We present a high-fidelity simulation tool for accurate acoustic modeling across a wide range of applications. The numerical method is based on diagonal-norm Summation-By-Parts (SBP) finite-difference operators, which guarantee linear stability on piecewise curvilinear multi-block grids. Realistic three-dimensional atmospheric and topographic data are directly incorporated into the simulations, and the solver is implemented in CUDA to achieve high computational efficiency. Verification is performed through convergence studies against highly resolved benchmark problems in both two and three spatial dimensions, while validation is carried out using high-quality infrasound measurements from two modern wind farms in Sweden. The results show that modern, large-scale wind turbines generate infrasound levels significantly higher than those reported for older, smaller turbines. These findings advance the understanding of the acoustic characteristics of contemporary wind turbines and provide important guidance for assessing their potential environmental and societal impacts.

Infrasound measurements have evolved from simply identifying sources to analyzing key parameters such as intensity, frequency content, and propagation characteristics in various environments. This study focuses on infrasound emissions generated by wind turbines and presents measurements from three wind farms using a synchronized network of four infrasound microphones. The methodology enables detailed analysis of spatial and temporal variations in infrasound propagation under different operational conditions. Our findings highlight the prominent frequency range of 2–10 Hz, where turbine-generated infrasound is most significant, and reveal spatial variability influenced by turbine layout, environmental factors, and geography. By introducing a synchronized, distributed sensor network, this study provides a tool for gaining new insights into the propagation patterns of infrasound and their potential environmental impacts, offering a robust foundation for optimizing wind farm design and mitigating noise-related concerns.

The TECSKILL project (Erasmus+ KA220-HED) addresses the need to integrate sustainability and digital competences into the education of European Engineering PhD students. By adapting competences from the GreenComp and DigComp 2.2 frameworks, an integrated framework was developed, encompassing 12 sustainability and 21 digital competences. To monitor the learning progress, 137 knowledge indicators were defined across four proficiency levels, ensuring a structured assessment of skill acquisition. The framework was implemented through four transnational training programs at universities in Spain, Italy, Portugal, and Sweden, providing PhD students with specialized lectures and hands-on workshops. A mid-term survey assessing student satisfaction confirmed the program’s effectiveness in enhancing competences and fostering international collaboration. Future research activities will focus on evaluating competence acquisition over the whole project, assessing the impact of the trainings on students' research and professional development, and optimizing the developed competence framework based on the final outcomes.

In recent years, commercial exoskeletons have seen widespread use across various industrial processes, offering assistance in tasks that are physically demanding for humans. While exoskeletons enhance task performance, it's crucial to understand their impact during periods of non-task activities, such as walking between workstations. This study utilized IMU sensors to investigate potential differences in gait dynamics when individuals walk with and without exoskeleton assistance. Our research reveals notable discrepancies in both range of motion (ROM) and step length when individuals employ exoskeletons during non-task-related walking. These disparities were investigated through the extraction of gait joint angles' features and the application of classification algorithms.

This study presents a sound-based tool-wear monitoring system designed to overcome the limitations of conventional methods that focus solely on gradual and predictable wear patterns. The proposed system employs low-cost, high-frequency microphones and advanced signal processing—featuring analog/digital filtering, oversampling, signal conditioning, PLL-based synchronization, and feature extraction (ZCR, RMS)—to capture acoustic emissions during machining. Key innovations include optimized microphone placement, a custom PCB, and real-time data transfer via WiFi to MATLAB for analysis. Using the TreeBagger machine-learning algorithm, the system accurately predicts tool wear, detecting both gradual and abrupt wear patterns. Tested on EN 1.4307 (AISI/ASTM 304L) stainless steel, the system demonstrated robust performance in real-time tool-condition assessment. Its scalable and cost-effective design allows for the integration of additional sensors and features, providing a non-invasive and adaptive solution to enhance machining efficiency and reduce operational costs.

Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.

In this research, we investigate the efficacy of information fusion techniques for the purpose of gait pattern recognition using hip joint angle data captured by smartphone sensors. The classifiers tested were the Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB) algorithms, which gave the highest classification accuracy. But, to further enhance the classification accuracy, we integrated score-level fusion (SLF) and decision-level fusion (DLF), leveraging multiple classifier algorithms. Our experiment results reveal that information fusion techniques improve the overall accuracy with 90.5% for the score-level fusion and 91% for the decision-level fusion (voting scheme), indicating the effectiveness of ensemble methods in hip joint angle-based recognition systems. Lastly, some Limitations of the study, such as the use of treadmills and the focus on healthy adult gait patterns are recognized, highlighting areas for future research, including the application to individuals with gait-affecting conditions.

Lower limb rehabilitation is crucial for improving mobility and quality of life for individuals with movement impairments. To address this need, a lower limb exoskeleton is being developed at the University of Gävle, marking the transition from laboratory-based research to the possibility of home-based rehabilitation solutions. The current prototype is constructed from aluminum, but ongoing investigations are exploring alternative materials, including Glass Fiber Reinforced Composites (GFRC) and Polycarbonate or Polylactic acid (PC/PLA), which can be produced via 3D printing. This study used SimaPro v9.3 software along with the Ecoinvent v3.8 environmental database and the ReCiPe 2016 impact assessment method, to perform a comprehensive Life Cycle Assessment (LCA) of an exoskeleton and their weight for each selected material. The analysis considered multiple environmental impact categories, including midpoint and endpoint scores, to evaluate the sustainability of different material choices. The results showed that GFRC and PC/PLA materials are less harmful and offer better solutions compared to the aluminum alloy. Therefore, PC/PLA appears to be the best option for reducing environmental impacts and weight while maintaining the exoskeleton's functionality, making it a promising choice for widespread use in home rehabilitation settings.

Rural areas often face communication challenges due to limited mobile coverage on remote roads, posing significant difficulties in reporting emergencies and accidents. This study presented an autonomous vehicle tracking system using low-cost radar sensors to detect possible emergencies in the sections of roads with low cell coverage. The radar sensor system could determine the number of vehicles that passed through the nodes and classify them based on the vehicle type. Each node within the system is equipped with ten radars, a processor unit, and a radio transmitter to communicate with the network in real-time, achieving a rapid response time of just 0.2 s. To ensure seamless connectivity, two distinct wireless communication networks are employed, one for the connection between the towers in the same node and the other for the connection between nodes and a center with cellular coverage. The results of this study can be useful in conveying emergency messages, as well as traffic management.

The quality of wine is checked both during the production process and upon consumption. Therefore, manual wine-tasting work is still valuable. Due to the nature of wine, many volatile components are released, and it is therefore difficult to determine which elements need to be controlled. Acetic acid is one of the substances found in wine and is a crucial substance for wine quality. Gas sensor systems may be a potential alternative for manual wine tasting. In this work, we have developed a TGS2620 gas sensor module to analyze acetic acid levels in red wine. The gas sensor module was refined according to the Venturi effect along with signal slope analysis, providing promising results. The example included in this paper demonstrates that there is a direct relationship between the slope of the MOS gas sensor response and the acetic acid concentration. This relationship is useful to evaluate the ethanol oxidation in acetic acid in red wine during its production process.