A study of the lower gastrointestinal tract cancer with emphasis on gender and age of the patients in western Iran (Kermanshah) over 2006-2011

Given that the incidence of cancers in the coming years will have a growing trend due to the increased average age of the world's population, the partial control of communicable diseases, and the rapid growth of the environmental risk factors. The present work was a descriptive, comparative and analytical study. The statistical population consisted of all patients residing in Kermanshah who had been suffering from the lower gastrointestinal tract cancer for five years. The results of the present study revealed that 46.10% were male, and 53.90% were female. Further, the results indicated that the age of patients and the intensity of cancer differentiation were significantly correlated.It seemed that the lack of early diagnosis would ensue from a lack of periodic screening programs at early ages and lack of forums in which specialists could get together due to the unavailability of comparative statistics.Keywords: Cancer, Lower Gastrointestinal Tract Cancer, Western Iran, Kermanshah Cit

Perception of medical students participated in community-oriented medical education in Kermanshah (2007-2011)

Introduction: Physicians have a fundamental role in the health system. Therefore, investigation of different medical education approaches would have a major impact on improving the quality of education. The aim of this study was investigating the performance of community-based education center of Kermanshah from the viewpoint of medical interns. Methods: In this cross sectional analytic study, medical interns who were trained in community-oriented education center (n=175) over one month were enrolled and completed the researcher built questionnaire. Alpha reliability was 0.88 and its content validity was confirmed by the approval of five faculty members who were expert in the field of education. Data was analyzed by SPSS software and using of descriptive statistics, ANOVA and Friedman tests.  Results: Based on the viewpoint of our participants, community oriented education were desirable compare to education in hospital in terms of quality of education (42/4%), experienced of practical tips (48/3%), observing different cases (57/2%), preventive medicine education (55/6%)and time for communicating about patient (74%). Among reasons of difference between training in the COME center  compare to teaching hospitals, "possibility of learning of practical tips" was considered more important than other reasons (P<0.05). Conclusion: In this study, most participants have evaluated the training performance of the COME center as good and also more useful compared to training in teaching hospitals. Reinforcing of COME centers could be a priority in making future educational policies

Application of enhanced softening process in slaughterhouse wastewater treatment

217-221Enhanced softening has been applied for the removal of soluble chemical oxygen demand (SCOD), phosphorous and colour of the abattoir effluent, and the effect of increasing dose of coagulants is investigated. The study is performed on a laboratory scale by using jar apparatus, lime as softener and pH adjuster agent. Coagulants such as alum and ferric chloride are used to increase the size of flocs in various pH solutions. The results show that the enhanced softening is an efficient method for SCOD, and colour and phosphorous removal from abattoir wastewater. The maximum SCOD (93%) and phosphorous (94%) removal is attained at pH 11.65. Use of alum as coagulant combined with enhanced softening does not have positive effect on enhanced softening efficiency in SCOD, and phosphorous and colour removal, but ferric chloride increases the enhanced softening efficiency except its SCOD removal efficiency. The results also show that ferric chloride removes 90% and 97% SCOD and phosphorous respectively. This efficiency is obtained at 11.57 pH and 90 mg/L ferric chloride. The optimum pH and dose of ferric chloride (90 mg/L) increase the luminance of effluent from 40% to 75% at pH 7.6 and decrease its purity from 47% to 14% at pH 7.6

Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study

Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time

Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study

Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models&rsquo; predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time