The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against cancer.These neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-relat...The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against cancer.These neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related fatalities.The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments.Addressing this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of accuracy.The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model.The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST cases.The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN.展开更多
Wireless Sensor Networks (WSNs) are universally being used and deployed to monitor the surrounding physical environments and detail events of interest. In wireless Sensor Networks energy is one of the primary issues a...Wireless Sensor Networks (WSNs) are universally being used and deployed to monitor the surrounding physical environments and detail events of interest. In wireless Sensor Networks energy is one of the primary issues and requires energy conservation of the sensor nodes, so that network lifetime can be maximized. To minimize the energy loss in dense WSNs a Color Based Topology Control (CBTC) algorithm is introduced and implemented in Visual Studio 6.0. The results are compared with Traditional dense WSNs. In the evaluation process it was observed that the numbers of CPU ticks required in traditional WSNs are much more than that’s of CBTC Algorithm, both in Normal and Random deployments. So by using CBTC, delay in network can be minimized. Using CBTC algorithm, the energy conservation and removal of coverage holes was also achieved in the present study.展开更多
基金Science and Technology Innovation 2030-“New Generation Artificial Intelligence”Major Project,Grant/Award Number:2022ZD0116305Anhui Province Natural Science Funds for Distinguished Young Scholar,Grant/Award Number:2308085J02+3 种基金National Natural Science Foundation of China,Grant/Award Numbers:U2013601,U20A20225,42107112,32070670Innovation Leading Talent of Anhui Province TeZhi planCAAI-Huawei Mind Spore Open Fund,Grant/Award Number:CAAIXSJLJJ-2022-011ANatural Science Foundation of Hefei,China,Grant/Award Number:202321。
文摘The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against cancer.These neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related fatalities.The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments.Addressing this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of accuracy.The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model.The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST cases.The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN.
文摘Wireless Sensor Networks (WSNs) are universally being used and deployed to monitor the surrounding physical environments and detail events of interest. In wireless Sensor Networks energy is one of the primary issues and requires energy conservation of the sensor nodes, so that network lifetime can be maximized. To minimize the energy loss in dense WSNs a Color Based Topology Control (CBTC) algorithm is introduced and implemented in Visual Studio 6.0. The results are compared with Traditional dense WSNs. In the evaluation process it was observed that the numbers of CPU ticks required in traditional WSNs are much more than that’s of CBTC Algorithm, both in Normal and Random deployments. So by using CBTC, delay in network can be minimized. Using CBTC algorithm, the energy conservation and removal of coverage holes was also achieved in the present study.
