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Background: As stressed by COVID-19 pandemic, urbanicity might represent a risk factor for chronic non-communicable diseases or generally impacting on healthy lifestyle, among them physical activity. Methods: In light of this, we performed a systematic review aimed to explore the association between urban greenspaces and two important health indicators for both mental and physical health. In particular, our aims were to analyze the association between publicly accessible urban greenspaces exposure, and mental health outcomes (MH) and objectively-measured physical activity (PA). The review was conducted from 2000/ 01/01 to 2020/09/30 searching in two electronic databases: PubMed/Medline and Excerpta Medica dataBASE (EMBASE). Only articles in English were included. Results: Out of 356, a total of 34 studies were included in our review, of which 19 assessed MH outcomes, the remaining dealt with PA. Only a few included studies found a non-effect or a negative effect on MH outcomes, whereas, all the others demonstrated a positive effect of urban greenspace and both MH and PA. However, our results stressed not only the importance of green space presence, but also the importance of maintenance, renovation, closeness to residential areas, the presence of interactive activities, and perceived security aspects. Conclusions: To conclude, even some methodological limitations of the included studies, results are concordant in demonstrating that urban greenspaces show potentially beneficial effects on mental health and physical activity. Our results are significant for public health experts and policymakers involved in urban planning, community health promotion, and improvement of health and social equity. Key messages: • Urban green spaces are important factors, impacting on both physical and mental health. • Policymakers involved in urban planning should pay more attention in urban green spaces. [ABSTRACT FROM AUTHOR]

Background: In Europe, Public Health Residents (PHR) have participated in the response against COVID-19.The pandemic has had a profound mental health (MH) impact on healthcare workers, but little evidence has been accrued about the Public Health (PH) workforce.The current study aims at assessing prevalence and risk factors for pandemic-related depression (D), anxiety (A) and stress (S). Methods: Between March 22 and April 11, 2021 we administered an online survey to PHR from France, Italy, Portugal and Spain, recruited through the national associations of PHR.We collected socio-demographic data and assessed changes in the working conditions due to COVID-19.We used the Depression Anxiety Stress Scales-21.Cut-off scores for D, A and S were: 9, 7, and 14.Multivariable logistic regression models were applied to determine risk factors for pandemic-related MH outcomes.Data analysis was performed with SPSS version 27. Results: 443 out of approximately 2000 PHR completed the survey, response rate: 22.15%. Most responders (n=226; 51%) were from Italy, 87 (19.6%) from Portugal, 74 (16.47%) from Spain and 56 (12.6%) from France. Median age: 29 years (IQR 25 33). Female/male ratio: 1.6. PHR above the cut-offs for D, A and S were: 60.5%, 43.1%, 61.2%.DASS-21 scores were 13.3 (± 10.27), 8.13 (± 7.79), 19.06 (± 9.71), respectively.Across the multivariable logistic regression models, the main identified predictors were: for D - the COVID-19 impact on PH training (OR 1.78, 95% CI [1.12-2.80], p=0.014); for A - the loss of research opportunities (OR 1.94, 95% CI [1.28-2.93], p=0.002) and for psychological S - the year of residency (last year vs other years of residency: OR 2.3, 95% CI [1.204.38], p=0.012). Conclusions: Our sample featured mild-to-moderate levels of prevalent D, A and moderate levels of psychological stress.Overall, the main risk factors associated with negative psychometric outcomes were training challenges, loss of research opportunities and residency seniority. Key messages: During the pandemic it is vital to increase knowledge and awareness about the impact of the health emergency on mental health in the Public Health workforce. It is important to deploy concrete efforts into building healthy work environments, guarantee adequate training and research opportunities for Public Health Residents. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for the classification of targets based on the fusion of the class information provided by different imaging sensors. The outputs of the different sensors are combined to obtain an accurate estimate of the target class. The performance of each imaging sensor is modelled by means of its confusion matrix (CM), whose elements are the conditional error probabilities in the classification and the conditional correct classification probabilities. These probabilities are used by each sensor to make a decision on the target class. Then, a final decision on the class is made using a suitable fusion rule in order to combine the local decisions provided by the sensors. The overall performance of the classification process is evaluated by means of the "fused" confusion matrix, i.e. the CM pertinent to the final decision on the target class. Two fusion rules are considered: a majority voting (MV) rule and a maximum likelihood (ML) rule. A case study is then presented, where the developed algorithm is applied to three imaging sensors located on a generic air platform: a video camera, an infrared camera (IR), and a spotlight Synthetic Aperture Radar (SAR). [ABSTRACT FROM AUTHOR]