Xia Yan¹, Wei Wang¹, Kai Shen^2,*, Yanqing Feng¹, Junyi Sun¹, Xiaogang Li¹, Wentao Zhu¹, Binbin Shi¹, Guanglong Sheng^2,3

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070360 - 27 December 2025

Abstract In the development of coalbed methane (CBM) reservoirs using multistage fractured horizontal wells, there often exist areas that are either repeatedly stimulated or completely unstimulated between fracturing stages, leading to suboptimal reservoir performance. Currently, there is no well-established method for accurately evaluating the effectiveness of such stimulation. This study introduces, for the first time, the concept of the Fracture Network Bridging Coefficient (FNBC) as a novel metric to assess stimulation performance. By quantitatively coupling the proportions of unstimulated and overstimulated volumes, the FNBC effectively characterizes the connectivity and efficiency of the fracture network. A background… More >

Malvinder Singh Bali¹, Weiwei Jiang^2,*, Saurav Verma³, Kanwalpreet Kour⁴, Ashwini Rao³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070866 - 09 December 2025

Abstract In recent years, Blockchain Technology has become a paradigm shift, providing Transparent, Secure, and Decentralized platforms for diverse applications, ranging from Cryptocurrency to supply chain management. Nevertheless, the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency, scalability, and energy consumption. This study proposes an innovative approach to Blockchain network optimization, drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms. Specifically, we explore the application of genetic algorithms, particle swarm optimization, and related evolutionary techniques to enhance the performance of blockchain networks. The proposed methodologies More >

Zeeshan Ali Haider¹, Inam Ullah^2,*, Ahmad Abu Shareha³, Rashid Nasimov⁴, Sufyan Ali Memon^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.071042 - 10 November 2025

Abstract The advent of sixth-generation (6G) networks introduces unprecedented challenges in achieving seamless connectivity, ultra-low latency, and efficient resource management in highly dynamic environments. Although fifth-generation (5G) networks transformed mobile broadband and machine-type communications at massive scales, their properties of scaling, interference management, and latency remain a limitation in dense high mobility settings. To overcome these limitations, artificial intelligence (AI) and unmanned aerial vehicles (UAVs) have emerged as potential solutions to develop versatile, dynamic, and energy-efficient communication systems. The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning (CoRL) to manage an autonomous network.… More >

Guo-Feng Fan^1,2, Jia-Jing Qian¹, Li-Ling Peng¹, Xin-Hang Jia¹, Ling-Han Zuo¹, Jia-Can Yan¹, Jiang-Yan Chen¹, Anantkumar J. Umbarkar³, Wei-Chiang Hong^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3813-3837, 2025, DOI:10.32604/cmes.2025.073615 - 23 December 2025

Abstract Accurate prediction of perovskite photovoltaic materials’ optoelectronic properties is crucial for developing efficient and stable materials, advancing solar technology. To address poor interpretability, high computational complexity, and inaccurate predictions in relevant machine learning models, this paper proposes a novel methodology. The technical route of this paper mainly centers on the random forest-knowledge distillation-bidirectional gated recurrent unit with attention technology (namely RF-KD-BIGRUA), which is applied in perovskite photovoltaic materials. Primarily, it combines random forest to quantitatively assess feature importance, selecting variables with significant impacts on photoelectric conversion efficiency. Subsequently, statistical techniques analyze the weight distribution of More >

R. Rajeev^a,b,*, C. M. S. Negi^a

Chalcogenide Letters, Vol.22, No.5, pp. 469-480, 2025, DOI:10.15251/CL.2025.225.469

Abstract Cobalt-doped cadmium sulphide nanoparticles of semiconductors (CDs: Co NPs) were synthesised using various cobalt concentrations utilising a microwave-assisted approach. Debye-Scherer equation revealed the nanoparticles' size range to be between 2 and 4 nm. Diffraction from X-rays revealed a zinc mix structure. According to the structure in the optical bandgap energies indicates that, doping has systematically raised the bandgap energy as the doping concentration raises. The composition of the nanoparticles which was verified by EDAX, validated the effective integration of cobalt into the CdS structure. The detection of different functional and vibrational groups was performed at More >

Jie Jin¹, Tianci Wang¹, Yaning Wang¹, Jingqi Yao², Jinxiu Song^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3611-3632, 2025, DOI:10.32604/phyton.2025.070680 - 01 December 2025

Abstract In plant factory with artificial lighting, precise regulation of environmental and nutritional factors is essential to optimize both growth and quality of leafy vegetables. This study systematically evaluated the combined effects of light intensity (150, 200, 250 μmol/(m²·s)) and calcium supply in the nutrient solution (0.5, 1.0, 1.5 mmol/L) on lettuce morphology, photosynthesis, quality indices, and tipburn incidence. Elevating light from 150 to 200 μmol/(m²·s) significantly enhanced leaf number, area, photosynthetic rate, biomass, and foliar calcium. These gains plateaued at 250 μmol/(m²·s), where tipburn incidence surged to 76.5%. Photosynthetic pigments progressively rose with light intensity. Calcium supply… More >

Markapudi Bhanu Prasad¹, Borhen Louhichi², Santosh Kumar Sahu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 483-519, 2025, DOI:10.32604/cmes.2025.069395 - 30 October 2025

Abstract This study investigates the mechanical behavior of polyurethane (PU) nanocomposites reinforced with nanodiamonds (NDs) and proposes an integrated optimization–prediction framework that combines the Taguchi method with machine learning (ML). The Taguchi design of experiments (DOE), based on an L9 orthogonal array, was applied to investigate the influence of composite type (pure PU, 0.1 wt.% ND, 0.5 wt.% ND), temperature (145°C–165°C), screw speed (50–70 rpm), and pressure (40–60 bar). The mechanical tests included tensile, hardness, and modulus measurements, performed under varying process parameters. Results showed that the addition of 0.5 wt.% ND substantially improved PU performance,… More >

Hay Mon Aung¹, Aung Htay Naing^2,*, Chang Kil Kim², Kyeung II Park^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2719-2727, 2025, DOI:10.32604/phyton.2025.068713 - 30 September 2025

Abstract Phalaenopsis orchids are economically important ornamental crops; however, their commercial micropropagation is often limited by poor rooting efficiency and inconsistent growth. In this study, we investigated the effects of silver nanoparticles (Ag-NPs) on the in vitro regeneration and growth of Phalaenopsis cultivar 611B to determine the optimal concentration of Ag-NPs for improved micropropagation outcomes. Shoot tip explants (2–3 mm)—derived from protocorm-like bodies were cultured on a regeneration medium containing Hyponex (20:20:20 and 6.5:6.5:19), 18 g/L sugar, 2 g/L peptone, 0.8 g/L activated charcoal, 12.5 g/L potato extract, 50 mL/L apple juice, and 10 mg/L 6-benzylaminopurine (6-BA), with… More >

Hoon Ko¹, Marek R. Ogiela², Libor Mesicek³, Sangheon Kim^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2985-2997, 2025, DOI:10.32604/cmc.2025.065885 - 23 September 2025

Abstract The rapid evolution of AI-driven cybersecurity solutions has led to increasingly complex network infrastructures, which in turn increases their exposure to sophisticated threats. This study proposes a Graph Neural Network (GNN)-based feature selection strategy specifically tailored for Network Intrusion Detection Systems (NIDS). By modeling feature correlations and leveraging their topological relationships, this method addresses challenges such as feature redundancy and class imbalance. Experimental analysis using the KDDTest+ dataset demonstrates that the proposed model achieves 98.5% detection accuracy, showing notable gains in both computational efficiency and minority class detection. Compared to conventional machine learning methods, the More >

Gaurav Saxena¹, Kiran Napte², Neeraj Kumar Shukla^3,4, Sushma Parihar^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2307-2323, 2025, DOI:10.32604/cmes.2025.067530 - 31 August 2025

Abstract The ill-posed character of haze or fog makes it difficult to remove from a single image. While most existing methods rely on a transmission map refined through depth estimation and assume a constant scattering coefficient, this assumption limits their effectiveness. In this paper, we propose an enhanced transmission map that incorporates spatially varying scattering information inherent in hazy images. To improve linearity, the model utilizes the ratio of the difference between intensity and saturation to their sum. Our approach also addresses critical issues such as edge preservation and color fidelity. In terms of qualitative as More >