"Sweet space" strategy for solvent system selection in countercurrent chromatography: A case study on separation of polyunsaturated fatty acids from borage oil.

This study presents a comprehensive strategy for the selection and optimization of solvent systems in countercurrent chromatography (CCC) for the effective separation of compounds. With a focus on traditional organic solvent systems, the research introduces a "sweet space" strategy that merges intuitive understanding with mathematical accuracy, addressing the significant challenges in solvent system selection, a critical bottleneck in the widespread application of CCC. By employing a combination of volume ratios and graphical representations, including both regular and trirectangular tetrahedron models, the proposed approach facilitates a more inclusive and user-friendly strategy for solvent system selection. This study demonstrates the potential of the proposed strategy through the successful separation of gamma-linolenic acid, oleic acid, and linoleic acid from borage oil, highlighting the strategy's effectiveness and practical applicability in CCC separations.

A numerical study on capillary-evaporation behavior of porous wick in electronic cigarettes.

A mathematical model based on heat and mass transfer processes in the porous wick of electronic cigarettes was established to describe the atomization of e-liquids according to max liquid temperature, vaporization rate and thermal efficiency in a single puff. Dominant capillary-evaporation effects were defined in the model to account for the effects of electrical power, e-liquid composition and porosity of the wick material on atomization and energy transmission processes. Liquid temperature, vaporization rate, and thermal efficiency were predicted using the mathematical model in 64 groups, varying with electrical power, e-liquid composition and wick porosity. Experimental studies were carried out using a scaled-model test bench to validate the model's prediction. A higher PG/VG ratio in the e-liquid promoted energy transfer for vaporization, and the e-liquid temperature was comparatively reduced at a relatively high power, which was helpful to avoid atomizer overheating. Compared with the other factors, wick porosity affected the thermal efficiency more significantly. The vaporization rate increased with a higher wick porosity in a certain range. The modelling results suggested that a greater wick porosity and a higher PG ratio in e-liquids helped to improve the overall thermal efficiency.

Vaporization characteristics and aerosol optical properties of electronic cigarettes.

The aerosols generated from electronic cigarettes have a significant impact on the human respiratory system. Understanding the vaporization characteristics and aerosol optical properties of electronic cigarettes is important for assessing human exposure to aerosols. An experimental platform was designed and built to simulate the atomization process of electronic cigarette and detect the laser transmissivity of aerosols. The optical properties of single particles and polydispersed particle system for aerosols in the visible wavelength ranges of 400-780 nm were analyzed based on Mie theory. The results show that a higher heating power supplied by coil results in a larger average vaporization rate of e-liquid. Meanwhile, the steady-state transmissivity of the laser beam for aerosols reduces as the heating power increases. Under the same heating power and puffing topography, the total particulate mass (TPM) of aerosols generated by the e-liquid composed of higher vegetable glycerin (VG) content decreases. The scattering efficiency factor of aerosol particle of electronic cigarette increases with an increase in particle size. The volume scattering coefficients of a polydispersed particle system of aerosols decrease as the incident visible wavelengths increase. A higher VG content in e-liquid results in decreased TPM and particle number concentration of aerosols and increased the volume scattering coefficient in the visible wavelength range. It can explain an interesting phenomenon that a lower TPM and a better visual effect brought by the aerosols generated by the e-liquid with a higher VG content could be observed concurrently. The mass indexes (e.g., TPM, average vaporization rate, average mass concentration) and optical indexes (e.g., volume scattering coefficient, laser transmissivity) are suggested to be used for the comprehensive evaluation of relative amounts of aerosols. The results have potential significances for the objective and quantitative assessments of aerosols generated from electronic cigarettes.

Re-examination of Rule-Based Methods in Deidentification of Electronic Health Records: Algorithm Development and Validation.

BACKGROUND: Deidentification of clinical records is a critical step before their publication. This is usually treated as a type of sequence labeling task, and ensemble learning is one of the best performing solutions. Under the framework of multi-learner ensemble, the significance of a candidate rule-based learner remains an open issue. OBJECTIVE: The aim of this study is to investigate whether a rule-based learner is useful in a hybrid deidentification system and offer suggestions on how to build and integrate a rule-based learner. METHODS: We chose a data-driven rule-learner named transformation-based error-driven learning (TBED) and integrated it into the best performing hybrid system in this task. RESULTS: On the popular Informatics for Integrating Biology and the Bedside (i2b2) deidentification data set, experiments showed that TBED can offer high performance with its generated rules, and integrating the rule-based model into an ensemble framework, which reached an F1 score of 96.76%, achieved the best performance reported in the community. CONCLUSIONS: We proved the rule-based method offers an effective contribution to the current ensemble learning approach for the deidentification of clinical records. Such a rule system could be automatically learned by TBED, avoiding the high cost and low reliability of manual rule composition. In particular, we boosted the ensemble model with rules to create the best performance of the deidentification of clinical records.