Plots Of Data Research Articles

The rate of regeneration of mangrove ecosystems on post-bauxite mining areas in the waters of Sei Carang, Tanjungpinang City

The mangrove ecosystem in the Sei Carang estuary waters, Tanjungpinang city is a mangrove ecosystem that has land conversion activities and leaves the environment with open land. The purpose of this study was to determine the density and regeneration rate of mangrove ecosystems on post-bauxite mining areas as seen from seedling, sapling, and trees. This research was conducted in July-October 2022. This study was conducted at four stations determined by the purposive sampling method, based on the existence of a mangrove ecosystem in which there are post-bauxite mine openings in the Sei Carang estuary waters, Tanjungpinang City. For each station, 3 plots of data were collected, with a plot size of 1x1m for the seedling category, a plot of 5x5m for sampling and a plot of 10x10m for trees. Data analysis used the formula for species density, for the regeneration rate by comparing the data from the calculation of the density of seedling, sapling and trees and then the formula for the diversity index. The results of the study found eight types of mangroves, namely Rhizophora apiculata, Rhizophora mucronata, Bruguiera gymnorhiza, Soneratia alba, Avicennia lanata, Nypa fruticans, and Lumnitzera litorea. Density values at station 1 for seedling 40,000 individual/Ha, sapling 2,667 individual/Ha, and trees 1,533 individual/Ha, at station 2 for seedling 30,000 individual/Ha, sapling 1,467 individual/Ha, and trees 500 individual/Ha, at station 3 for seedling 36,667 individual/Ha, sapling 5,200 individual/Ha, and trees 1,600 individual/Ha then at station 4 for seedling 53,333 individual/Ha, sapling 3,200 individual/Ha, and trees 2,133 individual/Ha. The level of mangrove regeneration at all observation stations is quite good.

PEMODELAN DATA CURAH HUJAN DI KOTA LANGSA DENGAN MODEL ARIMA

The aim of this research is to provide the results of ARIMA modeling on rainfall data in Langsa City in 2017-2021. The initial stage of ARIMA modeling is the identification of data stationarity. Meanwhile, stationarity in the mean can be done with data plots and ACF forms. Identification of ACF and PACF forms from data that is already stationary is used to determine the order of the alleged ARIMA model. The next stage is parameter estimation to see the suitability of the model. The diagnostic check process is carried out to evaluate whether the residual model meets the white noise requirements and is normally distributed. The Ljung-Box test is a test that can be used to validate white noise requirements. Rainfall data forms a stationary time series. Furthermore, from the model fit test it was found that the MA(1) model was suitable for predicting the model. Meanwhile, AR(1) and ARMA(1,1) are not used to predict because they do not meet the model fit test. The model obtained with the MA(1) model is as follows, namely .

Integration of Two-Dimensional Liquid Chromatography-Mass Spectrometry and Molecular Docking to Characterize and Predict Polar Active Compounds in Curcuma kwangsiensis.

Curcuma kwangsiensis, one species of Curcumae zedoaria Ros. c, is a commonly used traditional Chinese medicine (TCM) for treating cardiovascular disease, cancer, asthma and inflammation. Polar compounds are abundant in water decoction, which would be responsible for critical pharmacological effects. However, current research on polar compounds in Curcumae zedoaria Ros. c remains scarce. In this study, the polar fraction from Curcuma kwangsiensis was firstly profiled on G protein-coupled receptor 109A (GPR109A), β2-adrenergic receptor (β2-AR), neurotensin receptor (NTSR), muscarinic-3 acetylcholine receptor (M3) and G protein-coupled receptor 35 (GPR35), which were involved in its clinical indications and exhibited excellent β2-AR and GPR109A receptor activities. Then, an offline two-dimensional reversed-phase liquid chromatography (RPLC) coupled with the hydrophilic interaction chromatography (HILIC) method was developed to separate polar compounds. By the combination of a polar-copolymerized XAqua C18 column and an amide-bonded XAmide column, an orthogonality of 47.6% was achieved. As a result of coupling with the mass spectrometry (MS), a four-dimensional data plot was presented in which 373 mass peaks were detected and 22 polar compounds tentatively identified, including the GPR109A agonist niacin. Finally, molecular docking of these 22 identified compounds to β2-AR, M3, GPR35 and GPR109A receptors was performed to predict potential active ingredients, and compound 9 was predicted to have a similar interaction to the β2-AR partial agonist salmeterol. These results were supplementary to the material basis of Curcuma kwangsiensis and facilitated the bioactivity research of polar compounds. The integration of RPLC×HILIC-MS and molecular docking can be a powerful tool for characterizing and predicting polar active components in TCM.

Constraints of C[sbnd]H[sbnd]O[sbnd]S[sbnd]Pb isotopes and fluid inclusions on the origin of the giant Zhugongtang carbonate-hosted Pb–Zn deposit in South China

The Sichuan–Yunnan–Guizhou (SYG) metallogenic province on the western margin of the Yangtze Block has long been a research focus and is an important region of Pb–Zn mineralization in China, but its genesis is poorly constrained. This study examines the Zhugongtang carbonate-hosted Pb–Zn deposit (>3.0 Mt, 0.09–35.04 wt% Pb + Zn), the largest discovery made in the past decade in the southeastern part of the SYG metallogenic province. Fluid inclusions from sphalerite and calcite, CHO–isotopes from calcite, and S–Pb isotopes from sulfide minerals in the Zhugongtang Pb–Zn deposit were analyzed to understand the sources and genesis of the metals and hydrothermal fluids. Fluid inclusions in sphalerite and calcite from the largest ore body indicate that the fluids of the main mineralization stage had low temperatures (105 °C–210 °C) and moderate salinities (0.35–12.89 wt% NaCl equiv.). The sulfides are characterized by narrow ranges of sulfur isotope compositions (δ34Spyrite = +14.27 ‰ to +15.89 ‰, δ34Ssphalerite = +14.31 ‰ to +15.53 ‰, and δ34Sgalena = +10.64 ‰ to +12.97 ‰), indicating that reduced sulfur was derived from sedimentary sulfate in the Devonian to Permian carbonate host rocks by thermochemical sulfate reduction with the addition of organic matter. The δ13CPDB and δ18OSMOW values of calcite range from − 8.6 ‰ to +0.82 ‰ and +15.45 ‰ to +19.41 ‰, respectively, which are similar to values for mantle, marine carbonate rocks, and sedimentary organic matter. The negative correlation between δ13CPDB and δ18OSMOW values indicates that CO2 in the hydrothermal fluids was sourced from marine carbonate rocks and sedimentary organic matter. Calculated δ18OH2O and δDH2O values range from +3.5 ‰ to +7.5 ‰, and −93 ‰ to −52 ‰, respectively, indicating that the ore-forming fluids were dominantly metamorphic, but diluted by meteoric water that had reacted with organic matter. The galena samples yield 206Pb/204Pb values of 18.52 to 18.82, 207Pb/204Pb of 15.68 to 15.93, and 208Pb/204Pb values of 38.97 to 39.96. The μ and ω values of the sulfides range from 9.61 to 10.07 and 38.84 to 43.67, respectively. These data plot on the upper crust Pb evolution curve, generally in the field of Devonian to lower Permian sedimentary rocks, although the value for one sample falls in the field of Proterozoic metamorphic rocks, pointing to a complex origin for the ore-forming materials. Overall, the fluid inclusion and isotope data, together with geological evidence, suggest that the ore-forming fluids of the Pb–Zn deposit were sourced from deep metamorphic water with the features of low temperatures and moderate salinities, and the ore-forming metals were sourced from Devonian to lower Permian sedimentary rocks and partly from basement rocks. The genesis of the Zhugongtang ore is typical of SYG-type deposits.

Impact of the COVID-19 Pandemic on Solid Organ Transplant and Rejection Episodes in Brazil's Unified Healthcare System.

Brazil has the world's largest public organ transplant program, which was severely affected by the COVID-19 pandemic. The primary aim of the study was to evaluate differences in solid organ transplants and rejection episodes during the COVID-19 pandemic compared to the five years before the pandemic in the country. A seven-year database was built by downloading data from the DATASUS server. The pandemic period was defined as March 2020 to December 2021. The pre-pandemic period was from January 2015 to March 2020. During the pandemic, the number of solid organ transplants decreased by 19.3% in 2020 and 22.6% in 2021 compared to 2019. We found a decrease for each evaluated organ, which was more pronounced for lung, pancreas, and kidney transplants. The seasonal plot of rejection data indicated a high rejection rate between 2018 and 2021. There was also an 18% (IRR 1.18 (95% CI 1.01 to 1.37), p = 0.04) increase in the rejection rate during the COVID-19 pandemic. The total number of organ transplants performed in 2021 represents a setback of six years. Transplant procedures were concentrated in the Southeast region of the country, and a higher proportion of rejections occurred during the pandemic. Together, these findings could have an impact on transplant procedures and outcomes in Brazil.

Modelling and mapping forest structure from aerial LiDAR data

A methodology for estimating forest structure and fuel variables from airborne LiDAR data is presented, based on the following steps: (1) data pre-treatment to obtain the digital terrain model and the canopy height model; (2) extraction of features or attributes from the LiDAR data for each sampling plot; (3) generation of prediction models for structure variables at plot level; and (4) its extension for the generation of maps of forest variables in larger areas. The methodology was applied to a study area of 4,100 ha in the municipality of Cuenca, where a LiDAR flight was carried out with a nominal density of 4 points/m2. For the generation of the prediction models, stepwise multiple regression was used, taking as reference measurements made in 110 field plots, obtaining values of R2 greater than 0.9 for total biomass, volume and fraction of canopy cover, and above 0.86 for basal area, stem biomass and root biomass. The generated models were applied to the entire work area for the creation of maps of forest structure variables, and the entire procedure was integrated into a specific software application.

The utility of probability plotting in palaeobiology

AbstractWe introduce probability plotting for general use in the analysis of biological and palaeontological morphometric data. When applied to morphometric data, this analytical tool can yield palaeoecological, ontogenetic and demographic information. We have adapted and expanded a statistical tool used in reliability engineering, mechanical or electrical lifetime analysis, geochemistry and ore mineral analysis to analyse fossil populations. The technique is explained stepwise in a ‘user's manual’ format. Its power is to characterize statistical distribution types, to determine whether these distributions are skewed or truncated, and to resolve and quantify the components of mixed distributions, which in palaeontological data potentially represent morphological groupings such as cohorts or sexual dimorphs. The ease of application and simplicity are surprising. A probability plot of growth series data (e.g. skull lengths, femoral lengths, snout‐to‐vent lengths or other morphometrics) typically shows complex non‐normal distribution of the data because of multimodal composition. We show that these component modes, which are generally, but not always normal (i.e. Gaussian), can be resolved, and that they correspond to yearly age groups (cohorts), sexual dimorphs, pre‐ and post‐metamorphic forms, other ontogenetic stages or possibly mixed taxa. When these component size distributions are associated with an age group or sex, several important palaeoecological calculations become possible. These include growth curves, age and sex distributions, sexual dimorphism indices, etc. We demonstrate the biological and palaeontological utility of probability plots with several examples including a Permian wingless insect, an extant unionid bivalve, a Triassic metoposaurid amphibian, an extant lizard, and a Triassic theropod dinosaur.

Development and Testing a Method for Retrieving Atmospheric Aerosol Optical Thickness based on the Solar Intensity from the Sun-photometer Data

The context of atmospheric aerosols is an indispensable aspect in studying the Earth’s radiation budget, climate change, and air quality. Therefore, the quality technique in retrieving aerosol parameters is important for a better understanding their characteristics. The precise calculating of the aerosol physical parameter in the planetary boundary layer will increase the accuracy of evaluation of their impact on environmental conditions. In several atmospheric corrections of optical remote sensing using satellite sensors, the AOT’s values play an important role in arranging a Look Up Table (LUT) for scattering parameters. Therefore, this study aims to develop a method for processing and correcting the sun-photometer data to obtain the original AOT in the planetary boundary layer. In AOT calculation using the sun-photometer data, the solar radiation at the extraterritorial of the atmosphere is determined using the Langley plot. Then, using the target data at the same season as the data for the Langley plot, the temporal change of AOT is estimated by employing the Lambert-Beer Law with some corrections. The major correction for the AOT’s values computation in the measurement target is the contribution of molecule from the local station and Ozone (O3) from the GOME-2 satellite data. The result has been compared with an independent measurement using a sky-radiometer at the same time as the sun-photometer monitoring. From the overall procedure, the AOT’s values have uncertainties at approximately 2-5% compared to the sky-radiometer. Therefore, the procedure will be useful for studying aerosol optical properties in the lower troposphere.

Target decomposition using dual-polarization sentinel-1 SAR data: Study on crop growth analysis

Decomposition of synthetic aperture radar data has been carried out using fully polarised microwave bands. Considering the economical point of view, fully polarimetric SAR data is expensive to use for many applications like soil and agriculture, where, it is important to monitor frequently. With the advancement of human civilization, new agricultural techniques and crops are being developed to meet global food demand. With the development of crop growth, crop texture and dielectric properties varies, which is depicted in the backscattering values along with the crop growth. In this study, Sentinel-1 SAR data is used, which is freely available in dual polarimetric mode with a temporal revisit of 12 days with respect to the transmitter polarizations. In this work, we attempt to decompose the targets from dual polarised SAR data using entropy and alpha bands of H-A-α decomposition. The entropy and alpha band clusters are obtained by using K-means unsupervised classification is utilized for target decomposition. The clustering process is repeated 30 times for 100 iterations to obtain the optimum grouping of pixels. The clusters are plotted on the H-α plane to get an H-α plot of dual-polarization SAR data for target decomposition. The obtained H-α plot is used to identify the crop stages and its scattering mechanism at different growth stages. Crops grown in selected crop fields during the considered period include ginger, tobacco, rice, cabbage, and pumpkin. An attempt is made by plotting the time-series trends of early and late-planted crops with peak mature stages in terms of backscattering analysis, and the results were compared to the H-α plot to gain a better understanding of crop growth scattering mechanisms at various growth stages. Although the backscattering values for the VH and VV polarizations of crops are different, the temporal backscattering study showed the same trend for both polarizations with good similarity of VH polarization than VV for analyzing crop growth stages. The crop growth scattering mechanism on the H-α plane produced similar results to the temporal analysis.

Transit GIS Applications in Fairfax County, Virginia

The Fairfax County Department of Transportation (DOT) manages a fixed-route bus system (the Fairfax Connector) that encompasses 58 routes. To better support the planning, operation, and marketing of this bus system, the Fairfax County DOT and the Fairfax County Department of Information Technology formed a team to develop a pilot project of Geographic Information Systems (GIS) transit applications. These applications would serve as a demonstration to facilitate automation, analysis, accessing, and plotting of transit data. To be successful, the applications had to be cost effective and match users’ technical needs with their abilities.Paramount to the success of this project was having a transit database capable of supporting all the applications identified by the development team. The database had to handle problems such as bus stops serving more than one route and routing varying by time of day. To effectively model the bus routes in GIS, each path had to be uniquely identified even though the same route could travel on different paths. Dynamic segmentation of ARC/INFO v.7.1 was used to develop the route database. A bus stop database, in the format of ARC/INFO coverage, was then related to the route coverage.More than 15 applications were identified for three areas of transit management: planning, operation, and marketing. Planning applications focused on transit service improvement and route restructuring. They included routing adjustment, route demographic and land-use analysis, and reporting of statistics required by the National Transit Database. Operation applications were designed for daily service monitoring and consisted of route running times, loading at bus stops, and emergency service. Marketing applications emphasized functions for public outreach, which included creating specialized route and stop maps and publishing route information on the Internet. Most end-user applications were built in ArcVìew 3.0 using Avenue and Dialog Designer. Some applications were built with ESRIs Map Objects (MO) and Map Objects Internet Map Server.

Development of regression models to forecast the CO2 emissions from fossil fuels in the BRICS and MINT countries

This study focuses to propose regression models to forecast the fossil fuel-related carbon dioxide (FFCO2) emissions in the BRICS (Brazil, the Russian Federation, India, China, South Africa) and MINT (Mexico, Indonesia, Nigeria, Turkey) countries. In order to get an overview, a short review on the current status of the fossil fuel use and FFCO2 emissions is firstly presented. Then, statistical regression models are developed and verified by various statistical tests including coefficient of determination, F-and t-tests, and the actual versus predicted data plots. Additionally, the most significant variables affecting the FFCO2 emissions of the BRICS and MINT countries are statistically determined. Moreover, the forecasting accuracies and effectiveness of the proposed models are measured using the mean absolute percentage error. Using the proposed models, furthermore, the future FFCO2 emissions of the BRICS and MINT countries are estimated and discussed. The results show that BRICS and MINT countries will come to a position that will guide the world economy in the near future due to their significant superiorities. The modeling results reveal that the proposed models have a strong practical applicability potential. Moreover, forecasting results clearly demonstrate that significant increases are expected in the future FFCO2 emissions of the BRICS and MINT countries.

Quality control of total carbon dioxide (CO2) in serum or plasma using the Abbott Architect is affected by environmental pCO2 concentrations.

Introduction: Total CO2, or bicarbonate, is a parameter in clinical chemistry often applied to assess the metabolic status of a patient. This article discusses the observations and interventions during an episode of assay instability on an Abbott Architect routine chemistry analyser.Results: The Levey-Jennings plot of QC data showed a circadian pattern, having an overestimation of total CO2 during periods of high personnel attendance. A qualitative analysis revealed a correlation between atmospheric CO2 in the lab environment and the acquired total CO2 value in a quality control sample. Assessment of total CO2 is hence influenced by the equilibrium between atmospheric CO2, dissolved CO2 and bicarbonate. The effect is more pronounced on samples containing low concentrations of total CO2. The bias related to environmental CO2 is also noticeable on patient samples, patient means between periods with high and low atmospheric CO2 levels differed by 2mmol/L.Discussion: Passive ventilation of the laboratory environment is proven insufficient during weather conditions in which the lab is not exposed to wind. Consistent reduction of atmospheric CO2 could only be guaranteed with an active ventilation infrastructure. Systematic closure of analyser lids also reduced analyser variance.Conclusion: The lab environment is an important source of parameter variance. Both environmental and infrastructural aspects must be considered when assessing the potential cause of the instability.

Analysis of Ground Level Enhancement Events of 1989 September 29; 2001 April 15 and 2005 January 20

We present the results of analyses of the ground level enhancements (GLEs) of cosmic ray (CR) events on 1989 September 29; 2001 April 15 and 2005 January 20. This involves examination of hourly raw CR counts of an array of neutron monitors (NMs) spread across different geographical latitudes and longitudes. Using awk script and computer codes implemented in R software, the pressure corrected raw data plots of the NMs were grouped into low-, mid- and high-latitudes. The results show both similarities and differences in the structural patterns of the GLE signals. In an attempt to explain why the CR count during the decay phase of GLEs is always higher than the count before peak, we interpreted all counts prior to the peak as coming from direct solar neutrons and those in the decay phase including the peak as coming from secondary CR neutrons generated by the interactions of primary CRs with the atoms and molecules in the atmosphere. We identified NMs that detected these primary neutrons and found that they are close in longitude. Previous authors seemingly identified these two species as impulsive and gradual events. Although there are a number of unexplained manifestations of GLE signals, some of the results suggest that geomagnetic rigidity effectively determines the intensity of CRs at low- and mid-latitudes. Its impact is apparently insignificant in high-latitude regions. Nevertheless, the results presented should be validated before making any firm statements. Principally, the contributions of the ever-present and intractable CR diurnal anisotropies to GLE signals should be accounted for in future work.

VISUALISING AND FORECASTING THE STOCK PRICE

Stock investments provide one of the highest returns in the market. Even though they are volatile in nature, one can visualize share prices and other statistical factors which helps the keen investors carefully decide on which company they want to spend their earnings on. Developing this simple project idea using the Dash (a Python framework) to create web application, we can make dynamic plots of the financial data of a specific company by using the tabular data provided by yfinance python library and some machine learning models which will show company information and stock plots based on the user input stock code. On top of it, we can use a machine learning algorithm-Support Vector Regression (SVR) module from the sklearn library and then train the SVR model with the training dataset to predict the upcoming stock prices for the date inputted by the user.

Fat fraction quantification of bone marrow in the lumbar spine using the LiverLab assessment tool in healthy adult volunteers and patients with Gaucher disease.

Magnetic Resonance Imaging is used for evaluation of bone in Gaucher disease (GD), but a widely available quantitative scoring method remains elusive. The study purpose was to assess the reproducibility of the LiverLab tool for assessing bone marrow fat fraction (FF) and determine whether it could differentiate GD patients from healthy subjects. Ten healthy volunteers and 18 GD patients were prospectively recruited. FF was calculated at L3, L4 and L5. GD patient bone marrow burden (BMB) score assessed by one observer. Inter and intra-rater agreement assessed with Bland-Altman data plots. Differences in FF between healthy volunteers versus GD patients and between subjects treated versus not treated assessed using two-sample t-tests. In GD patients, the relationship between FF, BMB and glucosylsphingosine was determined using the Pearson's correlation coefficient. Healthy volunteer mean FF was 0.36, standard deviation (SD) 0.10 (range 0.20-0.57). Intra and inter-rater SD were both 0.02. GD patient mean FF was 0.40, SD 0.13 (range 0.09-0.57). No statistical difference was shown between healthy volunteers and GD patients (P=0.447) or between GD patients whether on enzyme replacement therapy or not (P=0.090). No significant correlation between mean FF and total BMB (r=-0.525, P=0.253) or between FF and glucosylsphingosine levels (r=0.287, P=0.248). Excellent reproducibility of LiverLab FF measurements across studies and observers is comparable to Dixon quantitative chemical shift imaging (QCSI). Lack of statistical difference between GD patients and controls may be explained by limited patient numbers, active treatment or mild disease severity in untreated patients.

An integrative insight into the diversity, distribution, and biogeography of the freshwater endemic clade of the Ponticola syrman group (Teleostei: Gobiidae) in the Caucasus biodiversity hotspot.

Freshwater habitats of the Caucasus biodiversity hotspot represent a center of endemism for the gobiid genus Ponticola Iljin, 1927. Hitherto, large‐scale molecular studies, owing to restricted taxon and geographical sampling, have failed to give an elaborate picture of diversity and evolutionary history of these species. Here, to contribute to filling this gap, we assessed taxonomic diversity, phylogeography and evolutionary history for the south Caspian populations of Ponticola presently classified as P. iranicus and P. patimari, using an integrative taxonomic approach comprising an entire geographic range sampling, and analyses of mitochondrial DNA haplotypes, the head lateral line system, otolith shape, and meristic and morphometric variation. All freshwater samples of the P. syrman group belong to a monophyletic clade with two main subclades: a small subclade confined to the upper Sefidroud sub‐basin including the type locality of P. iranicus and a large subclade with three geographically constrained haplogroups (Hg1, Hg2, and Hg3), comprising the rest of the distribution. Hg1 showed an eastern distribution including the type locality of P. patimari, while Hg2 and Hg3 are sister groups with central and western‐central distributions, respectively. The freshwater clade diverged from P. syrman during the Tyurkyanian low stand (~150 m b.s.l. lasting ~0.1 Myr), while the divergence of P. iranicus and P. patimari and radiations within P. patimari took place during the Bakunian high stand (up to 50 m a.s.l. lasting ~378–480 kya). Species delimitation analyses indicated two distinct species, corresponding to each main subclade. Although the otolith shape and lateral line analyses did not reflect with phylogeographic pattern, PCA and DFA plots of meristic and morphometric data showed a clear separation of the two major subclades corresponding to P. iranicus and P. patimari, suggesting the presence of significant morphological variation meriting formal taxonomic recognition. Overall, our findings (i) reveal the presence of two freshwater endemic species in the P. syrman group, and pending further investigation, hypothesize the presence of a third cryptic species; (ii) revise and document a narrow distributional range and low diversity for P. iranicus, in contrast to a wider distributional range and high diversity for P. patimari; (iii) suggest that the climatic oscillations of the Pleistocene were associated with the cladogenesis within the P. syrman group; and (iv) allowed for the recognition of conservation units and proposition of management measures.

An isotope study of the Shule River Basin, Northwest China: Sources and groundwater residence time, sulfate sources and climate change

• The Indian summer monsoon invaded into the upper Shule River Basin in August 2018, interrupting the usual pattern of westerly moisture flow. • Surface water and groundwater originate from both recent precipitation and ancient precipitation archived in ice. • Groundwater with pre-evaporation δ 18 O < −14‰ may represent incursions of monsoonal moisture prior to 12 ka in the Hexi Corridor. • δ 34 S SO4 and δ 18 O SO4 in both surface water and groundwater fail to distinguish agricultural sulfate pollution from natural sulfate sources. Isotopes (δ 18 O and δD, δ 34 S SO4 and δ 18 O SO4 , tritium and 14 C) were employed to reveal moisture sources in precipitation and sources of surface water and groundwater, as well as groundwater residence times and sulfate sources in the Shule River Basin (SRB). Groundwater originates in the Qilian Mountains as high-altitude precipitation and meltwater from ice archives. The local meteoric water line (LMWL) is δD = 7.8δ 18 O + 18.1. Precipitation from westerly circulation has a characteristic annual cycle of δ 18 O and δD, high (δ 18 O > −5‰) in summer and low (δ 18 O < −10‰) at other times. This pattern was interrupted by an incursion of the Indian summer monsoon in August 2018, resulting in abnormally low δ 18 O and δD values. Surface water in the upper SRB yields an evaporation trend of slope near 5, with an origin near δ 18 O = −10‰ on the LMWL. Other catchments of similar altitude in the Qilian Mountains have evaporation trends with different origin points, indicating different input fractions of meltwater from ancient ice for each catchment. Groundwater δ 18 O and δD data plot along mixing trends, different in each sub-basin, between three water types: (1) recent Shule River runoff; (2) water like that archived in the Dunde ice sheet, representing precipitation over the last 12 ka; and (3) evaporated water that cannot be explained as precipitation from the last 12 ka. Type (3) water originated as water with δ 18 O values between −14 and −20‰ on the LMWL, and may represent incursion of monsoonal circulation prior to 12 ka. Tritium and 14 C data identify post-bomb recharge, but 14 C is of limited use in dating older groundwater mixtures. Sulfate isotopes (δ 34 S SO4 and δ 18 O SO4 ) in dissolved sulfate from groundwater and surface water indicate mixing of sulfur derived from evaporite and sulfide, but do not identify sulfate pollution from fertilizer. Future climate change may lead to water shortage as ancient ice is consumed by melting.

Development of the LCMAP annual land cover product across Hawaiʻi

• Generated several training datasets by integrating existing datasets. • Produced annual land cover maps for the Hawaiian Islands at 30-m resolution over 20 years with different training datasets and Change detection and classification (CCDC) algorithm. • Summarized the effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types. • Revealed annual land cover changes in Hawaiʻi. Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the LCMAP framework to process annual land cover maps in Hawaiʻi is to collect sufficient high-quality training data. Although multiple datasets depicting land cover information are available in Hawaiʻi, they covered limited time frames and were produced from various remote sensing sources with different, classification categories, spatial resolution, and mapping accuracies. No solo product is suitable to provide LCMAP training data labels on its own. In this paper, we focused on enhancing the LCMAP training datasets to generate land cover products from 2000 to 2019 in Hawaiʻi. A total of 200 independent reference data plots were generated and manually interpreted for validating the mapping results produced by the training datasets. The results revealed that using the appropriate filter of multiple products as training data pools improved the classification model performance. The effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types were summarized. The LCMAP land surface change products for Hawaiʻi are available at https://doi.org/10.5066/P91E8M23 .

PERAMALAN GARIS KEMISKINAN PROVINSI DAERAH ISTIMEWA YOGYAKARTA MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING

Poverty is the inability to meet the minimum standard of living. This situation can be seen from the low income in meeting basic needs, both in the form of needs for food, clothing, and shelter. Based on Law No. 24 of 2004, poverty is a socioeconomic condition of a person or group of people for which the non-fulfillment of its basic rights to maintain and develop a dignified life. The issuance of this policy from the government is due to differences in social, economic, and natural characteristics and conditions in each region, with the aim of maximizing the potential in each region, so that it is expected to be able to improve welfare and reduce poverty. Yogyakarta Special Region (D.I.Y) is a province-level Special Region in Indonesia located in the southern part of Java Island, and bordering Central Java Province. According to the BPS report, the number of diy poor people until September 2021 was 474.49 thousand people. This study used Double Exponential Smoothing as the analysis method. The results of the analysis obtained are data on the poverty line of the Yogyakarta Special Region from 2004 to 2022 patterned on upward trand. the MAPE value of the model is 1.8% which indicates that the forecasting model uses the DES method accurately. 4. After looking at the comparison plot of the actual data and forecasting. From 2023 to 2027, the poverty line in the Special Region of Yogyakarta will continue to increase compared to the previous year

Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern.

A prototype application for machine-readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data-driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top-ranked papers together with a stack plot of the user data alongside the top-five database entries. The paper describes the approach and explores successes and challenges.