Dragonfly-based swarm system model for node identification in ultra-reliable low-latency communication

Neural Computing and Applications - Latency and reliability are essential parameters for enabling ultra-reliable low-latency communication (URLLC). Therefore, an approach for node identification...     

Methane pyrolysis in a molten gallium bubble column reactor for sustainable hydrogen production: Proof of concept & techno-economic assessment

Nowadays, nearly 50% of the hydrogen produced worldwide comes from Steam Methane Reforming (SMR) at an environmental burden of 10.5 t_CO2,eq/t_H2, accelerating the consequences of global warming. One way to produce clean hydrogen is via methane pyrolysis using melts of metals and salts. Compared to SMR, significant less CO₂ is produced due to conversion of methane into hydrogen and carbon, making this route more sustainable to generate hydrogen. Hydrogen is produced with high purity, and solid carbon is segregated and deposited on the molten bath. Carbon may be sold as valuable co-product, making industrial scale promising. In this work, methane pyrolysis was performed in a quartz bubble column using molten gallium as heat transfer agent and catalyst. A maximum conversion of 91% was achieved at 1119 °C and ambient pressure, with a residence time of the bubbles in the liquid of 0.5 s. Based on in-depth analysis of the carbon, it can be characterized as carbon black. Techno-economic and sensitivity analyses of the industrial concept were done for different scenarios. The results showed that, if co-product carbon is saleable and a CO₂ tax of 50 euro per tonne is imposed to the processes, the molten metal technology can be competitive with SMR.     

A high payload reversible data hiding algorithm for homomorphic encrypted absolute moment block truncation coding compressed images

Multimedia Tools and Applications - With the aim to ensure privacy and security of secret message, an enhanced reversible data hiding algorithm in encrypted as well as compressed domain is proposed...     

Influence of Asymmetric Cyclic Loading on Structural Evolution and Deformation Behavior of Cu-5 at.% Zr Alloy: An Atomistic Simulation-Based Study

Molecular dynamics (MD) simulation-based studies of tensile test and structural evolution of Cu-5 at.% Zr alloy under asymmetric cyclic loading (i.e., ratcheting behavior) considering various stress ratios such as ? 0.2, ? 0.4 and ? 0.6 for different temperatures, viz.≈ 100, 300 and 500 K have been performed using embedded atom model Finnis–Sinclair potential. According to obtained stress–strain response from MD calculation, Cu-5 at.% Zr alloy specimen is pristine in nature as sudden drop in stress just after yield stress and subsequent elastic type deformation are observed for this alloy. Predicted ratcheting strain by MD simulation for Cu-5 at.% Zr alloy varies from 4.5 to 5%. Significant increase in ratcheting strain has been observed with the increase in temperature. Slight reduction in crystallinity is identified at the middle of the each loading cycle from the performed radial distribution function analysis and cluster analysis.     

Fruit Extract Mediated Green Synthesis of Metallic Nanoparticles: A New Avenue in Pomology Applications

Fruit extracts have natural bioactive molecules that are known to possess significant therapeutic potential. Traditionally, metallic nanoparticles were synthesized via chemical methods, in which the chemical act as the reducing agent. Later, these traditional metallic nanoparticles emerged as the biological risk, which prompted researchers to explore an eco-friendly approach. There are different eco-friendly methods employed for synthesizing these metallic nanoparticles via the usage of microbes and plants, primarily via fruit extract. These explorations have paved the way for using fruit extracts for developing nanoparticles, as they eliminate the usage of reducing and stabilizing agents. Metallic nanoparticles have gained significant attention, and are used for diverse biological applications. The present review discusses the potential activities of phytochemicals, and it intends to summarize the different metallic nanoparticles synthesized using fruit extracts and their associated pharmacological activities like anti-cancerous, antimicrobial, antioxidant and catalytic efficiency.     

Corrosion and Fire Protective Behavior of Advanced Phosphatic Geopolymeric Coating on Mild Steel Substrate

The research objectives of this study were to investigate and compare corrosion and fire protective properties of conventional and advanced phosphatic geopolymeric coating on mild steel substrate using spray coating technique For these studies two composition were developed using conventional geopolymerisation route by adding alkali activator solution to fly ash and six compositions were developed using advanced geopolymerisation process in which water was added to solid precursor powder obtained by together co-ginding of raw materials for a period of 8 h. Coated plates were tested for adhesion strength, water resistance, fire protection and corrosion resistance. Results indicated that coating developed from two passes with thickness 100 ± 15 μm showed better adhesion as compared to single pass and also proved to be promising corrosion protective coating material for mild steel substrate under sea water conditions. The developed material is able to withstand flame for more than 45 min and also no cracks were observed in coating by direct heating on liquefied petroleum gas flame. Thus developed phosphatic geopolymeric material is well suited for protecting the mild steel structures from fire and corrosion.     

Development of a Glacio-hydrological Model for Discharge and Mass Balance Reconstruction

The reconstruction of glacio-hydrological records for the data deficient Himalayan catchments is needed in order to study the past and future water availability. The study provides outcomes of a glacio-hydrological model based on the degree-day approach. The model simulates the discharge and mass balance for glacierised Shaune Garang catchment. The degree-day factors for different land covers, used in the model, were estimated using daily stake measurements on Shaune Garang glacier and they were found to be varying between 2.6?±?0.4 and 9.3?±?0.3 mm °C^?1day^?1. The model is validated using observed discharge during ablation season of 2014 with coefficient of determination (R²) 0.90 and root mean square error (RMSE) 1.05 m³ sec^?1. The model is used to simulate discharge from 1985 to 2008 and mass balance from 2001 to 2008. The model results show significant contribution of seasonal snow and ice melt in total discharge of the catchment, especially during summer. We observe the maximum discharge in July having maximum contribution from snow and ice melt. The annual melt season discharge shows following a decreasing trend in the simulation period. The reconstructed mass balance shows mass loss of 0.89 m we per year between 2001 and 2008 with slight mass gain during 2000/01 and 2004/05 hydrological years.     

Using topic models for OCR correction

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers perform adequately on constrained handwritten documents which typically use a restricted vocabulary (lexicon). But in the case of unconstrained handwritten documents, state-of-the-art word recognition accuracy is still below the acceptable limits. The objective of this research is to improve word recognition accuracy on unconstrained handwritten documents by applying a post-processing or OCR correction technique to the word recognition output. In this paper, we present two different methods for this purpose. First, we describe a lexicon reduction-based method by topic categorization of handwritten documents which is used to generate smaller topic-specific lexicons for improving the recognition accuracy. Second, we describe a method which uses topic-specific language models and a maximum-entropy based topic categorization model to refine the recognition output. We present the relative merits of each of these methods and report results on the publicly available IAM database.