338 research outputs found
Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector
We provide a fast approach incorporating the usage of deep learning for
evaluating the effects of photon sensors in an antineutrino detector on the
event reconstruction performance therein. This work is an attempt to harness
the power of deep learning for detector designing and upgrade planning. Using
the Daya Bay detector as a benchmark case and the vertex reconstruction
performance as the objective for the deep neural network, we find that the
photomultiplier tubes (PMTs) have different relative importance to the vertex
reconstruction. More importantly, the vertex position resolutions for the Daya
Bay detector follow approximately a multi-exponential relationship with respect
to the number of PMTs and hence, the coverage. This could also assist in
deciding on the merits of installing additional PMTs for future detector plans.
The approach could easily be used with other objectives in place of vertex
reconstruction
Pore structure characterization of Chang-7 tight sandstone using MICP combined with N2GA techniques and its geological control factors
Understanding the pore networks of unconventional tight reservoirs such as tight sandstones and shales is crucial for extracting oil/gas from such reservoirs. Mercury injection capillary pressure (MICP) and N(2) gas adsorption (N(2)GA) are performed to evaluate pore structure of Chang-7 tight sandstone. Thin section observation, scanning electron microscope, grain size analysis, mineral composition analysis, and porosity measurement are applied to investigate geological control factors of pore structure. Grain size is positively correlated with detrital mineral content and grain size standard deviation while negatively related to clay content. Detrital mineral content and grain size are positively correlated with porosity, pore throat radius and withdrawal efficiency and negatively related to capillary pressure and pore-to-throat size ratio; while interstitial material is negatively correlated with above mentioned factors. Well sorted sediments with high debris usually possess strong compaction resistance to preserve original pores. Although many inter-crystalline pores are produced in clay minerals, this type of pores is not the most important contributor to porosity. Besides this, pore shape determined by N(2)GA hysteresis loop is consistent with SEM observation on clay inter-crystalline pores while BJH pore volume is positively related with clay content, suggesting N(2)GA is suitable for describing clay inter-crystalline pores in tight sandstones
Toward 6G TK Extreme Connectivity: Architecture, Key Technologies and Experiments
Sixth-generation (6G) networks are evolving towards new features and
order-of-magnitude enhancement of systematic performance metrics compared to
the current 5G. In particular, the 6G networks are expected to achieve extreme
connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral
efficiency, and s-scale latency. To this end, an original three-layer 6G
network architecture is designed to realise uniform full-spectrum cell-free
radio access and provide task-centric agile proximate support for diverse
applications. The designed architecture is featured by super edge node (SEN)
which integrates connectivity, computing, AI, data, etc. On this basis, a
technological framework of pervasive multi-level (PML) AI is established in the
centralised unit to enable task-centric near-real-time resource allocation and
network automation. We then introduce a radio access network (RAN) architecture
of full spectrum uniform cell-free networks, which is among the most attractive
RAN candidates for 6G TK extreme connectivity. A few most promising key
technologies, i.e., cell-free massive MIMO, photonics-assisted Terahertz
wireless access and spatiotemporal two-dimensional channel coding are further
discussed. A testbed is implemented and extensive trials are conducted to
evaluate innovative technologies and methodologies. The proposed 6G network
architecture and technological framework demonstrate exciting potentials for
full-service and full-scenario applications.Comment: 15 pages, 12 figure
Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers
Patients at different ages have different rates of cell development and metabolisms. As a result, age should be an essential part of how a disease diagnosis model is trained and optimized. Unfortunately, most of the existing studies have not taken age into account. This study demonstrated that disease diagnosis models could be improved by merely applying individual models for patients of different age groups. Both transcriptomes and methylomes of the TCGA breast cancer dataset (TCGA-BRCA) were utilized for the analysis procedure of feature selection and classification. Our experimental data strongly suggested that disease diagnosis modeling should integrate patient age into the whole experimental design
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