Analyzing runoff processes through conceptual hydrological modeling in the Upper Blue Nile Basin, Ethiopia

Understanding runoff processes in a basin is of paramount importance for the effective planning and management of water resources, in particular in data-scarce regions such as the Upper Blue Nile. Hydrological models representing the underlying hydrological processes can predict river discharges from ungauged catchments and allow for an understanding of the rainfall-runoff processes in those catchments. In this paper, such a conceptual process-based hydrological model is developed and applied to the upper Gumara and Gilgel Abay catchments (both located within the Upper Blue Nile Basin, the Lake Tana sub-basin) to study the runoff mechanisms and rainfall-runoff processes in the basin. Topography is considered as a proxy for the variability of most of the catchment characteristics. We divided the catchments into different runoff production areas using topographic criteria. Impermeable surfaces (rock outcrops and hard soil pans, common in the Upper Blue Nile Basin) were considered separately in the conceptual model. Based on model results, it can be inferred that about 65% of the runoff appears in the form of interflow in the Gumara study catchment, and baseflow constitutes the larger proportion of runoff (44-48%) in the Gilgel Abay catchment. Direct runoff represents a smaller fraction of the runoff in both catchments (18-19% for the Gumara, and 20% for the Gilgel Abay) and most of this direct runoff is generated through infiltration excess runoff mechanism from the impermeable rocks or hard soil pans. The study reveals that the hillslopes are recharge areas (sources of interflow and deep percolation) and direct runoff as saturated excess flow prevails from the flat slope areas. Overall, the model study suggests that identifying the catchments into different runoff production areas based on topography and including the impermeable rocky areas separately in the modeling process mimics the rainfall-runoff process in the Upper Blue Nile Basin well and yields a useful result for operational management of water resources in this data-scarce region

Training Single Walled Carbon Nanotube based Materials to perform computation

This thesis illustrates the use of Single Walled Carbon Nanotube based materials for the solution of various computational problems by using the process of computer controlled evolution. The study aims to explore and identify three dimensions of a form of unconventional computing called, `Evolution-in-materio'. First, it focuses on identifying suitable materials for computation. Second, it explores suitable methods, i.e. optimisation and evolutionary algorithms to train these materials to perform computation. And third, it aims to identify suitable computational problems to test with these materials. Different carbon based materials, mainly single walled carbon nano-tubes with their varying concentrations in polymers have been studied to be trained for different computational problems using the principal of `evolution-in-materio'. The conductive property of the materials is used to train these materials to perform some meaningful computation. The training process is formulated as an optimisation problem with hardware in loop. It involves the application of an external stimuli (voltages) on the material which brings changes in its electrical properties. In order to train the material for a specific computational problem, a large number of configuration signals need to be tested to find the one that transforms the incident signal in such a way that a meaningful computation can be extracted from the material. An evolutionary algorithm is used to identify this configuration data and using a hardware platform, this data is transformed into incident signals. Depending on the computational problem, the specific voltages signals when applied at specific points on to the material, as identified by an evolutionary algorithm, can make the material behave as a Logic gate, a tone discriminator or a data classifier. The problem is implemented on two types of hardware platforms, one a more simple implementation using mbed ( a micro- controller) and other is a purpose-built platform for `Evolution-in-materio" called Mecobo. The results of this study showed that the single walled carbon nanotube composites can be trained to perform simple computational tasks (such as tone discriminator, AND, OR logic gates and a Half adder circuit), as well as complex computational problems such as Full Adder circuit and various binary and multiple class machine learning problems. The study has also identified the suitability of using evolutionary algorithms such as Particle Swarm Optimisation algorithm (PSO) and Differential evolution for finding solutions of complex computational problems such as complex logic gates and various machine learning classification problems. The implementation of classification problem with the carbon nanotube based materials also identified the role of a classifier. It has been found that K-nearest neighbour method and its variant kNN ball tree algorithm are more suitable to train carbon nanotube based materials for different classification problems. The study of varying concentrations of single walled carbon nanotubes in fixed polymer ratio for the solution of different computational problems provided an indication of the link between single walled carbon nanotubes concentration and ability to solve computational problem. The materials used in this study showed stability in the results for all the considered computational problems. These material systems can compliment the current electronic technology and can be used to create a new type of low energy and low cost electronic devices. This offers a promising new direction for evolutionary computation

Electrospun polymeric nanohybrids with outstanding pollutants adsorption and electroactivity for water treatment and sensing devices

Graphene oxide (GO) and carbon nanotubes (CNTs) were loaded at different mutual ratios into poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-co-HFP) matrix and electrospun to construct mats that were assessed as smart sorbents for decontaminating water from methylene blue (MB) pollutant, while ensuring the additional possibility of detecting the dye amounts. The results revealed that sorption capacity enhances upon increasing GO content, which is beneficial to wettability and active area. Equilibrium adsorption of these materials is precisely predicted by the Langmuir isotherm model and the maximum capacities herein achieved, ranging from 120 to 555 mg/g depending on the formulation, are higher than those reported for similar systems. The evolution of the structure and properties of such materials as a function of dye adsorption was studied. The results reveal that MB molecules prompted the increase of electrical conductivity of the samples in a dose-dependent manner. Mats containing solely CNTs, while displaying the worst sorption performance, showed the highest electrical performances, displaying interesting changes in their electrical response as a function of the dye amount adsorbed, with a linear response and high sensitivity (309.4 mu S cm-1 mg-1) in the range 0-235 mu g of dye adsorbed. Beyond the possibility to monitor the presence of small amounts of MB in contaminated water and the saturation state of sorbents, this feature could even be exploited to transform waste sorbents into high-added value products, including flexible sensors for detecting low values of pressure, human motion, and so on

Modelling the impact of agroforestry on hydrology of Mara River Basin in East Africa

Land�use change is one of the main drivers of change of watershed hydrology. The effect of forestry related land�use changes (e.g. afforestation, deforestation, agroforestry) on water fluxes depends on climate, watershed characteristics and spatial scale. The Soil and Water Assessment Tool (SWAT) model was calibrated, validated and used to simulate the impact of agroforestry on the water balance in Mara River Basin (MRB) in East Africa. Model performance was assessed by Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE). The NSE (and KGE) values for calibration and validation were: 0.77 (0.88) and 0.74 (0.85) for the Nyangores sub-watershed, and 0.78 (0.89) and 0.79 (0.63) for the entire MRB. It was found that agroforestry in the watershed would generally reduce surface runoff, mainly due to enhanced infiltration. However, it would also increase evapotranspiration and consequently reduce the baseflow and the overall water yield, which was attributed to increased water use by trees. Spatial scale was found to have a significant effect on water balance; the impact of agroforestry was higher at the smaller headwater catchment (Nyangores) than for the larger watershed (entire MRB). However, the rate of change in water yield with increase in area under agroforestry was different for the two and could be attributed to the spatial variability of climate within MRB. Our results suggest that direct extrapolation of the findings from a small sub-catchment to a larger watershed may not always be accurate. These findings could guide watershed managers on the level of trade-offs to make between reduced water yields and other benefits (e.g. soil erosion control, improved soil productivity) offered by agroforestry. This article is protected by copyright. All rights reserved

Evolutionary computation based on nanocomposite training: application to data classification

Research into novel materials and computation frameworks by-passing the limitations of the current paradigm, has been identified as crucial for the development of the next generation of computing technology. Within this context, evolution in materio (EiM) proposes an approach where evolutionary algorithms (EAs) are used to explore and exploit the properties of un-configured materials until they reach a state where they can perform a computational task. Following an EiM approach, this thesis demonstrates the ability of EAs to evolve dynamic nanocomposites into data classifiers. Material-based computation is treated as an optimisation problem with a hybrid search space consisting of configuration voltages creating an electric field applied to the material, and the infinite space of possible states the material can reach in response to this field. In a first set of investigations, two different algorithms, differential evolution (DE) and particle swarm optimisation (PSO), are used to evolve single-walled carbon nanotube (SWCNT) / liquid crystal (LC) composites capable of classifying artificial, two-dimensional, binary linear and non-linear separable and merged datasets at low SWCNT concentrations. The difference in search behaviour between the two algorithms is found to affect differently the composite’ state during training, which in turn affects the accuracy, consistency and generalisation of evolved solutions. SWCNT/LC processors are also able to scale to complex, real-life classification problems. Crucially, results suggest that problem complexity influences the properties of the processors. For more complex problems, networks of SWCNT structures tend to form within the composite, creating stable devices requiring no configuration voltages to classify data, and with computational capabilities that can be recovered more than several hours after training. A method of programming the dynamic composites is demonstrated, based on the reapplication of sequences of configuration voltages which have produced good quality SWCNT/LC classifiers. A second set of investigations aims at exploiting the properties presented by the dynamic nanocomposites, whilst also providing a means for evolved device encapsulation, making their use easier in out-of-the lab applications. Novel composites based on SWCNTs dispersed in one-part UV-cure epoxies are introduced. Results obtained with these composites support their choice for use in subsequent EiM research. A final discussion is concerned with evolving an electro-biological processor and a memristive processor. Overall, the work reported in the thesis suggests that dynamic nanocomposites present a number of unexpected, potentially attractive properties not found in other materials investigated in the context of EiM

복잡계 네트워크에서 확산 현상의 예측 및 제어

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 물리·천문학부(물리학전공), 2023. 8. 백용주.지난 20년간 복잡계 네트워크의 창발현상에 대해 많은 연구가 이루어져왔다. 이런 현상의 예측과 제어는 복잡계 과학에서 중요한 주제이다. 복잡계 네트워크의 그래프 표현은 이런 주제를 효과적으로 다룬다. 복잡계 중에서는 두개 이상의 개체들이 동시에 상호작용하는 경우가 있다. 예를 들어 두명 이상의 연구자가 동시에 협업을 할 수 있다. 네트워크의 엣지를 통한 전파는 단순한 전파라 불린다. 단순한 전파과정으로 나타낼 수 없는 많은 현상들이 있다. 그 예로는 하이퍼그래프 전파과정, 양자 전파과정, 그리고 사회기반시설에서의 연쇄파멸현상이 있다. 구체적으로 이 학위논문에서는 복잡한 전파과정의 예측과 제어를 다룬다. 하이퍼그래프에서의 전염병 전파 모델인 simplicial SIS 모델의 상전이와 제어 전략을 다룬다. 또한 불균일한 치명률을 가진 인구분포에서 최적 백신 전략의 상전이에 대해서도 다룬다. 추가로 degree 분포가 균일한 네트워크와 불균일한 네트워크에서의 양자 상전이에 대해서도 연구한다. 마지막으로 기계학습을 적용하여 전염병 전파와 연쇄파멸현상을 예측하고 제어한 연구에 대해 소개한다.In past decades, extensive research has been done on emerging phenomena in complex systems. An important issue for such emerging phenomena is their prediction and control. Complex networks represented by graphs enable researchers to study such issues successfully. In complex systems, however, interactions among constituents can be more complex than pairwise. For instance, more than two people can collaborate on a team. Contagion through an edge in a network is called simple contagion. There are contagion processes that cannot be reduced to simple contagions. Examples are hypergraph epidemic processes, quantum spreading processes, and cascading failures in infrastructure networks. In this dissertation, we study the prediction and control of these complex contagion processes. We study the phase transition and control strategy of the simplicial SIS model, which is an epidemic model in hypergraphs. We then study the transition of vaccination strategy in a population with heterogeneous fatality rates. Moreover, we study the phase transition of quantum spreading processes in homogeneous and heterogeneous networks. Lastly, we employ machine learning for the prediction and control of epidemic spreading and cascading failures in infrastructure networks.Abstract i Contents ii List of Figures v List of Tables xxiii 1 Introduction 1 1.1 Complex Network 1 1.2 Complex contagion 2 1.3 Overview of dissertation 3 2 Higher-order epidemics 5 2.1 Phase transition and critical phenomena of the simplicial susceptibleinfected- susceptible (s-SIS) model 5 2.2 Static model of uniform hypergraph 7 2.3 Simplicial SIS model 9 2.4 Heterogeneous mean-field theory (annealed approximation) 11 2.4.1 Self-consistency equation 11 2.5 Phase transition and critical behavior 14 2.5.1 Order parameter 14 2.5.2 Susceptibility 18 2.5.3 Correlation size 19 2.6 Numerical simulations 22 2.6.1 Numerical methods 22 2.6.2 Numerical results 24 2.7 Degree distribution of static model 30 2.8 Asymptotic behavior of G′(Θ) 30 2.9 Susceptibility 31 2.10 Containment strategy for simplicial SIS model 32 2.10.1 Hypergraph popularity-similarity optimization (h-PSO) model 35 2.10.2 Individual- and pair-based mean-field theories 37 2.10.3 Immunization strategies 41 2.10.4 Numerical Results 45 2.11 Summary and conclusion 50 3 Phase transition in vaccination strategy 53 3.1 Introduction 53 3.2 Susceptible-infected-recovered-dead (SIRD) model 55 3.3 Results 56 3.3.1 Fatality- and contact-based strategies 56 3.3.2 Transition and path-dependency of the optimal vaccination strategy 59 3.3.3 Real-world epidemic diseases 63 3.3.4 Complex epidemic stages, vaccine breakthrough infection, and reinfection 66 3.4 Conclusion 68 4 Application of graph neural network (GNN) on spreading processes 70 4.1 Introduction 70 4.2 Prediction and mitigation of avalanche dynamics in power grids using graph neural network 70 4.2.1 Avalanche dynamics 72 4.2.2 Avalanche mitigation strategy 75 4.2.3 Graph neural network (GNN) 78 4.2.4 Conclusion 84 4.3 Epidemic control using graph neural network ansatz 86 4.3.1 Model 88 4.3.2 Vaccination strategy 91 4.3.3 Results 97 4.3.4 Conclusion 103 5 Quantum spreading processes in complex networks 105 5.1 Introduction 105 5.2 Permutational symmetry 109 5.3 Quantum contact process 111 5.4 Dissipative Transverse Ising model 114 5.4.1 Transverse Ising model 114 5.4.2 Dissipative transverse Ising model 117 5.5 Comparison with quantum jump Monte Carlo simulation 127 5.6 Quantum contact process in scale-free networks 129 5.6.1 Annealed approximation and self-consistency equation 129 5.6.2 Phase transition and critical behavior 132 5.6.3 Numerical results 138 5.7 Summary and Discussion 141 6 Conclusion 145 Bibliography 146 Abstract in Korean 188박

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Haloes gone MAD: The Halo-Finder Comparison Project

[abridged] We present a detailed comparison of fundamental dark matter halo properties retrieved by a substantial number of different halo finders. These codes span a wide range of techniques including friends-of-friends (FOF), spherical-overdensity (SO) and phase-space based algorithms. We further introduce a robust (and publicly available) suite of test scenarios that allows halo finder developers to compare the performance of their codes against those presented here. This set includes mock haloes containing various levels and distributions of substructure at a range of resolutions as well as a cosmological simulation of the large-scale structure of the universe. All the halo finding codes tested could successfully recover the spatial location of our mock haloes. They further returned lists of particles (potentially) belonging to the object that led to coinciding values for the maximum of the circular velocity profile and the radius where it is reached. All the finders based in configuration space struggled to recover substructure that was located close to the centre of the host halo and the radial dependence of the mass recovered varies from finder to finder. Those finders based in phase space could resolve central substructure although they found difficulties in accurately recovering its properties. Via a resolution study we found that most of the finders could not reliably recover substructure containing fewer than 30-40 particles. However, also here the phase space finders excelled by resolving substructure down to 10-20 particles. By comparing the halo finders using a high resolution cosmological volume we found that they agree remarkably well on fundamental properties of astrophysical significance (e.g. mass, position, velocity, and peak of the rotation curve).Comment: 27 interesting pages, 20 beautiful figures, and 4 informative tables accepted for publication in MNRAS. The high-resolution version of the paper as well as all the test cases and analysis can be found at the web site http://popia.ft.uam.es/HaloesGoingMA

Particle-scale numerical study on screening processes

The present study aimed to increase the understanding of the industrial screening process by using the discrete element method simulation (DEM) and machine learning modelling. Thus, the study focused on understanding the fundamentals of the complicated screening processes by investigating the process model with different controlling factors through particle-scale analysis. The particle-scale analysis was also linked to several macroscopic models and screening processes such as percolation of particles under vibration, the local passing of particles from the screen, choking of screening, non-spherical shaped particles contact detection and packing and machine learning modelling. The computational and theoretical analyses as well as machine leaning helped to clarify the use of particle-scale analysis and screening processes in several areas. The outcomes of this thesis include: (i) the percolation of particles under vibration and the machine learning modelling of percolation velocity to predict the size ratio threshold; (ii) a better understanding of screening process based on local passing of inclined and multi-deck screen and physics informed machine learning modelling to predict the particles passing; (iii) a logical model to predict the choking judgement of screen while combining the numerical results and machine learning and (iv) a novel contact force model for non-spherical particles by Fourier transformation and packing. The research in this thesis is useful for the fundamental understanding of the effect of particles’ contact force, operational conditions, particle properties, percolation and sieving on the screening process. Moreover, the novel process models based on artificial intelligence modelling, DEM simulation, and physics laws can help the design, control and optimisation of screening processes