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  • Open Access

    ARTICLE

    Explicit ARL Computational for a Modified EWMA Control Chart in Autocorrelated Statistical Process Control Models

    Yadpirun Supharakonsakun1, Yupaporn Areepong2, Korakoch Silpakob3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 699-720, 2025, DOI:10.32604/cmes.2025.067702 - 30 October 2025

    Abstract This study presents an innovative development of the exponentially weighted moving average (EWMA) control chart, explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise. Unlike previous works that rely on simplified models such as AR(1) or assume independence, this research derives for the first time an exact two-sided Average Run Length (ARL) formula for the Modified EWMA chart under SARMA(1,1)L conditions, using a mathematically rigorous Fredholm integral approach. The derived formulas are validated against numerical integral equation (NIE) solutions, showing strong agreement and significantly reduced More > Graphic Abstract

    Explicit ARL Computational for a Modified EWMA Control Chart in Autocorrelated Statistical Process Control Models

  • Open Access

    PROCEEDINGS

    Intelligent Structural Strength Monitoring Method Using Dynamic Evolving Digital Twin Model

    Chenjun Ni, Kuo Tian*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.012069

    Abstract The development of large-scale, high-precision aerospace structures has imposed increasingly stringent requirements on mechanical response monitoring during ground testing. Aiming at the long-standing limitations of mechanical response monitoring for ground tests in terms of accuracy and real-time performance, this study introduces an intelligent structural strength monitoring method using a dynamically evolving digital twin model.
    First, a reduced-order modeling method that accounts for actual test deviations is established. By jointly sampling deviation and loading information as variables, a reduced-order model with full-field mechanical responses as output is constructed, enabling rapid updates to reflect the real test conditions.… More >

  • Open Access

    PROCEEDINGS

    CO2 Migration Monitoring and Leakage Risk Assessment in Deep Saline Aquifers for Geological Sequestration

    Mingyu Cai1,2, Xingchun Li1,2, Kunfeng Zhang1,2,*, Shugang Yang1,2, Shuangxing Liu1,2, Ming Xue1,2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-2, 2025, DOI:10.32604/icces.2025.010490

    Abstract Deep saline aquifers account for more than 90% of the global theoretical geological CO2 sequestration capacity, making them the dominant choice for large-scale CO2 storage. These aquifers offer vast storage potential, especially in comparison to oil and gas reservoirs, which are often considered for CO2 geological sequestration. Despite their significant storage capacity, deep saline aquifers face several challenges that hinder their practical application. In particular, the lack of adequate geological infrastructure and exploration conditions for deep saline aquifers presents major obstacles to the effective monitoring of CO2 migration and predicting leakage risks. These challenges are compounded by… More >

  • Open Access

    ARTICLE

    Thermal Performance and Application of a Self-Powered Coal Monitoring System with Heat Pipe and Thermoelectric Integration for Spontaneous Combustion Prevention

    Tao Lin1,*, Chengdai Chen1, Liyao Chen1, Fengqin Han1, Guanghui He2

    Frontiers in Heat and Mass Transfer, Vol.23, No.5, pp. 1661-1680, 2025, DOI:10.32604/fhmt.2025.070787 - 31 October 2025

    Abstract Targeting spontaneous coal combustion during stacking, we developed an efficient heat dissipation & self-supplied wireless temperature measurement system (SPWTM) with gravity heat pipe-thermoelectric integration for dual safety. The heat transfer characteristics and temperature measurement optimization of the system are experimentally investigated and verified in practical applications. The results show that, firstly, the effects of coal pile heat production power and burial depth, along with heat pipe startup and heat transfer characteristics. At 60 cm burial depth, the condensation section dissipates 98% coal pile heat via natural convection. Secondly, for the temperature measurement error caused by… More >

  • Open Access

    ARTICLE

    A Security Operation and Event Management (SOEM) Platform for Critical Infrastructures Protection

    Roberto Caviglia1, Daniyar Aliaskharov2, Alessio Aceti1, Mila Dalla Preda3, Paola Girdinio2, Giovanni Battista Gaggero2,*

    CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5327-5340, 2025, DOI:10.32604/cmc.2025.068509 - 23 October 2025

    Abstract Industrial Control Systems (ICS) in Operational Technology (OT) environments face unique cybersecurity challenges due to legacy systems, critical operational needs, and incompatibility with standard IT security practices. To address these challenges, this paper presents the Security Operation and Event Management (SOEM) platform, a software designed to support Security Operations Centers (SOCs) in reaching full visibility of OT environments. SOEM integrates diverse log sources and intrusion detection systems, including logs generated by the control system itself and additional on-the-shelf products, to enhance situational awareness and enable rapid incident response. The pilot project was carried out within More >

  • Open Access

    ARTICLE

    Leveraging Deep Learning for Precision-Aware Road Accident Detection

    Kunal Thakur1, Ashu Taneja1,*, Ali Alqahtani2, Nayef Alqahtani3

    CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4827-4848, 2025, DOI:10.32604/cmc.2025.067901 - 23 October 2025

    Abstract Accident detection plays a critical role in improving traffic safety by enabling timely emergency response and reducing the impact of road incidents. The main challenge lies in achieving real-time, reliable and highly accurate detection across diverse Internet-of-vehicles (IoV) environments. To overcome this challenge, this paper leverages deep learning to automatically learn patterns from visual data to detect accidents with high accuracy. A visual classification model based on the ResNet-50 architecture is presented for distinguishing between accident and non-accident images. The model is trained and tested on a labeled dataset and achieves an overall accuracy of… More >

  • Open Access

    ARTICLE

    Significance of CA125 Monitoring during Maintenance Treatment with Poly(ADP-Ribose) Polymerase Inhibitor in Ovarian Cancer after First-Line Chemotherapy: Multicenter, Observational Study

    Szymon Piątek1, Anna Dańska-Bidzińska2,*, Paweł Derlatka2, Bartosz Szymanowski3, Renata Duchnowska3, Aleksandra Zielińska4, Natalia Sawicka4, Aleksander Gorzeń5, Wojciech Michalski6, Mariusz Bidziński1

    Oncology Research, Vol.33, No.11, pp. 3405-3416, 2025, DOI:10.32604/or.2025.068609 - 22 October 2025

    Abstract Objectives: Monitoring of Cancer Antigen 125 (CA125) during ovarian cancer (OC) maintenance treatment with poly(ADP-ribose) polymerase inhibitors (PARPis) may be insufficient when using Gynecologic Cancer Intergroup (GCIG) biochemical progression criteria. This study aimed to evaluate the usefulness of CA125 monitoring in detecting OC recurrence during PARPis maintenance treatment. Methods: This multicenter retrospective cohort study included patients with primary OC who achieved complete or partial response after first-line platinum-based chemotherapy followed by PARPis maintenance treatment. Progression was defined using Response Evaluation Criteria in Solid Tumors (RECIST) and GCIG biochemical criteria. New biochemical progression definitions, based on… More >

  • Open Access

    ARTICLE

    A Digital Twin Driven IoT Architecture for Enhanced xEV Performance Monitoring

    J. S. V. Siva Kumar1, Mahmad Mustafa2, Sk. M. Unnisha Begum3, Badugu Suresh4, Rajanand Patnaik Narasipuram5,*

    Energy Engineering, Vol.122, No.10, pp. 3891-3904, 2025, DOI:10.32604/ee.2025.070052 - 30 September 2025

    Abstract Electric vehicle (EV) monitoring systems commonly depend on IoT-based sensor measurements to track key performance parameters such as vehicle speed, state of charge (SoC), battery temperature, power consumption, motor RPM, and regenerative braking. While these systems enable real-time data acquisition, they are often hindered by sensor noise, communication delays, and measurement uncertainties, which compromise their reliability for critical decision-making. To overcome these limitations, this study introduces a comparative framework that integrates reference signals, a digital twin model emulating ideal system behavior, and real-time IoT measurements. The digital twin provides a predictive and noise-resilient representation of More >

  • Open Access

    ARTICLE

    Deployable and Accurate Time Series Prediction Model for Earth-Retaining Wall Deformation Monitoring

    Seunghwan Seo1,2,*, Moonkyung Chung1

    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2893-2922, 2025, DOI:10.32604/cmes.2025.069668 - 30 September 2025

    Abstract Excavation-induced deformations of earth-retaining walls (ERWs) can critically affect the safety of surrounding structures, highlighting the need for reliable prediction models to support timely decision-making during construction. This study utilizes traditional statistical ARIMA (Auto-Regressive Integrated Moving Average) and deep learning-based LSTM (Long Short-Term Memory) models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models. The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time, whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within… More > Graphic Abstract

    Deployable and Accurate Time Series Prediction Model for Earth-Retaining Wall Deformation Monitoring

  • Open Access

    ARTICLE

    Deep Learning-Based Automated Inspection of Generic Personal Protective Equipment

    Atta Rahman*, Fahad Abdullah Alatallah, Abdullah Jafar Almubarak, Haider Ali Alkhazal, Hasan Ali Alzayer, Younis Zaki Shaaban, Nasro Min-Allah, Aghiad Bakry, Khalid Aloup

    CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3507-3525, 2025, DOI:10.32604/cmc.2025.067547 - 23 September 2025

    Abstract This study presents an automated system for monitoring Personal Protective Equipment (PPE) compliance using advanced computer vision techniques in industrial settings. Despite strict safety regulations, manual monitoring of PPE compliance remains inefficient and prone to human error, particularly in harsh environmental conditions like in Saudi Arabia’s Eastern Province. The proposed solution leverages the state-of-the-art YOLOv11 deep learning model to detect multiple safety equipment classes, including safety vests, hard hats, safety shoes, gloves, and their absence (no_hardhat, no_safety_vest, no_safety_shoes, no_gloves) along with person detection. The system is designed to perform real-time detection of safety gear while… More >

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