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Background: Multi-faults in rotating machines are critical and create an unfavourable working environment. Research on multi-faults is still in the early stages, and models are trained at constant speeds, which are not efficient under different types of uncertain speed conditions. Methodology: This paper proposes developing a multi-fault diagnosis system that can work irrespective of speed conditions. Vibration signatures were acquired from the different experimental multi-fault conditions under an uncertain operating environment. STFNet (Segmented Time-frequency Network) was applied to extract fault information and to create an intelligent multi-fault classification model. Results and validation: Rigorous testing of the proposed STFNet model was done under various uncertain speed conditions like constant, continuously accelerated, continuously decelerated and fluctuating speed, and a maximum accuracy of 97.87 % was achieved. The performance of the proposed model was compared with the existing algorithms and was validated on another bearing multi-fault dataset. Conclusion: As per the industrial requirement, the proposed approach will be very helpful for diagnosing multi-faults under various uncertain speed conditions. [ABSTRACT FROM AUTHOR]

Background: The widely used rolling element bearings in rotating machines undergo progressive degradation with continuous operation. To identify bearing faults, complex time-frequency based signal processing techniques and high-end deep neural network algorithms have been used to perform fault classification, which is time-consuming. Method: In this paper, the focus was given to replace the complex time-frequency domain signal processing techniques by incorporating a simple time-domain based methodology. Initially, the vibration signature of different bearing faults was acquired at three different speeds and was directly converted into images by 2D-Vibration Imaging (2D-VI) technique using an overlapping-based moving window. The extracted images were fed into Convolutional Neural Network (CNN) for automatic feature extraction, followed by classification using Support Vector Machine (SVM). Results and validation: Separately, time-frequency spectrums were also extracted to compare the effectiveness of the proposed methodology. Furthermore, the proposed methodology was validated on the bearing dataset of combined faults and Case Western Reserve University (CWRU). Conclusion: The experimental results showed that the proposed methodology has the potential to replace the conventional approach by consuming less computational time without affecting classification accuracy. [ABSTRACT FROM AUTHOR]

In this work, pure and Zn ion doped SnO2 nanorods (Zn:SnO2) were prepared by hydrothermal method. Structural and microstructural properties of these nanorods were characterized by X-ray diffraction and high-resolution transmission electron microscopy analysis. Further, the Zn:SnO2 nanorods have been systematically investigated through optical studies such as UV–visible, Raman and photoluminescence spectroscopies. The stochiometric composition of the Zn:SnO2 had been confirmed by X-ray photoelectron spectroscopy studies. The samples revealed out that the dopant Zn ions creating the oxygen vacancies, which can enhance the gas sensing performance of SnO2. As a result, the Zn doped SnO2 nanorods are very suitable for gas sensing applications. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is one of the challenging optimization problems in power domain. The complexity of the problem escalates with incorporation of uncertain and intermittent renewable sources into the electrical network. Flexible AC transmission system (FACTS) devices are also becoming more commonplace in modern power system to mitigate growing demand and to relieve congestion from the network. This paper aims to solve the OPF where the generation cost is optimized with incorporation of stochastic wind power and several types of FACTS devices such as static VAR compensator, thyristor-controlled series compensator and thyristor-controlled phase shifter. Case studies with both fixed and uncertain load demands are performed. The stochastic wind energy and load demand are modeled using suitable probability density functions. Optimization objective considers cost of thermal generation, direct cost of scheduled wind power, penalty cost for underestimation and reserve cost for overestimation of the wind power. In addition, both locations and ratings of the FACTS devices are optimized to minimize total generation cost of the system. Success history-based adaptive differential evolution (SHADE), a powerful evolutionary algorithm, is adopted to perform the optimization task. The constraints of OPF problem are handled using superiority of feasible solutions (SF) method. The integration approach of SF method with several popular metaheuristic algorithms has been proposed in this work, and a detailed comparative analysis among various algorithms establishes SHADE algorithm to be the best performer. [ABSTRACT FROM AUTHOR]

The radiation response, long-term performance, and reliability of HfO2-based gate dielectric materials play a critical role in metal oxide semiconductor (MOS) technology for space device applications. Al/HfO2/Si atomic layer-deposited devices were irradiated by gamma and swift heavy ions. An increase in the leakage current and charge trapping has been observed as the gamma irradiation dose varied from 25 to 100 krad. The density of oxide traps is found to increase with an increase in the gamma dose while the interface trap density is found to decrease. Another set of samples were irradiated by 120 MeV Au ions to study the SHI-induced defect annealing/creation of defects and intermixing effects in HfO2/Si-based devices. The formation of an interfacial layer of HfSiO at a fluence of at 5 × 1013 cm−2 is revealed by X-ray reflectivity analysis. The densities of interface- and oxide-trapped charges are found to decrease up to a critical fluence of 1 × 1012 cm−2 and then increase with further increase in fluence to 5 × 1013 cm−2. The presence of the interlayer, due to the swift heavy ion-induced intermixing, has been confirmed by X-ray photoelectron spectroscopy measurements. Various current conduction mechanisms in both substrate and gate injection cases were used to understand the basic mechanisms of direct, Fowler–Nordheim, and Poole–Frenkel tunneling, as well as Schottky emission in these devices. These studies elucidated the radiation tolerance and charge-trapping behavior of Al/HfO2/Si nMOS capacitors. [ABSTRACT FROM AUTHOR]

Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality medical care. In this research work, we proposed a novel method which utilizes a modified vesselness measurement and a Deep Convolutional Neural Network (DCNN) to identify the oral cancer region structure in IoT based smart healthcare system. The robust vesselness filtering scheme handles noise while reserving small structures, while the CNN framework considerably improves classification accuracy by deblurring focused region of interest (ROI) through integrating with multi-dimensional information from feature vector selection step. The marked feature vector points are extracted from each connected component in the region and used as input for training the CNN. During classification, each connected part is individually analysed using the trained DCNN by considering the feature vector values that belong to its region. For a training of 1500 image dataset, an accuracy of 96.8% and sensitivity of 92% is obtained. Hence, the results of this work validate that the proposed algorithm is effective and accurate in terms of classification of oral cancer region in accurate decision making. The developed system can be used in IoT based diagnosis in health care systems, where accuracy and real time diagnosis are essential. [ABSTRACT FROM AUTHOR]

Insertion of distributed generation (DG) in existing distribution system has effectively improved its performance and operation. Many different approaches for the planning of distribution system with DG insertion are presented by researchers. In this paper a multi objective approach has been proposed to maximize the mutual benefits of both the distribution system operator and DG owner. The contradictory relationship between reduction in MVA rating of DGs and reduction of power losses of the system is the motivation for this multi-objective approach. The best compromised size of DGs in MVA, their operating power factors and positions are obtained to reduce the system active power loss along with the reduction of DGs size. The 69-bus and 85-bus radial distribution system are considered as test systems. The Pareto-front of non-dominated solutions is obtained by using multi-objective differential evolution (MODE) optimization algorithm. The performance of MODE algorithm is also compared with that of multi-objective particle swarm optimization (MOPSO) algorithm. The different system operating indices such as active power loss, reactive power loss and voltage deviation are evaluated to show the effect of the best compromised solution of DGs placement in distribution system. [ABSTRACT FROM AUTHOR]

Flying Ad-Hoc networks (FANET) are the extended paradigm of the mobile Ad-Hoc networks and, perhaps, one of the most emerging research domains in the current era. A huge number of tangible applications have been developed in this domain. The main advantages of such networks are their easy deployment, scalability, and robustness. However, the sparseness of these networks is an inherent characteristic that is known to be a bottleneck. The main objective of this work was to provide an alternative solution for the intermittently connected FANET by considering the philosophy of the Delay Tolerant Network (DTN) approach. To realize the functionality of the DTN protocols in a three-dimensional (3D) space, a social FANET model is proposed. FANET nodes are supposed to have a sparse node density. Fundamentally, the proposed DTN assisted Flying Ad hoc Network exploits the DTN routing and mobility features. The new mobility modeling for 3D spaces was re-engineered and tested with well-known routing protocols to analyze the performance of the model based on node speed, density, buffer, latency, message overhead, and power consumption. The effectiveness of 3D mobility models has also been compared against the one of classical models. The obtained results reflect a significant enhanced performance of the suggested DTN protocol for sparse FANET in a social scenario. [ABSTRACT FROM AUTHOR]

We report the facile hydrothermal synthesis of p-NiO nanoflakes (NFs)/n-Si (100) heterojunction architecture and its photoresponse behavior. The vertically grown NiO NFs architecture is clearly seen on the Si substrate, as evidenced from the field emission scanning electron microscopy images. The elemental composition of the grown nanostructure on the Si substrate was estimated using energy-dispersive X-ray spectroscopy, and resonant Rutherford backscattering spectrometry measurements. The grazing incidence X-ray diffraction measurement reveals the grown NiO NFs architecture is of face centered cubic phase. The I-V characteristics of p-NiO NFs/n-Si heterojunction architecture shows good rectifying behavior in the dark and the increased photocurrent in the reverse bias imply the formation of p–n junction at the interface of p-NiO NFs and n-Si substrate. The voltage-dependent sensitivity of the device reveals the highest sensitivity at reverse bias of 3 V. The time-dependent photoresponse measurements of the device show good stability and repeatability under blue light illumination. The response and recovery time of the device is found as 6.18 s and 1.83 s under the reverse bias of 3 V. Our experimental studies demonstrate that the fabricated p-NiO NFs/n-Si heterojunction device has considerable promise for the practical photodetector applications. [ABSTRACT FROM AUTHOR]

Proteins have a significant role in animals and human health. Interactions among proteins are complex and large. Proteins separations are challenging process in molecular biology. Computational tools help to simulate the analysis in order to reduce the training data into small testing data. Large proteins have been mapped using self-organizing maps (SOMs). Neural network based SOMs has a significant role in reducing the irregular shapes of proteins interactions. Iterative checking enables the organizations of all proteins. In next stage, particle swarm intelligence is applied to classify the proteins' families. In the current work, secondary (Two dimensional) and tertiary proteins (Three dimensional) proteins have been grouped. Two dimensional proteins contain fewer hydro-carbons than three dimensional proteins. For faster analysis, the angles of the proteins are taken into account. The SOMs is compared with Bounding Box approach. In final, the experimental evolutions show that swarm intelligence achieved faster processing through enabling less memory consumptions and time. Since PSO combines proteins datasets in fuzzy values, the compactness or integration of similar proteins are strong. On the other hand, Bounding Box uses the Crisp value. Therefore, it needs more space to organize the whole data. Without SOMs, swarm intelligence also results are poor due to the excessive time consuming and required storage area. Moreover, for almost all classification and clustering tools, it is observed that the overall classification task becomes slow, time consuming, space consuming and also less sensitive because of noises, irrelevant data in input datasets. Thus, the proposed SOM based PSO approach achieved less time consuming with efficient classification into secondary and tertiary proteins. [ABSTRACT FROM AUTHOR]

In the study, we report the facile synthesis of reduced graphene oxide (rGO) conformally coated vertically aligned, densely packed NiO nanoflakes (NFs) architecture for high efficient electron field emission applications. The NiO NFs architecture was grown on the Si substrate by a simple hydrothermal method, followed by decoration with rGO by drop casting method. The grazing incidence X-ray diffraction measurements reveal the formation of cubic structured NiO. The grown NiO NFs architecture is in better stoichiometry as evidenced from the resonant Rutherford backscattering spectrometry measurements. The rGO conformally coated NiO NFs (GNiO NFs) shows higher mobility and lower sheet resistance in comparison with its counterparts. X-ray absorption near edge structure analyses at O K-edge reveals the increase in unoccupied density of states due to the charge transfer from NiO to rGO. The rGO conformally coated NiO NFs architecture exhibits low turn-on voltage and enhanced field emission (FE) current as compared to the NiO NFs architecture. The observed low turn-on voltage and enhanced FE current for the GNiO NFs architecture is ascribed to the synergistic effect of addition of rGO as the conductive channels, which results in better charge transport and the enhanced field emission current characteristics. [ABSTRACT FROM AUTHOR]

The influence of low chromium additions to a 0.25C-1.5Mn semikilled steel on microstructure, and tensile and impact behaviors of high strength rock bolt reinforcing bars has been investigated. Although chromium imparted adequate tensile properties at ambient temperature (yield stress: 624 MPa; ultimate tensile stress: 819 MPa; elongation: 12.5%) by forming transformation products such as tempered martensite, lower and upper bainite, and small amounts of acicular ferrite, it increased the ductile-to-brittle transition temperature due to coarser upper bainite in the core region of bar having larger unit crack paths. The synthesized steel is considered to be effective in realizing the desired tensile properties, and suitable for application in rock bolt, as well as other reinforced concrete structures. [ABSTRACT FROM AUTHOR]

Mechanical properties particularly Charpy impact toughness of two low-carbon [(a) 0.11% phosphorus and (b) 0.009% niobium] thermomechanically treated reinforcing bar steels were investigated. The phosphorus and niobium steels showed tensile to yield strength ratio of 1.25 and 1.19, ductile-brittle transition temperature of 223 K and below 193 K at yield strength levels of 428 and 472 MPa, respectively. The improved toughness of phosphorus steel is attributed to a mixed transformation microstructure comprising low-carbon bainite and fine polygonal ferrite. Lowest ductile-brittle transition temperature was observed in the niobium steel due to overall fineness of microstructure consisting mainly of low-carbon bainite, acicular ferrite, and polygonal ferrite. [ABSTRACT FROM AUTHOR]

Traditionally, Tinospora cordifolia (Willd.) Hook. F. & Thomson (Menispermaceae), Ocimum sanctum L. (Lamiaceae), Moringa oleifera Lam. (Moringaceae), and Phyllanthus niruri L. (Euphorbiaceae) are some of the commonly used medicinal plants in India for curing ailments ranging from common cold, skin diseases, and dental infections to major disorders like diabetes, hypertension, jaundice, rheumatism, etc. To understand and correlate their medicinal use, trace element studies on the aqueous extract of these medicinal plants have been carried out using particle-induced X-ray emission technique. A 2-MeV proton beam was used to identify and characterize major and minor elements namely Cl, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, Br, and Sr in them. Results have revealed that these elements are present in varying concentrations in the selected plants. Notable results include very high concentrations of Cl, K, and Ca in all the leaf samples, appreciable levels of Mn in all plants, high Zn content in T. cordifolia, and the aqueous extract of Moringa leaves compared to others and relative higher concentrations of Cr in all the plants. [ABSTRACT FROM AUTHOR]

Germanium atomic (Ge1 +) and molecular ions (Ge2 +) of equal energy per atom are implanted in silicon at an elevated temperature. The ion induced damage has been monitored by following the intensity variation of the LO Raman peak of Si. The germanium implanted samples have been labeled with 10 keV Au ions. The gettering of gold has been observed by Rutherford backscattering spectrometry in the post-annealed samples. This paper reports a first time observation of an enhanced gettering of gold in silicon implanted with molecular ions. [ABSTRACT FROM AUTHOR]