bluuebunny/crossref_metadata_2025_split huggingface dataset:
Edukasi Pemahaman Covid-19 pada Pemudik dengan Status ODP (Orang Dalam Pengawasan) Di Desa Sumber Agung Kabupaten OKU Timur
Authors: Sinensis Arini Rosa , Firdaus Thoha , Khusuma Ari 
Published: April 2021

Covid-19 merupakan virus yang telah menginfeksi manusia hampir diseluruh dunia pada awal tahun 2020. Virus ini telah masuk di Indonesia pada awal maret 2020 dan penyebarannya begitu cepat. Di Indonesia masa pandemik ini bertepatan dengan hari raya idul fitri 1441 H yang jatuh pada tanggal 23 mei 2020 oleh karena itu, pemerintah menerapkan Pembatasan sosial berskala besar (PSBB) karena dipandang efektif dalam menekan atau membatasi pergerakan orang antar wilayah dan menghentikan penyebaran covid-19 ke berbagai wilayah Indonesia. Pengabdian kepada masyarakat ini bertujuan untuk memberikan edukasi kepada masyarakat khususnya warga yang berstatus ODP (Orang dalam Pengawasan) tentang bahayanya virus Covid-19, menjelaskan pencegahannya dan membiasakan pola hidup bersih dan sehat (PHBS). Kegiatan pengabdian ini dilaksanakan di desa sumber agung kabupaten OKU Timur Sumsel. Sasaran pengabdian ini adalah warga OKU Timur yang mudik dari daerah berdampak Covid-19. Warga mudik tersebut telah ditandai dengan status ODP. Edukasi ini memberikan dampak terhadap bertambahnya wawasan dan pengetahuan masyarakat tentang bahayanya covid-19, menerapkan pola hidup bersih dan sehat serta membiasakan konsumsi madu untuk meningkatkan imunitas tubuh. Kegiatan ini mendapatkan respon positif oleh perangkat desa dan masyarakat sebagai upaya dalam pencegahan penyebaran Covid-19 di daerah.
How frequent is natural cloud seeding from ice cloud layers ( &lt; −35 °C) over Switzerland?
Authors: Proske Ulrike , Bessenbacher Verena , Dedekind Zane , Lohmann Ulrike , Neubauer David 
Published: April 2021

Abstract. Clouds and cloud feedbacks represent one of the largest uncertainties in climate projections. As the ice phase influences many key cloud properties and their lifetime, its formation needs to be better understood in order to improve climate and weather prediction models. Ice crystals sedimenting out of a cloud do not sublimate immediately but can survive certain distances and eventually fall into a cloud below. This natural cloud seeding can trigger glaciation and has been shown to enhance precipitation formation. However, to date, an estimate of its occurrence frequency is lacking.
In this study, we estimate the occurrence frequency of natural cloud seeding over Switzerland from satellite data and sublimation calculations. We use the DARDAR (radar lidar) satellite product between April 2006 and October 2017 to estimate the occurrence frequency of multi-layer cloud situations, where a cirrus cloud at T < −35 ∘C can provide seeds to a lower-lying feeder cloud. These situations are found to occur in 31 % of the observations. Of these, 42 % have a cirrus cloud above another cloud, separated, while in 58 % the cirrus is part of a thicker cloud, with a potential for in-cloud seeding. Vertical distances between the cirrus and the lower-lying cloud are distributed uniformly between 100 m and 10 km. They are found to not vary with topography. Seasonally, winter nights have the most multi-layer cloud occurrences, in 38 % of the measurements.
Additionally, in situ and liquid origin cirrus cloud size modes can be identified according to the ice crystal mean effective radius in the DARDAR data.
Using sublimation calculations, we show that in a significant number of cases the seeding ice crystals do not sublimate before reaching the lower-lying feeder cloud.
Depending on whether bullet rosette, plate-like or spherical crystals were assumed, 10 %, 11 % or 20 % of the crystals, respectively, could provide seeds after sedimenting 2 km. The high occurrence frequency of seeding situations and the survival of the ice crystals indicate that the seeder–feeder process and natural cloud seeding are widespread phenomena over Switzerland. This hints at a large potential for natural cloud seeding to influence cloud properties and thereby the Earth's radiative budget and water cycle, which should be studied globally. Further investigations of the magnitude of the seeding ice crystals' effect on lower-lying clouds are necessary to estimate the contribution of natural cloud seeding to precipitation.
COVID-19 lockdown-induced changes in NO2 levels across India observed by multi-satellite and surface observations
Authors: Biswal Akash , Singh Vikas , Singh Shweta , Kesarkar Amit P. , Ravindra Khaiwal , Sokhi Ranjeet S. , Chipperfield Martyn P. , Dhomse Sandip S. , Pope Richard J. , Singh Tanbir , Mor Suman 
Published: April 2021

Abstract. We have estimated the spatial changes in NO2 levels over different
regions of India during the COVID-19 lockdown (25 March–3 May 2020) using the satellite-based tropospheric column NO2 observed by
the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring
Instrument (TROPOMI), as well as surface NO2 concentrations obtained
from the Central Pollution Control Board (CPCB) monitoring network. A
substantial reduction in NO2 levels was observed across India during
the lockdown compared to the same period during previous business-as-usual
years, except for some regions that were influenced by anomalous fires in
2020. The reduction (negative change) over the urban agglomerations was
substantial (∼ 20 %–40 %) and directly proportional to the
urban size and population density. Rural regions across India also
experienced lower NO2 values by ∼ 15 %–25 %. Localised
enhancements in NO2 associated with isolated emission increase
scattered across India were also detected. Observed percentage changes in
satellite and surface observations were consistent across most regions and
cities, but the surface observations were subject to larger variability
depending on their proximity to the local emission sources. Observations
also indicate NO2 enhancements of up to ∼ 25 % during
the lockdown associated with fire emissions over the north-east of India
and some parts of the central regions. In addition, the cities located near the
large fire emission sources show much smaller NO2 reduction than other
urban areas as the decrease at the surface was masked by enhancement in
NO2 due to the transport of the fire emissions.
Synergy processing of diverse ground-based remote sensing and in situ data using the GRASP algorithm: applications to radiometer, lidar and radiosonde observations
Authors: Lopatin Anton , Dubovik Oleg , Fuertes David , Stenchikov Georgiy , Lapyonok Tatyana , Veselovskii Igor , Wienhold Frank G. , Shevchenko Illia , Hu Qiaoyun , Parajuli Sagar 
Published: April 2021

Abstract. The exploration of aerosol retrieval synergies from
diverse combinations of ground-based passive Sun-photometric measurements
with collocated active lidar ground-based and radiosonde observations using
versatile Generalized Retrieval of Atmosphere
and Surface Properties (GRASP) algorithm is presented. Several potentially fruitful aspects of observation synergy were considered. First, a set of passive and active ground-based observations collected
during both day- and nighttime was inverted simultaneously under the
assumption of temporal continuity of aerosol properties. Such an approach
explores the complementarity of the information in different observations
and results in a robust and consistent processing of all observations. For
example, the interpretation of the nighttime active observations usually
suffers from the lack of information about aerosol particles sizes, shapes
and complex refractive index. In the realized synergy retrievals, the
information propagating from the nearby Sun-photometric observations
provides sufficient constraints for reliable interpretation of both day- and nighttime lidar observations. Second, the synergetic processing of such complementary observations with
enhanced information content allows for optimizing the aerosol model used in the retrieval. Specifically, the external mixture of several aerosol
components with predetermined sizes, shapes and composition has been
identified as an efficient approach for achieving reliable retrieval of
aerosol properties in several situations. This approach allows for achieving consistent and accurate aerosol retrievals from processing stand-alone advanced lidar observations with reduced information content about aerosol columnar properties. Third, the potential of synergy processing of the ground-based
Sun-photometric and lidar observations, with the in situ backscatter sonde
measurements was explored using the data from KAUST.15 and KAUST.16 field
campaigns held at King Abdullah University of Science and Technology (KAUST) in the August of 2015 and 2016. The inclusion of radiosonde data has been demonstrated to provide significant additional constraints to validate and improve the accuracy and scope of aerosol profiling. The results of all retrieval setups used for processing both synergy and
stand-alone observation data sets are discussed and intercompared.
Feasibility of reconstructing the summer basin-scale sea surface partial pressure of carbon dioxide from sparse in situ observations over the South China Sea
Authors: Wang Guizhi , Shen Samuel S. P. , Chen Yao , Bai Yan , Qin Huan , Wang Zhixuan , Chen Baoshan , Guo Xianghui , Dai Minhan 
Published: April 2021

Abstract. Sea surface partial pressure of CO2 (pCO2) data with a high spatiotemporal resolution are important in studying the global carbon cycle and assessing the oceanic carbon uptake. However, the observed sea surface pCO2 data are usually limited in spatial and temporal coverage, especially in marginal seas. This study provides an approach to reconstruct the complete sea surface pCO2 field in the South China Sea (SCS) with a grid resolution of 0.5∘×0.5∘ over the period of 2000–2017 using both remote-sensing-derived pCO2 and observed underway pCO2, among which the gridded underway pCO2 data in 2004, 2005, and 2006 are presented for the first time. Empirical orthogonal functions (EOFs) were computed from the remote-sensing-derived pCO2. Then, a multilinear regression was applied to the observed pCO2 as the response variable with the EOFs as the explanatory variables. EOF1 explains the general spatial pattern of pCO2 in the SCS. EOF2 shows the pattern influenced by the Pearl River plume on the northern shelf and slope. EOF3 is consistent with the pattern influenced by coastal upwelling along the northern coast of the SCS. When pCO2 observations cover a sufficiently large area, the reconstructed fields successfully display a pattern of relatively high pCO2 in the mid and southern basin. The rate of sea surface pCO2 increase in the SCS is 2.4±0.8 µatm yr−1 based on the spatial average of the reconstructed pCO2 over the period of 2000–2017. This is consistent with the temporal trends at Station SEATS (SouthEast Asia Time-series Study; 18∘ N, 116∘ E) in the northern basin of the SCS and at Station ALOHA (A Long-Term Oligotrophic Habitat Assessment; 22∘45′ N, 158∘ W) in the North Pacific. We validated our reconstruction with a leave-one-out cross-validation approach, which yields the root-mean-square error (RMSE) in the range of 2.4–5.2 µatm, smaller than the spatial standard deviation of our reconstructed data and much smaller than the spatial standard deviation of the observed underway data. The RMSE between the reconstructed summer pCO2 and the observed underway pCO2 is no larger than 31.7 µatm, in contrast to (a) the RMSE from 12.8 to 89.0 µatm between the remote-sensing-derived pCO2 and the underway data and (b) the RMSE from 32.6 to 44.5 µatm between the neural-network-produced pCO2 and the underway data. The difference between the reconstructed pCO2 and those calculated from observations at Station SEATS is in the range from −7 to 10 µatm. These comparison results indicate the reliability of our reconstruction method and output. All the data for this paper are openly and freely available at PANGAEA under the link https://doi.org/10.1594/PANGAEA.921210 (Wang et al., 2020).
A national extent map of cropland and grassland for Switzerland based on Sentinel-2 data
Authors: Pazúr Robert , Huber Nica , Weber Dominique , Ginzler Christian , Price Bronwyn 
Published: April 2021

Abstract. Agricultural landscapes support multiple functions and are of great importance for biodiversity. Heterogeneous agricultural mosaics of cropland and grassland commonly result from variable land use practices and ecosystem service demands. Switzerland’s agricultural land use is considerably spatially heterogeneous due to strong variability in conditions, especially topography and climate, thus presenting challenges to automated agricultural mapping. Nation-wide knowledge of the location of cropland and grassland is necessary for effective conservation and land use planning. We mapped the distribution of cropland and grassland across Switzerland. We used several indices largely derived from Sentinel-2 satellite imagery captured over multiple growing seasons, and parcel-based training data derived from landholder reporting. The mapping was conducted within Google Earth Engine using a random forest classifier. The resulting map has high accuracy in lowlands as well as in mountainous areas. The map will act as a base agricultural land cover dataset for researchers and practitioners working in agricultural areas of Switzerland and interested in land cover and landscape structure. The map as well as the training data and calculation algorithms (using Google Earth Engine) are freely available for download on the Envidat platform (doi: 10.16904/envidat.205).
Geological monitoring networks for risk management close to large rock cliffs: the case history of Gallivaggio and Cataeggio in the italian Alps
Authors: Dei Cas Luca , Pastore Maria Luisa , Pavan Andrea , Petrella Nicola 
Published: April 2021

Abstract. In areas located near large rock cliffs, risk reduction by early warning monitoring systems highligts potentiality but also critical issues and limits. The paper examines two rock slope failures that occurred in a short time from each other near inhabited areas in the Italian Alps. The viscous behavior of the rock mass was reconstructed through data processing from ground-based Synthetic Aperture Radar Interferometry (InSAR), and elaboration of acceleration and speed curves. Landslides types and underlying complexity associated with rock detachment mechanisms suggest the identification of precautionary alarm thresholds for collapse forecasting. The analysis of financial outlay, both for mitigation works and for monitoring activities, highlight the adequacy and the opportunity to combine passive systems, like embankments or rockfall drapery meshes, with a reliable monitoring network for early warning.
Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0
Authors: McGuffin Dana L. , Huang Yuanlong , Flagan Richard C. , Petäjä Tuukka , Ydstie B. Erik , Adams Peter J. 
Published: April 2021

Abstract. Atmospheric aerosol microphysical processes are a
significant source of uncertainty in predicting climate change.
Specifically, aerosol nucleation, emissions, and growth rates, which are
simulated in chemical transport models to predict the particle size
distribution, are not understood well. However, long-term size distribution
measurements made at several ground-based sites across Europe implicitly
contain information about the processes that created those size
distributions. This work aims to extract that information by developing and
applying an inverse technique to constrain aerosol emissions as well as
nucleation and growth rates based on hourly size distribution measurements.
We developed an inverse method based upon process control theory into an
online estimation technique to scale aerosol nucleation, emissions, and
growth so that the model–measurement bias in three measured aerosol
properties exponentially decays. The properties, which are calculated from
the measured and predicted size distributions, used to constrain aerosol
nucleation, emission, and growth rates are the number of particles with
a diameter between 3 and 6 nm, the number with a diameter greater than 10 nm,
and the total dry volume of aerosol (N3–6, N10, Vdry),
respectively. In this paper, we focus on developing and applying the
estimation methodology in a zero-dimensional “box” model as a
proof of concept before applying it to a three-dimensional simulation in
subsequent work. The methodology is first tested on a dataset of synthetic
and perfect measurements that span diverse environments in which the true
particle emissions, growth, and nucleation rates are known. The inverse
technique accurately estimates the aerosol microphysical process rates with
an average and maximum error of 2 % and 13 %, respectively. Next, we
investigate the effect that measurement noise has on the estimated rates.
The method is robust to typical instrument noise in the aerosol properties
as there is a negligible increase in the bias of the estimated process rates.
Finally, the methodology is applied to long-term datasets of in situ size
distribution measurements in western Europe from May 2006 through June 2007.
At Melpitz, Germany, and Hyytiälä, Finland, the average diurnal
profiles of estimated 3 nm particle formation rates are reasonable, having
peaks near noon local time with average peak values of 1 and 0.15 cm−3 s−1, respectively. The
normalized absolute error in estimated N3–6, N10, and Vdry at
three European measurement sites is less than 15 %, showing that the
estimation framework developed here has potential to decrease
model–measurement bias while constraining uncertain aerosol microphysical
processes.
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
Authors: Wunsch Andreas , Liesch Tanja , Broda Stefan 
Published: April 2021

Abstract. It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep-learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, shallow recurrent networks frequently seem to be excluded from the study design due to the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANNs, namely non-linear autoregressive networks with exogenous input (NARX) and popular state-of-the-art DL techniques such as long short-term memory (LSTM) and convolutional neural networks (CNNs). We compare the performance on both sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. Further, we also investigate the data dependency in terms of time series length of the different ANN architectures. For seq2val forecasts, NARX models on average perform best; however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL techniques especially when only small amounts of training data are available, where they can clearly outperform LSTMs and CNNs; however, LSTMs and CNNs might perform substantially better with a larger dataset, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.
ANALISIS FAKTOR KEPATUHAN TERAPI OBAT ANTI TUBERKULOSIS KOMBINASI DOSIS TETAP PADA PENDERITA TUBERKULOSIS DI PUSKESMAS SERPONG 1 KOTA TANGERANG SELATAN
Authors: Andriati Riris , Sudrajat Ajat 
Published: April 2021

Latar belakang: Menurut WHO dalam Global Tuberculosis Report 2018, TB merupakan salah satu penyakit dari 10 penyebab kematian di dunia.Pada tahun 2017, diperkirakan terdapat 10 juta kasus baru (insidensi) TB di seluruh dunia yang setara dengan 133 kasus per 100.000 penduduk, diantaranya 5,8 juta laki-laki, 3,2 juta wanita dan 1 juta adalah anak-anak.Pada tahun 2018 di Puskesmas Serpong 1 didapatkan angka penemuan kasus (CNR) termasuk ke tiga tertinggi dari seluruh puskesmas di Tangerang Selatan.Puskesmas Serpong 1 tersebut memiliki sistem penjaringan kasus TB yang cukup baik di Tangerang Selatan. Tujuan:Untuk menganalisa Faktor kepatuhan Terapi Obat Anti Tuberkulodid (OAT) Kombinasi Dosis Tetap (KDT) pada pasien dengan tuberkulosis paru di Puskesmas Serpong 1 Kota Tangerang Selatan tahun 2019. Metode:Desain cross sectional (potong lintang) dengan sampel 47 responden. Sampel adalah pasien TB paru yang sedang menjalani pengobatan TB. Teknik pengambilan sampel menggunakan total sampling. Analisis dengan uji chi-square. Hasil:Berdasarkan analisis univariat responden berada dikelompok umur produkttif sebanyak 46 (97,9%),sedangkan berdasarkan analisis bivarat didapatkan ada hubungan dengan pengetahuan, efek samping AOT, jarang tempat tinggal , peran PMO dan peran kesehatan dengan kepatuhan Terapi Obat Anti Tuberkulodid (OAT) Kombinasi Dosis Tetap (KDT) dengan nilai P < 0,05.Sedangkan tidak ada hubungan antara usia, jenis kelamin, pendidikan dan status pekerjaaan dengan kepatuhan Terapi Obat Anti Tuberkulodid (OAT) Kombinasi Dosis Tetap (KDT)dengan nilai P > 0,05. Kesimpulan: Faktor pengetahuan,efek samping OAT,jarak tempuh ,peran PMO dan Peran petugas ada hubungan dengan kepatuhan berobat pasien TB paru. Sedangkan faktor karakteristik pasien yang tidak berhubungan yaitu usia, jenis kelamin, tingkat pendidikan, status pekerjaan.
HUBUNGAN KELULUSAN AKREDITASI RUMAH SAKIT DENGAN KEPUASAN PASIEN RAWAT INAP RSUD TAMANSARI PROVINSI DKI JAKARTA
Authors: Meilia Riska Edwi 
Published: April 2021

Akreditasi adalah pernyataan untuk kualitas pelayanan yang diberikan Rumah Sakit yang sesuai dengan standar serta telah dinyatakan Rumah Sakit tersebut telah memenuhi Standar Akreditasi. Kualitas pelayanan yang baik dapat menyebabkan kepuasan pelanggan meningkat. Kualitas dapat memberikan motivasi kepada pasien sebagai pelanggan untuk menjalin hubungan yang baik dengan Rumah sakit yang memberikan pelayanan. Kepuasan merupakan tolak ukur sebuah keputusan pembelian dimasa yang akan datang. Meningkatkan kualitas serta kinerja pelayanan sudah seyogyanya diupayakan secara berkelanjutan. Pihak independen atau pihak luar yang memberikan penilaian dibutuhkan untuk peningkatan kualitas dengan standar penilaian yang telah ditentukan pihak berwenang. Tujuan dari penelitian ini untuk mengetahui apakah ada hubungan kelulusan akreditasi rumah sakit terhadap kepuasan Pasien Rawat Inap RSUD Tamansari Provinsi DKI Jakarta tahun 2019. Jenis penelitian dalam penelitian ini adalah kuantitatif menggunakan pendekatan cross sectional. Penelitian ini dilakukan di Ruang Rawat Inap RSUD Tamansari Provinsi DKI Jakarta. Penelitian ini dilakukan pada bulan Febuari tahun 2019. Jumlah sampel sebanyak 79 pasien rawat inap Hasil penelitian diketahui dari 79responden yang menyatakan puas atas pelayanan pasca lulus akreditasi yaitu 51 (81%) sedangkan dari 13 responden yang menyatakan tidak puas karena indikator dalam akreditasi tidak lulus. Uji statistik menyatakan ada hubungan dengan nilai P value =0.001) < 0,05, maka Kesimpulan yang dapat diambil adalah ada hubungan antara kelulusan akreditasi rumah sakit dengan kepuasan Pasien Rawat Inap RSUD Tamansari Provinsi DKI Jakarta Saran dari hasil penelitian ini diharapkan Rumah sakit dapat berkomitmen memberikan pelayanan prima kepada pasien dengan memenuhi standar akreditasi RS.