Minghu Zhang

Measuring river surface velocity is crucial for hydrological monitoring. However, traditional methods exhibit significant limitations under extreme weather conditions and emergency scenarios. In this study, we propose OF-PhyNet, an innovative hybrid neural network architecture designed as an image-based velocimetry method to improve the accuracy of river surface velocity estimation. By integrating an optical flow algorithm with a physics-informed neural network, OF-PhyNet achieves deep coupling of motion feature extraction with fluid dynamics constraints. To address the challenges posed by uneven particle distribution and illumination variations in natural rivers, we introduce a feature extraction strategy based on K-means clustering and morphological operations, which enables effective flow field segmentation and reliable identification of particle regions. Experimental validation conducted in the Heihe River Basin, using Stalker Pro II surface velocity radar as a reference, demonstrates that OF-PhyNet achieves a mean absolute error of 0.054 m/s and a relative error of 7.22% across 12 cross-sectional measurement points, substantially outperforming PIVLab with 11.65% and the Horn–Schunck algorithm with 25.84% relative error. As a UAV-based image velocimetry approach, OF-PhyNet offers exceptional mobility for rapid flood monitoring and enables robust reconstruction of flow fields in particle-sparse regions, delivering precise noncontact river velocity measurements beyond the reach of traditional methods.

Ecosystem monitoring involves fully understanding the complex interactions of ecosystem progress and providing massive scientific data to answer research questions. Fetching data in extreme areas without a public network largely relies on human efforts. Along with the ongoing advances of the Internet of Things (IoT), the Internet of Remote Things (IoRT), as a new paradigm, is being pursued. In this paper, we improve the previous drone-enabled IoRT system and innovatively integrate the system with ecological monitoring devices for wildlife, phenology and environmental monitoring, and the monitored data are remotely retrieved by drones. The experimental results indicate that the data transmission rate between the drone relay and the terrestrial terminal reaches up to 10-15 MB/s. Experiments further demonstrate that in terms of wildlife monitoring, the required time to retrieve an image cached on a terrestrial terminal is approximately 0.35 s, the time required to transfer a set of phenology data is 0.29 and 0.34 s, and in regard to environmental monitoring data, the average time consumption reaches approximately 0.0302 s. Moreover, we introduce a signal strength-based priority strategy that can reduce the time consumption of data transmission between the drone relay and the terrestrial terminal. The demonstrated applications reveal that monitoring devices deployed in remote extreme regions can be connected in the IoRT network, and the way to retrieve data from these deployed devices is being revolutionized by the drone-enabled IoRT network.

In this article, we employed a satellite-enabled Internet of Remote Things (IoRT) network as a promising solution to retrieve data in the most remote areas of interest, where public networks are absent. This article presents a system network based on the satellite-enabled IoRT, a new paradigm that defines a network where each environmental monitoring device can autonomously establish a network with a remote data center. The Xingyun satellite constellation was employed for data retrieval on the Tibetan Plateau (TP). The monitoring system was mainly composed of a ground Internet of Things (IoT) terminal that was built with satellite transceivers, environmental monitoring devices, and system software. We deployed five of these newly developed terminals in harsh areas to monitor environmental variables, and accordingly, air temperature and relative humidity, precipitation, snow depth, land surface temperature, tree stemflow rate, and photosynthetically active radiation were retrieved with the satellite-enabled IoRT network. Field experiments were conducted to evaluate the performance of the proposed system network, and the results indicated that the average time delay with and without the packet creation mode reached 32 and 32.7 s, respectively, and the average packet loss rate with and without the packet creation mode reached 5.63% and 4.48%, respectively. The successful implementation of the satellite-enabled IoRT network for the rapid retrieval of monitoring data in remote glacier, forestland, and canyon areas at very high altitudes on the TP provides an entirely new and revolutionary data retrieval means for backhauling data from remote areas of interest.

Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F₂ score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.

Advancements in the Internet of Things (IoT) have led to the revolutionary potential to achieve intelligent environmental monitoring. However, extreme conditions across hard-to-reach areas, where public ground networks do not provide sufficient coverage, have resulted in the difficult backhaul of monitoring data from remote areas of interest. Because of the urgent demand for low-cost data collection in such areas, this paper proposes a novel drone-enabled IoT relay system to provide high-speed data collection to support remote environmental monitoring. The high-speed ability of 5 GHz communication technology was exploited to reduce the time required for data transmission between ground monitoring devices and the drone. Meanwhile, long-range (LoRa) technology, as a low-power and long-distance wireless communication technology, is adopted as a wake-up strategy for waking up the high-power 5 GHz module. Based on 5 GHz and LoRa technology, a drone-based on-board relay and ground intelligent terminal are designed. An application is used to demonstrate the feasibility of the designed system. Numerous real-world experiments validate the effectiveness of the designed drone-enabled IoT relay system and show its capability for high-speed data collection. The field experiments demonstrate that the system collects cached data with a stable 3.5 MB/s throughput at an altitude of 140 m. The breakthroughs in the drone-enabled IoT relay facilitate intelligent environmental monitoring in remote areas without public networks and provide a new perspective on data backhaul over areas of interest.

Diploid wild einkorn wheat, Triticum boeoticum Boiss (A^bA^b, 2n = 2x = 14), is a wheat-related species with a blue aleurone layer. In this study, six blue-grained wheat lines were developed from F₈ progeny of crosses between common wheat and T. boeoticum. The chromosome constitutions of these lines were characterized by fluorescence in situ hybridization (FISH) using the oligonucleotide probes Oligo-pTa535-1, Oligo-pSc119.2-1, Oligo-pTa71-2, and (AAC)₇. Multicolor FISH using Oligo-pTa535-1, Oligo-pSc119.2-1, and Oligo-pTa71-2 identified all 42 common wheat chromosomes, while Oligo-pTa535-1 and (AAC)₇ discriminated the 14 chromosomes of T. boeoticum. FISH revealed that all six blue-grained lines were wheat-T. boeoticum 4A^b (4B) disomic substitution lines. The substitution lines were validated by genotyping using the wheat 55 K single nucleotide polymorphism (SNP) array containing 53,063 markers. These 4A^b (4B) substitution lines represent novel germplasm for blue-grained wheat breeding. The FISH probes and SNP markers used here can be applied in the development of blue-grained wheat-Triticum boeoticum translocation lines.

Field strength measurements at 900 MHz were conducted with the field strength meter in suburban Beijing, China. Observed field strength values were converted into path losses and were compared with different path loss prediction methods like Hata, Cost 231 Walfisch-Ikegami. The regression fitting results showed that beyond a certain distance the Hata model can provide a more accurate result and within that distance the Cost 231 model's value becomes closer to the measurements.