Mustafa Qaid Khamisi¹, Zuping Zhang^1,*, Mohammed Al-Habib¹, Muhammad Asim², Sajid Shah²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077556 - 08 May 2026

Abstract Large Vision-Language models (VLMs) such as Contrastive Language-Image Pretraining (CLIP) have transformed open world image recognition. Nevertheless, few-shot classification, particularly in the extremely low-shot regime, requires not only high accuracy but also reliably calibrated uncertainty for decisions with high confidence. Existing training-free CLIP adapters are primarily designed to increase accuracy and efficiency; integrate the zero-shot text logits with the few-shot feature caches, but not definitely model predictive uncertainty and therefore often exhibit considerable miscalibration and weak selective performance. Bayesian adapters move in the direction of probabilistic modeling by placing priors over adapter parameters and employing… More >

Ali S. Alghamdi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079763 - 27 April 2026

Abstract The integration of wind-based DG introduces significant variability and uncertainty into the operation of distribution networks, which complicates the planning and decision-making process. This paper presents a dual-objective stochastic optimization framework for the optimal allocation of wind DG, considering dynamic network reconfiguration across multiple loading conditions. Probabilistic modeling of wind speed is integrated using the Weibull distribution and the associated wind power uncertainty is discretized through a scenario-based point estimation method. Variability in load is accounted for by considering multiple loading levels, and the integrated uncertainty space is constructed as the Cartesian product of wind… More >

Marvin Zager^1,*, Gianluca Manca², Alexander Fay², Felix Gehlhoff¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079058 - 27 April 2026

Abstract Autonomous robot teams operating in dynamic, uncertain environments require reliable mechanisms to build decentralized context models without centralized coordination. Traditional consensus methods often fail under uncertainty caused by inconsistent sensing, communication delays, or heterogeneous perception models. This paper introduces the Decentralized Belief Consensus (DBC) algorithm, a novel approach that integrates probabilistic reasoning with entropy-based certainty measures to enable adaptive and robust consensus formation in heterogeneous multi-robot systems. Each robot quantifies the uncertainty of its local observations using Shannon entropy, derives a certainty score, and fuses beliefs with neighbors through certainty-weighted averaging. This allows the team… More >

Cheng Yang¹, Xiuyu Yang^1,*, Gangui Yan¹, Hongda Dong², Chenggang Li²

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2025.072773 - 27 April 2026

Abstract The integration of renewable energy introduces significant uncertainty into daily power system operation scenarios. Traditional deterministic unit commitment methods struggle to adapt to these conditions, often resulting in poor economic performance and high curtailment rates in planning outcomes. To address these challenges, this paper proposes a coordinated thermal power-energy storage planning methodology for managing renewable energy uncertainty. First, the operational effectiveness of daily unit commitment under uncertain renewable energy scenarios is analyzed, with quantitative assessment of how different commitment strategies impact supply-demand balance and economic performance. Subsequently, by conducting flexibility evaluation under multiple renewable energy… More >

Zemin Liang, Songyu Gao, Qi Yao^*

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072877 - 27 March 2026

Abstract For mixed-integer programming (MIP) problems in new power systems with uncertainties, existing studies tend to address uncertainty modeling or MIP solution methods in isolation. They overlook core bottlenecks arising from their coupling, such as variable dimension explosion, disrupted constraint separability, and conflicts in solution logic. To address this gap, this paper focuses on the coupling effects between the two and systematically conducts three aspects of work: first, the paper summarizes the uncertainty optimization methods suitable for addressing uncertainty-related issues in power systems, along with their respective advantages and disadvantages. It also clarifies the specific forms… More >

Sara Tehsin^1,*, Inzamam Mashood Nasir¹, Wiem Abdelbaki², Fadwa Alrowais³, Reham Abualhamayel⁴, Abdulsamad Ebrahim Yahya⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076514 - 12 March 2026

Abstract The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise, adversarial manipulation, and semantic inconsistency between modalities. Existing multimodal fake news detection approaches often rely on deterministic fusion strategies, which limits their ability to model uncertainty and complex cross-modal dependencies. To address these challenges, we propose Q-ALIGNer, a quantum-inspired multimodal framework that integrates classical feature extraction with quantum state encoding, learnable cross-modal entanglement, and robustness-aware training objectives. The proposed framework adopts quantum formalism as a representational abstraction, enabling probabilistic modeling of multimodal alignment… More >

Sana Shahab¹, Vladimir Simic^2,*, Ashit Kumar Dutta^3,4, Mohd Anjum^5,*, Dragan Pamucar^6,7,8

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073945 - 29 January 2026

Abstract Environmental problems are intensifying due to the rapid growth of the population, industry, and urban infrastructure. This expansion has resulted in increased air and water pollution, intensified urban heat island effects, and greater runoff from parks and other green spaces. Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies. This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization (AAROM-TN), enhanced by a dual weighting strategy. The weighting approach integrates the Criteria Importance Through Intercriteria Correlation… More >

Fatemeh Mobasheri¹, Masoud Hosseinpoor^1,*, Ammar Yahia^1,2, Farhad Pourkamali-Anaraki³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3309-3347, 2025, DOI:10.32604/cmes.2025.072271 - 23 December 2025

Abstract Self-consolidating concrete (SCC) is an important innovation in concrete technology due to its superior properties. However, predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes. This study combines machine learning (ML) models with conformal prediction (CP) to address these issues, offering prediction intervals that quantify uncertainty and reliability. A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods, including Random Forest (RF), Gradient Boosting Regressor (GBR), Extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM), along with CP techniques, More >

Shohreh Moradpour^1,#, Shuai Zhao^2,#, Mojgan Entezari¹, Shamsollah Ayoubi^3,*, Seyed Roohollah Mousavi⁴

Revue Internationale de Géomatique, Vol.34, pp. 809-829, 2025, DOI:10.32604/rig.2025.069538 - 06 November 2025

Abstract Accurate spatial prediction of soil organic carbon (SOC) and soil inorganic carbon (SIC) is vital for land management decisions. This study targets SOC/SIC mapping challenges at the watershed scale in central Iran by addressing environmental heterogeneity through a random forest (RF) model combined with bootstrapping to assess prediction uncertainty. Thirty-eight environmental variables—categorized into climatic, soil physicochemical, topographic, geomorphic, and remote sensing (RS)-based factors—were considered. Variable importance analysis (via) and partial dependence plots (PDP) identified land use, RS indices, and topography as key predictors of SOC. For SIC, soil reflectance (Bands 5 and 7, ETM+), topography, More > Graphic Abstract

Yingying Guo¹, Ziyu Cui², Jibing Shen¹, Pei Li^3,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4519-4550, 2025, DOI:10.32604/cmc.2025.071504 - 23 October 2025

Abstract Accurate simulation of acoustic wave propagation in complex structures is of great importance in engineering design, noise control, and related research areas. Although traditional numerical simulation methods can provide precise results, they often face high computational costs when applied to complex models or problems involving parameter uncertainties, particularly in the presence of multiple coupled parameters or intricate geometries. To address these challenges, this study proposes an efficient algorithm for simulating the acoustic field of structures with adhered sound-absorbing materials while accounting for ground reflection effects. The proposed method integrates Catmull-Clark subdivision surfaces with the boundary… More >