Global Models of Sustainability and Values

Exploring the Futures of the Ummah

American Journal of Islam and Society ◽

10.35632/ajis.v15i1.2213 ◽

1998 ◽

Vol 15 (1) ◽

pp. 1-30

Author(s):

Sohail Lnayatullah

Keyword(s):

Turning Point ◽

Ways Of Knowing ◽

Global Models ◽

Limits To Growth ◽

Islamic World ◽

Poststructural Theory ◽

The Future

This article is both a critique of ways of approaching the future and a presentation of scenarios of the Islamic world a generation ahead. The critique covers various global models, including The Club of Rome's classic Limits to Growth (L TG), 1 Mankind at the Turning Point (MTP), and World 2000, and other approaches to the understanding of the future. Drawing from poststructural theory, we ask: What is missing, who does the analysis privilege, and what epistemological frames or ways of knowing are accentuated, are made primary, by the models used? What can the Islamic world learn from these models? We attempt to go a step further than merely asking the Marxist class question of who benefits financially. For us, the issue is deeper. We are concerned with what knowledge frames and (more appropriately, from an Islamic perspective) what civilizational frames are privileged, are considered more important. An appendix presents recommendations focused on making the Islamic urrunah more future oriented. However, global models are only one way of approaching or understanding the future. There are other ways of approaching the study of the future from which can be derived specific assertions about issues, trends, and scenarios as to the likely and possible shape of the future. We also inquire into the utility of these models for better understanding the future of the Islamic ummah. We conclude with visions of the future of the ummah ...

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ASSESSING THE RELIABILITY OF GLOBAL MODELS USING COMPARISONS WITH GRACE SATELLITE DATA

10.1130/abs/2019am-336140 ◽

2019 ◽

Author(s):

Bridget R. Scanlon ◽

◽

Zizhan Zhang

Keyword(s):

Satellite Data ◽

Global Models ◽

Grace Satellite

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Spectral damping scaling factors for horizontal components of ground motions from subduction earthquakes using NGA-Subduction data

Earthquake Spectra ◽

10.1177/87552930211027903 ◽

2021 ◽

pp. 875529302110279

Author(s):

Sanaz Rezaeian ◽

Linda Al Atik ◽

Nicolas M Kuehn ◽

Norman Abrahamson ◽

Yousef Bozorgnia ◽

...

Keyword(s):

Ground Motions ◽

Damping Ratio ◽

Spectral Shape ◽

Parametric Models ◽

Scaling Factors ◽

Global Models ◽

Motion Models ◽

Subduction Earthquakes ◽

Crustal Earthquakes ◽

Subduction Zone Earthquakes

This article develops global models of damping scaling factors (DSFs) for subduction zone earthquakes that are functions of the damping ratio, spectral period, earthquake magnitude, and distance. The Next Generation Attenuation for subduction earthquakes (NGA-Sub) project has developed the largest uniformly processed database of recorded ground motions to date from seven subduction regions: Alaska, Cascadia, Central America and Mexico, South America, Japan, Taiwan, and New Zealand. NGA-Sub used this database to develop new ground motion models (GMMs) at a reference 5% damping ratio. We worked with the NGA-Sub project team to develop an extended database that includes pseudo-spectral accelerations (PSA) for 11 damping ratios between 0.5% and 30%. We use this database to develop parametric models of DSF for both interface and intraslab subduction earthquakes that can be used to adjust any subduction GMM from a reference 5% damping ratio to other damping ratios. The DSF is strongly influenced by the response spectral shape and the duration of motion; therefore, in addition to the damping ratio, the median DSF model uses spectral period, magnitude, and distance as surrogate predictor variables to capture the effects of the spectral shape and the duration of motion. We also develop parametric models for the standard deviation of DSF. The models presented in this article are for the RotD50 horizontal component of PSA and are compared with the models for shallow crustal earthquakes in active tectonic regions. Some noticeable differences arise from the considerably longer duration of interface records for very large magnitude events and the enriched high-frequency content of intraslab records, compared with shallow crustal earthquakes. Regional differences are discussed by comparing the proposed global models with the data from each subduction region along with recommendations on the applicability of the models.

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Interface boundary conditions for global models of multi-chamber negative hydrogen ion sources

10.1063/5.0057416 ◽

2021 ◽

Author(s):

Sergey Averkin ◽

Seth Veitzer

Keyword(s):

Boundary Conditions ◽

Ion Sources ◽

Hydrogen Ion ◽

Interface Boundary ◽

Global Models ◽

Interface Boundary Conditions ◽

Negative Hydrogen Ion

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Towards closing the gap in weakly supervised semantic segmentation with DCNNs: Combining local and global models

Computer Vision and Image Understanding ◽

10.1016/j.cviu.2021.103209 ◽

2021 ◽

pp. 103209

Author(s):

Christoph Mayer ◽

Radu Timofte ◽

Grégory Paul

Keyword(s):

Semantic Segmentation ◽

Global Models ◽

Closing The Gap ◽

Weakly Supervised

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NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3450526 ◽

2021 ◽

Vol 15 (6) ◽

pp. 1-20

Author(s):

Dongsheng Li ◽

Haodong Liu ◽

Chao Chen ◽

Yingying Zhao ◽

Stephen M. Chu ◽

...

Keyword(s):

Collaborative Filtering ◽

Optimization Problems ◽

Empirical Studies ◽

Large Datasets ◽

Model Learning ◽

Global Models ◽

Convex Optimization Problems ◽

Memory Network ◽

Real World Datasets ◽

Performance Tradeoff

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a performance tradeoff among users/items, i.e., not all users/items are perfectly fitted by the global models due to the hard non-convex optimization problems in CF algorithms. Ensemble learning can address this issue by learning multiple diverse models but usually suffer from efficiency issue on large datasets or complex algorithms. In this article, we keep the intermediate models obtained during global model learning as the snapshot models, and then adaptively combine the snapshot models for individual user-item pairs using a memory network-based method. Empirical studies on three real-world datasets show that the proposed method can extensively and significantly improve the accuracy (up to 15.9% relatively) when applied to a variety of existing collaborative filtering methods.

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Synergizing Local and Global Models for Matrix Approximation

Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19 ◽

10.1145/3357384.3358082 ◽

2019 ◽

Author(s):

Chao Chen ◽

Hao Zhang ◽

Dongsheng Li ◽

Junchi Yan ◽

Xiaokang Yang

Keyword(s):

Matrix Approximation ◽

Global Models

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Projected future daily characteristics of African precipitation based on global (CMIP5, CMIP6) and regional (CORDEX, CORDEX-CORE) climate models

Climate Dynamics ◽

10.1007/s00382-021-05859-w ◽

2021 ◽

Author(s):

Alessandro Dosio ◽

Martin W. Jury ◽

Mansour Almazroui ◽

Moetasim Ashfaq ◽

Ismaila Diallo ◽

...

Keyword(s):

East Africa ◽

Southern Africa ◽

Climate Models ◽

Precipitation Intensity ◽

Regional Models ◽

The West ◽

Precipitation Frequency ◽

Global Models ◽

Dry Spells ◽

Precipitation Characteristics

AbstractWe provide an assessment of future daily characteristics of African precipitation by explicitly comparing the results of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models, specifically highlighting the similarities and inconsistencies between them. Results for seasonal mean precipitation are not always consistent amongst ensembles: in particular, global models tend to project a wetter future compared to regional models, especially over the Eastern Sahel, Central and East Africa. However, results for other precipitation characteristics are more consistent. In general, all ensembles project an increase in maximum precipitation intensity during the wet season over all regions and emission scenarios (except the West Sahel for CORE) and a decrease in precipitation frequency (under the Representative Concentration Pathways RCP8.5) especially over the West Sahel, the Atlas region, southern central Africa, East Africa and southern Africa. Depending on the season, the length of dry spells is projected to increase consistently by all ensembles and for most (if not all) models over southern Africa, the Ethiopian highlands and the Atlas region. Discrepancies exist between global and regional models on the projected change in precipitation characteristics over specific regions and seasons. For instance, over the Eastern Sahel in July–August most global models show an increase in precipitation frequency but regional models project a robust decrease. Global and regional models also project an opposite sign in the change of the length of dry spells. CORE results show a marked drying over the regions affected by the West Africa monsoon throughout the year, accompanied by a decrease in mean precipitation intensity between May and July that is not present in the other ensembles. This enhanced drying may be related to specific physical mechanisms that are better resolved by the higher resolution models and highlights the importance of a process-based evaluation of the mechanisms controlling precipitation over the region.

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