This paper describes the design and installation of a fire-fighting system in a high-rise building, the SAMA Residential Tower in Palestine. A sprinkler system, a standpipe system, a fire hose cabinet, a landing valve, externally accessible fire hydrants, and a Siamese connection are all included in this system. Additionally, it complies with NFPA 13, 14, and 20 codes, as well as those of the Palestinian and Jordanian governments. Hydraulic calculations were performed manually and confirmed using Elite Fire Protection software. The layouts were developed using AutoCAD, and 3D modeling was created in Revit to optimize the placement of components. All zones of the building will be protected by... More >

Gas leakage poses a significant hazard in chemical industry operations, where failure to respond rapidly to gas diffusion can lead to poisoning, fire, or explosion. Timely and accurate prediction of gas dispersion is therefore essential for emergency decision-making and operational safety. While existing methods such as computational fluid dynamics, spatiotemporal statistics, and surrogate models emphasize prediction accuracy, they often suffer from excessive computational delays—especially critical in leak scenarios where casualties can occur within minutes. To address this gap, this paper introduces a Gaussian process-Markov random field-Kriging (GP-MRF-K) model for fast and reliable pre... More >

In urban public transport networks, subway and bus systems complement each other and together form a coupled system that serves passenger travel. However, a disturbance in either subsystem can propagate through coupling nodes across the entire network, thereby reducing overall operational efficiency. Most existing studies focus only on the reliability of a single mode, and few have analyzed the overall reliability of the system while considering the coupling relationship between the two. To address this gap, this paper proposes a probabilistic evaluation model to assess the reliability of the subway and bus coupling system. System reliability is defined as the probability that the network ca... More >

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The capacity regeneration phenomenon in lithium-ion batteries is inevitable and leads to non-monotonic fluctuations in capacity degradation trajectories, significantly complicating accurate remaining useful life (RUL) prediction. To address this challenge, this paper proposes a hybrid prediction model based on CEEMDAN-SSA-SVR-BiGRU. The method first employs Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the original capacity sequence into multiple Intrinsic Mode Functions (IMFs) representing local regeneration fluctuations, and a residual component (RES) referring to the global degradation trend, thereby achieving effective signal decoupling. Subseq... More >

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 >