Annealing Effects on Structural and Optical Properties of Ge10Sb30Se60 Thin Film

The optical constants of as-prepared and thermally annealed Ge10Sb30Se60thin films were determined. Effect of heat treatment on the structure and optical properties of Ge10Sb30Se60 thin films in the range between the glass transition and crystallization temperature have been investigated. The glass transition and crystallization temperature of the synthesized sample was measured by non- isothermal DSC measurements. The microstructure and optical properties of these films were characterized by UV-VIS spectrum, scanning electron microscope (SEM) and X-ray diffraction (XRD). The optical band gap for as-prepared and annealed films have been calculated using Taucs low from the optical transmission and reflection spectra. The results indicate that the optical band gap Eopt increases when the annealing temperature (Ta) is lower than the glass transition temperature (Tg), while decreases with further increase of Ta. The XRD studies show that the as-prepared film is amorphous in nature, but the crystalline improved with increasing the annealing temperature. Furthermore the particle size and crystalline increases while the dislocation and strains decreases with increasing the annealing temperature. Thermal annealing was found to be accompanied by structural effects, which in turn, lead to change in the optical constants. The obtained results were explained in terms of the Mott and Davis model for amorphous materials and amorphous to crystalline structure transformations

An Edge Computing Based Smart Healthcare Framework for Resource Management

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time

A GAME THEORETIC MODEL OF COOPERATION AND NON-COOPERATION FOR SOCCER PLAYING ROBOTS

Abstract: This paper proposes a model which combines cooperative and noncooperative behaviors among autonomous mobile robots. This problem is well demonstrated by the Soccer playing robots, which consists of two sub-games, namely a non-cooperative game between the teams, and a cooperative game among the players of the same team. Game theory is used for modeling these games. The model consists of two layers, the first for the non-cooperative game which feeds its output to the cooperative game in the second layer. Fuzzy logic is used to evaluate the utility functional. The model is implemented using a soccer playing robot simulator

A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services

Fog-to-Fog (F2F) communication has been introduced to deliver services to clients with minimal reliance on the cloud through resource and capability sharing of cooperative fogs. Current solutions assume full cooperation among the fogs to deliver simple and composite services. Realistically, each fog might belong to a different network operator or service provider and thus will not participate in any form of collaboration unless self-monetary profit is incurred. In this paper, we introduce a fog collaboration approach for simple and complex multimedia service delivery to cloud subscribers while achieving shared profit gains for the cooperating fogs. The proposed work dynamically creates short-term service-level-agreements (SLA) offered to cloud subscribers for service delivery while maximizing user satisfaction and fog profit gains. The solution provides a learning mechanism that relies on online and offline simulation results to build guaranteed workflows for new service requests. The configuration parameters of the short-term SLAs are obtained using a modified tabu-based search mechanism that uses previous solutions when selecting new optimal choices. Performance evaluation results demonstrate significant gains in terms of service delivery success rate, service quality, reduced power consumption for fog and cloud datacenters, and increased fog profits

Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets

Most Internet of Things (IoT)-based service requests require excessive computation which exceeds an IoT device's capabilities. Cloud-based solutions were introduced to outsource most of the computation to the data center. The integration of multi-agent IoT systems with cloud computing technology makes it possible to provide faster, more efficient and real-time solutions. Multi-agent cooperation for distributed systems such as fog-based cloud computing has gained popularity in contemporary research areas such as service composition and IoT robotic systems. Enhanced cloud computing performance gains and fog site load distribution are direct achievements of such cooperation. In this article, we pro- pose a work ow-net based framework for agent cooperation to enable collaboration among fog computing devices and form a cooperative IoT service delivery system. A cooperation operator is used to find the topology and structure of the resulting cooperative set of fog computing agents. The operator shifts the problem defined as a set of work ow-nets into algebraic representations to provide a mechanism for solving the optimization problem mathematically. IoT device resource and collaboration capabilities are properties which are considered in the selection process of the cooperating IoT agents from di_erent fog computing sites. Experimental results in the form of simulation and implementation show that the cooperation process increases the number of achieved tasks and is performed in a timely manner

A novel primary and backup relaying scheme considering internal and external faults in HVDC transmission lines

Discrimination of different DC faults near a converter end of a DC section consisting of a filter, a smoothing reactor, and a transmission line is not an easy task. The faults occurring in the AC section can be easily distinguished, but the internal and near-side external faults in the DC section are very similar, and the relay may cause false tripping. This work proposes a method to distinguish external and internal faults occurring in the DC section. The inputs are the voltage signals at the start of the transmission line and the end of the converter filter. The difference in voltage signals is calculated and given to an intelligent controller to detect and discriminate the faults. The intelligent controller is designed using machine learning (ML) and deep learning (DL) techniques for fault detection. The long short-term memory (LSTM-) based relay gives better results than other ML methods. The proposed method can distinguish internal from external faults with 100% accuracy. Another advantage is that a primary relay is suggested that detects faults quickly within a fraction of milliseconds. Nevertheless, another advantage is that a backup relay has been designed in case the primary relay cannot operate. Results show that the LSTM-based protection scheme provides higher sensitivity and reliability under different operation modes than the conventional traveling wave-based relay

Screening, production and biochemical characterization of a new fibrinolytic enzyme produced by Streptomyces sp. (Streptomycetaceae) isolated from Amazonian lichens

Thrombosis is a pathophysiological disorder caused by accumulation of fibrin in the blood. Fibrinolytic proteases with potent thrombolytic activity have been produced by diverse microbial sources. Considering the microbial biodiversity of the Amazon region, this study aimed at the screening, production and biochemical characterization of a fibrinolytic enzyme produced by Streptomyces sp. isolated from Amazonian lichens. The strain Streptomyces DPUA1576 showed the highest fibrinolytic activity, which was 283 mm2. Three variables at two levels were used to assess their effects on the fibrinolytic production. The parameters studied were agitation (0.28 - 1.12 g), temperature (28 - 36 ºC) and pH (6.0 - 8.0); all of them had significant effects on the fibrinolytic production. The maximum fibrinolytic activity (304 mm2) was observed at 1.12 g, 28 ºC, and pH of 8.0. The crude extract of the fermentation broth was used to assess the biochemical properties of the enzyme. Protease and fibrinolytic activities were stable during 6 h, at a pH ranging from 6.8 to 8.4 and 5.8 to 9.2, respectively. Optimum temperature for protease activity ranged between 35 and 55 °C, while the highest fibrinolytic activity was observed at 45 ºC. Proteolytic activity was inhibited by Cu2+ and Co2+ ions, phenylmethylsulfonyl fluoride (PMSF) and pepstatin A, which suggests that the enzyme is a serine protease. Enzymatic extract cleaved fibrinogen at the subunits A-chain, A-chain, and -chain. The results indicated that Streptomyces sp. DPUA 1576 produces enzymes with fibrinolytic and fibrinogenolytic activity, enzymes with an important application in the pharmaceutical industry.The authors grateful acknowledge the financial support of Fundação de Amparo a Pesquisa do Estado de Pernambuco (FACEPE, Pernambuco, Brazil, N. 0158-2.12/11), CNPq/ RENORBIO (National Counsel of Technological and Scientific Development, N.55146/2010-3) and National Council for the Improvement of Higher Education (CAPES, Brazil) for the scholarship. The author thanks editor and reviewers for their review and comments.info:eu-repo/semantics/publishedVersio

Highly Frequent Mutations in Negative Regulators of Multiple Virulence Genes in Group A Streptococcal Toxic Shock Syndrome Isolates

Streptococcal toxic shock syndrome (STSS) is a severe invasive infection characterized by the sudden onset of shock and multiorgan failure; it has a high mortality rate. Although a number of studies have attempted to determine the crucial factors behind the onset of STSS, the responsible genes in group A Streptococcus have not been clarified. We previously reported that mutations of csrS/csrR genes, a two-component negative regulator system for multiple virulence genes of Streptococcus pyogenes, are found among the isolates from STSS patients. In the present study, mutations of another negative regulator, rgg, were also found in clinical isolates of STSS patients. The rgg mutants from STSS clinical isolates enhanced lethality and impaired various organs in the mouse models, similar to the csrS mutants, and precluded their being killed by human neutrophils, mainly due to an overproduction of SLO. When we assessed the mutation frequency of csrS, csrR, and rgg genes among S. pyogenes isolates from STSS (164 isolates) and non-invasive infections (59 isolates), 57.3% of the STSS isolates had mutations of one or more genes among three genes, while isolates from patients with non-invasive disease had significantly fewer mutations in these genes (1.7%). The results of the present study suggest that mutations in the negative regulators csrS/csrR and rgg of S. pyogenes are crucial factors in the pathogenesis of STSS, as they lead to the overproduction of multiple virulence factors