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Additive manufacturing with soil–cement mixtures is emerging as a disruptive approach to advancing sustainable manufacturing processes. However, its industrial scalability remains limited by material brittleness and a lack of process standardization. This study presents an integrative literature review that critically evaluates the influence of fiber reinforcement on the 3D printing process and the mechanical performance of soil–cement mixtures within the context of sustainable construction and circular economy principles. The analysis integrates fresh-state rheological behavior with hardened-state performance, showing that an optimized fiber dosage (0.3–0.5% by volume) shifts the failure mode from brittle to quasi-ductile while reducing crack propagation by approximately 60%. Additionally, the study compares various fiber types, including synthetic and natural alternatives. The results show that synthetic fibers used at low dosages (0.5–1.0% by volume) provide the greatest improvements in tensile strength and post-cracking ductility. In contrast, natural fibers, typically used at higher dosages (8.0–13.0% by volume), mainly improve toughness and thermal performance, with more limited gains in strength. The review also identifies key gaps in the existing literature, such as a lack of standardized protocols for measuring process parameters and the need for studies that address long-term durability and comprehensive lifecycle assessments. These findings outline a clear research roadmap to support the consolidation of reinforced soil–cement as a resilient and sustainable material for next-generation additive manufacturing. Full article

(This article belongs to the Special Issue Application of 3D Printing Technology in Manufacturing and Material Processing)

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16 pages, 1189 KB

Open AccessArticle

Magnesium Supplementation Improves Cortical Stratification and Neuronal Differentiation in Blood–Brain Barrier-Integrated Human Brain Organoids

by Sara Castiglioni, Antonella Tosoni, Manuela Nebuloni and Jeanette A. Maier

Biomedicines 2026, 14(6), 1242; https://doi.org/10.3390/biomedicines14061242 - 29 May 2026

Abstract

Background/Objectives: Magnesium (Mg) is essential for neuronal maturation, yet its role in human cortical development remains poorly defined. Here, we investigated the effects of physiological (1 mM) and elevated (5 mM) concentrations of MgSO₄ and magnesium pidolate (MgPid) on human brain organoids co-cultured with an in vitro blood–brain barrier (BBB) model. Methods: Human brain organoids derived from induced pluripotent stem cells were co-cultured with an in vitro BBB system and treated for 4 days with either MgSO₄ or MgPid at physiological and elevated concentrations. Cortical organization was assessed by transmission electron microscopy and immunofluorescence analysis. Western blotting for neurotransmitter receptors and Mg transporters, quantification of intraorganoid Mg²⁺ levels, ELISA-based measurement of GABA and dopamine, and analysis of glutamate were performed. Results: High Mg exposure enhanced cortical stratification and neuronal organization, as shown by the localization of CTIP2 in the outermost layer and TBR2 in the inner layer, together with ultrastructural features consistent with advanced differentiation. Elevated Mg increased intraorganoid Mg²⁺ levels without altering Mg transporter abundance and selectively modulated neurotransmitter receptor expression: NMDA-R levels were reduced by MgPid, whereas GABA_A-R and GABA_B-R were upregulated, particularly in response to MgPid. Levels of glutamate, GABA, and dopamine remained unchanged. Conclusions: These findings identify Mg, especially in the form of MgPid, as a modulator of cortical architecture and inhibitory–excitatory receptor balance in human organoids, supporting its potential relevance for neurodevelopmental regulation and Mg-based therapeutic strategies. These results also support organoids as human-relevant, animal-free tools for neuroscience and neuropharmacological research. Full article

(This article belongs to the Topic Applications of Biomedical Technology and Molecular Biological Approach in Brain Diseases, 2nd Edition)

30 pages, 37529 KB

Open AccessArticle

Morphometric and Multivariate Analysis of Geomorphological and Multi-Hazard Dynamics in the La Sabana River Basin, Acapulco–Mexico

by Jesús Alfonso Carreto-Gutiérrez, Oscar Frausto-Martínez, Benjamín Castillo Elías, Herlinda Gervacio Jiménez, Julio César Morales Hernández and José Ángel Vences Martínez

Water 2026, 18(11), 1324; https://doi.org/10.3390/w18111324 - 29 May 2026

Abstract

Coastal basins are systems highly susceptible to flooding and erosion, processes that intensify during extreme cyclonic events. This study aims to develop an integrated physical–geographic framework to characterize the geomorphological and multi-hazard dynamics of the La Sabana River basin in Acapulco, Guerrero, in southeastern Mexico. The methodology integrates the analysis of natural and anthropogenic landscape components, 19 morphometric indicators, and Principal Component Analysis (PCA) at the sub-basin scale. The results reveal a high drainage network density (3.8–5.4 km/km²) and short concentration times (0.98–2.75 h), indicating a rapid hydrological response and high susceptibility to flash floods and active erosion. Six critical sub-basins with concentration times ≤ 1.5 h have been identified, spatially coinciding with areas of high anthropogenic exposure. The hypsometric index values (0.04–0.388) indicate advanced geomorphological evolution in most sub-basins. Principal component analysis (PCA) explained 65.8% of the total variance in the first two components: component 1 (52.7%) is linked to basin size and drainage network organization, and component 2 (13.1%) is associated with basin shape. The findings of this research have provided a spatially explicit, robust, and replicable framework that helps strengthen risk governance and guide land-use planning in tropical coastal basins exposed to hydrometeorological hazards. Full article

(This article belongs to the Special Issue Spatial Analysis of Flooding Phenomena: Challenges and Case Studies)

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34 pages, 2148 KB

Open AccessReview

Recent Advances in Microenvironment-Responsive Materials for Periodontitis Therapy

by Wenhan Ma, Yutong Han, Tong Cui, Jinfeng He and Haishan Shi

Int. J. Mol. Sci. 2026, 27(11), 4943; https://doi.org/10.3390/ijms27114943 (registering DOI) - 29 May 2026

Abstract

Periodontitis is a chronic inflammatory condition characterized by the progressive destruction of periodontal supporting tissues. With a global prevalence exceeding 60%, it poses a significant public health challenge. Traditional therapeutic approaches, primarily mechanical debridement, systemic antibiotics, and surgical interventions, often face limitations such as incomplete biofilm removal, rapid drug clearance, and systemic adverse effects. To overcome these challenges, recent research has shifted towards the development of intelligent biomaterials capable of modulating the pathological microenvironment. These microenvironment-responsive strategies leverage unique biochemical signatures, including acidic pH, elevated reactive oxygen species (ROS), and enzymatic dysregulation, to facilitate precise, on-demand drug delivery at the lesion site. This review examines recent advances from three integrated perspectives: (1) material platforms (hydrogels, microneedles, fiber membranes, microspheres, inorganic nanoparticles, and vesicles); (2) responsive design (pH, ROS, enzyme, glucose, and multi-stimulus cascade logic); and (3) spatiotemporal functional orchestration (early-stage microecological remodeling, mid-stage osteoimmunomodulation, and late-stage tissue regeneration). Additionally, we analyze critical translational challenges, including manufacturing scalability, clinical sterilization, and long-term biosafety, while discussing prospects for clinical implementation. This review aims to provide a strategic roadmap and theoretical guidance for the development of next-generation precision therapies for periodontitis. Full article

(This article belongs to the Special Issue Decoding the Bone Microenvironment: Molecular Control of Regeneration and Repair)

20 pages, 3446 KB

Open AccessArticle

V-Shaped Liquid Crystal: Structural Variation on Phase Transition

by Rajni Chaudhary, Ashok Singh Bahota, Neelam Agrawal, Arti Yadav, Ayush Shukla, Veena Prasad, Alejandro Pedro Ayala, Swapnil Singh and Poonam Tandon

Optics 2026, 7(3), 40; https://doi.org/10.3390/opt7030040 - 29 May 2026

Abstract

Bent-core liquid crystals are renowned for their remarkable optical and ferro-electrical properties, making them highly sought after for various applications. However, to harness their full potential, a thorough understanding of their structural mechanisms and fluctuations during phase transitions is imperative. In this study, we conducted an in-depth analysis of the structural conformation of a V-shaped liquid crystal, specifically (E) 1,2-phenylene bis[4-((E)-(4-pentyloxy chloro phenyl) diazenyl) benzoate], referred to as V1, utilizing density functional theory (DFT) calculations at the B3LYP/6-311G(d,p) level. Geometry optimization and frequency calculations of the most stable conformers were performed at the same theoretical level. Our investigation into the mesomorphic behavior of V1 unveiled two enantiotropic phase transitions: Isotropic (Iso) → Nematic (N) → Smectic A (SmA) → Crystalline (Cry), with decreasing temperature. To elucidate the molecular alterations of V1 at the microscopic level, Fourier Transform Infrared (FT-IR) and Fourier Transform Raman (FT-Raman) spectra were recorded across various temperature ranges. Remarkably, the simulated vibrational spectra exhibited a striking resemblance to the experimentally observed vibrational spectra at room temperature, validating the accuracy of our computational approach. These findings hold immense promise for advancing further research and facilitating the development of novel applications leveraging the unique properties of bent-core liquid crystals. Full article

(This article belongs to the Special Issue Multimodal Vibrational Spectroscopy and Laser Applications in Molecular and Material Research)

18 pages, 1685 KB

Open AccessReview

Tracheal Tissue Engineering: Advances and Challenges

by Nina D. Kosciuszek, Joanne Walker, Heather Wanczyk and Christine Finck

Bioengineering 2026, 13(6), 641; https://doi.org/10.3390/bioengineering13060641 (registering DOI) - 29 May 2026

Abstract

Traumatic tracheal injuries and congenital defects can be life-threatening and are associated with substantial morbidity and mortality. Regenerating the trachea through tissue-engineered scaffolds has emerged as an innovative alternative to traditional therapies that involve tracheal resection with primary end-to-end anastomosis or tracheostomies. Despite significant advances in biomaterial developments, stem cell biology, and novel scaffold fabrication, successful clinical translation of tracheal constructs remains limited. Major challenges include inadequate vascularization following implantation, epithelial regeneration, immune reactions, mechanical instability, infection, and inability of adaptive scaffold systems to withstand long-term tissue remodeling. While general tracheal tissue-engineering techniques and the materials, cell lines, and fabrication methodologies have been previously explored, this review summarizes current advancements in tracheal tissue engineering while emphasizing the mechanobiological and translational barriers that preclude functional tracheal regeneration and clinical success. Emerging knowledge in immunomodulatory biomaterials, dynamic scaffolds, strategic vascularization methods, and adaptable constructs has paved the way for researchers to develop a tracheal scaffold that can be translated into clinical use. This review provides a critical framework that discusses the advantages and potential pitfalls of the aforementioned technologies. Full article

(This article belongs to the Section Regenerative Engineering)

26 pages, 1872 KB

Open AccessReview

Advances in CO₂ Capture Technologies: A Review

by Yuzheng Liang and Yuzhong Li

Energies 2026, 19(11), 2633; https://doi.org/10.3390/en19112633 - 29 May 2026

Abstract

The rapid increase in atmospheric

{CO}_{2}

concentration has made carbon capture an essential strategy for mitigating climate change. This review systematically summarizes

{CO}_{2}

capture technologies following the complete process chain. First, three major routes based on combustion stages are introduced: pre-combustion [...] Read more.

The rapid increase in atmospheric

{CO}_{2}

concentration has made carbon capture an essential strategy for mitigating climate change. This review systematically summarizes

{CO}_{2}

capture technologies following the complete process chain. First, three major routes based on combustion stages are introduced: pre-combustion (e.g., coal gasification, biomass co-firing), combustion-based (oxy-fuel combustion and chemical looping combustion), and post-combustion capture. For post-combustion capture, which is the most widely applicable to existing emission sources, three core separation methods are further elaborated: absorption (amine blends, ionic liquids, deep eutectic solvents), adsorption (zeolites, activated carbon, MOFs, COFs, solid amine sorbents), and membrane separation (polymeric, inorganic, and mixed matrix membranes). Key strategies for performance enhancement—such as functionalization, pore engineering, and composite systems—are highlighted. Despite significant advances, large-scale deployment remains challenged by high costs, high energy consumption, and inadequate material stability. Future research should prioritize low-cost, energy-efficient, and robust capture materials and processes to enable net-zero and negative carbon emissions. Full article

(This article belongs to the Special Issue Catalytic Hydrogen Production and Hydrogen Energy Utilization—2nd Edition)

36 pages, 913 KB

Open AccessArticle

LR Linear Regression Model and FNN Feed-Forward Neural Network: Hybrid Approach to Predict SOH of Lithium Ion Batteries

by Alice Cervellieri

World Electr. Veh. J. 2026, 17(6), 289; https://doi.org/10.3390/wevj17060289 - 29 May 2026

Abstract

The integration of electric vehicles with grid vehicles promotes the creation of multi-energy microgrid models. One of the aims of these models is to decrease electricity usage through Vehicle-to-Grid planning. Effective management of microgrids necessitates sophisticated automation and control systems, which can prove challenging to establish and sustain. To tackle these challenges, the author introduces a hybrid model that merges a Linear Regression model and a Feedforward Neural Network, created using Matlab software. This combined algorithm adjusts the quantity of hidden neurons to enhance performance, guided by the evaluation criteria of Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error based on batteries B0005, B0006, and B0007 from the NASA PCoE Research Center Dataset. The author forecasts the lifespan of the battery that most accurately reflects its degradation, revealing important implications for the future advancement of systems that employ Linear Regression and Feedforward Neural Networks for integrating electric vehicles into Vehicle-to-Grid systems. The comparison among the training, testing, and validation stages of the methodology serves to thoroughly demonstrate its effectiveness. Furthermore, the author indicates that the LR-FFN algorithm provides predictive tools relevant for the management of V2G-compatible EV systems and performs superiorly compared to other methods noted in the existing literature. Additionally, the author aimed to specifically identify the attributes of the LR-FNN model for prospective usages, emphasizing its efficacy in developing effective microgrid management, promoting energy efficiency, and ensuring that microgrids remain secure and resilient against failures or threats. Full article

(This article belongs to the Section Charging Infrastructure and Grid Integration)

12 pages, 5276 KB

Open AccessArticle

Wind Profiling from Boundary Layer to Stratosphere Using a Scanning Rayleigh Doppler Lidar and a Coherent Lidar

by Hengjia Liu, Jie Liu, Sijiang Wu, Shuhua Zhang, Jiawei Li, Chong Chen, Dongsong Sun and Yuli Han

Photonics 2026, 13(6), 533; https://doi.org/10.3390/photonics13060533 (registering DOI) - 29 May 2026

Abstract

Accurate measurements of wind fields in the troposphere and stratosphere are essential for advancing atmospheric dynamics research, improving weather prediction, and supporting aerospace operations. However, a single Doppler lidar technique usually has limited capability to provide vertically extended wind profiles across both aerosol-rich lower altitudes and molecular-dominated higher altitudes. In this paper, we present a hybrid Doppler lidar system that combines a 355 nm scanning incoherent Rayleigh Doppler lidar with a 1550 nm coherent aerosol Doppler lidar for multi-scale wind field detection. The coherent Doppler lidar is used for boundary-layer wind retrievals, while the Rayleigh Doppler lidar, based on the double-edge technique, extends wind profiling from the upper boundary layer to approximately 40 km. Field deployments demonstrate continuous wind profiling from 50 m to 40 km, extending from the boundary layer to the stratosphere. Comparisons with radiosonde measurements show good agreement during the field campaigns, supporting the feasibility of this hybrid configuration for vertically extended wind profiling. The resulting high-resolution wind measurements across multiple atmospheric regions provide valuable data sources for studies of multi-scale circulation research, gravity wave dynamics, and climate-related atmospheric processes. Full article

(This article belongs to the Special Issue Advanced Lasers and Their Applications, 3rd Edition)

33 pages, 15846 KB

Open AccessArticle

AI for Garden Design Visualization: Development and Validation of the GardenDiff Model

by Xiaolong Sun, Xi Chen, Chao Zhou, Shengsha Wu, Hongbo Zhao and Kun Li

Buildings 2026, 16(11), 2195; https://doi.org/10.3390/buildings16112195 - 29 May 2026

Abstract

The rapid advancement of AI-driven generative design brings new opportunities, but its application in landscape garden design remains limited by two gaps: (1) semantic misalignment between generated images and the designer’s intent, and (2) low-resolution outputs with insufficient details. To address these gaps, we developed GardenDiff, a domain-adapted diffusion model trained via parameter optimization and a specialized landscape garden dataset. Central to this approach is Structured Design Captioning (SDC), a hierarchical annotation system specifically designed for garden design that encodes design elements, style features, and auxiliary scene information. To develop this model, we designed a three-stage experimental framework. In Stage 1, we examined the effects of training caption systems and training resolution on generated landscape garden imagery by controlled experiments. In Stage 2, we conducted joint training across five garden styles (Chinese, Japanese, Mediterranean, Nordic, and English) based on the optimized parameter settings from Stage 1 to construct the GardenDiff model. In Stage 3, we validated the model performance through expert evaluation (N = 36) and public evaluation (N = 136) and analyzed style-specific variations in the generated outcomes. Research results showed that Structured Design Captioning (SDC) improved Spatial Rationale by 19.67–39.46% compared with generic captions, and training at 1536 × 1536 pixels improved image quality by 23.2% compared with 768 × 768-pixel training. GardenDiff trained with these optimized parameters showed notable advantages. Its overall scores (5.06) exceeded those of Stable Diffusion XL base 1.0 (SDXL 1.0) by 16.4% and DreamShaper XL by 22.4%. The model improved across four dimensions, including Design Rationale, Design Professionalism, Design Accuracy, and Design Satisfaction. Our study offers a new model to improve the perspective visualization of generative garden design and provides insights into AI-informed landscape and urban design. Full article

(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

24 pages, 1482 KB

Open AccessReview

Advancements in Dual-Load Antibody–Drug Conjugates and Challenges with Quality Analysis

by Xiaojuan Yu, Xiao Ke, Yao Tang, Tao Tang, Yongbo Ni, Luyun Guo, Yongfei Cui, Yuting Mei, Gangling Xu, Gang Wu, Yalan Yang, Maoqin Duan, Jialiang Du, Meng Li, Jiao Tang, Shijun Yin, Jiali Zuo, Yanhua Xu, Yonghao Zhao, Yan Li and Chuanfei Yu Show full author list Hide full author list

Pharmaceuticals 2026, 19(6), 860; https://doi.org/10.3390/ph19060860 (registering DOI) - 29 May 2026

Abstract

Antibody–drug conjugates (ADCs) are a pivotal technology for precision cancer therapy, harnessing the synergistic effects of antibody targeting and toxin delivery. However, traditional ADCs encounter limitations in efficacy that stem from tumor resistance, heterogeneity, and intense target competition. Dual-payload ADCs (DP-ADCs) represent a promising solution to these challenges, as they leverage dual mechanisms of action that mitigate acquired drug resistance and enhance adaptability to tumor heterogeneity. The complex structure of DP-ADCs presents substantial quality control hurdles. In this manuscript, we review the current payload selection and conjugation strategies of DP-ADCs and examine recent advances in quality control research. Specifically, we analyze the analytical challenges related to the quantification of free toxins, the determination of the total antibody content, and the characterization of the drug-to-antibody ratio and its distribution. Ultimately, the aim of this work is to provide valuable guidance for future DP-ADC quality control analyses to facilitate their clinical translation and application. Full article

(This article belongs to the Section Biopharmaceuticals)

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17 pages, 543 KB

Open AccessReview

Cultural Alignment and Psychological Well-Being: Insights from Person–Culture Match Research

by Vera Vogel

Healthcare 2026, 14(11), 1513; https://doi.org/10.3390/healthcare14111513 - 29 May 2026

Abstract

Background: Research on psychological well-being has traditionally focused on individual characteristics such as personality traits, values, and beliefs. However, comparatively less attention has been paid to the sociocultural contexts in which individuals are embedded and that influence how the individual characteristics are expressed, evaluated, and rewarded. One theoretical framework that captures this interaction is person–culture match (PCM), defined as the alignment between individual traits, values, or beliefs and those prevalent within the surrounding culture. Objectives: This narrative review synthesizes conceptual and empirical research on PCM and discusses its implications for psychological well-being and broader societal consequences. Methods: A narrative review of the literature was conducted to identify key theoretical contributions and empirical studies on PCM. The reviewed literature includes cross-cultural research examining the alignment between personal characteristics and corresponding cultural characteristics, as well as its implications for well-being and broader societal processes. Results: Across a wide range of studies, individuals tend to report higher well-being when their personal traits, values, or beliefs align with characteristics prevalent within their sociocultural context. This pattern has been documented across multiple characteristics, including personality traits, religiosity, political ideology, and personal values. Higher PCM has been associated with higher life satisfaction, greater positive affect, stronger self-esteem, and lower levels of stress and depressive symptoms. Conclusions: The literature suggests that well-being is shaped not only by individual characteristics but also by their alignment with one’s sociocultural contexts. Future research is needed to clarify the mechanisms underlying these effects and to explore the broader societal consequences of PCM. Considering cultural alignment may therefore be valuable for both advancing research and informing public health strategies and policy interventions aimed at enhancing well-being and social cohesion. Full article

(This article belongs to the Special Issue Global Health and Health Care Services: Social Psychological Determinants of Health and Well-Being)

19 pages, 1604 KB

Open AccessReview

Teaching and Teacher Educating Data Literacy in K-12 STEM Education: Looking Back, Moving Forward (AA)

by Azita Manouchehri and Aula Andika Fikrullah Al Balad

Educ. Sci. 2026, 16(6), 860; https://doi.org/10.3390/educsci16060860 (registering DOI) - 29 May 2026

Abstract

The growing centrality of data in contemporary society has intensified calls to expand data literacy across K–12 education, positioning teachers as key agents in this effort. This article traces the emergence of data literacy as a domain of educational research and reports findings from a systematic review of empirical studies on K–12 STEM teacher data literacy published between 2015 and 2025. Guided by the PRISMA framework, searches of Academic Search Complete, APA PsycINFO, and supplementary sources yielded a final sample of 26 studies. The review examines (1) what has been prioritized in research on teaching data literacy and (2) the conceptual models used to study data literacy in educational contexts. Findings indicate that existing research primarily emphasizes teachers’ knowledge, beliefs, and use of technological tools, with comparatively limited attention to classroom enactment and student learning. Conceptually, the field is characterized by the use of diverse and often disconnected frameworks, including competency-based, statistical reasoning, and pedagogical models, resulting in a fragmented knowledge base. We argue that this fragmentation stems from underlying epistemological, methodological, and contextual tensions that have yet to be theoretically reconciled. In response, we propose an integrative perspective that conceptualizes data literacy as a situated, practice-based, and socio-epistemic phenomenon. This framing highlights the dynamic interplay among interpretive reasoning, instructional design, mediational tools, and contextual conditions. Advancing the field requires moving beyond isolated lines of inquiry toward theoretically coherent approaches that connect teacher cognition, instructional practice, and student learning in order to support meaningful and equitable participation in a data-driven world. Full article

(This article belongs to the Special Issue Data Literacy in STEM Education)

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24 pages, 552 KB

Open AccessArticle

How Corporate Social Responsibility Influences Loyalty and Willingness to Pay Through Trust and Attitudes Among Young Consumers

by Jorge Figueiredo, Isabel Oliveira, Ricardo Jorge Pinto, Manuel Sousa Pereira, Amândio F. C. Silva and António Cardoso

Adm. Sci. 2026, 16(6), 261; https://doi.org/10.3390/admsci16060261 - 29 May 2026

Abstract

Corporate social responsibility (CSR) has become an increasingly important factor in shaping consumer responses, particularly among younger generations who are often portrayed as ethically sensitive yet behaviorally ambivalent. While prior research has established that CSR is associated with favorable consumer outcomes, the mechanisms through which CSR perceptions translate into loyalty and economic value remain insufficiently understood. Addressing this gap, the present study adopts a mediation perspective to examine how perceived CSR influences consumer loyalty and willingness to pay through the sequential roles of trust and attitudes among young consumers. Using survey data collected from 307 young consumers in Portugal, the study tests a conceptual framework in which CSR perceptions influence trust and attitudes, which in turn shape loyalty and willingness to pay. Data were analyzed using reliability analysis, exploratory factor analysis, regression-based mediation analysis, and bootstrapping procedures. The results provide strong support for the proposed mediation framework, revealing that CSR does not exert direct effects on loyalty or willingness to pay. Instead, its relationship with these outcomes is indirectly transmitted through trust, attitudes, and loyalty. Specifically, trust and attitudes sequentially mediate the relationship between CSR and loyalty, while the relationship between CSR and willingness to pay operates indirectly through loyalty. These findings contribute to the CSR and consumer behavior literature by clarifying the mechanisms through which CSR creates value for firms and by advancing a process-oriented understanding of CSR-driven consumer behavior. From a managerial perspective, the results highlight the importance of designing and communicating CSR initiatives that foster trust and positive attitudes, thereby strengthening long-term consumer relationships and enhancing willingness to pay among young consumers. Full article

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25 pages, 742 KB

Open AccessReview

Advances in Optimized and Safe Path Planning of Marine Autonomous Surface Vehicles: A Review

by Lirong Kou and Xiaoyang Gao

Sensors 2026, 26(11), 3445; https://doi.org/10.3390/s26113445 - 29 May 2026

Abstract

With the rapid development of intelligent shipping and the autonomy of marine engineering equipment, numerous studies have focused on the advancement of Autonomous Surface Vehicles (ASVs). As a fundamental component of ASV automation systems, path planning directly determines the safety and economy of ship navigation. This paper systematically reviews recent research progress in ASV path planning. First, five key issues are identified for ASV path planning: navigation environment, environment modeling, ship motion model, collision avoidance for safety, and optimization. Second, existing algorithms are classified into four categories: graph search-based, sampling-based, numerical optimization-based, and artificial intelligence-based. The improvement directions and application scenarios of each category are elaborated. Finally, the four types of algorithms are evaluated against three indicators: path quality, scalability and extensibility, and algorithm performance. Analysis of the reviewed literature shows that traditional graph search and sampling algorithms perform well in various aspects under static environments, but are insufficient in adapting to multiple constraints and generalizing to different environments. In contrast, artificial intelligence algorithms represented by deep reinforcement learning exhibit significant advantages in dynamic collision avoidance decision-making, multi-agent coordination, and environmental generalization, and have become the mainstream direction of current research. This paper summarizes the existing challenges in safety and optimization in current ASV path planning research and prospects future development directions. Full article

(This article belongs to the Special Issue Intelligent Optimization for Marine Vehicle Navigation and Positioning)

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