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Intrusion detection is essential to cybersecurity. However, the curse of dimensionality and class imbalance limit detection accuracy and impede the identification of rare attacks. To address these challenges, this paper proposes the high-dimensional feature sequence temporal convolutional network (HiSeq-TCN) for intrusion detection. The proposed HiSeq-TCN transforms high-dimensional feature vectors into pseudo-temporal sequences, enabling the network to capture contextual dependencies across feature dimensions. This enhances feature representation and detection robustness. In addition, a few-shot reinforcement strategy adaptively assigns larger loss weights to minority classes, mitigating class imbalance and improving the recognition of rare attacks. Experiments on the NSL-KDD dataset show that HiSeq-TCN achieves an overall accuracy of 99.44%, outperforming support vector machines, deep neural networks, and long short-term memory models. More importantly, it significantly improves the detection of rare attack types such as remote-to-local and user-to-root attacks. These results highlight the potential of HiSeq-TCN for robust and reliable intrusion detection in practical cybersecurity environments. Full article

(This article belongs to the Special Issue Applications of Deep Learning in Cyber Threat Detection)

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24 pages, 3293 KB

Open AccessArticle

Short-Term Forecasting of Photovoltaic Clusters Based on Spatiotemporal Graph Neural Networks

by Zhong Wang, Mao Yang, Yitao Li, Bo Wang, Zhao Wang and Zheng Wang

Processes 2025, 13(11), 3422; https://doi.org/10.3390/pr13113422 (registering DOI) - 24 Oct 2025

Abstract

Driven by the dual-carbon goals, photovoltaic (PV) battery systems at renewable energy stations are increasingly clustered on the distribution side. The rapid expansion of these clusters, together with the pronounced uncertainty and spatio-temporal heterogeneity of PV generation, degrades battery utilization and forces conservative dispatch. To address this, we propose a “spatio-temporal clustering–deep estimation” framework for short-term interval forecasting of PV clusters. First, a graph is built from meteorological–geographical similarity and partitioned into sub-clusters by a self-supervised DAEGC. Second, an attention-based spatio-temporal graph convolutional network (ASTGCN) is trained independently for each sub-cluster to capture local dynamics; the individual forecasts are then aggregated to yield the cluster-wide point prediction. Finally, kernel density estimation (KDE) non-parametrically models the residuals, producing probabilistic power intervals for the entire cluster. At the 90% confidence level, the proposed framework improves PICP by 4.01% and reduces PINAW by 7.20% compared with the ASTGCN-KDE baseline without spatio-temporal clustering, demonstrating enhanced interval forecasting performance. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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19 pages, 679 KB

Open AccessArticle

Parental Dietary Knowledge, Income and Students’ Consumption of Sugar-Sweetened Beverages in China: Evidence from Longitudinal Study

by Yi Cui, Yunli Bai and Chengfang Liu

Nutrients 2025, 17(21), 3356; https://doi.org/10.3390/nu17213356 (registering DOI) - 24 Oct 2025

Abstract

Background/Objectives: Sugar-sweetened beverage (SSB) consumption has increased globally among children and adolescents, posing significant health risks. Parental dietary knowledge and income play important roles in shaping children’s food-choice and consumption behaviors. This study aimed to examine the effects of parental dietary knowledge and income on students’ SSB consumption at both extensive and intensive margins. Methods: A two-way fixed-effects model was estimated using longitudinal data from 3962 primary and junior high school students in the Jining District of Ulanqab City, Inner Mongolia Autonomous Region, northern China, collected in 2019 and 2020. Results: SSB consumption among Chinese students increased from 2019 to 2020 in both extensive (82.51% to 86.90%) and intensive margins (686.09 mL/week to 891.21 mL/week). Each one-point increase in parental dietary knowledge score was linked to a 13.39 mL (p < 0.05) reduction in weekly SSB consumption, and 9.90 mL (p < 0.05) reduction in juice beverages, correspondingly reductions in weekly added sugar intake from SSBs (1.26 g, p < 0.10) and juice beverages (0.79 g, p < 0.05), with stronger association among rural hukou students. Parental income showed minimal association with students’ SSB consumption, but had a stronger association among rural hukou and junior high school students. Conclusions: Parental dietary knowledge plays a crucial role in reducing students’ SSB consumption, with particularly strong association in rural hukou students. Targeted interventions enhancing parental dietary knowledge could reduce SSB consumption and added sugar intake among school-aged children. Full article

(This article belongs to the Special Issue Food Labeling and Consumer Behaviors)

23 pages, 4969 KB

Open AccessArticle

Experimental Study on Mechanical Properties of Hybrid Fiber Desert Sand Recycled Aggregate Concrete

by Yanlin Guan, Yaqiang Yang, Jianzhe Shi, Daochuan Zhou, Bitao Wu, Wenping Du, Shanshan Yu and Jing Cui

Buildings 2025, 15(21), 3857; https://doi.org/10.3390/buildings15213857 (registering DOI) - 24 Oct 2025

Abstract

In response to the issues of microcrack susceptibility, high brittleness, and unstable mechanical properties of desert sand recycled aggregate concrete (DSRAC), this study experimentally investigated the mechanical performance of DSRAC reinforced with hybrid steel–FERRO fibers. By testing macroscopic properties (compressive, splitting tensile, and flexural strengths) under different desert sand replacement ratios and fiber dosages, combined with microscopic analysis, the fiber-matrix interfacial behavior and toughening mechanism were clarified. The results showed that (1) DSRAC achieved optimal compressive strength when desert sand replaced 30% natural sand, with an obvious early strength enhancement; (2) both steel fibers and FERRO fibers independently improved DSRAC’s mechanical properties, while their hybrid combination (especially F0.15-S0.5 group) exhibited a superior synergistic strengthening effect, significantly outperforming single-fiber groups; (3) the established constitutive model accurately described the stress–strain response of hybrid fiber-reinforced DSRAC; (4) microscopic observations confirmed fibers inhibited crack propagation via bridging and stress dispersion, with hybrid fibers exerting multi-scale synergistic effects. This study provided theoretical–technical support for resource utilization of desert sand and recycled aggregates, and offered practical references for localized infrastructure materials (e.g., rural road subgrades and small-span culverts) in desert-rich regions and high-value reuse of construction waste in prefabricated components, advancing eco-friendly concrete in sustainable construction. Full article

(This article belongs to the Special Issue Fiber-Reinforced Polymers and Fiber-Reinforced Concrete in Civil Engineering—2nd Edition)

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19 pages, 2115 KB

Open AccessArticle

Application of Digital Twin Platform for Prefabricated Assembled Superimposed Stations Based on SERIC and IoT Integration

by Linhai Lu, Jiahai Liu, Bingbing Hu, Yingqi Gao, Qianwei Xu, Yanyun Lu and Guanlin Huang

Buildings 2025, 15(21), 3856; https://doi.org/10.3390/buildings15213856 (registering DOI) - 24 Oct 2025

Abstract

Prefabricated stations utilizing digital modeling techniques demonstrate significant advantages over traditional cast-in-place methods, including improved dimensional accuracy, reduced environmental impact, and minimized material waste. To maximize these benefits, this study develops a digital twin platform for prefabricated assembled superimposed stations through the integration of Digital Twin Scene–Entity–Relationship–Incident–Control (SERIC) modeling with IoT technology. The platform adopts a “1+5+N” architecture that implements model-data separation, lightweight processing, and model-data association for SERIC model management, while IoT-enabled data acquisition facilitates lifecycle data sharing. By integrating BIM models, engineering data, and IoT sensor inputs, the platform employs multi-source analytics to monitor construction progress, enhance safety surveillance, ensure quality control, and optimize designs. Implementation at Jinan Metro Line 8’s prefabricated underground station confirms the SERIC-IoT digital twin’s efficacy in advancing sustainable, high-quality rail transit development. Results demonstrate the platform’s capacity to improve construction efficiency and operational management, aligning with urban rail objectives prioritizing sustainability and technological innovation. This study establishes that integrating SERIC modeling with IoT in digital twin frameworks offers a robust approach to modernizing prefabricated station construction, with scalable applications for future smart transit infrastructure. Full article

(This article belongs to the Section Building Structures)

22 pages, 1467 KB

Open AccessArticle

Reactivity of Curcumin: Theoretical Insight from a Systematic Density Functional Theory-Based Review

by Marcin Molski

Int. J. Mol. Sci. 2025, 26(21), 10374; https://doi.org/10.3390/ijms262110374 (registering DOI) - 24 Oct 2025

Abstract

A comprehensive analysis of key findings derived from density functional theory (DFT) studies reveals that current theoretical data on curcumin remain incomplete, underscoring the need for further computational investigation to achieve a more thorough understanding of its chemical and biological reactivity. This study addresses these gaps through four primary objectives: (i) determination of a complete set of thermodynamic descriptors and elucidation of the multi-step anti-radical mechanisms of the neutral, radical, anionic, and radical–anionic forms of curcumin; (ii) calculation of global chemical reactivity descriptors of curcumin in various solvent environments; (iii) theoretical reproduction of experimentally determined pK_a values for all active sites within the molecule; and (iv) examination of the effects of dispersion interactions and solvent polarity on the reactivity descriptors of keto–enol forms of curcumin. The results obtained provide enhanced insight into the molecular behavior of curcumin, facilitating improved predictions of its reactivity under diverse conditions. Moreover, the findings indicate a potential structural modification of the keto form of curcumin, involving the attachment of two 4-hydroxy-3-methoxyphenyl-prop-1-en-2-one moieties to the methylene group. The resulting modeled compound, referred to as di-curcumin, exhibits enhanced chemical reactivity and increased anti-radical potential. Full article

(This article belongs to the Section Physical Chemistry and Chemical Physics)

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21 pages, 5544 KB

Open AccessArticle

Revealing Guangdong’s Bridging Role in Embodied Energy Flows Through International and Domestic Trade

by Qiqi Liu, Yu Yang, Yi Liu and Xiaoying Qian

Energies 2025, 18(21), 5607; https://doi.org/10.3390/en18215607 (registering DOI) - 24 Oct 2025

Abstract

Embodied energy flows link production systems with the energy sector, reflecting dependencies and structural risks under globalization and regional coordination. Guangdong, China’s most manufacturing-intensive, open, and energy-consuming province, is a central hub in both global value chains and domestic production networks, playing a pivotal role in national energy security. Understanding Guangdong’s embodied energy flows is essential for revealing the transmission of energy across multi-level spatial systems and the resilience of China’s energy infrastructure. This study integrates international (EXIOBASE) and Chinese inter-provincial input–output data to build a province-level nested global MRIO model, combined with Structural Path Analysis (SPA), to characterize Guangdong’s manufacturing embodied energy flows in domestic and international dual circulation from 2002 to 2017. Our findings confirm Guangdong’s pivotal bridging role in embodied energy transfers. First, flows are dual-directional and dominated by international transfers. Second, energy efficiency has improved, narrowing the intensity gap between export- and domestic-oriented industries. Third, flows have diversified spatially from concentration in developed regions toward developing regions, with domestic inter-provincial flows more dispersed. Finally, embodied energy remains highly concentrated across sectors, with leading industries shifting from labor- and capital-intensive to capital- and technology-intensive sectors. This research offers vital empirical evidence and policy reference for enhancing national energy security and optimizing spatial energy allocation. Full article

(This article belongs to the Special Issue Energy Security, Transition, and Sustainable Development)

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20 pages, 944 KB

Open AccessArticle

Predicting Corrosion Behaviour of Magnesium Alloy Using Machine Learning Approaches

by Tülay Yıldırım and Hüseyin Zengin

Metals 2025, 15(11), 1183; https://doi.org/10.3390/met15111183 (registering DOI) - 24 Oct 2025

Abstract

The primary objective of this study is to develop a machine learning-based predictive model using corrosion rate data for magnesium alloys compiled from the literature. Corrosion rates measured under different deformation rates and heat treatment parameters were analyzed using artificial intelligence algorithms. Variables such as chemical composition, heat treatment temperature and time, deformation state, pH, test method, and test duration were used as inputs in the dataset. Various regression algorithms were compared with the PyCaret AutoML library, and the models with the highest accuracy scores were analyzed with Gradient Extra Trees and AdaBoost regression methods. The findings of this study demonstrate that modelling corrosion behaviour by integrating chemical composition with experimental conditions and processing parameters substantially enhances predictive accuracy. The regression models, developed using the PyCaret library, achieved high accuracy scores, producing corrosion rate predictions that are remarkably consistent with experimental values reported in the literature. Detailed tables and figures confirm that the most influential factors governing corrosion were successfully identified, providing valuable insights into the underlying mechanisms. These results highlight the potential of AI-assisted decision systems as powerful tools for material selection and experimental design, and, when supported by larger databases, for predicting the corrosion life of magnesium alloys and guiding the development of new alloys. Full article

(This article belongs to the Section Computation and Simulation on Metals)

22 pages, 690 KB

Open AccessReview

Artificial Intelligence-Assisted CRISPR/Cas Systems for Targeting Plant Viruses

by Nurgul Iksat, Almas Madirov, Kuralay Zhanassova and Zhaksylyk Masalimov

Genes 2025, 16(11), 1258; https://doi.org/10.3390/genes16111258 (registering DOI) - 24 Oct 2025

Abstract

Plant viral infections continue to pose a significant and ongoing threat to global food security, especially in the context of climatic instability and intensive agricultural practices. The CRISPR/Cas system has emerged as a powerful tool for developing virus-resistant crops by enabling precise modifications to viral genomes or plant susceptibility factors. Nonetheless, the efficacy and dependability of CRISPR-based antiviral approaches are limited by challenges in guide RNA design, off-target effects, insufficiently annotated datasets, and the intricate biological dynamics of plant–virus interactions. This paper summarizes the latest advancements in the incorporation of artificial intelligence (AI) methodologies, including machine learning and deep learning algorithms, into the CRISPR design and optimization framework. It examines how convolutional and recurrent neural networks, transformer architectures, and generative models like AlphaFold2, RoseTTAFold, and ESMFold can be used to predict protein structures, score sgRNAs, and model host–virus interactions. AI-enhanced methods have been proven to improve target specificity, Cas protein performance, and in silico validation. This paper aims to establish a foundation for next-generation genome editing strategies against plant viruses and promote the adoption of AI-powered CRISPR technologies in sustainable agriculture. Full article

(This article belongs to the Section Plant Genetics and Genomics)

22 pages, 319 KB

Open AccessArticle

Integrated Spatiotemporal Life Cycle Assessment Framework for Hydroelectric Power Generation in Brazil

by Vanessa Cardoso de Albuquerque, Rodrigo Flora Calili, Maria Fatima Ludovico de Almeida, Rodolpho Albuquerque, Tarcisio Castro and Rafael Kelman

Energies 2025, 18(21), 5606; https://doi.org/10.3390/en18215606 (registering DOI) - 24 Oct 2025

Abstract

This study proposes and empirically validates a spatiotemporal life cycle assessment (LCA) framework for hydroelectric power generation applied to the Sinop Hydroelectric Power Plant in Brazil. Unlike conventional LCA, which assumes spatial and temporal homogeneity, the framework incorporates annual temporal discretisation and geographically differentiated impacts across all phases of assessment. The methodology combines the Enhanced Structural Path Analysis (ESPA) method with temporal modeling and region-specific inventory data. The results indicate that environmental impacts peak in the fourth year of the ‘Construction and Assembly’ stage, primarily due to the intensive production of concrete and steel. A spatial analysis shows that these impacts extend beyond Brazil, with notable contributions from international supply chains. By identifying temporal and geographical hotspots, the framework offers a refined understanding of impact dynamics and drivers. Uncertainty analysis further demonstrates that temporal discretisation significantly affects impact attribution, with the ‘Construction and Assembly’ stage results varying by up to ±15%, depending on scheduling assumptions. Overall, the study advances the LCA methodology while offering robust empirical evidence to guide sustainable decision-making in Brazil’s power sector and to inform global debates on low-carbon energy transitions. Full article

(This article belongs to the Section A: Sustainable Energy)

18 pages, 5537 KB

Open AccessArticle

Prior-Guided Residual Reinforcement Learning for Active Suspension Control

by Jiansen Yang, Shengkun Wang, Fan Bai, Min Wei, Xuan Sun and Yan Wang

Machines 2025, 13(11), 983; https://doi.org/10.3390/machines13110983 (registering DOI) - 24 Oct 2025

Abstract

Active suspension systems have gained significant attention for their capability to improve vehicle dynamics and energy efficiency. However, achieving consistent control performance under diverse and uncertain road conditions remains challenging. This paper proposes a prior-guided residual reinforcement learning framework for active suspension control. The approach integrates a Linear Quadratic Regulator (LQR) as a prior controller to ensure baseline stability, while an enhanced Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm learns the residual control policy to improve adaptability and robustness. Moreover, residual connections and Long Short-Term Memory (LSTM) layers are incorporated into the TD3 structure to enhance dynamic modeling and training stability. The simulation results demonstrate that the proposed method achieves better control performance than passive suspension, a standalone LQR, and conventional TD3 algorithms. Full article

(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)

19 pages, 936 KB

Open AccessStudy Protocol

The Effectiveness of the Safety and Home Injury Prevention for Seniors: A Study Protocol for a Randomized Controlled Trial

by Ok-Hee Cho, Hyekyung Kim and Kyung-Hye Hwang

Healthcare 2025, 13(21), 2695; https://doi.org/10.3390/healthcare13212695 (registering DOI) - 24 Oct 2025

Abstract

Background: The majority of injuries among older adults occur due to unexpected and sudden incidents in the home environment. This study aimed to develop a protocol for the design of the health belief model-based program for preventing unintentional home injuries in older adults and to evaluate the effectiveness of the program. Methods: The study proposed in this protocol, Safety and Home Injury Prevention for Seniors (SHIPs), is a single-blind, parallel-group, randomized controlled trial. A total of 54 Korean older adults (≥65 years) will be randomly assigned to either (1) the intervention group (n = 27), which will receive the SHIPs program, or (2) the control group (n = 27), which will attend four lecture-only sessions. The efficacy of the program will be assessed via tests performed at baseline, 1 week after program completion, and 1 month after program completion, and analyses of the changes in injury occurrences, risk factors, preventive behaviors, beliefs about safety and injury prevention, psychological health, physiological function, and health-related quality of life. Expected Results: The SHIPs intervention is expected to reduce home injuries and enhance awareness and preventive behaviors among community-dwelling older adults. It may also improve their physical and psychological health and overall quality of life. Conclusions: The SHIPs intervention may serve as an effective community-based strategy to promote injury prevention and improve the overall well-being of older adults. Full article

(This article belongs to the Section Public Health and Preventive Medicine)

18 pages, 1387 KB

Open AccessArticle

Spatiotemporal Dynamics of Carbon Sequestration Potential Across South Korea: A CASA Model-Based Assessment of NPP, Heterotrophic Respiration, and NEP

by Nam-Shin Kim, Jae-Ho Lee and Chang-Seok Lee

Sustainability 2025, 17(21), 9490; https://doi.org/10.3390/su17219490 (registering DOI) - 24 Oct 2025

Abstract

Achieving carbon neutrality requires a comprehensive understanding of terrestrial carbon dynamics, particularly the capacity of ecosystems to act as carbon sinks. This study quantified the temporal and spatial variability of net primary production (NPP) and net ecosystem production (NEP) across South Korea from 2010 to 2024, assessing long-term carbon sink trends and their implications for carbon neutrality and nature-based solutions (NbSs). Using the Carnegie–Ames–Stanford Approach (CASA) model driven by Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data and climate variables, we estimated ecosystem carbon fluxes at high spatial and temporal resolutions. In 2024, national NPP totaled 78.63 Mt CO₂ yr⁻¹, with a mean value of 1956.63 t CO₂ ha⁻¹ yr⁻¹. High productivity was concentrated in upland forests of Gangwon-do, Mt. Jirisan, and northern Gyeongsangbuk-do, where favorable vegetation indices and climatic conditions enhanced photosynthesis. Lower productivity occurred in urbanized areas and intensively farmed lowlands. Heterotrophic respiration (RH) was estimated at 15.35 Mt CO₂ yr⁻¹, with elevated rates in warm, humid lowlands and reduced values in high-elevation forests. The resulting NEP in 2024 was 63.29 Mt CO₂ yr⁻¹, with strong sinks along the Baekdudaegan Range and localized negative NEP pockets in lowlands dominated by urban development or agriculture. From 2010 to 2024, the spatially averaged NPP increased from 1170 to 1543 g C m⁻² yr⁻¹, indicating a general upward trend in ecosystem productivity. However, interannual variability was influenced by climatic fluctuations, land-cover changes, and data masking adjustments. These findings provide critical insights into the spatiotemporal dynamics of terrestrial carbon sinks in South Korea, offering essential baseline data for national greenhouse gas inventories and the strategic integration of NbSs into carbon-neutral policies. Full article

23 pages, 1986 KB

Open AccessArticle

Solvent Fractionation Improves the Functional Properties of Sheep Rump Fat: Effects of Different Lipid Fractions on Lipid Metabolism and Gut Health in Mice

by Xin Ma, Junfei Yu, Zequan Xu, Jian Wei, Lingyan Wu, Hongjiao Han, Jianzhong Zhou and Zirong Wang

Foods 2025, 14(21), 3641; https://doi.org/10.3390/foods14213641 (registering DOI) - 24 Oct 2025

Abstract

To enhance the nutritional value of sheep fat, high-melting-point solid fat (HSO) and low-melting-point liquid oil (LSO) were prepared from Altay sheep rump fat via solvent fractionation. The effects of HSO and LSO on lipid metabolism and intestinal health were evaluated in a mouse model. Results showed that HSO, rich in saturated fatty acids (SFA), induced obesity, dyslipidemia, and colonic inflammation in mice. These adverse effects were associated with the upregulation of hepatic lipid synthesis genes such as Sterol regulatory element-binding protein 1c (SREBP-1c) and Fatty acid synthase (FAS), as well as increased expression of pro-inflammatory cytokines including Tumor necrosis factor-alpha (TNF-α) and Interleukin-6 (IL-6) in the colon. In contrast, LSO, which was predominantly composed of unsaturated fatty acids (UFA), did not cause significant metabolic disorders. Instead, it promoted the upregulation of fatty acid oxidation-related genes such as Peroxisome proliferator-activated receptor alpha (PPARα) and Acyl-CoA oxidase 1 (Acox1), helped maintain intestinal microbial balance, and enhanced the production of beneficial short-chain fatty acids (SCFAs), particularly butyrate and propionate. In conclusion, solvent fractionation effectively modulates the fatty acid composition of sheep fat, thereby influencing lipid metabolism and inflammatory responses through the regulation of key gene expression and modulation of the gut microenvironment. Full article

(This article belongs to the Section Food Nutrition)

24 pages, 2388 KB

Open AccessArticle

Enhancing the Chloride Adsorption and Durability of Sulfate-Resistant Cement-Based Materials by Controlling the Calcination Temperature of CaFeAl-LDO

by Lei Yang, Xin Zhao, Shaonan Cai, Minqi Hua, Jijiang Liu, Hui Liu, Junyi Wu, Liming Pang and Xinyu Gui

Materials 2025, 18(21), 4884; https://doi.org/10.3390/ma18214884 (registering DOI) - 24 Oct 2025

Abstract

Chloride-ion (Cl⁻)-induced corrosion of steel bars is a major threat to the durability of marine concrete structures. To address this, a type of calcined CaFeAl-layered double oxide (LDO-CFA) with different calcination temperatures was used to enhanced the Cl⁻ adsorption, compressive strength, and corrosion resistance of sulphate-resistant Portland cement (SRPC)-based materials. Experimental results demonstrated that LDO-CFA exhibited high Cl⁻ adsorption capacity in both CPSs and cement-based materials. Specifically, LDO-750-CFA reached 1.98 mmol/g in CPSs—60.1% higher than LDHs-CFA—and followed the Langmuir model, indicating monolayer adsorption. It also reduced the free Cl⁻ content of SRPC paste to 0.255–0.293% after 28 days, confirming its sustained adsorption over extended curing. Furthermore, LDO-CFA positively influenced the compressive strength at all curing ages. At an optimal dosage of 0.8 wt.%, LDO-750-CFA paste significantly improved the compressive strength, increasing it by 22.1% at 7 days and 15.6% at 28 days compared to the control. Electrochemical analysis confirmed the superior corrosion resistance of the LDO-750-CFA system. The property enhancement originated from LDO-750-CFA’s synergistic effects, which included pore refinement, increased tortuosity, Cl⁻ adsorption by structural memory, a PVP-induced passive film, and PVP-improved dispersion. Overall, this work provides a framework for developing LDO-750-CFA-based composites, paving the way for more durable marine concrete. Full article

(This article belongs to the Special Issue Advanced Experimental Technology, Theory and Numerical Methods in Geomaterials and Concrete Materials)

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