Rui Huang¹, Ziwei Zhang^2,*, Kari Tusongjiang¹, Bowen Zhang³, Ning Yang³, Xiaowei Li¹, Aimudula Maierdan¹

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086232 - 15 September 2026

Abstract The accuracy of partial discharge (PD) pattern recognition is essential for assessing the insulation condition of gas-insulated switchgear (GIS). However, in practical recognition tasks, phase-resolved partial discharge (PRPD) patterns often exhibit complex feature distributions, and key discharge characteristics may be weakened during feature extraction. This study proposes an improved sparrow search algorithm (ISSA)-optimized attention-enhanced ConvNeXt model for GIS PD pattern recognition. A multi-criterion grayscale evaluation scheme is first employed to select the most suitable grayscale conversion for PRPD patterns, aiming to preserve informative discharge regions and reduce redundant color interference. Subsequently, an attention-enhanced ConvNeXt model… More >

Dimitrios Papakonstantinou¹, Vasileios Vardas¹, Despoina M. Varouhaki², Aikaterini Kotzamouratoglou¹, Karolina Mangani¹, Julia A. Ju³, Catherine Alix-Panabières^4,5,6, Stuart S. Martin³, Constantinos M. Athanassopoulos², Galatea Kallergi^1,*

Oncology Research, Vol.34, No.10, 2026, DOI:10.32604/or.2026.085665 - 14 September 2026

Abstract Objectives: Combretastatin A-4 (CA-4) is a microtubule-disrupting agent with established anti-tumor properties. This study aimed to evaluate the effects of CA-4 on key metastatic traits of cancer cells, including migration, clonogenic potential, cytoskeletal protein expression, and microtentacle (McTN) formation, using multiple cancer cell models, including the colon patient-derived circulating tumor cell line CTC-MCC-41. Methods: H1299 (non-small cell lung cancer), MDA-MB-231 (triple-negative breast cancer), HT-29 (colorectal cancer), and CTC-MCC-41 (derived from the blood of a colon cancer patient) cells were treated with CA-4 (10 μM) for 24 and 48 h. Colony formation was assessed with a clonogenic… More > Graphic Abstract

Shounak Mandal¹, Subhadip Pati^1,#, Nirmallyadeb Ray^1,#, Bipasha Guha Roy^2,#, Priyanka Saha³, Deepsubhra Guha Roy^2,*

Digital Engineering and Digital Twin, Vol.4, pp. 27-47, 2026, DOI:10.32604/dedt.2026.081859 - 14 August 2026

Abstract HealthyBrain is a scalable, interoperable, and intelligent Remote Patient Monitoring (RPM) platform built on Internet of Things (IoT) technologies and a modular microservices architecture. The system integrates wearable IoT devices, MQTT (Message Queuing Telemetry Transport)-based lightweight messaging, and high-throughput real-time data streaming via Apache Kafka. Edge-side preprocessing enables low-latency analytics, while machine learning-based anomaly detection models facilitate early identification of critical health events. To ensure clinical interoperability, the platform adheres to the HL7 FHIR (Fast Healthcare Interoperability Resources) standard for electronic health record exchange. The system’s novel contribution lies in the unified integration of edge… More >

Ikenna D. Arungwa^1,2,*, Elochukwu C. Moka²

Revue Internationale de Géomatique, Vol.35, pp. 423-459, 2026, DOI:10.32604/rig.2026.083164 - 29 July 2026

Abstract This study utilized nearly two decades of temporal gravity field observations from the Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) satellite missions to analyze the spatial and temporal variations of terrestrial water storage (TWS) and groundwater storage (GWS) in Nigeria. Advanced statistical techniques, including Singular Spectrum Analysis (SSA), Principal Component Analysis (PCA), and Empirical Orthogonal Function (EOF), were applied to characterize and quantify these variations at a basin scale. Results show that TWS exhibits biennial, annual seasonal fluctuations (between ±10 to ±55 mm), reaching its lowest levels during the dry season (February–June) and peaking in the… More >

Jiawang Zou, Annan Jiang^*, Xinzhi Wang, Hao Huang

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082647 - 27 July 2026

Abstract Accurate forecasting of settlement in buildings adjacent to shield tunnels remains a critical challenge in underground engineering due to complex spatiotemporal interactions and nonlinear relationships among multi-source monitoring data and construction parameters. To address this issue, a Sparrow Search Algorithm (SSA)-optimized Convolutional Neural Network–Long Short-Term Memory–Transformer (CNN-LSTM-Transformer) hybrid framework is proposed, explicitly incorporating the relative spatial relationship between the shield excavation face and adjacent structures. In this framework, the Convolutional Neural Network (CNN) module extracts spatial features from monitoring data and tunneling parameters, capturing interdependencies among different construction indicators and reflecting local spatial heterogeneity of… More > Graphic Abstract

Lijun Yang¹, Qiuwei Yang^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083976 - 23 July 2026

Abstract This study proposes a Multi-Stage Sparrow Search Algorithm (MS-SSA) for precise structural damage identification. Initially, the structural static displacement sensitivity formulation is derived via the Sherman-Morrison-Woodbury formula, and an objective function is constructed by integrating the sensitivity equations with the L2-norm penalty. Subsequently, MS-SSA is implemented to sequentially achieve preliminary damage localization and accurate quantification. In the localization phase, a constrained narrow-bound search space is predefined to identify potential damage regions. Leveraging this feedback, the sensitivity equations are condensed, and the search boundaries are adaptively refined for the quantification phase, where SSA is reapplied to… More >

Byeongjeong Kim, Jeonghyun Park, Joonho Yang, Hwanhee Lee^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082460 - 23 July 2026

Abstract Long-context question answering over narrative documents remains challenging because many questions require reconstructing event sequences while preserving local contextual flow under limited context budgets. Existing retrieval-augmented generation (RAG) methods typically retrieve document snippets independently, which can fragment narratives and harm temporal dependencies. We propose ChronoRAG, a retrieval framework for narrative question answering that first converts sequential document chunks into concise relation descriptions and then retrieves relevant units together with their adjacent chronological context. This design preserves retrieval precision while providing the generator with coherent local narrative structure. Experiments on NarrativeQA and GutenQA show that ChronoRAG More >

Qiya Wang^1,2, Jia Liu^1,2,*, Yuwei Lu^1,2, Yujie Liu^1,2, Peng Luo^1,2

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081589 - 23 July 2026

Abstract Implicit Neural Representation (INR) is a technique that models continuous signals using neural networks, replacing traditional discrete grid representations with a coordinate-to-value mapping function. As a data carrier, INR is gradually being adopted as the target for steganographic processing. However, existing INR-based steganographic schemes typically require modifying network structures (e.g., weights, nodes) and retraining to obtain stego INRs, leading to high time consumption and the need for re-training when replacing cover images. To address this issue, this paper proposes StegaMIR (Steganography via Modulated Implicit Representations), an image steganographic scheme based on modulated implicit representations. It… More >

Shasha Tian^1,2, Zhengyang Chen^1,3, Kai Ren^1,2, Na Li^1,2, Chongwei Ruan⁴, Zhijia Cui^1,3, Mian Wu^4,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081556 - 23 July 2026

Abstract To address the issues of low exploration efficiency and “geometric myopia” caused by the lack of high-level environmental structure modeling for mobile robots in complex indoor environments, this paper proposes an active SLAM object navigation method based on Situational Semantic Augmented Graph (SSAG). Unlike methods that learn policies solely on pixel-level semantic maps or exploit only object-level relations for implicit association, this work elevates local observations online into a room-level topological graph and performs explicit semantic reasoning over unobserved regions. First, an online room segmentation algorithm is employed to transform unstructured sensory data into a… More >

Xiaolan Li^1,2,*, Jinyu Shen^1,2, Jinhuang Liang^1,2, Yanting Wang^1,2

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.076521 - 18 June 2026

Abstract The intrinsic volatility and stochasticity of large-scale wind power generation pose significant challenges to grid stability. To address the limitations of conventional models in capturing strong non-stationarity, this study proposes a novel Multi-Stage Adaptive Forecasting Network (MSAF-Net). The framework features a hierarchical signal refinement strategy coupled with an intelligently optimized hybrid predictor. Initially, input redundancy is minimized via Pearson Correlation Coefficient (PCC) analysis to isolate significant meteorological variables. A two-phase decomposition-reconstruction mechanism is then implemented: the wind power series is first decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). To optimize the… More >