The nativity of Populus alba in the Mediterranean has only been confirmed in the last decade, following the discovery of 8,000-year-old leaf imprints in Southern France. Recent evidence has even emerged from molecular studies suggesting that the species is native to some of the islands, and these populations may be relicts of a native flora that arrived there much earlier than previously thought. In view of this, samples obtained from the Central Mediterranean archipelago of Malta and other neighbouring regions were analysed to determine the native status of the Maltese populations and possibly trace their origins. All 38 samples were investigated in order to assess the genetic diversity and origin of Maltese trees. Nuclear microsatellite analysis revealed that all 28 trees sampled from the two islands of Malta belonged to one clone. Chloroplast data suggested relatedness of the Maltese clone to Italian P. alba samples. However, nuclear data suggested additional admixture through pollen from North Africa. Existing archival and palaeontological records were also examined for any supporting evidence. On considering the latter records in combination with molecular evidence, we arrived to the conclusion that arrival of this clone in Malta through human introduction in the sixteenth century is the most likely explanation, since alternative scenarios like autovegetative propagation or arrival by seed seem highly unlikely.

Difficulties in business liquidity and the consequent financial distress are usually an extremely costly and disruptive event. For this reason, this study attempts to provide a set of features that can help us predict the sustainability of a company. This study involves the building of a financial prediction system which after training on a set of companies' historical final accounts (ranging over a period of 3 to 5 years), the models built are then capable of evaluating the nature of another companies' financial data. Consequently, the company's financial position in the following financial period is predicted (whether a company is active or failing). After predicting firm financial health, the outputs of the Decision Tree, the Naïve Bayes classifier and the Artificial Neural Net are evaluated to see which algorithm is the most accurate in finding a set of features for this problem. The research findings over a reallife datasets confirmed the strength and ability of the proposed model in predicting eminent business failure. Moreover, Base-year and year-over-year comparison both produce good results, therefore both techniques can be used for financial analysis. The optimal feature set included ratios from all categories, meaning, company profitability, liquidity, leverage, management efficiency, industry type and company size are all crucial to distress prediction. The prototype implemented in this study attempts to answer open questions, such as whether ML techniques are capable of predicting financial distress and whether financial ratios and industry variables are indicative of financial sustainability.

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The purpose and scope of this studyThis study was part of a two-year research project financed by the European Regional Development Fund (ERDF) called Research Services in Manufacturing: ICT in Manufacturing. The objective of this process was to create a software solution that allows operators to model production lines and determine and classify the scheduling problems represented by the model. It has long been felt that not enough information is available regarding the scheduling problems affecting the manufacturing industry in Malta. This project was undertaken to gain additional information on the state of scheduling in the local manufacturing industry and to provide an extensible framework that can be used for current analysis and future research.This study focused on providing a graphical means of representing scheduling problems. This representation describes the abstraction of the production line involved in manufacturing a product. The second aim of the study is to create a set of heuristics using the model to classify the scheduling problem according to its computational complexity, and when possible, pointing to the relevant literature used to derive the classification in order to help researchers understand the problem better.Scheduling involves efficient utilisation of scarce resources. While this study focuses on scheduling in manufacturing industry, Leung draws a parallel to the problems encountered by computer scientists in the 1960s when computational resources (CPU, memory and I/O devices) were scarce [8]. This results of this study are of interest to computer scientists, operators in the manufacturing industry whose work deals with scheduling and to researchers in the field of optimisation. AbstractOptimisation of production lines is known to be NP-Hard in the general case so many near-optimal approximation algorithms have been researched to overcome the challenge [1]. In this paper we describe an approach to modelling production lines using a graph theoretic model. In particular, we focus on single machine and job shop problems. We show that the model can be extended to open shop problems. We also discuss how the model can be used to classify scheduling problems from the generated models. Int J Comput Softw EngIJCSE, an open access journal