WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed

Machine Vision for Ripeness Estimation in Viticulture Automation

Ripeness estimation of fruits and vegetables is a key factor for the optimization of field management and the harvesting of the desired product quality. Typical ripeness estimation involves multiple manual samplings before harvest followed by chemical analyses. Machine vision has paved the way for agricultural automation by introducing quicker, cost-effective, and non-destructive methods. This work comprehensively surveys the most recent applications of machine vision techniques for ripeness estimation. Due to the broad area of machine vision applications in agriculture, this review is limited only to the most recent techniques related to grapes. The aim of this work is to provide an overview of the state-of-the-art algorithms by covering a wide range of applications. The potential of current machine vision techniques for specific viticulture applications is also analyzed. Problems, limitations of each technique, and future trends are discussed. Moreover, the integration of machine vision algorithms in grape harvesting robots for real-time in-field maturity assessment is additionally examined

A Partially Hydrolyzed Whey Infant Formula Supports Appropriate Growth: A Randomized Controlled Non-Inferiority Trial

The aim of the current study was to investigate the effects of a partially hydrolyzed whey infant formula (PHF) on growth in healthy term infants as compared to a standard infant formula with intact protein (IPF). In a double-blind, non-inferiority, randomized controlled trial, a total of 163 healthy formula-fed infants, 55–80 days old, were recruited and randomly allocated to either the PHF (test) or the IPF (control) group. They were followed up for three months during which they were evaluated monthly on growth and development. In total, 21 infants discontinued the study, while 142 infants completed the study (test n = 72, control n = 70). The primary outcome was daily weight gain during the three months. Secondary outcomes included additional anthropometric indices at every timepoint over the intervention period. Daily weight gain during the three-month intervention period was similar in both groups with the lower bound of 95% confidence interval (CI) above the non-inferiority margin of −3 g/day [mean difference (95% CI) test vs. control: −0.474 (−2.460, 1.512) g/day]. Regarding secondary outcomes, i.e., infants’ weight, length, head circumference, body mass index (BMI), and their Z-scores, no differences were observed between the two groups at any time point. The PHF resulted in similar infant growth outcomes as the standard IPF. Based on these results, it can be concluded that the partially hydrolyzed whey infant formula supports adequate growth in healthy term infants

Toward Big Data Manipulation for Grape Harvest Time Prediction by Intervals' Numbers Techniques

The automation of agricultural production calls for accurate prediction of the harvest time. Our interest in particular here is in grape harvest time. Nevertheless, the latter prediction is not trivial also due to the scale of data involved. We propose a novel neural network architecture that processes whole histograms induced from digital images. A histogram is represented by an Intervals' Number (IN); hence, all-order data statistics are represented. In conclusion, the proposed IN Neural Network, or INNN for short, emerges with the capacity of predicting an IN from past INs. We demonstrate a proof-of-concept, preliminary application on a time series of digital images of grapes taken during their growth to maturity. Compared to a conventional Back Propagation Neural Network (BPNN), the results by INNN are superior not only in terms of prediction accuracy but also because the BPNN predicts only first-order data statistics, whereas the INNN predicts all-order data statistics

Opportunistic screening for hypertension in the general population in Greece: International Society of Hypertension May Measurement Month 2019

Hypertension remains a major public health issue with inadequate control worldwide. The May Measurement Month (MMM) initiative by the International Society of Hypertension was implemented in Greece in 2019 aiming to raise hypertension awareness and control. Adult volunteers (>= 18years) were recruited through opportunistic screening in five urban areas. Information on medical history and triplicate sitting blood pressure (BP) measurements were obtained using validated automated upper-arm devices. Hypertension was defined as systolic BP >= 140mmHg and/or diastolic >= 90mmHg, and/or self-reported use of drugs for hypertension. A total of 5727 were analysed [mean age 52.7 (SD 16.6) years, men 46.5%, 88.3% had BP measurement in the last 18months]. The prevalence of hypertension was (41.6%) and was higher in men and in older individuals. Among individuals with hypertension, 78.7% were diagnosed, 73.1% treated, and 48.3% controlled. Awareness, treatment, and control of hypertension were higher in women and in older individuals. Hypertensives had a higher body mass index (BMI) and were more likely to have diabetes, myocardial infarction and stroke, and less likely to smoke than normotensives (all P<0.001). Among treated hypertensives, 65.1% were on monotherapy, and with increasing number of antihypertensive drugs the BP levels were higher and hypertension control rates lower. The prevalence of hypertension in Greece is high, with considerable potential for improving awareness, treatment, and control. Screening programmes, such as MMM, need to be widely implemented at the population level, together with training programmes for healthcare professionals aiming to optimise management and control