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The collaboration of Yale, the University of California, Davis, and United Imaging Healthcare has successfully developed the NeuroEXPLORER, a dedicated human brain PET imager with high spatial resolution, high sensitivity, and a built-in 3-dimensional camera for markerless continuous motion tracking. It has high depth-of-interaction and time-of-flight resolutions, along with a 52.4-cm transverse field of view (FOV) and an extended axial FOV (49.5 cm) to enhance sensitivity. Here, we present the physical characterization, performance evaluation, and first human images of the NeuroEXPLORER. Methods: Measurements of spatial resolution, sensitivity, count rate performance, energy and timing resolution, and image quality were performed adhering to the National Electrical Manufacturers Association (NEMA) NU 2-2018 standard. The systems performance was demonstrated through imaging studies of the Hoffman 3-dimensional brain phantom and the mini-Derenzo phantom. Initial 18F-FDG images from a healthy volunteer are presented. Results: With filtered backprojection reconstruction, the radial and tangential spatial resolutions (full width at half maximum) averaged 1.64, 2.06, and 2.51 mm, with axial resolutions of 2.73, 2.89, and 2.93 mm for radial offsets of 1, 10, and 20 cm, respectively. The average time-of-flight resolution was 236 ps, and the energy resolution was 10.5%. NEMA sensitivities were 46.0 and 47.6 kcps/MBq at the center and 10-cm offset, respectively. A sensitivity of 11.8% was achieved at the FOV center. The peak noise-equivalent count rate was 1.31 Mcps at 58.0 kBq/mL, and the scatter fraction at 5.3 kBq/mL was 36.5%. The maximum count rate error at the peak noise-equivalent count rate was less than 5%. At 3 iterations, the NEMA image-quality contrast recovery coefficients varied from 74.5% (10-mm sphere) to 92.6% (37-mm sphere), and background variability ranged from 3.1% to 1.4% at a contrast of 4.0:1. An example human brain 18F-FDG image exhibited very high resolution, capturing intricate details in the cortex and subcortical structures. Conclusion: The NeuroEXPLORER offers high sensitivity and high spatial resolution. With its long axial length, it also enables high-quality spinal cord imaging and image-derived input functions from the carotid arteries. These performance enhancements will substantially broaden the range of human brain PET paradigms, protocols, and thereby clinical research applications.

OBJECTIVE: The study objective was to evaluate intraoperative experience with newly developed high-spatial-resolution microelectrode grids composed of poly(3,4-ethylenedioxythiophene) with polystyrene sulfonate (PEDOT:PSS), and those composed of platinum nanorods (PtNRs). METHODS: A cohort of patients who underwent craniotomy for pathological tissue resection and who had high-spatial-resolution microelectrode grids placed intraoperatively were evaluated. Patient demographic and baseline clinical variables as well as relevant microelectrode grid characteristic data were collected. The primary and secondary outcome measures of interest were successful microelectrode grid utilization with usable resting-state or task-related data, and grid-related adverse intraoperative events and/or grid dysfunction. RESULTS: Included in the analysis were 89 cases of patients who underwent a craniotomy for resection of neoplasms (n = 58) or epileptogenic tissue (n = 31). These cases accounted for 94 grids: 58 PEDOT:PSS and 36 PtNR grids. Of these 94 grids, 86 were functional and used successfully to obtain cortical recordings from 82 patients. The mean cortical grid recording duration was 15.3 ± 1.15 minutes. Most recordings in patients were obtained during experimental tasks (n = 52, 58.4%), involving language and sensorimotor testing paradigms, or were obtained passively during resting state (n = 32, 36.0%). There were no intraoperative adverse events related to grid placement. However, there were instances of PtNR grid dysfunction (n = 8) related to damage incurred by suboptimal preoperative sterilization (n = 7) and improper handling (n = 1); intraoperative recordings were not performed. Vaporized peroxide sterilization was the most optimal sterilization method for PtNR grids, providing a significantly greater number of usable channels poststerilization than did steam-based sterilization techniques (median 905.0 [IQR 650.8-935.5] vs 356.0 [IQR 18.0-597.8], p = 0.0031). CONCLUSIONS: High-spatial-resolution microelectrode grids can be readily incorporated into appropriately selected craniotomy cases for clinical and research purposes. Grids are reliable when preoperative handling and sterilization considerations are accounted for. Future investigations should compare the diagnostic utility of these high-resolution grids to commercially available counterparts and assess whether diagnostic discrepancies relate to clinical outcomes.

This paper provides a survey and analyzes the distribution of third-order non-linear weighted essentially non-oscillatory (WENO) weights with respect to the smoothness of the input data and corresponding approximation accuracy of the computed solution of underlying partial differential equations. Existing prevailing non-linear WENO3 weights are detailed, and it is demonstrated that even for smooth data, these constructed weights deviate from the optimal weights, and thus resulting WENO schemes fail to achieve third-order accuracy in such smooth solution regions. Based on the observations, a CFL-informed smooth solution region is introduced to devise third-order enhanced hybrid WENO schemes such that the non-linear WENO3 weights retain the optimal weights in smooth solution region. Numerical results are presented and compared for various benchmark test problems using hybrid-WENO schemes and respective underlying WENO3 schemes. The computational results clearly demonstrate that the hybrid scheme provides enhanced resolution of discontinuities and attains the third-order convergence rate for smooth solutions. [ABSTRACT FROM AUTHOR]

Single-channel seismic (SCS) methods play a crucial role in offshore wind farm assessments, offering rapid and continuous imaging of the subsurface. Conventional SCS methods often fall short in resolution and signal completeness, leading to potential misinterpretations of geological structures. In this study, we propose the application of seismic interferometry as a powerful tool to address these challenges by utilizing multiple reflections that are usually considered as noise. First, we demonstrate the feasibility of using seismic interferometry to approximate the primary wavefield. Then, we evaluate a series of seismic interferometry applied in SCS data, including cross-correlation, deconvolution, and cross-coherence, and determine the most appropriate one for our purpose. Finally, by comparing and analyzing the differences in amplitude, continuity, time–frequency properties, etc., between conventional primary wavefield information and reconstructed primary wavefield information by seismic interferometry, it is proved that incorporating multiples as supplementary information through seismic interferometry significantly enhances data reliability and resolution. The introduction of seismic interferometry provides a more detailed and accurate geological assessment crucial for optimal site selection in offshore wind farm development. [ABSTRACT FROM AUTHOR]

In the field of target detection using synthetic aperture radar (SAR) images, deep learning-based supervised learning methods have demonstrated outstanding performance. However, the effectiveness of deep learning methods is largely influenced by the quantity and diversity of samples in the dataset. Unfortunately, due to various constraints, the availability of labeled image data for training SAR vehicle detection networks is quite limited. This scarcity of data has become one of the main obstacles hindering the further development of SAR vehicle detection. In response to this issue, this paper collects SAR images of the Ka, Ku, and X bands to construct a labeled dataset for training Stable Diffusion and then propose a framework for data augmentation for SAR vehicle detection based on the Diffusion model, which consists of a fine-tuned Stable Diffusion model, a ControlNet, and a series of methods for processing and filtering images based on image clarity, histogram, and an influence function to enhance the diversity of the original dataset, thereby improving the performance of deep learning detection models. In the experiment, the samples we generated and screened achieved an average improvement of 2.32%, with a maximum of 6.6% in m A P 75 on five different strong baseline detectors. [ABSTRACT FROM AUTHOR]

With increasing demands for SAR image applications, spaceborne SAR systems are evolving towards higher resolution and wider swath capabilities. However, these advancements lead to more pronounced range-dependent variance effects. Previous research has primarily focused on the impact of range-dependent variance on range cell migration and the quadratic phase of range frequency, while often neglecting the effects of the cubic phase of range frequency. Although some block processing methods have addressed the range-dependent variant cubic phase of range frequency, they lack detailed impact analyses and segmentation criteria. This paper first proposes a Circle–Sphere geometric model to establish a theoretical framework for analyzing range-dependent variance impacts. Through simulation experiments, we validate the theoretical model's accuracy and develop a numerical analysis of range-dependent variance effects. Building upon this foundation, we introduce a range-dependent variance correction method based on block processing in the range dimension. This method, incorporating valid data area and buffered data area, effectively corrects range-dependent variance while preventing data wrapping and fragmentation. Moreover, our theoretical model for analyzing range-dependent variance impacts provides both theoretical and numerical foundations for block segmentation criteria. The effectiveness and applicability of the proposed method are validated through both simulation experiments and real data experiments using GF3. [ABSTRACT FROM AUTHOR]

Purpose: Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a slice thickness below 5 mm suffers from high image noise and blurring. The purpose of this prospective study was to improve image quality and accelerate imaging acquisition by using single-breath-hold T2-weighted HASTE with deep learning (DL) reconstruction (DL-HASTE) with a 3 mm slice thickness. Method: MRI of the upper abdomen with DL-HASTE was performed in 35 participants (5 healthy volunteers and 30 patients) at 3 Tesla. In a subgroup of five healthy participants, signal-to-noise ratio (SNR) analysis was used after DL reconstruction to identify the smallest possible layer thickness (1, 2, 3, 4, 5 mm). DL-HASTE was acquired with a 3 mm slice thickness (DL-HASTE-3 mm) in 30 patients and compared with 5 mm DL-HASTE (DL-HASTE-5 mm) and with standard HASTE (standard-HASTE-5 mm). Image quality and motion artifacts were assessed quantitatively using Laplacian variance and semi-quantitatively by two radiologists using five-point Likert scales. Results: In the five healthy participants, DL-HASTE-3 mm was identified as the optimal slice (SNR 23.227 ± 3.901). Both DL-HASTE-3 mm and DL-HASTE-5 mm were assigned significantly higher overall image quality scores than standard-HASTE-5 mm (Laplacian variance, both p < 0.001; Likert scale, p < 0.001). Compared with DL-HASTE-5 mm (1.10 × 10−5 ± 6.93 × 10−6), DL-HASTE-3 mm (1.56 × 10−5 ± 8.69 × 10−6) provided a significantly higher SNR Laplacian variance (p < 0.001) and sharpness sub-scores for the intestinal tract, adrenal glands, and small anatomic structures (bile ducts, pancreatic ducts, and vessels; p < 0.05). Lesion detectability was rated excellent for both DL-HASTE-3 mm and DL-HASTE-5 mm (both: 5 [IQR4–5]) and was assigned higher scores than standard-HASTE-5 mm (4 [IQR4–5]; p < 0.001). DL-HASTE reduced the acquisition time by 63–69% compared with standard-HASTE-5 mm (p < 0.001). Conclusions: DL-HASTE is a robust abdominal MRI technique that improves image quality while at the same time reducing acquisition time compared with the routine clinical HASTE sequence. Using ultra-thin DL-HASTE-3 mm results in an even greater improvement with a similar SNR. [ABSTRACT FROM AUTHOR]

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Background: The angiographic features of moyamoya disease (MMD) and atherosclerosis‐associated moyamoya vasculopathy (AS‐MMV) are similar, but the etiology and clinical treatment strategies are different. Differentiating MMD from AS‐MMV helps to choose the appropriate treatment. Purpose: To investigate the feasibility of a nomogram based on high‐resolution vessel wall (HR‐VWI) MRI features to differentiate MMD from AS‐MMV. Study Type: Retrospective. Subjects: One hundred and two patients with MMD (N = 52) or AS‐MMV (N = 50) in the training cohort (9–72 years; 54 females) and 70 patients with MMD (N = 42) or AS‐MMV (N = 28) in the validation cohort (7–69 years; 33 females). Field Strength/Sequence: 3‐T, three‐dimensional time‐of‐flight MR angiography (3D‐TOF‐MRA), spin echo high‐resolution 3D T1‐weighted imaging (3D‐T1WI), 3D T2‐weighted imaging (3D‐T2WI), and contrast‐enhanced 3D‐T1WI. Assessment: Image assessment was performed by three neuroradiologists (with 10, 15, and 18 years of experience). Demographic characteristic and image features were evaluated and compared. Independent factors of MMD were screened to construct a nomogram model in the training cohort. The validation cohort was used to validated its generality. Statistical Tests: Interclass correlation coefficient (ICC), kappa, t‐test, χ2 test, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve and concordance index (C‐index). A P‐value <0.05 was considered statistically significant. Results: Significant differences were observed between MMD and AS‐MMV in terms of age, vessel outer diameter, vessel wall thickening pattern, maximum thickness, dot sign, and anterior cerebral artery (ACA) involved. Age, outer diameter, dot sign, and ACA involved were independent factors. The C‐index was 0.886 in the training cohort and 0.859 in the validation cohort. The ROC demonstrated high diagnostic efficacy with an AUC of 0.884 in the training cohort and 0.857 in the validation cohort. Data Conclusion: A nomogram model based on age, vessel outer diameter, dot sign and ACA involved may effectively distinguish MMD from AS‐MMV with good reliability and accuracy. Evidence Level: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Non‐line‐of‐sight (NLOS) tracking has the ability to detect and locate moving objects which are hidden from the direct line of sight. It has a number of potential applications in autonomous driving, security and search‐and‐rescue scenarios. The current approaches for NLOS tracking mostly rely on time‐of‐flight (ToF) LiDAR, mmWave radar or RGB camera. However, they may either have a limited resolution or need accumulation to detect the position and velocity. Here a real‐time comb‐calibrated frequency‐modulated‐continuous‐wave (FMCW) LiDAR system is proposed and demonstrated for high‐resolution NLOS tracking. A 3D positioning (and velocity) accuracy of 2 mm (and 2 mm s−1) and the capability to realize real‐time multi‐object tracking are demonstrated in experiment. Importantly, the FMCW‐LiDAR approach can obtain the position and velocity of multiple objects in a snapshot, thus resolving the problems of measurement origin uncertainty, false alarm, and detection overlap in multi‐target tracking. The proposed approach shows significant potential for practical applications in NLOS detection and sensing. [ABSTRACT FROM AUTHOR]

Planar piezoelectric actuators, which can achieve high-precision motions over a wide range, have been widely applied in high-end equipment fields. However, the existing planar piezoelectric actuators can no longer meet the high actuation characteristics required for the rapidly development of these fields, which include large travel, high resolution, fast speed, planar three degrees of freedom (3-DOF) and a compact structure. A planar piezoelectric actuator with resonant actuation is proposed and tested in this work. The actuator consists of four symmetrically configured uniform bending-bending hybrid piezoelectric units, which coordinate to achieve large travel and planar 3-DOF characteristics. A vibration isolation mechanism adopting thin beam structure with low stiffness in coupling directions is designed to achieve vibration isolation among the units. The actuator can operate in high frequency resonance mode and low frequency stepping mode, allowing for the achievement of high-speed and high-resolution characteristics. A prototype is fabricated and tested. A compact structure measuring 53 mm × 53 mm × 13 mm and weighing only 30.2 g has been achieved. The maximum linear and rotary speeds have been tested to be over 400 mm/s and 13 rad/s, respectively. The linear and rotary resolutions are 0.42 μm and 12.9 μrad, respectively. The proposed actuator achieves characteristics of large travel, high resolution, fast speed, 3-DOF and a compact structure. [ABSTRACT FROM AUTHOR]

This article reports a 110.2 MHz ultra-low-power phase-locked loop (PLL) for MEMS timing/frequency reference oscillator applications. It utilizes a 6.89 MHz MEMS-based oscillator as an input reference. An ultra-low-power, high-resolution phase-frequency detector (PFD) is utilized to achieve low-noise performance. Eliminating the reset feedback path used in conventional PFDs leads to dead/blind zone-free phase characteristics, which are crucial for low-noise applications within a wide operating frequency range. The PFD operates up to 2.5 GHz and achieves a linear resolution of 100 ps input time difference ( Δ t i n ), without the need for any additional calibration circuits. The linearity of the proposed PFD is tested over a phase difference corresponding to aa Δ t i n ranging from 100 ps to 50 ns. At a 1 V supply voltage, it shows an error of <±1.6% with a resolution of 100 ps and a frequency-normalized power consumption ( P n ) of 0.106 pW/Hz. The PLL is designed and fabricated using a TSMC 65 nm CMOS process instrument and interfaced with the MEMS-based oscillator. The system reports phase noises of −106.21 dBc/Hz and −135.36 dBc/Hz at 1 kHz and 1 MHz offsets, respectively. It consumes 6.709 μ W at a 1 V supply and occupies an active CMOS area of 0.1095 mm2. [ABSTRACT FROM AUTHOR]

Remotely sensed evapotranspiration (ET) at a high spatial resolution (30 m) has wide-ranging applications in agriculture, hydrology and meteorology. The original optical trapezoid model for ET (O_OPTRAM-ET), which does not require thermal remote sensing, shows potential for high-resolution ET estimation. However, the non-automated O_OPTRAM-ET heavily depends on visual interpretation or optimization with in situ measurements, limiting its practical utility. In this study, a SpatioTemporal Aggregated Regression algorithm (STAR) is proposed to develop an automatic trapezoid model for ET (A_OPTRAM-ET), implemented within the Google Earth Engine environment, and evaluated globally at both moderate and high resolutions (500 m and 30 m, respectively). Through the integration of an aggregation algorithm across multiple dimensions to automatically determine its parameters, A_OPTRAM-ET can operate efficiently without the need for ground-based measurements as input. Evaluation against in situ ET demonstrates that the proposed A_OPTRAM-ET model effectively estimates ET across various land cover types and satellite platforms. The overall root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC) when compared with in situ latent heat flux (LE) measurements are 35.5 W·m−2 (41.3 W·m−2, 40.0 W·m−2, 36.1 W·m−2,), 26.3 W·m−2 (28.9 W·m−2, 28.7 W·m−2, 25.8 W·m−2,), and 0.78 (0.73, 0.70, 0.72) for Sentinel-2 (Landsat-8, Landsat-5, MOD09GA), respectively. The A_OPTRAM-ET model exhibits a stable accuracy over long time periods (approximately 10 years). When compared with other published ET datasets, ET estimated by the A_OPTRAM-ET model is better with the land cover types of cropland and shrubland. Additionally, global ET derived from the A_OPTRAM-ET model shows trends consistent with other published ET datasets over the period 2001–2020, while offering enhanced spatial details. Therefore, the proposed A_OPTRAM-ET model provides an efficient, high-resolution, and globally applicable method for ET estimation, with significant practical values for agriculture, hydrology, and related fields. [ABSTRACT FROM AUTHOR]

Technological advances in fMRI including ultra-high magnetic fields (≥ 7 T) and acquisition methods that increase spatial specificity have paved the way for studies of the human cortex at the scale of layers and columns. This mesoscopic scale promises an improved mechanistic understanding of human cortical function so far only accessible to invasive animal neurophysiology. In recent years, an increasing number of studies have applied such methods to better understand the cortical function in perception and cognition. This future perspective article asks whether closed-loop fMRI studies could equally benefit from these methods to achieve layer and columnar specificity. We outline potential applications and discuss the conceptual and concrete challenges, including data acquisition and volitional control of mesoscopic brain activity. We anticipate an important role of fMRI with mesoscopic resolution for closed-loop fMRI and neurofeedback, yielding new insights into brain function and potentially clinical applications. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'. [ABSTRACT FROM AUTHOR]

The net primary productivity (NPP) of vegetation is the key indicator for assessing ecosystem productivity and carbon cycling. The Ulan Mulun River Basin (UMRB) in Northwest China is a typical coal mining area, including open-pit mining (OPM) and underground coal mining (UGM). There are limited studies utilizing long-term, high-resolution data to investigate the spatiotemporal and driving mechanisms of NPP in different types of mining and non-coal mining (NCM) areas. In this study, NPP was estimated using high-resolution Landsat data (30 m) and an improved CASA model for the period 1987–2022. The spatiotemporal variations in NPP across the basin were systematically investigated using Theil–Sen–MK trend analysis, partial derivatives, and multivariate regression residual to explore and quantify the impacts of climate variability (CV) and human activities (HAs) on the different coal mining and NCM areas. The research results show that the overall fluctuating upward trend of vegetation cover in the country is 64.84% during the period from 1987 to 2022. However, there is a decreasing trend of NPP in the coal mining areas. Precipitation was the major factor influencing the change in NPP (21.835 gC/m2/a), while HAs had a lesser effect (4.667 gC/m2/a). In addition, UGM and NCM were more positively affected by HAs than OPM, while OPM was more positively affected by CV than UGM and NCM. These findings can guide scientific ecological restoration strategies, assess carbon balance impacts, and optimize land management and planning in mining areas to achieve a balance between resource development and environmental protection. [ABSTRACT FROM AUTHOR]

A resolution-improving method for multiband imaging based on an extrapolated RELAX algorithm (ERA) is proposed. The proposed method improves image resolution by reconstructing a wideband signal through nonadjacent narrow subbands. The key points of this method are the coherent compensation of the subbands and the accurate extraction of target point scatters' parameters from each subband. For coherent compensation, a two-step phase compensation (TSPC) is used to precisely compensate for the phase incoherence terms between the subbands. In order to accurately extract the parameters of point scatters (PSs), the ERA is proposed, which establishes a cost function between the extrapolated subbands and the PSs' parameters. Furthermore, to enhance the robustness of PSs' parameters extraction, a local band purification (LBP) process is proposed to resist the non-Gaussian clutter in the subbands. To validate the performance of the proposed method, Monte Carlo experiments using simulated data are carried out, whose results show that the proposed method can successfully improve the image resolution through the subbands under low signal-to-clutter ratio (SCR). Moreover, experiments with real measured data are conducted, using two 0.75 GHz subbands to reconstruct a 5 GHz signal, whose 2D image result is approximated to that based on the real 5 GHz signal. Image parameters are also compared, whose results demonstrate that the proposed method has satisfactory accuracy and robustness on improving image resolution. [ABSTRACT FROM AUTHOR]

Dichotomous Image Segmentation is a category-agnostic task aims to segment highly accurate objects from natural images. In semantic segmentation tasks, the utilization of high-resolution input, due to its richer contextual information, can effectively enhance the precision of segmentation and the accuracy of the boundaries. However, in DIS tasks, owing to the increased complexity and diversity of the targets, it is challenging to generate complete segmentation results. Directly employing high-resolution images as input may result in missed detection due to insufficient receptive field coverage. Furthermore, the additional potential details introduced by high resolution, which may not directly relate to the targets, can negatively impact the accuracy of the model's boundary predictions. To address the above problems, a dual-branch network structure is adopted, where the high-resolution input branch learns detailed information, and the low-resolution input branch captures global semantic information. Specifically, we use the Feature Pyramid Transfer module to enlarge the receptive field and enhance the semantic consistency between different Resnet blocks. In the decoder, we propose the Boundary-Aware structure to fuse features from different backbones and use boundary information to generate more accurate segmentation results. The experimental results demonstrate that our method achieves leading performance in four of the six evaluation metrics used in experiments on the complete DIS-TE testset. For instance, the F-measure reaches 0.801, which is 10.3% higher than that of the ISNet, the baseline method for the DIS task. [ABSTRACT FROM AUTHOR]

Miniaturized spectrometers have significantly advanced real-time analytical capabilities in fields such as environmental monitoring, healthcare diagnostics, and industrial quality control by enabling precise on-site spectral analysis. However, achieving high sensitivity and spectral resolution within compact devices remains a significant challenge, particularly when detecting low-concentration analytes or subtle spectral variations critical for chemical and molecular analysis. This study introduces an innovative approach employing guided-mode resonance filters (GMRFs) to address these limitations. Functioning similarly to notch filters, GMRFs selectively block specific spectral bands while allowing others to pass, maximizing energy extraction from incident light and enhancing spectral encoding. Our design incorporates narrow band-stop filters, which are essential for accurate spectrum reconstruction, resulting in improved resolution and sensitivity. Our spectrometer delivers a spectral resolution of 0.8 nm over a range of 370–810 nm. It achieves sensitivity values that are more than ten times greater than those of conventional grating spectrometers during fluorescence spectroscopy of mouse jejunum. This enhanced sensitivity and resolution are particularly beneficial for chemical and biological applications, facilitating the detection of trace analytes in complex matrices. Furthermore, the spectrometer's compatibility with complementary metal oxide semiconductor (CMOS) technology enables scalable and cost-effective production, fostering broader adoption in chemical analysis, materials science, and biomedical research. This study underscores the transformative potential of the GMRF-based spectrometer as an innovative tool for advancing chemical and interdisciplinary analytical applications. [ABSTRACT FROM AUTHOR]

Estimating high-resolution ocean subsurface temperature has great importance for the refined study of ocean climate variability and change. However, the insufficient resolution and accuracy of subsurface temperature data greatly limits our comprehensive understanding of mesoscale and other fine-scale ocean processes. In this study, we integrated multiple remote sensing data and in situ observations to compare four models within two frameworks (gradient boosting and deep learning). The optimal model, Deep Forest, was selected to generate a high-resolution subsurface temperature dataset (DORS0.25°) for the upper 2000 m from 1993 to 2023. DORS0.25° exhibits excellent reconstruction accuracy, with an average R 2 of 0.980 and RMSE of 0.579 °C, and the monthly average accuracy is higher than IAP and ORAS5 datasets. Particularly, DORS0.25° can effectively capture detailed ocean warming characteristics in complex dynamic regions such as the Gulf Stream and the Kuroshio Extension, facilitating the study of mesoscale processes and warming within the global-scale ocean. Moreover, the research highlights that the rate of warming over the past decade has been significant, and ocean warming has consistently reached new highs since 2019. This study has demonstrated that DORS0.25° is a crucial dataset for understanding and monitoring the spatiotemporal characteristics and processes of global ocean warming, providing valuable data support for the sustainable development of the marine environment and climate change actions. [ABSTRACT FROM AUTHOR]

A high-resolution and low-cost image-based water level sensor was developed using an image processing algorithm. The sensor measures water levels in six channels simultaneously. The image processing algorithm automatically identifies water level images and determines the water levels by analyzing the brightness of the images. The measured water levels were verified by comparison with the calibrated water levels using known length standards. The performance test results of the developed water level sensor were compared with those of commercial water level sensors, demonstrating a superior resolution of 0.06 mm and an inexpensive cost of USD 80. In addition, the developed sensor demonstrated an accuracy of 0.9%, a stability of 0.3%, an adjustable measurement range, and an instantaneous response time. In conclusion, the image-based water level sensor that was developed provides a reliable method for real-time visual monitoring of water levels in six channels simultaneously. [ABSTRACT FROM AUTHOR]

This research seeks to develop and test a rapid mapping approach using unmanned aerial vehicles (UAVs) and terrestrial laser scanning to provide precise, high-resolution spatial data for urban areas right after disasters. This mapping aims to support efforts to protect the population and infrastructure while analyzing the situation in affected areas. It focuses on flood-prone regions lacking modern hydrological data and where regular monitoring is absent. This study was conducted in resort villages and adjacent catchments in Bulgaria's southern Black Sea coast with leading maritime tourism features, after a flash flood on 5 September 2023 caused human casualties and severe material damage. The resulting field data with a spatial resolution of 3 to 5 cm/px were used to trace the effects of the flood on topographic surface changes and structural disturbances. Flood simulation using UAV data and a digital elevation model was performed. The appropriateness of contemporary land use forms and infrastructure location in catchments is discussed. The role of spatial data in the analysis of genetic factors in risk assessment is commented on. The results confirm the applicability of rapid mapping in informing the activities of responders in a period of increased vulnerability following a flood. The results were used by Bulgaria's Ministry of Environment and Water to analyze the situation shortly after the disaster. [ABSTRACT FROM AUTHOR]

The utilisation of high-resolution in situ computed tomography (CT) in the (sub-)μm range is typically only viable in synchrotron facilities, as the deployment of a conventional loading stage in laboratory CTs with a cone beam source does not facilitate a corresponding geometric magnification. This publication presents a CT system with a novel in situ concept that allows spatial resolutions down to 0.5 μm, enabling the analysis of weakly absorbing materials capable of applying loads of up to 5 kN in both the compression and tension directions to the sample during the measurement. The necessity for a highly precise mechanical design to ensure successful measurements at magnifications approaching the theoretical limit makes the system's development particularly demanding. The components employed are presented, along with the requisite considerations and methodologies. It can be demonstrated that the intended specifications with regard to precision and quality are met. The experimental results of a fibre-reinforced polymer demonstrate the system's ability to detect matrix damage features below a single fibre diameter, thereby highlighting its potential for applications in materials science where traditional laboratory CTs are insufficient and synchrotron access is limited. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time and high-resolution current system for underwater vehicle simulation and testing based on global ocean current data. The goal was to address the issue of the existing systems for underwater vehicle simulation, whose tests cannot provide real-time and continuous current velocity data. Thus, a three-dimensional ocean current model (3D-OCM) was built for depths of 0~4000 m via the reconstruction of raw current data, fast-access information retrieval, and three-dimensional interpolation. The three interpolation algorithms' data smoothness and computational times were contrasted. The three-dimensional spline and bilinear algorithm performed the best, taking about 22 milliseconds to acquire the current information anywhere underwater. The comparative analysis revealed that the constructed current system performed strongly in real time and had good velocity data consistency compared with the current data from the National Marine Data Center (NMDC). Furthermore, the running trajectories of the profiling float without interpolation and with three interpolations were contrasted, where the trajectories were more consistent between the three-dimensional spline and bilinear and the three-dimensional Newton and bilinear interpolations. The system can support various marine phenomena for the underwater vehicle's hardware-in-the-loop (HIL) simulation and testing, and it is meaningful and valuable for increasing the effectiveness of the underwater vehicle's research and development. [ABSTRACT FROM AUTHOR]