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Recognising the literature of a field is vital for advancement in that field. Yet, there has not been a systematic analysis of recent publications published in the Journal of Hospitality and Tourism Insights (JHTI). Therefore, this research aims to do a bibliometric analysis of articles published in JHTI during the previous five years.

This study used bibliometric techniques and indicators to analyse JHTI publications from 2018 to 2022. The data utilised in the study were obtained from Scopus and subsequently subjected to analysis through the Bibliometrix software.

The findings show good collaboration between the production components (country, institution and author) in JHTI. The co-occurrence analysis of keywords comprises five clusters; the co-citation analysis comprises six; and a group of articles connected with psychological aspects and areas such as motivation, attitude, customer engagement, place attachment and behavioural intention was the most remarkable cluster. Sharing economy, destination marketing, destination image and some, to an extent, social media and revenue management are just a few of the niche themes that have the potential to come up.

This study will be helpful as a roadmap for researchers in different fields who are interested in such studies, as well as for editorial board members and those who work in JHTI.

Scholars and practitioners may benefit the most from this research by obtaining insight into the development of JHTI's research and the areas of the hospitality and tourism industries that need more study.

The current study is both necessary and valuable because it is the first to provide insight into the effectiveness and intellectual framework of the hospitality and tourism literature selected by the JHTI.

The conceptual foundations of performance measurement and management (PMM) are predominantly rooted in control systems research. However, the appropriateness of this paradigm for volatile and uncertain environments has been questioned. This paper explores whether grounding PMM in social systems theory and viewing uncertainty from an organisational behaviour perspective provides new insights into the PMM theory–practice gap.

A framework, rooted in social systems theory and practice theory, is created that describes how organisational behaviour shapes the social processes associated with organisational change. Semi-structured interviews of 35 people from 16 organisations coupled with thematic analysis are employed to identify the organisational behavioural characteristics that influence how PMM is executed in practice. PMM is then reconceptualised from the perspective of this social systems-based framework.

This investigation proposes (1) performance management is concerned with elements of PMM-related practices open to flexible interpretation by human agents that change the effectiveness of organisational practices, whereas performance measurement is concerned with elements of PMM-related practices not open to interpretation but deliberately reproduced to provide a consistent comparison with the past; (2) the purpose of PMM should be to achieve organisational effectiveness (OE) and (3) the mechanisms underlying performance management and performance measurement are social intervention and embeddedness, respectively.

This first social systems perspective of PMM advances the development of PMM's theoretical foundations by providing a behaviour-based interpretation of, and framework for, PMM-mediated organisational change. This competing approach has strong links to practice.

The purpose of this study is to improve the control performance of wind energy conversion systems (WECSs) by proposing a new sensorless, robust control strategy based on a Smith predictor active disturbance rejection control (SP-ADRC) associated with a speed/position estimator.

The estimator consists of a sliding mode observer (SMO) in combination with a phase-locked loop (PLL) to estimate the permanent magnet synchronous generator (PMSG) rotor position and speed. At the same time, the SP-ADRC is applied to the speed control loop of the variable-speed WECS control system to adapt strongly to dynamic characteristics under parameter uncertainties and disturbances.

Numerical simulations are conducted to evaluate the speed tracking performances under various wind speed profiles. The results show that the proposed sensorless speed control improves the accuracy of rotor speed and position estimation and provides better power tracking performance than a regular ADRC controller under fast wind speed variations.

This paper offers a new approach for designing sensorless, robust control for PMSG-based WECSs.

A new sensorless, robust control is proposed to improve the stability and tracking performance of PMSG-based WECSs. The SP-ADRC control attenuates the effects of parameter uncertainties and disturbances and eliminates the time-delay impact. The sensorless control design based on SMO and PLL improves the accuracy of rotor speed estimation and reduces the chattering problem of traditional SMO. The obtained results support the theoretical findings.

This study aims to identify the suitable indicators and their scale for measuring the level of logistic halal implementation and then develops the measurement system based on those indicators and scales. Moreover, this research also applies the measurement system in food, beverage, and ingredient companies.

This study collected data through web-based closed questionnaires and short telephone interviews. This study succeeded in obtaining seven valid data sets from filling out validation and analytical hierarchy process questionnaires by a panel of experts. This study also obtained 97 valid data sets from filling out the questionnaire about the current condition of each indicator related to halal logistics (HL) implementation. Then, the framework for measuring HL implementation was designed based on the multi-attribute value theory approach.

The aggregate value of HL implementation in ingredient companies belongs to the excellent implementation category. In contrast, the aggregate value of HL implementation in food and beverage companies only belongs to the good implementation category. Then, according to the size of the company, the large-sized companies have the higher means of the aggregate value of HL implementation for preferred indicators than small- and medium-sized companies. However, all size companies belong to the good implementation category.

This research has several limitations. The preferred indicators are only measured by the Likert scales. Future research may benefit from inducing the qualitative approaches to measure the condition of each indicator better. Secondly, this research only consisted of 97 food, beverages and ingredients companies. Thirdly, this research only measured the HL implementations to a particular sector or industry. Future research could benefit from replicating the work in similar and dissimilar contexts, adding the sample size and comparing the level of HL implementation between Muslim and non-Muslim companies, and moreover, enhance the measurement of HL implementation by including consumers and other actors involved, such as the government.

This research provided a basis for helping the government and policymakers understand and evaluate non-performing indicators in HL implementation before formulating several actions.

Understanding the current HL implementation conditions could facilitate a more effective response in designing some appropriate alternative actions.

This research contributed to measuring HL implementation with a new indicator, a new scale and a new object in an empirical case of food, beverages and ingredient companies in Indonesia.

The purpose of this study is to test the performance of the designed automatic control system based on the Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) algorithms during landing of the helicopter on the ship deck. This paper is a further development of the series based on Topczewski et al. (2020).

The system consists of two automatic control algorithms based on LQR and the LQG. It is integrated with the ship motion prediction system based on autoregressive algorithm with parameters calculated using Burg’s method. It is assumed that the source of necessary navigation data is integrated Inertial Navigation System with Global Positioning System. Landing of the helicopter on the ship deck is performed in automatic way, based on the preselected procedure. Performance of the control system is analyzed when all necessary navigation data is available for the system and in case when one of the parameters is unavailable during performing the procedure.

In this paper, description of the designed control system developed for performing the approach and landing of the helicopter using selected procedure is presented. Helicopter dynamic model is validated using the manufacturer data and by test pilots, overview is presented. Necessary information about ship motion model is also included. Tests showing mission performance while using LQR and LQG algorithms applied to the control system are presented and analyzed, taking into account both situations when full navigation data is available/unavailable for the control system.

Results of the system performance analyses can be used for selection of the proper control methodology for prospective helicopters autopilots. Furthermore, the system can be used to analyze the mission safety when information about one of the navigation parameters is identified by the navigation system as unavailable or incorrect and therefore unavailable during landing on the ship deck.

In this paper, control system dedicated for the automatic landing of the helicopter on the ship deck, based on two different control algorithms is presented. Influence of lack of information about one of the navigation parameters on the mission performance is analyzed.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

Due to consumption changes in the post-pandemic era, the production safety of agricultural products is affecting global consumers. This paper constructs an evaluation index of the agricultural Internet of things (IOT) traceability system and evaluates it using the dynamic hesitant-fuzzy linguistic term sets (HFLTS)-based DEMATEL method to improve agricultural supply-chain links and improve production quality.

The agricultural IOT traceability index system is constructed using the literature and expert interviews; it comprises 6 first-level indices and 20 second-level indices. The agricultural IOT traceability system is evaluated using the dynamic HFLTS-DEMATEL method.

Producers' awareness of agricultural-production safety (A11) has the most significant impact on production and processing links, while warehouse location and storage capacity (A31) have the largest impact on the circulation link. Inspection authenticity and transparency and quarantine information (A41) have the largest impact on the detection-consumption link. The extent to which the traceability-platform construction is complete (A62) has the largest impact on technical support.

The present paper may be limited to the era of post-pandemic, and it is hard to consider all the indices. Further research can broaden the research context and establish a more comprehensive index system.

The index system constructed in this study will surely help relevant regulatory authorities in China to promote the construction of agricultural IOT traceability system and establish a unified standard, so as to provide a basis for future developers to enter the field. Accordingly, it also can help every subject to identify the key indices of each process in the agricultural-product supply chain and guide relevant departments to conduct targeted information tracking and management. The consumers could also understand the standards of traceable agricultural products and effectively protect their own rights and interests.

The existing literature does not provide an objective, unified standard for measuring a decentralized traceability system or identifying key processes. This study therefore proposes a new evaluation index system and uses a dynamic evaluation method to determine the importance of key indices. This study identifies the most important indices in each process, making it possible to discover, improve, and enhance the quality of agricultural products at a practical level.

This conceptual paper presents a proposal for improving a performance measurement (PM) system implementation process based on enterprise engineering (EE) guidelines, which gives the process a sense of completeness.

This paper analyzes a well-known process for PM systems implementation organized in two phases: identifying, designing and implementing the top-level performance measures; and cascading the top-level measures and identify appropriate lower-level performance measures. The proposed improvements to the studied process derive from the EE guidelines, which establish a basis for the structure of an organizational management system, the formalization and synchronization of processes, performance expectations, exception handling and change management.

The study reveals that not all EE guidelines are covered by the analyzed process, with four of them having no evidence of being adopted: involvement of people in process design and implementation; ensuring interoperability between different systems in the information structure; addressing of all possible exceptions; coherence and consistency of semantics across all processes.

By the lens of EE guidelines, this paper advances a how-to-guide. This paper can support managers and researchers on PM system design and implementation, given the importance and relevance of EE recommendations having a consistent and well-structured procedure.

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

Flood resilience is a critical concept in flood risk management (FRM). Meanwhile, flood resilience measurement has become vital for making the business case for investment in FRM. However, information is sparse on measuring the level of resilience of flood-prone communities in Nigeria. Therefore, this study aims to develop a fuzzy logic-based model for measuring the resilience of flood-prone communities towards achieving the United Nations Sustainable Development Goals (SDGs) 11 and 13.

This study describes the development of a fuzzy logic-based flood resilience measuring model, drawing on a synthesis of fuzzy logic literature and extant flood resilience. A generalisation of the flood system for a typical Nigerian community was made. It was followed by an identification and characterisation of the variables and parameters of the system based on SDGs 11 and 13. The generated data was transformed into a fuzzy inference system (FIS) using three input community flood resilience dimensions: natural, socio-technical and socio-economic factors (SEF). The model was then validated with primary data obtained from selected flood-prone communities in Ibadan, Southwest Nigeria. Expert opinions were used in rating the input dimensions for the selected communities.

In spite of various inputs from experts in the same study area (Apete, Ibadan, Nigeria), the resulting FIS generated consistent resilience indices for various natural, socio-technical and SEF. This approach can strengthen flood resilience measurement at the community level.

Although previous attempts have been made to measure flood resilience at the individual property level (Oladokun et al., 2017; Adebimpe et al., 2020), this research focuses on measuring flood resilience at the community level by adapting the fuzzy logic approach. The fuzzy logic-based model can be a tool for flood resilience measurement at the community level. It can also be developed further for regional and national level applications.