The export competitiveness of Mozambique's cashew nut industry: Applying Porter's diamond model

Export expansion propitiates scale economies, positive externalities, technological advancement, foreign currency earnings, and efficient resource use towards competitive advantage creation and consolidation. Fuelled by the export-driven economic growth hypothesis, some countries meet their export imperative through export promotion activities that enhance competitiveness. In this Thesis we analysed the factors influencing export competitiveness of the cashew nut industry in Mozambique. This industry is the main source of income for 1.4 million rural households. It reached in 1973 its peak global market share of 50%, having lost this position since 1975. International competitiveness analysis is needed to determine focus areas. We present results of the use of Porter’s Model whose determinants (factor conditions, demand conditions, and related industries) plus government (jointly exogenous constructs) interact and stimulate firm strategy representing competitiveness (endogenous construct). We analysed a quantitative longitudinal 80-observation secondary dataset, and a qualitative primary 310-observation dataset, collected through a structured questionnaire. We used a partial least squares structural equation modelling (PLS-SEM) on both datasets, applying SmartPLS 3.3.9 statistical tool. Results suggest all exogenous constructs influence positively competitiveness. Factor conditions’ impact leads with highest β coefficient of 0.265. Around 89% of respondents highlighted in-shell cashew nut availability, while 82% emphasised quality. Study recommends strategies to improve in-shell cashew nut availability and quality, electricity reliability, physical infrastructure, adherence to international standards, "Zambique" brand, traceability, R&D. Strategies need to be extended to upgrading and updating of labour legislation, taxation, fiscal incentives, and tackling economy’s informality, aiming to entice bigger and faster investments for Mozambique to regain market share.A expansão da exportação propicia economias de escala, externalidades positivas, avanço tecnológico, divisas e uso eficiente dos recursos para criação e consolidação da vantagem competitiva. Alimentados pela hipótese do crescimento económico induzido pela exportação, países realizam o imperativo de exportação realizando actividades de promoção da exportação que melhoram a competitividade. Nesta Tese analisamos os factores que influenciam a competitividade das exportações da indústria do caju em Moçambique. Esta indústria é a principal fonte de renda para 1.4 milhões de famílias rurais. Ela atingiu 50% da quota de mercado global, tendo perdido esta posição desde 1975. A análise da competitividade internacional é necessária para determinar as áreas de foco. Apresentamos resultados do uso do Modelo de Porter cujos determinantes (condições dos factores, condições da procura e indústrias relacionadas) mais governo (constructos exógenos) interagem e estimulam a estratégia da firma, representante da competitividade (constructo endógeno). Analisámos um conjunto de dados quantitativos secundários de 80 observações longitudinais e outro conjunto de dados qualitativos primários recolhidos via questionário estruturado. Usámos uma modelagem da equação estrutural dos mínimos quadrados parciais em ambos os conjuntos de dados, aplicando a ferramenta estatística SmartPLS 3.3.9. Os resultados sugerem que todos os constructos exógenos influenciam positivamente a competitividade. O impacto das condições dos factores lidera com o mais alto coeficiente β=0.265. Cerca de 89% dos inquiridos destacaram a disponibilidade da castanha com casca, enquanto 82% enfatizaram a qualidade. O estudo recomenda estratégias para melhorar a disponibilidade e qualidade da castanha com casca, fiabilidade da electricidade, infra-estruturas físicas, adesão aos padrões internacionais, marca "Zambique", rastreabilidade, pesquisa e desenvolvimento. As estratégias precisam ser extensivas ao melhoramento da legislação laboral, tributação, incentivos fiscais e combate à informalidade da economia, para atrair investimentos maiores e mais rápidos para Moçambique reconquistar a quota de mercado

SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis

To represent the biological variability of clinical neuroimaging populations, it is vital to be able to combine data across scanners and studies. However, different MRI scanners produce images with different characteristics, resulting in a domain shift known as the `harmonisation problem'. Additionally, neuroimaging data is inherently personal in nature, leading to data privacy concerns when sharing the data. To overcome these barriers, we propose an Unsupervised Source-Free Domain Adaptation (SFDA) method, SFHarmony. Through modelling the imaging features as a Gaussian Mixture Model and minimising an adapted Bhattacharyya distance between the source and target features, we can create a model that performs well for the target data whilst having a shared feature representation across the data domains, without needing access to the source data for adaptation or target labels. We demonstrate the performance of our method on simulated and real domain shifts, showing that the approach is applicable to classification, segmentation and regression tasks, requiring no changes to the algorithm. Our method outperforms existing SFDA approaches across a range of realistic data scenarios, demonstrating the potential utility of our approach for MRI harmonisation and general SFDA problems. Our code is available at \url{https://github.com/nkdinsdale/SFHarmony}

Prototype learning for explainable brain age prediction

The lack of explainability of deep learning models limits the adoption of such models in clinical practice. Prototype-based models can provide inherent explainable predictions, but these have predominantly been designed for classification tasks, despite many important tasks in medical imaging being continuous regression problems. Therefore, in this work, we present ExPeRT: an explainable prototype-based model specifically designed for regression tasks. Our proposed model makes a sample prediction from the distances to a set of learned prototypes in latent space, using a weighted mean of prototype labels. The distances in latent space are regularized to be relative to label differences, and each of the prototypes can be visualized as a sample from the training set. The image-level distances are further constructed from patch-level distances, in which the patches of both images are structurally matched using optimal transport. This thus provides an example-based explanation with patch-level detail at inference time. We demonstrate our proposed model for brain age prediction on two imaging datasets: adult MR and fetal ultrasound. Our approach achieved state-of-the-art prediction performance while providing insight into the model’s reasoning process

A Content Analysis of Mozambican Newspapers' Coverage of the 2004 Presidential Election

This study focuses on the amount of coverage given by four major tabloid newspapers-Demos, Zambeze, Savana and Domingo-to the candidates of the major political parties Renamo and Frelimo, during the 2004 presidential race. The number of stories of both parties in those newspapers were counted and calculated by chi-square to determine how much one party was covered than the other identifying signs of balance or bias. The research showed that there was a significant result of 42 percent of likelihood that stories in the four newspapers would either be about Frelimo or Renamo. However, the study also revealed that Frelimo was the party covered most often by Demos, Zambeze and Savana while Renamo was covered most often by Domingo

Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks

To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, regardless of the pose variation of the subject, the scale, and even partial feature-obstruction in the image, outperforming all current alternatives.Comment: 13 pages, 7 figures, MIUA conferenc

Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis

Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) is employed. Although expert sonographers are adept at reading US images, MR images which closely resemble anatomical images are much easier for non-experts to interpret. Thus in this paper we propose to generate MR-like images directly from clinical US images. In medical image analysis such a capability is potentially useful as well, for instance for automatic US-MRI registration and fusion. The proposed model is end-to-end trainable and self-supervised without any external annotations. Specifically, based on an assumption that the US and MRI data share a similar anatomical latent space, we first utilise a network to extract the shared latent features, which are then used for MRI synthesis. Since paired data is unavailable for our study (and rare in practice), pixel-level constraints are infeasible to apply. We instead propose to enforce the distributions to be statistically indistinguishable, by adversarial learning in both the image domain and feature space. To regularise the anatomical structures between US and MRI during synthesis, we further propose an adversarial structural constraint. A new cross-modal attention technique is proposed to utilise non-local spatial information, by encouraging multi-modal knowledge fusion and propagation. We extend the approach to consider the case where 3D auxiliary information (e.g., 3D neighbours and a 3D location index) from volumetric data is also available, and show that this improves image synthesis. The proposed approach is evaluated quantitatively and qualitatively with comparison to real fetal MR images and other approaches to synthesis, demonstrating its feasibility of synthesising realistic MR images.Comment: IEEE Transactions on Medical Imaging 202

Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration

We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approach consists of two sequential networks with a specifically designed loss function to address the challenges in 3D first trimester ultrasound. The first part learns the affine transformation and the second part learns the voxelwise nonrigid deformation between the target image and the atlas. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challenges present in 3D first trimester ultrasound. The method was tested on a dataset of human embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed alignment of the brain in some cases and gave insight in open challenges for the proposed method. We conclude that our method is a promising approach towards fully automated spatial alignment and segmentation of embryonic brains in 3D ultrasound

An automated method for tendon image segmentation on ultrasound using grey-level co-occurrence matrix features and hidden Gaussian Markov random fields

Background: Despite knowledge of qualitative changes that occur on ultrasound in tendinopathy, there is currently no objective and reliable means to quantify the severity or prognosis of tendinopathy on ultrasound. Objective: The primary objective of this study is to produce a quantitative and automated means of inferring potential structural changes in tendinopathy by developing and implementing an algorithm which performs a texture based segmentation of tendon ultrasound (US) images. Method: A model-based segmentation approach is used which combines Gaussian mixture models, Markov random field theory and grey-level co-occurrence (GLCM) features. The algorithm is trained and tested on 49 longitudinal B-mode ultrasound images of the Achilles tendons which are labelled as tendinopathic (24) or healthy (25). Hyperparameters are tuned, using a training set of 25 images, to optimise a decision tree based classification of the images from texture class proportions. We segment and classify the remaining test images using the decision tree. Results: Our approach successfully detects a difference in the texture profiles of tendinopathic and healthy tendons, with 22/24 of the test images accurately classified based on a simple texture proportion cut-off threshold. Results for the tendinopathic images are also collated to gain insight into the topology of structural changes that occur with tendinopathy. It is evident that distinct textures, which are predominantly present in tendinopathic tendons, appear most commonly near the transverse boundary of the tendon, though there was a large variability among diseased tendons. Conclusion: The GLCM based segmentation of tendons under ultrasound resulted in distinct segmentations between healthy and tendinopathic tendons and provides a potential tool to objectively quantify damage in tendinopathy

CheckList [assistive memory system]

There are countless times where cellphones, wallets, or keys have been forgotten at home, in the office or on public transit.  Our solution to this problem is the CheckList. With this handy device, anyone will be able to tag an item and add it onto their CheckList.  Before walking out of any place, the CheckList will detect whether or not you have everything you need.  Simply press the handy \u27Check\u27 button and the CheckList will inform the user if anything is missing.  Forgetting will be a thing of the past.  Just remember the CheckList