CNN-LSTM and clustering-based spatial-temporal demand forecasting for on-demand ride services

Passenger demand forecasting is of great importance to the on-demand ride systems. With the accurate forecasting of demand, it can be determined from which regions and when the passengers demand a vehicle. In this way, passenger and vehicle waiting times, fuel costs of vehicles can be reduced. In the literature, various models such as time series, long short-term memory (LSTM), convolutional neural network (CNN), and hybrid of CNN-LSTM are used for demand forecasting in on-demand ride service systems. These models forecast demands by considering temporal and spatial data separately or together. In models that use spatial and spatial-temporal data, generally, the city is divided into zones in the form of a grid. This partitioning method has some disadvantages, such as misleading the forecasting accuracy by considering regions without demand and ignoring the geographical conditions. In this study, two new models, ConvLSTM2D-clustering and CNN-LSTM-clustering are proposed to overcome these disadvantages and make more accurate and robust forecasts. The proposed models use clustering instead of grid partitioning in dividing the city into zones and take time-of-day, time-of-week variables into account in forecasting as well as passenger demand. The presented models have been used in the passenger demand forecasting of Turkcell Technology Company, which provides on-demand ride services for its employees in Istanbul, Turkey. Experimental results, validated on real-world data provided by Turkcell, show that the proposed models partition the city more effectively and achieve 14-55% better short- and long-term forecasting performances than the compared models in terms of mean squared error.Algorithms and the Foundations of Software technolog

Therapeutic potential of cristobalite in the treatment of calf diarrhea

Calf diarrhea continues to be the major problem of calves in the neonatal period. The effect of zeolites has been increasingly studied in ruminant health in recent years. In the present study, the efficacy of cristobalite, a zeolite, in neonatal calf diarrhea was studied first time. For this purpose, twenty-five neonatal calves with diarrheas were divided into two groups, and Group 1 (n=12) received conventional treatment and Group 2 (n=13) received cristobalite (Zoosorb 10 mg/kg) orally 3 times a day in addition to conventional treatment. Escherichia coli k99 and CS31a, bovine rotavirus and bovine coronavirus were isolated from fecal samples at the beginning of the treatment, on the third day and before discharge. It was determined that the recovery period in Group 2 was 0.95 (20.6%) days shorter than in Group 1 (p<0.05) while no viral agents were found on the fifth day in Group 2, viral shedding continued in 4 of 5 calves in Group 1. In conclusion, the study revealed that cristobalite speeds the recovery time and possibly decreases viral shedding in neonatal calf diarrhea, demonstrating a remarkable efficiency in the treatment