Effects of tea garden soil on aroma components and related gene expression in tea leaves

In order to explore the effect of soil on the synthesis of aroma components in tea leaves, tea seedlings replanted in tea rhizosphere soil of different ages were used as research materials. Tea seedlings were replanted in soils aged 0, 4, 9, and 30 years, and after one year of growth, 34, 37, 29, and 26 substances were detected in the tea leaves, respectively, using gas chromatography-mass spectrometry (GC-MS). The relative contents of terpenoids and alcohols in the tea leaves dropped from 66.40% to 44.52% and 5.21% to 2.61%, respectively, as the age of the rhizosphere soil increased. Aldehydes, esters, and nitrogen compounds increased from 3.80% to 22.36%, 1.33% to 12.02%, and 3.13% to 19.96%, respectively, as the age of the rhizosphere soil increased. Gene differential expression measured by fluorescence quantitative PCR (qRT-PCR) showed that the number of nerolidol synthetase and linalool synthase genes in tea leaves increased significantly, and the terpineol synthetase, phellandrene synthase, myrcene synthetase, ocimene synthase, limonene synthetase, germacrene synthase, and farnesene synthase genes declined significantly with the increase in soil age. In summary, as the number of years tea had been planted in the soil increased, the soil significantly affected the expression of terpene synthase genes in tea leaves, and then the composition and content of aroma substances in tea leaves changed. The results provide a theoretical basis for the improvement of tea quality

Scalable Mask Annotation for Video Text Spotting

Video text spotting refers to localizing, recognizing, and tracking textual elements such as captions, logos, license plates, signs, and other forms of text within consecutive video frames. However, current datasets available for this task rely on quadrilateral ground truth annotations, which may result in including excessive background content and inaccurate text boundaries. Furthermore, methods trained on these datasets often produce prediction results in the form of quadrilateral boxes, which limits their ability to handle complex scenarios such as dense or curved text. To address these issues, we propose a scalable mask annotation pipeline called SAMText for video text spotting. SAMText leverages the SAM model to generate mask annotations for scene text images or video frames at scale. Using SAMText, we have created a large-scale dataset, SAMText-9M, that contains over 2,400 video clips sourced from existing datasets and over 9 million mask annotations. We have also conducted a thorough statistical analysis of the generated masks and their quality, identifying several research topics that could be further explored based on this dataset. The code and dataset will be released at \url{https://github.com/ViTAE-Transformer/SAMText}.Comment: Technical report. Work in progres

Service differentiation in OFDM-Based IEEE 802.16 networks

IEEE 802.16 network is widely viewed as a strong candidate solution for broadband wireless access systems. Various flexible mechanisms related to QoS provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the standards. Among the mechanisms, bandwidth request scheme can be used to indicate and request bandwidth demands to the base station for different services. Due to the diverse QoS requirements of the applications, service differentiation (SD) is desirable for the bandwidth request scheme. In this paper, we propose several SD approaches. The approaches are based on the contention-based bandwidth request scheme and achieved by the means of assigning different channel access parameters and/or bandwidth allocation priorities to different services. Additionally, we propose effective analytical model to study the impacts of the SD approaches, which can be used for the configuration and optimization of the SD services. It is observed from simulations that the analytical model has high accuracy. Service can be efficiently differentiated with initial backoff window in terms of throughput and channel access delay. Moreover, the service differentiation can be improved if combined with the bandwidth allocation priority approach without adverse impacts on the overall system throughput