Adjusting
the local coordination environment of lanthanide luminescent
ions is a useful method to manipulate the relevant photoluminescence
(PL) property. K3Lu(PO4)2 is a phase-change
material, and according to the stable temperature range from low to
high, the related polymorphs are phase I [P21/m, coordination number (CN) of Lu3+ = 7], phase II (P21/m, CN = 6), and phase III (P3̅, CN = 6), respectively.
Based on the temperature-dependent PL analysis of K3Lu(PO4)2:Pr3+, we find that Pr3+ ions occupy the noninversion sites (C
s
) in the two low-temperature phases but preferentially
enter into the inversion ones (C
3i
) in phase III. Compared to Pr3+-doped phase I (78
K), Pr3+ ions in phase III (300 K) manifest a weaker fluorescence
intensity (170-fold lower). To enhance the room-temperature PL property
of K3Lu(PO4)2:Pr3+, a
polymorphous adjustment strategy was proposed by the use of the ion-doping
method. By introducing the Gd3+ ions into the lattice,
Pr3+-doped phase I is successfully stabilized to room temperature,
manifesting a 27-fold fluorescence increase in comparison to K3Lu(PO4)2:Pr3+ (0.1 at. %).
The finding discussed in this study highlights the significance of
site engineering for luminescent ions and also presents the application
value of phase-change hosts in the development of high-performance
luminescent materials.
Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures.
Udder conformation traits interact with cow milk yield, and it is essential to study the udder characteristics at different levels of production to predict milk yield for managing cows on farms. This study aims to develop an effective method based on instance segmentation and an improved neural network to divide cow production groups according to udders of high- and low-yielding cows. Firstly, the SOLOv2 (Segmenting Objects by LOcations) method was utilized to finely segment the cow udders. Secondly, feature extraction and data processing were conducted to define several cow udder features. Finally, the improved CNN-LSTM (Convolution Neural Network-Long Short-Term Memory) neural network was adopted to classify high- and low-yielding udders. The research compared the improved CNN-LSTM model and the other five classifiers, and the results show that CNN-LSTM achieved an overall accuracy of 96.44%. The proposed method indicates that the SOLOv2 and CNN-LSTM methods combined with analysis of udder traits have the potential for assigning cows to different production groups.
Accurate prediction of PM2.5 concentration is one of the key tasks of air pollution assessment, early warning, and treatment. In this paper, four monitoring sites were arranged in Jiangbei New District of Nanjing City, China. The environmental parameters such as PM2.5/PM10 concentration, temperature, and humidity were monitored from January to February 2020. A gated recurrent unit (GRU) network based on the PM2.5 concentration prediction model was established to predict PM2.5 concentration. The mean relative error (MRE), root mean square error (RMSE), and Pearson correlation coefficient were selected as the evaluation criteria for the accuracy of the GRU model. The data set was divided into a training set, a test set and a validation set at a ratio of 7:2:1, and the GRU model was used to predict the hourly value of PM2.5 concentration in the next week. The prediction results show that the Pearson correlation coefficients between the predicted values and the monitored values of the four monitoring sites have reached more than 0.9, reflecting a strong correlation. The relative average errors are around 10%. The GRU model prediction of NJAU (Nanjing Agricultural University)-Pukou Campus Site is the most accurate, and the correlation coefficient, MRE, and RMSE are 0.970, 7.85%, and 9.6049, respectively, reflecting the good prediction performance of the model. Therefore, this research supports the prediction of air quality in different cities and regions, so people can take protective measures in advance and reduce the damage caused by air pollution to human bodies.
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