Precise Size Research Articles

The performance of optic disc-macula distance to disc-diameter ratio in qualitative and quantitative assessment of the optic disc size.

We aimed to evaluate the performance of the optic disc-macula distance to disc-diameter ratio (DM/DD) in qualitative and quantitative assessment of optic disc size. In 300 apparently normal eyes, we determined the correlation between DM/DD and the planimetric disc area (DA) and evaluated the performance of DM/DD in discriminating between small and large discs. The ability of DM/DD to predict the actual DA was validated in a separate cohort of 200 eyes. The disc size in eyes with glaucoma or related diseases was compared with DA- or DM/DD-based disc size. The DM/DD and its square of inverse (DD2/DM2) showed excellent non-linear and linear correlations, respectively, with DA (p < 0.001 and p < 0.001, respectively), demonstrating their performance in discriminating between small (<2.04 mm2) and large (>2.94 mm2) disc sizes with an area under the receiver operating characteristic curve of 0.901 and 0.959, respectively. The difference between the predicted DA and planimetric DA was within ±10% in more than 80% of the eyes. Eyes with suspected and normal-tension glaucoma exhibited larger discs (p < 0.01) when evaluated using both DM/DD and DA. The variation in disc-macula distance seemed to be a confounding factor for DM/DD in inferring the precise disc size. DM/DD, which can be easily calculated from various kinds of fundus photographs or images, was an excellent and interchangeable tool for qualitative and quantitative assessment of disc size, which did not require correction for ocular magnification.

Vacuum‐Assisted Confined Growth of MOF@COF Composite Membranes with Enhanced Hydrogen Permselectivity

With ordered and periodic network structures, adjustable pore sizes and high chemical stability, covalent organic frameworks (COFs) have drawn much attention for the fabrication of superior separation membranes. However, it is challenging to prepare COF membranes with a molecular sieving property for gas separation due to their relatively large pore size. In this work, we develop the MOF‐in‐COF concept for vacuum‐assisted synthesis of metal‐organic framework (MOF) ZIF‐8 inside the pores of TB‐COF formed from trihydroxy‐benzene‐tricarbaldehyde (T) and diamino‐biphenyl‐disulfonic acid (B), thus constructing a novel ZIF‐8@TB‐COF membrane. Attributing to the formation of a well‐defined one‐dimensional (1D) nanoscale transport channel, the ZIF‐8@TB‐COF membrane displays a high hydrogen permselectivity. At 100 °C and 200 kPa, the mixture separation factors of H2/CO2, H2/CH4 and H2/C3H8 are 21.9, 63.1 and 134.4, respectively, which are much higher than those of the pristine TB‐COF membrane due to the precise size sieving channels brought about by the incorporated MOF. The synthesis of ZIF‐67@TB‐COF membrane demonstrates the versatility of the synthesis strategy.

Theoretical study of the threshing process of corn cob under the influence of the rasp bars

Abstract The device described in this scientific article can be used to cut off the unnecessary (top and bottom) part of the corn ears grown for seed, and to thresh and separate the seeds of the precise size. In the course of our research, it was determined that the speed of rotation of the ear corn after impact Ucr , the radius of the drum Rd , the angular velocity ωr of the drum at what number of revolutions, the angular speed ωcr of the ear corn after impact, and how it affects the threshing process. According to the physical meaning of the established inequality, the ear corn does not advance from the threshing rasp bar under the impact of the strike of the threshing rasp bar, on the contrary, the threshing rasp bar passes over the corn ear and threshes or removes the seed grains. Also, during the research, Vc and Vrbr speeds were analyzed from a theoretical point of view, and their influence on the technological process was studied.

Polymer Nano‐Carrier‐Mediated Gene Delivery: Visualizing and Quantifying DNA Encapsulation Using dSTORM

AbstractThe success of gene therapy hinges on the effective encapsulation, protection, and compression of genes. These processes deliver therapeutic genes into designated cells for genetic repair, cellular behavior modification, or therapeutic effect induction. However, quantifying the encapsulation efficiency of small molecules of interest like DNA or RNA into delivery carriers remains challenging. This work shows how super‐resolution microscopy, specifically direct stochastic optical reconstruction microscopy (dSTORM), can be employed to visualize and measure the quantity of DNA entering a single carrier. Utilizing pNIPAM/bPEI microgels as model nano‐carriers to form polyplexes, DNA entry into the carrier is revealed across different charge ratios at temperatures below and above the volume phase transition of the microgel core. The encapsulation efficiency also depends on DNA length and shape. This work demonstrates the uptake of the carrier entity by primary derived macro‐phages and showcases the cell viability of the polyplexes. The study shows that dSTORM is a potent tool for fine‐tuning and creating polyplex microgel carrier systems with precise size, shape, and loading capacity at the individual particle level. This advancement shall contribute significantly to optimizing gene delivery systems.

Development of ultrasonic phased array technique for inspection of fir-tree type turbine blade root and disc-rim in nuclear power plant

ABSTRACT Steam turbines are crucial in both nuclear and thermal power plants, serving a key role in electricity generation. However, the operational lifespan of these turbines is often limited by failures in the turbine blade roots and disc rims due to fatigue, stress corrosion cracking and creep. Usage of reliable non-destructive technique during in-service inspection of turbine assembly is very important for decision making and safe operation of power plant. The conventional ultrasonic technique which is being employed currently suffers from access limitation, high inspection time and false calls during defect detection due to complex geometry of blade roots and disc rims. To address these issues, an advanced ultrasonic inspection technique using phased array technology was developed, specifically tailored for fir-tree type blade roots and disc rims. Ultrasonic Simulation studies were performed to determine the optimal parameters for phased array probes, ensuring the effective detection and precise sizing of crack-like defects. This manuscript presents the findings from these simulations, along with experimental results from phased array inspections of turbine blade root and disc rim mockup of turbine assembly in nuclear power plant.

Evolution of Ultrathin CoFe-Nanomesh for Oxygen Evolution Reaction: From Slit Pores to Ink-Bottle Pores.

The time-dependent mechanism underlying the formation of Co0.8Fe0.2(OH)x-t nanomesh (nanomesh having 80% Co and 20% Fe, "t" represents materials synthesis time) has been identified towards the development of a highly effective catalyst for the OER. Utilizing 2-ethyl imidazole as an etching reagent and the Ostwald ripening process enabled the evolution of nanomesh formation with a precise pore size of inkbottle shape. Material characterization confirmed the evolution of pore structure from layered double hydroxide-like structure to hierarchical slit-pores to uniform ink-bottle pores after 24 h of synthesis with limited pore shrinkage attributable to iron redeposition at the pore entrances. AFM showed a gradual reduction in nanomesh thickness with an increase in synthesis time up to 24 h, indicative of successful exfoliation. The best catalyst (Co0.8Fe0.2(OH)x-24h) was developed after 24 h of synthesis, having 3.8 nm ink-bottle-shaped pores on the basal plane of nanosheets with only 3-4 layers. Co0.8Fe0.2(OH)x-24h exhibited the best catalytic performance, characterized by a 330 mV overpotential, a mass activity of 309.1 A/g, and a turnover frequency of 2.28 s-1. An increased electrochemical surface area (70.74 cm²) and a high roughness factor of approximately 1010 underlined the importance of narrow mesopores in facilitating catalyst-electrolyte interactions and improving mass transport.

Estimation of the Partial Discharge Inception Voltage of Electrical Asset Components at Variable Environmental Pressure: A Modelling Approach

Since partial discharges, PD, are a major accelerated degradation mechanism of organic electrical insulation systems, measuring partial discharge inception voltage, PDIV, of electrical asset components in aviation and aerospace is a fundamental tool to cope with life, safety, and reliability requirements. Partial discharge phenomenology and inception voltage depend on pressure, specifically, PDIV decreases with pressure. To avoid PD inception during aircraft or aerospace vehicle operation, the value of PDIV must be known at any pressure that electrical asset components will experience. However, a lack of experimental facilities adequate to test PD on real-size asset components might prevent from having PD-related information at pressures lower (or higher) than standard atmospheric pressure, SAP. This paper presents a heuristic approach, based on physics-derived PD field inception models, that allows the estimation of PDIV at low pressure to be carried out based on measurements made at SAP, when the typology of the defect causing partial discharge is known (from SAP PD measurements), but the precise type, size, and location of the PD-generating defect is unknown. It is shown that PDIV estimates obtained by the proposed models for internal and surface discharges are in good agreement with measured values in a range from SAP to 0.05 bar, testing simple insulation geometries but also real asset components, such as motors.

Development of a murine laser interstitial thermotherapy system.

The objective of this study was to develop a murine system for the delivery of laser interstitial thermotherapy (LITT) with probe-based thermometry as a model for human glioblastoma treatment to investigate thermal diffusion in heterogeneous brain tissue. First, the tissue heating properties were characterized using a diode-pumped solid-state near-infrared laser in a homogeneous tissue model. The laser was adapted for use with a repurposed stereotactic surgery frame utilizing a micro laser probe and Hamilton syringe. The authors designed and manufactured a stereotactic frame attachment to work as a temperature probe stabilizer. Application of this novel design was used as a precise method for real-time thermometry at known distances from the thermal ablative center mass during murine LITT studies. Temperature measurements were achieved during LITT that verified the direct thermometry capability of the system without the need for MR-based thermal monitoring. Application of multiple stereotactic design iterations led to an accurately reproducible surgical laser ablation procedure. Histological staining confirmed precise thermal ablation and controllable lesion size based on time and temperature control. Treatment of a syngeneic intracranial glioma model highly resistant to conventional therapy resulted in a modest survival benefit. The authors have successfully developed a murine model system of LITT with direct in situ thermometry for investigation into the effects of thermal ablation and combinatorial treatments in murine brain tumor models.

Hydrogel interfaced glass nanopore for high-resolution sizing of short DNA fragments

Solid-state nanopores, known for their label-free detection and operational simplicity, face challenges in accurately sizing the short nucleic acids due to fast translocation and a lack of enzyme-based control mechanisms as compared to their biological counterparts with sizing resolutions still limited to ≥100 bp. Here, we present a facile polyethylene glycol-dimethacrylate (PEG-DMA) hydrogel interfaced glass nanopore (HIGN) system by inserting glass nanopore into the hydrogel to achieve sub-100 base pair (bp) resolution in short DNA sizing analysis. We systematically investigated the effects of hydrogel mesh size, spatial configurations of glass nanopores about the hydrogel, applied bias voltage, and analyte concentration on the transport dynamics of 200 bp double-stranded DNA (dsDNA). A 7.5 w/v% PEG-DMA hydrogel induced ∼11x increase in the mean dwell times compared with bare solution nanopore system. The insertion locations and depths of the glass nanopore into the hydrogel resulted in 7.16% and 5.28% coefficients of variation (CV) for mean normalized event frequencies. This enhancement of dwell times and invariability in translocation characteristics enables precise sizing of dsDNA fragments under 400 bp using HIGN, with an achieved size resolution of 50 bp with observable mean normalized peak amplitude (ΔI/Io) of ∼0.005. Furthermore, we have demonstrated the capability of HIGN to perform multiplex detection of influenza A virus (IAV) and severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) through reverse transcriptase-polymerase chain reaction (RT-PCR). These results demonstrated the potential of HIGN as a versatile tool in nucleic acid analysis and multiplexed label-free molecular diagnostics.

Point-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model.

Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly expert delineation labeling. However, due to the lack of precise size and boundary guidance, the effectiveness of PSS often falls short of expectations. Although recent vision foundational models, such as the medical segment anything model (MedSAM), have made significant advancements in bounding-box-prompted segmentation, it is not straightforward to utilize point annotation, and is prone to semantic ambiguity. In this preliminary study, we introduce an iterative framework to facilitate semantic-aware point-supervised MedSAM. Specifically, the semantic box-prompt generator (SBPG) module has the capacity to convert the point input into potential pseudo bounding box suggestions, which are explicitly refined by the prototype-based semantic similarity. This is then succeeded by a prompt-guided spatial refinement (PGSR) module that harnesses the exceptional generalizability of MedSAM to infer the segmentation mask, which also updates the box proposal seed in SBPG. Performance can be progressively improved with adequate iterations. We conducted an evaluation on BraTS2018 for the segmentation of whole brain tumors and demonstrated its superior performance compared to traditional PSS methods and on par with box-supervised methods.

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization.

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure-property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co3O4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the size-dependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an "onset radius," which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

Plastid LPAT1 is an integral inner envelope membrane protein with the acyltransferase domain located in the stroma.

The N-terminal transmembrane domain of LPAT1 crosses the inner membrane placing the N terminus in the intermembrane space and the C-terminal enzymatic domain in the stroma. Galactolipids mono- and di-galactosyl diacylglycerol are the major and vital lipids of photosynthetic membranes. They are synthesized by five enzymes hosted at different sub-chloroplast locations. However, localization and topology of the second-acting enzyme, lysophosphatidic acid acyltransferase 1 (LPAT1), which acylates thesn-2 position of glycerol-3-phosphate (G3P) to produce phosphatidic acid (PA), remain unclear. It is not known whether LPAT1 is located at the outer or the inner envelope membrane and whether its enzymatic domain faces the cytosol, the intermembrane space, or the stroma. Even the size of mature LPAT1 in chloroplasts is not known. More information is essential for understanding the pathways of metabolite flow and for future engineering endeavors to modify glycerolipid biosynthesis. We used LPAT1 preproteins translatedin vitrofor import assays to determine the precise size of the mature protein and found that the LPAT1 transit peptide is at least 85 residues in length, substantially longer than previously predicted. A construct comprising LPAT1 fused to the Venus fluorescent protein and driven by theLPAT1promoter was used to complement an Arabidopsislpat1 knockout mutant. To determine the sub-chloroplast location and topology of LPAT1, we performed protease treatment and alkaline extraction using chloroplasts containingin vitro-imported LPAT1 and chloroplasts isolated from LPAT1-Venus-complemented transgenic plants. We show that LPAT1 traverses the inner membrane via an N-terminal transmembrane domain, with its N terminus protruding into the intermembrane space and the C-terminal enzymatic domain residing in the stroma, hence displaying a different membrane topology from its bacterial homolog, PlsC.

Precise Size Determination of Supported Catalyst Nanoparticles via Generative AI and Scanning Transmission Electron Microscopy.

Transmission electron microscopy (TEM) plays a crucial role in heterogeneous catalysis for assessing the size distribution of supported metal nanoparticles. Typically, nanoparticle size is quantified by measuring the diameter under the assumption of spherical geometry, a simplification that limits the precision needed for advancing synthesis-structure-performance relationships. Currently, there is a lack of techniques that can reliably extract more meaningful information from atomically resolved TEM images, like nuclearity or geometry. Here, cycle-consistent generative adversarial networks (CycleGANs) are explored to bridge experimental and simulated images, directly linking experimental observations with information from their underlying atomic structure. Using the versatile Pt/CeO2 (Pt particles centered ≈2nm) catalyst synthesized by impregnation, large datasets of experimental scanning transmission electron micrographs and physical image simulations are created to train a CycleGAN. A subsequent size-estimation network is developed to determine the nuclearity of imaged nanoparticles, providing plausible estimates for ≈70% of experimentally observed particles. This automatic approach enables precise size determination of supported nanoparticle-based catalysts overcoming crystal orientation limitations of conventional techniques, promising high accuracy with sufficient training data. Tools like this are envisioned to be of great use in designing and characterizing catalytic materials with improved atomic precision.

Detection of Camellia oleifera fruit maturity in orchards based on modified lightweight YOLO

Fruit maturity is the main factor affecting the quality and yield of Camellia oil. At present, accurate and efficient detection of the maturity level of Camellia oleifera fruits in orchards and enabling selective picking for harvesting robots is a crucial issue. Aiming at the challenge of low detection efficiency of Camellia oleifera fruit maturity and complex object detection models that are difficult to deploy to Camellia oleifera selective harvesting robots, a modified lightweight You Only Look Once model (YOLO-LM) based on YOLOv7-tiny is proposed. Specifically, three Criss-Cross Attention (CCA) modules are introduced to the backbone network to reduce the influence of leaves and branches occlusion as well as mutual occlusion between fruits. Then, the Adaptively Spatial Feature Fusion (ASFF) module is applied as the head to solve the limitation of inconsistency across different feature scales on the feature pyramid. Furthermore, the GSConv is introduced to replace the standard convolution of the Neck network to enable the model to simultaneously preserve model accuracy and reduce model complexity. Experiment results demonstrate that the precision, recall, mAP@0.5, parameters, FLOPs, and model size of YOLO-LM reached 93.96 %, 93.32 %, 93.18 %, 10.17 million, 19.46 G, and 19.82 MB, which outperformed YOLOv3, Faster R-CNN (VGG16), Faster R-CNN (ResNet50), YOLOv4, and YOLOv5m. Compared to YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv8s, the mAP@0.5 of YOLO-LM increased by 6.24 %, 6.11 %, 2.52 %, 2.45 %, and 0.57 %, respectively. Meanwhile, the parameters and model size of YOLO-LM are slightly higher than that of YOLOv3-tiny, YOLOv4-tiny, YOLOv5s and YOLOv7-tiny, and significantly lower than that of YOLOv8s. In addition, the detection heatmaps of YOLOv7-tiny and YOLO-LM demonstrate these optimization operations contribute to the enhanced performance of maturity detection for Camellia oleifera fruits within natural orchard environments. Overall, the YOLO-LM model can be used for the maturity detection of Camellia oleifera fruits in orchards, which is expected to provide theoretical reference for orchard yield estimation, growth monitoring, planting management optimization and the development of Camellia oleifera selective harvesting robots.

A deep learning-powered intelligent microdroplet analysis workflow for in-situ monitoring and evaluation of a dynamic emulsion

Accurately determining microdroplet size and uniformity in dispersed phase systems is key for assessing emulsion stability, and essential for quality monitoring and control of emulsion products. Microscopic imaging probes have provided intuitive and advanced insights for characterizing particle microscopic morphology and dispersion in multiphase flows. However, wide size distributions, edge-cutting, overlapping, and high-density target droplets can compromise the image processing accuracy, thereby affecting the precise assessment of emulsion status. In this study, we present a deep learning-based intelligent workflow for microdroplet characterization using an in-situ high-speed particle imaging probe combined with advanced image processing techniques. With the assistance of progressive automatic annotation, a droplet image database was constructed for training the Mask R-CNN model. By incorporating image patching and supervised data augmentation strategies, our well-trained model with accuracy of 95.5% can precisely detect, localize, and segment microscale droplets with various patterns. Additionally, the shape reconstruction module visualizes true shapes of overlapping and edge-cutting droplets, facilitating precise quantification of droplet size information. To validate the feasibility and generalizability of the proposed workflow, we obtained sufficient droplet images through offline sampling during emulsion processes while employing back and front illumination for in-situ monitoring. The automated method achieves precise droplet detection and size measurement, demonstrating its immense potential in online monitoring, evaluation, and control of emulsion quality.

Hydrophobic iron oxide nanoparticles: Controlled synthesis and phase transfer via flash nanoprecipitation

Iron oxide nanoparticles (IONPs) synthesized via thermal decomposition find diverse applications in biomedicine owing to precise control of their physico-chemical properties. However, use in such applications requires phase transfer from organic solvent to water, which remains a bottleneck.Through the thermal decomposition of iron oleate (FeOl), we systematically investigate the impact of synthesis conditions such as oleic acid (OA) amount, temperature increase rate, dwell time, and solvent on the size, magnetic saturation, and crystallinity of IONPs. Solvent choice significantly influences these properties, manipulating which, synthesis of monodisperse IONPs within a tunable size range (10-30 nm) and magnetic properties (75 to 42 Am2Kg-1) is obtained.To enable phase transfer of IONPs, we employ flash nanoprecipitation (FNP) for the first time as a method for scalable and precise size control, demonstrating its potential over conventional methods. Poly(lactic-co-glycolic acid) (PLGA)-coated IONPs with hydrodynamic diameter (Hd) in the range of 250 nm, high colloidal stability and high IONPs loadings up to 43% were obtained, such physicochemical properties being tuned exclusively by the size and hydrophobicity of starting IONPs. They showed no discernible cytotoxicity in human dermal fibroblasts, highlighting the applicability of FNP as a novel method for the functionalization of hydrophobic IONPs for biomedicine.

Механизмы электронного транспорта в низкоразмерных полупроводниковых структурах: обзор

Background: Low-dimensional semiconductor structures such as quantum wells, wires, and dots have sparked widespread attention due to their distinct electron transport properties that differ dramatically from bulk materials. These characteristics are critical for advances in nanoscale electrical and optoelectronic devices. Objective: The article aims to summarize current knowledge of electron transport processes in low-dimensional semiconductor devices, focusing on the impact of reduced dimensionality on electron behaviour. Methods: A thorough literature analysis was carried out, emphasizing experimental and theoretical investigations that shed light on electron transport dynamics in quantum wells, wires, and dots. The analysis looked into scattering mechanisms, quantum confinement effects, and the significance of material composition and structure. Results: The findings show that quantum confinement produces discrete energy states that drastically change electron mobility and conductivity. Furthermore, electron scattering by phonons, contaminants, and surfaces is shown to be heavily impacted by decreased dimensionality, which either enhances or suppresses electron transport depending on the precise configuration and size of the structures. Conclusion: Low-dimensional semiconductor structures have complex electron transport behaviours that differ significantly from their bulk counterparts. Understanding these mechanisms is critical for designing and developing future electronic devices, and this overview serves as a starting point for ongoing articles on this technologically significant topic.

High-sensitivity nanostructure-based sensor using Fano resonance for noninvasive EEG monitoring

Noninvasive electroencephalogram signal detection with high sensitivity and a high signal-to-noise ratio has attracted much attention. To achieve high-sensitivity electroencephalogram (EEG) sensing, a photonic crystal side-coupled microcavity nanoscale sensor based on Fano resonance is proposed with nano electro-optic sensing technology as the core support. This configuration comprises a nanobeam resonant cavity, a waveguide, and an electrical slab. The substantial overlap between the optical and electric fields within the resonant cavity is analyzed, and the sensor’s electric field sensing performance is assessed. Due to the optically coupled nanoscale sensing structure that is adopted in the sensor, the device has an ultrahigh voltage sensing sensitivity of up to 142.64 nm/V and a compact footprint as small as 4.29 mm2, enabling real-time sensing of EEG signals of 10 μV and below. These improvements overcome the technical challenges of low precision and large size as are associated with existing optical devices used for medical monitoring.

Controlled Fabrication of Unimolecular Micelles as Versatile Nanoplatform for Multifunctional Applications.

Unimolecular micelles (UMs) are nano-sized structures that are composed of single molecules with precise composition. Compared to self-assembled polymeric micelles, UMs possess ultra-stable property even in complex biological environment. With the development of controllable polymerization and coupling chemistry, the preparation of narrowly monodispersed UMs with precise morphology and size has been realized, which further facilitates their multifunctional applications. After brief introduction, state-of-the-art advances in the synthesis and applications of UMs are discussed with an emphasis on their bioapplications. It is believed that these UMs have great potential in future fabrication of multifunctional nanoplatforms.

Time-series satellite remote sensing reveals gradually increasing war damage in the Gaza Strip.

War-related urban destruction is a significant global concern, impacting national security, social stability, people's survival and economic development. The effects of urban geomorphology and complex geological contexts during conflicts, characterized by different levels of structural damage, are not yet fully understood globally. Here we report how integrating deep learning with data from the independently developed LuoJia3-01 satellite enables near real-time detection of explosions and assessment of different building damage levels in the Israel-Palestine conflict. We found that the damage continually increased from 17 October 2023 to 2 March 2024. We found 3747 missile craters with precision positions and sizes, and timing on vital infrastructure across five governorates in the Gaza Strip on 2 March 2024, providing accurate estimates of potential unexploded ordnance locations and assisting in demining and chemical decontamination. Our findings reveal a significant increase in damage to residential and educational structures, accounting for 58.4% of the total-15.4% destroyed, 18.7% severely damaged, 11.8% moderately damaged and 12.5% slightly damaged-which exacerbates the housing crisis and potential population displacement. Additionally, there is a 34.1% decline in the cultivated area of agricultural land, posing a risk to food security. The LuoJia3-01 satellite data are crucial for impartial conflict monitoring, and our innovative methodology offers a cost-effective, scalable approach to assess future conflicts in various global contexts. These first-time findings highlight the urgent need for an immediate ceasefire to prevent further damage and support the release of hostages and subsequent reconstruction efforts.