Shahbaz Juneja

Sustainable materials development of epoxy reinforced composites with Sansevieria cylindrica fibers and nano TiO₂ particles

Decellularized extracellular matrix bioinks through a polymer engineering lens: tissue-source-dependent macromolecular architecture, crosslinking chemistry, rheological behavior, and 3D bioprinting performance.

Decellularized extracellular matrix (dECM) bioinks are widely regarded as one of the more compositionally faithful hydrogel platforms for three-dimensional (3D) bioprinting in tissue engineering. However, the field still lacks a unified polymer engineering framework that links tissue-specific macromolecular architecture to rheological behavior and printing performance. This review synthesizes recent experimental studies on dECM bioinks derived from six tissue sources: cardiac, cartilage, liver, adipose, dermal, and neural tissues. These systems are analyzed through polymer network design, focusing on matrix composition, crosslinking chemistry, rheological properties, and printability. Tissue origin defines the polymeric composition of dECM and therefore influences the crosslinking strategies required to produce printable constructs with relevant mechanical properties. Across several composite systems, dECM incorporation creates a rheological paradox: storage modulus and viscosity may decrease compared with single-component matrices, likely because bioactive ECM macromolecules interfere with pre-formed polymer networks. Methacrylation partially resolves this limitation by separating mechanical tunability from native compositional constraints, enabling concentration-dependent stiffness modulation across approximately two orders of magnitude. Decellularization methodology also emerges as a critical, yet often underestimated, determinant of bioink performance. A polymer engineering perspective provides a more mechanistically useful basis for dECM bioink design than biological fidelity alone. Matching tissue-specific matrix composition, crosslinking architecture, rheological behavior, and printing parameters is essential for advancing dECM-based constructs toward clinical translation.

AI-aided metaheuristic optimized framework for job sequencing and machine failure prediction in T-shirt production system

This research proposes a study on AI-assisted optimized Job-Shop Production System (JPS) to improve performance, reduce cost and time, and detect machine failure. In the current industrial landscape, optimizing Job Shop Production Systems (JPS) is critical for achieving higher efficiency, reduced costs, and improved resource utilization. This study presents a comprehensive review of both conventional and artificial intelligence (AI)/machine learning (ML)-based approaches applied to JPS, with particular emphasis on job sequencing and machine failure prediction. The system is modeled as a multi-stage job-shop with diverse tasks and constraints, highlighting challenges in sequencing, resource allocation, and machine reliability. This work addresses job-shop scheduling in a T-shirt manufacturing system using Grey Wolf Optimization (GWO). While conventional GWO provides feasible job sequencing, it suffers from premature convergence. An Improved Grey Wolf Optimization (IGWO) algorithm is therefore proposed to enhance exploration and convergence efficiency. Simulation results show that IGWO achieves better job sequencing with reduced make-span, lower production cost, and improved resource utilization compared to standard GWO.