International Journal of Advanced Engineering Application.

Ethical Imperatives of CRISPR-Modified Crops in Global Food Systems

The emergence of CRISPR-Cas9 and related gene-editing technologies as commercially viable tools for crop improvement has generated unprecedented tension between the promises of agricultural biotechnology and the ethical imperatives of equitable food governance. Unlike classical genetic modification, CRISPR's site-specificity, relative affordability, and potential for gene-drive deployment across wild and cultivated plant populations introduce a qualitatively distinct set of moral hazards — including irreversible ecological modification, accelerated corporate enclosure of the seed commons, and systematic marginalisation of smallholder subsistence farming communities who collectively steward over 70% of global crop genetic diversity. This study combines applied bioethics, ecological risk modelling, and comparative regulatory analysis to construct a balanced normative framework capable of guiding international biotechnology governance beyond the current regulatory vacuum. Deploying a multi-stakeholder ethical risk matrix calibrated against eight risk dimensions — gene-drive persistence, cross-species gene flow, monopoly concentration, allergenicity unknowns, soil microbiome disruption, farmer intellectual property vulnerability, pollinator impacts, and seed sovereignty erosion — the study identifies gene-drive persistence and seed sovereignty as the highest-magnitude threats requiring immediate policy intervention. Simulation modelling of gene-drive allele propagation across twenty crop generations demonstrates that population-level irreversibility is achievable within ten to fourteen generations under realistic field diffusion assumptions, establishing a narrow pre-deployment window for precautionary governance. Stakeholder cost-benefit analysis reveals a deeply asymmetric distribution of CRISPR benefits and harms: large agrichemical corporations and consumers accrue net positive scores, while smallholder farmers, indigenous seed-keeping communities, and ecosystem stakeholders register significant net negative exposures. A six-phase international regulatory blueprint spanning 2024 to 2035 is proposed, incorporating mandatory ecological impact assessments with fifty-year gene-flow modelling, compulsory open-access licensing for food-security crops, farmer royalty exemptions codified in national seed laws, and binding benefit-sharing mechanisms analogous to the Nagoya Protocol.

Wearable PPG-Based Atrial Fibrillation Detection Using a Hybrid CNN-LSTM Model: Performance Evaluation Across Signal Quality and Noise Conditions

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a leading independent risk factor for ischaemic stroke, yet its frequently paroxysmal and asymptomatic presentation makes opportunistic, continuous screening through low-cost wearable photoplethysmography (PPG) sensors an attractive complement to intermittent clinical electrocardiography. The principal barrier to reliable wearable-based AF detection is motion-artefact and ambient-noise corruption of the PPG signal, which degrades the rhythm-irregularity features that distinguish AF from normal sinus rhythm and necessitates explicit signal-quality assessment ahead of classification. This study develops and evaluates a hybrid convolutional neural network-long short-term memory (CNN-LSTM) architecture for AF detection from single-channel wrist-worn PPG, incorporating a signal quality index (SQI)-based segment rejection stage trained and validated on a dataset of 41,600 thirty-second PPG segments (8,360 unique recording sessions) collected from 214 subjects, of which 3,680 segments were retained as AF-positive following cardiologist-verified ECG-synchronised labelling. The proposed CNN-LSTM model is benchmarked against a Random Forest baseline using handcrafted heart-rate-variability features, a standalone CNN, and a standalone LSTM. The CNN-LSTM model achieves the highest test-set performance with an accuracy of 94.5%, sensitivity of 91.5%, specificity of 95.7%, F1-score of 90.3%, and area under the receiver operating characteristic curve (AUC) of 0.978, outperforming the Random Forest baseline (AUC 0.912) by a statistically significant margin. Stratified performance analysis across signal-to-noise ratio (SNR) bins reveals that sensitivity falls from 97.8% at SNR greater than 15 dB to 58.2% at SNR below 0 dB, underscoring the necessity of the SQI-based rejection stage, which excludes 480 of 4,160 (11.5%) candidate segments per session-equivalent batch as unsuitable for reliable classification. Power spectral density and RR-interval Poincaré analysis confirm that the model's discriminative capacity derives from the characteristic beat-to-beat irregularity and dominant-frequency dispersion that distinguish AF from sinus rhythm. The results support the feasibility of CNN-LSTM-based PPG screening as a pre-clinical triage tool for opportunistic AF detection in continuous wearable monitoring contexts, contingent on robust signal-quality gating to maintain diagnostic reliability under real-world motion and noise conditions.

Microencapsulated Bacillus-Based Self-Healing Concrete: Crack Closure Kinetics, Durability Recovery, and Lifecycle Cost-Benefit Analysis

Microcracking in reinforced concrete structures provides ingress pathways for moisture, chloride, and carbon dioxide that accelerate reinforcement corrosion and reduce service life, with conventional repair approaches requiring reactive maintenance interventions that are costly and disruptive over a structure's operational lifetime. Microbially induced calcium carbonate precipitation (MICP), using ureolytic bacteria of the Bacillus genus, offers an autonomous crack-healing mechanism in which encapsulated bacterial spores activate upon crack-induced water ingress and precipitate calcite that fills and seals the crack, but the comparative performance of available encapsulation strategies and the resulting durability and economic benefits under realistic crack-width distributions remain incompletely characterised.This study evaluates Bacillus pseudofirmus spores encapsulated via five methods - direct mixing, lightweight aggregate impregnation, hydrogel encapsulation, and microcapsules with melamine-formaldehyde or sodium alginate shells - across bacterial concentrations of 10⁵-10⁹ cells/mL in M30 grade concrete. Crack closure was monitored over 28-day wet-dry healing cycles for cracks ranging 0.1-0.8 mm using digital image correlation, with compressive strength recovery, water permeability, rapid chloride migration coefficient, and SEM/EDX precipitate characterisation assessed at 28 and 56 days. A lifecycle cost model compared cumulative costs of conventional concrete with periodic repair against bacterial self-healing concrete over a 30-year service horizon. The optimum bacterial concentration of 10⁷ cells/mL achieved 93% crack closure by 28 days for cracks up to 0.3 mm, with healing efficiency falling below the practically significant 80% closure threshold for initial crack widths exceeding approximately 0.45 mm. Alginate microcapsule encapsulation achieved the highest bacterial survival (83% at 28 days) and healing efficiency (93%) among the five methods tested, substantially outperforming direct mixing (18% survival, 22% efficiency). Compressive strength recovery reached 96.7% of uncracked control strength after 56 days of healing, and water permeability was reduced by 83% relative to control at optimum bacterial concentration. Lifecycle cost analysis indicated a break-even point at approximately 6.5 years, beyond which bacterial self-healing concrete's avoided repair costs outweighed its higher initial material cost, with cumulative 30-year costs 62.7% lower than conventional concrete requiring periodic repair.

Performance, Combustion and Emission Characteristics of a Single-Cylinder CI Engine Fuelled with Waste Cooking Oil Biodiesel Blends Doped with Alumina Nanoparticles

The depletion of fossil diesel reserves and the tightening of emission legislation have intensified interest in waste cooking oil (WCO) biodiesel as a renewable, low-cost substitute fuel that simultaneously addresses the disposal burden of an otherwise discarded waste stream. However, biodiesel's inherently lower calorific value, higher viscosity, and delayed combustion relative to mineral diesel limit its unblended adoption in unmodified compression ignition (CI) engines, motivating the use of metal-oxide nanoparticle additives to restore combustion quality through enhanced fuel oxidation and improved spray atomisation. This study evaluates the performance, combustion, and emission behaviour of a single-cylinder, four-stroke CI engine operated on waste cooking oil biodiesel-diesel blends (B20, B40) doped with alumina (Al₂O₃) nanoparticles at 50 and 100 ppm dosages, benchmarked against neat diesel (D100) across five engine load conditions (20-100% of rated load). Performance parameters (brake thermal efficiency, brake specific fuel consumption, exhaust gas temperature), regulated emissions (NOx, CO, HC, smoke opacity), and in-cylinder combustion characteristics (cylinder pressure and heat release rate versus crank angle) were measured using a calibrated eddy-current dynamometer test rig instrumented with a piezoelectric pressure transducer and AVL-class exhaust gas analyser. The B20+100ppm Al₂O₃ blend achieves the highest brake thermal efficiency of 32.3% at full load, a 9.1% improvement over neat diesel, alongside the lowest brake specific fuel consumption of 0.30 kg/kWh. Nanoparticle dosing reduces CO, HC, and smoke opacity across all loads, with B20+100ppm Al₂O₃ recording a 45% reduction in smoke opacity at full load relative to diesel, at the expense of a 17.4% increase in NOx attributable to elevated in-cylinder peak pressure and heat release rate. Combustion analysis confirms that Al₂O₃ dosing advances and intensifies the premixed combustion phase, with peak heat release rate increasing from 42.1 J/° for diesel to 48.3 J/° for the nano-dosed B20 blend. The results establish B20+100ppm Al₂O₃ as the optimum fuel formulation for unmodified CI engines seeking simultaneous gains in thermal efficiency and particulate emission reduction, with NOx after-treatment recommended to offset the associated trade-off.

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.

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