Textural pattern classification for oral squamous cell carcinoma

Despite being an area of cancer with highest worldwide incidence, oral cancer yet remains to be widely researched. Studies on computer‐aided analysis of pathological slides of oral cancer contribute a lot to the diagnosis and treatment of the disease. Some researches in this direction have been carried out on oral submucous fibrosis. In this work an approach for analysing abnormality based on textural features present in squamous cell carcinoma histological slides have been considered. Histogram and grey‐level co‐occurrence matrix approaches for extraction of textural features from biopsy images with normal and malignant cells are used here. Further, we have used linear support vector machine classifier for automated diagnosis of the oral cancer, which gives 100% accuracy.     

Plant growth-promoting rhizobacteria (PGPR) and its mechanisms against plant diseases for sustainable agriculture and better productivity

Plant growth-promoting rhizobacteria (PGPR) are specialized bacterial communities inhabiting the root rhizosphere and the secretion of root exudates helps to, regulate the microbial dynamics and their interactions with the plants. These bacteria viz., Agrobacterium, Arthobacter, Azospirillum, Bacillus, Burkholderia, Flavobacterium, Pseudomonas, Rhizobium, etc., play important role in plant growth promotion. In addition, such symbiotic associations of PGPRs in the rhizospheric region also confer protection against several diseases caused by bacterial, fungal and viral pathogens. The biocontrol mechanism utilized by PGPR includes direct and indirect mechanisms direct PGPR mechanisms include the production of antibiotic, siderophore, and hydrolytic enzymes, competition for space and nutrients, and quorum sensing whereas, indirect mechanisms include rhizomicrobiome regulation via. secretion of root exudates, phytostimulation through the release of phytohormones viz., auxin, cytokinin, gibberellic acid, 1-aminocyclopropane-1-carboxylate and induction of systemic resistance through expression of antioxidant defense enzymes viz., phenylalanine ammonia lyase (PAL), peroxidase (PO), polyphenyloxidases (PPO), superoxide dismutase (SOD), chitinase and β-glucanases. For the suppression of plant diseases potent bio inoculants can be developed by modulating the rhizomicrobiome through rhizospheric engineering. In addition, understandings of different strategies to improve PGPR strains, their competence, colonization efficiency, persistence and its future implications should also be taken into consideration.

     

Uncertainty modelling of atmospheric dispersion by stochastic response surface method under aleatory and epistemic uncertainties

The parameters associated to a environmental dispersion model may include different kinds of variability, imprecision and uncertainty. More often, it is seen that available information is interpreted in probabilistic sense. Probability theory is a well-established theory to measure such kind of variability. However, not all available information, data or model parameters affected by variability, imprecision and uncertainty, can be handled by traditional probability theory. Uncertainty or imprecision may occur due to incomplete information or data, measurement error or data obtained from expert judgement or subjective interpretation of available data or information. Thus for model parameters, data may be affected by subjective uncertainty. Traditional probability theory is inappropriate to represent subjective uncertainty. Possibility theory is used as a tool to describe parameters with insufficient knowledge. Based on the polynomial chaos expansion, stochastic response surface method has been utilized in this article for the uncertainty propagation of atmospheric dispersion model under consideration of both probabilistic and possibility information. The proposed method has been demonstrated through a hypothetical case study of atmospheric dispersion.     

Adaptive integral higher order sliding mode controller for uncertain systems

This paper proposes an adaptive integral higher order sliding mode (HOSM) controller for uncertain systems. Instead of a regular control input, the derivative of the control input is used in the proposed control law. The discontinuous sign function in the controller is made to act on the time derivative of the control input. The actual control signal obtained by integrating the derivative control signal is smooth and chattering free. The adaptive tuning law used in the proposed controller eliminates the need of prior knowledge about the upper bound of the system uncertainties. Stability and robustness of the proposed controller are proved by using the classical Lyapunov criterion. Simulation results demonstrate the advantages of the proposed control scheme.