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This paper aims to solve the problem that strong noise interference seriously affects the direction of arrival (DOA) estimation in complex underwater acoustic environment. In this paper, a combined noise reduction algorithm and micro-electro-mechanical system (MEMS) vector hydrophone DOA estimation algorithm based on singular value decomposition (SVD), variational mode decomposition (VMD) and wavelet threshold denoising (WTD) is proposed.

Firstly, the parameters of VMD are determined by SVD, and the VMD method can decompose the signal into multiple intrinsic mode functions (IMFs). Secondly, the effective IMF component is determined according to the correlation coefficient criterion and the IMF less than the threshold is processed by WTD. Then, reconstruction is carried out to achieve the purpose of denoising and calibration baseline drift. Finally, DOA estimation is achieved by the combined directional algorithm of preprocessed signal.

Simulation and field experiments results show that the algorithm has good noise reduction and baseline drift correction effects for nonstationary underwater signals, and high-precision azimuth estimation is realized.

This research provides the basis for MEMS hydrophone detection and positioning and has great engineering significance in underwater detection system.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Most offender narrative being studied has been in oral forms, produced in the reciprocal process of researcher-(ex) offender interviews. This chapter offers an introduction to a variation of offender narrative study within the prison and rehabilitation context: the narrative of written autobiography. Since the early 1940s, Chinese reform institutions have required written autobiographies from new admitters, provided with clear presubscripted guidelines of instructions as well as postcensorship. For this chapter, we trace back and analyse this model based on 28 prisoners' autobiographies in mainland China between 2007 and 2009, as well as archive documents in different historical periods. We have found that the mandatory offender autobiographies are highly functional writings with clear requirements that embody the existing power structure. We have also found considerable commonality with findings in Western contexts on the presence and problems of narrative compliance in rehabilitation. We argue that narrative criminology should further engage in understanding the practice of narrative censorship and co-authorship in criminal justice processes, as it takes on different forms in different historical–social contexts.

The purpose of this paper is to show that advertising as part of product promotion is in strict correlation with the specific regulation of each product category, food supplements and herbal/over-the-counter medicines. Main contributions of the paper are two-fold. First, it demonstrates that there are products when certain botanical is registered according to legislation as herbal medicine, but at the same time it can be found in sales as food supplement. This can happen within the same country, but it is more frequent within different countries. Second, the possibility for regulation harmonization is discussed.

In this paper, a comparative analysis of regulation for concerned products in European Union and countries of interest is made. In two case studies, a comparative analysis of marketing of the products in Macedonia and Serbia is made.

Food supplements are subject to more liberal regulation in comparison with herbal/over-the-counter medicines, but should not be attributed indications and properties that they do not possess. Regulation is stricter and more complex for over-the-counter medicines as a separate class of medicines, these must be correlated with registered properties and indications and are subject to approval by regulatory bodies (print media: advertisement in newspaper/magazine, poster, brochure, flyer, banner and billboard and electronic media: TV spot, radio advertising and internet advertisement).

Countries of interest: EU, Macedonia, Serbia, Montenegro, Albania, Kosovo, Bosnia and Herzegovina.

The paper will contribute toward the creation of promotional and marketing steps in placement of these products on the market in countries of interest, based on regulation of the product category.

The discussed opportunities for harmonization are applicable and realistic and can positively contribute for better flow and placement of food supplements and herbal/over-the-counter medicines in different countries. The results of the case studies can also be used for regulatory activities and preparation of marketing materials for other products on other markets of interest with same or similar regulation.

For the first time, a comparative analysis of regulation is made for concerned products in countries of interest. Possibility for regulation harmonization is discussed.

Recently, interest and necessity for cloud-based hospital information systems (HISs) have emerged as an appropriate alternative for revitalizing medical information exchange between hospitals, analyzing “big data” medical information and developing the use of new medical technologies. The purpose of this paper is to investigate factors that affect the switching of information systems in existing on-premise environments into cloud-based HISs.

A research model was developed using the push–pull–mooring model based on migration theory. The research model was analyzed using confirmatory factor analysis and path analysis using partial least squares structural equation modeling.

The results of this study showed that low compatibility, perceived value, low cost and inertia influenced the intention to switch to cloud-based HISs; low flexibility and low compatibility influenced dissatisfaction; and low cost, ease of maintenance and ease of managing indicators influenced perceived value.

This study is expected to be used as the basis for developing a research model in subsequent studies to analyze the transition to new innovative technologies. Also, in practice, it is expected to contribute to the activation of cloud computing environments in hospitals.

The purpose of this paper was to assess and determine the impact of the five core technologies of Industry 4.0 (3D Printing, Big Data Analytics, Cloud Computing, Internet of Things (IoT) and Robotics) on the organizational performance of the retail industry in the context of Pakistan.

Pakistan's retail industry was chosen as the target sector, and the target population was composed of senior-level employees, including managers from first-level positions to top-level positions, as well as subordinate employees working under the supervision of first-level managers, possessing the technological know-how of Industry 4.0. The data were collected through a matrix-based survey questionnaire that was based on a five-point Likert scale, ranging from “strongly agree” to “strongly disagree.” The process of data analysis was conducted using IBM SPSS Statistics.

The findings obtained by this research work showed a significant relationship among the five core pillars of Industry 4.0 and the organizational performance of Pakistan's retail industry. Besides, the obtained findings provided preliminary evidence that Industry 4.0's disruptive technologies, particularly, 3D printing, big data analytics, cloud computing, IoT and robotics, could help Pakistan's retail industry solve various problems and challenges, such as meager revenues, increased expenses and unorganized systems.

The present study extended the theoretical body of knowledge through studying and examining Industry 4.0's five crucial factors that significantly contribute to the service sector, particularly, the retail industry, of the big emerging markets (BEM) economies, including Pakistan.

The purpose of this paper is to explore the contemporary paradigm of business leadership vis-à-vis China’s reform and transitional context.

The paper employs an evidence-based approach to explore the business leadership issues influenced by economic reform and within the context of societal transition in China. A qualitative research method was adopted based on in-depth interviews with a number of middle managers from a variety of Chinese enterprises, including state-owned, domestic-private and foreign-invested enterprises. Content analysis of several rounds of interviews added depth to the data analysis.

The findings complement existing thoughts and illustrate concepts, issues, and characteristics not yet emphasized in mainstream literature. General patterns and associated characteristics of business leadership in China, as well as specific patterns associated with different forms of enterprise ownerships, are identified.

The study makes a timely and necessary contribution that enriches context-specific understandings of business leadership against the backdrop of surrounding economic, social, and cultural changes.

The study enriches understandings of commonalities and differences in leadership across the globe, facilitating working collaboratively to achieve common goals in a global community.

The study offers new insights into business leadership by linking contextual, personal, and cognitional factors together and demonstrates some unique characteristics of leadership styles in transitional economies like China.

The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity in any given learning management system based on learners’ learning styles.

This paper offers a brief review of current frameworks and systems to support adaptivity in e-learning environments. A framework to support adaptivity is designed and discussed, reflecting the hybrid approach in detail. A system prototype is developed incorporating different adaptive features based on the Felder-Silverman learning styles model. Finally, the prototype is implemented in Moodle.

The system prototype supports real-time adaptivity in any given learning management system based on learners’ learning styles. It can deal with any type of content provided by course designers and instructors in the learning management system. Moreover, it can support adaptivity at both course and learner levels.

To the best of the authors’ knowledge, no previous work has been done incorporating the concept of the ECA model and intelligent agents as hybrid architecture to support adaptivity in e-learning environments. The system prototype has wider applicability and can be adapted to support different types of adaptivity.

This paper aims to use an ionic liquid (IL, [HMIM]PF₆) to improve the lubrication performance of liquid metal (LM) as a lithium grease additive and to expand the application range of LM.

In this paper, the different mass ratios of [HMIM]PF₆/LM mixtures were added into the lithium grease on a four-ball tribo-meter to investigate the effects of its tribological behavior. Scanning electron mircoscope/energy dispersive spectroscopy and X-ray photoelectron spectroscopy were used to reveal the anti-wear and friction-reducing mechanism of the additives.

When the load was used at 461 N, the average coefficient of friction (ACOF) and average wear scar diameter (AWSD) of steel ball Lubricated with grease with an optimal ratio of 2:3 ([HMIM]PF₆/LM) were reduced by 32.8% and 30.5%, respectively. Friction and wear mechanisms are ascribed to friction-induced additive components that can simultaneously form a composite lubrication film consisting of FePO₄, FeF₃, Ga₂O₃, In₂O₃ and SnO₂.

Compared with the pure lithium-based grease, when [HMIM]PF₆/LM was added with an optimal ratio of 2:3, the ACOF and AWSD were reduced by 12.4% from 0.097 to 0.085 and 23.8% from 552.117 µm to 420.590 µm under 392 N, respectively. When at 461 N, the ACOF and AWSD of steel ball were reduced by 32.8% from 0.122 to 0.082 and 30.5% from 715.714 µm to 497.472 µm, respectively. It was shown that the simultaneous addition of LM and [HMIM]PF₆ can form a composite lubrication film consisting of FePO₄, FeF₃, Ga₂O₃, In₂O₃ and SnO₂.

In this paper, [HMIM]P F₆ is added with LM simultaneously to improve the lubrication properties of lithium grease, and expand the application scope of LM.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0017/