Facilitating sanitation governance in small town DRC

This paper describes the initial results of formative research on Sanitation Governance carried out through the Sanitation Marketing (SanMark) programme in the Equateur province of the Democratic Republic of the Congo (DRC). The DRC is a fragile state slowly emerging from a dictatorship and a war. The development of a local sanitation market is hampered by a high degree of distrust towards the state amongst citizens, and the expectation that international development organisations will provide free goods and services. The SanMark programme has sought to overcome these challenges by working with the Comité Locale de Pilotage (CLP), a local steering committee, composed of a broad coalition of representatives from local government, civil society and the private sector. The work of the CLP has contributed towards ensuring the legitimacy of the SanMark programme within the local community, the business community, and within the local state structures

Strong coupling between excitons in organic semiconductors and Bloch Surface Waves

We report on the strong coupling between the Bloch surface wave supported by an inorganic multilayer structure and $J$-aggregate excitons in an organic semiconductor. The dispersion curves of the resulting polariton modes are investigated by means of angle-resolved attenuated total reflection as well as photoluminescence experiments. The measured Rabi splitting is 290 meV. These results are in good agreement with those obtained from our theoretical model

Julolidine fluorescent molecular rotors as vapour sensing probes in polystyrene films

We introduce a new sensing polymer system for detection of volatile organic compounds (VOCs) based on the optical response of polystyrene (PS) films doped with julolidine fluorescent molecular rotors (FMRs). The julolidine FMRs exhibited viscosity-dependent changes in the fluorescence intensity, that was enhanced when glycerol was added to ethanol solutions and when they were dispersed in PS films. Thus, reduction in medium mobility slowed down internal motions and allowed for a major radiative decay pathway. The FMR/PS films were exposed to several VOCs, and showed a significant decrease in fluorescence emission when exposed to chloroform, whereas a negligible variation in their emission occurred when methanol was utilized. This vapour sensing behaviour was much more evident when a perfluorodecyl chain was linked to the julolidine core being the molecule segregated at the film surface. This responsive behaviour was affected by solvent composition and its reproducible response was easily determined by luminescence experiments

Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry

Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization

Validation and Reliability of a Novel Vagus Nerve Neurodynamic Test and Its Effects on Heart Rate in Healthy Subjects: Little Differences Between Sexes

BACKGROUND: The vagus nerve (VN), also called the pneumogastric nerve, connects the brainstem to organs contained in the chest and abdomen. Physiologically, VN stimulation can rapidly affect cardiac activity and heart rate (HR). VN neuropathy can increase the risk of arrhythmias and sudden death. Therefore, a selective test of VN function may be very useful. Since peripheral neurodynamic tests (NDT) are reliable for the assessment of neuropathies in somatic nerves, we aimed to validate a novel NDT to assess VN activity, namely, the VN-NTD. METHODS: In this cross-sectional double-blind, sex-balanced study, 30 participants (15 females) completed a checklist of autonomic dysfunction symptoms. During the VN-NDT administration, HR and symptoms (i.e., mechanical allodynia) were monitored in parallel to a real-time ultrasonography imaging (USI) and motion capture analysis of the neck. The VN-NDT impact on HR and its accuracy for autonomic symptoms reported in the last 7 days were tested. RESULTS: The VN-NDT induced a significant HR reduction of about 12 and 8 bpm in males and females [t(1, 119) = 2.425; p < 0.017; η(p)(2) = 0.047, 95% confidence interval (CI): 0.93–9.18], respectively. No adverse events were observed during VN-NDT. A substantial interexaminer agreement between the evaluators in symptoms induction by VN-NDT was detected [F(1, 119) = 0.540; p = 0.464; η(p)(2) = 0.005, low effect]. Notably, mechanical allodynia accuracy for gastrointestinal dysfunctions was excellent (p < 0.05; 95% CI: 0.52–0.73; p < 0.001; 95% CI: 0.81–0.96). CONCLUSIONS: The novel VN-NDT is a valid and accurate test capable of detecting VN activation with high sensitivity. Data provided are suitable for both sexes as a hallmark of HR variation due to VN normal response. The proposed VN-NDT may be reliable as daily routine neurological examination tests for the evaluation of neuropathic signs related to neuroinflammation of the VN. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, identifier NCT04192877

Machine-Learning for Prescription Patterns: Random Forest in the Prediction of Dose and Number of Antipsychotics Prescribed to People with Schizophrenia

Objective: We aimed to predict antipsychotic prescription patterns for people with schizophrenia using machine learning (ML) algorithms.Methods: In a cross-sectional design, a sample of community mental health service users (SUs; n = 368) with a primary diagnosis of schizophrenia was randomly selected. Socio-demographic and clinical features, including the number, total dose, and route of administration of the antipsychotic treatment were recorded. Information about the number and the length of psychiatric hospitalization was retrieved. Ordinary Least Square (OLS) regression and ML algorithms (i.e., random forest [RF], supported vector machine, K-nearest neighborhood, and Naive Bayes) were used to estimate the predictors of total antipsychotic dosage and prescription of antipsychotic polytherapy (APP).Results: The strongest predictor of the total dose was APP. The number of Community Mental Health Centers (CMHC) contacts was the most important predictor of APP and, with APP omitted, of dosage. Treatment with anticholinergics predicted APP, emphasizing the strong correlation between APP and higher antipsychotic dose. RF performed better than OLS regression and the other ML algorithms in predicting both antipsychotic dose (root square mean error = 0.70, R-2 = 0.31) and APP (area under the receiving operator curve = 0.66, true positive rate = 0.41, and true negative rate = 0.78).Conclusion: APP is associated with the prescription of higher total doses of antipsychotics. Frequent attenders at CMHCs, and SUs recently hospitalized are often treated with APP and higher doses of antipsychotics. Future prospective studies incorporating standardized clinical assessments for both psychopathological severity and treatment efficacy are needed to confirm these findings

Users' choice and change of allocated primary mental health professional in community-based mental health services: A scoping review

Background. The recovery model in mental health care emphasizes users\u2019 right to be involved in key decisions of their care, including choice of one\u2019s primary mental health professional (PMHP). Aims. The aim of this paper was to provide a scoping review of the literature on the topic of users\u2019 choice, request of change and preferences for the PMHP in community mental health services. Method. A search of Pubmed, Cochrane Library, Web of Science and PsycINFO for papers in English was performed. Additional relevant research articles were identified through authors\u2019 personal bibliography. Results. 2774 articles were screened and 38 papers were finally included. Four main aspects emerged: 1) the importance, for users, to be involved in the choice of their PMHP; 2) the importance, for users, of the continuity of care in the relationship with their PMHP; 3) factors of the user/PMHP dyad influencing users\u2019 preferences; 4) the effect of choice on treatments\u2019 outcomes. Conclusions. While it is generally agreed that it is important to consider users\u2019 preferences in choosing or requesting to change their PMHP, little research on this topic is available. PMHPs\u2019 and other stakeholders\u2019 views should also be explored in order to discuss ethical and practical issues

Retrieval of phase relation and emission profile of quantum cascade laser frequency combs

The major development recently undergone by quantum cascade lasers has effectively extended frequency comb emission to longer-wavelength spectral regions, i.e. the mid and far infrared. Unlike classical pulsed frequency combs, their mode-locking mechanism relies on four-wave mixing nonlinear processes, with a temporal intensity profile different from conventional short-pulses trains. Measuring the absolute phase pattern of the modes in these combs enables a thorough characterization of the onset of mode-locking in absence of short-pulses emission, as well as of the coherence properties. Here, by combining dual-comb multi-heterodyne detection with Fourier-transform analysis, we show how to simultaneously acquire and monitor over a wide range of timescales the phase pattern of a generic frequency comb. The technique is applied to characterize a mid-infrared and a terahertz quantum cascade laser frequency comb, conclusively proving the high degree of coherence and the remarkable long-term stability of these sources. Moreover, the technique allows also the reconstruction of electric field, intensity profile and instantaneous frequency of the emission.Comment: 20 pages. Submitted to Nature Photonic

Forecasting Operation Metrics for Virtualized Network Functions

Network Function Virtualization (NFV) is the key technology that allows modern network operators to provide flexible and efficient services, by leveraging on general-purpose private cloud infrastructures. In this work, we investigate the performance of a number of metric forecasting techniques based on machine learning and artificial intelligence, and provide insights on how they can support the decisions of NFV operation teams. Our analysis focuses on both infrastructure-level and service-level metrics. The former can be fetched directly from the monitoring system of an NFV infrastructure, whereas the latter are typically provided by the monitoring components of the individual virtualized network functions. Our selected forecasting techniques are experimentally evaluated using real-life data, exported from a production environment deployed within some Vodafone NFV data centers. The results show what the compared techniques can achieve in terms of the forecasting accuracy and computational cost required to train them on production data