Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm DOI Creative Commons

Nuttawan Thongtam,

Sukree Sinthupinyo, Achara Chandrachai

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(24), С. 11973 - 11973

Опубликована: Дек. 20, 2024

With the continuous growth of Industry 4.0 (I4.0), industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for sustainable development organizations. Machinery is a critical component and key to success production in factory. Minimizing unplanned downtime (UPDT) poses significant challenge designing an effective maintenance system. In era 4.0, most widely adopted frameworks are intelligent systems (IMSs), which integrate predictive with computerized systems. IMSs tools designed efficiently plan cycles each machine This research presents application search algorithm named state space (SSS) conjunction newly IMS, aimed at optimizing routines by identifying optimal timing cycles. The design began new IMS concept that incorporates three elements: automation pyramid standard, Industrial Internet Things (IIoT) sensors, management system (CMMS). CMMS collects data from database, real-time parameters gathered via IIoT sensors supervisory control acquisition (SCADA) provides summary total cost remaining lifetime equipment. By integrating SSS algorithms, optimized cycle solutions manager, focusing on minimizing costs maximizing Moreover, algorithms take account risks associated routines, following factory standards such as failure mode effects analysis (FMEA). approach well suited factories helps reduce UPDT.

Язык: Английский

Unlocking the metaverse: Determinants of voluntary adoption in e-commerce DOI
Radka Bauerová,

Michal Halaška

Sustainable Futures, Год журнала: 2025, Номер 9, С. 100436 - 100436

Опубликована: Янв. 16, 2025

Язык: Английский

Процитировано

1

Factors Determining the Continuance Intention to Use Mobile English Learning Applications: An Application and Extension of Expectation Confirmation Model in the Mobile‐Assisted Language Learning Context DOI Open Access

Kun Dou,

Huzaina Abdul Halim,

Mohd Rashid Mohd Saad

и другие.

European Journal of Education, Год журнала: 2025, Номер 60(2)

Опубликована: Март 31, 2025

ABSTRACT Despite the rapid growth of integrating language learning applications into educational settings, limited studies have reported students' continuance intention to use mobile English in mobile‐assisted (MALL) context. This study extended expectation confirmation model (ECM) with self‐efficacy and perceived enjoyment. Data were collected from 251 university students through an online questionnaire analysed using SPSS AMOS. The findings revealed that: (1) satisfaction, usefulness, significantly affected intention, satisfaction acting as key influencing factor; (2) had a positive influence on enjoyment; (3) enjoyment did not usefulness failed predict intention. this provide practical insights for educators application developers improve teaching effectiveness user experience settings.

Язык: Английский

Процитировано

0

Real vs. Perceived Use DOI Open Access
Sarbjit Singh Oberoi, Debarun Chakraborty, Abhishek Singh

и другие.

Journal of Global Information Management, Год журнала: 2025, Номер 33(1), С. 1 - 24

Опубликована: Апрель 3, 2025

This study examines the elements of matchmaking intention, highlighting a significant gap in behaviors within Metaverse platforms. The explores socio-psychological and behavioral factors impacting intention this digital landscape. We developed conceptual model integrating social presence theory status quo bias with technology acceptance (TAM). Using survey data from 512 participants across various regions India, we tested proposed relationships through structural equation modeling. results indicate that intimacy, interactivity, connectedness, responsiveness contribute to increased agreeableness regarding on Furthermore, real, non-perceived usage significantly moderates relationship between intention. Our findings provide valuable insights for creating effective marketing strategies enhance user intentions Metaverse. can be used launch any new service

Язык: Английский

Процитировано

0

Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm DOI Creative Commons

Nuttawan Thongtam,

Sukree Sinthupinyo, Achara Chandrachai

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(24), С. 11973 - 11973

Опубликована: Дек. 20, 2024

With the continuous growth of Industry 4.0 (I4.0), industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for sustainable development organizations. Machinery is a critical component and key to success production in factory. Minimizing unplanned downtime (UPDT) poses significant challenge designing an effective maintenance system. In era 4.0, most widely adopted frameworks are intelligent systems (IMSs), which integrate predictive with computerized systems. IMSs tools designed efficiently plan cycles each machine This research presents application search algorithm named state space (SSS) conjunction newly IMS, aimed at optimizing routines by identifying optimal timing cycles. The design began new IMS concept that incorporates three elements: automation pyramid standard, Industrial Internet Things (IIoT) sensors, management system (CMMS). CMMS collects data from database, real-time parameters gathered via IIoT sensors supervisory control acquisition (SCADA) provides summary total cost remaining lifetime equipment. By integrating SSS algorithms, optimized cycle solutions manager, focusing on minimizing costs maximizing Moreover, algorithms take account risks associated routines, following factory standards such as failure mode effects analysis (FMEA). approach well suited factories helps reduce UPDT.

Язык: Английский

Процитировано

0