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This paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.

First, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.

The experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.

The proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.

The adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Manufacturing capability is a crucial component of every nation’s economy and pharmaceuticals are frequently a significant part of the manufacturing sector. Pharmaceutical supply chains are essential to health-care systems, contributing to living quality and shorter hospital stays. This study aims to examine the role of multiple integrations on business performance (BP) through supply chain flexibility (SCF) and supply chain agility (SCA).

Data was collected from 198 supply chain professionals in the pharmaceutical sector of the developing economy of Pakistan. The sample was collected based on a nonprobability purposive sampling approach. A five-point Likert-scale survey was used and analyzed with the PLS-SEM technique using SmartPLS 4.

This study found that process integration (PI) does not affect SCA, whereas relationship integration and measurement integration positively affect SCA. SCA positively impacts BP. In contrast, all integrations significantly influenced supply flexibility and BP except for PI. Finally, SCF significantly mediates the relationship between all integrations and BP.

This study examined the relationships of multiple integrations on BP, directly and indirectly, through SCF and agility. The theory of dynamic capabilities has been applied and extended to increase the comprehensiveness of the findings. A developing economy’s pharmaceutical industry supply chain was examined, producing empirical evidence of the results.

This paper aims to create a comprehensive framework for selecting alternative materials in construction projects, integrating building information modeling (BIM) and the particle swarm optimization (PSO) algorithm. Materials comprise 60%–65% of the total project cost, and current methods require significant time and human resources.

A prototype framework is developed that considers multiple criteria to optimize the material selection process, addressing the significant investment of time and resources required in current methods. The study uses surveys and interviews with construction professionals to collect primary data on alternative materials selection.

The results show that integrating BIM and the PSO algorithm improves cost optimization and material selection outcomes.

This comprehensive tool enhances decision-making capabilities and resource utilization, improving project outcomes and resource utilization. It offers a systematic approach to evaluating and selecting materials, making it a valuable resource for construction professionals.

This study aims to identify factors affecting attitudes towards social media influencers (SMI) on halal cosmetics products. It subsequently examines how the latter stimulates consumers’ attitudes towards purchase intentions of halal cosmetics moderated by religiosity.

This study uses a survey of samples of Muslim cosmetic consumers in Indonesia. In total, 234 respondents were selected using the purposive sampling method; a self-administered questionnaire was used to collect data from respondents. The survey data were analysed using partial least squares for structural equation modelling (PLS-SEM) to achieve the study’s objectives.

The findings revealed that trust, perceived expertise and perceived behavioural control positively and significantly affect attitudes towards SMI. However, the relationship between perceived credibility, subjective norm and attitude towards SMI is insignificant. In addition, the findings revealed that attitude towards SMI has a positive effect on explaining purchase intention. Notably, religiosity does not moderate attitudes towards SMI and purchase intention.

This study provides guidelines for companies in understanding the influence of religiosity on consumption behaviour in SMI marketing era. Thus, it will enable marketers to identify the factors influencing customers’ attitudes towards SMI and develop marketing strategies that effectively increase the customers’ intentions to purchase halal cosmetics. This will result in Indonesia becoming the epicentre of the global halal industry, particularly in the cosmetic sector.

This research provides a novel perspective by investigating how SMI shapes the purchase intention of Muslim consumers on halal cosmetics.

There are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.

First, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.

Through simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.

Both the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.

As laser scanning technology becomes readily available and affordable, there is an increasing demand of using point cloud data collected from a laser scanner to create as-built building information modeling (BIM) models for quality assessment, schedule control and energy performance within construction projects. To enhance the as-built modeling efficiency, this study explores an integrated system, called Auto-Scan-To-BIM (ASTB), with an aim to automatically generate a complete Industry Foundation Classes (IFC) model consisted of the 3D building elements for the given building based on its point cloud without requiring additional modeling tools.

ASTB has been developed with three function modules. Taking the scanned point data as input, Module 1 is built on the basis of the widely used region segmentation methodology and expanded with enhanced plane boundary line detection methods and corner recalibration algorithms. Then, Module 2 is developed with a domain knowledge-based heuristic method to analyze the features of the recognized planes, to associate them with corresponding building elements and to create BIM models. Based on the spatial relationships between these building elements, Module 3 generates a complete IFC model for the entire project compatible with any BIM software.

A case study validated the ASTB with an application with five common types of building elements (e.g. wall, floor, ceiling, window and door).

First, an integrated system, ASTB, is developed to generate a BIM model from scanned point cloud data without using additional modeling tools. Second, an enhanced plane boundary line detection method and a corner recalibration algorithm are developed in ASTB with high accuracy in obtaining the true surface planes. At last, the research contributes to develop a module, which can automatically convert the identified building elements into an IFC format based on the geometry and spatial relationships of each plan.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.