A new framework for resolving conflicts over transboundary rivers using bankruptcy methods

A novel bankruptcy approach is proposed for resolving transboundary river conflicts in which the total water demand or claim of the riparian parties is more than the available water. Bankruptcy solution methods can allocate the available water to the conflicting parties with respect to their claims. Four commonly used bankruptcy methods in the economic literature are used here to develop new river bankruptcy solution methods for allocating water to the riparian parties of river systems. Given the non-uniform spatial and temporal distribution of water across river basins, the proposed solution methods are formulated as non-linear network flow optimization models to allocate water with respect to time sensitivity of water deliveries at different locations in a river network during the planning horizon. Once allocation optimization solutions are developed, their acceptability and stability must be evaluated. Thus, a new bankruptcy allocation stability index (BASI) is developed for evaluating the acceptability of river bankruptcy solutions. To show how the proposed river bankruptcy framework can be helpful in practice, the suggested methods are applied to a real-world transboundary river system with eight riparians under various hydrologic regimes. Stability analysis based on the proposed stability evaluation method suggests that the acceptability of allocation rules is sensitive to hydrologic conditions and demand values. This finding has an important policy implication suggesting that fixed allocation rules and treaties may not be reliable for securing cooperation over transboundary water resources as they are vulnerable to changing socioeconomic and climatic conditions as well as hydrologic non-stationarity

Leveraging Time Series Data in Similarity Based Healthcare Predictive Models: The Case of Early ICU Mortality Prediction

Patient time series classification faces challenges in high degrees of dimensionality and missingness. In light of patient similarity theory, this study explores effective temporal feature engineering and reduction, missing value imputation, and change point detection methods that can afford similarity-based classification models with desirable accuracy enhancement. We select a piecewise aggregation approximation method to extract fine-grain temporal features and propose a minimalist method to impute missing values in temporal features. For dimensionality reduction, we adopt a gradient descent search method for feature weight assignment. We propose new patient status and directional change definitions based on medical knowledge or clinical guidelines about the value ranges for different patient status levels, and develop a method to detect change points indicating positive or negative patient status changes. We evaluate the effectiveness of the proposed methods in the context of early Intensive Care Unit mortality prediction. The evaluation results show that the k-Nearest Neighbor algorithm that incorporates methods we select and propose significantly outperform the relevant benchmarks for early ICU mortality prediction. This study makes contributions to time series classification and early ICU mortality prediction via identifying and enhancing temporal feature engineering and reduction methods for similarity-based time series classification. Keywords: time-series classification, similarity-based classification, mortality prediction, directional change poin

FPGA-Patch: Mitigating Remote Side-Channel Attacks on FPGAs using Dynamic Patch Generation

We propose FPGA-Patch, the first-of-its-kind defense that leverages automated program repair concepts to thwart power side-channel attacks on cloud FPGAs. FPGA-Patch generates isofunctional variants of the target hardware by injecting faults and finding transformations that eliminate failure. The obtained variants display different hardware characteristics, ensuring a maximal diversity in power traces once dynamically swapped at run-time. Yet, FPGA-Patch forces the variants to have enough similarity, enabling bitstream compression and minimizing dynamic exchange costs. Considering AES running on AMD/Xilinx FPGA, FPGA-Patch increases the attacker's effort by three orders of magnitude, while preserving the performance of AES and a minimal area overhead of 14.2%.Comment: 6 page

Political Economy of Iran: Institutions versus Culture

The Iranian revolution of 1979 is widely acknowledged as an event of world-historic significance with great empirical and theoretical implications. After almost four decades, there are still wide disagreements on its causes and consequences. The endurance and increasing power and influence of its most important institution, the state of the Islamic Republic of Iran, in the face of open internal and external coercion and violence is a political success story. But, declining per capita national income, continued dependence on oil revenues, and inefficient public finance and tax structure, all indicate an opposite direction economically. This thesis is an analysis of post-1979 revolution of the Iranian political economy, critically engaging the conceptual framework developed in North et al. (2009). The framework, a theoretical conceptualization of dynamic institutional change, focused on violence, beliefs, institutions and social orders, allows for investigation of the contextual and historical factors influencing and shaping post-revolution institutional changes in Iran. This study highlights not only the importance of endogenous institutional framework underlying political and economic developments, but also the critical role of exogenous factors, underplayed by the conceptual framework. It is an attempt to demonstrate that institutional frameworks viewed as cultural heritage are the outcomes of historical interplay of internal institutional frameworks and external environment in an uncertain non-ergodic world. Not only their origins, changes, and persistence are shaped and influenced by beliefs and ideas, but also their performances shape, change and sustain beliefs and ideas as well

Application of Hybrid Gamma-SVM Approach for River Flow Prediction in Zarinehrud Basin

Prediction of river flow is an important issue in planning water resources and management of supply and demand in future conditions. Hence, it has attracted researchers’ notice due to its importancein the designing, planning, management and operation of water facilitiesand also management the critical conditions such as flood and drought. In the present study, it was attempted to improve Zarinehrud river inflow prediction for use in water resource planning using a hybrid approach based on gamma test and supporting vector machine model (GSVM). For this purpose, the best possible combination of predictors was selected from the different combinations of 10 meteorological and hydrological variables in the basin. Then, based on the best combination of predictors, the potential of river inflow was predicted using a support vector machine. Comparison of predicted and observed flow indicated the good performance of hybrid approach in prediction of potential river inflow for application in basin management plans. In this case, the overall accuracy of the model to predict drought management levels based on Zarinehrud flow is 71.4%, and the upper and under estimation error are 8.2 and 20.4% respectively. These results show the acceptable precision of GSVM model for flow prediction in different hydrological situations of basin. Application of Hybrid Gamma-SVM Approach for River Flow Prediction in Zarinehrud Basi