Firework inspired load balancing approach for wireless sensor networks

Wireless Networks - In Wireless Sensor Networks (WSNs), where power consumption is a huge concern, the improvement of the network’s lifetime is an area of constant study and innovation. The...     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

Healing substrates with mobile, particle-filled microcapsules: designing a 'repair and go' system.

We model the rolling motion of a fluid-driven, particle-filled microcapsule along a heterogeneous, adhesive substrate to determine how the release of the encapsulated nanoparticles can be harnessed to repair damage on the underlying surface. We integrate the lattice Boltzmann model for hydrodynamics and the lattice spring model for the micromechanics of elastic solids to capture the interactions between the elastic shell of the microcapsule and the surrounding fluids. A Brownian dynamics model is used to simulate the release of nanoparticles from the capsule and their diffusion into the surrounding solution. We focus on a substrate that contains a damaged region (e.g. a crack or eroded surface coating), which prevents the otherwise mobile capsule from rolling along the surface. We isolate conditions where nanoparticles released from the arrested capsule can repair the damage and thereby enable the capsules to again move along the substrate. Through these studies, we establish guidelines for designing particle-filled microcapsules that perform a 'repair and go' function and thus, can be utilized to repair damage in microchannels and microfluidic devices.     

Plasma immersion ion implantation for SOI synthesis: SIMOX and ion-cut

We have demonstrated feasibility to form silicon-on-insulator (SOI) substrates using plasma immersion ion implantation (PIII) for both separation by implantation of oxygen and ion-cut. This high throughput technique can substantially lower the high cost of SOI substrates due to the simpler implanter design as well as ease of maintenance. For separation by plasma implantation of oxygen wafers, secondary ion mass spectrometry analysis and cross-sectional transmission electron micrographs show continuous buried oxide formation under a single-crystal silicon overlayer with sharp Si/SiO₂ interfaces after oxygen plasma implantation and high-temperature (1300°C) annealing. Ion-cut SOI wafer fabrication technique is implemented for the first time using PIII. The hydrogen plasma can be optimized so that only one ion species is dominant in concentration and there are minimal effects by other residual ions on the ion-cut process. The physical mechanism of hydrogen induced silicon surface layer cleavage has been investigated. An ideal gas law model of the microcavity internal pressure combined with a two-dimensional finite element fracture mechanics model is used to approximate the fracture driving force which is sufficient to overcome the silicon fracture resistance.     

Microwave-Assisted Synthesis of Fine Particle Oxides Employing Wet Redox Mixtures

Interaction of electromagnetic radiation with a physical mixture of metal nitrates and amides/hydrazides is observed to initiate high-temperature reactions, useful for realizing several high-temperature ceramic materials. A judicious choice of such redox mixtures undergoes exothermic reactions when they couple with microwave radiation. The coupling of electromagnetic radiation with metal salts and amides/hydrazides depends on the dielectric properties of the individual components in the reaction mixture. The approach has been used to prepare γ-Fe₂O₃, Fe₃O₄, MgCr₂O₄, α-CaCr₂O₄, and La_0.7Ba_0.3MnO₃.     

IMPROVEMENTS IN GAS INDUCING IMPELLER DESIGN

Rate of gas induction, static pressure, mixing time and power consumption have been measured in 0.57 m i.d. vessel. Different types of impellers namely shrouded disc turbine, shrouded curved blade turbine and pitched blade turbine were used. The impeller diameter was varied from 0.15-0.25 m and the impeller speed was varied from 3 to 20 r/s.

The pitched blade turbine was found to give 30-60 per cent higher rates of gas induction as compared with the best design reported in the literature. The mixing time was found to be lower by a similar magnitude. Moreover in the case of pitched blade turbine it was found that the gas was getting induced radially as well as axially. This eliminates the necessity of the diffuser and hence reducing the complexities in the mechanical structure.     

Thio Sol–Gel Process for the Synthesis of Titanium Disulfide

A thio sol–gel process for the synthesis of titanium disulfide using titanium alkoxide as the metal source is demonstrated. The alkoxide reacts at room temperature with H₂S to form a precipitate which is a precursor to the sulfide. Through infrared spectroscopy and chemical analysis of the powder and gas chromatographic analysis of the liquid byproducts of the reaction, it is shown that a partially sulfidized alkoxide precursor forms through the displacement of alkoxy groups from the alkoxide by sulfur-bearing thiol groups. This thiolysis reaction is very similar to that which occurs in the case of sol–gel reactions to form oxides. The alkoxy–thiol species then undergo condensation–polymerization by the liberation of H₂S. When it is heat-treated in H₂S, this precursor transforms to TiS₂ at ∼700°C. Systematic heat treatments have been performed and the formation of TiS₂ has been observed using X-ray diffractometry. The change in the morphology has also been studied using scanning electron microscopy.     

Thiophene-based dendronized macromonomers and polymers

The synthesis of thiophene-containing second (G2) and third generation (G3) dendronized macromonomers with methacrylate polymerizable units as well as their corresponding dendronized polymers is reported. The dendrons are prepared from branched thiophene oligomers and are decorated with straight alkyl chains for solubility reasons. The polymerization reactions were done with AIBN as initiator and the polymers were characterized by NMR spectroscopy, elemental analysis and GPC. Molar masses are in the range of 2.2-5.4 × 10⁵ g mol⁻¹ (G2) and 1.3-3.0 × 10⁴ g mol⁻¹ (G3) for different runs. These polymers are investigated by cyclic voltammetry and optical spectroscopy.     

Damage Development and Moduli Reductions in Nicalon—Calcium Aluminosilicate Composites under Static Fatigue and Cyclic Fatigue

Unidirectional and cross-ply Nicalon fiber-reinforced calcium aluminosilicate (CAS) glass-ceramic composite specimens were subjected to tension–tension cyclic fatigue and static fatigue loadings. Microcrack densities, longitudinal Young's modulus, and major Poisson's ratio were measured at regular intervals of load cycles and load time. The matrix crack (0° plies) density and transverse crack (90° plies) density increased gradually with fatigue cycles and load time. The crack growth is environmentally driven and depends on the maximum load and time. Young's modulus and Poisson's ratio decreased gradually with fatigue cycles and load time. The saturation crack densities under fatigue loadings were found to be comparable to those under monotonic loading. A matrix crack growth limit strain exists, below which matrix cracks do not grow significantly under fatigue loading. This limit coincides with the matrix crack initiation strain. Linear correlations between crack density and moduli reductions obtained from quasi-static data can predict the moduli reductions under cyclic loading, using experimentally measured crack densities. A logarithmic correlation can predict the Young's modulus reduction in a limited stress range. A fatigue crack growth model is proposed to explain the presence of two distinct regimes of crack growth and Young's modulus reduction.