Miracle-dz/UniFAR-FTUs huggingface dataset:
Remote Triggering in the Koyna‐Warna Reservoir‐Induced Seismic Zone, Western India

Dynamic triggering following large distant earthquakes has been observed in many regions globally. In this study, we present evidence for remote dynamic triggering in the Koyna‐Warna region of Western India, which is known to be a premier site of reservoir‐induced seismicity. Using data from a closely spaced broadband network of 11 stations operated in the region since 2005, we conduct a systematic search for dynamic triggering following 20 large distant earthquakes with dynamic stresses of at least 1 kPa in the region. We find that the only positive cases of dynamic triggering occurred during 11 April 2012, Mw8.6 Indian Ocean earthquake and its largest aftershock of Mw8.2. In the first case, microearthquakes started to occur in the first few cycles of the Love waves, and the largest event of magnitude 3.3 occurred during the first few cycles of the Rayleigh waves. The increase of microseismicity lasted for up to five days, including a magnitude 4.8 event occurred approximately three days later. Our results suggest that the Koyna‐Warna region is stress sensitive and susceptible for remote dynamic triggering, although the apparent triggering threshold appears to be slightly higher than other regions.
Variability in the correlation between satellite-derived liquid cloud droplet effective radius and aerosol index over the northern Pacific Ocean

Abstract The relationship between aerosol index (AI) and cloud effective radius (CER) was examined over five sea regions in the northern Pacific using daily Moderate Resolution Imaging Spectroradiometer data from 2002–2014. The results show that there tends to be a negative relationship between AI and CER at lower AI, becoming positive at higher AI, suggesting the Twomey effect held in low aerosol environment. Over the entire AI range, the correlation between AI and CER was significantly positive over the two marginal seas (the Bohai–Yellow Sea and the Sea of Japan), and it was significantly negative over the three open oceans (the North Pacific subtropical gyre, the western and eastern subarctic North Pacific) for all seasons, except that the correlations in summer were significantly positive over the two subarctic North Pacific regions. A series of statistical analyses showed that the AI–CER relationship (the regression slope) is significantly correlated with relative humidity (RH) and precipitable water vapour (PWV); on average, PWV accounts for 60% of the variance of the slope for the two marginal seas and higher for the three open oceans (79%). The slope can change from negative to positive at high PWV levels, suggesting that water vapour plays an important role in the variability of the slope. Aerosol hygroscopic growth and the growth of CER in humid condition may counteract the Twomey effect at high AI, particularly over the two marginal seas. The error high contribution of the low liquid cloud fraction (LCF) to AI–CER relationship may partially account for the positive slope over the two marginal seas, while the low LCF has a negligible impact on the three open oceans. Additionally, over the three open oceans, stable thermodynamic state may prevent the effect of aerosol on cloud at high AI.
Response of Applied Phosphorus Fertilizer Rate and Plant Spacing for Potato (Solanum tuberosum L.) Production on Nitisols in Central Highland of Ethiopia

DOI: 10.15580/GJAS.2017. 9.110917164 The use of low phosphorus fertilize rate as well as inappropriate plant spacing are one of the main factors constraining potato production in Ethiopia. Therefore, a field experiment was conducted during the 2015 and 2016 main growing season in Welmera and Ada’a Berga district in the central highland of Ethiopia. The experiment was aimed at determining the effect of phosphorus fertilizer rates and plant spacing on yield and yield components of potato. The treatments consisted of five phosphorus fertilizer rate and four levels of plant spacing (65 cm x 30 cm, 75 cm x 30 cm, 85 cm x 30 cm and 95 cm x 30 cm). The experiment was laid out as a randomized complete block design in a factorial arrangement and replicated three times. Analysis of the results revealed that phosphorus fertilizer and plant spacing significantly affected leaf area index, marketable tuber number, total tuber numbers, total yields, specific gravity and tuber dry matter. However, days to follower and days to maturity significantly affected by phosphorus fertilizer but did not affect by plant spacing. Increasing or widening plant spacing significantly reduced total as well as unmarketable tuber yields whereas decreasing or narrowing it significantly increased this yield parameter. On the other hand, increasing plant spacing significantly increased marketable tuber yields. In conclusion, the phosphorus fertilizer and plant spacing produced the highest tuber yields (ton ha -1 ) as well as marketable tuber yield in response to planting at phosphorus fertilizer in the form of P2O5 115kg ha -1 and spacing of 85 cm between rows and 30 cm between plants for Belete variety was advisably. Submitted: 09/11/2017 Accepted: 22/11/2017 Published: 30/11/2017
Journal of International Women's Studies Journal of International Women's Studies

Saptari is the smallest district in Nepal; it lies in the country’s eastern development region. There have been limited studies conducted about Maithali women's status and domestic violence they face, however, no studies were conducted about the psychological resilience and social support they receive to overcome these adversities specifically in the Saptari district. The position of Madhesi women is worse because of analphabetism, and political, religious, and superstitious beliefs. The study's primary objective is to examine the relationship between resilience and social support among women facing traumatic events in life. A descriptive cross-sectional study consisted of 200 respondents from the paralegal committee and the Kanchanrup municipality community, in the Saptari district. Quantitative data was collected for this research. The Connor-Davidson Resilience Scale (CD-RISC) and the Multidimensional Scale of Perceived Social Support (MSPSS) were used to measure resilience and social support. Pearson's correlation and t-test analyses were performed to examine associations between resilience and independent variables. The collected data was analyzed using SPSS 16. The study findings indicated significant associations between resilience and social support (r==.853, p<0.01) and demographic variables, which included employment, education, and resilience (r=0.173, p<0.01) (r=.264, p<0.05). There was an insignificant association between resilience and age, marital status, and religion. Our study showed a significant negative correlation between resilience and ethnicity, which indicates that the participants belonging to untouchable groups had lower resilience (r=−0.410, p<0.01). In a simple linear regression analysis, social support was able to explain 72% of the variance in resilience (R2 =0.72, F (6, 75) =12.53, p<0.001). The Cronbach's alpha for the CD-RISC and MSPSS were . 91 and . 80, respectively. From this study, there is a significant difference in the scores of resilience and perceived social support among women. Psychological resilience, perceived social support, and demographic factors were associated with resilience in women who experienced traumatic life events. The present research could help planners, decision-makers, and responsible government in micro-level development planning uplift women's status and improve their mental health status, especially of marginalized groups.
Midlatitude Cirrus Clouds at the SACOL Site: Macrophysical Properties and Large‐Scale Atmospheric States

Two‐year observations of a Ka‐band Zenith Radar at the Semi‐Arid Climate and Environment Observatory of Lanzhou University (SACOL) are used to document the midlatitude cirrus cloud macroproperties. Generally, cirrus occurs 41.6% of the observation time and most frequently appear at about 7.2 km above ground level. The cirrus macroproperties are strongly coupled with large‐scale atmospheric states; thus, its occurrence and location over the SACOL have significant seasonal variations. A k‐mean clustering method is used to classify cirrus into four distinct regimes without a prior knowledge about the meteorological process. Contrasting to the different cirrus physical properties in each regime, the cirrus event of each regime has a distinct seasonal distribution and the synoptic conditions from the ERA‐Interim reanalysis responsible for each cirrus regime are also quite different. Since global climate models typically overestimate cirrus cloud thickness due to inadequate parameterization or coarse grid resolution, we examined the probability density functions of large‐scale vertical velocity associated with each cirrus regime and the relationship between cirrus thickness and vertical velocity. It is found that the differences of the vertical velocity probability density functions among the cirrus regimes are as distinct as their macroproperties and a significant correlation exists between cirrus thickness and the vertical velocity, although the large‐scale vertical motion is nearly as likely to be descending as ascending when cirrus clouds are observed. This may imply that large‐scale vertical velocity can be used to constrain the variations of cirrus thickness simulated by global climate models.
Designing Efﬁcient Observing Networks for ENSO Prediction

The Tropical Atmosphere Ocean (TAO) array of moored buoys in the tropical Paciﬁc Ocean is a major source of data for understanding and predicting El Nin˜o–Southern Oscillation (ENSO). Despite the importance of the TAO array, limited work has been performed where observations are most important for predicting ENSO effectively. To address this issue, this study performs a series of observing system simulation experiments (OSSEs) with a linearized intermediate coupled ENSO model, stochastically forced. ENSO forecasts aresimulated for a variety of observing network conﬁgurations, and forecast skill averaged over many simulated ENSO events is compared. Theﬁrstpart of this study examined the relative importance of sea surface temperature (SST) and subsurface ocean observations, requirements for spacing and meridional extent of observations, and important regions for observations in this system. Using these results as a starting point, this paper develops efﬁcient observing networks for forecasting ENSO in this system, where efﬁcient is deﬁned as providing reasonably skillful forecasts for relatively few observations. First, efﬁcient networks that provide SST and thermocline depth data at the same locations are developed and discussed. Second, efﬁcient networks of only thermocline depth observations are addressed, assuming that many SST observations are available from another source (e.g., satellites). The dependence of the OSSE results on the duration of the simulated data record is also explored. The results suggest that several decades of data may be sufﬁcient for evaluating the effects of observing networks on ENSO forecast skill, despite being insufﬁcient for evaluating the long-term potential predictability of ENSO.
Improving El Niño prediction using a space‐time integration of Indo‐Pacific winds and equatorial Pacific upper ocean heat content

Equatorial westerly (easterly) wind anomalies, phase‐locked to the seasonal cycle, typically ‘propagate’ from the eastern equatorial Indian Ocean and into the western Pacific immediately before an El Niño (La Niña). A space‐time integration of this Indo‐Pacific signal yields an index τ that, for 11 out of 12 months of the calendar year, leads the El Niño index NINO3.4 with a correlation of 0.5 or greater for at least some lead in the range 10–15 months. Cross‐validated hindcasts suggest that a linear combination of this atmospheric index and the ocean indices NINO3.4 and (t), the anomalous equatorial Pacific upper ocean heat content, is an excellent predictor of El Niño. It can predict across the nearest spring persistence barrier, but not the one after that. The present El Niño should die over the spring, leaving near neutral conditions for the rest of 2003.
Estimation of Throughfall and Stemflow Bacterial Flux in a Subtropical Oak‐Cedar Forest

Transport pathways of microbes between ecosystem spheres (atmosphere, phyllosphere, and pedosphere) represent major fluxes in nutrient cycles and have the potential to affect microbially mediated biogeochemical processes. Novel data on bacterial fluxes from the phyllosphere to the pedosphere during rainfall via throughfall (rain dripping from/through the canopy) and stemflow (rain funneled down tree stems) are reported. Bacterial concentrations were quantified using flow cytometry and validated with quantitative polymerase chain reaction assays in rainfall samples from an oak‐cedar forest in coastal Georgia (southeastern U.S.). Bacteria concentrations (cells mL−1) and storm‐normalized fluxes (cells m−2 h−1, cells m−2 mm−1) were greater for cedar versus oak. Total bacterial flux was 1.5 × 1016 cells ha−1 yr−1. These previously unexamined bacterial fluxes are interpreted in the context of major elemental pools and fluxes in forests and could represent inoculum‐level sources of bacteria (if alive), and organic matter and inorganic solute inputs (if lysed) to soils.
A consumer perspective of AI certi ﬁ cation – the current certi ﬁ cation landscape, consumer approval and directions for future research

Purpose – In spite of the merits of arti ﬁ cial intelligence (AI) in marketing and social media, harm to consumers has prompted calls for AI auditing/certi ﬁ cation. Understanding consumers ’ approval of AI certi ﬁ cation entities is vital for its effectiveness and companies ’ choice of certi ﬁ cation. This study aims to generate important insights into the consumer perspective of AI certi ﬁ cations and stimulate future research. Design/methodology/approach – A literature and status-quo-driven search of the AI certi ﬁ cation landscape identi ﬁ es entities and related concepts. This study empirically explores consumer approval of the most discussed entities in four AI decision domains using an online experiment and outline a research agenda forAI certi ﬁ cationin marketing/socialmedia. Findings – Trust in AI certi ﬁ cation is complex. The empirical ﬁ ndings show that consumers seem to approve moreof non-pro ﬁ t entitiesthan for-pro ﬁ t entities,withthe government approving themost. Research limitations/implications – The introduction of AI certi ﬁ cation to marketing/social media contributes to work on consumer trust and AI acceptance and structures AI certi ﬁ cation research from outside marketing to facilitatefutureresearch onAI certi ﬁ cation for marketing/social media scholars. Practical implications – For businesses,the authors provide a ﬁ rst insight into consumerpreferences for AI-certifying entities, guiding the choice of which entity to use. For policymakers, this work guides their ongoing discussionon “ who should certify AI ” from a consumerperspective. ©
Corrosion Performance of Electrodeposited Ni Nanocomposite Coatings Reinforced with Metal Oxide Particles Elektrokimyasal Olarak Çöktürülmüş Metal Oksit Takviyelendirilmiş Ni Nanokompozit Kaplamaların Korozyon Performansı

The properties of traditional nickel coatings were enhanced by preparing Ni nanocomposite coatings reinforced with metal oxide nanoparticles by using electrodeposition technique. The impact of applied current density was investigated by changing SiO 2 nanoparticle concentration. Phase structure study and morphological investigation of the samples were performed by X-Ray Diffractometer and Scanning Electron Microscopy, respectively. Vickers indentation method was utilized to determine the mechanical properties of obtained nanocomposite coatings. Potentiodynamic polarization technique was performed to evaluate electrochemical behavior of nanocomposite coatings. As a result, introducing SiO 2 nanoparticles to Ni matrix improves both mechanical and electrochemical properties of produced nanocomposite coatings.
The genetic architecture of human brainstem structures and their The genetic architecture of human brainstem structures and their involvement in common brain disorders involvement in common brain disorders

Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the ﬁ rst linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are signi ﬁ cant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson ’ s disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.
Bias Correction of Climate Modeled Temperature and Precipitation Using Artificial Neural Networks

AbstractClimate studies and effective environmental management require unbiased climate datasets. This study develops a new bias correction approach using a three-layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, and net longwave and shortwave radiation are used as inputs to the network for bias correction of 6-hourly temperature. Inputs to the network for bias correction of monthly precipitation are precipitation at lag 0, 1, 2, and 3 months, and also the standard deviation of precipitation from 3 × 3 neighbors around the pixel of interest. The climate model data are provided by the Community Climate System Model, version 3 (CCSM3). Results show that the trained artificial neural network (ANN) can improve the estimation error and correlation of the variables for both calibration and validation periods even when there is a low temporal consistency between the time series of th.
Identifiable attribution maps using regularized contrastive learning

Gradient-based attribution methods aim to explain decisions of deep learning models, but so far lack identifiability guarantees. Here, we propose a method to generate attribution maps with identifiability guarantees by developing a regularized contrastive learning algorithm trained on time series data with continuous target labels. We show theoretically that our formulation of hybrid contrastive learning has favorable properties for identifying the Jacobian matrix of the data generating process, and is unable to overfit to random training distributions. Empirically, we demonstrate robust approximation of the ground-truth attribution map on synthetic data, and significant improvements across previous attribution methods based on feature ablation, Shapley values, and other gradient-based methods. Our work constitutes a first example of identifiable inference of attribution maps, and opens avenues for improving future attribution tools and better understanding neural dynamics and neural networks.
Does Inflation and The Number of Taxpayers Affect Income Tax?

Income tax is the largest state revenue, and the COVID-19 pandemic impacted decreasing income tax in 2020. This study examines factors from outside the policy of the Directorate General of Taxes, such as inflation and factors from inside the Directorate General of Taxes, namely the number of taxpayers affecting income tax. This study uses secondary data from monthly realization data and income tax art. 25 targets during 2017-2020 at the Yogyakarta Tax Office (KPP Pratama Yogyakarta). The sample retrieval technique in this study uses a saturated sample method. Sample this study of 48 data. The data analysis technique used is multiple linear regression analysis. The results show that inflation does not affect income tax, while the number of taxpayers has a negative effect on income tax. From the results of this study, the greater the number of registered taxpayers, the lower the income tax revenue. Indonesia uses an internal self-assessment system to calculate taxes, and the taxes paid depend on a financial report sent to the tax office. So, corporate taxpayer compliance greatly determines the amount of income tax. This result implies that the government can optimise corporate taxpayer compliance through the Ministry of Finance or the general directorate of taxes. This study only uses income tax art. 25 deposited by corporate taxpayers.
Comparative Study on Dimensional Stability and Biological Durability of Poplar Wood Modified by Chemical and Heat Treatment Usporedna studija dimenzijske stabilnosti i biološke trajnosti kemijski i toplinski modificirane topolovine

• The fast-growing species of Populus deltoides is of significant commercial importance to the Indian wood merchants. Despite its abundant availability and better treatability, the use of P. deltoides is limited due to its perishable nature. Heat treated and chemically modified Populus deltoides L. was thus explored for dimensional stability and biological durability to commercialise its use. Chemical modification was performed using a combination of citric acid and sodium hypophosphite. Heat treatment was done in a laboratory oven in air at 140 °C for 8h. Both chemically modified and heat-treated samples exhibited improvements in dimensional stability and biological durability in comparison to the untreated control sets. Chemical modification resulted in better dimensional stability. Anti-swelling efficiency of Populus deltoides was 2-2.5 times more for the chemically modified set compared to heat treatment in both radial and tangential directions. Chemically modified set became significantly more resistant to biodeterioration showing 5 times less mass loss by termites, whereas controls and heat-treated sets returned loss percentages which were comparable. Soil block bioassay with Trametes versicolor (TV) exhibited mass loss of 9.46 % for chemically modified samples, 40.26 % for heat treated samples and 50.02 % for the untreated controls, respectively. Rhodonia placenta (OP) followed a similar trend with mass loss of chemically modified set being 5.72 % and heat-treated set being 37 %, respectively, with the controls showing 43.85 % mass loss. Mass loss exhibited by the heat-treated samples were less than the controls, but the values suggested that heat treatment at 140 °C for 8 h failed to impart any substantial resistance against rotting fungi and termites.
2016 Recent progress in the description of positron scattering from atoms using the Convergent Close-Coupling Theory

Much progress in the theory of positron scattering on atoms has been made in the ten years since the review of Surko et al. [1]. We review this progress for few-electron targets with a particular emphasis on the two-centre convergent close-coupling and other theories which explicitly treat positronium (Ps) formation. While substantial progress has been made for Ps formation in positron scattering on few-electron targets, considerable theoretical development is still required for multielectron atomic and molecular targets.