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The shift from traditional on-site to off-site construction marks a significant evolution in the construction industry, characterized by increasing levels of prefabrication. These advancements enhance construction efficiency, reduce lead times, and mitigate environmental impacts, leading to modular integrated construction (MiC). However, MiC presents complex supply chain challenges, particularly in the transportation of prefabricated components and fully integrated modules. This study addresses these challenges by employing a multi-agent simulation using AnyLogic to optimize MiC transport logistics. The simulation models the interactions of various agents involved in the MiC process to improve operational efficiency and reduce costs. Results demonstrate that using three vehicles per supplier minimizes total transport costs, effectively balancing fixed and variable expenses while eliminating penalties for project delays. The findings highlight the cost efficiency of MiC, showing potential savings due to centralized assembly and optimized logistics. These significantly reduce material transportation and related costs, contributing to the overall efficiency and sustainability of construction projects. These insights underscore the value of multi-agent simulation in addressing the complexities of MiC supply chains. Full article

(This article belongs to the Special Issue Application of Building Information Modeling in Construction Management)

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19 pages, 13369 KiB

Open AccessArticle

Advancing Vehicle Technology Exploration with an Open-Source Simulink Model Featuring Commercial Truck Solutions

by Chi-Jui Peng, Yi-Ting Liu and Kuei-Yuan Chan

Vehicles 2024, 6(2), 1008-1026; https://doi.org/10.3390/vehicles6020048 (registering DOI) - 19 Jun 2024

Abstract

In response to the EU’s stringent zero-carbon emission standards for 2035 and global initiatives to phase out fossil-fuel-powered vehicles, there is an urgent need for innovative solutions in vehicle propulsion systems. While much of the current research focuses on electric passenger cars, commercial vehicles remain relatively underexplored despite their significant potential impact on carbon neutrality goals. This study presents an open-source Simulink model specifically tailored for the analysis of electric commercial trucks, concentrating on the 6.5-ton category. Developed to assess the influence of various power components and control strategies on driving range, the model incorporates three validated powertrain configurations and features such as regenerative braking and one-pedal drive. Simulations are conducted under two real-world driving scenarios in the city of Taipei in Taiwan to evaluate different configurations’ effects on energy consumption and efficiency. Results indicate that optimizing the vehicle configuration can reduce power consumption by 26.3% and extend driving range by an additional 25.1 km on a single battery charge. By making the model and its source code publicly available, this research not only fills a critical gap in specialized evaluation tools for electric commercial vehicles but also serves as a valuable resource for both industrial assessments and educational purposes in the field of vehicle electrification. Full article

(This article belongs to the Special Issue Feature Papers on Advanced Vehicle Technologies)

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11 pages, 4736 KiB

Open AccessArticle

Study on Formability Improvement of Zr-4 Sheets Based on Texture Optimization

by Huan Liu, Hong-Wu Song, Si-Ying Deng, Shuai-Feng Chen and Shi-Hong Zhang

Metals 2024, 14(6), 725; https://doi.org/10.3390/met14060725 (registering DOI) - 19 Jun 2024

Abstract

A positioning grid is a key clamping structure for fixing the transverse and axial positions of fuel assemblies in nuclear reactors, and it is generally prepared by the transverse stamping of a Zr-4 sheet. However, the texture formed in the processing process of Zr-4 sheets can affect formability, resulting in cracking in the stamping process. Therefore, the relationship between the formability of Zr-4 sheets and the normal Kearns factor (Fn) of basal texture was studied in this paper. The results showed that the Zr-4 sheet with an Fn equaling 0.720, prepared by an isobaric reduction rolling process, would crack in the stamping process. To avoid the cracking during stamping, the formability improvement of Zr-4 sheets based on texture optimization was discussed. By using the finite element model (FEM) and a visco plastic self-consistent (VPSC) model coupled simulation, the relationship between the initial textures and formabilities of Zr-4 sheet is established. It is found that the hardening exponents (n) decreased with increasing Fns in VPSC simulations. Meanwhile, as the Fn increases, cracks are prone to occur at the bottom corner of the stamped sheet in finite element simulation. Given the results from FEM and VPSC simulations, it is proposed that the Fn should be controlled to be less than 0.7 for preventing cracks in the sheet during stamping. Additionally, a new rolling process named non-isobaric reduction rolling was designed in which the Fn of the Zr-4 sheet is successfully reduced to 0.690. The stamping results indicate that the sheet is free of cracks under an Fn of 0.690. Therefore, texture optimization with the proposed rolling process can improve the formability of Zr-4 sheets, which effectively solves the cracking problem of Zr-4 sheets. Full article

(This article belongs to the Section Metal Casting, Forming and Heat Treatment)

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22 pages, 5808 KiB

Open AccessReview

Lubricating Polymer Gels/Coatings: Syntheses and Measurement Strategies

by Panpan Zhao and Jacob Klein

Gels 2024, 10(6), 407; https://doi.org/10.3390/gels10060407 (registering DOI) - 19 Jun 2024

Abstract

Straightforward design and long-term functionality for tribological considerations has prompted an extensive substitution of polymers for metals across various applications, from industrial machinery to medical devices. Lubrication of and by polymer gels/coatings, essential for ensuring the cost-effective operation and reliability of applications, has gained strong momentum by benefiting from the structural characteristics of natural lubrication systems (such as articular cartilage). The optimal synthetic strategy for lubricating polymer gels/coatings would be a holistic approach, wherein the lubrication mechanism in relation to the structural properties offers a pathway to design tailor-made materials. This review considers recent synthesis strategies for creating lubricating polymer gels/coatings from the molecular level (including polymer brushes, loops, microgels, and hydrogels), and assessing their frictional properties, as well as considering the underlying mechanism of their lubrication. Full article

(This article belongs to the Special Issue Hydrogel Surface/Coating for Smart Drug Delivery and Medical Devices)

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16 pages, 2858 KiB

Open AccessArticle

Robot Learning Method for Human-like Arm Skills Based on the Hybrid Primitive Framework

by Jiaxin Li, Hasiaoqier Han, Jinxin Hu, Junwei Lin and Peiyi Li

Sensors 2024, 24(12), 3964; https://doi.org/10.3390/s24123964 (registering DOI) - 19 Jun 2024

Abstract

This paper addresses the issue of how to endow robots with motion skills, flexibility, and adaptability similar to human arms. It innovatively proposes a hybrid-primitive-frame-based robot skill learning algorithm and utilizes the policy improvement with a path integral algorithm to optimize the parameters of the hybrid primitive framework, enabling robots to possess skills similar to human arms. Firstly, the end of the robot is dynamically modeled using an admittance control model to give the robot flexibility. Secondly, the dynamic movement primitives are employed to model the robot’s motion trajectory. Additionally, novel stiffness primitives and damping primitives are introduced to model the stiffness and damping parameters in the impedance model. The combination of the dynamic movement primitives, stiffness primitives, and damping primitives is called the hybrid primitive framework. Simulated experiments are designed to validate the effectiveness of the hybrid-primitive-frame-based robot skill learning algorithm, including point-to-point motion under external force disturbance and trajectory tracking under variable stiffness conditions. Full article

(This article belongs to the Section Sensors and Robotics)

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17 pages, 10625 KiB

Open AccessArticle

A Predictive Model for Voltage Transformer Ratio Error Considering Load Variations

by Zhenhua Li, Jiuxi Cui, Paulo R. F. Rocha, Ahmed Abu-Siada, Hongbin Li and Li Qiu

World Electr. Veh. J. 2024, 15(6), 269; https://doi.org/10.3390/wevj15060269 (registering DOI) - 19 Jun 2024

Abstract

The accuracy of voltage transformer (VT) measurements is imperative for the security and reliability of power systems and the equitability of energy transactions. The integration of a substantial number of electric vehicles (EVs) and their charging infrastructures into the grid poses new challenges for VT measurement fidelity, including voltage instabilities and harmonic disruptions. This paper introduces an innovative transformer measurement error prediction model that synthesizes Multivariate Variational Mode Decomposition (MVMD) with a deep learning framework integrating Bidirectional Temporal Convolutional Network and Multi-Head Attention mechanism (BiTCN-MHA). The paper is aimed at enhancing VT measurement accuracy under fluctuating load conditions. Initially, the optimization of parameter selection within the MVMD algorithm enhances the accuracy and interpretability of bi-channel signal decomposition. Subsequently, the model applies the Spearman rank correlation coefficient to extract dominant modal components from both the decomposed load and original ratio error sequences to form the basis for input signal channels in the BiTCN-MHA model. By superimposing predictive components, an effective prediction of future VT measurement error trends can be achieved. This comprehensive approach, accounting for input load correlations and temporal dynamics, facilitates robust predictions of future VT measurement error trends. Computational example analysis of empirical operational VT data shows that, compared to before decomposition, the proposed method reduces the Root-Mean-Square Error (RMSE) by 17.9% and the Mean Absolute Error (MAE) by 23.2%, confirming the method’s robustness and superiority in accurately forecasting VT measurement error trends. Full article

(This article belongs to the Topic Modern Power Systems and Units)

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15 pages, 8343 KiB

Open AccessArticle

Structural Behavior of High Durability FRP Helical Screw Piles Installed in Reclaimed Saline Land

by Sun-Hee Kim, Hyung-Joong Joo and Wonchang Choi

Polymers 2024, 16(12), 1733; https://doi.org/10.3390/polym16121733 (registering DOI) - 19 Jun 2024

Abstract

The bearing capacity of fiber-reinforced plastic (FRP) helical screw piles is determined by the lesser of the breaking load at the bolted joint and the resistance provided by the screw tip area. In this study, compression and tensile tests were performed with the number of bolts and edge distance as variables. It showed similar strength when compared to the failure stress derived from material testing. In addition, considering load resistance performance, the optimal screw cross section was obtained through parametric analysis. Considering the structural behavior of the screw, a prediction equation was presented to design the screw cross-section as a tapered cross-section using a theoretical method. As a result of comparing the screw cross-section with the finite element analysis results, it was confirmed that the design stress and analysis stress showed an error of 1.1 MPa and were within the allowable stress of 80 MPa. Full article

(This article belongs to the Special Issue Advances in Reinforced Polymer Materials for Additive Manufacturing, Product Design, Processing and Their Applications)

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12 pages, 1118 KiB

Open AccessArticle

Bifenthrin Residues in Table Grapevine: Method Optimization, Dissipation and Removal of Residues in Grapes and Grape Leaves

by Saleh S. Alhewairini, Rania M. Abd El-Hamid, Nevein S. Ahmed, Sherif B. Abdel Ghani and Osama I. Abdallah

Plants 2024, 13(12), 1695; https://doi.org/10.3390/plants13121695 (registering DOI) - 19 Jun 2024

Abstract

The QuEChERS method was adjusted to determine bifenthrin residues in grapes and grape leaves. Extraction and cleanup procedures were optimized to decrease co-extracted materials and enhance the detection of bifenthrin. The method was validated per the European Union (EU) Guidelines criteria. Accuracy ranged from 98.8% to 93.5% for grapes and grape leaves, respectively. Precision values were 5.5 and 6.4 (RSDr) and 7.4 and 6.7 (RSD_R) for grapes and grape leaves, respectively. LOQs (the lowest spiking level) were 2 and 20 µg/kg for grapes and grape leaves, respectively. Linearity as determination coefficient (R²) values were 0.9997 and 0.9964 for grapes and grape leaves, respectively, in a matrix over 1–100 µg/L range of analyte concentration. This was very close to the value in the pure solvent (0.9999), showing the efficiency of the cleanup in removing the co-extracted and co-injected materials; the matrix effect was close to zero in both sample matrices. Dissipation of bifenthrin was studied in a supervised trial conducted in a grapevine field during the summer of 2023 at the recommended dose and double the dose. Dissipation factor k values were 0.1549 and 0.1672 (recommended dose) and 0.235 and 0.208 (double dose) for grapes and grape leaves, respectively. Pre-harvest interval (PHI) was calculated for the Maximum Residue Limit (MRL) values of the EU database. Residues of bifenthrin were removed effectively from grapes using simple washing with tap water in a laboratory study. Residues reached the MRL level of 0.3 mg/kg in both washing treatments, running or soaking in tap water treatments for 5 min. Removal from leaves did not decrease residue levels to the MRL in grape leaves. Full article

(This article belongs to the Special Issue Pesticide Residues in Plants)

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12 pages, 3347 KiB

Open AccessArticle

Impact of the Fly Ash/Alkaline Activator Ratio on the Microstructure and Dielectric Properties of Fly Ash KOH-Based Geopolymer

by Meenakshi Yadav, Neha Saini, Lalit Kumar, Vidya Nand Singh, Karthikeyan Jagannathan and V. Ezhilselvi

CivilEng 2024, 5(2), 537-548; https://doi.org/10.3390/civileng5020028 (registering DOI) - 19 Jun 2024

Abstract

Geopolymer materials, alternatives to cement that are synthesized using industrial byproducts, have emerged as some of the leading champion materials due to their environmentally friendly attributes. They can significantly reduce pollution by utilizing a plethora of waste products and conserving natural resources that would otherwise be used in the production of conventional cement. Much work is being carried out to study geopolymers’ characteristics under different conditions. Here, a geopolymer derived from fly ash (FA) was synthesized using a combination of sodium silicate and potassium hydroxide (KOH) (2.5:1 ratio) as an alkali activator (AA) liquid. The FA/AA ratios were optimized, resulting in distinct geopolymer samples with ratios of 1.00, 1.25, 1.50, and 1.75. By adjusting the contribution of alkaline liquid, we investigated the impacts of subtle changes in the FA/AA ratio on the morphology and microstructure using X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM) techniques. The FESEM analysis illustrated a mixed matrix and morphology, with the sample with a ratio of 1.00 displaying consistently fused and homogenous morphology. The XRD results revealed the prevalent amorphous nature of geopolymer with a few crystalline phases of quartz, sodalite, hematite, and mullite. An electrical study confirmed the insulating nature of the geopolymer samples. Insulating geopolymers can provide energy-efficient buildings and resistance to fire, hurricanes, and tornadoes. Additionally, using KOH as a part of the alkali activator introduced a less-explored aspect compared to conventional sodium hydroxide-based activators, highlighting the novelty in the synthesis process. Full article

(This article belongs to the Collection Recent Advances and Development in Civil Engineering)

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17 pages, 885 KiB

Open AccessArticle

SSA-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

by Fei Wang, Yinxi Liang, Zhizhe Lin, Jinglin Zhou and Teng Zhou

Mathematics 2024, 12(12), 1895; https://doi.org/10.3390/math12121895 (registering DOI) - 19 Jun 2024

Abstract

Nowadays, accurate and efficient short-term traffic flow forecasting plays a critical role in intelligent transportation systems (ITS). However, due to the fact that traffic flow is susceptible to factors such as weather and road conditions, traffic flow data tend to exhibit dynamic uncertainty and nonlinearity, making the construction of a robust and reliable forecasting model still a challenging task. Aiming at this nonlinear and complex traffic flow forecasting problem, this paper constructs a short-term traffic flow forecasting hybrid optimization model, SSA-ELM, based on extreme learning machine by embedding the sparrow search algorithm in order to solve the above problem. Extreme learning machine has been widely used in short-term traffic flow forecasting due to its characteristics such as low computational complexity and fast learning speed. By using the sparrow search algorithm to optimize the input weight values and hidden layer deviations in the extreme learning machine, the sparrow search algorithm is utilized to search for the global optimal solution while taking into account the original characteristics of the extreme learning machine, so that the model improves stability while increasing prediction accuracy. Experimental results on the Amsterdam A10 road traffic flow dataset show that the traffic flow forecasting model proposed in this paper has higher forecasting accuracy and stability, revealing the potential of hybrid optimization models in the field of short-term traffic flow forecasting. Full article

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9 pages, 2649 KiB

Open AccessOpinion

Keep Fingers on the CpG Islands

by Xing Zhang, Robert M. Blumenthal and Xiaodong Cheng

Epigenomes 2024, 8(2), 23; https://doi.org/10.3390/epigenomes8020023 (registering DOI) - 19 Jun 2024

Abstract

The post-genomic era has ushered in the extensive application of epigenetic editing tools, allowing for precise alterations of gene expression. The use of reprogrammable editors that carry transcriptional corepressors has significant potential for long-term epigenetic silencing for the treatment of human diseases. The ideal scenario involves precise targeting of a specific genomic location by a DNA-binding domain, ensuring there are no off-target effects and that the process yields no genetic remnants aside from specific epigenetic modifications (i.e., DNA methylation). A notable example is a recent study on the mouse Pcsk9 gene, crucial for cholesterol regulation and expressed in hepatocytes, which identified synthetic zinc-finger (ZF) proteins as the most effective DNA-binding editors for silencing Pcsk9 efficiently, specifically, and persistently. This discussion focuses on enhancing the specificity of ZF-array DNA binding by optimizing interactions between specific amino acids and DNA bases across three promoters containing CpG islands. Full article

(This article belongs to the Special Issue A Commemorative Issue in Honor of the 30th Anniversary of the Epigenetics Society)

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23 pages, 5350 KiB

Open AccessArticle

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

by Akmalbek Abdusalomov, Mekhriddin Rakhimov, Jakhongir Karimberdiyev, Guzal Belalova and Young Im Cho

Bioengineering 2024, 11(6), 627; https://doi.org/10.3390/bioengineering11060627 (registering DOI) - 19 Jun 2024

Abstract

Medical imaging and deep learning models are essential to the early identification and diagnosis of brain cancers, facilitating timely intervention and improving patient outcomes. This research paper investigates the integration of YOLOv5, a state-of-the-art object detection framework, with non-local neural networks (NLNNs) to improve brain tumor detection’s robustness and accuracy. This study begins by curating a comprehensive dataset comprising brain MRI scans from various sources. To facilitate effective fusion, the YOLOv5 and NLNNs, K-means+, and spatial pyramid pooling fast+ (SPPF+) modules are integrated within a unified framework. The brain tumor dataset is used to refine the YOLOv5 model through the application of transfer learning techniques, adapting it specifically to the task of tumor detection. The results indicate that the combination of YOLOv5 and other modules results in enhanced detection capabilities in comparison to the utilization of YOLOv5 exclusively, proving recall rates of 86% and 83% respectively. Moreover, the research explores the interpretability aspect of the combined model. By visualizing the attention maps generated by the NLNNs module, the regions of interest associated with tumor presence are highlighted, aiding in the understanding and validation of the decision-making procedure of the methodology. Additionally, the impact of hyperparameters, such as NLNNs kernel size, fusion strategy, and training data augmentation, is investigated to optimize the performance of the combined model. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedicine)

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25 pages, 5138 KiB

Open AccessArticle

Game-Theory-Based Design and Analysis of a Peer-to-Peer Energy Exchange System between Multi-Solar-Hydrogen-Battery Storage Electric Vehicle Charging Stations

by Lijia Duan, Yujie Yuan, Gareth Taylor and Chun Sing Lai

Electronics 2024, 13(12), 2392; https://doi.org/10.3390/electronics13122392 (registering DOI) - 19 Jun 2024

Abstract

As subsidies for renewable energy are progressively reduced worldwide, electric vehicle charging stations (EVCSs) powered by renewable energy must adopt market-driven approaches to stay competitive. The unpredictable nature of renewable energy production poses major challenges for strategic planning. To tackle the uncertainties stemming from forecast inaccuracies of renewable energy, this study introduces a peer-to-peer (P2P) energy trading strategy based on game theory for solar-hydrogen-battery storage electric vehicle charging stations (SHS-EVCSs). Firstly, the incorporation of prediction errors in renewable energy forecasts within four SHS-EVCSs enhances the resilience and efficiency of energy management. Secondly, employing game theory’s optimization principles, this work presents a day-ahead P2P interactive energy trading model specifically designed for mitigating the variability issues associated with renewable energy sources. Thirdly, the model is converted into a mixed integer linear programming (MILP) problem through dual theory, allowing for resolution via CPLEX optimization techniques. Case study results demonstrate that the method not only increases SHS-EVCS revenue by up to 24.6% through P2P transactions but also helps manage operational and maintenance expenses, contributing to the growth of the renewable energy sector. Full article

(This article belongs to the Special Issue Hydrogen and Fuel Cells: Innovations and Challenges)

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13 pages, 5419 KiB

Open AccessArticle

Design and Test of a 2-DOF Compliant Positioning Stage with Antagonistic Piezoelectric Actuation

by Haitao Wu, Hui Tang and Yanding Qin

Machines 2024, 12(6), 420; https://doi.org/10.3390/machines12060420 (registering DOI) - 19 Jun 2024

Abstract

This paper designs a two-degrees-of-freedom (DOF) compliant positioning stage with antagonistic piezoelectric actuation. Two pairs of PEAs are arranged in an antagonistic configuration to generate reciprocating motions. Flexure mechanisms are intentionally adopted to construct the fixtures for PEAs, whose elastic deformations can help to reduce the stress concentration on the PEA caused by the extension of the PEA in the other direction. Subsequently, the parameter and performance of the 2-DOF compliant positioning stage is optimized and verified by finite element analysis. Finally, a prototype is fabricated and tested. The experimental results show that the developed positioning stage achieves a working stroke of 28.27 μm × 27.62 μm. Motion resolutions of both axes are 8 nm and natural frequencies in the working directions are up to 2018 Hz, which is promising for high-precision positioning control. Full article

(This article belongs to the Special Issue Optimization and Design of Compliant Mechanisms)

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15 pages, 3133 KiB

Open AccessArticle

Development and Validation of Ultra-Performance Liquid Chromatography (UPLC) Method for Simultaneous Quantification of Hydrochlorothiazide, Amlodipine Besylate, and Valsartan in Marketed Fixed-Dose Combination Tablet

by Doaa Hasan Alshora, Abdelrahman Y. Sherif and Mohamed Abbas Ibrahim

Processes 2024, 12(6), 1259; https://doi.org/10.3390/pr12061259 (registering DOI) - 19 Jun 2024

Abstract

Fixed-dose combination therapy is considered a practical approach in the treatment of various diseases, as it can simultaneously target different mechanisms of action that achieve the required therapeutic efficacy through a synergistic effect. A combination of hydrochlorothiazide (HTZ), amlodipine (AMD), and valsartan (VLS) has been created for the treatment of hypertension. Therefore, the aim of this study was to develop an optimized UPLC method for the simultaneous quantification of this combination. A DoE at a level of 32 was used to investigate the effects of column temperature (20, 30, and 40 °C) and formic acid concentration (0.05, 0.15, and 0.25%) on the retention time of each active pharmaceutical ingredient (API), the peak area, and the peak symmetry, as well as the resolution between HTZ-AMD and AMD-VLS peaks. The optimized analytical method was validated and used to extract the three APIs from the marketed product. The optimized analytical condition with a column temperature of 27.86 °C and a formic acid concentration of 0.172% showed good separation of the three APIs in 1.62 ± 0.006, 3.59 ± 0.002, and 3.94 ± 0.002 min for HTZ, AMD, and VST, respectively. The developed method was linear with the LOQ for a HTC, AMD, and VST of 0.028, 0.038, and 0.101 ppm, respectively. Moreover, the developed assay was sustainable and robust, with an RSD % of less than 2%. The application of this method in the extraction of HTZ, AMD, and VST from the Exforge^® marketed product showed good separation with a measurable drug content of 23.5 ± 0.7, 9.68 ± 0.1, and 165.2 ± 5.2 mg compared to the label claims of 25/10/160 for HTZ, AMD, and VST, respectively. Full article

(This article belongs to the Special Issue Applications of Chromatographic Separation Techniques in Food and Chemistry—Second Edition)

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