Cell culture media used in industrial mammalian cell are complex aqueous solutions that inherently difficult to analyze comprehensively. The analysis of quality and variance is utmost importance efficient manufacturing. We exploring the use rapid "holistic" analytical methods can be for routine screening biotechnology. application fluorescence spectroscopic techniques (Chinese hamster ovary cell-based manufacture) was investigated. have developed robust which identify compositional changes...

A robust and accurate analytical methodology for low-content (<0.1%) quantification in the solid-state using Raman spectroscopy, subsampling, chemometrics was demonstrated a piracetam-proline model. The method involved 5-step process: collection of relatively large number spectra (8410) from each sample by mapping, meticulous data pretreatment to remove spectral artifacts, use 0-100% concentration range partial least-squares (PLS) regression model estimate at pixel, more accurate, reduced...

The application of fluorescence excitation–emission matrix (EEM) spectroscopy to the quantitative analysis complex, aqueous solutions cell culture media components was investigated. These components, yeastolate, phytone, recombinant human insulin, eRDF basal medium, and four different chemically defined (CD) media, are used for formulation feed employed in production proteins using a Chinese Hamster Ovary (CHO) based process. comprehensive (either identification or quality assessment) these...

Yeastolate or yeast extract, which are hydrolysates produced by autolysis of yeast, often employed as a raw material in the media used for industrial mammalian cell culture. The source and quality yeastolate can significantly affect growth production; however, analysis these complex biologically derived materials is not straightforward. best current method, liquid chromatography–mass spectrometry (LC‐MS), time‐consuming requires extensive expertise. This study describes use surface‐enhanced...

Volumetric video streaming offers immersive 3D experiences but faces significant challenges due to high bandwidth requirements and latency issues in transmitting detailed content real time. Traditional methods like point cloud compromise visual quality when zoomed in, neural rendering techniques are too computationally intensive for real-time use. Though mesh-based stands out by preserving surface detail connectivity, offering a more refined representation content, traditional mesh typically...

In this study, a novel chemometric algorithm is presented to facilitate the comparison of relevant chemical components from different herbal samples. This so-called multicomponent spectral correlative chromatography (MSCC) developed detect and decide whether two chromatographic clusters are correlated spectrally with each other. The target cluster first partitioned one spectrochromatogram obtained by hyphenated chromatography. Then, projection operator constructed principal features...

The low-content quantification (LCQ) of active pharmaceutical ingredients or impurities in solid mixtures is important manufacturing and analysis.

Discrete-continuous hybrid action space is a natural setting in many practical problems, such as robot control and game AI. However, most previous Reinforcement Learning (RL) works only demonstrate the success controlling with either discrete or continuous space, while seldom take into account space. One naive way to address RL convert unified homogeneous by discretization continualization, so that conventional algorithms can be applied. this ignores underlying structure of also induces...

A subspace-projection method is developed to construct orthogonal block variable, which originally from some kinds of series topological indices or quantum chemical parameters. With the help canonical correlation analysis, variables were used establish structure-retention index model. The regression only few new obtained by analysis against retention shows significant improvement both in fitting and prediction ability Moreover, quantitative intercorrelation between different can also be...

As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific requirements due their unique attributes. Traditional methods for classification, typically relying on manual examination chemical assays, lack efficiency fail offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based reflectance spectrum machine learning. This method approach facilitates rapid...

In this study, multi-objective genetic algorithms (GAs) are introduced to partial least squares (PLS) model building. This method aims improve the performance and robustness of PLS by removing samples with systematic errors, including outliers, from original data. Multi-objective GA optimizes combination these be removed. Training validation sets were used reduce undesirable effects over-fitting on training set GA. The reduction leads accurate robust models. To clearly visualize factors an...

In cadmium (Cd) contaminated farmland soil, antagonism between elements can be used to control the absorption and accumulation of Cd in crops through external application zinc (Zn) manganese (Mn). Dithiocarbamates (DTCs) are highly effective fungicides commonly farmlands, DTCs rich Zn Mn. We selected three representative (propineb, mancozeb, zineb) for a field experiment Henan province, China. The effects DTC on wheat interaction Zn, Mn, after spraying were studied using different times at...

Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments prevent widespread application of DRL agents real-world scenarios. Although advances have made recently, most prior works assume sufficient online interaction on training environments, which can be costly practical cases. To this end, we focus an offline-training-online-adaptation setting, agent first learns from...

The changes in temperature may arise risks many industries. To solve this problem, the National Meteorological Center and Dalian Commodity Exchange jointly compiled a index which includes 5 cities. Therefore, forecasting time series data those cities is an important subject. Traditionally, we use statistic method ARIMA to predict next lags of series. With advancement computational power computers introduction more advanced machine learning algorithms, paper develops by integrating with...