Comparison and evaluation of statistical error models for scRNA-seq

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate. Here, we analyze 58 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation. Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

Seismogravimetric Methodology: Specifics and Applicability to the Siberian Platform

—The paper presents a new seismogravimetric method for estimating static corrections used in processing of seismic data and in construction of time and depth sections. The method efficiency is demonstrated by comparison of the results of industrial and new experimental processing of data for the western slope of the Nepa–Botuoba anteclise.

Design and Fabrication of Large-Size Powersphere for Wireless Energy Transmission via Laser

The powersphere is a device used for maximizing the conversion of light in wireless energy transmission via laser. It is a spherical structure made up of thousands of photovoltaic cells. Due to the large dimensions and existence of many holes in the spherical surface, there are some drawbacks in machining, such as limited movement space of the machines, long cycle, low precision, and high cost. In this context, with a powersphere irradiated by the laser as the model, the principle of powersphere is deduced theoretically. It is proven that the illuminance value at any position on the inner wall of the powersphere is equal, and the calculation formula of this value is derived. Based on this theory and the comparative analysis of processing methods and the results of processing experiments, the structure of the powersphere is designed. The experimental processing of the powersphere is carried out by selecting the welding method. Finally, two hemispherical powersphere frames are processed, which are connected by screws to form a ball frame for the installation of photovoltaic cells. The results show that the improved design and fabricating method can process the powersphere quickly, accurately, and economically. A comparative experiment of powersphere and photovoltaic panel was carried out. The experimental results show that the powersphere has the function of light uniformity and repeated use of laser. So, the designed and processed powersphere is consistent with the theoretical analysis.

Research on Model Slice and Forming Method of Thin-walled Parts

According to the structural characteristics of thin-walled parts, a model slicing method is proposed, and its mathematical process is established. The three-dimensional transient temperature field in the process of synchronous powder feeding laser cladding is studied and verified by numerical simulation method, and the thin-walled parts formed by later experimental processing are processed by the results of numerical simulation. Using the simulation results of temperature field as the basis for optimizing the processing parameters, the forming path of thin-walled parts is programmed and optimized, and the experimental verification shows the reliability of this method.

Optimization of the Bulk Heterojunction of All-smallmolecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches

All-small-molecule organic photovoltaic (OPV) cells based upon the small molecule donor, DRCN5T, and non-fullerene acceptors, ITIC, IT-M, and IT-4F, were optimized using Design of Experiments (DOE) and machine learning (ML) approaches. This combination enables rational sampling of large parameter spaces in a sparse but mathematically deliberate fashion and promises economies of precious resources and time. The work focused upon the optimization of the core layer of the OPV device, the bulk heterojunction (BHJ). Many experimental processing parameters play critical roles in the overall efficiency of a given device and are often correlated, and thus are difficult to parse individually. DOE was applied to the (i) solution concentration of the donor and acceptor ink used for spin-coating, (ii) the donor fraction, and (iii) the temperature and (iv) duration of the annealing of these films. The ML-based approach was then used to derive maps of the PCE landscape for the first and second rounds of optimization to be used as guides to determine the optimal values of experimental processing parameters with respect to device efficiency. This work shows that with little knowledge of a potential combination of components for a given BHJ, a large parameter space can be effectively screened and investigated to rapidly determine its potential for high efficiency OPVs.

Optimization of the Bulk Heterojunction of All-smallmolecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches

All-small-molecule organic photovoltaic (OPV) cells based upon the small molecule donor, DRCN5T, and non-fullerene acceptors, ITIC, IT-M, and IT-4F, were optimized using Design of Experiments (DOE) and machine learning (ML) approaches. This combination enables rational sampling of large parameter spaces in a sparse but mathematically deliberate fashion and promises economies of precious resources and time. The work focused upon the optimization of the core layer of the OPV device, the bulk heterojunction (BHJ). Many experimental processing parameters play critical roles in the overall efficiency of a given device and are often correlated, and thus are difficult to parse individually. DOE was applied to the (i) solution concentration of the donor and acceptor ink used for spin-coating, (ii) the donor fraction, and (iii) the temperature and (iv) duration of the annealing of these films. The ML-based approach was then used to derive maps of the PCE landscape for the first and second rounds of optimization to be used as guides to determine the optimal values of experimental processing parameters with respect to device efficiency. This work shows that with little knowledge of a potential combination of components for a given BHJ, a large parameter space can be effectively screened and investigated to rapidly determine its potential for high efficiency OPVs.