506 research outputs found

    Climate Change and Critical Agrarian Studies

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    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    Social interactions in bacteria mediated by bacteriocins and horizontal gene transfer

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    Bacteria are highly social organisms that frequently engage in cooperative and competitive interactions to successfully survive and reproduce. Examples include cell-to-cell communication, nutrient scavenging, and chemical warfare. However, the vast majority of our understanding of bacterial sociality has come from the laboratory strains of a small number of gram-negative social evolution model organisms, such as Pseudomonas spp. and Escherichia coli. In my thesis, I aim to expand our understanding of bacterial sociality in natural populations and further across the bacterial tree of life. I do this using two different approaches. Firstly, I use laboratory experiments and sequence analysis to study the evolution and ecology of bacteriocin-mediated competition in natural S. aureus populations, sampled as part of a carriage study on human nasal passages. Theory and laboratory experiments to date have provided extensive evidence that bacteriocin production plays a key role in determining the competitive dynamics of bacterial strains, however evidence from natural populations to support this hypothesis is lacking. I find that inhibitory strains were associated with the propensity to displace competing strains from the nasal cavity, which occurs despite inhibitory activity not being displayed by the majority of strains and targeting interspecific over intraspecific competitors. I also provide evidence for the genetic underpinnings of bacteriocin activity, by identifying five bacteriocin gene clusters associated with inhibition. Secondly, I use a comparative approach to study the role of horizontal gene transfer in stabilising cooperation across bacteria. Bacterial cooperation is typically mediated by the secretion of extracellular public goods, which are costly molecules that provide a fitness benefit to neighbouring cells. Cooperation can be destabilised by the invasion of selfish ‘cheats’ that reap the benefit of public good production without paying a cost. It is widely accepted that horizontal gene transfer, especially via plasmids, can allow cooperators to ‘re-infect’ cheats with the gene for a cooperative trait, thus stabilising cooperation. Although theoretical and experimental studies have provided evidence to support this hypothesis, a comprehensive genomic study that controls for phylogenetic non-independence across species remains to be conducted. The results from our analysis of plasmid genes from 51 diverse bacterial species do not support the cooperation hypothesis across bacteria and are instead supportive of environmental variability as a determining factor in the relationship between horizontal gene transfer and extracellular proteins. Taken together, this thesis provides a body of work that emphasises the importance of testing predictions from theoretical and laboratory experiments in natural populations, and across diverse species

    Identifying the hazard boundary of ML-enabled autonomous systems using cooperative co-evolutionary search

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    In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis. Given that such boundary captures the conditions in terms of MLC behavior and system context that can lead to hazards, it can then be used to, for example, build a safety monitor that can take any predefined fallback mechanisms at runtime when reaching the hazard boundary. However, determining such hazard boundary for an ML component is challenging. This is due to the problem space combining system contexts (i.e., scenarios) and MLC behaviors (i.e., inputs and outputs) being far too large for exhaustive exploration and even to handle using conventional metaheuristics, such as genetic algorithms. Additionally, the high computational cost of simulations required to determine any MLAS safety violations makes the problem even more challenging. Furthermore, it is unrealistic to consider a region in the problem space deterministically safe or unsafe due to the uncontrollable parameters in simulations and the non-linear behaviors of ML models (e.g., deep neural networks) in the MLAS under analysis. To address the challenges, we propose MLCSHE (ML Component Safety Hazard Envelope), a novel method based on a Cooperative Co-Evolutionary Algorithm (CCEA), which aims to tackle a high-dimensional problem by decomposing it into two lower-dimensional search subproblems. Moreover, we take a probabilistic view of safe and unsafe regions and define a novel fitness function to measure the distance from the probabilistic hazard boundary and thus drive the search effectively. We evaluate the effectiveness and efficiency of MLCSHE on a complex Autonomous Vehicle (AV) case study. Our evaluation results show that MLCSHE is significantly more effective and efficient compared to a standard genetic algorithm and random search

    The Influence of Allostery Governing the Changes in Protein Dynamics Upon Substitution

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    The focus of this research is to investigate the effects of allostery on the function/activity of an enzyme, human immunodeficiency virus type 1 (HIV-1) protease, using well-defined statistical analyses of the dynamic changes of the protein and variants with unique single point substitutions 1. The experimental data1 evaluated here only characterized HIV-1 protease with one of its potential target substrates. Probing the dynamic interactions of the residues of an enzyme and its variants can offer insight of the developmental importance for allosteric signaling and their connection to a protein’s function. The realignment of the secondary structure elements can modulate the mobility along with the frequency of residue contacts as well as which residues are making contact together2-5. We postulate that if there are more contacts occurring within a structure the mobility is being constrained and therefore gaining novel contacts can negatively influence the function of a protein. The evolutionary importance of protein dynamics is probed by analyzing the residue positions possessing significant correlations and the relationship between experimental information1 (variant activities). We propose that the correlated dynamics of residues observed to have considerable correlations, if disrupted, can be used to infer the function of HIV-1 protease and its variants. Given the robustness of HIV-1 protease the identification of any significant constraint imposed on the dynamics from a potential allosteric site found to disrupt the catalytic activity of the variant is not plainly evident. We also develop machine learning (ML) algorithms to predict the protein function/activity change caused by a single point substitution by using the DCC of each residue pair. Recognition of any substantial association between the dynamics of specific residues and allosteric communication or mechanism requires detailed examination of the dynamics of HIV-1 protease and its variants. We also explore the non-linear dependency between each pair of residues using Mutual Information (MI) and how it can influence the dynamics of HIV-1 protease and its variants. We suggest that if the residues of a protein receive more or less information than that of the WT it will adversely impact the function of the protein and can be used to support the classification of a variant structure. Furthermore, using the MI of residues obtained from the MD simulations for the HIV-1 protease structure, we build a ML model to predict a protein’s change in function caused by a single point substitution. Effectively the mobility, dynamics, and non-linear features tested in these studies are found to be useful towards the prediction of potentially drug resistant substitutions related to the catalytic efficiency of HIV-1 protease and the variants

    Evolutionary Reinforcement Learning: A Survey

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    Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impressive achievements in a wide range of challenging tasks, including board games, arcade games, and robot control. Despite these successes, there remain several crucial challenges, including brittle convergence properties caused by sensitive hyperparameters, difficulties in temporal credit assignment with long time horizons and sparse rewards, a lack of diverse exploration, especially in continuous search space scenarios, difficulties in credit assignment in multi-agent reinforcement learning, and conflicting objectives for rewards. Evolutionary computation (EC), which maintains a population of learning agents, has demonstrated promising performance in addressing these limitations. This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL). We categorize EvoRL methods according to key research fields in RL, including hyperparameter optimization, policy search, exploration, reward shaping, meta-RL, and multi-objective RL. We then discuss future research directions in terms of efficient methods, benchmarks, and scalable platforms. This survey serves as a resource for researchers and practitioners interested in the field of EvoRL, highlighting the important challenges and opportunities for future research. With the help of this survey, researchers and practitioners can develop more efficient methods and tailored benchmarks for EvoRL, further advancing this promising cross-disciplinary research field

    Process Knowledge-guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

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    Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central Universities and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235hj2023Electrical, Electronic and Computer Engineerin

    Breaking together: a freedom-loving response to collapse

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    The collapse of modern societies has already begun. That is the conclusion of two years of research by the interdisciplinary team behind the book 'Breaking Together'. How did it come to this? Because monetary systems caused us to harm each other and nature to such an extent it broke the foundations of our societies. So what can we do? This book describes people allowing the full pain of our predicament to liberate them into living more courageously and creatively. They demonstrate we can be breaking together, not apart, in this era of collapse. Professor Jem Bendell argues that reclaiming our freedoms is essential to soften the fall and regenerate the natural world. Escaping the efforts of panicking elites, we can advance an ecolibertarian agenda for both politics and practical action in a broken world. Endorsing the text, the founder of Schumacher College, Satish Kumar, remarked: “this is a prophetic book.

    A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

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    Population clustering methods, which consider the position and fitness of the individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method

    Dynamic multi-objective optimization using evolutionary algorithms

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    Dynamic Multi-objective Optimization Problems (DMOPs) offer an opportunity to examine and solve challenging real world scenarios where trade-off solutions between conflicting objectives change over time. Definition of benchmark problems allows modelling of industry scenarios across transport, power and communications networks, manufacturing and logistics. Recently, significant progress has been made in the variety and complexity of DMOP benchmarks and the incorporation of realistic dynamic characteristics. However, significant gaps still exist in standardised methodology for DMOPs, specific problem domain examples and in the understanding of the impacts and explanations of dynamic characteristics. This thesis provides major contributions on these three topics within evolutionary dynamic multi-objective optimization. Firstly, experimental protocols for DMOPs are varied. This limits the applicability and relevance of results produced and conclusions made in the field. A major source of the inconsistency lies in the parameters used to define specific problem instances being examined. The uninformed selection of these has historically held back understanding of their impacts and standardisation in experimental approach to these parameters in the multi-objective problem domain. Using the frequency and severity (or magnitude) of change events, a more informed approach to DMOP experimentation is conceptualized, implemented and evaluated. Establishment of a baseline performance expectation across a comprehensive range of dynamic instances for well-studied DMOP benchmarks is analyzed. To maximize relevance, these profiles are composed from the performance of evolutionary algorithms commonly used for baseline comparisons and those with simple dynamic responses. Comparison and contrast with the coverage of parameter combinations in the sampled literature highlights the importance of these contributions. Secondly, the provision of useful and realistic DMOPs in the combinatorial domain is limited in previous literature. A novel dynamic benchmark problem is presented by the extension of the Travelling Thief Problem (TTP) to include a variety of realistic and contextually justified dynamic changes. Investigation of problem information exploitation and it's potential application as a dynamic response is a key output of these results and context is provided through comparison to results obtained by adapting existing TTP heuristics. Observation driven iterative development prompted the investigation of multi-population island model strategies, together with improvements in the approaches to accurately describe and compare the performance of algorithm models for DMOPs, a contribution which is applicable beyond the dynamic TTP. Thirdly, the purpose of DMOPs is to reconstruct realistic scenarios, or features from them, to allow for experimentation and development of better optimization algorithms. However, numerous important characteristics from real systems still require implementation and will drive research and development of algorithms and mechanisms to handle these industrially relevant problem classes. The novel challenges associated with these implementations are significant and diverse, even for a simple development such as consideration of DMOPs with multiple time dependencies. Real world systems with dynamics are likely to contain multiple temporally changing aspects, particularly in energy and transport domains. Problems with more than one dynamic problem component allow for asynchronous changes and a differing severity between components that leads to an explosion in the size of the possible dynamic instance space. Both continuous and combinatorial problem domains require structured investigation into the best practices for experimental design, algorithm application and performance measurement, comparison and visualization. Highlighting the challenges, the key requirements for effective progress and recommendations on experimentation are explored here

    Operational research:methods and applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
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