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The different sources of noise in a vehicle have long been known, and they include noise from the engine and other mechanical parts, aerodynamic noise, and rolling noise. More specifically, the latter concerns the interaction between the tire and the road surface, and so it is also known as Tire–Road Noise (TRN). One of the parameters influencing TRN is pavement stiffness. The empirical measurement of pavement stiffness, and in particular, its frequency spectrum (dynamic stiffness), is not easy to determine, and only in the last decade have studies emerged about this subject. In these works, two different instrumental chains are employed as follows: the impact hammer one and the dynamic exciter (shaker) one, which has established itself over time as a reference. The objective of this work is to develop a system for the dynamic stiffness measurements of road pavements using the impact hammer capable of producing a similar performance to the shaker while minimizing costs. During the work, a measurement aid device named Test Automation Device (TAD) was designed and implemented to increase the quality of the measurements. In line with the practical execution of the measurement, the analysis and the representation of the results were optimized to obtain results that adhere to the stiffness model proposed in the literature. In the present paper, the TAD, the measurement optimization work, the data analysis performed, and the proposed representation method will be described. Finally, we will present the results obtained and possible future perspectives. Full article

(This article belongs to the Special Issue Modelling, Simulation and Data Analysis in Acoustical Problems–Volume 3)

16 pages, 3784 KiB

Open AccessArticle

The Effect of Variable Ratios of Na₂O/K₂O Oxides in Glazes Containing BaO, ZnO, and ZrO₂: Structural Analysis, Characteristic Temperatures, and Surface Properties

by Janusz Partyka, Dawid Kozień and Katarzyna Pasiut

Appl. Sci. 2025, 15(2), 648; https://doi.org/10.3390/app15020648 - 10 Jan 2025

Abstract

In this paper, the glazes of a multicomponent system (SiO₂-Al₂O₃-CaO-MgO-Na₂O-K₂O-BaO-ZnO-ZrO₂) were examined. This work focuses on five glazes and the difference between them as the molar ratio of the alkali oxides Na₂O/K₂O. Analysis of fired glazes focused on changes in phase composition (qualitative and quantitative) microstructure performed during observations made by scanning electron microscopy (SEM). The changing molar ratios were also studied in the structure analysis based on the result of data obtained by middle infrared (MIR) and Raman spectroscopy. The characteristic temperatures of the analyzed glazes were also designated using high-stage microscopy. Surface properties such as the color and roughness of the fired glazes were measured by means of a spectrometer and confocal microscopy as well. The amount and type of crystalline phases with the molar ratio of alkali oxides in the analyzed glazes were changed. In the glazes, the crystalline phase of a solid solution of plagioclase was obtained. The results obtained indicate that glazes with a predominant potassium oxide are characterized by lower characteristic temperatures and greater surface smoothness. Structure analysis indicates a different role for the five-molar ratio of Na₂O/K₂O. Full article

(This article belongs to the Special Issue Novel Ceramic Materials: Processes, Properties and Applications)

22 pages, 11309 KiB

Open AccessArticle

Exploring Adsorption Performance of Functionalized Mesoporous Silicas with a Different Pore Structure as Strong Cation-Exchange Sorbents for Solid-Phase Extraction of Atropine and Scopolamine

by Fernando L. Vera-Baquero, Sonia Morante-Zarcero, Damián Pérez-Quintanilla and Isabel Sierra

Appl. Sci. 2025, 15(2), 646; https://doi.org/10.3390/app15020646 - 10 Jan 2025

Abstract

In this work, mesoporous silicas with two types of mesoporous structures were synthesized and functionalized with sulfonic acid groups: MCM-41-SO₃H (honeycomb-like hexagonal structure) and MSU-2-SO₃H (three-dimensional porous structure with wormhole pores). The synthesized materials were characterized by X-ray diffraction, transmission electron microscopy, scanning electron microscopy, nitrogen adsorption–desorption, Fourier-transform infrared spectroscopy, ²⁹Si solid-state nuclear magnetic resonance spectroscopy, and elemental analysis. The obtained functionalized materials were evaluated as sorbents for strong cation-exchange solid-phase extraction (SPE) to determine their efficiency in the adsorption and desorption of tropane alkaloids (atropine and scopolamine). The loading solvents, loading volume, analyte concentration, and elution volume were studied, using 50 mg of both materials. Analyses were carried out by ultra-high performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry. The synthesized MCM-41-SO₃H material presented the highest recovery efficiency and has proven to be a promising sorbent for strong cation-exchange SPE of atropine and scopolamine in aqueous media. The high degree of functionalization of MCM-41-SO₃H and the high accessibility of the sulfonic groups for the target analytes, due to the regularity and uniformity of their pores, maximize the contact between the alkaloids and the sorbent, favoring efficient adsorption. Full article

(This article belongs to the Special Issue New Trends in Functional and Multifunctional Advanced Materials)

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18 pages, 2159 KiB

Open AccessArticle

Influence of the Thermoplastic Fiber Ratio on the Mechanical Properties of Recycled Carbon Fibers During the Carding Process

by Jean Ivars, Ahmad Rashed Labanieh and Damien Soulat

Materials 2025, 18(2), 302; https://doi.org/10.3390/ma18020302 - 10 Jan 2025

Abstract

This study investigates the impact of carding and blending recycled carbon fibers (rCF) with crimped thermoplastic polypropylene (PP) fibers on the mechanical properties of rCF, using a Weibull statistical approach. Tensile properties of rCF were evaluated before and after carding with varying rCF/PP blend ratios (100/0%, 85/15%, 70/30%, and 50/50%). A comparison between the two-parameter and three-parameter Weibull models showed that the two-parameter model provided a better fit for rCF properties before carding. The results show that adding crimped PP fibers during carding helps to decrease the stress-at-break disparity and move their distribution to higher values. Furthermore, a slight increase in tensile modulus was observed in carded rCF, with higher PP ratios associated with smaller scatter modulus distributions. Elongation at break remained consistent, with the Weibull modulus increasing slightly with carding and the inclusion of PP fibers, indicating improved consistency. Overall, carding rCF with PP fibers helped in the mechanical property uniformity of the resulting carded webs without compromising tensile performance. This work shows the potential of the carding process with or without thermoplastic fibers to efficiently realign and give continuity to discontinuous recycled carbon fibers. Full article

(This article belongs to the Topic Advanced Composites Manufacturing and Plastics Processing)

22 pages, 1648 KiB

Open AccessArticle

Hake Fish Preservation Using Plant-Based Impregnated Polylactic Acid Food Films as Active Packaging

by Fini Sánchez-García, Noelia D. Machado, María Tirado-Fernández, Cristina Cejudo-Bastante, Ana M. Roldán, Casimiro Mantell-Serrano and Lourdes Casas-Cardoso

Appl. Sci. 2025, 15(2), 643; https://doi.org/10.3390/app15020643 - 10 Jan 2025

Abstract

Global fish consumption has steadily increased; however, fishery products are difficult to preserve. Active packaging has emerged as an alternative to improve its conservation. In this work, fresh hake fillets were packaged in commercial polylactic acid films impregnated with olive leaf extract using supercritical CO₂. The impregnation was performed at 35 °C and 400 bar for 1 h. The ABTS assay was used to determine the antioxidant activity, and migration tests were performed using food simulants A and D2 for 10 days at 5 °C. The fresh fillets were packaged in impregnated and control films and stored for 12 days at 4 °C. The microbiological, physical (drip loss, a_w, pH, and color) and chemical parameters (total volatile base and trimethylamine) were analyzed. The impregnated films presented a 706 μg extract mg⁻¹ polymer, showing a 2-fold extract release using food simulant D2 than simulant A. After hake storage using impregnated films, reduced microbial count, and drip loss, maintaining the pH stability was obtained. The color turned yellowish and no detectable olfactory presence of the extract was noted. The chemical parameters were similar in both types of films. The proposed biodegradable packaging with olive by-products preserves moisture and controls microbial growth, representing an eco-friendly alternative. Full article

(This article belongs to the Special Issue Applications of Analytical Chemistry in Food Science)

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30 pages, 1888 KiB

Open AccessArticle

Non-Stoichiometric Ba_xMn_0.7Cu_0.3O₃ Perovskites as Catalysts for CO Oxidation: Optimizing the Ba Content

by Álvaro Díaz-Verde, Emerson Luiz dos Santos Veiga, Héctor Beltrán-Mir, María José Illán-Gómez and Eloísa Cordoncillo-Cordoncillo

Nanomaterials 2025, 15(2), 103; https://doi.org/10.3390/nano15020103 - 10 Jan 2025

Abstract

In this work, a series of Ba_xMn_0.7Cu_0.3O₃ samples (x: 1, 0.9, 0.8, and 0.7, BxMC) was synthesized, characterized, and used as catalysts for CO oxidation reaction. All formulations were active for CO oxidation in the tested conditions. A correlation between the electrical conductivity, obtained by impedance spectroscopy, and the reducibility of the samples, obtained by H₂-TPR, was observed. The Ba_0.8Mn_0.7Cu_0.3O₃ composition (B0.8MC) showed the best catalytic performance (comparable to that of the 1% Pt/Al₂O₃ reference sample) during tests conducted under conditions similar to those found in the exhaust gases of current gasoline engines. The characterization data suggest the simultaneous presence of a high Mn(IV)/Mn(III) surface ratio, oxygen vacancies, and reduced copper species, these two latter being key properties for ensuring a high CO conversion percentage as both are active sites for CO oxidation. The reaction temperature and the reactant atmosphere composition seem to be the most important factors for achieving a good catalytic performance, as they strongly determine the location and stability of the reduced copper species. Full article

(This article belongs to the Section Energy and Catalysis)

20 pages, 973 KiB

Open AccessArticle

A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm

by Pratap Singh, Nitin Mittal, Vikas Mittal, Tapankumar Trivedi, Ashish Singh, Szymon Łukasik and Rohit Salgotra

Mathematics 2025, 13(2), 224; https://doi.org/10.3390/math13020224 - 10 Jan 2025

Abstract

Magnetic induction (MI)-operated wireless sensor networks (WSNs), due to their similar performance in air, underwater, and underground mediums, are rapidly emerging networks that offer a wide range of applications, including mine prevention, power grid maintenance, underground pipeline monitoring, and upstream oil monitoring. MI-based wireless underground sensor networks (WUSNs), utilizing small antenna coils, offer a viable solution by providing consistent channel conditions. The cross-layer protocols address the specific challenges of WUSNs, leading to improved network performance and enhanced operational capabilities in real-world applications. This work proposes a distributed cross-layer solution, leveraging the hybrid marine predator naked mole rat algorithm (MPNMRA) for MI-operated WUSNs. The solution, called DECMN (distributed energy-throughput efficient cross-layer network using MPNMRA), is designed to optimize the MI communication channels, MI relay coils (MI waveguide), and MI waveguide with 3D coils to fulfill quality of service (QoS) parameters, while achieving energy savings and throughput gains. DECMN utilizes the interactions between various layers to develop cross-layer protocols based on MPNMRA. Simulation results demonstrate the effectiveness of DECMN, offering energy savings, increased throughput, and reliable transmissions within the performance limits. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 3rd Edition)

19 pages, 4686 KiB

Open AccessArticle

Comparison of Aging Performances and Mechanisms: Super-Durable Fire-Resistant “Xuan Paper” Versus Chinese Traditional Xuan Paper

by Li-Ying Dong, Ying-Jie Zhu, Jin Wu and Han-Ping Yu

Molecules 2025, 30(2), 263; https://doi.org/10.3390/molecules30020263 - 10 Jan 2025

Abstract

Paper is a thin nonwoven material made from cellulose fibers as the main raw material together with some additives. Paper is highly flammable, leading to the destruction of countless precious ancient books, documents, and art works in fire disasters. In recent years, researchers have made a lot of efforts in order to obtain more durable and fire-retardant paper. Owing to the successful synthesis of ultralong hydroxyapatite (HAP) nanowires as a new kind of inorganic nanofiber material, it becomes possible to develop a new kind of super-durable and fire-resistant paper. Recently, the authors’ research group prepared a new kind of fire-resistant “Xuan paper” consisting of ultralong HAP nanowires. In this article, the super-durable fire-resistant “Xuan paper” based on ultralong HAP nanowires and the traditional Xuan paper based on cellulose fibers were evaluated by the accelerated aging method for 1200 days at 105 °C in air, which is the equivalent of 10,000 years of natural aging in the ambient environment. The aging mechanism of the traditional Xuan paper was further investigated by studying the fiber length/width and their distributions, morphology, infrared spectroscopy, thermogravimetric analysis, H–nuclear magnetic resonance spectra, and C–nuclear magnetic resonance spectra of cellulose fibers before and after the accelerated aging. The durability, properties, and mechanism of the fire-resistant “Xuan paper” based on ultralong HAP nanowires during the accelerated aging were studied. The experiments reveal the reasons for the deteriorated properties and reduced durability by aging of the traditional Xuan paper based on cellulose fibers, and the mechanism for the super-durability and excellent performances of the fire-resistant “Xuan paper” based on ultralong HAP nanowires during the accelerated aging process. Full article

(This article belongs to the Section Nanochemistry)

11 pages, 4553 KiB

Open AccessArticle

Safety Autonomous Platform for Data-Driven Risk Management Based on an On-Site AI Engine in the Electric Power Industry

by Dongyeop Lee, Daesik Lim and Joonwon Lee

Appl. Sci. 2025, 15(2), 630; https://doi.org/10.3390/app15020630 - 10 Jan 2025

Abstract

The electric power industry poses significant risks to workers with a wide range of hazards such as electrocution, electric shock, burns, and falls. Regardless of the types and characteristics of these hazards, electric power companies should protect their workers and provide a safe and healthy working environment, but it is difficult to identify the potential health and safety risks present in their workplace and take appropriate action to keep their workers free from harm. Therefore, this paper proposes a novel safety autonomous platform (SAP) for data-driven risk management in the electric power industry. It can automatically and precisely provide a safe and healthy working environment with the cooperation of safety mobility gateways (SMGs) according to the safety rule and risk index data created by the risk level of a current task, a worker profile, and the output of an on-site artificial intelligence (AI) engine in the SMGs. We practically implemented the proposed SAP architecture using the Hadoop ecosystem and verified its feasibility through a performance evaluation of the on-site AI engine and real-time operation of risk assessment and alarm notification for data-driven risk management. Full article

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Safety autonomous platform architecture and its overall cooperation with safety mobility gateways on providing data-driven risk management. Full article ">Figure 2
Data-driven risk management procedure among the safety autonomous platform, safety mobility gateways, supervisors, and workers. Full article ">Figure 3
On-site AI engine with multimodal ensemble models for risk assessment and alarm notification in safety mobility gateways (SMGs). Full article ">Figure 4
Estimation of the risk index with the safety rule considering the risk level of a current task, a worker profile, the output of an ensemble model in an on-site AI engine, etc. Full article ">Figure 5
Experimental results of facial detection with the customized Dlib/CNN model (upper) and anomaly detection of biometric index (BI) data with the customized TadGAN model (lower) that can be applied to ensemble models in an on-site AI engine. Full article ">Figure 6
Experimental results of object detection with the customized YOLOv7 model applied to ensemble models in an on-site AI engine. Full article ">Figure 7
Experimental results of data-driven risk management (assessment and alarm notification) between the safety autonomous platform (left) and the supervisor with a tablet PC (right). Full article ">

20 pages, 10708 KiB

Open AccessArticle

Evaluation of 3D Models of Archaeological Remains of Almenara Castle Using Two UAVs with Different Navigation Systems

by Juan López-Herrera, Serafín López-Cuervo, Enrique Pérez-Martín, Miguel Ángel Maté-González, Consuelo Vara Izquierdo, José Martínez Peñarroya and Tomás R. Herrero-Tejedor

Heritage 2025, 8(1), 22; https://doi.org/10.3390/heritage8010022 - 10 Jan 2025

Abstract

Improvements in the navigation systems incorporated into unmanned aerial vehicles (UAVs) and new sensors are improving the quality of 3D mapping results. In this study, two flights were compared over the archaeological remains of the castle of Almenara, situated in Cuenca, Spain. We performed one with a DJI Phantom 4 (DJI Innovations Co., Ltd., Shenzhen, China) and the other with a Matrice 300 RTK (DJI Innovations Co., Ltd., Shenzhen, China) and the new Zenmuse P1 camera (45 mp, RGB sensor). With the help of the new software incorporated into the Zenmuse P1 camera gimbal, we could significantly reduce the flight time. We analysed the data obtained with these two UAVs and the built-in RGB sensors, comparing the flight time, the point cloud, and its resolution and obtaining a three-dimensional reconstruction of the castle. We describe the work and the flights carried out, depending on the type of UAV and its RTK positioning system. The improvement in the positioning system provides improvements in flight accuracy and data acquisition. We compared the results obtained in similar studies, and thanks to the advances in UAVs and their sensors with better resolution, we managed to reduce the data collection time and obtained 3D models with the same results as those from other types of sensors. The accuracies obtained with the RTK and the P1 camera are very high. The volumes calculated for a future archaeological excavation are precise, and the 3D models obtained by these means are excellent for the preservation of the cultural asset. These models can have various uses, such as the preservation of an asset of cultural interest, or even its dissemination and analysis in various studies. We propose to use this technology for similar studies of archaeological documentation and the three-dimensional reconstruction and visualisation of cultural heritage in virtual visits on the web. Full article

(This article belongs to the Special Issue 3D Reconstruction of Cultural Heritage and 3D Assets Utilisation)

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27 pages, 1282 KiB

Open AccessArticle

Plucking Point and Posture Determination of Tea Buds Based on Deep Learning

by Chengju Dong, Weibin Wu, Chongyang Han, Zhiheng Zeng, Ting Tang and Wenwei Liu

Agriculture 2025, 15(2), 144; https://doi.org/10.3390/agriculture15020144 - 10 Jan 2025

Abstract

Tea is a significant cash crop grown widely around the world. Currently, tea plucking predominantly relies on manual work. However, due to the aging population and increasing labor costs, machine plucking has become an important trend in the tea industry. The determination of the plucking position and plucking posture is a critical prerequisite for machine plucking tea leaves. In order to improve the accuracy and efficiency of machine plucking tea leaves, a method is presented in this paper to determine the plucking point and plucking posture based on the instance segmentation deep learning network. In this study, tea images in the dataset were first labeled using the Labelme software (version 4.5.13), and then the LDS-YOLOv8-seg model was proposed to identify the tea bud region and plucking area. The plucking points and the central points of the tea bud’s bounding box were calculated and matched as pairs using the nearest point method (NPM) and the point in range method (PIRM) proposed in this study. Finally, the plucking posture was obtained according to the results of the feature points matching. The matching results on the test dataset show that the PIRM has superior performance, with a matching accuracy of 99.229% and an average matching time of 2.363 milliseconds. In addition, failure cases of feature points matching in the plucking posture determination process were also analyzed in this study. The test results show that the plucking position and posture determination method proposed in this paper is feasible for machine plucking tea. Full article

(This article belongs to the Topic Intelligent Agriculture: Perception Technologies and Agricultural Equipment for Crop Production Processes)

14 pages, 4009 KiB

Open AccessArticle

Synergistic Integration of Mesocarbon Microbeads, Graphitic Nanofibers, and Mesoporous Carbon for Advanced Supercapacitor Electrodes

by Palanisamy Rajkumar, Vediyappan Thirumal, Kisoro Yoo and Jinho Kim

Crystals 2025, 15(1), 64; https://doi.org/10.3390/cryst15010064 - 10 Jan 2025

Abstract

In this study, a novel multiscale carbon architecture was developed by integrating mesocarbon microbeads (MCMBs), graphitic nanofibers (GNFs), and mesoporous carbon, aimed at enhancing the performance of symmetric supercapacitors. The unique combination of spherical MCMB particles, conductive GNF nanofibers, and mesoporous carbon sheets resulted in a highly effective electrode material, offering improved conductivity, increased active sites for charge storage, and enhanced structural stability. The fabricated MCMB/GNF/MC architecture demonstrated a remarkable specific capacitance of 393 F g⁻¹ at 1 A g⁻¹ in a three-electrode system, significantly surpassing the performance of individual MCMBs and MCMB/GNF electrodes. Furthermore, the architecture was incorporated into a symmetric supercapacitor (SSC) device, where it achieved a capacitance of 86 F g⁻¹ at 1 A g⁻¹. The device exhibited excellent cycling stability, retaining 92% of its initial capacitance after 10,000 charge–discharge cycles, with an outstanding coulombic efficiency of 99%. At optimal operating conditions, the SSC device delivered an energy density of 11 Wh kg⁻¹ at a power density of 500 W kg⁻¹, making it a promising candidate for high-performance energy-storage applications. This multiscale carbon architecture represents a significant advancement in the design of electrode materials for symmetric supercapacitors, offering a balance of high energy and power density, long-term stability, and excellent scalability for practical applications. This work not only contributes to the development of high-performance electrode materials but also paves the way for scalable, long-lasting supercapacitors for future energy-storage technologies. Full article

(This article belongs to the Special Issue Feature Papers on "Hybrid and Composite Crystalline Materials" 2023–2024)

13 pages, 7720 KiB

Open AccessArticle

Calculation of the Optimal Magnetic Duty Cycle for a Graded Coaxial Magnet of a Rotary Type Magnetic Refrigerator

by Chih-Hao Lee, Pai-Hsiang Cheng, Keh-Chyang Leou, Chih-Ming Hsieh and Yu-Chuan Su

Energies 2025, 18(2), 289; https://doi.org/10.3390/en18020289 - 10 Jan 2025

Abstract

In the design of a rotary-type magnetic refrigerator, a high field of a coaxial magnet is desired. Typically, a high-field design can be achieved with a small duty cycle, which might not be optimized from the viewpoint of the thermal hydraulics of a magnetic refrigerator. In this work, a numerical simulation analysis of a graded coaxial magnet designed using a COMSOL program for a rotary-type active magnetic refrigeration (AMR) system was performed. The magnet structures are based on neodymium–iron–boron permanent magnets with thin gadolinium (Gd) and gadolinium-terbium alloy (Gd-Tb) plates as AMR materials. For a rotary-type magnetic cooling system, from the thermal–hydraulic point of view, the best duty cycle of a coaxial magnet should be 50% if the magnetic field can be kept constant during the period of duty cycles. However, the simulation calculation shows a serious reduction in the magnetic field strength at higher duty cycles, resulting in lower magnetic cooling efficiency. After considering the thermos-hydraulic part, the optimized duty cycle is around 30% in the case of a temperature span of 8 K between the hot and cold ends on a rotary-type magnetic cooling system. By applying graded Gd-Tb alloy along the flow direction, the performance of magnetic refrigeration improves significantly. Compared to a pure Gd AMR system, it is demonstrated that more than three times the increase in the cooling capacity can be achieved. Full article

(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)

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19 pages, 731 KiB

Open AccessArticle

Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering

by Yunqiao Fei, Jingchao Fan and Guomin Zhou

Appl. Sci. 2025, 15(2), 628; https://doi.org/10.3390/app15020628 - 10 Jan 2025

Abstract

In China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of professional knowledge that represent the cutting-edge progress in fruit disease research. Traditional knowledge engineering methods for knowledge acquisition require extensive and cumbersome preparatory work, and they demand a high level of professional background and information technology skills from the handlers. This paper, from the perspective of fruit tree industry knowledge dissemination, aims at users such as fruit farmers, fruit tree experts, fruit tree knowledge communicators, and information gatherers. It proposes a fast, cost-effective, and low-technical-barrier method for extracting fruit tree disease knowledge from research paper abstracts—K-Extract, based on large language models (LLMs) and prompt engineering. Under zero-shot conditions, K-Extract utilizes conversational LLMs to automate the extraction of fruit tree disease knowledge. The K-Extract method has constructed a comprehensive classification system for fruit tree diseases and, through a series of optimized prompt questions, effectively overcomes the deficiencies of LLM models in providing factual accuracy. This paper tests multiple LLM models available in the Chinese market, and the results show that K-Extract can seamlessly integrate with any conversational LLM model, with the DeepSeek model and the Kimi model performing particularly well. The experimental results indicate that LLM models have a high accuracy rate in handling judgment tasks and simple knowledge Q&A tasks. The K-Extract method is simple, efficient, and accurate, and can serve as a convenient tool for knowledge extraction in the agricultural field. Full article

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Complete workflow diagram of the K-Extract method. Full article ">Figure 2
Knowledge extraction process. Full article ">Figure 3
Accuracy of the five large language models. Full article ">Figure 4
Precision of the five large language models. Full article ">

27 pages, 4655 KiB

Open AccessArticle

Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization

by Emad A. Abood, Mustafa Kamal Al-Kamal, Sabih Hashim Muhodir, Nadia Moneem Al-Abdaly, Luís Filipe Almeida Bernardo, Dario De Domenico and Hamza Imran

Buildings 2025, 15(2), 191; https://doi.org/10.3390/buildings15020191 - 10 Jan 2025

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Abstract

In structures with reinforced concrete walls, coupling beams join individual walls to produce a rigid assembly that withstands sideways forces. A precise forecasting of the critical shear capacity is essential to avoid early shear failure and attain the desired ductility performance of coupled shear wall systems in earthquake design. This paper examines the ability of Support Vector Regression (SVR) in predicting the shear performance of coupling beams. SVR is a distinguished machine learning regression method that has been positively utilized in former works to forecast the performance of several structural members. Nevertheless, the capability of this regression method deeply relies on picking its best hyperparameters. To handle this, a heuristic optimization procedure named Particle Swarm Optimization (PSO) was merged with SVR to select the optimal hyperparameters. The data of RC coupling beams collected from the previous works were utilized to build the proposed model. Several performance metrics, including RMSE, R2, and MAE, were employed to compare the performance of the optimized model against a baseline SVR model and previous approaches. Analytical results indicate that the new optimized prediction model can assist civil engineers in designing RC coupling beam structures more effectively and outperforms existing models in predicting the shear strength of such beams. Full article

(This article belongs to the Section Building Structures)

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