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In the process of climate warming, drought has increased the vulnerability of ecosystems. Due to the extreme sensitivity of grasslands to drought, grassland drought stress vulnerability assessment has become a current issue to be addressed. First, correlation analysis was used to determine the characteristics of the normalized precipitation evapotranspiration index (SPEI) response of the grassland normalized difference vegetation index (NDVI) to multiscale drought stress (SPEI-1 ~ SPEI-24) in the study area. Then, the response of grassland vegetation to drought stress at different growth periods was modeled using conjugate function analysis. Conditional probabilities were used to explore the probability of NDVI decline to the lower percentile in grasslands under different levels of drought stress (moderate, severe and extreme drought) and to further analyze the differences in drought vulnerability across climate zones and grassland types. Finally, the main influencing factors of drought stress in grassland at different periods were identified. The results of the study showed that the spatial pattern of drought response time of grassland in Xinjiang had obvious seasonality, with an increasing trend from January to March and November to December in the nongrowing season and a decreasing trend from June to October in the growing season. August was the most vulnerable period for grassland drought stress, with the highest probability of grassland loss. When the grasslands experience a certain degree of loss, they develop strategies to mitigate the effects of drought stress, thereby decreasing the probability of falling into the lower percentile. Among them, the highest probability of drought vulnerability was found in semiarid grasslands, as well as in plains grasslands and alpine subalpine grasslands. In addition, the primary drivers of April and August were temperature, whereas for September, the most significant influencing factor was evapotranspiration. The results of the study will not only deepen our understanding of the dynamics of drought stress in grasslands under climate change but also provide a scientific basis for the management of grassland ecosystems in response to drought and the allocation of water in the future.

Inspired by the mechanism of visual attentional selection, a color feature selection unit consisting of photoreceptors and an attentional selection circuit (ASC) is presented. The detection and wavelength recognition of trichromatic light are accomplished by the photoreceptors with RGB filters. The ASC, fabricated in a standard CMOS technology, activates the competition among photoreceptors to select the principal color-component feature defined as the attentional focus. The number of focuses and resolution can be changed with a top-down stimulus. Electrical test results show that the ASC can select and interpret the most important color-component feature. In optical tests, the ASC chip-based unit can extract the principal color-component of single-color and dual-color light in real-time to complete color recognition. The percentage ratio between the intensity of stray light relative to the light with maximum intensity is up to 93.58%. And the maximum achievable recognition frame rate is 1 kHz, which is more than one order of magnitude higher than the frequency of the biological vision systems. Besides, the unit's outputs can be transformed into binary codes as labels for color recognition, and the color recognition process does not require the participation of ADC, CPU, and memory, consequently avoiding the von Neumann bottleneck.

A self-powered IoT system with high integration, robust performance, and adaptability to complex environments is one of the current research hotspots. Piezoelectric materials have been widely used in pressure sensing and energy harvesting due to their stable output electrical properties. In this paper, a heterogeneous integrated self-powered IoT system based on a lithium niobate (LiNbO3) piezoelectric device and CMOS readout circuit is proposed. The piezoelectric sensor is fabricated by depositing electrodes on the surface of the 36°Y-cut LiNbO3 piezoelectric material. The sensitivity of the fabricated sensor is 17.5 mV/kPa. Based on the CSMC 0.18 μm BCD process, a ring voltage-controlled oscillator (VCO) based on the current starvation delay element is designed as a wireless data transmission unit. The oscillator has two tuning terminals, which can realize frequency alignment and voltage threshold judgment. Using photolithography, wire bonding technology, etc., the heterogeneous integration of the Si-based chip and LiNbO3 piezoelectric device is realized. The experimental results show that below 1.8 V supply voltage, the oscillation frequency of the chip increases with the increase in the control terminal voltage, which provides an idea for solving the energy supply problem of an IoT system. This system has great application potential in the field of self-powered sensing.

Inspired by the mechanism of visual attentional selection, a wireless optical communication (WOC) Integrated receiver consisting of a vertical double junction photodetector (VDJ-PD) and an attentional selection circuit (ASC) is presented. The whole receiver is fabricated in standard CMOS technology. The VDJ-PD can realize optical signal detection and wavelength identification. The attentional selection circuit activates the competition among the two PN junctions in VDJ-PD to select the signal with significant feature in real-time while discarding non-significant signals. A higher-order signal modulation scheme is applied by the proposed receiver to obtain a high transfer rate, and all data are decoded using color features. The optical testing results show that parallel and real-time communication of the red and blue light can be achieved, and the rise time is 4.47 μs. To measure the anti-interference performance of the receiver, a water tank is used to simulate a harsh communication environment. Results indicate that the real-time and reliable communication requirements in a harsh channel condition are satisfied. When the percentage ratio between the intensity of stray light relative to the light with maximum intensity is 84%, an accurate information decoding can still be achieved. Moreover, the whole processing procedure does not require the participation of ADC, CPU and memory, consequently avoiding the von Neumann bottleneck.

Two-dimensional material has many novel features, which can be used to significantly improve the performance of traditional photonic and electronic devices. Therefore, the development of silicon/two-dimensional material monolithic heterogeneous integrated photodetector has attracted extensive attention worldwide. In this paper, we present a method to enhance the response of photocurrent of silicon-based PN junction photodetectors by using two-dimensional material Van der Waals heterostructures. The MoS _2 /graphene/N+ silicon monolithic heterogeneous integrated Van der Waals heterostructure is used as an NPN-type phototransistor to realize the amplification of photocurrent. When the device is irradiated, the photogenerated electron hole pairs in the semiconductor are separated by the applied electric field. However, graphene has a low density of defect states, and only a few electrons from N+ silicon can be recombined in graphene. Meanwhile, the graphene layer is very thin, and the positively biased graphene/N+ silicon junction and reversed-biased MoS _2 /graphene junction will accelerate the electrons to across the graphene layer and directly into MoS _2 . Using MXenes as the contact electrode of the MoS _2 can eliminate the Fermi level pinning effect. The experimental results show that the photoresponsivity and photocurrent gain increase with the bias voltage, in the range of 0 to 5 V bias voltage. And the optical I _on /I _off ratio increases by nearly 50 times. This research provides new insights for the detection of weak light and design for the photon computing device.

Flexible sensing devices provide a convenient and effective solution for real-time human motion monitoring, but achieving efficient and low-cost assembly of pressure sensors with high performance remains a considerable challenge. Herein, a highly compressible and sensitive flexible foam-shaped piezoresistive pressure sensor was prepared by sequential fixing multiwalled carbon nanotubes and Ti3C2Tx MXene on the skeleton of melamine foam. Due to the porous skeleton of the melamine foam and the extraordinary electrical properties of the conductive fillers, the obtained MWCNTs/Ti3C2Tx MXene @ melamine foam device features high sensitivity of 0.339 kPa−1, a wide working range up to 180 kPa, a desirable response time and excellent cyclic stability. The sensing mechanism of the composite foam device is attributed to the change in the conductive pathways between adjacent porous skeletons. The proposed sensor can be used successfully to monitor human gestures in real-time, such as finger bending and tilting, scrolling the mouse and stretching fingers. By combining with the decision tree algorithm, the sensor can unambiguously classify different Arabic numeral gestures with an average recognition accuracy of 98.9%. Therefore, our fabricated foam-shaped sensor may have great potential as next-generation wearable electronics to accurately acquire and recognize human gesture signals in various practical applications.