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"Jennings, Andrew"
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Everything Vikings
An introduction to the Viking way of life including fearless voyages of discovery and fierce raids.
Book
New characterizations of strategy-proofness under single-peakedness
We provide novel representations of strategy-proof voting rules applicable when voters have uni-dimensional single-peaked preferences. In particular, we introduce a ‘grading curve’ representation which is particularly useful when introducing variable electorates. Our analysis recovers, links and unifies existing results in the literature, and provides new characterizations when strategy-proofness is combined with other desirable properties such as ordinality, participation, consistency, and proportionality. Finally, the new representations are used to compute the strategy-proof methods that maximize the ex-ante social welfare for the
L
2
-norm and a uniform prior. The resulting strategy-proof welfare maximizer is the linear median (or ‘uniform median’), that we also characterize as the unique proportional strategy-proof voting rule.
Journal Article
Mongooses of the world
Mongooses are a remarkable and fascinating group of small carnivores, with 25 species occurring in Africa and nine in Asia. They live within a wide variety of habitats, from open savannah to dense rainforest, and display an amazing diversity in social behaviour, with both solitary and group-living species. Yet this family is one of the least-known group of carnivores. The general lack of public awareness about most mongoose species, and the scare ecological knowledge of what they need to survive in the wild, are two of the many conservation threats that this group of carnivores faces, which highlights the urgent need to promote an interest in these amazing animals. As well as popularising mongooses, the book will be a valuable source of information on general scientific and conservation topics, such as social behaviour and how the loss of suitable habitats impacts animal species.
Book
The Market for Corporate Criminals
This Article identifies problems and opportunities at the intersection of mergers and acquisitions (M&A) and corporate crime and compliance. InM&A, criminal successor liability is of particular importance, because it is quantitatively less predictable and qualitatively more threatening to buyers than successor liability in tort or contract Private successor liability requires a buyer to bear bounded economic costs, which can in turn be reallocated to sellers via the contracting process. Criminal successor liability, however, threatens a buyer with non-indemnifiable and potentially ruinous punishment for another firms wrongful acts. This threat may inhibit the marketability of businesses that have criminal exposure, creating social cost in the form of inefficient allocations of corporate control. Such a result would be unfortunate because M&A could instead be a lever for promoting compliance. Yet criminal successor liability undermines this possibility and, in turn, the public s interest in compliance. To countervail these problems, this Article proposes new prosecutorial policies that, through bettertargeted sanctions and compliance-enhancing mergers, would promote M&A markets, deter corporate crime, andfoster corporate reform.
Journal Article
A Robust Artificial Intelligence Approach with Explainability for Measurement and Verification of Energy Efficient Infrastructure for Net Zero Carbon Emissions
Rapid urbanization across the world has led to an exponential increase in demand for utilities, electricity, gas and water. The building infrastructure sector is one of the largest global consumers of electricity and thereby one of the largest emitters of greenhouse gas emissions. Reducing building energy consumption directly contributes to achieving energy sustainability, emissions reduction, and addressing the challenges of a warming planet, while also supporting the rapid urbanization of human society. Energy Conservation Measures (ECM) that are digitalized using advanced sensor technologies are a formal approach that is widely adopted to reduce the energy consumption of building infrastructure. Measurement and Verification (M&V) protocols are a repeatable and transparent methodology to evaluate and formally report on energy savings. As savings cannot be directly measured, they are determined by comparing pre-retrofit and post-retrofit usage of an ECM initiative. Given the computational nature of M&V, artificial intelligence (AI) algorithms can be leveraged to improve the accuracy, efficiency, and consistency of M&V protocols. However, AI has been limited to a singular performance metric based on default parameters in recent M&V research. In this paper, we address this gap by proposing a comprehensive AI approach for M&V protocols in energy-efficient infrastructure. The novelty of the framework lies in its use of all relevant data (pre and post-ECM) to build robust and explainable predictive AI models for energy savings estimation. The framework was implemented and evaluated in a multi-campus tertiary education institution setting, comprising 200 buildings of diverse sensor technologies and operational functions. The results of this empirical evaluation confirm the validity and contribution of the proposed framework for robust and explainable M&V for energy-efficient building infrastructure and net zero carbon emissions.
Journal Article
Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation
Electric vehicles (EVs) are advancing the transport sector towards a robust and reliable carbon-neutral future. Given this increasing uptake of EVs, electrical grids and power networks are faced with the challenges of distributed energy resources, specifically the charge and discharge requirements of the electric vehicle infrastructure (EVI). Simultaneously, the rapid digitalisation of electrical grids and EVs has led to the generation of large volumes of data on the supply, distribution and consumption of energy. Artificial intelligence (AI) algorithms can be leveraged to draw insights and decisions from these datasets. Despite several recent work in this space, a comprehensive study of the practical value of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability and charge optimisation of the EVI has not been formally investigated. The objective of this study was to design, develop and evaluate a comprehensive AI framework that addresses this gap in EVI. Results from the empirical evaluation of this AI framework on a real-world EVI case study confirm its contribution towards addressing the emerging challenges of distributed energy resources in EV adoption.
Journal Article
Optimizing Generative AI Chatbots for Net-Zero Emissions Energy Internet-of-Things Infrastructure
Internet-of-Things (IoT) technologies have been steadily adopted and embedded into energy infrastructure following the rapid transformation of energy grids through distributed consumption, renewables generation, and battery storage. The data streams produced by such energy IoT infrastructure can be extracted, processed, analyzed, and synthesized for informed decision-making that delivers optimized grid operations, reduced costs, and net-zero carbon emissions. However, the voluminous nature of such data streams leads to an equally large number of analysis outcomes that have proven ineffective in decision-making by energy grid operators. This gap can be addressed by introducing artificial intelligence (AI) chatbots, or more formally conversational agents, to proactively assist human operators in analyzing and identifying decision opportunities in energy grids. In this research, we draw upon the recent success of generative AI for optimized AI chatbots with natural language understanding and generation capabilities for the complex information needs of energy IoT infrastructure and net-zero emissions. The proposed approach for optimized generative AI chatbots is composed of six core modules: Intent Classifier, Knowledge Extractor, Database Retriever, Cached Hierarchical Vector Storage, Secure Prompting, and Conversational Interface with Language Generator. We empirically evaluate the proposed approach and the optimized generative AI chatbot in the real-world setting of an energy IoT infrastructure deployed at a large, multi-campus tertiary education institution. The results of these experiments confirm the contribution of generative AI chatbots in simplifying the complexity of energy IoT infrastructure for optimized grid operations and net-zero carbon emissions.
Journal Article
Comprehension Ninja for Ages 6-7: Fiction & Poetry: Comprehension Worksheets for Year 2
Comprehension Ninja for Ages 6-7: Fiction Poetry is an exciting reading comprehension resource from the creator of the hugely popular Vocabulary Ninja and Comprehension Ninja: Non-Fiction series.Containing 24 immersive and imaginative fiction and poetry texts accompanied by photocopiable activities to boost reading retrieval skills, this book will ensure every pupil has what it takes to become a comprehension ninja!Ideal for KS1 SATs practice, the reading texts are high-quality, rich in vocabulary and perfectly matched to the National Curriculum. They cover a wide variety of genres including myths and legends, fantasy, contemporary stories, traditional tales and poetry. If you're searching for engaging resources to help pupils practise comprehension strategies and question types such as true or false, labelling, matching, highlighting, filling in the gap, sequencing and multiple choice, Comprehension Ninja for Ages 6-7: Fiction Poetry is the book for you.
eBook
Comprehension Ninja for Ages 5-6: Fiction & Poetry: Comprehension Worksheets for Year 1
Comprehension Ninja for Ages 5-6: Fiction Poetry is an exciting reading comprehension resource from the creator of the hugely popular Vocabulary Ninja and Comprehension Ninja: Non-Fiction series.Containing 24 immersive and imaginative fiction and poetry texts accompanied by photocopiable activities to boost reading retrieval skills, this book will ensure every pupil has what it takes to become a comprehension ninja!Ideal for KS1 SATs practice, the reading texts are high-quality, rich in vocabulary and perfectly matched to the National Curriculum. They cover a wide variety of genres including myths and legends, fantasy, contemporary stories, traditional tales and poetry. If you're searching for engaging resources to help pupils practise comprehension strategies and question types such as true or false, labelling, matching, highlighting, filling in the gap, sequencing and multiple choice, Comprehension Ninja for Ages 5-6: Fiction Poetry is the book for you.
eBook
A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types
Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems.
Journal Article