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Tomato is the second most important vegetable in the world, both in terms of production and consumption. Especially for the cultivation of industrial tomato, harvest is conducted when the total soluble solids, a major quality characteristic, are as high as possible. Advancements in technology have made Decision Support Systems simpler and more applicable in an everyday basis. Data Analysis, combined with Machine Learning algorithms are considered the future of sustainable agriculture, allowing farmers to be advised about the best possible decisions for their cultivation. Farmers need to adopt this kind of technology in order to be able to know when the quality of tomatoes is at its peak, in order to gather their product from the field. The implementation of a Decision Support System to predict the total soluble solids was conducted, based on data from previous years, including quality data (pH, Bostwick, L, a/b, Mean Weight, °Brix), the type of hybrid used, weather data and soil data from the fields. Data derived from fields in 6 different regions in the northwestern Peloponnese, Greece over 6 cultivation periods, created a dataset of 33 different inputs. Thirteen different algorithms were put into evaluation in order to find the best one in terms of speed and efficiency. In this research, we developed a Decision Support System using the K-nearest algorithm, which proved to be the best for our dataset. The predicted °Brix were following the same pattern as the actual °Brix. This means that the DSS could advise the farmer about the ideal harvesting period where the °Brix will be maximized. This DSS which is using real time weather data as an input is expected to be a valuable tool for the farmers. © 2022

Background: Autoimmune rheumatic diseases (ARDs) may affect both the heart and the brain. However, little is known about the interaction between these organs in ARD patients. We asked whether brain lesions are more frequent in ARD patients with cardiac symptoms compared with non-ARD patients with cardiovascular disease (CVD). Methods: 57 ARD patients with mean age of 48 ± 13 years presenting with shortness of breath, chest pain, and/or palpitations, and 30 age-matched disease-controls with non-autoimmune CVD, were evaluated using combined brain– heart magnetic resonance imaging (MRI) in a 1.5T system. Results: 52 (91%) ARD patients and 16 (53%) controls had white matter hyperintensities (p < 0.001) in at least one brain area (subcortical/deep/periventricular white matter, basal ganglia, pons, brainstem, or mesial temporal lobe). Only the frequency and number of subcortical and deep white matter lesions were significantly greater in ARD patients (p < 0.001 and 0.014, respectively). ARD vs. control status was the only independent predictor of having any brain lesion. Specifically for deep white matter lesions, each increase in ECV independently predicted a higher number of lesions [odds ratio (95% confidence interval): 1.16 (1.01–1.33), p = 0.031] in ordered logistic regression. Penalized logistic regression selected only ARD vs. control status as the most important feature for predicting whether brain lesions were present on brain MRI (odds ratio: 5.46, marginal false discovery rate = 0.011). Conclusions: Subclinical brain involvement was highly prevalent in this cohort of ARD patients and was mostly independent of the severity of cardiac involvement. However, further research is required to determine the clinical relevance of these findings. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Background: Acute cardiac events are a significant contributor to mortality in systemic sclerosis (SSc). However, echocardiographic evaluation may be deceptively normal during an acute presentation. We hypothesized that in diffiuse SSc patients presenting with acute cardiac events and a normal echocardiogram, cardiovascular magnetic resonance (CMR) would have incremental diagnostic/prognostic value. Methods. 50 consecutive diffiuse SSc patients with normal echocardiograms were evaluated using a 1.5T system. A total of 27 (63%) had experienced an acute cardiac event three to tendays beforeCMRevaluation (rhythm disturbances, angina pectoris, shortness of breath). Left/right ventricular (LV/RV) volumes and ejection fractions (EF), as well as LV mass, the T2-signal ratio, early/late gadolinium enhancement (EGE/LGE), native/post-contrast T1-mapping, T2-mapping and extracellular volume fraction (ECV) were compared between the event and no-event groups. Results: No differences were identified in LV/RV volumes/EF/mass. In logistic regression analyses, independent predictors of belonging to the event group were EGE (odds ratio (95% CI): 1.55 (1.06-2.26), p = 0.024), LGE (1.81 (1.23-2.67), p = 0.003), T2 mapping (1.20 (1.06-1.36), p = 0.004) and native/post-contrast T1 mapping (1.17 (1.04-1.32), p = 0.007 and 0.86 (0.75-0.98), p = 0.025). At a median follow-up of ∼1.2 years, 42% vs. 11% of the event/no-event group respectively reached a combined endpoint of event occurrence/recurrence or cardiovascular mortality. Of the independent predictors resulting from logistic regression analyses, only LGE (hazard ratio (95% CI): 1.20 (1.11-1.30), p < 0.001), T2-mapping (1.07 (1.01-1.14), p = 0.025) and native T1-mapping (1.08 (1.01-1.15), p = 0.017) independently predicted the combined endpoint. Conclusions: A normal echocardiogram does not preclude myocardial lesions in diffiuse SSc patients, which can be detected by CMR especially in symptomatic patients. © 2019 by the authors.

The new knowledge-based economy necessitates increasingly the collaboration between different organisations. Despite the recent upsurge in knowledge management and decision support systems, the vast majority of these systems focus on individual organisations. This article introduces the concept of the Learning Network - inter-organisational structures, formally established to increase the participants' knowledge and innovative capability - and examines the main functions and roles of a Learning Network. It presents an integrated toolkit for supporting knowledge sharing and decision making in Learning Networks that consists of a software system and a methodology. It also briefly presents how the toolkit has been piloted in an automotive cluster. Finally it provides a constructive set of recommendations for using IT to support learning and knowledge sharing in Learning Networks.

Cystic adventitial disease (CAD) of the popliteal artery is a rare but well-known cause of intermittent claudication, especially in young patients. The etiology of the disease is still controversial and the literature reports various hypotheses for its origin. Diagnosis starts with thorough history taking and physical examination; non invasive diagnostic studies comprise color duplex scanner (ECD), computed tomography (CT), better if elicoidal (3D CT) and magnetic resonance imaging (MRI), which can aid in establishing correct recognition of the disease in most cases. A 48-year-old man presented with intermittent right calf claudication that had begun 4 months earlier; the symptom-free interval was about 100 m. MRI and MR angiography of right popliteal fossa revealed the presence of an oval cystic (maximum diameter 45 mm). The caudal aspect of the cyst showed pedicles protruding between the popliteal vein and the popliteal artery that compressed the artery, causing complete occlusion of its lumen. Surgery was performed through the posterior approach using an S-shaped incision; the affected segment of the popliteal artery was successfully excised and replaced with an autogenous external saphenous vein graft. A follow-up is underway, both clinical and with; no cyst recurrence has so far been detected either clinically or by duplex scanner during the 15-month postoperative follow-up period; the graft is patent and the patient is completely symptom free. Severe claudication in young patients, possibly without significant vascular risk factors, should prompt the clinical suspicion of adventitial cystic disease of the popliteal artery. Medical history, clinical examination and non invasive instrumental investigations, such as duplex scanner, elicoidal CT and/or MRI, may aid in establishing the correct diagnosis.

The overall objective of the INKASS ("Intelligent Knowledge Asset Sharing and Trading") project is to develop an intelligent Internet-based marketplace of knowledge assets. At the core of our technological approach stands a case-based reasoning approach for ascertaining knowledge assets for relevant user contexts and a comprehensive metadata description of information objects which represent tradeable knowledge products. The research approach and the e-marketplace developed for a management consultancy is discussed in its first version in this paper

The increasing need of small knowledge-intensive companies for loosely-coupled collaboration and ad-hoc knowledge sharing has led to a strong requirement for an alternative approach to developing knowledge management systems. This paper proposes a framework for managing organisational knowledge that builds on a socio-technical perspective and considers people as well as technology as two highly interconnected components. We introduce a conceptualised system architecture that merges enterprise social software characteristics from the realm of Enterprise 2.0, and information processing techniques from the domain of Semantic Web technologies. In order to deliver a KM approach that could assist in reducing the socio-technical gap, we suggest deploying such a solution using an integrated sociotechnical implementation methodology.