The impact of multiskilling on UK construction manpower

The overall significance of the construction and building services sector internationally cannot be overemphasised. In the UK, the industry currently accounts for 10% gross domestic product (GDP) and employs 2 million people, which is more than 1 in 14 of the total workforce. However, regardless of its output (approximately £65 billion annually) there has been a steady decline in the number of trade entrants into the construction and building services sector. Consequently, the available ‘pool of labour’ is inadequately resourced; productivity is low; the existing labour force is overstressed; there is an increase in site deaths; and a long-term labour shortage is envisaged. Today, the evidence seems to suggest that multiskilling is a tentative redress for ameliorating the skills crisis in the construction and building sectors. A 43-year time-series of data on 23 manpower attributes was evaluated as part of this investigation. The developed linear regression models show that the concept of multiskilling obeys the ‘law of diminishing returns'. That is, a weak relation was found between construction output and a three or more combination of manpower attributes. An optimisation model is prescribed for traditional trades.

Strategic capacitor placement in distribution systems by minimisation of harmonics amplification because of resonance

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A matheuristic for the selection of beam directions and dose distribution in radiotherapy planning

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optiminación Dinámica en la gestión del cultivo de la dorada en la región mediterranea y Canarias

[ES] El trabajo presenta un modelo de optimización dinámica aplicado a la gestión de un sistema de cultivo de la dorada en la región mediterránea española y canaria. El modelo incluye una función de crecimiento ajustada a partir de datos reales de cultivo de la especie. Las variables económicas incorporan las peculiaridades de ambas regiones, siendo el coste del transporte el factor diferenciador más relevante. Se obtienen recomendaciones de tasas de racionamiento a lo largo del periodo de engorde, que se encuentran siempre por debajo del nivel de saturación. Las tallas de mercado óptimas resultan mayores en la región canaria, debido a sus ventajas medioambientales, destacándose en esta decisión de producto la existencia de un factor de competitividad diferenciada.

Sustainability of irrigated farming systems in a Tunisian region: A recursive stochastic programming analysis

The aim of this study was to evaluate the sustainability of farm irrigation systems in the Cébalat district in northern Tunisia. It addressed the challenging topic of sustainable agriculture through a bio-economic approach linking a biophysical model to an economic optimisation model. A crop growth simulation model (CropSyst) was used to build a database to determine the relationships between agricultural practices, crop yields and environmental effects (salt accumulation in soil and leaching of nitrates) in a context of high climatic variability. The database was then fed into a recursive stochastic model set for a 10-year plan that allowed analysing the effects of cropping patterns on farm income, salt accumulation and nitrate leaching. We assumed that the long-term sustainability of soil productivity might be in conflict with farm profitability in the short-term. Assuming a discount rate of 10% (for the base scenario), the model closely reproduced the current system and allowed to predict the degradation of soil quality due to long-term salt accumulation. The results showed that there was more accumulation of salt in the soil for the base scenario than for the alternative scenario (discount rate of 0%). This result was induced by applying a higher quantity of water per hectare for the alternative as compared to a base scenario. The results also showed that nitrogen leaching is very low for the two discount rates and all climate scenarios. In conclusion, the results show that the difference in farm income between the alternative and base scenarios increases over time to attain 45% after 10 years.

Drivers in adopting adaptation strategies to face increased water scarcity in agriculture. A case study in Doñana (Spain) and Makueni (Kenya)

La agricultura es uno de los sectores más afectados por el cambio climático. A pesar de haber demostrado a lo largo de la historia una gran capacidad para adaptarse a nuevas situaciones, hoy en día la agricultura se enfrenta a nuevos retos tales como satisfacer un elevado crecimiento en la demanda de alimentos, desarrollar una agricultura sostenible con el medio ambiente y reducir las emisiones de gases de efecto invernadero. El potencial de adaptación debe ser definido en un contexto que incluya el comportamiento humano, ya que éste juega un papel decisivo en la implementación final de las medidas. Por este motivo, y para desarrollar correctamente políticas que busquen influir en el comportamiento de los agricultores para fomentar la adaptación a estas nuevas condiciones, es necesario entender previamente los procesos de toma de decisiones a nivel individual o de explotación, así como los efectos de los factores que determinan las barreras o motivaciones de la implementación de medidas. Esta Tesis doctoral trata de profundizar en el análisis de factores que influyen en la toma de decisiones de los agricultores para adoptar estrategias de adaptación al cambio climático. Este trabajo revisa la literatura actual y desarrolla un marco metodológico a nivel local y regional. Dos casos de estudio a nivel local (Doñana, España y Makueni, Kenia) han sido llevados a cabo con el fin de explorar el comportamiento de los agricultores hacia la adaptación. Estos casos de estudio representan regiones con notables diferencias en climatología, impactos del cambio climático, barreras para la adaptación y niveles de desarrollo e influencia de las instituciones públicas y privadas en la agricultura. Mientras el caso de estudio de Doñana representa un ejemplo de problemas asociados al uso y escasez del agua donde se espera que se agraven en el futuro, el caso de estudio de Makueni ejemplifica una zona fuertemente amenazada por las predicciones de cambio climático, donde adicionalmente la falta de infraestructura y la tecnología juegan un papel crucial para la implementación de la adaptación. El caso de estudio a nivel regional trata de generalizar en África el comportamiento de los agricultores sobre la implementación de medidas. El marco metodológico que se ha seguido en este trabajo abarca una amplia gama de enfoques y métodos para la recolección y análisis de datos. Los métodos utilizados para la toma de datos incluyen la implementación de encuestas, entrevistas, talleres con grupos de interés, grupos focales de discusión, revisión de estudios previos y bases de datos públicas. Los métodos analíticos incluyen métodos estadísticos, análisis multi‐criterio para la toma de decisiones, modelos de optimización de uso del suelo y un índice compuesto calculado a través de indicadores. Los métodos estadísticos se han utilizado con el fin de evaluar la influencia de los factores socio‐económicos y psicológicos sobre la adopción de medidas de adaptación. Dentro de estos métodos se incluyen regresiones logísticas, análisis de componentes principales y modelos de ecuaciones estructurales. Mientras que el análisis multi‐criterio se ha utilizado con el fin de evaluar las opciones de adaptación de acuerdo a las opiniones de las diferentes partes interesadas, el modelo de optimización ha tenido como fin analizar la combinación óptima de medidas de adaptación. El índice compuesto se ha utilizado para evaluar a nivel regional la implementación de medidas de adaptación en África. En general, los resultados del estudio ponen de relieve la gran importancia de considerar diferentes escalas espaciales a la hora de evaluar la implementación de medidas de adaptación al cambio climático. El comportamiento de los agricultores es diferente entre lugares considerados a una escala local relativamente pequeña, por lo que la generalización de los patrones del comportamiento a escalas regionales o globales resulta relativamente compleja. Los resultados obtenidos han permitido identificar factores determinantes tanto socioeconómicos como psicológicos y calcular su efecto sobre la adopción de medidas de adaptación. Además han proporcionado una mejor comprensión del distinto papel que desempeñan los cinco tipos de capital (natural, físico, financiero, social y humano) en la implementación de estrategias de adaptación. Con este trabajo se proporciona información de gran interés en los procesos de desarrollo de políticas destinadas a mejorar el apoyo de la sociedad a tomar medidas contra el cambio climático. Por último, en el análisis a nivel regional se desarrolla un índice compuesto que muestra la probabilidad de adoptar medidas de adaptación en las regiones de África y se analizan las causas que determinan dicha probabilidad de adopción de medidas. ABSTRACT Agriculture is and will continue to be one of the sectors most affected by climate change. Despite having demonstrated throughout history a great ability to adapt, agriculture today faces new challenges such as meeting growing food demands, developing sustainable agriculture and reducing greenhouse gas emissions. Adaptation policies planned on global, regional or local scales are ultimately implemented in decision‐making processes at the farm or individual level so adaptation potentials have to be set within the context of individual behaviour and regional institutions. Policy instruments can play a formative role in the adoption of such policies by addressing incentives/disincentives that influence farmer’s behaviour. Hence understanding farm‐level decision‐making processes and the influence of determinants of adoption is crucial when designing policies aimed at fostering adoption. This thesis seeks to analyse the factors that influence decision‐making by farmers in relation to the uptake of adaptation options. This work reviews the current knowledge and develops a methodological framework at local and regional level. Whilst the case studies at the local level are conducted with the purpose of exploring farmer’s behaviour towards adaptation the case study at the regional level attempts to up‐scale and generalise theory on adoption of farmlevel adaptation options. The two case studies at the local level (Doñana, Spain and Makueni, Kenya) encompass areas with different; climates, impacts of climate change, adaptation constraints and limits, levels of development, institutional support for agriculture and influence from public and private institutions. Whilst the Doñana Case Study represents an area plagued with water‐usage issues, set to be aggravated further by climate change, Makueni Case study exemplifies an area decidedly threatened by climate change where a lack of infrastructure and technology plays a crucial role in the uptake of adaptation options. The proposed framework is based on a wide range of approaches for collecting and analysing data. The approaches used for data collection include the implementation of surveys, interviews, stakeholder workshops, focus group discussions, a review of previous case studies, and public databases. The analytical methods include statistical approaches, multi criteria analysis for decision‐making, land use optimisation models, and a composite index based on public databases. Statistical approaches are used to assess the influence of socio‐economic and psychological factors on the adoption or support for adaptation measures. The statistical approaches used are logistic regressions, principal component analysis and structural equation modelling. Whilst a multi criteria analysis approach is used to evaluate adaptation options according to the different perspectives of stakeholders, the optimisation model analyses the optimal combination of adaptation options. The composite index is developed to assess adoption of adaptation measures in Africa. Overall, the results of the study highlight the importance of considering various scales when assessing adoption of adaptation measures to climate change. As farmer’s behaviour varies at a local scale there is elevated complexity when generalising behavioural patterns for farmers at regional or global scales. The results identify and estimate the effect of most relevant socioeconomic and psychological factors that influence adoption of adaptation measures to climate change. They also provide a better understanding of the role of the five types of capital (natural, physical, financial, social, and human) on the uptake of farm‐level adaptation options. These assessments of determinants help to explain adoption of climate change measures and provide helpful information in order to design polices aimed at enhancing societal support for adaptation policies. Finally the analysis at the regional level develops a composite index which suggests the likelihood of the regions in Africa to adopt farm‐level adaptation measures and analyses the main causes of this likelihood of adoption.

Logistical framework for last mile relief distribution in humanitarian supply chains:considerations from the field

Recent years large scale natural disasters: (e.g. 2004 Tsunami, 2005 Earthquake in South Asia, 2010 Earthquake in Haiti, 2010 flood in Pakistan, 2011 Earthquake in Japan etc.) have captured international attention and led to the advance of research of disaster management. To cope with these huge impact disasters, the involved stakeholders have to learn how quickly and efficiently the relief organisations are able to respond. After a disaster strikes, it is necessary to get the relief aid to the affected people by the prompt action of relief organisations. This supply chain process has to be very fast and efficient. The purpose of this paper is to define the last mile relief distribution in humanitarian supply chain and develop a logistical framework by identifying the factors that affect this process. Seventeen interviews were conducted with field officers and the data analysed to identify which are the critical factors for last mile relief distribution of disaster relief operation. A framework is presented classifying these factors according to the ability to implement them in an optimisation model of humanitarian logistics.

Hierarchical Forest Management Planning: A Bilevel Wood Supply Modelling Approach

Le processus de planification forestière hiérarchique présentement en place sur les terres publiques risque d’échouer à deux niveaux. Au niveau supérieur, le processus en place ne fournit pas une preuve suffisante de la durabilité du niveau de récolte actuel. À un niveau inférieur, le processus en place n’appuie pas la réalisation du plein potentiel de création de valeur de la ressource forestière, contraignant parfois inutilement la planification à court terme de la récolte. Ces échecs sont attribuables à certaines hypothèses implicites au modèle d’optimisation de la possibilité forestière, ce qui pourrait expliquer pourquoi ce problème n’est pas bien documenté dans la littérature. Nous utilisons la théorie de l’agence pour modéliser le processus de planification forestière hiérarchique sur les terres publiques. Nous développons un cadre de simulation itératif en deux étapes pour estimer l’effet à long terme de l’interaction entre l’État et le consommateur de fibre, nous permettant ainsi d’établir certaines conditions pouvant mener à des ruptures de stock. Nous proposons ensuite une formulation améliorée du modèle d’optimisation de la possibilité forestière. La formulation classique du modèle d’optimisation de la possibilité forestière (c.-à-d., maximisation du rendement soutenu en fibre) ne considère pas que le consommateur de fibre industriel souhaite maximiser son profit, mais suppose plutôt la consommation totale de l’offre de fibre à chaque période, peu importe le potentiel de création de valeur de celle-ci. Nous étendons la formulation classique du modèle d’optimisation de la possibilité forestière afin de permettre l’anticipation du comportement du consommateur de fibre, augmentant ainsi la probabilité que l’offre de fibre soit entièrement consommée, rétablissant ainsi la validité de l’hypothèse de consommation totale de l’offre de fibre implicite au modèle d’optimisation. Nous modélisons la relation principal-agent entre le gouvernement et l’industrie à l’aide d’une formulation biniveau du modèle optimisation, où le niveau supérieur représente le processus de détermination de la possibilité forestière (responsabilité du gouvernement), et le niveau inférieur représente le processus de consommation de la fibre (responsabilité de l’industrie). Nous montrons que la formulation biniveau peux atténuer le risque de ruptures de stock, améliorant ainsi la crédibilité du processus de planification forestière hiérarchique. Ensemble, le modèle biniveau d’optimisation de la possibilité forestière et la méthodologie que nous avons développée pour résoudre celui-ci à l’optimalité, représentent une alternative aux méthodes actuellement utilisées. Notre modèle biniveau et le cadre de simulation itérative représentent un pas vers l’avant en matière de technologie de planification forestière axée sur la création de valeur. L’intégration explicite d’objectifs et de contraintes industrielles au processus de planification forestière, dès la détermination de la possibilité forestière, devrait favoriser une collaboration accrue entre les instances gouvernementales et industrielles, permettant ainsi d’exploiter le plein potentiel de création de valeur de la ressource forestière.

Optimisation of the lipase-catalysed preparation of a nucleoside prodrug model using an experimental design methodology

The preparation of 2', 3'-di-O-hexanoyluridine (2) by a Candida antarctica B lipase-catalysed alcoholysis of 2', 3', 5'-tri-O-hexanoyluridine (1) was optimised using an experimental design. At 25 ºC better experimental conditions allowed an increase in the yield of 2 from 80% to 96%. In addition to the yield improvement, the volume reaction could be diminished in a factor of 5 and the reaction time significantly shortened.

Design and optimisation of a large-area process-based model for annual crops

The formulation of a new process-based crop model, the general large-area model (GLAM) for annual crops is presented. The model has been designed to operate on spatial scales commensurate with those of global and regional climate models. It aims to simulate the impact of climate on crop yield. Procedures for model parameter determination and optimisation are described, and demonstrated for the prediction of groundnut (i.e. peanut; Arachis hypogaea L.) yields across India for the period 1966-1989. Optimal parameters (e.g. extinction coefficient, transpiration efficiency, rate of change of harvest index) were stable over space and time, provided the estimate of the yield technology trend was based on the full 24-year period. The model has two location-specific parameters, the planting date, and the yield gap parameter. The latter varies spatially and is determined by calibration. The optimal value varies slightly when different input data are used. The model was tested using a historical data set on a 2.5degrees x 2.5degrees grid to simulate yields. Three sites are examined in detail-grid cells from Gujarat in the west, Andhra Pradesh towards the south, and Uttar Pradesh in the north. Agreement between observed and modelled yield was variable, with correlation coefficients of 0.74, 0.42 and 0, respectively. Skill was highest where the climate signal was greatest, and correlations were comparable to or greater than correlations with seasonal mean rainfall. Yields from all 35 cells were aggregated to simulate all-India yield. The correlation coefficient between observed and simulated yields was 0.76, and the root mean square error was 8.4% of the mean yield. The model can be easily extended to any annual crop for the investigation of the impacts of climate variability (or change) on crop yield over large areas. (C) 2004 Elsevier B.V. All rights reserved.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Model predictive control using dynamic integrated system optimisation and parameter estimation (DISOPE)

DISOPE is a technique for solving optimal control problems where there are differences in structure and parameter values between reality and the model employed in the computations. The model reality differences can also allow for deliberate simplification of model characteristics and performance indices in order to facilitate the solution of the optimal control problem. The technique was developed originally in continuous time and later extended to discrete time. The main property of the procedure is that by iterating on appropriately modified model based problems the correct optimal solution is achieved in spite of the model-reality differences. Algorithms have been developed in both continuous and discrete time for a general nonlinear optimal control problem with terminal weighting, bounded controls and terminal constraints. The aim of this paper is to show how the DISOPE technique can aid receding horizon optimal control computation in nonlinear model predictive control.

Physical model parameter optimisation for calibrated emulation of the Dallas Rangemaster Treble Booster guitar pedal

In this work we explore optimising parameters of a physical circuit model relative to input/output measurements, using the Dallas Rangemaster Treble Booster as a case study. A hybrid metaheuristic/gradient descent algorithm is implemented, where the initial parameter sets for the optimisation are informed by nominal values from schematics and datasheets. Sensitivity analysis is used to screen parameters, which informs a study of the optimisation algorithm against model complexity by fixing parameters. The results of the optimisation show a significant increase in the accuracy of model behaviour, but also highlight several key issues regarding the recovery of parameters.