Berhampur University

• Introduces a novel method using Hilbert-Huang Transform (HHT) with LSTM networks for fault detection in DC microgrids. • Targets marine and satellite DC microgrids, addressing challenges with no zero-crossing points in traditional methods. • Demonstrates superior performance, outperforming SVM and Decision Tree techniques in fault detection time and accuracy. • Validates the method using MATLAB/Simulink, effectively detecting faults with resistance up to 1.3 ohms. • Ensures compliance with IEEE 8570 standards, enhancing reliability and applicability in real-world microgrid systems. Fault detection in DC microgrids is a major challenge because traditional protection methods like wavelet and Fourier transforms rely on zero-crossing points, which the DC signal lacks. This paper introduces a new approach combining the Hilbert-Huang Transform (HHT) and a long short-term memory (LSTM) neural network to improve fault detection and classification in DC microgrids. The proposed method first uses Empirical Mode Decomposition (EMD) to break down the fault current signal into multiple intrinsic mode function (IMFs). These IMFs capture important fault-related information at different frequency levels. Next, the Hilbert Transform is applied to these IMFs to obtain amplitude and frequency features over time. These features are used to create a set of numerical descriptors that summarize the fault characteristics. These extracted features are then fed into a two-layer LSTM neural network that can learn patterns in time-series data. The LSTM model is trained to classify the input into three categories: no fault, Pole-Ground (P-G) Fault, or Pole-Pole (P-P) Fault. Simulation tests were carried out using a MATLAB Simulink model of a 20kWatt DC microgrid under different fault conditions, noise levels, and load transients. Results show that the proposed HHT-LSTM model detects faults faster and more accurately than conventional techniques like Support Vector Machines (SVM) and Decision Trees (DT). It also meets the fault detection time requirements set by IEEE8570 and aligns with IEC standards. The model remains robust even under noisy conditions, making it reliable for real-time fault protection in marine, satellite, and an effective solution for enhancing fault detection in modern DC microgrids.

This study examines the mechanisms by which cognitive diversity affects team performance in India’s banking sector, an industry characterized by digital transformation and hierarchical organizational norms. Grounded in the social cognitive theory, we propose a triadic model in which team efficacy mediates the relationship between cognitive diversity and performance, while team learning moderates the efficacy–performance pathway. Data were collected from 39 banking teams (392 respondents: 33 team leaders and 359 team members) using a three-wave, time-lagged survey design. Psychometric analyses confirmed strong internal consistency ( α = 0.869–0.905) and appropriate team-level aggregation (ICC (1) = 0.18–0.23; ICC (2) = 0.72–0.78). Covariance-based structural equation modelling revealed that cognitive diversity exhibited both a direct positive association with team performance ( β = 0.113, p < .01) and an indirect association mediated through team efficacy, with bootstrapped confidence intervals (0.071–0.152) confirming robustness. Team learning significantly strengthened the efficacy–performance relationship, with conditional indirect effects increasing from 0.034–0.104 under low-learning conditions to 0.099–0.208 under high learning conditions. These findings advance the social cognitive theory by establishing team efficacy as the psychological mechanism linking diverse cognitive inputs to coordinated action and team learning as a critical boundary condition in high-power-distance contexts. Practically, this study demonstrates that banking organizations must systematically cultivate collective efficacy and embed continuous learning routines to harness the performance benefits of cognitive diversity.

Natural catastrophes like earthquakes, landslides, floods, cyclones, and droughts happen almost year as a result of climate change, and controlling these occurrences is essential in countries like India. Extreme weather occurrences brought on by rising temperatures make disaster patterns unpredictable. An increase in heat waves, floods and cyclones floods puts additional strain on disaster management resources. Unplanned expansion has left many cities and towns susceptible to natural calamities. The effects of disasters are exacerbated by encroachment on natural rivers, inadequate drainage systems, and weak building structures. Despite the fact that a number of models and strategies have been established to manage these disasters, none of them can fully capture all the issues. In a nation like India, preventing natural floods and droughts necessitates a combination of environmental preservation, better infrastructure and sustainable water management. Measures to control flooding in flood-prone areas include limiting construction in flood-prone areas, enforcing land-use restrictions, constructing multipurpose dams to control river flow by holding excess water and putting integrated river basin management into practice to control water flow and reduce the risk of flooding. In India, flood control necessitates a mix of structural and non-structural solutions, with hydrological models being essential for flood prediction, management, and mitigation. The popular hydraulic modeling program developed by U.S. Army Engineers for River hydraulics, flood levels, and water flow is known as HEC-RAS (Hydrologic Engineering Center–River Analysis System). HEC-RAS is extensively used in river engineering, urban drainage planning, and flood risk management in India. This paper highlights the developments in hydraulics models during the past ten years (2015–2025) for improved water management in Indian rivers using the HEC-RAC model.