Modeling air pollution by integrating ANFIS and metaheuristic algorithms.

Air pollution is increasing for many reasons, such as the crowding of cities, the failure of planning to consider the benefit of society and nature, and the non-implementation of environmental legislation. In the recent era, the impacts of air pollution on human health and the ecosystem have become a primary global concern. Thus, the prediction of air pollution is a crucial issue. ANFIS is an artificial intelligence technique consisting of artificial neural networks and fuzzy inference systems, and it is widely used in estimating studies. To obtain effective results with ANFIS, the training process, which includes optimizing its premise and consequent parameters, is very important. In this study, ANFIS training has been performed using three popular metaheuristic methods: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) for modeling air pollution. Various air pollution parameters which are particular matters: PM2.5 and PM10, sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and several meteorological parameters such as wind speed, wind gust, temperature, pressure, and humidity were utilized. Daily air pollution predictions in Istanbul were obtained using these particular matters and parameters via trained ANFIS approaches with metaheuristics. The prediction results from GA, PSO, and DE-trained ANFIS were compared with classical ANFIS results. In conclusion, it can be said that the trained ANFIS approaches are more successful than classical ANFIS for modeling and predicting air pollution.

State of the art in total pulse production in major states of India using ARIMA techniques.

Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. The present investigation is an attempt to study the trend in the production of total pulses in India using the autoregressive integrated moving average (ARIMA) method. For stochastic trend estimation, yearly data were used for the period from 1961 to 2019. On the basis of the performance of several goodness of model fit criteria, the most suitable ARIMA model is chosen to capture the trend of pulse production. Forecasting for the 10 years from 2020 to 2029 is done, and it is observed that India has the highest forecast value (31.03302 million tonnes) in 2029. This study will play an important role in determining the gap between production of and demand for pulses in the future.

Determination of production losses related to lumpy skin disease among cattle in Turkey and analysis using SEIR epidemic model.

BACKGROUND: Lumpy Skin Disease (LSD) is an infectious disease induced by the Capripoxvirus, causing epidemics in Turkey and several countries worldwide and inducing significant economic losses. Although this disease occurs in Turkish cattle every year, it is a notifiable disease. In this study, LSD in Turkey was modelled using the Susceptible, Exposed, Infectious, and Recovered (SEIR) epidemiological model, and production losses were estimated with predictions of the course of the disease. The animal population was categorized into four groups: Susceptible, Exposed, Infectious, and Recovered, and model parameters were obtained. The SEIR model was formulated with an outbreak calculator simulator applied for demonstration purposes. RESULTS: Production losses caused by the LSD epidemic and the SEIR model's predictions on the disease's course were evaluated. Although 1282 cases were identified in Turkey during the study period, the prevalence of LSD was calculated as 4.51%, and the mortality rate was 1.09%. The relationship between the disease duration and incubation period was emphasized in the simulated SEIR model to understand the dynamics of LSD. Early detection of the disease during the incubation period significantly affected the peak time of the disease. According to the model, if the disease was detected during the incubation period, the sick animal's time could transmit the disease (Tinf) was calculated as 2.66 days. Production loss from LSD infection was estimated at US $ 886.34 for dairy cattle and the US $ 1,066.61 for beef cattle per animal. CONCLUSION: Detection of LSD infection during the incubation period changes the course of the disease and may reduce the resulting economic loss.

Association of endocan expression with clinicopathological prognostic parameters in breast carcinoma.

BACKGROUND: Breast cancer (BC) is a common malignancy in women. Some molecules, including endocan, are still under investigation as potential prognostic factors in BC. OBJECTIVE: In the present study, we aimed to determine the relationship between endocan expression and clinicopathological prognostic parameters in BC. METHODS: Two hundred and fifty-five patients diagnosed with BC were included in the present study. The immuno-reactivity scoring (IRS) system was used to reveal the tissue endocan expression levels. RESULTS: We found that endocan expression is associated with tumor necrosis, tumor size, and the presence of lobular carcinoma in situ (LCIS) in BC. There was no relationship between endocan expression and survival as well as other clinicopathological prognostic parameters. CONCLUSION: Endocan overexpression in BC is associated with some prognostic parameters, including tumor necrosis, tumor size, and the presence of LCIS. Further studies with larger series are needed to reveal the clinical and therapeutic implications of endocan in BC.

Lifestyle and Chronic Constipation in Medical Students.

AIM: Constipation is one of the most common complaints of the digestive system indicated with an increase in defecation frequency, difficulty in defecation, and hard and strained defecation. Environmental, personal, and genetic factors may be affecting constipation although the affecting factors have not yet been thoroughly explained. The aim of this study was to investigate constipation frequency and lifestyles in medical students. METHOD: The population was selected among medical students for the study, which was planned as a survey study. Demographic data of all the participants and the factors suggested to affect constipation were questioned and analyzed. RESULTS: The study covered a total of 425 medical students. Among the students reporting constipation, 2.86% were in their first year of medical school, while 7.53% were in the third year and 9.09% were in the sixth year. The rate of students reporting constipation and familial history was statistically significant. While regular eating habits were reported in the first and third years, this rate was much lower in the sixth year group working at clinical departments. The results of our study did not reveal any significant relationship between daily intake of water and constipation. There was, however, a significant relationship between stress and constipation. CONCLUSION: The results of our study showed that medical education did not curb constipation frequency. We believe that stress is significant in constipation. The data we collected indicate that regular eating habits and excess liquid intake are not as effective as suggested in the treatment of constipation.