a wealth of archives and historical ethnography techniques to surface complex status and organizational dynamics that reveal how financiers and industrialists intermediated this transition and how organizations are deeply interwoven into social change.

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

This chapter focuses on the US Civil War of 1861–1864, the application of the laws of war to a civil war, and gives great attention to US Army General Order 100 (aka The Lieber Code), the first set of laws to direct and constrain the behavior of troops in the field.

Most of the existing time-cost-quality-environmental impact trade-off (TCQET) analysis models have focused on solving a simple project representation without taking typical activity and project characteristics into account. This study aims to present a novel approach called the “hybrid opposition learning-based Aquila Optimizer” (HOLAO) for optimizing TCQET decisions in generalized construction projects.

In this paper, a HOLAO algorithm is designed, incorporating the quasi-opposition-based learning (QOBL) and quasi-reflection-based learning (QRBL) strategies in the initial population and generation jumping phases, respectively. The crowded distance rank (CDR) mechanism is utilized to rank the optimal Pareto-front solutions to assist decision-makers (DMs) in achieving a single compromise solution.

The efficacy of the proposed methodology is evaluated by examining TCQET problems, involving 69 and 290 activities, respectively. Results indicate that the HOLAO provides competitive solutions for TCQET problems in construction projects. It is observed that the algorithm surpasses multiple objective social group optimization (MOSGO), plain Aquila Optimization (AO), QRBL and QOBL algorithms in terms of both number of function evaluations (NFE) and hypervolume (HV) indicator.

This paper introduces a novel concept called hybrid opposition-based learning (HOL), which incorporates two opposition strategies: QOBL as an explorative opposition and QRBL as an exploitative opposition. Achieving an effective balance between exploration and exploitation is crucial for the success of any algorithm. To this end, QOBL and QRBL are developed to ensure a proper equilibrium between the exploration and exploitation phases of the basic AO algorithm. The third contribution is to provide TCQET resource utilizations (construction plans) to evaluate the impact of these resources on the construction project performance.

Drawing on dual process theory as the overarching framework, this study investigates how different types of incidental vocabulary learning yield different performance, repetition, and continuance intention outcomes and uncovers the underlying mechanism.

We identify four popular types of incidental learning: traditional, a murder mystery game, noneducational live streaming, and VTuber. We propose that the underlying mechanism is the mediating role of perceived novelty as heuristic processing, and effort and performance expectancy as systematic processing. We conduct a between-subject experiment with four groups for the four types of incidental learning. From a total of 220 subjects, 55 valid responses were collected from each group. Analysis of variance and a partial least squares structural equation model are employed to examine the differences and mechanism.

The results show that noneducational live streaming performs significantly best for all three outcomes. The mechanism test demonstrates that perceived novelty and performance expectancy play significantly positive mediating roles, whereas effort expectancy has a null mediating effect.

The research provides both theoretical and practical implications.

Online learning is increasingly popular, and educational platforms provide a wealth of courses. Improving course sales is the key to promoting sustainable development of online course platforms. However, limited research has explored the marketing of online courses. We study how to drive online course sales by leveraging teacher information.

We performed an empirical study. We collected data through a crawler and image recognition from Tencent classroom.

Our results show that providing teacher information and profile images helps promote online course sales. However, detailed course descriptions weaken the positive impact of teachers' profile images on online course sales. Furthermore, our study shows an inverted U-shaped relationship between the intensity of smiling in teacher profile photos and online course sales, and teacher descriptions negatively moderate this relationship.

Our study contributes to the research on online course sales and extends the context of the research on smiling as well as the studies of visual and textual information.

The results have practical implications for online course sellers and platforms.

Existing scholarly efforts have explored online courses mainly from an education perspective. More research is needed to advance the understanding of online course sales. Our study advances research in the marketing of online courses.