Search Results (2,068)

Land subsidence constitutes a critical hazard to coastal megacities globally, amplifying flood risks and damaging infrastructure. Taking Tianjin—a major port city underlain by compressible sediments and affected by groundwater over-exploitation—as a case study, we address two key research gaps: the absence of a quantitative framework coupling groundwater extraction with construction land expansion, and the inadequate separation of seasonal and long-term subsidence drivers. We developed an integrated remote-sensing-based approach: high-resolution subsidence time series (2016–2023) were derived via Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, validated against leveling measurements (R > 0.885, error < 20 mm). This subsidence dataset was fused with groundwater level records and annual construction land maps. Seasonal-Trend Decomposition using Loess (STL) isolated trend, seasonal, and residual components, which were input into a Random Forest (RF) model to quantify the relative contributions of subsidence drivers. Dynamic Time Warping (DTW) and Cross-Wavelet Transform (CWT) were further employed to characterize temporal patterns and lag effects between subsidence and its drivers. Our results reveal a distinct shifting subsidence pattern: “areal expansion but intensity weakening.” Groundwater control policies mitigated five historical subsidence funnels, reducing areas with severe subsidence from 72.36% to <5%, while the total subsiding area expanded by 1024.74 km², with new zones emerging (e.g., northern Dongli District). The RF model identified the long-term groundwater level trend as the dominant driver (59.5% contribution), followed by residual (23.3%) and seasonal (17.2%) components. Cross-spectral analysis confirmed high coherence between subsidence and long-term groundwater trends; the seasonal component exhibited a dominant resonance period of 12 months and a consistent subsidence response lag of 3–4 months. Construction impacts were conceptualized as a “load accumulation-soil compression-time lag” mechanism, with high-intensity engineering projects inducing significant local subsidence. This study provides a robust quantitative framework for disentangling the complex interactions between subsidence, groundwater, and urban expansion, offering critical insights for evidence-based hazard mitigation and sustainable urban planning in vulnerable coastal environments worldwide. Full article

On 7 January 2025, an Mw7.1 earthquake struck Dingri County, Tibet, causing severe damage in a high-altitude, sparsely instrumented region where traditional damage assessment methods are limited. To address this, we developed an integrated "source simulation–nighttime light validation" framework. First, a kinematic source model (constrained by InSAR and teleseismic data) and the Unified Seismic Tomography models for continental China lithosphere 2.0 (USTClitho2.0) velocity model were used with the curved-grid finite difference method to simulate high-resolution ground motion and intensity fields. Second, NASA Black Marble (VNP46A2) nighttime light data, processed with the Block-Matching and 3D filtering (BM3D) algorithm, were analyzed to compute pixel-level radiance changes and township-level total nighttime light loss rates (TNLR). The results reveal a high spatial consistency between simulated high-intensity zones and areas of significant light loss. For instance, Mangpu Township, within a simulated high-intensity zone, exhibited a TNLR of 44.7%. This demonstrates that nighttime light remote sensing can effectively validate physical simulations in areas lacking dense seismic networks. Our framework provides a novel, complementary methodology for rapid and reliable post-earthquake damage assessment in high-mountain, data-sparse regions. Full article

To address the susceptibility of traditional concrete to explosive spalling and the lack of in situ damage-monitoring methods at high temperatures, in this study, a novel self-sensing, high-temperature-resistant Engineered Cementitious Composite (ECC) was developed. The matrix contains eco-friendly glass sand reinforced with a hybrid system of polypropylene fibers (PPFs) and carbon fibers (CFs). The evolution of mechanical properties and the multimodal self-sensing characteristics of the ECC were systematically investigated following thermal treatment from 20 °C to 800 °C. The results indicate that the hybrid system exhibits a significant synergistic effect: through PFFs’ pore-forming mechanism, internal vapor pressure is effectively released to mitigate spalling, while CFs provide residual strength compensation. Mechanically, the compressive strength increased by 51.32% (0.9% CF + 1.0% PPF) at 400 °C compared to ambient temperature, attributed to high-temperature-activated secondary hydration. Regarding self-sensing, the composite containing 1.1% CF and 1.5% PPF displayed superior thermosensitivity during heating (resistivity reduction of 49.1%), indicating potential for early fire warnings. Notably, pressure sensitivity was enhanced after high-temperature exposure, with the 0.7% CF + 0.5% PPF group achieving a Fractional Change in Resistivity of 31.1% at 600 °C. Conversely, flexural sensitivity presented a “thermally induced attenuation effect” primarily attributed to high-temperature-induced interfacial weakening. This study confirms that the “pore-formation” mechanism, combined with the reconstruction of the conductive network, governs the material’s macroscopic properties, providing a theoretical basis for green, intelligent, and fire-safe infrastructure. Full article

In recent years, the rapid development of commercial satellite projects, such as low-Earth orbit (LEO) communication and remote sensing constellations, has driven the satellite industry toward low-cost, rapid development, and large-scale deployment. Commercial off-the-shelf (COTS) components have been widely adopted across various commercial satellite platforms due to their advantages of low cost, high performance, and plug-and-play availability. However, the space environment is complex and hostile. COTS components were not originally designed for such conditions, and they often lack systematically flight-verified protective frameworks, making their reliability issues a core bottleneck limiting their extensive application in critical missions. This paper focuses on COTS solid-state drives (SSDs) onboard the Jilin-1 KF satellite and presents a full-lifecycle reliability practice covering component selection, system design, on-orbit operation, and failure feedback. The core contribution lies in proposing a full-lifecycle methodology that integrates proactive design—including multi-module redundancy architecture and targeted environmental stress screening—with on-orbit data monitoring and failure cause analysis. Through fault tree analysis, on-orbit data mining, and statistical analysis, it was found that SSD failures show a significant correlation with high-energy particle radiation in the South Atlantic Anomaly region. Building on this key spatial correlation, the on-orbit failure mode was successfully reproduced via proton irradiation experiments, confirming the mechanism of radiation-induced SSD damage and providing a basis for subsequent model development and management decisions. The study demonstrates that although individual COTS SSDs exhibit a certain failure rate, reasonable design, protection, and testing can enhance the on-orbit survivability of storage systems using COTS components. More broadly, by providing a validated closed-loop paradigm—encompassing design, flight verification and feedback, and iterative improvement—we enable the reliable use of COTS components in future cost-sensitive, high-performance satellite missions, adopting system-level solutions to balance cost and reliability without being confined to expensive radiation-hardened products. Full article

This paper analyses the role of landscape as a fundamental dimension of post-earthquake recovery in the inland areas of Central Italy, arguing that reconstruction must be understood not only as the repair of damaged buildings but as a broader territorial process affecting identity, spatial organization, and long-term settlement trajectories. In this sense, post-earthquake recovery is also interpreted as a strategic opportunity to reinforce coast–inland relationships, acknowledging the structural interdependence between inland Apennine areas and coastal urban systems. Drawing on insights from applied research conducted in the L’Aquila 2009 crater and on the conceptual framework developed within the PRIN TRIALS project, the paper discusses how seismic events accelerate pre-existing territorial dynamics and produce enduring transformations, particularly in the proximity landscapes surrounding historic centres. Rather than presenting empirical findings, the contribution offers a theoretical and operational framework aimed at integrating landscape considerations into reconstruction processes. It outlines key concepts such as landscape quality, transformative resilience, and permanent temporariness; reviews critical normative aspects linked to emergency procedures; and proposes a set of landscape-oriented guidelines and criteria for the contextual integration of reconstruction projects. These include landscape quality objectives, multiscalar readings of identity values, and operational tools such as visual-impact assessment, Project Reference Context analysis, and principles for managing transformations in peri-urban and historic environments. Overall, the paper argues that adopting a landscape-based perspective can strengthen territorial cohesion, support the sustainable redevelopment of historic centres and their surroundings, and embed post-earthquake reconstruction within broader coast–inland territorial strategies aimed at long-term resilience and balanced regional development in Apennine communities. Full article

Background: Catheter ablation is a validated treatment for ventricular arrhythmias (VA), but conventional radiofrequency (RF) energy may cause collateral injury due to non-selective thermal damage. Pulsed Field Ablation (PFA), a non-thermal modality based on irreversible electroporation, offers myocardial tissue selectivity and enhanced safety. While PFA is widely adopted for atrial arrhythmias’ ablation, its application in the ventricles remains an evolving frontier. Methods: We report a single-center experience using the Centauri PFA system integrated with a focal, contact-force sensing irrigated catheter (Tacticath™ SE, Abbott Laboratories, St. Paul, MN, USA) in four consecutive patients with drug-refractory VA. Two patients presented with frequent premature ventricular complexes (PVC) arising from the right and left ventricular outflow tract, respectively, while two had ischemic cardiomyopathy with recurrent scar-related ventricular tachycardia (VT). All procedures were guided by high-density mapping using the EnSite X system (Abbott Laboratories, St. Paul, MN, USA). Procedural safety, acute efficacy, and early follow-up outcomes were assessed. Results: All ablations achieved acute procedural success without complications. In both PVC cases, PFA led to immediate and complete suppression of ectopy, with a ≥95% reduction in arrhythmic burden at 12- and 9-months follow-up, respectively. In the VT cases, the arrhythmogenic substrate was effectively modified, rendering the clinical VT non-inducible. ICD interrogation during a 9-month follow-up showed complete absence of recurrent sustained VT. No coronary spasm, atrioventricular block, pericardial effusion, or other adverse events occurred. Conclusions: In this initial experience, focal PFA using a contact-force sensing catheter appeared feasible and effective for both focal and scar-related VA. This system provides an intuitive workflow similar to RF ablation. While our data suggest a favourable safety profile, larger studies are required to definitively confirm safety margins near critical structures. Full article

Microparticle detection technology uses materials that can specifically recognize complex biostructures, such as antibodies and aptamers, as trapping agents. The development of antibody production technology and simplification of sensing signal output methods have facilitated commercialization of disposable biosensors, making rapid diagnosis possible. Although this contributed to the early resolution of pandemics, traditional biosensors face issues with sensitivity, durability, and rapid response times. We aimed to fabricate microspaces using metallic materials to further enhance durability of mold fabrication technologies, such as molecular imprinting. Low-damage metal deposition was performed on target protozoa and Norovirus-like particles (NoV-LPs) to produce thin metallic films that adhere to the material. The procedure for fitting the object into the bio structured space formed on the thin metal film took less than a minute, and sensitivity was 10 fg/mL for NoV-LPs. Furthermore, because it was a metal film, no decrease in reactivity was observed even when the same substrate was stored at room temperature and reused repeatedly after fabrication. These findings underscore the potential of integrating stable metallic structures with bio-recognition elements to significantly enhance robustness and reliability of environmental monitoring. This contributes to public health strategies aimed at early detection and containment of infectious diseases. Full article

Under the intensifying water shortages in the vegetation season, early identification of Ips typographus L. damage is crucial for preventing wide outbreaks, which undermine the economic potential of commercial stands of Norway spruce (Picea abies Karst.) across Europe. For this purpose, remote sensing based on satellite images is considered one of the most efficient methods, particularly in homogenous and wide forested landscapes. However, under highly heterogeneous seminatural managed forest landscapes in lowland Central and Northern Europe, as illustrated by the eastern Baltic region and Latvia in particular, the efficiency of such an approach can lack the desired accuracy. Hence, the identification of smaller damage patches by I. typographus, which can act as a source of wider outbreaks, can be overlooked, and situational awareness can be further aggravated by infrastructure artefacts. In this study, the accuracy of satellite imaging for the identification of I. typographus damage was evaluated, focusing on the occurrence of false positives and particularly false negatives obtained from the comparison with UAV imaging. Across the studied landscapes, correct or partially correct identification of damage patches larger than 30 m² occurred in 73% of cases. Still, the satellite image analysis of the highly heterogeneous landscape resulted in quite a common occurrence of false negatives (up to one-third of cases), which were related to stand and patch properties. The high rate of false negatives, however, is crucial for the prevention of outbreaks, as the sources of outbreaks can be underestimated, burdening prompt and hence effective implication of countermeasures. Accordingly, elaborating an analysis of satellite images by incorporating stand inventory data could improve the efficiency of early detection systems, especially when coupled with UAV reconnaissance of heterogeneous landscapes, as in the eastern Baltic region. Full article

The increasing severity of extreme weather events necessitates rapid methods for post-disaster damage assessment. Current remote sensing datasets often lack the spatial resolution required for a detailed evaluation of critical waterfront infrastructure, which is vulnerable during hurricanes. To address this limitation, we introduce the Flood and Waterfront Infrastructure Segmentation Dataset (FWISD), a new dataset constructed from high-resolution unmanned aerial vehicle imagery captured after a major hurricane, comprising 3750 annotated 1024 × 1024 pixel image patches. The dataset provides semantic labels for 11 classes, specifically designed to distinguish between intact and damaged structures. We conducted comprehensive experiments to evaluate the performance of both convolution and Transformer-based models. Our results indicate that hybrid models integrating Transformer encoders with convolutional decoders achieve a superior balance of contextual understanding and spatial precision. Regression analysis indicates that the distance to water has the maximum influence on the detection success rate, while comparative experiments emphasize the unique complexity of waterfront infrastructure compared to homogenous datasets. In summary, FWISD provides a valuable resource for developing and evaluating advanced models, establishing a foundation for automated systems that can improve the timeliness and precision of post-disaster response. Full article

Natural disasters occur worldwide and cause significant financial and human losses. Wildfires are among the most important natural disasters, occurring more frequently in recent years due to global warming. Fast and accurate post-disaster damage detection could play an essential role in swift rescue planning and operations. Remote sensing (RS) data is an important source for tracking damage detection. Deep learning (DL) methods, as efficient tools, can extract valuable information from RS data to generate an accurate damage map for future operations. The present study proposes an encoder–decoder architecture composed of pre-trained Yolov11 blocks as the encoder path and Modified UNet (MUNet) blocks as the decoder path. The proposed network includes three main steps: (1) pre-processing, (2) network training, (3) prediction multilabel damage map and accuracy evaluation. To evaluate the network’s performance, the US and Canada datasets were considered. The datasets are satellite images of the 2023 wildfires in the US and Canada. The proposed method reaches the Overall Accuracy (OA) of 97.36, 97.47, and Kappa Coefficient (KC) of 0.96, 0.87 for the US and Canada 2023 wildfire datasets, respectively. Regarding the high OA and KC, an accurate final burnt map can be generated to assist in rescue and recovery efforts after the wildfire. The proposed YOLOv11–MUNet framework introduces an efficient and accurate post-event-only approach for wildfire damage detection. By overcoming the dependency on pre-event imagery and reducing model complexity, this method enhances the applicability of DL in rapid post-disaster assessment and management. Full article

Efficient and reliable road damage detection is a critical component of intelligent transportation and infrastructure control systems that rely on visual sensing technologies. Existing road damage detection models are facing challenges such as missed detection of fine cracks, poor adaptability to lighting changes, and false positives under complex backgrounds. In this study, we propose an enhanced YOLO-based framework, YOLO-ERCD, designed to improve the accuracy and robustness of sensor-acquired image data for road crack detection. The datasets used in this work were collected from vehicle-mounted and traffic surveillance camera sensors, representing typical visual sensing systems in automated road inspection. The proposed architecture integrates three key components: (1) a residual convolutional block attention module, which preserves original feature information through residual connections while strengthening spatial and channel feature representation; (2) a channel-wise adaptive gamma correction module that models the nonlinear response of the human visual system to light intensity, adaptively enhancing brightness details for improved robustness under diverse lighting conditions; (3) a visual focus noise modulation module that reduces background interference by selectively introducing noise, emphasizing damage-specific features. These three modules are specifically designed to address the limitations of YOLOv10 in feature representation, lighting adaptation, and background interference suppression, working synergistically to enhance the model’s detection accuracy and robustness, and closely aligning with the practical needs of road monitoring applications. Experimental results on both proprietary and public datasets demonstrate that YOLO-ERCD outperforms recent road damage detection models in accuracy and computational efficiency. The lightweight design also supports real-time deployment on edge sensing and control devices. These findings highlight the potential of integrating AI-based visual sensing and intelligent control, contributing to the development of robust, efficient, and perception-aware road monitoring systems. Full article

With the intensification of global climate change, high temperatures have emerged as a major abiotic stressor adversely affecting summer maize yields in North China. This study presents a high-resolution monitoring framework for Henan Province. First, an hourly, high-resolution (0.02° × 0.02°) near-surface air temperature dataset was generated by fusing Himawari-8 satellite observations, ERA5 reanalysis data, and ground-based measurements through a machine learning approach. Among the tested algorithms (support vector regression, random forest, and XGBoost), XGBoost achieved the best performance (R² = 0.933 and RMSE = 0.841 °C). Second, a High-Temperature Damage Index (HTDI) was constructed using hourly temperature thresholds of 32 °C and 35 °C, respectively. The index exhibited a statistically significant but modest negative correlation with ear grain number (R² = 0.054 and p = 0.0007). Spatial assessment revealed intensified heat damage in 2024 (average HTDI = 0.51; over 67% of the area experienced moderate or worse damage) compared to 2023 (average HTDI = 0.22), with severe damage concentrated in south–central and east–central Henan. This approach surpasses the limitations of conventional daily scale assessments by enabling refined, hourly monitoring of high-temperature heat stress. It not only advances the deep integration of remote sensing and machine learning in agricultural meteorology but also provides technical support for addressing food security challenges under climate change. Full article

The presence of aberrant double-stranded DNA (dsDNA) in the cytoplasm of cells is sensed by unique pattern recognition receptors (PRRs) to trigger innate immune response. The cyclic GMP–AMP synthase (cGAS)–stimulator of interferon genes (STING) signaling pathway is activated by the presence of non-self or mislocalized self-dsDNA from nucleus or mitochondria released in response to DNA damage or cellular stress in the cytoplasm. Activation of cGAS leads to the synthesis of the second messenger cyclic GMP–AMP (cGAMP), which binds and activates STING, triggering downstream signaling cascades that result in the production of type I interferons (IFNs) and proinflammatory cytokines. Here, we show that diverse immunostimulatory dsDNA ligands and chemotherapy agents like Doxorubicin and Taxol trigger the integrated stress response (ISR) by activating endoplasmic reticulum (ER) stress kinase, protein kinase RNA-like ER kinase (PERK), in addition to the canonical IFN pathways. PERK-mediated phosphorylation and inactivation of the alpha subunit of eukaryotic translation initiation factor-2 (eIF2α) result in the formation of stress granules (SGs). SG formation by dsDNA was significantly reduced in PERK knockout cells or by inhibiting PERK activity. Transcriptional induction of IFNβ and cytokines, ISR signaling, and SG formation by dsDNA was dampened in cells lacking PERK activity, STING, or key stress-granule nucleating protein, Ras-GAP SH3 domain-binding protein 1 (G3BP1), demonstrating an important role of the signal transduction pathway mediated by STING and SG assembly. Lastly, STING regulates reactive oxygen species (ROS) production in response to DNA damage, highlighting the crosstalk between DNA sensing and oxidative stress pathways. Together, our data identify STING–PERK–G3BP1 signaling axis that couples cytosolic DNA sensing to stress response pathways in maintaining cellular homeostasis. Full article

Microneedle (MN) technologies have emerged as a groundbreaking platform for transdermal and intradermal drug delivery, offering a minimally invasive alternative to oral and parenteral routes. Unlike passive transdermal systems, MNs allow the permeation of hydrophilic macromolecules, such as peptides, proteins, and vaccines, by penetrating the stratum corneum barrier without causing pain or tissue damage, unlike hypodermic needles. Recent advances in materials science, microfabrication, and biomedical engineering have enabled the development of various MN types, including solid, coated, dissolving, hollow, hydrogel-forming, and hybrid designs. Each type has unique mechanisms, fabrication techniques, and pharmacokinetic profiles, providing customized solutions for a range of therapeutic applications. The integration of 3D printing technologies and stimulus-responsive polymers into MN systems has enabled patches that combine drug delivery with real-time physiological sensing. Over the years, MN applications have grown beyond vaccines to include the delivery of insulin, anticancer agents, contraceptives, and various cosmeceutical ingredients, highlighting the versatility of this platform. Despite this progress, broader clinical and commercial adoption is still limited by issues such as scalable and reliable manufacturing, patient acceptance, and meeting regulatory expectations. Overcoming these barriers will require coordinated efforts across engineering, clinical research, and regulatory science. This review thoroughly summarizes MN technologies, beginning with their classification and drug-delivery mechanisms, and then explores innovations, therapeutic uses, and translational challenges. It concludes with a critical analysis of clinical case studies and a future outlook for global healthcare. By comparing technological progress with regulatory and commercial hurdles, this article highlights the opportunities and limitations of MN systems as a next-generation drug-delivery platform. Full article

Microbial contamination of food is a crucial cause of food spoilage and foodborne diseases, posing a severe threat to global public health. Although chemical preservatives are effective, their potential hazards to human health and the environment, coupled with the growing demand for “clean label” products, have driven the search for natural alternatives. Lactic acid bacteria (LAB), recognized as the Generally Recognized as Safe (GRAS) microorganisms, have emerged as the promising bio-preservatives due to their safety, effectiveness, and multifunctionality. This review systematically summarized the core antimicrobial properties of LAB, including their inhibitory spectrum against foodborne pathogens, spoilage microorganisms, viruses, parasites, and their ability to degrade toxic substances such as mycotoxins, pesticides, and heavy metals. Key inhibitory mechanisms of LAB are highlighted, encompassing the production of antimicrobial metabolites, leading to metabolism disruption and cell membrane damage, nutrition and niche competition, quorum-sensing interference, and anti-biofilm formation. Furthermore, recent advances in LAB applications in preserving various food matrices (meat, dairy products, fruits and vegetables, cereals) are integrated, including their roles in enhancing food sensory quality, extending shelf life, and retaining nutritional value. The review also discusses critical factors influencing LAB’s inhibitory activity (medium composition, culture conditions, ionic components, pathway regulator, etc.) and the challenges associated with the application of LAB. Finally, future research directions are outlined, including the novel LAB and metabolites exploration, AI-driven cultural condition optimization, genetic engineering application, nano-encapsulation and active packaging development, and building up the LAB-based cellular factories. In conclusion, LAB and their antimicrobial metabolites hold great promise as green and safe food preservatives. This review is to provide comprehensive theoretical support for the rational improvement and efficient application of LAB-based natural food preservatives, contributing to the development of a safer and more sustainable food processing and preservation systems. Full article