Vivekananda Institute of Technology

Globally, greater yam (Dioscorea alata L.) is an economically important tuber crop that contributes to food security, household income and rural livelihoods through its production and value-added utilization. However, systematic evaluation of its genetic resources remains limited, particularly under semi-arid North-West Indian conditions. Therefore, the present study aimed to assess 30 greater yam genotypes for genetic variability, trait associations, and genetic diversity to identify suitable genotypes for yield and quality traits. A randomized block design (RBD) experiment comprising three blocks, with all 30 greater yam genotypes represented once in each block, was conducted at Udaipur, India, during three kharif seasons (2018–2020) to evaluate the genotypes. A total of 23 morphological, yield, and biochemical traits were subjected to analyses of variability, correlation, path coefficient, and cluster analyses. Highly significant genotypic differences for all traits indicated the availability of substantial variability for selection. High genotypic and phenotypic coefficients of variation were observed for tuber weight (33.40

Background Globally, greater yam ( Dioscorea alata L.) is an important tuber crop owing of its high nutritional value, industrial utility, and contribution to food security. Despite its economic importance, systematic evaluation of its genetic resources remains limited, particularly under semi-arid North West Indian conditions. Therefore, understanding genetic diversity, trait associations, and heritability is essential for the identification of superior genotypes and the development of high-yielding, nutritionally improved varieties for sustainable crop improvement and conservation programs. Methods A randomized block design with three replications was conducted at Udaipur, India, during two kharif seasons (2018–2020) to evaluate 30 greater yam genotypes. A total of 23 morphological, yield, and biochemical traits were subjected to analyses of variability, correlation, path coefficient, and cluster analyses. Results and discussion Highly significant genotypic differences for all traits indicated substantial variability for selection. High genotypic and phenotypic coefficients of variation were observed for tuber weight (33.40%, 28.43%) and yield per vine (37.59%, 30.62%), while total soluble solids also showed high variation (24.23%, 24.40%). Vine length exhibited high heritability (84.28%) and genetic advance (95.78%), indicating additive gene action. Tuber yield per vine showed strong positive correlations with tuber length (0.99), leaf number (0.99), tuber weight (0.97), and vine length (0.96). Path analysis identified vine length (1.033) and tuber length (0.760) as major direct contributors to yield. Tocher’s clustering grouped genotypes into four clusters, with maximum divergence between Clusters III and II (400.62), while Cluster II showed superior yield and quality traits. Genotypes TGy-14-9, MPY-8, and MPY-4 were identified as promising for high yield, starch, and ascorbic acid content, respectively. Conclusion The results provide a strong basis for selection and hybridization programs in greater yam improvement. Vine length and tuber length should be prioritized as selection indices, and the identified promising genotypes can serve as valuable genetic resources for breeding high-yielding, nutritionally enhanced varieties suitable for semi-arid regions.

This paper proposes artificial intelligence-enhanced secure routing (AIRS), a lightweight AI-enhanced secure routing protocol for internet of things (IoT) networks operating under advanced routing attacks. Unlike existing approaches that treat intrusion detection and routing separately, AIRS tightly integrates anomaly scoring into trust-aware routing decisions using a compact random forest model designed for constrained nodes. The anomaly detector is trained offline on simulated IoT traffic features and deployed for real-time inference during routing. Extensive Cooja simulations demonstrate that AIRS improves intrusion detection accuracy and packet delivery while reducing energy consumption compared to secure-RPL and trust-LEACH. The current validation is limited to simulation environments, and real-world testbed evaluation is left for future work.

Air pollution caused by soot particles poses environmental and health risks, while traditional ink production relies on petroleum-based carbon. By collecting airborne soot and turning it into usable ink, this project offers a sustainable solution. A high-voltage electrostatic precipitation system is used to charge and collect soot particles onto a metal mesh surface. Real-time monitoring and IoT-based data logging via ThingSpeak are made possible by a secondary subsystem that combines a NodeMCU ESP8266 with air-quality and level sensors. The collected soot is purified and formulated into ink, demonstrating a circular, ecofriendly approach to resource utilization. The system demonstrates the viability of pollution-toproduct technology by reducing pollution while creating a valuable material. The proposed system consists of a dualsubsystem architecture designed to convert airborne soot into usable ink while monitoring environmental conditions. The first subsystem uses a high-voltage electrostatic precipitation method, powered by a Li ion battery, to charge soot particles and collect them on a grounded metal mesh surface. This mechanism enables efficient soot extraction from the surrounding air. The second subsystem includes a NodeMCU ESP8266 microcontroller integrated with a MQ-air quality sensor and awater-level sensor for ink storage monitoring. A buck converter regulates power from a dual battery setup to ensure stable operation. For real-time analytics and visualization, the subsystem wirelessly sends data to the ThingSpeak cloud platform. In order to create ink, the collected soot is cleaned and processed, encouraging a sustainable pollution-toproduct strategy.