National Institute of Technology

Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm design, and adaptive decision-making. This survey reviews the bidirectional interaction between these two paradigms and examines how their complementary strengths can be leveraged in hybrid intelligent systems. First, we analyze how EC can enhance LLM-based systems through prompt optimization, hyperparameter tuning, and architecture search. Second, we review how LLMs can im- prove EC by supporting metaheuristic design, surrogate reasoning, adaptive operator control, and heuristic generation. We further discuss emerging co-adaptive frameworks in which LLMs and EC interact through iterative feedback loops. Beyond summarizing recent developments, the survey provides a structured perspec- tive on interaction mechanisms, application patterns, and methodological challenges, including computational cost, reproducibility, interpretability, benchmarking, and generalization. The paper concludes by outlining open research questions and future directions for developing more robust, transparent, and scalable LLM-EC systems.

For q∈R﹨{0}, the generalized Laplacian of a graph X is the matrix L=Δ+qA, where Δ is the degree matrix and A is the adjacency matrix of X. This paper investigates perfect state transfer (PST) on graphs with possible loops equipped with non-trivial involutions, where the generalized Laplacian matrix is taken as the Hamiltonian of the underlying spin network. We establish an equivalence between the existence of PST between pair states (or plus states) in such a graph and PST between vertices in a subgraph induced by the involution. This allows us to prove that almost all simple unweighted planar graphs (resp., almost all simple unweighted trees) can be assigned loops of weight one to exactly two vertices to produce PST between pair states relative to L. We also construct an infinite family of graphs with loops admitting PST between pair or plus states at arbitrarily long distances. For paths on n vertices, we show that PST occurs between end vertices relative to L if and only if n=2, or (n,q)=(3,k2−ℓ28ℓ2), where k and ℓ are integers with k>ℓ and k≢ℓ(mod2). For cycles, we show that the addition of an edge does not yield vertex PST relative to Laplacian and signless Laplacian matrices. Finally, we show that the addition of a few suitable edges (including loops) in complete bipartite graphs, cycles, and paths yields PST between pair states.

Structural integrity of a structure is the ability of it’s components to hold together under the adverse action of rarely occurring excessive loading and aggressive environment condition. Any civil structure must be able to prevent catastrophic failure by undergoing deformation in a manner predicted during the design phase, thereby avoiding untoward incidents that can claim precious human lives and cause extensive damages. A promising technique to ensure that such catastrophic failures will be prevented is through the implementation of a robust Structural Health Monitoring (SHM) system. An efficient SHM system should be able to measure the structural deformation with minimal noise, analyse the output from the sensors installed, provide effective communication and enable the formulation of appropriate corrective action. This paper explains a methodology for integrating vibration based monitoring of large engineering structures based on operational modal analysis and Strain outlier detection in Internet of Things Platform. The methodology proposed can be applied to any engineering structure

Rapid urbanization in emerging Indian urban cities is increasing the pressure on municipal solid waste management. This review evaluates anaerobic digestion as an integrated option for urban organic waste management, renewable energy recovery, and circular economy development. A structured narrative review was used to assess feedstock characteristics, digester configurations, operating conditions, and biogas uses. Policy support and reforms are discussed, followed by techno-economic evaluation of decentralized anaerobic digestion, along with opportunities to diversify income through bio-CNG, electricity, and bio-fertilizer production. Life cycle studies indicate a potential to reduce greenhouse gas through methane capture, fossil fuel substitution, and comparison with conventional methods. The reviewed evidence indicates that decentralized anaerobic digestion is most suitable if the waste is source-separated, moisture-rich, and available within a short distance. These work best if the local demand for energy and digestate exists. The economic performance of this technology depends substantially on digester scale, capacity utilization, feedstock availability, capital and operating and maintenance costs, etc. Direct thermal use may require lower treatment and conversion. At the same time, electricity generation and biomethane upgrading provide higher-value energy products, but the latter involves greater capital cost, operational complexity, and energy demand. Environmental benefits are higher when methane leakage is controlled, energy recovered replaces conventional energy resources, and digestate is used safely as a nutrient source. From a circular economy perspective, decentralized anaerobic digestion can reduce the disposal of biodegradable waste, promote energy and resource recovery, and support localized waste-to-resource systems. However, poor waste segregation, inconsistent feeding, limited capacity utilization, poor maintenance, and inadequate digestate management remain major barriers. This review article concludes that decentralized anaerobic digestion can support sustainable urban waste management in India only if technology selection, feedstock management, energy utilization, financing, policy support, and community participation are integrated.

Wireless sensor networks (WSNs) play a critical role in environmental monitoring applications such as pollution detection, forest surveillance, and disaster management. However, conventional centralized monitoring approaches require frequent data transmission to a fusion center (FC), resulting in significant communication overhead, uneven energy consumption, and reduced network lifetime. To address these challenges, this paper proposes a distributed framework for the estimation of convex event regions based on diffusion cooperation and minimum volume ellipsoid (MVE) representation. The proposed Cooperation-Then-Minimum-Volume-Ellipsoid Formation (CTMVEF) strategy enables sensor nodes (SNs) to collaboratively estimate the geometric boundary of a convex event region using only local communication with neighboring nodes, thereby eliminating the need for centralized processing. An adaptive covariance-weighted diffusion mechanism is introduced to improve convergence speed and robustness under noisy measurements. In addition, a boundary-node-based estimation strategy is employed to reduce computational complexity and communication overhead while preserving estimation accuracy. Extensive simulations demonstrate accurate convex event-region estimation and fast convergence while significantly reducing communication cost and energy consumption compared with conventional centralized approaches, confirming that the proposed framework provides an effective and scalable solution for energy-efficient environmental monitoring in large-scale WSNs.