Search results

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

This paper aims to comprehensively explore techniques for reducing solution time in finite element analysis (FEA), addressing the critical need for expediting computations to facilitate agile design exploration within project timelines.

Drawing from a wide array of literature sources, this paper synthesizes and analyzes various methodologies used to enhance the efficiency of FEA. Techniques are scrutinized in terms of their applicability, effectiveness and potential limitations.

The review signifies application of linear assumptions across multiple facets of analysis and delves into matrix order reduction strategies, geometry simplification, symmetry exploitation, submodeling and mesh attribute control. It reveals how these techniques can effectively reduce computational burdens while maintaining acceptable levels of accuracy.

While this review provides a comprehensive overview of existing efficiency enhancement techniques in FEA, it acknowledges inherent limitations of any synthesis-based study. Future research should focus on refining these methodologies.

The insights provided in this paper offer practical guidance for structural engineers and researchers seeking to optimize FEA workflows. By implementing these techniques, practitioners can expedite solution times and enhance their ability to explore design alternatives efficiently ultimately leading to cost savings and more robust structures.

This review contributes to the existing literature by offering a comprehensive synthesis of efficiency enhancement techniques in FEA. By highlighting the originality and value of each discussed methodology, this paper provides a roadmap for future research and practical implementation in the field of structural engineering.

This study investigates the perceptions and challenges faced by academic library professionals in utilising infographics as a communication tool. It aims to understand how infographics are perceived in libraries, their effectiveness in conveying information and the obstacles encountered during implementation.

The research employed a descriptive survey design, collecting data through a closed-ended questionnaire distributed electronically to 144 library and information science professionals. Stratified random sampling was used to ensure representation from various types of libraries. Pilot testing was conducted to validate the questionnaire, and data analysis was performed using statistical software.

Library professionals perceive infographics as valuable tools for conveying complex concepts quickly, promoting library events and appealingly presenting resources. They also believe that infographics simplify complex information and enhance the user experience. However, challenges include lacking design skills and resources, difficulty finding relevant data, integrating infographics into traditional library services and time constraints.

This study provides unique insights into the role of infographics in library communication, highlighting both their positive perceptions and critical challenges. It offers recommendations for enhancing the use of infographics, including training for library professionals, improved data management and integration into traditional library services. The study also suggests promoting best practices and exploring emerging technologies like 3D interactive infographics.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.

The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.

It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.

This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.

The dynamic of the business environment has escalated the competition and uncertainty, which is challenging business survivability, particularly for small and medium enterprises (SMEs). SMEs attract researchers due to their unique characteristics that have limited resources but great flexibility and adaptability. Furthermore, Collaborative Networks (CNs) have been proposed by business scholars as a critical strategy to gain resilience and antifragility. However, the concept of antifragility and its relation with CNs is still vague in the SME sector. Therefore, this study aims to develop a complete understanding regarding: (1) the emerging knowledge that is critical in explaining antifragility in the business sector based on co-citation and thematic analysis; (2) the relation between resilience and antifragility in emerging business research; (3) the relation between CNs and antifragility in emerging business research and (4) a framework of antifragility in the SME context.

Bibliographic Analysis and Systematic Literature Review are performed to reach the research objectives. We use co-citation and thematic analysis to identify the map of emerging knowledge and the related concepts, which are the fundamentals of antifragility. Furthermore, we use a systematic literature review to determine the relation of antifragility, resilience and CNs in the SME context.

Antifragility is a higher level of survivability compared to resilience. Antifragile SMEs could gain an advantage from the uncertain business environment. However, both in resilience and antifragility, SMEs should become active learners. Furthermore, CNs are proposed as the gateway for SMEs to manage their resource limitations. The conceptual framework of Antifragile SMEs is presented as the theoretical contribution of this manuscript.

This article explains the knowledge structure of antifragility in the business sector, particularly among SMEs. Based on bibliometric data, we describe critical characteristics or mental states entrepreneurs should have when facing uncertainty. Furthermore, we propose a conceptual framework for antifragile SMEs where active learning and positive psychology are the pillars, and CNs are critical ingredients of antifragility in SMEs.

This study aims to evaluate the readiness of library professionals in Pakistan for metaverse technology adoption. It assesses their familiarity with the metaverse, identifies potential applications within libraries, pinpoints skill gaps, and unravels challenges hindering integration.

Employing a quantitative survey design, a survey was conducted from November 15, 2023, to December 15, 2023, targeting library professionals in Pakistan. The survey gathered data on familiarity with the metaverse, perceived applications in libraries, self-assessed proficiency in metaverse-related skills, and perceived challenges to metaverse adoption.

Library professionals in Pakistan exhibit moderate familiarity with the metaverse, demonstrating higher proficiency in certain aspects such as virtual reality and digital assets but a lesser understanding of legal/regulatory challenges and societal impacts. Perceived highly impactful applications in libraries include virtual meetups, immersive learning programs, collaborative research spaces, and virtual librarian-led tutorials. Challenges hindering metaverse adoption include budget constraints, limited technical expertise, concerns about privacy/security, and resistance to change.

The study’s limitations include the sample size and the specific data collection timeframe, which might limit generalizability. The findings offer implications for training programs, resource allocation, and policy formulation to facilitate metaverse integration in Pakistani libraries.

This study contributes original insights into the metaverse readiness of library professionals in Pakistan, offering a comprehensive analysis of their familiarity, perceived applications, skill gaps, and challenges, enabling tailored strategies for metaverse integration in library services.

In this study, a different approach is introduced to generate the kinetic sub-model for the modeling of solid-state pyrolysis reactions based on the thermogravimetric (TG) experimental data over a specified range of heating rates. Gene Expression Programming (GEP) is used to produce a correlation for the single-step global reaction rate as a function of determining kinetic variables, namely conversion, temperature, and heating rate.

For a case study on the coal pyrolysis, a coefficient of determination (R²) of 0.99 was obtained using the generated model according to the experimental benchmark data. Comparison of the model results with the experimental data proves the applicability, reliability, and convenience of GEP as a powerful tool for modeling purposes in the solid-state pyrolysis reactions.

The resulting kinetic sub-model takes advantage of particular characteristics, to be highly efficient, simple, accurate, and computationally attractive, which facilitates the CFD simulation of real pyrolizers under isothermal and non-isothermal conditions.

It should be emphasized that the above-mentioned manuscript is not under evaluation in any journals and submitted exclusively for consideration for possible publication in this journal. The generated kinetic model is in the final form of an algebraic correlation which, in comparison to the conventional kinetic models, suggests several advantages: to be relatively simpler, more accurate, and numerically efficient. These characteristics make the proposed model computationally attractive when used as a sub-model in CFD applications to simulate real pyrolizers under complex heating conditions.