Emerging regulatory challenges facing the Indian rural electrification programme

The purpose of this paper is to present and analyse the regulatory issues emerging from the newly launched programme of rural electricity access in India. We focus on two broad areas, namely regulatory issues related to the organisation/structuring of the activities and issues related to subsidy and tariffs.     

Control of Bacterial Diseases of Banana Using CRISPR/Cas-Based Gene Editing

Banana is an important staple food crop and a source of income for smallholder farmers in about 150 tropical and sub-tropical countries. Several bacterial diseases, such as banana Xanthomonas wilt (BXW), blood, and moko disease, cause substantial impacts on banana production. There is a vast yield gap in the production of bananas in regions where bacterial pathogens and several other pathogens and pests are present together in the same field. BXW disease caused by Xanthomonas campestris pv. musacearum is reported to be the most destructive banana disease in East Africa. The disease affects all the banana varieties grown in the region. Only the wild-type diploid banana, Musa balbisiana, is resistant to BXW disease. Developing disease-resistant varieties of bananas is one of the most effective strategies to manage diseases. Recent advances in CRISPR/Cas-based gene editing techniques can accelerate banana improvement. Some progress has been made to create resistance against bacterial pathogens using CRISPR/Cas9-mediated gene editing by knocking out the disease-causing susceptibility (S) genes or activating the expression of the plant defense genes. A synopsis of recent advancements and perspectives on the application of gene editing for the control of bacterial wilt diseases are presented in this article.     

A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

Melanoma or skin cancer is the most dangerous and deadliest disease. As the incidence and mortality rate of skin cancer increases worldwide, an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer. In this study, a Hybrid Artificial Intelligence Model (HAIM) is designed for skin cancer classification. It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron (EWHMLP) for the classification. Though the wavelet transform is a powerful tool for signal and image processing, it is unable to detect the intermediate dimensional structures of a medical image. Thus the proposed HAIM uses Curvelet (CurT), Contourlet (ConT) and Shearlet (SheT) transforms as feature extraction techniques. Though MLP is very flexible and well suitable for the classification problem, the learning of weights is a challenging task. Also, the optimization process does not converge, and the model may not be stable. To overcome these drawbacks, EWHMLP is developed. Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33% in a multi-class approach on PH² database.     

Polyfunctional bioceramics based on calcium phosphate and M-type hexagonal ferrite for medical applications

Magnetic biologically active ceramics based on calcium phosphate with a content of M-type hexagonal ferrite (HF) particles varying from 10 to 50 wt % has been synthesized and characterized. It has been found that the ceramics synthesized consists of a biocompatible carbonated hydroxyapatite (CHA) with the matrix containing M-type HF particles, leading to the magnetic characteristics of the ceramics synthesized being significantly higher than those of iron-oxide-modified bioglass ceramics used in medicine.     

Bayesian Scale Space Analysis of Differences in Images

This article considers the detection of image features in different spatial scales. The main focus is on capturing the scale-dependent differences in a pair of noisy images, but the technique developed can also be applied to the analysis of single images. The approach proposed uses Bayesian statistical modeling and simulation-based inference, and it can be viewed as a further development of SiZer technology, originally designed for nonparametric curve fitting. Numerical examples include artificial test images and a preliminary analysis of a pair of Landsat images used in satellite-based forest inventory. This article has supplementary material online.     

Analysis and detection of mimicked speech based on prosodic features

This paper describes a work aimed towards understanding the art of mimicking by professional mimicry artists while imitating the speech characteristics of known persons, and also explores the possibility of detecting a given speech as genuine or impostor. This includes a systematic approach of collecting three categories of speech data, namely original speech of the mimicry artists, speech while mimicking chosen celebrities and original speech of the chosen celebrities, to analyze the variations in prosodic features. A?method is described for the automatic extraction of relevant prosodic features in order to model speaker characteristics. Speech is automatically segmented as intonation phrases using speech/nonspeech classification. Further segmentation is done using valleys in energy contour. Intonation, duration and energy features are extracted for each of these segments. Intonation curve is approximated using Legendre polynomials. Other useful prosodic features include average jitter, average shimmer, total duration, voiced duration and change in energy. These prosodic features extracted from original speech of celebrities and mimicry artists are used for creating speaker models. Support Vector Machine (SVM) is used for creating speaker models, and detection of a given speech as genuine or impostor is attempted using a speaker verification framework of SVM models.     

Study on Reuse of Coconut Fiber Chemical Retting Bath. Part II---Recovery and Characterization of Lignin

Study aimed at recovery of lignin from the reused residual liquor of chemical retting bath of coconut fiber caused by partial delignification to make it soft. The lignin of 8.9% was obtained after recovery (maintaining pH 2.0 for 1 h at ambient temperature) and further purification. Extracted lignin is characterized through FTIR, TGA and particle size analysis. FTIR showed absence of polysaccharides in purified lignin. TGA indicates presence of higher methyl-aryl ether groups than aromatic groups in lignin of coconut fibre. Analysis reveals about 91% of particles in the rage of 1–500 µm, which shows its good dispersiblity in fluid. Recovery of lignin associated with two benefits i.e., diversified use of extracted pure lignin and reduction of organic load of the waste liquor to make it suitable for discharge.     

Characterization of diesel particles: effects of fuel reformulation, exhaust aftertreatment, and engine operation on particle carbon composition and volatility

Diesel exhaust particles are the major constituent of urban carbonaceous aerosol being linked to a large range of adverse environmental and health effects. In this work, the effects of fuel reformulation, oxidation catalyst, engine type, and engine operation parameters on diesel particle emission characteristics were investigated. Particle emissions from an indirect injection (IDI) and a direct injection (DI) engine car operating under steady-state conditions with a reformulated low-sulfur, low-aromatic fuel and a standard-grade fuel were analyzed. Organic (OC) and elemental (EC) carbon fractions of the particles were quantified by a thermal-optical transmission analysis method and particle size distributions measured with a scanning mobility particle sizer (SMPS). The particle volatility characteristics were studied with a configuration that consisted of a thermal desorption unit and an SMPS. In addition, the volatility of size-selected particles was determined with a tandem differential mobility analyzer technique. The reformulated fuel was found to produce 10-40% less particulate carbon mass compared to the standard fuel. On the basis of the carbon analysis, the organic carbon contributed 27-61% to the carbon mass of the IDI engine particle emissions, depending on the fuel and engine operation parameters. The fuel reformulation reduced the particulate organic carbon emissions by 10-55%. In the particles of the DI engine, the organic carbon contributed 14-26% to the total carbon emissions, the advanced engine technology, and the oxidation catalyst, thus reducing the OC/EC ratio of particles considerably. A relatively good consistency between the particulate organic fraction quantified with the thermal optical method and the volatile fraction measured with the thermal desorption unit and SMPS was found.     

Medium chain triglycerides (MCT): State-of-the-art on chemistry,synthesis, health benefits and applications in food industry

Medium chain triglycerides (MCT) are esters of fatty acids with 6 to 12 carbon atom chains. Naturally, they occur in various sources; their composition and bioactivity are source and extraction process-linked. The molecular size of MCT oil permits unique metabolic pathways and energy production rates, making MCT oil a high-value functional food. This review details the common sources of MCT oil, presenting critical information on the various approaches for MCT oil extraction or synthesis. Apart from conventional techniques, non-thermal processing methods that show promising prospects are analyzed. The biological effects of MCT oil are summarized, and the range of need-driven modification approaches are elaborated. A section is devoted to highlighting the recent trends in the application of MCT oil for food, nutraceuticals, and allied applications. While much is debated about the role of MCT oil in human health and wellness, there is limited information on daily requirements, impact on specific population groups, and effects of long-term consumption. Nonetheless, several studies have been conducted and continue to identify the most effective methods for MCT oil extraction, processing, handling, and storage. A knowledge gap exists and future research must focus on technology packages for scalability and sustainability.