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During the Bronze Age, the inhabitants of regions of Crete, mainland Greece, and Cyprus inscribed their languages using, among other scripts, a writing system called Linear A. These symbols, mainly characterized by combinations of lines, have, since their discovery, remained a mystery. Not only is the corpus very small, but it is challenging to link Minoan, the language behind Linear A, to any known language. Most decipherment attempts involve using the phonetic values of Linear B, a grammatological offspring of Linear A, to 'read' Linear A. However, this yields meaningless words. Recently, novel approaches to deciphering the script have emerged which involve a computational component. In this paper, two such approaches are combined to account for the biases involved in provisionally assigning Linear B phonetic values to Linear A and to shed more light on the possible connections of Linear A with other scripts and languages from the region. Additionally, the limitations inherent in such approaches are discussed. Firstly, a feature-based similarity measure is used to compare Linear A with the Carian Alphabet and the Cypriot Syllabary. A few Linear A symbols are matched with symbols from the Carian Alphabet and the Cypriot Syllabary. Finally, using the derived phonetic values, Linear A is compared with Ancient Egyptian, Luwian, Hittite, Proto-Celtic, and Uralic using a consonantal approach. Some possible word matches are identified from each language. [ABSTRACT FROM AUTHOR]

In viticulture, downy mildew is one of the most common diseases that, if not adequately treated, can diminish production yield. However, the uncontrolled use of pesticides to alleviate its occurrence can pose significant risks for farmers, consumers, and the environment. This paper presents a new framework for the early detection and estimation of the mildew's appearance in viticulture fields. The framework utilizes a protocol for the real-time acquisition of drones' high-resolution RGB images and a cloud-docker-based video or image inference process using object detection CNN models. The authors implemented their framework proposition using open-source tools and experimented with their proposed implementation on the debina grape variety in Zitsa, Greece, during downy mildew outbursts. The authors present evaluation results of deep learning Faster R-CNN object detection models trained on their downy mildew annotated dataset, using the different object classifiers of VGG16, ViTDet, MobileNetV3, EfficientNet, SqueezeNet, and ResNet. The authors compare Faster R-CNN and YOLO object detectors in terms of accuracy and speed. From their experimentation, the embedded device model ViTDet showed the worst accuracy results compared to the fast inferences of YOLOv8, while MobileNetV3 significantly outperformed YOLOv8 in terms of both accuracy and speed. Regarding cloud inferences, large ResNet models performed well in terms of accuracy, while YOLOv5 faster inferences presented significant object classification losses. [ABSTRACT FROM AUTHOR]

In this research, we present the first steps toward developing a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research holds significant importance as it can offer valuable insights into disease transmission patterns and assist in devising effective intervention strategies. To the best of our knowledge, no similar study has been conducted in Greece. We constructed a prototype ABM that utilizes publicly accessible data to accurately represent the complex interactions and dynamics of disease spread in the Greek population. By incorporating demographic information and behavioral patterns, our model captures the specific characteristics of Greece, enabling accurate and context-specific simulations. By using our proposed ABM, we aim to assist policymakers in making informed decisions regarding disease control and prevention. Through the use of simulations, policymakers have the opportunity to explore different scenarios and predict the possible results of various intervention measures. These may include strategies like testing approaches, contact tracing, vaccination campaigns, and social distancing measures. Through these simulations, policymakers can assess the effectiveness and feasibility of these interventions, leading to the development of well-informed strategies aimed at reducing the impact of infectious diseases on the Greek population. This study is an initial exploration toward understanding disease transmission patterns and a first step towards formulating effective intervention strategies for Greece. [ABSTRACT FROM AUTHOR]

The digitalization of agriculture generates a new environment for the actors involved in agrifood production. In such a context, Agricultural Knowledge and Innovation Systems (AKISs) face the challenge of reconsidering their operational paradigms, redefining priorities, and designing strategies to achieve new aims. To do so, the actors participating in AKISs should develop and exploit a set of competencies known as dynamic capabilities, including the aptitude to sense the change in the external environment, the capacity to seize the opportunities that this change creates, and an ability to transform and adapt themselves to the new conditions that digitalization generates. In this study, using as examples the AKISs operating in Greece and Italy, we aimed to uncover if and how actors participating in these systems attempt and manage to deploy such capabilities. Based on a qualitative approach and drawing on data from two workshops, we discovered that seizing the opportunities sensed is a challenging task for AKIS actors. Our results also indicate that knowledge is a pivotal resource for AKISs, allowing actors to enhance their transformative capacity. However, to create a "collective" knowledge base, AKISs should ensure a functional connection between stakeholders and strengthen the roles of actors not actively engaged with the system, like public advisory organizations, universities, and technology providers. [ABSTRACT FROM AUTHOR]