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We have built a weather app take the current location using latitude & longitude and sends this information to the API next it gets the temperature of popular areas along with the latitude & longitude of them, then recommends the coolest area near user and shows the temperature of the user location. To get the location of the user we have used location listener. The current location of the user is fetched through location listener and data is arranged into an array list. Async Task is a method which is used to access the data at any time required and the task is ran continuously in back ground. A Weather API is used to fetch the current temperature of the popular areas within the radius of 150 km then the data is stored into an array list. From the array lists the data is assigned by a temporary variable to get the minimum temperature and the area. The outcome of the app is in format of suggestion of area with minimum temperature and current location temperature.

We revisit the full variety of observed temporal and spatial distributions of energetic solar protons in “gradual” solar energetic-particle (SEP) events resulting from the spatial variations in the shock waves that accelerate them. Differences in the shock strength at the solar longitude of a spacecraft and at the footpoint of its connecting magnetic field line, curved by solar rotation nominally 55 ∘ to the west, drive much of that variation. The shock wave itself, together with energetic particles trapped near it by self-amplified hydromagnetic or Alfvén waves, forms an underlying autonomous structure. This structure can drive across magnetic field lines intact, spreading proton intensities in a widening SEP longitude distribution. During the formation of this fundamental structure, historically called an “energetic storm particle” (ESP) event, many SEPs leak away early, amplifying waves as they flow along well-connected field lines and broaden the distribution outward; behind the structure, between the shock and the Sun, a “reservoir” of quasi-trapped SEPs forms. Very large SEP events are complicated by additional extensive wave growth that can spread an extended ESP-like trapping region around the Sun throughout most of the pre-shock event. Here SEP intensities are bounded at the “streaming limit,” a balance between proton streaming, which amplifies waves, and scattering, which reduces the streaming. The multiplicity of shock-related processes contributing to the observed SEP profiles causes correlations of the events to be poorly represented by the single peak intensity commonly used. In fact, the extensive spatial distributions of SEPs are sometimes free and sometimes interwoven with the structures of the shocks that have accelerated them. We should consider new questions: Which extremes of the shock contribute most to a local SEPs profile of an event, (1) the shock at the longitude of a spacecraft, (2) the shock ∼ 55 ∘ to the west at the footpoint of the field, or (3) SEPs that have collected in the reservoir? How does the space-time distribution of SEPs correspond with the underlying space-time distribution of shock strength?

This work analyzes the appearance of wide-spread deka-MeV solar energetic proton (SEP) events, in particular the arrival of the first protons within ≈ 4.5 – 45 MeV measured at Earth–Sun L1, and their relationship with their relative solar source longitude. The definition of “wide-spread SEP event” for this study refers to events that are observed as a 25 MeV proton intensity increase at near 1 AU locations that are separated by at least 130 ∘ in solar longitude. Many of these events are seen at all three of the spacecraft, STEREO (Solar-Terrestrial Relations Observatory) A, STEREO B, and SOHO (Solar and Heliospheric Observatory), and may therefore extend far beyond 130 ∘ in longitude around the Sun. A large subset of these events have already been part of a study by Richardson et al. ( Solar Phys ., 289 , 3059, 2014). The event source region identifications draw from this study; more recent events have also been added. Our focus is on answering two specific questions: (1) What is the maximum longitude over which SEP protons show energy dispersion, i.e., a clear sign of arrival of higher-energy protons before those of lower energy? (2) What implications can be drawn from the ensemble of events observed regarding either direct magnetic connectivity to shocks and/or cross-field transport from the site of the eruption in the onset phase of the event?

Drug-induced acute kidney injury (AKI) with a high morbidity and mortality is poorly diagnosed in hospitals and deficiently evaluated in drug discovery. Here, we report the development of molecular renal probes (MRPs) with high renal clearance efficiency for in vivo optical imaging of drug-induced AKI. MRPs specifically activate their near-infrared fluorescence or chemiluminescence signals towards the prodromal biomarkers of AKI including the superoxide anion, N-acetyl-β-d-glucosaminidase and caspase-3, enabling an example of longitudinal imaging of multiple molecular events in the kidneys of living mice. Importantly, they in situ report the sequential occurrence of oxidative stress, lysosomal damage and cellular apoptosis, which precedes clinical manifestation of AKI (decreased glomerular filtration). Such an active imaging mechanism allows MRPs to non-invasively detect the onset of cisplatin-induced AKI at least 36 h earlier than the existing imaging methods. MRPs can also act as exogenous tracers for optical urinalysis that outperforms typical clinical/preclinical assays, demonstrating their clinical promise for early diagnosis of AKI.Chemiluminescent molecular renal probes have been developed and are shown to be capable of non-invasive real-time imaging of early-stage oxidative stress biomarkers of drug-induced acute kidney injury, and high renal clearance.

The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere.

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients 1 . To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records 2 – 17 and using acute kidney injury—a common and potentially life-threatening condition 18 —as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests 9 . Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment. A deep learning approach that predicts the risk of acute kidney injury may help to identify patients at risk of health deterioration within a time window that enables early treatment.

The Tycho-2 proper motions and five-band Tycho-2 and 2MASS photometry for approximately 2.5 million common stars have been used to select OB stars and to determine the extinction and photometric distance for each of them. We have selected 37 485 stars and calculated their reddenings based on their positions in the two-color V ^sub ^sub T^^-H, J-Ks diagrams relative to the zero-age main sequence and the theoretical reddening line for B5 stars. Tests confirm that the selected stars belong to the spectral types O-B with a small admixture of later types. We calculate the extinction coefficient R and its variations with Galactic longitude based on the positions of the selected stars in the two-color B ^sub ^sub T^^-V ^sub ^sub T^^, V ^sub ^sub T^^-Ks diagram. The interstellar extinction for each star is calculated as the product of the reddening found and the coefficient R. The extinction and its variations with Galactic longitude agree well with the extinction based on the model by Arenou et al. (1992). Calibration of the relation between the absolute magnitude and reduced proper motion (ProQuest: Formulae and/or non-USASCII text omitted; see image) for Hipparcos stars has allowed us to calculate the absolute magnitudes and photometric distances for the selected stars. The distances found agree with those derived from the Hipparcos parallaxes within 500 pc. The distribution of the stars and the extinction variations with distance found show that the selected stars form an almost complete sample of stars with spectral types earlier than B5 within about 750 pc of the Sun. The sample includes many noticeably reddened stars in the first and second Galactic quadrants that are absent from the Hipparcos and Tycho Spectral Types Catalogues. This slightly changes the pattern of the distribution of OB stars compared to the classical pattern based on Hipparcos.[PUBLICATION ABSTRACT]

Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. Therefore, soil‐related information can increase the predictive ability of genotype's performances across different locations. However, soil information is not always readily available worldwide or before the site or plot level growing season. Thus, in the current version, this tool has two functions. The first function retrieves soil samples and soil (from WoSIS Soil Profile Database) near your target location. Then it predicts 13 soil characteristics (physical and chemical). If the number of samples per location is greater than five, the function uses random forest to predict soil characteristics otherwise it averages the information. The output is a table with the target location and its latitude and longitude coordinates—the number of samples used, the root mean square error (RMSE), and the R‐square for each prediction. From a couple of instances in a trial (location), the second function predicts soil characteristics at the plot level via random forest. The output is a table with the plot IDs, coordinates, number of samples used to make predictions, the RMSE and R‐square for each prediction, the trait predicted, and the predicted value. As a proof‐of‐concept, we used the first function in the LSU Rice Breeding multi‐environmental trials (24 locations), identified the most important soil covariates to determine rice yield, and then clustered the locations. This tool can support breeders in better allocating trials in advance, borrow information from other regions, identify the best variety for each location, reduce costs, and increase accuracy. Core Ideas Soil characteristics are a valuable source of information to explain G × E. SoilType is a useful tool to receive and predict soil features for any geographical position. It will allow researchers to better model G × E at the plot level rather than using trial‐level measurements, as most studies do.

Geospace phenomena such as the aurora, plasma motion, ionospheric currents and associated magnetic field disturbances are highly organized by Earth’s main magnetic field. This is due to the fact that the charged particles that comprise space plasma can move almost freely along magnetic field lines, but not across them. For this reason it is sensible to present such phenomena relative to Earth’s magnetic field. A large variety of magnetic coordinate systems exist, designed for different purposes and regions, ranging from the magnetopause to the ionosphere. In this paper we review the most common magnetic coordinate systems and describe how they are defined, where they are used, and how to convert between them. The definitions are presented based on the spherical harmonic expansion coefficients of the International Geomagnetic Reference Field (IGRF) and, in some of the coordinate systems, the position of the Sun which we show how to calculate from the time and date. The most detailed coordinate systems take the full IGRF into account and define magnetic latitude and longitude such that they are constant along field lines. These coordinate systems, which are useful at ionospheric altitudes, are non-orthogonal. We show how to handle vectors and vector calculus in such coordinates, and discuss how systematic errors may appear if this is not done correctly.

This paper presents for the first time the global latitude structure and seasonal variability of the ionospheric response to the forced from below DE3 and DE2 tides during the period of time January 2008–March 2009. The COSMIC hmF2 and SABER temperature data have been utilized in order to define the ionospheric DE3 and DE2 tidal response to the DE3 and DE2 temperature tides propagating from below. The COSMIC DE3 and DE2 hmF2 tidal oscillations are derived by the same method as the tides seen the SABER temperatures. It has been shown that the longitude wave‐4 and wave‐3 hmF2 structures observed respectively in September and May 2008 are forced mainly by DE3 (wave‐4) and DE2 (wave‐3) temperature tides coming from below. The longitude wave‐3 hmF2 structure observed in January 2008 however is forced by the combined action of the DE2 temperature tide coming from below and the SPW3 probably generated in‐situ.