Structural and optical properties of Co-doped ZnS nanoparticles synthesized by a capping agent

Cobalt-doped Zinc sulfide (ZnS) nanoparticles were prepared by a simple chemical method using alkyl hydroxyl ethyl dimethyl ammonium chloride (YH) as capping agent. The structural and optical properties of prepared cobalt-doped ZnS nanoparticles have been characterized. X-ray diffraction patterns and transmission electron microscope images reveal pure cubic ZnS phase with size of about 5–2 nm for all cobalt-doped ZnS nanoparticles. The lattice constant of the samples decreases slightly by the introduction of Co²⁺ The absorption edge of the ZnS:Co²⁺ nanoparticles is blue-shifted as compared with that of bulk ZnS, indicating the quantum confinement effect. The photoluminescence emission band exhibits a blue shift for Co-doped ZnS nanoparticles as compared to the ZnS nanoparticles.     

A modelling framework to support design of complex engineering systems in early design stages

Production, assembly or logistic systems exist in widespread domains. It is agreed that more than 50% of life-cycle performance, costs and environmental impacts of such systems are due to those decisions that are made in their early design stages (Reich, Res Eng Design 28(4):411–419, https://doi.org/10.1007/s00163-017-0270-7, 2017). However, the large scale and multi-disciplinary essence of such systems make their design considerably challenging. Most of the design approaches follow a sequential approach such that the design in each lower level is finalized/frozen before proceeding to the next level. However, such approaches do not properly address the interaction between different design disciplines which may later lead to design inconsistencies. Therefore, this paper aimed to propose a modelling framework that allows having an integrated approach in the early design stages of such systems. To this end, first the framework prescribed developing an executable meta-architecture that can embody all the design requirements. Second, the framework describes the interconnections between the meta-architecture with certain supporting algorithms and optimization models. This allows generating and simulating different design alternatives and observing the impact of different design decisions on system integrated performance. Therefore, the proposed framework with its providing outcomes can be used to support the decision making in early design stages of such systems. The framework is applied in a real case study from the warehousing domain, which serves to show the practical application of the proposed framework.     

Grain Boundary Effects and AC-Susceptibility Measurements in Aluminum-Doped MgB2Superconductor

We have used the AC susceptibility for MgAlB₂ samples to investigate the grain boundary effects and the Al-doping effects on the critical current density. The Cole-Cole plots were used to obtain the grain boundary effects on the critical current density. We found that the grain boundaries are highly transparent to current flow and have virtually no effects on the critical current density in MgB₂pure and Al-doped samples. Moreover, the lower critical field H _c1 is found to be enhanced with Al doping. The highest H _c1 value is achieved with the 4.5 % concentration of Al.     

Can social media usage of scientific literature predict journal indices of AJG,SNIP and JCR? An altmetric study of economics

Scientometrics - Altmetrics are often praised as an alternative or complement to classic bibliometric metrics, especially in the social sciences discipline. However, empirical investigations of...     

Strength,toughness and R-curve behaviour of SiC whisker-reinforced composite Si3N4 with reference to monolithic Si3N4

The flexural strength and fracture toughness of 30 vol% SiC whisker-reinforced Si₃N₄ material were determined as a function of temperature from 25 to 1400°C in an air environment. It was found that both strength and toughness of the composite material were almost the same as those of the monolithic counterpart. The room-temperature strength was retained up to 1100°C; however, appreciable strength degradation started at 1200°C and reached a maximum at 1400°C due to stable crack growth. In contrast, the fracture toughness of the two materials was independent of temperature with an average value of 5.66 MPam^1/2. It was also observed that the composite material exhibited no rising R-curve behaviour at room temperature, as was the case for the monolithic material. These results indicate that SiC whisker addition to the Si₃N₄ matrix did not provide any favourable effects on strength, toughness and R-curve behaviour.     

A novel ratiometric fluorescent Yb3+ sensor based on a N′-(1-oxoacenaphthylen-2(1H)-ylidene)furan-2-carbohydrazide as a suitable fluorophore

N′-(1-oxoacenaphthylen-2(1H)-ylidene)furan-2-carbohydrazide (L) was synthesized for the first time and used as a ratiometric fluorescent chemosensor for high selective recognition of Yb³⁺ ions in acetonitrile (MeCN) solution. The L–Yb³⁺ complexation quench the fluorescence of L at 420 nm and induces new fluorescent enhancement at 516 nm. Due to the formation of a 2:1 metal ligand complex in acetonitrile solution, the red shift of fluorescent emission spectrum occurred. The sensor shows a linear response toward Yb³⁺ ion concentration in the range of 3.3 × 10^? 7 M to 1.0 × 10^? 4 M with detection limit of 1.2 × 10^? 7 M. The fluorescent probe exhibits high selectivity for Yb³⁺ ion over the other common mono-, di-, and trivalent cations.     

Active XML-based Web data integration

Today, the Web is the largest source of information worldwide. There is currently a strong trend for decision-making applications such as Data Warehousing (DW) and Business Intelligence (BI) to move onto the Web, especially in the cloud. Integrating data into DW/BI applications is a critical and time-consuming task. To make better decisions in DW/BI applications, next generation data integration poses new requirements to data integration systems, over those posed by traditional data integration. In this paper, we propose a generic, metadata-based, service-oriented, and event-driven approach for integrating Web data timely and autonomously. Beside handling data heterogeneity, distribution and interoperability, our approach satisfies near real-time requirements and realize active data integration. For this sake, we design and develop a framework that utilizes Web standards (e.g., XML and Web services) for tackling data heterogeneity, distribution and interoperability issues. Moreover, our framework utilizes Active XML (AXML) to warehouse passive data as well as services to integrate active and dynamic data on-the-fly. AXML embedded services and changes detection services ensure near real-time data integration. Furthermore, the idea of integrating Web data actively and autonomously revolves around mining events logged by the data integration environment. Therefore, we propose an incremental XML-based algorithm for mining association rules from logged events. Then, we define active rules dynamically upon mined data to automate and reactivate integration tasks. Finally, as a proof of concept, we implement a framework prototype as a Web application using open-source tools.     

Coronavirus disease 2019 (COVID-19): survival analysis using deep learning and Cox regression model

Pattern Analysis and Applications - Coronavirus (COVID-19) is one of the most serious problems that has caused stopping the wheel of life all over the world. It is widely spread to the extent that...     

TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.