Morphology,Mineralogy and Mixing of Individual Atmospheric Particles Over Kanpur (IGP): Relevance of Homogeneous Equivalent Sphere Approximation in Radiative Models

Estimation of the direct radiative forcing (DRF) by atmospheric particles is uncertain to a large extent owing to uncertainties in their morphology (shape and size), mixing states, and chemical composition. A region-specific database of the aforementioned physico-chemical properties (at individual particle level) is necessary to improve numerically-estimated optical and radiative properties. Till date, there is no detailed observation of the above mentioned properties over Kanpur in the Indo-Gangetic Plain (IGP). To fill this gap, an experiment was carried out at Kanpur (IITK; 26.52°N, 80.23°E, 142 m msl), India from April to July, 2011. Particle types broadly classified as (a) Cu-rich particles mixed with carbon and sulphur (b) dust and clays mixed with carbonaceous species (c) Fe-rich particles mixed with carbon and sulfur and (d) calcite (CaCO₃) particles aged with nitrate, were observed. The frequency distributions of aspect ratio (AR; indicator of extent of particle non-sphericity) of total 708 particles from April to June reveal that particles with aspect ratio range >1.2 to ≤1.4 were abundant throughout the experiment except during June when it was found to shift to high AR range, >1.4 to ≤1.6 (followed with another peak of AR i.e. >2 to ≤2.4) due to dust storm conditions enhancing the occurrence of more non-spherical particles over the sampling site. The spherical particles (and close to spherical shape; AR range, 1.0 to ≤1.2) were found to be <20% throughout the experiment with a minimum (11.5%) during June. Consideration of Homogeneous Equivalent Sphere Approximation (HESA) in the optical/radiative model over the study region is found to be irrelevant during the campaign.     

Particulate matter emitted from ultrasonic humidifiers—Chemical composition and implication to indoor air

Household humidification is widely practiced to combat dry indoor air. While the benefits of household humidification are widely perceived, its implications to the indoor air have not been critically appraised. In particular, ultrasonic humidifiers are known to generate fine particulate matter (PM). In this study, we first conducted laboratory experiments to investigate the size, quantity, and chemical composition of PM generated by an ultrasonic humidifier. The mass of PM generated showed a correlation with the total alkalinity of charge water, suggesting that CaCO₃ is likely making a major contribution to PM. Ion chromatography analysis revealed a large amount of SO₄²⁻ in PM, representing a previously unrecognized indoor source. Preliminary results of organic compounds being present in humidifier PM are also presented. A whole-house experiment was further conducted at an actual residential house, with five low-cost sensors (AirBeam) monitoring PM in real time. Operation of a single ultrasonic humidifier resulted in PM_2.5 concentrations up to hundreds of μg m⁻³, and its influence extended across the entire household. The transport and loss of PM_2.5 depended on the rate of air circulation and ventilation. This study emphasizes the need to further investigate the impact of humidifier operation, both on human health and on the indoor atmospheric chemistry, for example, partitioning of acidic and basic compounds.     

Linguistic frequent pattern mining using a compressed structure

Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information from quantitative databases is not a trivial task compared to conventional algorithms in ARM. Fuzzy-set theory was invented to represent a more valuable form of knowledge for human reasoning, which can also be applied and utilized for quantitative databases. Many approaches have adopted fuzzy-set theory to transform the quantitative value into linguistic terms with its corresponding degree based on defined membership functions for the discovery of FFIs, also known as fuzzy frequent itemsets. Only linguistic terms with maximal scalar cardinality are considered in traditional fuzzy frequent itemset mining, but the uncertainty factor is not involved in past approaches. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to quickly discover multiple FFIs from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed for reducing the search space and speeding up the mining process. Several experiments are carried out to verify the efficiency and effectiveness of the designed approach in terms of runtime, the number of examined nodes, memory usage, and scalability under different minimum support thresholds and different linguistic terms used in the membership functions.

     

Property of Self-Similarity Between Baseband and Modulated Signals

Mobile Networks and Applications - The increment of communication technologies and the&nbsp;development of signal processing require efficient identification techniques for communication...     

An intelligent wavelet transform-based framework to detect subsurface fires with NOAA–AVHRR images

Subsurface coal fires (in this article, termed as hotspots), responsible for atmospheric pollution, human fatalities and perilous land subsidence, pose a big threat to major coal-producing countries in the world. The majority of the research performed to date has focused on providing hotspot allocation information for a specific region of interest and most has explored quite expensive high-resolution Landsat Thematic Mapper (TM) satellite images for the same. This article aims to investigate the applicability of a wavelet transform-based model to detect subsurface fires (hotspots) with freely available National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA–AVHRR) images and find out the possibility of extracting novel hotspot features by applying a wavelet transform-based analysis technique. The proposed feature vector consists of wavelet variance coefficients (WVCs) obtained from scale-by-scale decomposition of the AVHRR image variance and builds up a strong base for designing an accurate classification system. Furthermore, the support vector machine (SVM), an efficient machine learning tool, is applied to the proposed feature vector in order to develop a classification model. The demonstrated results successfully prove the effectiveness of the proposed framework as the classified images show a good correspondence with records of subsurface fires mapped by the Bharat Coking Coal Limited (BCCL), India. The effectiveness of the SVM method is also evaluated in comparison with the classical neural network-based approach.     

Analysis of Mechanical Milling in Simoloyer: An Energy Modeling Approach

A model was developed to estimate the energy transfer from milling media to the powder during milling carried out in Simoloyer CM 01 (Zoz GmbH, Wenden, Germany), a horizontal attritor high-energy ball mill. The model was then used to estimate the energy transfer in milling of iron at 1000 rpm. Furthermore, the time required to achieve a particular grain size was formulated as a function of milling speed, using the model developed for the energy transfer. The results were verified at 500 rpm and 1500 rpm for iron and aluminum.     

Modelling and kinetic aspects of a BTEX contaminated air-treating biofilter

In this study, the overall performance of a biofilter was evaluated in terms of its elimination capacity by using 3-D mesh techniques. The overall results indicate that the agreement between experimental data and model predictions is excellent for benzene, toluene, ethylbenzene and o-xylene (BTEX). In this study, the maximum removal rate (r _max) values for BTEX were 0.0117, 0.0126, 0.0081 and 0.0146 g m^–3 h^–1, and the half-saturation constant (K_S ) values were calculated to be 0.269, 0.297, 0.156 and 0.394 g m^–3, respectively. For this system, the coefficients of determination (r ²) of BTEX compounds were greater than 0.97. The BTEX concentration profiles along the depth were also determined using a convection–diffusion reactor (CDR) model. The sums of squares of the errors (SSEs) of BTEX were 0.0078, 0.0059, 0.0129 and 0.0269, respectively, with r ² values greater than 0.99 for all four compounds at low concentrations.     

Analysis of a SiGe MOSFET at 22nm

The present paper proposes a novel concept which can successfully reduce threshold voltage and increase switching speed of a conventional MOSFET. The proposed structures have been incorporated with a silicon germanium (SiGe) layer as a channel at the 22 nm technology node. Also, extensive analyses have been done to study the effects of replacing conventional polysilicon by graded dopent profile polysilicon, use of a high-k/silicon dioxide stack as a dielectric with graded dopent profile polysilicon and by using a high-k/silicon dioxide stack as a dielectric with a metal gate. Hafnium dioxide is used as a high-k material. Silvaco Athena and Atlas simulators are used for simulation as well as for finding electrical characteristics of the structures. For all the proposed structures two important parameters are studied in detail, threshold voltage and subthreshold slope. Comparing the three structures, it can be seen that using the high-k/silicon dioxide stack as a dielectric with a metal gate yields the best threshold voltage as well as good subthreshold slope which is directly related to the switching behaviour of the device. The required fabrication aspects of the modelled structures are also elaborated in detail.     

Nanocrystalline Barium Strontium Titanate Ceramics Synthesized via the “Organosol” Route and Spark Plasma Sintering

Dense nanocrystalline barium strontium titanate Ba_0.6Sr_0.4TiO₃ (BST) ceramics with an average grain size around 40 nm and very small dispersion were obtained by spark plasma sintering at 950°C and 1050°C starting from nonagglomerated nanopowders (~20 nm). The powders were synthesized by a modified “Organosol” process. X‐ray diffraction (XRD) and dielectric measurements in the temperature range 173–313 K were used to investigate the evolution of crystal structure and the ferroelectric to paraelectric phase transformation behavior for the sintered BST ceramics with different grain sizes. The Curie temperature T_C decreases, whereas the phase transition becomes diffuse for the particle size decreasing from about 190 to 40 nm with matching XRD and permittivity data. Even the ceramics with an average grain size as small as 40 nm show the transition into the ferroelectric state. The dielectric permittivity ε shows relatively good thermal stability over a wide temperature range. The dielectric losses are smaller than 2%–4% in the frequency range of 100 Hz–1 MHz and temperature interval 160–320 K. A decrease in the dielectric permittivity in nanocrystalline ceramics was observed compared to submicrometer‐sized ceramics.