Search Results (76)

Pitch control is the standard approach to regulating the rotational speed of large wind energy systems when the wind speed goes over its rated value. However, the pitch control system can also be used to reduce blade loads. In this last case, it is necessary to extend the classic collective pitch control system by including a complicated mechanism, which involves a Coleman or a Clarke transformation. This extension is known as the individual pitch control (IPC). While the performance of the IPC is satisfactory regarding the load alleviation, its dynamics remain insufficiently comprehended, especially due to the previously mentioned embedded transformations. Hence, the tuning of the IPC is sometimes challenging, and the controller can exhibit unexpected behaviours. The idea of this work is to formulate the IPC as a multivariable controller in the input/output representation such that the classic tools for the analysis and control of linear systems can be applied. As a result, some lesser-known properties as well as limitations are disclosed. Specifically, the approach makes apparent the existence of proportional-resonant controllers, which are crucial for dynamical behaviour. This additional knowledge can assist in the design of control systems and the tuning of controllers. A simulation study completes the presentation, including qualitative and quantitative analysis. Full article

A novel method based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) combined with a modified multiple branch convolutional neural network (MMBCNN) is designed to detect fault events in distribution networks and to classify various faults in a distribution system. Given the presence of noise components in transient voltage signals, a moving time window technique integrated with the NA-MEMD method is employed to process high-frequency sampling and long-term series signals. This method is also utilized to reliably identify noise components in modal components through permutation entropy. On this basis, the Clarke transform is employed to convert transient voltage signals into the d–q axis, and three-phase voltage waveforms are transformed into a ring image. Moreover, an MMBCNN is developed to accurately detect and classify distribution network faults, and a modified pooling function is introduced to improve feature extraction ability and model convergence performance. Finally, the accuracy and effectiveness of the proposed algorithm are estimated and analyzed using measurement and fault simulation data from distribution networks. Full article

The digital transformation has intensified demands for university teachers to develop technology-enhanced teaching competence, especially under China’s High-Quality Course initiative for blended learning excellence. While existing well-recognized frameworks (e.g., TPACK, DigCompEdu) provide valuable foundational guidance, they inadequately capture the dynamic, ecological processes through which teachers systematically reconstruct curricula and professional identities in blended contexts. This study addresses this gap by proposing an ecological model of competence development, building on the strengths of existing frameworks while capturing the dynamic interplay between teachers, technology, and blended environments. Using a qualitative multiple-case design, we conducted semi-structured interviews with six national recognized exemplary university English teachers. Data were analyzed via Braun & Clarke’s six-phase thematic analysis in MaxQDA. Findings reveal that technology-enhanced teaching competence comprises five co-evolving dimensions: Curriculum Empowerment (systematic course redesign), Role Transformation (shifting from lecturer to learning designer), Environment Integration (orchestrating online-offline spaces), Technology Application (selective tool use), and Competence Spanning (transferring expertise across contexts). These dimensions form an ecological system: when teachers redesign curricula, they simultaneously rethink their professional identities; when they adopt technologies, they reshape classroom environments; and when all four dimensions align, higher-order spanning competence emerges naturally. Theoretically, this ecological model advances beyond technology addition by illuminating relational mechanisms and emergent properties of competence. Practically, it informs a shift from fragmented tool-training to systemic faculty support architectures that honor the complexity of blended teaching transformation. Full article

Transformers serve as crucial hubs for power transmission, but during no-load energization, the nonlinear magnetization of their cores frequently induces extreme magnetizing inrush currents. Current suppression methods encounter challenges regarding transient feature extraction and excessive circuit complexity. To overcome these limitations, this study develops a high-fidelity model of a 100 kVA transformer using MATLAB/Simulink to investigate the interaction between residual flux and the closing angle. Extensive simulations were executed across a closing phase angle range of 0° to 360° and a residual flux domain of −0.8 p.u. to 0.8 p.u. Furthermore, this study utilizes Wavelet and Clarke transforms to extract characteristic parameters and quantitatively analyze the transients within the energy domain, enabling a multi-scale assessment of the mitigation efficacy based on these extracted features. The analytical results demonstrate that an optimal pre-magnetization distribution of −0.8 p.u. for Phase A, 0 p.u. for Phase B, and 0.8 p.u. for Phase C, coupled with a target closing angle of 330°, achieves the best suppression. This strategy strictly clamps the peak inrush current to 1.5 times the rated current, significantly outperforming conventional demagnetization alone. Consequently, this highly pronounced mitigation effect provides robust support for reliable transformer protection and overall power grid security. Full article

Background/Objective: In Spain, 99% of births occur in hospital settings, and planned home birth is neither funded nor regulated by the Public Health System. Despite growing interest in this birth option, qualitative evidence exploring the experiences of women who opt for a planned home birth after a previous hospital birth remains scarce, particularly in contexts where this practice is not integrated into the healthcare system. This study aimed to explore the perceptions and experiences of Spanish women who opted for a planned home birth following a previous hospital birth, focusing on the reasons that motivated this decision and the care received during the process. Methods: A qualitative descriptive design was employed. Semi-structured interviews were conducted between July and December 2025 with 19 women who had experienced a planned home birth in Spain after a previous hospital birth. Data were analysed using inductive thematic analysis following Braun and Clarke’s approach. The study adhered to the Standards for Reporting Qualitative Research (SRQR). Results: Three main themes emerged: (1) motives related to choosing a planned home birth, including negative hospital experiences characterised by loss of autonomy, medicalisation of birth without consent, and fragmented care; (2) seeking a physiological and humanised birth, reflecting women’s desire for empowerment, control, and a transformative experience, alongside barriers such as lack of professional support and financial burden; and (3) the need to increase visibility and establish regulation, highlighting demands for professional training, dissemination strategies, and integration of planned home birth into the Public Health System to ensure equitable access. Conclusions: Women who opted for a planned home birth after a hospital experience reported highly positive and empowering outcomes. However, the absence of regulation, professional support, and public funding creates significant inequalities. Integrating planned home birth into the Public Health System, educating healthcare professionals, and developing strategies to increase the visibility of planned home births are essential to guarantee women’s right to choose where they give birth. Full article

This paper presents a systematic methodology for time-domain modeling of three-phase power transformers aimed at electromagnetic transient analysis in shipboard and embedded electrical systems. Accurate modeling of transformers in such environments is critical, as naval power systems are subject to strict electromagnetic compatibility (EMC) requirements and are particularly susceptible to fast transients caused by switching operations, fault events, and nonlinear loads operating in confined and isolated grids. The proposed approach combines the Vector Fitting (VF) algorithm with Clarke modal decomposition to obtain stable, passive, and causal rational approximations of the frequency-dependent admittance matrix over a wide frequency range. The admittance matrix is first identified from frequency-domain measurements or simulations, capturing the transformer’s terminal behavior across multiple frequency sub-bands. Clarke’s transformation is then applied to decouple the three-phase system into independent modal components—namely the zero-sequence and positive-sequence modes, reducing the original multi-phase problem to a set of independent single-phase systems. This modal decoupling significantly improves computational efficiency without sacrificing accuracy, as each mode can be fitted and simulated independently. Full article

Background/Objectives: Patients with liver cirrhosis experience a high symptom burden and low Health-Related Quality of Life (HR-QoL). Hepatic encephalopathy (HE) occurs in 75% of patients with cirrhosis but is sparsely described from the patient’s perspective. Due to recurrent cognitive impairment, a marginalized diagnosis, and a healthcare discourse emphasizing involvement and self-responsibility, these patients appear vulnerable when navigating a complex healthcare system. This study aims to explore how patients with chronic liver disease experience living with HE as a recurring condition, and how these patients are met by healthcare professionals (HCPs). Methods: Eight semi-structured interviews were conducted with four patients and four HCPs. Data were analyzed thematically following Braun and Clarke’s six-step analysis within the framework of Interpretive Description. The study was reported according to COREQ Guidelines. Results: The overarching theme “Becoming a different person” captured the profound identity changes experienced by patients. Three main themes emerged: 1. change and loss—in identity and self-understanding, in relationships, in relation to losing control, and in relation to experiencing isolation; 2. new paths—mental and practical alternative strategies; 3. HE in clinical encounters—requiring empathy, flexibility, and continuity. Stigma related to cirrhosis and its association with alcohol further intensified patients’ vulnerability. Conclusions: HE is experienced as a transformative and isolating condition, deeply affecting patients’ autonomy and social roles through vulnerability. The clinical encounter is shaped by the cognitive impairment due to HE, requiring tailored and sensitive care. Full article

Noise elimination in current signals of three-phase induction motors, considered as energy systems for electromechanical conversion, is a critical preprocessing step for reliable condition monitoring and fault diagnosis. However, conventional wavelet-based denoising approaches often treat noise suppression as a generic filtering task, which may distort diagnostically relevant spectral components and inter-phase relationships. To address this limitation, this paper presents a physically constrained denoising framework that integrates the Clarke transformation with dyadic wavelet analysis to enable diagnostic-safe noise attenuation. The proposed method explicitly preserves frequency bands associated with supply harmonics, mechanical phenomena, and fault-related sidebands, while enforcing inter-phase coherence and zero-sequence stability in the Clarke domain. Wavelet parameters are selected through a diagnostic-oriented multi-criteria framework that jointly balances disturbance attenuation, harmonic fidelity, coherence retention, zero-sequence stability, and time-domain waveform integrity. Experimental validation using real three-phase induction motor current measurements under steady-state conditions shows that the proposed framework achieves noise reduction ratios of approximately 8–10 dB, while preserving the amplitudes of the main harmonic components with deviations below ${10}^{-3}$ dB. These results demonstrate that the proposed method provides a robust and physically consistent preprocessing stage for current-based monitoring of three-phase AC machines. Full article

Generative artificial intelligence (GenAI) has rapidly emerged as a transformative educational technology, raising questions about how educators and pre-service teachers critically engage with AI-produced content. This case study investigates how WebQuests, a long-established, inquiry-based pedagogical model, can foster critical engagement with GenAI tools. Situated within an initial teacher education programme, a WebQuest, incorporating GenAI sources, was implemented with 24 pre-service language teachers, who engaged with curated resources alongside ChatGPT and Copilot to produce infographics for secondary school audiences. Data were collected through semi-structured interviews and were analysed using Braun and Clarke’s thematic analysis. Findings indicate that scaffolded engagement with GenAI encouraged participants to compare AI-generated outputs with trusted sources, critically evaluate accuracy and reliability, and reflect on integration into their future practice. Whilst pre-service teachers valued GenAI’s accessibility and efficiency, they expressed concerns about clarity, verbosity, and trustworthiness. The WebQuest model effectively supported synthesis of multiple information sources, fostering functional AI engagement and critical evaluation of its affordances and limitations. This case study concludes that integrating GenAI within structured, inquiry-based pedagogies advances digital and AI literacy in initial teacher education, whilst highlighting the need for institutional guidance, professional development, and further research in this area. Full article

Background: The master’s thesis defense requires students to demonstrate research maturity, a high-stakes phase often causing significant stress. Understanding student challenges and the process of emotional regulation is crucial to improving pedagogical support, promoting academic well-being, and reducing the associated anxiety. Aim: To explore master’s nursing students’ experiences of the interplay between mental well-being and emotion regulation, during their thesis defense preparation. Methods: A qualitative study, conducted in accordance with COREQ guidelines, explored the perceptions of 29 master’s nursing students (average age of 35.62 years) in Portugal. Data was collected through four face-to-face focus groups, each comprising six to eight students, between October and November 2024, and was analyzed using Braun and Clarke’s systematic thematic analysis. Results: We found three main themes: (i) the pervasive nature of performance anxiety, characterized by significant fear of judgment and cognitive blocks; (ii) preparedness as a central strategy for fostering mental well-being, which included emotional regulation strategies such as researching the jury and practice sessions to manage uncertainty; and (iii) institutional support as a key mediator of well-being, which highlighted a demand for clearer information and formal training in oral communication skills to mitigate anxiety. Conclusions: The findings suggest that relying solely on students’ informal emotional regulation strategies creates vulnerability. To reduce defense-related anxiety and enhance mental well-being, structured institutional support, including clear guidelines, simulated rehearsals, and communication training focused on emotional regulation, is essential to transform the defense process into an opportunity for professional growth and academic well-being. Full article

Three-leg AC-DC Vienna rectifiers are susceptible to single open-switch faults, which make DC-link voltage ripple and make three-leg input AC currents distorted and unbalanced. Thus, this paper presents a quick identification method for single open-switch faults based on three-leg fault currents and output capacitors voltage difference. Fault-leg identification depended on zero-plateaus in the three-leg fault currents, whereas fault-side identification was dependent on reconstruction variables obtained through Clark transformation and phase shifting. In order to improve the reliability of the diagnosis system, the harmonic component of capacitor voltage difference is used to realize the missed diagnosis detection and adjust the time threshold automatically. This method requires no additional hardware and is easy to implement. Experimental results verify the effectiveness of this strategy. It is shown that the fault diagnosis method proposed in this paper has the advantages of fast diagnosis speed, high accuracy and good robustness. Full article

This paper presents a detection algorithm for identifying when a sinusoidal signal becomes zero, which can provide information about its amplitude. This method can be used to detect voltage interruptions in a single-phase sinusoidal waveform, which may be applied in the rapid recognition of power outages in single-phase electrical systems. The method requires the measurement of a voltage signal. Other analysis methods, like calculating the Root Mean Square (RMS), are based on window sampling and require storing a relatively larger amount of samples in the system memory; an advantage of the proposed method is that it does not require as many samples, but its main advantage is its ability to reduce the detection time compared to other approaches. Techniques like the RMS value or amplitude detection through FFT typically require one full AC cycle to change from a 100% to 0% output signal and then detect a blackout, whereas the proposed method achieves detection within only a quarter cycle without considering additional rate-of-change enhancements, which can be further applied. The algorithm treats the measured single-phase voltage as the α component of an αβ Clarke pair and generates the β component by introducing a 90° electrical delay through a delayed replica of the original signal. The resulting αβ signals are then transformed into the dq reference frame in which the d component is used for outage detection, as it rapidly decreases from 100% to 0% within a quarter cycle following an interruption. This rapid response makes the proposed method suitable for applications that demand minimal detection latency, such as battery backup systems. Both simulation and experimental results validate the effectiveness of the approach. Full article

Recent advances in wearable devices and physiological signal monitoring technologies have motivated research into non-invasive glucose estimation for diabetes management. However, the existing studies are often limited by sample constraints, in terms of relatively small numbers of subjects, and few address personalized differences. Physiological signals vary considerably for different individuals, affecting the reliability of accuracy measurements, and training data and test data are both used from the same subjects, which makes the test result more affirmative than the truth. This study aims to compare the two scenarios mentioned above, regardless of whether the testing/training involves the same individual, in order to determine whether the proposed training method has better generalization ability. The publicly available MIMIC-III dataset, which contains 700,000 data points for 10,000 subjects, is used to create a more generalized model. The model architecture uses a ResNet CNN + Transformer block, and data quality is graded during preprocessing to select signals with less interference for training to increase data quality. This preprocessing method allows the model to extract useful features without being adversely affected by noise and anomalous data that decreases performance; therefore, the model’s training results and generalization capability are increased. This study creates a model to predict blood glucose values from 70 to 250 for 180 classes, using mean absolute relative difference (MARD) as the evaluation metric and a Clarke error grid (CEG) to determine a reasonable error tolerance. For personalized cases (specific individual data during model training), the MARD is 11.69%, and the optimal Zone A (representing no clinical risk) in the Clarke error grid is 82.7%. Non-personalized cases (test subjects not included in the model training samples) using 60,000 unseen data yields MARD = 15.16%, and the optimal Zone A in the Clarke error grid is 75.4%. Across multiple testing runs, the proportion of predictions falling within Clarke error grid zones A and B consistently approached 100%. The small performance difference suggests that the proposed method has the potential to improve subject-independent estimation; however, further validation in broader populations is required. Therefore, the primary objective of this study is to improve subject-independent, non-personalized PPG-based glucose estimation and reduce the performance gap between personalized and non-personalized measurements. Full article

The aim of this research is to present both a sensorless control and a torque derating algorithm in the overload region of a permanent magnet motor for e-bikes. First, the theoretical backgrounds and the field-oriented control are presented. Then, a sensorless control is designed based on the back-emf estimation with a second-order generalized integral flux observer for the permanent magnet motor. The second-order generalized integral flux observer is an adaptive filter which can eliminate the DC offset and strongly attenuate the harmonics of the estimated rotor flux. The algorithms have been simulated and then validated by means of tests on a permanent magnet motor for e-bikes. Full article

This study examines Lean 4.0, defined as the integration of Lean soft practices (LSPs) and Industry 4.0 technologies (I4Ts), from a socio-technical systems perspective. While prior research has mainly linked Lean and I4Ts to operational and cost-based performance indicators, far less is known about how their human and technological elements interact as one socio-technical system during digital transformation. We investigate how LSPs and I4Ts combine to form social and technical subsystems, how their interaction reshapes work systems, and how these configurations relate to organisational performance. An inductive qualitative design was used. Fifteen managers and professionals with direct experience in continuous improvement and digital transformation completed an open-ended online questionnaire. Data were analysed using Braun and Clarke thematic analysis, guided by socio-technical systems theory and complemented by a cross-case synthesis. The findings identify four interrelated subsystems, social, technical, work, and outcomes, that co-evolve in Lean 4.0 initiatives. LSPs such as training, empowerment, and stakeholder involvement constitute a social system that enables the adoption and effective use of I4Ts in the technical system. When both subsystems are strong, their combined operation drives more extensive digital transformation of operational processes and customer facing activities, and in some cases business models, and is associated with broader improvements in efficiency, quality, customer satisfaction, employee engagement, and financial performance than medium or unbalanced configurations. The analysis also highlights recurrent integration challenges, including skill gaps, legacy system constraints, resistance to change, and data security concerns. Overall, the study conceptualises Lean 4.0 as an integrated socio-technical configuration and extends socio-technical systems theory by showing how LSPs mediate and amplify the value created by I4Ts, providing an empirically grounded framework and configuration-based insights for future testing. Full article