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Although Industry 4.0 has successfully advanced lean manufacturing through digitalization and automation, its primary focus on operational efficiency leaves emerging strategic priorities—human-centricity, sustainability, and resilience—outside its original scope. The Industry 5.0 agenda explicitly elevates these three pillars, creating new potential to drive lean transformation. However, how Industry 5.0 can systematically drive lean manufacturing transformation remains unclear. To address this knowledge gap, this study develops a strategic roadmap. First, a content-centric literature review identifies 12 key enablers for Industry 5.0-driven lean manufacturing. Second, Fuzzy Interpretive Structural Modeling (FISM) and expert opinions determine hierarchical relationships among the enablers and construct a multi-level structural model. Third, Matrices d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis evaluates the driving power and dependence of each enabler. Finally, a strategic roadmap is developed based on expert synthesis. The findings reveal that “government regulation and incentives” and “employee skill training” are the most critical enablers, while “value chain design and improvement” and “resource input and return” are the most complex and difficult to develop. The roadmap highlights the mediating role of “stakeholder participation and collaboration.” Importantly, the roadmap addresses potential tensions in lean implementation—such as the carbon footprint trade-off of frequent small-batch transport—by embedding sustainability assessment into value chain design and technology governance. This study offers a practical guide for manufacturers to prioritize investments and sequence actions toward lean transformation in the Industry 5.0 era. The main contribution of this study is a strategic roadmap that explains how Industry 5.0 can enable lean manufacturing transformation through prioritized actions and hierarchical enablers, while reconciling efficiency with sustainability and resilience goals. This roadmap offers a practical guide for manufacturers and policymakers to sequence investments and actions toward lean transformation in the Industry 5.0 era. Full article

(This article belongs to the Special Issue Intelligent Manufacturing and Lean Production for Circular Economy Under Industry 4.0/5.0)

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17 pages, 2455 KB

Open AccessArticle

Waterborne Polyurethane Reinforced with SiO₂-Modified TiO₂: Enhanced Mechanical Properties and Retained Hydrostatic Pressure Resistance

by Shuyi Wang, Weiping Yao, Xia Lin, Yamin Xu, Kemei Pei and Yuhai Lu

Polymers 2026, 18(12), 1492; https://doi.org/10.3390/polym18121492 (registering DOI) - 13 Jun 2026

Abstract

Driven by the growing demand for functional textiles featuring excellent waterproofness, moisture permeability and mechanical robustness in outdoor sportswear, medical protection and technical apparel, traditional pongee—despite its desirable softness, high wrinkle resistance and good stability as an ideal substrate fabric—is severely restricted in further application by its intrinsically poor hydrostatic pressure resistance in extremely wet environments. Accordingly, we developed a modified waterborne polyurethane (WPU) coating for pongee substrates to fabricate functional textiles that maintain high hydrostatic pressure resistance while possessing good mechanical properties and increased UV absorption. In this study, by using the sol–gel method, an amorphous silicon dioxide (SiO₂) coating layer was constructed on the surface of titanium dioxide (TiO₂) particles, forming silica-modified titania particles (SiO₂/TiO₂). These SiO₂-modified particles were subsequently physically blended with an anionic waterborne polyurethane system that had been previously modified with a polyester-type modifier A to enhance its hydrostatic pressure resistance. The resulting composite coating was designed to combine the high hydrostatic pressure resistance inherited from the modified WPU matrix, the mechanical reinforcement and increased UV absorption contributed by SiO₂/TiO₂, and satisfactory water repellency on fabric substrates. The results indicate that the incorporation of an appropriate amount of modifier A into the prepolymer system significantly enhances hydrostatic pressure resistance while maintaining high elongation at break. At a SiO₂/TiO₂ loading of 0.2 wt%, the composite film exhibits optimal comprehensive performance, characterized by superior mechanical properties, low water absorption, and static water contact angles exceeding 100° for coated fabrics. SiO₂/TiO₂ composite WPU coatings substantially improve hydrostatic pressure resistance across various fabrics, with 380T polyester taffeta demonstrating the best performance. This resistance remains remarkably stable after standard washing, indicating excellent wash fastness and practical applicability. Full article

(This article belongs to the Section Polymer Applications)

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29 pages, 2813 KB

Open AccessArticle

A Conceptual Framework for Sustainable Vertical Growth in the Housing Sector: A Case Study of the Dammam Metropolitan Area

by Saqr Mohammed Al-Absi, Ali M. Alqahtany and Umar Lawal Dano

Sustainability 2026, 18(12), 6101; https://doi.org/10.3390/su18126101 (registering DOI) - 13 Jun 2026

Abstract

The housing sector in major cities is facing escalating challenges due to rapid population growth and land scarcity. Consequently, vertical growth has been adopted as a strategic solution to optimize land use while balancing economic, social, and environmental needs. This study examines the phenomenon of vertical growth of the Dammam Metropolitan Area (DMA) in Saudi Arabia, from an urban sustainability perspective, focusing on evaluating the current state of multi-story buildings, their determinants, and their impact on quality of life and infrastructure efficiency. This study utilizes a systematic review methodology and a conceptual approach to develop an integrated framework for sustainable vertical growth. Furthermore, an empirical validation was conducted by projecting this framework onto vertical housing projects in Dammam, focusing on challenges related to design, construction quality, shared service management, and the suitability of apartments for family needs. The results indicate that the shift toward vertical growth achieves land-use efficiency, limits random horizontal expansion, and provides economic opportunities. However, it faces social and cultural constraints, most notably the resistance of some families to changing traditional ownership patterns, limited privacy and green spaces, and challenges in building maintenance and operations. The study highlights the importance of integrating urban planning, governance, architectural design, and infrastructure to ensure the sustainability of vertical growth and provide suitable housing alternatives. The study recommends further field research to assess social acceptance, improve quality-of-life indicators, and develop policies encouraging sustainable vertical expansion in alignment with Saudi Vision 2030 and the 2030 Sustainable Development Goals (SDGs), ensuring cities are more resilient, efficient, sustainable, and liveable. Full article

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29 pages, 3497 KB

Open AccessReview

Numerical Simulation for Natural Gas and Hydrogen-Blended Natural Gas Pipeline Safety: A Comprehensive Analysis of the “Leakage–Dispersion–Evolution–Consequence” Disaster Chain

by Bingyuan Hong, Ting Pan, Huizhong Xu, Fubin Wang, Xingyu Wang, Siyan Hong, Zhenglong Li, Zhanghua Yin and Zhipeng Yu

Processes 2026, 14(12), 1939; https://doi.org/10.3390/pr14121939 (registering DOI) - 13 Jun 2026

Abstract

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

21 pages, 4405 KB

Open AccessArticle

Robust Tightly-Coupled Multi-Source Navigation Using Acoustic-Geometric Constraints for Underwater Vehicles in Tunnels

by Xiangbin Wang, Mingyu Yang, Bing Zhao, Tengfei Ma, Lijia Liu and Xinyu Li

J. Mar. Sci. Eng. 2026, 14(12), 1097; https://doi.org/10.3390/jmse14121097 (registering DOI) - 13 Jun 2026

Abstract

Utilizing underwater vehicles for hydropower infrastructure inspection is increasingly vital. However, these GNSS-denied and confined environments pose significant navigation challenges: Inertial Navigation Systems (INSs) suffer cumulative drift, Doppler Velocity Logs (DVLs) face acoustic blind zones near walls, and visual navigation frequently fails in highly turbid waters. To address these issues, this paper proposes a tightly coupled multi-source (INS/acoustic/optical/vision) navigation algorithm leveraging prior wall geometry constraints. Developed within an Error-State Kalman Filter (ESKF) framework, the model seamlessly accommodates sensor spatiotemporal heterogeneity. To overcome optical failures, a structural surface constraint model is innovatively constructed using single-beam sonar ranging. The core contribution involves transforming sonar ranging data into 6-DOF spatial pose constraints based on the dam’s planar characteristics, effectively bounding the localization drift perpendicular to the surface. Field experiments at the hydropower station dam demonstrate that under extreme conditions with total visual failure, the proposed algorithm effectively constrains critical motion degrees of freedom. By maintaining the wall-tracking error within 0.08 m (Root Mean Square Error, RMSE)—which effectively represents the relative localization error given the known absolute position of the structural wall—this method significantly enhances the operational robustness and precision of close-wall inspections in extreme underwater environments. Full article

(This article belongs to the Section Ocean Engineering)

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12 pages, 581 KB

Open AccessArticle

A Nomogram Prediction Model and Scoring System for Resistance in Acute Myeloid Leukemia Patients Treated with Venetoclax Combined with Hypomethylating Agents

by Qingqing Fan, Yujiao Guo, Xiang Hui, Yu Zhang, Jianrui Li, Jinhua Liang and Yongqing Wang

Curr. Oncol. 2026, 33(6), 357; https://doi.org/10.3390/curroncol33060357 (registering DOI) - 13 Jun 2026

Abstract

To investigate the predictive factors for resistance to VEN combined with HMAs in the treatment of AML, construct a drug resistance prediction model, and visualize the model. A retrospective analysis was conducted on 74 AML patients. Multivariate logistic regression was used to identify independent predictors of primary resistance, based on which a nomogram model and a risk scoring system for drug resistance were constructed. The results showed that KIT (p = 0.012), TP53 (p = 0.010), and FAB-M5 (p = 0.059) were significantly associated with primary resistance to VEN. A nomogram prediction model incorporating FAB-M5, KIT, and TP53 was established. Based on the nomogram model, a drug resistance prediction scoring tool comprising three variables was developed, categorizing patients into high-risk (6–10 points), intermediate-risk (3–5 points), and low-risk (0–2 points) groups. Significant differences in NR rates were observed among the three risk groups (p < 0.001). KIT, TP53, and FAB-M5 are independent factors influencing VEN resistance. The constructed nomogram prediction model and scoring system may provide valuable references for predicting primary resistance to VEN. Full article

(This article belongs to the Special Issue Deep Insights into Acute Myeloid Leukemia: Molecular Targets and Evolving Treatment Paradigms)

23 pages, 9007 KB

Open AccessArticle

Modeling, Comparative Investigation and Compensation for Hysteresis Response of Actuator Using Nonlinear Transformation

by Zhisheng Ren, Yuguo Cui, Xingyang Xie, Pan Chen and Yang Yu

Actuators 2026, 15(6), 338; https://doi.org/10.3390/act15060338 (registering DOI) - 13 Jun 2026

Abstract

Aiming at problems such as the reduced positioning accuracy and insufficient dynamic response caused by the inherent hysteresis nonlinearity of piezoelectric actuators, this paper proposes a hysteresis modeling and compensation method for piezoelectric actuators based on nonlinear transformation. The proposed NT hysteresis model utilizes the nonlinear characteristics of activation functions to describe the complex nonlinearity in the hysteresis response of piezoelectric actuators. On this basis, a feedforward compensation controller is further designed based on the proposed inverse NT hysteresis model. Moreover, a composite controller is constructed by combining it with PID feedback to improve the dynamic response speed and trajectory tracking accuracy of piezoelectric actuators. Based on experimental data, the fitting performance of the proposed model is compared with that of several common hysteresis models using evaluation indicators including RMSE, MAPE and SMAPE. Finally, the performance of the proposed control method is verified through step response and sinusoidal trajectory tracking experiments. Experimental results show that the proposed NT hysteresis model performs best in characterizing the hysteresis characteristics of piezoelectric actuators, and the feedforward compensation controller constructed based on its inverse model exhibits superior control performance. Full article

(This article belongs to the Section Actuator Materials)

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76 pages, 9266 KB

Open AccessReview

Recent Advances in Quinoline Synthesis: Sustainable Catalytic Strategies and Emerging Methodologies

by Ignacio M. López-Coca, Shima Ghafouriraz, Silvia Izquierdo, Carlos J. Durán-Valle, Mohammad Qandalee and Alireza Soltani

Molecules 2026, 31(12), 2081; https://doi.org/10.3390/molecules31122081 (registering DOI) - 13 Jun 2026

Abstract

Quinoline derivatives constitute a privileged class of nitrogen-containing heterocycles with extensive applications in medicinal chemistry, agrochemicals, materials science, and functional organic materials. Owing to their broad biological and industrial relevance, the development of efficient, selective, and sustainable synthetic methodologies for quinoline construction remains an active area of research. This review provides a comprehensive overview of recent advances in quinoline synthesis, with particular emphasis on catalytic strategies aligned with the principles of green and sustainable chemistry. Classical transformations, including the Friedländer, Skraup, and Povarov reactions, are revisited in the context of modern catalytic developments that improve reaction efficiency, substrate scope, selectivity, and environmental compatibility. Special attention is devoted to homogeneous and heterogeneous catalytic systems based on both platinum-group and earth-abundant transition metals, highlighting the growing importance of borrowing-hydrogen and acceptorless dehydrogenative coupling methodologies. Recent progress in nanocatalysis, photocatalysis, multicomponent reactions, ionic-liquid-mediated transformations, and metal-free protocols is also critically discussed. Furthermore, solvent-free processes, microwave-assisted synthesis, and recyclable catalytic systems are examined as practical approaches toward minimizing waste generation and energy consumption. Mechanistic aspects, catalytic design principles, substrate limitations, and sustainability metrics are evaluated throughout the review to provide a critical perspective on current methodologies. Collectively, the advances summarized herein demonstrate the rapid evolution of quinoline synthesis toward more atom-economical, environmentally benign, and operationally efficient processes, while also identifying future opportunities for the development of next-generation catalytic platforms for quinoline-based heterocycle construction. Full article

(This article belongs to the Special Issue Exploring Organic Synthesis in Green Chemistry: From Catalytic Reactions to Advanced Applications)

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23 pages, 19029 KB

Open AccessArticle

CETransUNet: An Intelligent Landslide Identification Method Based on Collaborative Optimization of Global Context and Dual Attention Mechanisms

by Tianli Sun, Chengsheng Yang, Jifeng Wu, Zewei Liu, Ziqian Wang and Xiaoqiang Cheng

Remote Sens. 2026, 18(12), 1974; https://doi.org/10.3390/rs18121974 (registering DOI) - 13 Jun 2026

Abstract

Accurate landslide identification is crucial for enhancing emergency response capabilities during destructive geological hazards. Although deep-learning-based semantic segmentation has demonstrated effectiveness, substantial variations in landslide scales and environmental similarities continue to challenge existing methods. This paper systematically constructs a new co-seismic landslide dataset for the Yarlung Zangbo River basin based on the 2017 Nyingchi earthquake, effectively filling a critical regional data gap. This paper proposes CETransUNet (coordinate attention and edge-guided attention transformer UNet), a novel landslide detection model that integrates ResNet and Transformer architectures. Specifically, a coordinate attention (CA) module is introduced within the skip connections between the encoder and decoder. This module encodes positional information along both horizontal and vertical spatial directions and dynamically re-weights the feature maps, thereby effectively suppressing background noise caused by semantic gaps and enhancing the model’s ability to localize landslide regions. Additionally, an edge-guided attention (EGA) module is incorporated into the decoder. This module extracts explicit edge priors from the input image using a Laplacian operator and imposes geometric constraints on the predictions via a boundary reverse attention mechanism, thereby significantly alleviating boundary ambiguity and morphological distortion of landslides. Evaluations across datasets from the Yarlung Zangbo River, Iburi-Tobu, and Bijie regions demonstrate that CETransUNet significantly outperforms state-of-the-art models—including TransUNet, SegFormer, and SwinUNet—in terms of IoU, MIoU, and F1-score. Overall, through the synergistic optimization of the coordinate attention and edge-guided attention modules, the CETransUNet model achieves synchronous enhancement of boundary integrity and geometric precision in complex scenarios, providing a reliable technical solution for large-scale intelligent landslide identification. Full article

(This article belongs to the Special Issue Advances in Geological Hazard Characterization and Assessment: Merging Remote Sensing with Direct Surveys)

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29 pages, 28758 KB

Open AccessArticle

Spatio-Temporal Feature Enhancement for Recognizing Strongly Correlated Sequential Actions in Aircraft Assembly

by Jiaming Shi, Xiang Huang, Guoyi Hou, Chengda Guo, Qingxue Wang and Yumin Chen

Sensors 2026, 26(12), 3781; https://doi.org/10.3390/s26123781 (registering DOI) - 13 Jun 2026

Abstract

The positioning and clamping process in aircraft assembly exhibits pronounced long-term temporal correlations and intense human–machine interactions. Consequently, assembly quality depends heavily on operator compliance and consistency. Capturing long-term, strongly correlated features in complex industrial environments remains a significant challenge. To overcome this, this study proposes a Long-Term Strongly Associated Action Recognition Network (LTSA-Net) tailored for aircraft assembly positioning and clamping tasks. Based on the C3D backbone, the model first incorporates the SimAM attention mechanism and BN modules to significantly enhance focus on critical spatiotemporal features. To address the challenge of capturing long-term temporal dependencies, LTSFEM is designed to extract global temporal information accurately. Furthermore, to balance structural lightweight design with real-time inference requirements, the CWSTB module is integrated to achieve substantial parameter compression. In addition, a dedicated aircraft assembly positioning and clamping dataset was constructed, and a robust training framework was established using the AdamW optimizer and Mixup data augmentation. Experimental results demonstrate that LTSA-Net achieves a recognition accuracy of 98.82% on the LTSA-Dataset, with a per-frame inference time of 42 ms, successfully meeting the dual requirements of high precision and real-time performance in industrial scenarios, and providing a practical technical solution for intelligent monitoring of aircraft assembly processes. Full article

(This article belongs to the Section Industrial Sensors)

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24 pages, 4952 KB

Open AccessArticle

A Comprehensive Evaluation Method for Reservoir Fracability and Fracturing Applicability Based on Multiple Influencing Factors

by Fuchun Tian, Liyong Yang, Xiaonan Ma, Xuewei Liu, Qi Chen, Yingxi Zhang, Shuzhao Guo, Yuwei Li and Genbo Peng

Processes 2026, 14(12), 1935; https://doi.org/10.3390/pr14121935 (registering DOI) - 13 Jun 2026

Abstract

Hydraulic fracturing is the core technology for stimulation and reform of low-permeability and unconventional oil and gas reservoirs. Reservoir fracability directly determines fracture morphology, complexity, and stimulated reservoir volume. To address the shortcomings of existing fracability evaluation models, such as poor applicability, subjective weighting and insufficient accuracy, five key indicators are selected, including brittleness index, brittle mineral index, stress difference coefficient, minimum horizontal principal stress and porosity. First, the three-dimensional discrete lattice method is used to clarify the influence of each parameter on fracture complexity. Then, the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) are combined to determine the indicator weights, a continuous fracability evaluation model is constructed, and a classification standard for fracturing applicability is established. The results show that the brittleness index has the greatest influence on fracture complexity with a weight of 0.3559, followed by brittle mineral index (0.2986), minimum principal stress (0.1994), stress difference coefficient (0.0993) and porosity (0.0467). The reservoir fracability indices of 0.37 and 0.59 are the mutation points of fracture complexity. Based on microseismic evaluation of stimulated reservoir volume (SRV) using an envelope surface method, it is found that reservoirs with low fracability are more suitable for fracturing designs characterized by large cluster spacing, fewer clusters, and smaller stage spacing. In contrast, reservoirs with medium and high fracability can develop more complex fracture networks by reducing cluster spacing, increasing the number of clusters, and adopting higher pumping rates. The research results can provide theoretical basis and technical support for hydraulic fracturing operation design. Full article

(This article belongs to the Special Issue Oil and Gas Well Engineering: Experimental and Numerical Investigation, 2nd Edition)

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16 pages, 2628 KB

Open AccessArticle

Prediction of Rainfall-Induced Slope Stability Spatiotemporal Evolution Based on a Hybrid Transformer–LSTM Deep Learning Framework

by Xin Zhang, Fang Wang, Hao Yang and Shixiao Liu

GeoHazards 2026, 7(2), 75; https://doi.org/10.3390/geohazards7020075 (registering DOI) - 13 Jun 2026

Abstract

Rainfall is a critical factor inducing slope instability, and accurate prediction of the factor of safety (FOS) of slopes under rainfall conditions is of paramount importance for disaster prevention and mitigation. Conventional numerical simulation methods incur high computational costs, while individual machine learning models are often insufficient to adequately capture the nonlinear spatiotemporal evolution characteristics of multiple factors under coupled multi-physics fields. To address these limitations, this paper proposes a Transformer–LSTM prediction framework. First, a fluid–structure coupling model for rainfall-affected slopes is constructed using COMSOL, and multi-factor orthogonal experiments are performed to generate multi-dimensional time-series data. Subsequently, a Transformer–LSTM fusion deep learning model is built, in which LSTM is employed to extract the temporal dynamic characteristics of rainfall infiltration, and the self-attention mechanism of the Transformer is leveraged to enhance feature extraction and global dependency modeling of key disaster-causing factors. Experimental results demonstrate that the Transformer–LSTM model significantly outperforms traditional PSO-LSTM, PSO-SVM, and standalone Transformer or LSTM models in terms of both prediction accuracy and generalization capability. Its coefficient of determination (R²) remains above 0.94, and key evaluation metrics—including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE)—attain the lowest values among the compared models. Furthermore, the SHAP (SHapley Additive exPlanations) interpretability framework is introduced to quantitatively elucidate the model’s predictive decision-making and to establish a physically grounded causal mapping with geotechnical mechanisms. It is confirmed that effective cohesion and slope angle exert a dominant interactive effect on the degradation of slope stability, providing data-driven support for wide-area monitoring of rainfall-induced landslides. Full article

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11 pages, 719 KB

Open AccessProceeding Paper

Enhancing Power Tool Stability and Safety: A Portable Drill and Grinder Holder with Integrated Measurement Guide

by Cerelo T. Tabat, Jay R. De La Serna, Louie O. Besing, Mj N. Zamora and Vince Rowen F. Lopez

Eng. Proc. 2026, 143(1), 12; https://doi.org/10.3390/engproc2026143012 (registering DOI) - 13 Jun 2026

Abstract

This study designed and developed a Portable Drill and Grinder Holder with an Integrated Measurement Guide to improve stability, safety, and accuracy in hand-held power tool operations. Addressing workshop challenges like excessive vibration and uncontrolled tool movement, the project employed a developmental research design involving sixteen (16) welding experts. The prototype was constructed using durable, locally available materials to ensure affordability. Evaluation results showed significant improvements in operator control, with Safety receiving the highest rating (M = 3.66). The findings confirm that the tool meets industry standards for instructional and workshop use. Full article

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33 pages, 12965 KB

Open AccessArticle

Hjorth Reliability Analysis and Its Applications Under Newly Adaptive Progressively First-Failure Censoring Plan

by Mazen Nassar, Refah Alotaibi and Ahmed Elshahhat

Axioms 2026, 15(6), 443; https://doi.org/10.3390/axioms15060443 (registering DOI) - 13 Jun 2026

Abstract

This paper investigates classical and Bayesian inferences for the parameters and reliability metrics of the Hjorth distribution using a new censoring mechanism called the adaptive progressive first-failure censoring scheme. This new strategy combines guaranteed observation of a fixed number of failures with adaptive control of test duration, providing a flexible and practically efficient framework for modern reliability experiments. The Hjorth distribution is considered due to its capability to model various hazard-rate shapes within a simple two-parameter structure. Maximum likelihood estimation is developed, and approximate confidence intervals are constructed using normal approximation and logarithmic transformation methods based on the observed Fisher information matrix and the delta method. A Bayesian framework is also established using independent gamma prior distributions, with posterior inference carried out through maximum a posteriori estimation and Markov chain Monte Carlo simulation. Bayes estimates and both equal-tail and highest-posterior-density credible intervals are obtained. The performance of the proposed methods is evaluated through simulation studies and illustrated using real lifetime data from an engineering domain consisting of the tensile strength of polyester fibers, demonstrating their effectiveness under adaptive censoring settings. Full article

(This article belongs to the Special Issue Reliability and Risk of Complex Systems: Modelling, Analysis and Optimization, 2nd Edition)

46 pages, 10634 KB

Open AccessReview

A Roadmap to Perfused Skin: Defining the Next Generation of Research Questions in Cutaneous Tissue Engineering

by Ahmet Akif Kızılkurtlu and Özgür Yılmaz

Int. J. Mol. Sci. 2026, 27(12), 5350; https://doi.org/10.3390/ijms27125350 (registering DOI) - 13 Jun 2026

Abstract

Cutaneous tissue engineering has advanced from simple coverage substitutes to increasingly complex living constructs, yet the field remains constrained by a decisive problem: timely and durable perfusion. Many engineered skin substitutes can appear vascular in static culture or in small-animal models. However, they still fail when blood flow must be established quickly enough to rescue cells across clinically relevant tissue thickness. Rather than re-catalog platforms already summarized in recent reviews, this critical narrative review reframes the field around perfusion as the master functional endpoint rather than vessel density alone. We analyze the vascularization bottleneck as a sequence, internal network formation, host inosculation, flow initiation, and perfusion stability—and use that sequence to reassess biomaterial design, cell-based strategies, immunomodulation, decellularized matrices, bioprinting, microfluidics, and prevascularization. We intentionally distinguish implantable skin substitutes from perfused in vitro platforms such as skin-on-chip systems, arguing that these are linked but non-interchangeable application spaces with different success criteria. Building on this distinction, we propose a research agenda centered on functional benchmarking of perfusion, spatiotemporal coordination of scaffold dynamics, immune–mural–lymphatic–vascular crosstalk, scalable hierarchical vascular fabrication, and predictive human test platforms. The central argument is that translation will depend not on ever more isolated pro-angiogenic interventions but on integrated systems that survive the ischemic interval, connect rapidly, tolerate blood entry, maintain a workable inflow–outflow balance, and remodel into a stable, skin-specific microvasculature. Full article

(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)

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