A Bio-Inspired Routing Optimization in UAV-enabled Internet of Everything

Internet of Everything (IoE) indicates a fantastic vision of the future, where everything is connected to the internet, providing intelligent services and facilitating decision making. IoE is the collection of static and moving objects able to coordinate and communicate with each other. The moving objects may consist of ground segments and flying segments. The speed of flying segment e.g., Unmanned Ariel Vehicles (UAVs) may high as compared to ground segment objects. The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE (Ue-IoE). The routing maintenance overhead may increase when scaling the Ue-IoE (number of objects increases). A single change in topology can force all the objects of the Ue-IoE to update their routing tables. Similarly, the frequent updating in routing table entries will result more energy dissipation and the lifetime of the Ue-IoE may decrease. The objects consume more energy on routing computations. To prevent the frequent updation of routing tables associated with each object, the computation of routes from source to destination may be limited to optimum number of objects in the Ue-IoE. In this article, we propose a routing scheme in which the responsibility of route computation (from neighbor objects to destination) is assigned to some IoE-objects in the Ue-IoE. The route computation objects (RCO) are selected on the basis of certain parameters like remaining energy and mobility. The RCO send the routing information of destination objects to their neighbors once they want to communicate with other objects. The proposed protocol is simulated and the results show that it outperform state-of-the-art protocols in terms of average energy consumption, messages overhead, throughput, delay etc.     

Optical and physical behaviours of newly developed germanium-tellurium (GeTe) glasses: a comprehensive experimental and in-silico study with commercial glasses and ordinary shields

Journal of Materials Science: Materials in Electronics - In this study, synthesis and determination of physical, optical, and radiation shielding properties of glasses based on TeO2 and GeO2 were...     

Towards superior permeability and antifouling performance of sulfonated polyethersulfone ultrafiltration membranes modified with sulfopropyl methacrylate functionalized SBA-15

A non-solvent induced phase separation (NIPS) process was used to fabricate a series of sulfonated polyethersulfone (SPES) membranes blending with different concentrations of SBA-15-g-PSPA with the applications in the ultrafiltration (UF) process. SBA-15 was modified with 3-methacrylate-propyltrimethoxysilane (MPS) to form SBA-15-g-MPS. It was further modified with the charge tailorable polymer chains by reacting with 3-sulfopropyl methacrylate potassium salt. The nanoparticles were uniformly dispersed and finger-like channels were developed within the membrane. The adding of surface modified SBA-15-g-PSPA nanoparticles has significantly improved membrane water permeability, hydrophilicity, and antifouling properties. The pure water fluxes of the composite SPES membranes were significantly higher than the pristine SPES membrane. For the membrane containing 5% (mass) of SBA-15-g-PSPA (MSSPA5), the pure water flux was increased dramatically to 402.15 L·m^-2·h^-1, which is ~1.5 times that of MSSPA0 (268.0 L·m^-2·h^-1). The high flux rate was achieved with 3% (mass) of SBA-15 nanoparticles with retained high rejection ratio 98% for natural organic matter. The results indicate that the fashioned composite membrane comprising SBA-15-g-PSPA nanoparticles have a promising future in ultrafiltration applications.     

In situ aqueous synthesis of silver nanoparticles supported on titanium as active electrocatalyst for the hydrogen evolution reaction

Dispersed silver nanoparticles (AgNPs) were successfully assembled on titanium (Ti) substrates by electroless deposition without using reducing agents, stabilizers, or any chemical pre-treatments. The substrate was immersed in aqueous solutions of AgNO₃ of various concentrations (0.001–0.5 M) for different durations (5 s–2 h) at room temperature. Subsequently, Ti substrates with various AgNP densities (number of AgNPs per unit surface area) were obtained. Nitrate anions in solution were found to destabilize the passivity of Ti proportionally to C_nitrate. This in turn activated the reducing ability of Ti, and hence resulted in an obvious increase in the population of AgNPs on Ti. This study was complemented with SEM/EDS, TEM, XPS and XRD examinations. The mechanism by which Ag ions are reduced to metallic Ag and the catalytic influence of destabilizing the passivity of Ti in enhancing its reducing ability are discussed. The electrocatalytic properties of as-prepared Ti-AgNPs catalysts for the hydrogen evolution reaction (HER) were investigated in 0.1 M HCl solution by means of cathodic polarization and impedance studies as a function of various experimental parameters. The results show that the Ti substrate loaded with the highest population of AgNPs exhibits the most effective electrocatalytic activity towards the HER, even better than platinum. Hence, the as-prepared Ti-AgNPs catalysts look promising in catalyzing the HER.     

MCBC-SMOTE: A Majority Clustering Model for Classification ofImbalanced Data

Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem. Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different under-sampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling. To over-come these problems, this paper proposes a majority clustering model for classification of imbalanced datasets known as MCBC-SMOTE (Majority Clustering for balanced Classification-SMOTE). The model provides a method to convert the problem of binary classification into a multi-class problem. In the proposed algorithm, the number of clusters for the majority class is calculated using the elbow method and the minority class is over-sampled as an average of clustered majority classes to generate a symmetrical class distribution. The proposed technique is cost-effective, reduces the problem of noise generation and successfully disables the imbalances present in between and within classes. The results of the evaluations on diverse real datasets proved to provide better classification results as compared to state of the art existing methodologies based on several performance metrics.     

Impact of Tools and Techniques for Securing Consultancy Services

In a digital world moving at a breakneck speed, consultancy services have emerged as one of the prominent resources for seeking effective, sustainable and economically viable solutions to a given crisis. The present day consultancy services are aided by the use of multiple tools and techniques. However, ensuring the security of these tools and techniques is an important concern for the consultants because even a slight malfunction of any tool could alter the results drastically. Consultants usually tackle these functions after establishing the clients’ needs and developing the appropriate strategy. Nevertheless, most of the consultants tend to focus more on the intended outcomes only and often ignore the security-specific issues. Our research study is an initiative to recommend the use of a hybrid computational technique based on fuzzy Analytical Hierarchy Process (AHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) for prioritizing the tools and techniques that are used in consultancy services on the basis of their security features and efficacy. The empirical analysis conducted in this context shows that after implementing the assessment process, the rank of the tools and techniques obtained is: A7 > A1 > A4 > A2 > A3 > A5 > A6 > A7, and General Electric McKinsey (GE-McKinsey) Nine-box Matrix (A7) obtained the highest rank. Thus, the outcomes show that this order of selection of the tools and techniques will give the most effective and secure services. The awareness about using the best tools and techniques in consultancy services is as important as selecting the most secure tool for solving a given problem. In this league, the results obtained in this study would be a conclusive and a reliable reference for the consultants.     

An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

COVID-19, being the virus of fear and anxiety, is one of the most recent and emergent of various respiratory disorders. It is similar to the MERS-COV and SARS-COV, the viruses that affected a large population of different countries in the year 2012 and 2002, respectively. Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty. The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution, and Random Forest Model. The analysis upon dataset has been performed considering the time duration from January 1^st 2020 to16^th July 2021. The model has been developed to obtain the forecast values till September 2021. This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country. In India, the cases are rapidly increasing day-by-day since mid of Feb 2021. The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave. To empower the prediction for future validation, the proposed model works effectively.     

A Layout Sensitivity Model for Estimating Electromigration-vulnerable Narrow Interconnects

During back-end manufacturing process of IC, intervention of spot defects induces extra and missing material of interconnects causing circuit failures. Interconnect narrowing occurs when spot defects induce interconnects missing material without resulting in a complete cut. The narrow sites of defective interconnects favor electromigration that makes narrow interconnects more likely to induce a chip failure than regular interconnects. In this paper, an innovative layout sensitivity model accounting for “narrow” defects is derived. The paper also pioneers estimation of the probability of narrow interconnects in the die. The layout sensitivity model for narrow interconnects is tested and compared to actual and simulated data. Our layout sensitivity model predicts the probability of narrowing with 3.1% error, on average. The model is then combined with electromigration constraints to predict mean-time-to-failure of chips manufactured in future technologies down to 32 nm node. The paper concludes with some other possible applications of the narrow interconnect predictive model.

An Ensemble Approach to Identify Firearm Listing on Tor Hidden-Services

The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online. The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it. To aid the law enforcement agencies in curbing the illicit trade of firearms on cryptomarkets, this paper has proposed an automated technique employing ensemble machine learning models to detect the firearms listings on cryptomarkets. In this work, we have used part-of-speech (PoS) tagged features in conjunction with n-gram models to construct the feature set for the ensemble model. We studied the effectiveness of the proposed features in the performance of the classification model and the relative change in the dimensionality of the feature set. The experiments and evaluations are performed on the data belonging to the three popular cryptomarkets on the Tor dark web from a publicly available dataset. The prediction of the classification model can be utilized to identify the key vendors in the ecosystem of the illegal trade of firearms. This information can then be used by law enforcement agencies to bust firearm trafficking on the dark web.     

Construction of Nanophase Novel Coatings-Based Titanium for the Enhancement of Protein Adsorption

In the recent years,biological nanostructures coatings have been incorporated into orthopedic and dental implants in order to accelerate osseointegration and reducing surgical restrictions.In the present work,chemical etching,anodization and metal doping surface modification methods were integrated in one strategy to fabricate innovative titanium surfaces denominated by titanium nanoporous,anodized titanium nanoporous,silver-anodized titanium nanoporous and gold-anodized titanium nanoporous.The stability properties of nanostructures-coated surfaces were elucidated using electrochemical impedance spectroscopy(EIS) after 7 days of immersion in simulated biological fluids.Morphology and chemical compositions of new surfaces were characterized by scanning electron microscope and energy-dispersive X-ray analysis.The EIS results and data fitting to the electrical equivalent circuit model demonstrated the influence of adsorption of bovine serum albumin on new surfaces as a function of protein concentration.Adsorption process was described by the very well-known model of the Langmuir adsorption isotherm.The thermodynamic parameter DGADS(-50 to 59 kJ mol~(-1)) is calculated,which supports the instantaneous adsorption of protein from biological fluids to new surfaces and refers to their good biocompatibility.Ultimately,this study explores new surface strategy to gain new implants as a means of improving clinical outcomes of patients undergoing orthopedic surgery.