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Synergistic Enhancement of Mechanical, Tribological, and Thermal Properties of UHMWPE Nanocomposites Through Hybrid MXene (Ti₃C₂Tₓ) and Hexagonal Boron Nitride Reinforcement: An Experimental Investigation

Ultra-High Molecular Weight Polyethylene (UHMWPE) occupies a unique position in engineering polymer science by virtue of its exceptional abrasion resistance, chemical inertness, and biocompatibility, yet its thermal conductivity (0.40–0.44 W/m·K) and moderate tensile strength (25–35 MPa) constrain its deployment in thermally demanding tribological applications such as orthopaedic bearing surfaces, industrial seal components, and high-load conveyor liners. This study presents a systematic experimental investigation of eight nanocomposite formulations incorporating two-dimensional MXene nanosheets (Ti₃C₂Tₓ, 1–5 wt%), hexagonal boron nitride (h-BN, 5–10 wt%), and dual hybrid combinations (MX3-BN5 and MX5-BN5), processed via bath sonication and dual-step ball-milling routes followed by uniaxial hot compression moulding at 180°C. Characterisation encompasses X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and field-emission scanning electron microscopy with energy-dispersive X-ray analysis (FESEM-EDX) for microstructural evaluation; uniaxial tensile testing, Shore D hardness, and pin-on-disc tribometry for mechanical and wear performance; laser flash diffusivity for thermal conductivity; and thermogravimetric analysis (TGA) for thermal stability. The MX3-BN5 hybrid achieves the optimal property balance: tensile strength 42.3 MPa (+49% vs. control), thermal conductivity 1.31 W/m·K (+220%), wear rate 3.21 × 10⁻⁶ mm³/N·m (−63%), and friction coefficient 0.16 (−33%), with TGA onset temperature elevated to 374°C. XRD confirms intercalation-driven d-spacing expansion of the MXene (002) plane from 13.24 Å to 13.51 Å, and FESEM-EDX reveals uniform nanofiller dispersion with strong interfacial adhesion in the hybrid formulation. These results establish MXene–h-BN hybrid UHMWPE nanocomposites as high-performance candidates for next-generation orthopaedic implant bearing surfaces and industrial tribological components.

Deep Learning and Signal Processing Approaches for Real-Time Damage Detection and Prognosis in Large-Scale Civil Infrastructure: A Comparative Study on a Cable-Stayed Bridge

Structural Health Monitoring (SHM) of large-scale civil infrastructure such as cable-stayed bridges demands continuous acquisition and interpretation of multi-channel sensor data across heterogeneous modalities — accelerometers, fibre-optic strain gauges, acoustic emission transducers, and corrosion probes — generating data volumes that overwhelm traditional signal processing paradigms. This study presents a comparative evaluation of six machine learning architectures — Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory networks (LSTM), a convolutional-LSTM hybrid (CNN-LSTM), Autoencoder with multilayer perceptron classifier (AE-MLP), and a Vision Transformer (ViT) adapted for multivariate time-series — applied to a 1.2 km cable-stayed bridge instrumented with 196 sensors over a 36-month monitoring period. Damage scenarios simulated include wire fatigue in hangers, bearing degradation, anchor bolt loosening, and deck delamination across four severity levels (L1–L4). The CNN-LSTM hybrid achieves the highest overall detection accuracy of 97.4% (F1 = 0.974) with a mean time-to-detection of 4.2 minutes for L3 damage events, outperforming the baseline SVM by 6.2 percentage points. Explainability analysis via Gradient-weighted Class Activation Mapping (Grad-CAM) identifies frequency bands 0.3–2.1 Hz and 8.4–12.6 Hz as primary discriminative features for hanger wire fatigue and bearing degradation respectively. A lifecycle-integrated cost model demonstrates that early AI-driven detection of L2 damage reduces maintenance intervention cost by 38% relative to periodic manual inspection schedules, with a net present value improvement of INR 4.2 crore over 30 years.

Mechanical Performance, Microstructural Analysis with Metakaolin and Fly Ash as Supplementary Cementitious Materials

The growing environmental urgency to decarbonise cement-intensive construction has accelerated interest in high-volume supplementary cementitious material (SCM) blends that simultaneously reduce clinker factor and enhance structural performance. Metakaolin (MK), produced by calcining kaolin clay at 600–800°C, and Fly Ash (FA), the aluminosilicate by-product of coal combustion, are both established SCMs whose individual effects on concrete are well characterised; however, systematic multi-variable data on M30 grade concrete incorporating varying MK and FA replacement levels under Indian temperature and humidity conditions remain limited. This study investigates the fresh, mechanical, and long-term durability properties of M30 concrete mixes incorporating MK at 5%, 10%, 15%, and 20% cement replacement and FA at 5% and 10% replacement, with a control mix for baseline. Properties evaluated include slump, compressive strength at 28, 56, and 90 days, flexural strength, split tensile strength, water absorption, Rapid Chloride Permeability Test (RCPT) charge passed, and CO₂ emissions. Load-deflection response of reinforced beams and Mercury Intrusion Porosimetry (MIP) pore-structure evolution at ages 3–90 days characterise structural performance and microstructural development. M30 + 15% MK achieves 90-day compressive strength of 49.7 MPa — 31.5% above the control — and the lowest RCPT value (680 C, classified "Very Low" per ASTM C1202), accompanied by a 13% CO₂ reduction versus the plain M30 control. SEM analysis confirms dense, well-knit interfacial transition zones in MK-modified specimens, while EDX reveals elevated Si/Ca and Al/Ca ratios consistent with extensive secondary pozzolanic C-S-H and zeolitic product formation.

Machine Learning-Optimised Lattice Architectures for Lightweight Aerospace Brackets

Mass reduction in aerospace structural brackets directly improves fuel efficiency, payload capacity, and lifecycle emissions, yet conventional topology optimisation and manually designed lattice infill strategies struggle to navigate the vast design space spanned by unit-cell topology, strut diameter, relative density gradients, and laser powder bed fusion (LPBF) process parameters simultaneously. This study develops a machine learning (ML) surrogate-assisted optimisation framework that couples a gradient-boosted regression surrogate model, trained on 4,200 finite element analysis (FEA) simulations, with a generative design search to identify hybrid lattice architectures that maximise specific stiffness subject to manufacturability and fatigue-life constraints. Gyroid triply periodic minimal surface (TPMS), octet-truss, and ML-optimised hybrid lattices were evaluated across relative densities of 0.10-0.40, fabricated in Ti-6Al-4V via LPBF, and characterised through quasi-static compression testing, micro-computed tomography (CT) porosity quantification, and high-cycle axial fatigue testing (R = 0.1) against wrought Ti-6Al-4V baselines. Process-parameter sensitivity was mapped across laser power (150-300 W) and scan speed (800-1200 mm/s) to identify a low-porosity build window, and the ML surrogate's feature importance was extracted via SHAP analysis to identify the dominant design drivers. The ML-optimised hybrid lattice achieved 41.8% mass reduction relative to a solid topology-optimised baseline while retaining 87% of the baseline's fatigue strength at 10^6 cycles, outperforming octet-truss (35.2% mass reduction, 68% fatigue retention) and gyroid TPMS (31.8% mass reduction, 79% fatigue retention) alternatives. The ML surrogate model achieved R² = 0.97 against held-out FEA validation data, with strut diameter, unit-cell type, and relative density identified as the three dominant predictors of stiffness. The recommended LPBF process window (210-240 W laser power, 800 mm/s scan speed) reduced CT-measured porosity to below 0.4%. The ML-optimised hybrid lattice also reduced embodied CO₂ per bracket by 41.4% relative to the solid baseline, combining structural and environmental performance gains.

Supply Chain Resilience Capabilities, Visibility, and Firm Performance

The COVID-19 pandemic exposed the structural fragility of globally integrated supply chains, triggering a fundamental reassessment of the trade-off between efficiency and resilience in supply chain design. Indian manufacturing firms — embedded in global value chains across automotive, pharmaceuticals, electronics, FMCG, and textiles — experienced disruption intensities that varied dramatically by supply chain resilience capability (SCR), revealing that pre-pandemic investments in visibility, flexibility, and collaborative partnerships were not merely cost centres but strategic insurance assets whose performance value was only realised under conditions of severe external shock. Grounded in the Dynamic Capabilities View (DCV) and the Resource Dependence Theory (RDT), this study develops and tests a structural model in which supply chain visibility and transparency, flexibility and agility, and collaborative partnerships jointly mediate the SCR capability–firm performance relationship, with disruption severity as a moderating context variable. A cross-sectional survey of 596 supply chain directors and operations managers across 102 manufacturing firms in Kerala, Uttarakhand, and Rajasthan, spanning automotive (n=26), pharmaceuticals (n=24), FMCG (n=22), electronics (n=18), and textiles (n=12), was analysed using PLS-SEM with 5,000-sample bootstrapping. Results confirm that supply chain visibility (β=0.46, p<0.001) and collaborative partnerships (β=0.49, p<0.001) are the dominant mediating pathways to financial risk mitigation, while flexibility and agility drive operational performance (β=0.41, p<0.001). The total indirect SCR–performance effect (β=0.53; 95% CI: 0.41–0.65) substantially exceeds the direct effect (β=0.15; p<0.05). A 60-month recovery time trajectory analysis confirms that high-resilience-tier firms recover from disruptions in 3 weeks versus 10–18 weeks for low-resilience counterparts. Digital technology adoption S-curves reveal ERP/MRP as the most adopted tool (92% ceiling) while digital twin simulation (28%) and blockchain provenance (38%) remain emergent. Risk heat map analysis identifies single-source dependency, cybersecurity breach, and supplier financial failure as the three highest-priority supply chain risks for Indian manufacturing firms. The return on resilience investment is estimated at ₹1.8 of loss avoided per ₹1 invested, with pharmaceutical and automotive firms achieving the highest absolute returns. Implications for the National Logistics Policy 2022, Production Linked Incentive (PLI) scheme design, and firm-level supply chain strategy are discussed.

Particle Swarm Optimisation-Based Global Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions: Boost Converter Design and Experimental Validation

Photovoltaic (PV) energy systems deployed in rooftop and ground-mounted configurations across Indian climatic zones routinely experience partial shading conditions arising from clouds, adjacent structures, and inter-row self-shading in large solar farms. Under partial shading, the PV array power–voltage characteristic exhibits multiple local maxima, rendering conventional perturb-and-observe (P&O) and incremental conductance (INC) maximum power point tracking (MPPT) algorithms susceptible to premature convergence at local maxima that may deliver 20–40% less power than the true global maximum power point (GMPP). This paper presents a Particle Swarm Optimisation (PSO)-based MPPT controller integrated with a high-efficiency synchronous boost DC–DC converter for GMPP extraction under partial shading in a 310 W, two-module series-connected PV array. The PSO controller employs fifteen particles, an inertia weight linearly decayed from 0.9 to 0.4 over 100 iterations, and cognitive and social acceleration coefficients of 2.0 each, operating on the converter duty cycle search space. The boost converter is designed for 24–48 V step-up conversion at 40 kHz switching frequency with synchronous rectification achieving 96.8% peak conversion efficiency. Experimental and simulation results on the MATLAB/Simulink platform with a dSPACE DS1104 rapid-control-prototyping board confirm that PSO-MPPT achieves 97.1% tracking efficiency under uniform irradiance and 93.4% under a two-step partial shading pattern, outperforming P&O (87.2%, 71.6%) and INC (89.8%, 76.3%) under the same conditions. Annual energy yield simulation for Coimbatore meteorological conditions projects a 9.7% increase in annual generation relative to P&O, corresponding to a payback period reduction of 0.8 years for a 5 kWp residential system. Total harmonic distortion of the grid-injected current is 2.8% with PSO-MPPT, well within the IEEE 1547-2018 limit of 5%.

Fine-Tuning Multilingual BERT for Hindi Text Classification: Sentiment Analysis and Topic Categorisation Using the HindiSentiment-6 Corpus

Formulation, Characterisation, and In Vitro Anticancer Evaluation of Curcumin-Loaded PLGA and PLGA-PEG Nanoparticles Against MCF-7 Breast Cancer Cells

Optimisation of Turning Parameters for Inconel 718 Superalloy Using Taguchi–Grey Relational Analysis and Response Surface Methodology Under Dry, Flood, and Minimum Quantity Lubrication Conditions

Mechanical and Tribological Behaviour of Woven Fiber-Reinforced Polymer Matrix Composites: Effect of Fiber Type and Volume Fraction on Static, Dynamic, and Wear Performance