To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

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.

In order to promote the application of noise map in high-speed railway noise management, the high-speed railway noise map drawing technology based on the combination of noise prediction model and geographic information system (GIS) is studied. Firstly, according to the distribution characteristics of noise sources and line structure characteristics of high-speed railway, the prediction model of multi equivalent sound sources and the calculation method of sound barrier insertion loss of high-speed railway are optimized; secondly, a three-dimensional geographic information model of a high-speed railway is built in GIS software, and the railway noise prediction technology based on the model is developed again; then, the noise of discrete nodes is calculated, and the continuous noise distribution map is drawn by spatial interpolation. The research results show that the comparison error between the noise map of a high-speed railway drawn by this technology and the measured results is less than 1 dB (A), which verifies the accuracy and practicality of the high-speed railway noise map, and can be used as a reference for the railway noise management department to formulate noise control countermeasures.

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.