Contactless Medical Equipment AI Big Data Risk Control and Quasi Thinking Iterative Planning

Contactless medical equipment AI big data risk control and quasi thinking iterative planning，The tanh equilibrium state of heavy core clustering based on hierarchical fuzzy clustering system based on differential incremental equilibrium theory is adopted. Successfully control the parameter group of CT / MR machine internal data, big data AI mathematical model risk. The polar graph of high-dimensional heavy core clustering processing data is regular and scientific. Compared with the discrete characteristics of the polar graph of the original data. So as to correctly detect and control the dynamic change process of CT / MR in the whole life cycle. It provides help for the predictive maintenance of early pre inspection and orderly maintenance of the medical system. It also puts forward and designs the big data depth statistics of AI risk control medical equipment, and establishes the standardized model software. Scientifically evaluated the exposure time and heat capacity MHU% of CT tubes, as well as the internal law of MR (nuclear magnetic resonance ), and processed big data twice and three times in heavy nuclear clustering. After optimizing the algorithm, hundreds of thousands of nonlinear random vibrations are carried out in the operation and maintenance database every second, and at least 30 concurrent operations are formed, which greatly improves and shortens the operation time. Finally, after adding micro vibration quasi thinking iterative planning to the uncertain structure of AI operation, we can successfully obtain the scientific and correct results required by high-dimensional information and images. This kind of AI big data risk control improves the intelligent management ability of medical institutions, establishes the software for predictable maintenance of AI big data, which is cross platform and embedded into the web system.

Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions

A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over fifteen growth time points and multispectral (RGB, red-edge and near infrared) bands over twelve time points were compared across 280 unique maize hybrids. Through cross validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP) outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in three other cross validation scenarios. Genome wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5 percent of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51 percent of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, temporal phenomic prediction appeared to work successfully on unrelated individuals unlike genomic prediction.

Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions

A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over fifteen growth time points and multispectral (RGB, red-edge and near infrared) bands over twelve time points were compared across 280 unique maize hybrids. Through cross validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP) outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in three other cross validation scenarios. Genome wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5 percent of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51 percent of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, temporal phenomic prediction appeared to work successfully on unrelated individuals unlike genomic prediction.

Recognizing VSC DC Cable Fault Types Using Bayesian Functional Data Depth

Diagnostics of power and energy systems is obviously an important matter. In this paper we present a contribution of using new methodology for the purpose of signal type recognition (for example, faulty/healthy or different types of faults). Our approach uses Bayesian functional data analysis with data depths distributions to detect differing signals. We present our approach for discrimination of pole-to-pole and pole-to-ground short circuits in VSC DC cables. We provide a detailed case study with Monte Carlo analysis. Our results show potential for applications in diagnostics under uncertainty.

Quantifying Intratumor Heterogeneity by Key Genes Selected Using Concrete Autoencoder

The tumor cell population in a cancer tissue has distinct molecular characteristics and exhibits different phenotypes, thus, resulting in different subpopulations. This phenomenon is known as Intratumor Heterogeneity (ITH), which is a major contributor in drug resistance, poor prognosis, etc. Therefore, quantifying the levels of ITH in cancer patients is essential and there are many algorithms which do so in different ways, using different types of omics data. DEPTH (Deviating gene Expression Profiling Tumor Heterogeneity) is the latest algorithm that uses transcriptomic data to evaluate the ITH score. It shows promising performance, has strong similarity with six other algorithms, and has advantage over two algorithms that uses same type of data (tITH, sITH). However, it has a major drawback that it uses expression values of all the genes (~20K genes) in quantifying ITH levels. We hypothesize that a subset of key genes is sufficient to quantify the ITH level for a tumor. To prove our hypothesis, we developed a deep learning-based computational framework using unsupervised Concrete Autoencoder (CAE) to select a set of cancer-specific key genes that can be used to evaluate the ITH score. For experiment, we used gene expression profile data of tumor cohorts of breast, kidney and lung cancer from TCGA repository. We selected three sets of key genes, each set related to breast, kidney, and lung tumor cohorts, using multi-run CAE. For the three cancers stated and three molecular subtypes of lung cancer, we calculated ITH level using all genes and key genes selected by CAE and performed a side-by-side comparison. It was found that similar conclusions can be reached for survival and prognostic outcomes based on ITH scores derived from all genes and the sets of key genes. Additionally, for subtypes of lung cancer, the comparative distribution of ITH scores derived from all genes and key genes remains similar. Based on these observations, it can be stated that, a subset of key genes, instead of all genes, is sufficient for ITH quantification. Our results also showed that many of the key genes are prognostically significant, which can be used as possible therapeutic targets.

Modeling Seasonal Distribution of Irrawaddy Dolphins (Orcaella brevirostris) in a Transnational Important Marine Mammal Area

Fishing activities continue to decimate populations of marine mammals, fish, and their habitats in the coastal waters of the Kep Archipelago, a cluster of tropical islands on the Cambodia-Vietnam border. In 2019, the area was recognized as an Important Marine Mammal Area, largely owing to the significant presence of Irrawaddy dolphins (Orcaella brevirostris). Understanding habitat preferences and distribution aids in the identification of areas to target for monitoring and conservation, which is particularly challenging in data-limited nations of Southeast Asia. Here, we test the hypothesis that accurate seasonal habitat models, relying on environmental data and species occurrences alone, can be used to describe the ecological processes governing abundance for the resident dolphin population of the Kep Archipelago, Cambodia. Leveraging two years of species and oceanographic data—depth, slope, distance to shore and rivers, sea surface temperature, and chlorophyll-a concentration—we built temporally stratified models to estimate distribution and infer seasonal habitat importance. Overall, Irrawaddy dolphins of Kep displayed habitat preferences similar to other populations, and were predominately encountered in three situations: (1) water depths ranging from 3.0 to 5.3 m, (2) surface water temperatures of 27–32°C, and (3) in close proximity to offshore islands (< 7.5 km). With respect to seasonality, statistical tests detected significant differences for all environment variables considered except seafloor slope. Four predictor sets, each with a unique combination of variables, were used to map seasonal variation in dolphin habitat suitability. Models with highest variable importance scores were water depth, pre- and during monsoon season (61–62%), and sea surface temperature, post-monsoon (71%), which suggests that greater freshwater flow during the wet season may alter primary productivity and dolphin prey abundance. Importantly, findings show the majority of areas with highest habitat suitability are not currently surveyed for dolphins and located outside Kep’s Marine Fisheries Management Area. This research confirms the need to expand monitoring to new areas where high-impact fisheries and other human activities operate. Baseline knowledge on dolphin distribution can guide regional conservation efforts by taking into account the seasonality of the species and support the design of tailored management strategies that address transboundary threats to an Important Marine Mammal Area.

A continuum limit for the PageRank algorithm

Semi-supervised and unsupervised machine learning methods often rely on graphs to model data, prompting research on how theoretical properties of operators on graphs are leveraged in learning problems. While most of the existing literature focuses on undirected graphs, directed graphs are very important in practice, giving models for physical, biological or transportation networks, among many other applications. In this paper, we propose a new framework for rigorously studying continuum limits of learning algorithms on directed graphs. We use the new framework to study the PageRank algorithm and show how it can be interpreted as a numerical scheme on a directed graph involving a type of normalised graph Laplacian. We show that the corresponding continuum limit problem, which is taken as the number of webpages grows to infinity, is a second-order, possibly degenerate, elliptic equation that contains reaction, diffusion and advection terms. We prove that the numerical scheme is consistent and stable and compute explicit rates of convergence of the discrete solution to the solution of the continuum limit partial differential equation. We give applications to proving stability and asymptotic regularity of the PageRank vector. Finally, we illustrate our results with numerical experiments and explore an application to data depth.

Robust multivariate estimation based on statistical depth filters

In the classical contamination models, such as the gross-error (Huber and Tukey contamination model or case-wise contamination), observations are considered as the units to be identified as outliers or not. This model is very useful when the number of considered variables is moderately small. Alqallaf et al. (Ann Stat 37(1):311–331, 2009) show the limits of this approach for a larger number of variables and introduced the independent contamination model (cell-wise contamination) where now the cells are the units to be identified as outliers or not. One approach to deal, at the same time, with both type of contamination is filter out the contaminated cells from the data set and then apply a robust procedure able to handle case-wise outliers and missing values. Here, we develop a general framework to build filters in any dimension based on statistical data depth functions. We show that previous approaches, e.g., Agostinelli et al. (TEST 24(3):441–461, 2015b) and Leung et al. (Comput Stat Data Anal 111:59–76, 2017), are special cases. We illustrate our method by using the half-space depth.