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A finite element framework has been developed to explore the nonlinear dynamic instability exhibited by stiffened plates subjected to in-plane harmonic edge loading. In this framework, an eight-noded isoparametric degenerated shell element is used to model plate skin, while a three-noded curved beam element is employed to model the stiffener. The formulation of system matrices for both the plate skin and the stiffener is based on the Green-Lagrange strain displacement relationship, using the total Lagrangian approach. The Bolotin method is then applied to identify the region of linear dynamic instability. The Incremental Harmonic Balance (IHB) method is utilized to investigate the nonlinear frequency response of stiffened plates.

Escalating global temperatures driven by unchecked CO2 emissions, inefficient waste management, and deforestation have intensified the search for sustainable, multifunctional building materials capable of addressing energy efficiency, fire safety, and carbon mitigation simultaneously. In this article, we report a novel bio-based xerogel that combines excellent thermal insulation, outstanding flame-retardancy, and significant CO2 adsorption, offering a practical and eco-friendly solution in one innovative material. The xerogel was carefully engineered using cellulose nanofibers (CNFs), lignin, and polyvinyl alcohol (PVA), crosslinked with glutaraldehyde (GA). Diammonium phosphate (DAP) was added to impart flame resistance, while diethylenetriamine (DETA) synergistically enhanced structural integrity and CO2 capture. The resulting xerogel exhibited high porosity (96.1%), low density (30.1 mg cm-3), and excellent compressive strength (569.3 +/- 14 kPa at 50% strain). The compressive strength was found to be 308% higher than that of the neat CNF-xerogels (139.4 +/- 11 kPa). It also demonstrated superior thermal insulation (27.6 mW m-1 K-1), 18.1% higher than CNF-xerogels (33.7 mW m-1 K-1), and remarkable fire resistance, outperforming many commercial insulators and recently reported aerogels/xerogels. In CO2 adsorption tests, the xerogel achieved a high capacity of 3.09 mmol g-1 and maintained over 98.6% regeneration efficiency across six cycles. To the best of our knowledge, this work presents the first bio-based xerogel that simultaneously addresses thermal insulation, fire safety, and CO2 capture, offering a promising pathway toward advanced green materials and circular sustainability.

This study presents a first-time comprehensive characterization of Alpinia malaccensis (A. malaccensis) fibers extracted from the pseudostems of the Zingiberaceae family to evaluate their suitability as reinforcement in bio-composite applications. The physicochemical composition, thermal stability, crystalline structure, morphology, and tensile properties of the fibers were systematically investigated using FTIR, TGA, XRD, SEM, and single-fiber tensile testing. The fibers exhibited a high cellulose content of 75.94%, resulting in superior tensile strength (1164.99 MPa) and stiffness (28.44 GPa). Moderate hemicellulose (14.27%) and lignin (6.14%) contents contributed to balanced flexibility and good interfacial compatibility with polymer matrices. SEM analysis revealed a fine fiber diameter of 40-50 & micro;m with uniform longitudinal alignment, promoting efficient stress transfer. In addition, the low density (0.53 g/cm & sup3;) and moderate moisture content (10.19%) indicate suitability for lightweight and dimensionally stable composite structures. The results demonstrate that A. malaccensis fibers are a high-performance, sustainable reinforcement material with strong potential to replace synthetic fibers in eco-friendly composite applications.

Muntingia calabura, whose leaves are rich in phenolic and flavonoid content, was studied for the synergistic effects of UV-C pretreatment during microwave drying (from 200 watts to 360 watts). Fresh leaves of the plant were subjected to UV-C pretreatment for durations of 5, 10, 15 and 20 min. The results were further used to evaluate the leaf drying kinetics, energy performance and quality characteristics. Results showed that moderate UV-C (5-10 min) in conjunction with moderate microwave (200 W) drying improved drying performance. Improvements were indicated by higher effective moisture diffusivity, lower activation energy, decreased drying time and reduced energy consumption. Extended UV-C treatment (15-20 min) with high microwave power resulted in tissue damage, pigment loss, and decreases in model performance. The Midilli model was the best fit (R 2 = 0.999) among semi-empirical thin-layer models for all time and energy treatments. Moderate UV-C pretreatment improved retention of phytochemicals and maintained optical properties. Overall, the research has shown for the first time that UV-C assisted microwave drying is a novel and efficient method for the sustainable preservation of M. calabura leaves.

PurposeThe purpose of this study is to address the challenge of optimizing joint pricing and inventory decisions in supply chains by proposing an Agentic Automation-driven Multi-Agent Reinforcement Learning (MARL) framework. By embedding autonomy, goal-directed behavior and self-improving capabilities into each decision-making entity, this research overcomes limitations of traditional optimization methods in handling demand heterogeneity, variable lead times, dynamic pricing and shared resources such as warehouse capacity. Design/methodology/approachThe methodology integrates principles of multi-agent automation within a Centralized-Training–Decentralized-Execution (CTDE) architecture, enabling agents to exhibit proactive, coordinated behavior. Agents are trained using shared global information (e.g. warehouse constraints and cross-product demand patterns) but execute specialized, independent policies for individual Stock Keeping Units (SKUs). The approach combines Deep Reinforcement Learning (RL) with inventory theory to jointly optimize pricing and replenishment decisions under stochastic demand, while enabling autonomous adaptation to changing market conditions. FindingsThe framework is benchmarked against eight popular optimization and learning approaches: Bayesian Optimization, Genetic Algorithm (GA), Evolutionary Algorithm, Deep Q-Networks (DQN), Newsvendor Model, Economic Order Quantity (EOQ), Proximal Policy Optimization (PPO) and Soft Q-Learning (SQL). The results of this study show that the agentic MARL system achieves strong, balanced performance ($524k profit, 94.9% service) with robust adaptability. The EOQ model offers higher profit ($584k, 98.9% service level) but only in stable environments because of its limited adaptiveness. Other RL methods (PPO and SQL) exhibit high variability, while traditional approaches (GA, rule-based, Bayesian) underperform, lacking the autonomy and learning capacity needed for dynamic business environments. Originality/valueThis study provides: a scalable architecture enabling autonomous, goal-driven coordination for supply chain optimization; empirical evidence showing the advantages of agentic RL over traditional methods in complex, uncertain settings; and foundational insights for extending agentic AI to real-world applications such as promotion planning, supplier collaboration and end-to-end retail automation. Overall, this work bridges academic research and operational practice, providing a pathway toward intelligent, adaptive and agentic supply chain systems.