Epidemiology of Squamous Cell Carcinoma in Response to Different Hazardous Chewing Causes in Lahore, Pakistan-a Unicenter study epidemiology of oral carcinoma in Lahore, Pakistan

Oral squamous cell carcinoma (OSCC)being the world’s most prevailing and frightening cancerous disorder lacks the sufficient data in Pakistan despite of its higher magnitude and prevalence. Objective: This study was specifically designed and conducted with the aim to identify the frequency of this disorder along with causative factors in past three years in a tertiary care hospital of Lahore, Pakistan. Methods: Epidemiological study was conducted using retrospective randomized method and all pre-requisites were filled. The clinical profiles of patients were collected from Maxillofacial and Oral Surgery Departmentof Pathology, Mayo Hospital Lahore. Patients who had undergone treatment for OSCC were contacted and interviewed for information about demographic regions, previous history of malignancy, disease onset, chewing habits, exposure to pesticides, industrial exposure to metals etc. And all particulars were not and compiled on questionnaire. Results: A total of 54 patients from different districts of Punjab participated in the study. Percentages for each possible causative chewing habit were calculated and 87.50 % of population was found addicted to different habits. Genetic factor might have contributed in remaining for development of OSCC. Conclusions: Informative data provided in this study will be helpful to be used by the government and private health agencies while designing and planning management of oral health problems and allocating health budgets in focusing this issue

Efficient Utilization of Energy Employing Meta-Heuristic Techniques With the Incorporation of Green Energy Resources in Smart Cities

A smart city is an efficient, reliable, and sustainable urban center that facilitates its inhabitants with a high quality of life standards via optimal management of its resources. Energy management of smart homes (SHs) is one of the most challenging and demanding issues which needs significant effort and attention. Demand side management in smart grids authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. In demand side management, scheduling of appliances based on consumer-defined priorities is an important task performed by a home energy management controller. However, user discomfort is caused by the scheduling of home appliances based on the demand response or limiting its time of use. Further, rebound peaks that are regenerated in the off-peak hours are also a major challenge in demand side management. An increase in the world’s population results in high energy demand; thus, causing a huge consumption of fossil fuels. This ultimately results in severe environmental problems for mankind and nature. Renewable energy sources (RESs) emerge as an alternative to fossil fuels. The RESs are eco-friendly and sustainable, which are incorporated in SHs via two modes: grid-connected or stand-alone. The reliability of RESs is usually met with the use of hybrid RESs along with the integration of energy storage systems(ESS).The efficient usage of these components in the hybrid RESs requires an optimum unit sizing that achieves the objectives of cost minimization and reliability in stand-alone mode. These are some of the main concerns of a decision-maker. This thesis focuses on employing meta-heuristic techniq ues for efficient utilization of energy and RESs in SH. At first,an evolutionary accretive comfort algorithm is developed based on four postulations which allow the time-varying priorities to be quantified in time and device based features. Based on the input data, considering the appliances’ power ratings, its time ofuse,andabsolutecomfortderivedfrompriorities,theevolutionaryaccretivecomfortalgorithm generates an optimal energy consumption pattern which gives maximum satisfaction atapredetermineduserbudget. Acostperunitcomfortindex, whichrelatestheconsumer’s expenditure to the achievable comfort is also demonstrated. To test the applicability of theproposed evolutionaryaccretive comfort algorithm, three budget scenariosof 1.5 $/day, 2.0 $/day,and2.5$/dayaretaken. Secondly,apriority-induceddemandsidemanagementstrategybasedontheloadshiftingtechniqueconsideringvariousenergycyclesofanapplianceis presented. Theday-aheadloadshiftingtechniqueismathematicallyformulatedandmapped with multiple knapsack problem to mitigate the rebound peaks. The proposed autonomous home energy management controller embeds three meta-heuristic optimization techniques: genetic algorithm, enhanced differential evolution, and binary particle swarm optimization along with the optimal stopping rule, which is used for solving the load shifting problem. Next, the RESs and ESS are integrated into a residential sector considering grid-connected mode. The proposed optimized home energy management system minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of theelectricitymarket. Heretheappliancesareclassifiedintoshiftableandnon-shiftablecategories, and a hybrid genetic particle optimization scheme outperforms to other algorithms in terms of cost and a peak-to-average ratio. Besides, meta-heuristic schemes that do not depend on algorithmic-specific parameters are considered for integrating the RESs and ESS in a stand-alone system. Preliminary, the Jaya algorithmisusedforfindingthe optimalunit sizingofRESs, including photovoltaicpanels, windturbines,andfuelcellstoreducetheconsumer’stotalannualcost. Themethodologyis applied to real solar irradiation and wind speed data taken from Hawksbay, Pakistan. Next, animprovedJayaandthelearningphaseasdepictedinteachinglearning-basedoptimization isproposedforoptimalunitsizingofphotovoltaics,windturbines,andbatterysystemsusing real data obtained from another site, located in Rafsanjan, Iran. The system’s reliability is consideredusingthemaximumallowablelossofpowersupplyprobabilityconcept. Finally, a diesel generator is integrated into the RESs to assess its environmental and economic aspects. Thus, the thesis objectives achieved are to have a green, reliable, economical, and sustainable power supply in the SH.