Strike Hard, Strike Sure - Operation Musketeer : British Military Planning during the Suez Crisis, 1956

This study analyses British military planning and actions during the Suez Crisis in 1956. It seeks to find military reasons for the change of concepts during the planning and compares these reasons with the tactical doctrines of the time. The thesis takes extensive advantage of military documents preserved in the National Archives, London. In order to expand the understanding of the exchange of views during the planning process, the private papers of high ranking military officials have also been consulted. French military documents preserved in the Service Historique de la Defence, Paris, have provided an important point of comparison. The Suez Crisis caught the British armed forces in the middle of a transition phase. The main objective of the armed forces was to establish a credible deterrence against the Soviet Union. However, due to overseas commitments the Middle East playing a paramount role because of its economic importance the armed forces were compelled to also prepare for Limited War and the Cold War. The armed forces were not fully prepared to meet this demand. The Middle Eastern garrison was being re-organised after the withdrawal from the Canal Base and the concept for a strategic reserve was unimplemented. The tactical doctrines of the time were based on experiences from the Second World War. As a result, the British view of amphibious operations and the subsequent campaigns emphasised careful planning, mastery of the sea and the air, sufficient superiority in numbers and firepower, centralised command and extensive administrative preparations. The British military had realized that Nasser could nationalise the Suez Canal and prepared an outline plan to meet this contingency. Although the plan was nothing more than a concept, it was accepted as a basis for further planning when the Canal was nationalised at the end of July. This plan was short-lived. The nominated Task Force Commanders shifted the landing site from Port Said to Alexandria because it enabled faster expansion of the bridgehead. In addition, further operations towards Cairo the hub of Nasser s power would be easier to conduct. The operational concept can be described as being traditional and was in accordance with the amphibious warfare doctrine. This plan was completely changed at the beginning of September. Apparently, General Charles Keightley, the Commander-in-Chief, and the Chairman of the Chiefs of Staff Committee developed the idea of prolonged aerial operations. The essence of the concept was to break the Egyptian will to resist by attacking the oil facilities, the transportation system and the armed forces. This victory through air concept would be supported by carefully planned psychological operations. This concept was in accordance with the Royal Air Force doctrine, which promoted a bomber offensive against selected target categories. General Keightley s plan was accepted despite suspicions at every planning level. The Joint Planning Staff and the Task Force Commanders opposed the concept from the beginning to the end because of its unpredictability. There was no information that suggested the bombing would persuade the Egyptians to submit. This problem was worsened by the fact that British intelligence was unable to provide reliable strategic information. The Task Force Commanders, who were responsible for the tactical plans, were not able to change Keightley s mind, but the concept was expanded to include a traditional amphibious assault on Port Said due to their resistance. The bombing campaign was never tested as the Royal Air Force was denied authorisation to destroy the transportation and oil targets. The Chiefs of Staff and General Keightley were too slow to realise that the execution of the plan depended on the determination of the Prime Minister. However, poor health, a lack of American and domestic support and the indecisiveness of the military had ruined Eden s resolve. In the end, a very traditional amphibious assault, which was bound to succeed at the tactical level but fail at the strategic level, was launched against Port Said.

Learning challenges of NGOs in development : Co-operation of Finnish NGOs in Morogoro, Tanzania

Non-governmental organisations (NGOs) have gained an important role in development co-operation during the last two decades. The development funding channelled through NGOs has increased and the number of NGOs engaged in development activities, both North and South, has been growing. Supporting NGOs has been seen as one way to strengthen civil society in the South and to provide potential for enhancing more effective development than the state, and to exercise participatory development and partnership in their North-South relationships. This study focuses on learning in the co-operation practices of small Finnish NGOs in Morogoro, Tanzania. Drawing on the cultural-historical activity theory and the theory of expansive learning, in this study I understand learning as a qualitative change in the actual co-operation practices. The qualitative change, for its part, emerges out of attempts to deal with the contradictions in the present activity. I use the concept of developmental contradiction in exploring the co-operation of the small Finnish NGOs with their Tanzanian counterparts. Developmental contradiction connects learning to actual practice and its historical development. By history, in this study I refer to multiple developmental trajectories, such as trajectories of individual participants, organisations, co-operation practices and the institutional system in which the NGO-development co-operation is embedded. In the empirical chapters I explore the co-operation both in the development co-operation projects and in micro-level interaction between partners taking place within the projects. I analyse the perceptions of the Finnish participants about the different developmental trajectories, the tensions, inclusions and exclusions in the evolving object of co-operation in one project, the construction of power relations in project meetings in three projects, and the collision of explicated partnership with the emerging practice of trusteeship in one project. On the basis of the empirical analyses I elaborate four developmental contradictions and learning challenges for the co-operation. The developmental contradictions include: 1) implementing a ready-made Finnish project idea vs. taking the current activities of Tanzanian NGO as a starting point; 2) gaining experiences and cultural interaction vs. access to outside funding; 3) promoting the official tools of development co-operation in training vs. use of tools and procedures taken from the prior activities of both partners in actual practice; and 4) asymmetric relations between the partners vs. rhetoric of equal partnership. Consequently, on the basis of developmental contradictions four learning challenges are suggested: a shift from legitimation of Finnish ideas to negotiation, transcending the separate objects and finding a partly joint object, developing locally shared tools for the co-operation, and identification and reflection of the power relations in the practice of co-operation. Keywords: activity theory; expansive learning; NGO development co-operation; partnership; power

Bayesian fisheries stock assessment: integrating and updating knowledge

In this thesis the use of the Bayesian approach to statistical inference in fisheries stock assessment is studied. The work was conducted in collaboration of the Finnish Game and Fisheries Research Institute by using the problem of monitoring and prediction of the juvenile salmon population in the River Tornionjoki as an example application. The River Tornionjoki is the largest salmon river flowing into the Baltic Sea. This thesis tackles the issues of model formulation and model checking as well as computational problems related to Bayesian modelling in the context of fisheries stock assessment. Each article of the thesis provides a novel method either for extracting information from data obtained via a particular type of sampling system or for integrating the information about the fish stock from multiple sources in terms of a population dynamics model. Mark-recapture and removal sampling schemes and a random catch sampling method are covered for the estimation of the population size. In addition, a method for estimating the stock composition of a salmon catch based on DNA samples is also presented. For most of the articles, Markov chain Monte Carlo (MCMC) simulation has been used as a tool to approximate the posterior distribution. Problems arising from the sampling method are also briefly discussed and potential solutions for these problems are proposed. Special emphasis in the discussion is given to the philosophical foundation of the Bayesian approach in the context of fisheries stock assessment. It is argued that the role of subjective prior knowledge needed in practically all parts of a Bayesian model should be recognized and consequently fully utilised in the process of model formulation.

Discovering hidden structures in molecular data using a Bayesian partition model approach

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.

Bayesian Inference for Retrospective Population Genetics Models Using Markov Chain Monte Carlo Methods

Genetics, the science of heredity and variation in living organisms, has a central role in medicine, in breeding crops and livestock, and in studying fundamental topics of biological sciences such as evolution and cell functioning. Currently the field of genetics is under a rapid development because of the recent advances in technologies by which molecular data can be obtained from living organisms. In order that most information from such data can be extracted, the analyses need to be carried out using statistical models that are tailored to take account of the particular genetic processes. In this thesis we formulate and analyze Bayesian models for genetic marker data of contemporary individuals. The major focus is on the modeling of the unobserved recent ancestry of the sampled individuals (say, for tens of generations or so), which is carried out by using explicit probabilistic reconstructions of the pedigree structures accompanied by the gene flows at the marker loci. For such a recent history, the recombination process is the major genetic force that shapes the genomes of the individuals, and it is included in the model by assuming that the recombination fractions between the adjacent markers are known. The posterior distribution of the unobserved history of the individuals is studied conditionally on the observed marker data by using a Markov chain Monte Carlo algorithm (MCMC). The example analyses consider estimation of the population structure, relatedness structure (both at the level of whole genomes as well as at each marker separately), and haplotype configurations. For situations where the pedigree structure is partially known, an algorithm to create an initial state for the MCMC algorithm is given. Furthermore, the thesis includes an extension of the model for the recent genetic history to situations where also a quantitative phenotype has been measured from the contemporary individuals. In that case the goal is to identify positions on the genome that affect the observed phenotypic values. This task is carried out within the Bayesian framework, where the number and the relative effects of the quantitative trait loci are treated as random variables whose posterior distribution is studied conditionally on the observed genetic and phenotypic data. In addition, the thesis contains an extension of a widely-used haplotyping method, the PHASE algorithm, to settings where genetic material from several individuals has been pooled together, and the allele frequencies of each pool are determined in a single genotyping.

Bayesian analysis of community dynamics

Elucidating the mechanisms responsible for the patterns of species abundance, diversity, and distribution within and across ecological systems is a fundamental research focus in ecology. Species abundance patterns are shaped in a convoluted way by interplays between inter-/intra-specific interactions, environmental forcing, demographic stochasticity, and dispersal. Comprehensive models and suitable inferential and computational tools for teasing out these different factors are quite limited, even though such tools are critically needed to guide the implementation of management and conservation strategies, the efficacy of which rests on a realistic evaluation of the underlying mechanisms. This is even more so in the prevailing context of concerns over climate change progress and its potential impacts on ecosystems. This thesis utilized the flexible hierarchical Bayesian modelling framework in combination with the computer intensive methods known as Markov chain Monte Carlo, to develop methodologies for identifying and evaluating the factors that control the structure and dynamics of ecological communities. These methodologies were used to analyze data from a range of taxa: macro-moths (Lepidoptera), fish, crustaceans, birds, and rodents. Environmental stochasticity emerged as the most important driver of community dynamics, followed by density dependent regulation; the influence of inter-specific interactions on community-level variances was broadly minor. This thesis contributes to the understanding of the mechanisms underlying the structure and dynamics of ecological communities, by showing directly that environmental fluctuations rather than inter-specific competition dominate the dynamics of several systems. This finding emphasizes the need to better understand how species are affected by the environment and acknowledge species differences in their responses to environmental heterogeneity, if we are to effectively model and predict their dynamics (e.g. for management and conservation purposes). The thesis also proposes a model-based approach to integrating the niche and neutral perspectives on community structure and dynamics, making it possible for the relative importance of each category of factors to be evaluated in light of field data.

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.

The Most Probable Bayesian Network and Beyond

This doctoral dissertation introduces an algorithm for constructing the most probable Bayesian network from data for small domains. The algorithm is used to show that a popular goodness criterion for the Bayesian networks has a severe sensitivity problem. The dissertation then proposes an information theoretic criterion that avoids the problem.

NORDEK : A Plan for Increased Nordic Economic Co-operation and Integration 1968-1970

For the first time the attempt of Denmark, Finland, Norway and Sweden to increase Nordic economic co-operation and integration (NORDEK 1968-1970) is analysed by using records from the four governments archives and interviews with central actors participating. A dominating argument has until now been that dynamics in Nordic economic integration is different from dynamics in European integration. This archive based study disproves the myth however of ideological Nordism and of short term political developments outside Norden as most important for the NORDEK initiative. The NORDEK initiative was actually more a consequence of a long term socioeconomic and socio-political path dependant process. The study also disproves the myth that the NORDEK plan was a political and ideological symbol without socioeconomic substance. The purpose with NORDEK was to create a better basis for generating economic growth and social welfare. The proposed NORDEK institutions were therefore developed to promote economic progress. The study finally shows that the NORDEK failure in 1970 was not a result of lacking economic rationale or incompatible economic interests. The failure was a result of a power struggle in Finnish domestic policy and lacking political will in the other Nordic countries to continue without Finland.

Accelerator Mass Spectrometry and Bayesian Data Analysis

Accelerator mass spectrometry (AMS) is an ultrasensitive technique for measuring the concentration of a single isotope. The electric and magnetic fields of an electrostatic accelerator system are used to filter out other isotopes from the ion beam. The high velocity means that molecules can be destroyed and removed from the measurement background. As a result, concentrations down to one atom in 10^16 atoms are measurable. This thesis describes the construction of the new AMS system in the Accelerator Laboratory of the University of Helsinki. The system is described in detail along with the relevant ion optics. System performance and some of the 14C measurements done with the system are described. In a second part of the thesis, a novel statistical model for the analysis of AMS data is presented. Bayesian methods are used in order to make the best use of the available information. In the new model, instrumental drift is modelled with a continuous first-order autoregressive process. This enables rigorous normalization to standards measured at different times. The Poisson statistical nature of a 14C measurement is also taken into account properly, so that uncertainty estimates are much more stable. It is shown that, overall, the new model improves both the accuracy and the precision of AMS measurements. In particular, the results can be improved for samples with very low 14C concentrations or measured only a few times.