The growing demand for lightweight, high-strength materials in automotive and aerospace industries has positioned aluminum-based hybrid metal matrix composites (HMMCs) as game-changing alternatives to conventional alloys. However, their enhanced mechanical properties present significant machinability challenges that require advanced processing strategies. This study addresses this critical gap by systematically investigating the electrical discharge machining (EDM) characteristics of a novel Al6063‐10SiC‐5B4C‐Mg hybrid composite fabricated through pressurized stir casting. Employing Response Surface Methodology with Central Composite Rotatable Design (CCRD), we developed robust second-order predictive models for three critical performance indicators: material removal rate (MRR), electrode wear rate (EWR), and surface roughness (SR). Comprehensive statistical validation through ANOVA and residual diagnostics confirmed excellent model adequacy at a 95
Bismuth-doped molybdenum vanadium-phosphate glasses of composition xBi2O3–(1–x) (0.35MoO₃-0.35 V₂O₅-0.30P₂O₅) where x = 0.05, 0.15, 0.25, and 0.35 were prepared by a conventional melt quenching process. The addition of Bi2O3 increases the glass samples’ density from (4.05–5.27 g/cm3). Several elastic moduli were theoretically determined using both the bond compression model and the Makishima and Mackenzie model. Obtained bulk modulus (decreased from 36.88 to 35.46 GPa), longitudinal modulus (decreased from 69.97 to 66.08 GPa), shear modulus (decreased from 24.89 to 23.03 GPa), and Young’s modulus (decreased from 59.98 to 56.79 GPa), which alter with the Bi2O3 incorporation. As the Bi₂O₃ content rises, the bulk, longitudinal, shear, and Young’s moduli decrease progressively, indicating a reduction in cross-link density and a decrease in average bond strength due to structural depolymerisation. Dielectric studies across a broad frequency (25 Hz–5 MHz) and temperature (383–533 K) range show strong frequency dispersion and thermally activated polarization, primarily driven by Maxwell–Wagner interfacial effects and hopping conduction. Electrical modulus analysis reveals non-Debye relaxation, accurately modelled by the Bergman-modified Kohlrausch–Williams–Watts equation, where relaxation times follow Arrhenius behaviour linked to small-polaron hopping between mixed-valence V⁴⁺/V⁵⁺ and Mo⁵⁺/Mo⁶⁺ sites. These findings highlight robust structure–property relationships, with targeted Bi₂O₃ doping effectively tuning mechanical strength, thermal stability, and dielectric performance in phosphate-vanadate-molybdate glasses, positioning them as strong contenders for dielectric, optoelectronic, and energy storage applications.
Industry 5.0’s rapid smart manufacturing growth has increased demand for intelligent, resilient, and sustainable anomaly detection frameworks. Traditional machine learning methods require large labeled datasets, have low interpretability, scalability difficulties, and high processing costs, limiting their real-world usefulness. This paper suggests a football player optimization algorithm (FPOA)-tuned variational autoencoder (VAE) for robust industrial anomaly detection to address these issues. The 100,000 recordings with 13 sensor properties record machine operating conditions like vibration, temperature, pressure, energy consumption, downtime risk, and system statuses. To counter balancing the experimental apparatus, erroneous conditions such as overheating, over-vibration and pressure dip were indicated against base operations. The proposed framework was experimented on the basis of truthful and faulty environments with 70 training, 15 validation and 15 testing. The FPOA-tuned VAE performed better than typical models as well as the Optuna baseline with F1-score of 0.983, AUC of 0.991 and accuracy of 0.982 and stable convergence between training and validation loss. The comparative research studies revealed that the method identified rare anomalies with minimal false positives, which guaranteed predictive maintenance reliability. The framework fosters human-centricity, resilience, and sustainability of Industry 5.0, as opposed to accuracy. The proposed model integrates technical innovation, social and ecological objectives, and explainability (through SHAP-based feature attributions), resistance to noisy inputs, and predictive maintenance, which reduces resource wastage. These findings indicate that FPOA-tuned VAE is a light, interpretable, and potent anomaly detecting framework of Industry 5.0 manufacturing.
This paper identifies the important role in gesture and recognition of sign language systems in improving accessibility, particularly through the development of contactless systems for people who are deaf or hard of hearing. It introduces a deep convolutional neural network (CNN) tailored for hand gesture recognition, showcasing impressive validation results across diverse datasets. This innovative method is not only highly effective but also promising for advancing the interpretation and analysis of sign language, marking a significant advancement in accessibility solutions. The research compares modern contactless technologies, such as smart wearables that track physiological metrics, with traditional sign language recognition systems. This comparison highlights the evolution of assistive technologies and their influence on communication for those with hearing impairments. By contrasting conventional methods with state-of-the-art technologies, the study emphasizes how technological advancements can enhance inclusivity and bridge communication gaps. Additionally, the paper investigates sign language recognition, visualization, and synthesis, revealing new opportunities and areas for exploration. The technological integration of sign language fosters more nuanced and meaningful interactions within deaf and hard-of- hearing communities. The study also proposes a detailed framework for future research in gesture and sign language recognition, stressing the need for ongoing research and innovation. This framework advocates for a comprehensive approach that combines established and emerging technologies, aiming to create a more accessible and inclusive technological landscape for individuals with hearing impairments.
In Indian higher education, conversations about gender equality and inclusivity are becoming more common, especially among Generation Z students who are perceived to be more aware because of digital media. Grounded in the theory of planned behaviour and gender socialisation, it attempts to understand the relationship between gender-sensitive behaviour and attitude towards gender equality. Using a mixed-methods approach, it explores how various factors, such as age, gender, and income, influence gender-sensitive behaviour patterns among Generation Z in selected higher educational institutions. It aims to explore how institutional and digital spaces shape gender-sensitive behaviours. Quantitative results from 125 participants indicate moderate-to-high theoretical gender sensitivity, with female participants and those under 18 demonstrating higher sensitivity levels. However, qualitative findings from focus group discussions reveal a gap in the awareness paradox, where high theoretical empathy coexists with deeply entrenched biases, such as the delegitimisation of female technical competence in STEM, restrictive campus curfews, and identifying gender issues as women’s issues. The paper argues that to address the gap between awareness and action, there is a need to shift from rights-based awareness to gender- transformative interventions that focus on experiential learning, accountability and active involvement in shaping gender-sensitive behaviour in higher educational spaces that dismantle the structural patriarchal barriers.
