No abstract

ED (Economic Dispatch) problem is one of the vital step in operational planning. It is a nonconvex constrained optimization problem. However, it is solved as convex problem by approximation of machine input/output characteristics, thus resulting in an inaccurate result. Reliable, secure and cheapest supply of electrical energy to the consumers is the prime objective in power system operational planning. Increase in fuel cost, reduction in fossil-fuel assets and ecological concerns have forced to integrate renewable energy resources in the generation mix. However, the instability of wind and solar power output affects the power network. For solution of such solar and wind integrated economic dispatch problems, evolutionary approaches are considered potential solution methodologies. These approaches are considered as potential solution methodologies for nonconvex ED problem. This paper presents CEED (Combined Emission Economic Dispatch) of a power system comprising of multiple solar, wind and thermal units using continuous and binary FPA (Flower Pollination Algorithm). Proposed algorithm is applied on 5, 6, 15, 26 and 40 thermal generators by integrating several solar and wind plants, for both convex and non-convex ED problems. Proposed algorithm is simulated in MATLAB 2014b. Results of simulations, when compared with other approaches, show promise of the approach.

Abstract:The accessibility to Electroencephalogram (EEG) recording systems has enabled the healthcare providers to record the brain activity of patients under treatment, during multiple sessions. Thus brain changes can be observed and evaluated. It has been shown in many studies that the EEG data are never exactly the same when recordings are done in different sessions inducing a shift between the data of multiple sessions. This shift is induced due to the changes in parameters such as: the physical /mental state of the patient, the ambient environment, location of the electrodes, and impedance of the electrodes. The shift can be modelled as a covariate shift between multiple sessions. However, the algorithms that have been developed to tackle this shift assume the presence of training as well as testing data apriori to calculate the importance weights which are then used in the learning algorithm to reduce the mismatch. This major problem makes them impractical. In this paper, we tackle this, using marginalized stacked denoising autoencoder (mSDAs) while using the data from seven healthy subjects recorded over eightsessions distributed over four weeks. We compare our results with kernel mean matching, a popular approach for covariate shift adaption. Using support vector machines for classification and reduced complexity of mSDA, we get promising accuracy.

Aim: to explore the mandibular bone fractures in elderly patients with reference to etiology of trauma. Methodology: This was a descriptive study conducted in the department of Oral and Maxillofacial Surgery KEMU/ Mayo Hospital Lahore. Elderly patients (age 60-100years) with mandibular bone fractures. Results: Aetiology of trauma leading to mandibular fractures was as follows; there were 79(65.83%) cases of RTA, 32(26.66%) falls, 4(3.33%) assaults and there were only 3(2.5%) cases of industrial injury. Conclusion: To conclude, this study depicts that road traffic accidents were the predominant cause of injury in the studied age group. Elderly patients need more care and attention, especially after traumatic incidents and lead to financial burden in hospitals Keywords: Maxillofacial trauma, Elderly population, Mandibular injuries, Elderly fractures