Sensitivity assessment of diphacinone by pharmacokinetic analysis in invasive black rats in the Bonin (Ogasawara) Archipelago, Japan

Takeda, Kazuki; Shimizu, Keita; Sato, Miyu; Katayama, Masafumi; Nakayama, Shouta M.M.; Tanaka, Kotaro; Ikenaka, Yoshinori; Hashimoto, Takuma; Minato, Ryuichi; Oyamada, Yusuke; Tanaka, Kazuyuki D.; Kimura, Goro; Tanikawa, Tsutomu; Kato, Keisuke; Kusakabe, Taichi; Ishizuka, Mayumi; Kamata, Ryo

doi:10.1016/j.pestbp.2023.105767

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The sensitivity coefficient represents the relative measure of the degree of influence of uncertainty factors on evaluation indicators [34,35]. The formula for calculating the sensitivity coefficient is s AF = (∆A/A) (∆F/F) , where ∆A/A is the corresponding rate of change of the evaluation indicator A, and ∆F/F is the rate of change of the uncertainty factor F. The larger the absolute value of the sensitivity coefficient, the more sensitive the evaluation indicator A is to the uncertainty factor F. It can be seen from Figure 5 that the critical depth of the wellbore structural instability after the adoption of optimized construction measures is affected by several factors to varying degrees.…”

Section: Multi-factor Sensitivity Discussionmentioning

confidence: 99%

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Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints

Pan,

Liu,

Cheng

et al. 2024

Applied Sciences

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With the development of coal resource extraction and wellbore construction proceeding towards deeper depths, the stability of drilling wellbore structures has become increasingly severe, even posing a barrier to the use of drilling method technology in deep wellbore construction. To address this issue, this study raised an optimized constraints method involving pre-throwing cement slurry to the bottom before wellbore decent, altering bottom constraints. Firstly, the critical depth and instability criterion of this optimized method was derived by catastrophe theory. Subsequently, the role of single-factor and multi-factor sensitivity analyses on critical depth was discussed. The engineering effects of optimized constraint methods were contrasted and examined in several drilling projects. Finally, the characteristic values of real engineering were computed using numerical techniques and ABAQUS2020 software, and the efficacy of optimization approaches was examined and validated. The results revealed that the critical depth increased by 41.39 ± 5%. The influence factors described in order of the degree were the counterweight water height, the elastic modulus, the thickness of the wellbore, and the self-weight of the wellbore, sequentially. The conclusion on structural stability between the numerical calculation solution and theoretical calculation solution was completely the same. The optimized constraints method can effectively improve the stability of the wellbore structure.

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Section: Multi-factor Sensitivity Discussionmentioning

confidence: 99%

“…According to Sections 2.3.1 and 2.3.2, the sufficient and necessary conditions for vertical instability to occur are that the depth of the wellbore is within the range shown in Equation (35), and Equation ( 36) is the critical depth for the occurrence of vertical instability of the wellbore.…”

Section: Equation For Critical Depth Of Wellbore Destabilizationmentioning

confidence: 99%

Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints

Pan,

Liu,

Cheng

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

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AI in Predictive Toxicology

Yingngam

2024

Advances in Medical Technologies and Clinical Practice

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The field of toxicology is undergoing a significant transformation due to the integration of artificial intelligence (AI). In addition to traditional reliance on empirical studies and animal testing, AI-powered predictive toxicology is now used to predict the toxic effects of chemicals and drugs. This chapter examines the role of AI in enhancing the accuracy, efficiency, and breadth of toxicological assessments by bridging the gap between traditional approaches and advanced AI techniques. It explores various AI methodologies, such as machine learning, deep learning, and neural networks, focusing on their application in toxicity prediction. Furthermore, this chapter investigates the integration of AI with toxicological databases and the development and empirical validation of predictive models. It also addresses various challenges associated with AI-powered toxicology, including data quality, model interpretability, and scalability. The chapter concludes that despite facing challenges, AI is a powerful tool in modern toxicological analysis.

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Sensitivity assessment of diphacinone by pharmacokinetic analysis in invasive black rats in the Bonin (Ogasawara) Archipelago, Japan

Cited by 2 publications

References 26 publications

Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints

Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints

AI in Predictive Toxicology

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