Why physicians and lay people smoke and how can it be reduced?

Objective: The objective of this study was to find out the level of knowledge the physicians and lay people have pertaining to the effect of cigarettes, why certain physicians smoke and what measures could be applied to reduce the rate of smoking. Methods: A questionnaire was administered to the one hundred physicians who smoke, one hundred non-smoking physicians and one hundred lay people who smoke to determine their attitude towards this addition. Subjects were chosen using convenience sampling. The physicians were picked from six hospitals of Karachi. Results: When the smoking physicians were asked what could motivate them to stop smoking, majority of them said that an occurrence of a smoking related illness would. Majority of the physicians who do not smoke felt that individual will was the greatest force keeping them from smoking. When asked how smoking can be reduced in Pakistan, majority of the physicians, both smoking and non-smoking, favoured mass health education. Lay smokers expressed marked ignorance about deleterious effects of cigarette smoke. Like smoking physicians, majority of them said that occurrence of an illness related to smoking would effectively motivate them to stop smoking. CONCLUSION: Based on this survey we conclude that mass health education and enforcement of the ban on smoking in public places will effectively reduce the number of smokers. There is a need to educate physicians and the general public about the cardiac and carcinogenic effects of smoking

Emergency Intraperitoneal Onlay Mesh Repair of Incarcerated Spigelian Hernia

This report suggests that Spigelian hernia in an emergency setting may be easily and quickly repaired with an intraperitoneal onlay method using composite mesh

Testing Onion Market Integration in Pakistan

This paper analyses spatial market integration using monthly wholesale real price of onion in four regional markets located in each of the four provinces of Pakistan. Unit root test indicates that the price series in each location are stationary, and the series are represented as autoregressive model for eachlocation. The error correction model results show that the regional markets of onion have strong price linkages, and thus are spatially integrated.

A taxonomic study of some red algae commonly growing on the coast of Karachi

Eighteen commonly occurring species of marine benthic red algae, i.e., Asparagopsis taxiformis (Delile) Trevisan, Bangia atropurpurea (Roth) C. Agardh, Centroceras clavulatum (C. Agardh) Montagne, Calliblepharis fimbriata (Greville) Kiitzing, Coelarthrum muelleri (Sonder) B¢rgesen, Cottoniella filamentosa (Howe) B¢rgesen, Gracilariafoliifera (Forsskai) B¢rgesen, Halymenia porphyraeformis (B¢rgesen) Parkinson, Hypnea muscifor:mis (Wulfen) Lamouroux, Hypnea valentiae (Turner) Montagne, Laurencia obtusa (Hudson) Lamouroux, Me!anothamnus somalensis Bomet et Falkenberg, Porphyra vietnamensis Tanaka et Pham-hoang H6, Sarconema filiforme (Sonder) Kylin, Sebdenia flabellata (J. Agardh) Parkinson, Scinaia fascicularis (B¢rgesen) Huisman, Scinaia hatei B¢rgesen, and Solieria robusta (Greville) Kylin were collected from coastal areas near Karachi (Pakistan) and taxonomically investigated. All the investigated seaweeds are taxonomically known species. During this study, Melanothamnus somalensis is reported for the first time from northern Arabian Sea and Asparagopsis taxiformis, Bangia atropurpurea, Cottoniella filamentosa, Gracilaria foliifera, Halymenia porphyraeformis, Melanothamnus somalensis, Sarconema filiforme, Sebdenia flabellata, Scinaia fascicularis, and Solieria robusta are taxonomically described for the first time from the coast of Pakistan

MODLEACH: A Variant of LEACH for WSNs

Wireless sensor networks are appearing as an emerging need for mankind. Though, Such networks are still in research phase however, they have high potential to be applied in almost every field of life. Lots of research is done and a lot more is awaiting to be standardized. In this work, cluster based routing in wireless sensor networks is studied precisely. Further, we modify one of the most prominent wireless sensor network's routing protocol "LEACH" as modified LEACH (MODLEACH) by introducing \emph{efficient cluster head replacement scheme} and \emph{dual transmitting power levels}. Our modified LEACH, in comparison with LEACH out performs it using metrics of cluster head formation, through put and network life. Afterwards, hard and soft thresholds are implemented on modified LEACH (MODLEACH) that boast the performance even more. Finally a brief performance analysis of LEACH, Modified LEACH (MODLEACH), MODLEACH with hard threshold (MODLEACHHT) and MODLEACH with soft threshold (MODLEACHST) is undertaken considering metrics of throughput, network life and cluster head replacements.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions

Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The application of the coarse ground truth (GT) annotations of these datasets without detriment to the accuracy of semantic image segmentation (by the mean intersection over union - mIoU) could simplify and speedup the dataset preparation and model fine tuning before its practical application. Here the results of the comparative analysis for semantic segmentation accuracy obtained by PSPNet deep learning architecture are presented for fine and coarse annotated images from Cityscapes dataset. Two scenarios were investigated: scenario 1 - the fine GT images for training and prediction, and scenario 2 - the fine GT images for training and the coarse GT images for prediction. The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa. It means that for some applications some unimportant classes can be excluded and the model can be tuned further for some classes and specific regions on the coarse GT dataset without loss of the accuracy even. Moreover, this opens the perspectives to use deep neural networks for the preparation of such coarse GT datasets.Comment: 10 pages, 6 figures, 2 tables, The Second International Conference on Computer Science, Engineering and Education Applications (ICCSEEA2019) 26-27 January 2019, Kiev, Ukrain