Abstract: A method for predicting an air quality index (AQI) based on a fusion model proposes a differential fusion seasonal prediction model (DF-SPM) based on a random forest (RF) model and a convolutional neural network (CNN)-long short-term memory (LSTM)-attention (CLA) model. This method uses the optimal threshold interval (OTI) search algorithm to search and learn the OTI of four seasons during the prediction process, and obtains the final prediction results according to the predicted values of RF model and CLA model. The fusion model combines the prediction advantages of two independent models, and fully considers the seasonal and periodic characteristics of AQI, so as to accurately search OTI in different time periods with the seasonal scale, so as to achieve higher prediction accuracy. The OTI strategy of fusion model is superior to the single threshold strategy, which can extract the historical fluctuation characteristics of AQI and achieve higher prediction accuracy.

Abstract: The disclosure discloses a wide-concentration multi-component hazardous gas detector and an implementation method thereof, solving the problems of the existing technology that false negative results, ultra-limit concentration and sensor poisoning often occur in a gas detector used in fire fighting forces. The wide-concentration multi-component hazardous gas detector includes a gas diluting and sampling connector, a sensor integrated module, electrochemical sensors, ADC (analog to digital converter) circuits, MCU (microprogrammed control unit) single chip microcomputers, acousto-optic alarms, a 4-button keyboard module, an LED (light-emitting diode) display module, an SD card data memory module, a power supply control and electric quantity display module, a high-performance lithium battery pack, a small evacuation pump, 433M signal transmission modules and a remote command platform signal collection terminal.

Abstract: the disclosure discloses a wide-concentration multi-component hazardous gas detector and an implementation method thereof, solving the problems of the existing, technology that false negative results, ultra-limit concentration and sensor poisoning often occur in a gas detector used in fire fighting forces. The wide-concentration multi-component hazardous gas detector includes a gas diluting and sampling connector, a sensor integrated module, electrochemical sensors, ADC (analog to digital converter) circuits, MCU (microprogrammed control unit) single chip microcomputers, acousto-optic alarms, a 4-button keyboard module, an LED (light-emitting diode) display module, an SD card data memory module, a power supply control and electric quantity display module, a high-performance lithium battery pack, a small evacuation pump, 433M signal transmission modules and a remote command platform signal collection terminal.

Abstract: Techniques are disclosed for generating data provenance associated with a computing system. For example, a method comprises the following steps. Information associated with the execution of a given process in a given computing environment in accordance with a given process data set is captured. A provenance data set is generated based on the captured information. The generated provenance data set comprises one or more states associated with one or more execution components of the given computing environment that existed during execution of the given process, the one or more execution components comprising one or more virtual machines and one or more storage units. At least a portion of the generated provenance data set may be utilized to revert the computing environment back to the one or more states associated with the one or more execution components of the given computing environment that existed during the execution of the given process.

Abstract: Techniques are disclosed for generating data provenance associated with a computing system. For example, a method comprises the following steps. Information associated with the execution of a given process in a given computing environment in accordance with a given process data set is captured. A provenance data set is generated based on the captured information. The generated provenance data set comprises one or more states associated with one or more execution components of the given computing environment that existed during the execution of the given process. At least a portion of the generated provenance data set may be utilized to revert the computing environment back to the one or more states associated with the one or more execution components of the given computing environment that existed during the execution of the given process.