Inter-blockchain protocols with the Isabelle Infrastructure framework

The main incentives of blockchain technology are distribution and distributed change, consistency, and consensus. Beyond just being a distributed ledger for digital currency, smart contracts add transaction protocols to blockchains to execute terms of a contract in a blockchain network. Inter-blockchain (IBC) protocols define and control exchanges between different blockchains. The Isabelle Infrastructure framework has been designed to serve security and privacy for IoT architectures by formal specification and stepwise attack analysis and refinement. A major case study of this framework is a distributed health care scenario for data consistency for GDPR compliance. This application led to the development of an abstract system specification of blockchains for IoT infrastructures. In this paper, we first give a summary of the concept of IBC. We then introduce an instantiation of the Isabelle Infrastructure framework to model blockchains. Based on this we extend this model to instantiate different blockchains and formalize IBC protocols. We prove the concept by defining the generic property of global consistency and prove it in Isabelle

Characterizations of non-normalized discrete probability distributions and their application in statistics

From the distributional characterizations that lie at the heart of Stein’s method we derive explicit formulae for the mass functions of discrete probability laws that identify those distributions. These identities are applied to develop tools for the solution of statistical problems. Our characterizations, and hence the applications built on them, do not require any knowledge about normalization constants of the probability laws. To demonstrate that our statistical methods are sound, we provide comparative simulation studies for the testing of fit to the Poisson distribution and for parameter estimation of the negative binomial family when both parameters are unknown. We also consider the problem of parameter estimation for discrete exponential-polynomial models which generally are non-normalized

Simulation of flood flow in a river system using artificial neural networks

International audienceArtificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets

Modelling spring flood in the area of the Upper Volga basin

Integrated river-basin management for the Volga river requires understanding and modelling of the flow process in its macro-scale tributary catchments. At the example of the Kostroma catchment (16 000 km²), a method combining existing hydrologic simulation tools was developed that allows operational modelling even when data are scarce. Emphasis was placed on simulation of three processes: snow cover development using a snow-compaction model, runoff generation using a conceptual approach with parameters for seasonal antecedent moisture conditions, and runoff concentration using a regionalised unit hydrograph approach. Based on this method, specific regional characteristics of the precipitation-runoff process were identified, in particular a distinct threshold behaviour of runoff generation in catchments with clay-rich soils. With a plausible overall parameterisation of involved tools, spring flood events could successfully be simulated. Present paper mainly focuses on the simulation of a 16-year sample of snowmelt events in a meso-scale catchment. An example of regionalised simulation in the scope of the modelling system "Flussgebietsmodell" shows the capabilities of developed method for application in macro-scale tributary catchments of the Upper Volga basin

Application of soft computing models with input vectors of snow cover area in addition to hydro-climatic data to predict the sediment loads

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R$^{2}$ value of 0.85 and 0.74 during the training and testing period, respectively

Comparative assessment of spatial variability and trends of flows and sediments under the impact of climate change in the upper Indus basin

Extensive research of the variability of flows under the impact of climate change has been conducted for the Upper Indus Basin (UIB). However, limited literature is available on the spatial distribution and trends of suspended sediment concentrations (SSC) in the sub-basins of UIB. This study covers the comparative assessment of flows and SSC trends measured at 13 stations in the UIB along with the variability of precipitation and temperatures possibly due to climate change for the past three decades. In the course of this period, the country’s largest reservoir, Tarbela, on the Indus River was depleted rapidly due to heavy sediment influx from the UIB. Sediment management of existing storage and future planned hydraulic structures (to tap 30,000 MW in the region) depends on the correct assessment of SSC, their variation patterns, and trends. In this study, the SSC trends are determined along with trends of discharges, precipitation, and temperatures using the non-parametric Mann–Kendall test and Sen’s slope estimator. The results reveal that the annual flows and SSC are in a balanced state for the Indus River at Besham Qila, whereas the SSC are significantly reduced ranging from 18.56%–28.20% per decade in the rivers of Gilgit at Alam Bridge, Indus at Kachura, and Brandu at Daggar. The SSC significantly increase ranging from 20.08%–40.72% per decade in the winter together with a significant increase of average air temperature. During summers, the SSC are decreased significantly ranging from 18.63%–27.79% per decade along with flows in the Hindukush and Western–Karakorum regions, which is partly due to the Karakorum climate anomaly, and in rainfall-dominated basins due to rainfall reduction. In Himalayan regions, the SSC are generally increased slightly during summers. These findings will be helpful for understanding the sediment trends associated with flow, precipitation, and temperature variations, and may be used for the operational management of current reservoirs and the design of several hydroelectric power plants that are planned for construction in the UIB

A criterion for separating process calculi

We introduce a new criterion, replacement freeness, to discern the relative expressiveness of process calculi. Intuitively, a calculus is strongly replacement free if replacing, within an enclosing context, a process that cannot perform any visible action by an arbitrary process never inhibits the capability of the resulting process to perform a visible action. We prove that there exists no compositional and interaction sensitive encoding of a not strongly replacement free calculus into any strongly replacement free one. We then define a weaker version of replacement freeness, by only considering replacement of closed processes, and prove that, if we additionally require the encoding to preserve name independence, it is not even possible to encode a non replacement free calculus into a weakly replacement free one. As a consequence of our encodability results, we get that many calculi equipped with priority are not replacement free and hence are not encodable into mainstream calculi like CCS and pi-calculus, that instead are strongly replacement free. We also prove that variants of pi-calculus with match among names, pattern matching or polyadic synchronization are only weakly replacement free, hence they are separated both from process calculi with priority and from mainstream calculi.Comment: In Proceedings EXPRESS'10, arXiv:1011.601

Adapted hydropower-driven water supply system: assessment of an underground application in an Indonesian karst area

Populated karst landscapes can be found all over the world, although their natural boundary conditions mostly lead to distinct challenges regarding a sustainable water supply. Especially in developing and emerging countries, this situation aggravates since appropriate technologies and water management concepts are rarely available. Against this background, the interdisciplinary, German-Indonesian joint project ‘‘Integrated Water Resources Management (IWRM) Indonesia’’, funded by the German Federal Ministry of Education and Research (BMBF), focused on the development and exemplary implementation of adapted techniques to remedy the partly severe water scarcity in the region Gunung Sewu. This karst area, widely known as ‘‘Java’s poorhouse’’, is located on the southern coast of Java Island and distinctly suffers from the mentioned constraints. Under the aegis of the Karlsruhe Institute of Technology (KIT), the conceptual and technical achievements of the ‘‘IWRM Indonesia’’ joint research project are characterized by a high potential for multiplication not only for karst areas but also for nonkarst regions. One of the project’s major accomplishments is the erection of an innovative hydropower-driven water supply facility located in a karst cave 100 m below ground and continuously supplying tens of thousands of people with fresh water. Referring to the plant’s innovative character and the demanding conditions on-site, the implementation was a highly iterative process leading to today’s autonomous operation by an Indonesian public authority. Based on the experiences gained during design, construction, operation and monitoring phase, this paper introduces an implementation approach for adapted technologies as well as a comprising technical and economical assessment of the plant’s operation