Non-invasive continuous glucose monitoring (CGM) systems offer the advantage of non-invasive, real-time dynamic glucose monitoring, marking a significant advancement in diabetes management. However, the complexity of their sensing principles and operational mechanisms make systems vulnerable to various factors, which may introduce measurement bias or cause system interruptions and thereby compromise patient safety and monitoring effectiveness. To address these challenges, the Design Failure Mode and Effects Analysis (DFMEA) method is employed to identify and prioritize risks by assigning expert-based scores to critical components, ultimately enabling targeted improvements for high-risk failu... More >

Remaining Useful Life (RUL) prediction is critical for ensuring equipment safety and optimizing maintenance schedules, directly impacting system reliability and maintenance efficiency. However, in real-world industrial scenarios, factors such as operating condition fluctuations and load variations lead to inconsistent data distributions, making it challenging for existing models to achieve satisfactory adaptability and accuracy. To address this issue, this paper proposes a deep learning framework based on a multi-branch serial-parallel fusion of CNN-BiLSTM-Transformer architectures. Through innovative model architecture design and optimized training strategies, the framework aims to enhance... More >

This study introduces a novel approach for enhancing production decision-making by applying Reinforcement Learning to optimize the Economic Manufacturing Quantity (EMQ) model within discrete-time production-inventory systems. By incorporating machine status, inventory levels, and production choices, a Markov Decision Process (MDP) is constructed and combined with the Q-learning algorithm to derive an adaptive control method. This method enables the dynamic adaptation of production decisions, by effectively balancing the normal operation and shutdown for rest states. Numerical simulations show that the suggested Reinforcement Learning model surpasses conventional EMQ models and steady-state p... More >

Large-scale computing systems, such as cloud data centers, grid infrastructures, and high-performance computing clusters, are the backbone of modern information technology ecosystems. These systems typically consist of numerous heterogeneous, multi-state computing nodes that exhibit varying performance levels due to component failures, degradation, or dynamic resource allocation. Performability analysis, which integrates both system reliability and performance evaluations to quantify the probability of the system operating at a specified performance level, is critical for ensuring the efficient, reliable, and cost-effective operation of these complex systems. This paper presents a comprehens... More >

Business model innovation (BMI) constitutes a structural catalyst for competitive advantage within contemporary business ecosystems (BEs), transcending firm-level adaptation to reconfigure multi-stakeholder value generation networks. This research theorizes the reciprocal dynamism between BMI and ecosystem evolution through systematic literature synthesis and longitudinal analysis of a keystone technology enterprise. The study establishes four constitutive dimensions: (1) BMI’s steering effect on BE trajectories and resilience under sustainability pressures; (2) Its mediation of environmental integrity, social equity, and economic viability across ecosystem lifecycles; (3) The generative m... More >

This literature review offers an in-depth overview of recent advances in routing optimization for Unmanned Aerial Vehicles (UAVs), a field central to improving the performance, reliability, and flexibility of UAV systems. The review is organized into five categories: (1) multi-objective mission planning, (2) algorithmic design and optimization techniques, (3) energy efficiency and resource allocation, (4) communication protocols and network management, and (5) context-specific applications and environmental adaptability. The review highlights methodological progress and algorithmic approaches developed to balance competing demands such as mission effectiveness, energy use, and communication... More >

In the after-sales service market, understanding both the internal degradation of products and the external incentives within warranty period is crucial. Efforts into preventive maintenance can slow down the internal degradation, but these efforts are also influenced by external strategic services. Enabling an Industrial Internet of Things (IIoT) platform for preventive maintenance requires carefully considering the benefits instead of merely increasing efforts. This paper addresses these complexities by first proposing an additive degradation model to characterize the internal deterioration of products and the impact of efforts into preventive maintenance. It then introduces a sequential ga... More >

This paper comprehensively reviews literature on the operations management of metro systems, which are crucial for urban mass transit. It classifies the existing research into five categories: 1) passenger demand prediction; 2) timetabling and scheduling; 3) system vulnerability, resilience and performance; 4) resource planning; and 5) evacuation optimization. The paper focuses on publications in the last decade in order to reflect the latest research and industrial trends. In addition, some limitations of the existing literature are located and the potential knowledge gaps are identified. The paper provides a useful reference for developing sustainable and resilient metro systems to meet th... More >