Assessing the Response of Snow Avalanche Runout Altitudes to Climate Fluctuations Using Hierarchical Modeling: Application to 61 Winters of Data in France

Abstract Snow avalanches are natural hazards strongly controlled by the mountain winter climate, but their recent response to climate change has thus far been poorly documented. In this paper, hierarchical modeling is used to obtain robust indexes of the annual fluctuations of runout altitudes. The proposed model includes a possible level shift, and distinguishes common large-scale signals in both mean- and high-magnitude events from the interannual variability. Application to the data available in France over the last 61 winters shows that the mean runout altitude is not different now than it was 60 yr ago, but that snow avalanches have been retreating since 1977. This trend is of particular note for high-magnitude events, which have seen their probability rates halved, a crucial result in terms of hazard assessment. Avalanche control measures, observation errors, and model limitations are insufficient explanations for these trends. On the other hand, strong similarities in the pattern of behavior of the proposed runout indexes and several climate datasets are shown, as well as a consistent evolution of the preferred flow regime. The proposed runout indexes may therefore be usable as indicators of climate change at high altitudes.

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Climate dependent diatom production is preserved in biogenic Si isotope signatures

Biogeosciences Discussions ◽

10.5194/bgd-8-3771-2011 ◽

2011 ◽

Vol 8 (2) ◽

pp. 3771-3795

Author(s):

X. Sun ◽

P. Andersson ◽

C. Humborg ◽

D. J. Conley ◽

P. Crill ◽

...

Keyword(s):

Climate Change ◽

Anthropogenic Disturbance ◽

Large Scale ◽

Isotope Composition ◽

Temperature Variations ◽

Climate Fluctuations ◽

Time Period ◽

Si Isotope ◽

Bothnian Bay ◽

Isotope Analyses

Abstract. Climate change is likely to have the greatest impact on high-latitude ecosystems which are sensitive to climate fluctuations due to the relatively short growth season of diatoms. Biogenic silica (BSi) derived from diatoms provides a direct measure for tracking these processes although the knowledge gained from the amount of BSi preserved may be limited. In this study we report an application of a method based on Si isotope analyses of diatoms derived from a Gulf of Bothnia sediment core to reconstruct diatom production during the last two hundred years. During this time period large scale anthropogenic disturbances, such as the damming of rivers, can be observed. There is a relationship between measured Si isotope values of BSi, e.g. the silica incorporated into diatom frustules, in Bothnian Bay sediments and air temperature. This relationship suggests that the diatom Si isotope composition can be used to trace temperature variations over time. This method can be applied to other diatom dominated aquatic systems, i.e., a large part of the world's ocean and coastal seas, in order to improve our knowledge of impacts of temperature variations and thus also climate change and anthropogenic disturbance.

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Implications of a Decadal Climate Shift over East Asia in Winter: A Modeling Study

Journal of Climate ◽

10.1175/2010jcli3637.1 ◽

2010 ◽

Vol 23 (18) ◽

pp. 4989-5001 ◽

Cited By ~ 9

Author(s):

Song-You Hong ◽

Yoo-Bin Yhang

Keyword(s):

Climate Change ◽

East Asia ◽

Large Scale ◽

Hydrological Cycle ◽

East Asian ◽

Lower Troposphere ◽

Large Scale Circulation ◽

Winter Climate ◽

Climate Shift ◽

Decadal Climate

Abstract This study investigates a decadal climate shift over East Asia in winter, focusing on the changes in hydrological cycle as well as large-scale circulation using the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM). The RSM is forced by perfect boundary conditions for winter (December–February) from 1979 to 2007. Analyses for two separate periods (1979–87 and 1999–2007) are performed to investigate the regional climate model’s ability to simulate climate change in precipitation as well as large-scale circulation. The RSM reproduces differences in large-scale features associated with winter climate change over East Asia when the winter monsoon is modulated on decadal time scales with its weakening pattern observed since the late 1980s. The model adequately reproduces a weakening of the Siberian high and shallowness of the Aleutian low in the lower troposphere and a weakened East Asian coastal trough and East Asian jet in the upper troposphere during 1999–2007, as compared to the first nine winters of 1979–87. Conversely, the decadal shift in precipitation is not well reproduced by the model. The model is capable of reproducing the power spectrum of daily precipitation with maxima at 8.5 days and 45 days in 1979–87, whereas widely spread peaks in 1999–2007 are not captured. The increase of precipitation due to parameterized convection is prominent. This study shows that the dynamical numerical model has a limited capability to reproduce the wintertime hydrological climate over East Asia associated with global warming in recent years.

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Assessment of quality of life in patients with the effects of transient ischemic stroke

East European Journal of Neurology ◽

10.33444/2411-5797.2016.6(12).37-39 ◽

2016 ◽

pp. 37-39

Author(s):

A. Babirad

Keyword(s):

Quality Of Life ◽

Ischemic Stroke ◽

Large Scale ◽

Cerebrovascular Diseases ◽

Control Measures ◽

Sociological Research ◽

Cerebrovascular Events ◽

Sf 36 ◽

Angioplasty And Stenting

Cerebrovascular diseases are a problem of the world today, and according to the forecast, the problem of the near future arises. The main risk factors for the development of ischemic disorders of the cerebral circulation include oblique and aging, arterial hypertension, smoking, diabetes mellitus and heart disease. An effective strategy for the prevention of cerebrovascular events is based on the implementation of large-scale risk control measures, including the use of antiagregant and anticoagulant therapy, invasive interventions such as atheromectomy, angioplasty and stenting. In this connection, the efforts of neurologists, cardiologists, angiosurgery, endocrinologists and other specialists are the basis for achieving an acceptable clinical outcome. A review of the SF-36 method for assessing the quality of life in patients with the effects of transient ischemic stroke is presented. The assessment of quality of life is recognized in world medical practice and research, an indicator that is also used to assess the quality of the health system and in general sociological research.

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Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

Information and Control Systems ◽

10.31799/1684-8853-2018-4-45-51 ◽

2018 ◽

pp. 45-51

Author(s):

A. V. Ponomarev

Keyword(s):

Large Scale ◽

Wide Spectrum ◽

Preference Relation ◽

Pairwise Comparison ◽

Ground Truth ◽

Comparison Method ◽

Characteristic Matrix ◽

Image Tagging ◽

Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems).

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Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

10.2196/preprints.22865 ◽

2020 ◽

Author(s):

Anusha Ampavathi ◽

Vijaya Saradhi T

Keyword(s):

Feature Extraction ◽

Big Data ◽

Deep Learning ◽

Weight Function ◽

Optimization Algorithm ◽

Large Scale ◽

Heuristic Algorithms ◽

Disease Prediction ◽

Health Care Decisions ◽

Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

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Denouement: World Politics, Systemic Leadership, and Climate Change

10.1093/oso/9780190699680.003.0013 ◽

2018 ◽

Author(s):

William R. Thompson ◽

Leila Zakhirova

Keyword(s):

Climate Change ◽

Global Warming ◽

World Politics ◽

First Century ◽

Modern World ◽

Climate Fluctuations ◽

The Past ◽

Agrarian Economy ◽

Historical Processes

In this final chapter, we conclude by recapitulating our argument and evidence. One goal of this work has been to improve our understanding of the patterns underlying the evolution of world politics over the past one thousand years. How did we get to where we are now? Where and when did the “modern” world begin? How did we shift from a primarily agrarian economy to a primarily industrial one? How did these changes shape world politics? A related goal was to examine more closely the factors that led to the most serious attempts by states to break free of agrarian constraints. We developed an interactive model of the factors that we thought were most likely to be significant. Finally, a third goal was to examine the linkages between the systemic leadership that emerged from these historical processes and the global warming crisis of the twenty-first century. Climate change means that the traditional energy platforms for system leadership—coal, petroleum, and natural gas—have become counterproductive. The ultimate irony is that we thought that the harnessing of carbon fuels made us invulnerable to climate fluctuations, while the exact opposite turns out to be true. The more carbon fuels are consumed, the greater the damage done to the atmosphere. In many respects, the competition for systemic leadership generated this problem. Yet it is unclear whether systemic leadership will be up to the task of resolving it.

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Complete genomes of two extinct New Zealand passerines show responses to climate fluctuations but no evidence for genomic erosion prior to extinction

Biology Letters ◽

10.1098/rsbl.2019.0491 ◽

2019 ◽

Vol 15 (9) ◽

pp. 20190491 ◽

Cited By ~ 2

Author(s):

Nicolas Dussex ◽

Johanna von Seth ◽

Michael Knapp ◽

Olga Kardailsky ◽

Bruce C. Robertson ◽

...

Keyword(s):

Climate Change ◽

New Zealand ◽

Human Impacts ◽

Oceanic Islands ◽

Genomic Diversity ◽

Climate Fluctuations ◽

Forest Clearing ◽

Mammalian Predators ◽

Demographic Trajectories ◽

Human Climate

Human intervention, pre-human climate change (or a combination of both), as well as genetic effects, contribute to species extinctions. While many species from oceanic islands have gone extinct due to direct human impacts, the effects of pre-human climate change and human settlement on the genomic diversity of insular species and the role that loss of genomic diversity played in their extinctions remains largely unexplored. To address this question, we sequenced whole genomes of two extinct New Zealand passerines, the huia ( Heteralocha acutirostris ) and South Island kōkako ( Callaeas cinereus ). Both species showed similar demographic trajectories throughout the Pleistocene. However, the South Island kōkako continued to decline after the last glaciation, while the huia experienced some recovery. Moreover, there was no indication of inbreeding resulting from recent mating among closely related individuals in either species. This latter result indicates that population fragmentation associated with forest clearing by Maōri may not have been strong enough to lead to an increase in inbreeding and exposure to genomic erosion. While genomic erosion may not have directly contributed to their extinctions, further habitat fragmentation and the introduction of mammalian predators by Europeans may have been an important driver of extinction in huia and South Island kōkako.

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Public Awareness: What Climate Change Scientists Should Consider

Sustainability ◽

10.3390/su12208369 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8369

Author(s):

Mohammad Rahimi

Keyword(s):

Climate Change ◽

Social Media ◽

Large Scale ◽

Public Awareness ◽

Connected Network ◽

Multi Scale ◽

Valuable Addition ◽

Scientific Approach ◽

Design Solutions ◽

Mitigate Climate Change

In this Opinion, the importance of public awareness to design solutions to mitigate climate change issues is highlighted. A large-scale acknowledgment of the climate change consequences has great potential to build social momentum. Momentum, in turn, builds motivation and demand, which can be leveraged to develop a multi-scale strategy to tackle the issue. The pursuit of public awareness is a valuable addition to the scientific approach to addressing climate change issues. The Opinion is concluded by providing strategies on how to effectively raise public awareness on climate change-related topics through an integrated, well-connected network of mavens (e.g., scientists) and connectors (e.g., social media influencers).

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Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽

10.3390/electronics10141670 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1670

Author(s):

Waheeb Abu-Ulbeh ◽

Maryam Altalhi ◽

Laith Abualigah ◽

Abdulwahab Ali Almazroi ◽

Putra Sumari ◽

...

Keyword(s):

Data Analysis ◽

Structural Equation ◽

Large Scale ◽

Review Paper ◽

Essential Element ◽

Routine Activities ◽

Criminological Theory ◽

Equation Modeling ◽

Future Research ◽

Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

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Impact of air–sea coupling on the climate change signal over the Iberian Peninsula

Climate Dynamics ◽

10.1007/s00382-021-05812-x ◽

2021 ◽

Author(s):

Alba de la Vara ◽

William Cabos ◽

Dmitry V. Sein ◽

Claas Teichmann ◽

Daniela Jacob

Keyword(s):

Climate Change ◽

Iberian Peninsula ◽

Mediterranean Sea ◽

Large Scale ◽

Regional Distribution ◽

Coupled Model ◽

Climate Change Signal ◽

The North ◽

The Mediterranean ◽

The Impact

AbstractIn this work we use a regional atmosphere–ocean coupled model (RAOCM) and its stand-alone atmospheric component to gain insight into the impact of atmosphere–ocean coupling on the climate change signal over the Iberian Peninsula (IP). The IP climate is influenced by both the Atlantic Ocean and the Mediterranean sea. Complex interactions with the orography take place there and high-resolution models are required to realistically reproduce its current and future climate. We find that under the RCP8.5 scenario, the generalized 2-m air temperature (T2M) increase by the end of the twenty-first century (2070–2099) in the atmospheric-only simulation is tempered by the coupling. The impact of coupling is specially seen in summer, when the warming is stronger. Precipitation shows regionally-dependent changes in winter, whilst a drier climate is found in summer. The coupling generally reduces the magnitude of the changes. Differences in T2M and precipitation between the coupled and uncoupled simulations are caused by changes in the Atlantic large-scale circulation and in the Mediterranean Sea. Additionally, the differences in projected changes of T2M and precipitation with the RAOCM under the RCP8.5 and RCP4.5 scenarios are tackled. Results show that in winter and summer T2M increases less and precipitation changes are of a smaller magnitude with the RCP4.5. Whilst in summer changes present a similar regional distribution in both runs, in winter there are some differences in the NW of the IP due to differences in the North Atlantic circulation. The differences in the climate change signal from the RAOCM and the driving Global Coupled Model show that regionalization has an effect in terms of higher resolution over the land and ocean.

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