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Appl. Sci., Volume 15, Issue 10 (May-2 2025) – 570 articles

Cover Story (view full-size image): Heterogeneous photocatalysis has emerged as a versatile and sustainable technology for the degradation of emerging contaminants in water. This review highlights recent advancements in photocatalysts design, including band gap engineering, heterojunction formation, and plasmonic enhancement to enable visible-light activation. Various reactor configurations are examined in terms of their efficiency, scalability, and operational challenges. Hybrid systems combining photocatalysis with membrane filtration, adsorption, Fenton processes, and biological treatments demonstrate improved removal efficiency and broader applicability. The integration of material innovation, system design, and data-driven optimization underlines the potential of photocatalysis to contribute to global efforts in environmental protection and sustainable development. View this paper
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16 pages, 8174 KiB  
Article
An Improved Power Optimizer Architecture for Photovoltaic (PV) String Under Partial Shading Conditions
by Ali Faisal Murtaza, Abdulhakeem Alsaleem and Filippo Spertino
Appl. Sci. 2025, 15(10), 5791; https://doi.org/10.3390/app15105791 - 21 May 2025
Viewed by 145
Abstract
In this paper, a better power optimizer architecture has been presented for PV strings, using a buck converter for each PV module to address partial shading conditions. The buck converter, though rarely used, is a natural converter for partial shading effects, as it [...] Read more.
In this paper, a better power optimizer architecture has been presented for PV strings, using a buck converter for each PV module to address partial shading conditions. The buck converter, though rarely used, is a natural converter for partial shading effects, as it converts the lower current of the shaded module to a higher output current. Usually, the advanced architecture activates the isolated converters (complex) of only shaded modules to draw extra current from the inverter’s DC-link node to maintain the string current (Istring). On the other hand, the conventional architecture activates converters (basic) of all modules regardless of their shading status. The proposed architecture contains a unique design with a new schematic layout, where it activates the buck converters of only shaded modules without drawing extra current from the DC-link. Thus, it combines the benefits of both architectures—selective converter operation, basic topology, high efficiency, low voltage stress, and low control complexity—while eliminating their drawbacks. The designing philosophy, control mechanism, and fundamental operation of the proposed architecture have been comprehensively explained and validated through simulation experiments. Three levels of shading are used to test the proposed architecture for string containing three PV modules: (1) a single module moderate (15%) shading level, (2) a single module strong (50%) shading level, and (3) a double module extreme (75%) and moderate (25%) shading levels. The results show a successful operation of the proposed architecture as it maintains a common Istring for an inverter, where all the shaded modules remain active. The architecture exhibits an average efficiency over 97% under normal conditions. A comparative analysis of architectures has been presented to indicate the enhanced features of the proposed architecture. Full article
(This article belongs to the Special Issue Energy and Power Systems: Control and Management)
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23 pages, 1237 KiB  
Review
The Health-Promoting Potential of Fruit Pomace and Its Application in the Confectionery Industry
by Anna Tama and Monika Karaś
Appl. Sci. 2025, 15(10), 5790; https://doi.org/10.3390/app15105790 - 21 May 2025
Viewed by 118
Abstract
Every year, around 1.3 billion tons of food is wasted globally, with fruits and vegetables making up a significant portion. One by-product of this waste is pomace—the solid remains after juice extraction—which is rich in valuable nutrients like fiber, polyphenols, flavonoids, carotenoids, organic [...] Read more.
Every year, around 1.3 billion tons of food is wasted globally, with fruits and vegetables making up a significant portion. One by-product of this waste is pomace—the solid remains after juice extraction—which is rich in valuable nutrients like fiber, polyphenols, flavonoids, carotenoids, organic acids, vitamins, and minerals. Common sources of pomace are apples, grapes, citrus fruits, and berries. Researchers have highlighted its potential use in the confectionery industry. For example, replacing flour with pomace in cookies can improve antioxidant content and reduce hardness. Adding grape pomace to gummy candies increases levels of anthocyanins, flavanols, and proanthocyanidins while enhancing texture. Fortifying waffles with raspberry pomace boosts their nutritional value and may inhibit enzymes linked to free radical production. As a functional ingredient, pomace could help lower the risk of cardiovascular disease, diabetes, obesity, and colon cancer. Using fruit waste in food production supports sustainability by reducing waste and improving nutrition. Public awareness efforts, such as the NRDC’s Save the Food campaign, underscore the importance of repurposing food waste. Investing in functional confectionery made with pomace offers both health and environmental benefits, making it a key ingredient for sustainable food innovation. However, despite increasing attention to functional foods, the potential of fruit pomace specifically in confectionery has not been reviewed comprehensively. This review aims to fill this gap, providing a focused synthesis on the use of fruit pomace in the confectionery industry, identifying research trends, challenges, and practical applications. Full article
(This article belongs to the Special Issue Bioactive Compounds for Functional Foods and Sustainability)
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27 pages, 2622 KiB  
Article
Enhancing Interoperability Between Building Information Modeling and Building Energy Modeling: Alphanumerical Information Exchange for Energy Optimization in Early Design Stages
by Josef Miller, Larissa Schneiderbauer, Martin Hauer, Alexandra Jäger, Georg Fröch, Rainer Pfluger and Stephan Moser
Appl. Sci. 2025, 15(10), 5789; https://doi.org/10.3390/app15105789 - 21 May 2025
Viewed by 108
Abstract
Building information modeling (BIM) has revolutionized integrated planning by optimizing costs, schedule, and material use. However, building energy modeling (BEM) remains underutilized in early design stages due to interoperability challenges between BIM and BEM tools. This study addresses these challenges by exploring standardized [...] Read more.
Building information modeling (BIM) has revolutionized integrated planning by optimizing costs, schedule, and material use. However, building energy modeling (BEM) remains underutilized in early design stages due to interoperability challenges between BIM and BEM tools. This study addresses these challenges by exploring standardized exchange requirements and introducing a novel toolchain that bridges BIM and BEM workflows. In the BIM2IndiLight project, over 400 standardized properties for daylighting, artificial lighting, and façade systems were validated, revealing the advantages and limitations of parameter standardization. Building on these insights, the BIM2BEM-Flow project developed a three-step toolchain that efficiently manages project- and company-specific properties, defines mapping rules, and integrates parameters via a BIM plugin for validated IFC export. The results demonstrate that combining standardized properties with a flexible, workflow-driven toolchain significantly enhances data exchange and interoperability between BIM and BEM. This integrated approach supports early-stage energy performance optimization and offers a promising pathway toward more efficient design processes in the AECO industry. Full article
(This article belongs to the Special Issue Recent Advances in the Digital Construction Industry and Its Applications)
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24 pages, 5920 KiB  
Article
Numerical Investigations on Boil-Off Gas Generation Characteristics of LCO2 in Type C Storage Tanks Under Different Sloshing Conditions
by Mengke Sun, Zhongchao Zhao and Jiwei Gong
Appl. Sci. 2025, 15(10), 5788; https://doi.org/10.3390/app15105788 - 21 May 2025
Viewed by 121
Abstract
Marine transportation of liquefied carbon dioxide (LCO2) is crucial for Carbon Capture, Transportation, Utilization, and Storage (CCTUS) technology, aiding in CO2 emission reduction and greenhouse effect control. This study investigates the thermodynamic and fluid dynamic characteristics of LCO2 in [...] Read more.
Marine transportation of liquefied carbon dioxide (LCO2) is crucial for Carbon Capture, Transportation, Utilization, and Storage (CCTUS) technology, aiding in CO2 emission reduction and greenhouse effect control. This study investigates the thermodynamic and fluid dynamic characteristics of LCO2 in Type C storage tanks using numerical simulations, focusing on heat transfer, flow phenomena, and boil-off gas (BOG) generation under varying storage pressures. Results show that heated liquid rises along the tank wall, forming vortices, while gas-phase vortices are driven by central upward airflow. Over time, liquid velocity near the wall increases, enhancing flow field mixing. Gas-phase temperatures rise significantly, while liquid-phase temperature gradients remain minimal. Higher storage pressures reduce fluid velocity, vortex range, and thermal response speed. BOG generation is higher at low pressures and decreases as pressure rises, slowing beyond 1.5 MPa. Under sloshing conditions, interfacial fluctuations enhance heat and mass transfer, reducing thermal stratification. Resonance periods amplify interfacial disturbances, improving thermal mixing and minimizing temperature gradients (ΔT ≈ 0.1 K). Higher filling rates suppress surface rupture, while lower rates exhibit gas-dominated instabilities and larger thermal gradients (ΔT ≈ 0.3 K). Full article
(This article belongs to the Special Issue Research on Heat Transfer Analysis in Fluid Dynamics)
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15 pages, 1467 KiB  
Article
Prediction of Corroded Pipeline Failure Pressure Based on Empirical Knowledge and Machine Learning
by Hongbo Liu and Xiangzhao Meng
Appl. Sci. 2025, 15(10), 5787; https://doi.org/10.3390/app15105787 - 21 May 2025
Viewed by 104
Abstract
This paper presents a novel approach for predicting the failure pressure of corroded pipelines by integrating empirical formulas into the loss function of a neural network-based prediction model. Traditional empirical formulas, such as ASME-B31G, DNV RP-F101, and PCORRC, have been widely used for [...] Read more.
This paper presents a novel approach for predicting the failure pressure of corroded pipelines by integrating empirical formulas into the loss function of a neural network-based prediction model. Traditional empirical formulas, such as ASME-B31G, DNV RP-F101, and PCORRC, have been widely used for their simplicity but often suffer from significant prediction errors due to the complex interactions between defect parameters and material properties. In contrast, artificial neural networks (ANNs) offer more accurate predictions but require substantial training data. To address these limitations, we propose an integrated loss function that combines the strengths of empirical formulas and the powerful fitting capabilities of ANNs. The proposed loss function incorporates an additional defect factor term predicted by the neural network to compensate for errors caused by varying defect conditions, thereby enhancing the model′s adaptability and accuracy. The model is trained using a diverse dataset of 60 burst test results from various literature sources, covering a wide range of corrosion scenarios. The results demonstrate that the proposed method significantly improves prediction accuracy compared to traditional empirical formulas and ANN models trained with standard loss functions. The proposed approach achieves a mean absolute percentage error (MAPE) of 2.52%, a root mean square error (RMSE) of 0.39 MPa, and a coefficient of determination (R2) of 0.9886 on the validation set. This study highlights the effectiveness of integrating empirical knowledge with data-driven models and provides a robust and accurate solution for predicting the failure pressure of corroded pipelines, contributing to enhanced pipeline integrity assessment and safety management. Full article
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14 pages, 2206 KiB  
Article
CNN-Based Automatic Detection of Beachlines Using UAVs for Enhanced Waste Management in Tailings Storage Facilities
by Sergii Anufriiev, Paweł Stefaniak, Wioletta Koperska, Maria Stachowiak, Artur Skoczylas and Paweł Stefanek
Appl. Sci. 2025, 15(10), 5786; https://doi.org/10.3390/app15105786 - 21 May 2025
Viewed by 107
Abstract
Continuous monitoring is key to the safety of such critical infrastructure as Tailings storage facilities. Due to the high risk of liquification of the dams, it is crucial to move the water as far as possible from the dam crest. In order to [...] Read more.
Continuous monitoring is key to the safety of such critical infrastructure as Tailings storage facilities. Due to the high risk of liquification of the dams, it is crucial to move the water as far as possible from the dam crest. In order to control the distance from the water to the dam, regular manual inspections need to be carried out. In this article, we propose a method for automatic detection of the water-beach line based on photographs from an unmanned aerial vehicle (UAV). An algorithm based on MobileNet v2 convolutional neural network architecture was developed for the classification of images collected by the UAV. Based on the results of this classification, the border between the water and the beach is defined. Several approaches to the model training were tested. Accuracy for the validation set reaches up to 97% for particular image fragments. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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32 pages, 2199 KiB  
Article
Transforming Learning with Generative AI: From Student Perceptions to the Design of an Educational Solution
by Corina-Marina Mirea, Răzvan Bologa, Andrei Toma, Antonio Clim, Dimitrie-Daniel Plăcintă and Andrei Bobocea
Appl. Sci. 2025, 15(10), 5785; https://doi.org/10.3390/app15105785 - 21 May 2025
Viewed by 326
Abstract
Education is another field which generative artificial intelligence has made its way into, intervening in students’ learning processes. This article explores students’ perspectives on the use of generative AI tools, specifically ChatGPT-3.5 (free version) and ChatGPT-4 (with a subscription). The results of the [...] Read more.
Education is another field which generative artificial intelligence has made its way into, intervening in students’ learning processes. This article explores students’ perspectives on the use of generative AI tools, specifically ChatGPT-3.5 (free version) and ChatGPT-4 (with a subscription). The results of the survey revealed a correlation between the use of ChatGPT and the perception of grade improvement by students. In addition, this article proposes an architecture for an adaptive learning system based on generative artificial intelligence (AI). To develop the architectural proposal, we incorporated the results of the student survey along with insights gained from analyzing the architectures of other learning platforms. The proposed architecture is based on a study of adaptive learning platforms with classically virtual assistants. The main question from which the current research started was how artificial intelligence can be integrated into a learning system to improve student outcomes based on their experience with generative AI. This has been sectioned into two more specific questions: 1. How do students perceive the use of generative artificial intelligence tools, such as ChatGPT, in enhancing their learning journey? 2. Is it possible to integrate generative AI into a learning system used in education? Consequently, this article concludes with a proposed architecture for a learning platform incorporating generative artificial intelligence technologies. This article aims to present a way to understand how generative AI technologies support education and contribute to improving academic performance. Full article
(This article belongs to the Special Issue Unlocking Scientific Insights: Data Mining, Large Models, and AI-Driven Discovery)
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17 pages, 1324 KiB  
Article
Efficacy of a Low-Cost Weight-Bearing Sensitivity Incentivator After Lower Limb Surgery: A Pilot Randomised Controlled Trial
by Alessandro Manelli, Isella Carola, Fabrizio Mancini, Schiavone Nicola, Rathlef Daniel, Marina Protasoni, Andrea Brambilla and Piero Antonio Zecca
Appl. Sci. 2025, 15(10), 5784; https://doi.org/10.3390/app15105784 - 21 May 2025
Viewed by 83
Abstract
Background: Accurate partial weight-bearing (PWB) is essential for postoperative recovery after lower limb surgery, yet patients often fail to maintain load within clinically meaningful thresholds. Methods: In this pilot randomised controlled trial with 1:1 concealed allocation, 34 inpatients aged 18–85 who underwent femoral [...] Read more.
Background: Accurate partial weight-bearing (PWB) is essential for postoperative recovery after lower limb surgery, yet patients often fail to maintain load within clinically meaningful thresholds. Methods: In this pilot randomised controlled trial with 1:1 concealed allocation, 34 inpatients aged 18–85 who underwent femoral fracture fixation, hip arthroplasty, or knee arthroplasty were enrolled and followed for 14 days. Participants were randomly assigned to either standard physiotherapy or the same protocol with a low-cost tactile insole (“incentiviser”) that provides mechanical feedback when the prescribed 20% body weight (BW) load is exceeded. The primary outcome was absolute deviation from target load, with a minimal clinically important difference (MCID) of ±2 kg. Results: In total, 88% of the intervention group achieved the MCID at discharge versus 24% of controls. The between-group difference in final load error was 10.8 kg (95% CI: −15.2 to −6.4), with a large effect size (Cohen’s d = 1.71). No significant differences were found in pain (NRS) or walking distance (6MWT) between groups. Conclusions: The tactile incentiviser significantly improved PWB accuracy within 14 days, meeting MCID thresholds in most cases. Its low cost and simplicity make it promising for routine or home-based use. Limitations include the small sample, diagnosis heterogeneity, and absence of a sham control. Larger multicentre trials are needed to confirm generalisability and long-term clinical impact. Full article
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12 pages, 921 KiB  
Article
Comparison of ECG Between Gameplay and Seated Rest: Machine Learning-Based Classification
by Emi Yuda, Hiroyuki Edamatsu, Yutaka Yoshida and Takahiro Ueno
Appl. Sci. 2025, 15(10), 5783; https://doi.org/10.3390/app15105783 - 21 May 2025
Viewed by 80
Abstract
The influence of gameplay on autonomic nervous system activity was investigated by comparing electrocardiogram (ECG) data during seated rest and gameplay. A total of 13 participants (6 in the gameplay group and 7 in the control group) were analyzed. RR interval time series [...] Read more.
The influence of gameplay on autonomic nervous system activity was investigated by comparing electrocardiogram (ECG) data during seated rest and gameplay. A total of 13 participants (6 in the gameplay group and 7 in the control group) were analyzed. RR interval time series (2 Hz) and heart-rate variability (HRV) indices, including mean RR, SDRR, VLF, LF, HF, LF/HF, and HF peak frequency, were extracted from ECG signals over 5 min and 10 min segments. HRV indices were calculated using fast Fourier transform (FFT). The classification was performed using Logistic Regression (LGR), Random Forest (RF), XGBoost (XGB, v2.9.2), One-Class SVM (OCS), Isolation Forest (ILF), and Local Outlier Factor (LOF). A balanced dataset of 5 min and 10 min segments was evaluated using k-fold cross-validation (k = 3, 4, 5). Performance metrics, including recall, F-score, and PR-AUC, were computed for each classifier. Grid search was applied to optimize parameters for LGR, RF, and XGB, while default settings were used for the other classifiers. Among all models, OCS with k = 3 achieved the highest classification accuracy for both 5 min and 10 min data. These findings suggest that machine learning-based classification can effectively distinguish ECG patterns between gameplay and rest. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Bioinformatics)
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22 pages, 6256 KiB  
Article
Structural Design of Segmented Linings for High-Pressure CAES in Underground Workings: Method and Case Study
by Sheng Wang, Mengfan Gao and Caichu Xia
Appl. Sci. 2025, 15(10), 5782; https://doi.org/10.3390/app15105782 - 21 May 2025
Viewed by 98
Abstract
This study aims to ensure that the maximum crack width of underground working linings for compressed air energy storage (CAES) meets the allowable limit under high internal pressure conditions. Drawing on crack width calculation methods from hydraulic tunnels, this study proposes a design [...] Read more.
This study aims to ensure that the maximum crack width of underground working linings for compressed air energy storage (CAES) meets the allowable limit under high internal pressure conditions. Drawing on crack width calculation methods from hydraulic tunnels, this study proposes a design method for segmented linings with preset seams. The method accounts for the shear mechanical behavior of the sliding layer, with parameters determined through laboratory testing. A typical case study validates the reliability of the crack width calculation method that accounts for lining damage and plasticity. The study determined, from an engineering case, that six seams are optimal when the lateral pressure coefficient λ is below 1, while four seams are more suitable when λ > 1. Additionally, reinforcement ratios and retractable joints of the segmented lining were designed for the case. When the surrounding rock quality is lower than that of hard rock mass and gas pressure exceeds 12 MPa, monolithic cast-reinforced concrete linings often fail to meet the allowable crack width limits. However, segmented linings offer greater flexibility, as they can still meet the requirements even with fair-quality rock mass. These findings provide critical theoretical foundations for the design of CAES workings under high internal pressure. Full article
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22 pages, 8325 KiB  
Article
Stability Analysis of the Huasushu Slope Under the Coupling of Reservoir Level Decline and Rainfall
by Hao Yang, Yingfa Lu and Jin Wang
Appl. Sci. 2025, 15(10), 5781; https://doi.org/10.3390/app15105781 - 21 May 2025
Viewed by 71
Abstract
The coupling of water level fluctuations and heavy rainfall in the Three Gorges reservoir area poses a significant threat to the stability of bank slopes, especially in landslide areas with complex geological conditions. In this study, the Huasushu slope in Fengjie County, Chongqing, [...] Read more.
The coupling of water level fluctuations and heavy rainfall in the Three Gorges reservoir area poses a significant threat to the stability of bank slopes, especially in landslide areas with complex geological conditions. In this study, the Huasushu slope in Fengjie County, Chongqing, was taken as the research object and, based on a field investigation and monitoring data, two- and three-dimensional numerical models were constructed to analyze the response mechanism of the slope under the combined effects of different reservoir water level decreases and rainfall. In addition, the safety coefficients under each working condition were calculated using the Morgenstern–Price method. The results show that it is difficult to trigger significant deformation with a single water level drop or rainfall. However, when the reservoir water level drops more than 10 m within a short period of time and is superimposed with strong rainfall, the landslide body is prone to plastic zone extension and significant displacement, showing typical strain localization characteristics. The three-dimensional model further reveals the spatial distribution characteristics of the landslide deformation area, which helps to accurately identify potential destabilization locations. The research results provide theoretical support for the construction of early warning systems for reservoir bank slopes and have reference value for the development of disaster mitigation engineering measures based on the coupling mechanism of rainwater and reservoir water. Full article
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24 pages, 7505 KiB  
Article
Investigations on an Ancient Mortar from Ulpia Traiana Sarmizegetusa Archaeological Site, Romania
by Zeno Dorian Ghizdavet, Corina Anca Simion, Anton Ficai, Ovidiu-Cristian Oprea, Radu Claudiu Fierascu, Maria Loredana Marin, Doina-Roxana Trușcă, Vasile-Adrian Surdu, Ludmila Motelica, Iuliana Madalina Stanciu, Alexandru Razvan Petre and Ileana Radulescu
Appl. Sci. 2025, 15(10), 5780; https://doi.org/10.3390/app15105780 - 21 May 2025
Viewed by 95
Abstract
A fragment of mortar from the pedestal ruin belonging to the central statue in Forum Vetus, Ulpia Traiana archaeological site, Romania, was investigated. The ruin is well-documented and unrestored, and radiocarbon dating was deemed suitable to determine its moment of construction. Preliminary analyses [...] Read more.
A fragment of mortar from the pedestal ruin belonging to the central statue in Forum Vetus, Ulpia Traiana archaeological site, Romania, was investigated. The ruin is well-documented and unrestored, and radiocarbon dating was deemed suitable to determine its moment of construction. Preliminary analyses were used to establish the composition of the material and the sources of carbon-14, selecting the most reliable fraction for radiocarbon dating by the AMS method. Although sampling was carried out according to the recommendations, a younger apparent age was obtained than that expected. This is in fact a concrete-like mortar according to the analyses, and the phenomenon of delayed hardening of mortar in masonry was detected. The difference between the real and apparent ages quantifies this phenomenon. X-ray diffraction, scanning electron microscopy with energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, differential scanning calorimetry with thermogravimetric analysis, and gamma spectrometry were used. Pyrogenic calcium carbonate and carbonates from calcium silicate/calcium aluminate hydrates were the only forms present in mini-nodules/lumps. The reactivation of binder calcite or geogenic calcite, the other problems encountered when dating mortars, were not spotted. This case study highlights the limitations of the radiocarbon dating method, and we introduce gamma spectrometry as a technique for additional investigations into direct exposure to the environment or the origins of raw materials. Full article
(This article belongs to the Special Issue Innovative Building Materials: Design, Properties and Applications)
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13 pages, 4561 KiB  
Article
Noise and Vibration Analysis of Electric Oil Pump with Asymmetric Pitch Control for Gearbox in Hybrid and Battery Electric Vehicle
by Chinchul Choi
Appl. Sci. 2025, 15(10), 5779; https://doi.org/10.3390/app15105779 - 21 May 2025
Viewed by 82
Abstract
This study proposes an asymmetric pitch control technique for electric oil pumps with symmetric gear-type pumps in order to reduce noise and vibration. For vane pump noise reduction, mechanical asymmetric pitch arrangements of each vane are widely used. However, the mechanical asymmetric pitch [...] Read more.
This study proposes an asymmetric pitch control technique for electric oil pumps with symmetric gear-type pumps in order to reduce noise and vibration. For vane pump noise reduction, mechanical asymmetric pitch arrangements of each vane are widely used. However, the mechanical asymmetric pitch arrangement approach is not applicable in gear-type pumps due to structural limitations. The proposed asymmetric pitch control method provides similar effects to the mechanical asymmetric pitch arrangement by employing instantaneous motor torque controls for an electric oil pump with a gear-type pump. The magnitude of motor torque for each pump tooth is determined with an asymmetric pitch formula, which has been widely used for mechanical vane pumps in previous studies and patents. A formula for the shape of instantaneous motor torque is proposed for the analysis of pressure fluctuations of pumps, which is a combination of trigonometric and exponential functions. The calibration factors for the magnitude and shape can be adjusted according to the characteristics of a given pump. The experimental results for a 400 W electric pump show that the proposed method reduced and dispersed the noise peak by approximately 4 dB(A) in comparison with the normal control, and affected hydraulic performance with a less than 1% decrease in flow rate in not only pump-level but also gearbox-level test environments. Full article
(This article belongs to the Special Issue Noise Measurement, Acoustic Signal Processing and Noise Control)
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17 pages, 1253 KiB  
Article
Retrieving Memory Content from a Cognitive Architecture by Impressions from Language Models for Use in a Social Robot
by Thomas Sievers and Nele Russwinkel
Appl. Sci. 2025, 15(10), 5778; https://doi.org/10.3390/app15105778 - 21 May 2025
Viewed by 114
Abstract
Large Language Models (LLMs) and Vision-Language Models (VLMs) have the potential to significantly advance the development and application of cognitive architectures for human–robot interaction (HRI) to enable social robots with enhanced cognitive capabilities. An essential cognitive ability of humans is the use of [...] Read more.
Large Language Models (LLMs) and Vision-Language Models (VLMs) have the potential to significantly advance the development and application of cognitive architectures for human–robot interaction (HRI) to enable social robots with enhanced cognitive capabilities. An essential cognitive ability of humans is the use of memory. We investigate a way to create a social robot with a human-like memory and recollection based on cognitive processes for a better comprehensible and situational behavior of the robot. Using a combined system consisting of an Adaptive Control of Thought-Rational (ACT-R) model and a humanoid social robot, we show how recollections from the declarative memory of the ACT-R model can be retrieved using data obtained by the robot via an LLM or VLM, processed according to the procedural memory of the cognitive model and returned to the robot as instructions for action. Real-world data captured by the robot can be stored as memory chunks in the cognitive model and recalled, for example by means of associations. This opens up possibilities for using human-like judgment and decision-making capabilities inherent in cognitive architectures with social robots and practically offers opportunities of augmenting the prompt for LLM-driven utterances with content from declarative memory, thus keeping them more contextually relevant. We illustrate the use of such an approach in HRI scenarios with the social robot Pepper. Full article
(This article belongs to the Special Issue Advances in Cognitive Robotics and Control)
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18 pages, 2601 KiB  
Article
Refined Consolidation Settlement Calculation Based on the Oedometer Tests for Normally and Overconsolidated Clays
by Nopakun Phonchamni, Thammanun Chatwong, Artit Udomchai, Sivarit Sultornsanee, Niwat Angkawisittpan, Noppadol Sangiamsak and Nopanom Kaewhanam
Appl. Sci. 2025, 15(10), 5777; https://doi.org/10.3390/app15105777 - 21 May 2025
Viewed by 444
Abstract
This study presents an enhanced analytical approach for one-dimensional consolidation settlement by introducing a revised AJOP (arc joint via optimum parameters) equation assuming creep and strain rate effects can be neglected for both normally and overconsolidated clays. This modified equation integrates both curved [...] Read more.
This study presents an enhanced analytical approach for one-dimensional consolidation settlement by introducing a revised AJOP (arc joint via optimum parameters) equation assuming creep and strain rate effects can be neglected for both normally and overconsolidated clays. This modified equation integrates both curved and linear segments within a unified framework, enhancing accuracy across varying stress levels for normally consolidated clay. Additionally, the revised AJOP function, coupled with newly proposed equations for symmetrical and asymmetrical hysteresis, improves the modeling of overconsolidated clay. The findings from a comparative investigation using benchmark datasets and conventional methods, including the linear function (LF) and the curved function (CF), reveal that the revised AJOP method was found to reduce settlement prediction errors by up to 85% compared to LF method (particularly at shallow layers) and by 10–15% compared to the CF method (particularly at deep layers). The revised AJOP equation effectively resolves this error with a wide range of depths. Furthermore, results highlight the crucial impact of clay layering techniques on consolidation settlement predictions. Non-layered models yield lower settlement estimates compared to multilayer approaches, emphasizing the significance of the proper e−logσ′v relationship and layering techniques in enhancing prediction reliability. Full article
(This article belongs to the Special Issue Geotechnical Engineering: Principles and Applications)
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20 pages, 3630 KiB  
Systematic Review
Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
by Alexandru Catalin Motofelea, Adelina Mihaescu, Nicu Olariu, Luciana Marc, Lazar Chisavu, Gheorghe Nicusor Pop, Andreea Crintea, Ana Maria Cristina Jura, Viviana Mihaela Ivan, Adrian Apostol and Adalbert Schiller
Appl. Sci. 2025, 15(10), 5776; https://doi.org/10.3390/app15105776 - 21 May 2025
Viewed by 81
Abstract
Background: Hemodialysis (HD) patients have significantly higher mortality rates compared to the general population, primarily due to complex comorbidities. This systematic review and meta-analysis aimed to evaluate and compare the performance of various machine learning (ML) models in predicting mortality among HD patients. [...] Read more.
Background: Hemodialysis (HD) patients have significantly higher mortality rates compared to the general population, primarily due to complex comorbidities. This systematic review and meta-analysis aimed to evaluate and compare the performance of various machine learning (ML) models in predicting mortality among HD patients. Methods: The analysis followed PRISMA guidelines, including studies that assessed the predictive capabilities of ML models for mortality in HD patients. Review Manager software version 5.4.1. was used for meta-analysis, and the performance of ML models was compared, including logistic regression, XGBoost, and Random Forest models. Results: The meta-analysis indicated that the logistic regression model predicted a true positive mortality rate of 8.23%, close to the actual rate of 10.53%. In contrast, the XGBoost and Random Forest models predicted rates of 9.93% and 8.94%, respectively, compared to the actual mortality rate of 13.73%. The highest area under the curve (AUC) was reported for the Random Forest model at a 3-year follow-up (AUC = 0.89). No significant difference was found between the performance of logistic regression and Random Forest models (p = 0.82). Conclusions: ML models, particularly Random Forest and logistic regression, demonstrated effective predictive capabilities for mortality in HD patients. These models can help identify high-risk patients early, facilitating personalized treatment strategies and potentially improving long-term outcomes. However, the observed heterogeneity among studies indicates a need for further research to refine model performance and standardize predictive features. Full article
(This article belongs to the Special Issue Applied Machine Learning III)
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20 pages, 6422 KiB  
Article
Intelligent Automation in Knitting Manufacturing: Advanced Software Integration and Structural Optimisation for Complex Textile Design
by Radostina A. Angelova, Daniela Sofronova, Violina Raycheva and Elena Borisova
Appl. Sci. 2025, 15(10), 5775; https://doi.org/10.3390/app15105775 - 21 May 2025
Viewed by 115
Abstract
Automation in textile manufacturing plays a pivotal role in enhancing production efficiency, precision, and innovation. This study investigates the integration of intelligent technologies in the knitting sector, focusing on industrial flat knitting machines from a leading manufacturer and the use of the advanced [...] Read more.
Automation in textile manufacturing plays a pivotal role in enhancing production efficiency, precision, and innovation. This study investigates the integration of intelligent technologies in the knitting sector, focusing on industrial flat knitting machines from a leading manufacturer and the use of the advanced software platform M1plus V7.5. The software’s capabilities for the digital design and simulation of complex patterned and structural knits are explored through the development and production of five experimental knitted designs. Each sample is evaluated in terms of its structural characteristics and dimensional behaviour after washing. The results highlight the potential of software-driven optimisation to improve product accuracy, reduce shrinkage variability, and support smart manufacturing practices in the textile industry. Full article
(This article belongs to the Special Issue New Technologies in Intelligent Manufacturing and Industrial Engineering)
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35 pages, 8710 KiB  
Article
Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
by Yuyang Lian, Jianuo Zhu, Quanzhi Zuo, Mingyang Cai and Shuai Jiang
Appl. Sci. 2025, 15(10), 5774; https://doi.org/10.3390/app15105774 - 21 May 2025
Viewed by 70
Abstract
In planar linkage mechanisms, due to various influencing factors, the existence of joint clearance becomes an inevitable phenomenon, which substantially diminishes the precision of the system’s movement. Currently, the majority of studies are largely confined to simple mechanisms with a single clearance, whereas [...] Read more.
In planar linkage mechanisms, due to various influencing factors, the existence of joint clearance becomes an inevitable phenomenon, which substantially diminishes the precision of the system’s movement. Currently, the majority of studies are largely confined to simple mechanisms with a single clearance, whereas investigations into more intricate systems with multiple types of clearances are still lacking. In view of this, this paper proposes an innovative dynamic algorithm for complex multilink mechanisms, aiming to deeply explore the specific impacts of multiple factors on dynamic response and nonlinear rigid-body properties, as well as its reliability analysis. Taking an eight-bar mechanism as an example, a dynamic model with mixed clearances is constructed, based on which the dynamic responses of the mechanism to different types of clearances are studied. Simultaneously, the effects of different variation ranges of clearance values and traveling speeds on the dynamic response, nonlinear characteristics, and dynamic accuracy reliability analysis of the mechanism were investigated. This research not only lays a robust theoretical foundation for the dynamics of multilink mechanisms but also demonstrates significant value and significance in both academic research and engineering application fields. Full article
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16 pages, 5111 KiB  
Article
One-Pot Synthesis of Magnetic Core-Shell Fe3O4@C Nanospheres with Pt Nanoparticle Immobilization for Catalytic Hydrogenation of Nitroarenes
by Jun Qiao, Yang Gao, Kai Zheng, Chao Shen, Aiquan Jia and Qianfeng Zhang
Appl. Sci. 2025, 15(10), 5773; https://doi.org/10.3390/app15105773 - 21 May 2025
Viewed by 94
Abstract
Magnetic materials with intriguing structural and functional modifications demonstrate broad application potential in various fields, including drug delivery, absorption, extraction, separation, and catalysis. In particular, the catalytic hydrogenation of functionalized organic nitro compounds represents a significant research focus in contemporary catalysis studies. A [...] Read more.
Magnetic materials with intriguing structural and functional modifications demonstrate broad application potential in various fields, including drug delivery, absorption, extraction, separation, and catalysis. In particular, the catalytic hydrogenation of functionalized organic nitro compounds represents a significant research focus in contemporary catalysis studies. A facile synthesis of Fe3O4@C–Pt core-shell nanocatalysts was developed in this work through a sequential process involving (1) one-pot hydrothermal synthesis followed by N2-annealing to obtain the Fe3O4@C core and (2) subsequent solvothermal deposition of platinum nanoparticles. Comprehensive characterization was performed using FT-IR, XRD, Raman spectroscopy, TEM, XPS, BET surface area analysis, TGA, and VSM techniques. The resulting magnetic nanocatalysts exhibited uniformly dispersed Pt nanoparticles and demonstrated exceptional catalytic performance in nitroarene hydrogenation reactions. Remarkably, the system showed excellent functional group tolerance across all 20 substituted nitroarenes, consistently yielding corresponding aromatic amine products with >93% conversion efficiency. Furthermore, the magnetic responsiveness of Fe3O4@C–Pt enabled convenient catalyst recovery through simple magnetic separation, with maintained catalytic activity over 10 consecutive reuse cycles without significant performance degradation. Full article
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19 pages, 3299 KiB  
Article
Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
by Tanvir Alam Shifat, Ryan Coe, Gioegio Bacelli and Ted Brekken
Appl. Sci. 2025, 15(10), 5772; https://doi.org/10.3390/app15105772 - 21 May 2025
Viewed by 77
Abstract
Wave energy converters (WECs) harness the untapped power of ocean waves to generate renewable energy, offering a promising solution to sustainable energy. An optimal WEC control strategy is essential to maximize power capture that dynamically adjusts system parameters in response to rapidly changing [...] Read more.
Wave energy converters (WECs) harness the untapped power of ocean waves to generate renewable energy, offering a promising solution to sustainable energy. An optimal WEC control strategy is essential to maximize power capture that dynamically adjusts system parameters in response to rapidly changing sea states. This study presents a novel control approach that leverages neural networks to estimate sea states from onboard WEC measurements such as position, velocity, and force. Using a point absorber WEC device as a test platform, our proposed approach estimates sea states in real-time and subsequently adjusts PID controller gains to maximize energy extraction. Simulation results across diverse sea conditions demonstrate that our strategy eliminates the need for external wave monitoring equipment while maintaining power capture efficiency. The results show that our neural network-based control technique can improve power capture by 25.6% while significantly reducing system complexity. This approach offers a practical alternative for WEC deployments where direct wave measurements are either infeasible or cost prohibitive. Full article
(This article belongs to the Special Issue Dynamics and Control with Applications to Ocean Renewables)
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22 pages, 632 KiB  
Article
Enhancing Multi-Key Fully Homomorphic Encryption with Efficient Key Switching and Batched Multi-Hop Computations
by Liang Zhou, Ruwei Huang and Bingbing Wang
Appl. Sci. 2025, 15(10), 5771; https://doi.org/10.3390/app15105771 - 21 May 2025
Viewed by 78
Abstract
Multi-Key Fully Homomorphic Encryption (MKFHE) offers a powerful solution for secure multi-party computations, where data encrypted under different keys can be jointly computed without decryption. However, existing MKFHE schemes still face challenges such as large parameter sizes, inefficient evaluation key generation, complex homomorphic [...] Read more.
Multi-Key Fully Homomorphic Encryption (MKFHE) offers a powerful solution for secure multi-party computations, where data encrypted under different keys can be jointly computed without decryption. However, existing MKFHE schemes still face challenges such as large parameter sizes, inefficient evaluation key generation, complex homomorphic multiplication processes, and limited scalability in multi-hop scenarios. In this paper, we propose an enhanced multi-hop MKFHE scheme based on the Brakerski-Gentry-Vaikuntanathan (BGV) framework. Our approach eliminates the need for an auxiliary Gentry-Sahai-Waters (GSW)-type scheme, simplifying the design and significantly reducing the public key size. We propose novel algorithms for evaluation key generation and key switching that simplify the computation while allowing each party to independently precompute and share its evaluation keys, thereby reducing both computational overhead and storage costs. Additionally, we combine the tensor product and key switching processes through homomorphic gadget decomposition, developing a new homomorphic multiplication algorithm and achieving linear complexity with respect to the number of parties. Furthermore, by leveraging the Polynomial Chinese Remainder Theorem (Polynomial CRT), we design a ciphertext packing technique that transforms our BGV-type MKFHE scheme into a batched scheme with improved amortized performance. Our schemes feature stronger multi-hop properties and operate without requiring a predefined maximum number of parties, offering enhanced flexibility and scalability compared to existing similar schemes. Full article
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12 pages, 2451 KiB  
Article
An Improved Dual-Sorting NSGA-II Method for Optimal Radiation Shielding Design
by Shenghan Cheng, Zhilin Chen, Yu Li, Wenxiang Jiang, Yang Yang, Po Huang and Taiping Peng
Appl. Sci. 2025, 15(10), 5770; https://doi.org/10.3390/app15105770 - 21 May 2025
Viewed by 72
Abstract
The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a widely used approach for solving multi-objective radiation shielding optimization problems, but it struggles to distinguish equally ranked solutions near the Pareto front, leading to reduced selection pressure and slower convergence. This study proposes an [...] Read more.
The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a widely used approach for solving multi-objective radiation shielding optimization problems, but it struggles to distinguish equally ranked solutions near the Pareto front, leading to reduced selection pressure and slower convergence. This study proposes an improved dual-sorting NSGA-II method that incorporates a novel dominance determination strategy and an O-based sorting mechanism to overcome these limitations. By leveraging the concave characteristics of Pareto-optimal solutions, the dual-sorting approach improves solution ranking accuracy and maintains population diversity. A case study on a multilayer shielding design demonstrates that the proposed method converges faster than the classical NSGA-II and achieves over 40% higher optimization efficiency in later evolutionary stages. These findings highlight the practical significance of the improved algorithm and its potential to accelerate radiation shielding optimization for nuclear facilities, offering a promising tool for efficient shielding design under multi-objective constraints. Full article
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46 pages, 676 KiB  
Review
From Ocean to Market: Technical Applications of Fish Protein Hydrolysates in Human Functional Food, Pet Wellness, Aquaculture and Agricultural Bio-Stimulant Product Sectors
by Dolly Bhati and Maria Hayes
Appl. Sci. 2025, 15(10), 5769; https://doi.org/10.3390/app15105769 - 21 May 2025
Viewed by 117
Abstract
Sustainability in food production is a pressing priority due to environmental and political crises, the need for long-term food security, and feeding the populace. Food producers need to increasingly adopt sustainable practices to reduce negative environmental impacts and food waste. The ocean is [...] Read more.
Sustainability in food production is a pressing priority due to environmental and political crises, the need for long-term food security, and feeding the populace. Food producers need to increasingly adopt sustainable practices to reduce negative environmental impacts and food waste. The ocean is a source for sustainable food systems; deforestation, water scarcity, and greenhouse gas emissions burden traditional, terrestrial resources. Our oceans contain the largest unexploited resource in the world in the form of mesopelagic fish species, with an estimated biomass of 10 billion metric tons. This resource is largely untapped due in part to the difficulties in harvesting these species. To ensure sustainability of this resource, management of fish stocks and fish processing practices must be optimised. Generation of fish protein hydrolysates from by-catch/underutilised species creates high-value, functional ingredients while also reducing waste. Marine hydrolysates offer a renewable source of nutrition and align with the principles of the circular economy, where waste is minimised and resources are reused efficiently. Ocean-derived solutions demand fewer inputs, generate less pollution, and have a smaller carbon footprint compared to traditional agriculture. This review collates clearly and succinctly the current and potential uses of FPHs for different market sectors and highlights the advantages of their use in terms of the scientifically validated health benefits for humans and animals and fish, and the protection and crop yield benefits that are documented to date from scientific studies. Full article
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13 pages, 2136 KiB  
Article
Synthesizing Time-Series Gene Expression Data to Enhance Network Inference Performance Using Autoencoder
by Cao-Tuan Anh and Yung-Keun Kwon
Appl. Sci. 2025, 15(10), 5768; https://doi.org/10.3390/app15105768 - 21 May 2025
Viewed by 59
Abstract
It is a challenge to infer a gene regulatory network from time-series gene expression data in the systems biology field. A lack of gene expression data samples is a factor limiting the performance of the inference methods. To resolve this problem, we propose [...] Read more.
It is a challenge to infer a gene regulatory network from time-series gene expression data in the systems biology field. A lack of gene expression data samples is a factor limiting the performance of the inference methods. To resolve this problem, we propose a novel autoencoder-based approach that synthesizes virtual gene expression data to be used as input to the inference method. Through intensive experiments, we showed that using synthetic gene expression as input improves the performance of the network inference method compared to that without it. In particular, the performance improvement was stable against the discretization level of gene expression, the number of time steps in the observed gene expression, and the number of genes. Full article
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29 pages, 2702 KiB  
Article
IFMIR-VR: Visual Relocalization for Autonomous Vehicles Using Integrated Feature Matching and Image Retrieval
by Gang Li, Xiaoman Xu, Jian Yu and Hao Luo
Appl. Sci. 2025, 15(10), 5767; https://doi.org/10.3390/app15105767 - 21 May 2025
Viewed by 79
Abstract
Relocalization technology is an important part of autonomous vehicle navigation. It allows the vehicle to find its position on the map after a reboot. This paper presents a relocalization algorithm framework that uses image retrieval techniques. An integrated matching algorithm is applied during [...] Read more.
Relocalization technology is an important part of autonomous vehicle navigation. It allows the vehicle to find its position on the map after a reboot. This paper presents a relocalization algorithm framework that uses image retrieval techniques. An integrated matching algorithm is applied during the feature matching process. This improves the accuracy of the vehicle’s relocalization. We use image retrieval to select the most relevant image from the map database. The integrated matching algorithm then finds precise feature correspondences. Using these correspondences and depth information, we calculate the vehicle’s global pose with the Perspective-n-Point (PnP) and Levenberg–Marquardt (L-M) algorithms. This process helps the vehicle determine its position on the map. Experimental results on public datasets show that the proposed framework outperforms existing methods like LightGlue and LoFTR in terms of matching accuracy. Full article
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23 pages, 2620 KiB  
Article
A Novel Overload Control Algorithm for Distributed Control Systems to Enhance Reliability in Industrial Automation
by Taikyeong Jeong
Appl. Sci. 2025, 15(10), 5766; https://doi.org/10.3390/app15105766 - 21 May 2025
Viewed by 117
Abstract
This paper presents a novel real-time overload detection algorithm for distributed control systems (DCSs), particularly applied to thermoelectric power plant environments. The proposed method is integrated with a modular multi-functional processor (MFP) architecture, designed to enhance system reliability, optimize resource utilization, and improve [...] Read more.
This paper presents a novel real-time overload detection algorithm for distributed control systems (DCSs), particularly applied to thermoelectric power plant environments. The proposed method is integrated with a modular multi-functional processor (MFP) architecture, designed to enhance system reliability, optimize resource utilization, and improve fault resilience under dynamic operational conditions. As legacy DCS platforms, such as those installed at the Tae-An Thermoelectric Power Plant, face limitations in applying advanced logic mechanisms, a simulation-based test bench was developed to validate the algorithm in anticipation of future DCS upgrades. The algorithm operates by partitioning function code executions into segment groups, enabling fine-grained, real-time CPU and memory utilization monitoring. Simulation studies, including a modeled denitrification process, demonstrated the system’s effectiveness in maintaining load balance, reducing power consumption to 17 mW under a 2 Gbps data throughput, and mitigating overload levels by approximately 31.7%, thereby outperforming conventional control mechanisms. The segmentation strategy, combined with summation logic, further supports scalable deployment across both legacy and next-generation DCS infrastructures. By enabling proactive overload mitigation and intelligent energy utilization, the proposed solution contributes to the advancement of self-regulating power control systems. Its applicability extends to energy management, production scheduling, and digital signal processing—domains where real-time optimization and operational reliability are essential. Full article
(This article belongs to the Special Issue Motivation AI and Its Application to Smart Systems and Industrial Innovations)
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40 pages, 2547 KiB  
Article
Digital Twin Framework for Road Infrastructure Management
by Munkhbaatar Buuveibaatar, Sungpil Shin and Wonhee Lee
Appl. Sci. 2025, 15(10), 5765; https://doi.org/10.3390/app15105765 - 21 May 2025
Viewed by 123
Abstract
Digital twin (DT) technology has garnered increasing attention across various sectors, particularly in the construction and road infrastructure domains. To fully realize its potential and systematically apply it in practice, adherence to a formalized approach is necessary. However, numerous DT-related standards and models [...] Read more.
Digital twin (DT) technology has garnered increasing attention across various sectors, particularly in the construction and road infrastructure domains. To fully realize its potential and systematically apply it in practice, adherence to a formalized approach is necessary. However, numerous DT-related standards and models currently exist, creating uncertainty in the selection of appropriate frameworks. Moreover, no widely accepted standard or reference model has yet been developed in the field of road infrastructure management. Therefore, this study examined the current standards and models employed in the adoption and implementation of DTs in road infrastructure management, focusing on their dimensions (layers) and functional components. A bottom-up approach was adopted by comprehensively reviewing the existing literature on road networks, bridges, tunnels, and other civil infrastructures and urban DTs. Ultimately, a DT framework was developed, comprising five core layers with their respective components and functionalities, to facilitate network-level integrated road infrastructure management. Moreover, the proposed framework’s implementation scenario enhances its applicability in the field. Overall, this study provides valuable insights for researchers and practitioners involved in DT implementation in infrastructure management and supports future standardization efforts in this domain. Full article
(This article belongs to the Special Issue Advances in Intelligent Road Design and Application)
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16 pages, 2205 KiB  
Article
Supersonic Pulse-Jet System for Filter Regeneration: Molecular Tagging Velocimetry Study and Computational Fluid Dynamics Validation
by Giancarlo Lenci, Charles Fort, Matthieu A. André, Victor Petrov, Ryan E. Jones, Chuck R. Marks and Philippe M. Bardet
Appl. Sci. 2025, 15(10), 5764; https://doi.org/10.3390/app15105764 - 21 May 2025
Viewed by 101
Abstract
This paper provides shadowgraphy and molecular tagging velocimetry (MTV) acquisition results and validates a computational fluid dynamics (CFDs) simulation for an underexpanded supersonic gas jet in a plenum pointed toward a wall with an aligned converging pipe outlet. Flow configurations of this type [...] Read more.
This paper provides shadowgraphy and molecular tagging velocimetry (MTV) acquisition results and validates a computational fluid dynamics (CFDs) simulation for an underexpanded supersonic gas jet in a plenum pointed toward a wall with an aligned converging pipe outlet. Flow configurations of this type are encountered in pulse-jet systems for online industrial gas filter regeneration. Although previous CFD validation efforts for pulse-jet systems have relied on static pressure measurements, this work expands the validation data using high-resolution flow visualization and velocimetry techniques. Simulations were performed with an axisymmetric two-dimensional Reynolds-averaged Navier-Stokes model and are in close agreement with the shadowgraphy and MTV data, including the description of Mach disks, barrel shocks, and reflected shocks in the underexpanded jet. The CFD model was finally applied to study the role of the converging tube downstream of the jet. Full article
(This article belongs to the Section Fluid Science and Technology)
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15 pages, 2902 KiB  
Article
Transcranial Doppler-Based Neurofeedback to Improve Hemispheric Lateralization
by Rosita Rabbito, Leonardo Ermini, Caterina Guiot and Silvestro Roatta
Appl. Sci. 2025, 15(10), 5763; https://doi.org/10.3390/app15105763 - 21 May 2025
Viewed by 64
Abstract
Functional transcranial Doppler (fTCD) ultrasound can detect cerebral blood flow lateralization to the left/right hemisphere during different tasks. This study aims to test the effectiveness of neurofeedback in improving the individual capacity to lateralize blood flow with mental activity. Bilateral monitoring of blood [...] Read more.
Functional transcranial Doppler (fTCD) ultrasound can detect cerebral blood flow lateralization to the left/right hemisphere during different tasks. This study aims to test the effectiveness of neurofeedback in improving the individual capacity to lateralize blood flow with mental activity. Bilateral monitoring of blood velocity (CBV) in the middle cerebral arteries was performed in 14 subjects engaged in 15 min of training, followed by a 15 min test in each of four sessions. A ball, displayed on a screen, moved right or left, according to the current right/left difference in normalized CBVs, thus providing a visual neurofeedback of lateralization. The subjects were invited to control the left/right movement of the depicted ball by appropriately orienting their mental activity, freely exploring different strategies. These attempts were completely free and unsupervised during training, while during the test, the subjects were required to follow randomized left/right cues lasting 35 s. Performance was assessed using receiver operating characteristic (ROC) analysis. With training, responses to left and right cues diverged more rapidly and consistently. Accuracy improved significantly from 0.51 to 0.65, and the area under the ROC increased from 0.55 to 0.69. These results demonstrate the effectiveness of neurofeedback in improving lateralization capacity, with implications for the development of fTCD-based brain–computer interfaces. Full article
(This article belongs to the Special Issue Innovations in Brain-Computer Interfaces: From Healthcare to Entertainment)
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4 pages, 178 KiB  
Editorial
Human Activity Recognition (HAR) in Healthcare, 2nd Edition
by Luigi Bibbò and Artur Serrano
Appl. Sci. 2025, 15(10), 5762; https://doi.org/10.3390/app15105762 - 21 May 2025
Viewed by 66
Abstract
Technological advances, particularly in the medical field, have significantly improved patients’ quality of life [...] Full article
(This article belongs to the Special Issue Human Activity Recognition (HAR) in Healthcare, 2nd Edition)
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