Experimental assessment of the acoustic performance of nozzles designed for clean agent fire suppression

Discharge through nozzles used in gas-based fire protection of data centers may generate noise that causes the performance of hard drives to decay considerably; silent nozzles are employed to limit this harmful effect. This work focuses on proposing an experimental methodology to assess the impact of sound emitted by gaseous jets by comparing various nozzles under several operating conditions, together with relating that impact to design parameters. A setup was developed and repeatability of the experiments was evaluated; standard and silent nozzles were tested regarding the discharge of inert gases and halocarbon compounds. The ability of silent nozzles to contain the emitted noise—generally below the 110 dB reference threshold—was proven effective; a relationship between Reynolds number and peak noise level is suggested to support the reported increase in noise maxima as released flow rate increases. Hard drives with lower speed were the most affected. Spectral analysis was conducted, with sound at the higher frequency range causing performance decay even if lower than the acknowledged threshold. Independence of emitted noise from the selected clean agent was also observed in terms of released volumetric flow rate, yet the denser the fluid, the lower the generated noise under the same released mass flow rate

Experimental assessment and predictive model of the performance of Ti-based nanofluids

The need for innovative propulsion technologies (e.g., fuel cells) in the mobility sector is posing a higher-than-ever burden on thermal management. When low operative temperature shall be ensured, dissipation of a significant amount of heat is requested, together with limited temperature variation of the coolant; mobile applications also yield limitations in terms of space available for cooling subsystems. Nanofluids have recently become one of the most promising solutions to replace conventional coolants. However, the prediction of their effectiveness in terms of heat-transfer enhancement and required pumping power still appears a challenge, being limited by the lack of a general methodology that assesses them simultaneously in various flow regimes. To this end, an experiment was developed to compare a conventional coolant (ethylene glycol/water) and a TiO2-based nanofluid (1% particle loading), focusing on heat transfer and pressure loss. The experimental dataset was used as an input for a physical model based on two independent figures of merit, aiming at an a priori evaluation of the potential simultaneous gain in heat transfer and parasitic power. The model showed conditions of combined gain specifically for the laminar flow regime, whereas turbulent flows proved inherently associated to higher pumping power; overall, criteria are presented to evaluate nanofluid performance as compared to that of conventional coolants. The model is generally applicable to the design of cooling systems and emphasizes laminar flow regime as promising in conjunction with the use of nanofluids, proposing indices for a quantitative a priori evaluation and leading to an advancement with respect to an a posteriori assessment of their performance

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

The Sex-Specific Detrimental Effect of Diabetes and Gender-Related Factors on Pre-admission Medication Adherence Among Patients Hospitalized for Ischemic Heart Disease: Insights From EVA Study

Background: Sex and gender-related factors have been under-investigated as relevant determinants of health outcomes across non-communicable chronic diseases. Poor medication adherence results in adverse clinical outcomes and sex differences have been reported among patients at high cardiovascular risk, such as diabetics. The effect of diabetes and gender-related factors on medication adherence among women and men at high risk for ischemic heart disease (IHD) has not yet been fully investigated.Aim: To explore the role of sex, gender-related factors, and diabetes in pre-admission medication adherence among patients hospitalized for IHD.Materials and Methods: Data were obtained from the Endocrine Vascular disease Approach (EVA) (ClinicalTrials.gov Identifier: NCT02737982), a prospective cohort of patients admitted for IHD. We selected patients with baseline information regarding the presence of diabetes, cardiovascular risk factors, and gender-related variables (i.e., gender identity, gender role, gender relations, institutionalized gender). Our primary outcome was the proportion of pre-admission medication adherence defined through a self-reported questionnaire. We performed a sex-stratified analysis of clinical and gender-related factors associated with pre-admission medication adherence.Results: Two-hundred eighty patients admitted for IHD (35% women, mean age 70), were included. Around one-fourth of the patients were low-adherent to therapy before hospitalization, regardless of sex. Low-adherent patients were more likely diabetic (40%) and employed (40%). Sex-stratified analysis showed that low-adherent men were more likely to be employed (58 vs. 33%) and not primary earners (73 vs. 54%), with more masculine traits of personality, as compared with medium-high adherent men. Interestingly, women reporting medication low-adherence were similar for clinical and gender-related factors to those with medium-high adherence, except for diabetes (42 vs. 20%, p = 0.004). In a multivariate adjusted model only employed status was associated with poor medication adherence (OR 0.55, 95%CI 0.31–0.97). However, in the sex-stratified analysis, diabetes was independently associated with medication adherence only in women (OR 0.36; 95%CI 0.13–0.96), whereas a higher masculine BSRI was the only factor associated with medication adherence in men (OR 0.59, 95%CI 0.35–0.99).Conclusion: Pre-admission medication adherence is common in patients hospitalized for IHD, regardless of sex. However, patient-related factors such as diabetes, employment, and personality traits are associated with adherence in a sex-specific manner

Characterization of high-pressure water-mist sprays: Experimental analysis of droplet size and dispersion

Popularity of water mist is increasing for a variety of applications within the broad areas of fire suppression and surface cooling. The present study has been focused on characterizing the solid-cone water-mist spray produced by a typical atomizer at high operative pressure (in the range 60-80. bar). To this end, an experimental campaign has been conducted, mainly employing optical techniques: drop-size and flux distribution, initial velocity and cone angle have been investigated to provide a quantitative description of atomization and dispersion. Most notably, a laser-diffraction-based instrument (Malvern Spraytec) has been used to evaluate drop size, while velocity field and spray-cone angle have been studied by Particle Image Velocimetry (PIV) technique. Appropriate measurement methodologies have been developed to the purpose. Moreover, a theoretical discussion based on inviscid-fluid assumption is presented and some relations have been evaluated as predictive of the considered parameters. © 2010 Elsevier Inc

Full scale experiments on water-mist fire-suppression systems in High-Hazard Storages (HHS) \u2013 A temperature-based comparison between sole water and water/additive flow

Water-mist systems have gained wide popularity over the last twenty years as an innovative technology in fire protection. Moreover, insertion of additives in the flow is typically applied to provide additional improvements in terms of suppression effectiveness and temperature control. The present work consists of an experimental approach in a real-scale facility, which has been aimed at challenging water mist against severe fire scenarios. The sole water flow is compared to water endowed with a commercial additive, the F-500 by Hazard Control Technologies Inc. As the fire setting, a high-rise storage has been explored: this real scenario is commonly recognized as severely hazardous even by technical standards, because of both its nominal fire load and the designed physical domain. The thermal transient within the test chamber during the fire development has been measured as the main quantitative parameter: K-type thermocouples have been employed to the purpose over a set of remarkable locations. Moreover, the fire evolution has been evaluated through a post-fire estimation of the damages. Despite the large amount of released smoke and smoldering materials, water mist is shown to be efficient in fire control, if endowed with the chosen additive, while the sole water flow does not appear suitable for such hazardous conditions