Η φυσική δραστηριότητα που προσφέρουν τα αθλήματα της καλαθοσφαίρισης και ποδοσφαίρισης σε παιδιά ηλικίας 6-8 ετών

Τα τελευταία χρόνια η χώρα μας αντιμετωπίζει οξύ πρόβλημα παιδικής παχυσαρκίας. Ένας σημαντικός παράγοντας για την καταπολέμηση του φαινομένου είναι η Φυσική Δραστηριότητα (ΦΔ), ωστόσο, τα επίπεδα ΦΔ των παιδιών σήμερα είναι χαμηλά, γεγονός που επιτείνει την ανάγκη για την ενίσχυσή της. Σε αυτή την κατεύθυνση, μπορεί να συμβάλλει σημαντικά η συμμετοχή των παιδιών στον αθλητισμό, εντούτοις, τα ερευνητικά ευρήματα είναι ιδιαίτερα περιορισμένα, με αποτέλεσμα να μη δίνεται μια ξεκάθαρη εικόνα για τη ΦΔ που προσφέρει σε ένα παιδί η συμμετοχή του σε κάποιο άθλημα. Σκοπός αυτής της έρευνας ήταν να μελετηθεί η ΦΔ που προσφέρει η συμμετοχή αγοριών παιδικής ηλικίας στα αθλήματα της Ποδοσφαίρισης και της Καλαθοσφαίρισης. Για τον σκοπό αυτό, 27 αγόρια ηλικίας 6-8 ετών (Μ=7,26, SD= 0,86), τα οποία συμμετείχαν σε προγράμματα Ποδοσφαίρισης (n=12) και Καλαθοσφαίρισης (n=15), φόρεσαν βηματόμετρα Omron HJ-720IT-E2, κατά τη διάρκεια μίας τυχαία επιλεγμένης προπόνησης του αθλήματος στο οποίο συμμετείχαν. Επίσης, καταγράφηκαν τα σωματομετρικά χαρακτηριστικά των συμμετεχόντων και υπολογίστηκε ο Δείκτης Μάζας Σώματος (ΔΜΣ). Για τη στατιστική ανάλυση των δεδομένων χρησιμοποιήθηκαν t-tests για ανεξάρτητα δείγματα, ορίζοντας ως ανεξάρτητη μεταβλητή το άθλημα (Ποδοσφαίριση vs Καλαθοσφαίριση) και ως εξαρτημένες μεταβλητές τον ΔΜΣ των παιδιών, το σύνολο των βημάτων που συγκέντρωσαν και τα αερόβια βήματά τους. Από τα αποτελέσματα φάνηκε ότι υπήρξε στατιστικά σημαντική διαφορά στον ΔΜΣ μεταξύ των δύο ομάδων (t25=3.011, p<.006), με τα παιδιά της Ποδοσφαίρισης να έχουν υψηλότερο ΔΜΣ. Αναφορικά με τη βηματομετρική δραστηριότητα των παιδιών, παρότι τα παιδιά που συμμετείχαν στο άθλημα της Καλαθοσφαίρισης συγκέντρωσαν συνολικά περισσότερα βήματα (M=2.431,8 ± 503) από αυτά της Ποδοσφαίρισης (M=2.125,58 ± 199,49), η διαφορά μεταξύ τους δεν ήταν στατιστικά σημαντική (p=.06). Ωστόσο, στατιστικά σημαντική ήταν η διαφορά μεταξύ των δύο ομάδων στα αερόβια βήματα (t25=4.11, p<.001), με τα παιδιά της Καλαθοσφαίρισης να συγκεντρώνουν περισσότερα. Από τα παραπάνω συμπεραίνεται ότι και τα δύο αθλήματα προσφέρουν μια σημαντική ποσότητα ΦΔ στα παιδιά που συμμετέχουν σε αυτά. Περαιτέρω έρευνα είναι απαραίτητη, ώστε να μελετηθεί η ΦΔ που προσφέρει μια ευρεία γκάμα αθλημάτων, ενώ κρίνεται αναγκαία η ευαισθητοποίηση των αθλητικών φορέων για τον σημαντικό ρόλο που μπορούν να διαδραματίσουν στην κατεύθυνση της ενίσχυσης της ΦΔ και της υγείας των παιδιών.ΟΧ

Detection of Neolithic Settlements in Thessaly (Greece) Through Multispectral and Hyperspectral Satellite Imagery

Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 – 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate its potential to detect Neolithic settlements. Hundreds of sites were geo-referenced through systematic GPS surveying throughout the region. Data from four primary sensors were used, namely Landsat ETM, ASTER, EO1 - HYPERION and IKONOS. A range of image processing techniques were originally applied to the hyperspectral imagery in order to detect the settlements and validate the results of GPS surveying. Although specific difficulties were encountered in the automatic classification of archaeological features composed by a similar parent material with the surrounding landscape, the results of the research suggested a different response of each sensor to the detection of the Neolithic settlements, according to their spectral and spatial resolution

Outcomes of elective liver surgery worldwide: a global, prospective, multicenter, cross-sectional study

Background: The outcomes of liver surgery worldwide remain unknown. The true population-based outcomes are likely different to those vastly reported that reflect the activity of highly specialized academic centers. The aim of this study was to measure the true worldwide practice of liver surgery and associated outcomes by recruiting from centers across the globe. The geographic distribution of liver surgery activity and complexity was also evaluated to further understand variations in outcomes. Methods: LiverGroup.org was an international, prospective, multicenter, cross-sectional study following the Global Surgery Collaborative Snapshot Research approach with a 3-month prospective, consecutive patient enrollment within January–December 2019. Each patient was followed up for 90 days postoperatively. All patients undergoing liver surgery at their respective centers were eligible for study inclusion. Basic demographics, patient and operation characteristics were collected. Morbidity was recorded according to the Clavien–Dindo Classification of Surgical Complications. Country-based and hospital-based data were collected, including the Human Development Index (HDI). (NCT03768141). Results: A total of 2159 patients were included from six continents. Surgery was performed for cancer in 1785 (83%) patients. Of all patients, 912 (42%) experienced a postoperative complication of any severity, while the major complication rate was 16% (341/2159). The overall 90-day mortality rate after liver surgery was 3.8% (82/2,159). The overall failure to rescue rate was 11% (82/ 722) ranging from 5 to 35% among the higher and lower HDI groups, respectively. Conclusions: This is the first to our knowledge global surgery study specifically designed and conducted for specialized liver surgery. The authors identified failure to rescue as a significant potentially modifiable factor for mortality after liver surgery, mostly related to lower Human Development Index countries. Members of the LiverGroup.org network could now work together to develop quality improvement collaboratives

Horticulture Into Landscape: The Career Of Warren Manning

This dissertation focuses on the career of one of the founding members of the American Society of Landscape Architects: Warren Henry Manning (1860-1938). It uses texts that Manning published about gardening, his garden commissions, and the reception of his garden work to explain: (i) how Manning parleyed his horticultural expertise into the design of gardens, (ii) what professional identity he crafted for himself, and (iii) how garden-making ultimately failed to satisfy his professional ambitions. In doing so, it situates Manning’s work in a historical continuum, and it identifies certain inhibitions that seem to pervade his professional practice. The significance of these inhibitions is explained by transposing cognitive models that have been developed for works of literary fiction to the context of landscape architecture

Survey on Security Threats in Agricultural IoT and Smart Farming

The agriculture sector has held a major role in human societies across the planet throughout history. The rapid evolution in Information and Communication Technologies (ICT) strongly affects the structure and the procedures of modern agriculture. Despite the advantages gained from this evolution, there are several existing as well as emerging security threats that can severely impact the agricultural domain. The present paper provides an overview of the main existing and potential threats for agriculture. Initially, the paper presents an overview of the evolution of ICT solutions and how these may be utilized and affect the agriculture sector. It then conducts an extensive literature review on the use of ICT in agriculture, as well as on the associated emerging threats and vulnerabilities. The authors highlight the main ICT innovations, techniques, benefits, threats and mitigation measures by studying the literature on them and by providing a concise discussion on the possible impacts these could have on the agri-sector

Blockchain in Agriculture Traceability Systems: A Review

Food holds a major role in human beings’ lives and in human societies in general across the planet. The food and agriculture sector is considered to be a major employer at a worldwide level. The large number and heterogeneity of the stakeholders involved from different sectors, such as farmers, distributers, retailers, consumers, etc., renders the agricultural supply chain management as one of the most complex and challenging tasks. It is the same vast complexity of the agriproducts supply chain that limits the development of global and efficient transparency and traceability solutions. The present paper provides an overview of the application of blockchain technologies for enabling traceability in the agri-food domain. Initially, the paper presents definitions, levels of adoption, tools and advantages of traceability, accompanied with a brief overview of the functionality and advantages of blockchain technology. It then conducts an extensive literature review on the integration of blockchain into traceability systems. It proceeds with discussing relevant existing commercial applications, highlighting the relevant challenges and future prospects of the application of blockchain technologies in the agri-food supply chain

The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers

Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this context, this study proposes and describes a holistic methodology starting from the generation of zero-day-type, yet realistic, data in tabular format and concluding to the evaluation of a Neural Network zero-day attacks’ detector which is trained with and without synthetic data. This methodology involves the design and employment of Generative Adversarial Networks (GANs) for synthetically generating a new and larger dataset of zero-day attacks data. The newly generated, by the Zero-Day GAN (ZDGAN), dataset is then used to train and evaluate a Neural Network classifier for zero-day attacks. The results show that the generation of zero-day attacks data in tabular format reaches an equilibrium after about 5000 iterations and produces data that are almost identical to the original data samples. Last but not least, it should be mentioned that the Neural Network model that was trained with the dataset containing the ZDGAN generated samples outperformed the same model when the later was trained with only the original dataset and achieved results of high validation accuracy and minimal validation loss

An Artificial Intelligence-Based Approach for the Controlled Access Ramp Metering Problem

The ever-increasing demand for transportation of people and goods as well as the massive accumulation of population in urban centers have increased the need for appropriate infrastructure and system development in order to efficiently manage the constantly increasing and diverse traffic flows. Moreover, given the rapid growth and the evolution of Information and Communication Technologies (ICT), the development of intelligent traffic management systems that go beyond traditional approaches is now more feasible than ever. Nowadays, highways often have sensors installed across their range that collect data such as speed, density, direction and so on. In addition, the rapid evolution of vehicles with installed computer systems and sensors on board, provides a very large amount of data, ranging from very simple features such as speed, acceleration, etc. to very complex data like the driver’s situation and driving behavior. However, these data alone and without any further processing, cannot solve the congestion problem. Therefore, the development of complex computational methods and algorithms underpins the chance to process these data in a fast and reliable way. The purpose of this paper is to present a traffic control ramp metering (RM) method based on machine learning and to study its impact on a selected highway segment