Proportional similarity-based Openmax classifier for open set recognition in SAR images

Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the “closed world” assumption, i.e., they only work with what was previously observed. Nevertheless, the real world is relatively “open” in the sense that the knowledge of the environment is incomplete. Therefore, unknown targets can feed the recognition system at any time while it is operational. Addressing this issue, the Openmax classifier has been recently proposed in the optical domain to make convolutional neural networks (CNN) able to reject unknown targets. There are some fundamental limitations in the Openmax classifier that can end up with two potential errors: (1) rejecting a known target and (2) classifying an unknown target. In this paper, we propose a new classifier to increase the robustness and accuracy. The proposed classifier, which is inspired by the limitations of the Openmax classifier, is based on proportional similarity between the test image and different training classes. We evaluate our method by radar images of man-made targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Moreover, a more in-depth discussion on the Openmax hyper-parameters and a detailed description of the Openmax functioning are given

Three-Dimensional Polarimetric InISAR Imaging of Non-Cooperative Targets

A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3D imaging method for non-cooperative targets is proposed in this paper. 3D imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2D ISAR imaging. In the previously reported single-polarimetry InISAR based 3D imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3D ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3D imaging of the target is possible. To validate this proposed Pol-InISAR based 3D imaging approach, both simulated and real datasets are taken under consideration

Optimizing Planting Density for Increased Resource Use Efficiency in Baby-Leaf Production of Lettuce (Lactuca sativa L.) and Basil (Ocimum basilicum L.) in Vertical Farms

Vertical farming is gaining popularity as a sustainable solution to global food demand, particularly in urban areas where space is limited. However, optimizing key factors such as planting density remains a critical issue, as it directly affects light interception, energy efficiency, and crop yield. Lettuce and basil, the most commonly grown crops in vertical farms, were chosen for this study, with the aim of addressing the impact of planting density on light interception and overall productivity for improving the performance and sustainability of vertical farming systems. Plants were grown in an ebb-and-flow system of a fully controlled experimental vertical farm, where light was provided by light-emitting diode fixtures delivering a photoperiod of 16 h d−1 and 200 μmol m−2 s−1 of photosynthetic photon flux density. Experimental treatments included three planting densities, namely 123 (low density, LD), 237 (medium density, MD), and 680 (high density, HD) plant m−2. At the final harvest (29 days after sowing), the adoption of the highest planting density (680 plant m−2) resulted in greater fresh yield (kg FW m−2), leaf area index (LAI, m2 m−2), light use efficiency (LUE, g DW mol−1) and light energy use efficiency (L-EUE, g FW kWh−1) for both lettuce (+207%, +227%, +142%, +206%, respectively), and basil (+312%, +316%, +291, +309%, respectively), as compared to the lowest density (123 plant m−2). However, the fresh and dry weights of the individual plants were lowered, probably as a result of the reduced light availability due to the highly dense plants’ canopy. Overall, these findings underscore the potential of increasing planting density in vertical farms to enhance yield and resource efficiency

Healthcare and long-term care workforce: demographic challenges and potential contribution of migration and digital technology

In the EU, the right to timely access the ‘affordable, preventive and curative health care of good quality’ and the right to ‘affordable long-term services of good quality’ are enshrined in the European Pillar of Social Rights (C(2017) 2600 final). The backbone of health and long-term care (LTC) systems’ capacity to ensure that EU citizens can exercise these rights is its workforce. The COVID-19 pandemic has put the resilience of national health and LTC systems to the test and has made it even more tangible that ‘health is a precondition for our society and economy to function’ (COM(2020)724 final). However, even prior to the COVID-19 outbreak, the national health and LTC systems were faced with an unprecedented challenge with regard to the progressive ageing population in the EU. The rise in the number of elderly people has been increasing the demand for health and LTC services which, in turn, has generated a rising demand for a qualified health and LTC workforce. In the period 2018-2030 alone, the EU-27 will need 10.9 million newly trained or imported health and LTC workers to satisfy the rising demand in the health and LTC sectors. Planning a health and LTC workforce that has the size and skills suitable to satisfy the demand is a challenging task, given the numerous and often interrelated factors in play. These factors range from the demographic and health characteristics of a population, a country’s economic growth, technology, the migration of health and LTC professionals, to education and retirement policies. This implies a need for a holistic approach in workforce planning, capable of incorporating and coordinating various policy domains at local, national and EU level. Drawing on research activities carried out at the Joint Research Centre (JRC) – specifically within the framework of the Commission’s Knowledge Centre on Migration and Demography (KCMD) and the Centre for Advanced Studies HUMAINT project – this report aims to contribute to workforce planning by enhancing the scientific knowledge in three specific domains: demography, migration and digital technology. More specifically, the aim of this report is to provide scientific insights into the role of demographic change, migration and intra-EU mobility, as well as digital technology, in determining the demand and the supply of health and LTC workers in an effort to inform the EU’s workforce planning policies.JRC.E.6 - Demography, Migration and Governanc

The Demographic Landscape of EU Territories

This report aims at detailing the territorial diversities of ageing across the EU, understanding the main drivers behind such differences and explore their relations with data on access to services and amenities, regional economic performance, political attitudes and behaviours.JRC.E.6 - Demography, Migration and Governanc

Atlas of Demography

The Atlas of Demography is a new interactive tool from the European Commission. It brings together demographic data from official statistics and projections as well as new data produced by the Joint Research Centre. With this Atlas the EU citizen can better understand how demographic change is shaping the future of Europe. You can approach the Atlas in two main ways: You can look at geographic areas, from an EU overview to the national, regional and local dimensions (top menu). Or you can look at specific themes, which are presented as stories (bottom-right menu). Each tab opens a dashboard. A dashboard is a collection of interactive maps and charts that can be explored and customised through filters. By hovering the mouse on text, chart and maps, additional information will be shown. The HOME button leads back to this page.JRC.E.5 - Demography and Migratio

Access Rate to the Emergency Department for Venous Thromboembolism in Relationship with Coarse and Fine Particulate Matter Air Pollution

Particulate matter (PM) air pollution has been associated with cardiovascular and respiratory disease. Recent studies have proposed also a link with venous thromboembolism (VTE) risk. This study was aimed to evaluate the possible influence of air pollution-related changes on the daily flux of patients referring to the Emergency Department (ED) for VTE, dissecting the different effects of coarse and fine PM. From July 1st, 2007, to June 30th, 2009, data about ED accesses for VTE and about daily concentrations of PM air pollution in Verona district (Italy) were collected. Coarse PM (PM10-2.5) was calculated by subtracting the finest PM2.5 from the whole PM10. During the index period a total of 302 accesses for VTE were observed (135 males and 167 females; mean age 68.3±16.7 years). In multiple regression models adjusted for other atmospheric parameters PM10-2.5, but not PM2.5, concentrations were positively correlated with VTE (beta-coefficient = 0.237; P = 0.020). During the days with high levels of PM10-2.5 (≥75th percentile) there was an increased risk of ED accesses for VTE (OR 1.69 with 95%CI 1.13–2.53). By analysing days of exposure using distributed lag non-linear models, the increase of VTE risk was limited to PM10-2.5 peaks in the short-term period. Consistently with these results, in another cohort of subjects without active thrombosis (n = 102) an inverse correlation between PM10-2.5 and prothrombin time was found (R = −0.247; P = 0.012). Our results suggest that short-time exposure to high concentrations of PM10-2.5 may favour an increased rate of ED accesses for VTE through the induction of a prothrombotic state

Mapping the Demand Side of Computational Social Science for Policy

This report aims at collecting novel and pressing policy issues that can be addressed by Computational Social Science (CSS), an emerging discipline that is rooted in the increasing availability of digital trace data and computational resources and seeks to apply data science methods to social sciences. The questions were sourced from researchers at the European Commission who work at the interface between science and policy and who are well positioned to formulate research questions that are likely to anticipate future policy needs. The attempt is to identify possible directions for Computational Social Science starting from the demand side, making it an effort to consider not only how science can ultimately provide policy support — “Science for Policy – but also how policymakers can be involved in the process of defining and co-creating the CSS4P agenda from the outset — ‘Policy for Science’. The report is expected to raise awareness on the latest scientific advances in Computational Social Science and on its potential for policy, integrating the knowledge of policymakers and stimulating further questions in the context of future developments of this initiativeJRC.A.5 - Scientific Developmen

Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients