bluuebunny/crossref_metadata_2025_split huggingface dataset:
NATURALEZA Y EVOLUCIÓN DE LAS TRANSFERENCIAS MUNICIPALES EN LOS TERRITORIOS FORALES
Authors: ANGLÉS JUANPERE BENJAMÍ 
Published: February 2021

Entre los recursos económicos de las Haciendas Locales se encuentran las participaciones en los ingresos de las Comunidades Autónomas, recursos considerados como fundamentales por la Constitución Española. El objetivo del presente trabajo es comprobar, en el caso de los Territorios Forales, si tales recursos a disposición de los municipios son verdaderas participaciones en los ingresos de las Haciendas Forales o transferencias de otra índole, así como si efectivamente juegan el papel fundamental que les otorga la Carta Magna. Para ello, se han recopilado datos económicos de los distintos Fondos de Financiación Municipal, procedentes mayoritariamente de los sucesivos Presupuestos Generales de la Comunidad Foral de Navarra y de las Diputaciones Forales de Álava, Guipúzcoa y Vizcaya.Toki-ogasunen baliabide ekonomikoetako bat dira partaidetzak autonomia-erkidegoen diru-sarreretan, eta Espainiako Konstituzioak oinarrizko baliabidetzat jotzen ditu. Lan honek helburu du foru-lurraldeei dagokienez egiaztatzea ea udalerrien esku dauden baliabideak benetan diren foru-ogasunen diru-sarreretako partaidetzak, edo, aldiz, bestelako transferentziak diren. Halaber, ziurtatu nahi du baliabide horiek Magna Cartan aitortuta duten oinarrizko rola egiatan betetzen duten. Horretarako, udal-finantzaketarako funtsetako datu ekonomikoak bildu dira; gehienak Nafarroako Foru Erkidegoaren eta Arabako, Gipuzkoako zein Bizkaiko foru- aldundien aurrekontu orokorretatik atera dira.Among the financial resources of Local Treasures there are the shares in the Autonomus Communities¿ revenues, resources that are considered crucial for the Spanish Constitution. The aim of this study is to check, in the case of Foral territories, whether those resources in hands of municipalities are true shares in the Foral treasures revenue or other different transfers as well as whether they effectively play the crucial role attributed to them by the Magna Carta. For that, economic data have been compiled regarding the different municipal sources of funding from mainly the successive general budgets of the Foral Community of Navarra and the Foral councils of Alava, Guipuzcoa and Vizcaya.
Upaya Meningkatkan Hasil Belajar Matematika Siswa Kelas VIII-F SMP Negeri 22 Surabaya Melalui Penerapan Model Pembelajaran Problem Based Learning Tahun Pelajaran 2018-2019
Authors: Asmana Arezqi Tunggal 
Published: February 2021

Students' experience in solving problems through discovery is a prerequisite for students' critical and analytical thinking in constructing their learning outcomes, so that a knowledge system process will be formed in their students' memories that is ready to be reused when such knowledge is needed.
Less optimal student learning outcomes are caused by several factors, one of which is the low attention and lack of concentration of students in following mathematics learning. Teachers often provide lessons in the form of lectures and questions and answers, so students are less motivated to develop creative thinking skills in solving problems.
The purpose of this study is to determine whether there is an increase in student mathematics learning outcomes after the implementation of problem based learning learning models.
This type of research is classroom action research with two cycles, where each cycle consists of activities of planning, implementation, observation, and reflection. Data retrieval is carried out by a written test on two linear variables. This research was conducted in class VIII-F of SMP Negeri 22 Surabaya with 32 students.
PENINGKATAN PRESTASI BELAJAR EFEKTIF DAN EFISIEN MELALUI BIMBINGAN PRIBADI DAN SOSIAL PADA SISWA KELAS XI IPS 1 SMA NEGERI 1 KAYUAGUNG
Authors: Ismail Ina 
Published: February 2021

Tujuan Penelitian Tindakan Kelas (PTK) ini adalah untuk mengatasi bahwa bimbingan pribadi dan sosial dapat Peningkatan Prestasi belajar siswa. Lokasi yang dimaksud meliputi aspek tempat ialah lokasi dimana proses pembelajaran berlangsung yaitu kelas XI IPS 1 SMA Negeri 1 Kayuagung. Aspek pelaku, ialah peneliti, sebagai guru dan siswa kelas XI IPS 1 yang terlibat dalam interaksi pembelajaran. Aspek kegiatan ialah layanan kelompok uintuk Peningkatan Prestasi Belajar Melalui Bidang Bimbingan Pribadi dan Sosial Materi Belajar Efektif dan Efisien dengan layanan kelompok pada siswa kelas XI IPS 1 SMA Negeri 1 Kayuagung Tahun Pelajaran 2017/2018. Pada materi belajar efektif dan efisien. Berdasarkan hasil penelitian dan hasil belajar maka dapat disimpulkan bahwa bimbinga pribadi dan sosial dapat meningkatan Prestasi belajar Bidang Bimbingan Belajar pada Materi Belajar Efektif dan Efisien siswa kelas XI IPS 1 SMA Negeri 1 Kayuagung tahun pelajaran 2017/2018 nilai persentase ketuntasan prestasi belajar siklus I sebesar 77.54% dan siklus II 86.11%
Literary Meaning of Anxiety reflected by the Servant found in ‘Appointment in Samarra’ by Somerset Maugham
Authors: Fatimah Siti , Koestiani Septi 
Published: February 2021

This research was conducted to describe the semantics analysis of the literary meaning of anxiety in the story ‘Appointment in Samara’ written by Somerset Maugham. This research applied descriptive qualitative method. The instrument was the researcher himself, and supporting by the secondary instrument called the scripted text of the story examined. The data collection were taken from internet, it was the text of the story ‘Appointment in Samara’. To analyze all data, this study used the theory of literary meaning proposed by Lyons (1977). The data were indentified, then were classified, were displayed, and were described. The results of the study showed there were three kinds defense mechanism of reactions in anxiety namely: (1) regression, (2) repression, and (3) rationalization.
Investigating Indonesian Pre-Service Teachers' Speaking Anxiety in EFL Classroom
Authors: Ahmad Syafi'i  
Published: February 2021

The objective of this research are to investigate pre-service teachers’ speaking anxiety in EFL classroom. The subject of this research was 40 pre-service teachers of STKIP Al Hikmah Surabaya Indonesia. The research design of this study is descriptive qualitative research. The main single instrument used in this research was questionnaire. The questionnaire used in the form of Likert scale which was adopted and developed from Horwitz. In this study, questionnaire was applied as the single instruments. In analyzing data, an interactive data analysis model proposed by Miles M.B and Huberman A.M (1984) was used. The result of the study were: (1) All pre service teachers experienced speaking anxiety in EFL classroom. (2) There are five major areas of speaking anxiety experienced by pre service teachers in EFL classroom namely fear of interaction, fear of perception of others, low self esteem and lack of confidence, language proficiency, and error correction or fear of negative corection.
The Use of Instagram Picture Series as Media to Improve Students’ Ability in Writing Procedure Text
Authors: Rahmawati Vebriary Eka , Sulistyaningsih  
Published: February 2021

This research aims to know whether the students’ ability in writing procedure text could be improved through the use of Instagram picture series as teaching media. This is a classroom action research (CAR) implemented in two cycles, on the eleventh-grade students of Madrasah Aliyah NU Sidoarjo in 2019/2020 academic year. This research uses quasi-experimental design which has these criteria based on Mueller: 1). No random selection of subjects; 2). Pre-test both groups; 3). Experimental group receives new treatment; 4). Control group receive no treatment or the normal treatment; 5). Post-test both groups. The population is 126 students. The samples are ten students of XI MIPA 1 as the experimental group and ten students of XI MIPA 2 as the control group. The independent variable is Instagram picture series as media of teaching writing. The dependent variable is the students’ ability in writing procedure text. In collecting the data, the researcher applied instrument: interview, questionnaire, Instagram application, writing pre-test and writing post-test. The data was analyzed statistically using T-test. The researcher examines two cycles of test. As the result in the first cycle, the pre-test average score of the control group is 44 and the experimental group is 45.5. In the first cycle of pot-test, the average score of the control group is 70 and the experimental group is 78. Both of them don’t get the Kriteria Ketuntasan Minimum (KKM) or the level of basic competence score which is 80. So, the second cycle of post-test is held. As the result, the average score of the control group is 74 and the experimental group is 82.5. The next cycle of post-test isn’t needed because the average score of the experimental group is already passed over the KKM. It means that, the use of Instagram picture series as teaching media has the significant positive impact to the improvement of the students’ ability in writing procedure text.
Photochemical environment over Southeast Asia primed for hazardous ozone levels with influx of nitrogen oxides from seasonal biomass burning
Authors: Marvin Margaret R. , Palmer Paul I. , Latter Barry G. , Siddans Richard , Kerridge Brian J. , Latif Mohd Talib , Khan Md Firoz 
Published: February 2021

Abstract. Mainland and maritime Southeast Asia is home to more than 655 million
people, representing nearly 10 % of the global population. The dry
season in this region is typically associated with intense biomass
burning activity, which leads to a significant increase in surface air
pollutants that are harmful to human health, including ozone
(O3). Latitude-based differences in the dry season and land use
distinguish two regional biomass burning regimes: (1) burning on the
peninsular mainland peaking in March and (2) burning across Indonesia
peaking in September. The type and amount of material burned in each
regime impact the emissions of nitrogen oxides (NOx = NO + NO2) and
volatile organic compounds (VOCs), which combine to
produce ozone. Here, we use the nested GEOS-Chem atmospheric chemistry
transport model (horizontal resolution of 0.25∘ × 0.3125∘),
in combination with satellite observations
from the Ozone Monitoring Instrument (OMI) and ground-based
observations from Malaysia, to investigate ozone photochemistry
over Southeast Asia in 2014. Seasonal cycles
of tropospheric ozone columns from OMI and GEOS-Chem
peak with biomass burning emissions.
Compared to OMI, the model has a mean annual bias of −11 %
but tends to overestimate tropospheric ozone near areas of seasonal fire activity.
We find that outside these burning areas,
the underlying photochemical environment is generally
NOx-limited and dominated by anthropogenic NOx and
biogenic non-methane VOC emissions. Pyrogenic emissions of NOx play a key
role in photochemistry, shifting towards more VOC-limited ozone production
and contributing about 30 % of the regional
ozone formation potential during both biomass burning seasons. Using the
GEOS-Chem model, we find that biomass burning activity coincides with
widespread ozone exposure at levels that exceed world public health
guidelines, resulting in about 260 premature deaths across
Southeast Asia in March 2014 and another 160 deaths
in September. Despite a positive model bias,
hazardous ozone levels are confirmed by surface observations during
both burning seasons.
Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System
Authors: Jiang Fei , Wang Hengmao , Chen Jing M. , Ju Weimin , Tian Xiangjun , Feng Shuzhuang , Li Guicai , Chen Zhuoqi , Zhang Shupeng , Lu Xuehe , Liu Jane , Wang Haikun , Wang Jun , He Wei , Wu Mousong 
Published: February 2021

Abstract. Satellite retrievals of the column-averaged dry air
mole fractions of CO2 (XCO2) could help to improve carbon flux estimation
due to their good spatial coverage. In this study, in order to assimilate the
GOSAT (Greenhouse Gases Observing
Satellite) XCO2 retrievals, the Global Carbon Assimilation System (GCAS) is
upgraded with new assimilation algorithms, procedures, a localization
scheme, and a higher assimilation parameter resolution. This upgraded system is
referred to as GCASv2. Based on this new system, the global terrestrial ecosystem
(BIO) and ocean (OCN) carbon fluxes from 1 May 2009 to 31 December 2015 are
constrained using the GOSAT ACOS (Atmospheric CO2 Observations from Space) XCO2 retrievals (Version 7.3). The
posterior carbon fluxes from 2010 to 2015 are independently evaluated using
CO2 observations from 52 surface flask sites. The results show that the
posterior carbon fluxes could significantly improve the modeling of
atmospheric CO2 concentrations, with global mean bias decreases from a
prior value of 1.6 ± 1.8 ppm to −0.5 ± 1.8 ppm. The uncertainty
reduction (UR) of the global BIO flux is 17 %, and the highest monthly
regional UR could reach 51 %. Globally, the mean annual BIO and OCN carbon
sinks and their interannual variations inferred in this study are very close
to the estimates of CarbonTracker 2017 (CT2017) during the study period, and the inferred mean
atmospheric CO2 growth rate and its interannual changes are also very
close to the observations. Regionally, over the northern lands, the
strongest carbon sinks are seen in temperate North America, followed by Europe,
boreal Asia, and temperate Asia; in the tropics, there are strong sinks in
tropical South America and tropical Asia, but a very weak sink in Africa.
This pattern is significantly different from the estimates of CT2017, but
the estimated carbon sinks for each continent and some key regions like
boreal Asia and the Amazon are comparable or within the range of previous bottom-up
estimates. The inversion also changes the interannual variations in carbon
fluxes in most TransCom land regions, which have a better relationship with
the changes in severe drought area (SDA) or leaf area index (LAI), or are more consistent with
previous estimates for the impact of drought. These results suggest that the
GCASv2 system works well with the GOSAT XCO2 retrievals and shows good
performance with respect to estimating the surface carbon fluxes; meanwhile, our results
also indicate that the GOSAT XCO2 retrievals could help to better
understand the interannual variations in regional carbon fluxes.
D-region impact area of energetic electron precipitation during pulsating aurora
Authors: Bland Emma , Tesema Fasil , Partamies Noora 
Published: February 2021

Abstract. A total of 10 radars from the Super Dual Auroral Radar Network (SuperDARN) in Antarctica were used to estimate the spatial area over which energetic electron precipitation (EEP) impacts the D-region ionosphere during pulsating aurora (PsA) events. We use an all-sky camera (ASC) located at Syowa Station to confirm the presence of optical PsAs, and then we use the SuperDARN radars to detect high frequency (HF) radio attenuation caused by enhanced ionisation in the D-region ionosphere. The HF radio attenuation was identified visually by examining quick-look plots of the background HF radio noise and backscatter power from each radar. The EEP impact area was determined for 74 PsA events. Approximately one-third of these events have an EEP impact area that covers at least 12∘ of magnetic latitude, and three-quarters cover at least 4∘ of magnetic latitude. At the equatorward edge of the auroral oval, 44 % of events have a magnetic local time extent of at least 7 h, but this reduces to 17 % at the poleward edge. We use these results to estimate the average size of the EEP impact area during PsAs, which could be used as a model input for determining the impact of PsA-related EEP on the atmospheric chemistry.
Identifying meteorological drivers of extreme impacts: an application to simulated crop yields
Authors: Vogel Johannes , Rivoire Pauline , Deidda Cristina , Rahimi Leila , Sauter Christoph A. , Tschumi Elisabeth , van der Wiel Karin , Zhang Tianyi , Zscheischler Jakob 
Published: February 2021

Abstract. Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.
The use of personal weather station observations to improve precipitation estimation and interpolation
Authors: Bárdossy András , Seidel Jochen , El Hachem Abbas 
Published: February 2021

Abstract. The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.
Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation
Authors: Sattari Mohammad Taghi , Apaydin Halit , Band Shahab S. , Mosavi Amir , Prasad Ramendra 
Published: February 2021

Abstract. Timely and accurate estimation of reference evapotranspiration (ET0) is indispensable for agricultural water management for efficient water use. This study aims to estimate the amount of ET0 with machine learning approaches by using minimum meteorological parameters in the Corum region, which has an arid and semi-arid climate and is regarded as an important agricultural centre of Turkey. In this context, monthly averages of meteorological variables, i.e. maximum and minimum temperature; sunshine duration; wind speed; and average, maximum, and minimum relative humidity, are used as inputs. Two different kernel-based methods, i.e. Gaussian process regression (GPR) and support vector regression (SVR), together with a Broyden–Fletcher–Goldfarb–Shanno artificial neural network (BFGS-ANN) and long short-term memory (LSTM) models were used to estimate ET0 amounts in 10 different combinations. The results showed that all four methods predicted ET0 amounts with acceptable accuracy and error levels. The BFGS-ANN model showed higher success (R2=0.9781) than the others. In kernel-based GPR and SVR methods, the Pearson VII function-based universal kernel was the most successful (R2=0.9771). Scenario 5, with temperatures including average temperature, maximum and minimum temperature, and sunshine duration as inputs, gave the best results. The second best scenario had only the sunshine duration as the input to the BFGS-ANN, which estimated ET0 having a correlation coefficient of 0.971 (Scenario 8). Conclusively, this study shows the better efficacy of the BFGS in ANNs for enhanced performance of the ANN model in ET0 estimation for drought-prone arid and semi-arid regions.