Supported-Metal Oxide Nanoparticles-Potential Photocatalysts

Recently, nanosized metal oxides play an essential role in the photocatalytic system due to their ability to create charge carriers during the light irradiation. Metal oxide nanoparticles display excellent light absorption properties, outstanding charge transport characteristics, which are suitable in the photocatalytic system for the treatment of wastewater. Most of the photocatalysts found in the literature are in the form of powders. Only a few supported photocatalytic systems have been reported. The advantages of supported photocatalysts, such as that they produce a small pressure drop, have good mechanical stability and are easily separated from the reaction medium, make them superior to conventional powder photocatalysts. In this chapter, the definition of supported-metal oxide nanoparticles as the photocatalyst and their synthesis methodology are detailed discussed

Facile Template In-Situ Fabrication of ZnCo2O4 Nanoparticles with Highly Photocatalytic Activities under Visible-Light Irradiation

High specific surface area ZnCo2O4 nanoparticles were prepared via a sacrificial template accelerated hydrolysis by using nanoparticles of ZnO with highly polar properties as a template. The obtained ZnCo2O4 nanoparticles were characterized by the method of scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) surface area measurements, Transmission electron microscopy (TEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). The obtained nanoparticles were performed as a photocatalyst for the degradation of methylene blue in aqueous solution under visible irradiation. The photocatalytic degradation rate of methylene blue onto the synthesized ZnCo2O4 was higher than that of commercial ZnO and synthesized ZnO template. Copyright © 2019 BCREC Group. All rights reserved

Energy-loss Function for Lead

We study the energy-loss function for lead in the framework of the time-dependent density functional theory, using the full-potential linearized augmented plane-wave plus local orbitals method. The ab initio calculations are performed in the adiabatic local density approximation. The comparison between the obtained energy-loss function for zero momentum transfer with those from reflection electron energy loss spectroscopy measurements and from first-principles calculations shows good agreement

Achromobacter xylosoxidans respiratory tract infection in cystic fibrosis patients

The aims of this study were to evaluate the frequency of Achromobacter xylosoxidans infection in a cohort of cystic fibrosis patients, to investigate antimicrobial sensitivity, to establish possible clonal likeness among strains, and to address the clinical impact of this infection or colonization on the general outcome of these patients. The study was undertaken between January 2004 and December 2008 on 300 patients receiving care at the Regional Cystic Fibrosis Center of the Naples University “Federico II”. Sputum samples were checked for bacterial identification. For DNA fingerprinting, pulsed-field gel electrophoresis (PFGE) was carried out. Fifty-three patients (17.6%) had at least one positive culture for A. xylosoxidans; of these, 6/53 (11.3%) patients were defined as chronically infected and all were co-colonized by Pseudomonas aeruginosa. Of the patients, 18.8% persistently carried multidrug-resistant isolates. Macrorestriction analysis showed the presence of seven major clusters. DNA fingerprinting also showed a genetic relationship among strains isolated from the same patients at different times. The results of DNA fingerprinting indicate evidence of bacterial clonal likeness among the enrolled infected patients. We found no significant differences in the forced expiratory volume in 1 s (FEV1) and body mass index (BMI) when comparing the case group of A. xylosoxidans chronically infected patients with the control group of P. aeruginosa chronically infected patients

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice

Viral Etiology of Encephalitis in Children in Southern Vietnam: Results of a One-Year Prospective Descriptive Study

Viral encephalitis is associated with high morbidity and mortality in Vietnam. However little is known about the causes of the disease due to a lack of diagnostic facilities in this relatively resource-poor setting. Knowledge about the etiologies and clinical outcome of viral encephalitis is necessary for future design of intervention studies targeted at improvement of clinical management, treatment and prevention of the disease. We report the viral agents, clinical outcome and prognostic factors of mortality of encephalitis in children admitted to a referral hospital for children in southern Vietnam. We show that about one third of the enrolled patients die acutely, and that mortality is independently associated with patient age and Glasgow Coma Scale on admission. Japanese encephalitis, dengue virus and enterovirus (including enterovirus 71) are the major viruses detected in our patients. However, more than half of the patients remain undiagnosed, while mortality in this group is as high as in the diagnosed group. This study will benefit clinicians and public health in terms of clinical management and prevention of childhood encephalitis in Vietnam

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs