8-[(1E)-1-(2-Aminophenyl­iminio)eth­yl]-2-oxo-2H-chromen-7-olate

The title Schiff base, C17H14N2O3, exists as an NH tautomer with the H atom of the phenol group transferred to the imine N atom. The iminium H atom is involved in a strong intra­molecular N+—H⋯O− hydrogen bond to the phenolate O atom, forming an S(6) motif. In the crystal structure, N—H⋯O hydrogen bonds form a C(9) chain parallel to [100] and a C(11) chain parallel to [010], while C—H⋯O hydrogen bonds form a C(11) chain parallel to [010]. The combination of N—H⋯O and C—H⋯O hydrogen bonds generates R 4 3(30) rings parallel to the ab plan

An agent-based industrial cyber-physical system deployed in an automobile multi-stage production system

Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based on Multi-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers. © 2020, Springer Nature Switzerland AG.info:eu-repo/semantics/publishedVersio

An Architecture of IoT Service Delegation and Resource Allocation Based on Collaboration between Fog and Cloud Computing

Despite the wide utilization of cloud computing (e.g., services, applications, and resources), some of the services, applications, and smart devices are not able to fully benefit from this attractive cloud computing paradigm due to the following issues: (1) smart devices might be lacking in their capacity (e.g., processing, memory, storage, battery, and resource allocation), (2) they might be lacking in their network resources, and (3) the high network latency to centralized server in cloud might not be efficient for delay-sensitive application, services, and resource allocations requests. Fog computing is promising paradigm that can extend cloud resources to edge of network, solving the abovementioned issue. As a result, in this work, we propose an architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing. We provide new algorithm that is decision rules of linearized decision tree based on three conditions (services size, completion time, and VMs capacity) for managing and delegating user request in order to balance workload. Moreover, we propose algorithm to allocate resources to meet service level agreement (SLA) and quality of services (QoS) as well as optimizing big data distribution in fog and cloud computing. Our simulation result shows that our proposed approach can efficiently balance workload, improve resource allocation efficiently, optimize big data distribution, and show better performance than other existing methods

Informal payments and intra-household allocation of resources for health care in Albania

<p>Abstract</p> <p>Background</p> <p>Informal payments for health care services can impose financial hardship on households. Many studies have found that the position within the household can influence the decision on how much is spent on each household member. This study analyses the intra-household differences in spending on informal payments for health care services by comparing the resources allocated between household heads, spouses and children.</p> <p>Methods</p> <p>Pooled data from two cross sectional surveys, the Albanian Living Standard Measurement Survey 2002 and 2005, are used to analyse both the probability and the amount paid in inpatient and outpatient health care services. A generalised Hausman specification test is used to compare the coefficients of probit and OLS models for nuclear and extended households.</p> <p>Results</p> <p>We find that due to the widespread informal payments there are no significant differences between households in the incidence of informal payments for households' members, but there are more differences in the amount paid informally. Results suggest that households strategically allocate their resources on health care by favouring individuals with higher earning potential who have invested more in human capital. Extended households pay higher amounts for spouses with higher education compared to nuclear households. On the other hand, nuclear households choose to pay higher amounts for children with a higher level of education compared to extended households.</p> <p>Conclusions</p> <p>The differences between households should be taken into account by public policies which should compensate this by redistribution mechanisms targeting disadvantaged groups. Governments should implement effective measures to deal with informal patient payments.</p> <p><b>JEL Codes: </b>I10, I19, D10</p

Profit-aware Resource Management for Edge Computing Systems

Edge Computing (EC) represents the most promising solution to the real-time or near-real-time processing needs of the data generated by Internet of Things devices. The emergence of Edge Infrastructure Providers (EIPs) will bring the EC benefits to those enterprises that cannot afford to purchase, deploy, and manage their own edge infrastructures. The main goal of EIPs will be that of maximizing their profit, i.e. the difference of the revenues they make to host applications, and the cost they incur to run the infrastructure plus the penalty they have to pay when QoS requirements of hosted applications are not met. To maximize profit, an EIP must strike a balance between the above two factors. In this paper we present the Online Profit Maximization (OPM) algorithm, an approximation algorithm that aims at increasing the profit of an EIP without a priori knowledge. We assess the performance of OPM by simulating its behavior for a variety of realistic scenarios, in which data are generated by a population of moving users, and by comparing the results it yields against those attained by an oracle (i.e., an unrealistic algorithm able to always make optimal decisions) and by a state-of-the-art alternative. Our results indicate that OPM is able to achieve results that are always within 1% of the optimal ones, and that always outperforms the alternative solution

Photocatalytic Oxidation of Carbon Monoxide over Nanocomposites under UV Irradiation

The NiO/SnO2 nanocomposites have been prepared by the simple coprecipitation method and further characterized by the XRD, SEM, TEM, UV-Vis, and BET. X-ray diffraction (XRD) data analyses indicate the exclusive formation of nanosized particles with rutile-type phase (tetragonal SnO2) for Ni contents below 10 mol%. Only above 10 mol% Ni, the formation of a second NiO-related phase has been determined. The particle size is in the range from 12 to 6 nm. It decreases with increasing amounts of doping NiO. The morphology of NiO-doped SnO2 nanocrystalline powders is spherical, and the distribution of particle size is uniform, as seen from transmission electron microscopy (TEM). The photocatalytic oxidation of CO over NiO/SnO2 photocatalyst has been investigated under UV irradiation. Effects of NiO loading on SnO2, photocatalyst loading, and reaction time on photocatalytic oxidation of CO have been systematically studied. Compared with pure SnO2, the 33.3 mol% NiO/SnO2 composite exhibited approximately twentyfold enhancement of photocatalytic oxidation of CO. Our results provide a method for pollutants removal. Due to simple preparation, high photocatalytic oxidation of CO, and low cost, the NiO/SnO2 photocatalyst will find wide application in the coming future of photocatalytic oxidation of CO

Synthesis and Characterization of CeO2-SiO2 Nanoparticles by Microwave-Assisted Irradiation Method for Photocatalytic Oxidation of Methylene Blue Dye

CeO2-SiO2 nanoparticles were synthesized for the first time by a facile microwave-assisted irradiation process. The effect of irradiation time of microwave was studied. The materials were characterized by N2 adsorption, XRD, UV-vis/DR, and TEM. All solids showed mesoporous textures with high surface areas, relatively small pore size diameters, and large pore volume. The X-ray diffraction results indicated that the as-synthesized nanoparticles exhibited cubic CeO2 without impurities and amorphous silica. The transmission electron microscopy (TEM) images revealed that the particle size of CeO2-SiO2 nanoparticles, which were prepared by microwave method for 30 min irradiation times, was around 8 nm. The photocatalytic activities were evaluated by the decomposition of methylene blue dye under UV light irradiations. The results showed that the irradiation under the microwave produced CeO2-SiO2 nanoparticles, which have the best crystallinity under a shorter irradiation time. This indicates that the introduction of the microwave really can save energy and time with faster kinetics of crystallization. The sample prepared by 30 min microwave irradiation time exhibited the highest photocatalytic activity. The photocatalytic activity of CeO2-SiO2 nanoparticles, which were prepared by 30 min irradiation times was found to have better performance than commercial reference P25

Characterization and Catalytic Properties of Nano-Sized Au Metal Catalyst on Titanium Containing High Mesoporous Silica (Ti-HMS) Synthesized by Photo-Assisted Deposition and Impregnation Methods

The photo-assisted deposition (PAD) and impregnation (img) synthesis of nano-sized Au metal on Ti-HMS are reported. The prepared catalysts were characterized by different techniques such as XRD, XAFS, TEM and nitrogen adsorption analysis. Photocatalytic reactivity using Au/Ti-HMS catalysts under visible-light condition on the oxidation of CO with O2 reaction was evaluated. The results have shown notable photocatalytic activity of PAD-Au/Ti-HMS which was 2.1 and 5.7 times higher than that of img-Au/Ti-HMS and Ti-HMS, respectively

Using DEVS for modeling and simulating a Fog Computing environment

With the increase in popularity of Internet of Things (IoT), pervasive computing, healthcare services, sensor networks, and mobile devices, a lot of data is being generated at the perception layer. Cloud is the most viable solution for data storage, processing, and management. Cloud also helps in the creation of further services, refined according to the context and requirement. However, being reachable through the Internet, cloud is not efficient enough for latency sensitive multimedia services and other time-sensitive services, like emergency and healthcare. Fog, an extended cloud lying within the proximity of underlying nodes, can mitigate the issues traditional cloud cannot solve being standalone. Fog can provide quick response to the requiring applications. Moreover, it can preprocess and filter data according to the requirements. Trimmed data is then sent to the cloud for further analysis and enhanced service provisioning. However, how much better is it to have a fog in any particular scenario instead of a standalone cloud working without fog is a question right now. In this paper, we provide an answer by analyzing both cloud-only and cloud-fog scenarios in the context of processing delay and power consumption according to increasing number of users, on the basis of varying server load. The simulation is done through Discrete Event System Specification (DEVS). Simulation results demonstrate that by the use of fog networks, users experienced lower waiting times and increased data rates

RACE: Relinquishment-Aware Cloud Economics Model

A lot of work on resource estimation has been carried out in the area of cloud computing. For example, some recent models assign resources based on the history of the users and the utilization of the cloud resource pool. Although these models have generally shown an increase in server utilization, they lack a cost-benefit analysis to know the profit margin obtained by the respective resource allocation schemes. Therefore, a complete model to analyze the cost could be a valuable tool for IaaS cloud service providers (CSP) to compare various resource assignment mechanisms. In this paper, we introduce a Relinquishment-Aware Cloud Economics Model (RACE) to calculate the net profit in a cloud provider environment. Our model includes various parameters such as service price, income from resources used by cloud service customers (CSC), service utilization, number of servers, electricity cost, and service relinquishment cost. The noteworthy contribution of our model is that it includes the cost incurred when users are leaving the cloud provider before their scheduled end time. We consider this loss as relinquishment cost or opportunity cost loss. After implementing our model, we evaluate different resource allocation schemes in a finite resource pool environment. The preliminary results show that blindly assigning more resources does not necessarily generate more profit