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This paper presents implementation of locally weighted projection regression (LWPR) network method for concurrency control while developing dial of a fork using Autodesk inventor 2008. The LWPR learns the objects and the type of transactions to be done based on which node in the output layer of the network exceeds a threshold value. Learning stops once all the objects are exposed to LWPR. During testing performance, metrics are analyzed. We have attempted to use LWPR for storing lock information when multi users are working on computer Aided Design (CAD). The memory requirements of the proposed method are minimal in processing locks during transaction.
Road accident is a common problem that claims a considerable number of lives every year. According to a study conducted in India, about 77% of road accidents in the country are due to the driver not following the traffic rules and regulations. In the proposed model, we consider the speeding problem and rectify it with the help of a combined effect of Global positioning Systems via commercial navigation maps and automatic Speed control of electric vehicles. The location of the automobile is tracked periodically in real-time. This is achieved with the help of the commercially available online navigation maps. We have used the Google Maps here. The data from the navigation maps are intercepted by a microcontroller. We use the Node MCU ESP-12 in this presented model, owing to its simplicity in usage. The microcontroller accesses the required data from Google Maps by making use of its own Java library. The data thus obtained is used by the Node MCU ESP-12 to know the exact location of the vehicle and the speed limit of that place. The speed limit data can be gathered with respect to both roads and road segments. The microcontroller is also interfaced with the Motor Driver Controller Module (L298n). The Driver Module controls the speed of the DC Motors in the electric vehicle influenced by the commands from the microcontroller. The Node MCU ESP-12 sends out the commands to change the speed of the DC Motors based on the speed limit values previously acquired from the Navigation map. In this way, the suggested model makes sure that the rules for the speed limit are followed in every road segment and thus reducing the possibility of accidents and ensures safer automobile systems.
Abstract GTEx_Pro is a Nextflow-based pipeline for preprocessing GTEx v8 transcriptomic data, enhancing multi-tissue comparability. It integrates TMM + CPM normalization and SVA batch effect correction to improve biological signal recovery while reducing systematic variations across 54 GTEx tissues. Designed for scalability and reproducibility, GTEx_Pro facilitates accurate multi-tissue transcriptomic analysis and a similar framework can be adapted to other large-scale transcriptome datasets.
In RNA-seq workflows, hidden batch effects, technical noise, or unknown sources of variation can silently degrade downstream analyses, lead…