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Introduction: This was a feasibility pilot study aiming to develop and validate an activity recognition system based on a custom-made body-fixed sensor and driven by an algorithm for recognising basic kinetic movements in military personnel. The findings of this study are deemed essential in informing our development process and contributing to our ultimate aim which is to develop a low-cost and easy-to-use body-fixed sensor for military applications., Methods: Fifty military participants performed a series of trials involving walking, running and jumping under laboratory conditions in order to determine the optimal, among five machine learning (ML), classifiers. Thereafter, the accuracy of the classifier was tested towards the prediction of these movements (15 183 measurements) and in relation to participants' gender and fitness level., Results: Random forest classifier showed the highest training and validation accuracy (98.5% and 92.9%, respectively) and classified participants with differences in type of activity, gender and fitness level with an accuracy level of 83.6%, 70.0% and 62.2%, respectively., Conclusions: The study showed that accurate prediction of various dynamic activities can be achieved with high sensitivity using a low-cost easy-to-use sensor and a specific ML model. While this technique is in a development stage, our findings demonstrate that our body-fixed sensor prototype alongside a fully trained validated algorithm can strategically support military operations and offer valuable information to commanders controlling operations remotely. Further stages of our developments include the validation of our refined technique on a larger range of military activities and groups by combining activity data with physiological variables to predict phenomena relating to the onset of fatigue and performance decline., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

The purpose of this article is to assess the justness or unjustness of the securitization of Covid-19 in the Greek case. To do so, the Just Securitization Theory, is used as articulated by Rita Floyd to examine the following research question: Was there a just initiation and just conduct in the case of the securitization of Covid-19 in Greece? To answer this question, the methodology employed is a mix of methods proposed by Rita Floyd, such as the recourse to natural sciences to establish the actuality of the threat in combination with discourse analysis in political and scientific actors' speech acts and qualitative analysis of legal policy documents. In total, 85 statements and legal documents were examined for the period 26/02/2020 to 15/06/2020 which is considered to be the first "phase" of the pandemic. Overall, the analysis shows that the securitization of the Covid-19 pandemic in Greece was just. More specifically, there was indeed an existential threat, the securitizing actors were sincere in their intentions to securitize Covid-19, whereas the good gained from this securitization (physical survival and protection of public health) was greater than the harm inflicted (suspension of liberties). In addition, the results show that the securitization of Covid-19 in Greece did not cause more insecurity than it aimed to solve and that the overall response from the Greek Government was proportionate to the threat and respectful of the human rights of the people secured by the threat., Competing Interests: Conflict of interestAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022.)

Global warming affects the aquatic ecosystems, accelerating pathogenic microorganisms' and toxic microalgae's growth and spread in marine habitats, and in bivalve molluscs. New parasite invasions are directly linked to oceanic warming. Consumption of pathogen-infected molluscs impacts human health at different rates, depending, inter alia, on the bacteria taxa. It is therefore necessary to monitor microbiological and chemical contamination of food. Many global cases of poisoning from bivalve consumption can be traced back to Mediterranean regions. This article aims to examine the marine bivalve's infestation rate within the scope of climate change, as well as to evaluate the risk posed by climate change to bivalve welfare and public health. Biological and climatic data literature review was performed from international scientific sources, Greek authorities and State organizations. Focusing on Greek aquaculture and bivalve fisheries, high-risk index pathogenic parasites and microalgae were observed during summer months, particularly in Thermaikos Gulf. Considering the climate models that predict further temperature increases, it seems that marine organisms will be subjected in the long term to higher temperatures. Due to the positive linkage between temperature and microbial load, the marine areas most affected by this phenomenon are characterized as 'high risk' for consumer health., (© 2021 Society for Applied Microbiology and John Wiley & Sons Ltd.)

Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the outbreak of the novel COVID-19 there has been a rush for developing automatic techniques for expert-level disease identification on Chest X-ray data. In particular, the use of deep supervised learning has become the go-to paradigm. However, the performance of such models is heavily dependent on the availability of a large and representative labelled dataset. The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease. Semi-supervised learning has shown the ability to match the incredible performance of supervised models whilst requiring a small fraction of the labelled examples. This makes the semi supervised paradigm an attractive option for identifying COVID-19. In this work, we introduce a graph based deep semi-supervised framework for classifying COVID-19 from chest X-rays. Our framework introduces an optimisation model for graph diffusion that reinforces the natural relation among the tiny labelled set and the vast unlabelled data. We then connect the diffusion prediction output as pseudo-labels that are used in an iterative scheme in a deep net. We demonstrate, through our experiments, that our model is able to outperform the current leading supervised model with a tiny fraction of the labelled examples. Finally, we provide attention maps to accommodate the radiologist's mental model, better fitting their perceptual and cognitive abilities. These visualisation aims to assist the radiologist in judging whether the diagnostic is correct or not, and in consequence to accelerate the decision., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier Ltd. All rights reserved.)