Prospective predictors of electronic nicotine delivery system initiation in tobacco naive young adults: A machine learning approach

Atuegwu, Nkiruka C.; Mortensen, Eric M.; Krishnan‐Sarin, Suchitra; Laubenbacher, Reinhard; Litt, Mark D.

doi:10.1016/j.pmedr.2023.102148

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…Studi pemantauan kualitas pengukuran NT secara realtime dengan EWMA efektif tingkatkan performa sonografer A hingga F dominan mencapai signifikansi 0,058 (94,2%) [7]. Studi prediksi ini juga konsisten pada kontribusi 11% pengguna rokok elektrik baru pada remaja awalnya bukan perokok di Inggris [8], [9]. Studi industri jasa, layanan memperkirakan permintaan tabung gas LPG menggunakan Double Exponential Smoothing (DES),…”

Section: Studi Yang Telah Ada Mengungkapkan Prediksi Deteksi Wabah In...unclassified

Perbandingan Pendekatan Metode Peramalan Naive Approach, Simple Moving Average dan Weighted Moving Average dalam Upaya Meningkatkan Prediksi Penjualan JNE Kopma UNY

Cahya Wulan,

Pasca Riani

2024

jatiunik. jurnal. ilm. teknik. manaj. industri.

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JNE Kopma UNY's logistics demand increases 2% per week, the manual courier allocation system is unable to keep up, predictions are accurate and real-time is needed. However, there is no exact accuracy for this case. The study was conducted to determine the accuracy of the smallest Mean Percentage Error (MPE) at JNE Kopma UNY by observing sales data for 2022 to predict demand. The data was analyzed using the Naive Approach, SMA and WMA methods, then measured using the MPE to determine the best forecasting method. The software used is Microsoft Excel 2016. The 3 Month (SMA) produces the highest sales prediction (Rp. 38,668,850) for January 2023 compared to Naive Approach (Rp. 35,086,330) and 3 Month WMA (Rp. 37,993,380). 3 Month SMA is proven to be superior with the lowest MPE (12.07%) and consistent performance. Naive Approach is inaccurate (MPE 20.26%) and 3 Month WMA performance is unstable (MPE 14.27%). This research recommends 3 Month SMA for JNE Kopma UNY to increase the accuracy of sales predictions and optimize stock. Opportunities for further research are open regarding the application of 3 Month SMA in various contexts.

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Section: Studi Yang Telah Ada Mengungkapkan Prediksi Deteksi Wabah In...unclassified

Perbandingan Pendekatan Metode Peramalan Naive Approach, Simple Moving Average dan Weighted Moving Average dalam Upaya Meningkatkan Prediksi Penjualan JNE Kopma UNY

Cahya Wulan,

Pasca Riani

2024

jatiunik. jurnal. ilm. teknik. manaj. industri.

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“…Secondly, tobacco is one of the most abundant plants in nature, and the whole plant is densely covered with glandular trichomes which secrete abundant numbers of secondary metabolites [24]. Furthermore, the tobacco inflorescence can be topped and collected several times such that the 10 selected diterpene-rich germplasms can be used as special resources for the cultivation and extraction of diterpenoids [25]. Finally, biosynthesis is an effective method for obtaining diterpenoids, but the metabolic pathways must be well defined.…”

Section: Obtaining Diterpenoid Componentsmentioning

confidence: 99%

Identification and Evaluation of Diterpenoids from Glandular Trichome Secretions of Air/Sun-Cured Tobacco Germplasm Resources

Qi,

Liu,

et al. 2023

Agronomy

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To explore the multipurpose utilization of tobacco germplasm resources, 80 representative air/sun-cured tobacco germplasms were used as experimental materials to determine the composition and content of the diterpenoids of glandular trichome secretions using ultra-performance liquid chromatography (UPLC). Correlation analysis, cluster analysis, and principal component analysis (PCA) were used to comprehensively evaluate 80 air/sun-cured tobacco germplasms. The results are as follows: (1) 11 chromatographic peaks (Peak1–Peak11) were detected by UPLC, and the coefficient of variation of their contents ranged from 26.3 to 143%. (2) Correlation analysis showed that alpha-cembratriene-diol (α-CBT-diol) and beta-cembratriene-diol (β-CBT-diol) had the highest correlation coefficients (0.97), showing a significantly negative correlation with cis-abienol and a significantly positive correlation with the other diterpenoids. (3) PCA showed that the cumulative contribution rate of the three principal components was 75.70%, and the diterpenoid-rich germplasms were ranked in the following order: X40 (Maiduo) > X48 (Jianpingpiaoba) > X58 (Mianzhu) > X54 (Shifangpipaliu) > X63 (Xuejia5) > X41 (Tangpeng) > X29 (OLOR) > X73 (Criollo) > X44 (Tiebanqing) > X70 (Nicaragua Changxin). The diterpenoid content of X36 was the lowest. These results provide excellent germplasms for plant-based pesticides and fragrance compounds and genetic studies of the diterpenoid metabolism.

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“…Here, longitudinal work has demonstrated that impulsivity‐related traits prospectively predict the frequency of e‐cigarette use among young adults [37], that higher depressive symptoms and impulsivity predict e‐cigarette initiation in youth [36], and that higher depressive symptoms predict e‐cigarette initiation among university students [32]. Other research highlights the importance of health predictors of vaping uptake over time, showing that susceptibility to both smoking and vaping, increased physical exercise related to muscle building, and social media use, were all important predictors of future vaping uptake in tobacco‐naïve young adults in the Population Assessment of Tobacco and Health longitudinal study [40]. However, there is a need for longitudinal research to examine the extent to which a broader range of psychological factors predict vaping uptake over time among adult non‐users.…”

Section: Introductionmentioning

confidence: 99%

Psychological predictors of vaping uptake among non‐smokers: A longitudinal investigation of New Zealand adults

Conner,

Teah,

Sibley

et al. 2024

Drug and Alcohol Review

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IntroductionDemographic and health factors are known to predict vaping. Less is known about psychological predictors of vaping uptake, particularly among non‐smoking adults using longitudinal designs. We aimed to model how psychological factors related to personality and mental health predicted the likelihood of vaping uptake over time in non‐smoking adults ages 18+ using longitudinal data.MethodsLongitudinal regression models utilised data from the 2018–2020 waves of the New Zealand Attitudes and Values Study to assess how the Big Five personality traits, mental distress and self‐control predicted who began vaping over time among non‐users (non‐vapers and non‐smokers), controlling for gender, age, ethnicity and economic deprivation.ResultsAnalyses included 36,309 adults overall (ages 18 to 99; M = 51.0). The number of non‐users who transitioned into current vaping was small (transitioned from 2018 to 2019, n = 147; 0.48%; 2019 to 2020, n = 189, 0.63%). Fully adjusted models showed that adults with higher mental distress (adjusted odds ratio [aOR] 1.43; 95% confidence interval [CI] 1.09–1.88), lower self‐control (aOR 0.79; 95% CI 0.69–0.89) and higher extraversion (aOR 1.09; 95% CI 1.06–1.13) were more likely to begin vaping at the next time point compared to adults who remained non‐users. Higher neuroticism and lower conscientiousness also predicted vaping uptake in initial models, but inclusion of mental distress and self‐control superseded these traits.Discussion and ConclusionsPsychological factors related to mental distress, impulse control and sociability predicted who was more likely to begin vaping as non‐smoking adults. Harm prevention interventions could target these factors to reduce vaping uptake in non‐smokers.

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Prospective predictors of electronic nicotine delivery system initiation in tobacco naive young adults: A machine learning approach

Cited by 5 publications

References 44 publications

Perbandingan Pendekatan Metode Peramalan Naive Approach, Simple Moving Average dan Weighted Moving Average dalam Upaya Meningkatkan Prediksi Penjualan JNE Kopma UNY

Perbandingan Pendekatan Metode Peramalan Naive Approach, Simple Moving Average dan Weighted Moving Average dalam Upaya Meningkatkan Prediksi Penjualan JNE Kopma UNY

Identification and Evaluation of Diterpenoids from Glandular Trichome Secretions of Air/Sun-Cured Tobacco Germplasm Resources

Psychological predictors of vaping uptake among non‐smokers: A longitudinal investigation of New Zealand adults

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