Do Energy Resources matter for Growth Level? The dynamic effects of different strategies of renewable energy,carbon emissions on sustainable economic growth

Clean Technologies and Environmental Policy - This study examines the association between renewable energy strategies and carbon emissions on sustainable economic growth under affordable and clean...     

Real geo-time-based secured access computation model for e-Health systems

Role Back Access Control model (RBAC) allows devices to access cloud services after authentication of requests. However, it does not give priority in Big Data to devices located in certain geolocations. Regarding the crisis in a specific region, RBAC did not provide a facility to give priority access to such geolocations. In this paper, we planned to incorporate Location Time- (GEOTime) based condition alongside Priority Attribute role-based access control model (PARBAC), so requesters can be allowed/prevented from access based on their location and time. The priority concept helped to improve the performance of the existing access model. TIME-PARBAC also ensures service priorities based on geographical condition. For this purpose, the session is encrypted using a secret key. The secret key is created by mapping location, time, speed, acceleration and other information into a unique number, that is, K(Unique_Value) = location, time, speed, accelerator, other information. Spatial entities are used to model objects, user position, and geographically bounded roles. The role is activated based on the position and attributes of the user. To enhance usability and flexibility, we designed a role schema to include the name of the role and the type of role associated with the logical position and the rest of the PARBAC model proposed using official documentation available on the website for Azure internet of things (IoT) Cloud. The implementation results utilizing a health use case signified the importance of geology, time, priority and attribute parameters with supporting features to improve the flexibility of the existing access control model in the IoT Cloud.     

Pythagorean Neutrosophic Planar Graphs with an Application in Decision-Making

Graph theory has a significant impact and is crucial in the structure of many real-life situations. To simulate uncertainty and ambiguity, many extensions of graph theoretical notions were created. Planar graphs play a vital role in modelling which has the property of non-crossing edges. Although crossing edges benefit, they have some drawbacks, which paved the way for the introduction of planar graphs. The overall purpose of the study is to contribute to the conceptual development of the Pythagorean Neutrosophic graph. The basic methodology of our research is the incorporation of the analogous concepts of planar graphs in the Pythagorean Neutrosophic graphs. The significant finding of our research is the introduction of Pythagorean Neutrosophic Planar graphs, a conceptual blending of Pythagorean Neutrosophic and Planar graphs. The idea of Pythagorean Neutrosophic multigraphs and dual graphs are also introduced to deal with the ambiguous situations. This paper investigates the Pythagorean Neutrosophic planar values, which form the edges of the Pythagorean neutrosophic graphs. The concept of Pythagorean Neutrosophic dual graphs, isomorphism, co-weak and weak isomorphism have also been explored for Pythagorean Neutrosophic planar graphs. A decision-making algorithm was proposed with a numerical illustration by using the Pythagorean Neutrosophic fuzzy graph.     

Recent Progress in Synthesis and Application of Biomass-Based Hybrid Electrodes for Rechargeable Batteries

The rapid growth in electronic and portable devices demands safe, durable, light weight, low cost, high energy, and power density electrode materials for rechargeable batteries. In this context, biomass-based materials and their hybrids are extensively used for energy generation research, which is primarily due to their properties such as large specific surface area, fast ion/electron kinetics, restricted volume expansion, and restrained shuttle effect. In this review, the key advancements in the preparation of biomass derived porous carbons using different synthesis strategies and their modifications with species such as heteroatoms, metal oxides, metal sulfides, silicon, and other carbon forms are discussed. The electrochemical performances of these materials and the ion storage mechanisms in different batteries including lithium-ion, lithium–sulfur, sodium-ion, and potassium-ion batteries are discussed. Special attention will be paid to the challenges in using porous biomass-derived carbons and the current strategies employed for maximizing the specific capacity and lifetime for battery applications. Finally, the drawbacks in current technology and endeavors for the future research and development in the field to catapult the performances of the biomass derived materials in order to equip them to meet the demands of commercialization are highlighted.     

Industrial decarbonization: A revolution ahead

Clean Technologies and Environmental Policy -     

Application of artificial neural network for prediction of 10 crude oil properties

This study aims to develop an industrially reliable and accurate method to estimate crude oil properties from their Fourier transform infrared spectroscopy (FTIR) spectra. We used the complete FTIR spectral data of selected crude oil samples from seven different Canadian oil fields to predict 10 important crude oil properties using artificial neural networks (ANNs). The predicted properties include specific gravity, kinematic viscosity, total acid number, micro carbon content, and production of light and heavy naphtha, Kero, and distillate in oil refineries. The 107 different (65 light oil and 42 heavy/medium oil samples) crude oil samples used in this study came from seven oil fields and reservoirs across Canada. In line with standard practice, we used 80% of the dataset for training the ANN models and used the remaining 20% of the crude oil samples to test the models. In the ANN analysis, the mean squared error (MSE) was used as the loss function in models, and the mean absolute prediction error (MAPE) was used as a reference to compare the performance of different neural networks constructed with different numbers of layers. This work demonstrates that FTIR spectroscopy is a promising technique that provides rapid and accurate estimates for the oil properties of interest to the industry. A comparison of the values predicted by the validated ANN models and their corresponding measured (actual) values showed excellent prediction with the acceptable range of error (below 15%) aimed for by our industry partner for all properties except viscosity, for which building models based on the natural logarithmic values of measured viscosities significantly improved the results.     

Structural Characterization and Protective Properties of Spray-Deposited Titania Coating Obtained from Oxime-Modified Titanium(IV) Precursor

Protection of Metals and Physical Chemistry of Surfaces - Spray pyrolytic deposition (SPD) of titania coatings on steel substrates have been carried out using acetoxime-modified titanium(IV)...     

Snoek-Dominated Internal Friction Response in bcc Steel: Relating Experiments With a Multi-scale Atomistic Computational Framework

Metallurgical and Materials Transactions A - Internal friction is often sensitive to microstructural features. However, there is a clear absence of rational approach for decoupling internal...     

Characteristics Study of Physical,Mechanical and Tribological Behaviour of SiC/TiB2 Dispersed Aluminium Matrix Composite

Silicon - SiC and TiB2 particle reinforced Al7075 matrix composite has numerous applications such as aircraft structures, mould tool manufacturing, and structural application due to less weight to...     

Recovery and purification of copper sulfide nanoparticles from acid mine drainage by biological sulfate reduction and microfiltration using low-cost ceramic membrane

Clean Technologies and Environmental Policy - Acid mine drainage (AMD) typically has a high amount of copper that can be recovered in pure form for different industrial applications. This study...