Mn1-xCuxO2/ reduced graphene oxide nanocomposites: Synthesis,characterization, and evaluation of visible light mediated catalytic studies

The narrow optical band gap, higher electrical conductivity, and wider-absorption range are three key features that a good photocatalyst must possess. Herein, we have fabricated Cu-doped MnO₂ (Mn_1-xCu_xO₂) nanostructure by facile wet chemical approach and formed its nanocomposite with r-GO (Mn_1-xCu_xO₂/r-GO) via ultra-sonication approach. The successful replacement of host metal ions (Mn⁴⁺) with the dopant metal ions (Cu²⁺) was supported with the PXRD, FT-IR, and EDX characterizations. The effect of Cu-doping on the band gap and r-GO matrix on the conductivity of the fabricated nanocomposite was also evaluated via Tauc plots and I–V tests, respectively. The photocatalytic efficiency of the fabricated photocatalysts was tested and compared against the methylene blue (MB) under visible light irradiation. The photocatalytic experiments revealed that Mn_1-xCu_xO₂/r-GO photocatalyst exhibited superior photocatalytic aptitude than that of pristine MnO₂ and Mn_1-xCu_xO₂ photocatalysts. More precisely, the Mn_1-xCu_xO₂ photocatalysts degraded 86.89% MB dye at the rate of 0.021 min^?1 after a 90-min exposure to the visible light. Observed superior catalytic activity of the nanocomposite can be attributed to the synergistic effects between the Cu doped MnO₂ and r-GO nanosheets that resulted in its narrow band-gap (2.19 eV) and excellent conductivity (2.217 × 10^?2 Scm^?1).     

Improved fire safety of composites for naval applications

This study is based on the use of integral, hybrid thermal barrier to protect the core of the composite structure. Thermal barrier treatments evaluated in this study include ceramic fabric, ceramic coating, intumescent coating, hybrid of ceramic and intumescent coating, silicone foam, and phenolic skin. The composite systems evaluated in combination with thermal barrier treatments included glass/vinyl ester, graphite/epoxy, graphite/bismaleimide, and graphite/phenolic. All configurations were tested for flammability characteristics. These included smoke density and combustion gas generation (ASTM E-662), residual flexural strength (ASTM D-790), heat release rate, and ignitability (ASTM E-1354). ASTM E-662 test method covers the determination of specific optical density of smoke generated by solid materials. ASTM D-790 test method covers the determination of flexural properties of composite materials in the form of rectangular bars. ASTM E-1354 (cone calorimeter) covers the measurement of the response of materials exposed to controlled levels of radiant heating with or without an external ignitor, and is used to determine the ignitability, heat release rates, mass loss rates, effective heat of combustion, and visible smoke development. Without any fire barrier treatments, all composite systems evaluated in this study failed to meet ignitability and peak heat release requirements of MIL-STD-2031 (SH) at radiant heat fluxes of 75 and 100 kW m^?2, respectively. Intumescent coating and a hybrid system consisting of intumescent coating over ceramic coating were the most effective fire barrier treatments for composite systems evaluated in this study. Using either of these treatments, all composite systems met the ignitability requirements of 90 and 60 at 75 and 100 kW m^?2, respectively. Except for glass/vinyl ester, all systems also met the peak and average heat release requirements of MIL-STD-2031 (SH) at radiant heat fluxes of 25, 75, and 100 kW m^?2, respectively.     

Tailoring of optical band gap and electrical conductivity in a-axis oriented Ni doped Chromium Oxide thin films

Ni doped Cr₂O₃ (NCO) films have attracted much attention due to their applications in the field of photovoltaics. This study reports the tailoring of structural, electrical and optical properties as a function of Ni doping in Chromium oxide (Cr₂O₃). NCO thin films were grown by Pulsed laser deposition (PLD) using 2nd harmonic Nd:YAG Laser on n-Si (100) with in-situ annealing of 450?°C. Structural analyses based on X-ray diffractometry (XRD) and Raman Spectroscopy showed the inconsistent variation in crystallinity and shift in $A_{1g}$ band in turn revealing the successful incorporation of Ni into Chromium oxide host lattice. In addition, electrical measurements also showed an inconsistent variation in resistivity ranging from 10² to 10⁴$\Omega ?\mathit{cm}$. The properties showed widening of band gap energy (E_g) from 3.41 to 3.60?eV as a function of Ni doping concentration with significantly decreased reflectance in the range of 500–600?nm thereby increasing the absorption, a pre-requisite for solar absorbers.     

Flexure of a fluid-saturated cuboid

Summary The flexure of a cuboid mixture of a nonlinearly elastic solid and an ideal fluid is considered in the context of the theory of interacting continua. The Helmholtz free energy function for the mixture is assumed to be of a Neo-Hookean type. The effect of flexure on the distribution of the fluid content and the radial stresses in the deformed, swollen, cylindrical configuration is investigated. Classical results for the flexure of a cuboid of an incompressible nonlinearly elastic material [1] are also derived by considering the special case of the mixture with zero fluid content. It is anticipated that the results presented herein will be valuable to practicing engineers, and also guide and motivate future experimental work.     

Fast-Converging and Low-Power LDPC Decoding: Algorithm,Architecture, and VLSI Implementation

Low-latency and energy-efficient multi-Gbps LDPC decoding requires fast-converging iterative schedules. Hardware decoder architectures based on such schedules can achieve high throughput at low clock speeds, resulting in reduced power consumption and relaxed timing closure requirements for physical VLSI design. In this work, a fast column message-passing (FCMP) schedule for decoding LDPC codes is presented and investigated. FCMP converges in half the number of iterations compared to existing serial decoding schedules, has a significantly lower computational complexity than residual-belief-propagation (RBP)-based schedules, and consumes less power compared to state-of-the-art schedules. An FCMP decoder architecture supporting IEEE 802.11ad (WiGig) LDPC codes is presented. The decoder is fully pipelined to decode two frames with no idle cycles. The architecture is synthesized using the TSMC 40 nm and 65 nm CMOS technology nodes, and operates at a clock-frequency of 200 MHz. The decoder achieves a throughput of 8.4 Gbps, and it consumes 72 mW of power when synthesized using the 40 nm technology node. This results in an energy efficiency of 8.6 pJ/bit, which is the best-reported energy-efficiency in the literature for a WiGig LDPC decoder.

     

A survey on Urdu and Urdu like language stemmers and stemming techniques

Stemming is one of the basic steps in natural language processing applications such as information retrieval, parts of speech tagging, syntactic parsing and machine translation, etc. It is a morphological process that intends to convert the inflected forms of a word into its root form. Urdu is a morphologically rich language, emerged from different languages, that includes prefix, suffix, infix, co-suffix and circumfixes in inflected and multi-gram words that need to be edited in order to convert them into their stems. This editing (insertion, deletion and substitution) makes the stemming process difficult due to language morphological richness and inclusion of words of foreign languages like Persian and Arabic. In this paper, we present a comprehensive review of different algorithms and techniques of stemming Urdu text and also considering the syntax, morphological similarity and other common features and stemming approaches used in Urdu like languages, i.e. Arabic and Persian analyzed, extract main features, merits and shortcomings of the used stemming approaches. In this paper, we also discuss stemming errors, basic difference between stemming and lemmatization and coin a metric for classification of stemming algorithms. In the final phase, we have presented the future work directions.     

Application of feature selection methods for automated clustering analysis: a review on synthetic datasets

The effective modelling of high-dimensional data with hundreds to thousands of features remains a challenging task in the field of machine learning. This process is a manually intensive task and requires skilled data scientists to apply exploratory data analysis techniques and statistical methods in pre-processing datasets for meaningful analysis with machine learning methods. However, the massive growth of data has brought about the need for fully automated data analysis methods. One of the key challenges is the accurate selection of a set of relevant features, which can be buried in high-dimensional data along with irrelevant noisy features, by choosing a subset of the complete set of input features that predicts the output with higher accuracy comparable to the performance of the complete input set. Kohonen’s self-organising neural network map has been utilised in various ways for this task, such as with the weighted self-organising map (WSOM) approach and this method is reviewed for its efficacy. The study demonstrates that the WSOM approach can result in different results on different runs on a given dataset due to the inappropriate use of the steepest descent optimisation method to minimise the weighted SOM’s cost function. An alternative feature weighting approach based on analysis of the SOM after training is presented; the proposed approach allows the SOM to converge before analysing the input relevance, unlike the WSOM that aims to apply weighting to the inputs during the training which distorts the SOM’s cost function, resulting in multiple local minimums meaning the SOM does not consistently converge to the same state. We demonstrate the superiority of the proposed method over the WSOM and a standard SOM in feature selection with improved clustering analysis.

     

An optimized hybrid algorithm in term of energy and performance for mapping real time workloads on 2d based on-chip networks

In this paper, we propose an optimized, search based near-optimal mapping heuristic, named as ONMAP for mapping real time embedded application workloads on 2D based on-chip interconnection network platforms. ONMAP exploits NMAP, a well-known and fast nearest neighbor heuristic algorithm by using the modular exact optimization method. The proposed hybrid algorithm minimizes the on-chip inter-processor communication energy consumption and optimizes the interconnection network performance parameters. The algorithm inherits the constructive search based heuristic nature of the NMAP algorithm, as well as the property of exact optimization for mapping embedded applications on the target communication architecture. To verify the efficiency and effectiveness of the algorithm, we have compared the proposed algorithm with NMAP and random mapping algorithm under similar simulation environments and traffic conditions. The mapping results of the exemplary real world applications such as VOPD, PIP, MPEG4, MWD, MMS and WiFi-80211arx indicate that ONMAP algorithm is more efficient than its competitors for most of the performance parameters of the on-chip network designs. The algorithm successfully optimized the energy consumption, up to 20 % and 26% in comparison to NMAP and random algorithms, respectively. Similarly, the cost is optimized up to 10% and 60% as compared to NMAP and random mapping algorithms, respectively.     

GGA and GGA + <Emphasis Type="Italic">U</Emphasis> Study of Rare Earth-Based Perovskites in Cubic Phase

The structural, elastic, electronic, and magnetic properties of cubic perovskite RAlO₃ (R = Sm, Eu, Gd, Dy, Tb, Ho, Tm, Er, Yb) compounds have been calculated using a full-potential linearized augmented plane wave (FP-LAPW) method within the density functional theory. The exchange-correlation potential was treated with the generalized gradient approximation of Wu and Cohen (WC-GGA) to calculate the total energy. Moreover, the GGA + U?based potential was also applied for the electronic and magnetic properties. The calculated structural properties such as lattice parameter are consistent with the accessible data. The spin-polarized electronic band structure and the calculation of density of state show that DyAlO₃, EuAlO₃, SmAlo₃, TmAlO₃, HoAlO₃, YbAlO₃, and TbAlO₃ compounds have a half-metallic nature, and only GdAlO₃ and ErAlO₃ have a semiconductor nature. The spin-polarized magnetic moment of these compounds reveals that they show a ferromagnetic nature.     

Role of Ni<Superscript>2+</Superscript> Ions in Magnetite Nano-particles Synthesized by Co-precipitation Method

In the present work, nickel-doped iron oxide (Ni_xFe_3?x O ₄) nanoparticles with different concentration of nickel (x = 0, 0.05, 0.1, and 0.15) have been prepared by co-precipitation method. These prepared nanoparticles have been characterized by using x-ray diffractometer, thermo gravimetric analysis and differential scanning calorimetry, Fourier transform infrared spectroscopy, scanning electron microscopy, vibrating sample magnetometer, and UV-Visible spectroscopy to study their structural, thermal, morphological, magnetic, and optical properties, respectively. The x-ray diffraction confirms the formation of single-phase inverse spinel cubic structure of NiFe₃ O ₄ nanoparticles. Crystallite size has been estimated by the full width at half maximum of the most intense x-ray diffraction peak where vibrational and stretching modes of metal-oxygen bonds in 872 cm are shown in Fourier transform infrared spectra which confirms the formation of nanoparticles. The thermal analysis revealed that the transition temperature and stability increases with increasing Ni concentration. The surface morphology indicated that the particles are spherical in shape with some agglomeration. The magnetic measurement revealed that the coercivity and anisotropy increases with nickel doping in magnetite nanoparticles. The optical analysis revealed that direct and indirect both types of band gap increases when the particle size decreases because the absorption spectra shift toward smaller wavelength. The blue shift confirms the formation of nanoparticles.