Research on the Lake Surface Water Temperature Downscaling Based on Deep Learning

An innovative intelligent system based on remote sensing and mathematical models for improving crop yield estimation

Neutron scattering facilities like those available at ORNL are enabling the discovery and development of advanced materials include those to be used for next generation energy storage systems. In spite of all the success that neutron imaging has enjoyed in recent years, even higher-impact research is limited by the current temporal and spatial resolution of neutron detection devices. In particular, the spatial resolution of all neutron imaging methods in use and under development is fundamentally limited by the variance introduced by the charged particles emitted from neutron absorption. Thus, we are investigating, designing and demonstrating an array of ~1 micron-sized glass scintillating fibers arranged via photonics-crystal-fiber patterning techniques-- with particle tracking (a must) so that position resolution can be finer than than that allowed by the charged particle ranges. The processes, knowledge and algorithms developed will allow for spatially (1 micron) and time resolved (<100 ns), high throughput neutron transmission imaging in large areas at reasonable costs.

The formation dynamics of periodic ripples induced by femtosecond laser pulses (pulse duration τ = 50 fs and central wavelength λ = 800 nm) are studied by a collinear pump-probe imaging technique with a temporal resolution of 1 ps and a spatial resolution of 440 nm. The ripples with periods close to the laser wavelength begin to appear upon irradiation of two pump pulses at surface defects produced by the prior one. The rudiments of periodic ripples emerge in the initial tens of picoseconds after fs laser irradiation, and the ripple positions keep unmoved until the formation processes complete mainly in a temporal span of 1500 ps. The results suggest that the periodic deposition of laser energy during the interaction between femtosecond laser pulses and sample surface plays a dominant role in the formation of periodic ripples.

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

Transient Membrane-Linked FtsZ Assemblies Precede Z-Ring Formation in Escherichia coli.

Echocardiography is a reliable and non-invasive method for assessing cardiac structure and function in both clinical and experimental settings, offering valuable insights into disease progression and treatment efficacy. The successful application of echocardiography in murine models of disease has enabled the evaluation of disease severity, drug testing, and continuous monitoring of cardiac function in these animals. However, there is insufficient standardization of echocardiographic measurements for smaller animals. This article aims to address this gap by providing a guide and practical tips for the appropriate acquisition and analysis of echocardiographic parameters in adult rats, which may also be applicable in other small rodents used for scientific purposes, like mice. With advancements in technology, such as ultrahigh-frequency ultrasonic transducers, echocardiography has become a highly sophisticated imaging modality, offering high temporal and spatial resolution imaging, thereby allowing for real-time monitoring of cardiac function throughout the lifespan of small animals. Moreover, it allows the assessment of cardiac complications associated with aging, cancer, diabetes, and obesity, as well as the monitoring of cardiotoxicity induced by therapeutic interventions in preclinical models, providing important information for translational research. Finally, this paper discusses the future directions of cardiac preclinical ultrasound, highlighting the need for continued standardization to advance research and improve clinical outcomes to facilitate early disease detection and the translation of findings into clinical practice.

Many algorithms have been proposed for spatiotemporal image fusion on simulated data, yet only a few deal with spectral changes in real satellite images. An innovative spatiotemporal sparse representation (STSR) image fusion approach is introduced in this study to generate global dense high spatial and temporal resolution images from real satellite images. It aimed to minimize the data gap, especially when fine spatial resolution images are unavailable for a specific period. The proposed approach uses a set of real coarse- and fine-spatial resolution satellite images acquired simultaneously and another coarse image acquired at a different time to predict the corresponding unknown fine image. During the fusion process, pixels located between object classes with different spectral responses are more vulnerable to spectral distortion. Therefore, firstly, a rule-based fuzzy classification algorithm is used in STSR to classify input data and extract accurate edge candidates. Then, an object-based estimation of physical constraints and brightness shift between input data is utilized to construct the proposed sparse representation (SR) model that can deal with real input satellite images. Initial rules to adjust spatial covariance and equalize spectral response of object classes between input images are introduced as prior information to the model, followed by an optimization step to improve the STSR approach. The proposed method is applied to real fine Sentinel-2 and coarse Landsat-8 satellite data. The results showed that introducing objects in the fusion process improved spatial detail, especially over the edge candidates, and eliminated spectral distortion by preserving the spectral continuity of extracted objects. Experiments revealed the promising performance of the proposed object-based STSR image fusion approach based on its quantitative results, where it preserved almost 96.9% and 93.8% of the spectral detail over the smooth and urban areas, respectively.

The gold standard for diagnosis of coronary artery disease remains X-ray angiography, recently combined with intravascular ultrasound (IVUS) [13]. The main advantages of this technique are the high spatial and temporal resolution and good image contrast.

Spatiotemporal fusion is an effective and cost-effective method to obtain both high temporal resolution and high spatial resolution images. However, existing methods do not sufficiently extract the deeper features of the image, resulting in fused images which do not recover good topographic detail and poor fusion quality. In order to obtain higher quality spatiotemporal fusion images, a novel spatiotemporal fusion method based on deep learning is proposed in this paper. The method combines an attention mechanism and a multiscale feature fusion network to design a network that more scientifically explores deeper features of the image for different input image characteristics. Specifically, a multiscale feature fusion module is introduced into the spatiotemporal fusion task and combined with an efficient spatial-channel attention module to improve the capture of spatial and channel information while obtaining more effective information. In addition, we design a new edge loss function and incorporate it into the compound loss function, which helps to generate fused images with richer edge information. In terms of both index performance and image details, our proposed model has excellent results on both datasets compared with the current mainstream spatiotemporal fusion methods.

Future space-borne synthetic aperture radar, hyper-spectral imaging, and high spatial and temporal resolution imaging will place increasing demands on space network data throughput. It is expected that space data relay systems will require capacities of multiple Terabits per second (Tbps). Free-space optical communication is likely the only technology capable of supporting these data demands. A previous study1 has shown that implementing these high-capacity optical feeder links through a traditional geostationary constellation will pose significant development challenges. Meshed satellite constellations in low earth orbit (LEO), operating at significantly shorter ranges, were shown to have the potential to support Tbps feeder links using the technical capabilities of current free-space optical communication systems. Meshed LEO constellations, however, provide unique challenges, including complex constellation maintenance, dynamic meshing and data routing, and short contact periods with ground stations. We review the design requirements for high capacity LEO relay optical terminals and satellites. The global coverage and ground station downlink opportunities of constellation options are evaluated to develop constellation designs utilizing the minimum number of satellites while providing required feeder link performance. The constellations are designed to provide mesh links with Tbps feeder link capabilities using optical terminals comprised of components within current state-of-the-art and with demonstrated capabilities. These meshed feeder link constellations were then modelled to demonstrate the ability of a LEO constellation to gather Tbps of data over a global network, relay this information around the constellation, and downlink this data to a limited number of ground stations.

Magnetic resonance imaging (MRI) technology and availability has rapidly improved over recent years with advances in magnet and gradient design, phased array coils and software that permit high spatial and temporal resolution imaging in a clinically practical scan time. MRI combined with MR cholangiopancreatography (MRCP) provides anatomic, physiologic and potentially biochemical information without the need for ionizing radiation or iodinated contrast agents. Compared to computed tomography (CT), MRI provides superior soft tissue contrast, direct multiplanar capability and three-dimensional (3D) data acquisition that is particularly beneficial for pancreatic imaging. Although CT has traditionally been the gold standard for imaging in acute pancreatitis, MRI is a reliable alternative for staging, assessing severity and predicting outcome in patients with acute pancreatitis (; ; ; ; ).

Unmanned Aerial Vehicles (UAVs), can be controlled automatically or with the help of a remote control system, provide high spatial and temporal resolution images in addition to its advantages in terms of cost and time. Due to these features, UAV’s have been widely used in studies such as map production, 3D modeling and volume calculation in recent years. In this study, it is aimed to investigate the effects of different flight parameters on the accuracy of Orthomosaic and DEM produced with UAV Systems. In addition, the rolling shutter effect and the use of pre-calibrated and automatic camera calibration parameters were also tested. For this purpose, a total of seven UAV flights were carried out at different heights, overlap ratios, angles and grids at a mining area located within the borders of Aksaray province. Agisoft MetaShape and Pix4D Mapper, which are widely used commercial software, were used to process the UAV images. In this study, 27 points were established and 3D coordinates of these points were measured using GNSS technique. 15 of these points were used as reference points and the remaining 12 points were used as check points. Accuracy analysis was performed by comparing the produced Orthomosaics and DEMs with the check points in the study area. CloudCompare and ArcGIS software were used in the comparison of the products as a result of the evaluation of the images. It is concluded that the Orthomosaics produced using 450 angle and DEMs with 600 angle has higher accuracy than the others. Point clouds and DEMs’ produced using automatic and pre-calibrated camera calibration parameters show that the pre-calibrated images provide higher accuracy. Also, it is seen that when the rolling shutter effect is modeled, the horizontal and vertical accuracy is increased in all three flights with different flight parameters in this study.

Abstract. Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an evident indicator of climate change. By combining the strengths of satellite-based observation and modeling, we have produced an integrated daily LSWT for 160 lakes across the Tibetan Plateau where in situ observation is limited. The MODIS-based lake-wide mean LSWT in the integrated dataset includes the daytime, nighttime, and daily mean for the period 2000–2017. The MODIS-based daily mean LSWT is used to calibrate a simplified physically based model (i.e., modified air2water model), upon which a complete and consistent daily LSWT dataset is reconstructed for the period 1978–2017. The reconstructed LSWT dataset is validated by comparing it with both the satellite-based and in situ observations. The validation shows that the reconstructed LSWT is in good agreement with the observations. According to the reconstructed LSWT dataset, the annual LSWT of lakes in the Tibetan Plateau has increased significantly in the period 1978–2017 with an increase rate ranging from 0.01 to 0.47 ∘C per 10 years. The warming rate is higher in winter than in summer. The integrated dataset is unique for its relatively large temporospatial span (1978–2017) and high temporal resolution. The dataset together with the methods developed can contribute to research in exploring water and heat balance changes and the consequent ecological effects at the Tibetan Plateau. Data from this study are openly available via the Zenodo portal, with DOI https://doi.org/10.5281/zenodo.6637526 (Guo et al., 2022).

Lake surface water temperature (LSWT) is a crucial water quality parameter that modulates many lake and reservoir processes. Therefore, it is necessary to monitor it from a long-term perspective. Over the last decades, many methods to retrieve LSWT fields from satellite imagery have been developed. This work aims to test, implement and automate six methods. These are performed in the Google Earth Engine (GEE) platform, using 30 m spatial resolution images from Landsat 7 and 8 satellites for 2000–2020. Automated methods deliver long-term time series. Series are then calibrated with in situ data. Two-dimensional (2D) × time data fields are built on the lakes with the calibration, and a subsequent LSWT climatology is derived. Our study area is two urban lagoons with areas smaller than two (2) km2 of the city of San Pedro de la Paz, South-Central Chile. The six methods describe the seasonal variation of LSWT (Willmott’s index of agreement &gt; 0.91, R2 &gt; 0.67). The main difference between series is their bias. Thus, after a simple calibration, all series adequately describe the LSWT. We utilized the Pedro de la Paz lagoons to demonstrate the method’s utility. Our research demonstrates that these adjacent lagoons exhibit comparable LSWT spatial (15.5–17 ∘C) and temporal (7–25 ∘C) trends throughout the year. Differences in geographical pattern might result from the northern island’s heat impact and the existence of the Biobío river to the east. Our work represents an efficient alternative for obtaining LSWT in particular lakes and reservoirs, especially useful in medium and small-sized ones.

The ability of cells to copy their DNA allows them to transmit their genetic information to their progeny. In such, this central biological process preserves the instructions that direct the entire development of a cell. Earlier biochemical analysis in vitro and genetic analysis in yeast laid the basis of our understanding of the highly conserved mechanism of DNA replication. Recent advances on labeling and live-cell microscopy permit now the dissection of this fundamental process in vivo within the context of intact cells. In this chapter, we describe in detail how to perform multiple DNA replication labeling and detection allowing high spatial resolution imaging, as well as how to follow DNA replication in living cells allowing high temporal resolution imaging.