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Hundreds of Proteaceae species in Australia and South Africa typically grow on phosphorus (P)‐impoverished soils, exhibiting a carboxylate‐releasing P‐mobilizing strategy. In the Southwest Australian Biodiversity Hotspot, two Xylomelum (Proteaceae) species are widely distributed, but restricted within that distribution.We grew Xylomelum occidentale in hydroponics at 1 μM P. Leaves, seeds, rhizosheath and bulk soil were collected in natural habitats.Xylomelum occidentale did not produce functional cluster roots and occupied soils that are somewhat less P‐impoverished than those in typical Proteaceae habitats in the region. Based on measurements of foliar manganese concentrations (a proxy for rhizosphere carboxylate concentrations) and P fractions in bulk and rhizosheath soil, we conclude that X. occidentale accesses organic P, without releasing carboxylates. Solution 31P‐NMR spectroscopy revealed which organic P forms X. occidentale accessed.Xylomelum occidentale uses a strategy that differs fundamentally from that typical in Proteaceae, accessing soil organic P without carboxylates. We surmise that this novel strategy is likely expressed also in co‐occurring non‐Proteaceae that lack a carboxylate‐exuding strategy. These co‐occurring species are unlikely to benefit from mycorrhizal associations, because plant‐available soil P concentrations are too low.Synthesis. Our findings show the first field evidence of effectively utilizing soil organic P by X. occidentale without carboxylate exudation and explain their relatively restricted distribution in an old P‐impoverished landscape, contributing to a better understanding of how diverse P‐acquisition strategies coexist in a megadiverse ecosystem. [ABSTRACT FROM AUTHOR]

Plants that produce specialised cluster roots, which mobilise large quantities of poorly available nutrients such as phosphorus (P), can provide a benefit to neighbouring plants that produce roots in the cluster rhizosphere, as demonstrated previously in pot studies. To be effective, such roots must be present within the short time of peak cluster activity. We tested if this requirement is met, and quantified potential P benefits, in a hyperdiverse Mediterranean woodland of southwest Australia where cluster-rooted species are prominent. Using minirhizotrons, we monitored root dynamics during the wet season in the natural habitat. We found non-cluster roots intermingling with all 57 of the observed cluster roots of the studied tree species, Banksia attenuata. Almost all (95%) of these cases were observed in a high-moisture treatment simulating the 45-year average, but not present when we intercepted some of the rainfall. We estimate that cluster-root activity can increase P availability to intermingling roots to a theoretical maximum of 80% of total P in the studied soil. Due to their high P-remobilisation efficiency (89%), which results from P rapidly being relocated from cluster roots within the plant, senesced Banksia cluster roots are a negligible P source for other roots. We conclude that, rather than serving as a P source, it is the cluster-root activity, particularly the exudation of carboxylates, that may improve the coexistence of interacting species that are capable of root intermingling, thus potentially promoting species diversity in nutrient-poor habitats, and that this mechanism will be less effective in a drying climate. [ABSTRACT FROM AUTHOR]

Objective: The aims of this study were to evaluate a subset of sleep-related cognitions and to examine whether dysfunctional beliefs and attitudes about sleep were associated with sleep quality in college students. Patients and methods: A total of 1,333 college students were enrolled in this study by randomized cluster sampling. A brief version of Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16) was administered to college students at several colleges. Sleep quality was also assessed using the Pittsburgh Sleep Quality Index (PSQI). The DBAS-16 scores were analyzed across different demographic variables, corresponding subscales of 7-item PSQI, and relevant sleep behavior variables. Results: A total of 343 participants were poor sleepers, while 990 were good sleepers, as defined by PSQI. The DBAS-16 scores were lower in poor sleepers than in good sleepers (46.32 ± 7.851 vs 49.87 ± 8.349, p , 0.001), and DBAS-16 scores were lower in females and nonmedical students when compared with those in males and medical students, respectively (48.20 ± 8.711 vs 49.73 ± 7.923, p = 0.001; 48.56 ± 8.406 vs 49.88 ± 8.208, p = 0.009, respectively). The total score for sleep quality, as measured by PSQI, was negatively correlated with the DBAS-16 total score (r =−0.197, p , 0.01). There were significant differences in PSQI scores between individuals with attitudes and those without attitudes about sleep with respect to good sleep habits (p , 0.001), self-relaxation (p = 0.001), physical exercise (p , 0.001), taking sleeping pills (p = 0.004), and taking no action (p , 0.001).Conclusion: Dysfunctional beliefs about sleep are associated with sleep quality and should be discouraged, especially for females and nonmedical college students. [ABSTRACT FROM AUTHOR]

Microseismic monitoring is a widely used technique in coal mine safe production. Microseismic events classification is one of the most important steps in microseismic monitoring. CNN has been proven to be the potential tool to identify microseismic events automatically. When the noise is serious and the signal is weak, the recognition ability of CNN is limited. Some scholars use denoising methods, such as wavelet transform, to enhance the signal-to-noise ratio (SNR) of the input data. However, the effective signal will be broken if the incorrect threshold parameters are set in this wavelet transform-based denoising method. In this paper, we intend to make the data volume of the time-frequency graph obtained by WPT as the input of CNN. But it may lead to a dimensional disaster if taking the time-frequency domain signals as input directly. To utilize the effective information extraction and reduce the data dimension, we propose a dual-channel CNN model by combining time domain information and wavelet packet decomposition coefficients (T-WPD CNN). The wavelet packet decomposition coefficients highlight the characteristics of the signal and suppress the characteristics of noise. In addition, the coefficients have the same dimension as the original signal. These advantages are useful for enhancing the performance of CNN. Two field datasets are used to test the network. One from Australia and another from Jiayang coal mine, Sichuan, China. The feature map of the convolutional layer with different inputs are shown to illustrate the influence effect of raw time domain signal and WPD coefficient on the classification performance. The final results show that the T-WPD CNN is superior to the traditional CNN method in accuracy and robustness. • The network combines time-domain information and wavelet packet decomposition coefficients of the microseismic data. • The more obvious feature map of the microseismic is extracted, which makes the classification performance better. • The wavelet packet decomposition coefficients can avoid the dimensional disaster of the input data. [ABSTRACT FROM AUTHOR]

Drought indices based on thermal remote sensing have been developed and have merit for effective early warning of agricultural droughts, but approaches so far are relatively complex or sensitive to land surface temperature (LST) estimation uncertainties. Here, we propose the temperature rise index (TRI), a drought index that is comparatively robust and easy to calculate, as the anomaly of the intrinsic morning rise of LST. The underlying principle is that the rate of LST rise between 1.5 and 3.5 h after the sunrise is approximately linear and occurs more rapidly under dry conditions than under wet conditions over vegetated surfaces as a consequence of stomatal control. TRI during the growing seasons of 2010–2014 was calculated over the Australian wheatbelt from LST retrievals from the geostationary Multifunction Transport Satellite-2 (MTSAT-2) instrument. The calculated TRI was compared with indices based on precipitation integrated over 1-, 3- and 6-month time scales, on Soil Moisture and Ocean Salinity (SMOS) soil moisture derived from passive microwave remote sensing, and on vegetation condition (normalized difference vegetation index, NDVI) derived from optical remote sensing. The various indices were also compared to annual wheat yield over large areas. The correlation coefficient between TRI and precipitation anomaly that serves as an operational drought index in Australia was above 0.6 in general with 3-month integrative time scale for precipitation. TRI produced spatiotemporal dryness patterns that were very similar to those in soil moisture, but with more detail due to its finer resolution. A time lag of >1 month was found between TRI and observed vegetation condition, supporting the use of TRI in early warning. Among the compared drought indices, TRI explained the largest fraction (35%) of wheat yield variations. TRI correlations with wheat yields peaked higher and earlier by almost one month in comparison to other indices. We conclude that the thermal drought index proposed here shows considerable potential for use in drought early warning as an effective complement. • Land surface temperature morning rise at 1.5–3.5 h after sunrise is quasi-linear. • Vegetation canopy under water stress has a faster temperature morning rise. • Temperature rise index provides earlier drought monitoring than greenness indices. • Temperature rise index peaked higher and earlier by 1 month in correlation to yields. [ABSTRACT FROM AUTHOR]

Amongst various remote sensing-based agricultural drought indices that have been developed, indices based on thermal remote sensing have merit for effective early warning of agricultural droughts. These approaches, however, are relatively complex or sensitive to uncertainties in land surface temperature (LST) estimation. Here, we propose the temperature rise index (TRI), a drought index that is easy to calculate directly from brightness temperatures in the split-window channels, as the anomaly of the intrinsic morning rise of LST. The underlying principle is that the rate of LST rise between 1.5 and 3.5 h after the sunrise is approximately linear and over vegetated surfaces occurs more rapidly under dry conditions as a consequence of stomatal control. TRI during the growing seasons of 2015−2018 was calculated over the Australian wheatbelt from LST retrievals from the newly launched geostationary Himawari-8 instrument. The calculated TRI was compared with indices based on precipitation over different time scales, on soil moisture derived from passive microwave remote sensing, and on vegetation condition derived from optical remote sensing. The various indices were also compared to annual wheat yield over large areas. The correlation coefficient between TRI and precipitation anomaly that serves as an operational drought index in Australia was above 0.6 in general with a 3-month integrative timescale for precipitation. TRI produced spatiotemporal dryness patterns that were very similar to those in soil moisture, but with more detail due to its finer resolution. A time lag was found between TRI and observed vegetation condition, supporting the use of TRI in early warning. Among the compared drought indices, TRI explained the largest fraction of wheat yield variations. TRI correlations with wheat yields peaked higher and earlier by almost one month in comparison to other indices. We conclude that the thermal drought index proposed here shows considerable potential as an effective complement for early warning. [ABSTRACT FROM AUTHOR]