Hybrid Performance-Measurement Model of Elevators

Zubair, Muhammad Umer; Zhang, Xueqing

doi:10.1061/(asce)cf.1943-5509.0001406

Cited by 10 publications

(4 citation statements)

References 55 publications

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“…Measurement models have their origin in social sciences and statistics [32]. They also appear in more quantitative applications; some recent examples include management, manufacturing, and computer vision [33][34][35]. These measurement models can take on more complexity than the examples we provided, depending on the unique needs of the problems in these areas.…”

Section: Discussionmentioning

confidence: 99%

“…The use of measurement models in these areas is motivated by a desire to define and quantify observations of nuanced and/or subjective phenomena; and connect those observations to an underlying theory. Our use of the measurement model to learn phenomenological links between an underlying process and qualitative biological observations is inspired by the use of measurement models in these fields [32][33][34][35]. Future, demonstrations of this concept might employ causal formalisms to distinguish between molecular markers and molecular drivers of a phenotype.…”

Section: Discussionmentioning

confidence: 99%

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Model certainty in cellular network-driven processes with missing data

2023

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Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a combined Bayesian and Machine Learning Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context. We find model prediction accuracy and certainty strongly depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. For instance, two orders of magnitude more ordinal (e.g., immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g., fluorescence) data for calibration of an apoptosis execution model. Notably, ordinal and nominal (e.g., cell fate observations) non-quantitative data synergize to reduce model uncertainty and improve accuracy. Finally, we demonstrate the potential of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Model certainty in cellular network-driven processes with missing data

2023

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“…Measurement models have their origin in social sciences and statistics 30 . They also appear in more quantitative applications; some recent examples include management, manufacturing, and computer vision [33][34][35] . These measurement models can take on more complexity than the examples we provided, depending on the unique needs of the problems in these areas.…”

Section: Discussionmentioning

confidence: 99%

Predictive uncertainty in mechanistic models of cellular processes calibrated to experimental data

Ramanathan

López

2021

Preprint

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Mathematical models are often used to study the structure and dynamics of network-driven cellular processes. In cell biology, models representing biochemical reaction networks have provided significant insights but are often plagued by a dearth of available quantitative data necessary for simulation and analysis. This has in turn led to questions about the usefulness of biochemical network models with unidentifiable parameters and high-degree of parameter sloppiness. In response, approaches to incorporate highly-available non-quantitative data and use this data to improve model certainty have been undertaken with various degrees of success. Here we employ a Bayesian inference and Machine Learning approach to first explore how quantitative and non-quantitative data can constrain a mechanistic model of apoptosis execution, in which all models can be identified. We find that two orders of magnitude more ordinal data measurements than those typically collected are necessary to achieve the same accuracy as that obtained from a quantitative dataset. We also find that ordinal and nominal non-quantitative data on their own can be combined to reduce model uncertainty and thus improve model accuracy. Further analysis demonstrates that the accuracy and certainty of model predictions strongly depends on accurate formulations of the measurement as well as the size and make-up of the nonquantitative datasets. Finally, we demonstrate the potential of a data-driven Machine Learning measurement model to identify informative mechanistic features that predict or define nonquantitative cellular phenotypes, from a systems perspective.

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“…The modified constructs considered in the papers include the technological scheme of the hydraulic elevator installation, the electro-hydraulic servo system for controlling the speed of the elevator, the improved design for the hydraulic system of the elevator, the hydraulic elevator with a tool tray, and the automated design of elevator systems using AI techniques. These modifications and additions were made to address the unique characteristics and requirements of hydraulic elevators, such as the need for speed control, real-time speed correction, increased bearing platform area, and efficient design (Mohammed, Hashim and Beram, 2020;Zubair and Zhang, 2020;Medvedeva, Ivashechkin and Satsuta, 2022). UTAUT has been applied in various contexts to understand the factors influencing technology adoption.…”

Section: Adaptation Of Utaut Constructsmentioning

confidence: 99%

Unveiling Technology Acceptance in the Elevator Industry: An Extension of the Unified Theory of Acceptance and Use of Technology Model

Suwankanit,

Bowonchaiyarit,

2024

AJSSS

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This study aimed to examine the factors influencing individuals' behavioral intentions to use hydraulic elevators through Structural Equation Modeling (SEM) analysis and assess their feasibility across five crucial aspects: Economic, Technical, Legal, Operational, and Scheduling. We found that perceived benefits, perceived risks, and social influence significantly impact behavioral intentions. The feasibility analysis results indicate that hydraulic elevators are economically, technically, and operationally viable, with mean scores of 0.68, 5.03, and 4.98, respectively. However, potential legal and scheduling challenges were identified, with mean scores of 0.33 for both aspects. The study's implications suggest addressing individuals' perceptions of benefits, risks, and social influence, as well as informing decision-makers about practical considerations for successful hydraulic lift implementation. Despite limitations, such as self-reported data and cross-sectional survey design, the study provides valuable insights into factors influencing behavioral intentions and feasibility considerations. Future research could explore longitudinal data and additional factors, such as environmental concerns, accessibility, and user experience.

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Hybrid Performance-Measurement Model of Elevators

Cited by 10 publications

References 55 publications

Model certainty in cellular network-driven processes with missing data

Model certainty in cellular network-driven processes with missing data

Predictive uncertainty in mechanistic models of cellular processes calibrated to experimental data

Unveiling Technology Acceptance in the Elevator Industry: An Extension of the Unified Theory of Acceptance and Use of Technology Model

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