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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]

We for the first time demonstrate ground-based remote sensing of Nitrous Oxide (N2O) over Hefei in eastern China from high resolution Fourier Transform Infra-Red (FTIR) solar spectra. We have retrieved Column-averaged Abundance of N2O (${X_{{{\rm{N}}_2}{\rm{O}}}}$ X N 2 O ) from both Near-Infrared (NIR, 4000 to 11,000 cm−1) and Mid-Infrared (MIR, 2400 to 3200 cm−1) solar spectra and inspected their agreement. Generally, NIR and MIR measurements agree well with a correlation coefficient of 0.86 and an average difference of (1.33 ± 4.05) ppbv (NIR – MIR). By correcting the bias of the two datasets, we combine the NIR and MIR measurements to investigate seasonality and inter-annual trend of ${X_{{{\rm{N}}_2}{\rm{O}}}}$ X N 2 O over Hefei. The observed monthly mean time series of ${X_{{{\rm{N}}_2}{\rm{O}}}}$ X N 2 O minimize in June and maximize in September, with values of (316.55 ± 12.22) ppbv and (322.05 ± 12.93) ppbv, respectively. The ${X_{{{\rm{N}}_2}{\rm{O}}}}$ X N 2 O time series from 2015 to 2020 showed an inter-annual trend of (0.53 ± 0.10) %/year over Hefei, China. We also compared the FTIR ${X_{{{\rm{N}}_2}{\rm{O}}}}$ X N 2 O observations with GEOS-Chem model ${X_{{{\rm{N}}_2}{\rm{O}}}}$ X N 2 O simulations. They are in reasonable agreement with a correlation coefficient (R) of 0.71, but GEOS-Chem model underestimated the seasonality of the observations. This study can enhance current knowledge of ground-based high-resolution FTIR remote sensing of N2O in the atmosphere and contribute to generating a new reliable N2O dataset for climate change research. [ABSTRACT FROM AUTHOR]

Emerging high‐resolution global ocean climate models are expected to improve both hindcasts and forecasts of coastal sea level variability by better resolving ocean turbulence and other small‐scale phenomena. To examine this hypothesis, we compare annual to multidecadal coastal sea level variability over the 1993–2018 period, as observed by tide gauges and as simulated by two identically forced ocean models, at ∼1° ${\sim} 1{}^{\circ}$ (LR) and ∼0.1° ${\sim} 0.1{}^{\circ}$ (HR) horizontal resolution. Differences between HR and LR, and misfits with tide gauges, are spatially coherent at regional alongcoast scales. Resolution‐related improvements are largest in, and near, marginal seas. Near attached western boundary currents, sea level variance is several times greater in HR than LR, but correlations with observations may be reduced, due to intrinsic ocean variability. Globally, in HR simulations, intrinsic variability comprises from zero to over 80% of coastal sea level variance. Outside of eddy‐rich regions, simulated coastal sea level variability is generally damped relative to observations. We hypothesize that weak coastal variability is related to large‐scale, remotely forced, variability; in both HR and LR, tropical sea level variance is underestimated by ∼ ${\sim} $50% relative to satellite altimetric observations. Similar coastal dynamical regimes (e.g., attached western boundary currents) exhibit a consistent sensitivity to horizontal resolution, suggesting that these findings are generalizable to regions with limited coastal observations. Plain Language Summary: Recent advancements in global ocean climate models, particularly increases in horizontal resolution, are expected to improve predictions of coastal sea level changes. In this study, we compared sea level data recorded by tide gauges from 1993 to 2018 with Community Earth System Model (CESM) simulations at a lower (∼1°) $(\sim 1{}^{\circ})$ and higher (∼0.1°) $(\sim 0.1{}^{\circ})$ horizontal resolution. The higher‐resolution model better represents the influence of small‐scale turbulent ocean behavior. In some regions, turbulent ocean variability plays a significant role in coastal sea level changes, and can account for more than half of the variance. However, despite these improvements, simulated sea level changes remain smaller than observations in many regions, even at high model resolution. This underestimate of variability is apparent in CESM across much of the tropical oceans. Key Points: While resolution‐related improvements are pervasive, their magnitude and nature vary widely across coastal dynamic regimesNear western boundary currents, coastal sea level exhibits dramatic resolution‐related increases in intrinsic variabilityOutside of eddy‐rich regions, simulated coastal sea level variability is damped relative to observations, even at 0.1° resolution [ABSTRACT FROM AUTHOR]

Abstract Follicle count, a pivotal metric in the adjunct diagnosis of polycystic ovary syndrome (PCOS), is often underestimated when assessed via transvaginal ultrasonography compared to MRI. Nevertheless, the repeatability of follicle counting using traditional MR images is still compromised by motion artifacts or inadequate spatial resolution. In this prospective study involving 22 PCOS patients, we employed periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) and single-shot fast spin-echo (SSFSE) T2-weighted sequences to suppress motion artifacts in high-resolution ovarian MRI. Additionally, deep learning (DL) reconstruction was utilized to compensate noise in SSFSE imaging. We compared the performance of DL reconstruction SSFSE (SSFSE-DL) images with conventional reconstruction SSFSE (SSFSE-C) and PROPELLER images in follicle detection, employing qualitative indices (blurring artifacts, subjective noise, and conspicuity of follicles) and the repeatability of follicle number per ovary (FNPO) assessment. Despite similar subjective noise between SSFSE-DL and PROPELLER as assessed by one observer, SSFSE-DL images outperformed SSFSE-C and PROPELLER images across all three qualitative indices, resulting in enhanced repeatability in FNPO assessment. These results highlighted the potential of DL reconstruction high-resolution SSFSE imaging as a more dependable method for identifying polycystic ovary, thus facilitating more accurate diagnosis of PCOS in future clinical practices.

With the widespread application of array signal processing, the estimation of direction of arrival(DOA) as the core problem of array signal processing has made significant progress. This paper first summarizes the traditional algorithms based on beamforming for narrowband target direction estimation relying on uniform linear arrays and emerging algorithms in the past decade. Then, it analyzes the reasons for the limited resolution of traditional beamforming-based methods and discusses higher-resolution methods such as adaptive beamforming direction spectrum, subspace methods, and compressed sensing. Furthermore, for the needs of practical applications, the paper summarizes the progress of broadband target DOA estimation methods, sparse array-based DOA estimation methods, and two-dimensional DOA estimation methods. Finally, the new advances of artificial intelligence-based methods in DOA estimation are introduced. The research in this paper can be applied to modern radar/sonar detection, radio communication, and navigation, showing high application value.

We for the first time demonstrate ground-based remote sensing of Nitrous Oxide (N2O) over Hefei in eastern China from high resolution Fourier Transform Infra-Red (FTIR) solar spectra. We have retrieved Column-averaged Abundance of N2O ([Formula: see text]) from both Near-Infrared (NIR, 4000 to 11,000 cm−1) and Mid-Infrared (MIR, 2400 to 3200 cm−1) solar spectra and inspected their agreement. Generally, NIR and MIR measurements agree well with a correlation coefficient of 0.86 and an average difference of (1.33 ± 4.05) ppbv (NIR – MIR). By correcting the bias of the two datasets, we combine the NIR and MIR measurements to investigate seasonality and inter-annual trend of [Formula: see text] over Hefei. The observed monthly mean time series of [Formula: see text] minimize in June and maximize in September, with values of (316.55 ± 12.22) ppbv and (322.05 ± 12.93) ppbv, respectively. The [Formula: see text] time series from 2015 to 2020 showed an inter-annual trend of (0.53 ± 0.10) %/year over Hefei, China. We also compared the FTIR [Formula: see text] observations with GEOS-Chem model [Formula: see text] simulations. They are in reasonable agreement with a correlation coefficient (R) of 0.71, but GEOS-Chem model underestimated the seasonality of the observations. This study can enhance current knowledge of ground-based high-resolution FTIR remote sensing of N2O in the atmosphere and contribute to generating a new reliable N2O dataset for climate change research.

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers’ ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R2 = 0.9566) with a reference system and performed well in field tests, providing a Lin’s concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection.

The Köppen‐Geiger (K‐G) climate classification is the most commonly used climate classification method in the world, and there are many K‐G climate classification studies focusing on Türkiye using different datasets. However, the differences in the datasets used in these studies lead to substantial differences and errors in K‐G climate zone maps. The differences and disagreements in these maps also cause significant discrepancies in climate studies. In this respect, accurate identification of climate classes and types is very important for understanding the distribution of climate types and for many climate‐based studies to achieve accurate results. In this study, the K‐G climate types of Türkiye and the regime characteristics of these climate types were determined using the CHELSA dataset corrected based on the measurements of 337 meteorological stations. According to the results that were obtained, 14 climate types were identified in Türkiye. Since the CHELSA dataset reflected topographic conditions well, many microclimates were identified within broad areas of climate types. The distribution of the microclimate types was compared to the distribution of the vegetation, and the accuracy of the results was evaluated. Apart from microclimates, other prominent features of this study were the co‐occurrence of multiple climate types in a limited area in the Eastern Black Sea Region and the detection of the EF climate type for the first time at the summit of Mount Ararat. Climate types vary according to altitude conditions, and temperature changes due to altitude are an important factor in the formation of climate sub‐types within the same main climate type in Türkiye. [ABSTRACT FROM AUTHOR]

We describe the bias adjustment and downscaling to a resolution of 1 × 1 km of daily temperature, precipitation and solar radiation of a multimodel ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs with the local observational data set BhutanClim for Bhutan and an analysis of the results for 1996–2100. The GCMs were selected based on their ability to reproduce local temporal and spatial precipitation patterns, resulting in a total of 14 models for each shared socioeconomic pathway (SSP). The results are shown for five global warming levels (GWLs) 1.5, 2, 3, 4 and 5°C, providing insight into the projected impacts of climate change at different warming levels. The daily temperature and precipitation data are publicly available, which is of great benefit for climate impact studies, such as ecological, hydrological or infrastructure‐related studies. In this paper, we discuss showcase climate indicators. Peak‐over‐threshold climate indicators such as hot days above 30°C are relevant for human health. In the capital city of Thimphu, these are currently rare, at 4 days per year, but might increase to more than 80 for GWL5.0. Precipitation increases in the summer months, but slightly decreases in the drier winter months. Accordingly, the 3‐month SPEI drought index is projected to increase in summer, but strongly decreases in the winter months, mostly pronounced in February. The increasing severity of late‐winter and spring droughts might have a negative impact on vegetation, wildfires and water supply. [ABSTRACT FROM AUTHOR]

Advanced remote sensing technology is rapidly improving the ability to map plantations with specialized satellite sensors and advanced image analysis. This technology has the potential to transform the industry by allowing for precise vegetation mapping, health assessments, growth monitoring, and climate impact evaluations. Based on a review of relevant agricultural literature, the study emphasizes the importance of sub-meter pixel sizes in remote sensing applications for differentiating cover types and identifying landscape changes at the individual tree crown level. The integration of high-density data with geospatial processing and machine learning enables precise feature identification and classification, which is an important aspect of precision agriculture. This investigates the specific functions of various sensor types used in plantation monitoring, such as multispectral, thermal, LiDAR, and radar platforms, advocating for a approach that takes advantage of each's distinct strengths. This gives an overview of the most recent high-resolution optical satellites, demonstrating their ability to distinguish vegetation types, track changes over time, and extract useful attributes from images also discusses current monitoring techniques and their limitations before introducing advanced analytical methods for extracting valuable insights from dense data streams, such as classification, object-based image analysis, and deep learning. It discusses upcoming advancements such as improved spatial, spectral, and temporal resolution, increased 3D point cloud density through new LiDAR technologies, and AI-powered cloud computing tools for automated processing and analysis. Despite current challenges, the increasing availability of high-resolution remote sensing technology points to changes in the complexity of plantation mapping and improved management via data-rich insights. [ABSTRACT FROM AUTHOR]

The phase shift method is generally used for wavelength (chromatic) dispersion measurement of optical fibers. However, the phase shift method requires a long fiber under test (FUT) of several hundred meters or more to obtain high temporal resolution. Therefore, measuring specialty fibers with short production lengths and amplification fibers that absorb light at specific wavelengths used for fiber lasers is difficult with this method. We have developed a method to measure the beat spectrum, by incident, an incoherent tunable light source (I-TLS) composed of amplified spontaneous emission (ASE) to a FUT. The FUT constitutes a Fabry–Perot resonator using Fresnel reflections at both end faces of the fiber. Chromatic dispersion can be calculated from the wavelength dependence of the refractive indices of the FUT, enabling measurement with short fiber lengths. The standard deviation of the chromatic dispersion @1550 nm was 0.352 ps/(nm km) based on five measurements of a FUT of approximately 3 m. The error between the manufacturer's measurement of chromatic dispersion @1550 nm, and the average value measured in this study was measured at 0.56%, indicating that it can be measured with very high accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hyper spectral imaging (HSI) has emerged as a powerful technique for accurate disease diagnosis in medical environments. It provides high-resolution images with detailed spectral information, making it possible to identify subtle differences between healthy and diseased tissues. However, current HSI systems face challenges in terms of accuracy and efficiency, limiting their widespread application in sustainable medical environments. To overcome these challenges, our team has developed an improved HSI system that utilizes state-of-the-art spectral imaging technology and machine learning algorithms. This system is capable of capturing and analyzing a wider range of spectral data, enabling more precise identification of disease-specific spectral signatures. Furthermore, our system has been optimized to operate with minimal power consumption, making it environmentally friendly and suitable for sustainable medical environments. The improved HSI system has been successfully tested in clinical settings and has shown promising results in accurately diagnosing various diseases such as cancer, dermatitis, and cardiovascular conditions. Its high accuracy and fast processing time make it a valuable tool for early disease detection and treatment planning. Moreover, the ability to operate with low energy consumption makes it a sustainable solution for medical facilities in resourcelimited areas. In addition to its accuracy and efficiency, our improved HSI system is also user-friendly and can be easily integrated into existing medical imaging systems. [ABSTRACT FROM AUTHOR]

Oceanic stratocumulus decks of clouds are among the largest contributors to the Earth's radiation budget, covering around a fifth of the planet's surface and reflecting a large part of the incoming solar radiation. Unfortunately, these clouds are not well represented in modern climate models, resulting in one of the leading causes of uncertainty in climate change projections. This contribution analyses this issue from a novel perspective and sheds light on the mechanisms behind the misrepresentation and resolution dependence of these large clouds. The analysis is based on realistic week‐long simulations performed over a 563×563 km 2 oceanic domain. Four horizontal resolutions, between 4.4 and 0.55 km, are employed, resulting in a timely investigation, especially in light of the high resolutions employed by present and near‐future climate projections. Results show that the liquid cloud water, the main contributor to the simulated grid‐scale clouds, decreases with a power‐law decay as the resolution increases, whereas the water vapour, responsible for subgrid‐scale clouds, is much less affected by the grid spacing. The leading cause is identified as an imbalance between the rates of change of the advection and turbulence parametrisation terms. In order to verify this observation and provide a possible mitigation to the issue, a second set of simulations is performed where the turbulence parametrisation is tuned. The strategy proves to be successful, confirming the hypotheses and resulting not only in a resolution‐independent radiation budget but also cloud coverage. [ABSTRACT FROM AUTHOR]

The backscatter characteristics of ocean surfaces are of great importance in active marine remote-sensing fields. This paper presents the high spatial and temporal resolution dual co-polarized (VV and HH) and cross-polarized (HV) Ka-band sea-surface backscattering measurements taken from the Yellow Sea research platform at incidence angles ranging from 30° to 50° and in the wind speed range from 5.8 to 8.6 m/s. The experimental results show that the backscattering coefficient in HH polarization is close to (or even surpassing) that in VV polarization within a wind speed range of 7.1 to 8.6 m/s for Ka band under high resolution at medium incidence angles (30°–50°). Further analysis of the 10-ms short-time observation samples found that the sea surface echoes in VV polarization are more sensitive to wave motions, exhibiting more complex scattering characteristics such as multi-peaks and reducing scattering energy, especially at high wind speeds and large incident angles. The Doppler velocity analysis also confirms that rapid ocean wave changes can be detected within a short observation period, especially in VV polarization. The research in this article not only demonstrates the high spatial and temporal resolution capabilities of Ka-band radar for ocean surface observation but also reveals its great potential in interpreting and inversing rapidly evolving marine phenomena. [ABSTRACT FROM AUTHOR]

This paper proposes an ultrasound imaging algorithm based on sub-beamformer and multi-apodization with cross-correlation (SUB-MAX), aiming to achieve high resolution close to the minimum variance (MV) beamforming with low complexity and to enhance image contrast while maintaining background quality. The output of two (N /2)-element DAS beamformers with asymmetric phase centers is subtracted, resulting in a large drop in the main-lobe amplitude, while the sidelobe maintains a relatively high amplitude level. Inspired by this characteristic, the coefficients with opposite trends compared with the subtracted output are obtained and fused with the normalized cross-correlation (NCC) weighting matrix acquired by using multi-pair received apodization, the proposed SUB-MAX obtains a new weighting matrix to weight the output of the DAS beamformer. For ats_wire point targets, the average full-width at half-maximum (FWHM) of SUB-MAX compared with DAS, DMAS, CF, and MAX decreases by 52.7%, 43.5%, 33.3%, and 52.7%, respectively. For geabr_0 cysts, the average contrast ratio (CR) of SUB-MAX compared with DAS, MV, DMAS, and CF increases by 57.7%, 86.8%, 2.5%, and 14.4%, respectively. Experiments on rat_tumor dataset also indicate that SUB-MAX has a superior comprehensive imaging performance. The experimental results indicate that the superior comprehensive imaging performance of the proposed SUB-MAX is expected to be suitable for real-time imaging systems due to its non-reliance on covariance matrix inversion. [ABSTRACT FROM AUTHOR]

Gridless direction of arrival (DOA) estimation methods have garnered significant attention due to their ability to avoid grid mismatch errors, which can adversely affect the performance of high-resolution DOA estimation algorithms. However, most existing gridless methods are primarily restricted to applications involving uniform linear arrays or sparse linear arrays. In this paper, we derive the relationship between the element-domain covariance matrix and the angular-domain covariance matrix for arbitrary array geometries by expanding the steering vector using a Fourier series. Then, a deep neural network is designed to reconstruct the angular-domain covariance matrix from the sample covariance matrix and the gridless DOA estimation can be obtained by Root-MUSIC. Simulation results on arbitrary array geometries demonstrate that the proposed method outperforms existing methods like MUSIC, SPICE, and SBL in terms of resolution probability and DOA estimation accuracy, especially when the angular separation between targets is small. Additionally, the proposed method does not require any hyperparameter tuning, is robust to varying snapshot numbers, and has a lower computational complexity. Finally, real hydrophone data from the SWellEx-96 ocean experiment validates the effectiveness of the proposed method in practical underwater acoustic environments. [ABSTRACT FROM AUTHOR]

Background: High-resolution computed tomography (HRCT) is dependent on detailed morphology in diagnostic assessment of interstitial lung diseases. Photon-counting CT (PCCT) enables improved resolution while reducing radiation. Purpose: To compare if the image quality, detailed morphology, and radiation dose in HRCT of the lung improves with PCCT compared to energy-integrated CT (EICT). Material and Methods: HRCT with PCCT in patients with body mass index (BMI) from normal to obese, previously examined with different EICT were included. They were evaluated in a five-step scale for image quality according to Quality Criteria for CT (Diagnostic Requirement of the ImPACT group-European standardization). In addition, ground-glass opacities, bronchiectasis, emphysema, nodules, and subpleural detailed morphology (≤1 cm from the pleural border) were evaluated by three independent thoracic and/or pediatric radiologists. Visual grading characteristics (VGC) were used for comparison of image quality and detailed morphology and Fleiss kappa for intra-observer variability. Dose-length product (DLP) and CT dose index-volume (CTDIvol) were collected to calculate effective radiation dose. Results: HRCT with PCCT in 52 women and 48 men (mean age=67.2 ± 13.6 years; age range=27–87 years; BMI=26.9 kg/m2; range=18.6–45 kg/m2) previously examined with EICT (mean age=65.3 ± 13.6 years; age range=27–85 years; BMI=27 kg/m2; range=18.9–45 kg/m2) were included. There were significant differences in image quality for all entities in favor of PCCT. The radiation dose was reduced with PCCT by 47% in all, particularly pronounced in obese with 48.5%. Conclusion: Image quality, detailed morphology, and radiation dose, particularly in obese patients, were significantly improved in HRCT with PCCT compared to conventional EICT. The new technique enables visualization of subpleural structures. [ABSTRACT FROM AUTHOR]

Purpose: Dysphagia is common in people with Parkinson's disease (PD). Yet, literature describing swallow function in PD using high-resolution manometry is limited. This study explored swallowing pressure metrics for varied bolus conditions in people with PD. Method: A solid-state unidirectional catheter was used to acquire manometric data for triplicate swallows (5 ml, 10 ml, 20 ml; IDDSI 0, 2 & 4). Penetration-aspiration severity was rated during videofluoroscopy. Patient-reported measures included PDQ-8: Parkinson's Disease Questionnaire-8 and EAT-10: Eating Assessment Tool-10. Quantitative manometric swallow analysis was completed through Swallow Gateway™. Metrics were compared to published normative values and generalized linear model tests explored modulatory effects. Results: 21 participants (76% male; mean age 69.6 years, SD 7.1) with mild-moderate severity PD were studied. Two patients (9%) aspirated for single bolus thin liquid and paste trials and 15 patients (73%) scored > 3 EAT-10. Standardized PDQ-8 scores correlated with EAT-10 (p < 0.05). Abnormality in UES relaxation and distension was demonstrated by high UES integrated relaxation pressure and low UES maximum admittance (UES MaxAdm) values across varied bolus conditions. Participants demonstrated abnormally elevated pharyngeal contractility and increased post-swallow upper-esophageal sphincter (UES) contractility for thinner liquid trials. Alterations in volume and viscosity had significant effects on the bolus timing metric—distention to contraction latency. UES peak pressure measures were altered in relation to bolus viscosity. Conclusion: This study identifies early pharyngoesophageal contractile changes in relation to bolus volume and viscosity in PD patients, associated with subtle deterioration of self-reported swallow scores. Manometric evaluation may offer insight into PD-related swallowing changes and help optimize diagnostics and treatment planning [ABSTRACT FROM AUTHOR]

High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement, there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion modelddenoising diffusion probabilistic model (DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement, but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the continuous development of science and technology, it has become possible to acquire and process massive high-resolution image data. The amount of high-resolution image data is huge, and the traditional single-machine computing and processing methods may become inefficient, which is difficult to meet the needs of real-time or large-scale data processing. This article selected a high resolution satellite remote sensing image from the Landsat dataset for processing. Gaussian filtering was used to denoise the image, followed by K-means algorithm for image segmentation. The image data was then transmitted and stored and the results of image data processing were merged. Data processing efficiency and storage space utilization were analyzed for different data segmentation and storage methods. According to the experimental results, it could be concluded that the method of image resolution segmentation not only had fast processing speed, but also produced higher data quality. The storage space utilization rate using AWS S3 (Amazon Simple Storage Service) storage solution was the highest, reaching a maximum of 0.98. The response time was the shortest, around 100 ms. AWS S3 showed the highest read speed, between 147 MB and 154 MB per second. It could be seen that when processing massive high resolution image data, appropriate segmentation methods and storage schemes should be selected. The research and application of distributed computing and storage strategies for massive high resolution image data posed certain theoretical and technical challenges, which could promote the development of distributed computing and storage technology, technological progress and innovation in related fields. [ABSTRACT FROM AUTHOR]

Ferromanganese nodules are important marine storehouses for critical metals and windows for changing oceans. Although advanced in situ analytical techniques have been applied to visualize the elemental distribution in the nodule cross‐sections, their spatial distribution remains largely uncertain. This study addresses this gap by employing micro X‐ray fluorescence mapping of parallel nodule cross‐sections to delineate the spatial distributions of critical metals (Mn, Fe, Co, Ti, Ni, and Cu) in three Co‐rich ferromanganese nodules from the northwestern Pacific Ocean. The 10‐layer Os isotopic compositions of one nodule closely align with the well‐documented marine Os isotope evolution of seawater, providing a chronological framework and a maximum age of ∼36 Ma for these nodules. Three concentric chemostratigraphic layers, labeled L1, L2, and L3, were identified from the inside out, based on microscopic structures and the distributions of critical metals. The early growth stage was marked by Mn‐rich, Si‐rich, and high Mn/Fe ratios, suggesting a diagenetic‐driven process attributed to high paleoproductivity conditions because of the low latitude of the study area at that time. The subsequent growth stages are all hydrogenetic in origin to be rich in Fe, Co, and Ti with low Mn/Fe ratios. The apparent detritus present during the second growth stage of the nodules may correspond to the stronger bottom currents in the early Miocene. The final mineralization stage indicates a more stable environment with diminished bottom current activity, leading to the formation of a dense, laminar hydrogenetic layer. Plain Language Summary: The present study bridges the knowledge gap on the spatial distribution of critical metals through the innovative use of micro X‐ray fluorescence mapping on parallel sections of three nodules in the northwestern Pacific Ocean. Os isotope stratigraphy establishes a chronological framework and a maximum age of ∼36 Ma for these nodules. Distinct concentric chemostratigraphic layers, labeled L1, L2, and L3, have been identified in these nodules. The early growth stage of the nodules is characterized by Mn‐rich, Si‐rich and high Mn/Fe ratios, indicative of a diagenetic‐driven process under high paleoproductivity conditions due to the low historical latitude. The subsequent stages are all hydrogenetic in origin to be rich in Fe, Co, and Ti with low Mn/Fe ratios. The apparent detritus present during the second growth stage of the nodules may correspond to the stronger bottom currents in the early Miocene. The final mineralization stage suggested a period of environmental stability with reduced current activity, forming of a hydrogenetic layer enriched in Fe, Co, and Ti. This study not only enhances our understanding of the complex processes that govern the formation and evolution of ferromanganese nodules but also provides deeper insight into the historical shifts within our oceans. Key Points: Ferromanganese nodule was dated via Os isotope stratigraphy and has a maximum age of ∼36 MaMulti‐element integrated maps were produced to assist in obtaining clearer chemostratigraphy images of ferromanganese nodulesSpatial distribution of critical metals and chemostratigraphy in ferromanganese nodules were visualized via multiple parallel cross‐sections [ABSTRACT FROM AUTHOR]

The microtopography of tidal freshwater forested wetlands (TFFWs) impacts biogeochemical processes affecting the carbon and nitrogen dynamics, ecological parameters, and habitat diversity. However, it is challenging to quantify low-relief microtopographic features that might only vary by a few tens of centimeters. We assess the high-resolution fine-scale microtopographic features of a TFFW with terrestrial LiDAR and aerial LiDAR to test a method appropriate to quantify microtopography in low-relief forested wetlands. Our method uses a combination of water-level and elevation thresholding (WALET) to delineate hollows in terrestrial and aerial LiDAR data. Close-range remote sensing technologies can be used for microtopography in forested regions. However, the aerial and terrestrial LiDAR technologies have not been used to analyze or compare microtopographic features in TFFW ecosystems. Therefore, the objectives of this study were (1) to characterize and assess the microtopography of low-relief tidal freshwater forested wetlands and (2) to identify optimal elevation thresholds for widely available aerial LiDAR data to characterize low-relief microtopography. Our results suggest that the WALET method can correctly characterize the microtopography in this area of low-relief topography. The microtopography characterization method described here provides a basis for advanced applications and scaling mechanistic models. [ABSTRACT FROM AUTHOR]

Accurate on-orbit wavelength calibration of the spaceborne hyperspectral payload is the key to the quantitative analysis and application of observational data. Due to the high spectral resolution of general spaceborne hyperspectral greenhouse gas (GHG) detection instruments, the common Fraunhofer lines in the solar atmosphere can be used as a reference for on-orbit wavelength calibration. Based on the performances of a GHG detection instrument under development, this study simulated the instrument's solar-viewing measurement spectra and analyzed the main sources of errors in the on-orbit wavelength calibration method of the instrument using the solar Fraunhofer lines, including the Doppler shift correction error, the instrumental measurement error, and the peak-seek algorithm error. The calibration accuracy was independently calculated for 65 Fraunhofer lines within the spectral range of the instrument. The results show that the wavelength calibration accuracy is mainly affected by the asymmetry of the Fraunhofer lines and the random error associated with instrument measurement, and it can cause calibration errors of more than 1/10 of the spectral resolution at maximum. A total of 49 Fraunhofer lines that meet the requirements for calibration accuracy were screened based on the design parameters of the instrument. Due to the uncertainty of simulation, the results in this study have inherent limitations, but provide valuable insights for quantitatively analyzing the errors of the on-orbit wavelength calibration method using the Fraunhofer lines, evaluating the influence of instrumental parameters on the calibration accuracy, and enhancing the accuracy of on-orbit wavelength calibration for similar GHG detection payloads. [ABSTRACT FROM AUTHOR]

The optimized derivative fast Fourier transform (dFFT) simultaneously increases resolution and reduces noise in spectra reconstructed from encoded time signals. The pertinent applications have recently been published for time signals encoded with and without water suppression by in vitro and in vivo magnetic resonance spectroscopy (MRS). Even with the employed lower derivative orders, genuine resonances were narrowed, their intensities enhanced and the background baselines flattened. This unequivocally separated many overlapped peaks that are the thorniest problem in data analysis by signal processing. However, it has been common knowledge that higher-order derivative spectra quickly deteriorate with the increased derivative order. The optimized dFFT can challenge such findings. An unprecedented resilience of this processor to derivative-induced distortions is presently demonstrated for high derivative orders (up to 20). The salient illustrations are given for the water residual, lactate quartet and lactate doublet alongside their close surroundings. These applications of diagnostic relevance for patients with cancer are reported for time signals encoded with water suppression by in vitro proton MRS of human ovary. [ABSTRACT FROM AUTHOR]

The computational balance between the model grid resolution and the complexity of the data assimilation technique is essential for accurate aerosol forecasting and obtaining aerosol reanalysis data sets. This study aimed to develop a high‐resolution aerosol assimilation system. A 2‐dimensional variational method (2DVar) was implemented in a non‐hydrostatic icosahedral atmospheric model (NICAM). This new model (NICAM/2DVar), with a global grid size of 56 km, assimilated the observed aerosol optical depth (AOD) that is estimated by combining multiple products of geostationary and polar‐orbital satellites. The model results were evaluated against ground‐based AOD observations on a global scale. They exhibited higher correlations, lower uncertainties, and lower biases than those obtained without the 2DVar. The model also reproduced the observed surface aerosols (PM2.5) mass concentrations, especially in Kyushu, Japan. This occurred because the satellite‐estimated AODs over ocean close to air pollution sources were obtained for many occasions. The correlation coefficient values against the PM2.5 observations increased from 0.44 to 0.65 compared to the results without the 2DVar. The impact of the 2DVar on the forecast results was investigated, and the forecast values for 2–3 days were improved. Because satellite‐retrieved AODs are often lacking over land owing to retrieval difficulties, the use of ground‐based AODs in assimilations is essential for precise processing the of aerosol reanalysis data sets. The computational cost with the use of the 2DVar was only 0.6% more than that without its use. Thus, aerosol assimilation using the NICAM/2DVar can be realistically extended to finer grid sizes. Plain Language Summary: High‐resolution models and data assimilation techniques should be used to obtain more accurate aerosol distributions. We combined a high‐resolution model and an assimilation method at a low computational cost. We also integrated multiple aerosol satellite products. The new procedure successfully reproduced the aerosol observations. The results were significantly better when observations for multiple grids and times were available. Surface measurements on land, for which satellites often lack aerosol signals, are helpful. Key Points: Combined geostationary and polar orbital satellite aerosol products were assimilatedThe aerosol assimilation results improved with increasing spatiotemporal coverage of the observationsEven though the column‐integrated aerosol optical depth was assimilated, surface aerosol (PM2.5) concentration improved [ABSTRACT FROM AUTHOR]

Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by equipment ambient noise, azimuth-distance coupling, wave scattering, and transmission power. Both the amplitude and phase of the signal suffer from the interference of multiple clutter, so they cannot be effectively utilized. To address these issues, this paper introduces a covering penetration detection system based on an improved Range Migration Algorithm (IMRMA) imaging method. Firstly, the proposed method minimizes interferences from the front end of the system using an optimized window to balance denoising and information preservation. Next, interval non-uniform interpolation, instead of Stolt interpolation decoupling, is employed to reduce the computational overhead significantly. To minimize the effects due to wave scattering and propagation loss, distance information is enhanced using amplitude and phase compensation. This reduces scattering effects and enhances image quality. An experimental system is constructed based on a vector network analyzer (VNA) to image the target. The proposed method takes about half the time of traditional RMA. The PSNR in the chunky bowl experiment is higher than 14 dB, which is higher than all the compared methods in the paper. The test results show that the designed system and the reported method can effectively achieve high-resolution images by strengthening the target intensity and suppressing the environmental artifacts. [ABSTRACT FROM AUTHOR]

This work presents a novel ΣΔ analog-to-digital converter (ADC) architecture for a high-resolution sensor interface. The concept is to reduce the effect of kT/C noise generated by the loop filter by placing the gain stage in front of the loop filter. The proposed architecture effectively reduces the kT/C noise power from the loop filter by as much as the squared gain of the added gain stage. The gain stage greatly relaxes the loop filter's sampling capacitor requirements. The target resolution is 20 bit. The sampling frequency is 512 kHz, and the oversampling ratio (OSR) is only 256 for a target resolution. Therefore, the proposed ΔΣ ADC structure allows for high-resolution ADC design in an environment with a limited OSR. The proposed ADC designed in 65 nm CMOS technology operates at supply voltages of 1.2 V and achieves a peak signal-to-noise ratio (SNR) and Schreier Figure of Merit (FoMs) of 117.7 dB and 180.4 dB, respectively. [ABSTRACT FROM AUTHOR]

This study aims to assess the ability of the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 4.7.1 to reproduce historical monthly mean temperatures and precipitation in different physical geographical conditions of Georgia. RegCM4.7.1 simulation has been done at 12 km resolution over the territory of Georgia from 1985 to 2014, with the hourly ERA5 high-resolution atmospheric reanalysis of the global climate data of the European Centre for Medium-Range Weather Forecasts (ECMWF) as boundary conditions. Standard deviation is used to evaluate the model's performance against Georgia's meteorological station data. This metric helps quantify the agreement between model outputs and observed data. Conducting historical runs and validating the model against observed data contributes to understanding how regional climate models perform in regions with diverse geographical features and is crucial for ensuring the reliability of future climate projections. A group of weather stations with the best data modeling results in all months of the year when the difference between the actual and model data does not exceed the standard deviation value is Kutaisi, Gori, Sagarejo, Tbilisi, and Tsalka for air temperature and Akhalkalaki, Akhaltsikhe, Dedoplistskaro, Gori, Sagarejo, Tbilisi, Telavi, and Tianeti for precipitation. The modeling results are generally satisfactory, indicating that the model can be used effectively for future climate projections in Georgia. These findings provide valuable insights for policymakers, stakeholders, and researchers working on climate change adaptation in Georgia. [ABSTRACT FROM AUTHOR]

This study assesses the performance of the Weather Research and Forecasting-Hydrological modeling system (WRF-Hydro) in the simulation of street-scale flood inundation. The case study is the Hackensack River Watershed in New Jersey, US, which is part of the operational Stevens Flood Advisory System (SFAS), a one-way coupled hydrodynamic-hydrologic system that currently uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to simulate streamflow. The performance of the 50-m gridded WRF-Hydro model was assessed for potential integration into the operational SFAS system. The model was calibrated with the dynamically dimensioned search algorithm using streamflow observations. The model performance was assessed using (i) streamflow observations, (ii) USGS HWMs, and (iii) crowdsourced data on street inundation. Results show that WRF-Hydro outperformed the HEC-HMS model. WRF-Hydro over and underestimated flood inundation extent due to the inaccuracy of the synthetic rating curves and the modeling structure errors. An agreement was noticed between WRF-Hydro and crowdsourced data on flood extent. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers' ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R2 = 0.9566) with a reference system and performed well in field tests, providing a Lin's concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection. [ABSTRACT FROM AUTHOR]

A high resolution, acceptable accuracy and low power consumption time-domain temperature sensor is proposed and simulated in this paper based on a 180 nm standard CMOS technology. A diode stacking structure is introduced to enhance the accuracy of the temperature sensing core. To improve the resolution of the sensor, a dual-input capacitor multiplexing voltage-to-time converter (VTC) is implemented. Additionally, a low-temperature drift voltage-mode relaxation oscillator (ROSC) is proposed, effectively reducing the large oscillation frequency drift caused by significant temperature impacts on delay errors. The simulated results show that the resolution is as high as 0.0071 °C over 0∼120 °C with +0.43 °C/−0.38 °C inaccuracy and 1.9 pJ · K2 resolution FoM, consuming only 1.48 μ W at a 1.2 V supply voltage. [ABSTRACT FROM AUTHOR]

Purpose: QSM provides insight into healthy brain aging and neuropathologies such as multiple sclerosis (MS), traumatic brain injuries, brain tumors, and neurodegenerative diseases. Phase data for QSM are usually acquired from 3D gradient‐echo (3D GRE) scans with long acquisition times that are detrimental to patient comfort and susceptible to patient motion. This is particularly true for scans requiring whole‐brain coverage and submillimeter resolutions. In this work, we use a multishot 3D echo plannar imaging (3D EPI) sequence with shot‐selective 2D CAIPIRIHANA to acquire high‐resolution, whole‐brain data for QSM with minimal distortion and blurring. Methods: To test clinical viability, the 3D EPI sequence was used to image a cohort of MS patients at 1‐mm isotropic resolution at 3 T. Additionally, 3D EPI data of healthy subjects were acquired at 1‐mm, 0.78‐mm, and 0.65‐mm isotropic resolution with varying echo train lengths (ETLs) and compared with a reference 3D GRE acquisition. Results: The appearance of the susceptibility maps and the susceptibility values for segmented regions of interest were comparable between 3D EPI and 3D GRE acquisitions for both healthy and MS participants. Additionally, all lesions visible in the MS patients on the 3D GRE susceptibility maps were also visible on the 3D EPI susceptibility maps. The interplay among acquisition time, resolution, echo train length, and the effect of distortion on the calculated susceptibility maps was investigated. Conclusion: We demonstrate that the 3D EPI sequence is capable of rapidly acquiring submillimeter resolutions and providing high‐quality, clinically relevant susceptibility maps. [ABSTRACT FROM AUTHOR]

The data presented here were collected independently for 6 real buildings by researchers of different institutions and gathered in the context of the IEA EBC Annex 81 Data-driven Smart Buildings, as a joint effort to compile a diverse range of datasets suitable for advanced control applications of indoor climate and energy use in buildings. The data were acquired by energy meters, both consumption and PV generation, and sensors of technical installation and indoor climate variables, such as temperature, flow rate, relative humidity, CO2 level, illuminance. Weather variables were either acquired by local sensors or obtained from a close by meteorological station. The data were collected either during normal operation of the building, with observation periods between 2 weeks and 2 months, or during experiments designed to excite the thermal mass of the building, with observation periods of approximately one week. The data have a time resolution varying between 1 min and 15 min; in some case the highest resolution data are also averaged at larger intervals, up to 30 min.

Abstract Surface plasmon resonance (SPR) sensors are based on photon-excited surface charge density oscillations confined at metal-dielectric interfaces, which makes them highly sensitive to biological or chemical molecular bindings to functional metallic surfaces. Metal nanostructures further concentrate surface plasmons into a smaller area than the diffraction limit, thus strengthening photon-sample interactions. However, plasmonic sensors based on intensity detection provide limited resolution with long acquisition time owing to their high vulnerability to environmental and instrumental noises. Here, we demonstrate fast and precise detection of noble gas dynamics at single molecular resolution via frequency-comb-referenced plasmonic phase spectroscopy. The photon-sample interaction was enhanced by a factor of 3,852 than the physical sample thickness owing to plasmon resonance and thermophoresis-assisted optical confinement effects. By utilizing a sharp plasmonic phase slope and a high heterodyne information carrier, a small atomic-density modulation was clearly resolved at 5 Hz with a resolution of 0.06 Ar atoms per nano-hole (in 10–11 RIU) in Allan deviation at 0.2 s; a faster motion up to 200 Hz was clearly resolved. This fast and precise sensing technique can enable the in-depth analysis of fast fluid dynamics with the utmost resolution for a better understanding of biomedical, chemical, and physical events and interactions.

Abstract Currently, flexible capacitive sensors have a wide range of application scenarios in the field of wearable electronic devices. In order to detect more subtle joint movements of the human body, a method of fabricating stacked capacitive sensors is demonstrated. An ultrathin dielectric elastomer film of about 110 μm by the “secondary calendering” method was prepared. The shape of the electrode layers was designed, printed the electrode materials on the dielectric elastomer film by screen-printing, realized the stacked-layer technology, and connected each sensor unit in parallel by the electrode columns formed inside. A 12-layer flexible capacitive sensor with an initial capacitance of 10.2nF, good resolution (1% strain), high sensitivity (1.09) and stability under 10,000 cycles is fabricated. The sensor fabricated in this paper can recognize the motion at various joints of the human body, such as elbow and knee joints. This paper provides a new method for fabrication of stacked flexible capacitive sensors, which opens up new applications in flexible sensors, wearable electronic devices and human-computer interaction.

Conventional microscopes designed for submicron resolution in biological research are hindered by a limited field of view, typically around 1 mm. This restriction poses a challenge when attempting to simultaneously analyze various parts of a sample, such as different brain areas. In addition, conventional objective lenses struggle to perform consistently across the required range of wavelengths for brain imaging in vivo. Here we present a novel mesoscopic objective lens with an impressive field of view of 8 mm, a numerical aperture of 0.5, and a working wavelength range from 400 to 1000 nm. We achieved a resolution of 0.74 μm in fluorescent beads imaging. The versatility of this lens was further demonstrated through high-quality images of mouse brain and kidney sections in a wide-field imaging system, a confocal laser scanning system, and a two-photon imaging system. This mesoscopic objective lens holds immense promise for advancing multi-wavelength imaging of large fields of view at high resolution.

In this review, we summarize the latest advances in the design of optical frequency-domain reflectometers (OFDRs), digital signal processing, and sensors based on special optical fibers. We discuss state-of-the-art approaches to improving metrological characteristics, such as spatial resolution, SNR, dynamic range, and the accuracy of determining back reflection coefficients. We also analyze the latest achievements in the OFDR-based sensors: the accuracy of spatial localization of the impact, the error in detecting temperatures, deformation, and other quantities, and the features of separate measurement of various physical quantities. We also pay attention to the trend of mutual integration of frequency-domain optical reflectometry methods with time-domain optical reflectometry, which provides completely new sensing possibilities. We believe that this review may be useful to engineers and scientists focused on developing a lab setup, complete measurement instrument, or sensing system with specific requirements. [ABSTRACT FROM AUTHOR]

Surface plasmon resonance (SPR) sensors are based on photon-excited surface charge density oscillations confined at metal-dielectric interfaces, which makes them highly sensitive to biological or chemical molecular bindings to functional metallic surfaces. Metal nanostructures further concentrate surface plasmons into a smaller area than the diffraction limit, thus strengthening photon-sample interactions. However, plasmonic sensors based on intensity detection provide limited resolution with long acquisition time owing to their high vulnerability to environmental and instrumental noises. Here, we demonstrate fast and precise detection of noble gas dynamics at single molecular resolution via frequency-comb-referenced plasmonic phase spectroscopy. The photon-sample interaction was enhanced by a factor of 3,852 than the physical sample thickness owing to plasmon resonance and thermophoresis-assisted optical confinement effects. By utilizing a sharp plasmonic phase slope and a high heterodyne information carrier, a small atomic-density modulation was clearly resolved at 5 Hz with a resolution of 0.06 Ar atoms per nano-hole (in 10–11 RIU) in Allan deviation at 0.2 s; a faster motion up to 200 Hz was clearly resolved. This fast and precise sensing technique can enable the in-depth analysis of fast fluid dynamics with the utmost resolution for a better understanding of biomedical, chemical, and physical events and interactions. [ABSTRACT FROM AUTHOR]

Polystyrene (PS) has not been much used as an optical material due to the considerably high birefringence of PS. For the extensive use of PS in the imaging-lens systems, lower birefringence should be attained for the enhancement of the resolution. Previously, we have developed PS with extremely low birefringence (LB-PS) even when the molecular chain orientation of PS occurred. It indicates that PS may now become an effective material with excellent properties for optical lenses. In this paper, we compared the birefringence of our developed LB-PS with ones of commercial optical polymers that are most commonly utilized in imaging-lens systems. It was confirmed that the birefringence of LB-PS was comparable or even better than that of the commercial ones with high resolution. By designing an imaging-lens system, it was also confirmed that employing PS with the Abbe number of 31.5 could be highly effective in compensating for chromatic aberration as well as obtaining high optical resolution. Moreover, the resolution performance of several lens systems was calculated, analyzed, and compared by considering the birefringence of cyclo-olefin polymer (COP), normal PS, and LB-PS. It was found that LB-PS could maintain high resolution, while normal PS with high birefringence exhibited significant decrease in resolution. It was also found that LB-PS generated higher resolution than COP. All these results revealed that LB-PS could be an excellent optical material to compensate for the chromatic aberration of lens systems, leading to the enhancement of the actual resolution performance. It was, therefore, expected that LB-PS could contribute to improving the resolution performance of smartphone cameras and security cameras. [ABSTRACT FROM AUTHOR]

CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO2 dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO2 dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO2 in China and its surroundings (region: 0°–60° N, 70°–140° E) from 2019 to 2020. The gapless and fine-scale CO2 generation method enhances people's understanding of CO2 spatiotemporal variations, supporting carbon-related research. [ABSTRACT FROM AUTHOR]

Infant meningitis remains a severe burden on global health, particularly for young infants. Traditional ultrasound imaging techniques are limited in spatial resolution to visualize white blood cells (WBCs) in the cerebrospinal fluid (CSF), which is considered a well-established marker for meningitis detection. This work presents a novel platform that uses high-resolution ultrasound to detect the backscatter signals from microscopic CSF WBCs through the anterior fontanelle of neonates and young infants. The whole system was built around a custom probe that allows for a 20 MHz focused transducer to be mechanically controlled to map the area of interest in the CSF. Data processing can be performed internally in the device without the need to extract the images for further analysis. The in vitro feasibility of the proposed solution was evaluated in imaging 7 μ m particle suspensions at different concentrations relevant to meningitis diagnosis ranging from 7- to 646-particles (pp)/ μ L. The experimental tests were conducted from a simple setup using a sample container to a more realistic setup based on an anatomical phantom of the neonatal head. The results show high-quality images, where 7 μ m particles can be resolved for the different concentrations. [ABSTRACT FROM AUTHOR]

Considerable spring precipitation occurs over South China (SC), a region that is adjacent to large-scale Asian topography and oceans. Its reasonable simulation is crucial for improving regional climate predictability. This study investigates spring precipitation biases over SC and their possible causes in atmosphere-only and coupled Flexible Global Ocean–Atmosphere–Land System finite-volume version 3 (FGOALS-f3) models with different horizontal resolutions. The performance of spring precipitation simulation over SC varies across different FGOALS-f3 model versions, with the best reproducibility in the high-resolution coupled model (25 km). In the low-resolution atmosphere-only model (100–125 km), the precipitation dry bias over SC is closely linked to overestimated surface sensible forcing over the eastern Tibetan Plateau (TP), which weakens the subtropical anticyclone over the western Pacific (SAWP) through regional circulation responses. By contrast, the high-resolution atmosphere-only model further amplifies surface thermal forcing in the Asian continents, causing intensified land-sea thermal contrast between the Southeast Asian continents and western Pacific, enhanced southerly winds and SAWP, and increased water vapor transport into SC. Meanwhile, the reduced middle-high level cold bias over 10°–30°N in the high-resolution atmosphere-only model intensifies the East Asian westerly jet and ascent over SC, leading to enhanced spring precipitation there. The high-resolution coupled model simulation not only reduces sea surface cold bias over the Bay of Bengal, thus intensifying the Indian-Burma trough and strengthening low-level water vapor transport into SC, but also enhances ascent over SC. As a result, the high-resolution coupled model better reproduces the magnitude and pattern of spring precipitation over SC than its atmosphere-only model. Compared with low-resolution models, the domain-mean spring precipitation dry bias decreases by 11.2% over SC in the high-resolution atmosphere-only model and by 35.9% in the coupled model. These results demonstrate that the high-resolution FGOALS-f3 models can improve simulations of the influencing atmospheric circulations and spring precipitation over SC. [ABSTRACT FROM AUTHOR]

Reflectivity inversion plays a pivotal role in reservoir prediction. Conventional sparse-spike deconvolution assumes that the reflectivity (reflection coefficient) is sparse, which is solved based on the l 1 norm. However, the restricted isometry property (RIP) of wavelet matrix and seismic effective bandwidth limits the accuracy of the sparse-spike reflectivity inversion. Consequently, we investigate the connection between seismic amplitude shape and reflectivity. When the reflectivity contains more non-zero values, the wavelet bandwidth within the effective seismic data bandwidth approaches a limit corresponding to the Sinc wavelet, where the main-lobe amplitude closely approximates the reflectivity. Conversely, when the reflectivity has fewer non-zero values, a wavelet with a smaller sidelobe provides a more accurate approximation of the reflectivity. In this paper, we propose a high-resolution inversion optimization method based on joint l 2 norm and l 1 norm constraints. By parameter tuning, we construct the Sinc wavelet or the wavelet with a weak-sidelobe corresponding to the seismic spectrum. Subsequently, we determine the extremum to approximate the reflectivity. To mitigate the RIP condition's constraints, we employ the l 2 norm to balance the l 1 norm (joint constraint) by introducing l 2 norm with low-pass filtering characteristics. This approach yields more accurate reflectivity estimates. By taking the extremum, this approach yields more accurate reflectivity estimates. The synthetic test demonstrates that our method achieves better reflectivity inversion accuracy compared to sparse-spike inversion with l 1– l 2 norm constraint. Furthermore, field tests indicate that the proposed reflectivity inversion method not only can better match the well curve, but also exhibits excellent resolution. [ABSTRACT FROM AUTHOR]