Economic evidence to inform recovery planning in the wake of Covid-19: a discussion paper for Nottinghamshire County Council

This paper has been commissioned by Nottinghamshire County Council to inform future recovery planning in the wake of the ongoing Covid-19 Pandemic. The views expressed in this report are those of the authors and do not represent the views of Nottinghamshire County Council. Furthermore, it is not the place of the authors to produce a recovery plan for Nottinghamshire County Council. Recovery planning requires both an understanding of the available economic evidence – interpreted in light of the wider body of knowledge relating to local economic development – and a detailed understanding of the County’s operating context, capabilities, resources and, importantly, local communities across the County. This report is intended to address the former requirement rather than the latter. The approach adopted in preparation of this report reflects the proposition that, in planning for recovery, Nottinghamshire County Council and its partners must respond to the exigencies of a unique social, economic and public health crisis, but do so without losing sight of the long term structural challenges faced by the County

UK Competitiveness Index 2019

First introduced and published in 2000, this UK Competitiveness Index (UKCI) represents the 2019 edition of the report. The UKCI provides a benchmarking of the competitiveness of the UK’s localities2 , and it has been designed to be an integrated measure of competitiveness focusing on both the development and sustainability of businesses and the economic welfare of individuals. In this respect, competitiveness is considered to consist of the capability of an economy to attract and maintain firms with stable or rising market shares in an activity, while maintaining stable or increasing standards of living for those who participate in it. The above definition makes clear that competitiveness is not a zero-sum game, and does not rely on the shifting of a finite amount of resources from one place to another. Competitiveness involves the upgrading and economic development of all places together, rather than the improvement of one place at the expense of another. However, competitiveness does involve balancing the different types of advantages that one place may hold over another, i.e. the range of differing strengths that the socio-economic environment affords to a particular place compared to elsewhere. This report publishes competitiveness indices that incorporate the most up-to-date data available in 2019, as well as an updated version of the indices presented in the 2016 report, which provides a means of comparison and an examination of the UK’s changing competitiveness landscape. In light of Brexit, published before the UK’s departure from the EU, it will also act as a future benchmark for the performance of UK localities. The key findings of the 2019 UKCI are analysed and outlined in the following sections. For those readers interested in the score and rank of a particular locality or localities they may wish to refer directly to Appendix 2, which provides a ranked order list of all localities, and/or Appendix 3, which ranks localities within their relevant regional grouping

UK Competitiveness Index 2023

The report covers UK Competitiveness Index for 2023. This is a benchmarking study covering the competitiveness of localities (local authority districts) in Great Britain. Measures are also included for Local Enterprise Partnerships (LEPs) and City Regions

Predicción del IPC mexicano combinando modelos econométricos e inteligencia artificial

El objetivo de este trabajo es descomponer los factores de comportamiento del Índice de Precios y Cotizaciones (IPC) mexicano para ser pronosticado mediante modelos econométricos y redes neuronales artificiales evolutivas. La metodología empleada consiste en reducir la complejidad de análisis y eliminar el ruido en los datos del IPC mediante la descomposición empírica en modos (DEM), combinando las funciones de modo intrínseco (FMIs) resultantes con las variantes de los modelos autorregresivo integrado de promedio móvil (ARIMA) y autorregresivo con heterocedasticidad condicional (ARCH), y el algoritmo de selección de características de programación evolutiva de redes (FS-EPNet) para pronosticar su comportamiento. La configuración experimental y resultados se presentan y analizan mediante tres fases de predicción del IPC. Las limitaciones son que el IPC mexicano no es estacionario, implicando que algunas FMIs tampoco lo sean. La originalidad consiste en la combinación de la DEM con el algoritmo FS-EPNet para analizar la evolución del mercado bursátil mexicano a través de su IPC, con lo cual se demuestra y concluye que genera una mejor predicción que la obtenida a partir de los datos originales.(Mexican IPC Prediction Combining Econometric Models and Artificial Intelligence)The purpose of this paper is to decompose the behavioral factors of the Mexican Price and Quotation Index (IPC for its acronym in Spanish) to be forecast using econometric models and evolutionary artificial neural networks. The methodology used consists on reducing the analysis complexity and eliminating the noise in the IPC data through empirical mode decomposition (EMD), combining the intrinsic mode functions (IMFs) resulting with the variants of the autoregressive integrated mobile average (ARIMA) and autoregressive conditional heteroskedasticity (ARCH) models, as well as the algorithm for selection of characteristics of evolutionary network programing (FS-EPNet) to forecast its behavior. The experimental configuration and results are shown and are analyzed using three prediction phases of the IPC. The limitations are that the Mexican IPC is not stationary, which implies that some IMFs are also not stationary. The originality of this consists on the combination of DEM with the FS-EPNet algorithm to analyze the evolution of the Mexican Stock Exchange through its IPC, which is used to show and conclude that it generates a better prediction than that obtained from the original data

Detection of dish manufacturing defects using a deep learning-based approach

Quality control is essential to ensure the smooth running of an industrial process. This work proposes to use and adapt a deep learning-based algorithm that will integrate an automatic quality control system at a porcelain dish factory. This system will receive images acquired in real time by high resolution cameras directly placed on production line. The algorithm proposed in this research work will classify the dishes presented in the images as "defective" or "without defect". Therefore, the objective of the system will be the detection of defective dishes, causing fewer defective dishes to reach the market, thus contributing to a better reputation of the factory. This system is based on the application of an algorithm called Convolutional Neural Network. This algorithm requires a large amount of data to be trained and to perform the image classification. Since the COVID-19 pandemic was felt on a larger scale in Portugal at the time of the development of this research work, it was impossible to obtain data directly from the factory. Due to this setback, the data used in this work was artificially generated. By providing the complete images of dishes to the algorithm, it achieved a defect detection accuracy of 92.7% with the first dataset and 91.9%. with the second. When providing the algorithm 100x100 pixel segments of the original images, using the second created dataset, it reached 91.6% accuracy in the classification of these segments, which translated into a 52.0% accuracy rate in the classification of the complete dish images.O controlo de qualidade é fundamental para assegurar o bom funcionamento de um processo industrial. Este trabalho propõe a utilização e adaptação de um algoritmo, baseado em aprendizagem profunda, como parte integrante de um sistema automático de controlo de qualidade numa fábrica de pratos de porcelana. Este sistema receberá imagens adquiridas em tempo real por câmaras fotográficas colocadas diretamente sobre a linha de produção. O algoritmo utilizado classificará os pratos presentes nas imagens como "defeituoso" ou "sem defeito". O objetivo do sistema será, portanto, a deteção de pratos defeituosos, fazendo com que menos pratos com defeito cheguem ao mercado, contribuindo assim para uma melhor reputação da fábrica. Este sistema é baseado na aplicação de uma rede neuronal convolucional. Este tipo de redes requer um elevado número de dados para ser treinado de modo a conseguir realizar a classificação de imagens. Uma vez que a pandemia de COVID-19 se fez sentir em maior escala em Portugal na altura do desenvolvimento deste trabalho, foi impossível a obtenção de imagens provenientes da fábrica. Devido a este contratempo, os dados utilizados neste trabalho foram gerados artificialmente. Ao fornecer imagens completas de pratos ao algoritmo, o mesmo atingiu uma taxa de acerto da deteção de defeitos de 92,7% com o primeiro conjunto de dados e 91,9% com o segundo. Ao fornecer ao algoritmo segmentos de 100x100 pixéis da imagem original, o mesmo atingiu 91,6% de taxa de acerto, o que se traduziu numa taxa de acerto de 52,0% na classificação das imagens completas de pratos

A framework for Lean implementation in infrastructure construction in the UK

The UK government is keen to have a world class modern transport infrastructure operational in the UK that will provide opportunities for regeneration and enable the nation to be competitive in the global market. Modern transport infrastructure is the economic backbone of many first world nations. The UK government has plans to increase spending on infrastructure that will rival any spending in the sector since the 1970s (IPA, 2017). A five-year, £135.3bn investment is planned to be spent on transport infrastructure between 2017 and 2026. The stakes are high, and therefore, there is a need for increased efficiency and effectiveness in the pre, during and post construction process. The industry is now vehemently pushing for the adoption of Lean construction to guide the allocation of resources and the execution of the works on budget, time and at an appropriate quality (IPA, 2017). Lean has brought about many benefits in manufacturing, such as; increased customer engagement, increased customer satisfaction, time and cost savings, and enhanced quality (Aziz et al., 2016). Lean construction, therefore, will bring about an effective system to generate the kind of efficiency savings desired within infrastructure construction. However, for optimum efficiency, the project team and the entire supply-chain must be committed to the Lean process. There has to be a full management buy-in, and commitment. The supply-chain also has to be fully motivated to achieve the desired efficiency targets. Many Lean implementation frameworks have been provided for use in the construction industry, but none have successfully incorporated the necessary elements that will drive motivation and commitment on the part of top management, project teams, and the supply-chain. It is imperative that the Lean initiative is beneficial for everybody involved. Therefore, this research set out to develop a framework to drive motivation and ensure commitment from project stakeholders in Lean implementations within infrastructure construction. Using purposive sampling, semi-structured interviews were conducted with 27 Lean managers and practitioners in the infrastructure sector for which rich and informative responses were received satisfying many of the study’s queries on Lean implementation in infrastructure construction. The research found that the nature of contracts determines the level of motivation and commitment given to any Lean initiative. Furthermore, it was found that leadership, aligned with the objectives of the supply-chain and that of the client, including collaborative planning, monitoring and control, performance evaluation, and rewarding and incentivising of good performance make for a successful implementation of Lean within infrastructure projects

Crossover control in selection hyper-heuristics: case studies using MKP and HyFlex

Hyper-heuristics are a class of high-level search methodologies which operate over a search space of heuristics rather than a search space of solutions. Hyper-heuristic research has set out to develop methods which are more general than traditional search and optimisation techniques. In recent years, focus has shifted considerably towards cross-domain heuristic search. The intention is to develop methods which are able to deliver an acceptable level of performance over a variety of different problem domains, given a set of low-level heuristics to work with. This thesis presents a body of work investigating the use of selection hyper-heuristics in a number of different problem domains. Specifically the use of crossover operators, prevalent in many evolutionary algorithms, is explored within the context of single-point search hyper-heuristics. A number of traditional selection hyper-heuristics are applied to instances of a well-known NP-hard combinatorial optimisation problem, the multidimensional knapsack problem. This domain is chosen as a benchmark for the variety of existing problem instances and solution methods available. The results suggest that selection hyper-heuristics are a viable method to solve some instances of this problem domain. Following this, a framework is defined to describe the conceptual level at which crossover low-level heuristics are managed in single-point selection hyper-heuristics. HyFlex is an existing software framework which supports the design of heuristic search methods over multiple problem domains, i.e. cross-domain optimisation. A traditional heuristic selection mechanism is modified in order to improve results in the context of cross-domain optimisation. Finally the effect of crossover use in cross-domain optimisation is explored