Exploring Why Organisations Differ in Board Configuration: Do Organisations’ Age, Size and their Years Being Public Matter? Findings from Greek Listed Manufacturing Organisations

In this paper building upon several theories (agency theory, stakeholder theory and resource dependence theory) and by utilising data from 161 Greek manufacturing companies that were listed in the Athens Stock Exchange on the 31st December 2008, we explore the relationships between the organisational characteristics of the firms (organisational age, organisational size and years listed in the stock market) and the Board configuration (board size, board leadership structure and directors’ dependence/independence). Both descriptive and inferential statistics (ANOVA tests) were utilised to answer the research questions. Interestingly and in alignment with the literature, the findings showed that larger organizations tend to have larger boards and greater proportions of external and independent directors. However, no more strong relationships have been identified between the organisational characteristics and the board configuration. Finally, it is worth mentioning that this study examines the listed Greek manufacturing companies during very turbulent times, the start of the financial crisis in Greece, which may have an impact on the configuration of the boards at that time

An agent-based intelligent environmental monitoring system

Fairly rapid environmental changes call for continuous surveillance and on-line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi-agent system for monitoring and assessing air-quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air-quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data-driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real-world test case is demonstrated.Comment: Multi-Agent Systems, Intelligent Applications, Data Mining, Inductive Agents, Air-Quality Monitorin

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

Updated version of final design and of the architecture of SEAMLESS-IF

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

DKM: Dense Kernelized Feature Matching for Geometry Estimation

Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense methods have previously shown inferior performance to their sparse and semi-sparse counterparts for estimation of two-view geometry. This changes with our novel dense method, which outperforms both dense and sparse methods on geometry estimation. The novelty is threefold: First, we propose a kernel regression global matcher. Secondly, we propose warp refinement through stacked feature maps and depthwise convolution kernels. Thirdly, we propose learning dense confidence through consistent depth and a balanced sampling approach for dense confidence maps. Through extensive experiments we confirm that our proposed dense method, \textbf{D}ense \textbf{K}ernelized Feature \textbf{M}atching, sets a new state-of-the-art on multiple geometry estimation benchmarks. In particular, we achieve an improvement on MegaDepth-1500 of +4.9 and +8.9 AUC$@5^{\circ}$ compared to the best previous sparse method and dense method respectively. Our code is provided at https://github.com/Parskatt/dk

Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous

Crystalline Lens Staining with Intracameral Phenylephrine During Cataract Surgery

This is a Photo Essay and does not have an abstract. Please download the PDF or view the article in HTML

Herpes Zoster Ophthalmicus: A Devastating Disease Coming Back with Vengeance or Finding Its Nemesis?

Herpes zoster ophthalmicus is a frequent, painful, and debilitating condition caused by the reactivation of the varicella-zoster virus alongside the ophthalmic branch of the trigeminal nerve. Twenty-five percent of adults will develop the disease during their lifetime with the risk increasing to one in two over the age of 50. Herpes zoster ophthalmicus presents with a plethora of ocular manifestations ranging from the characteristic rash in the distribution of the ophthalmic branch of the fifth cranial nerve to more severe keratouveitis, disciform keratitis, and even retinal necrosis. Up to 20% of affected patients develop post-herpetic neuralgia which can persist for years after the acute episode, resulting in potentially devastating consequences for the patient’s social, financial, and professional circumstances, as well as their quality of life and daily activities. Shingles prevention studies indicated that the herpes zoster vaccine markedly reduces the burden of the disease, as well as the incidence of both infection and post-herpetic neuralgia. Here we review the vaccinations available for herpes zoster, the reasons behind their limited adoption so far, as well as the future perspectives and challenges associated with this debilitating disease in the era of herpes zoster vaccination and coronavirus disease pandemic