Academy of Technology

The integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies in underground coal mining has received growing attention due to its potential to improve operational efficiency and reduce energy consumption. IoT-based sensing systems enable real-time monitoring of environmental and operational conditions, while AI techniques support predictive analysis and decision-making. Existing studies indicate that such integration can contribute to improved ventilation control, predictive maintenance, and system-level energy management. This review synthesizes recent developments in IoT and AI applications in underground mining and examines their role in supporting energy-efficient operations. The analysis also highlights current limitations related to scalability, long-term validation, and data reliability. Overall, the literature suggests that IoT–AI integration offers promising opportunities for improving energy performance and sustainability in underground coal mining.

Perovskite solar cells (PSCs) have rapidly established themselves as a leading contender for next-generation photovoltaics, offering tunable band gaps, structural flexibility, and impressive power conversion efficiency (PCE). However, significant stability issues—such as moisture sensitivity, thermal instability, halide segregation, and ion migration—have persistently been observed, limiting commercial viability. Traditional methods such as Density Functional Theory (DFT), chemical substitution, and synthesis have been used, but those are computationally intensive and experimentally demanding processes. So, data-driven methodologies have been explored as powerful allies to experimental research via high-throughput screening. In this paper, a hierarchical ensemble machine learning (ML) framework named PerovLearn has been proposed for accurate band gap prediction of perovskite materials to expedite photovoltaic design. Twenty-two ML algorithms from six different families have been investigated using Root Mean Squared Error (RMSE), Coefficient of Determination (R2), and Pearson and Spearman correlation coefficients. Top-performing models from each model family have been identified, and then ensemble strategies have been applied to enhance the generalization and to improve predictive stability. It is found that blended averaging and stacked generalization model with a Random Forest (RF) meta-learner has attained near-experimental accuracy (RMSE = 0.008751, R2 = 0.999176) in bandgap prediction. In the PerovLearn framework, SHapley Additive exPlanations (SHAP) based interpretability has been incorporated to unveil compositional influences on bandgap, bridging predictive power and scientific insight. The proposed PerovLearn framework has delivered a generalized, adaptable, scalable, interpretable, and high-fidelity approach for bandgap engineering, offering a robust data-driven pathway for accelerated design and optimization of perovskite photovoltaic materials.

Parity-time (PT) symmetry in time-delay oscillators such as lasers and optoelectronic oscillators provides a potential route to enhanced spectral purity, including reduced phase noise and improved sidemode suppression. Existing theoretical descriptions are typically based on coupled-mode formulations derived under slowly varying envelope and near-degeneracy assumptions, which restrict their validity to weak coupling, small gain/loss contrast, and small detuning. In this work, a non-perturbative formulation of PT symmetric time-delay oscillators is developed based on a delay-difference equation and a scattering matrix representation of the coupling network. The approach treats propagation delay explicitly and does not rely on modal truncation, remaining valid for arbitrary coupling strength, gain/loss imbalance, and resonance detuning. The exact eigenvalue structure of the system is obtained in closed form, yielding a complete characterization of the unbroken and broken PT symmetric regimes as well as the associated exceptional points. A dimensionless order parameter is introduced that governs the symmetry transition over the full parameter space. It is further shown that conventional coupled-mode theory is recovered as an asymptotic limit of the exact formulation for small parameters. The results provide a unified and physically transparent framework for analysing PT symmetric delay systems beyond the weak-coupling limit, with direct implications for the design and optimisation of low-noise oscillators and photonic systems.

This article discusses a topology adaptive self-organizing circular neural network (TASOCNN) that can be used to cluster circular data. TASOCNN consists of a set of interconnected processors that are trained to capture the topological structure of the circular data. The connections (edges) between the processors are assigned strength values which are updated during the training process. However, the naive TASOCNN model has some limitations, such as its inability to accurately distinguish between inter-cluster and intra-cluster edges. The article proposes significant modifications to naive TASOCNN, including updates to strength equations for generating stronger and shorter edges in dense areas of data. Additionally, a novel non-iterative clustering procedure based on a two-component Beta mixture model is introduced. This model isolates intra-cluster edges by analyzing strength values, leading to the removal of inter-cluster edges. The application of TASOCNN is demonstrated in the context of color image segmentation, a crucial task in various applications. The results show that the proposed method outperforms the naive TASOCNN model and other state-of-the-art self-organizing neural models as well as mixture models.

For accurate detection of thyroid cancer stages, including I, II, III, IVA, and IVB, proper treatment planning is necessary in a modern, sustainable medical system. Nowadays, researchers suggest the use of machine learning models for detecting complex patterns from laboratory test data. Hence, this work presents a novel, secure, and reliable fusion model that efficiently predicts stages of thyroid cancer. To design a strong ensemble model, it integrates BERT and XGBoost, which exploits both structural and categorical data to identify patterns from clinical data. Along with this, to improve security and transparency, this model was then integrated with a Blockchain platform, where patient records were securely deployed in an immutable platform. Finally, Performance analysis shows that the proposed framework outperforms other competitive classifiers in detecting the stages accurately. Thus, this work demonstrates an effective and secure framework that has the power to identify deep contextual meaning for predicting thyroid cancer stages and utilizes blockchain technology for enhancing privacy and transparency for securing diagnostic information.