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The anchorage system at column bases plays a critical role in transferring forces between the superstructure and foundation in steel structure-reinforced concrete systems, thereby governing overall seismic performance. This study investigates the seismic behavior of reinforced concrete foundation columns using two anchorage systems: traditional foundation bolts (TFB) and friction-welded rebar anchor bolts (FRAB). A total of six full-scale specimens were tested under quasi-static cyclic loading to evaluate strength, deformation capacity, and failure mechanisms. The FRAB system integrates reinforcing bars with threaded rods through friction welding, aiming to enhance bond performance compared to conventional smooth anchor bolts. Test results indicate that specimens with FRAB exhibit improved seismic capacity and more stable hysteretic behavior than those with TFB. The enhanced performance is attributed to the superior bond characteristics of the welded reinforcing bars, which provide more effective force transfer between steel columns and concrete foundations. Full article

(This article belongs to the Special Issue Innovations and Emerging Technologies in Modular and Prefabricated Building Systems)

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20 pages, 1328 KB

Open AccessArticle

Enhancing Long-Term Forecasting Stability in Smart Grids: A Hybrid Mamba-LSTM-Attention Framework

by Fusheng Chen, Chong Fo Lei, Te Guo and Chiawei Chu

Energies 2026, 19(8), 1855; https://doi.org/10.3390/en19081855 (registering DOI) - 9 Apr 2026

Abstract

Accurate multivariate long-term time series forecasting (LTSF) is critical for smart grid operations. However, non-stationary distribution shifts frequently induce compounding error accumulation in conventional architectures. This study proposes the Mamba-LSTM-Attention (MLA) framework, a distribution-aware architecture engineered for forecasting stability. The pipeline integrates Reversible Instance Normalization (RevIN) to neutralize statistical drift. To address computational bottlenecks, the architecture utilizes a linear-time Selective State Space Model (Mamba) to capture global trend dynamics, cascaded with a single-layer gated Long Short-Term Memory (LSTM) unit to model localized non-linear residuals. A terminal information bottleneck structurally bounds cross-step error propagation. Empirical results across standard ETT and Electricity benchmarks reveal a precision–stability trade-off. By prioritizing structural resilience, the MLA framework limits error accumulation on highly volatile datasets, yielding MSEs of 0.210 and 0.128 on ETTh2 and ETTm2 at the T = 96 horizon. This structural bottleneck inherently smooths high-frequency periodic patterns, yielding lower absolute accuracy on stationary benchmarks such as ETTh1 and ETTm1. Ultimately, the architecture establishes a computationally efficient, structurally stable baseline tailored for non-stationary anomaly tracking in smart grids. Full article

(This article belongs to the Special Issue Forecasting Electricity Demand Using AI and Machine Learning)

16 pages, 1008 KB

Open AccessReview

CEUS in Atypical Renal Cystic Masses: How, When and Why

by Michele Bertolotto, Irene Campo, Alessandra Oliva, Antonio Granata and Vito Cantisani

Medicina 2026, 62(4), 721; https://doi.org/10.3390/medicina62040721 (registering DOI) - 9 Apr 2026

Abstract

Background/Objectives: Cysts are the most common kidney lesions identified in patients undergoing abdominal imaging, with ultrasound (US) typically serving as the initial diagnostic tool. Contrast-enhanced ultrasound (CEUS) has emerged as a highly effective modality for the evaluation of cystic renal lesions, particularly when conventional B-mode ultrasound (US) or CE-CT are inconclusive. While simple renal cysts are readily characterised on US, cystic renal lesions require further assessment. Methods: The Bosniak classification, originally developed for CE-CT, remains the cornerstone for categorising cystic renal lesions, guiding management from surveillance to surgical intervention. Recent efforts to standardise CEUS-specific imaging parameters and adapt the Bosniak criteria aim to improve interobserver agreement, reduce subjectivity, and enhance diagnostic accuracy. Results: CEUS offers superior sensitivity for detecting slow blood flow and minimal vascularity within septa, wall or solid components, often outperforming CE-CT in real-time vascular assessment. However, the high sensitivity of CEUS can reveal additional septa or subtle enhancement, potentially leading to lesion overscoring, if the different sensitivity of CEUS and CT/MRI for detecting enhancement is not taken into account. CEUS also plays a crucial role in the follow-up of non-surgical cystic lesions, providing a radiation-free and cost-effective alternative for long-term monitoring. Certain scenarios, such as post-interventional changes, traumatic cystic rupture, or infected cysts, fall outside the scope of the Bosniak system and require careful clinical correlation. Conclusions: By integrating CEUS into the diagnostic pathway, sonologists can achieve accurate lesion characterisation, optimise patient management, and minimise unnecessary invasive procedures, reinforcing CEUS as an essential tool in the evaluation and follow-up of complex renal cystic masses. Full article

(This article belongs to the Special Issue Interventional Radiology and Imaging in Cancer Diagnosis)

17 pages, 1783 KB

Open AccessArticle

Non-Infectious Anterior Uveitis Is Associated with Functional Retinal Changes Demonstrable by Multifocal Electroretinography

by Danijela Mrazovac Zimak, Nenad Vukojević, Igor Petriček, Tomislav Jukić, Kristina Ana Škreb and Snježana Kaštelan

J. Clin. Med. 2026, 15(8), 2865; https://doi.org/10.3390/jcm15082865 (registering DOI) - 9 Apr 2026

Abstract

Introduction: Although anterior non-infectious uveitis affects the structures of the anterior segment of the eye, (inflammatory) disruption of the hemato–ocular barrier may lead to changes in the structures of the posterior segment of the eye. Objective: To evaluate functional retinal changes using multifocal electroretinography (mfERG) and their relationship with structural optical coherence tomography (OCT) parameters in patients with acute anterior non-infectious uveitis (AANU). Methods: This prospective study included 38 eyes of 19 patients diagnosed with unilateral AANU and age-matched healthy fellow eyes as controls. All subjects underwent comprehensive ophthalmological examination, including best-corrected visual acuity (BCVA), spectral-domain OCT, and mfERG testing at baseline, 3 months, and 6 months. mfERG parameters (amplitude and implicit times) were analyzed alongside central field thickness (CFT), macular volume (MV), and average macular thickness (AMT). Results: Eyes affected by AANU demonstrated a significant reduction in mfERG response amplitude in the central retinal region compared with control eyes, particularly during the acute phase. Although OCT parameters showed partial structural normalization during follow-up, functional recovery was less pronounced in selected retinal regions. Latency values showed minimal variation over time. These findings indicate a potential dissociation between electrophysiological function and structural morphology during disease resolution. Conclusions: Acute anterior uveitis is associated with measurable macular functional impairment detectable by mfERG, even when structural OCT parameters appear relatively stable. These results suggest that inflammatory processes in AAU may extend beyond the anterior segment and transiently affect retinal function. mfERG may therefore serve as a sensitive adjunct tool for detecting and monitoring subclinical macular dysfunction in AANU. Clinical Relevance: Functional retinal impairment may persist despite apparent structural recovery in acute anterior uveitis. Incorporating mfERG into clinical evaluation may improve the detection of subtle macular involvement and enhance understanding of disease dynamics beyond conventional imaging findings. Full article

(This article belongs to the Section Ophthalmology)

17 pages, 892 KB

Open AccessArticle

Research on Carbon Emission Calculation and Emission Reduction Strategies for Buildings Based on the Whole Life Cycle

by Xiaolong Xu, Suyun Yu, Hongmei Lu, Zhengyi Sun, Kelin Zheng, Zede Liang and Zhenjun Xu

Buildings 2026, 16(8), 1487; https://doi.org/10.3390/buildings16081487 (registering DOI) - 9 Apr 2026

Abstract

Global climate change necessitates urgent carbon reduction, with the building sector being a major contributor. This study conducts a comprehensive life cycle carbon emission analysis of a nearly zero-energy office building in Shenyang, China, using the LCA theory and the carbon emission factor method. The calculation covers the production and transportation of building materials, construction, operation, and demolition stages. The results show that the building’s average annual carbon emission intensity is 56.36 kgCO₂e/(m²·a). The operation stage contributes the largest share, with an intensity of 37.83 kgCO₂e/(m²·a), primarily due to HVAC energy consumption. The material production and transportation stage follows, accounting for 31.67% of total emissions. Compared to conventional buildings, the proportion of operational emissions in this nearly zero-energy building is relatively lower, while the share from material production is significantly higher due to the use of high-performance insulation and components. Based on these findings, targeted carbon reduction strategies are proposed for each life cycle stage, emphasizing low-carbon material selection, renewable energy utilization, and efficient design. This study provides a quantitative reference for achieving carbon reduction goals in the building sector. Full article

(This article belongs to the Section Building Energy, Physics, Environment, and Systems)

29 pages, 2439 KB

Open AccessReview

Agentic and LLM-Based Multimodal Anomaly Detection: Architectures, Challenges, and Prospects

by Mohammed Ayalew Belay, Amirshayan Haghipour, Adil Rasheed and Pierluigi Salvo Rossi

Sensors 2026, 26(8), 2330; https://doi.org/10.3390/s26082330 (registering DOI) - 9 Apr 2026

Abstract

Anomaly detection is crucial in maintaining the safety, reliability, and optimal performance of complex systems across diverse domains, such as industrial manufacturing, cybersecurity, and autonomous systems. While conventional methods typically handle single data modalities, recently, there has been an increase in the application of multimodal detection in dynamic real-world environments. This paper presents a comprehensive review of recent research at the intersection of agentic artificial intelligence and large language-based multimodal anomaly detection. We systematically analyze and categorize existing studies based on the agent architecture, reasoning capabilities, tool integration, and modality scope. The main contribution of this work is a novel taxonomy that unifies agentic and multimodal anomaly detection methods, alongside benchmark datasets, evaluation methods, key challenges, and mitigation strategies. Furthermore, we identify major open issues, including data alignment, scalability, reliability, explainability, and evaluation standardization. Finally, we outline future research directions, with a particular emphasis on trustworthy autonomous agents, efficient multimodal fusion, human-in-the-loop systems, and real-world deployment in safety-critical applications. Full article

(This article belongs to the Special Issue Intelligent Sensors for Security and Attack Detection)

16 pages, 5731 KB

Open AccessArticle

Bacillus subtilis Biofertilizer Mitigates N₂O Emissions from Saline-Alkali Farmland

by Rui Li, Xingjie Lin, Yu Miao, Chi Zhang, Fangze Li, Ge Zhang, Qiwei Sun, Tianci Hua and Jiachen Wang

Life 2026, 16(4), 635; https://doi.org/10.3390/life16040635 (registering DOI) - 9 Apr 2026

Abstract

Nitrous oxide (N₂O) emissions from agricultural soils are an important source of greenhouse gases and are strongly influenced by fertilization practices. In this study, a field experiment was conducted from 24 June to 12 October 2024, at a saline-alkali farmland site in Binzhou, Shandong Province, China, to evaluate the effect of Bacillus subtilis biofertilizer on N₂O emissions and to explore the underlying mechanisms. Compared with conventional chemical fertilization, the Bacillus subtilis biofertilizer treatment reduced the cumulative N₂O emission flux by 39%. At the N₂O emission peak, the emission flux under the biofertilizer treatment was 40.7%, 18.2% lower than that under the CF and CBF treatments, respectively. Functional gene analysis further showed that at the N₂O emission peak, the biofertilizer treatment reduced the copy number of Bacterial-amoA by 94% and 83% relative to CF and CBF, respectively, while the hao gene abundance in the CF treatment was 7.67, 24 times higher than that in the BF and CBF treatments, indicating that the reduction in N₂O emissions was closely associated with suppression of the nitrification process. In addition, the biofertilizer treatment showed the highest plant nitrogen uptake. All fertilization treatments significantly increased crop yield compared with the control, whereas there was no significant difference in yield among BF, CF, and CBF treatments (p > 0.05). These findings indicate that B. subtilis biofertilizer can mitigate N₂O emissions from saline-alkali farmland without reducing crop yield and may represent a promising strategy for sustainable agricultural management. Full article

(This article belongs to the Special Issue Advances in the Structure and Function of Microbial Communities)

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16 pages, 4011 KB

Open AccessArticle

Adaptive Multi-Order Penalty and Dual-Driven Weighting: aisPLS Algorithm for Raman Baseline Correction with Weak Peak Preservation

by Jiawei He, Yonglin Bai, Zishang Jv, Zhen Chen and Bo Wang

Molecules 2026, 31(8), 1243; https://doi.org/10.3390/molecules31081243 (registering DOI) - 9 Apr 2026

Abstract

Baseline correction of Raman spectra is a critical step for achieving high-precision quantitative analysis. However, the presence of complex background noise, nonlinear baseline drift, and spectral peak distortion due to peak overlap in real spectral data severely limits the performance of conventional correction methods. To better preserve spectral details, this study proposes an improved penalized least squares method for Raman spectral baseline correction. Compared with common baseline correction approaches, the proposed method optimizes the iterative weight function through precise noise classification, significantly enhancing the algorithm’s flexibility. The traditional single smoothing parameter is extended into a smoothing vector, and a classification strategy consistent with that of the penalty parameter is adopted, enabling synchronous optimization and coordinated adjustment of both during iteration. Furthermore, based on the physical constraints of Raman spectra, the algorithm eliminates non-physical solutions that may arise in traditional iterative processes, ensuring the fidelity of the corrected spectra. Experimental results demonstrate that the proposed method exhibits strong robustness under various noise conditions and significantly improves correction accuracy. Full article

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29 pages, 3924 KB

Open AccessArticle

Effect of Nozzle Parameters and Spindle Speed on the Oil Mist Penetration Mechanism in MQL High-Speed Milling of a GH4169 Alloy

by Wenjie Mei, Ziyang Cao, Xin Zhao and Qiang Wu

Machines 2026, 14(4), 420; https://doi.org/10.3390/machines14040420 (registering DOI) - 9 Apr 2026

Abstract

Minimum quantity lubrication (MQL) is a promising green technology for high-speed milling of GH4169. However, the full-chain oil mist penetration mechanism remains unclear, limiting precise parameter regulation. Based on a cross-scale mechanism, this study develops a semi-empirical oil mist penetration efficiency model coupling four key parameters and conducts single-factor and orthogonal high-speed milling experiments to validate the model and analyze the regulation mechanism using milling force and surface roughness. The experimental results show relative deviations below 6%, demonstrating good model validity and robustness. The influence hierarchy is spindle speed > nozzle orientation > nozzle angle > nozzle distance. Spindle speed and nozzle orientation are strongly coupled dominant parameters with a “drive-adaptation” mechanism, while nozzle distance and nozzle angle are weakly coupled, only notable under extreme conditions. The optimal parameters obtained via BP neural network and NSGA-II are nozzle orientation −X, angle 22.43°, distance 14.96 mm, and spindle speed 16,581 rpm. Under this combination, minimum Surface Roughness Ra of 0.17 μm and milling force of 24.27 N are achieved, reducing surface roughness by 85.32% and milling force by 53.52% versus the worst condition and reducing roughness by 28.57% versus the baseline while maintaining milling force within a reasonable range. This study clarifies the physical mechanism of MQL oil mist penetration, extending conventional macroscopic parameter optimization. The proposed cross-scale framework offers theoretical and engineering guidance for MQL parameter design in green precision machining of nickel-based superalloys. Full article

(This article belongs to the Special Issue Sustainable Manufacturing and Green Processing Methods, 2nd Edition)

12 pages, 3551 KB

Open AccessArticle

Determination of HOMO–LUMO Energy Levels of Carbon Dots via Electron Transfer Kinetics and Marcus Theory

by Mengli Yang, Xiaoyu Yu, Yang Yang, Huiqi Shi, Bianyang He, Weishuang Li, Yaoyao Zhang and Lei Zhu

Molecules 2026, 31(8), 1247; https://doi.org/10.3390/molecules31081247 (registering DOI) - 9 Apr 2026

Abstract

The precise determination of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels is critical for understanding the photophysical and photochemical properties of carbon dots (C-dots), which directly govern their performance in optoelectronic, catalytic, and sensing applications. However, the lack of distinct redox peaks in cyclic voltammetry (CV) curves of C-dots poses a major challenge to conventional energy level calculation methods. Herein, we propose a novel strategy to calculate the HOMO–LUMO energy levels of C-dots by combining electron transfer (ET) kinetics with Marcus theory. A series of quinones (electron acceptors, EAs) and ferrocene derivatives (electron donors, EDs) were employed to quench the fluorescence of C-dots, and the ET rate constants (K) were derived from fluorescence lifetime measurements. The CV curves of EAs and EDs provided their respective oxidation and reduction potentials, which were used as reference energy levels. The UV–Vis absorption spectra confirmed that the fluorescence quenching mechanism was dominated by ET rather than energy transfer. Based on Marcus theory, the free energy change (ΔG) of ET reactions was correlated with K, and the HOMO and LUMO energy levels of C-dots were calculated to be −1.84 V (vs. SCE) and +1.60 V (vs. SCE), respectively. This study not only provides a reliable method for determining the energy levels of C-dots without distinct redox peaks but also deepens the understanding of ET mechanisms between C-dots and small molecules. The proposed strategy is expected to be extended to other fluorescent nanomaterials with similar CV limitations. Full article

(This article belongs to the Special Issue Advances in Organic Materials for Energy, Optoelectronics, and Electronics)

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12 pages, 1273 KB

Open AccessArticle

Logistics-Mediated Artificial Sympatry and Its Implications for Molecular Detection of Hylurgus ligniperda

by Jijing Han, Jiaying Wang, Junxia Cui, Li Liu, Xianfeng Chen, Yuhao Cao, Jiaojiao Chen and Xuemei Song

Insects 2026, 17(4), 408; https://doi.org/10.3390/insects17040408 (registering DOI) - 9 Apr 2026

Abstract

International timber trade has accelerated the global spread of the invasive red-haired pine bark beetle H. ligniperda, posing persistent challenges to phytosanitary inspection and border biosecurity. Rapid isothermal amplification assays are increasingly deployed in frontline quarantine settings to support timely regulatory decisions. However, their performance under the heterogeneous biological backgrounds typical of traded timber remains insufficiently evaluated, particularly with respect to the practical implications of low-level false-positive signals. We re-evaluated a previously reported isothermal assay for H. ligniperda using conditions that simulate timber transport and routine customs workflows. Fifty non-target arthropod species (predominantly insects), selected from quarantine interception records, were included to represent taxa likely to co-occur in operational contexts. Material from Lema decempunctata consistently generated weak but reproducible amplification signals across replicates. Sanger sequencing excluded contamination, confirming low-level non-target amplification in complex biological matrices. Although the signals were faint, ambiguous results in quarantine settings may trigger shipment detention, confirmatory laboratory testing, or temporary trade restrictions, thereby increasing inspection workload, delaying clearance, and generating avoidable compliance costs. These findings indicate that trade-mediated species assemblages can compromise assay performance beyond expectations derived from conventional taxonomy-based specificity testing. To reduce interpretive uncertainty and associated regulatory burden, we propose a tiered diagnostic workflow combining rapid on-site isothermal screening with specificity-oriented SYBR Green qPCR confirmation. This strategy enhances diagnostic reliability while preserving operational efficiency in applied biosecurity surveillance. Full article

(This article belongs to the Special Issue Insect Molecular Biology: From Bioinformatics to Pest Management Applications)

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24 pages, 6223 KB

Open AccessArticle

Admission C-Reactive Protein and Mortality After STEMI: A Retrospective Cohort Study Identifying Subgroup-Specific Risk Thresholds

by Kristen Kopp, Magdalena Leitner, Nikolaus Clodi, Michael Lichtenauer, Matthias Hammerer, Uta C. Hoppe, Elke Boxhammer and Mathias C. Brandt

J. Clin. Med. 2026, 15(8), 2864; https://doi.org/10.3390/jcm15082864 (registering DOI) - 9 Apr 2026

Abstract

Background: Inflammation is central to myocardial injury and repair after ST-segment elevation myocardial infarction (STEMI). C-reactive protein (CRP) is an established biomarker of systemic inflammation, but its prognostic thresholds across patient subgroups are not well defined. Methods: In this retrospective cohort study, admission CRP was analyzed in 958 consecutive STEMI patients admitted to University Hospital Salzburg 2018–2020 and categorized into four groups (Serum CRP < 5.0, 5.0–9.9, 10.0–15, and >15.0 mg/dL). Mortality was assessed during short- (30, 90, and 180 days) and long-term (1, 3, and 5 years) follow-up. Kaplan–Meier analyses compared survival, Cox regression tested associations, and receiver operating characteristic (ROC) curves determined discriminatory value and optimal cut-offs. Results: Elevated admission CRP was associated with larger infarct size, impaired left ventricular function, and increased mortality. Kaplan–Meier curves showed progressively poorer survival with higher CRP, with worst outcomes at >15 mg/dL. At 30, 90, and 180 days, CRP demonstrated moderate discrimination (AUC 0.628, 0.653, and 0.654; all p < 0.001), with predictive cut-offs 11–15 mg/dL in the overall cohort. Subgroup analyses revealed markedly lower thresholds in vulnerable populations. Diabetic patients showed cut-offs 5–6 mg/dL with the highest AUC values (up to 0.714). Younger patients and smokers exhibited thresholds near 9–10 mg/dL, while subacute STEMI presentations demonstrated lower cut-offs compared with acute infarction. These findings indicate that the prognostic value of CRP is context-dependent rather than uniform. Conclusions: Admission CRP predicts short-term mortality after STEMI, with subgroup-specific cut-offs emerging below conventional thresholds, highlighting profiles where modest inflammatory activation carries disproportionate risk. Full article

(This article belongs to the Special Issue Cardiovascular Prevention and Epidemiology: Innovations and Insights Bridging Research and Practice for Effective Risk Reduction)

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20 pages, 1741 KB

Open AccessArticle

Analysis of High-Power Electromagnetic Pulses Effect on Unmanned Aerial Vehicles

by Kyoung Joo Lee, Sung-Man Kang, Dong-Wook Park, Ji-Hun Kim and Jeong Min Woo

Drones 2026, 10(4), 272; https://doi.org/10.3390/drones10040272 (registering DOI) - 9 Apr 2026

Abstract

This study investigates the “soft-kill” mechanism of unmanned aerial vehicles (UAVs) under high-power electromagnetic pulse (EMP) exposure. Unlike previous research focused on hardware destruction, we identify flight control paralysis caused by Pulse Width Modulation (PWM) signal logic threshold violation as the primary failure mode. To resolve discrepancies between theory and experiment, a 1 × 1 m loop antenna model was implemented in CST Studio Suite. Results demonstrate that EMP coupling in drone arm wiring predominantly generates differential mode (DM) noise. This explains why conventional ferrite beads fail while full-body shielding remains effective. Our findings provide a theoretical basis for low-power anti-drone system optimization and hardened UAV design guides. Full article

21 pages, 968 KB

Open AccessArticle

ViTUNet: Vision Transformer U-Net Hybrid Model for Carious Lesions Segmentation on Bitewing Dental Images

by Vincent Majanga, Ernest Mnkandla, Ekundayo Olufisayo Sunday, Bosun Ajala and Thottempundi Sree

Appl. Sci. 2026, 16(8), 3693; https://doi.org/10.3390/app16083693 (registering DOI) - 9 Apr 2026

Abstract

Meticulous segmentation of medical images requires obtaining both local and global spatial detailed information. The conventional U-Net model excels at local spatial feature extraction through residual convolutional blocks but struggles to capture global features. To resolve this issue, we propose the vision transformer U-NeT (ViTUNet) model framework, which combines the self-attention mechanism of the vision transformer (ViT) to capture global information while maintaining the extraction of local features via U-NeT. This proposed architecture introduces vision transformers to the existing residual convolution blocks in the U-Net encoder path, thereby capturing both local and global features. The decoder path then rebuilds this information into high-quality segmentation maps with accurately highlighted boundaries/edges. This model is utilized to segment carious lesions in bitewing dental radiographs. These images are pre-processed using augmentation, morphological operations, and segmentation to identify the boundaries/edges of the regions of interest (caries/cavity). The proposed method is evaluated on an augmented dataset containing 3000 image–watershed mask pairs. It was trained on 2400 training images and tested on 600 testing images. The experimental results exemplified significant improvements in segmentation performance, achieving

98.45 %

validation accuracy,

97.88 %

validation Dice coefficient, and

95.87 %

validation intersection over union (IoU) metric scores. These results are superior compared to other conventional and state-of-the-art U-NeT models, thus highlighting the impact of transformer-based hybrid architectures in improving medical image segmentation tasks. Full article

(This article belongs to the Special Issue Advances in Medical Physics and Quantitative Imaging)

29 pages, 7569 KB

Open AccessArticle

Urban Ecological Zoning and Optimization from the ES-ERI-RES Perspective: A Case Study of Ganzhou City

by Ting Zhang, Xiaosheng Liu, Zihang Lin and Xiaobin Huang

Appl. Sci. 2026, 16(8), 3686; https://doi.org/10.3390/app16083686 (registering DOI) - 9 Apr 2026

Abstract

Regional sustainable development requires integrated assessments that capture ecosystem function, risk exposure, and recovery capacity. Conventional two-dimensional frameworks based on ecosystem services (ESs) and landscape ecological risk (ERI) often overlook the self-regulation potential of ecosystems following disturbance. This study proposes that incorporating RES as a third zoning dimension enables functional differentiation between areas that share similar ES–ERI profiles but differ substantially in recovery capacity, thereby revealing management priorities that a conventional two-dimensional framework cannot detect. This study develops a three-dimensional zoning framework integrating ES, ERI, and ecological resilience (RES) in the main urban area of Ganzhou City, a representative hilly city in southern China. Land-use dynamics from 1990 to 2020 and under four 2050 scenarios were simulated using a coupled PLUS-InVEST approach. Differentiated ecological zones were delineated, and the optimal-parameter geographic detector (OPGD) was applied to examine driving factor interactions. Results indicate that cultivated land and forestland dominated the study area throughout the period. ES supply remained favorable with stage-wise fluctuations, while ERI showed progressive convergence of high-risk patches toward the central basin. RES exhibited a sharp decline in higher-resilience areas during 1990–2000 (91.0%), followed by partial recovery during 2010–2020 (47.3%). The three-dimensional zoning delineated 35.9% of the area as Ecological control zones that may require priority intervention. Driver analysis revealed that DEM, precipitation, and river proximity, along with their interactions, strongly influenced regional ecological patterns. The proposed framework extends conventional ES-ERI assessments and provides spatial guidance for differentiated ecological management in hilly regions. Full article

(This article belongs to the Section Environmental Sciences)

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