Applied Sciences

13 pages, 543 KiB

Open AccessArticle

Nutritional Values, Physicochemical and Microbiological Properties of Turkey Thigh Muscle—Effect of Wild Garlic (Allinum ursinum L.) Supplementation

by Małgorzata Karwowska, Patrycja Skwarek, Elżbieta Solska, Agata Nowaczyk, Dariusz M. Stasiak and Mateusz Niedzielak

Appl. Sci. 2025, 15(5), 2715; https://doi.org/10.3390/app15052715 (registering DOI) - 3 Mar 2025

Abstract

The current study assessed the impact of adding wild garlic (Allinum ursinum L.) to the diets of turkeys on the physicochemical and microbiological properties as well as the fatty acid profile and antioxidant potential of thigh muscle. The tested materials were thigh [...] Read more.

The current study assessed the impact of adding wild garlic (Allinum ursinum L.) to the diets of turkeys on the physicochemical and microbiological properties as well as the fatty acid profile and antioxidant potential of thigh muscle. The tested materials were thigh muscle from 40 turkeys (Big-6 type) divided into two groups (P1—control group and P2—sample supplemented with wild garlic at a level of 0.2% in the feed mixture). Measurements of chemical composition, pH, water activity, color, total heme pigments, lipid oxidation, and microbiological analysis (Enterobacteriaceae, E. coli, Salmonella spp., L. monocytogenes) showed that the addition of wild garlic to a turkey diet did not affect thigh meat quality. The effect of adding wild garlic (Allinum ursinum L.) to the diets of turkeys on the antioxidant potential (against the DPPH radical) and fatty acid profile of the thigh muscle was noted. Thigh muscles obtained from turkeys reared in group P2 were characterized by a significantly higher PUFA content compared to meat from the control group. Further studies should consider turkey diet supplementation with a higher level of wild garlic and an in-depth analysis of meat quality. Full article

(This article belongs to the Special Issue Quality and Safety of Fresh and Processed Meat: The Impact of Factors from Farm to Fork)

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19 pages, 2788 KiB

Open AccessArticle

Design and Development of Supersonic Shockwave Soil-Loosening Device That Can Improve the Aeration of Crop Root Zone

by Ming-Sen Hu, Uzu-Kuei Hsu, Shang-Han Tsai and Chia-Hsing Lee

Appl. Sci. 2025, 15(5), 2714; https://doi.org/10.3390/app15052714 (registering DOI) - 3 Mar 2025

Abstract

When the soil at the plant roots is poorly ventilated due to few pores, the root system will grow short and shallow, leading to poor growth. In this paper, we developed a shockwave soil-loosening device. It can first drill a hollow drill bit [...] Read more.

When the soil at the plant roots is poorly ventilated due to few pores, the root system will grow short and shallow, leading to poor growth. In this paper, we developed a shockwave soil-loosening device. It can first drill a hollow drill bit containing multi-directional holes into the soil near the roots of the crops and then generate high-pressure gas to impact the soil outside the drill bit to increase the soil pores. Therefore, this can quickly improve soil aeration. We conducted numerical simulations of shockwave loosening to explore how 3.4 atm shockwaves are emitted from the drill bit’s porous nozzles and analyze the behavior and efficiency of shockwave loosening. We also performed visual observation experiments of shockwave multi-directional impact in a transparent acrylic water tank. Furthermore, we used eight pressure sensors to automatically measure the range of shockwave impact and found that when the storage tank volume was 5000 cm³, we could achieve a soil loosening range of 30 cm. Finally, this shockwave-loosening mechanism ensures that the soil surface will not be damaged during the loosening process, thus avoiding large-scale tillage disturbance of the soil. This will reduce carbon emissions stored in soil and released into the atmosphere. Full article

23 pages, 7068 KiB

Open AccessArticle

Thermal Management for Electric Motorcycles—Multi-Scale Modelling and Battery Thermal Design Evaluation

by Tao Zhu, Mehmet Kirca, Shilei Zhou, Truong Dinh and Andrew McGordon

Appl. Sci. 2025, 15(5), 2713; https://doi.org/10.3390/app15052713 (registering DOI) - 3 Mar 2025

Abstract

Electric motorcycles feature a smaller size and lower weight than electric cars, meaning they have greater manoeuvrability and energy efficiency, which translate to a dynamic riding experience and reduced environmental footprint. From a thermal management perspective, one major challenge is how to maximise [...] Read more.

Electric motorcycles feature a smaller size and lower weight than electric cars, meaning they have greater manoeuvrability and energy efficiency, which translate to a dynamic riding experience and reduced environmental footprint. From a thermal management perspective, one major challenge is how to maximise the heat dissipation efficiency of the battery system within the limited space available onboard since the battery system represents one of the largest thermal loads onboard. This paper investigates electric motorcycle modelling to facilitate prototype development, emphasising a compact, integrated cooling system for high-voltage powertrain components, including the battery, inverter, and motor. Particularly, the proposed battery model is structured across the pack–module–cell hierarchy, which makes it capable of distinguishing the thermal state of each individual cell and the cell-to-cell performance variations resulting from temperature effects. The integrated cooling system and multi-scale battery modelling method proposed in this paper allow for a quick comparison of performances between different battery module thermal designs, which is specifically suited for early-stage investigation of different concepts. A series and a parallel battery module thermal design are proposed and compared, with a focus on evaluating their impacts on system-level and component-level thermal performances as well as cell-level performance variations, including but not limited to temperature, state of charge, voltage, and state of power. Specifically, the serial thermal design provides better overall cooling efficiency and lower battery pack temperatures, while the parallel design significantly reduces cell-to-cell variations. Full article

(This article belongs to the Special Issue New Insights into Lithium-Ion Batteries: Technologies and Challenges)

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13 pages, 3245 KiB

Open AccessArticle

Accurate Identification of Grade of Grape Damage by Brevipalpus spp. Based on the Improved YOLOv8n Model

by Chaoxue Wang, Wenxi Tian, Gang Ma and Liang Zhu

Appl. Sci. 2025, 15(5), 2712; https://doi.org/10.3390/app15052712 (registering DOI) - 3 Mar 2025

Abstract

Brevipalpus spp. are widespread pests on wine grapes in northwest China and have generated a major threat to the local wine grape industry in recent years. We advanced the YOLOv8n model (object detection algorithm), termed SEM-YOLOv8n, to predict the degree of damage from [...] Read more.

Brevipalpus spp. are widespread pests on wine grapes in northwest China and have generated a major threat to the local wine grape industry in recent years. We advanced the YOLOv8n model (object detection algorithm), termed SEM-YOLOv8n, to predict the degree of damage from these mites, and thereby provided the appropriate time for pest management. The damage symptoms of Brevipalpus spp. were classified into the following five grades: non-infested, slight, moderate, severe, and extremely severe; the pictures of different grades were structured into a self-constructed dataset. Regarding algorithm improvements, to improve the ability to recognize subtle differences between the various grades of damage symptoms in complex natural backgrounds, the EMA attention mechanism was introduced after the SPPF layer of the backbone network. Secondly, to address the problem of target omission caused by grapevine fruit overlapping, the MPDIoU loss function was used instead of the CIoU loss function. Finally, the Slim-Neck structure was adopted in the neck of YOLOv8n to generate a lightweight model. The experimental results showed that the improved model increased the mean accuracy by 1.1% and decreased the number of parameters by about 13.3% compared with the original model. Compared with the related authoritative YOLO series algorithms, the improved model proposed in this study had a better detection performance in terms of both the accuracy and model size. Full article

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18 pages, 1157 KiB

Open AccessArticle

A Computationally Efficient p-Refinement Finite Element Method Approach for the Fracture Analysis of Axially Cracked Pipes with Composite Patch Reinforcement

by Jae S. Ahn

Appl. Sci. 2025, 15(5), 2711; https://doi.org/10.3390/app15052711 - 3 Mar 2025

Abstract

Cylindrical shells are widely used in pipelines, pressure vessels, and aircraft fuselages due to their efficient internal pressure distribution. However, axial cracks caused by fatigue, environmental effects, or mechanical loading compromise structural integrity, requiring effective reinforcement. This study presents a finite element modeling [...] Read more.

Cylindrical shells are widely used in pipelines, pressure vessels, and aircraft fuselages due to their efficient internal pressure distribution. However, axial cracks caused by fatigue, environmental effects, or mechanical loading compromise structural integrity, requiring effective reinforcement. This study presents a finite element modeling approach integrating p-refinement techniques for the efficient analysis of axially cracked pipes reinforced with composite patches. The proposed method unifies equivalent single-layer and layer-wise theories into a single finite element type, improving computational efficiency and eliminating the need for multiple element types in transition elements. Benchmark studies show that the proposed model accurately predicts mechanical behavior, with maximum displacement and stress intensity factors (SIFs) deviating by less than 5% from reference solutions. Fracture analysis using the virtual crack closure technique confirms the accuracy of the SIF calculations. In patched cracked pipes, the proposed model achieves a 67% reduction in degrees of freedom compared to conventional p-refinement layer-wise models, while maintaining computational accuracy. Additionally, boron–epoxy composite patches reduce SIFs by up to 40%, demonstrating effective crack reinforcement. These findings support computationally efficient damage-tolerant design strategies for pressurized cylindrical structures in aerospace, marine, and mechanical engineering. Full article

(This article belongs to the Section Civil Engineering)

15 pages, 5735 KiB

Open AccessArticle

Fiber Bragg Grating Detection and Nonlinear Thermal Strain Response of Annular Concrete Structures Under Stress Constraints

by Chuan Li, Yusi Ran, Jiaqi Li, Qiang Xu, Hao Liu and Xiaorong Wan

Appl. Sci. 2025, 15(5), 2710; https://doi.org/10.3390/app15052710 - 3 Mar 2025

Abstract

As a typical engineering structure, the annular concrete structure demonstrates remarkable performance in bearing radial stress and environmental loads. However, when applied to projects in cold areas with large temperature difference between day and night and large seasonal temperature difference, the structure is [...] Read more.

As a typical engineering structure, the annular concrete structure demonstrates remarkable performance in bearing radial stress and environmental loads. However, when applied to projects in cold areas with large temperature difference between day and night and large seasonal temperature difference, the structure is prone to degradation cracking under the combined action of temperature and external stress, resulting in reduced stability and safety risks. To investigate the complex thermodynamic response of annular concrete structures under the combined constraints of stress and temperature, a detection model was developed to measure structural strain by combining the structural mechanics model with fiber Bragg grating (FBG) detection technology, while a numerical simulation model was created to reflect the mechanical response of the annular concrete structures. Through the performance test of the structure, the bearing capacity range of the structure was determined. Then, the gradient cooling experiments under different stress constraints are carried out to measure the total strain of the structure, extract the thermal strain data and carry out statistical evaluation, and analyze the nonlinear thermal strain response of the structure. The results show that, within the bearing capacity range of 3108 N, the strain of the annular concrete structure gradually increases as the temperature decreases. When the temperature drops from 20

^{\circ}

C to −40

^{\circ}

C, the range of thermal strain initially increases and then decreases. The thermal strain amplitude of the concrete exhibits a significant turning point. Moreover, under different pressure conditions, the thermal strain sensitivity of concrete increases with the gradual increase in pressure confinement, that is, the greater the pressure, the greater the influence on the cooling process of concrete. Full article

25 pages, 1706 KiB

Open AccessArticle

Field Strength Prediction in High-Speed Train Carriages Using a Multi-Neural Network Ensemble Model with Optimized Output Weights

by Zhou Fang, Hengkai Zhao, Yichen Feng, Yating Wu, Yanqiong Sun, Qi Yang and Guoxin Zheng

Appl. Sci. 2025, 15(5), 2709; https://doi.org/10.3390/app15052709 - 3 Mar 2025

Abstract

Accurate path loss prediction within train carriages is crucial for deploying base stations along high-speed railway lines. The field strength at receiving points inside carriages is influenced by outdoor signal transmission, penetration through window glass, and multiple reflections within the carriage, making it [...] Read more.

Accurate path loss prediction within train carriages is crucial for deploying base stations along high-speed railway lines. The field strength at receiving points inside carriages is influenced by outdoor signal transmission, penetration through window glass, and multiple reflections within the carriage, making it challenging for traditional models to predict the field strength distribution accurately. To address this issue, this paper proposes a machine learning-based path loss prediction method that incorporates ensemble techniques of multiple neural networks to enhance prediction stability and accuracy. The Whale Optimization Algorithm (WOA) is used to optimize the output weight configuration of each neural network in the ensemble model, thereby significantly improving the overall model performance. Specifically, on the test set, the WOA-optimized ensemble model reduces RMSE by 1.47 dB for CI, 0.47 dB for CNN, 0.93 dB for RNN, 1.38 dB for GNN, 0.1 dB for Transformer, 0.09 dB for AutoML, 0.33 dB for the GA-optimized ensemble model, and 0.18 dB for the PSO-optimized ensemble model. Full article

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

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16 pages, 1372 KiB

Open AccessReview

Bioconvection in Microalgae: Review of Mathematical Models

by Laura Barsanti, Lorenzo Birindelli, Angelo Di Garbo and Paolo Gualtieri

Appl. Sci. 2025, 15(5), 2708; https://doi.org/10.3390/app15052708 - 3 Mar 2025

Abstract

Bioconvection can be defined as an aggregation pattern-generating phenomenon characterized by the collective behavior of swimming microorganisms in response to external influences and self-induced fluid flow, and it has attracted significant interest in the study of microalgae. This review aims to provide a [...] Read more.

Bioconvection can be defined as an aggregation pattern-generating phenomenon characterized by the collective behavior of swimming microorganisms in response to external influences and self-induced fluid flow, and it has attracted significant interest in the study of microalgae. This review aims to provide a comprehensive examination of this phenomenon in a concentrated population of suspended microalgae definable as nonlinear dynamical systems (i.e., generators of spatial and temporal patterns), describing and clarifying the underlying mechanisms, ecological implications, and possible biotechnological applications in different fields for the improvement of bioreactor design for biomass cultivation, wastewater treatment, or bioremediation. The most influential models used to capture the complexity of bioconvection are perused in order to explore the intricate interplay of internal and external influences (biological, physical, and environmental) governing pattern formation dynamics alongside recent advancements in modeling techniques and experimental approaches. Full article

(This article belongs to the Section Environmental Sciences)

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17 pages, 5550 KiB

Open AccessArticle

Groundwater Tracer Tests as a Supporting Method for Interpreting the Complex Hydrogeological Environment of the Urbas Landslide in NW Slovenia

by Luka Serianz and Mitja Janža

Appl. Sci. 2025, 15(5), 2707; https://doi.org/10.3390/app15052707 - 3 Mar 2025

Abstract

This study investigates groundwater flow patterns in a landslide area above the settlement of Koroška Bela in NW Slovenia using a series of tracer tests with sodium chloride (NaCl) and fluorescein (uranine). The tracer experiments, using a combination of pumping tests and continuous [...] Read more.

This study investigates groundwater flow patterns in a landslide area above the settlement of Koroška Bela in NW Slovenia using a series of tracer tests with sodium chloride (NaCl) and fluorescein (uranine). The tracer experiments, using a combination of pumping tests and continuous groundwater observations, reveal two distinct groundwater flow horizons within the landslide body: a prevailing shallower flow within highly permeable gravel layers and a slower deep flow in the weathered low-permeability clastic layers. Uranine injections suggest longer retentions, indicating complex hydrogeological conditions. Groundwater is recharged by the infiltration of precipitation and subsurface inflow from the upper-lying carbonate rocks. In the upper landslide, highly permeable gravel layers accelerate flow, especially during heavy rainfall, while downstream interactions between permeable gravel and less permeable clastic materials create local aquifers and springs. These groundwater dynamics significantly influence landslide stability, as rapid infiltration during intense precipitation events can lead to transient increases in pore water pressure, reducing shear strength and potentially triggering slope movement. Meanwhile, slow deep flows contribute to prolonged saturation of critical failure surfaces, which may weaken the landslide structure over time. The study emphasizes the region’s geological heterogeneity and landslide stability, providing valuable insights into the groundwater dynamics of this challenging environment. By integrating hydrogeological assessments with engineering measures, the study provides supportive information for mitigating landslide risks and improving groundwater management strategies. Full article

(This article belongs to the Section Earth Sciences)

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14 pages, 1257 KiB

Open AccessArticle

Surface Deformation and Straightness Detection of Electromagnetic Launcher Based on Laser Point Clouds

by Kangwei Yan, Delin Zeng, Long Cheng and Sai Tan

Appl. Sci. 2025, 15(5), 2706; https://doi.org/10.3390/app15052706 - 3 Mar 2025

Abstract

Bore deterioration phenomena, such as surface ablation, wear, aluminum deposition, and structural bending, severely restrict the service life and performance of electromagnetic launchers. Efficient bore inspection is necessary to study the deterioration mechanism, guide design, and health management. In this paper, an inspection [...] Read more.

Bore deterioration phenomena, such as surface ablation, wear, aluminum deposition, and structural bending, severely restrict the service life and performance of electromagnetic launchers. Efficient bore inspection is necessary to study the deterioration mechanism, guide design, and health management. In this paper, an inspection system for electromagnetic launchers is presented which utilizes structured light scanning, time-of-flight, and laser alignment methods to acquire bore laser point clouds, and ultimately extracts the surface deformation of rails and insulators, as well as the straightness of the bore, through the registration of point cloud data. First, the system composition and detection principles are introduced. Second, the impacts of the detection device’s attitude deflection are analyzed. Next, focusing on the key registration issue of laser point clouds, a coarse registration method is proposed which utilizes the arc features of the rail by combining circle and parabola equations, thereby maximizing registration efficiency. Finally, the trimmed iterative closest-point (TrICP) algorithm is employed for fine registration to handle non-axisymmetric bore deformations. The experimental results show that the proposed method can detect bore surface deformation and straightness efficiently and precisely. Full article

(This article belongs to the Special Issue Optical Sensors: Applications, Performance and Challenges)

44 pages, 872 KiB

Open AccessArticle

Perceptions of a ‘Pelvic-Floor Friendly’ Group Exercise Class in Women with Urinary Incontinence

by Amanda J. M. Gard and David Lavallee

Appl. Sci. 2025, 15(5), 2705; https://doi.org/10.3390/app15052705 - 3 Mar 2025

Abstract

This mixed methods intervention study investigated perceptions of a tailored group exercise intervention designed to increase physical activity (PA) participation among women with urinary incontinence (UI). Recognising UI as a barrier to PA, the study aimed to minimise UI risk during exercise, enhance [...] Read more.

This mixed methods intervention study investigated perceptions of a tailored group exercise intervention designed to increase physical activity (PA) participation among women with urinary incontinence (UI). Recognising UI as a barrier to PA, the study aimed to minimise UI risk during exercise, enhance enjoyment, and foster social inclusion. The study included 15 participants, across intervention and control groups. Modifications included low-impact exercises, the presence of a pelvic health physiotherapist, and age-matched female instructors. Data from focus groups, surveys, and pre/post-intervention measures revealed that the intervention improved participants’ confidence, action planning, and perceptions of PA. Participants valued social opportunities and tailored modifications but noted the need for better advertising and instructor education. The findings suggest that such interventions can positively influence PA participation and reduce UI-related stigma. Full article

(This article belongs to the Special Issue Sports Medicine, Exercise, and Health: Latest Advances and Prospects)

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17 pages, 2335 KiB

Open AccessArticle

Attention-Based Color Difference Perception for Photographic Images

by Hua Qiang, Xuande Zhang and Jinliang Hou

Appl. Sci. 2025, 15(5), 2704; https://doi.org/10.3390/app15052704 - 3 Mar 2025

Abstract

Traditional color difference (CD) measurement methods cannot adapt to large sizes and complex content of photographic images. Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. The objective of this paper is [...] Read more.

Traditional color difference (CD) measurement methods cannot adapt to large sizes and complex content of photographic images. Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. The objective of this paper is to propose a high-precision image CD measurement model that simulates the perceptual process of the human visual system and apply it to the CD perception of smartphone photography images. Based on this, a CD measurement network called CD-Attention is proposed, which integrates CNN and Vision Transformer features. First, a CNN and the ViT are used separately to extract local features and global semantic features from the reference image and the distorted image. Secondly, deformable convolution is used for attention guidance, utilizing the global semantic features of the ViT to direct CNN to focus on salient regions of the image, enhancing the transformation modeling capability of CNN features. Thirdly, through the feature fusion module, the CNN features that have been guided by attention are fused with the global semantic features of the ViT. Finally, a dual-branch network for high-frequency and low-frequency predictions is used for score estimation, and the final score is obtained through a weighted sum. Validated on the large-scale SPCD dataset, the CD-Attention model has achieved state-of-the-art performance, outperforming 30 existing CD measurement methods and demonstrating useful generalization ability. It has been demonstrated that CD-Attention can achieve CD measurement for large-sized and content-complex smartphone photography images. At the same time, the effectiveness of CD-Attention’s feature extraction and attention guidance are verified by ablation experiments. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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27 pages, 1983 KiB

Open AccessArticle

Fréchet-Derivative-Based Global Sensitivity Analysis and Its Physical Meanings in Structural Design

by Weifeng Tao, Zhiqiang Wan and Xiuli Wang

Appl. Sci. 2025, 15(5), 2703; https://doi.org/10.3390/app15052703 - 3 Mar 2025

Abstract

Sensitivity analysis is essential for uncertainty-based structural design and analysis, especially global sensitivity analysis, which can reflect the overall physical properties of large and complex computational models with stochastic parameters. In recent decades, a variety of global sensitivity indices (GSIs) have been extensively [...] Read more.

Sensitivity analysis is essential for uncertainty-based structural design and analysis, especially global sensitivity analysis, which can reflect the overall physical properties of large and complex computational models with stochastic parameters. In recent decades, a variety of global sensitivity indices (GSIs) have been extensively developed based on the distinct perspectives of global sensitivity analysis, in which the most common GSIs are variance-based, moment-independent, and failure-probability-based. In this work, a newly developed Fréchet-derivative-based GSI (Fre-GSI) is discussed. Properties of the Fre-GSI related to the measure and direction are first investigated. Then, a functional perspective of global sensitivity analysis is proposed, with the physical meanings of the four GSIs illustrated. Practical links of the Fre-GSI with the other three classical GSIs are derived analytically. Numerical examples are studied to verify the proposed links, and the specific advantages of the four GSIs are discussed. Full article

(This article belongs to the Special Issue Structural Design of Buildings and Civil Engineering Constructions)

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27 pages, 5921 KiB

Open AccessArticle

Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments

by Zichong Wang and Yingying Zheng

Appl. Sci. 2025, 15(5), 2702; https://doi.org/10.3390/app15052702 - 3 Mar 2025

Abstract

The microgrid is a small-scale, independent power system that plays a crucial role in the transition to carbon-neutral energy systems. Combined heat and power (CHP) systems with energy storage reduce energy waste within microgrids, enhancing energy utilization efficiency. The key challenge for a [...] Read more.

The microgrid is a small-scale, independent power system that plays a crucial role in the transition to carbon-neutral energy systems. Combined heat and power (CHP) systems with energy storage reduce energy waste within microgrids, enhancing energy utilization efficiency. The key challenge for a microgrid integrated with a combined heat and power system is determining the optimal configuration and operation duration under different scenarios to meet users’ electricity and heat demands while minimizing both economic and environmental costs. Thus, this paper presents a bi-objective mathematical model to solve the optimal scheduling problem of the microgrid. The Long Short-Term Memory–Parallel Multi-Objective Energy Valley Optimizer (LSTM-PMOEVO) framework incorporates energy load prediction using LSTM and scheduling planning solved via PMOEVO. These strategies address the challenges posed by unpredictable energy load fluctuations and the complexity of solving such systems. Finally, a public dataset was utilized for the experiments to verify the performance of the proposed algorithm. Comparisons and discussions show that the proposed optimization strategies significantly improve the performance of PMOEVO, demonstrating marked advantages over six classical algorithms. In conclusion, the PMOEVO developed in this paper performs excellently in solving the Scheduling Problem of Biomass-Hybrid microgrids with energy storage considering uncertainty. The work presented in this paper provides a new solution framework for the microgrid-scheduling problem considering uncertainty. In future research, this solution framework will be further advanced for application in real-world scenarios. Full article

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15 pages, 1824 KiB

Open AccessArticle

SPN-Based Dynamic Risk Modeling of Fire Incidents in a Smart City

by Menghan Hui, Feng Ni, Wencheng Liu, Jiang Liu, Niannian Chen and Xingjun Zhou

Appl. Sci. 2025, 15(5), 2701; https://doi.org/10.3390/app15052701 - 3 Mar 2025

Abstract

Smart cities are confronted with a variety of disaster threats. Among them, natural fires pose a serious threat to human lives, the environment, and asset security. In view of the fact that existing research mostly focuses on the analysis of accident precursors, this [...] Read more.

Smart cities are confronted with a variety of disaster threats. Among them, natural fires pose a serious threat to human lives, the environment, and asset security. In view of the fact that existing research mostly focuses on the analysis of accident precursors, this paper proposes a dynamic risk-modeling method based on Stochastic Petri Nets (SPN) and Bayesian theory to deeply explore the evolution mechanism of urban natural fires. The SPN model is constructed through natural language processing techniques, which discretize the accident evolution process. Then, the Bayesian theory is introduced to dynamically update the model parameters, enabling the accurate assessment of key event nodes. The research results show that this method can effectively identify high-risk nodes in the evolution of fires. Their dynamic probabilities increase significantly over time, and key transition nodes have a remarkable impact on the emergency response efficiency. This method can increase the fire prevention and control efficiency by approximately 30% and reduce potential losses by more than 20%. The dynamic update mechanism significantly improves the accuracy of risk prediction by integrating real-time observation data and provides quantitative support for emergency decision making. It is recommended that urban management departments focus on strengthening the maintenance of facilities in high-risk areas (such as fire alarm systems and emergency passages), optimize cross-departmental cooperation processes, and build an intelligent monitoring and early-warning system to shorten the emergency response time. This study provides a new theoretical tool for urban fire risk management. In the future, it can be extended to other types of disasters to enhance the universality of the model. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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18 pages, 5111 KiB

Open AccessArticle

Improving Engineering Design Using Smart Evaluation of Indoor Acoustical Climate of Dining Places

by Elzbieta Nowicka

Appl. Sci. 2025, 15(5), 2700; https://doi.org/10.3390/app15052700 - 3 Mar 2025

Abstract

The indoor acoustic climate of dining places often presents challenges for designers and their projects. In this type of enclosure, one of the fundamental factors determining the acoustic climate of a space is the acceptable speech reception in relation to sound alarm systems. [...] Read more.

The indoor acoustic climate of dining places often presents challenges for designers and their projects. In this type of enclosure, one of the fundamental factors determining the acoustic climate of a space is the acceptable speech reception in relation to sound alarm systems. This study discusses an index-based method for assessing the acoustic climate of dining places, such as restaurants and cafeterias, during the design process. These studies focused on the reverberation time and speech intelligibility as key parameters defining the acoustical climate. The analyses considered dining places (primarily restaurants) with different dimensions, geometries, and layouts. The method combines architectural parameters (such as volume or shape) and acoustical parameters (such as absorption or background sound) that influence the acoustical quality of dining places. The proposed method is assumed to provide a convenient evaluation of planned dining locations in terms of speech intelligibility. The method is based on the W_R index parameter and serves as a functional tool for architects and designers in their initial drawings and when making design decisions. The method was verified using subjective assessments. Implementing this method provides possibilities to increase the comfort of restaurants and cafeterias and lower or avoid costs associated with improving the acoustic comfort of ready-to-use dining places. Full article

(This article belongs to the Special Issue Recent Advances in Architectural Acoustics and Noise Control)

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33 pages, 15107 KiB

Open AccessArticle

Experimental Study on Dynamic Force–Thermal Loading for Multi-Stage Telescopic Wings Based on the Dynamic Multi-Point Equivalent Method

by Hong Xiao, Shuailin Li, Hongwei Guo, Hualiang Liu, Guang Yang, Chunfeng Li and Jianguo Tao

Appl. Sci. 2025, 15(5), 2699; https://doi.org/10.3390/app15052699 - 3 Mar 2025

Abstract

To address the challenge of simulating force–thermal environmental loads on morphing wings during flight, this study proposes and validates a force–thermal simulation method based on servo loading. First, the aerodynamic loads on a multi-stage telescopic wing under extreme conditions were systematically analyzed to [...] Read more.

To address the challenge of simulating force–thermal environmental loads on morphing wings during flight, this study proposes and validates a force–thermal simulation method based on servo loading. First, the aerodynamic loads on a multi-stage telescopic wing under extreme conditions were systematically analyzed to identify the critical design loads. Subsequently, a force–thermal servo loading platform for multi-stage telescopic wings was designed and constructed to evaluate the performance of the wing’s morphing mechanism during flight. A dynamic multi-point equivalent method based on grid reconstruction was proposed and theoretically derived, along with simulations using a traditional multi-point load distribution method. Compared to the conventional equal-area division method, the simulation results demonstrated a significant improvement in deformation fitting accuracy using the proposed method. Finally, force–thermal servo loading experiments were conducted on a prototype of the multi-stage telescopic wing. The results verified that the proposed loading method can accurately simulate load variations during flight, with experimental trends closely aligning with simulation predictions. Additionally, the experiments demonstrated the loading system’s rapid response capability, confirming the feasibility and potential of the designed loading platform and theoretical model. This research provides critical technical support and theoretical foundations for the design, validation, and force–thermal environment simulation of future multidimensional morphing wings. Full article

(This article belongs to the Special Issue Applications of Aerodynamics in Aeronautical Engineering)

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17 pages, 1614 KiB

Open AccessArticle

A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention

by Ziyun Zhang, Peng Liu, Chen Su and Shoufeng Tong

Appl. Sci. 2025, 15(5), 2698; https://doi.org/10.3390/app15052698 - 3 Mar 2025

Abstract

With the advancement of technology, biometric recognition technology has gained widespread attention in identity authentication due to its high security and convenience. Finger vein recognition, as a biometric technology, utilizes near-infrared imaging to extract subcutaneous vein patterns, offering high security, stability, and anti-spoofing [...] Read more.

With the advancement of technology, biometric recognition technology has gained widespread attention in identity authentication due to its high security and convenience. Finger vein recognition, as a biometric technology, utilizes near-infrared imaging to extract subcutaneous vein patterns, offering high security, stability, and anti-spoofing capabilities. Existing research primarily focuses on improving recognition accuracy; however, this often comes at the cost of increased model complexity, which, in turn, affects recognition efficiency, making it difficult to balance accuracy and speed in practical applications. To address this issue, this paper proposes a high-accuracy and high-efficiency finger vein recognition model called Faster Multi-Scale Finger Vein Recognition Network (FMFVNet), which optimizes recognition speed through the FasterNet Block module while ensuring recognition accuracy with the Multi-Scale Convolutional Attention (MSCA) module. Experimental results show that on the FV-USM and SDUMLA-HMT datasets, FMFVNet achieves recognition accuracies of 99.80% and 99.06%, respectively. Furthermore, the model’s inference time is reduced to 1.75 ms, representing a 20.8% improvement over the fastest baseline model and a 62.7% improvement over the slowest, achieving more efficient finger vein recognition. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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20 pages, 7927 KiB

Open AccessArticle

DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact Lenses

by Sung-Hoon Kim, Seong-Jong Joo and Kwan-Hee Yoo

Appl. Sci. 2025, 15(5), 2697; https://doi.org/10.3390/app15052697 - 3 Mar 2025

Abstract

Vision-based inspection systems are essential for quality control in manufacturing industries, and advances in artificial intelligence (AI) have significantly enhanced their accuracy. However, the high-precision requirements of products such as contact lenses demand even more robust inspection methods. This paper introduces a novel [...] Read more.

Vision-based inspection systems are essential for quality control in manufacturing industries, and advances in artificial intelligence (AI) have significantly enhanced their accuracy. However, the high-precision requirements of products such as contact lenses demand even more robust inspection methods. This paper introduces a novel defect-adaptive hierarchical structure convolution neural network (DHS-CNN) model based on InceptionV4. The proposed model architecture reflects the manufacturing process and defect types, and we developed a custom loss function to suit this multi-output hierarchical design. Experimental results on a dataset of 2800 contact lens images revealed that the proposed model improved accuracy by 2.08% over the baseline model. These findings suggest that the defect-adaptive hierarchical structure and customized loss function offer substantial improvements in the vision-based inspection of contact lenses and may enhance AI-driven quality control processes in other manufacturing sectors. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing Technologies and Applications)

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