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This study investigates managers’ perceptions of digitalization and artificial intelligence (AI) adoption within the framework of Industry 4.0, emphasizing the relationship between technological modernization, organizational culture, and sustainability. Drawing on empirical data collected in 2025 from 150 Romanian companies ’managers by applying a structured questionnaire, followed by a multivariate analytical approach supported by the Benjamini–Hochberg correction, the research identifies critical managerial perceptions that influence the success of digital transformation. The findings show that managers recognize digitalization as a strategic opportunity for process optimization and competitiveness. At the same time, they perceive it as a structural challenge driven by legacy systems, financial constraints, and limited digital competencies. Similarly, managers view AI as a valuable tool for data analysis and market forecasting, while also expressing concerns related to ethical, technical, and cybersecurity risks. The study further reveals managerial ambivalence toward Industry 4.0. Although automation and IoT are considered inevitable for maintaining competitiveness, their implementation remains constrained by logistical and cultural barriers. By integrating technological, organizational, and human dimensions, this research contributes to the literature on sustainable digital transformation. It provides an in-depth understanding of how managerial perceptions mediate the balance between innovation, responsibility, and long-term resilience. Finally, the results offer actionable insights for policymakers and business leaders seeking to align digitalization and AI initiatives with sustainable development objectives. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

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28 pages, 3763 KB

Open AccessArticle

Directional Access to the Sky as a Criterion of Residential Environmental Quality in Sustainable Urban Design

by Zdzisław Pelczarski and Michał Pelczarski

Sustainability 2026, 18(5), 2569; https://doi.org/10.3390/su18052569 (registering DOI) - 5 Mar 2026

Abstract

Access to the sky is a key element of residential environmental quality. In densely built-up urban areas, exposure to the sky is often limited not only quantitatively but, above all, directionally. Traditional illuminance metrics, such as the Sky View Factor (SVF) or Daylight Factor (DF), describe the proportion of visible sky or the amount of light in an averaged manner, without considering its relationship to the functional organisation of the human field of view.This article introduces the Relative Retinal Image (RRI) metric, which evaluates directional access to the sky through geometric analysis of viewing directions in relation to functional zones of the visual field, without reconstructing perceived images or simulating physiological processes. Within this geometric framework, human vision is interpreted as operating simultaneously in two visual cones: a narrow central cone responsible for acute, conscious vision (RRI-A), and a wider peripheral cone enabling the reception of low-resolution but spatially stable stimuli (RRI-B). For clarity, three concentric central ranges are distinguished: foveal (0–2.5°), sharp central (0–5°), and extended interpretative central vision (up to 10°). The proposed approach provides a geometry-based analytical tool that complements existing daylight metrics in the assessment of sustainable residential environments, without formulating normative or biological design prescriptions. Based on geometric and graphical analyses and a case study of the Józefowiec housing estate in Katowice, the results indicate that the directional structure of the sky view may be lost despite compliance with conventional planning criteria. Full article

(This article belongs to the Special Issue Shaping Sustainable Environments: Advances in Building and Infrastructure Design)

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27 pages, 1809 KB

Open AccessArticle

A Stability-Aware Adaptive Fractional-Order Speed Control Framework for IPMSM Electric Vehicles in Field-Weakening Operation

by Chih-Chung Chiu, Wei-Lung Mao and Feng-Chun Tai

Energies 2026, 19(5), 1326; https://doi.org/10.3390/en19051326 (registering DOI) - 5 Mar 2026

Abstract

High-performance speed regulation of interior permanent magnet synchronous motor (IPMSM) drives in electric vehicle (EV) applications becomes particularly challenging in the field-weakening region, where voltage constraints, parameter variations, and nonlinear aerodynamic loads significantly affect the closed-loop stability. To address these challenges, this paper proposes a stability-aware adaptive fractional-order speed control framework for EV traction systems. The framework integrates a fractional-order PI (FOPI) core to provide iso-damping robustness, a bounded fuzzy gain-scheduling mechanism for real-time adaptation, and an offline multi-objective optimization layer for systematic parameter tuning. A Lyapunov-based qualitative analysis is provided to justify closed-loop ultimate boundedness under adaptive gain modulation and field-weakening constraints. The fuzzy scheduler is explicitly structured to regulate the error energy dissipation rate by modulating the proportional and integral gains while preserving the gain boundedness. The controller parameters are optimized using a diversity-driven fractional-order multi-objective PSO algorithm to balance the tracking accuracy and control effort. The proposed framework was validated using a high-fidelity MATLAB/Simulink–CarSim 2023 co-simulation platform under the aggressive US06 driving cycle. The results demonstrated a zero-overshoot transient response, robustness against a 2.5× inertia mismatch, and sustained performance under flux-linkage and inductance variations in deep field-weakening operation. Compared with conventional PI-based strategies, the proposed approach reduced the speed RMSE by 82%, lowered the current THD from 18.5% to 3.2%, and reduced the cumulative DC-link current-squared index by 6.7%. These results validate the practical robustness and computational feasibility of the proposed stability-aware framework for EV traction control. Full article

(This article belongs to the Topic Advanced Electrical Machines and Drives Technologies, 3rd Edition)

33 pages, 3309 KB

Open AccessArticle

A Multidimensional Traffic Accident Causation Index for Severity Modeling Using Explainable Machine Learning

by Halil İbrahim Şenol and Gencay Sarıışık

Systems 2026, 14(3), 282; https://doi.org/10.3390/systems14030282 (registering DOI) - 5 Mar 2026

Abstract

Road traffic accidents remain a major public health concern, and effective safety management requires interpretable tools that integrate multiple causal dimensions. This study proposes a Traffic Accident Causation Index (TACI) to provide a holistic representation of severity-related drivers by combining six theoretically grounded domains: Accident Infrastructure, Driver, Pedestrian, Road Condition, Emergency and Response, and Severity. Using a national police-reported dataset from Türkiye (N = 13,639), operational variables are mapped to normalized risk scores, aggregated into domain indices, and combined into a 0–100 composite TACI score. To assess the robustness and compatibility of the proposed index framework, we develop ensemble machine learning models (Random Forest, Gradient Boosting, LightGBM, XGBoost, and CatBoost) under two feature configurations: an Extended Feature Set (EFS) with the original variables and a Core Feature Set (CFS) consisting of the six domain indices. The results indicate that domain-level aggregation improves predictive stability, and the best-performing boosting models (XGBoost/CatBoost) achieve near-perfect agreement with the constructed index (test R² > 0.99) and very high classification performance (AUC > 0.999). SHAP-based explainability highlights pedestrian exposure and vulnerability as the dominant contributors, followed by lighting/visibility conditions, road surface quality, and adverse road–environment factors, whereas emergency-response and infrastructural attributes show comparatively indirect effects. Overall, the proposed framework supports interpretable, domain-oriented evidence for prioritizing safety interventions and monitoring high-risk accident conditions. Full article

(This article belongs to the Section Complex Systems and Cybernetics)

34 pages, 2334 KB

Open AccessReview

Survey on Reconnaissance Autonomous Robotic Systems for Disaster Management

by Sahaj Sinha, Sinjae Lee and Saurabh Singh

Sensors 2026, 26(5), 1659; https://doi.org/10.3390/s26051659 (registering DOI) - 5 Mar 2026

Abstract

Systems that operate in dangerous environments are becoming essential in case of emergencies. This survey reviews the latest ground reconnaissance robots using computer vision (CV), machine learning (ML), MCU-based control, LoRa communication, DC motors, and dual-power systems. The analysis includes hardware and algorithms, and their performance in the field and lab. There has been clear progress in navigation, sensor fusion, and situational awareness. The main challenges which remain include the use of energy and standardization of benchmarks. This survey focuses exclusively on Unmanned Ground Vehicles (UGVs) for disaster reconnaissance, examining recent advances in hardware, software, and autonomy. The survey highlights the improvements in navigation, sensor fusion, and intelligence, and identifies remaining challenges such as energy limitations, robustness in harsh conditions, and the lack of standardized benchmarks. The analysis synthesizes findings from over 190 recent studies (2020–2025) in ground-based disaster robotics, providing a comprehensive overview of current capabilities and research gaps. It encapsulates all issues with their remedy for future disaster-response systems. Full article

(This article belongs to the Special Issue Advanced Sensors and AI Integration for Human–Robot Teaming)

26 pages, 9231 KB

Open AccessArticle

Quantitative Risk Assessment of Buildings and Infrastructures: A Natural Hazard Perspective Under Extreme Rainfall Scenarios

by Guangming Li, Zizheng Guo, Haojie Wang, Zhanxu Guo, Lejun Zhao, Rujiao Tan and Yuhua Zhang

Appl. Sci. 2026, 16(5), 2522; https://doi.org/10.3390/app16052522 (registering DOI) - 5 Mar 2026

Abstract

The increasing frequency and intensity of extreme climate events have posed more geohazards worldwide. It is therefore crucial to quantify and map risk to reduce disaster-related losses. The main objective of this study is to propose a quantitative framework to conduct risk assessment of buildings and infrastructures impacted by geohazards. A debris flow hazard in Tianjin, North China was taken as a case study. A physically based model and the Gumbel extreme value distribution were utilized to construct a range of extreme rainfall and runoff scenarios. The FLO-2D and ABAQUS software were subsequently employed to simulate the surging behavior of the debris flow and assess the structural vulnerability of buildings, respectively. Furthermore, the number of elements at risk and economic values were estimated to generate risk maps. The results revealed that variations in peak discharge in the channel evidently affected flow velocity and depth, thus elevating the debris flow intensity and the likelihood of the materials threatening buildings. The stiffness degradation of concrete was strategically used as the indicator to quantify structure vulnerability and effectively present the dynamic responses under the impacts of the debris flow. Under a 100-year return period rainfall scenario, the proportion of very high- and high-risk areas reached 31%, with the estimated economic loss approximately ¥167.7 million. This highlighted the critical role that extreme rainfall played in shaping both the spatial distribution and severity of debris flow risks. The proposed method provides a scientific basis for enhancing the resilience of mountainous regions to compound natural disasters exacerbated by climate change. Full article

(This article belongs to the Special Issue Dynamics of Geohazards)

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21 pages, 285 KB

Open AccessArticle

Long-Term Mental Health Effects of Mother–Child Separation Due to Adoption

by Lynn Roche Zubov

Soc. Sci. 2026, 15(3), 167; https://doi.org/10.3390/socsci15030167 (registering DOI) - 5 Mar 2026

Abstract

The Preliminary Exploration into Adoption Reunions (PEAR) survey examined the mental health issues faced by adoptees and first mothers. Data were collected from 1313 adoptees, first mothers, and first fathers. Study results indicate that adoption has lasting adverse effects on both adoptees and first mothers. Adoptees and first mothers are significantly more likely to attempt suicide (35 times and 37.7 times, respectively), abuse alcohol, display hypersexual behaviors, and restrict their eating compared to their peers: While first mothers have a lower life expectancy and are more likely to die by suicide than women who did not lose their children to adoption, adoptees frequently struggle with their identity and sense of belonging. They expressed experiencing trauma from their separation from their first mothers, regardless of the quality of their adoptive parents. The findings also highlight the negative impact of the secrecy surrounding adoption. Themes of secrecy and shame were prevalent in the responses from both adoptees and first mothers. The findings highlight the importance of listening to and validating the experiences of adoptees and first mothers and that there needs to be transparency in adoption practices, which may reduce the stigma associated with adoption, and facilitate healing. Full article

(This article belongs to the Section Family Studies)

22 pages, 5157 KB

Open AccessArticle

Accelerating and Improving the Accuracy of Parameter Calibration in a Phenomenological Crystal Plasticity Model Through High-Volume Machine Learning Simulations

by Dayalan R. Gunasegaram, Najmeh Samadiani, Nathan G. March, Indrajeet Katti, David Howard and Mark Easton

Metals 2026, 16(3), 295; https://doi.org/10.3390/met16030295 (registering DOI) - 5 Mar 2026

Abstract

Phenomenological crystal plasticity (CP) models are widely used in Integrated Computational Materials Engineering (ICME) to link microstructural features with engineering-scale mechanical behaviour. Their practical use, however, is limited by the high computational cost of physics-based simulations and the labour-intensive nature of parameter calibration, challenges that are amplified in additively manufactured materials with location-dependent properties. To address these obstacles, we first developed deep neural network (DNN) surrogate models of physics simulations to predict the stress–strain response of an additively manufactured AlSi10Mg alloy. Twenty-five experimentally derived scenarios (five microstructures × five sets of grain orientations) were used for training 25 separate DNNs, with datasets for validated material behaviour generated using the Düsseldorf Advanced Material Simulation Kit (DAMASK) platform and a Fast Fourier Transform (FFT)-based solver. Once trained, the DNNs produced stress–strain curves almost instantaneously, enabling an exhaustive grid-search exploration of a vast parameter space. Our approach yielded significant efficiency gains, which were comprehensively quantified. The best-fit CP parameters obtained through this approach are expected to be more accurate than those derived from conventional trial-and-error calibration, which is restricted to a limited number of candidate values. In addition, the minimum number of CP-FFT simulations required to train the DNNs with sufficient accuracy was identified, reducing the need for costly physics simulations in future studies. The proposed framework enhances the practical utility of CP models for simulation-informed materials engineering and optimisation and is broadly applicable to parameter identification in phenomenological models of other domains. Full article

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

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22 pages, 1669 KB

Open AccessArticle

Investigating the Socioeconomic Determinants of Solar Pump Adoption Among Respondents in Bangladesh: A Firth’s Penalized Likelihood Logistic Regression Approach

by Anika Tahsin Mou, Kentaka Aruga and Md. Monirul Islam

Sustainability 2026, 18(5), 2562; https://doi.org/10.3390/su18052562 (registering DOI) - 5 Mar 2026

Abstract

This study examines the socioeconomic and behavioral determinants, together with spatial heterogeneity, influencing the adoption of solar irrigation pumps in Bangladesh. Five study regions of Bangladesh were sampled using stratified random sampling to collect 257 respondents, who were familiar with both solar and diesel pumps, to justify the energy transition, ensuring sample equity throughout the regions. Income inequality among respondents was assessed using the Lorenz curve, revealing that the bottom 50% of respondents only earned 20% of total income, while a Gini coefficient of 0.46 indicated moderate to high income disparity. To determine whether socioeconomic factors and spatial heterogeneity significantly influence adoption decisions, a Firth’s penalized likelihood logistic regression model was employed, complemented by predictive and average marginal effects for regional categories. The results identified that training, social influence, large household size and income are the prominent drivers for solar pump adoption. Based on the significant spatial heterogeneity, we further recorded a five-point Likert scale response to design region-wise policy recommendations for the fast diffusion of solar pumps. Financial incentives emerged as the most critical policy lever, with 89.10% of respondents expressing strong agreement and a mean score of 4.83. Overall, these findings highlight the central role of socioeconomic and spatial factors in shaping adoption behavior and suggest that policy interventions should prioritize targeted financial and technical support to promote the equitable and rapid diffusion of solar irrigation technologies. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

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21 pages, 7945 KB

Open AccessArticle

Response-Surface-Based Optimization of Pyrolysis Parameters for Enhanced Fixed-Carbon Content and High Heating Value of Pili (Canarium ovatum Engl.) Nutshell-Derived Biochar

by Arly Morico, Jeffrey Lavarias, Wendy Mateo, Antonio Barroga, Melba Denson, Kaye Papa, Marvin Valentin and Andrzej Białowiec

Biomass 2026, 6(2), 22; https://doi.org/10.3390/biomass6020022 (registering DOI) - 5 Mar 2026

Abstract

Waste is increasingly recognized as misplaced biomass, underscoring its potential for reintegration into sustainable environmental management strategies. Biomass pyrolysis has emerged as a promising value-adding process capable of enhancing material properties for diverse applications. In this study, discarded Pili (Canarium ovatum Engl.) nutshells (PS) were utilized as a pyrolysis feedstock to upgrade their fuel characteristics. Pyrolysis conditions were optimized using response surface methodology (RSM) based on a central composite design (CCD) to maximize fixed-carbon content and higher heating value (HHV). The optimized biochar achieved a maximum fixed-carbon content of 86.15% and an HHV of 32.10 MJ/kg at a pyrolysis temperature of 600 °C and a residence time of 60 min, values comparable to those of conventional coal. Under these optimized conditions, the fixed-carbon content and HHV of the precursor biomass were enhanced by up to 254.7% and 58.4%, respectively. Statistical analysis indicated that pyrolysis temperature was the most significant factor influencing both fixed-carbon content and HHV (p < 0.05). The optimized biochar exhibited low volatile matter (8.88%), low ash content (4.97%), and low atomic ratios (H:C = 0.291; O:C = 0.077), indicating a high degree of carbonization and thermal stability. Energy-dispersive X-ray (EDX) analysis identified alkali and alkaline earth metals (Ca, Mg, Na), which contributed to the ash fraction, with minor heavy metals present, predominantly Pb. Hence, these findings enhance understanding of how pyrolysis conditions affect PS–biochar properties, improving fuel quality indicators. Full article

(This article belongs to the Special Issue Biochar and the Circular Bioeconomy: Innovations in Biomass Utilisation)

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22 pages, 1098 KB

Open AccessReview

Chemokine Networks in Blood–Brain Barrier Regulation: Bidirectional Mechanisms, Clinical Translation, and Precision Therapeutic Prospects

by Qiang Wu, Zhengjie Miao, Wen Lei, Xuewen Wu, Jingjing Zhao and Jun Sun

Biomolecules 2026, 16(3), 395; https://doi.org/10.3390/biom16030395 (registering DOI) - 5 Mar 2026

Abstract

The blood–brain barrier (BBB), a core component of the neurovascular unit (NVU), meticulously regulates material exchange between the blood and brain parenchyma, serving as a critical barrier for maintaining the homeostasis of the central nervous system (CNS). Neuroinflammation, a pivotal response of the CNS to injury and disease, can disrupt NVU homeostasis when excessive or persistent, acting as a core pathogenic driver of various intractable neurological disorders. Chemokines, as key signaling molecules guiding the directional migration of immune cells, form the central hub mediating the dynamic regulation of neuroinflammation and the BBB. However, existing studies mostly focus on single disease systems or chemokine families, neglecting the bidirectional heterogeneity of different chemokine axes in BBB regulation and the common regulatory rules across diseases, while lacking systematic exploration of clinical translation challenges caused by the redundancy and spatiotemporal heterogeneity of the chemokine network. This review systematically clarifies the bidirectional regulatory effects of the core axes of the three major chemokine families (e.g., CCL2/CCR2, CXCL12/CXCR4, CX3CL1/CX3CR1) on the BBB. For the first time, we integrate a multi-dimensional regulatory model based on concentration, location, and time to analyze their molecular mechanisms and regulatory heterogeneity in promoting BBB disruption under pathological conditions versus mediating barrier repair and neuroprotection under specific spatiotemporal conditions. Combined with advancements in cutting-edge models such as microfluidic chips, we discuss the clinical translation progress of chemokine research, including potential biomarkers and targeted therapeutic strategies, and propose precise breakthrough paths for the two core challenges of network redundancy and spatiotemporal heterogeneity. Finally, we construct a complete research framework for chemokine-mediated regulation of NVU homeostasis, providing novel insights and directions for restoring BBB function and treating intractable neurological diseases. Full article

(This article belongs to the Special Issue Advancing Molecular Regulation in Brain Injury Research: Mechanisms, Diagnosis, and Rehabilitation)

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12 pages, 398 KB

Open AccessPerspective

Periodization in Orthobiologics Rehabilitation

by Georgios Kakavas, George Skarpas, Trifon Totlis, Panagiotis Kouloumentas, Nikolaos Malliaropoulos and Florian Forelli

J. Clin. Med. 2026, 15(5), 2006; https://doi.org/10.3390/jcm15052006 (registering DOI) - 5 Mar 2026

Abstract

Orthobiologic treatments such as platelet-rich plasma and stem cell therapies are increasingly used to support the healing of tendons, ligaments, and joints. This perspective proposes applying periodization—a structured, progressive model borrowed from athletic training—to rehabilitation following orthobiologic interventions in order to improve functional outcomes. The framework is organized into sequential phases that align with biological stages of healing. Early phases emphasize pain control, inflammation management, and safe, controlled mobility. Rehabilitation then progresses toward gradually increasing load bearing and strength, and toward more specific exercises to promote tissue regeneration while reducing the risk of re-injury. In later phases (mesocycles), the model highlights the importance of neuroplastic adaptations for sustained functional recovery, including neurogenesis, synaptic plasticity, and functional remodeling to safer RTP for athletes. A key advantage of this approach is its adaptability: progression can be individualized according to a patient’s recovery trajectory and response to loading. By aligning rehabilitation progression with intrinsic healing processes and integrating physiological and neuromuscular goals, the proposed model aims to maximize regenerative potential across both athletic and non-athletic populations. Overall, this neuroplastic periodized approach offers a practical, evidence-informed structure to guide clinicians in delivering patient-centered regenerative rehabilitation and may help standardize care after orthobiologic procedures. Full article

(This article belongs to the Special Issue Musculoskeletal Rehabilitation and Regenerative Medicine: Current Challenges and New Perspectives)

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35 pages, 581 KB

Open AccessArticle

Synergistic Impact Mechanism of Digital Technology on Inter-Provincial Ecology in River Basins—Taking the Middle Reaches of the Yangtze River Basin as an Example

by Wanhua Huang, Panni Yue, Qian Chen, Jiantuan Hu, Honggui Gao and Changzheng Zhou

Sustainability 2026, 18(5), 2567; https://doi.org/10.3390/su18052567 - 5 Mar 2026

Abstract

How digital technology can effectively drive ecological collaborative governance in trans-administrative river basins is a core prerequisite for achieving intelligent ecological governance of river basins. Based on 402 micro survey responses collected from water conservancy, environmental protection and other relevant departments in the middle reaches of the Yangtze River Basin, this study identifies the characteristics of digital technology from three dimensions: tool, power and capacity. By integrating factor analysis and the mediating effect model, it empirically examines the impact of digital technology on inter-provincial ecological collaboration in river basins and its underlying mechanism. The results show that: (1) Digital technology exerts a significantly positive driving effect on inter-provincial ecological collaboration in the middle reaches of the Yangtze River Basin, and this conclusion remains robust after conducting robustness tests including the re-measurement of digital technology and the exclusion of interference from smart water conservancy pilot projects. (2) Mechanism analysis reveals that central government support and public participation play partial mediating roles in the relationship between digital technology and inter-provincial ecological collaboration, and both variables exert a masking mediating effect on the sustainability of inter-provincial ecological collaboration. These findings provide micro-evidence-based policy implications for optimizing the digital collaborative governance system of river basins. Full article

(This article belongs to the Section Social Ecology and Sustainability)

31 pages, 3164 KB

Open AccessArticle

Multi-Objective Optimization of Mechanical and Geometric Properties of 3D-Printed PLA Porous Scaffolds for Biomedical Applications

by Alejandro González González, Patricia C. Zambrano-Robledo, Deivis Avila, Marcelino Rivas and Ramón Quiza

Materials 2026, 19(5), 1008; https://doi.org/10.3390/ma19051008 - 5 Mar 2026

Abstract

Porous scaffolds fabricated via fused deposition modeling (FDM) are promising for bone tissue engineering, but their mechanical performance and geometric fidelity are governed by complex interactions between process parameters and architectural design. This study presents a multi-objective optimization framework for poly (lactic acid) (PLA) scaffolds based on three triply periodic minimal surface (TPMS) topologies—Gyroid, Primitive, and Diamond. A Box–Behnken design combined with response surface methodology was used to model compressive strength, elastic modulus, yield strength, energy absorption density, and discrepancies in volume and porosity as functions of layer thickness (0.05–0.15 mm), extrusion temperature (210–220 °C), and target porosity (50–70%). The resulting quadratic models exhibited strong predictive capability (R² > 77%, with most >90%) and were validated experimentally at extreme parameter combinations, yielding relative errors below 10% for 83% of measurements. Multi-objective optimization using NSGA-II, coupled with principal component analysis and correlation-based objective reduction, revealed that the six original objectives collapse to topology-specific essential pairs: absorbed energy density and porosity discrepancy for Gyroid; Young’s modulus and volume discrepancy for Primitive; and Young’s modulus and porosity discrepancy for Diamond. The generated Pareto fronts quantify the inherent trade-off between mechanical performance and geometric fidelity for each topology, providing designers with explicit decision maps. This framework enables rational, application-driven selection of printing parameters and scaffold architecture, advancing the clinical translation of patient-specific FDM-printed bone scaffolds. Full article

(This article belongs to the Special Issue Advances in Synthesis and Multiscale Characterization of Functional Polymeric Materials)

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20 pages, 655 KB

Open AccessArticle

The Non-Simulation Resilience Assessment for Electric–Gas Distribution Networks

by Chun Xiao, Tingjun Li and Xiaoqing Han

Algorithms 2026, 19(3), 196; https://doi.org/10.3390/a19030196 - 5 Mar 2026

Abstract

Unlike traditional power systems, the heterogeneous energy support of electric–gas regional distribution networks brings new challenges to resilience assessment. On the basis of identifying N-k fault uncertainty risks, establishing a resilience assessment methodology is one of the important issues in resilience research. Existing reliability assessment methods cannot accurately quantify resilience under N-k extreme fault scenarios. To address this limitation, we propose a non-simulation resilience assessment method. The approach can simultaneously quantify the dynamic interactions of heterogeneous energy flows and the impact of repair process time uncertainty on system resilience under extreme fault scenarios. Specifically, the resilience indexes are established by combining the load outage and mathematical expectation during/after the extreme fault and applying probabilistic knowledge to express the N-k load outage event, so as to effectively offset the data scarcity due to the limited N-k fault data samples. The internal consistency and parametric responsiveness of the proposed non-simulation method are demonstrated through systematic case comparisons under varying failure rates, repair times, and coupling conditions. Full article

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