The biodiversity and stability of alpine meadow plant communities in relation to altitude gradient in three headwater resource regions

Kobresia pygmaea meadow community diversities in relation to altitude gradients (4200, 4300, 4400, 4450) on free grazing grassland was studied in the range of Chenduo county, Yushu prefecture, Qinghai province. Species richness and diversity index of vegetations in the four altitudes were comparatively analyzed. The results showed that the shape of species richness responsive curves to altitude gradient is “Bell-shape”. There were the same 11 common species in the four communities. The relative abundance of K. pygmaea decreased along increasing altitude. Moreover, the fuzzy membership functions were used to calculate the degree of stability, showing medium altitude > high altitude > low altitude, which suggested that grass land vegetation in low altitude of the sampling site had lower diversity, and the grade of species vulnerability risks may be decided with the help of the degree of stability.Key words: Alpine meadow, Yangtze, Yellow and Yalu Tsangpo river source region, altitude gradient, species diversity, membership functions

Application of high-resolution remote sensing technology for the iron ore deposits of the West Kunlun Mountains in China

This study focuses on the iron ore of Taxkorgan and Heiqia in the West Kunlun mountains as a case study, for the application of WorldView−2 and IKONOS remote sensing images as major data sources in the fabrication of a standard image map and in the adoption of image enhancement methods to extract information on the ore-controlling factors and mineralization, to interpret remote sensing for the mineral resources in these areas. ASTER, WorldView−2, and IKONOS data were applied for the extraction of alteration anomaly information. With an appropriate amount of field sampling and verification tests, this was used to establish a remote sensing geology prospecting model, that would provide the basis for future remote sensing of metallogenic belts in  West Kunlun in the hope of discovering similar minerals. Survey results showed four additional iron ore mineralization belts could be delineated in the Taxkorgan area. A comparative analysis conducted for part of the field confirmation and the known mineral deposits indicated good reliability. In Heiqia, a siderite-haematite mineralization zone was observed with copperlead- zinc formation, 60-km in length and 200–500 m wide, which includes several mineralized bodies. The ore bodies, appear as stratoid, lenticular, or podiform morphologies and were located in the transition site from clastic to carbonate rocks of the D segment in the Wenquangou Group. The ore bodies generally occur within 40°–50° strike and 68°–81° dip, in accordance to the strata. The length of the single body varies from several hundred metres to more than 9500 m. Its exposed thickness on the surface ranges from 2–50 m, and the general thickness was approximately 15 m. The surface ore minerals were mainly haematite and limonite, with a small amount of siderite. Therefore, high-resolution remote sensing technology is suitable for iron ore geological and mineral remote sensing surveying. It is advantageous in both high-ground resolution of optical characteristics and a certain spectral recognition capability, and is effective not only for information extraction from a large area, but also for recognition of local mineralization outcrops. Therefore, high-resolution remote sensing technology is valuable for popularization.

Application of an airborne hyper-spectral survey system CASI/SASI in the gold-silver-lead-zinc ore district of Huaniushan, Gansu, China

The airborne hyper-spectral survey system CASI/SASI, which has an integrated system for gathering both image an spectral data, is at the cutting edge developments in the remote-sensing field. It can be used to directly identify surface objects based on diagnostic spectral characteristics. In this paper, the CASI/SASI were used in the Huaniushan gold-silver-lead-zinc ore district–Gansu to produce a lithologic map, identify altered minerals, and map the mineralized-alteration zones. Radiometric correction, radiometric calibration, atmospheric correction (spectral reconstruction), and geometric corrections were carried out in ENVI to pre-process the measured data. A FieldSpec ® Pro FR portable spectrometer was used to obtain the spectral signatures of all types of rock samples, ore deposits, and mineralized-alteration zones. We extracted and analyzed the spectral characteristics of typical alteration minerals. On the basis of hyper-spectral data, ground-spectral data processing, and comparative analysis of the measured image spectrum, we used the spectral-angle-mapping (SAM) and mixture-tuned matchedfiltering (MTMF) methods to perform hyperspectral-alteration mineral  mapping of wall rock and mineralized-alteration-zone hyperspectral identification. Hyperspectral- remote- sensing geological- classification maps were produced as well as distribution maps of all kinds of alteration minerals and mineralized-alteration zones. Based on geological comprehensive analysis and field investigations, the range of mineral alteration was proven to be the same as shown by the remote-sensing imagery. Indications are that airborne hyperspectral- remote-sensing -image CASI/SASI offer good application results and show a promising potential as a tool in geological investigations. The results will provide the basis for hyperspectral remote-sensing prospecting in the same or similar unexplored areas.

Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network

Automatic modulation recognition (AMR) plays an essential role in modern communication systems. In recent years, various modulation recognition algorithms based on deep learning have been emerging, but the problem of low recognition accuracy has not been solved well. To solve this problem, based on the existing MCLDNN algorithm, in this paper, we proposed an improved spatiotemporal multi-channel network (IQ-related features Multi-channel Convolutional Bi-LSTM with Gaussian noise, IQGMCL). Firstly, dividing the input IQ signals into three channels, time sequence feature extraction is carried out for route I, route Q, and route IQ, respectively. For route IQ, convolution kernel (2,1) is first used to extract relevant features. Two layers of the small convolution kernel (1,3) are used to extract time sequence features further, and the three channels are used to extract features further. Then, a two-layer short-length memory network is used to extract features from time and space more effectively. Through comparison experiments, Bi-LSTM is introduced to replace one layer of LSTM, and a fully connected layer is removed to prevent overfitting. Finally, multiplicative Gaussian noise is introduced to naturally corrode the feature parameters, further improving the robustness and accuracy of the model. Experiments are carried out on three public datasets RML2016.10a, RML2016.10b, and RML2016.04C. The experiments show that the IQGMCL network has higher recognition accuracies on all datasets, especially on the RML2016.10a dataset. When the SNR is 4 dB, the recognition accuracy reaches 93.52%. When the SNR is greater than 0 dB, the average recognition accuracy reaches 92.3%, 1.31%, and 1.2% higher than the original MCLDNN network, respectively

Effective and Efficient Steiner Maximum Path-Connected Subgraph Search in Large Social Internet of Things

Social Internet of Things (SIoT) is extended to integrate social networks in the Internet of Things (IoT). SIoT enriches IoT, and thus resource (or service) discovery and consolidation in SIoT becomes an important and challenging problem. In this paper, we represent the SIoT as the heterogeneous information networks (HINs) with multi-typed entities and relations, and resolve this problem from the perspective of cohesive subgraph search. Specifically, given a query node $q$ , we discover a cohesive subgraph containing $q$ from a HIN, where all nodes are of the same type as $q$ and have dense relationships. Yet existing solutions cannot be applied to various HINs, and the result subgraph is not accurate and close enough due to ignoring the connectivity among nodes within the subgraph. To this end, 1) we extend the connectivity with novel meta-path-based edge-disjoint paths to HINs, and propose the $k$ -path connected component ( $k$ -PCC) to measure the cohesiveness of subgraph in HINs; 2) we model the densest connected subgraph containing $q$ as the $k$ -PCC with the maximum connectivity including $q$ , called the Steiner Maximum Path-Connected Subgraph (SMPCS); 3) we develop efficient algorithms based on an index tree for searching the SMPCS. Extensive experiments on four real HINs are conducted to demonstrate the effectiveness and efficiency of our proposed approaches

TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN

Reconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal Reconstruction GAN). Reconstructing IQ sequences using a CNN and RNN presents challenges in capturing the correlations between two signals. To tackle this issue, the VIT (vision in transformer) approach was introduced into the discriminator network. The IQ signal is treated as a single-channel, two-dimensional image, divided into blocks of 2 × 2 pixels, with absolute position embedding added. The generator network maps the input noise to the same dimension as the IQ signal dimension × embedding vector dimension and adds two identical position embedding data points to the network learning. In the transformer network, prob sparse attention is employed as a replacement for multi-head attention to tackle the issue of high computational complexity. Finally, combined with the MLP structure, the transformer-based generator and discriminator are designed. The signal similarity evaluation index was constructed, and experiments showed that the reconstructed signal under QPSK and BPSK modulation had good reconstruction quality in the time-domain waveform, constellation diagram, and spectrogram at a high SNR. Compared with other reconstruction algorithms, the proposed algorithm improved the quality of the reconstructed signal while reducing the complexity of the algorithm

Application of Improved YOLOv5 in Aerial Photographing Infrared Vehicle Detection

Aiming to solve the problems of false detection, missed detection, and insufficient detection ability of infrared vehicle images, an infrared vehicle target detection algorithm based on the improved YOLOv5 is proposed. The article analyzes the image characteristics of infrared vehicle detection, and then discusses the improved YOLOv5 algorithm in detail. The algorithm uses the DenseBlock module to increase the ability of shallow feature extraction. The Ghost convolution layer is used to replace the ordinary convolution layer, which increases the redundant feature graph based on linear calculation, improves the network feature extraction ability, and increases the amount of information from the original image. The detection accuracy of the whole network is enhanced by adding a channel attention mechanism and modifying loss function. Finally, the improved performance and comprehensive improved performance of each module are compared with common algorithms. Experimental results show that the detection accuracy of the DenseBlock and EIOU module added alone are improved by 2.5% and 3% compared with the original YOLOv5 algorithm, respectively, and the addition of the Ghost convolution module and SE module alone does not increase significantly. By using the EIOU module as the loss function, the three modules of DenseBlock, Ghost convolution and SE Layer are added to the YOLOv5 algorithm for comparative analysis, of which the combination of DenseBlock and Ghost convolution has the best effect. When adding three modules at the same time, the mAP fluctuation is smaller, which can reach 73.1%, which is 4.6% higher than the original YOLOv5 algorithm

Duplicated adrenal veins in primary aldosteronism misdiagnosed with ectopic aldosteronoma due to apparent bilateral aldosterone suppression

Background Primary aldosteronism (PA) is considered the number one aetiology for secondary hypertension. Apart from confirmatory tests and localisation of PA determined by computed tomography (CT), adrenal venous sampling (AVS) is used to define whether aldosterone hypersecretion occurs inside one or both adrenal glands. However, even correctly-performed AVS may lead to undiagnostic results such as apparent bilateral adrenal suppression (apparent bilateral aldosterone suppression), in which the adrenal aldosterone-to-cortisol ratios (AC ratios) are decreased bilaterally compared to the peripheral blood sample, with several causes contributing to it. Case description Here, we describe the case of a 48-year-old man who was referred to our department for further investigation with a history of refractory hypertension, hypokalaemia, and aortic dissection. His hypertension and hypokalaemia were initially attributed to ectopic aldosteronoma due to his adrenal CT scan and AVS results. However, the correct diagnosis of an adenoma with duplicated right adrenal veins (duplicated adrenal veins) due to apparent bilateral aldosterone suppression was confirmed during surgery. Conclusion AVS is the gold standard accepted for PA subtyping, but sometimes when apparent bilateral aldosterone suppression is present, it can give ambiguous results. Duplicated right adrenal veins, may impact results, thus, AVS may not accurately provide evidence of unilateral hypersecretion for all PA patients. Repeat AVS or adrenal surgery can provide worthwhile diagnostic conclusions

Extreme zircon O isotopic compositions from 3.8 to 2.5Ga magmatic rocks from the Anshan area, North China Craton

Most zircon from Archean (3.8-2.5Ga) trondhjemitic rocks, meta-gabbro, meta-diorite and monzogranite from the Anshan area, North China Craton, has δ18O values in the range of 4.6-7.5‰, but some has extreme compositions (0.02-11.0‰, with one value a