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

Atmospheric Boundary Layer Height: Inter-Comparison of Different Estimation Approaches Using the Raman Lidar as Benchmark

This work stems from the idea of improving the capability to measure the atmospheric boundary layer height (ABLH) in variable or unstable weather conditions or in the presence of turbulence and precipitation events. A new approach based on the use of rotational and roto-vibrational Raman lidar signals is considered and tested. The traditional gradient approach based on the elastic signals at wavelength 532 nm is also considered. Lidar data collected by the University of Basilicata Raman lidar (BASIL) within the Special Observation Period 1 (SOP 1) in Cardillargues (Ceveninnes-CV supersite) during the Hydrological Cycle in the Mediterranean Experiment (HyMeX) were used. Our attention was specifically focused on the data collected during the period 16-21 October 2012. ABLH estimates from the Raman lidar were compared against other innovative methods, such as the recently established Morphological Image Processing Approach (MIPA) and the temperature gradient technique applied to potential temperature obtained from radio-sounding data. For each considered methodology, a statistical analysis was carried out. In general, the results from the different methodologies are in good agreement. Some deviations have been observed in correspondence with quite unstable weather conditions

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

Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution

The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR). Yet the development of unsupervised deep networks remains challenging for this task. To this end, we propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet for short, to enhance the spatial resolution of HSI by means of higher-spatial-resolution multispectral image (MSI). Inspired by coupled spectral unmixing, a two-stream convolutional autoencoder framework is taken as backbone to jointly decompose MS and HS data into a spectrally meaningful basis and corresponding coefficients. CUCaNet is capable of adaptively learning spectral and spatial response functions from HS-MS correspondences by enforcing reasonable consistency assumptions on the networks. Moreover, a cross-attention module is devised to yield more effective spatial-spectral information transfer in networks. Extensive experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models, demonstrating the superiority of the CUCaNet in the HSI-SR application. Furthermore, the codes and datasets will be available at: https://github.com/danfenghong/ECCV2020_CUCaNet

Robust band-dependent spatial-detail approaches for panchromatic sharpening

Pansharpening refers to the fusion of a multispectral (MS) image with a finer spectral resolution but coarser spatial resolution than a panchromatic (PAN) image. The classical pansharpening problem can be dealt with component substitution or multiresolution analysis techniques. One of the most notable approaches in the former class is the band-dependent spatial-detail (BDSD) method. It has been shown state-of-the-art performance, in particular, when the fusion of four band data sets is addressed. However, new sensors, such as the WorldView-2/-3 ones, usually acquire MS images with more than four spectral bands to be fused with the PAN image. The BDSD method has shown limitations in performance in these cases. Thus, in this paper, several BDSD-based approaches are provided to solve this issue getting a robustness of the BDSD with respect to the spectral bands to be fused. The experimental results conducted both at reduced and at full resolutions on four real data sets acquired by the IKONOS, the QuickBird, the WorldView-2, and the WorldView-3 sensors demonstrate the validity of the proposed approaches against the benchmark

Joint probabilistic data association tracker for extended target tracking applied to X-band marine radar data

X-band marine radar systems are flexible and low-cost tools for monitoring multiple targets in a surveillance area. Although they may suffer from several sources of interference, e.g., sea clutter, they can provide high-resolution measurements in both space and time. Such features offer the opportunity to get accurate information not only about the target kinematics, i.e., positions and velocities, as other conventional radars, but also about the targets' extents. This research area is named extended target tracking (ETT). In this paper, we propose a signal processing chain composed by a detector and a joint probabilistic data association (JPDA) tracker to handle the problem of multiple ETT and to jointly estimate both the targets' kinematics and their sizes, i.e., length and width. The performance assessment is conducted on real data acquired by an X-band marine radar located in the Gulf of La Spezia, Italy. The experimental results demonstrate the ability of the processing chain to reach high performance with a limited computational burden

A Combiner-Based Full Resolution Quality Assessment Index for Pansharpening

Pansharpening refers to the problem of fusing a multispectral (MS) image and a panchromatic image in order to get an MS image at a finer spatial resolution than the one of the original MS image. Due to the lack of a reference image, the assessment of the quality of a pansharpened product is a challenging task. Typical solutions are related to the reduced resolution assessment by exploiting Wald's protocol or to indexes without reference. In this letter, an efficient approach based on the combination of the two above-mentioned solutions is proposed. The performance is assessed using real data acquired by sensors with very different features mounted on-board of the Pléiades, the WorldView-3, and the WorldView-4 satellite platforms