Priyadarshini College of Engineering

Wire-arc additive manufacturing (WAAM) is increasingly adopted for fabricating large-scale metallic components. However, reliable prediction and multi-objective optimization of weld-bead geometry remain critical challenges. This study systematically investigates the influence of voltage, wire feed speed (WFS), and torch travel speed (TS) on bead width (BW), bead height (BH), and microhardness (MH) using a structured Taguchi L18 experimental design. Statistical regression modeling and analysis of variance (ANOVA) were employed to identify significant process parameters and establish physically interpretable predictive relationships. To further assess potential nonlinear behavior, the XGBoost machine-learning algorithm was implemented as a surrogate model. Given the limited dataset size (n = 18), leave-one-out cross-validation (LOOCV) was adopted to obtain unbiased predictive performance estimates. Cross-validated results indicate that XGBoost provides performance comparable to linear regression within the investigated parameter window, achieving R²_LOOCV values of 0.879 (BW), 0.770 (BH), and 0.870 (MH). The comparatively lower prediction accuracy for BH is attributed to inherent melt pool instability and droplet transfer variability. Multi-objective grey relational analysis identified a locally optimal parameter combination of 15 V, 5.61 m/min WFS, and 10.9 mm/s TS within the defined design space, enabling simultaneous minimization of BW and maximization of BH and MH. The developed framework provides a physically grounded and cross-validated predictive optimization methodology for WAAM parameter selection within constrained single-bead deposition conditions.

The rapid usage of recycled aggregate concrete and nano-modified binders necessitates prediction frameworks that can handle tightly correlated mechanical, durability, and functional properties with limited experimental data samples. Traditional empirical formulations and single-output learning models cannot represent nonlinear, multiscale recycled aggregate, nano-admixture, curing history, and microstructure interactions. These limits limit material optimization reliability and prevent the design of durable and intelligent concrete systems for sustainable infrastructure sets. Next generation artificial intelligence frameworks using graph neural networks, capsule networks, neural ordinary differential equations, and neural architecture search predict nano-modified recycled aggregate concrete’s compressive, tensile, flexural, freeze–thaw, chloride penetration, and self-sensing electrical behavior. Microstructural interaction models and physical limitations ensure material believability across varied compositions and curing regimes. A quantitative analysis of over 100 experimental mix configurations indicates significant accuracy gains over multi-output baselines. EvoConcreteNet predicted flexural strength with a R² of 0.95, while GraphSenseNet achieved a coefficient of determination of 0.96 for compressive strength with mean absolute errors < 2.5 MPa. CapsuleRACNet achieved a R² above 0.97 for electrical resistance estimate, while ContinuousConcreteODE reduced freeze-thaw cycle prediction errors by over 40

Lead (Pb) is a well-known xenobiotic and neurotoxin. Chronic Pb exposure remains a major public health concern, particularly in developing countries, and is associated with cognitive impairment, memory deficits, and peripheral and central nervous system toxicity. Pb readily crosses the blood-brain barrier by competing with iron for transport via divalent metal transporter 1 and mimicking calcium to enter through Ca-permeable ion channels, thereby disrupting Fe homeostasis and blood-brain barrier integrity. Pb accumulation promotes excessive generation of reactive oxygen species, mitochondrial dysfunction, lipid peroxidation, and neuroinflammatory responses in the brain. These events alter apoptotic signaling pathways, impair Ca-dependent neuronal communication, and disrupt cholinergic neurotransmission, leading to synaptic dysfunction and neuronal loss in the hippocampus. Proteomic studies have provided insights into the molecular mechanisms underlying Pb-induced neurotoxicity by identifying various Pb-interacting proteins involved in metal transport, oxidative stress regulation, apoptosis, synaptic plasticity, and neurotransmitter signaling. In the absence of effective pharmaceutical treatments for Pb poisoning, these proteomic insights highlight potential diagnostic biomarkers and therapeutic targets that play a central role in antioxidant defense and inflammation control. Emerging enzymatic biosensors also offer promising tools for the rapid and sensitive detection of Pb exposure. Collectively, this review integrates mechanistic, proteomic, and translational perspectives to understand Pb-induced neurotoxicity and support the development of improved diagnostic and mitigation strategies for Pb exposure.

To meet rapidly growing demands for multifunctional smart materials with dynamic energy-responsive capabilities, a unified modeling framework must integrate material design, structural optimization, and real-time adaptability. Existing methodologies tend to be disparate, thus separating topology optimization, inverse design, and structural analysis. A result of this fragmentation is inefficiency, nonoptimal performance under dynamic conditions, and limited adaptiveness to real-world uncertainties. This research proposes the Unified Deep Hybrid Analytical Framework for Smart Material Design and Energy-Responsive Structural Optimization (UDHF-SMERO) to fill these gaps. In under five tightly coupled stages of processing, each stage will focus on an engineered aspect of the design-to-deployment pipeline. The first region is Co-Evolutionary Physics Informed Graph Transformers (Co-PIGT), which model the concurrent co-learning of the patterning material cores and energy-dissipation pathways while guided by the physics Informed loss functions. The output is optimally attained graphs and generated spatio-temporal energy paths with R2> 0.95 and dissipation error < 3

This study presents a novel semi-analytical framework for modeling the hygro-thermoelastic response of infinite porous cylinders under environmental loading. Unlike conventional models, the proposed formulation uniquely integrates several advanced physical mechanisms: a tunable memory-dependent transport law with customizable kernel functions (uniform, exponential, and polynomial), Eringen-type nonlocal elasticity to capture long-range microstructural interactions, dynamic porosity evolution coupled with stress, temperature, and moisture fields, and the explicit inclusion of cross-diffusion effects (Dufour and Soret). The problem considers an axisymmetric porous solid cylinder subjected to coupled hygro-thermal-mechanical loads. The solution methodology employs the Laplace transformation and Bessel function expansions, with numerical inversion carried out via the fixed Talbot contour. Key findings reveal that the choice of memory kernel significantly influences the distributions of temperature, moisture, displacement, and stress, while the nonlocal parameter smooths field gradients and delays peak responses. The model provides a unified platform for benchmarking porous media behavior, with direct applications in the design and optimization of thermal insulation systems, biomedical implants, and microstructured devices. This fully coupled approach, in which memory-dependent transport, nonlocal stress, and dynamic porosity evolution are solved simultaneously within a single governing system, has not been previously reported and offers new avenues for experimental validation and multiphysics simulation.