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Abstract In recent years, there has been growing interest in digital twin technology in both industry and academia. This versatile technology has found applications across various industries. Wind energy systems are particularly suitable for digital twin platforms due to the integration of multiple subsystems. This study aims to explore the current state of predictive digital twin platforms for wind energy systems by surveying literature from the past five years, identifying challenges and limitations, and addressing future research opportunities. This review is structured around four main research questions. It examines commonly employed methodologies, including physics-based modeling, data-driven approaches, and hybrid modeling. Additionally, it explores the integration of data from various sources such as IoT sensors, historical databases, and external application programming interfaces. The review also delves into key features and technologies behind real-time systems, including communication networks, edge computing, and cloud computing. Finally, it addresses current challenges in predictive digital twin platforms. Addressing these research questions enables the development of hybrid modeling strategies with data fusion algorithms, which allow for interpretable predictive digital twin platforms in real time. Filter methods with dimensionality reduction algorithms minimize the computational resource demand in real-time operating algorithms. Moreover, advancements in high-bandwidth communication networks facilitate efficient data transmission between physical assets and digital twins with reduced latency.

Abstract Digital twin technology, aligned with Industry 4.0 standard, has witnessed widespread adoption in various industries, notably in manufacturing. Meanwhile, the concept of digital twin itself is yet to be clearly defined in wind farm sector. Our primary contribution lies in investigating the potential for transferring knowledge of industrial digital twins for the wind farm industry. Through a comprehensive literature study, we explored the digital twin concept within the context of wind farm applications. Also, we conducted a comparative analysis of digital twin frameworks employed in wind farm and manufacturing sectors. We aim to identify commonalities and differences between these frameworks and to determine how they could be adapted to the unique requirements of the offshore wind sector. A case study is presented, wherein the Industry 4.0 standard framework, Asset Administration Shell (AAS), is conceptually applied to the wind farm sector. Additionally, we briefly explored the AASX Package Explorer and concluded that implementing the AAS could be a promising option for enhancing digital twin functionality in offshore wind farms, and for achieving interoperability in line with Industry 4.0 standard.

This paper introduces a novel method called Wide-Array of Nonlinear Dynamics Approximation (WyNDA) for extracting mathematical models of dynamical systems from data. A key advantage of this method over existing approaches lies in its suitability for online implementation. Moreover, WyNDA stands out by not relying on optimization or machine learning, ensuring computational efficiency. The fundamental concept revolves around approximating the unknown function of a dynamical system through a diverse set of basis functions that encapsulate the available data. An adaptive observer is then employed to iteratively refine this approximation and estimate the associated parameters. The efficacy of the proposed method is demonstrated through numerical simulations encompassing linear systems, nonlinear systems, and control systems. The results underscore the method’s ability to successfully unveil the governing equations of dynamical systems, highlighting its potential for extracting intricate system dynamics from observational data. • WyNDA represents a novel approach for uncovering mathematical models of dynamical systems from data. • Utilizing a series of basis functions, WyNDA effectively approximates the unknown structure inherent in dynamical systems. • The validation of WyNDA involves benchmark equations of dynamical systems, confirming its efficacy in diverse scenarios.

This study explores the relationship between hedonic and functional value toward the intention to buy luxury brands as direct effects and through attitude toward luxury brands. This study used a survey to gather data from 113 respondents with birth years between 1981 and 2012 who had purchased luxury brands. This study used the partial least squares structural equation modelling (PLS-SEM) to evaluate the model and hypotheses. The result shows that hedonic value positively and significantly affects the intention to buy luxury brands. In addition, the result shows a positive relationship between hedonic value and intention for luxury brand purchases through attitude towards luxury brands. Functional value positively affects the intention to buy luxury brands. Meanwhile, functional value does not significantly affect attitude toward luxury brands. Furthermore, attitude toward luxury brands has a significant effect on the intention to buy luxury brands. Understanding these relationships can help marketers develop effective strategies to attract and retain consumers of luxury brands.

In this paper, we introduce a systematic methodology to discover state-space representations of dynamical systems from noisy data. Our approach utilizes a fusion of basis functions to adeptly estimate both the structure and parameters inherent in the systems under investigation. To strengthen the robustness of our methodology, we integrate estimation algorithms grounded in both deterministic and stochastic approaches. Through extensive simulations, we showcase the efficacy of our proposed method in accurately uncovering the state-space representations of diverse dynamical systems. The synergy of versatile basis functions and advanced estimation algorithms enhances our approach to navigate the complexity inherent in various systems, offering a reliable and accurate framework for the discovery of equations from empirical data. The results highlight the potential of the proposed method as a versatile tool for system identification, capable of providing accurate insights into the underlying dynamics of complex systems based on observed data.

A heavy-duty vehicle (HDV) finds applications in various fields, particularly logistics and freight transportation, involving the movement of containers from ports to local warehouses and eventual delivery destinations. In smart ports, the process has been automated, necessitating the vehicle’s autonomous docking and undocking. Such a cyber-physical system faces the risk of cyberattacks and internal disturbances posing hazardous situations within the terminal. Therefore, it becomes crucial to identify and mitigate any cyberattacks as well as disturbances during the docking and undocking procedures. This research aims to develop an observer-based detection and mitigation system for actuator attacks and disturbances for autonomous docking of HDVs, employing a point stabilization-based control strategy. The methodology outlined in this study involves analytical proof of the asymptotic stability of the detection and mitigation system, complemented by numerical simulations and experiments to provide a quantitative evaluation. The proposed system design utilizes the Lyapunov direct method to establish the asymptotic stability of the detection system and subsequently integrates it with the point stabilization controller to design the mitigation system, ultimately contributing to the asymptotic stability of the desired configuration for docking the vehicles. The simulation of autonomous docking is performed, demonstrating the ability of the detection and mitigation system, in conjunction with the point stabilization controller, to detect simulated actuator attacks and mitigate their effects, resulting in a smooth docking process at the desired configuration. Experimental results validate the effectiveness of the controller, as the vehicle consistently achieves the desired configuration. Although this work primarily focuses on cyberattacks, it is worth noting that, for safety reasons within container ports, the experimental investigation tackles actuator disturbances instead of actuator attacks, effectively showcasing the system’s ability to detect internal actuator malfunctions.

Urban management systems evolve from merely overseeing urban growth to comprehending interactions among cities’ natural, physical, cyber, and social systems. Urban digital twins leverage emerging technologies and serve as platforms to support city transitions. However, a generic urban digital twin framework with essential building blocks leading to a sustainable urban digital transformation is lacking, as most urban digital twin implementations focus on specific application areas and usages. In addition, as urban digital twins are rather new developments, it is important to clarify their concept, definition, and characteristics, including their joint components and functions. This paper presents findings from a domain analysis based on a systematic literature review on urban digital twins, including investigating the concept, definition, and characteristics of urban digital twins and identifying joint components and functions as building blocks for developing a conceptual framework of urban digital twins for sustainable digital transformation. In total, we identified six main components that could be subdivided into 16 sub-components. Moreover, we identified four main features that could be subdivided into ten sub-features. A conceptual framework for urban digital twins is proposed based on those components and features. Two case studies were conducted to illustrate the framework’s applicability. It is an overarching goal that the proposed framework will become a useful reference and decision-making tool for urban planners, designers, constructors, and others.

Wind energy is one of the most versatile and promising sustainable energy solutions. Wind energy can be harvested in both onshore and offshore environments. Due to the environmental and societal impacts of onshore wind farms, the focus on developing offshore wind farms has been steadily increasing. For deep-water areas, floating wind turbine solutions have been developed in the past two decades. Each wind turbine is mounted on a floating structure and connected to a mooring system. While the floating wind turbine technology enables electricity generation in water depths where fixed-foundation turbines are not feasible, operation and maintenance (O&M) have become serious issues. This paper investigates the challenges and potential enabling technologies for the development of autonomous floating offshore wind farms. In particular, the paper explores the potential utilization of information and communication technology (ICT) and robotics toward fully autonomous floating offshore wind farm O&M, aiming for cost reduction and improved operational safety. The presented solutions cover fundamental aspects in floating platform design, remote operation in the form of digital twins, autonomous underwater robots and surface vehicles, and eco-friendly energy storage.

This paper presents a novel approach to optimize wind farm layout during operation based on digital twin and dynamic repositioning. The technology offers a possibility to increase the total energy production of floating wind farms by minimizing the wake effect. We assume each of the individual wind turbine is completed with a propulsion system and is allowed to reposition itself anywhere in the restricted domain. The simulations are performed in a digital twin platform based on Unity 3D, where wind speed and direction are used as inputs for the digital twin. Furthermore, Jensen’s wake model is implemented to model the dynamic behaviour of the wind. To find an optimal position for all individual wind turbines, two methods are proposed. The first method is based on reactive control, while the second method is based on optimal control. The possible energy gains of dynamic repositioning with real-life scenarios are assessed to show the feasibility of the proposed method.

This paper presents energy modeling of Hywind Tampen floating wind farm based on digital twin technology. Upon its completion, the Hywind Tampen wind farm is the largest floating wind farm in the World and the first floating wind farm to supply electricity for oil and gas fields. The wind farm is located about 140 km off the Norwegian coast with water depth between 260 and 300 m and consists of eleven wind turbines with a capacity of 8.6 MW each. Together with ten gas turbines, it will supply electricity for two oil and gas fields at the Norwegian Continental Shelf (NCS), namely the Gullfaks field with three platforms and the Snorre field with two platforms. In this paper, digital twins of the wind turbines and the oil platforms are created in Unity 3D. Energy from the wind farm is modeled from the first principle and is calculated using inputs from the historic weather data all year round. Simulation results show the wind farm can supply up to one-third of the total electricity needed by the oil and gas platforms almost constantly, which associated with reduction of 200,000 tonnes of CO2 emissions and 1000 tonnes of NOx emissions annually.

In this paper, three model-based fault diagnosis algorithms for robotic systems are designed, compared, simulated, and implemented. The first algorithm is based on a nonlinear adaptive observer (NLAO), where a sufficient condition for the convergence of the estimator is derived in terms of linear matrix inequality (LMI) under persistence of excitation condition. The second algorithm is based on an adaptive extended Kalman filter (AEKF). Unlike traditional approaches, where the fault parameters are considered as augmented state variables, the AEKF directly estimates the fault parameters from measurement data. The third algorithm is based on a cascade of a nonlinear observer (NLO) and a linearized adaptive Kalman filter (LAKF), called the adaptive exogenous Kalman filter (AXKF). The pros and cons for each algorithm are discussed. The performance of the algorithms is compared in a single-link joint robot system. Furthermore, the algorithms are implemented in a ball-balancing robot to detect and estimate the magnitude of the actuator faults.

Abstract As wind turbines continue to grow in size, they are increasingly being deployed offshore. This causes operation and maintenance of wind turbines becoming more challenging. Digitalization is a key enabling technology to manage wind farms in hostile environments and potentially increasing safety and reducing operational and maintenance costs. Digital infrastructure based on Industry 4.0 concept, such as digital twin, enables data collection, visualization, and analysis of wind power analytic at either individual turbine or wind farm level. In this paper, the concept of predictive digital twin for wind farm applications is introduced and demonstrated. To this end, a digital twin platform based on Unity3D for visualization and OPC Unified Architecture (OPC-UA) for data communication is developed. The platform is completed with the Prophet prediction algorithm to detect potential failure of wind turbine components in the near future and presented in augmented reality to enhance user experience. The presentation is intuitive and easy to use. The limitations of the platform include a lack of support for specific features like electronic signature, enhanced failover, and historical data sources. Simulation results based on the Hywind Tampen floating wind farm configuration show our proposed platform has promising potentials for offshore wind farm applications.

This paper presents a digital twin-driven fault diagnosis approach based on a graphical model and an adaptive extended Kalman filter algorithm for autonomous surface vehicles. In contrast with the traditional adaptive Kalman filter algorithm, where the fault parameters are treated as extended state variables, the newly proposed adaptive extended Kalman filter algorithm estimates the magnitude of the faults by calculating the parameter estimation gains directly from the sensor systems. To this end, the algorithm utilizes data from a global navigation satellite system receiver assuming the persistence of excitation conditions on the control inputs. The algorithm is tested in the Otter, an autonomous surface vehicle developed by Maritime Robotics, in which one of its propellers is faulty. Technically, the digital twin receives real-time data from the sensor system, estimates the magnitude of the actuator faults, and visualizes the results in a web-based application using JavaScript with Three.js library and Mapbox for real-world 3D map generation. Information regarding the magnitude of the faults is important for fault-tolerant control. Simulation and experimental results show the proposed approach is able to detect and estimate the actuator faults accurately.

Spatiotemporal data visualization is of paramount importance in several applications, especially for analyzing urban mobility data encoded on topologies. This paper introduces a novel solution, called Temporal Topology Density Map (TTDM), to represent 2D discrete spatial data with temporal variations into a 2D continuous spatial space constrained by a topology. The proposed approach combines topological density maps with Change Frequency Heatmap (CFH) to convey visual information regarding temporal changes, leading to a new visualization method. Two case studies related to the analysis of response time associated with emergency services and walkability changes over time in specific areas of interest demonstrated the effectiveness of TTDM in challenging scenarios. The proposed solution provides an intuitive visualization for supporting the accurate analysis of spatiotemporal data changes over time using topology density maps.

This paper presents mathematical modeling and quantitative evaluation of Large Scale Social Restriction (LSSR) in Jakarta between 10 April and 4 June 2020. The special capital region of Jakarta is the only province among 34 provinces in Indonesia with an average Testing Positivity Rate (TPR) below 5% recommended by the World Health Organization (WHO). The transmission model is based on a discrete-time compartmental epidemiological model incorporating suspected cases. The quantitative evaluation is measured based on the estimation of the time-varying effective reproduction number (Rt). Our results show the LSSR has been successfully suppressed the spread of COVID-19 in Jakarta, which was indicated by Rt < 1. However, once the LSSR was relaxed, the effective reproduction number increased significantly. The model is further used for short-term forecasting to mitigate the course of the pandemic.

Abstract We propose a new method to estimate the time-varying effective (or instantaneous) reproduction number of the novel coronavirus disease (COVID-19). The method is based on a discrete-time stochastic augmented compartmental model that describes the virus transmission. A two-stage estimation method, which combines the Extended Kalman Filter (EKF) to estimate the reported state variables (active and removed cases) and a low pass filter based on a rational transfer function to remove short term fluctuations of the reported cases, is used with case uncertainties that are assumed to follow a Gaussian distribution. Our method does not require information regarding serial intervals, which makes the estimation procedure simpler without reducing the quality of the estimate. We show that the proposed method is comparable to common approaches, e.g., age-structured and new cases based sequential Bayesian models. We also apply it to COVID-19 cases in the Scandinavian countries: Denmark, Sweden, and Norway, where the positive rates were below 5% recommended by WHO.

In this paper, we design a Nonlinear Observer (NLO) to estimate the effective reproduction number (Rt) of infectious diseases. The NLO is designed from a discrete-time augmented Susceptible-Infectious-Removed (SIR) model. The observer gain is obtained by solving a Linear Matrix Inequality (LMI). The method is used to estimate Rt in Jakarta using epidemiological data during COVID-19 pandemic. If the observer gain is tuned properly, this approach produces similar result compared to existing approach such as Extended Kalman filter (EKF).

Abstract This paper presents a study of early epidemiological assessment of COVID-19 transmission dynamics in Indonesia. The aim is to quantify heterogeneity in the numbers of secondary infections. To this end, we estimate the basic reproduction number $$\mathscr {R}_0$$ R 0 and the overdispersion parameter $$\mathscr {K}$$ K at two regions in Indonesia: Jakarta–Depok and Batam. The method to estimate $$\mathscr {R}_0$$ R 0 is based on a sequential Bayesian method, while the parameter $$\mathscr {K}$$ K is estimated by fitting the secondary case data with a negative binomial distribution. Based on the first 1288 confirmed cases collected from both regions, we find a high degree of individual-level variation in the transmission. The basic reproduction number $$\mathscr {R}_0$$ R 0 is estimated at 6.79 and 2.47, while the overdispersion parameter $$\mathscr {K}$$ K of a negative-binomial distribution is estimated at 0.06 and 0.2 for Jakarta–Depok and Batam, respectively. This suggests that superspreading events played a key role in the early stage of the outbreak, i.e., a small number of infected individuals are responsible for large numbers of COVID-19 transmission. This finding can be used to determine effective public measures, such as rapid isolation and identification, which are critical since delay of diagnosis is the most common cause of superspreading events.

Tabayyun or tatsabbut or tahaqquq (verification) and taakkud (confirmation) of the news are important in order to prevent doing something wrong, injustice and regret. While in science and religion, tatsabbut is more important because the consequences could be dangerous. Therefore, Muslims as ummatan wasatan have been given by God the privilege to be the people that their religion and science has sanad. At the time of the salaf, tatsabbut with sanad was common through talaqqi and historical verification. In order to revive the culture of this tatsabbut, the researcher see the important of doing critical study on the sanad concept in a famous and wide spread of Nahj al-Balaghah book. Tabayyun atau tatsabbut atau tahaqquq (verifikasi) dan taakkud (konfirmasi) dalam berita adalah penting agar kita tidak berbuat salah, zhalim dan menyesal. Sedangkan di dalam ilmu dan agama maka tatsabbut itu lebih penting lagi sebab akibatnya bisa lebih fatal. Oleh karena itu umat Islam yang dijadikan oleh Allah sebagai ummatan wasatan diberi keistimewaan sebagai umat yang agama dan ilmunya ber-sanad. Di jaman salaf, tatsabbut dengan sanad sudah membudaya dalam talaqqi dan riwayat. Maka untuk menghidupkan kembali budaya tastabbut ini peneliti memandang perlu melakukan studi kritis terhadap konsep sanad yang ada dalam kitab Nahj al-Balaghah yang kesohor dan tersebar luas itu.

Objective: To observe the success of platinum, vincristin, bleomycin and mitomycin C (PVB-MMc) combination chemotherapy on cervical carcinoma by evaluating apoptosis feature. Methods: This study was performed at Dr. Wahidin Sudirohusodo Hospital and several other hospitals. The method of this study was prospective longitudinal. Cervical biopsy was performed on 15 cervical carcinoma patients before and after PVB-MMc chemotherapy. Six patients were at early stage and 9 patients were at advanced stage. Biopsy result were sent to pathologic anatomic laboratory to be made into paraffin block and further examined for apoptosis with Tunel method. Data were analyzed by using Wilcoxon Signed Rank test. Results: Study results indicating no significant difference for apoptosis index before and after PVB-MMc chemotherapy for early stage (p=0.463), advance stage (p=0.594), but quantitatively there were increasing apoptosis index before and after chemotherapy. Conclusion: Chemotherapy combination PVB-MMc for cervix carcinoma can increase apoptosis index and give a good response in term of reducing the tumor size although statistically not significant. [Indones J Obstet Gynecol 2012; 36-4: 200-5] Keywords: apoptosis, cervical carcinoma, PVB-MMc combination chemotherapy.