Profiling Metacognition in Binge Eating Disorder

© 2020, The Author(s). Research has shown that metacognition may play a role in problem eating. In this study we explored whether aspects of metacognition are relevant to the understanding of binge eating in patients with Binge Eating Disorder. We aimed to ascertain: (1) the presence of metacognitive beliefs about binge eating; (2) the goal of, and stop signal for, binge eating; and (3) the impact of binge eating on self-consciousness. Ten Binge Eating Disorder patients took part in the study and were assessed using the metacognitive profiling semi-structured interview. Results suggested that all patients endorsed both positive and negative metacognitive beliefs about binge eating. The goals of binge eating were stop thinking about personal concerns and improve emotional state. All patients reported that they did not know when these goals had been reached. The stop signals for binge eating included physical discomfort, beliefs about binge eating not being the best way to solve problems, and environmental stimuli. All patients also confirmed that a reduction in self-consciousness occurred during a binge eating episode. The results of this study confirm that metacognition may indeed be relevant to the understanding of Binge Eating Disorder

Inverse design of a Raman amplifier in frequency and distance domains using convolutional neural networks

We present a convolutional neural network architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution in both distance along the fiber and in frequency. Using the proposed framework, the prediction of the pump configuration required to achieve a target power profile is demonstrated numerically with high accuracy in C-band considering both counter-propagating and bidirectional pumping schemes. For a distributed Raman amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54, and 0.64 dB) and standard deviation set (0.62, 0.43, and 0.38 dB) of the maximum test error are obtained numerically employing two and three counter-, and four bidirectional propagating pumps, respectively

Distance and spectral power profile shaping using machine learning enabled Raman amplifiers

We propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, (distance and frequency), and the Raman pumps. Using the CNN, the pump powers and wavelengths for arbitrary 2D profiles can be determined with high accuracy

Methodologies for determining staffing needs in healthcare: systematic literature review

The determination of staffing needs in healthcare is not just calculating the optimal number of professionals but is defining how the professional contingent accompanies the development of the healthcare organisation and of the population’s care needs. This research investigates the existence of a gold standard for determining health personnel requirements. We perform a systematic literature review to explore several approaches worldwide, examining a wide range of contextual variables, useful for the definition of an omni-comprehensive approach. A total of 557 articles was initially detected, then reduced to 57 after excluding everything not related to healthcare context and staff planning models. Results do not reveal a recognized standard for determining staffing needs. Approaches to the definition of staffing standards are mainly ex-ante (31%), based on the characteristics of specific models and organisational needs, or ex-post (62%), based on production analysis and historical trends. Most of these refer to the medical and nursing category (68.4%), while the minority proposes a multi-professional approach (17.5%). This review highlights innovative approaches based on algorithms which, starting from historical data, are adjusted by moderating key variables such as contextual factors, healthcare organisation models and professional attributes. The review suggests: 1. Develop and share a unique tool for defining standards based on several variables that identify the characteristics of the context 2. Use up-to-date information flows and quality data 3. Consider a multi-professional approach 4. Adopt a long-term vision and continuous dialogue with the training process It is clear the need to develop a tool for the definition of personnel requirements in line with internal and external changes in the health system. Therefore, such models need to account for an adequate number of variables, useful to identify the characteristics of the overall context. Key messages: The development of staffing needs estimates must necessarily rely on a certain level of standardisation, but at the same time must take into account the variability characterising different contexts. In order to respond to recent demographic and epidemiological trends, it is crucial to include in the model skill mix and task shifting strategies involving health professionals as a whole

Dual-polarization nonlinear Fourier transform-based optical communication system

New services and applications are causing an exponential increase in Internet traffic. In a few years, the current fiber optic communication system infrastructure will not be able to meet this demand because fiber nonlinearity dramatically limits the information transmission rate. Eigenvalue communication could potentially overcome these limitations. It relies on a mathematical technique called “nonlinear Fourier transform (NFT)” to exploit the “hidden” linearity of the nonlinear Schrödinger equation as the master model for signal propagation in an optical fiber. We present here the theoretical tools describing the NFT for the Manakov system and report on experimental transmission results for dual polarization in fiber optic eigenvalue communications. A transmission of up to 373.5 km with a bit error rate less than the hard-decision forward error correction threshold has been achieved. Our results demonstrate that dual-polarization NFT can work in practice and enable an increased spectral efficiency in NFT-based communication systems, which are currently based on single polarization channels

Generalization Properties of Machine Learning-based Raman Models

We investigate the generalization capabilities of neural network-based Raman amplifier models. The new proposed model architecture, including fiber parameters as inputs, can predict Raman gains of fiber types unseen during training, unlike previous fiber-specific models

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to the pump powers and noise figures. The obtained results show highly accurate gain profile designs and noise figure predictions, with a maximum error on average of ∼ 0.3 dB. This framework provides a comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of next-generation optical communication systems, expected to employ Raman amplification

Experimental Characterization of Raman Amplifier Optimization through Inverse System Design

Optical communication systems are always evolving to support the need for ever-increasing transmission rates. This demand is supported by the growth in complexity of communication systems which are moving towards ultra-wideband transmission and space-division multiplexing. Both directions will challenge the design, modeling, and optimization of devices, subsystems, and full systems. Amplification is a key functionality to support this growth and in this context, we recently demonstrated a versatile machine learning framework for designing and modeling Raman amplifiers with arbitrary gains. In this article, we perform a thorough experimental characterization of such machine learning framework. The applicability of the proposed approach, as well as its ability to accurately provide flat and tilted gain-profiles, are tested on several practical fiber types, showing errors below 0.5 dB. Moreover, as channel power optimization is heavily employed to further enhance the transmission rate, the tolerance of the framework to variations in the input signal spectral profile is investigated. Results show that the inverse design can provide highly accurate gain-profile adjustments for different input signal power profiles even not considering this information during the training phase

Optimization of Raman amplifiers using machine learning

It has been recently demonstrated that neural networks can learn the complex pump–signal relations in Raman amplifiers. Here we experimentally show how these neural network models are applied to provide highly–accurate Raman amplifier designs and flexible configuration for ultra–wideband optical communication systems