Applied Sciences

23 pages, 1289 KiB

Open AccessArticle

Analytical Investigations of Nonlinear Stiffness Characteristics of Halbach-Cylinder Magnetic Springs for Heavy-Load Capacity

by Zhongsheng Chen, Yangyi Zhang, Yeping Xiong and Ankang Wang

Appl. Sci. 2025, 15(9), 5099; https://doi.org/10.3390/app15095099 (registering DOI) - 3 May 2025

Abstract

Quasi-zero stiffness (QZS) has become a promising way of realizing low-frequency vibration isolation, where magnetic springs have been widely adopted for constructing negative stiffness. However, existing single-layer magnetic springs often have a small-amplitude negative stiffness, so the loading capacity is low. In order [...] Read more.

Quasi-zero stiffness (QZS) has become a promising way of realizing low-frequency vibration isolation, where magnetic springs have been widely adopted for constructing negative stiffness. However, existing single-layer magnetic springs often have a small-amplitude negative stiffness, so the loading capacity is low. In order to address this issue, this paper presents novel Halbach-cylinder magnetic springs (HCMSs) by using the Halbach array. Firstly, stiffness formulas of basic single-layer magnetic springs are analytically built based on the Amperian current model. The stiffness of the HCMS is derived from combining multiple single-layer magnetic springs. Then, nonlinear stiffness characteristics of both single-layer magnetic springs and HCMSs are investigated in terms of the amplitude, the uniformity, and the displacement range of negative stiffness. Analytical results show that HCMSs can generate negative stiffness with different equilibrium positions, and the amplitude of negative stiffness of HCMSs is much larger than that of single-layer magnetic springs. The amplitude of negative stiffness is in conflict with the uniformity, so a trade-off design is needed. In addition, increasing the number of layers of Halbach cylinders can be adopted to realize larger-amplitude and wider-range negative stiffness. This study will provide new insights into designing QZS with heavy-load capacity. Full article

12 pages, 687 KiB

Open AccessArticle

Exploring Interactions of Older Adults with Mild Dementia Participating in Robotherapy with Robot Cats in a Day Hospital in Spain: A Qualitative Study

by Cristina Perdomo-Delgado, Minoo Dabiri-Golchin, Patricia Sánchez-Herrera-Baeza, Almudena Muñoz-Martínez, Nereida Reyes-Sosa, Marta Pérez-de-Heredia-Torres and Paula Obeso-Benítez

Appl. Sci. 2025, 15(9), 5098; https://doi.org/10.3390/app15095098 (registering DOI) - 3 May 2025

Abstract

The growing aging population has raised concerns about the treatment of age-related diseases, such as dementia. An emerging technology that could assist individuals with dementia is the development of social robots. However, interactions with these robots have been underexplored. This study aimed to [...] Read more.

The growing aging population has raised concerns about the treatment of age-related diseases, such as dementia. An emerging technology that could assist individuals with dementia is the development of social robots. However, interactions with these robots have been underexplored. This study aimed to examine the interactions between a robot cat and older adults with mild dementia during robotherapy sessions. Thirteen older adults with mild dementia who used a robot cat during robotherapy sessions in a day hospital in Spain were interviewed. Following semi-structured interviews, a thematic qualitative analysis of the data was conducted, revealing four main themes: (1) therapeutic effects of robotherapy (relaxation, mood improvement, cognitive stimulation, and increased social interaction); (2) preferences regarding the types of robotherapy activities; (3) interaction with the robot cat during the sessions; and (4) technological characteristics of the robot cat. Overall, the findings indicate positive interactions and suggest promising benefits for older adults with mild dementia participating in robotherapy with robot cats. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

19 pages, 1851 KiB

Open AccessArticle

Exploring the Potential of Desmodesmus sp. KNUA231 for Bioenergy and Biofertilizer Applications and Its Adaptability to Environmental Stress

by Yeonsu Shin, Jeong-Mi Do, Hae-Seo Noh and Ho-Sung Yoon

Appl. Sci. 2025, 15(9), 5097; https://doi.org/10.3390/app15095097 (registering DOI) - 3 May 2025

Abstract

As global energy demand continues to rise, microalgae have gained attention as a promising feedstock for biofuel production due to their environmental adaptability and renewable nature. This study investigated the growth performance and stress tolerance of Desmodesmus sp. KNUA231 under varying pH and [...] Read more.

As global energy demand continues to rise, microalgae have gained attention as a promising feedstock for biofuel production due to their environmental adaptability and renewable nature. This study investigated the growth performance and stress tolerance of Desmodesmus sp. KNUA231 under varying pH and salinity conditions to evaluate its potential as a biofuel candidate. The strain was cultivated under controlled laboratory conditions and exhibited stable growth across a broad pH range (4–10) and moderate salinity levels (up to 5 g L⁻¹ NaCl), indicating its resilience to diverse environmental conditions. Fatty acid methyl ester (FAME) analysis revealed that the biodiesel properties of Desmodesmus sp. KNUA231 comply with ASTM and EN standards in specific parameters, reinforcing its feasibility as a renewable biofuel feedstock. Additionally, its high calorific value (CV) suggests its potential as an energy-dense biomass source. The results of inductively coupled plasma mass spectrometry (ICP) analysis show that the soil is supplied with essential nutrients while minimizing heavy metal contamination, suggesting the possibility of biofertilizers. Although Desmodesmus sp. KNUA231 demonstrated promising characteristics for biofuel applications, further research is required to optimize large-scale cultivation and improve productivity for industrial applications. These findings highlight the potential of Desmodesmus sp. KNUA231 as a biofuel resource, particularly in non-optimal environmental conditions where pH and salinity fluctuations are common, contributing to the ongoing search for sustainable bioenergy solutions. Full article

(This article belongs to the Special Issue Bioprocessing and Fermentation Technology for Biomass Conversion)

31 pages, 3581 KiB

Open AccessArticle

Team-Communication Modeling Framework for Multi-Unit Nuclear Power Plant Accident Analysis

by Shelly Salim, Seon-Yeong Yeom and Dong-Han Ham

Appl. Sci. 2025, 15(9), 5096; https://doi.org/10.3390/app15095096 (registering DOI) - 3 May 2025

Abstract

A multi-unit nuclear accident is an accident that damages the nuclear core and occurs in multiple units simultaneously in a nuclear power plant. Its massive consequences have triggered extensive studies on accident prevention and mitigation. In this study, we are interested in the [...] Read more.

A multi-unit nuclear accident is an accident that damages the nuclear core and occurs in multiple units simultaneously in a nuclear power plant. Its massive consequences have triggered extensive studies on accident prevention and mitigation. In this study, we are interested in the emergency-response teams involved in multi-unit accident management. Multiple emergency-response teams need to interact with each other during multi-unit accident-management tasks, and the basis of team interactions is communication. Thus, we propose a team-communication modeling framework consisting of three models, i.e., a team-communication investigation model, process model, and performance-factor model. The methods used to develop each model are described. Also, an analysis methodology is provided to guide the application of the modeling framework, and a case study based on a multi-unit accident scenario is performed to confirm the feasibility of our study. The final results of the case study are four specialized models developed by applying the functional resonance analysis method (FRAM), each covering a specific scope of the accident scenario, with the functions and aspects elaborated to support team-communication analysis. Modeling insights are collected and can be applied to improve accident-training scenarios, procedure/guideline development, and accident-management support systems. Full article

(This article belongs to the Special Issue Human Factors Engineering in Complex Socio-Technical Systems)

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13 pages, 272 KiB

Open AccessArticle

First-Time Meniscal Surgeries Reveal Age-Linked Rise in Medial Tears and Sex-Based Injury Difference

by Sorin Florescu, Daliana Minda and Cosmin Grațian Damian

Appl. Sci. 2025, 15(9), 5095; https://doi.org/10.3390/app15095095 (registering DOI) - 3 May 2025

Abstract

Background and Objectives: Although gender and age are high-profile risk factors for meniscal injuries, their interplay is not yet fully understood. Little information is available about patients undergoing first-time meniscus surgery and the epidemiology of meniscus tears in Eastern Europe. This study investigated [...] Read more.

Background and Objectives: Although gender and age are high-profile risk factors for meniscal injuries, their interplay is not yet fully understood. Little information is available about patients undergoing first-time meniscus surgery and the epidemiology of meniscus tears in Eastern Europe. This study investigated sex- and age-related trends in meniscal injuries (medial and/or lateral, with or without co-occurring knee chondropathy) in a substantial cohort of patients undergoing first-time meniscus surgery at a high-volume medical center in western Romania. Materials and Methods: This retrospective study included 488 patients of different ages. Logistic regression was applied across the age continuum, followed by analyses stratified by age quartiles to investigate age-related variations. Results: Increasing age was significantly associated with increased odds of having medial meniscus tears (O.R. = 1.04 (1.01–1.07)) and associated knee chondropathy (O.R. = 1.03 (1.00–1.05)). Women displayed significant, twofold lower odds of having lateral meniscus damage (O.R. = 0.54 (0.31–0.96)). Men tended to have a higher prevalence of associated knee chondropathy (O.R. = 1.71 (0.96–3.04)). The frequency of medial meniscus tears and patellar damage rose significantly, starting from the third age quartile (33–42 years) and the second quartile (23–32 years), respectively. Significant positive associations between medial and lateral meniscal damage were found across all age quartiles (r_s ≥ 0.42), with their magnitude increasing with age. Medial damage also correlated significantly with patellar damage, but weaker and only from the third quartile onward (r_s ≥ 0.30). Conclusions: Increasing age was linked to a higher risk of medial meniscal injuries and knee chondropathy, with the latter tending to occur earlier. Male sex was associated with a higher likelihood of lateral meniscal damage. These findings highlight the importance of considering age and sex in the diagnosis and management of meniscal injuries and provide valuable data for an Eastern European population. Full article

25 pages, 14247 KiB

Open AccessArticle

Energy Efficiency and Environmental Sustainability in Rural Buildings: A Life Cycle Assessment of Photovoltaic Integration in Poultry Tunnels—A Case Study in Central Italy

by Stefano Bigiotti, Carlo Costantino, Alessio Patriarca, Giulia Mancini, Giorgio Provolo, Fabio Recanatesi, Maria Nicolina Ripa and Alvaro Marucci

Appl. Sci. 2025, 15(9), 5094; https://doi.org/10.3390/app15095094 (registering DOI) - 3 May 2025

Abstract

Livestock buildings in rural areas are increasingly recognized for their environmental impact, yet few studies provide applied, scenario-based evaluations to guide retrofit interventions. While the existing literature acknowledges the environmental burden of livestock facilities, it often lacks operationally grounded analyses applicable to real-world [...] Read more.

Livestock buildings in rural areas are increasingly recognized for their environmental impact, yet few studies provide applied, scenario-based evaluations to guide retrofit interventions. While the existing literature acknowledges the environmental burden of livestock facilities, it often lacks operationally grounded analyses applicable to real-world agricultural contexts. This paper proposes an original integration of experimental climatic monitoring and life cycle assessment (LCA) to evaluate retrofit scenarios for energy efficiency in real poultry farming contexts. Based on an accurate climatic monitoring campaign conducted on-site during the spring and summer periods, relevant data were collected on air temperature, humidity, wind speed, and solar radiation affecting two poultry tunnels in central Italy, highlighting the need for thermal mitigation. The comparison between the observed operational scenario and the hypothesized improved scenario, involving energy supply from photovoltaic sources, evaluated using the PVGIS tool, demonstrated a significant reduction in environmental impact, with a 33.4% decrease in global warming potential and a 26.1% reduction in energy consumption. This study combines experimental on-site climatic data collection with comparative environmental evaluation using LCA methodology. The LCA approach, which guided the entire study, highlighted how the energy efficiency gained through solar panels adequately offsets their production and maintenance costs over the long term. These findings offer a replicable model for energy retrofits in rural livestock facilities, contributing to both environmental goals and rural resilience. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Technologies, Materials and Their Applications)

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25 pages, 7571 KiB

Open AccessArticle

The Effect of Industrial Byproducts Fly Ash and Quartz Powder on Cement Properties and Environmental Benefits Analysis

by Yonghong Miao, Yudong Luo, Yulong Zheng, Zenian Wang, Zhaochang Zhang, Xiaoyong Wang and Guiyu Zhang

Appl. Sci. 2025, 15(9), 5093; https://doi.org/10.3390/app15095093 (registering DOI) - 3 May 2025

Abstract

Using industrial byproducts to replace cement is an important way to reduce carbon emissions from the cement industry. In this study, the effects of two industrial byproducts, fly ash (FA) and quartz powder (QZ), as supplementary cementitious materials (SCMs) on the macroscopic properties [...] Read more.

Using industrial byproducts to replace cement is an important way to reduce carbon emissions from the cement industry. In this study, the effects of two industrial byproducts, fly ash (FA) and quartz powder (QZ), as supplementary cementitious materials (SCMs) on the macroscopic properties and microstructure of cement-based materials were experimentally investigated. The results of the compressive strength and ultrasonic pulse velocity experiments showed that QZ significantly mitigated the decrease in strength and ultrasonic pulse velocity caused by the reduction in cement dosage in the early stage. Moreover, the 28-day compressive strength of the FA group was comparable to that of the control group, and regression analysis indicated a negligible effect of FA addition on 28-day compressive strength. X-ray diffraction and Fourier transform infrared spectroscopy experiments showed that QZ can promote the hydration reaction in the early stage. Scanning electron microscopy images revealed that a layer of hydration products can form on the surface of FA after 28 days of hydration. Hydration heat experiments indicated that FA significantly reduces the release of hydration heat, while QZ promotes the formation of ettringite through nucleation effects in the early stage of hydration, thereby accelerating the release of hydration heat. Thermogravimetric analysis after 28 days showed that the amount of hydration products and calcium hydroxide produced decreased with the addition of cementitious materials. Finally, the use of FA and QZ was analyzed for carbon emissions and energy consumption. The results showed that using these two cementitious materials significantly reduces carbon dioxide emissions and energy consumption. Full article

(This article belongs to the Section Civil Engineering)

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14 pages, 2750 KiB

Open AccessArticle

Subjective Evaluation of Generative AI-Driven Dialogues in Paired Dyadic and Topic-Sharing Triadic Interaction Structures

by Kaori Abe, Changqin Quan, Sheng Cao and Zhiwei Luo

Appl. Sci. 2025, 15(9), 5092; https://doi.org/10.3390/app15095092 (registering DOI) - 3 May 2025

Abstract

As the linguistic capabilities of dialogue systems improve, the importance of how they interact with humans and build trustworthy relationships is increasing. This study investigated the effect of interaction structures in a generative AI-driven dialogue system to improve relationships through interactions. The dialogue [...] Read more.

As the linguistic capabilities of dialogue systems improve, the importance of how they interact with humans and build trustworthy relationships is increasing. This study investigated the effect of interaction structures in a generative AI-driven dialogue system to improve relationships through interactions. The dialogue system communicated with subjects in natural language via voice and included a facial expression function. The settings of dyadic and triadic interaction structures were applied to the system. The one-to-one dyadic interaction and triadic interaction with joint attention to a topic were designed following the developmental stages of children’s social communication ability. Subjective evaluations of the dialogues and the system were conducted through a questionnaire. As a result, positive evaluations were based on well-constructed structures. The system’s inappropriate behavior under failed structures reduced the quality of the dialogues and worsened the evaluation of the system. The interaction structures in the system settings needed to match the structures intended by the subjects, whether the structures were dyadic or triadic. Under the matching and successful construction, the system fully demonstrated its dialogue capability and behaved pleasantly with the subjects. By switching interaction structures to adapt to users’ demands, system behavior becomes more appropriate for users. Full article

(This article belongs to the Special Issue Applications, Challenges and Future Direction of Natural Language Processing Based on Deep Learning)

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38 pages, 5064 KiB

Open AccessArticle

BPriS: Disciplined Agile Delivery Planning Method Based on Work Items List Pattern Applied to Prioritized Semantically Coupled Software Functions Derived from Business Process Model and Software Functional Pattern

by Ljubica Kazi and Zoltan Kazi

Appl. Sci. 2025, 15(9), 5091; https://doi.org/10.3390/app15095091 (registering DOI) - 3 May 2025

Abstract

Common practice in agile delivery planning is based on user requirements-related artifacts. However, an aspect of business process alignment to software product functions comes into focus in the phase of inception of enterprise-aware disciplined agile software projects. This research proposes a method for [...] Read more.

Common practice in agile delivery planning is based on user requirements-related artifacts. However, an aspect of business process alignment to software product functions comes into focus in the phase of inception of enterprise-aware disciplined agile software projects. This research proposes a method for mapping business process model elements to sets of semantically coupled and prioritized software functions to obtain ordered software product backlog, i.e., work items list. These software functions are derived from primitive business processes and software functional patterns. The mapping table enables assignment of primitive business processes to categorized software functions. Derived and prioritized software functions are related to a work item list pattern according to selected technology implementation. This way, a prioritized work items list is formulated, which enables development iteration planning. This method could be useful in software functional design alternatives comparison, change management, multi-project integration of software modules to support business processes in information systems, etc. Feasibility of the proposed method has been demonstrated with a case study, related to the development of a billing and reporting software utilized in a private hospital. This case study shows usability of the proposed method in the case of two related development projects that enable software functionality enhancement. Full article

(This article belongs to the Special Issue Advances in Digital Information System)

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13 pages, 4211 KiB

Open AccessArticle

Biochar’s Adsorption of Escherichia coli and Probiotics Lactiplantibacillus plantarum and Limosilactobacillus reuteri and Its Impact on Bacterial Growth Post In Vitro Digestion

by Serena Reggi, Sara Frazzini, Eleonora Fusi, Marianna Guagliano, Cinzia Cristiani, Elisabetta Onelli, Alessandra Moscatelli and Luciana Rossi

Appl. Sci. 2025, 15(9), 5090; https://doi.org/10.3390/app15095090 (registering DOI) - 3 May 2025

Abstract

Background: Biochar has gained increasing attention for its potential benefits in improving animal health. Its physical and chemical properties depend on the starting biomass and production technology. This study investigates its functional properties versus bacteria. Methods: The morphology and physical properties of biochar [...] Read more.

Background: Biochar has gained increasing attention for its potential benefits in improving animal health. Its physical and chemical properties depend on the starting biomass and production technology. This study investigates its functional properties versus bacteria. Methods: The morphology and physical properties of biochar from vine pruning were evaluated by SEM. The adsorption capacity for pathogenic E. coli F4+ and F18+ and probiotic microorganisms such as Lactobacillus sp. was assessed by plate count after contact with biochar. The growth activity on pathogenic and probiotic bacteria was tested after in vitro digestion. Results: Biochar from grapevine pruning did not maintain the original structure and showed both smooth and rough surfaces. The binding capacity varied across bacterial species. At concentrations of 20 mg/mL, up to 74% of E. coli adhered to the biochar surface, while the maximum adsorption rate of Lactobacillus sp. was around 38%. An inhibitory activity against E. coli (maximum reduction: 35%) and a growth-promoting effect for Lactobacillus sp. were observed (maximum promotion: 6%). Conclusions: These findings highlight the potential of biochar as a functional feed ingredient and that its functional properties are preserved after in vitro digestion. Full article

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19 pages, 2393 KiB

Open AccessArticle

CLSTM-MT (a Combination of 2-Conv CNN and BiLSTM Under the Mean Teacher Collaborative Learning Framework): Encryption Traffic Classification Based on CLSTM (a Combination of 2-Conv CNN and BiLSTM) and Mean Teacher Collaborative Learning

by Xiaozong Qiu, Guohua Yan and Lihua Yin

Appl. Sci. 2025, 15(9), 5089; https://doi.org/10.3390/app15095089 (registering DOI) - 3 May 2025

Abstract

The identification and classification of network traffic are crucial for maintaining network security, optimizing network management, and ensuring reliable service quality. These functions help prevent malicious activities, such as network attacks and illegal intrusions, while supporting the efficient allocation of network resources and [...] Read more.

The identification and classification of network traffic are crucial for maintaining network security, optimizing network management, and ensuring reliable service quality. These functions help prevent malicious activities, such as network attacks and illegal intrusions, while supporting the efficient allocation of network resources and enhancing user experience. However, the widespread use of traffic encryption technology, while improving data transmission security, also obscures the content of traffic, making it challenging to accurately classify and identify encrypted traffic. This limitation hampers both network security maintenance and further improvements in service quality. Therefore, there is an urgent need to develop an efficient and accurate encryption traffic identification method. This study addresses three key challenges: First, existing methods fail to explore the potential relationship between flow load features and sequence features during feature extraction. Second, there is a need for approaches that can adapt to the diverse characteristics of different protocols, ensuring the accuracy and robustness of encrypted traffic identification. Third, traditional deep learning models need large amounts of labeled data, which are expensive to acquire. To overcome these challenges, we propose an encrypted traffic recognition method based on a CLSTM model (a combination of 2-conv CNN and BiLSTM) and Mean Teacher collaborative learning. This approach detects and integrates traffic load features with sequence features to improve the accuracy and robustness of encrypted traffic identification while reducing the model’s reliance on labeled data through the consistency constraint of unlabeled data using Mean Teacher. Experimental results demonstrate that the CLSTM-MT collaborative learning method outperforms traditional methods in encrypted traffic identification and classification, achieving superior performance even with limited labeled data, thus addressing the high cost of data labeling. Full article

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20 pages, 7332 KiB

Open AccessArticle

Modeling and Predicting Human Actions in Soccer Using Tensor-SOM

by Moeko Tominaga, Yasunori Takemura and Kazuo Ishii

Appl. Sci. 2025, 15(9), 5088; https://doi.org/10.3390/app15095088 (registering DOI) - 3 May 2025

Abstract

As robots become increasingly integrated into society, a future in which humans and robots collaborate is expected. In such a cooperative society, robots must possess the ability to predict human behavior. This study investigates a human–robot cooperation system using RoboCup soccer as a [...] Read more.

As robots become increasingly integrated into society, a future in which humans and robots collaborate is expected. In such a cooperative society, robots must possess the ability to predict human behavior. This study investigates a human–robot cooperation system using RoboCup soccer as a testbed, where a robot observes human actions, infers their intentions, and determines its own actions accordingly. Such problems have typically been addressed within the framework of multi-agent systems, where the entity performing an action is referred to as an ‘agent’, and multiple agents cooperate to complete a task. However, a system capable of performing cooperative actions in an environment where both humans and robots coexist has yet to be fully developed. This study proposes an action decision system based on self-organizing maps (SOM), a widely used unsupervised learning model, and evaluates its effectiveness in promoting cooperative play within human teams. Specifically, we analyze futsal game data, where the agents are professional futsal players, as a test case for the multi-agent system. To this end, we employ Tensor-SOM, an extension of SOM that can handle multi-relational datasets. The system learns from this data to determine the optimal movement speeds in x and y directions for each agent’s position. The results demonstrate that the proposed system successfully determines optimal movement speeds, suggesting its potential for integrating robots into human team coordination. Full article

(This article belongs to the Special Issue Recent Advances in Human-Robot Interactions)

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21 pages, 2876 KiB

Open AccessArticle

A Computer-Aided Approach to Canine Hip Dysplasia Assessment: Measuring Femoral Head–Acetabulum Distance with Deep Learning

by Pedro Franco-Gonçalo, Pedro Leite, Sofia Alves-Pimenta, Bruno Colaço, Lio Gonçalves, Vítor Filipe, Fintan McEvoy, Manuel Ferreira and Mário Ginja

Appl. Sci. 2025, 15(9), 5087; https://doi.org/10.3390/app15095087 (registering DOI) - 3 May 2025

Abstract

Canine hip dysplasia (CHD) screening relies on radiographic assessment, but traditional scoring methods often lack consistency due to inter-rater variability. This study presents an AI-driven system for automated measurement of the femoral head center to dorsal acetabular edge (FHC/DAE) distance, a key metric [...] Read more.

Canine hip dysplasia (CHD) screening relies on radiographic assessment, but traditional scoring methods often lack consistency due to inter-rater variability. This study presents an AI-driven system for automated measurement of the femoral head center to dorsal acetabular edge (FHC/DAE) distance, a key metric in CHD evaluation. Unlike most AI models that directly classify CHD severity using convolutional neural networks, this system provides an interpretable, measurement-based output to support a more transparent evaluation. The system combines a keypoint regression model for femoral head center localization with a U-Net-based segmentation model for acetabular edge delineation. It was trained on 7967 images for hip joint detection, 571 for keypoints, and 624 for acetabulum segmentation, all from ventrodorsal hip-extended radiographs. On a test set of 70 images, the keypoint model achieved high precision (Euclidean Distance = 0.055 mm; Mean Absolute Error = 0.0034 mm; Mean Squared Error = 2.52 × 10⁻⁵ mm²), while the segmentation model showed strong performance (Dice Score = 0.96; Intersection over Union = 0.92). Comparison with expert annotations demonstrated strong agreement (Intraclass Correlation Coefficients = 0.97 and 0.93; Weighted Kappa = 0.86 and 0.79; Standard Error of Measurement = 0.92 to 1.34 mm). By automating anatomical landmark detection, the system enhances standardization, reproducibility, and interpretability in CHD radiographic assessment. Its strong alignment with expert evaluations supports its integration into CHD screening workflows for more objective and efficient diagnosis and CHD scoring. Full article

(This article belongs to the Special Issue Research on Machine Learning in Computer Vision)

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16 pages, 3698 KiB

Open AccessArticle

Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks

by Jiazhu Dai and Yubing Lu

Appl. Sci. 2025, 15(9), 5086; https://doi.org/10.3390/app15095086 (registering DOI) - 3 May 2025

Abstract

Graph neural networks (GNNs) are widely used for graph-structured data. However, GNNs are vulnerable to membership inference attacks (MIAs) in graph classification tasks, which determine whether a graph was in the training set, risking the leakage of sensitive data. Existing MIAs rely on [...] Read more.

Graph neural networks (GNNs) are widely used for graph-structured data. However, GNNs are vulnerable to membership inference attacks (MIAs) in graph classification tasks, which determine whether a graph was in the training set, risking the leakage of sensitive data. Existing MIAs rely on prediction probability vectors, but they become ineffective when only prediction labels are available. We propose a Graph-level Label-Only Membership Inference Attack (GLO-MIA), which is based on the intuition that the target model’s predictions on training data are more stable than those on testing data. GLO-MIA generates a set of perturbed graphs for the target graph by adding perturbations to its effective features and queries the target model with the perturbed graphs to obtain their prediction labels, which are then used to calculate the robustness score of the target graph. Finally, by comparing the robustness score with a predefined threshold, the membership of the target graph can be inferred correctly with high probability. Experimental evaluations on three datasets and four GNN models demonstrate that GLO-MIA achieves an attack accuracy of up to 0.825, outperforming baseline work by 8.5% and closely matching the performance of probability-based MIAs, even with only prediction labels. Full article

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17 pages, 5745 KiB

Open AccessArticle

Effect of Inertial and Kinetic Forces of a Soil–Pile–Structure System on the Behavior of a Superstructure Under Earthquake

by Sun-Yong Kwon

Appl. Sci. 2025, 15(9), 5085; https://doi.org/10.3390/app15095085 (registering DOI) - 3 May 2025

Abstract

The seismic behavior of pile-supported structures is influenced by complex interactions between inertial force and kinematic force mainly drawn by soil properties and superstructure characteristics. This study aims to investigate the combined effects of inertial and kinematic interaction on the dynamic response of [...] Read more.

The seismic behavior of pile-supported structures is influenced by complex interactions between inertial force and kinematic force mainly drawn by soil properties and superstructure characteristics. This study aims to investigate the combined effects of inertial and kinematic interaction on the dynamic response of pile foundations under seismic loading. To achieve this, three-dimensional numerical simulations were conducted using FLAC3D, based on a bridge substructure model. A total of twelve analysis cases were developed by varying input seismic motion levels, soil relative densities, and pile cap masses. The results demonstrate that kinematic force effects become more dominant in dense soils as seismic intensity increases, resulting in greater velocity responses and internal forces in the pile cap. Meanwhile, inertial forces from heavier superstructures interacted with kinematic force effects in a resistive manner, particularly under embedded pile cap conditions. The displacement of pile foundations remained within serviceable limits in all cases, although structural demands would be elevated under certain conditions. These findings confirm the significance of accounting for both inertial and kinematic effects in seismic design and highlight the importance of site-specific soil conditions. Full article

(This article belongs to the Special Issue Seismic Resistant Design and Analysis for Building Structures and Infrastructure Systems)

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19 pages, 4752 KiB

Open AccessArticle

Mixed Sedimentation in the Transition Zone Between a Shallow-Water Delta and Tidal Flat and Its Influence on Reservoir Quality: A Case Study of Member B of the Asmari Formation in C Oilfield, South Iraq

by Juan He, Li Yang, Yunjie Fan, Chen Hui, Bowen Zhang, Wen Zhou and Meiyan Fu

Appl. Sci. 2025, 15(9), 5084; https://doi.org/10.3390/app15095084 (registering DOI) - 3 May 2025

Abstract

The transition zone between a shallow-water delta and tidal flat is characterized by a high degree of mixed siliciclastic–carbonate sedimentation. There are frequent lateral and vertical variations in sandstone, dolostone, limestone, and mixed siliciclastic–carbonate rock (MSR); however, their influence on reservoir quality remains [...] Read more.

The transition zone between a shallow-water delta and tidal flat is characterized by a high degree of mixed siliciclastic–carbonate sedimentation. There are frequent lateral and vertical variations in sandstone, dolostone, limestone, and mixed siliciclastic–carbonate rock (MSR); however, their influence on reservoir quality remains uncertain. Member B of the Asmari Formation (Asmari B) in Iraq’s C Oilfield was deposited in a remnant ocean basin formed by the closure of the Neo-Tethys Ocean. During the Oligocene–Miocene, frequent exposure of the Arabian Shield provided intermittent sediment sources to the study area. Under shallow water and relatively arid conditions, widespread mixed sedimentation of siliciclastic sand and dolomitic components occurred. Taking Asmari B as a case study, this research employs core and thin-section observations, trace element analyses, and quantitative mineralogical interpretations of logging data to investigate the characteristics of mixed sedimentation and to evaluate its impact on reservoir quality. Four key aspects were identified: (1) Four main types of mixed lithofacies developed in Member B of the Asmari Formation, namely sandstone-bearing dolomite, dolomitic sandstone, dolostone-bearing sand, and sandy dolostone. These lithofacies were deposited in the transition zone between distributary channels and intertidal zone with different water depths. As the terrigenous input decreased, the water depth for sand-bearing facies increased. In particular, sandy dolostone was predominantly formed in subtidal settings under the influence of storm events. (2) MSRs are categorized based on the proportion of the minor component into high and low mixing degrees. Based on mineral compositions interpreted from well logging data, the mixing degree of MSRs was characterized by the thickness ratio, using the thickness of high- and low-mixing-degree MSRs relative to the total thickness of the formation. The MSRs mainly developed in the B1, B2, B3-1, B3-2, and B4 sublayers, where moderate provenance supply facilitated the high mixing of terrigenous clastic and carbonate components. (3) The pore and throat patterns of MSR reservoirs change with the mixing degree index. When the dolomite content in sandstone exceeds 25%, the pore–throat structure changes significantly. A small amount of sand in dolostone has little effect on the pore and throat. Sandy dolostone exhibits the poorest reservoir quality. (4) Mixed sandstone reservoirs are distributed on both sides of the distributary channels and mouth bar. The dolostone-bearing sand reservoirs are distributed in the transition zone between the sandy flat and dolomite flat. Sandy dolostone is mainly thin and isolated due to the influence of storm events. This study provides guidance for understanding the development patterns of MSR reservoirs under similar geological settings, facilitating the next step of oil and gas exploration in these special reservoirs. Full article

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16 pages, 1483 KiB

Open AccessArticle

Multi-Source Information Fusion Diagnosis Method for Aero Engine

by Kai Yin, Yawen Shen, Yifan Chen and Huisheng Zhang

Appl. Sci. 2025, 15(9), 5083; https://doi.org/10.3390/app15095083 - 2 May 2025

Abstract

Aero engines are complex coupled systems in which faults in one subsystem can propagate and affect the performance of others. Relying on single-source performance parameters is often insufficient for accurately assessing component degradation. Although multi-source fusion diagnosis methods, such as those based on [...] Read more.

Aero engines are complex coupled systems in which faults in one subsystem can propagate and affect the performance of others. Relying on single-source performance parameters is often insufficient for accurately assessing component degradation. Although multi-source fusion diagnosis methods, such as those based on Bayesian networks, have been widely applied, their diagnostic performance remains limited when prior knowledge is scarce. To address this challenge, this paper proposes a multi-source information fusion diagnosis method for aero engine fault detection based on Dempster–Shafer (D-S) evidence theory. Data from gas path and vibration subsystems are separately processed to extract fault features, and a decision-level fusion strategy is employed to achieve comprehensive diagnoses. A case study based on real operational data from a two-shaft aero engine demonstrates that the proposed method significantly improves diagnostic performance. Specifically, the Bayesian-network-based fusion method achieves a diagnostic confidence of 87.2% without prior knowledge and 91.2% with prior knowledge incorporated, whereas D-S evidence theory attains a higher fault confidence of 99.6% without requiring any prior information. Full article

25 pages, 569 KiB

Open AccessReview

A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives

by Luka Živković, Tin Benšić, Marinko Barukčić and Goran Kurtović

Appl. Sci. 2025, 15(9), 5082; https://doi.org/10.3390/app15095082 - 2 May 2025

Abstract

This paper provides a comprehensive overview of mission-profile-based stress testing methods for AC electric motor drives, focusing on the impact of stress factors on long-term performance. Special attention is given to the categorization of stress factors that make up the mission profile and [...] Read more.

This paper provides a comprehensive overview of mission-profile-based stress testing methods for AC electric motor drives, focusing on the impact of stress factors on long-term performance. Special attention is given to the categorization of stress factors that make up the mission profile and their influence on the reliability and lifespan of motor drives. The paper reviews existing testing methods, discussing their applications and highlighting their focus on individual components, such as electric motors and power converters, rather than the entire drive system. Methods aimed at improving the reliability and extending the lifespan of motor drives are also presented, alongside an overview of state-of-the-art testing approaches. Relevant research is examined to outline current practices in the field. Moreover, through this survey, attention is drawn to the importance of considering component interactions in future testing protocols, with the aim of supporting more realistic system-level assessments and improving overall drive performance. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

28 pages, 465 KiB

Open AccessArticle

Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry

by Edmund Radlbauer, Thomas Moser and Markus Wagner

Appl. Sci. 2025, 15(9), 5081; https://doi.org/10.3390/app15095081 - 2 May 2025

Abstract

This study develops and implements a scalable system architecture for dynamic data acquisition and knowledge modeling in industrial contexts. The objective is to efficiently process large datasets to support decision-making and process optimization within Industry 4.0. The architecture integrates modern technologies, such as [...] Read more.

This study develops and implements a scalable system architecture for dynamic data acquisition and knowledge modeling in industrial contexts. The objective is to efficiently process large datasets to support decision-making and process optimization within Industry 4.0. The architecture integrates modern technologies, such as the ibaPDA system for data acquisition, and employs communication standards like Modbus TCP and OPC UA to ensure broad compatibility with diverse equipment. In addition, it leverages native protocols offered by certain controllers, enabling direct data exchange without the need for conversion layers. A developed prototype demonstrates the practical applicability of the architecture, tested in a real industrial environment with a focus on processing speed, data integrity, and system reliability. The results indicate that the architecture not only meets the requirements for dynamic data acquisition but also enhances knowledge modeling. This leads to more efficient process control and opens new perspectives for managing and analyzing big data in production environments. The study emphasizes the importance of an integrated development approach and highlights the need for interdisciplinary collaboration to address operational challenges. Future extensions may include the implementation of Python interfaces and machine learning algorithms for data simulation, enabling more accurate predictive models. These findings provide valuable insights for industry, software development, data science, and academia, helping to tackle the challenges of Industry 4.0 and drive innovation forward. Full article

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