Applied Sciences

16 pages, 25742 KiB

Open AccessArticle

Theory of Refraction, Ray–Wave Tilt, Hidden Momentum, and Apparent Topological Phases in Isotropy-Broken Materials Based on Electromagnetism of Moving Media

by Maxim Durach

Appl. Sci. 2024, 14(15), 6851; https://doi.org/10.3390/app14156851 - 5 Aug 2024

Viewed by 1214

Abstract

The mysterious nature of electromagnetic momentum in materials is considered one of the most significant challenges in physics, surpassing even Hilbert’s mathematical problems. In this paper, we demonstrate that the difference between the Minkowski and Abraham momenta, which consists of Roentgen and Shockley [...] Read more.

The mysterious nature of electromagnetic momentum in materials is considered one of the most significant challenges in physics, surpassing even Hilbert’s mathematical problems. In this paper, we demonstrate that the difference between the Minkowski and Abraham momenta, which consists of Roentgen and Shockley hidden momenta, is directly related to the phenomenon of refraction and the tilt of rays from the wavefront propagation direction. We show that individual electromagnetic waves with non-unit indices of refraction (n) appear as quasistatic high-k waves to an observer in the proper frames of the waves. When Lorentz transformed into the material rest frames, these high-k waves are Fresnel–Fizeau dragged from rest to their phase velocities, acquiring longitudinal hidden momentum and related refractive properties. On a material level, all electromagnetic waves belong to Fresnel wave surfaces, which are topologically classified according to hyperbolic phases by Durach and determined by the electromagnetic material parameters. For moving observers, material parameters appear modified, leading to alterations in Fresnel wave surfaces and even the topological classes of the materials may appear differently in moving frames. We discuss the phenomenon of electromagnetic momentum tilt, defined as the non-zero angle between Abraham and Minkowski momenta or, equivalently, between the rays and the wavefront propagation direction. This momentum tilt is only possible in isotropy-broken media, where the E and H fields can be longitudinally polarized in the presence of electric and magnetic bound charge waves. The momentum tilt can be understood as a differential aberration of rays and waves when observed in the material rest frame. Full article

(This article belongs to the Section Optics and Lasers)

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18 pages, 6795 KiB

Open AccessArticle

A Meshless Method of Radial Basis Function-Finite Difference Approach to 3-Dimensional Numerical Simulation on Selective Laser Melting Process

by Chieh-Li Chen, Cheng-Hsuan Wu and Cha’o-Kuang Chen

Appl. Sci. 2024, 14(15), 6850; https://doi.org/10.3390/app14156850 - 5 Aug 2024

Viewed by 848

Abstract

Selective laser melting (SLM) is a rapidly evolving technology that requires extensive knowledge and management for broader industrial adoption due to the complexity of phenomena involved. The selection of parameters and numerical analysis for the SLM process are both costly and time-consuming. In [...] Read more.

Selective laser melting (SLM) is a rapidly evolving technology that requires extensive knowledge and management for broader industrial adoption due to the complexity of phenomena involved. The selection of parameters and numerical analysis for the SLM process are both costly and time-consuming. In this paper, a three-dimensional radial basis function-finite difference (RBF-FD) meshless model is introduced to accurately and efficiently simulate the molten pool size and temperature distribution during the SLM process for austenitic stainless steel (AISI 316L). Two different volumetric moving heat source models were presented, namely the ray-tracing method heat source model and the double-ellipsoidal shape heat source model. The temperature-dependent material properties and phase change process were also considered, based on experiments and effective models. Results of the model for the molten pool size were validated with those of the literature. The proposed approach can be used to predict the effect of different laser power and scan speed on the molten pool size and temperature gradient along the depth direction. The result reveals that the depth of the molten pool is more sensitive to laser power than scan speed. Under the same scan speed, a 22% change in laser power (45 ± 10 W) affects the maximum temperature proportionally by about 9%. The developed algorithm is computationally efficient and suitable for industrial applications. Full article

(This article belongs to the Special Issue 3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications)

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14 pages, 5497 KiB

Open AccessArticle

Management Solutions and Stabilization of a Pre-Existing Concealed Goaf Underneath an Open-Pit Slope

by Qing Na, Qiusong Chen, Yunbo Tao, Xiangyu Zhang and Yi Tan

Appl. Sci. 2024, 14(15), 6849; https://doi.org/10.3390/app14156849 - 5 Aug 2024

Viewed by 1200

Abstract

Pre-existing concealed goafs underneath open-pit slopes (PCO-goafs) pose a serious threat to the stability of open-pit slopes (OP-slopes), which is a common problem worldwide. In this paper, the variable weight-target approaching method, equilibrium beam theory, Pratt’s arch theory, and numerical simulation are used [...] Read more.

Pre-existing concealed goafs underneath open-pit slopes (PCO-goafs) pose a serious threat to the stability of open-pit slopes (OP-slopes), which is a common problem worldwide. In this paper, the variable weight-target approaching method, equilibrium beam theory, Pratt’s arch theory, and numerical simulation are used to analyze the management solutions and stability of five PCO-goaf groups in the Nannihu molybdenum mine located in Luoyang City, Henan Province, China. The five PCO-goaf groups, numbered 1#, 2#, 3#, 4#, and 5#, are divided into four hazard classes, ranging from extremely poor to good stability. The stability of 1#, 2#, and 4# is poor and must be managed by filling, and the design strength of backfill is 1.2 MPa; caving is used to treat 3# and 5#, and the safe thickness of the overlying roof is calculated to be 10.5–41 m. After treatment, the safety coefficient of the slope is greater than 1.2, indicating that the slope is stable. This study provides insight and guidance for the safe operation of open-pit mines threatened by the existence of PCO-goafs. Full article

(This article belongs to the Special Issue Mining Safety: Challenges and Prevention, 2nd Edition)

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23 pages, 1009 KiB

Open AccessArticle

Enhancement of English-Bengali Machine Translation Leveraging Back-Translation

by Subrota Kumar Mondal, Chengwei Wang, Yijun Chen, Yuning Cheng, Yanbo Huang, Hong-Ning Dai and H. M. Dipu Kabir

Appl. Sci. 2024, 14(15), 6848; https://doi.org/10.3390/app14156848 - 5 Aug 2024

Viewed by 974

Abstract

An English-Bengali machine translation (MT) application can convert an English text into a corresponding Bengali translation. To build a better model for this task, we can optimize English-Bengali MT. MT for languages with rich resources, like English-German, started decades ago. However, MT for [...] Read more.

An English-Bengali machine translation (MT) application can convert an English text into a corresponding Bengali translation. To build a better model for this task, we can optimize English-Bengali MT. MT for languages with rich resources, like English-German, started decades ago. However, MT for languages lacking many parallel corpora remains challenging. In our study, we employed back-translation to improve the translation accuracy. With back-translation, we can have a pseudo-parallel corpus, and the generated (pseudo) corpus can be added to the original dataset to obtain an augmented dataset. However, the new data can be regarded as noisy data because they are generated by models that may not be trained very well or not evaluated well, like human translators. Since the original output of a translation model is a probability distribution of candidate words, to make the model more robust, different decoding methods are used, such as beam search, top-k random sampling and random sampling with temperature T, and others. Notably, top-k random sampling and random sampling with temperature T are more commonly used and more optimal decoding methods than the beam search. To this end, our study compares LSTM (Long-Short Term Memory, as a baseline) and Transformer. Our results show that Transformer (BLEU:

27.80

in validation,

1.33

in test) outperforms LSTM (

3.62

in validation,

0.00

in test) by a large margin in the English-Bengali translation task. (Evaluating LSTM and Transformer without any augmented data is our baseline study.) We also incorporate two decoding methods, top-k random sampling and random sampling with temperature T, for back-translation that help improve the translation accuracy of the model. The results show that data generated by back-translation without top-k or temperature sampling (“no strategy”) help improve the accuracy (BLEU

38.22

, +

10.42

on validation,

2.07

, +

0.74

on test). Specifically, back-translation with top-k sampling is less effective (

k = 10

, BLEU

29.43

, +

1.83

on validation,

1.36

, +

0.03

on test), while sampling with a proper value of T,

T = 0.5

makes the model achieve a higher score (

T = 0.5

, BLEU

35.02

, +

7.22

on validation,

2.35

, +

1.02

on test). This implies that in English-Bengali MT, we can augment the training set through back-translation using random sampling with a proper temperature T. Full article

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10 pages, 4167 KiB

Open AccessArticle

Investigation of the Effect of Relative Density on the Dynamic Modulus and Damping Ratio for Coarse Grained Soil

by Ziying Huang, Sen Cai, Rongfen Hu, Jianfeng Wang, Mingjie Jiang and Jian Gong

Appl. Sci. 2024, 14(15), 6847; https://doi.org/10.3390/app14156847 - 5 Aug 2024

Viewed by 762

Abstract

As the critical dynamic parameters for soil, an extensive examination of the dynamic elastic modulus E_d and damping ratio λ in coarse-grained soil is of significant theoretical and practical importance. Currently, there is a scarcity of experimental equipment and methods for measuring [...] Read more.

As the critical dynamic parameters for soil, an extensive examination of the dynamic elastic modulus E_d and damping ratio λ in coarse-grained soil is of significant theoretical and practical importance. Currently, there is a scarcity of experimental equipment and methods for measuring the dynamic elastic modulus and damping ratio of coarse-grained soils. Moreover, studies examining the influence of relative density on these parameters in coarse-grained soils are largely absent. To investigate the behavior of the dynamic elastic modulus and damping ratio in coarse-grained soil under varying relative densities, a number of dynamic triaxial tests were conducted on a specific coarse-grained soil using the DYNTTS type dynamic triaxial test apparatus. The findings reveal that, under various gradations, the E_d of coarse-grained soils exhibits a decreasing trend with increasing dynamic strain, a trend that intensifies with higher relative densities. Additionally, as relative density increases, the degradation rate of the dynamic shear modulus ratio G_d/G_dmax to dynamic shear strain γ_d curve escalates. The maximum dynamic shear modulus G_dmax rises with increasing relative density D_r, displaying a linear relationship between G_dmax and D_r. Furthermore, both the increasing rate of λ to γ_d curve and the maximum damping ratio λ_max progressively diminish with the escalation of relative density D_r. Notably, the maximum damping ratio has a power function relationship with the relative density. Full article

(This article belongs to the Special Issue New Trends in Long-Life Road Infrastructures: Materials and Structures)

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13 pages, 4363 KiB

Open AccessArticle

Deep Learning Methods to Analyze the Forces and Torques in Joints Motion

by Rui Guo, Baoyi Chen and Yonghui Li

Appl. Sci. 2024, 14(15), 6846; https://doi.org/10.3390/app14156846 - 5 Aug 2024

Viewed by 889

Abstract

This paper proposes a composite model that combines convolutional neural network models and mechanical analysis to determine the forces acting on an object. First, we establish a model using Newtonian mechanics to analyze the forces experienced by the human body during movement, particularly [...] Read more.

This paper proposes a composite model that combines convolutional neural network models and mechanical analysis to determine the forces acting on an object. First, we establish a model using Newtonian mechanics to analyze the forces experienced by the human body during movement, particularly the forces on joints. The model calculates the mapping relationship between the object’s movement and the forces on the joints. Then, by analyzing a large number of fencing competition videos using a deep learning model, we extract video features to study the torques and forces on human joints. Our analysis of numerous images reveals that, in certain movement patterns, the peak pressure on the knee joint can be two to three times higher than in a normal state, while the driving knee can withstand peak torques of 400–600 Nm. This straightforward model can effectively capture the forces and torques on the human body during movement using a deep neural network. Furthermore, this model can also be applied to problems involving non-rigid body motion. Full article

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22 pages, 4293 KiB

Open AccessArticle

A Transformer Maintenance Interval Optimization Method Considering Imperfect Maintenance and Dynamic Maintenance Costs

by Jianzhong Yang, Hongduo Wu, Yue Yang, Xiayao Zhao, Hua Xun, Xingzheng Wei and Zhiqi Guo

Appl. Sci. 2024, 14(15), 6845; https://doi.org/10.3390/app14156845 - 5 Aug 2024

Viewed by 859

Abstract

As one of the most critical components of the power grid system, transformer maintenance strategy planning significantly influences the safe, economical, and sustainable operation of the power system. Periodic imperfect maintenance strategies have become a research focus in preventive maintenance strategies for large [...] Read more.

As one of the most critical components of the power grid system, transformer maintenance strategy planning significantly influences the safe, economical, and sustainable operation of the power system. Periodic imperfect maintenance strategies have become a research focus in preventive maintenance strategies for large power equipment due to their ease of implementation and better alignment with engineering realities. However, power transformers are characterized by long lifespans, high reliability, and limited defect samples. Existing maintenance methods have not accounted for the dynamic changes in maintenance costs over a transformer’s operational lifetime. Therefore, we propose a maintenance interval optimization method that considers imperfect maintenance and dynamic maintenance costs. Utilizing defect and maintenance cost data from 400 220 KV oil-immersed transformers in northern China, we employed Bayesian estimation for the first time to address the distribution fitting of defect data under small sample conditions. Subsequently, we introduced imperfect maintenance improvement factors to influence the number of defects occurring in each maintenance cycle, resulting in more realistic maintenance cost estimations. Finally, we established an optimization model for transformer maintenance cycles, aiming to minimize maintenance costs throughout the transformer’s entire lifespan while maintaining reliability constraints. Taking a transformer’s strong oil circulation cooling system as an example, our method demonstrates that while meeting the reliability threshold recognized by the power grid company, the system’s maintenance cost can be reduced by 41.443% over the transformer’s entire life cycle. Through parameter analysis of the optimization model, we conclude that as the maintenance cycle increases, the factors dominating maintenance costs shift from corrective maintenance to preventive maintenance. Full article

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15 pages, 8419 KiB

Open AccessArticle

Capturing Snowmelt Runoff Onset Date under Different Land Cover Types Using Synthetic Aperture Radar: Case Study of Sierra Nevada Mountains, USA

by Bing Gao and Wei Ma

Appl. Sci. 2024, 14(15), 6844; https://doi.org/10.3390/app14156844 - 5 Aug 2024

Viewed by 728

Abstract

Snow plays a crucial role in the global water and energy cycles, and its melting process can have a series of impacts on hydrological or climatic systems. Accurately capturing the timing of snowmelt runoff is essential for the utilization of snow resources and [...] Read more.

Snow plays a crucial role in the global water and energy cycles, and its melting process can have a series of impacts on hydrological or climatic systems. Accurately capturing the timing of snowmelt runoff is essential for the utilization of snow resources and the early warning of snow-related disasters. A synthetic aperture radar (SAR) offers an effective means for capturing snowmelt runoff onset dates (RODs) over large areas, but its accuracy under different land cover types remains unclear. This study focuses on the Sierra Nevada Mountains and surrounding areas in the western United States. Using a total of 3117 Sentinel-1 images from 2017 to 2023, we extracted the annual ROD based on the Google Earth Engine (GEE) platform. The satellite extraction results were validated using the ROD derived from the snow water equivalent (SWE) data from 125 stations within the study area. The mean absolute errors (MAEs) for the four land cover types—tree cover, shrubland, grassland, and bare land—are 24, 18, 18, and 16 d, respectively. It indicates that vegetation significantly influences the accuracy of the ROD captured from Sentinel-1 data. Furthermore, we analyze the variation trends in the ROD from 2017 to 2023. The average ROD captured by the stations shows an advancing trend under different land cover types, while that derived from Sentinel-1 data only exhibits an advancing trend in bare land areas. It indicates that vegetation leads to a delayed trend in the ROD captured by using Sentinel-1 data, opposite to the results from the stations. Meanwhile, the variation trends of the average ROD captured by both methods are not significant (p > 0.05) due to the impact of the extreme snowfall in 2023. Finally, we analyze the influence of the SWE on RODs under different land cover types. A significant correlation (p < 0.05) is observed between the SWE and ROD captured from both stations and Sentinel-1 data. An increase in the SWE causes a delay in the ROD, with a greater delay rate in vegetated areas. These findings will provide vital reference for the accurate acquisition of the ROD and water resources management in the study area. Full article

(This article belongs to the Special Issue Geographic Information System and Remote Sensing Applications in Digital Earth)

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17 pages, 2402 KiB

Open AccessArticle

High Accuracy Reconstruction of Airborne Streak Tube Imaging LiDAR Using Particle Swarm Optimization

by Xing Wang, Zhaodong Chen, Chaowei Dong, Zhiwei Dong, Deying Chen and Rongwei Fan

Appl. Sci. 2024, 14(15), 6843; https://doi.org/10.3390/app14156843 - 5 Aug 2024

Cited by 1 | Viewed by 660

Abstract

Airborne streak tube imaging LiDAR (STIL) consists of several different data-generating subsystems and introduces system errors each time it is installed on an aircraft. These errors change with each installation, which makes the parametric calibration of the LiDAR meaningless. In this study, we [...] Read more.

Airborne streak tube imaging LiDAR (STIL) consists of several different data-generating subsystems and introduces system errors each time it is installed on an aircraft. These errors change with each installation, which makes the parametric calibration of the LiDAR meaningless. In this study, we propose a high-precision reconstruction method for point clouds that can be used without calibrating the system parameters. In essence, after each remote sensing measurement, a self-checking process is performed with experimental data to replace the fixed system parameters. In this process, the splicing error of the same region measured under different conditions is used as a criterion to optimize the reconstruction parameters via a particle swarm optimization (PSO) algorithm. For a detection distance of 3000 m, the elevation error of the point cloud reconstruction reaches more than 1 m if the placement parameters are not optimized; after optimization, the elevation error can be controlled within 0.3 m. Full article

(This article belongs to the Special Issue Application of Signal Processing in Lidar)

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19 pages, 4466 KiB

Open AccessArticle

New Web-Based Ventilator Monitoring System Consisting of Central and Remote Mobile Applications in Intensive Care Units

by Kyuseok Kim, Yeonkyeong Kim, Young Sam Kim, Kyu Bom Kim and Su Hwan Lee

Appl. Sci. 2024, 14(15), 6842; https://doi.org/10.3390/app14156842 - 5 Aug 2024

Viewed by 1228

Abstract

A ventilator central monitoring system (VCMS) that can efficiently respond to and treat patients’ respiratory issues in intensive care units (ICUs) is critical. Using Internet of Things (IoT) technology without loss or delay in patient monitoring data, clinical staff can overcome spatial constraints [...] Read more.

A ventilator central monitoring system (VCMS) that can efficiently respond to and treat patients’ respiratory issues in intensive care units (ICUs) is critical. Using Internet of Things (IoT) technology without loss or delay in patient monitoring data, clinical staff can overcome spatial constraints in patient respiratory management by integrated monitoring of multiple ventilators and providing real-time information through remote mobile applications. This study aimed to establish a VCMS and assess its effectiveness in an ICU setting. A VCMS comprises central monitoring and mobile applications, with significant real-time information from multiple patient monitors and ventilator devices stored and managed through the VCMS server, establishing an integrated monitoring environment on a web-based platform. The developed VCMS was analyzed in terms of real-time display and data transmission. Twenty-one respiratory physicians and staff members participated in usability and satisfaction surveys on the developed VCMS. The data transfer capacity derived an error of approximately

10^{- 7}

, and the difference in data transmission capacity was approximately

{1.99 \times 10}^{- 7} \pm {9.97 \times 10}^{- 6}

with a 95% confidence interval of

{- 1.16 \times 10}^{- 7}

to

{5.13 \times 10}^{- 7}

among 18 ventilators and patient monitors. The proposed VCMS could transmit data from various devices without loss of information within the ICU. The medical software validation, consisting of 37 tasks and 9 scenarios, showed a task completion rate of approximately 92%, with a 95% confidence interval of 88.81–90.43. The satisfaction survey consisted of 23 items and showed results of approximately 4.66 points out of 5. These results demonstrated that the VCMS can be readily used by clinical ICU staff, confirming its clinical utility and applicability. The proposed VCMS can help clinical staff quickly respond to the alarm of abnormal events and diagnose and treat based on longitudinal patient data. The mobile applications overcame space constraints, such as isolation to prevent respiratory infection transmission of clinical staff for continuous monitoring of respiratory patients and enabled rapid consultation, ensuring consistent care. Full article

(This article belongs to the Special Issue Monitoring of Human Physiological Signals)

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18 pages, 7797 KiB

Open AccessArticle

A Prediction Method for the Average Winding Temperature of a Transformer Based on the Fully Connected Neural Network

by Junjie Feng, Ziyu Feng, Guojun Jiang, Guangyong Zhang, Wei Jin and Huijun Zhu

Appl. Sci. 2024, 14(15), 6841; https://doi.org/10.3390/app14156841 - 5 Aug 2024

Viewed by 1002

Abstract

The average winding temperature of a transformer (AWTT), serving as a key indicator for assessing the running state of the transformer, is of utmost importance in determining a transformer’s electrical properties and the insulation longevity of the transformer. An accurate prediction of AWTT [...] Read more.

The average winding temperature of a transformer (AWTT), serving as a key indicator for assessing the running state of the transformer, is of utmost importance in determining a transformer’s electrical properties and the insulation longevity of the transformer. An accurate prediction of AWTT is essential for ensuring the safe operation of the transformer. A novel method for predicting AWTT is introduced based on the analysis of field monitoring data. Firstly, the thermal characteristics and operational mechanisms of oil-immersed transformers are examined. Secondly, a factor analysis model is developed to streamline the network structure, accounting for the strong correlations among ambient temperature, load current, and top oil temperature. Thirdly, the independent temperature factor and load factor are extracted as pivotal features, and then input into the fully connected neural network to predict AWTT. Through a case study involving a 110 kV/10 kV oil-immersed transformer, the results show that the proposed method reduces redundant correlation information compared to traditional methods and improves the prediction accuracy of AWTT, establishing a foundation for further transformer state assessments. Full article

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21 pages, 7110 KiB

Open AccessArticle

Experimental and Numerical Study of Air Flow Reversal Induced by Fire in an Inclined Mine Working

by Lev Levin, Maksim Popov, Mikhail Semin and Sergey Zhikharev

Appl. Sci. 2024, 14(15), 6840; https://doi.org/10.3390/app14156840 - 5 Aug 2024

Viewed by 777

Abstract

Effective fire prevention in mine workings and tunnels requires a thorough theoretical analysis of the heat and mass transfer processes within these structures. This involves using established models to calculate non-isothermal air flow dynamics in long tunnels and mine workings. While the ventilation [...] Read more.

Effective fire prevention in mine workings and tunnels requires a thorough theoretical analysis of the heat and mass transfer processes within these structures. This involves using established models to calculate non-isothermal air flow dynamics in long tunnels and mine workings. While the ventilation of tunnels has been extensively studied, significant challenges persist regarding mine ventilation systems, particularly due to their complex and branched topology. This study aimed to address these challenges and gaps in mine ventilation. We designed a laboratory bench to simulate an inclined mine working with a heat source (fire) and validated a mathematical model of heat and mass transfer in such settings. Using experimental measurements, we verified the model’s accuracy. It is important to note that our experimental and theoretical analyses focused solely on the thermal effects of a fire, without considering the release of harmful impurities. Using the validated model, we conducted multiparameter simulations to identify the conditions leading to the formation of a thermal slug in an inclined mine working and the subsequent reversal of air flow. The simulation data enabled us to determine the dependency of the critical heat release rate on the aerodynamic parameters of the mine working. Additionally, we evaluated the changes in average air density within a mine working at the critical heat release rate. These findings are crucial for the further development of a network-based method to analyze air flow stability in mine ventilation networks during fires. Full article

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26 pages, 591 KiB

Open AccessReview

Can Complex Training Improve Acute and Long-Lasting Performance in Basketball Players? A Systematic Review

by Enrique Flórez Gil, Alejandro Vaquera, Rodrigo Ramírez-Campillo, Javier Sanchez-Sanchez and Alejandro Rodríguez Fernández

Appl. Sci. 2024, 14(15), 6839; https://doi.org/10.3390/app14156839 - 5 Aug 2024

Cited by 1 | Viewed by 3041

Abstract

Basketball demands a sophisticated blend of tactical, technical, physical, and psychological skills, and various methods have been proposed to prepare players for these demands, including resistance training to enhance strength, power, speed, agility, and endurance. Complex training (CT) integrates diverse strength training methodologies [...] Read more.

Basketball demands a sophisticated blend of tactical, technical, physical, and psychological skills, and various methods have been proposed to prepare players for these demands, including resistance training to enhance strength, power, speed, agility, and endurance. Complex training (CT) integrates diverse strength training methodologies by combining heavy-resistance exercises (e.g., squat at 90% of one repetition maximum) with high-velocity movements or plyometrics, both sharing the same biomechanical pattern. However, the optimal application of CT in basketball remains uncertain due to diverse protocols and a lack of consensus in the literature. The aim of this systematic review was to evaluate the acute and chronic effects of CT interventions on physical fitness performance in basketball players and identify the most effective characteristics of moderators. Methods: A bibliographic search was conducted using PubMed, SCOPUS, and Web of Science databases following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines using the PICOS strategy. Results: Fourteen studies met the inclusion criteria, three articles analyzed acute effects, and thirteen analyzed chronic effects. The total number of participants in the studies analyzing acute effects was 50, while for studies examining chronic effects, it was 362. Conclusions: Acutely, CT triggers post-activation potentiation and enhances sprint performance when coupled with brief rest intervals. Over time, these acute improvements contribute to more substantial, long-lasting benefits. Chronic effects of CT improve strength, as evidenced by enhanced 1 RM performance, jumps, sprints, and core muscle strength. Full article

(This article belongs to the Special Issue Advances in Assessment of Physical Performance)

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20 pages, 565 KiB

Open AccessArticle

INVSID 1.0: An Inverse System Identification Toolbox for MATLAB

by Runzhe Han, Christian Bohn and Georg Bauer

Appl. Sci. 2024, 14(15), 6838; https://doi.org/10.3390/app14156838 - 5 Aug 2024

Viewed by 823

Abstract

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process from theoretical derivation to toolbox creation of developing the toolbox is demonstrated. Afterwards, numerical examples are illustrated [...] Read more.

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process from theoretical derivation to toolbox creation of developing the toolbox is demonstrated. Afterwards, numerical examples are illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox. Full article

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

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11 pages, 2172 KiB

Open AccessArticle

Reduction Potential of Gaseous Emissions in European Ports Using Cold Ironing

by Krishna Pavan Peddi, Stefano Ricci and Luca Rizzetto

Appl. Sci. 2024, 14(15), 6837; https://doi.org/10.3390/app14156837 - 5 Aug 2024

Cited by 1 | Viewed by 954

Abstract

Providing electrical power to ships while they are docked, cold ironing allows ships to turn off their engines and reduces emissions of air pollutants and greenhouse gases. This study identifies and assesses ship and port emissions and analyzes the potential for emission reduction [...] Read more.

Providing electrical power to ships while they are docked, cold ironing allows ships to turn off their engines and reduces emissions of air pollutants and greenhouse gases. This study identifies and assesses ship and port emissions and analyzes the potential for emission reduction achievable by cold ironing in European ports. It includes (1) a review of the current state of cold ironing in European ports; (2) an analysis of the time spent in ports by ships; (3) a quantification of emissions potentially avoided by means of a larger-scale use of cold ironing in Europe; (4) an estimation of the benefits achievable and the perspective to play a role in meeting emission reduction targets, improving air quality in port cities; (5) an analysis of the challenges and limitations of larger-scale cold ironing implementation; (6) potential solutions to overcome them. The results of this study could have important implications for (a) the shipping industry, which could benefit from the need for additional standardized electrical equipment onboard; (b) port authorities, which could benefit from providing additional services to the ships; (c) policymakers working to reduce emissions and promote energy efficiency, who could better approach their local and global targets. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport and Ports: Challenges and Opportunities)

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15 pages, 3294 KiB

Open AccessArticle

Implementation of a Small-Sized Mobile Robot with Road Detection, Sign Recognition, and Obstacle Avoidance

by Ching-Chang Wong, Kun-Duo Weng, Bo-Yun Yu and Yung-Shan Chou

Appl. Sci. 2024, 14(15), 6836; https://doi.org/10.3390/app14156836 - 5 Aug 2024

Viewed by 1112

Abstract

In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to [...] Read more.

In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to let the robot have good computing processing and graphics processing capabilities. In addition, three functions of road detection, sign recognition, and obstacle avoidance are implemented on this small-sized robot. For road detection, we divide the captured image into four areas and use Intel NUC to perform road detection calculations. The proposed method can significantly reduce the system load and also has a high processing speed of 25 frames per second (fps). For sign recognition, we use the YOLOv4-tiny model and a data augmentation strategy to significantly improve the computing performance of this model. From the experimental results, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. For obstacle avoidance, a 2D LiDAR-based method with a distance-based filtering mechanism is proposed. The distance-based filtering mechanism is proposed to filter important data points and assign appropriate weights, which can effectively reduce the computational complexity and improve the robot’s response speed to avoid obstacles. Some results and actual experiments illustrate that the proposed methods for these three functions can be effectively completed in the implemented small-sized robot. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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20 pages, 2214 KiB

Open AccessArticle

Designing Digital Twin with IoT and AI in Warehouse to Support Optimization and Safety in Engineer-to-Order Manufacturing Process for Prefabricated Building Products

by Alessandro Pracucci

Appl. Sci. 2024, 14(15), 6835; https://doi.org/10.3390/app14156835 - 5 Aug 2024

Cited by 1 | Viewed by 1349

Abstract

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This [...] Read more.

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users’ requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi’s production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry. Full article

(This article belongs to the Special Issue Digital Twins: Technologies and Applications)

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17 pages, 896 KiB

Open AccessArticle

Tibetan Speech Synthesis Based on Pre-Traind Mixture Alignment FastSpeech2

by Qing Zhou, Xiaona Xu and Yue Zhao

Appl. Sci. 2024, 14(15), 6834; https://doi.org/10.3390/app14156834 - 5 Aug 2024

Viewed by 1129

Abstract

Most current research in Tibetan speech synthesis relies primarily on autoregressive models in deep learning. However, these models face challenges such as slow inference, skipped readings, and repetitions. To overcome these issues, we propose an enhanced non-autoregressive acoustic model combined with a vocoder [...] Read more.

Most current research in Tibetan speech synthesis relies primarily on autoregressive models in deep learning. However, these models face challenges such as slow inference, skipped readings, and repetitions. To overcome these issues, we propose an enhanced non-autoregressive acoustic model combined with a vocoder for Tibetan speech synthesis. Specifically, we introduce the mixture alignment FastSpeech2 method to correct errors caused by hard alignment in the original FastSpeech2 method. This new method employs soft alignment at the level of Latin letters and hard alignment at the level of Tibetan characters, thereby improving alignment accuracy between text and speech and enhancing the naturalness and intelligibility of the synthesized speech. Additionally, we integrate pitch and energy information into the model, further enhancing overall synthesis quality. Furthermore, Tibetan has relatively smaller text-to-audio datasets compared to widely studied languages. To address these limited resources, we employ a transfer learning approach to pre-train the model with data from resource-rich languages. Subsequently, this pre-trained mixture alignment FastSpeech2 model is fine-tuned for Tibetan speech synthesis. Experimental results demonstrate that the mixture alignment FastSpeech2 model produces higher-quality speech compared to the original FastSpeech2 model, particularly when pre-trained on an English dataset, resulting in further improvements in clarity and naturalness. Full article

(This article belongs to the Special Issue Deep Learning for Speech, Image and Language Processing)

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20 pages, 7821 KiB

Open AccessArticle

Optimizing the Influence of Fly Ash as an Anti-Sagging Additive in Highly Deviated Geothermal Well Drilling Fluids Using Surface Response Method

by Opeyemi Oni and Adesina Fadairo

Appl. Sci. 2024, 14(15), 6833; https://doi.org/10.3390/app14156833 - 5 Aug 2024

Viewed by 874

Abstract

Weighting materials such as barite and ilmenite are crucial for controlling fluid density during deep or ultra-deep drilling operations. However, sagging poses significant challenges, especially in highly deviated high-pressure and high-temperature (HP/HT) wells. This leads to inadequate well control, wellbore instability, and variations [...] Read more.

Weighting materials such as barite and ilmenite are crucial for controlling fluid density during deep or ultra-deep drilling operations. However, sagging poses significant challenges, especially in highly deviated high-pressure and high-temperature (HP/HT) wells. This leads to inadequate well control, wellbore instability, and variations in hydrostatic pressure in extended-reach wells. Given the challenges of experimental research, reliable prediction models are imperative for evaluating the interaction between the ratio of anti-sagging additives, temperature, and wellbore inclination on sag factor (SF). This research presents statistical-based empirical models for predicting the SF at various wellbore inclinations (0°, 30°, 45°, 60°, 70°, 80°, and 90°) and assessing the influence of fly ash on the SF. The regression equations, developed using the Response Surface Methodology in Minitab 18 software, show high reliability, with R² values approaching unity. Contour and surface response plots provide a clear understanding of the variable interactions. The analysis reveals that sagging is most severe at 60° to 65° inclination. At 400 °F and 60° inclination, adding 4 lb/bbl of fly ash reduces sagging in barite and ilmenite-densified fluid by 63.9% and 63.1%, respectively. Model validation shows high accuracy, with percentage errors below 3%. This study offers valuable insights for optimizing drilling fluid formulations in HP/HT well environments. Full article

(This article belongs to the Special Issue Recent Advances in Drilling Fluid Technologies)

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17 pages, 1054 KiB

Open AccessArticle

Integration of Relation Filtering and Multi-Task Learning in GlobalPointer for Entity and Relation Extraction

by Bin Liu, Jialin Tao, Wanyuan Chen, Yijie Zhang, Min Chen, Lei He and Dan Tang

Appl. Sci. 2024, 14(15), 6832; https://doi.org/10.3390/app14156832 - 5 Aug 2024

Viewed by 986

Abstract

The rise of knowledge graphs has been instrumental in advancing artificial intelligence (AI) research. Extracting entity and relation triples from unstructured text is crucial for the construction of knowledge graphs. However, Chinese text has a complex grammatical structure, which may lead to the [...] Read more.

The rise of knowledge graphs has been instrumental in advancing artificial intelligence (AI) research. Extracting entity and relation triples from unstructured text is crucial for the construction of knowledge graphs. However, Chinese text has a complex grammatical structure, which may lead to the problem of overlapping entities. Previous pipeline models have struggled to address such overlap problems effectively, while joint models require entity annotations for each predefined relation in the set, which results in redundant relations. In addition, the traditional models often lead to task imbalance by overlooking the differences between tasks. To tackle these challenges, this research proposes a global pointer network based on relation prediction and loss function improvement (GPRL) for joint extraction of entities and relations. Experimental evaluations on the publicly available Chinese datasets DuIE2.0 and CMeIE demonstrate that the GPRL model achieves a 1.2–26.1% improvement in F1 score compared with baseline models. Further, experiments of overlapping classification conducted on CMeIE have also verified the effectiveness of overlapping triad extraction and ablation experiments. The model is helpful in identifying entities and relations accurately and can reduce redundancy by leveraging relation filtering and the global pointer network. In addition, the incorporation of a multi-task learning framework balances the loss functions of multiple tasks and enhances task interactions. Full article

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14 pages, 3138 KiB

Open AccessArticle

Synthetic Medical Imaging Generation with Generative Adversarial Networks for Plain Radiographs

by John R. McNulty, Lee Kho, Alexandria L. Case, David Slater, Joshua M. Abzug and Sybil A. Russell

Appl. Sci. 2024, 14(15), 6831; https://doi.org/10.3390/app14156831 - 5 Aug 2024

Viewed by 998

Abstract

In medical imaging, access to data is commonly limited due to patient privacy restrictions, and it can be difficult to acquire enough data in the case of rare diseases. The purpose of this investigation was to develop a reusable open-source synthetic image-generation pipeline, [...] Read more.

In medical imaging, access to data is commonly limited due to patient privacy restrictions, and it can be difficult to acquire enough data in the case of rare diseases. The purpose of this investigation was to develop a reusable open-source synthetic image-generation pipeline, the GAN Image Synthesis Tool (GIST), that is easy to use as well as easy to deploy. The pipeline helps to improve and standardize AI algorithms in the digital health space by generating high quality synthetic image data that is not linked to specific patients. Its image generation capabilities include the ability to generate imaging of pathologies or injuries with low incidence rates. This improvement of digital health AI algorithms could improve diagnostic accuracy, aid in patient care, decrease medicolegal claims, and ultimately decrease the overall cost of healthcare. The pipeline builds on existing Generative Adversarial Networks (GANs) algorithms, and preprocessing and evaluation steps were included for completeness. For this work, we focused on ensuring the pipeline supports radiography, with a focus on synthetic knee and elbow X-ray images. In designing the pipeline, we evaluated the performance of current GAN architectures, studying the performance on available X-ray data. We show that the pipeline is capable of generating high-quality and clinically relevant images based on a lay person’s evaluation and the Fréchet Inception Distance (FID) metric. Full article

(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Processing)

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14 pages, 613 KiB

Open AccessArticle

Effects of Whole-Body Vibration Training on Improving Physical Function, Cognitive Function, and Sleep Quality for Older People with Dynapenia in Long-Term Care Institutions: A Randomized Controlled Study

by Yu-Chen Su and Shu-Fang Chang

Appl. Sci. 2024, 14(15), 6830; https://doi.org/10.3390/app14156830 - 5 Aug 2024

Viewed by 842

Abstract

As the global demographic shifts toward an aging population, aging-related problems, particularly in older individuals with dynapenia, are increasingly gaining attention. However, interventional studies focusing on physical and cognitive function and sleep quality in such individuals are limited, indicating a need for further [...] Read more.

As the global demographic shifts toward an aging population, aging-related problems, particularly in older individuals with dynapenia, are increasingly gaining attention. However, interventional studies focusing on physical and cognitive function and sleep quality in such individuals are limited, indicating a need for further exploration. The present study investigated the effects of whole-body vibration (WBV) training on physical and cognitive function and sleep quality in older people with dynapenia residing in long-term care institutions. This study was a randomized controlled trial. The experimental group underwent WBV training three times a week for 3 months, whereas the control group continued with their regular daily care routine. Statistical analyses were performed using the Traditional Chinese version of SAS Statistics version 9.4. Paired t tests, a one-way analysis of variance, independent t tests, and generalized estimating equation analysis were performed. The results revealed that compared with the control group, the experimental group experienced significant improvements in grip strength, instrumental activities of daily living, cognitive function, and sleep quality in terms of latency and duration. These findings suggest that 3 months of WBV training can effectively enhance physical and cognitive function and sleep quality in older people with dynapenia residing in long-term care institutions. Full article

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16 pages, 9647 KiB

Open AccessArticle

Experimental Study on Shear Creep Characteristics of Residual Soil with Different Stone Content

by Jinyu Dong, Tanyu Wang and Yawen Zhao

Appl. Sci. 2024, 14(15), 6829; https://doi.org/10.3390/app14156829 - 5 Aug 2024

Viewed by 614

Abstract

The residual soil on a slope can slowly move downward under the influence of gravity, forming a creep landslide. These types of landslides are known for their extensive coverage, significant magnitude, and prolonged duration of hazard. A systematic study of the creep properties [...] Read more.

The residual soil on a slope can slowly move downward under the influence of gravity, forming a creep landslide. These types of landslides are known for their extensive coverage, significant magnitude, and prolonged duration of hazard. A systematic study of the creep properties of creep landslide geotechnical bodies is essential for the analysis of the deformation process and long-term safety evaluation of landslides. This paper focuses on studying a creep landslide involving residual soil in western Henan Province. The creep characteristics of residual soil with different stone content are investigated through direct shear creep experiments. The findings reveal that stone content has a profound impact on the creep behavior of residual soil. As the stone content of the soil increased, the structure of the test soil changed significantly, resulting in a gradual decrease in its shear creep. The Burgers model can effectively fit the deceleration creep and steady-state creep stages of the residual soil. With the increase in stone content, the four parameters of the Burgers model show a significant increase, with the instantaneous elasticity coefficient G₁ and the viscosity coefficient η₁ experiencing more noticeable changes. The average long-term strength of specimens with different stone content is only 54% of their instantaneous strength. Additionally, as the stone content increases, the ratio of long-term strength to instantaneous strength also increases. Notably, the long-term strength of specimens with 10–30% stone content is significantly lower than that of specimens with 50–70% stone content. Full article

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15 pages, 1045 KiB

Open AccessArticle

Adaptive Imputation of Irregular Truncated Signals with Machine Learning

by Tyler Ward, Kouroush Jenab and Jorge Ortega-Moody

Appl. Sci. 2024, 14(15), 6828; https://doi.org/10.3390/app14156828 - 5 Aug 2024

Viewed by 711

Abstract

In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems [...] Read more.

In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems is the problem of connectivity, where a dropped Internet connection can lead to the loss of important condition data from a machine. Such gaps in the data, which we call irregular truncated signals, can lead to incorrect assumptions about the status of a machine and other flawed decision-making processes. This paper presents an adaptive data imputation framework based on machine learning (ML) algorithms to assess whether the missing data in a signal is missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) and automatically select an appropriate ML-based data imputation model to deal with the missing data. Our results demonstrate the potential for applying ML algorithms to the challenge of irregularly truncated signals, as well as the capability of our adaptive framework to intelligently solve this issue. Full article

(This article belongs to the Special Issue Smart Design and Advanced Manufacturing: Integrating Emerging Technologies for Improved Production Processes)

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31 pages, 5711 KiB

Open AccessArticle

Self-Adaptable Software for Pre-Programmed Internet Tasks: Enhancing Reliability and Efficiency

by Mario Martínez García, Luis Carlos G. Martínez Rodríguez and Ricardo Pérez Zúñiga

Appl. Sci. 2024, 14(15), 6827; https://doi.org/10.3390/app14156827 - 5 Aug 2024

Viewed by 719

Abstract

In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with [...] Read more.

In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software’s effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation. Full article

(This article belongs to the Special Issue Advances and Applications of Complex Data Analysis and Computing)

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19 pages, 4489 KiB

Open AccessArticle

Effect of Bedding Angle on Energy and Failure Characteristics of Soft–Hard Interbedded Rock-like Specimen under Uniaxial Compression

by Zheng Wang, Jiaqi Guo and Fan Chen

Appl. Sci. 2024, 14(15), 6826; https://doi.org/10.3390/app14156826 - 5 Aug 2024

Viewed by 758

Abstract

To investigate how bedding planes affect the energy evolution and failure characteristics of transversely isotropic rock, uniaxial compression tests were conducted on soft–hard interbedded rock-like specimens with varying bedding angles (α) using the RMT-150B rock mechanics loading system. The test results [...] Read more.

To investigate how bedding planes affect the energy evolution and failure characteristics of transversely isotropic rock, uniaxial compression tests were conducted on soft–hard interbedded rock-like specimens with varying bedding angles (α) using the RMT-150B rock mechanics loading system. The test results indicate that throughout the loading process, the energy evolution shows obvious stage characteristics, and the change of α mainly affects the accelerating energy dissipation stage and the full energy release stage. With the increase of α, the ability of rock to resist deformation under the action of energy shows the characteristics of “strong–weak–strong”. The energy dissipation process is accelerated by medium angle bedding planes (α = 45°~60°). The precursor points of the ratios of dissipation energy to total energy (R_DT) and elastic energy to dissipation energy (R_ED) can be used to effectively predict early failure. With the gradual increase of α, the difficulty of crack development is gradually reduced. The changes of energy storage limitation and release rate of releasable elastic energy are the immanent cause of different macroscopic failure modes of specimens with varying α. Full article

(This article belongs to the Special Issue Rock Mass Characterization: Failure and Mechanical Behavior)

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19 pages, 4827 KiB

Open AccessArticle

Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism

by Tingwei Wu, Zhaohan Huang, Shilong Li, Qijun Zhao and Fan Pan

Appl. Sci. 2024, 14(15), 6825; https://doi.org/10.3390/app14156825 - 5 Aug 2024

Viewed by 1013

Abstract

Heart murmurs play a critical role in assessing the condition of the heart. Murmur quality reflects the subjective human perception of heart murmurs and is an important characteristic strongly linked to cardiovascular diseases (CVDs). This study aims to use deep neural networks to [...] Read more.

Heart murmurs play a critical role in assessing the condition of the heart. Murmur quality reflects the subjective human perception of heart murmurs and is an important characteristic strongly linked to cardiovascular diseases (CVDs). This study aims to use deep neural networks to classify the patients’ murmur quality (i.e., harsh and blowing) from phonocardiogram (PCG) signals. The phonocardiogram recordings with murmurs used for this task are from the CirCor DigiScope Phonocardiogram dataset, which provides the murmur quality labels. The recordings were segmented, and a dataset of 1266 segments with average lengths of 4.1 s from 164 patients’ recordings was obtained. Each patient usually has multiple segments. A deep neural network model based on convolutional neural networks (CNNs) with channel attention and gated recurrent unit (GRU) networks was first used to extract features from the log-Mel spectrograms of segments. Then, the features of different segments from one patient were weighted by the proposed “Feature Attention” module based on the attention mechanism. The “Feature Attention” module contains a layer of global pooling and two fully connected layers. Through it, the different features can learn their weight, which can help the deep learning model distinguish the importance of different features of one patient. Finally, the detection results were produced. The cross-entropy loss function was used to train the model, and five-fold cross-validation was employed to evaluate the performance of the proposed methods. The accuracy of detecting the quality of patients’ murmurs is 73.6%. The F1-scores (precision and recall) for the murmurs of harsh and blowing are 76.8% (73.0%, 83.0%) and 67.8% (76.0%, 63.3%), respectively. The proposed methods have been thoroughly evaluated and have the potential to assist physicians with the diagnosis of cardiovascular diseases as well as explore the relationship between murmur quality and cardiovascular diseases in depth. Full article

(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis, 2nd Edition)

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25 pages, 636 KiB

Open AccessArticle

A User-Centered Framework for Data Privacy Protection Using Large Language Models and Attention Mechanisms

by Shutian Zhou, Zizhe Zhou, Chenxi Wang, Yuzhe Liang, Liangyu Wang, Jiahe Zhang, Jinming Zhang and Chunli Lv

Appl. Sci. 2024, 14(15), 6824; https://doi.org/10.3390/app14156824 - 5 Aug 2024

Cited by 1 | Viewed by 1234

Abstract

This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial computing and facial recognition. The innovation lies in a novel [...] Read more.

This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial computing and facial recognition. The innovation lies in a novel user attention mechanism that dynamically adjusts attention weights based on data characteristics and user privacy needs, enhancing the ability to identify and protect sensitive information effectively. Significant methodological advancements differentiate our approach from existing techniques by incorporating user-specific attention into traditional LLMs, ensuring both data accuracy and privacy. We succinctly highlight the enhanced performance of this framework through a selective presentation of experimental results across various applications. Notably, in computer vision, the application of our user attention mechanism led to improved metrics over traditional multi-head and self-attention methods: FasterRCNN models achieved precision, recall, and accuracy rates of 0.82, 0.79, and 0.80, respectively. Similar enhancements were observed with SSD, YOLO, and EfficientDet models with notable increases in all performance metrics. In natural language processing tasks, our framework significantly boosted the performance of models like Transformer, BERT, CLIP, BLIP, and BLIP2, demonstrating the framework’s adaptability and effectiveness. These streamlined results underscore the practical impact and the technological advancement of our proposed framework, confirming its superiority in enhancing privacy protection without compromising on data processing efficacy. Full article

(This article belongs to the Special Issue Cloud Computing: Privacy Protection and Data Security)

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13 pages, 8748 KiB

Open AccessArticle

Evaluation of the Mineral Manganese OXMN009 and OXMN009P in the Chemical Looping Combustion (CLC) Process Using Thermogravimetry

by Sandra Peña Murillo, Carmen Forero, Francisco Velasco-Sarria and Eduardo Arango

Appl. Sci. 2024, 14(15), 6823; https://doi.org/10.3390/app14156823 - 5 Aug 2024

Viewed by 646

Abstract

Indirect combustion with the chemical looping combustion (CLC) of solid oxygen carriers is one of the most promising technologies for capturing carbon dioxide (CO₂) in energy production from fossil fuels since the separation of the generated CO₂ is inherent to [...] Read more.

Indirect combustion with the chemical looping combustion (CLC) of solid oxygen carriers is one of the most promising technologies for capturing carbon dioxide (CO₂) in energy production from fossil fuels since the separation of the generated CO₂ is inherent to the process itself. Therefore, the cost associated with capturing this gas will be significantly reduced. This technology transfers oxygen from air to fuel through a metal oxide that acts as an oxygen carrier, avoiding direct contact between air and fuel. This oxygen carrier circulates in a fluidized bed reactor called a reduction reactor and an oxidation reactor. (1) This research work has focused on evaluating the behavior of oxygen carriers based on the original and improved manganese mineral (copper-impregnated mineral) named for this study, OXMN009 and OXMN009P, respectively. (2) Equilibrium experiments were carried out on a thermogravimetric balance (TGA) to evaluate the kinetic behavior of these oxygen transporters OXMN009 and OXMN009P, using the gases methane (CH₄), carbon monoxide (CO), and hydrogen (H₂). (3) The enhanced solid oxygen carrier OXMN009P exhibited good performance for the CLC process with gaseous fuels in terms of reactivity and combustion efficiency, having high reactivity and oxygen transfer properties due to copper impregnation. (4) The results show that OXMN009P has comparable reactivity to other manganese-based materials reported in the literature. It may be an effective option for carbon dioxide capture, as it uses metal oxides as the oxygen transporters (TO). (5) These oxygen transporters, OXMN009 and OXMN009P, are used in a cyclic process that prevents the formation of nitrogen oxides by keeping the air and fuel separate. (6) Thermogravimetric balance (TGA) experiments were conducted to evaluate the kinetic behavior of these copper-modified oxygen transporters. (7) It was found that OXMN009P improved the reactivity and oxygen transfer properties due to copper impregnation. The kinetic parameters obtained in the TGA indicate that the reaction is non-thermal and requires less energy to initiate. (8) The results show that OXMN009P has reactivity comparable to other manganese-based materials reported in the literature and can be an effective option for carbon dioxide capture. Full article

(This article belongs to the Section Energy Science and Technology)

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13 pages, 2939 KiB

Open AccessArticle

Study on the Length of the Effective Vibration Area of the Catenary in a Pantograph–Catenary Interaction System

by Liming Chen, Like Pan, Yan Xu and Chengbin Huang

Appl. Sci. 2024, 14(15), 6822; https://doi.org/10.3390/app14156822 - 5 Aug 2024

Cited by 1 | Viewed by 618

Abstract

The effective vibration area includes most of the catenary vibration caused by pantograph–catenary interactions and is the basis of the real-time catenary model for hardware-in-the-loop simulation. However, while the length of the effective vibration area is one of the most important parameters of [...] Read more.

The effective vibration area includes most of the catenary vibration caused by pantograph–catenary interactions and is the basis of the real-time catenary model for hardware-in-the-loop simulation. However, while the length of the effective vibration area is one of the most important parameters of the real-time catenary model, it has not been fully studied at present. In this paper, the length of the effective vibration area is first investigated. A pantograph–catenary interaction model is developed based on the modal superposition method. After the validation of the model, the vibration energy distribution of the catenary is used to determine the length of the effective vibration area based on the converged total energy. The influence of vehicle velocity and contact wire tension on the vibration energy distribution and length of the effective vibration area is investigated. The obtained appropriate length of effective vibration area is validated by a real-time catenary model and online measurement data of the contact force. The investigation results show that the energy distribution of the catenary can accurately determine the length of effective vibration area, and it increases with increasing vehicle velocity but decreases with increasing contact wire tension. The appropriate length of effective vibration area should be at least 160 m (approximately three spans) in the pantograph–catenary system. Full article

(This article belongs to the Special Issue Simulations and Experiments in Design of Transport Vehicles)

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Appl. Sci., Volume 14, Issue 15 (August-1 2024) – 462 articles

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