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Low-light (LL) stress is a major abiotic limiting factor in protected cherry tomato production, adversely affecting vegetative growth, inducing oxidative damage, and disrupting fruit sugar metabolism. To clarify the regulatory role of exogenous abscisic acid (ABA) in mitigating LL stress, we examined the effects of varying ABA concentrations on plant growth, antioxidant capacity, and fruit sugar metabolism in cherry tomatoes under low-light conditions. A two-factor randomized complete block design, with two light regimes—normal light (NL, 100% natural sunlight) and low light (LL, 25% natural sunlight)—and three ABA concentrations (CK: 0 mg·L⁻¹, T1: 10 mg·L⁻¹, T2: 20 mg·L⁻¹). Fruits were sampled at three typical ripening stages (green mature, breaker, and red ripe) to evaluate vegetative and reproductive physiological responses. The results showed that exogenous ABA application effectively suppressed LL-induced excessive stem elongation and alleviated LL-caused reductions in stem diameter and biomass accumulation. ABA treatment significantly increased peroxidase (POD) activity and reduced malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) accumulation, thereby relieving LL-triggered oxidative damage. In addition, ABA regulated key sugar-metabolizing enzymes (soluble acid invertase (SAI), sucrose synthase (SS), sucrose phosphate synthase (SPS), and amylase (Amy)) and the transcript levels of related functional genes (HXK1, SPS, SS, AI), thereby mediating stage-dependent fruit sugar metabolism under LL stress. In conclusion, exogenous ABA effectively modulates vegetative growth, antioxidant homeostasis, and stage-specific fruit sugar metabolism, ultimately alleviating low-light stress damage in cherry tomato. Among the tested treatments, 20 mg·L⁻¹ ABA exhibited the most pronounced mitigation effects, which can be recommended as an optimal foliar application concentration for cherry tomato cultivation in low-light protected facilities. Full article

(This article belongs to the Section Horticultural and Floricultural Crops)

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34 pages, 3315 KB

Open AccessArticle

Evolutionary Dynamics of Openness, Dependence, and Regulation in AI Computing Power Innovation Ecosystem

by Zhengrui Li, Qingjin Wang, Shuai Huang and Tian Lan

Systems 2026, 14(5), 505; https://doi.org/10.3390/systems14050505 (registering DOI) - 2 May 2026

Abstract

Driven by the rapid proliferation of generative artificial intelligence, the computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems. This study investigates the evolutionary dynamics of the computing power ecosystem, specifically examining the strategic interplay between antitrust regulation and vertical integration. We construct a tripartite evolutionary game framework involving the government regulators, leading computing power incumbents, and downstream AI innovators. By deriving evolutionarily stable strategies, we analyze the underlying mechanisms of system transitions and employ numerical simulations to explore key parametric sensitivities. The theoretical analysis suggests that the evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics—potentially transitioning from an initial efficiency-based state of “natural monopoly and passive dependence” during the industry’s emergence, through transitionary states such as the “comfort zone trap” or “regulatory stalemate” during the expansion phase, and ultimately converging toward a mature configuration of “co-opetition and endogenous growth.” The model suggests that downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures, The analysis further implies that the leading computing power firm could strengthen its ecological niche by opening its underlying interfaces and software stacks to maintain its ecological niche as the industry cornerstone in integrated form. For the government, it is necessary to establish precise dynamic intervention and orderly exit mechanisms. Full article

(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)

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26 pages, 11504 KB

Open AccessArticle

Diversity, Taxonomy, and Pathogenicity of Members of Fusarium tricinctum Species Complex Associated with Wild Rosaceae Fruits

by Asanka Madhushan, Paul W. J. Taylor, Ahmed Mahmoud Ismail, Jian-Kui Liu and Sajeewa S. N. Maharachchikumbura

J. Fungi 2026, 12(5), 333; https://doi.org/10.3390/jof12050333 (registering DOI) - 2 May 2026

Abstract

This study investigated Fusarium species associated with seven wild relatives of four economically important Rosaceae fruits in Sichuan Province, China, including wild strawberry (Fragaria sp. and Potentilla indica), wild raspberry (Rubus rosaefolius), wild cherry (Prunus sp., Maddenia sp. and Prunus leveilleana), and wild apple (Malus kansuensis). Based on multi-gene phylogenetic analyses and morphological characteristics, seven Fusarium species within the Fusarium tricinctum species complex (FTSC) were identified. Among these, four are described as new species (F. fragariae, F. potentillae, F. pruni and F. fructicola), while the remaining three represent new host records (F. avenaceum, F. diversisporum and F. paeoniae). In addition, phylogenetic and morphological evidence indicated that F. rosiradicicola is conspecific with F. diversisporum. Prioritizing the oldest epithet, we synonymized F. rosiradicicola under F. diversisporum. The pathogenicity of the isolates was evaluated on both their wild hosts and the corresponding cultivated fruits using detached, wound-inoculated assays. All tested isolates produced symptoms, showing pathogenic potential under experimental conditions. This study shows that selected wild Rosaceae fruits harbor several members of the FTSC and provides preliminary evidence of cross-host susceptibility under experimental conditions. However, further field-based investigations and non-wound inoculation studies are required to clarify their ecological roles, natural host susceptibility, and potential relevance in cultivated systems. Full article

(This article belongs to the Special Issue The Dark Side of Ascomycetes)

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36 pages, 1568 KB

Open AccessSystematic Review

Quality by Design Approach for Hot-Melt Extrusion Coupled Fused Deposition Modeling (HME-FDM) 3D Printing: A Systematic Review

by Petra Arany, Ádám Papp, Dániel Nemes, Pálma Fehér, Zoltán Ujhelyi and Ildikó Bácskay

Pharmaceutics 2026, 18(5), 569; https://doi.org/10.3390/pharmaceutics18050569 (registering DOI) - 2 May 2026

Abstract

Background: Fused deposition modeling (FDM) is one of the most well-known and often published methods for 3D-printed drug delivery systems. In early scientific reports, the active pharmaceutical ingredients were added by soaking, but later, a new milestone was established, after researchers started to manufacture their own filaments by hot-melt extrusion (HME). The number of publications covering this method has multiplied in the last decade, a wide range of natural and synthetic polymers have been tested with versatile active pharmaceutical ingredient components, and various printing parameters and their effects have been investigated. Objectives: In this review, we aim to synthesize how the available quality by design approaches and the scientific results established so far can facilitate the creation of a guideline for appropriate quality production of HME-FDM 3D-printed pharmaceuticals. Methods: Based on PRISMA 2020 guidelines, a systematic search of relevant publications from 2015 to 2025 was carried out using the PubMed database. Twenty-six articles were included, based on number of monitored parameters and methodological description. Reporting of important quality processes and material parameters was assessed. Results: HME, the FDM, and analytical testing experiences were compared and collected into three tables from the selected publications. In two different sections, the pharmacopeial dosage-form tests and the involvement of process analytical technologies (PAT) were also analyzed. We found that reporting of influential parameters is heterogenous, and lack of robust reporting schemes limits the development of QbD approaches. Conclusions: Regarding the data, trends were synthetized, and a guideline was created which is limited by inconsistent parameter reporting. Full article

(This article belongs to the Special Issue 3D Printing—Current Pharmaceutical Applications and Future Directions)

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17 pages, 4465 KB

Open AccessReview

Advances and Applications of Narrow-Linewidth Vertical-Cavity Surface-Emitting Lasers

by Xiaoru Li, Ning Cui and Baolu Guan

Photonics 2026, 13(5), 450; https://doi.org/10.3390/photonics13050450 (registering DOI) - 2 May 2026

Abstract

Vertical-cavity surface-emitting lasers (VCSELs) have emerged as essential light sources for atomic-precision measurement, quantum-secure communication, high-speed optical transmission, and laser coherent scanning detection, owing to their low power consumption, high-quality beam characteristics, and ease of two-dimensional integration. However, the fundamental limitation on linewidth narrowing in VCSELs arises from their inherently short resonator, resulting in a natural linewidth on the order of 50–100 MHz. This limitation prevents conventional VCSELs from meeting the stringent requirements of advanced applications, making the ultra-narrow linewidth a key focus in optoelectronics research. This review analyzes representative achievements and application scenarios of narrow-linewidth VCSELs, evaluates the merits and limitations of industrial-grade devices, and envisions future directions in next-generation optoelectronic systems. Distinct from existing reviews, it integrates key single-mode fabrication techniques, quantitative linewidth requirements across applications, silicon photonic integration, and scalable manufacturing trends, establishing a complete mechanism–technology–application–industry analytical framework. Full article

(This article belongs to the Special Issue Recent Progress in Vertical-Cavity Surface-Emitting Lasers (VCSELs))

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16 pages, 1838 KB

Open AccessArticle

Hydrological Variability and Socio-Ecological Responses in Flood-Prone Riverine Communities of the Niger Delta, Nigeria: Women’s Lived Experiences

by Turnwait Otu Michael

Limnol. Rev. 2026, 26(2), 18; https://doi.org/10.3390/limnolrev26020018 (registering DOI) - 2 May 2026

Abstract

Riverine systems in tropical deltaic environments are increasingly exposed to hydrological variability driven by climate change, sea level rise, and extreme precipitation. In Nigeria’s Niger Delta, recurrent flooding and environmental degradation are intensifying pressures on freshwater ecosystems and dependent communities. This study examines hydrological stressors in riverine settlements of Bayelsa State and explores associated socio-ecological responses. Using an exploratory qualitative design, data were collected from 51 women residing in highly vulnerable riverine communities through 24 in-depth interviews and three focus group discussions. Thematic analysis identified prolonged flooding, riverbank erosion, salinity intrusion, water quality deterioration, and oil pollution, as key drivers of declining fisheries, reduced agricultural productivity, and household water insecurity. These stressors have prompted relocation, livelihood diversification, and reliance on indigenous adaptation practices. The study recommends: (1) installation of community-based flood early warning systems; (2) routine monitoring of surface water quality and salinity; (3) enforcement of oil spill remediation and pollution control measures; (4) rehabilitation of wetlands and natural drainage channels; and (5) targeted support for climate-resilient livelihoods such as aquaculture and elevated farming systems. These measures are critical for sustaining freshwater ecosystems and strengthening resilience in vulnerable deltaic communities. Full article

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43 pages, 8067 KB

Open AccessReview

Phytohormone-Mediated Regulation of Plant Cold Stress Tolerance: Signaling, Hormonal Crosstalk, and Translational Perspectives

by Shafi Ullah, Mohammad Nurul Matin, Changxi Yin, Md. Atik Mas-ud, Atika Khan, Md. Shoffikul Islam, Irfanullah and Ijaz ul Haq

Int. J. Mol. Sci. 2026, 27(9), 4085; https://doi.org/10.3390/ijms27094085 (registering DOI) - 2 May 2026

Abstract

Cold stress (CS) represents a major environmental factor that adversely affects plant growth, development, and productivity. To cope with low-temperature conditions, plants have evolved sophisticated mechanisms for CS perception and response, mediated through complex cellular signaling networks and physiological processes. Central to these adaptive responses are phytohormones, which function either independently or through synergistic and antagonistic interactions to fine-tune CS tolerance. This review synthesizes current knowledge on the roles of major classical phytohormones and signaling metabolites in regulating CS tolerance in plants. We first outline the molecular mechanisms involved in CS sensing and signal transduction, highlighting the roles of membrane-associated sensors, calcium signaling, and downstream transcriptional networks. Then, we discuss the contributions of key classical phytohormones, including auxin, abscisic acid, ethylene, salicylic acid, cytokinin, jasmonic acid, brassinosteroids, gibberellic acid, strigolactones, and signaling metabolites, including melatonin and gamma-aminobutyric acid, to CS tolerance, highlighting their individual and interacting roles in modulating gene expression regulation, antioxidant defense and physiological adaptations. We also discuss the crosstalk between these hormones, emphasizing the dynamic and often context-dependent nature of their interactions in response to CS. Furthermore, the review highlights recent advances in CRISPR/Cas9-based genome editing strategies targeting phytohormone biosynthesis, signaling, and response pathways to improve CS tolerance in plants. By integrating hormonal signaling, molecular regulation, and modern biotechnological tools, this review provides a comprehensive framework for understanding phytohormone-mediated CS adaptation and offers perspectives for developing climate-resilient crops through genetic and agronomic approaches. Full article

(This article belongs to the Special Issue Molecular Genetic Mechanism of Stress Resistance in Plants)

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27 pages, 6364 KB

Open AccessArticle

Sonication–Freezing–Assisted Extraction of Chia Seed Mucilage: Functional and Structure–Rheology Relationships and Molecular Weight Determination

by Monserrat Sanpedro-Díaz, Esteban F. Medina-Bañuelos, Ma. de la Paz Salgado-Cruz, Benjamín M. Marín-Santibáñez, Alitzel Belem García-Hernández, Ana Luisa Gómez-Gómez and Diana Maylet Hernández-Martínez

Gels 2026, 12(5), 394; https://doi.org/10.3390/gels12050394 (registering DOI) - 2 May 2026

Abstract

Chia seed mucilage (CSM) is a promising plant-derived hydrocolloid characterized by unique physicochemical and functional properties that are strongly influenced by the extraction methodology. In this research, an optimized sonication–freezing-assisted extraction (SFAE) process was developed to obtain mucilage while preserving its structural integrity. Results indicate that the extracted mucilage has a high total dietary fiber content of 75.87% and a moderate protein level of 8.71%. Fourier transform infrared spectroscopy (FTIR) confirmed the presence of hydroxyl and ionized carboxylate (COO⁻) groups associated with uronic acids, highlighting the anionic and polyelectrolyte nature of the system. Rheological characterization of optimized-CSM revealed Newtonian behavior in dilute solutions, indicating minimal intermolecular interactions and permitting accurate measurement of intrinsic viscosity and viscosity-average molecular weight. A critical overlap concentration (c** ≈ 0.2% w/v) was identified, marking the transition to semi-dilute regimes, chain entanglement, and the onset of shear-thinning and viscoplastic behavior. Functionally, the optimized-CSM exhibited high water holding capacity and competitive emulsifying properties (emulsion activity index (EAI): 62.50%; emulsion stability index (ESI): 49.32%), attributed to synergistic interactions between proteins and polysaccharides. Overall, this work provides new insights into how processing conditions influence the chemical composition and molecular structure, which fundamentally govern the rheological and functional performance of CSM. These findings underscore its potential as a versatile hydrocolloid for food and biomedical applications. Full article

(This article belongs to the Special Issue Food Gels: Structure and Properties (3rd Edition))

27 pages, 3299 KB

Open AccessArticle

Neural Network Copulas for Generating Synthetic Test Data Preserving Psychometric Properties

by Juyoung Jung, Minho Lee and Won-Chan Lee

J. Intell. 2026, 14(5), 77; https://doi.org/10.3390/jintelligence14050077 (registering DOI) - 2 May 2026

Abstract

In intelligence research, the sharing of item response data from cognitive ability assessments is often restricted by privacy concerns, while traditional parametric simulation methods frequently fail to capture complex response dependencies. This study proposes a neural network copula (NNC) framework for generating synthetic dichotomous item response data that preserves essential psychometric properties without revealing sensitive examinee information. By decoupling the modeling of marginal item probabilities from the dependence structure using a deep autoencoder and kernel density estimation, the framework accommodates the discrete nature of binary item response data while minimizing distributional assumptions. Validation against large-scale empirical data demonstrated high correspondence across multiple facets. At the data consistency level, the NNC-based synthetic data reproduced total score distributions and inter-item correlations. Psychometrically, the method yielded consistent item characteristic curve parameter estimates, item fit statistics, and test information functions. Furthermore, Monte Carlo replications demonstrated algorithmic stability and inferential precision. Full article

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16 pages, 1952 KB

Open AccessArticle

The Influence of Cellulose Fiber Content on the Mechanical Properties of Composites Based on Modified Thermoplastic Starch

by Mariusz Fabijański and Jacek Garbarski

Processes 2026, 14(9), 1480; https://doi.org/10.3390/pr14091480 (registering DOI) - 2 May 2026

Abstract

This study presents the results of evaluating composites based on modified thermoplastic starch (TPS) with BWW40 and FD600/30 cellulose fibers at varying mass contents. The aim of this study was to assess the effect of filler type and quantity on mechanical properties and water absorption. Test samples were prepared using the injection molding method. It was shown that increasing fiber content led to a reduction in strength of approximately 36% for BWW40 fibers and approximately 37% for FD600/30 fibers at maximum fill. Similar results were observed for elongation at break. Young’s modulus increased by approximately 15% for BWW40 fibers and approximately 13% for FD600/30 fibers. Water absorption also increased with increasing fiber content, which is due to the hydrophilic nature of both the starch matrix and the reinforcing phase. The main conclusion drawn from the conducted research is that by properly selecting the type and content of fibers, it is possible to consciously shape the stiffness and dimensional stability of such composites while maintaining their biodegradability. The results obtained allow for a better assessment of the application potential of these materials in the context of developing sustainable material solutions. Full article

(This article belongs to the Section Materials Processes)

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29 pages, 1887 KB

Open AccessReview

Viscoelastic Hydrogels Governed by Molecular Interactions and Mechanochemical Effects

by Wenjie Zhang, Dianrui Zhang, Haocheng Niu, Junsheng Zhang and Yiran Li

Polymers 2026, 18(9), 1126; https://doi.org/10.3390/polym18091126 (registering DOI) - 2 May 2026

Abstract

Hydrogels, particularly those based on polymer networks, exhibit complex mechanical behaviors arising from the interplay between network architecture, molecular interactions, and external stimuli. In particular, their viscoelasticity, energy dissipation, and nonlinear mechanical responses arise from the dynamic nature of crosslinking and multiscale relaxation processes. This review provides a comprehensive overview of hydrogel mechanics from a multiscale perspective, covering viscoelastic behavior, relaxation dynamics, energy dissipation mechanisms, nonlinear deformation, and fracture properties. We summarize recent advances in experimental characterization, including bulk rheology and single-molecule force spectroscopy, and discuss how molecular-level interactions, bond kinetics and mechanochemical processes contribute to macroscopic mechanical performance. In addition, theoretical models and constitutive frameworks describing transient and dynamic polymer networks are critically evaluated to bridge microscopic dynamics with bulk responses. Emerging strategies that integrate dynamic bonding and force-responsive elements are also discussed in the context of tailoring mechanical adaptability and functionality. Finally, we outline current challenges and future directions toward the rational design of hydrogels with tunable viscoelasticity, enhanced mechanical robustness, and programmable mechanical functions. Full article

(This article belongs to the Special Issue Polymer Mechanochemistry: From Fundamentals to Applications)

28 pages, 10066 KB

Open AccessArticle

Pharmacological Mechanisms of Ursolic Acid Derivative Against Prostate Cancer via Regulating Cytoskeletal Homeostasis and Apoptotic Pathways

by Huiyue Shen, Zhaolan Ni, Haibo Guo, Xiaofeng Liu, Yaru Zhao, Xuan He, Yinghan Liu, Yan Zhao and Hongbo Teng

Pharmaceuticals 2026, 19(5), 726; https://doi.org/10.3390/ph19050726 (registering DOI) - 2 May 2026

Abstract

Background: Ursolic acid (UA) is a natural pentacyclic triterpenoid with notable antitumor activity, yet its poor water solubility and insufficient targeting restrict clinical translation. Methods: Forty novel ursolic acid-phosphine derivatives bearing seven distinct lipophilic cationic moieties were synthesized via C28 modification and structurally characterized by ¹H NMR and ¹³C NMR. Their antitumor activities in PC3-M cells were evaluated via in vitro assays. Mechanistic investigations were performed using transcriptomic analysis and Western blot. Molecular docking was performed to predict the binding profile of Compound 25 with FGFR1. In vivo antitumor efficacy and biosafety were assessed in RM-1 xenograft models in C57BL/6 mice. Results: Compound 25 (bearing a tris(3,5-dimethylphenyl)phosphine group at the C28 position with an alkyl chain length of five methylene units) exhibited the most potent activity against PC3-M cells, dose-dependently inhibiting proliferation, migration, and invasion and inducing apoptosis. It triggered mitochondrial apoptosis via ROS accumulation and disrupted cytoskeletal homeostasis by suppressing the FGFR1/KRAS/RAC1/PIP4K2 axis. Molecular docking results suggested its strong binding affinity and specificity. In vivo studies confirmed its significant antitumor effect and favorable safety. Conclusions: These results highlight the potential of Compound 25 as a promising lead compound and provide valuable insights for further medicinal chemistry optimization and the development of novel anticancer drugs derived from ursolic acid. Full article

(This article belongs to the Special Issue Natural Products for the Treatment of Prostate Cancer)

26 pages, 1313 KB

Open AccessArticle

CausalAgent: A Hierarchical Graph-Enhanced Multi-Agent Framework for Causal Question Answering in Production Safety Accident Reports

by Tianyi Wang, Tao Shen, Zhiyuan Zhang, Shuangping Huang, Huiguo He, Qingguang Chen and Houqiang Yang

Algorithms 2026, 19(5), 355; https://doi.org/10.3390/a19050355 (registering DOI) - 2 May 2026

Abstract

Accident reports provide a detailed account of environmental causes, unsafe human behaviors, and subsequent chain reactions. These records serve as essential resources for analyzing accident mechanisms and exploring potential risk patterns within production safety processes. Currently, Graph based Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with Knowledge Graphs (KGs), has emerged as a leading approach for complex causal question answering over extensive unstructured accident documentation. However, the application of this technology in the production safety domain still encounters two primary challenges. First, knowledge graph construction using a single granularity fails to capture fine-grained case details and macro-level standard systems. Second, traditional one-step retrieval paradigms lack the capacity to track deep causal chains or interpret the complex logic of multi-factor coupling. To address these limitations, we propose CausalAgent, a hierarchical graph-enhanced multi-agent framework for causal question answering in production safety accident reports. This framework innovatively combines a Hierarchical Causal Graph (HC-Graph) and a Multi-Agent Collaborative Reasoning (MACR) mechanism. Specifically, the HC-Graph employs a two-layer architecture that links a fine-grained instance layer with a national standard causation layer to resolve conflicts in semantic granularity. The MACR mechanism converts complex natural language queries into executable structured queries and logic verification steps through the sequential cooperation of four specialized agents, namely the Graph Parsing Agent, the Problem Analysis Agent, the Query Generation Agent, and the Reasoning Insight Agent. CausalAgent enables in-depth mining of accident causation mechanisms and provides scientific, robust and interpretable intelligent support for data-driven risk assessment and emergency decision-making. Experiments on real-world accident datasets demonstrate that CausalAgent achieves a

100.0 %

query execution rate and an

87.3 %

reasoning accuracy, outperforming the SOTA baseline by

45.2 %

in terms of absolute accuracy. Full article

(This article belongs to the Special Issue Intelligent Information Processing Methods in Interdisciplinary)

31 pages, 9109 KB

Open AccessArticle

Effects of Elevated Temperatures and Cooling Regimes on the Mechanical Properties and Toughness of Glass Fiber-Reinforced Geopolymer Concrete

by Xugang Tang, Kewei Liu, Xiang Li and Yi Zhang

Buildings 2026, 16(9), 1820; https://doi.org/10.3390/buildings16091820 (registering DOI) - 2 May 2026

Abstract

In this study, an eco-friendly geopolymer concrete (GPC) was synthesized using fly ash, slag, and rice husk ash as precursors, and glass fibers were incorporated to enhance its mechanical properties. And then this study investigates the residual mechanical properties and microstructure evolution of glass fiber-reinforced geopolymer concrete (GFGPC) following elevated temperature exposure and subsequent cooling. Specimens incorporating varying glass fiber volume fractions (0–2.5%) were subjected to temperatures ranging from 25 °C to 800 °C, followed by either natural cooling or water-spraying cooling. The uniaxial compressive strength, Brazilian splitting tensile strength, and three-point flexural strength of the glass fiber-reinforced GPC were experimentally determined. Furthermore, fracture performance indicators—including the energy absorption capacity at failure, characteristic length, and double-K fracture parameters—were systematically analyzed. Results indicate that a glass fiber content of 1.5% optimally enhances the composite’s mechanical performance. Under natural cooling, splitting tensile and flexural strengths exhibit a non-monotonic trend, peaking at 200 °C. Conversely, water-spraying cooling induced thermal shock generally degrades tensile and flexural properties. However, at extreme temperatures (600 °C and 800 °C), water-spray cooling facilitates matrix densification and secondary geopolymerization, resulting in a residual compressive strength increase of 12.16% and 20.77% compared to natural cooling. Furthermore, based on composite damage theory, a binary nonlinear prediction model was developed to accurately capture the coupled effects of temperature and fiber characteristics on the residual compressive strength (R² > 0.90). Coupled with scanning electron microscopy (SEM) observations, the profound effects of elevated temperatures and thermal shock on the GPC gel matrix were elucidated, and the microscopic mechanisms underlying the failure of the fiber-bridging effect at high temperatures were thoroughly investigated. The findings of this study provide a solid theoretical foundation and scientific reference for the performance assessment and repair decision-making of GPC structures post-fire exposure. Full article

(This article belongs to the Special Issue Green and Low-Carbon Comprehensive Utilization of Solid Waste Resources)

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17 pages, 3173 KB

Open AccessArticle

RaTDet: A Marine Radar Transformer Network for End-to-End Target Detection

by Huaxing Kuang, Haocheng Yang and Luxi Yang

Electronics 2026, 15(9), 1933; https://doi.org/10.3390/electronics15091933 (registering DOI) - 2 May 2026

Abstract

Recent advancements in deep learning have shown considerable potential to enhance radar target detection, particularly in improving detection probability under complex environmental conditions. However, existing deep learning approaches largely operate in the real number domain, neglecting the complex-valued nature of radar data, and often inherit vision-oriented architectures that fail to address radar-specific challenges—such as sparse target echoes, the necessity for phase preservation, and constraints imposed by scanning radar systems. Meanwhile, conventional radar signal processing methods, including CA-CFAR, are limited by their dependence on idealized statistical models and often underperform in dynamic and cluttered electromagnetic environments.To overcome these issues, this paper proposes Radar Transformer for Detection (RaTDet), an end-to-end detection network that integrates complex-valued convolutional neural networks (CNNs) and Transformers. RaTDet fully leverages complex-valued data to preserve critical phase and amplitude information, enabling automated feature learning directly from raw radar signals. The model operates effectively with very few pulses, making it suitable for resource-constrained scenarios, and can serve as a pre-trained foundation model for various radar downstream tasks. Experimental results demonstrate that RaTDet achieves excellent detection performance, characterized by high detection probability (Pd) and low false alarm rate (Pfa), outperforming both traditional signal processing and conventional deep learning methods. This work bridges the gap between deep learning and radar signal processing, offering a flexible and powerful network for next-generation radar systems. Full article

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