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The outbreak of COVID-19 significantly disrupted educational activities and forced universities to rapidly transition from the traditional face-to-face (F2F) environment to online learning formats. The purpose of this paper is to examine the effects of self-directed learning (SDL) on three instructional modalities (F2F, online and HyFlex) among emerging adults. The authors propose that class interaction enjoyment serves as a channel to understand how SDL relates to students’ satisfaction and stress reduction.

An online survey was distributed to the emerging adults, aged 18–25, at six universities across five different US states. Construct validity and reliability were tested by using confirmatory factor analysis. The moderated mediation relationship was examined by calculating the indirect effects of each course delivery format.

The results show that the positive indirect effect of SDL on stress reduction via interaction enjoyment was stronger for F2F classes. In addition, the positive indirect effect of SDL on class satisfaction via interaction enjoyment was stronger for HyFlex classes.

This literature has shown contradictory results: the effects of SDL on student satisfaction and stress reduction prove to be sometimes positive, sometimes non-significant. To better understand this relationship, the authors aim at a mediating variable – enjoyment of class interaction – as a mechanism, and a moderating variable – the instructional modality – as a boundary condition. This research contributes to emerging adults learning literature by involving the interplay among SDL, enjoyment of class interaction and the instructional modality.

Through Bourdieusian sociology, this study aims to interpret a globalized symbolic environment ward by the States and dominated by organizations through the States’ Nobilities enticing and the Euro-American influences disseminated by the cultural circuit of capitalism in the inculcation and incorporation of a class habitus conniving with this logic of domination.

This study has developed a theoretical essay based on the contributions of Bourdieusian sociology to discuss and understand the following concepts and their respective relationships: symbolic environment, globalization, organizations, State, State Nobility, Euro-American influences, cultural circuit of capitalism and class habitus.

The arguments built throughout this theoretical essay recognized how class habitus on environment contributes to organizations establishing themselves as a space that consolidates and replicates the domination logic. As indicated, the State Nobility is an intermediary element between dominant organizations and the State, as dominated.

This theoretical essay signals that less harmful alliances between organizations, the State Nobility and the State could culminate in social, environmental and economic scenarios provided with more inclusion, diversity and preservation.

This study presents an in-depth conceptual analysis to hold power structures responsible as direct and indirect drivers of environmental problems, with their different proportions and severity levels, affecting the planet.

This study proposes an alternative lens to debate and question how much the results presented by the contemporary world order compensate (if in any way) the damage that invades and deteriorate environmental assets.

Liquidity is a primary concern for businesses. The purpose of this study is to understand the impact of the collaborative liquidity management within the supply chain. Larger firms prescribe favorable trade terms in the transactions and do not engage in value chain vision sharing with their smaller counterparts. Smaller firms encounter challenges with liquidity and often face the risk of bankruptcy. Such practice can threaten the entire supply chain. Instead, collaborative liquidity management can offer a win–win scenario to both parties. In that case, what are the benefits of implementing a collaborative liquidity management approach across the value chain, and what is the reward?

The authors selected key liquidity metrics that matter most to the organizations from a cohort of 307 firms from the Indian automobile industry for 10 years (2012–2021). The authors classified the businesses into five distinct revenue-based categories. They emphasized the importance of expanded supply chain finance adoption and demonstrated how collaborative liquidity management strategies boosted return on assets.

The research confirms the tangible benefits of greater adoption of supply chain finance in realizing supply chain members’ shared vision. The authors challenged the age-old practice of power-based relationships in the supply chain. They recommended a win–win scenario through practical cooperation and increased adoption of SCF by value chain members.

Existing research predominantly focuses on dyadic relationships and is restricted to Europe and China. According to the authors, no comprehensive investigation has been conducted in India. This application of simulation techniques to improve the liquidity performance of companies in developing economies is innovative.

E-learning has become a polarizing issue. Some say that it enhances accessibility to education and some say that it hinders it. While the literature on the subject underscores the effectiveness of the pedagogical frameworks, strategies and distance learning technologies, the firsthand accounts of students, parents and practitioners challenge the validity of experts’ assessments. There is a gap between theory and practice and between the perceptions of providers and consumers of online learning. Following a period of lockdowns and a transition to online learning during the recent pandemic, the prevailing sentiment toward a distance mode of instruction became one of strong skepticism and negative bias. The aim of the study was to examine why e-learning has struggled to meet stakeholder expectations. Specifically, the study posed two research questions: 1. What are the reasons for dissatisfaction with online learning? 2. What are the implications for future research and practice?

The study used a mixed methods approach to examine the reasons behind negative perceptions of online learning by comparing the firsthand accounts posted on social media with the literature. To this end, n = 62,874 social media comments of secondary and postsecondary students, as well as parents, teachings staff and working professionals, covering the span of over 14 years (2008–2022), were collected and analyzed.

The study identified 28 themes that explain the stakeholder’s discontent with the online learning process and highlighted the importance of user-centric design. The analysis revealed that the perceived ineffectiveness of distance education stems from the failure to identify and address stakeholders’ needs and, more particularly, from the incongruence of instructional strategies, blindness to the cost of decisions related to instructional design, technology selection and insufficient levels of support. The findings also highlight the importance of user-centric design.

To address dissatisfaction with e-learning, it is imperative to remove barriers to learning and ensure alignment between technology and learners’ needs. In other words, the learning experience should be personalized to account for individual differences. Despite its cost-effectiveness, the one-size-fits-all approach hinders the learning process and experience and is likely to be met with resistance.

Drawing from the extensive literature, the study offers an explanation for stakeholders’ discontent with e-learning. Unlike survey research that is prone to social desirability bias, the sample provides a rare opportunity to observe and measure the visceral reactions that provide a more authentic sense of stakeholders’ perceptions toward online learning. The authors offer recommendations and identify areas for future research.

This paper investigates the constraints an organisation faces when using recruitment agencies and having to trade-off between the speed of hiring a candidate, the cost of a candidate and the match of the candidate against the job requirements across different job seniorities. We analyse how technology can shift the cost and hiring speed in spite of these constraints.

The research design is exploratory, quantitative and cross-sectional. The study employed a two-factor, unbalanced class Analysis of Variance (ANOVA) including interaction effects to test the difference between the means of the class of interest and a control class.

Our empirical findings confirm that (1) the technological innovation of a recruitment agency marketplace can liberate organisations from their time, cost and quality hiring constraints, accelerating the time to hire by four times and reducing costs by over 12%, and (2) these results hold across varying role seniority levels.

This study contributes to the existing literature in three ways: (1) it introduces the recruitment triangle from project management into the recruitment literature; (2) it demonstrates how technological innovations such as recruitment agency marketplaces are able to provide a shift in the constraints posed by the recruitment triangle.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

Data storytelling courses position students as agents in creating stories interpreted from data about a social problem or social justice issue. The purpose of this study is to explore two research questions: What themes characterized students’ iterative development of data story topics? Looking back at six years of iterative feedback, what categories of data literacy pedagogy did instructors engage for these themes?.

This project examines six years of data storytelling final projects using thematic analysis and three years of instructor feedback. Ten themes in final projects align with patterns in feedback. Reflections on pedagogical approaches to students’ topic development suggest extending data literacy pedagogy categories – formal, personal and folk (Pangrazio and Sefton-Green, 2020).

Data storytelling can develop students’ abilities to move from being consumers to creators of data and interpretations. The specific topic of personal data exposure or risk has presented some challenges for data literacy instruction (Bowler et al., 2017). What “personal” means in terms of data should be defined more broadly. Extending the data literacy pedagogy categories of formal, personal and folk (Pangrazio and Sefton-Green, 2020) could more effectively center social justice in data literacy instruction.

Implications for practice include positioning students as producers of data interpretation, such as role-playing data analysis or decision-making scenarios.

Data storytelling has the potential to address current challenges in data literacy pedagogy and in teaching critical data literacy.

Course descriptions provide a template for future data literacy pedagogy involving data storytelling, and findings suggest implications for expanding definitions and applications of personal and folk data literacies.

Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.

To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.

The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.

This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

The six-axis force/torque sensor based on a Y-type structure has the advantages of simple structure, small space volume, low cost and wide application prospects. To meet the overall structural stiffness requirements and sensor performance requirements in robot engineering applications, this paper aims to propose a Y-type six-axis force/torque sensor.

The performance indicators such as each component sensitivities and stiffnesses of the sensor were selected as optimization objectives. The multiobjective optimization equations were established. A multiple quadratic response surface in ANSYS Workbench was modeled by using the central composite design experimental method. The optimal manufacturing structural parameters were obtained by using multiobjective genetic algorithm.

The sensor was optimized and the simulation results show that the overload resistance of the sensor is 200%F.S., and the axial stiffness, radial stiffness, bending stiffness and torsional stiffness are 14.981 kN/mm, 16.855 kN/mm, 2.0939 kN m/rad and 6.4432 kN m/rad, respectively, which meet the design requirements, and the sensitivities of each component of the optimized sensor have been well increased to be 2.969, 2.762, 4.010, 2.762, 2.653 and 2.760 times as those of the sensor with initial structural parameters. The sensor prototype with optimized parameters was produced. According to the calibration experiment of the sensor, the maximum Class I and II errors and measurement uncertainty of each force/torque component of the sensor are 1.835%F.S., 1.018%F.S. and 1.606%F.S., respectively. All of them are below the required 2%F.S.

Hence, the conclusion can be drawn that the sensor has excellent comprehensive performance and meets the expected practical engineering requirements.

The paper aims is to see why social conflict caused by class segregation did not occur in Gading Serpong? What factors prevent conflict from occurring? This research seeks to find the causes of the nonoccurrence of social conflict due to class segregation in the Gading Serpong cluster area and explore the factors that restrain conflict there.

This research is qualitative research with data collection techniques through in-depth interviews with several parties identified as brokerages in the research object area. In this context, one of the media and analytical tools is to recognize agents or brokers who connect two groups of people. Brokerage occurs in sectors, patterns or forms of informal, personal relationships; to understand it, one must pay close attention to micro-level relationships and social psychological processes. However, brokerage can have a significant impact on macro-level social relations, as it is generally associated with social integration processes.

The lack of involvement of developers in overcoming social conflicts that occur between Gading Serpong natives and migrants in Gading Serpong housing has given rise to new actors. These new actors are what we can call brokers, where they have a role as brokers who are able to connect between migrants and natives in the Gading Serpong area. The broker phenomenon is actually familiar in academia, where in practice the broker acts as someone who is able to find solutions to problems. The broker is the reason even social segregation is created between migrant citizens and native citizens in Gading Serpong but never becomes a conflict between them.

Even if the brokerage phenomenon is the reason why there is no conflict over social segregation brokerage is not the only factor in this nonconflict segregation. Therefore, to cover the larger area of these suburban segregation problems, there must be further research on this topic.

The practical implication of this research is to encourage the housing developers that create urban housing, such as clusters or other gated communities, to evaluate the social factors, such as potential segregation and conflict management. Also to encourage the developers to get involved and create some social engineering systems, like brokerage, market and other social agents, to create some nonconflict segregation or even more inclusive communities.

This research is uncovering the main reason why social segregation between migrant and native people in Gading Serpong, which could potentially lead to conflict, is never a conflict. The main reason is social actors like brokerage.