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A switching depth controller based on a variable buoyancy system (VBS) is proposed to improve the performance of small autonomous underwater vehicles (AUVs). First, the requirements of VBS for small AUVs are analyzed. Second, a modular VBS with high extensibility and easy integration is proposed based on the concepts of generality and interchangeability. Subsequently, a depth-switching controller is proposed based on the modular VBS, which combines the best features of the linear active disturbance rejection controller and the nonlinear active disturbance rejection controller.

The controller design and endurance of tiny AUVs are challenging because of their low environmental adaptation, limited energy resources and nonlinear dynamics. Traditional and single linear controllers cannot solve these problems efficiently. Although the VBS can improve the endurance of AUVs, the current VBS is not extensible for small AUVs in terms of the differences in individuals and operating environments.

The switching controller’s performance was examined using simulation with water flow and external disturbances, and the controller’s performance was compared in pool experiments. The results show that switching controllers have greater effectiveness, disturbance rejection capability and robustness even in the face of various disturbances.

A high degree of standardization and integration of VBS significantly enhances the performance of small AUVs. This will help expand the market for small AUV applications.

This solution improves the extensibility of the VBS, making it easier to integrate into different models of small AUVs. The device enhances the endurance and maneuverability of the small AUVs by adjusting buoyancy and center of gravity for low-power hovering and pitch angle control.

Occupational stress is common in the workplace and leads to various negative outcomes such as burnout, turnover, and medical problems. Although occupational stress is associated with negative connotations, it also can foster workplace resiliency. Workplace resiliency involves the ability to recover quickly in the face of adversity. Emotionally laborious jobs, or jobs in which employees must modify, manage, or regulate their emotions as part of their work role, are inherently stressful. Thus, such jobs, while stress-inducing, may also offer employees opportunities to become more resilient at work. Currently, display rules, rules encouraging the suppression and expression of certain emotions, dictate workplace emotions and thus, interactions. Ultimately, display rule adherence makes it difficult for employees engaging in emotional labor to build resilience. In this chapter, the authors detail how and when emotional labor encounters lead to episodic and prolonged workplace resilience. Specifically, the authors outline instances in which employees engaging in emotional labor can create and sustain workplace resiliency by not deploying an acting strategy and instead, breaking character. The authors further discuss individual and organizational factors that may impact this process as well such as personality and organizational culture that serve as potential boundary conditions to workplace resilience capacity. The authors conclude with implications for both researchers and practitioners.

Standard stratified sampling (SSS) is a popular non-random sampling scheme. Maximum likelihood estimator (MLE) is inconsistent if some sampled strata depend on the response variable Y (‘endogenous samples’) or if some Y-dependent strata are not sampled at all (‘truncated sample’ – a missing data problem). Various versions of MLE have appeared in the literature, and this paper reviews practical likelihood-based estimators for endogenous or truncated samples in SSS. Also a new estimator ‘Estimated-EX MLE’ is introduced using an extra random sample on X (not on Y) to estimate the distribution EX of X. As information on Y may be hard to get, this estimator's data demand is weaker than an extra random sample on Y in some other estimators. The estimator can greatly improve the efficiency of ‘Fixed-X MLE’ which conditions on X, even if the extra sample size is small. In fact, Estimated-EX MLE does not estimate the full FX as it needs only a sample average using the extra sample. Estimated-EX MLE can be almost as efficient as the ‘Known-FX MLE’. A small-scale simulation study is provided to illustrate these points.

The construction industry's transformation of prefabrication into large-scale offsite manufacturing continues to grow. While the Offsite Construction (OSC) transition has brought positive changes to the industry, it also presents various challenges. Literature suggests that to address the challenges, advanced technologies such as Building Information Modeling (BIM) can be applied for effective mitigation. Nonetheless, the combined application of BIM and OSC is in its infancy and faces challenges in many countries. This study aims to investigate the factors influencing OSC and BIM integration, including challenges and strategies within the New Zealand construction industry.

This study explores which specific factors impact integration positively or negatively through a comprehensive literature review and confirmatory survey. Factor Analysis (FA) was used as the data analysis method, resulting in eight groups of underlying factors. Next, three Structural Equation Models (SEM) were developed and tested to analyze the impact of latent and observed variables on OSC and BIM integration.

Significant underlying factors affecting OSC and BIM integration were identified, and recommendations to enhance integration were provided. Specifically, the findings demonstrated that three underlying factors for challenges, two for strategies and three for integration were identified through EFA and confirmed by CFA. This step was followed by SEM, which employed three different path analysis models for integration variables. It clarified that not all variables have similar impacts on integration; some exhibit significant positive or negative effects, while others are not significant at all.

The identified challenges and strategies for OSC and BIM integration and their impact on integration factors can contribute to developing standardized integration guidelines and frameworks to assist stakeholders in focusing on their specific integration goals.

This paper provides an overview of the new Securities Law (“the Law”) of the People’s Republic of China, promulgated at the end of 1998. The analysis will be based on the objective of providing good quality control. The writer will ask whether the Law is sufficiently clear in setting up a securities law system in the PRC. The paper will conclude that the Law falls short of this goal by a considerable margin. This is mainly due to ambiguities in the Law. In addition, inherent characteristics of the PRC legal system also contribute towards this conclusion.

The purpose of this paper is to find the relationship between alliance partner selection and innovation performance of technological standard alliance in China.

The authors designed the selection factors of alliance partners in terms of reputation, compatibility and technological standardization ability and, based on the empirical research data, analyzed the correlation between partner selection and innovation performance using the method of cross validation statistically.

It was found that the selection of alliance partners would directly affect the operation and management of technological standard alliance in China. Appropriate partners among technological standard alliance will play a significant role in improving partners' independent innovation capability. The empirical results showed that potential cooperative partners' reputation, compatibility and standardization ability had an obvious effect on innovation performance.

The paper finds and verifies the key index of alliance partner selection. This result can provide some important references for managers when selecting appropriate cooperative partners. Technological standard alliance managers can evaluate potential cooperative partners according to the index of reputation, technological standardization ability and compatibility designed in this paper. High‐tech firms can cultivate their cooperative innovation ability to adapt to the demands of technological standard alliance according to the key index.

Adopting more sustainable and social-oriented perspectives is crucial for the emergence of the so-called humane entrepreneurial ecosystems (HEEs), the last ones supporting the improvement of both economic, environmental and social wealth. Entrepreneurs act as keystone players in each entrepreneurial ecosystem, thus the emergence of Humane Entrepreneurship (HumEnt) is crucial in shaping HEEs. Given the role of culture in affecting HumEnt, the relationships between Humane Orientation (HO) – as defined in the GLOBE project – and the basic components of Humane Entrepreneurship (HumEnt) were, particularly, explored in a selected sample of countries. Both Intellectual Capital (IC) and knowledge management (KM) perspectives were adopted in pursuing the research goal.

The study approaches this by the mean of the Ward method with Euclidean squared distance and the k-means method. The GLOBE project, the Global Entrepreneurship Monitor (GEM), the Environmental Performance Index (EPI) and the International Social Survey Program (ISSP) were used as data sources. Correlations between HO “as is” scores and each components of HumEnt were checked for the world sample (N = 36), as well as for the groups of innovation-driven countries (N = 17) and European countries (N = 14).

Research results show a conditional confirmation of the developed hypotheses, depending on countries cultural levels of HO, with a moderating role exercised by the economic development on the relationship between culture and HumEnt.

Given the increasing pressure of fundamental societal challenges, such as climate change, poverty and increasing inequality within and between countries intensified by pandemic (UN report, 2021), integrating the more traditional approaches to profit seeking with the more sustainable and human-centric perspective is a priority for both scholars and society at large. Previous researches do not provide explanation about the contextual factors responsible for the emergence of more humane-oriented entrepreneurial ecosystems, especially when referring to culture. This article broadens our understanding about the reason why both HumEntr and HEEs differently arise and develop in different cultural contexts.

This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques.

The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed.

The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k-means algorithm predicted users’ behavior model regarding their loyalty.

The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users.

The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.