Prediction of local geomagnetic activity on the example of data of “Lviv” Magnetic Observatory

For the first time in world practice, predictive models were constructed for X, Y, Z geomagnetic elements. Based on these models, the prediction was made with 3 hours lead time using data of the “Lviv” magnetic observatory. The properties of models are as follows: observatory — LVV, рredicted values — XYZ; lead time — 3 hours; correlation coefficients’ averaged measurement data — 0.824 (X), 0.811 (Y), 0.804 (Z); prediction efficiency — 0.816 (X), 0.803 (Y), 0.801 (Z); skill score — 0.115 (X), 0.095 (Y), 0.099 (Z). The developed models were tested in the Main Center of Special Monitoring, and they were found to meet the Basic Requirements for operational predictive models.

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Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation

Sensors ◽

10.3390/s21020601 ◽

2021 ◽

Vol 21 (2) ◽

pp. 601 ◽

Cited By ~ 1

Author(s):

Marco Germanotta ◽

Ilaria Mileti ◽

Ilaria Conforti ◽

Zaccaria Del Prete ◽

Irene Aprile ◽

...

Keyword(s):

Inertial Sensors ◽

Center Of Mass ◽

Correlation Coefficients ◽

Biomechanical Model ◽

Lower Limbs ◽

Accuracy Evaluation ◽

Postural Tasks ◽

Single Sensor ◽

Using Data ◽

Displacement Based

The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.

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Meteor head echo polarization at 930 MHz studied with the EISCAT UHF HPLA radar

Annales Geophysicae ◽

10.5194/angeo-29-1197-2011 ◽

2011 ◽

Vol 29 (6) ◽

pp. 1197-1208 ◽

Cited By ~ 5

Author(s):

G. Wannberg ◽

A. Westman ◽

A. Pellinen-Wannberg

Keyword(s):

Angle Of Incidence ◽

Resonance Effects ◽

Observation Volume ◽

Polarization Characteristics ◽

Radar Measurements ◽

Head Echo ◽

Using Data ◽

Static Head ◽

First Time ◽

Underdense Plasma

Abstract. The polarization characteristics of 930-MHz meteor head echoes have been studied for the first time, using data obtained in a series of radar measurements carried out with the tristatic EISCAT UHF high power, large aperture (HPLA) radar system in October 2009. An analysis of 44 tri-static head echo events shows that the polarization of the echo signal recorded by the Kiruna receiver often fluctuates strongly on time scales of tens of microseconds, illustrating that the scattering process is essentially stochastic. On longer timescales (> milliseconds), more than 90 % of the recorded events show an average polarization signature that is independent of meteor direction of arrival and echo strength and equal to that of an incoherent-scatter return from underdense plasma filling the tristatic observation volume. This shows that the head echo plasma targets scatter isotropically, which in turn implies that they are much smaller than the 33-cm wavelength and close to spherically symmetric, in very good agreement with results from a previous EISCAT UHF study of the head echo RCS/meteor angle-of-incidence relationship. Significant polarization is present in only three events with unique target trajectories. These all show a larger effective target cross section transverse to the trajectory than parallel to it. We propose that the observed polarization may be a signature of a transverse charge separation plasma resonance in the region immediately behind the meteor head, similar to the resonance effects previously discussed in connection with meteor trail echoes by Herlofson, Billam and Browne, Jones and Jones and others.

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What drives housing prices, rent and new construction in China

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-12-2016-0080 ◽

2017 ◽

Vol 10 (5) ◽

pp. 662-686

Author(s):

Dimitrios Staikos ◽

Wenjun Xue

Keyword(s):

Real Estate ◽

Empirical Test ◽

Housing Prices ◽

Panel Cointegration ◽

Real Estate Market ◽

Estimation Methods ◽

Content Type ◽

New Construction ◽

Using Data ◽

First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

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Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

Journal of Climate ◽

10.1175/jcli-d-15-0861.1 ◽

2016 ◽

Vol 29 (17) ◽

pp. 6065-6083 ◽

Cited By ~ 25

Author(s):

Yinghui Liu ◽

Jeffrey R. Key

Keyword(s):

Cloud Cover ◽

Climate Models ◽

Global Climate ◽

Correlation Coefficients ◽

Cloud Amount ◽

Satellite Observation ◽

Skill Score ◽

Global Climate Models ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

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Lagged Ensembles, Forecast Configuration, and Seasonal Predictions

Monthly Weather Review ◽

10.1175/mwr-d-12-00184.1 ◽

2013 ◽

Vol 141 (10) ◽

pp. 3477-3497 ◽

Cited By ~ 19

Author(s):

Mingyue Chen ◽

Wanqiu Wang ◽

Arun Kumar

Keyword(s):

Lead Time ◽

Geographical Location ◽

Seasonal Prediction ◽

Skill Score ◽

Negative Influence ◽

Positive Influence ◽

Prediction Skill ◽

Ensemble Size ◽

Seasonal Forecasts ◽

Ensemble Forecasts

Abstract An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

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Morphological and molecular characteristics of Bastiania sinensis sp. n., Tripyla aquatica Brzeski & Winiszewska-Ślipińska, 1993 and T. setifera Bütschli, 1873 (Nematoda: Triplonchida) from Shanxi Province, North China

Nematology ◽

10.1163/15685411-bja10090 ◽

2021 ◽

pp. 1-23

Author(s):

Mei Na Liu ◽

Yu Mei Xu ◽

Zeng Qi Zhao ◽

Jian Ming Wang

Keyword(s):

North China ◽

Large Subunit ◽

Small Subunit ◽

Rrna Genes ◽

Shanxi Province ◽

Molecular Characteristics ◽

Separate Species ◽

Using Data ◽

First Time ◽

Respective Species

Summary This paper describes a new species of Bastiania, presents a new record and redescribes a known species of Tripyla. These nematodes are all in the order Triplonchida and were collected from Shanxi Province, North China. Bastiania sinensis sp. n. is characterised by having the female with a relatively slender body 1049-1295 μm long, dorsally arcuate after heat relaxation, with outer labial setae and cephalic setae in a single circle, an oval amphid, 7-8 laterodorsal cervical setae scattered in the pharyngeal region, orthometamenes and pseudocoelomocytes present, tail conoid with a mucron 1-2 μm long, two pairs of caudal setae present, a = 58.1-75.5, b = 4.0-4.6, c = 12.7-19.7, c′ = 4.1-7.8 and V = 61.1-67.7. Males were not found. Tripyla aquatica is recorded for the first time from China, and is redescribed. Tripyla setifera has been reported from China but without a detailed description – now provided. In addition, phylogenetic relationships among the species were analysed using data from the near full length small subunit (SSU) and D2-D3 segments of large subunit (LSU) of rRNA genes. Bastiania sinensis sp. n. is monophyletic with the Bastiania sequences available in GenBank, but is on an independent branch supporting its status as a separate species; T. aquatica and T. setifera are monophyletically clustered with known Tripyla species and grouped together with sequences from their respective species.

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An Objective Search for Unrecognized Bias in Validated COVID-19 Prediction Models

10.1101/2021.10.28.21265629 ◽

2021 ◽

Author(s):

Hossein Estiri ◽

Zachary Strasser ◽

Sina Rashidian ◽

Jeffrey Klann ◽

Kavishwar Wagholikar ◽

...

Keyword(s):

Predictive Models ◽

Medical Records ◽

Prediction Models ◽

Evaluation Studies ◽

Protected Group ◽

Increased Risk ◽

Using Data ◽

Bias Evaluation ◽

Algorithmic Bias

The growing recognition of algorithmic bias has spurred discussions about fairness in artificial intelligence (AI) / machine learning (ML) algorithms. The increasing translation of predictive models into clinical practice brings an increased risk of direct harm from algorithmic bias; however, bias remains incompletely measured in many medical AI applications. Using data from over 56 thousand Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in four AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. We discuss that while a model can be biased against certain protected groups (i.e., perform worse) in certain tasks, it can be at the same time biased towards another protected group (i.e., perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations. If the goal is to make a change in a positive way, the underlying roots of bias need to be fully explored in medical AI. Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

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Remote sensing and GPR study of the Raddle glacier of the Ridge Munku-Sardyk (the East Sayan)

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/2414-9179-2020-2-26-275-285 ◽

2020 ◽

Vol 26 (2) ◽

pp. 275-285

Author(s):

Aleksandr Kitov ◽

Ivan Denisenko ◽

Oxana Lunina ◽

Andrey Gladkov ◽

Viktor Plyusnin ◽

...

Keyword(s):

Remote Sensing ◽

Main Part ◽

Surface Characteristics ◽

Open Part ◽

The North ◽

The Earth ◽

Using Data ◽

Eastern Sayan ◽

Geometric Changes ◽

First Time

The Munku-Sardyk (Eastern Sayan) glacier has been described and studied for more than 100 years. The first largest glacier of Peretolchina was studied in the most detailed detail. Radde's second-largest glacier is much weaker. Monitoring of surface characteristics of the Radde glacier by ground methods and using data of remote sensing of the Earth (RSE) has been carried out since 2006. In 2018, georadar profiling of this glacier was performed for the first time. As a result, it was possible not only to clarify its surface characteristics, but also to assess the power of the ice and the internal structure (a layer of firn, ice, bed). According to the RSE, its geometric changes have been revealed. Over 120 years, the open part of the Radde Glacier has shrunk from 0.4 to 0.09 km2, and the length from 1 to 0.4 km. It also revealed the division of the glacier into two parts and the intensive reservation of the bottom of the main part of the tongue by surface moraines and the formation of a glacial lake on the glacier itself in the lower part of the second half. Radar research using the Oko-2 georadar, allowed to determine the volume of ice of this glacier 0.003 km3 and the greatest thickness of the main ice body 42 m. The main glacier flows down from the Eskadriliy top, 3168 m, to the north, flows on the cross-bar and from it turns to the northeast, and at the bottom of the kar will continue to flow north again.

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Verification of modeling of convective events based on radar reflectivity

10.5194/egusphere-egu21-5224 ◽

2021 ◽

Author(s):

Ekaterina Svechnikova ◽

Nikolay Ilin ◽

Evgeny Mareev

Keyword(s):

General Pattern ◽

Measurement Data ◽

Quantitative Agreement ◽

Convective Event ◽

Simulation Results ◽

Verification Process ◽

Using Data ◽

Wrf Modeling ◽

Probabilistic Nature

The use of numerical modeling for atmospheric research is complicated by the problem of verification by a limited set of measurement data. Comparison with radar measurements is widely used for assessing the quality of the simulation. The probabilistic nature of the development of convective phenomena determines the complexity of the verification process: the reproduction of the pattern of the convective event is prior to the quantitative agreement of the values at a particular point at a particular moment.We propose a method for verifying the simulation results based on comparing areas with the same reflectivity. The method is applied for verification of WRF-modeling of convective events in the Aragats highland massif in Armenia. It is shown that numerical simulation demonstrates approximately the same form of distribution of areas of equal reflectivity as for radar-measured reflectivity. In this case, the model tends to overestimate on average reflectivity, while enabling us to obtain the qualitatively correct description of the convective phenomenon.The proposed technique can be used to verify the simulation results using data on reflectivity obtained by a satellite or a meteoradar. The technique allows one to avoid subjectivity in the interpretation of simulation results and estimate the quality of reproducing the &#8220;general pattern&#8221; of the convective event.

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Predicting the Evolution of Pain Relief

ACM Transactions on Computing for Healthcare ◽

10.1145/3466781 ◽

2021 ◽

Vol 2 (4) ◽

pp. 1-28

Author(s):

Anderson Bessa Da Costa ◽

Larissa Moreira ◽

Daniel Ciampi De Andrade ◽

Adriano Veloso ◽

Nivio Ziviani

Keyword(s):

Chronic Pain ◽

Pain Relief ◽

Predictive Models ◽

Pain Treatment ◽

Explanatory Models ◽

Robust Algorithms ◽

Local Structures ◽

Patient Will ◽

Data Points ◽

Using Data

Modeling from data usually has two distinct facets: building sound explanatory models or creating powerful predictive models for a system or phenomenon. Most of recent literature does not exploit the relationship between explanation and prediction while learning models from data. Recent algorithms are not taking advantage of the fact that many phenomena are actually defined by diverse sub-populations and local structures, and thus there are many possible predictive models providing contrasting interpretations or competing explanations for the same phenomenon. In this article, we propose to explore a complementary link between explanation and prediction. Our main intuition is that models having their decisions explained by the same factors are likely to perform better predictions for data points within the same local structures. We evaluate our methodology to model the evolution of pain relief in patients suffering from chronic pain under usual guideline-based treatment. The ensembles generated using our framework are compared with all-in-one approaches of robust algorithms to high-dimensional data, such as Random Forests and XGBoost. Chronic pain can be primary or secondary to diseases. Its symptomatology can be classified as nociceptive, nociplastic, or neuropathic, and is generally associated with many different causal structures, challenging typical modeling methodologies. Our data includes 631 patients receiving pain treatment. We considered 338 features providing information about pain sensation, socioeconomic status, and prescribed treatments. Our goal is to predict, using data from the first consultation only, if the patient will be successful in treatment for chronic pain relief. As a result of this work, we were able to build ensembles that are able to consistently improve performance by up to 33% when compared to models trained using all the available features. We also obtained relevant gains in interpretability, with resulting ensembles using only 15% of the total number of features. We show we can effectively generate ensembles from competing explanations, promoting diversity in ensemble learning and leading to significant gains in accuracy by enforcing a stable scenario in which models that are dissimilar in terms of their predictions are also dissimilar in terms of their explanation factors.

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