The paper investigates the potential of biomass pyrolysis as a sustainable and renewable energy solution. The study focuses on three biomass types: corn cob, vine rod, and sunflower, which are abundant agricultural residues with potential for biofuel production. The pyrolytic gas, oil, and char produced during pyrolysis at a heating rate of 10 °C/min were analyzed. At the pyrolysis temperature of 500 °C, the corn cob showed the smallest final residual mass of 24%, while the vine rod exhibited the largest mass loss of 40%. Gas analysis revealed the concentrations of CO₂, CO, H₂, and CH₄ in the pyrolytic gas, indicating its energy potential. Sunflower presented the largest calorific value of the produced biogas, while corn cob was the lowest. The chemical composition of the bio-oils was determined, with aliphatic acids identified as the dominant compounds, suggesting their potential for biodiesel production. Fourier Transform–Infrared Spectroscopy (FT-IR) analysis of raw biomass and char products demonstrated varying extents of decomposition among the biomass samples. A multicriteria assessment approach was employed to evaluate the differences between the selected three biomass feedstock and determined that sunflower biomass ranked the highest among the three, although the overall difference was small, confirming the suitability of all three biomass samples for pyrolysis conversion to higher-value-added fuels.

This study proposes a supervised machine learning framework for vibration-based fault diagnosis of rotating machinery using integrated data-driven and physics-informed feature sets. A dataset acquired under variable load and multiple operating conditions was used for model training. Parallel signal processing techniques were applied to capture fault-related information across multiple frequency bands including time-domain analysis, frequency-domain analysis, baseband analysis, and envelope analysis. From the corresponding signal representations, statistical, spectral, and physics-based features associated with characteristic fault frequencies were extracted and combined into integrated feature sets. The diagnostic performance of models trained using purely data-driven features was systematically compared with models incorporating integrated data-driven and physics-informed features. Support Vector Machine, Random Forests, Gradient Boosting, and an ensemble classifier were evaluated using accuracy, precision, recall, and F1-score metrics. The proposed framework employs a two-layer classification strategy, where the first layer performs multiclass fault identification, while the second layer evaluates the presence of imbalance as a coexisting fault. In addition, the influence of different feature groups as well as individual measurement axes and their combinations on diagnostic performance were analyzed. Validation using a new dataset measured in laboratory conditions confirmed the robustness and generalization capability of the proposed diagnostic framework.

Urban noise is a major environmental concern that affects public health and quality of life, demanding new approaches beyond conventional noise level monitoring. This study investigates the development of an AI-driven Acoustic Event Detection and Classification (AED/C) system designed for urban sound recognition and its integration into smart city application. Using the UrbanSound8K dataset, five acoustic parameters—Mel Frequency Cepstral Coefficients (MFCC), Mel Spectrogram (MS), Spectral Contrast (SC), Tonal Centroid (TC), and Chromagram (Ch)—were mathematically modeled and applied to feature extraction. Their combinations were tested with three classical machine learning algorithms: Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB) and a deep learning approach, i.e., Convolutional Neural Networks (CNN). A total of 52 models with the three ML algorithms were analyzed along with 4 models with CNN. The MFCC-based CNN models showed the highest accuracy, achieving up to 92.68% on test data. This achieved accuracy represents approximately +2% improvement compared to prior CNN-based approaches reported in similar studies. Additionally, the number of trained models, 56 in total, exceeds those presented in comparable research, ensuring more robust performance validation and statistical reliability. Real-time validation confirmed the applicability for IoT devices, and a low-cost wireless sensor unit (WSU) was developed with fog and cloud computing for scalable data processing. The constructed WSU demonstrates a cost reduction of at least four times compared to previously developed units, while maintaining good performance, enabling broader deployment potential in smart city applications. The findings demonstrate the potential of AI-based AED/C systems for continuous, source-specific noise classification, supporting sustainable urban planning and improved environmental management in smart cities.

The application of the advanced methods for noise analysis in the urban areas through the development of systems for classification of sound events significantly improves and simplifies the process of noise assessment. The main purpose of sound recognition and classification systems is to develop algorithms that can detect and classify sound events that occur in the chosen environment, giving an appropriate response to their users. In this research, a supervised system for recognition and classification of sound events has been established through the development of feature extraction techniques based on digital signal processing of the audio signals that are further used as an input parameter in the machine learning algorithms for classification of the sound events. Various audio parameters were extracted and processed in order to choose the best set of parameters that result in better recognition of the class to which the sounds belong. The created acoustic event detection and classification (AED/C) system could be further implemented in sound sensors for automatic control of environmental noise using the source classification that leads to reduced amount of required human validation of the sound level measurements since the target noise source is evidently defined.

The challenge of designing real-world robots continues due to the complexities of navigating inaccessible terrains and encountering unexpected conditions. Introducing smart materials like shape memory alloys (SMAs) in the robot body can be beneficial due to their shape memory effect for actuation; however, there is no systematic way to introduce SMAs in a robot design. This research aims to address these challenges by proposing a design framework for SMA-actuated smart structures in robotic applications. Drawing inspiration from nature, the initial step in this framework involves conceptualizing a multifunctional grasper. This grasper utilizes SMA springs actuated by electric current, enabling various movements such as crawling, grasping, and folding. Analytical modeling is employed to determine the necessary characteristics of the SMA springs for one segment of the grasper. A multi-body modeling approach is utilized for more comprehensive understanding of the robot performance. This approach verifies the results of the analytical modeling and allows for performance optimization. Grasper’s dynamics is enhanced by fine-tuning actuation input signals, resulting in a more precise, sustainable, and energy-efficient grasper that is capable of traveling 400% longer distance than the initial concept design. The conducted experiments confirm that the proposed design framework for mechanically intelligent grasper has the potential to streamline the SMA-actuated structure design process by reducing development time, minimizing the trial-and-error iterations, and yielding cost savings in both development and prototyping phases.

The increasing demand for autonomous wireless sensors in Internet of Things (IoT) applications has intensified research on vibration energy harvesting, particularly in the low-frequency range where ambient vibrations are most prevalent. However, most vibration energy harvesters operate efficiently only at a single resonance mode, resulting in a narrow operational bandwidth and pronounced performance degradation under frequency detuning. To address this limitation, this paper proposes a multimodal hybrid piezoelectric–electromagnetic vibration energy harvester that exploits both the first and second resonance modes of a cantilever-based structure to achieve broadband low-frequency operation. The design is guided by the complementary utilization of strain-dominated and velocity-dominated regions associated with different vibration modes. Numerical modeling and finite element simulations are employed to investigate the influence of mass distribution, deformation characteristics, and relative velocity on energy conversion performance. A secondary cantilever carrying the electromagnetic coil is introduced to enhance the relative motion between the coil and the magnetic field, thereby extending the effective operational bandwidth. The experimental results demonstrate increased harvested power, improved energy conversion efficiency, and a significantly broadened effective frequency range compared to conventional single-mode piezoelectric and electromagnetic energy harvesters.