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A tightly coupled global navigation satellite system (GNSS)-Vision-IMU-wheel odometer (GVIWO) system is proposed, which can realize robust positioning in extreme environments. The purpose of this study is to achieve adaptive initialization in complex environments, sensor anomaly detection and processing, and adaptive robust localization in extreme environments.

Adaptive initialization includes traditional dynamic and static initialization and extreme condition initialization. To deal with the unstable visual features in the state of excited motion, a method of wheel odometer assisted initialization is designed. According to the abnormal condition of the sensor, the anomaly detection and attenuation mechanism are designed to realize the accurate positioning of the sensor under abnormal condition.

Tight coupling optimization of GNSS signals, RGB+Depth Map cameras, inertial measurement units and wheel odometers ensures accurate positioning in both indoor and outdoor environments. Through open data sets and field validation experiments, the proposed tightly coupled system has strong adaptability, especially in extreme environments.

A new framework is proposed by integrating GNSS, visual, inertial measurement unit (IMU) and wheel odometer sensors to form an efficient positioning solution. An adaptive initialization method is proposed to enhance the robustness and real-time performance of the positioning system in complex and dynamic environments. A mechanism for detecting and attenuating sensor anomalies is designed, enabling quasideterministic positioning under sensor anomalies.

Wheeled robots have been widely used in People’s Daily life. Accurate positioning is the premise of autonomous navigation. In this paper, an optimization-based visual-inertial-wheel odometer tightly coupled system is proposed, which solves the problem of failure of visual inertia initialization due to unobservable scale.The aim of this paper is to achieve robust localization of visually challenging scenes.

During system initialization, the wheel odometer measurement and visual-inertial odometry (VIO) fusion are initialized using maximum a posteriori (MAP). Aiming at the visual challenge scene, a fusion method of wheel odometer and inertial measurement unit (IMU) measurement is proposed, which can still be robust initialization in the scene without visual features. To solve the problem of low track accuracy caused by cumulative errors of VIO, the local and global positioning accuracy is improved by integrating wheel odometer data. The system is validated on a public data set.

The results show that our system performs well in visual challenge scenarios, can achieve robust initialization with high efficiency and improves the state estimation accuracy of wheeled robots.

To realize robust initialization of wheeled robot, wheel odometer measurement and vision-inertia fusion are initialized using MAP. Aiming at the visual challenge scene, a fusion method of wheel odometer and IMU measurement is proposed. To improve the accuracy of state estimation of wheeled robot, wheel encoder measurement and plane constraint information are added to local and global BA, so as to achieve refined scale estimation.

The purpose of this study is to evaluate and address the energy efficiency prevalent within the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) countries, a diverse group that constitutes a significant portion of the global gross domestic product. By assessing energy efficiency, the study aims to inform strategies that foster sustainable economic growth and environmental sustainability within these nations.

This research uses a three-stage slacks-based measure data envelopment analysis (SBM-DEA) model, enhanced by stochastic frontier analysis (SFA), to assess the energy efficiency of CPTPP countries over the period from 2000 to 2015. The three-stage SBM-DEA model allows for a comprehensive analysis by first measuring energy efficiency, then adjusting for external environmental factors and statistical noise through SFA, and finally, reevaluating efficiency with adjusted inputs.

The analysis reveals significant disparities in energy efficiency among CPTPP nations, identifying both high-performing countries and those needing considerable improvements. Developed countries within the CPTPP generally exhibit higher energy efficiency levels, which can be attributed to their adoption of advanced technologies and strong policy frameworks. In contrast, developing nations demonstrate greater vulnerability to external environmental factors affecting their energy efficiency.

The research fills a gap in the literature by providing a nuanced and comprehensive evaluation of energy efficiency across a significant and economically diverse group of nations, offering valuable insights for sustainable development within the CPTPP framework.

Recognition of the complexity of job embeddedness in the work environment has grown, highlighting the need for a deeper understanding of the factors that contribute to this phenomenon. This study analyzed how and when job crafting and leisure crafting are linked to job embeddedness by investigating employee resilience as a mediator and employee adaptivity as a moderator.

Data were gathered from 568 Taiwanese hotel employees. The PROCESS macro was used to verify all hypotheses.

Both job crafting and leisure crafting increased job embeddedness. Employee resilience mediated the impacts of job and leisure crafting on job embeddedness. The positive relationship between employee resilience and job embeddedness was stronger when employee adaptivity was high. Employee adaptivity moderated the indirect impacts of job and leisure crafting on job embeddedness through employee resilience.

Hotel managers should foster a workplace culture that encourages employees to engage in job crafting. Additionally, managers can offer employee assistance programs to proactively encourage workers to participate in leisure crafting. Providing training and wellness programs to strengthen employee resilience, along with allocating resources and designing learning programs to enhance employee adaptability, can further promote job embeddedness.

This research contributes to the literature through the construction of a moderated mediation model that explored how and when job and leisure crafting affect job embeddedness.

Finite element analysis of underwater transducers typically requires a high level of expertise, and the iterative process of testing various sizes, material parameters and other factors is often inefficient. To address this challenge, this paper aims to introduce underwater transducer parametric simulation (UTPS) software to streamline the design and optimization process.

The design methodology integrates the strengths of ANSYS Parametric Design Language (APDL) for parametric design with the Qt Creator framework for developing a visual interface. C++ is used to encapsulate complex, hard-to-master APDL macros and interact with ANSYS software to execute the relevant APDL macros, performing finite element analysis on the underwater transducer in the background. The results are then processed and displayed on the visual interface.

UTPS enables parametric modeling, modal analysis, harmonic response analysis and directivity analysis of underwater transducers. Users only need to input parameters into the software interface to obtain the transducer’s performance, significantly improving work efficiency and lowering the professional threshold. A prototype transducer was fabricated and tested based on UTPS results, which confirmed the accuracy of the software.

This paper presents an innovative parametric simulation tool for underwater transducers, combining finite element analysis and APDL to simplify and expedite the design process. UTPS reduces the need for specialized knowledge, cutting down on training costs, while its parametric design capabilities accelerate the design process, saving resources.

This research paper aims to explore the added value of knowledge management (KM) and its antecedents for innovation and organizational performance (OP) in marginal healthcare organizations.

Using insights from the resource-based view and knowledge-based theory of the firm, the model explains the effects of technology capabilities (TC) and organizational culture (OC) on the KM process, process innovation (PIN), administrative innovation (AIN) and OP. The authors used partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to analyze data collected from 168 healthcare practitioners in Cameroon using a survey.

The authors reveal that TC and OC positively impact some KM components. Knowledge sharing (KS), knowledge acquisition (KA) and responsiveness to knowledge (RK) influence PIN, while only PIN and KA influence OP. FsQCA provided several configurations that lead to high OP within healthcare centers. As a result, the results are adaptable to any healthcare center that wishes to set up one or more KM processes.

Given that the results will help the health workforce make concerted decisions about medical care, the authors contribute significantly to the definition and optimization of KM in healthcare by implementing various processes and policies to ensure the continued existence of high-quality and outstanding healthcare systems. The KM propositions will enable healthcare centers to: (1) improve the quality of patient care through collegiality in medical practice; (2) optimize processes in the patient care chain; and (3) leverage knowledge gained though knowledge sharing among the medical team. The propositions open up avenues for future research in addition to providing practical implications for healthcare center practitioners.

This study sheds new empirical light on the relationships between KM antecedents and processes, innovation and OP in healthcare centers. This research is one of the few to examine the relationship between TC, OC, KM processes, innovation and OP in developing countries. This paper aims to fill this gap and inform future research concerning KM in the healthcare sector. Further, this study goes beyond testing the PLS-SEM approach's hypotheses by applying fsQCA to provide practical and comprehensive knowledge on how to increase the efficiency of a healthcare center through KM.

The review paper aims to understand the development of workplace ostracism as a field in organizational studies from 2000 to the present. The study provides a comprehensive synthesis of the current state of the domain by exploring its antecedents, consequences, underlying mechanisms and buffering mechanisms.

The present study analyses 134 published peer-reviewed empirical and non-empirical articles retrieved from the Scopus database. A systematic literature review and bibliometric analyses (using VOS viewer) have been used to gain insights into the development and trends within the field. Bibliometric analyses involved science mapping techniques such as co-citation analysis, co-occurrence of keywords and bibliographic coupling. Combining these three techniques, the study aimed to provide a comprehensive overview of the workplace ostracism research domain's historical, current and future landscape.

In the present study, through descriptive analyses, the authors uncovered publishing trends, productive journals, countries and industries that contribute to this research field. The systematic review enabled the showcasing of the current landscape of workplace ostracism. The bibliometric analyses shed light on major authors, influential articles, prominent journals and significant keywords in workplace ostracism.

This study enriches the existing literature by offering a comprehensive research framework for workplace ostracism. It goes beyond that by presenting significant bibliographic insights by applying bibliometric analyses. Furthermore, this study identifies and emphasizes future research directions using the theory, characteristics, construct and methodologies framework, aiming to expand the knowledge base and understanding of this topic.

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.

This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.

Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.

A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.

Developing technological capabilities to enhance innovation performance is essential for firms to respond to external changes and competition. Based on the effect of organizational structure on organizational capability development, this study assesses whether a specific R&D organizational structure design can be used to develop different technological capabilities.

Combining organizational theory and the resource-based view as an integrated view, we propose several contrasting hypotheses to show the effects of three general R&D organizational structure designs (centralized, decentralized, and hybrid) on developing exploitative and explorative capabilities. We propose R&D slack as a moderator. 82 Taiwanese listed manufacturing firms were selected. Data on the firms' annual reports and their patent applications to the Taiwan Patent Office from 2005 to 2017 were collected.

Firms’ adoption of centralized and decentralized R&D structures has a significant positive effect on developing exploitative capability and an opposite effect on developing explorative capability. A high or low R&D slack can moderate the impact of R&D organizational structure on non-routine capability development.

This study concludes that R&D organizational structure affects the development of different technological capabilities and that the effect of R&D organizational structure on the development of technological capabilities can be changed under the moderation of R&D slack, which means that the possibility of developing different technological capabilities under the same organizational structure will increase.

The top manager should consider the relationship between R&D structure design and technological capability development to manage the R&D routines to influence the generation of technological capabilities. Also, they must utilize the provision of R&D slack to modulate technological capability development.

This study reexamines the relationship between organizational structure and capability development. It shows that organizational structure can shape unique technological capabilities and that firms may be able to change structural elements through slack resources, enabling ambidexterity or dynamic capability development without organizational change.

The prevalence of live-streaming e-commerce leads to massive impulse purchases. Based on social learning theory, this study proposes a research model to explore the effect of streamers and viewers on impulsive buying by influencing consumers’ certainty perception.

This study adopts a questionnaire (N = 285) to collect data and conducts a partial least squares structural equation modelling (PLS-SEM) analysis to examine our research hypotheses.

Our results indicate that streamers and viewers improve product demand certainty and product quality certainty, but their effects on streamer quality certainty vary. In addition, product quality certainty is positively associated with impulsive buying, whereas streamer quality certainty and product demand certainty show a negative effect. Streamers can only drive impulse purchases by increasing product quality certainty, but the influence of viewers on consumers’ impulsive buying is fully mediated by product quality certainty and streamer quality certainty.

Our findings contribute to the literature on live-streaming e-commerce (LSE) by identifying the differential effects of streamers and viewers on impulsive buying. In addition, we broaden the area of certainty research in the LSE field to include product quality certainty, streamer quality certainty and product demand certainty and evaluate these certainty aspects as three psychological mechanisms that drive consumers’ impulse purchases.