A review on utilisation of biomass from rice industry as a source of renewable energy

Volatile oil price and growing emphasis on environmental conservation have stimulated the development and utilisation of biomass as a vital source of renewable energy. In reducing the global dependency on fossil fuels, rice husk and rice straw which are the widely abundant agricultural wastes from the rice industry have a vital role to play. This paper reviews the key aspects of the utilisation of rice husk and rice straw as important sources of renewable energy. The paper provides some essential background information that includes the physical and chemical characteristics that dictates the quality of these rice biomasses. This paper also describes the various chemical and physical pretreatment techniques that can facilitate handling and transportation of rice straw and husk. Finally, the paper presents the state-of-the-art on thermo-chemical and bio-chemical technologies to convert rice husk and rice straw into energy.     

An overview of cathode material and catalysts suitable for generating hydrogen in microbial electrolysis cell

Bio-electrohydrogenesis through Microbial Electrolysis Cell (MEC) is one of the promising technologies for generating hydrogen from wastewater through degradation of organic waste by microbes. While microbial activity occurs at anode, hydrogen gas is evolved at the cathode. Identifying a highly efficient and low cost cathode is very important for practical implication of MEC. In this review, we have summarized the efforts of different research groups to develop different types of efficient and low cost cathodes or cathode catalysts for hydrogen generation. Among all the materials used, stainless steel, Ni alloys Pd nanoparticle decorated cathode are worth mentioning and have very good efficiency. Industrial application of MEC should consider a balance of availability and efficiency of the cathode material.     

Electrical determination of bandgap narrowing in bipolartransistors with epitaxial Si, epitaxial Si1-xGex,and ion implanted bases

The apparent bandgap narrowing in bipolar transistors with epitaxial Si, epitaxial SiGe and ion implanted bases is measured from the temperature dependence of the collector current density J_c(T). A graph of InJ_c(T)/J_o(T) as a function of reciprocal temperature is plotted, and the apparent bandgap narrowing obtained from the slope. For epitaxial base transistors, in which the boron base profiles are abrupt, a linear J_c(T)/J _o(T) characteristic is obtained, which allows the unambiguous determination of the apparent bandgap narrowing. The measured values for epitaxial Si bases are in good agreement with the theoretical model of Klaassen over a range of base doping concentrations. For Si_0.88 Ge_0.12 and Si_0.87Ge_0.13 epitaxial base heterojunction bipolar transistors (HBT's), values of bandgap narrowing of 119 and 121 meV are obtained due to the presence of the Ge, which can be compared with theoretical values of 111 and 118 meV. For the implanted base transistor, the J_c(T)/J_o(T) characteristic is not linear, and its slope is larger at high temperatures than at low. This behaviour is explained by the presence of a tail on the ion implanted profile, which dominates the Gummel number of the transistor at low temperatures     

Maximizing Transfection Efficiency of Vertically Aligned Silicon Nanowire Arrays

Vertically aligned silicon nanowire (VA‐SiNW) arrays are emerging as a powerful new tool for gene delivery by means of mechanical transfection. In order to utilize this tool efficiently, uncertainties around the required design parameters need to be removed. Here, a combination of nanosphere lithography and templated metal‐assisted wet chemical etching is used to fabricate VA‐SiNW arrays with a range of diameters, heights, and densities. This fabrication strategy allows identification of critical parameters of surface topography and consequently the design of SiNW arrays that deliver plasmid with high transfection efficiency into a diverse range of human cells whilst maintaining high cell viability. These results illuminate the cell‐materials interactions that mediate VA‐SiNW transfection and have the potential to transform gene therapy and underpin future treatment modalities.     

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs. To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model. The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository. The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.     

Mobilising the potential towards low-carbon emissions society in Asia

Emission of CO₂, CH₄, and NO _x is among the main sources of greenhouse gases (GHGs) emitted through human activities such as fossil fuels combustion for power, heat and transportation, industrial processes, and land-use change. Low-carbon emission has become synonymous with GHG emission, which is often expressed in t CO₂ eq. as derived from the major GHG. However, CO₂ emission from fossil fuel constitutes just about 2/3 of GHGs. Low-carbon emission has received high publicity in recent years as a major reason for the potential mitigation of climate change. Achieving low GHG emission targets while decoupling the economic growth from high emissions, pollution, and resource use is desirable. This paper reviews the low-carbon emissions initiatives to develop resilient growth strategies to reduce GHG emissions in Asia and beyond. Four major initiatives, including the modelling of GHG emission and mitigation initiative; sustainable energy systems; sustainable waste management; and education and community outreach, are reviewed for mobilising the potential towards low-carbon emissions societies in Asia. Cooperation from major stakeholders, e.g. government, policy makers, financial institutions, private investors, industrial, commercial sector, residential, has been needed towards realising the goal.     

Measurement of Percentage Depth Dose and Half Value Layer of the <Emphasis Type="Italic">Rhizophora</Emphasis> spp. Particleboard Bonded by <Emphasis Type="Italic">Eremurus</Emphasis> spp. to 60, 80 and 100 kVp Diagnostic X-rays

Some researchers formerly provided the mechanical, physical, and attenuation properties of the fabricated Eremurus–Rhizophora spp. particleboard phantom. In this study, the percentage depth dose (PDD) and the half value layer (HVL) of fabricated Eremurus–Rhizophora spp. particleboard phantom were determined and compared with those of Perspex and water phantoms, with the same standard phantom size (30 cm?×?30 cm?×?30 cm) in the diagnostic energy range using TLD 100H. In addition, the energy range of X-ray was in diagnostic range of energy. The results indicated that the PDD and HVL of the fabricated Eremurus–Rhizophora spp. particleboard phantom were close to those of the Perspex phantom. Likewise, the PDD and HVL of the fabricated Eremurus–Rhizophora spp. particleboard phantom were found in good agreement with those of water phantom. According to the results of this study, the fabricated Eremurus–Rhizophora spp. particleboard phantom can be used as medical phantoms.     

Geometric and radiometric evaluation of Razaksat medium-sized aperture camera data

Razaksat, a high-resolution Malaysian remote-sensing satellite, was launched on 14 July 2009. It carries a medium-sized aperture camera (MAC) with one panchromatic and four multispectral bands, of 2.5 and 5 m spatial resolution, respectively. The satellite was placed in a near-equatorial orbit with a low inclination angle of 9° to enable an optimum 14 overpasses per day over the equatorial region (i.e. 9° N to 9° S) as compared to only three daily passes over Malaysia for near-polar orbiting satellites. This article reports on evaluation of the panchromatic and multispectral images of MAC: (i) a geometric evaluation of the panchromatic and multispectral MAC images; (ii) a radiometric evaluation, focusing particularly on the noise level and sharpness of the MAC images; (iii) an evaluation of the MAC panchromatic data for updating planimetric topographic features; and (iv) a classification of MAC multispectral data for land-cover mapping. The noise level within the image set was found to increase with the intensity, while the sharpness of edges tested on the images in all non-homogeneous targets was relatively marginal. However, the outcome of the analysis showed the utility and potential of high-resolution panchromatic and multispectral bands of the Razaksat as stipulated in the system mission for terrain mapping.     

Multiclass Support Vector Machines for Classification of ECG Data with Missing Values

The article presents an experimental study on multiclass Support Vector Machine (SVM) methods over a cardiac arrhythmia dataset that has missing attribute values for electrocardiogram (ECG) diagnostic application. The presence of an incomplete dataset and high data dimensionality can affect the performance of classifiers. Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets. The article proposes experiments to evaluate performance of One-Against-All (OAA) and One-Against-One (OAO) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques. The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values.     

Computing Bessel functions of the second kind in extreme parameter regimes

A useful method of computing the integral order Bessel functions of the second kind Y_n(x+iy) when either, the absolute value of the real part, or the imaginary part of the argument z=x+iy is small, is described. This method is based on computing the Bessel functions for extreme parameter regimes when x∼0 (or y∼0) and is useful because a number existing algorithms and methods fail to give correct results for small x or small y. The approximating equations are derived by expanding the Bessel function in Taylor series, are tested and discussed. The present work is a continuation of the previous one conducted in regard to the Bessel function of the first kind. The results of our formalism are compared to the available existing numerical methods used in Mathematica, IMSL, MATLAB, and the Amos library. Our numerical method is easy to implement, efficient, and produces reliable results. In addition, this method reduces the computation of the Bessel functions of the second complex argument to that of real argument which simplify the computation considerably.