Yida Shen¹, Bin Dong², Quan Ma¹, Chao Dang^1,*, Congjian Li^2,*, Guojian Ren³, Shaozhan Wang^1,2, Xiaozhe Sun¹, Yong Ding⁴

Frontiers in Heat and Mass Transfer, Vol.24, No.2, 2026, DOI:10.32604/fhmt.2025.077096 - 30 April 2026

Abstract This article explores the application of Physics-Informed Neural Networks (PINNs) in solving supersonic flow problems within a Laval nozzle, proposing innovative methods by integrating physical constraints and neural network optimization techniques. The main innovations of this study include the construction of a novel neural network architecture with shortcut connections to enhance the prediction of overall flow trends and local fluctuations, thereby improving convergence speed, reducing computational costs, and increasing the accuracy of flow field reconstruction. Additionally, this study designs a PINNs framework that incorporates specific physical knowledge (SPK) to improve model stability, generalization, and accuracy, More >

Jialun Liu^1,2,3,*, Yuchang Lu⁴, Jianjun Lin³, Shebing Li³, Ruixia Gao⁵, Zhao Li⁶

Frontiers in Heat and Mass Transfer, Vol.24, No.2, 2026, DOI:10.32604/fhmt.2026.076292 - 30 April 2026

Abstract A steady thermo-hydraulic model of the helical tube steam generator was first constructed to study the coupled heat transfer process between the primary and secondary sides based on a discrete modeling method, and obtain the heat flux density distribution along the steam generator. Then, taking the obtained coupled heat flux density distribution as the thermal boundary condition input, considering the dynamic variation of physical properties on the secondary side, a dynamic model based on the time-domain method suitable for two-phase flow instability among parallel multiple channels of the steam generator was constructed. Finally, taking the… More >

Ahmed Ghazy^*

Frontiers in Heat and Mass Transfer, Vol.24, No.2, 2026, DOI:10.32604/fhmt.2025.076192 - 30 April 2026

Abstract In this study, the covers of the conventional double slope solar distiller (CDSSD) were replaced with a glass air heater and a glass air-cooled condenser. Ambient air was circulated through the air heater and air-cooled condenser to recover unavoidable heat losses in air heating as an auxiliary product. The thermal performance of the double slope solar distiller integrated with an air heater and an air-cooled condenser (DSSD-AH-ACC) was mathematically evaluated under real weather conditions and varying air flows. The results showed that increasing air flow through the air heater and air-cooled condenser improved the efficiency More >

Yun-Jae Ahn^1,2,3, Moon-Seok Kang², Ki-Byung Lim^2,3,4,*

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.078884 - 28 April 2026

Abstract Artificial polyploidy induction is widely used in ornamental breeding but can yield aneuploidy derivatives that vary in developmental stability and breeding utility. In Hibiscus syriacus ‘Blue Bird’, in vivo colchicine and oryzalin treatments generated regenerated shoots in which genome-size shifts were detected by flow cytometry; among the candidate lines, a subset reached flowering maturity and was characterized in detail. These flowering aneuploids displayed diverse floral alterations, including reduced corolla size, altered pigmentation, and partial conversion of stamens into petaloid organs. Flow cytometry and somatic chromosome counts indicated aneuploid status (150–182 chromosomes). Pollen morphology was highly variable, with… More >

Tianzhe Jiao, Yuming Chen, Xiaoyue Feng, Chaopeng Guo, Jie Song^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080595 - 27 April 2026

Abstract Knowledge distillation bridges the performance gap between camera-based and LiDAR-based 3D detectors by leveraging the precise geometric information from LiDAR. However, cross-modal knowledge transfer remains challenging due to the inherent modality heterogeneity between LiDAR and camera data, which often leads to instability during training. In this work, we find that these instabilities are closely related to distribution mismatch in the cross-modal feature space and noisy teacher signals. To address this issue, we propose a novel distribution-aware cross-modal distillation framework, named DA-T3D. Specifically, we first explicitly model the LiDAR teacher’s Bird’s-Eye-View (BEV) feature distribution and use… More >

Mustaqeem Khan¹, Ufaq Khan², Mamoun Awad¹, Nazar Zaki¹, Guiyoung Son³, Soonil Kwon^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079697 - 27 April 2026

Abstract Knowledge distillation has shown impressive results in different fields, including detection, recognition, and generation. These models are excellent at tasks such as speech recognition, but they need to be shrunk down using adaptive knowledge distillation (AKD). The use of AKD can improve human-computer interactions and streamline data collection in the field of Speech Emotion Recognition (SER). This study presents a high-level approach that employs a novel adaptive knowledge distillation (AKD) with spatio-temporal transformers to acquire advanced semantic features from the input signal. This method uses an instance-by-instance correlation between the teacher and a student to determine the More >

Xuan Sun^1,2, Yueying Zhu³, Jiaxi Jin¹, Zhitong Li^1,*, Leizhi Wang⁴, Zhaobo Chen^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078891 - 27 April 2026

Abstract Piezoelectric smart materials have been widely used in applications such as soft robotic actuation, vibration control and sensing of aerospace structures. In such contexts, the smart structures are typically subjected to significant large deformations and strong electromechanical coupling effects, which pose considerable challenges for conventional analytical approaches and classical finite element models in accurately predicting their nonlinear dynamic responses and capturing multiphysics coupling behaviors. To address these challenges in modeling and analysis, this work develops a flexible–electrical coupled computational framework with a unified mesh description based on the absolute nodal coordinate formulation (ANCF). This coupling… More >

Nai-Wei Lo¹, Cheng-I Lin², Chih-Chieh Chang^3,*, Chi-Yang Chang⁴, Tran Thi Luu Ly¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077316 - 27 April 2026

Abstract The growing frequency of malicious attacks on Internet of Things (IoT) devices has rendered conventional approaches with static label-dependent risk assessment models obsolete, especially when coping with unknown and continuously evolving threats. To mitigate these challenges, a novel dynamic trust evaluation framework approach is proposed in this work. The proposed framework utilized unsupervised learning and zero-knowledge proofs to assess device risks in complex environments adaptively, with an accuracy rate of 98.96% for normal clustering and 95.39% for anomalies. K-means clustering algorithm is leveraged to distinguish risk patterns with an additional Decision Tree classification algorithm to More >

Serhiy Serbin^1,*, Bohdan Lychko², Kateryna Burunsuz¹

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.076966 - 27 April 2026

Abstract This study investigates the use of ammonia as a carbon-free fuel for gas turbines in decarbonized hybrid energy systems. The objective is to predict the emission characteristics of a gas turbine combustion chamber operating on gaseous ammonia by employing detailed combustion kinetics. The chamber is modeled as a network of chemical reactors to simulate the primary reaction zone and the secondary air-mixing zone. The model is based on solving mass and energy conservation equations for chemically reacting flows. Four high-temperature ammonia oxidation mechanisms, comprising 71 to 286 chemical reactions, were used as kinetic schemes. New More >

Xiang Xiao, Peng Zhang^*, Yuan Yuan, Zhiyuan Feng, Kui Hu, Yuan Xu, Yunhuang Zhang, Guoming Liu

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.073741 - 27 April 2026

Abstract As a promising solution to the challenges of future clean and reliable energy supply, the Gas-Cooled Micro-Reactor (GCMR) has attracted increasing attention due to its potential for decentralized power generation, carbon-free operation, and flexible deployment in remote or extreme environments. As a novel reactor concept, the GCMR offers advantages such as compact size, inherent safety, and high thermal efficiency. However, conventional core calculation methods face significant challenges due to the complex geometric configurations, heterogeneous material distribution, and pronounced neutron leakage characteristics of the GCMR. This study proposes a diffusion-based homogenization method for GCMR analysis. First,… More > Graphic Abstract