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The intelligent regulation and control strategies for rice and wheat combine harvesters' operation are lacking and the rule of parameter matching is fuzzy in China, around these issues, the dynamic correlation control law among the parameters of rice and wheat, cleaning operation parameters of combine harvesters, the cleaning loss rate and impurity rate are studied, and so on. The intelligent control model for the rice and wheat combine harvester is established based on case-based reasoning. According to different rice and wheat varieties, water content and other rice and wheat properties, the control scheme of cleaning fan speed, air distributor plate angle and upper sieve opening with low cleaning impurity rate and cleaning loss rate is provided. Through the development of web-based cleaning intelligent control expert system and conduct experimental evaluation, the feasibility and effectiveness of the case-based reasoning method in the intelligent control filed of rice and wheat combine harvesters is verified.

Due to the transcritical nature, some of the test methods used in the conventional subcritical refrigeration cycle, such as the liquid refrigerant flow metering method and the water-cooled condenser metering method, are no longer appropriate for evaluating the transcritical carbon dioxide (CO2) compressor performances. This paper introduces a performance test facility taken into account both the transcritical and the subcritical operating conditions for CO2 compressor, including design requirements and parameter control, the main technical challenges and solutions in the laboratory testing facility development. The compressor refrigerating capacity major test results with this setup are compared to the auxiliary test and found less than 4% discrepancies. In addition, this test facility has high test repeatability. Due to these advantages, it is being used to provide experimental data and technical support for the new China National Standard (GB) "Positive Displacement CO2 Refrigerant Compressor Unit".

Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.