http://lutpub.lut.fi:80/handle/10024/158303 2026-09-11T16:53:19Z http://lutpub.lut.fi:80/handle/10024/172943 Optimizing Hybrid Cruise Vessels Performance With Industry 5.0 Driven Digital Twins: Efficiency Improvement and Emissions Reduction Kuusisto, Teemu; Tariq, Adeel; Sumbal, Muhammad Saleem; Gunasekaran, Angappa; Torkkeli, Marko Despite the advances in Industry 5.0 potential through digital twins, limited research has examined how human-centered digital twins can support the evaluation of battery-supported hybrid cruise vessels to improve operational and environmental performance. This research examines the operational savings and emissions reduction (reducing emissions of Carbon Dioxide [CO2], Sulfur Oxide [SOx], Nitrogen Oxide [NOx], and Particulate Matter [PM]) that can be realized across different types of cruise vessels through the integration of data-driven batteries with Industry 5.0 digital twin. Data were collected from a case study of Wärtsilä, a leading Finnish maritime firm, and operationalized using human centered digital twins of two hybrid cruise vessels to assess the impact on operations, environmental performance, and carbon intensity. Findings show that battery-supported hybrid systems can reduce annual operating costs by €100,000–€600,000 and lower annual carbon emissions by up to 3000 tons. Integrating shore power further improves performance by generating additional annual savings of approximately €40,000 while substantially reducing port-side emissions. The results also show that battery performance depends on battery size, C-rate, and vessel operating profile. Larger batteries generally improve fuel efficiency and reduce engine running hours, although the economic benefits diminish beyond the optimal battery size. Smaller batteries experience faster degradation and provide lower operational benefits. This study extends the maritime sustainability literature by demonstrating how human-centered Industry 5.0 digital twins can support battery integration decisions in hybrid cruise vessels. Proposed framework provides a practical decision-support approach for improving operational efficiency while accelerating the decarbonization of the cruise industry. 2026-09-11T00:00:00Z http://lutpub.lut.fi:80/handle/10024/172940 Toward a Persona-Driven Approach in Cybersecurity: Insights From a Systematic Literature Review Levaniuk, Daria; Naqvi, Bilal; Knutas, Antti; Akbar, Muhammad Azeem In recent years, developing secure yet usable systems has been one of the top concerns in cybersecurity research and practice. Research indicates that many data breaches and other cybercrimes directed toward exploiting human factors could be prevented by an inclusive and human-centered cybersecurity design. When it comes to human-centered design, the use of the persona approach can assist in considering the users' needs and aspirations while designing and developing cybersecurity systems and services. In addition, personas can be useful to mitigate the risks of several attacks, increase security awareness, and also enable a better understanding of hacker behavior by modeling different threat scenarios. We conducted a systematic literature review (SLR) of 63 research articles from 2013 to 2024 across three digital databases (ACM, Scopus, and Web of Science). The study focuses on (1) key persona types proposed in the cybersecurity domain; (2) the areas of cybersecurity in which these personas have been proposed; and (3) opportunities and future directions for utilizing the persona approach to address cybersecurity issues. The findings identified the following four types of security personas: attackers, end-users, security workers, and others. Further analysis revealed a distribution matrix and a taxonomy presenting security awareness and threat modeling and mitigation as future research directions and opportunities that exist for improvement of the state of the art of social-media security and privacy, and integrating AI to Cybersec areas. The findings also have implications for practice, including improvements in developmental approaches, training and awareness programs, and increased resilience to cyber-attacks. 2026-08-19T00:00:00Z http://lutpub.lut.fi:80/handle/10024/172936 Estimation for AMB-Supported Rotor Systems: A Feasibility Study for Going Beyond the Kalman Filter Moharrami, Ali; Ranjan, Gyan; Sopanen, Jussi; Ebel, Henrik As a key enabler for rotating machinery operating at very high speeds with higher efficiency, active magnetic bearings (AMBs) bring many advantages, including lubricant-, and friction-free operation. As AMBs are open-loop unstable, they require feedback control, and good results for multiple-input multiple-output AMB-rotor-systems are typically achieved with state-space methods, requiring state estimators. Further, AMBs’ performance in rotor systems is impacted by effects such as unbalances and changing loading, which can be interpreted as unknown parameters that one may want to estimate. Today, in rotor-AMB systems, commonly, variants of the Kalman filter are employed. By design, they approximate the system dynamics, e.g., in the form of linearization, and are often tuned for a specific operation speed. Parameter estimation introduces further nonlinearities in the problem, and parameters are rarely subject to purely aleatoric uncertainty or following a Gaussian distribution. Often, bounds for physical parameters are known from engineering insight, but cannot be directly used in variants of the Kalman filter. In contrast, moving horizon estimation (MHE), an estimation method formulating and solving online the underlying estimation problem as a nonlinear optimization problem over a moving window of past measurements, can overcome these deficiencies. It can naturally deal with nonlinear dynamics and with bounded parameters to estimate, where the latter is useful also for diagnostic purposes. Due to its numerical and theoretical complexity, it is presently not as popular as Kalman filtering in engineering applications. This contribution investigates whether MHE can realize foundational theoretical advantages in a prototypical test scenario for AMB-rotor systems. The estimation results practically confirm the theoretical differences between the Kalman filter and the moving-horizon estimator. However, subsequent work will try to improve MHE’s estimates by using more of its capabilities. 2026-06-23T00:00:00Z http://lutpub.lut.fi:80/handle/10024/172935 Physics-guided neural network in gearbox fault diagnosis Ahmadi, Ghouchan Atigh Nastaran; Moharrami, Ali; Rohani, Bastami Abbas; Choudhury, Tuhin; Behzad, Mehdi; Sopanen, Jussi; Pourgol, Mohammad Intelligent fault diagnosis involves deep learning (DL) algorithms. A hybrid model that integrates both physics-based and data-driven approaches can offer effective health monitoring solutions. In this study, a physics-guided framework is developed, in comparison with classical feedforward neural network (FNN) and convolutional neural network (CNN) models, to be employed in gearbox fault detection. The proposed framework integrates a main vibration-data-driven model, that is called CNN-A trained on either envelope or fast Fourier transform (FFT) signals, with a complementary CNN-B that specifically learns from the gear mesh frequency (GMF) harmonics and their sidebands. The accuracy of fault detection is improved in the framework through a physics-and-data-driven (PDD) parameter fusion mechanism in which the model updates the weights in CNN-A by considering the weights of CNN-B, based on the healthy and faulty classes. The physics-guided DL approach is evaluated using experimental vibration measurements of a gearbox test rig. Experimental data with varying speeds and loads from another gearbox test bench is utilized for validation. It is concluded that the framework is robust under varying operating conditions, achieving higher and more stable accuracy, that is, higher performance reliability, compared to the CNN and FNN models. 2026-07-14T00:00:00Z