Technologies

21 pages, 4411 KB

Open AccessArticle

Life Damage Online Monitoring Technology of a Steam Turbine Rotor Start-Up Based on an Empirical-Statistical Model

by Wenhe Liu, Baoguo Liang, Xuhui Wu, Mengmeng Yang, Zhihe Sun, Yucong Li, Mingze Yao, Zhanyang Xu and Feng Zhang

Technologies 2025, 13(9), 417; https://doi.org/10.3390/technologies13090417 (registering DOI) - 15 Sep 2025

Abstract

In order to achieve fast and accurate life damage online monitoring of the steam turbine rotor, it was significant to propose an empirical-statistical model using a machine learning algorithm instead of finite element simulation to improve the effect of operation. The finite element [...] Read more.

In order to achieve fast and accurate life damage online monitoring of the steam turbine rotor, it was significant to propose an empirical-statistical model using a machine learning algorithm instead of finite element simulation to improve the effect of operation. The finite element method was used to calculate the maximum stress during the start-up schedule. The linear CDM (Continuum Damage Mechanics) and nonlinear CDM were applied to assess the creep-fatigue damage of the steam turbine rotor. A empirical-statistical model of a 600 MW steam turbine rotor was established by using temperature change rate and maximum stress according to the finite element result samples, which is proposed by compared R2 of SVR (Support Vector Regression), LSTM (Long Short-Term Memory) and RRM (Ridge Regression Method), which was also verified by finite element simulation under a random start-up parameters. The results showed that the creep-fatigue damage could be calculated by nonlinear CDM for more safety rather than linear CDM. The R2 of SVR (Support Vector Regression), LSTM (Long Short-Term Memory) and RRM were 0.9377, 0.9647 and 0.999, respectively. RRM was more suitable for the empirical-statistical model establishment of the steam turbine rotor. By comparing the empirical-statistical model result and finite element result under random parameters of the start-up schedule, the error is 0.51%. Full article

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24 pages, 5485 KB

Open AccessArticle

SQUbot: Enhancing Student Support Through a Personalized Chatbot System

by Zia Nadir, Hassan M. Al Lawati, Rayees A. Mohammed, Muna Al Subhi and Abdulnasir Hossen

Technologies 2025, 13(9), 416; https://doi.org/10.3390/technologies13090416 - 15 Sep 2025

Abstract

Educational institutions commonly receive numerous student requests regarding various services. Given the large population of students in a college, it becomes extremely overwhelming for the staff to address the inquiries of all the students while dealing with multiple administrative tasks at the same [...] Read more.

Educational institutions commonly receive numerous student requests regarding various services. Given the large population of students in a college, it becomes extremely overwhelming for the staff to address the inquiries of all the students while dealing with multiple administrative tasks at the same time. Furthermore, students often make multiple visits to the university’s administration, make multiple calls, or write emails about their concerns, which makes it difficult to respond to their queries promptly. AI-powered chatbots can act as virtual assistants that promptly help students in addressing their simple and complex queries. Most of the research work has focused on chatbots supporting the English language, and significant improvement is needed for implementing chatbots in the Arabic language. Existing studies supporting the Arabic language have either employed rule-based models or built custom deep learning models for chatbots. Rule-based models lack understanding of diverse contexts, whereas custom-built deep learning models, besides needing huge datasets for effective training, are difficult to integrate with other platforms. In this work, we leverage the services offered by IBM Watson to develop a chatbot that assists university students in both English and Arabic. IBM Watson employs natural language understanding and deep learning techniques to build a robust dialog and offers a more scalable, integrable, and customizable solution for enterprises. The chatbot not only provides information about the university’s general services but also customizes its response based on the individual needs of the students. The chatbot has been deployed at Sultan Qaboos University (SQU), Oman, and tested by the university’s staff and students. User testing shows that the chatbot achieves promising results. This first bilingual AI chatbot at SQU supports English and Arabic and offers secure, personalized services via OTP and student email verification. SQUbot delivers both general and individualized academic support. Pilot testing showed 84.9% intent recognition accuracy. Most unidentified queries were due to dialectal variation or out-of-scope inputs, which were addressed through fallback prompts and dataset refinement. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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17 pages, 581 KB

Open AccessCommunication

3D Localization of Near-Field Sources with Symmetric Enhanced Nested Arrays

by Linke Yu, Huayue Wu, Haifen Meng, Zheng Zhou and Hua Chen

Technologies 2025, 13(9), 415; https://doi.org/10.3390/technologies13090415 - 12 Sep 2025

Viewed by 135

Abstract

Sparse arrays can effectively reduce antenna cost and implementation complexity. However, most existing research in sparse array design mainly focuses on far-field scenarios, which cannot be directly applied to near-field (NF) source localization, where the delay term and source incident parameters exhibit a [...] Read more.

Sparse arrays can effectively reduce antenna cost and implementation complexity. However, most existing research in sparse array design mainly focuses on far-field scenarios, which cannot be directly applied to near-field (NF) source localization, where the delay term and source incident parameters exhibit a nonlinear relationship. In this paper, employing a symmetric enhanced nested array, a high-precision underdetermined three-dimensional (3D) NF localization method is proposed. Firstly, the symmetry of the array and the fourth-order cumulant are utilized to construct the equivalent virtual far-field (FF) received data. Then, a gridless, sparse, and parametric approach combined with an l₁-singular value decomposition-based pairing procedure is employed to obtain estimates of two paired angles. Finally, a one-dimensional (1D) spectral estimator is applied to obtain the estimate of the range parameter. By analyzing the virtual aperture, the optimal parameter configuration for a given number of elements is obtained. As shown by simulation results, the proposed method can handle underdetermined estimation. Compared with the other algorithms, the proposed algorithm achieves significant improvements in both angular and distance accuracy, with enhancements of 65% and 61.7%, respectively. Full article

(This article belongs to the Topic Advanced Propagation Channel Estimation Techniques for Sixth-Generation (6G) Wireless Communications)

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18 pages, 44725 KB

Open AccessArticle

BCP-YOLOv5: A High-Precision Object Detection Model for Peony Flower Recognition Based on YOLOv5

by Baofeng Ji, Xiaoshuai Hong, Ji Zhang, Chunhong Dong, Fazhan Tao, Gaoyuan Zhang and Huitao Fan

Technologies 2025, 13(9), 414; https://doi.org/10.3390/technologies13090414 - 11 Sep 2025

Viewed by 104

Abstract

Peony flowers in Luoyang are renowned for their diverse varieties and substantial economic value. However, recognizing multiple peony varieties in natural field conditions remains challenging due to limited datasets and the shortcomings of existing detection models. High intra-class similarity among peony varieties, frequent [...] Read more.

Peony flowers in Luoyang are renowned for their diverse varieties and substantial economic value. However, recognizing multiple peony varieties in natural field conditions remains challenging due to limited datasets and the shortcomings of existing detection models. High intra-class similarity among peony varieties, frequent occlusions, and imbalanced sample quality pose significant challenges to conventional approaches. To address these issues, we propose BCP-YOLOv5, an enhanced YOLOv5-based model designed for peony variety detection. The proposed model incorporates the Vision Transformer with Bi-Level Routing Attention (Biformer) to improve the detection accuracy of occluded targets. Inspired by Focal-EIoU, we redesign the loss function as Focal-CIoU to reduce the impact of low-quality samples and enhance bounding box localization. Additionally, Content-Aware Reassembly of Features (CARAFE) is employed to replace traditional upsampling, further improving performance. The experiments show that BCP-YOLOv5 improves precision by 3.4%, recall by 4.4%, and mAP@0.5 by 4.5% over baseline YOLOv5s. This work fills the gap in multi-variety peony detection and offers a practical, reproducible solution for intelligent agriculture. Full article

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28 pages, 4127 KB

Open AccessFeature PaperArticle

Deep Residual Learning for Face Anti-Spoofing: A Mathematical Framework for Optimized Skip Connections

by Ardak Nurpeisova, Anargul Shaushenova, Oleksandr Kuznetsov, Aidar Ispussinov, Zhazira Mutalova and Akmaral Kassymova

Technologies 2025, 13(9), 413; https://doi.org/10.3390/technologies13090413 - 11 Sep 2025

Viewed by 91

Abstract

Face anti-spoofing is crucial for protecting biometric authentication systems. Presentation attacks using 3D masks and high-resolution printed images present detection challenges for existing methods. In this paper, we introduce a family of specialized CNN architectures, AttackNet, designed for robust face anti-spoofing with optimized [...] Read more.

Face anti-spoofing is crucial for protecting biometric authentication systems. Presentation attacks using 3D masks and high-resolution printed images present detection challenges for existing methods. In this paper, we introduce a family of specialized CNN architectures, AttackNet, designed for robust face anti-spoofing with optimized residual connections and activation functions. The study includes the development of four architectures: baseline LivenessNet, AttackNetV1 with concatenation-based skip connections, AttackNetV2.1 with optimized activation functions, and AttackNetV2.2 with efficient addition-based residual learning. Our analysis demonstrates that element-wise addition in skip connections reduces parameters from 8.4 M to 4.2 M while maintaining performance. A comprehensive evaluation was conducted on four benchmark datasets: MSSpoof, 3DMAD, CSMAD, and Replay-Attack. Results show high accuracy (approaching 100%) on the 3DMAD, CSMAD, and Replay-Attack datasets. On the more challenging MSSpoof dataset, AttackNetV1 achieved 99.6% accuracy with an HTER of 0.004, outperforming the baseline LivenessNet (94.35% accuracy, 0.056 HTER). Comparative analysis with state-of-the-art methods confirms the superiority of the proposed approach. AttackNetV2.2 demonstrates an optimal balance between accuracy and computational efficiency, requiring 16.1 MB of memory compared to 32.1 MB for other AttackNet variants. Training time analysis shows twice the speed for AttackNetV2.2 compared to AttackNetV1. Architectural ablation studies highlight the crucial role of residual connections, batch normalization, and suitable dropout rates. Statistical significance testing verifies the reliability of the results (p-value < 0.001). The proposed architectures show excellent generalization ability and practical usefulness for real-world deployment in mobile and embedded systems. Full article

(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)

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36 pages, 1229 KB

Open AccessArticle

Redefining Transactions, Trust, and Transparency in the Energy Market from Blockchain-Driven Technology

by Manuel Uche-Soria, Antonio Martínez Raya, Alberto Muñoz Cabanes and Jorge Moya Velasco

Technologies 2025, 13(9), 412; https://doi.org/10.3390/technologies13090412 - 10 Sep 2025

Viewed by 262

Abstract

Rapid depletion of fossil fuel reserves forces the global energy sector to transition to sustainable energy sources. Specifically, distributed energy markets have emerged in the renewable energy sector in recent years, partly because blockchain technology is becoming a successful way to promote secure [...] Read more.

Rapid depletion of fossil fuel reserves forces the global energy sector to transition to sustainable energy sources. Specifically, distributed energy markets have emerged in the renewable energy sector in recent years, partly because blockchain technology is becoming a successful way to promote secure and transparent transactions. Using its decentralized structure, transparency, and even pseudonymity, blockchain is increasingly adopted worldwide for large-scale energy trading, peer-to-peer exchanges, project financing, supply chain management, and asset tracking. The research comprehensively analyzes blockchain applications across multiple fields related to energy, bibliographically evaluating their transformative potential. In addition, the study explores the architecture of various blockchain systems, assesses critical security and privacy challenges, and discusses how blockchain can enhance operational efficiency, transparency, and reliability in the energy sector. The paper’s findings provide a roadmap for future developments and the strategic adoption of blockchain technologies in the evolving energy landscape for an effective energy transition. Full article

(This article belongs to the Section Information and Communication Technologies)

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19 pages, 3154 KB

Open AccessArticle

Physiologically Explainable Ensemble Framework for Stress Classification via Respiratory Signals

by Chenxi Yang, Siyu Wei, Jianqing Li and Chengyu Liu

Technologies 2025, 13(9), 411; https://doi.org/10.3390/technologies13090411 - 10 Sep 2025

Viewed by 175

Abstract

This study proposes a physiologically interpretable framework for stress state classification using respiratory signals. The framework aims to assess whether integrating physiologically meaningful features with an interpretable model can enhance both the accuracy and interpretability of stress state classification. First, a 16-parameter feature [...] Read more.

This study proposes a physiologically interpretable framework for stress state classification using respiratory signals. The framework aims to assess whether integrating physiologically meaningful features with an interpretable model can enhance both the accuracy and interpretability of stress state classification. First, a 16-parameter feature set was constructed by extracting rhythm, depth, and nonlinear characteristics of respiratory signals. Subsequently, feature correlations and group differences across stress states were analyzed via heatmaps, multivariate analysis of variance (MANOVA), and box plots. A stacking ensemble model was then designed for three-state classification (normal/stress/meditation). Finally, Shapley additive explanations (SHAP) values were used to quantify feature contributions to classification outcomes. The leave-one-subject-out (LOSO) cross-validation results show that on the wearable stress and affect detection (WESAD) dataset, the model achieves an accuracy of 92.33% and a precision of 93.54%. Furthermore, initial validation shows key respiratory features like breath rate, inspiration time ratio, and expiratory variability coefficient align with autonomic regulation. Key respiratory metrics in other areas like rapid shallow breathing index also play an important role in the stress classification. Notably, increased respiratory depth under a stress state needs further study to clarify its physiological reasons. Overall, this framework enhances physiological interpretability while maintaining competitive performance, offering a promising approach for future applications in multimodal stress monitoring and clinical assessment. Full article

(This article belongs to the Section Assistive Technologies)

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26 pages, 24376 KB

Open AccessArticle

Enhancing Traffic Safety and Efficiency with GOLC: A Global Optimal Lane-Changing Model Integrating Real-Time Impact Prediction

by Jia He, Yanlei Hu, Wen Zhang, Zhengfei Zheng, Wenqi Lu and Tao Wang

Technologies 2025, 13(9), 410; https://doi.org/10.3390/technologies13090410 - 10 Sep 2025

Viewed by 166

Abstract

Lane-changing maneuvers critically influence traffic flow and safety. This study introduces the Global Optimal Lane-Changing (GOLC) model, a framework that optimizes decisions by quantitatively predicting their systemic effects on surrounding traffic. Unlike traditional models that focus on immediate neighbors, the GOLC model integrates [...] Read more.

Lane-changing maneuvers critically influence traffic flow and safety. This study introduces the Global Optimal Lane-Changing (GOLC) model, a framework that optimizes decisions by quantitatively predicting their systemic effects on surrounding traffic. Unlike traditional models that focus on immediate neighbors, the GOLC model integrates a kinematic wave model to precisely quantify the spatiotemporal impacts on the entire affected platoon, striking a balance between local vehicle actions and global traffic efficiency. Implemented in the Simulation of Urban Mobility (SUMO) environment, the GOLC model is evaluated against benchmark models Minimizing Overall Braking Induced by Lane Changes (MOBIL) and SUMO LC2013. Comparative evaluations demonstrate the GOLC model’s superior performance. In a three-lane scenario, the GOLC model significantly enhances traffic efficiency, reducing average delay by 3.4% to 46.8% compared to MOBIL under medium- to high-flow conditions. It also fosters a safer environment by reducing unnecessary lane changes by 1.1 times compared to the LC2013 model. In incident scenarios, the GOLC model shows greater adaptability, achieving higher average speeds and lower travel times while minimizing speed dispersion and deceleration. These findings validate the effectiveness of embedding macroscopic traffic theory into microscopic driving decisions. The model’s unique strength lies in its ability to predict and minimize the collective negative impact on all affected vehicles, representing a significant step towards real-world implementation in Advanced Driver-Assistance Systems (ADAS) and enhancing safety in next-generation intelligent transportation systems. Full article

(This article belongs to the Special Issue Advanced Intelligent Driving Technology)

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24 pages, 7601 KB

Open AccessArticle

Network Intrusion Detection Integrating Feature Dimensionality Reduction and Transfer Learning

by Hui Wang, Wei Jiang, Junjie Yang, Zitao Xu and Boxin Zhi

Technologies 2025, 13(9), 409; https://doi.org/10.3390/technologies13090409 - 10 Sep 2025

Viewed by 167

Abstract

In the Internet era, network malicious intrusion behaviors occur frequently and network intrusion detection is increasingly in demand. Addressing the challenges of high-dimensional data, nonlinearity and noisy network traffic data in network intrusion detection, a net-work intrusion detection model is proposed in this [...] Read more.

In the Internet era, network malicious intrusion behaviors occur frequently and network intrusion detection is increasingly in demand. Addressing the challenges of high-dimensional data, nonlinearity and noisy network traffic data in network intrusion detection, a net-work intrusion detection model is proposed in this paper. Firstly, a hybrid multi-model feature selection and kernel-based dimensionality reduction algorithm is proposed to map high-dimensional features to low-dimensional space to achieve feature dimensionality reduction and enhance nonlinear differentiability. Then the semantic feature mapping is introduced to convert the low-dimensional features into color images which represent distinct data characteristic. For classifying these images, an integrated convolutional neural network is constructed. Moreover, sub-model fine-tuning is performed through transfer learning and weights are assigned to improve the performance of multi-classification detection. Experiments on the UNSW-NB15 and CICIDS 2017 datasets show that the proposed model achieves accuracies of 99.99% and 99.96%. The F1-scores of 99.98% and 99.91% are achieved respectively. Full article

(This article belongs to the Section Information and Communication Technologies)

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8 pages, 181 KB

Open AccessEditorial

Empowering Independence: The Role of Assistive Technologies in Enhancing Quality of Life

by Daniele Giansanti

Technologies 2025, 13(9), 408; https://doi.org/10.3390/technologies13090408 - 8 Sep 2025

Viewed by 290

Abstract

Assistive technologies are increasingly central to improving quality of life across various settings, from rehabilitation to ongoing care [...] Full article

(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives)

14 pages, 2076 KB

Open AccessArticle

User Evaluation of Head-Level Obstacle Detector for Visually Impaired

by Iva Klimešová, Ján Lešták, Karel Hána, Tomáš Veselý and Pavel Smrčka

Technologies 2025, 13(9), 407; https://doi.org/10.3390/technologies13090407 - 6 Sep 2025

Viewed by 415

Abstract

The white cane is a reliable and often-used assistive aid; however, it does not protect against obstacles at the head level. We designed and built an ultrasonic-based obstacle detector with a limited detection field in front of the head. The detector is located [...] Read more.

The white cane is a reliable and often-used assistive aid; however, it does not protect against obstacles at the head level. We designed and built an ultrasonic-based obstacle detector with a limited detection field in front of the head. The detector is located on the chest and can be mounted on backpack straps or around the neck. We have performed testing with 74 blind people and their instructors. Blind people used the device for three to four weeks in their regular lives, and instructors tested it by themselves or with their clients. The testing showed that individualization by the type of mounting is helpful. The needed detection distance depends on the situation and the speed of movement. In total, 70% of the users were satisfied with the distance options 80 cm, 110 cm, and 140 cm. 81% of the testers were satisfied, or somewhat satisfied, with the sliding switches to control. It is simple, and its position (setting) can be detected by touch. The testers see the benefit of using the device, especially in unknown environments (outdoor and indoor), primarily because of the increased safety by movement (64%) or the feeling of security (41%). Full article

(This article belongs to the Section Assistive Technologies)

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20 pages, 2584 KB

Open AccessArticle

Dynamic Updating of Geological Models by Directly Interpolating Geological Logging Data

by Deyun Zhong, Zhaohao Wu, Liguan Wang and Jianhong Chen

Technologies 2025, 13(9), 406; https://doi.org/10.3390/technologies13090406 - 6 Sep 2025

Viewed by 294

Abstract

Traditional orebody modeling methods struggle to efficiently integrate new geological data. Therefore, we propose a novel framework for dynamically updating 3D geological models by directly interpolating geological logging data. The core innovation lies in the innovative interpolation of raw interpreted cross polylines into [...] Read more.

Traditional orebody modeling methods struggle to efficiently integrate new geological data. Therefore, we propose a novel framework for dynamically updating 3D geological models by directly interpolating geological logging data. The core innovation lies in the innovative interpolation of raw interpreted cross polylines into an implicit scalar field representation without intermediate explicit surface extraction or manual remodeling. To obtain reliable vectorized polylines, we developed image recognition and digitization techniques that are based on the pattern recognition of geological sketches. Moreover, different from existing implicit techniques, we present an improved approach to interpolate complex cross polylines that are dynamically based on the improved principal component analysis. The method allows specifying a priori constraints to adjust the erroneous estimated normal to improve the reliability of the normal estimation results of cross-contour polylines. The a priori information can be combined into the normal estimation algorithm to update the normals of the corresponding adjacent contour polylines in the process of normal estimation at the intersection points and in the process of normal propagation. By leveraging the radial basis functions-based spatial interpolators, the method continuously assimilates incremental geological observations into the interpolation constraints to update the implicit model. Case studies demonstrate a reduction in the modeling cycle time compared to conventional explicit methods while maintaining geologically coherent boundaries. The framework significantly enhances decision agility in resource estimation and mine planning workflows by bridging geological interpretation and dynamic model iteration. Full article

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30 pages, 6751 KB

Open AccessArticle

Web System for Solving the Inverse Kinematics of 6DoF Robotic Arm Using Deep Learning Models: CNN and LSTM

by Mayra A. Torres-Hernández, Teodoro Ibarra-Pérez, Eduardo García-Sánchez, Héctor A. Guerrero-Osuna, Luis O. Solís-Sánchez and Ma. del Rosario Martínez-Blanco

Technologies 2025, 13(9), 405; https://doi.org/10.3390/technologies13090405 - 5 Sep 2025

Viewed by 532

Abstract

This work presents the development of a web system using deep learning (DL) neural networks to solve the inverse kinematics problem of the Quetzal robotic arm, designed for academic and research purposes. Two architectures, LSTM and CNN, were designed, trained, and evaluated using [...] Read more.

This work presents the development of a web system using deep learning (DL) neural networks to solve the inverse kinematics problem of the Quetzal robotic arm, designed for academic and research purposes. Two architectures, LSTM and CNN, were designed, trained, and evaluated using data generated through the Denavit–Hartenberg (D-H) model, considering the robot’s workspace. The evaluation employed the mean squared error (MSE) as the loss metric and mean absolute error (MAE) and accuracy as performance metrics. The CNN model, featuring four convolutional layers and an input of 4 timesteps, achieved the best overall performance (95.9% accuracy, MSE of 0.003, and MAE of 0.040), significantly outperforming the LSTM model in training time. A hybrid web application was implemented, allowing offline training and real-time online inference under one second via an interactive interface developed with Streamlit 1.16. The solution integrates tools such as TensorFlow™ 2.15, Python 3.10, and Anaconda Distribution 2023.03-1, ensuring portability to fog or cloud computing environments. The proposed system stands out for its fast response times (1 s), low computational cost, and high scalability to collaborative robotics environments. It is a viable alternative for applications in educational or research settings, particularly in projects focused on industrial automation. Full article

(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)

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34 pages, 3473 KB

Open AccessArticle

Workspace Definition in Parallelogram Manipulators: A Theoretical Framework Based on Boundary Functions

by Luis F. Luque-Vega, Jorge A. Lizarraga, Dulce M. Navarro, Jose R. Navarro, Rocío Carrasco-Navarro, Emmanuel Lopez-Neri, Jesús Antonio Nava-Pintor, Fabián García-Vázquez and Héctor A. Guerrero-Osuna

Technologies 2025, 13(9), 404; https://doi.org/10.3390/technologies13090404 - 5 Sep 2025

Viewed by 387

Abstract

Robots with parallelogram mechanisms are widely employed in industrial applications due to their mechanical rigidity and precise motion control. However, the analytical definition of feasible workspace regions free from self-collisions remains an open challenge, especially considering the nonlinear and composite nature of such [...] Read more.

Robots with parallelogram mechanisms are widely employed in industrial applications due to their mechanical rigidity and precise motion control. However, the analytical definition of feasible workspace regions free from self-collisions remains an open challenge, especially considering the nonlinear and composite nature of such regions. This work introduces a mathematical model grounded in a collision theorem that formalizes boundary functions based on joint variables and geometric constraints. These functions explicitly define the envelope of safe configurations by evaluating relative positions between critical structural components. Using the MinervaBotV3 as a case study, the symbolic joint-space boundaries and their corresponding geometric regions in both 2D and 3D are computed and visualized. The feasible region is refined through centroid-based scaling to introduce safety margins and avoid singularities. The results show that this framework enables analytically continuous workspace representations, improving trajectory planning and reliability in constrained environments. Future work will extend this method to spatial mechanisms and real-time implementations in hybrid robotic systems. Full article

(This article belongs to the Special Issue Collaborative Robotics and Human-AI Interactions)

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26 pages, 3749 KB

Open AccessArticle

Synthesis of Pectin Hydrogels from Grapefruit Peel for the Adsorption of Heavy Metals from Water

by Vinusiya Vigneswararajah, Nirusha Thavarajah and Xavier Fernando

Technologies 2025, 13(9), 403; https://doi.org/10.3390/technologies13090403 - 5 Sep 2025

Viewed by 720

Abstract

The increasing presence of heavy metals in aquatic environments, driven by the production of industrial waste and consumer products, poses serious environmental and health risks due to their toxicity and persistence. Copper (Cu(II)) and nickel (Ni(II)) are particularly harmful, with high concentrations linked [...] Read more.

The increasing presence of heavy metals in aquatic environments, driven by the production of industrial waste and consumer products, poses serious environmental and health risks due to their toxicity and persistence. Copper (Cu(II)) and nickel (Ni(II)) are particularly harmful, with high concentrations linked to neurological, dermatological and carcinogenic effects. This proof-of-concept study explores the synthesis of sustainable hydrogels derived from grapefruit peel (biosorbents) for the adsorption of Cu(II) and Ni(II) from aqueous solutions. Pectin was extracted from the peels and was used to synthesize pectin-based hydrogels (PH) and pectin hydrogel metal–organic frameworks (PHM composites). The hydrogels were characterized using FT-IR, SEM, diameter size and water absorption capacity. Lyophilized hydrogels were significantly smaller than their wet counterparts, and adsorption performance was analyzed using FAAS. PHs demonstrated high Cu(II) removal efficiency, achieving 95.11% adsorption and 97.75 mg/g capacity at pH 5. PHM composites showed comparable Cu(II) adsorption with a maximum capacity of 67.53 mg/g. Notably, PHs also exhibited rapid Ni(II) adsorption, reaching 92.62% efficiency and 28.189 mg/g capacity within one minute. These findings highlight the potential of pectin-based hydrogels as an effective, low-cost and environmentally friendly method for heavy metal remediation in water. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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27 pages, 1630 KB

Open AccessArticle

Hybrid LSTM–FACTS Control Strategy for Voltage and Frequency Stability in EV-Penetrated Microgrids

by Paul Arévalo-Cordero, Félix González, Andrés Martínez, Diego Zarie, Augusto Rodas, Esteban Albornoz, Danny Ochoa-Correa and Darío Benavides

Technologies 2025, 13(9), 402; https://doi.org/10.3390/technologies13090402 - 4 Sep 2025

Viewed by 528

Abstract

This paper proposes a real-time energy management strategy for low-voltage microgrids that combines short-horizon forecasting with a rule-based supervisory controller to coordinate battery energy storage usage and reactive power support provided by flexible alternating current transmission technologies. The central contribution is the forecast-informed, [...] Read more.

This paper proposes a real-time energy management strategy for low-voltage microgrids that combines short-horizon forecasting with a rule-based supervisory controller to coordinate battery energy storage usage and reactive power support provided by flexible alternating current transmission technologies. The central contribution is the forecast-informed, joint orchestration of active charging and reactive power dispatch to regulate voltage and preserve stability under large photovoltaic variability and uncertain electric vehicle demand. The work also introduces a resilience response index that quantifies performance under external disturbances, forecasting delays, and increasing levels of electric-vehicle integration. Validation is carried out through time-domain numerical simulations in MATLAB/Simulink using realistic solar irradiance and electric vehicle charging profiles. The results show that the coordinated strategy reduces voltage deviation events, maintains stable operation across a wide range of scenarios, and enables electric vehicle charging to be supplied predominantly by renewable generation. Sensitivity analysis further indicates that support from flexible alternating current devices becomes particularly decisive at high charging demand and in the presence of forecasting latency, underscoring the practical value of the proposed approach for distribution-level microgrids. Full article

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27 pages, 6135 KB

Open AccessArticle

A Unified Deep Learning Framework for Robust Multi-Class Tumor Classification in Skin and Brain MRI

by Mohamed A. Sayedelahl, Ahmed G. Gad, Reham M. Essa, Zakaria G. Hussein and Amr A. Abohany

Technologies 2025, 13(9), 401; https://doi.org/10.3390/technologies13090401 - 3 Sep 2025

Viewed by 616

Abstract

Early detection of cancer is critical for effective treatment, particularly for aggressive malignancies like skin cancer and brain tumors. This research presents an integrated deep learning approach combining augmentation, segmentation, and classification techniques to identify diverse tumor types in skin lesions and brain [...] Read more.

Early detection of cancer is critical for effective treatment, particularly for aggressive malignancies like skin cancer and brain tumors. This research presents an integrated deep learning approach combining augmentation, segmentation, and classification techniques to identify diverse tumor types in skin lesions and brain MRI scans. Our method employs a fine-tuned InceptionV3 convolutional neural network trained on a multi-modal dataset comprising dermatoscopy images from the Human Against Machine archive and brain MRI scans from the ISIC 2023 repository. To address class imbalance, we implement advanced preprocessing and Generative Adversarial Network (GAN)-based augmentation. The model achieves 97% accuracy in classifying images across ten categories: seven skin cancer types, multiple brain tumor variants, and an “undefined” class. These results suggest clinical applicability for multi-cancer detection. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)

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22 pages, 763 KB

Open AccessArticle

Optimizing TSCH Scheduling for IIoT Networks Using Reinforcement Learning

by Sahar Ben Yaala, Sirine Ben Yaala and Ridha Bouallegue

Technologies 2025, 13(9), 400; https://doi.org/10.3390/technologies13090400 - 3 Sep 2025

Viewed by 390

Abstract

In the context of industrial applications, ensuring medium access control is a fundamental challenge. Industrial IoT devices are resource-constrained and must guarantee reliable communication while reducing energy consumption. The IEEE 802.15.4e standard proposed time-slotted channel hopping (TSCH) to meet the requirements of the [...] Read more.

In the context of industrial applications, ensuring medium access control is a fundamental challenge. Industrial IoT devices are resource-constrained and must guarantee reliable communication while reducing energy consumption. The IEEE 802.15.4e standard proposed time-slotted channel hopping (TSCH) to meet the requirements of the industrial Internet of Things. TSCH relies on time synchronization and channel hopping to improve performance and reduce energy consumption. Despite these characteristics, configuring an efficient schedule under varying traffic conditions and interference scenarios remains a challenging problem. The exploitation of reinforcement learning (RL) techniques offers a promising approach to address this challenge. AI enables TSCH to dynamically adapt its scheduling based on real-time network conditions, making decisions that optimize key performance criteria such as energy efficiency, reliability, and latency. By learning from the environment, reinforcement learning can reconfigure schedules to mitigate interference scenarios and meet traffic demands. In this work, we compare various reinforcement learning (RL) algorithms in the context of the TSCH environment. In particular, we evaluate the deep Q-network (DQN), double deep Q-network (DDQN), and prioritized DQN (PER-DQN). We focus on the convergence speed of these algorithms and their capacity to adapt the schedule. Our results show that the PER-DQN algorithm improves the packet delivery ratio and achieves faster convergence compared to DQN and DDQN, demonstrating its effectiveness for dynamic TSCH scheduling in Industrial IoT environments. These quantifiable improvements highlight the potential of prioritized experience replay to enhance reliability and efficiency under varying network conditions. Full article

(This article belongs to the Section Information and Communication Technologies)

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15 pages, 4611 KB

Open AccessArticle

Real-Time Prediction of Foot Placement and Step Height Using Stereo Vision Enhanced by Ground Object Awareness

by Chulyong Lim, Jaewon Baek, Junhee Han, Giuk Lee and Woochul Nam

Technologies 2025, 13(9), 399; https://doi.org/10.3390/technologies13090399 - 3 Sep 2025

Viewed by 419

Abstract

Foot placement position (FP) and step height (SH) are needed to control walking-assistive systems on uneven terrain. This study proposes a novel model that predicts FP and SH before a user takes a step. The model uses a stereo vision system mounted on [...] Read more.

Foot placement position (FP) and step height (SH) are needed to control walking-assistive systems on uneven terrain. This study proposes a novel model that predicts FP and SH before a user takes a step. The model uses a stereo vision system mounted on the upper body and adapts to various terrains by incorporating foot motions and terrain object information. First, FP was predicted by visually tracking foot positions and was corrected based on the types and locations of objects on the ground. Then, SH was estimated using depth maps captured by an RGB-D stereo camera. To predict SH, several RGB-D frames were considered with homography, feature matching, and image transformation. The results show that the heatmap trajectory improved FP prediction on the flat-walking dataset, reducing the root mean square error of FP from 20.89 to 17.70 cm. Furthermore, incorporating object preference significantly improved FP prediction, resulting in an accuracy improvement from 52.57% to 78.01% in identifying the object a user stepped on. The mean absolute error of SH was calculated to be 7.65 cm in scenes containing rocks and puddles. The proposed model can enhance the control of walking-assistive systems in complex environments. Full article

(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)

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23 pages, 5879 KB

Open AccessArticle

CAD Analysis of 3D Printed Parts for Material Extrusion—Pre-Processing Optimization Method

by Andrei Mario Ivan, Cozmin Adrian Cristoiu and Lidia Florentina Parpala

Technologies 2025, 13(9), 398; https://doi.org/10.3390/technologies13090398 - 3 Sep 2025

Viewed by 581

Abstract

Free form fabrication (FFF), also known as fused deposition modeling (FDM), is a widespread and accessible method for prototyping. Parts a with lattice structure having functional roles as mechanism elements is becoming more common. In the research field, the mechanical characteristics as well [...] Read more.

Free form fabrication (FFF), also known as fused deposition modeling (FDM), is a widespread and accessible method for prototyping. Parts a with lattice structure having functional roles as mechanism elements is becoming more common. In the research field, the mechanical characteristics as well as optimization methods for manufacturing these parts are major points of interest. One of the major aspects of FFF is part orientation during print, as it has influence over a wide range of variables, from tensile strength to surface quality and material consumption. For parts with a lattice structure, the printing orientation is important not only as a factor that influences the characteristics of the part itself, but also as a factor that determines the support requirements. However, due to the complex lattice structure, removing supports from these parts can be a challenging task. This study focuses on analyzing the reliability of available CAD optimization methods for FFF pre-processing. The analysis is performed using the Design for Additive Manufacturing module included in the Siemens NX software, version NX2406. The efficiency of CAD optimization was observed by taking into account the material consumption, printing times, surface quality, and support requirements. The study methods were based on the comparative analysis approach. The case studies used for the comparative analysis consider two-part inner structures: the solid structure approach with a rectilinear infill and the lattice structure approach. Full article

(This article belongs to the Section Innovations in Materials Science and Materials Processing)

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26 pages, 2682 KB

Open AccessArticle

A Novel Membrane Dehumidification Technology Using a Vacuum Mixing Condenser and a Multiphase Pump

by Jing Li, Chang Zhou, Xiaoli Ma, Xudong Zhao, Xiang Xu, Semali Perera, Joshua Nicks and Barry Crittenden

Technologies 2025, 13(9), 397; https://doi.org/10.3390/technologies13090397 - 3 Sep 2025

Viewed by 600

Abstract

Vacuum membrane-based air dehumidification (MAD) is potentially more efficient than refrigeration cycles. Air permeance through a membrane is inevitable, especially when there is a large pressure difference between the supply and permeate sides. Given the high specific gas volume under vacuum conditions, removing [...] Read more.

Vacuum membrane-based air dehumidification (MAD) is potentially more efficient than refrigeration cycles. Air permeance through a membrane is inevitable, especially when there is a large pressure difference between the supply and permeate sides. Given the high specific gas volume under vacuum conditions, removing the permeating air from the dehumidifier is crucial for the stable operation of the vacuum compressor. Energy-efficient air removal techniques are still lacking, thereby hindering the development of MAD technology. This paper proposes a novel MAD approach using a vacuum mixing condenser. The cooling water directly condenses moisture from the vacuum compressor without any heat exchanger. The permeating air and water mixture in the condenser then experiences a quasi-isothermal pressurization process through a multiphase pump, enabling continuous dehumidification and air removal with low power consumption. The fundamentals of the proposed approach are illustrated, and mathematical models are built. Influences of air permeance rate, cooling water flow rate, condenser pressure, membrane area, and gravitational work are investigated. The results show that a COP of 8~12 is achievable to dehumidify air to 50%RH, 25 °C. The vacuum compressor consumes about 80% of the power. A low air permeance rate, low condenser pressure, large membrane area, and high gravitational work positively impact the COP, while the cooling water flow rate has a more complex effect. The proposed dehumidifier can use less selective membranes for higher permeability and cost-effectiveness. Full article

(This article belongs to the Section Environmental Technology)

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28 pages, 1331 KB

Open AccessArticle

Rewired Leadership: Integrating AI-Powered Mediation and Decision-Making in Higher Education Institutions

by Margarita Aimilia Gkanatsiou, Sotiria Triantari, Georgios Tzartzas, Triantafyllos Kotopoulos and Stavros Gkanatsios

Technologies 2025, 13(9), 396; https://doi.org/10.3390/technologies13090396 - 2 Sep 2025

Viewed by 572

Abstract

This study examines how university students perceive AI-powered tools for mediation in higher education, with a focus on the influence of communication richness and social presence on trust and the intention to use such systems. Although AI is increasingly used in educational settings, [...] Read more.

This study examines how university students perceive AI-powered tools for mediation in higher education, with a focus on the influence of communication richness and social presence on trust and the intention to use such systems. Although AI is increasingly used in educational settings, its role in handling academic mediation, where ethical sensitivity, empathy, and trust are essential, remains underexplored. To fill this gap, this study presents a model that integrates Media Richness Theory, Social Presence Theory, Technology Acceptance Models, and Trust Theory, incorporating digital fluency and conflict ambiguity as key moderating elements. Using a convergent mixed-methods design, the research involves 287 students from a variety of academic institutions. The quantitative findings indicate that students’ willingness to adopt AI mediation tools is significantly influenced by automation, efficiency, and trust, while their perceptions are shaped by how clearly the conflict is understood and by students’ digital skills. The qualitative insights reveal concerns about emotional responsiveness, transparency, and institutional capacity. According to the results, user trust rooted in perceived presence, fairness, and emotional connection is a central factor in terms of AI acceptance, and emotionally aware, transparent, algorithmic and context-sensitive design strategies should be a system-level priority for institutions when integrating AI mediation tools into academic environments. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Information Systems: Trends and Innovations)

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21 pages, 5648 KB

Open AccessArticle

Investigation of Phase Segregation in Highly Doped InP by Selective Electrochemical Etching

by Yana Suchikova, Sergii Kovachov, Ihor Bohdanov, Anatoli I. Popov, Zhakyp T. Karipbayev, Artem L. Kozlovskiy and Marina Konuhova

Technologies 2025, 13(9), 395; https://doi.org/10.3390/technologies13090395 - 1 Sep 2025

Viewed by 869

Abstract

We demonstrate that selective electrochemical etching is a reliable method for detecting and observing the uneven concentration distribution of impurities in indium phosphide crystals, which accompanies the growth of highly doped crystals using the Czochralski method. Even though selective electrochemical etching, as a [...] Read more.

We demonstrate that selective electrochemical etching is a reliable method for detecting and observing the uneven concentration distribution of impurities in indium phosphide crystals, which accompanies the growth of highly doped crystals using the Czochralski method. Even though selective electrochemical etching, as a method of detecting defects in the crystal lattice, has been discussed many times in the literature, it has not yet been described for indium phosphide. In this work, we investigated etching in compositions of various selective electrolytes for InP of n- and p-type conductivity with different surface orientations. We present in detail the features of detecting the striped inhomogeneity of impurity distribution. The mechanisms and peculiarities of the formation of oxide crystallites on the surface of InP during electrochemical processing are presented, including structures like flower-like and parquet crystallites. The formation of porous surfaces, terraces, tracks, and crystallites is explained from the perspective of the defect-dislocation mechanism. Full article

(This article belongs to the Section Manufacturing Technology)

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24 pages, 6077 KB

Open AccessArticle

Trajectory Tracking Control of Intelligent Vehicles with Adaptive Model Predictive Control and Reinforcement Learning Under Variable Curvature Roads

by Yuying Fang, Pengwei Wang, Song Gao, Binbin Sun, Qing Zhang and Yuhua Zhang

Technologies 2025, 13(9), 394; https://doi.org/10.3390/technologies13090394 - 1 Sep 2025

Viewed by 411

Abstract

To improve the tracking accuracy and the adaptability of intelligent vehicles in various road conditions, an adaptive model predictive controller combining reinforcement learning is proposed in this paper. Firstly, to solve the problem of control accuracy decline caused by a fixed prediction time [...] Read more.

To improve the tracking accuracy and the adaptability of intelligent vehicles in various road conditions, an adaptive model predictive controller combining reinforcement learning is proposed in this paper. Firstly, to solve the problem of control accuracy decline caused by a fixed prediction time domain, a low-computational-cost adaptive prediction horizon strategy based on a two-dimensional Gaussian function is designed to realize the real-time adjustment of prediction time domain change with vehicle speed and road curvature. Secondly, to address the problem of tracking stability reduction under complex road conditions, the Deep Q-Network (DQN) algorithm is used to adjust the weight matrix of the Model Predictive Control (MPC) algorithm; then, the convergence speed and control effectiveness of the tracking controller are improved. Finally, hardware-in-the-loop tests and real vehicle tests are conducted. The results show that the proposed adaptive predictive horizon controller (DQN-AP-MPC) solves the problem of poor control performance caused by fixed predictive time domain and fixed weight matrix values, significantly improving the tracking accuracy of intelligent vehicles under different road conditions. Especially under variable curvature and high-speed conditions, the proposed controller reduces the maximum lateral error by 76.81% compared to the unimproved MPC controller, and reduces the average absolute error by 64.44%. The proposed controller has a faster convergence speed and better trajectory tracking performance when tested on variable curvature road conditions and double lane roads. Full article

(This article belongs to the Section Manufacturing Technology)

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24 pages, 3878 KB

Open AccessArticle

All-Grounded Passive Component Mixed-Mode Multifunction Biquadratic Filter and Dual-Mode Quadrature Oscillator Employing a Single Active Element

by Natchanai Roongmuanpha, Jetwara Tangjit, Mohammad Faseehuddin, Worapong Tangsrirat and Tattaya Pukkalanun

Technologies 2025, 13(9), 393; https://doi.org/10.3390/technologies13090393 - 1 Sep 2025

Viewed by 377

Abstract

This paper introduces a compact analog configuration that concurrently realizes a mixed-mode biquadratic filter and a dual-mode quadrature oscillator (QO) by employing a single differential differencing gain amplifier (DDGA) and all-grounded passive components. The proposed design supports four fundamental operation modes—voltage-mode (VM), current-mode [...] Read more.

This paper introduces a compact analog configuration that concurrently realizes a mixed-mode biquadratic filter and a dual-mode quadrature oscillator (QO) by employing a single differential differencing gain amplifier (DDGA) and all-grounded passive components. The proposed design supports four fundamental operation modes—voltage-mode (VM), current-mode (CM), trans-impedance-mode (TIM), and trans-admittance-mode (TAM)—utilizing the same circuit topology without structural modifications. In filter operation, it offers low-pass, high-pass, band-pass, band-stop, and all-pass responses with orthogonal and electronic pole frequency and quality factor. In oscillator operation, it delivers simultaneous voltage and current quadrature outputs with independent tuning of oscillator frequency and condition. The grounded-component configuration simplifies layout and enhances its suitability for monolithic integration. Numerical simulations in a 0.18-μm CMOS process with ±0.9 V supply confirm theoretical predictions, demonstrating precise gain-phase characteristics, low total harmonic distortion (<7%), modest sensitivity to 5% component variations, and stable operation from −40 °C to 120 °C. These results, combined with the circuit’s low component count and integration suitability, suggest strong potential for future development in low-power IoT devices, adaptive communication front-ends, and integrated biomedical systems. Full article

(This article belongs to the Section Information and Communication Technologies)

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27 pages, 9580 KB

Open AccessArticle

Structural Integrity Assessment of Stainless Steel Fabricated by GMAW-Assisted Wire Arc Additive Manufacturing

by Joel Sam John and Salman Pervaiz

Technologies 2025, 13(9), 392; https://doi.org/10.3390/technologies13090392 - 1 Sep 2025

Viewed by 591

Abstract

Metal additive manufacturing techniques have seen technological advancements in recent years, fueled by their ability to provide industrial use parts with excellent mechanical properties. Wire Arc Additive Manufacturing is a technology that is being widely used in critical industries, and much research is [...] Read more.

Metal additive manufacturing techniques have seen technological advancements in recent years, fueled by their ability to provide industrial use parts with excellent mechanical properties. Wire Arc Additive Manufacturing is a technology that is being widely used in critical industries, and much research is conducted in this field due to the multiple factors involved in the overall process. Within WAAM, gas metal arc welding stands out for its low cost, high production volume, high quality and capability for automation. In this study, a CNC router was retrofitted with a gas metal arc welding setup to facilitate precise metal printing. The flexibility in this process allows for rapid repairs on site without the need to replace the entire part. The literature predominantly focuses on the macro-mechanical properties of GMAW parts, and very few studies try to study the interaction and influence of different process parameters on the mechanical properties. Thus, this study focused on the GMAW WAAM of stainless-steel parts by studying the influence of the wire feed rate, arc voltage and strain rate on the UTS, yield strength, toughness and percentage elongation. ANOVA and interaction plots were analyzed to study the interaction between the input parameters on each output parameter. Results showed that printing stainless steel through the gas metal arc welding process with an arc voltage of 18.7 V and a wire feed rate of 6 m/min resulted in poor mechanical properties. The input parameter that influenced the mechanical properties the highest was the wire feed rate, followed by the arc voltage and strain rate. Printing with an arc voltage of 18.7 V and a wire feed rate of 5 m/min, tested at a crosshead speed of 1 mm/min, gave the best mechanical properties. Full article

(This article belongs to the Special Issue Wire Arc Additive Manufacturing: Pioneering Techniques, Materials, and Applications)

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22 pages, 17218 KB

Open AccessArticle

Exploring Attention Placement in YOLOv5 for Ship Detection in Infrared Maritime Scenes

by Ruian Zhu, Junchao Zhang, Degui Yang, Dongbo Zhao, Jiashu Chen and Zhengliang Zhu

Technologies 2025, 13(9), 391; https://doi.org/10.3390/technologies13090391 - 1 Sep 2025

Viewed by 379

Abstract

With the rapid expansion of global maritime transportation, infrared ship detection has become increasingly critical for ensuring navigational safety, enhancing maritime monitoring, and supporting environmental protection. To address the limitations of conventional methods in handling small-scale targets and complex background interference, in this [...] Read more.

With the rapid expansion of global maritime transportation, infrared ship detection has become increasingly critical for ensuring navigational safety, enhancing maritime monitoring, and supporting environmental protection. To address the limitations of conventional methods in handling small-scale targets and complex background interference, in this paper, we propose an improved approach by embedding the convolutional block attention module (CBAM) into different components of the YOLOv5 architecture. Specifically, three enhanced models are constructed: the YOLOv5n-H (CBAM embedded in the head), the YOLOv5n-N (CBAM embedded in the neck), and the YOLOv5n-HN (CBAM embedded in both the neck and head). The comprehensive experiments are conducted on a publicly available infrared ship dataset to evaluate the impact of attention placement on detection performance. The results demonstrate that the YOLOv5n-HN achieves the best overall performance, attaining the mAP@0.5 of

86.83 %

, significantly improving the detection of medium- and large-scale maritime targets. The YOLOv5n-N exhibits superior performance for small-scale target detection. Furthermore, the incorporation of the attention mechanism substantially enhances the model’s robustness against background clutter and its discriminative capacity. This work offers practical guidance for the development of lightweight and robust infrared ship detection models. Full article

(This article belongs to the Special Issue Intelligent Vision Systems for Remote Sensing and Environmental Monitoring)

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21 pages, 852 KB

Open AccessArticle

Classifying XAI Methods to Resolve Conceptual Ambiguity

by Lynda Dib and Laurence Capus

Technologies 2025, 13(9), 390; https://doi.org/10.3390/technologies13090390 - 1 Sep 2025

Viewed by 471

Abstract

This article provides an in-depth review of the concepts of interpretability and explainability in machine learning, which are two essential pillars for developing transparent, responsible, and trustworthy artificial intelligence (AI) systems. As algorithms become increasingly complex and are deployed in sensitive domains, the [...] Read more.

This article provides an in-depth review of the concepts of interpretability and explainability in machine learning, which are two essential pillars for developing transparent, responsible, and trustworthy artificial intelligence (AI) systems. As algorithms become increasingly complex and are deployed in sensitive domains, the need for interpretability has grown. However, the ongoing confusion between interpretability and explainability has hindered the adoption of clear methodological frameworks. To address this conceptual ambiguity, we draw on the formal distinction introduced by Dib, which rigorously separates interpretability from explainability. Based on this foundation, we propose a revised classification of explanatory approaches structured around three complementary axes: intrinsic vs. extrinsic, specific vs. agnostic, and local vs. global. Unlike many existing typologies that are limited to a single dichotomy, our framework provides a unified perspective that facilitates the understanding, comparison, and selection of methods according to their application context. We illustrate these elements through an experiment on the Breast Cancer dataset, where several models are analyzed: some through their intrinsically interpretable characteristics (logistic regression, decision tree) and others using post hoc explainability techniques such as treeinterpreter for random forests. Additionally, the LIME method is applied even to interpretable models to assess the relevance and robustness of the locally generated explanations. This contribution aims to structure the field of explainable AI (XAI) more rigorously, supporting a reasoned, contextualized, and operational use of explanatory methods. Full article

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24 pages, 2160 KB

Open AccessArticle

Enhancing the A Algorithm for Efficient Route Planning in Agricultural Environments with a Hybrid Heuristic Approach and Path Smoothing^*

by Antonios Chatzisavvas and Minas Dasygenis

Technologies 2025, 13(9), 389; https://doi.org/10.3390/technologies13090389 - 1 Sep 2025

Viewed by 406

Abstract

The A* algorithm is broadly identified for its application in diverse fields, such as agriculture, robotics and GPS technology, due to its effectiveness in route planning. Despite its broad utility, the algorithm faces inherent limitations regarding operational efficiency and the length of the [...] Read more.

The A* algorithm is broadly identified for its application in diverse fields, such as agriculture, robotics and GPS technology, due to its effectiveness in route planning. Despite its broad utility, the algorithm faces inherent limitations regarding operational efficiency and the length of the paths it generates. Addressing these constraints, this paper proposes an enhancement to the traditional A* algorithm that significantly improves its performance. Our innovative approach integrates Euclidean and Chebyshev distances into a single heuristic function, thereby enhancing pathfinding accuracy and flexibility. This combined heuristic leverages the strengths of both distance measures: the Euclidean distance provides an accurate straight-line measure between points, while the Chebyshev distance effectively handles scenarios allowing diagonal movement. Furthermore, we incorporate Bezier curves into the algorithm to smooth the generated paths. This addition is particularly advantageous in agricultural environments, where machinery must navigate complex terrains without causing damage to crops. The smooth paths produced by Bezier curves ensure more efficient and safer navigation in such settings. Comprehensive experiments conducted in various agricultural scenarios demonstrate the superior performance of the enhanced algorithm. These results reveal that the improved algorithm not only reduces the computation time needed for route planning but also generates shorter and smoother paths compared to the standard A* algorithm. The proposed approach significantly enhances the operational efficiency and route optimization capabilities of the A* algorithm, making it more suitable for complex and dynamic applications in agriculture. This advancement also holds promise for improving navigation systems in various other domains. Full article

(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies)

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30 pages, 19158 KB

Open AccessArticle

Enhanced Performance and Reduced Emissions in Aviation Microturboengines Using Biodiesel Blends and Ejector Integration

by Constantin Leventiu, Grigore Cican, Laurentiu-Lucian Cristea, Sibel Osman, Alina Bogoi, Daniel-Eugeniu Crunteanu and Andrei Vlad Cojocea

Technologies 2025, 13(9), 388; https://doi.org/10.3390/technologies13090388 - 1 Sep 2025

Viewed by 423

Abstract

This study examines the impact of using eco-friendly biodiesel blends with Jet A fuel in aviation microturbine engines, both with and without an ejector. Three biodiesel concentrations (10%, 20%, and 30%) were evaluated under three different operating conditions. Key performance indicators, including combustion [...] Read more.

This study examines the impact of using eco-friendly biodiesel blends with Jet A fuel in aviation microturbine engines, both with and without an ejector. Three biodiesel concentrations (10%, 20%, and 30%) were evaluated under three different operating conditions. Key performance indicators, including combustion temperature, fuel consumption, propulsive force, specific fuel consumption, and emissions, were analyzed. Results indicate that fuel consumption increases with higher biodiesel content, reaching a peak rise of 3.05% at idle for a 30% biodiesel blend. However, the ejector helps offset this increase, reducing fuel consumption by 3.82% for Jet A. A similar trend is observed for specific fuel consumption (SFC), which decreases by up to 19.67% when using Jet A with the ejector at idle. The addition of an ejector significantly enhances propulsive force, achieving improvements of up to 36.91% for a 30% biodiesel blend at idle. At higher operating regimes, biodiesel alone slightly reduces thrust, but the ejector effectively compensates for these losses. Emission analysis reveals that using biodiesel leads to a cleaner combustion process, significantly reducing CO and SO₂ emissions. The ejector further enhances this effect by improving airflow and combustion efficiency. Additionally, noise measurements conducted using five microphones demonstrate that the ejector contributes to noise reduction. Overall, this study concludes that integrating an ejector with sustainable biodiesel blends not only enhances engine performance but also significantly reduces the environmental footprint of aviation microturbine engines. Full article

(This article belongs to the Special Issue Aviation Science and Technology Applications)

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