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In 2023, Tropical Cyclone Freddy caused severe damage in southern Malawi, flooding much of the lowland area near Lake Chilwa and displacing many residents. This study evaluates long-term, region-specific mitigation strategies to lessen future risks, using a novel approach that combines drone and satellite data, building footprints, and 3D simulations to analyze how building elevation affects flood damage and assess Property-Level Flood Risk Adaptation measures. Results show a significant difference in ground elevation between affected and unaffected buildings, with damaged structures generally at lower levels. The 3D simulation confirmed a water-level rise of approximately 3.0 m caused by Freddy. Scenario analysis indicates that elevating buildings by 2.0, 2.5, and 3.0 m could reduce direct flood exposure and 64%, 76%, and 91% of damage, respectively. These insights can inform the development of targeted regional risk-mitigation strategies through Property-Level Flood Risk Adaptation in high-risk areas. Full article

(This article belongs to the Special Issue Remote Sensing for Hydrological Management)

29 pages, 3527 KB

Open AccessReview

Molecular Insights into Lignin Bioactivity: From Structural Architecture to Sustainable Food Industry Applications

by Akhmadjon Sultanov, Rakhmat Sultonov, Byung-Dae Park, Ju-Ock Nam, Soo Rin Kim and Deokyeol Jeong

Int. J. Mol. Sci. 2026, 27(10), 4458; https://doi.org/10.3390/ijms27104458 (registering DOI) - 15 May 2026

Abstract

This review explores the biological properties and application potential of native, technical, and modified lignins, with a focus on their antioxidant, antimicrobial, and anti-inflammatory activities. Native lignin generally preserves more of its original phenolic architecture and thus shows stronger intrinsic biological activity. This is likely due to its more homogeneous structure, which makes its physicochemical behavior more predictable compared with highly processed technical lignins. Among technical lignins, organosolv and soda lignin appear the most promising due to their sulfur-free nature, lower condensation, and higher reactivity. At the monomer level, catechol-type phenolics show the highest antioxidant potential, while vanillin remains the most attractive lignin-derived monomer because it combines bioactivity with direct application potential in food, pharmaceutical, and cosmetic systems. Comparison of modification strategies indicates that phenolic grafting, esterification, and carboxylation are more practical for scale-up than complex multistep polymer grafting. In particular, gallic acid grafting produced some of the strongest results, including near-complete 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) scavenging, 98.7% 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical inhibition, and a fourfold increase in phenolic hydroxyl content, whereas other modified lignins also showed improved antimicrobial and anti-inflammatory effects. Overall, mild and green lignin modification, especially with food-safe phenolic compounds, appears to be the most promising strategy for future food and human health applications. Full article

(This article belongs to the Section Molecular Plant Sciences)

20 pages, 574 KB

Open AccessArticle

Anger, Cynical Distrust, Nightmare Distress and Insomnia Among Nursing Personnel

by Athanasios Tselebis, Argyro Pachi, Christos Sikaras, Dimitrios Kasimis, Evgenia Kavourgia and Ioannis Ilias

J. Clin. Med. 2026, 15(10), 3837; https://doi.org/10.3390/jcm15103837 (registering DOI) - 15 May 2026

Abstract

Background: The nursing profession is recognized as a high-risk occupation, with the emotional toll on healthcare workers reaching a critical point. A complex interplay of anger and cynicism, often stemming from systemic pressures and chronic moral injury, seems to increasingly affect nurses’ professional and personal lives. This psychological strain does not end when the shift ends; rather, it often manifests as insomnia and nightmare distress, creating a vicious cycle of exhaustion and emotional instability. This article explores how anger, cynical distrust, nightmare distress and insomnia are interrelated and jeopardize the well-being of nursing staff and what these “invisible” symptoms reveal about the current state of healthcare by confirming their prevalence rates. Methods: This cross-sectional study was conducted online in October 2025 and included 441 hospital nurses who completed the Dimensions of Anger Reactions-5 (DAR-5), the 8-item Cynical Distrust scale (CDS-8), the Nightmare Distress Questionnaire (NDQ) and the Athens Insomnia Scale (AIS). Results: The prevalence rates of anger, nightmare distress and insomnia were 41.5%, 6.6%, and 62.1%, respectively. Based on the CDS-8 scores, a notable proportion (20.9%) of nurses fell within the highest quartile of CDS-8 scores (CDS-8 > 29), indicating relatively elevated cynical distrust within this sample; this threshold is sample-derived and does not correspond to a validated clinical cut-off. Hierarchical multiple regression analysis indicated that the DAR-5 explained 22.1% of the variance in AIS, while an additional 10.2% was explained by NDQ and another 1.5% by the CDS-8. Both cynical distrust and nightmare distress displayed a chain mediation pattern in the association between anger and insomnia; however, given the cross-sectional design, the temporal order of these variables cannot be confirmed. Conclusions: Anger exhibited significant direct and indirect associations with insomnia, with cynical distrust and nightmare distress acting as serial mediators in this cross-sectional model. Findings from this cross-sectional study tentatively suggest that future intervention efforts targeting insomnia in nurses might benefit from addressing anger alongside nightmare distress and cynical attitudes; however, experimental studies are needed to confirm whether such interventions would be effective. Full article

(This article belongs to the Special Issue Sleep and Circadian Rhythms in Patients with Physical and Mental Disorders)

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21 pages, 1471 KB

Open AccessArticle

Physical Deliverability-Oriented Carbon Cost-Constrained Low-Carbon Dispatch: A User-Centric Dispatch Framework with Demand Response

by Ke Liu, Wenhao Song, Chen Yang, Chunsheng Zhou, Haoran Feng, Zhonghua Zhao, Chunxiao Tian and Qiuyu Chen

Sustainability 2026, 18(10), 5019; https://doi.org/10.3390/su18105019 (registering DOI) - 15 May 2026

Abstract

Sustainable power-system operation requires carbon-reduction strategies that are emission-effective, physically deliverable, economically feasible, and compatible with user-side decarbonization claims. As Scope 2 carbon accounting increasingly emphasizes temporal, spatial, and physical consistency, dispatch models need to link user-level carbon claims with network-constrained power delivery. This paper proposes a User-Centric Carbon Cost-Constrained Low-Carbon Dispatch (CCC-LCD) framework that integrates carbon emission flow (CEF), nodal carbon intensity (NCI), network-constrained optimal dispatch, and endogenous demand response. A PTDF-based DC-OPF model represents active-power deliverability, while dual virtual flow variables determine carbon-flow directions endogenously. The model minimizes the target user’s physically traced Scope 2 emissions under a cost-tolerance budget and flexible-load constraints. Case studies on a modified IEEE 14-bus system show that nodal decarbonization is topology-dependent: high-load and high-NCI nodes obtain larger reductions from source-side generation substitution, whereas renewable-adjacent nodes exhibit limited marginal gains. The CEF-DR strategy outperforms single-mechanism cases, indicating the value of coordinating physical carbon-flow constraints with flexible demand. From a sustainability perspective, the proposed framework supports verifiable low-carbon electricity consumption, improves the economic feasibility of user-side decarbonization, and provides a practical dispatch tool for sustainable energy transition and corporate Scope 2 emission reduction. Full article

(This article belongs to the Special Issue Electrical Engineering Innovations for the Sustainable Renewable Energy Solutions)

30 pages, 3075 KB

Open AccessArticle

Metabolic Saliency as KL-Divergence Estimator: Information-Geometric Attribution of Systemic Stress in JSE Equity Network

by Ntebogang Dinah Moroke

Entropy 2026, 28(5), 559; https://doi.org/10.3390/e28050559 (registering DOI) - 15 May 2026

Abstract

The attribution of systemic financial stress to specific market sectors requires metrics that are faithful to the model’s computations, statistically consistent, and connected to a physically meaningful measure of directed information flow. This paper addresses all three requirements through information geometry, contributing to SDGs 7, 8, 9, and 17 through an entropic causal chain linking energy infrastructure failure to financial market stress. We conjecture and empirically verify the Entropy–Saliency Equivalence: Metabolic Saliency is an asymptotically unbiased estimator of the local Kullback–Leibler divergence between stressed and resting sector return distributions, with bias decaying at a parametric rate under Gaussian regularity conditions. The finite-sample bias–variance decomposition of the Kraskov–Stögbauer–Grassberger transfer entropy estimator is derived, establishing a minimax-optimal convergence rate. A novel metric, the Spatio-Temporal Information Flux (STIF), quantifies directed inter-sector stress transmission in bits per trading day, providing a bootstrap-calibrated audit trail aligned with the South African Financial Sector Regulation Act and MiFID II. Empirical validation on the JSE canonical panel (87 securities, 2857 trading days, 2015–2026) with Eskom load-shedding stages as exogenous stress injectors confirms the equivalence (

R^{2} = 0.810

\hat{ρ} = 0.90

), with walk-forward

R^{2} = 0.789

and placebo

R^{2} = 0.081

ruling out estimation artefacts. The energy sector is identified as the primary stress transmitter during Stage 4+ Eskom events (STIF rising from 0.14 to 0.43 bits/day, directional asymmetry ratio 4.7). Robustness checks confirm stability across non-Gaussian securities and rolling transfer entropy windows. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

29 pages, 12190 KB

Open AccessArticle

Identification, Screening and Mechanism Analysis of Anti-Parkinson’s Disease Peptides from Rapana venosa Protein Hydrolysates

by Qingzhong Wang, Shuqin Shao, Yizhuo Wang, Wenshuai Fan, Zilong Wang, Xuchang Liu, Kechun Liu and Shanshan Zhang

Mar. Drugs 2026, 24(5), 180; https://doi.org/10.3390/md24050180 (registering DOI) - 15 May 2026

Abstract

At present, there is still a lack of effective treatments to slow the progression of Parkinson’s disease. Naturally derived active substances, valued for their safety and multi-target potential, have become an important direction in anti-PD drug development, with marine organisms representing a valuable source of bioactive peptides. This study aimed to isolate and identify anti-PD peptides from Rapana venosa protein hydrolysates. Through bioactivity-guided screening combined with an MPTP-induced zebrafish PD model, three novel active peptides—KSTELLI, FLVKLPMFM, and SDSLSEILIS—were successfully identified. The study showed that these peptides significantly alleviated dopaminergic neuron loss, improved the cerebral vascular system, restored motor and sensory function, and alleviated oxidative stress. Molecular docking confirmed their stable binding to key PD targets (DDC, α-synuclein, and MAO-B). Further transcriptomic and gene expression analyses revealed that their neuroprotective effects involve the regulation of pathways related to metabolism, oxidative stress, inflammation, and apoptosis, with the three peptides exhibiting distinct mechanistic emphases. The research demonstrates that these marine-derived peptides exert neuroprotective effects through a synergistic multi-target mechanism, laying a foundation for the development of novel lead compounds against Parkinson’s disease. Full article

(This article belongs to the Special Issue Marine Proteins: Biological Activities and Applications)

24 pages, 828 KB

Open AccessArticle

E-Commerce and the Spatial Rebalancing of Market Entry: A Multi-Mechanism Analysis of Urban–Rural Market Vitality in China

by Manru Zhao and Yujia Lu

Systems 2026, 14(5), 567; https://doi.org/10.3390/systems14050567 (registering DOI) - 15 May 2026

Abstract

The rapid expansion of e-commerce has transformed market access in developing economies, yet its impact on the spatial structure of market participation remains insufficiently understood. While existing studies primarily examine welfare outcomes such as income growth and consumption smoothing, few investigate how digital platforms reshape the balance of market entry between urban and rural areas. Drawing on New Economic Geography and platform economics theory, this study proposes that e-commerce development rebalances urban–rural market vitality through three associative pathways: alleviating rural capital constraints, improving rural innovation environments, and promoting agricultural-industry agglomeration. Using county-level panel data covering 2725 Chinese counties from 2011 to 2022, we employ a Double Machine Learning (DML) framework to examine the association between designation as an “E-commerce into Rural Comprehensive Demonstration County” and changes in the urban–rural market vitality balance (URMAR). The results indicate that demonstration county designation is associated with a statistically significant reduction in urban–rural market disparity, as measured by both the Theil index and the absolute difference in new enterprise registrations. The directional URMAR indicator further reveals that this convergence is driven primarily by accelerated rural enterprise formation. Subsample analysis confirms that the rebalancing interpretation holds across counties with different baseline market structures. Mechanism analysis provides suggestive evidence consistent with all three proposed associative pathways. Heterogeneity analysis further reveals that these effects are stronger in economically developed eastern regions, in counties linked to higher-tier cities, and in secondary and tertiary industries. These findings advance a market-structure perspective on digital development that complements existing welfare-based approaches and offer policy insights for fostering balanced regional development through targeted digital and complementary investments. Full article

(This article belongs to the Special Issue Digital Platform Ecosystems and Platform Governance)

19 pages, 339 KB

Open AccessArticle

Impact of Natural Disasters on ESG Performance of Agricultural Firms

by Jinhui Ning, Fang Shi, Yu Cui and Zhenru Wang

Sustainability 2026, 18(10), 5017; https://doi.org/10.3390/su18105017 (registering DOI) - 15 May 2026

Abstract

Global climate warming has led to the frequent occurrence of natural disasters, threatening the stability of agricultural production and the survival of agricultural enterprises. The existing literature presents mixed evidence regarding the impact of natural disasters on corporate ESG performance. Some studies argue that natural disasters promote ESG performance; however, such conclusions only hold for non-agricultural enterprises. Agricultural enterprises are highly dependent on natural conditions, and their core production factors are vulnerable to direct damage from natural disasters. Meanwhile, they are characterized by long production cycles and high asset specificity. After disaster shocks, agricultural enterprises have to prioritize production recovery, so natural disasters exert a dominant negative effect on their ESG performance. Based on the above context, here we take the performance of Chinese A-share listed agricultural companies between 2010 and 2023 as the research sample to explore the impact of natural disasters on the ESG performance of agricultural enterprises. The empirical results show that natural disasters significantly inhibit the ESG performance of agricultural enterprises. Mechanism tests indicate that natural disasters weaken ESG performance by damaging supply chain resilience, hindering green innovation, and disrupting internal control. A cross-sectional heterogeneity analysis reveals that the inhibitory effect is more pronounced for large-scale enterprises, enterprises with lower executive green cognition, and enterprises located in areas that are not major grain-selling areas. This study enriches the research on the economic consequences of natural disasters and the factors influencing corporate ESG performance. It also provides important practical implications for strengthening the ESG fulfillment of agricultural enterprises and accelerating the cultivation of new productive forces in agriculture. Full article

(This article belongs to the Special Issue Agricultural Economics, Policies, and Sustainable Rural Development)

32 pages, 13955 KB

Open AccessArticle

A Finite Element Simulation-Informed Machine Learning Framework for Screening Average Thermal Stress Responses in SLM-Fabricated 316L Stainless Steel

by Yuan Zheng and Shaoding Sheng

Materials 2026, 19(10), 2088; https://doi.org/10.3390/ma19102088 (registering DOI) - 15 May 2026

Abstract

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

(This article belongs to the Section Materials Simulation and Design)

57 pages, 5985 KB

Open AccessReview

Mathematical Framework for Explainable Vehicle Systems Integrating Graph-Theoretic Road Geometry and Constrained Optimization

by Asif Mehmood and Faisal Mehmood

Mathematics 2026, 14(10), 1710; https://doi.org/10.3390/math14101710 (registering DOI) - 15 May 2026

Abstract

Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a unified mathematical framework for explainable deep learning which integrates multimodal inputs, graph-theoretic road geometry, uncertainty modeling, and intrinsically interpretable representations. Road-structured priors that include lane topology and spatial constraints are incorporated into learning and optimization processes for ensuring model predictions and explanations to remain physically and semantically grounded. The review synthesizes methods across saliency-based, concept-based, causal, and intrinsic explainability, and extends them to vision-language models. This enables language-grounded, human-interpretable reasoning in autonomous vehicle systems. While vision-language models offer a new paradigm for semantic explainability, their limitations such as hallucinations, misgrounding, and reduced reliability under distribution shifts are also critically examined. Along with the role of road priors in improving alignment and robustness, another key contribution of this work is its quantitative evaluation metrics for road-aware explainability. These evaluation metrics link the explanations to spatial consistency, uncertainty alignment, and graph-structured reasoning. The overall framework connects latent representations, predictions, and explanations within a single formulation, enabling systematic comparison and analysis across models. Based on a PRISMA-guided review of 164 studies, this research identifies gaps in real-world reliability, temporal reasoning, and standardized evaluation, and outlines future directions including human-in-the-loop systems, regulatory readiness, and language-based auditing. Overall, this study advances a mathematically grounded and road-aware perspective on explainable vehicle AI which significantly bridges the gap between high-performance models and transparent, trustworthy autonomous systems. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Convolutional Neural Network)

37 pages, 4112 KB

Open AccessReview

Digitisation of Procurement and Information Modelling—Literature Review on e-Procurement

by Eliana Basile, Francesca Porcellini, Enrico Pasquale Zitiello, Sonia Lupica Spagnolo, Antonio Salzano and Salvatore Antonio Biancardo

Buildings 2026, 16(10), 1969; https://doi.org/10.3390/buildings16101969 (registering DOI) - 15 May 2026

Abstract

In recent decades, the introduction of e-procurement has profoundly transformed the methods of procuring goods, services, and works, redefining traditional procurement processes and significantly impacting global economic, operational, and regulatory dynamics. The construction sector has also been affected by this transition, which has altered the operating models of public procurement and favoured the adoption of digital tools aimed at more efficient, transparent, and automated process management. This study proposes a systematic literature review based on the analysis of 95 scientific contributions, with the aim of outlining the evolution of the e-procurement paradigm in the construction sector and identifying the main directions for research development. Despite the widespread dissemination of studies on the topic, it emerges that the actual maturity of e-procurement systems is still limited, often resulting in a logic of document dematerialization rather than full process digitalization. In this context, the review critically analyses the role of Building Information Modelling as an enabling factor for the evolution of e-procurement, exploring the potential of its integration into procurement flows. Particular attention is paid to the contribution of the Digital Building Logbook, an information tool capable of extending the value of data generated during the tender phase throughout the building’s entire life cycle, supporting advanced management and maintenance strategies. The results highlight how, despite the significant potential of integrating e-procurement and BIM, significant technological, regulatory, and cultural issues persist that limit its large-scale adoption. This underscores the need to develop shared and interoperable methodological approaches capable of transforming procurement from a document-based process to an integrated information system, oriented toward value creation throughout the entire life cycle of projects. Full article

(This article belongs to the Section Construction Management, and Computers & Digitization)

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19 pages, 1387 KB

Open AccessArticle

Uniform in Bandwidth Consistency of the L¹-Modal Regression Estimator for High-Dimensional Data

by Fatimah A. Almulhim, Mohammed B. Alamari and Ali Laksaci

Entropy 2026, 28(5), 558; https://doi.org/10.3390/e28050558 (registering DOI) - 15 May 2026

Abstract

We propose a new nonparametric estimator of the conditional mode in a regression framework where the covariates are functional in nature. The estimator is constructed through a quantile regression approach, which provides a robust alternative to classical density-based procedures. It is well documented [...] Read more.

L^{1}

-structure in quantile regression, the estimation procedure improves robustness properties, particularly resistance to outliers and heavy-tailed error distributions. This feature makes the

L^{1}

estimation of the conditional mode more stable and reliable in complex and high-variability functional data settings. The main objective of this paper is to establish strong consistency, with explicit convergence rates, for the associated kernel estimators, uniformly over a range of bandwidth parameters. The latter is developed under general regularity conditions involving the concentration distribution of the functional regressor, smoothness assumptions on the structural components of the model, and entropy conditions ensuring adequate control of the functional class complexity. Uniformity in bandwidth is essential both from a theoretical and practical issues, as it guarantees stability of the estimator under data-driven smoothing parameter selection. Beyond its theoretical contribution, this paper has direct implications for applied statistics. Specifically, it provides mathematical support for the automatic bandwidth selection procedures in the high-dimensional data context. Furthermore, the main theoretical novelty is highlighted through simulation experiments and applications to real data. Full article

45 pages, 18550 KB

Open AccessReview

Cyberworthiness for Corporate Organisations: A Structured Review of Standards, Frameworks, and Future Directions

by Saad Almarri, Wael Issa, Marwa Keshk, Benjamin Turnbull and Nour Moustafa

Electronics 2026, 15(10), 2133; https://doi.org/10.3390/electronics15102133 (registering DOI) - 15 May 2026

Abstract

Cyberworthiness extends the concept of cybersecurity by evaluating whether systems and networks can perform their intended functions securely while maintaining protection against cyber threats. In corporate environments, cyberworthiness aims to ensure security, operational resilience, and trustworthiness across interconnected business processes and digital infrastructures. Modern organisations increasingly rely on complex cyber–physical and information systems, where vulnerabilities in software, networks, and devices can introduce significant operational and security risks. Cyberworthiness, therefore, encompasses security controls, risk management practices, and compliance with recognised cybersecurity standards and governance frameworks. It supports the assessment of information technology components and their exposure to both known and emerging cyber attacks, enabling organisations to evaluate system robustness and operational continuity. While cyberworthiness has historical foundations in system assurance and dependability, it also provides a conceptual basis for contemporary cyber resilience strategies. This paper discusses the concept of cyberworthiness in corporate organisations and identifies potential pathways for its practical implementation. It analyses existing cybersecurity standards and governance frameworks to support structured cyberworthiness assessment. This study presents a structured comparative review of fifteen cyberworthiness-relevant standards, supported by a Source Quality Appraisal Framework, a Framework Selection Guide specifying when each standard should be preferred and where conflicts arise, and a five-dimensional Cyberworthiness Assessment Readiness Model (CARM), a directional self-assessment instrument. The Efficient Automatic Safety and Security Assurance (EASSA) concept is proposed as a direction for future research, not a validated deployed system. Ensuring cyberworthiness remains challenging due to automation limitations in all reviewed standards, evolving threat landscapes, and governance complexity, requiring organisations to adopt integrated and measurable approaches to safeguard their digital assets and operational systems. Full article

(This article belongs to the Special Issue Recent Advances in Hardware-Oriented Security and Trust in IoT Systems)

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35 pages, 1628 KB

Open AccessPerspective

The Challenge of Machine Learning and Artificial Intelligence in the Construction Sector: The Lesson Learned from the Rome Technopole Project

by Luca Gugliermetti, Maria Michaela Pani, Marco Cimillo, Fabrizio Tucci and Federico Cinquepalmi

Appl. Sci. 2026, 16(10), 4964; https://doi.org/10.3390/app16104964 (registering DOI) - 15 May 2026

Abstract

Artificial Intelligence (AI) and Digital Twins (DTs) can support the digital and energy transition in the construction sector; however, their application to the built environment presents both opportunities and limitations. This study aims to give a critical perspective on the topic analyzing the related key challenges, including error assessment, model interpretability, data availability, cybersecurity risks, organizational constraints, and lifecycle costs. Where AI is nowadays developed as a context-dependent tool set, it is most effective when embedded within integrated socio-technical systems rather than adopted as a universal solution. Instead, DTs can be intended as an enabling framework, integrating AI, Internet of Things (IoT), Big Data, and Building Management Systems (BMS) to enhance energy performance, indoor environmental quality, safety, maintenance, and decision-making at both building and urban scales. The direction international research on these topics is facing is clear as evidenced by the wide number of research papers published. The future of these technologies moves towards a simulative approach oriented towards the sustainable and fair development goals and will bring a broad transformation of the building environment where they are ever more integrated into each social and technical aspect. The work is supported by a case study developed at Sapienza University of Rome founded by the Italian National Recovery and Resilience Plan within Flagship Project 2 (FP2), “Energy Transition and Digital Transition in Urban Regeneration and Construction,” of the Rome Technopole ecosystem. Full article

(This article belongs to the Special Issue The Rome Technopole Project: Energy Transition and Digital Transition in Urban Regeneration and Construction)

22 pages, 5279 KB

Open AccessArticle

Assessing POT Methods for Large-Displacement Landslide Measurement with Multi-Source Imagery: A Case Study of the Zhenba Landslide

by Yuyuan Zhang, Xuechi Yang, Shuai Yang, Penglin Zhao, Yuanye Cao, Xiuguo Liu, Liping Li and Qihao Chen

Remote Sens. 2026, 18(10), 1591; https://doi.org/10.3390/rs18101591 (registering DOI) - 15 May 2026

Abstract

A large landslide struck the Zhenba Dahekou area of Shaanxi Province, China, on 9 September 2021. To accurately extract landslide displacement in emergency situations, in this study, we explore the feasibility and effectiveness of using satellite images and post-failure emergency UAV images to investigate the large displacement of the landslide through the pixel offset tracking (POT) method and evaluate four different POT methods, including NCC operated in the spatial domain (NCC), the orientation correlation method in ImGRAFT software (ImGRAFT), the multi-pass method in GIV software (GIV), and the frequency method in COSI-Corr software (COSI-Corr). It is found that the Zhenba landslide has moved southwest by about 74.3 m~96.4 m with the sliding direction between 231°~258°. The southward displacement of the landslide gradually decreases from southeast to northwest, and the westward displacement on the west side is greater than that on the east side. The relative matching precision of the POT methods in stable areas reached 0.8 m, superimposed on an image registration RMSE of 1.2 m. Under the experimental conditions of this study, ImGRAFT demonstrated robust overall performance. In terms of matching ability, ImGRAFT and NCC outperform GIV and COSI-Corr. In terms of displacement gradients expression ability, ImGRAFT and COSI-Corr outperform NCC and GIV; in terms of matching efficiency, COSI-Corr, GIV, and ImGRAFT are far superior to NCC. This study expands the application of multi-source optical data to investigate landslides and provides suggestions for the displacement extraction of large-displacement landslides, which will be helpful for the emergency investigation and research of landslides in the future. Full article

(This article belongs to the Special Issue Remote Sensing-Based Geological Hazard Monitoring, Identification, Prediction, and Risk Assessment: Techniques and Applications)

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