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Comparative transcriptome evaluation reveals differential gene phrase within clean and sterile as well as fertile plastic tree varieties through blossom marijuana difference.
Electron interaction with methane molecule and accurate determination of its elastic cross-section is a demanding task for both experimental and theoretical standpoints and relevant for our better understanding of the processes in Earth's and Solar outer planet atmospheres, the greenhouse effect or in plasma physics applications like vapor deposition, complex plasma-wall interactions and edge plasma regions of Tokamak. Methane can serve as a test molecule for advancing novel electron-molecule collision theories. We present a combined experimental and theoretical study of the elastic electron differential cross-section from methane molecule, as well as integral and momentum transfer cross-sections in the intermediate energy range (50-300 eV). The experimental setup, based on a crossed beam technique, comprising of an electron gun, a single capillary gas needle and detection system with a channeltron is used in the measurements. The absolute values for cross-sections are obtained by relative-flow method, using argon as a reference. Theoretical results are acquired using two approximations simple sum of individual atomic cross-sections and the other with molecular effect taken into the account.Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and under varying lighting conditions, in a consistent manner is still a challenging problem. In this paper, we propose a novel approach for detecting multiple lanes across a large number of frames and under various lighting conditions. Instead of resorting to the conventional approach of processing each frame to detect lanes, we treat the overall problem as a multitarget tracking problem across space and time using the integrated probabilistic data association filter (IPDAF) as our basis filter. We use the intensity of the pixels as an augmented feature to correctly group multiple lane markings using the Hough transform. By representing these extracted lane markings as splines, we then identify a set of control points, which becomes a set of targets to be tracked over a period of time, and thus across a large number of frames. We evaluate our approach on two different fronts, covering both model- and machine-learning-based approaches, using two different datasets, namely the Caltech and TuSimple lane detection datasets, respectively. When tested against model-based approach, the proposed approach can offer as much as 5%, 12%, and 3% improvements on the true positive, false positive, and false positives per frame rates compared to the best alternative approach, respectively. When compared against a state-of-the-art machine learning technique, particularly against a supervised learning method, the proposed approach offers 57%, 31%, 4%, and 9× improvements on the false positive, false negative, accuracy, and frame rates. Furthemore, the proposed approach retains the explainability, or in other words, the cause of actions of the proposed approach can easily be understood or explained.In Japan, the world's most rapidly aging country, urban farming is attracting attention as an infrastructure for health activities. In Tokyo, urban residents generally participate in two types of farming programs allotments and experience farms. The availability of regular interaction among participants distinguishes these two programs. We quantitatively examined the difference in changes in self-reported health status between participants in these two types of urban farming. We obtained retrospective cross-sectional data from questionnaire surveys of 783 urban farming participants and 1254 nonparticipants and analyzed the data using ordinal logistic regressions. As a result, compared with nonparticipants, participants in both types of urban farming reported significantly improved self-rated health (SRH) and mental health (MH). After controlling for changes in their physical activity (PA), although participants in allotments did not report significant improvement in SRH and MH, those in experience farms did, suggesting that their health improvement was not only caused by an increase in PA but also by social interaction among participants. From the perspective of health promotion, public support is needed not only for the municipality's allotments but also for the experience farms operated by the farmers themselves.Maximal safe resection is the standard of care in the neurosurgical treatment of high-grade gliomas. To aid surgeons in the operating room, adjuvant techniques and technologies centered around improving intraoperative visualization of tumor tissue have been developed. In this review, we will discuss the most advanced technologies, specifically fluorescence-guided surgery, intraoperative imaging, neuromonitoring modalities, and microscopic imaging techniques. The goal of these technologies is to improve detection of tumor tissue beyond what conventional microsurgery has permitted. We describe the various advances, the current state of the literature that have tested the utility of the different adjuvants in clinical practice, and future directions for improving intraoperative technologies.As various types of data are generated on the social Internet of things (SIoT), which combine the Internet of things (IoT) and social networks, the relations of IoT devices should be established for necessary data exchange. In this paper, we propose a user recommendation scheme that facilitates data sharing through an analysis of an interaction between an IoT device and a user in the SIoT. An interrelation between a user and an IoT device as well as an interrelation between users exist simultaneously in the SIoT. Hence, the interaction between users must be analyzed to identify the interest keywords, and the interaction between IoT devices and users to determine the user's preference of IoT device. Moreover, the proposed scheme calculates the similarity between users based on the IoT device preference based on IoT device usage frequency and interest keywords, which are identified through an analysis between the user and IoT device and that between users. Orelabrutinib research buy Subsequently, it recommends top-N users who have a high similarity as the users for data sharing.
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