Solapur University

Three-dimensional (3D) parts are widely gaining popularity in industries like automotive, aerospace, medical healthcare, consumer goods, and electronics. The properties and performance of these parts vary depending on the parameters chosen at the design and manufacturing stages. This study experimentally evaluates the impact of topology on energy absorption and structural stability of eight strut-based lattice topologies fabricated using Fused Deposition Modeling (FDM) with Polylactic Acid (PLA). Quasi-static compression tests were conducted in accordance with ASTM D695. Specific Energy Absorption (SEA), crush force efficiency (CFE), and newly defined metrics—Peak-to-Plateau Load Ratio (PPLR) and Structural Efficiency (SE)—were used to determine collapse behavior and load stability. All lattices were manufactured within a narrow relative density range (0.29–0.30) to ensure topology-driven assessment. The Double Octagon lattice showed superior performance, achieving a maximum SEA of 6653.18 J kg⁻¹ , total absorbed energy of 57.55 J, and the highest structural efficiency among the investigated configurations. Substantial energy dissipation and trustworthy collapse mechanisms were shown by the Hex Truss and Kelvin cell; however, the Face Centered and Face Diagonal structures showed comparatively lower energy absorption. The proposed PPLR metric quantifies initial peak load severity relative to sustained collapse response, complementing conventional SEA and CFE measures. The findings highlight the critical role of topology optimization in balancing stiffness, weight, and energy absorption in guiding lightweight crashworthy lattice design.

Despite their versatility, polymers frequently lack the barrier qualities, mechanical strength, or thermal stability needed for high-performance applications. The development of polymer nanocomposites is a sophisticated solution to this constraint. Small amounts (usually 1–5% weight percentage) of nanoscale fillers—such as carbon nanotubes, graphene, silica, clay, and metal oxide nanoparticles—are added to a polymer matrix to create these materials. The nanoparticles' extraordinarily high surface area-to-volume ratio and their special interactions with the polymer chains are what make nanocomposites so magical. This frequently results in a synergistic improvement of attributes that surpasses what could be accomplished with straightforward additive effects or macroscopic fillers. It is possible to greatly enhance characteristics such as stiffness, tensile strength, electrical and thermal conductivity, and gas barrier performance. The "filler effect" describes the significant impact that precisely chosen and engineered nanoparticles have on the healing process, even though self-healing mechanisms in polymers can be extrinsic (such as embedded microcapsules, vascular networks) or intrinsic (such as dynamic covalent bonds, supramolecular interactions). This impact enables, accelerates, or enhances the self-repair capabilities rather than merely boosting strength. Innovation in material science has been fueled by the unrelenting search for materials with increased durability, lower maintenance requirements, and improved safety. Self-healing materials—substances that, like biological systems, can repair damage on their own—are among the most promising developments. Polymer nanocomposites are unique in this fascinating field, especially when their capacity for self-healing is deliberately boosted by the "filler effect." Conventional materials deteriorate with time as a result of environmental exposure, wear, and fatigue. Failure eventually results from the accumulation of cracks, microfractures, and other types of damage. In crucial applications including aerospace, automotive, electronics, and biomedical implants, this presents safety issues and mandates expensive repairs or early replacement. Self-healing materials provide a ground-breaking technology that increases system sustainability and dependability while decreasing waste and prolonging product lifespan.

Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in rare oncological conditions, and heterogeneous data fusion constraints. To address these challenges, we present a Quantum-Inspired Classical Convolutional Neural Network (QC-CNN) inspired by quantum analogies for automated bone cancer detection in radiographic images. The proposed architecture integrates classical convolutional layers for hierarchical feature extraction with a classical variational layer motivated by high-dimensional Hilbert space analogies for enhanced pattern discrimination. A curated and annotated dataset of bone X-ray images was utilized, partitioned into training, validation, and independent test cohorts. The QC-CNN was optimized using stochastic gradient descent (SGD) with adaptive learning rate scheduling, and regularization strategies were applied to mitigate overfitting. Quantitative evaluation demonstrated superior diagnostic performance, achieving high accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results highlight the ability of classical CNN with quantum-inspired design to capture non-linear correlations and subtle radiographic biomarkers that classical CNNs may overlook. This study establishes QC-CNN as a promising framework for quantum-analogy motivated medical image analysis, providing evidence of its utility in oncology and underscoring its potential for translation into clinical decision-support systems for early bone cancer diagnosis. All computations in the present study are performed using classical algorithms, with quantum-inspired concepts serving as a conceptual framework for model design and motivating future extensions.

Organic agrotech offers a transformative pathway for enhancing farmer income while ensuring ecological sustainability.This study explores the integration of organic farming practices with appropriate agrotechnological interventions in the cultivation of turmeric (Curcuma longa), tomato (Solanum lycopersicum), and custard apple (Annona squamosa).These crops were selected due to their high market demand, nutritional value, export potential, and adaptability to diverse agro-climatic conditions.The research highlights the role of organic inputs such as biofertilizers, vermicompost, botanical pesticides, and microbial consortia in improving soil health, crop productivity, and produce quality. Adoption of improved cultivation techniques-including precision irrigation,mulching, integrated nutrient management, and pest control through biological methods-demonstrated enhanced yield stability and reduced input costs.Value addition, grading, and better market linkage further contributed to increased profitability.

Recent progresses in genetic engineering and biotechnology are revolutionizing crop yields, resistance to pests and diseases and environmental stability. Techniques such as CRISPR-KAS9, transgenic modification and molecular marker-support selection are enabling specific feature improvement and rapid breeding cycle. These innovations contribute to climate-elastic agriculture, reduction of agrochemical use and improved crop nutrient profiles. Moreover, biotechnological programs are supporting land health and sustainable farming practices. This chapter detects current progresses, practical applications and future possibilities in agronomies that highlight their transformative impact on global food security and agricultural productivity.

Climate change poses escalating risks to agricultural productivity, food security, and ecosystem stability. It affects nearly every aspect of agricultural systems by altering growing seasons, reducing soil moisture, increasing evapotranspiration, intensifying pest and disease pressures, and disrupting nutrient cycling. This review summarises how increased temperatures, altered precipitation, and intensified extreme events are transforming agronomic practices and necessitating advanced crop adaptation strategies. Climate-smart agriculture, supportive policy frameworks, and robust extension systems are central to enhancing resilience. Emerging technologies, such as AI, machine learning, remote sensing, and genomic tools, are accelerating the development and adoption of climate-resilient cultivars and improving farm-level decision-making. Advanced breeding techniques such as CRISPR-Cas9 and genomic selection further strengthen tolerance to heat and drought stress. Long-term climate monitoring and predictive modelling provide critical data for planning and risk management. Coordinated global research, public-private partnerships, and integrated policy support remain essential for sustaining agricultural systems under a warming climate.