bluuebunny/crossref_metadata_2025 huggingface dataset:
DASKYLEİON ANTİK KENTİ VE MANYAS GÖLÜ ÇEVRELERİNDE DELGİ SONDAJ YÖNTEMLİ PALEOCOĞRAFYA - JEOARKEOLOJİ ARAŞTIRMALARININ İLK SONUÇLARI (MANYAS-BALIKESİR)
Authors: İlhan Rifat , Doğan Mehmet , Karadaş Aylin , Vardar Serdar , Öner Ertuğ
Published: March 2023

Daskyleion Höyüğü ve çevresinde delgi sondaj yöntemli ilk paleocoğrafya-jeoarkeoloji araştırmalarımız 2022 yaz döneminde 18 ve 24 Temmuz 2022 tarihleri arasında gerçekleştirilmiştir. Daskyleion antik kentindeki ilk yerleşimin, bazı antik yazarlara göre MÖ 12. Yüzyıla kadar gittiği ifade edilmekle birlikte, antik kent tarihini daha eskiye yani Kalkolitik Döneme götüren buluntular yüzey araştırmalarında gözlenmiştir. Tarihi bu kadar eskiye uzanan Daskyleion’un coğrafi çevresi geçen süre içinde kuşkusuz önemli değişiklikler göstermiştir. Bu değişmelerin belirlenmesi amacıyla başladığımız çalışmalarımızda çevrede 12 adet delgi sondaj yapılmıştır. Sondaj verileri ve genel bilgilerimiz, gözlemlerimizle birlikte değerlendirilerek ilk ön sonuçlarımız bu yazının içeriğini oluşturmuştur. Manyas Gölü kıyısında ve gölün ayağını oluşturan Karaçay’ın yakınında bulunması nedeniyle, Daskyleion çevresinde meydana gelen coğrafi değişmeler üzerinde, bu su kaynaklarının etkisi büyüktür. Özellikle Manyas Gölü seviye değişmeleri ile alüvyal birikime bağlı kıyı çizgisindeki değişmelerin, antik kent ve sakinleri için önemi fazladır. Karaçay’ın akım özellikleri ve taşkınları da aynı şekilde antik kent açısından önem taşımaktadır. Tarihsel süreçte meydana gelen bu etkilerin ve değişmelerin belirlenmesi, büyük ölçüde Daskyleion çevresindeki genç birikimlerin ayrıntılı incelenmesi ile ortaya konulabilecektir. İşte bu amaçla genç dolgular içinde yaptığımız alüvyon delgi sondajları, bize önemli bilgiler sağlamıştır. Bu çalışmalarımız sonucunda Daskyleion kentinin Neojen (Pliyosen) anakayadan oluşan bir tepe üzerinde kurulduğu, kabaca yayılış alanı ve ilk yerleşildiği dönemde göl kıyılarının uzanışı ortaya konmuş, seviye ve kıyı çizgisi değişmeleri yorumlanmıştır.
Unmasking Deepfakes: Using Resnext and LSTM to Detect Deepfake Videos
Authors: Shilpa B  , Anush Kamath  , Anush Kamath  , Hemanth Bhat  , Sathwik A M
Published: March 2023

This paper proposes an approach for detecting deepfake videos using Resnext CNN and LSTM. The proposed approach involves training a Resnext CNN on a dataset of real and deepfake videos to classify them accurately. The Resnext CNN takes video frames as input and outputs a probability score for each frame, which is then fed into an LSTM to model the temporal dynamics of the video. The approach was evaluated on a dataset of real and deepfake videos and achieved promising results. The proposed approach can be used to detect deepfake videos, which can help in preventing the spread of misinformation and safeguarding our society.
A Perspective Study on Speech Recognition
Authors: Sana Fatema N. Ali  , Prof. S. T. Khandare
Published: March 2023

Emotions play an extremely important role in human mental life. It is a medium of expression of one’s perspective or one’s mental state to others. Speech Emotion Recognition (SER) can be defined as extraction of the emotional state of the speaker from his or her speech signal. There are few universal emotions including Neutral, Anger, Happiness, and Sadness in which any intelligent system with finite computational resources can be trained to identify or synthesize as required. In this work spectral and prosodic features are used for speech emotion recognition because both of these features contain the emotional information. Mel-Frequency Cepstral Coefficients (MFCC) is one of the spectral features. Fundamental frequency, loudness, pitch and speech intensity and glottal parameters are the prosodic features which are used to model different emotions. The potential features are extracted from each utterance for the computational mapping between emotions and speech patterns. Pitch can be detected from the selected features, using which gender can be classified. The audio signal is filtered using a method known as feature extraction technique. In this article, the feature extraction technique for speech recognition and voice classification is analyzed and also centered to comparative analysis of different types of Mel frequency cepstral coefficients (MFCC) feature extraction method. The MFCC technique is used for deduction of noise in voice signals and also used for voice classification and speaker identification. The statistical results of the different MFCC techniques are discussed and finally concluded that the delta-delta MFCC feature extraction technique is better than the other feature extraction techniques..
A Modeling Approach to Understanding the Interrelated Nature of Cathodic Protection Current and AC Stray Current on Pipelines
Authors: Moran A.J. , Lillard R.S.
Published: March 2023

The change in AC current on a pipeline as a function of cathodic protection (CP) current is well known in the industry as is the change in CP current as a function of the interfering AC current. To better understand the underlying mechanisms responsible for these observations, the interrelated nature of AC and CP was reproduced here for pipeline steel in soil-simulating environments and the results were analyzed within the context of kinetics and transport-based models. The kinetics model combines the Butler-Volmer reaction kinetics for steel oxidation, oxygen reduction, and hydrogen reduction with the time evolution of potential at an interface subject to alternating potentials. The critical observation from these calculations was that changes to the measured CP on a pipeline with AC interference were not due to changes in the underlying electrochemical kinetics, but rather, due to asymmetric polarization of the steel along existing Tafel slopes resulting in a change in the time-averaged DC signal. To explore the effect of CP current on the magnitude of the AC interference, a transport-based model of grounding resistance at pipeline coating holidays was developed. For holiday sizes less than 20 cm2, calculations revealed that at a critical CP current density of 1 A/m2, local soil pH increases rapidly and the normalized grounding resistance decreases. Correspondingly, the corrosion rate increases dramatically.
Roberto J. Vernengo (1926-2021) e Eugenio Bulygin (1931-2021): um testemunho biográfico
Authors: Serbena César Antônio
Published: March 2023

A comunidade internacional da Filosofia do Direito perdeu dois de seus maiores expoentes no ano de 2021: Roberto José Vernengo e Eugenio Bulygin. O artigo trata da biografia intelectual e de algumas das principais contribuições originais dos dois professores da Faculdade de Direito da Universidade de Buenos Aires, os quais exerceram influência e impacto em diversos países da América do Sul e da Europa, e principalmente, de maneira pessoal, sobre as ideias e a formação intelectual do autor do presente artigo.
Eicosanoids in human heart failure: pilot study of plasma epoxyeicosatrienoic and dihydroxyeicosatrienoic acid levels
Authors: Kala Petr , Hnat Tomas , Padrova Karolina , Kotaška Karel , Veselka Josef
Published: March 2023

Introduction

The role of eicosanoids, metabolites of arachidonic acid with cardio-renal activity, remains unclear in human heart failure (HF).

Material and methods

We enrolled 50 patients with HF to measure plasma 14,15-EET and 14,15-DHET levels using commercial ELISA kits and compared them with 25 age- and sex-matched controls.

Results

Both of the measured eicosanoids were significantly higher in the HF group: 14,15-EET (91.3 ±25.7 ng/ml vs. 64.95 ±35.4 ng/ml) and 14,15-DHET (10.58 ±2.06 ng/ml vs. 9.07 ±1.60 ng/ml), p for both < 0.001.

Conclusions

We found that peripheral plasma eicosanoid (14,15-EET, 14,15-DHET) levels are raised in patients with HF compared to age- and sex-matched controls.
Investigating the effects of financial management practices on financial performance of a State-Owned Enterprise in Johannesburg
Authors:   , Ntuli Sizwe Perfect Ayanda
Published: March 2023

The effectiveness of financial management practices in the state-owned enterprises (SOEs) is becoming extremely indispensable and frugal in South Africa. However, the dynamics of the effectiveness of financial management practices on the SOEs is not much reconnoitred or not in existence in the collected works of academics with previous studies largely paid attention to the private sector whereas there is also a monumental necessity for the SOEs to be financially viable. Therefore, this study insightfully examined the employees’ perceptions on the effects of financial management practices (independent variable) on improving financial performance (dependent variable) in a selected South African SOE. Quantitative research design was employed in the study, and it was cross-sectional in nature. The survey questionnaire was utilised as a primary data collection tool. The study adopted a convenience sampling method which resulted in a sample size of 69 respondents at the response rate of 74%. The statistical results were determined using Statistical Package for Social Sciences (SPSS) (version 27®). The study revealed a positive relationship when measuring the independent variables against the financial performance. The study recommended a resilient application of cash management practices in the form of determining target cash balances on a regular basis as a working capital management practice to realise enhanced financial performance. The study further recommends that as the firm considers expanding the investments to real estates, that practice should be aimed at firm value maximisation. It is further recommended that future similar studies consider broadening the research scope by including all the operational divisions of an SOE from different regions throughout South Africa.
Eptinezumab (Vyepti)
Authors: CADTH
Published: March 2023

CADTH reimbursement reviews are comprehensive assessments of the clinical effectiveness and cost-effectiveness, as well as patient and clinician perspectives, of a drug or drug class.

The assessments inform non-binding recommendations that help guide the reimbursement decisions of Canada's federal, provincial, and territorial governments, with the exception of Quebec.

This review assesses eptinezumab (Vyepti), 100 mg/mL solution for IV infusion.

Indication: Indicated for the prevention of migraine in adults who have had at least 4 migraine days per month.
Global impact of the COVID-19 lockdown on surface concentration and health risk of atmospheric benzene
Authors: Ling Chaohao , Cui Lulu , Li Rui
Published: March 2023

Abstract. To curb the spread of the COVID-19 pandemic, many countries around the world
imposed an unprecedented lockdown, producing reductions in pollutant
emissions. Unfortunately, the lockdown-driven global ambient benzene changes
still remain unknown. An ensemble machine-learning model coupled with chemical transport models (CTMs) was applied to estimate global
high-resolution ambient benzene levels. Afterwards, the extreme gradient boosting (XGBoost) algorithm
was employed to decouple the contributions of meteorology and emission
reduction to ambient benzene. The change ratio (Pdew) of the deweathered
benzene concentration from the pre-lockdown to lockdown period was in the order
of India (−23.6 %) > Europe (−21.9 %) > the United
States (−16.2 %) > China (−15.6 %). The detrended change (P∗)
of the deweathered benzene level (change ratio in 2020 − change ratio in 2019)
followed the order of India (P*=-16.2 %) > Europe (P∗=-13.9 %) > China (P∗=-13.3 %) > the United States
(P∗=-6.00 %). Emission reductions derived from industrial activities
and transportation were major drivers for the benzene decrease during the
lockdown period. The highest decreasing ratio of ambient benzene in India
might be associated with local serious benzene pollution during the
business-as-usual period and restricted transportation after lockdown.
Substantial decreases in atmospheric benzene levels had significant health
benefits. The global average lifetime carcinogenic risk (LCR) and hazard
index (HI) decreased from 4.89×10-7 and 5.90×10-3 to 4.51×10-7 and 5.40×10-3,
respectively. China and India showed higher health benefits due to
benzene pollution mitigation compared with other countries, highlighting the
importance of benzene emission reduction.
Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties
Authors: Schmidt Luisa , Forkel Matthias , Zotta Ruxandra-Maria , Scherrer Samuel , Dorigo Wouter A. , Kuhn-Régnier Alexander , van der Schalie Robin , Yebra Marta
Published: March 2023

Abstract. Vegetation attenuates the microwave emission from the
land surface. The strength of this attenuation is quantified in models in
terms of the parameter vegetation optical depth (VOD) and is influenced by
the vegetation mass, structure, water content, and observation wavelength.
Earth observation satellite sensors operating in the microwave frequencies
are used for global VOD retrievals, enabling the monitoring of vegetation at
large scales. VOD has been used to determine above-ground biomass, monitor
phenology, or estimate vegetation water status. VOD can be also used for
constraining land surface models or modelling wildfires at large scales.
Several VOD products exist, differing by frequency/wavelength, sensor, and
retrieval algorithm. Numerous studies present correlations or empirical
functions between different VOD datasets and vegetation variables such as
the normalized difference vegetation index, leaf area index, gross primary
production, biomass, vegetation height, or vegetation water content. However,
an assessment of the joint impact of land cover, vegetation biomass, leaf
area, and moisture status on the VOD signal is challenging and has not yet
been done. This study aims to interpret the VOD signal as a multi-variate function of
several descriptive vegetation variables. The results will help to select
VOD at the most suitable wavelength for specific applications and can guide
the development of appropriate observation operators to integrate VOD with
large-scale land surface models. Here we use VOD from the Land Parameter
Retrieval Model (LPRM) in the Ku, X, and C bands from the harmonized Vegetation Optical Depth Climate Archive (VODCA)
dataset and L-band VOD derived from Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) sensors. The leaf area index,
live-fuel moisture content, above-ground biomass, and land cover are able to
explain up to 93 % and 95 % of the variance (Nash–Sutcliffe model
efficiency coefficient) in 8-daily and monthly VOD within a multi-variable
random forest regression. Thereby, the regression reproduces spatial
patterns of L-band VOD and spatial and temporal patterns of Ku-, X-, and
C-band VOD. Analyses of accumulated local effects demonstrate that Ku-, X-,
and C-band VOD are mostly sensitive to the leaf area index, and L-band VOD is most sensitive to
above-ground biomass. However, for all VODs the global relationships with
vegetation properties are non-monotonic and complex and differ with land
cover type. This indicates that the use of simple global regressions to
estimate single vegetation properties (e.g. above-ground biomass) from VOD
is over-simplistic.
Climate Adaptation and Disaster Assessment using Deep Learning and Earth Observation
Authors: Chen Thomas Y.
Published: March 2023

Due to anthropogenic climate change, the frequency and intensity of natural disasters is only increasing. As supercomputing capabilities increase in an era of computational geosciences, artificial intelligence has emerged as a key tool in assessing the progression and impact of these disasters. Recovery from extreme weather events is aided by machine learning-based systems trained on multitemporal satellite imagery data. We work on shifting paradigms by seeking to understand the inner decision-making process (interpretability) of convolutional neural networks (CNNs) for damage assessment in buildings after natural disasters, as these deep learning algorithms are typically black boxes. We compare the efficacy of models trained on different input modalities, including combinations of the pre-disaster image, the post-disaster image, the disaster type, and the ground truth of neighboring buildings. Furthermore, we experiment with different loss functions, and find that ordinal cross entropy loss is the most effective criterion for optimization. Finally, we visualize inputs by creating gradient-weighted class activation mapping (Grad-CAM) on the data, with the end goal of deployment. Earth observation data harnessed by deep learning and computer vision is not only useful for disaster assessment, but also in understanding the other impacts of our changing climate from marine ecology to agriculture in the Global South.
Improving Probabilistic Gas Hazard Assessment through HPC: Unveiling VIGIL-2.0, an automatic Python workflow for probabilistic gas dispersion modelling
Authors: Massaro Silvia , Dioguardi Fabio , Guerrero Alejandra , Costa Antonio , Folch Arnau , Sulpizio Roberto , Macedonio Giovanni , Mingari Leonardo
Published: March 2023

The atmospheric dispersion of gases (of natural or industrial origins) can be very hazardous to life and the environment if the concentration of some gas species overcome specie-specific thresholds. In this context, the natural variability associated to the natural phenomena has to be explored to provide robust probabilistic gas dispersion hazard assessments. 
VIGIL-1.3 (automatic probabilistic VolcanIc Gas dIspersion modeLling) is a Python simulation tool born to automatize the complex and time-consuming simulation workflow required to process a large number of gas dispersion numerical simulations. It is interfaced with two models: a dilute (DISGAS) and a dense gas (TWODEE-2) dispersion model. The former is used when the density of the gas plume at the source is lower than the atmospheric density (e.g. fumaroles), the latter when the gas density is higher than the atmosphere and the gas accumulates on the ground and may flow due to the density contrast with the atmosphere to form a gravity current (e.g. cold CO2 flows).
In the enhancement of the code towards a higher-scale computing, here we present the ongoing improvements aimed to extend some code functionalities such as memory management, modularity revision, and full-ensemble uncertainty on gas dispersal scenarios (e.g. sampling techniques for gas fluxes and source locations).
Optimizations are also provided in terms of tracking errors, redesignation of the input file, validation of data provided by the users, and addition of the Latin hypercube sampling (LHS) for the post-processing of model outputs.
All these new features will be issued in the future release of the code (VIGIL-2.0) in order to facilitate the users which could run VIGIL on laptops or large supercomputer, and to widen the spectrum of model applications from routinely operational forecast of volcanic gas to long-term hazard and/or risk assessments purposes.
Massively parallel inverse modelling on GPUs using the adjoint method
Authors: Utkin Ivan , Räss Ludovic
Published: March 2023

Continuum-based numerical modelling is a useful tool for interpreting field observations and geological or geotechical data. In order to match the available data and the results of numerical simulations, it is necessary to estimate the sensitivity of a particular model to changes in its parameters. Recent advances in hardware design, such as the development of massively parallel graphical processing units (GPUs), make it possible to run simulations at unprecedented resolution close to that of the original data. Thus, automated methods of calculating the sensitivity in a high-dimensional space of parameters are in demand.The adjoint method of computing sensitivities, i.e., gradients of the forward model solution with respect to the parameters of interest, gains more attention in the scientific and engineering communities. This method allows for computing the sensitivities for every point of computational domain using the results of only one forward solve, in contrast to the direct method that would require runninng the forward simulation for each point of the domain. This property of adjoint method significantly reduces the amount of computational resources required for sensitivity analysis and inverse modelling.In this work, we demonstrate the applications of the adjoint method to inverse modelling in geosciences. We developed massively parallel 3D forward and inverse solvers with full GPU support using Julia language. We present the results of performance and scalability tests on Piz Daint supercomputer at CSCS.
Towards exascale-prepared codes for tsunami simulation
Authors: de la Asunción Marc , Macías Jorge , Castro Manuel Jesús
Published: March 2023

The recently finished ChEESE European project aimed at establishing a center of excellence in the domain of solid earth by developing ten flagship European codes prepared for the upcoming exascale supercomputers. The EDANYA group, at the University of Malaga, Spain, has developed two of these flagship codes: Tsunami-HySEA and Landslide-HySEA, for the simulation of tsunamis generated by earthquakes and landslides, respectively. These two codes, although being already implemented for multi-gpu architectures at the beginning of the ChEESE project, underwent substantial changes during the lifetime of the project with the objective of improving several crucial aspects such as their efficiency, scaling or input/output requirements. Specifically, we added features such as an improved load balancing, direct GPU to GPU data transfers or compressed output files, among others. Additionally, we developed a version of Tsunami-HySEA, named Monte-Carlo, particularly suited for areas such as probabilistic tsunami forecast or machine learning, capable of performing multiple simulations in parallel. In this presentation we describe all these developments carried out during the ChEESE project, along with the two audits that the two codes went through, performed by the Performance Optimisation and Productivity Centre of Excellence in HPC (POP CoE). Finally, we show some comparative results using realistic scenarios achieved at the beginning and at the end of the project.
Optimal source selection for local probabilistic tsunami hazard analysis
Authors: Abbate Alice , Volpe Manuela , Romano Fabrizio , Tonini Roberto , Davies Gareth , Basili Roberto , Lorito Stefano
Published: March 2023

Local hazard models for evacuation planning should accurately describe the probability of exceeding a certain intensity (e.g. flow depth, current velocity, etc.) over a period of years. Computational-based probabilistic forecasting for earthquake-generated tsunamis deals with tens of thousands to millions of scenarios to be simulated over very large domains and with sufficient spatial resolution of the bathymetry model. The associated high computational cost can be tackled by means of  workflows that take advantage of HPC facilities and numerical models specifically designed for multi-GPU architectures.  For the sake of feasibility, Seismic Probabilistic Tsunami Hazard Assessment (S-PTHA) at local scale exploits some approximations in both source and tsunami modeling, but  uncertainty  quantification is still lacking in the estimates. Here, we propose a possible approach to reduce the computational cost of local-scale S-PTHA, while providing uncertainty quantification. The algorithm performs an  efficient selection of  scenarios  based on the tsunami impact on a site. The workflow is thought to take advantage of parallel execution on HPC clusters. Hence, as a first step, the whole ensemble of scenarios is split into a finite number of regions defined by the tectonic regionalization;  then the procedure selects the scenarios mainly contributing to the hazard at an offshore point (in front of the target site) and for specific intensity  levels. Finally, for each intensity level, the totality of synthetic tsunamigenic earthquakes is optimally sampled with replacement in a Monte Carlo Importance Sampling scheme. The tsunamis potentially triggered by the selected scenarios are explicitly simulated with the GPU-based Tsunami-HySEA nonlinear shallow water code on high spatial resolution grids (up to 10 m) and subsequently the Monte Carlo errors are finally propagated to the onshore estimates. This procedure allows for lessening the computational cost of local S-PTHA by reducing the number of simulations to be conducted while quantifying the epistemic uncertainties associated with the inundation modeling without appreciable losses of information content.
Exhaustive High-Performance Computing utilization in the estimation of the economic impact of tsunamis on Spanish coastlines
Authors: Gonzalez del Pino Alejandro , Fernández Marta , Llorente Miguel , Macías Jorge , García-Mayordomo Julián , Paredes Carlos
Published: March 2023

Tsunamis are low-probability phenomena with high-risk potential. Lack of field data emphasizes the need of using simulation software to model the potential devastating effects of a tsunami and use this information to develop safety, sustainable actions and social resilience for the future. These measures may include, among many others, spatial planning; designing of evacuation routes; or the allocation of economic resources through insurance or other instruments to mitigate tsunami impacts. Our work introduces a Monte Carlo-like method for simulating the potential impact of tsunamis on the Spanish coastlines, specifically in the provinces of Huelva and Cádiz for the Atlantic region, and Balearic Islands, Ceuta, Melilla and eastern Iberian coast for the Mediterranean region. The method introduces a pseudo-probabilistic seismic-triggered tsunami simulation approach, by considering a particular selection of active faults with associated probabilistic distributions for some of the source parameters, and a Sobol’s sequences-based sampling strategy to generate a synthetic seismic catalogue. All roughly 4000 crafted seismic events are simulated along the areas of interest in high-resolution grids (five meters pixel resolution) using a two-way nested mesh approach, retrieving maximum water height, maximum mass flow and maximum modulus of the velocity at each grid cell. These numerical simulations are computed in a GPU environment, harnessing resources allocated in several high-performance computing (HPC) centres. HPC infrastructures play a crucial role in the computing aspect of the project, as the calculation power required to complete full-fledge high-resolution tsunami simulations in a reasonably time is expensive. The numerical database of retrieved variables generated throughout this study offers an excellent foundation for evaluating various tsunami-related hazards and risks.The final resulting product focuses on generating frequency distributions for the economic impacts for the Spanish insurance sector (Consorcio de Compensación de Seguros, CCS). The CCS is a public-private entity insuring most natural catastrophic events in Spain. A consistent spatially-distributed economic database regarding insurance building-related values has been constructed and aggregated in conjunction with the numerical tsunami simulations. The proposed procedure allows to associate an economic impact indicator to each source. Further statistical analysis of the economic impact estimators yields to varied conclusions such as an improved definition of worst-case scenario (effect-based rather than worst-triggered), most and least likely economic impact, highest hazardous fault sources overall and locally and many others.
Characterization of Earthquake Near-fault Ground Motion Parameters Using an Artificial Neural Network on Synthetic Big Data
Authors: Kowsari Milad , Titos Manuel , Bayat Farnaz , Benitez Carmen , Monterrubio-Velasco Marisol , Rojas Otilio , de la Puente Josep , Halldorsson Benedikt
Published: March 2023

The South Iceland Seismic Zone (SISZ) and Reykjanes Peninsula Oblique Rift (RPOR) in Southwest Iceland together form one of the two major transform zones in the country that have the greatest capacity for the occurrence of destructive earthquakes. Therefore, in these regions, the seismic hazard is highest and performing a probabilistic seismic hazard assessment (PSHA) is vital as the foundation of earthquake resistant building design and seismic risk mitigation. It is well known both from observations as well as physics-based (PB) modeling of earthquake rupture and near-fault ground motion simulations, that the most damaging part of near-fault seismic motion is the velocity pulse, the large-amplitude and long-period pulse-like ground motions found along the fault and away from the ends of strike-slip faults. Such motions cause intense earthquake action primarily on large buildings, such as hydroelectric power plants, dams, powerlines, bridges and pipelines. However, the data is still too limited to enable the reliable calibration of a physically realistic, yet parsimonious, near-fault model that incorporate such effects into empirical ground motions models (GMMs), thereby allowing their incorporation into a formal PSHA. However, in the recent European H2020 ChEESE project, we established a new 3D finite-fault system model for the SISZ-RPOR system that now has facilitated the simulation of finite-fault earthquake catalogues. Moreover, the catalogues have been implemented into the CyberShake platform, the PB earthquake simulator that was adapted to the characteristics of the SISZ-RPOR earthquakes in the ChEESE project. The seismic ground motions of each earthquake in the catalogue have thus been simulated on a dense grid of 594 near-fault stations in Southwest Iceland. The simulation has been carried out on high-performance computing systems of the Barcelona Supercomputing Centre in Spain. Moreover, the hypocentral locations and slip distributions on each synthetic fault have been varied, resulting in approximately 1 million earthquake-station-specific pairs of synthetic low-frequency and high-amplitude near-fault ground motion time histories. In this study, we analyse this dataset using an artificial neural network to reveal its characteristics in terms of amplitudes and the characteristics of near-fault velocity pulses, capturing all key features of such effects. The results will facilitate the incorporation of the near-fault effects into new near-fault and far-field GMMs, that are a key element of conventional PSHA. This will both enable the near-fault PB-PSHA along with the comparison of PSHA from the synthetic dataset vs. the GMMs. This will usher in a new era of PB-PSHA in Iceland.
Modeling Depth averaged velocity and Boundary Shear Stress distribution with complex flows
Authors: Kedir Ebissa , Ojha Chandra , Prasad Hari
Published: March 2023

In the present study, the depth-averaged velocity and boundary shear stress in non-prismatic compound channels with three different converging floodplain angles ranging from 1.43ᶱ to 7.59ᶱ have been studied. The analytical solutions were derived by considering acting forces on the channel beds and walls. In the present study, five key parameters, i. e non-dimensional coefficient, secondary flow term, secondary flow coefficient, friction factor, and dimensionless eddy viscosity, were considered and discussed. A new expression for non-dimensional coefficient and integration constants were derived based on the novel boundary conditions. The model was applied to different data sets of the present experiments and experiments from other sources, respectively, to examine and analyse the influence of floodplain converging angles on depth-averaged velocity and boundary shear stress distributions. The results show that the non-dimensional parameter plays an important in portraying the variation of depth-averaged velocity and boundary shear stress distributions with different floodplain converging angles. Thus, the variation of the non-dimensional coefficient needs attention since it affects the secondary flow term and secondary flow coefficient in both the main channel and floodplains. The analysis shows that the depth-averaged velocities are sensitive to a shear stress-dependent model parameter non-dimensional coefficient, and the analytical solutions are well agreed with experimental data when five parameters are included. It is inferred that the developed model may facilitate the interest of others in complex flow modeling.Keywords: Depth-average velocity, Converging floodplain angles, Non-dimensional coefficient, Non-prismatic compound Channels
Hydro-mechanical modeling of injection-induced seismicity at the Deep Heat Mining Project of Basel, Switzerland
Authors: Boyet Auregan , De Simone Silvia , Vilarrasa Víctor
Published: March 2023

Fluid injection in subsurface reservoirs often induces seismicity, which is a limiting factor in the deployment of geo-energies, as it is the case for Enhanced Geothermal Systems (EGS). EGS are commonly deep granitic reservoirs subject to hydraulic stimulation in order to enhance the fracture permeability and consequently the heat production. Injection-induced seismicity occurs also after the stop of injection, and in many cases the largest earthquakes occur after the shut-in. The counterintuitive post-injection large magnitude seismicity is still not well understood and its modelling is necessary to improve the understanding of the processes triggering the seismicity. Pressure-driven processes, as pore pressure increase and poroelastic stress/strain variations, have been identified as triggers of seismicity, together with stress interactions, thermal disparities and geomechanical interactions. We design a coupled hydro-mechanical 2D model of the well-known case of post-injection induced seismicity of Basel EGS (Deep Heat Mining Project at Basel, Switzerland, 2006). We use CODE_BRIGHT, a finite element method software able to perform monolithic coupled thermo-hydro-mechanical analysis in geological media. The faults respond to a Mohr-Coulomb failure criterion with strain weakening and dilatancy, which allow to simulate fault reactivation and its aperture variation. The model is able to reproduce the pressure and stress variations, and the consequent fault reactivations through the simulations. The Basel EGS has been well documented and its characteristics are available. We are able to reproduce the spatio-temporal induced seismicity. Yet, our current numerical method takes long computational time. To speed up simulations, we simplify the model geometry by grouping faults that yield similar static stress transfer, computed with the code Coulomb3, which solves an analytical solution to compute stress changes caused by fault slip. The combination of numerical with analytical solutions is an effective way of obtaining faster computing models. By simultaneously assimilating monitoring data in real-time with an efficient computing model would enable a better understanding of the fluid-injection effects on the stability of the reservoir, and potentially the mitigation of the induced seismicity.
Machine Learning based Estimator for ground Shaking maps
Authors: Monterrubio-Velasco Marisol , Modesto David , Callaghan Scott , de la Puente Josep
Published: March 2023

Large earthquakes are among the most destructive natural phenomena. After a large-magnitude event occurs, a crucial task for hazard assessment is to rapidly and accurately estimate the ground shaking intensities in the affected region. To satisfy real-time constraints, ground shaking is traditionally evaluated with empirical relations called Ground Motion Prediction Equations (GMPE) which can be combined with local amplification factors and early data recordings, when available. Given their nature, GMPEs can be inaccurate to model rarely observed earthquakes, such as large earthquakes. Furthermore, even for very populated databases, GMPEs are characterized by large variances, as earthquakes of similar magnitude and location may have very different outcomes related to complex fault phenomena and wave physics. The ML Estimator for Ground Shaking maps (MLESmap) workflow is proposed as a novel procedure that exploits the predictive power of ML algorithms to estimate ground acceleration values a few seconds after a large earthquake occurs. The inferred model can produce peak (spectral) ground motion maps for quasi-real-time applications. Due to its fast assessment, it can further be used to explore uncertainties quickly and reliably. MLESmap is based upon large databases of physics-based seismic scenarios to feed the algorithms.Our approach (i.e. simulate, train, deploy) can help produce the next generation of ground shake maps, capturing physical information from wave propagation (directivity, topography, site effects) at the velocity of simple empirical GMPE. In this work, we will present the MLESmap workflow, its precision, and a use case.
Preparing Seismic Applications for Exascale Using Scientific Workflows
Authors: Callaghan Scott , Maechling Philip , Vahi Karan , Deelman Ewa , Silva Fabio , Milner Kevin , Olsen Kim , Graves Robert , Jordan Thomas , Ben-Zion Yehuda
Published: March 2023

Scientific workflows are key to supporting the execution of large-scale simulations in many scientific domains, including solid earth geophysics. Although many different workflow tools exist, they share common features, enabling application developers to express their simulations as a series of linked software elements with data dependencies and then execute the workflow efficiently on distributed resources.To illustrate the use and benefits of scientific workflows in seismic applications, this talk will describe CyberShake, a probabilistic seismic hazard analysis (PSHA) platform developed by the Southern California Earthquake Center (SCEC). CyberShake uses 3D physics-based wave propagation simulations with reciprocity to calculate ground motions for events from an earthquake rupture forecast (ERF). Typically, CyberShake considers over 500,000 events per site of interest, and then combines the individual ground motions with probabilities from the ERF to produce site-specific PSHA curves. CyberShake has integrated modules from another SCEC workflow application, the Broadband Platform (BBP), enabling CyberShake simulations to include both low-frequency deterministic and high-frequency stochastic content. This talk will discuss the workflow framework that CyberShake utilizes to support campaigns requiring hundreds of thousands of node-hours over months of wall clock time, and the lessons learned through 15 years of CyberShake simulations.This talk will also reflect on the growth and development of workflow-based simulations and explore the challenges faced by applications in the exascale era, such as managing massive volumes of data, taking full advantage of exascale systems, and the emergence of AI-informed simulations. The talk will discuss ways in which workflow technologies may help mitigate these challenges as we move our science forward.
Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index
Authors: Reyniers Nele , Osborn Timothy J. , Addor Nans , Darch Geoff
Published: March 2023

Abstract. Droughts cause enormous ecological, economical and societal damage, and they are already undergoing changes due to anthropogenic climate change.
The issue of defining and quantifying droughts has long been a substantial source of uncertainty in understanding observed and projected trends.
Atmosphere-based drought indicators, such as the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI), are often used to quantify drought characteristics and their changes, sometimes as the sole metric representing drought.
This study presents a detailed systematic analysis of SPI- and SPEI-based drought projections and their differences for Great Britain (GB), derived from the most recent set of regional climate projections for the United Kingdom (UK).
We show that the choice of drought indicator has a decisive influence on the resulting projected changes in drought frequency, extent, duration and seasonality using scenarios that are 2 and 4 ∘C above pre-industrial levels.
The projected increases in drought frequency and extent are far greater based on the SPEI than based on the SPI.
Importantly, compared with droughts of all intensities, isolated extreme droughts are projected to increase far more with respect to frequency and extent and are also expected to show more pronounced changes in the distribution of their event durations.
Further, projected intensification of the seasonal cycle is reflected in an increasing occurrence of years with (extremely) dry summers combined with wetter-than-average winters.
Increasing summer droughts also form the main contribution to increases in annual droughts, especially using the SPEI.
These results show that the choice of atmospheric drought index strongly influences the drought characteristics inferred from climate change projections, with a comparable impact to the uncertainty from the climate model parameters or the warming level; therefore, potential users of these indices should carefully consider the importance of potential evapotranspiration in their intended context.
The stark differences between SPI- and SPEI-based projections highlight the need to better understand the interplay between increasing atmospheric evaporative demand, moisture availability and drought impacts under a changing climate.
The region-dependent projected changes in drought characteristics by two warming levels have important implications for adaptation efforts in GB, and they further stress the need for rapid mitigation.
A non-extensive approach to probabilistic seismic hazard analysis
Authors: Motaghed Sasan , Khazaee Mozhgan , Eftekhari Nasrollah , Mohammadi Mohammad
Published: March 2023

Abstract. We modify the probabilistic seismic hazard analysis
(PSHA) formulation by replacing the Gutenberg–Richter power law with the SCP (Sotolongo-Costa and Posadas)
non-extensive model for earthquake size distribution and call it NEPSHA. The SCP
claimed to model the regional seismicity better than the classical models.
The proposed method (NEPSHA) is implemented in the Tehran region, and the
results are compared with the classic PSHA method. The hazard curves show
that NEPSHA gives a higher hazard, especially in the range of practical
return periods. The uniform hazard spectra of NEPSHA provide more spectral
accelerations, especially for the medium-height buildings, which are the
most common urban structures.
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
Authors: Guo Wenkai , Itkin Polona , Singha Suman , Doulgeris Anthony P. , Johansson Malin , Spreen Gunnar
Published: March 2023

Abstract. We provide sea ice classification maps of a sub-weekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and relatively high spatial resolution of TSX SC data and is a useful basic dataset for future MOSAiC studies on physical sea ice processes and ocean and climate modeling. Sea ice is classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with different degrees of deformation. We establish the per-class incidence angle (IA) dependencies of TSX SC intensities and gray-level co-occurrence matrix (GLCM) textures and use a classifier that corrects for the class-specific decreasing backscatter with increasing IAs, with both HH intensities and textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier are adjusted by Markov random field contextual smoothing to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 % and good correspondence to geometric ice surface roughness derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of classes representing ice openings (leads and young ice) show prominent increases in middle to late November 2019 and March 2020, corresponding well to ice-opening time series derived from in situ data in this study and those derived from satellite synthetic aperture radar (SAR) and optical data in other MOSAiC studies.
Siber Pornografi Kullanım Envanteri-9’un Türkçe Geçerlilik ve Güvenirlik Çalışması
Authors: Ünübol Hüseyin , Ünübol Başak , Tutgun-ünal Aylin , Hızlı Sayar Gökben
Published: March 2023

Amaç: Bu araştırmanın amacı, Grubbs ve arkadaşları tarafından geliştirilen Siber Pornografi Kullanım Envanteri-9’un (SPKE-9) Türkçe formunun dil eşdeğerliği, geçerlilik ve güvenirliğinin incelenmesidir.
Yöntem: SPKE-9 Türkiye genelinde 4390 kişiden oluşan bir çalışma grubuna uygulanmıştır. Envanter “Kesinlikle Katılmıyorum” ile “Kesinlikle Katılıyorum” aralığında yedili likert tipinde derecelendirilmekte ve bireylerin siber porno kullanım seviyesini ölçmektedir. SPKE-9’un İngilizce ve Türkçe formu arasındaki uyumu incelemek için yapılan Pearson korelasyon katsayıları sonucunda pozitif ve anlamlı değerlere ulaşılmıştır.
Bulgular: Orijinali 3 faktörden oluşan envanterin yapı geçerliliğini sağlamak için yapılan faktör analizi sonucunda Türkçe formunda da aynı yapının geçerli ortaya çıkmıştır. Birinci faktör olan “Algılanan Zorlanma” varyansın %47,57’sini, ikinci faktör olan “Duygusal Sıkıntı” varyansın %24,23’ünü, üçüncü faktör olan “Erişim Çabaları” varyansın %10,72’sini açıklamaktadır. Toplam 9 maddeden oluşan üç faktörlü yapı toplam varyansın %82,53’ünü açıklamıştır. Ayrıca yapılan doğrulayıcı faktör analizinde üç faktör SPKE-9’u doğrulanmıştır. Uyum iyiliği değerleri kabul edilebilir bulunmuştur (Ki-kare/serbestlik değeri: 3,97; RMSEA: 0,08; NFI: 0,95; NNFI: 0,93; CFI: 0,96; GFI:0,94; AGFI: 0,90). Ölçeğin iç tutarlılık katsayısı Cronbach Alfa değeri 0,84 bulunmuştur.
Sonuç: “Siber Pornografi Kullanım Envanteri-9” (SPKE-9) Türkçe formunun geçerli ve güvenilir bir ölçme aracı olarak kullanılabileceği bulunmuştur.
A relevância estratégica dos ministérios públicos de contas para a efetiva concretização do projeto constitucional
Authors: dos Anjos Bandeira de Mello João Augusto
Published: March 2023

This text aims to investigate the role of the Public Administration control bodies, notably the strategic relevance of the Public Ministry of Accounts, in order to guarantee the effectiveness of the norms, values and objectives of the Constitution (constitutional project), in the face of the historical deficits of implementation, and the still persistent perverse effects of the pandemic caused by Covid-19. It is argued that, based on Constitutionalization of Law thinking, all interpretation of the legal system, all the effort of the public services and the performance of control bodies must be reverent to the values and objectives of Constitution norms, especially the implementation of the fundamental rights. In the specific case of this article, this paper emphasizes the Public Ministry of Accounts role in order to evaluate the performance of the public services; to verify whether the mission of guaranteeing the implementation of the constitutional project is being fulfilled; and what to do if necessary, to reach the goal of correction. And, in this matter, considering that such task demands specific expertise in terms of control, such as: the vision of the whole in terms of public policies; the management of inducing, collaborative and articulating control functions; as well, the broad knowledge of the public budget; and bearing in mind that the Public Accounting Ministries have extensive experience in these matters; this text concludes that Public Ministry of Accounts are strategic and essential bodies in the control of public administration and in the defense and feasibility of the constitutional project.