Contraceptive use and sexual function: a comparison of Italian female medical students and women attending family planning services

Objectives: The aims of the study were to understand how education relates to contraceptive choice and how sexual function can vary in relation to the use of a contraceptive method. Methods: We surveyed female medical students and women attending a family planning service (FPS) in Italy. Participants completed an online questionnaire which asked for information on sociodemographics, lifestyle, sexuality and contraceptive use and also included items of the Female Sexual Function Index (FSFI). Results: The questionnaire was completed by 413 women (362 students and 51 women attending the FPS) between the ages of 18 and 30 years. FSFI scores revealed a lower risk of sexual dysfunction among women in the control group who did not use oral hormonal contraception. The differences in FSFI total scores between the two study groups, when subdivided by the primary contraceptive method used, was statistically significant (p < 0.005). Women using the vaginal ring had the lowest risk of sexual dysfunction, compared with all other women, and had a positive sexual function profile. In particular, the highest FSFI domain scores were lubrication, orgasm and satisfaction, also among the control group. Expensive contraception, such as long-acting reversible contraception, was not preferred by this young population, even though such methods are more contemporary and manageable. Compared with controls, students had lower compliance with contraception and a negative attitude towards voluntary termination of pregnancy. Conclusion: Despite their scientific knowledge, Italian female medical students were found to need sexual and contraceptive assistance. A woman's sexual function responds to her awareness of her body and varies in relation to how she is guided in her contraceptive choice. Contraceptive counselling is an excellent means to improve female sexuality

Guided Deep Decoder: Unsupervised Image Pair Fusion

The fusion of input and guidance images that have a tradeoff in their information (e.g., hyperspectral and RGB image fusion or pansharpening) can be interpreted as one general problem. However, previous studies applied a task-specific handcrafted prior and did not address the problems with a unified approach. To address this limitation, in this study, we propose a guided deep decoder network as a general prior. The proposed network is composed of an encoder-decoder network that exploits multi-scale features of a guidance image and a deep decoder network that generates an output image. The two networks are connected by feature refinement units to embed the multi-scale features of the guidance image into the deep decoder network. The proposed network allows the network parameters to be optimized in an unsupervised way without training data. Our results show that the proposed network can achieve state-of-the-art performance in various image fusion problems.Comment: ECCV 202

La progettazione psicosociale nei progetti del Sistema di Accoglienza e Integrazione/SAIUn modello di intervento

The authors, psychologists, social workers, educators, care workers of the "Don Vincenzo Matrangolo" Association, aware that in the field of Reception and Inte-gration there is no shared methodology to define in detail the process of psychosocial planning, present the validation of an accurate and meticulous proposal in which they define the "phases, tools and timing" of a professional work that faces the complex emergency of migration

Assessment of hyperspectral sharpening methods for the monitoring of natural areas using multiplatform remote sensing imagery

— The use of cutting-edge geospatial technologies to monitor ecosystems and the development of tailored tools for assessing such natural areas is a fundamental task. In this context, the growing availability of hyperspectral (HS) imagery from satellite and aerial platforms can provide valuable information for the sustainable management of ecosystems. However, in some cases, the spectral richness provided by HS sensors is at the expense of spatial quality. To alleviate this inconvenience, which can be critical to monitor some heterogeneous and mixed natural areas, a number of HS sharpening techniques have been developed to increase the spatial resolution while trying to preserve the spectral content. This image processing field has attracted the interest of the scientific community, and many research studies have been conducted to assess the performance of different HS sharpening algorithms. In the last decade, however, many comparative studies rely upon simulated data. In this work, the challenging application of sharpening methods in real situations using multiplatform or multisensor data is also addressed. Thus, experiments with real data have been conducted, in addition to a thorough assessment of HS sharpening techniques using simulated imagery in scenarios with different spatial resolution ratios and registration errors. In particular, airborne and satellite HS imageries have been pansharpened with drone, orthophotos, and satellite high spatial resolution data evaluating 11 fusion algorithms. After a comprehensive analysis, considering different visual and quantitative quality indicators, the algorithm characteristics have been summarized and the methods with higher performance and robustness have been identified

Automated Detection and Machine Learning‐Based Classification of Seismic Tremors Associated With a Non‐Volcanic Gas Emission (Mefite d’Ansanto, Southern Italy)

Abstract A major aim in the study of crustal fluids is the development of automatic methodologies for monitoring deep‐source, non‐volcanic gas emissions’ spatio‐temporal evolution. Crustal fluids play a significant role in the generation of large earthquakes and the characterization of their emissions on the surface can be essential for better understanding crustal processes generating earthquakes. We investigate seismic tremors recorded over 4 days in 2019 at the Mefite d’Ansanto (southern Apennines, Italy) that is located at the northern end of the fault system that generated the Mw 6.9 1980 Irpinia Earthquake. The Mefite d’Ansanto is hypothesized to be the largest natural, non‐volcanic, CO2‐rich gas emission on Earth. The seismic tremor is studied by employing a dense temporary seismic network and an automated detection algorithm based on non‐parametric statistics of the recorded signal amplitudes. We extracted signal characteristics (RMS amplitude and statistical moments of amplitudes both in time and frequency domains) for use in the subsequent supervised machine‐learning classification of the target tremor and accidently detected anthropogenic and background noise. The data set is used for the training and optimization of station‐based KNN (k‐Nearest‐Neighbors) binary classifiers obtaining good classification performances with a median overall accuracy across all stations of 92.8%. The classified tremor displayed common features at all stations: variable duration (16 s to 30–40 min), broad peak frequency (4–20 Hz) with varying amplitudes, and two types of signals: (a) long‐duration, high‐amplitude tremor and (b) pulsating tremor. Higher tremor amplitudes recorded at stations closer to local bubbling and pressurized vents suggest multiple local tremor sources

The 2022 IEEE GRSS data fusion contest: Semisupervised learning [technical committees]

Data availability plays a central role in any machine learning setup, especially since the rise of deep learning. Although input data are often available in abundance, reference data used to train and evaluate corresponding approaches are usually scarce due to the high cost of obtaining them. Although this is not limited to remote sensing, it is of particular importance in Earth-observation applications. Semisupervised learning is one approach to mitigate this challenge and leverage the large amount of available input data while relying only on a small, annotated training set