The possibility of preoperative prediction of pathologic complete response in rectal cancer has been studied in order to identify patients who would respond to neoadjuvant therapy and to individualize therapeutic strategies. Endoscopic ultrasound of the rectum is an accurate method for the evaluation of local tumor and lymph node invasion. Objective: To evaluate the potential of endoscopic ultrasound as a predictor of complete pathological response to neoadjuvant treatment in patients with locally advanced rectal cancer. Material and methods: Retrospective study of patients with rectal cancer from January 2014 to December 2016. Results: We obtained a statistical association between T stage by endoscopic ultrasound and complete pathological response (p = 0.015). It is not so for N, sphincter involvement, circumferential involvement and maximum tumor thickness (p = 0.723, p = 0.510, p = 0.233 and p = 0.114, respectively). When multivariate logistic regression analysis was applied to assess the degree of influence of the predictor variables on pathologic response, none of these variables was associated with complete pathologic response. Conclusion: Prediction of pathologic complete response in rectal cancer has been considered as the crucial point upon which treatments for rectal cancer could be individualized. So far, no imaging method has been able to demonstrate efficacy in predicting complete pathologic response, and in turn there is no direct association between any endosonographic finding that can accurately predict it.

Ebola virus is a potent infectious disease virus that can cause Ebola haemorrhagic fever caused by human and primate. It has high mortality and easy infectivity to form a great obstacle to the steady development of human society. The profound understanding of the virus is particularly important harm. In this paper, a number of mathematical models are established to solve this problem. The software is used to analyze and predict the propagation of Ebola virus. The residual analysis is used to test the model. Finally, the effects of various control measures on controlling the epidemic are analyzed. In order to solve the problem, we will establish the infectious disease model to dynamically describe the spread of the virus in the 'virtual orangutan population'. Considering that the latent population is analyzed in this question, we will improve the model. Join the latent group (), and the migrants are divided into self-healing () and the dead (), to establish a suitable solution to this problem model. According to the relevant data given in the title, differential equations were established. For the second question, this question involves the one-way transmission of the virus across the species, so we can improve the model, on the basis of human contact with orangutans infected groups, the establishment of a one-way model to solve this problem. On the basis of the problem one, the differential equation is established, the model is predicted and tested. In the case of question 3, the number of human susceptible groups is much higher than that of the orangutan infection group by comparing the relevant data with the increase of the cure rate to 80% after the intervention of the outside experts. Therefore, the original data of human populations from experts can be ignored. Since then the virus spreads within a single species, the differential equation can be established according to the model in question 1 and the data values in the virtual human population are predicted. For question 4, the effect of the measures such as the strict enforcement of the various epidemic control measures and the improvement of the drug effect on the control of the epidemic are analyzed by comparing the above-mentioned models with the control measures.

In order to explore the application of the new integrated intelligent spore capture system developed in China in the prediction of cucumber downy mildew and cucumber powdery mildew, the main working parameters of the integrated intelligent spore capture system, such as the presence or absence of air cutting head, the height of air collection port and the time of air collection, were optimized by identifying the morphology of captured spores in the case of natural disease in the field. The relationship between the disease index of cucumber downy mildew and cucumber powdery mildew in greenhouse and the amount of spores captured was analyzed through the dynamic monitoring of disease and spores. The results show that when the air cutting head is not installed, the height of the air collection port is 70 cm, and the period of 10: 00–10: 30 was beneficial to the capture of spores. The disease index of cucumber downy mildew and cucumber powdery mildew had a strong positive correlation with the total amount of spores captured for 7 consecutive days. Continuous monitoring of cucumber downy mildew sporangia and rapid increase in the number is a predictor of the occurrence or rapid increase of cucumber downy mildew. The conidia of cucumber powdery mildew were not detected before the disease onset, and the number of conidia captured was still small at the peak of the disease. The research shows that the integrated intelligent spore capture system is suitable for the prediction of cucumber downy mildew, but there are still some problems in the prediction of cucumber powdery mildew.

Every year, hundreds of fires occur in the forests and rangelands across the world and damage thousands hectare of trees, shrubs, and plants which cause environmental and economic damages. This study aims to establish a real time forest fire alert system for better forest management and monitoring in Golestan Province. In this study, in order to prepare fire hazard maps, the required layers were produced based on fire data in Golestan forests and MODIS sensor data. At first, the natural fire data was divided into two categories of training and test samples randomly. Then, the vegetation moisture stresses and greenness were considered using six indexes of NDVI, MSI, WDVI, OSAVI, GVMI and NDWI in natural fire area of training category on the day before fire occurrence and a long period of 15 years, and the risk threshold of the parameters was considered in addition to selecting the best spectral index of vegetation. Finally, the model output was validated for fire occurrences of the test category. The results showed the possibility of prediction of fire site before occurrence of fire with more than 80 percent accuracy.

To achieve sustainable development, detailed planning, control and management of land cover changes that occur naturally or by human caused artificial factors, are essential. Urban managers and planners need a tool that represents them the information accurate, fast and in exact time. In this study, land use changes of 3 periods, 1994-2002, 2002-2009, 2009-2015 and predictions of 2009, 2015 and 2023 were assessed. In this paper, Maximum Likelihood method was used to classify the images, so that after evaluation of accuracy, amount of overall accuracy for images of 2013 was 85.55% and its Kappa coefficient was 80.03%. To predict land use changes, Markov-CA model was used after assessing the accuracy, and the amount of overall accuracy for 2009 was 82.57% and for 2015 was 93.865%. Then web GIS application was designed via map server application and evoked shape files through map file and open layers to browser environment and for design of appearance of website CSS, HTML and JavaScript languages were used. HTML is  responsible for creating the foundation and overall structure of webpage but beautifying and layout design on CSS. 

Heat removal has become an increasingly crucial issue for microelectronic chips due to increasingly high speed and high performance. One solution is to increase the thermal conductivity of the corresponding dielectrics. However, traditional approach to adding solid heat conductive nanoparticles to polymer dielectrics led to a significant weight increase. Here we propose a dielectric polymer filled with heat conductive hollow nanoparticles to mitigate the weight gain. Our mesoscale simulation of heat conduction through this dielectric polymer composite microstructure using the phase-field spectral iterative perturbation method demonstrates the simultaneous achievement of enhanced effective thermal conductivity and the low density. It is shown that additional heat conductivity enhancement can be achieved by wrapping the hollow nanoparticles with graphene layers. The underlying mesoscale mechanism of such a microstructure design and the quantitative effect of interfacial thermal resistance will be discussed. This work is expected to stimulate future efforts to develop light-weight thermal conductive polymer nanocomposites.