Nancy Abril Martínez-López¹, Betsy Anaid Peña-Ocaña², Rodolfo García-Contreras³, Toshinari Maeda⁴, Reiner Rincón-Rosales¹, Federico Antonio Gutiérrez-Miceli¹, Víctor Manuel Ruíz-Valdiviezo^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2729-2743, 2025, DOI:10.32604/phyton.2025.068176 - 30 September 2025

Abstract Species of the genus Paenibacillus, especially those from extreme environments that have been reported, are known for producing bioactive compounds with agricultural and biotechnological applications. In this study, we investigated the genomic and biochemical potential of Paenibacillus sp. VCA1 strain isolated from a thermophilic environment. Taxonomic identification was performed using whole genome similarity analysis, TETRA four-nucleotide frequency of occurrence analysis, ANI average nucleotide identity analysis, and gene distance analysis using digital DNA-DNA hybridization (dDDH). Functional analysis of the strain VCA1 was performed by detecting genes, enzymes, and genome subsystems involved in biocontrol and plant growth promotion,… More >

Hafiza Saima Gul^1,2,*, Mumtaz Hussain¹, Tayyaba Sanaullah³, Habib-ur-Rehman Athar², Ibrahim Al-Ashkar⁴, Muhammad Kamran⁵, Mohammed Antar⁶, Ayman El Sabagh^7,*

Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2893-2909, 2025, DOI:10.32604/phyton.2025.058816 - 30 September 2025

Abstract The low efficiency of phytoextraction of lead (Pb) from agricultural fields poses a significant agricultural challenge. Organic chelating agents can influence Pb bioavailability in soil, affecting its uptake, transport, and toxicity in plants. This study aimed to assess the impact of citric acid (CA) and diethylenetriaminepentaacetic acid (DTPA) on chelate-assisted phytoextraction of Pb and its effect on growth and physiology of two cultivars (07001; 07002) of mung bean (Vigna radiata). The cultivars of mung bean were exposed to 60 mg·L⁻¹ lead chloride (PbCl₂) solution, with or without the addition of 300 mg·L⁻¹ CA or 500 mg·L⁻¹ DTPA, until… More >

Shigen Shen^1,2, Xiaojun Ji^1,*, Yimeng Liu¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3997-4021, 2025, DOI:10.32604/cmc.2025.069332 - 23 September 2025

Abstract The Industrial Internet of Things (IIoT) is increasingly vulnerable to sophisticated cyber threats, particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures. To address this critical challenge, this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient (ZSG-MAD3PG). The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient (MAD3PG) algorithm and incorporates defensive deception (DD) strategies to achieve adaptive and efficient protection. While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,… More >

Yann-Horng Lin¹, Wen-Jer Chang^1,*, Yi-Chen Lee^2,*, Muhammad Shamrooz Aslam³, Cheung-Chieh Ku⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2231-2262, 2025, DOI:10.32604/cmes.2025.067464 - 31 August 2025

Abstract An Interval Type-2 (IT-2) fuzzy controller design approach is proposed in this research to simultaneously achieve multiple control objectives in Nonlinear Multi-Agent Systems (NMASs), including formation, containment, and collision avoidance. However, inherent nonlinearities and uncertainties present in practical control systems contribute to the challenge of achieving precise control performance. Based on the IT-2 Takagi-Sugeno Fuzzy Model (T-SFM), the fuzzy control approach can offer a more effective solution for NMASs facing uncertainties. Unlike existing control methods for NMASs, the Formation and Containment (F-and-C) control problem with collision avoidance capability under uncertainties based on the IT-2 T-SFM… More >

Abubakar Elsafi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1749-1766, 2025, DOI:10.32604/cmes.2025.066449 - 31 August 2025

Abstract Domain randomization is a widely adopted technique in deep reinforcement learning (DRL) to improve agent generalization by exposing policies to diverse environmental conditions. This paper investigates the impact of different reset strategies, normal, non-randomized, and randomized, on agent performance using the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3) algorithms within the CarRacing-v2 environment. Two experimental setups were conducted: an extended training regime with DDPG for 1000 steps per episode across 1000 episodes, and a fast execution setup comparing DDPG and TD3 for 30 episodes with 50 steps per episode under constrained computational… More >

Raffaele Carrano^1,#, Carlotta Zucca^1,#, Nicla Cristina¹, Martina Grande¹, Eleonora Leti Maggio¹, Riccardo Bei², Antonio Infante², Chiara Focaccetti¹, Valeria Lucarini³, Loredana Cifaldi¹, Laura Masuelli⁴, Luciano Mutti⁵, Camilla Palumbo¹, Monica Benvenuto¹, Roberto Bei^1,*

Oncology Research, Vol.33, No.9, pp. 2181-2204, 2025, DOI:10.32604/or.2025.066708 - 28 August 2025

Abstract Mesothelioma is a rare and aggressive cancer with a poor prognosis and limited therapeutic options. Despite recent advances, conventional treatment approaches remain largely ineffective due to late diagnosis, chemoresistance and immunosuppressive tumor microenvironment. This review reports the latest studies on combination therapies for mesothelioma, focusing on the potential of integrating chemotherapeutic agents, molecularly targeted agents, vaccines and natural bioactive compounds such as polyphenols. Clinical and preclinical studies demonstrate that integrating immune-modulating drugs or molecular inhibitors with chemotherapy can improve survival and reduce tumor progression in mesothelioma models and patients. Vaccine-based strategies show potential for inducing More > Graphic Abstract

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2071-2094, 2025, DOI:10.32604/cmc.2025.068533 - 29 August 2025

Abstract Smart learning environments have been considered as vital sources and essential needs in modern digital education systems. With the rapid proliferation of smart and assistive technologies, smart learning processes have become quite convenient, comfortable, and financially affordable. This shift has led to the emergence of pervasive computing environments, where user’s intelligent behavior is supported by smart gadgets; however, it is becoming more challenging due to inconsistent behavior of Artificial intelligence (AI) assistive technologies in terms of networking issues, slow user responses to technologies and limited computational resources. This paper presents a context-aware predictive reasoning based… More >

Lei Lei¹, Chengfu Wu^2,*, Huaimin Chen²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1339-1363, 2025, DOI:10.32604/cmc.2025.067117 - 29 August 2025

Abstract This paper proposes a Multi-Agent Attention Proximal Policy Optimization (MA2PPO) algorithm aiming at the problems such as credit assignment, low collaboration efficiency and weak strategy generalization ability existing in the cooperative pursuit tasks of multiple unmanned aerial vehicles (UAVs). Traditional algorithms often fail to effectively identify critical cooperative relationships in such tasks, leading to low capture efficiency and a significant decline in performance when the scale expands. To tackle these issues, based on the proximal policy optimization (PPO) algorithm, MA2PPO adopts the centralized training with decentralized execution (CTDE) framework and introduces a dynamic decoupling mechanism,… More >

Qamar H. Naith^1,*, H. Mancy^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 387-405, 2025, DOI:10.32604/cmes.2025.066917 - 31 July 2025

Abstract Effective water distribution and transparency are threatened with being outrightly undermined unless the good name of urban infrastructure is maintained. With improved control systems in place to check leakage, variability of pressure, and conscientiousness of energy, issues that previously went unnoticed are now becoming recognized. This paper presents a grandiose hybrid framework that combines Multi-Agent Deep Reinforcement Learning (MADRL) with Shapley Additive Explanations (SHAP)-based Explainable AI (XAI) for adaptive and interpretable water resource management. In the methodology, the agents perform decentralized learning of the control policies for the pumps and valves based on the real-time… More >

Bismillah Mubeen^1,2,#, Ammarah Hasnain^2,3,#,*, Syed Atif Hasan Naqvi⁴, Fahad Hakim⁵, Syed Sheharyar Hassan Naqvi⁶, Muhammad Zeeshan Hassan⁴, Muhammad Umer Iqbal⁷, Mahmoud Moustafa⁸, Mohammed O. Alshaharni⁸, Mingzheng Duan^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 1941-1971, 2025, DOI:10.32604/phyton.2025.064056 - 31 July 2025

Abstract Plants have long served as a cornerstone for drug discovery, offering a vast repertoire of bioactive compounds with proven efficacy in combating oxidative stress, a pivotal driver of chronic diseases such as cancer, diabetes, cardiovascular disorders, and neurodegenerative conditions. This review synthesizes current knowledge on plant-derived antioxidants, emphasizing their mechanisms, therapeutic potential, and quantitative efficacy validated through standardized assays. Key phytochemicals, including polyphenols, carotenoids, flavonoids, and terpenoids, neutralize reactive oxygen species (ROS) through radical scavenging, enzyme modulation, and gene regulation. For instance, lutein, a carotenoid found in leafy greens, demonstrates potent antioxidant activity with IC₅₀ values… More >