Adwitiya Sinha

Nanotechnology has evolved from empirical traditional material practices to a highly advanced field based on the controlled design and manipulation of matter at the nanoscale. This article aims to provide a critical narrative overview of the evolution of the field of nanotechnology from traditional empirical approaches to modern scientific approaches. Particular emphasis has been placed on traditional materials exhibiting subsequently identified nanoscale features, and traditional formulations such as Bhasma, which might have inadvertently used nanostructures. Additionally, the scientific basis of modern nanotechnology and their roots in Indian civilization have been discussed. Furthermore, the main applications of nanotechnology in medicine, electronics, energy, sensing, and environmental remediation have been emphasized. Additionally, the article also emphasizes the green synthesis of nanoparticles, such as the plant‐mediated synthesis of nanoparticles, which might act as a bridge to connect the traditional practices with the modern advancements in the field of nanotechnology. The review points out that for any successful merging of traditional material knowledge with contemporary nanotechnology, there is a need to ensure characterization, repeatability, understanding of the mechanism involved, safety issues, scalability and proper validation. Historical nanoscale properties are also constrained by lack of modern characterization evidence.

The social network is an interconnected online web of people spanning across the digital space for sharing information, reflecting opinions, and building societal trends. This necessitates quantification of relative prominence of an individual in the online community. The significance of a node is measured by centrality metrics, with different indicators accentuating different characteristics of the network. Though there are a myriad of centrality indicators however, it would be interesting to explore which centrality indicators are highly correlated. The literature survey shows that extensive study is still required to establish the network-specific choice of centrality measures for defining node importance. In our work, we have analysed different classes of networks by calculating various centrality indicators. We have computed the pair-wise correlation of the centrality indicators for each network. This is followed by setting threshold value to find the strongly associated centrality indictors. These indicators are further partitioned into clusters, which are validated by comparing the top 50 ranked nodes based on the strongly correlated centrality measures. We have proposed technique to find the similarity in clustering of centrality indictors for each category of networks. Also, we have explored the similarity of centrality indices irrespective of the network class for generalization. Our findings have established that each member of a cluster of correlated centralities is capable of serving as a suitable indicator for detecting the influential nodes based on the structural properties of a specific class of network. Furthermore, we employed association rule mining, a machine learning approach, to uncover frequently co-occurring centrality indicators that exhibit consistent patterns across networks. This AI-driven analysis provides deeper insight into the interdependencies among centrality measures, facilitating more efficient and interpretable network analysis.

The sensor network consists of several low‐powered electronic sensing devices that are strategically deployed in a predefined area to gather important information. The sensors have low computing capacity, constrained battery energy, and limited storage. Therefore, the sensed data should be communicated to the sink efficiently for further processing. In this work, a clustering‐based multihop data transmission method is proposed using fuzzy logic. The proposed method, termed fuzzy‐based cluster head (CH) selection for unequal clustering (FSUC), utilizes four sensor factors: residual energy, distance to the sink node, node density, and the average distance of neighboring nodes to select optimal CHs. The FSUC selects CHs locally through a distributed competition process, enhancing the scalability of the sensor network. The comparative analysis of FSUC is performed with six existing models that involve LEACH, EEUC, FL‐SEP, MOFCA, FMCR‐CT, and FLPSOC in three different scenarios. The performance evaluation metrics are the number of alive nodes, total energy of alive nodes, and energy consumption in each round. The performance is also evaluated for the metrics occurrence of first node dead, half node dead, and last node dead. The energy consumption prediction across all compared models is conducted using exponential weighted mean (EWM), autoregressive integrated moving average (ARIMA), and linear regression (LR). Furthermore, statistical validation through t ‐test is employed to examine the reliability of the proposed model. The computational complexity of the model is also analyzed along with the complexity–performance curve to assess its practical feasibility.