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Increased air pollution is driven by anthropogenic pollution emissions and climate change, which pose great challenges to environmental governance. Strengthening the monitoring of regional air quality levels and analyzing the causes of regional pollution is conducive to the management and sustainable development of the regional atmosphere. Functional data obtained on a wavelet basis were used in the fitting of air quality data of Fujian Province, and wavelet decomposition was performed to obtain low-frequency and high-frequency information. While the Fourier basis cannot adaptively adjust the time–frequency window, resulting in the loss of location information of special frequencies, the wavelet basis solves this problem. Functional analysis of variance was utilized for analyzing spatial differences in air pollution characteristics. Furthermore, the study established a multivariate functional linear regression model to explore the impact of meteorological factors and ozone precursor factors. The results indicated that the overall air quality was gradually improving in Fujian Province, but the concentration of ozone was progressively increasing. Air pollution in coastal areas was higher than that in inland areas. The p-values of the functional analysis of variance for energy values and crest values were less than 0.05. Moreover, the energy entropy and kurtosis values were greater than 0.05. There were significant differences of AQI in the fluctuation amplitude and variation characteristics of different cities. The total squared multiple correlation of regression model was above 50% on average. Ozone is currently the most serious pollution factor, mainly affected by wind speed, temperature, NO2, and CO. In summer, it was principally influenced by VOCs. The findings of this study could act as a reference in exploring the time–frequency characteristics of air quality data and support of air pollution control.

Leaf diseases such as Mosaic disease and Black Rot are among the most common diseases affecting apple leaves, significantly reducing apple yield and quality. Detecting leaf diseases is crucial for the prevention and control of these conditions. In this paper, we propose incorporating rotated bounding boxes into deep learning-based detection, introducing the ProbIoU loss function to better quantify the difference between model predictions and real results in practice. Specifically, we integrated the Plant Village dataset with an on-site dataset of apple leaves from an orchard in Weifang City, Shandong Province, China. Additionally, data augmentation techniques were employed to expand the dataset and address the class imbalance issue. We utilized the EfficientNetV2 architecture with inverted residual structures (FusedMBConv and S-MBConv modules) in the backbone network to build sparse features using a top–down approach, minimizing information loss. The inclusion of the SimAM attention mechanism effectively captures both channel and spatial attention, expanding the receptive field and enhancing feature extraction. Furthermore, we introduced depth-wise separable convolution and the CAFM in the neck network to improve feature fusion capabilities. Finally, experimental results demonstrate that our model outperforms other detection models, achieving 93.3% mAP@0.5, 88.7% Precision, and 89.6% Recall. This approach provides a highly effective solution for the early detection of apple leaf diseases, with the potential to significantly improve disease management in apple orchards.

It is generally accepted that air quality is closely related to human health. In this study, to investigate the dynamic characteristics of air quality and explore the driving factors of air pollution, the Air Quality Index (AQI) and concentration data of six air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2) were fitted to functional curves using the B-spline basis function. Compared with discrete data, functional data can better express the dynamic characteristics of data and reduce information loss. Additionally, functional clustering based on the principal component coefficient was established to analyze the spatiotemporal dynamic characteristics of air quality, and a functional linear model was established to analyze the relationship between pollutants and anthropogenic factors. The results showed that air pollutants in Fujian Province were found to have certain temporal and spatial heterogeneity, among which the seasonal characteristics of NO2 and O3 (high in summer, low in winter) were opposite to those of the other pollutants considered. The spatial distribution of air pollution was low (high) pollution in inland (coastal) areas, and the primary air pollutants in Fujian Province were PM10 and PM2.5. The functional linear model indicated that anthropogenic factors (e.g., vehicle numbers and emissions of industrial NOX emissions) were found to have a notable impact on air pollutants. The findings of this study could act as a reference in support of air pollution control.

Terahertz spectroscopy is a powerful tool for investigating the properties and states of biological matter. Here, a systematic investigation of the interaction of THz wave with “bright mode” resonators and “dark mode” resonators has been conducted, and a simple general principle of obtaining multiple resonant bands has been developed. By manipulating the number and positions of bright mode and dark mode resonant elements in metamaterials, we realized multi-resonant bands terahertz metamaterial structures with three electromagnetic-induced transparency in four-frequency bands. Different carbohydrates in the state of dried films were selected for detection, and the results showed that the multi-resonant bands metamaterial have high response sensitivity at the resonance frequency similar to the characteristic frequency of the biomolecule. Furthermore, by increasing the biomolecule mass in a specific frequency band, the frequency shift in glucose was found to be larger than that of maltose. The frequency shift in glucose in the fourth frequency band is larger than that of the second band, whereas maltose exhibits an opposing trend, thus enabling recognition of maltose and glucose. Our findings provide new insights into the design of functional multi-resonant bands metamaterials, as well as new strategies for developing multi-band metamaterial biosensing devices.

In this paper, we investigate the application of using software-defined radio (SDR) and surface acoustic wave (SAW) device for wireless measurement of the response of in situ sensors. SDR uses software to realize different communication functions. After collecting the magnitude and phase of the response at discrete frequencies, we apply inverse Fourier transform to analyze the time domain responses which, in turn, allows for monitoring the changes of the response of the in situ sensor. We employ microwave signal flow graph concepts to improve the quality of the received signals. Comparing the normalized results obtained by SDR with those obtained from a commercial vector network analyzer (VNA), we demonstrate that the results are sufficiently close, and the SDR-based experiments can provide satisfactory measurement of the in-situ sensors. The objective is to eventually employ this wireless measurement system for soil nutrient sensing.

We report the development of a new ultrasensitive approach for label-free DNA detection using [...]