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Heated tobacco products (HTPs) are a recent category of tobacco products, with their relative safety compared to cigarette smoking and potential to help smokers to quit being two reasons why regulators may consider their market approval. Designed to heat tobacco rather than to burn in order to produce aerosol, different heating techniques are applied to commercial HTPs, which may result in differing aerosol formation. Therefore, each product requires separate assessment. This work focuses on a closed-end HTP (coded as HTP-A), which is electrically heated and designed to allow puffing air flow to bypass its tobacco section, resulting in reduced oxygen concentration within the tobacco section during heating and aerosol forming. To provide a preliminary aerosol chemistry and in vitro toxicological screening, this study assessed HTP-A against a commercial electrically heated HTP (IQOSTM, coded as HTP-B) and a 3R4F reference cigarette. Under Health Canada Intense (HCI) smoking regime, the levels of 9 regulatory priority toxicants in the aerosol of HTP-A were either reduced or comparable to those in HTP-B on a per-stick basis. Additionally, both HTPs showed significant reduction (greater than 90%) in comparison to those measured in mainstream smoke of 3R4F cigarette for these toxicants. Using a set of standard in vitro toxicological assays (Ames, Micronucleus and Neutral Red Uptake), the two HTPs showed no observable responses while significant toxicity responses were recorded for 3R4F’s total particulate matter. Based on these preliminary results, the novel closed-end HTP-A design may provide similar toxicological profiles to the comparator HTP-B. Further toxicological and clinical assessments are warranted to evaluate HTP-A’s potential for exposure or disease risk reduction. [Contrib. Tob. Nicotine Res. 32 (2023) 146–156]

Kriging interpolation is a spatial interpolation method widely employed in the field of data analytics and prediction of environmental variables, which provides the best linear unbiased prediction of intermediate values. The core principle of Kriging interpolation is searching for data distribution regularity and predicting regionalised variable value, and it can be transferred into two descriptions of learning process: function fitting problem and coefficient optimisation problem. Although these two problems could be solved by many traditional algorithms like multiple linear regression method, the parameter estimation of variogram model becomes quite difficult when there are drifts or noises in the raw data. The purpose of this paper is to improve the Kriging interpolation algorithm with learning kernels based on Estimation of Distribution Algorithms (EDAs) and Least-Squares Support Vector Machine (LSSVM). The experiments have been carried out based on a real-world case with environmental variables. Compared with other machine learning methods, experimental results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Metastatic pheochromocytoma and paraganglioma (MPPGL) is a rare neuroendocrine tumour in which genetic factors play an important role. In recent years, with the continuous progress of genetic testing technology, more and more susceptibility genes have been proved to be associated with MPPGL, making early identification of MPPGL possible. Recent studies have shown that genes associated with the development of MPPGL include SDHA, SDHB, SDHC, SDHD, SDHAF2, FH, MDH2, VHL, IDH1, PDH1/2, SLC25A11, GOT2, DLST, CSDE1, MAML3, H3F3A, MERTK, PCDHGC3, and KIF1B, with SDHA, SDHB, SDHC, SDHD, and SDHAF2 being the common pathogenic genes. Potential mutations affect the clinical manifestations of MPPGL, such as malignant potential and genetic prediction, which can help to better understand the clinical course and treat accordingly. Genetic testing for pheochromocytomas and paragangliomas allows for early detection of genetic syndromes and facilitates close follow-up of high-risk patients. This article provides a review of the progress of research on susceptibility genes identified in MPPGL in recent years, with a view to providing a certain theoretical basis for further related research. [ABSTRACT FROM AUTHOR]

Steel-making production is a dynamic process that has the characteristics of high temperature and heat, and a complex reaction mechanism that causes the mechanism model possibly to be unavailable and the system to be a black box. In this article, a dynamic operation optimization (DOO) problem is refined from the basic oxygen furnace (BOF) steel-making process, and the system model is formulated by a data analytics method. This prevents to solve the optimization problem with derivative-based optimization methods. To circumvent these difficulties, a surrogate-model-based derivative-free optimization algorithm is proposed for solving the DOO problem. In order to establish the surrogate model with the least number of function evaluations, a subset selection strategy is designed to find a sparse structure for the optimization problem, based on which a set of simple bases is determined to establish the surrogate model. Moreover, this also reduces the scale of the parameter optimization problem. Numerical experiments on actual production data verify the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a "decomposition" style (in the abstract of the well-known "Transformer" article "Attention is All You Need", they use "attention" instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or "gathered" for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two "where to go" strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation. [ABSTRACT FROM AUTHOR]

Use of electronic cigarettes (e‐cigarettes) has increased significantly over the past decade due to consumer perception that these products represent a less risky alternative to combustible cigarettes. E‐liquids generally contain a simple mix of vegetable glycerin, propylene glycerol, nicotine, organic acids, and flavourings. Regulators require that harmful and potentially harmful constituents (HPHCs) that might cause harm to the consumer must be monitored in the aerosol generated by e‐cigarettes and in cigarette smoke (CS). To quantify HPHCs in aerosols from commercial flavoured e‐cigarettes in Chinese market, this study has systematically compared levels of HPHCs, including eight carbonyls, five volatile organic compounds, four tobacco‐specific nitrosamines, 16 polycyclic aromatic hydrocarbons, and seven heavy metals, in the aerosols of four market‐leading flavoured e‐cigarettes and mainstream CS, alongside in vitro cytotoxicity and mutagenicity assays. The vast majority of HPHCs were either undetected or significantly lower in the e‐cigarette aerosols than in commercial CS or reference CS (3R4F). Where HPHCs were detected, there were small variations among the different flavoured e‐cigarettes. In the neutral red uptake and Ames assays, aqueous extracts of the e‐cigarette aerosols did not induce obvious cytotoxicity or mutagenicity, whereas CS aqueous extract showed dose‐related cytotoxicity and mutagenicity. Collectively, these results indicate that use of e‐cigarettes might potentially lead to a significant reduction in exposure to harmful substances, with fewer cytotoxic and mutagenic effects, as compared with conventional smoking. Further studies based on human puffing conditions and longer evaluation periods will be needed to substantiate this potential. [ABSTRACT FROM AUTHOR]