Shaoyan Wu^1,2, Jie Tian^1,2, Yiyan Wang^1,2, Muhammad Arif^1,2, Shuyao Wang^1,2, Jing Wang^1,2, Zhuoyao Yang^1,2, Ruhong Xu^1,2,*, Luhua Li^1,2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 3913-3924, 2025, DOI:10.32604/phyton.2025.072229 - 29 December 2025

Abstract Wheat grain morphology, particularly grain length (GL) and width (GW), is a key determinant of yield. To improve the suboptimal grain dimensions of the local anthocyanin-rich variety Guizi 1 (GZ1), we crossed it with Zhongyan 96-3 (ZY96-3), an elite germplasm known for faster grain filling and superior grain size. A genotyping-by-sequencing (GBS) approach was applied to an F₂ population of 110 individuals derived from GZ1 × ZY96-3, resulting in the identification of 23,134 high-quality SNPs. Most of the SNPs associated with GL and GW were clustered on chromosomes 2B, 3A, and 3B. QTL mapping for GL… More >

Ling Li^1,#, Ninghui Xu^1,#, Wenjing Wang^2,*, Jianfen Ying^1,*

Journal of Psychology in Africa, Vol.35, No.6, pp. 833-842, 2025, DOI:10.32604/jpa.2025.071557 - 30 December 2025

Abstract This study explores the role of teachers’ professional identity (TPI) on employee learning engagement (LE), with mediation by basic needs satisfaction (BNS). Participants were 255 Chinese pre-service teachers (191 females = 74.9%, 16 freshmen = 6.2%, 135 sophomores = 52.9%, 35 juniors = 12.5%, 72 seniors = 28.2%). They completed surveys on the “QuestionStar” online survey platform and 12 of the teachers completed interviews for sharing their personal insights. The results of Structural Equation Modeling (SEM) indicated that teachers’ professional identity significantly predicted both learning engagement and basic needs satisfaction, with basic needs satisfaction partially More >

R. K. Mishra^a,*, M. N. Anwar^a, M. A. Hasan^b

Chalcogenide Letters, Vol.22, No.10, pp. 871-882, 2025, DOI:10.15251/CL.2025.2210.871

Abstract This study introduces a novel approach to enhancing the performance of CdTe/IGZO-based heterojunction solar cells by utilizing IGZO as both a window layer and an electron transport layer (ETL). A comprehensive simulation using SCAPS-1D was conducted to evaluate the impact of various transparent conductive oxides (TCOs), including ITO, SnO_₂, ZnO, and FTO, on key photovoltaic parameters such as power conversion efficiency (PCE), open-circuit voltage (Voc), short-circuit current density (Jsc), and fill factor (FF). The research also explores the critical role of transport layers (HTL/ETL) and their material properties, band alignment, carrier mobility, and defect density, in More >

Yang Lei^1,*, Bo Jiang¹, Yucai Zhao², Gaofeng Fu³, Falin Qi¹, Tian Tian¹, Qiankuan Feng¹, Qiming Qu¹

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1657-1679, 2025, DOI:10.32604/sdhm.2025.069415 - 17 November 2025

Abstract Detecting internal defects, particularly voids behind linings, is critical for ensuring the structural integrity of aging high-speed rail (HSR) tunnel networks. While ground-penetrating radar (GPR) is widely employed, systematic quantification of performance boundaries for air-coupled (A-CGPR) and ground-coupled (G-CGPR) systems within the complex electromagnetic environment of multilayer reinforced HSR tunnels remains limited. This study establishes physics-based quantitative performance limits for A-CGPR and G-CGPR through rigorously validated GPRMax finite-difference time-domain (FDTD) simulations and comprehensive field validation over a 300 m operational HSR tunnel section. Key performance metrics were quantified as functions of: (a) detection distance (A-CGPR:… More >

Hanyu Tao^1,2, Dongye Sun^1,2, Tao Fang^1,2, Wenhu Zhao^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 521-536, 2025, DOI:10.32604/cmes.2025.072089 - 30 October 2025

Abstract Structural internal flaws often weaken the performance and integral stability, while traditional nondestructive testing or inversion methods face challenges of high cost and low efficiency in quantitative flaw identification. To quickly identify internal flaws within structures, a deep learning model for flaw detection is proposed based on the image quadtree scaled boundary finite element method (SBFEM) combined with a deep neural network (DNN). The training dataset is generated from the numerical simulations using the balanced quadtree algorithm and SBFEM, where the structural domain is discretized based on recursive decomposition principles and mesh refinement is automatically… More >

Khawaja Tahir Mehmood^1,*, Muhammad Majid Hussain²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5643-5670, 2025, DOI:10.32604/cmc.2025.068404 - 23 October 2025

Abstract In the modern era of 5th generation (5G) networks, the data generated by User Equipments (UE) has increased significantly, with data file sizes varying from modest sensor logs to enormous multimedia files. In modern telecommunications networks, the need for high-end security and efficient management of these large data files is a great challenge for network designers. The proposed model provides the efficient real-time virtual data storage of UE data files (light and heavy) using an object storage system MinIO having inbuilt Software Development Kits (SDKs) that are compatible with Amazon (S3) Application Program Interface (API)… More >

J. S. V. Siva Kumar¹, Mahmad Mustafa², Sk. M. Unnisha Begum³, Badugu Suresh⁴, Rajanand Patnaik Narasipuram^5,*

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 >

Amita Biswal, Dah-Jing Jwo^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 927-944, 2025, DOI:10.32604/cmes.2025.067299 - 31 July 2025

Abstract The extended Kalman filter (EKF) is extensively applied in integrated navigation systems that combine the global navigation satellite system (GNSS) and strap-down inertial navigation system (SINS). However, the performance of the EKF can be severely impacted by non-Gaussian noise and measurement noise uncertainties, making it difficult to achieve optimal GNSS/INS integration. Dealing with non-Gaussian noise remains a significant challenge in filter development today. Therefore, the maximum correntropy criterion (MCC) is utilized in EKFs to manage heavy-tailed measurement noise. However, its capability to handle non-Gaussian process noise and unknown disturbances remains largely unexplored. In this paper,… More >

Yunfeng Zhang, Dong Hu^*, Yuan Liu

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.7, pp. 1641-1655, 2025, DOI:10.32604/fdmp.2025.065820 - 31 July 2025

Abstract To address the challenges of poor surface quality and high energy consumption in marble cutting, this study introduces an auxiliary abrasive jet cutting technology enhanced by the use of polyacrylamide (PAM) as a drag-reducing additive. The effects of feed rate (50–300 mm/min), polymer concentration (0–0.5 g/L), and nozzle spacing (4–12 mm) on kerf width and surface roughness are systematically investigated through an orthogonal experimental design. Results reveal that feed rate emerges as the most significant factor (p < 0.01), followed by PAM concentration and nozzle spacing. The optimal set of parameters, comprising a 200 mm/min feed More >

Sarmad K. Ibrahim^1,*, Saif A. Abdulhussien², Hazim M. ALkargole¹, Hassan H. Qasim¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5223-5238, 2025, DOI:10.32604/cmc.2025.066548 - 30 July 2025

Abstract The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and Massive Machine Type Communication (mMTC)—present tremendous challenges to conventional methods of bandwidth allocation. A new deep reinforcement learning-based (DRL-based) bandwidth allocation system for real-time, dynamic management of 5G radio access networks is proposed in this paper. Unlike rule-based and static strategies, the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput, fairness, and compliance with QoS requirements. By using… More >