The paper is devoted to the turbomachinery vibration processes investigation. It is based on the hypothesis that such process as forced vibration, high temperature and centrifugal force, constructional damping parameters put the main impact in the gas turbine rotors decrease. For these studies the special refined mathematical model was used. For these model the special space finite elements have been developed, that gives an opportunity to make the approximation of various turbine blades geometry. The model verification has been done by the usage the modern highly automatized experimental equipment that is based on the holographic interferometry method application in real time. The first impeller cooled blades forced vibration parameters have been determined for several most dangerous forced vibration modes, temperatures up to 1000oC and angular velocities of shaft rotation, corresponding with the gas turbine engine normal working regime. Results of calculations data together with the previous studies in the fields of gas turbine engines flow dynamics, heat exchange, stress-strain state can be used in further studies of turbomachinery fracture mechanics area.
A more stable grid is achieved by storage systems, with Alternating Current (AC) microgrids providing more continuous grid operation by supplying energy to electrical loads from distributed energy sources and energy sources from the old distribution grid. The article provides specific content of a three-port converter that connects distributed energy sources (renewable energy sources), storage systems to the AC microgrid. The converter uses Direct Current (DC) Sinusoidal Pulse-Width Modulation (SPWM) with the design foundation of Direct Current/Direct Current (DC/DC) energy converters such as H-bridge and Buck-Boost. It aims to realize the operation of a microgrid system that provides more continuous and stable power to the system when operating independently and connected to the main grid. The results of the whole project are verified by simulation and experiment with the power value of 2-5.5kW achieving an average efficiency of each case together close to 97% and are compared with some reference documents.
Graphene Nano-Sheet Field Effect Transistor (GNSFET) is designed using a new material (graphene) in the channel, marking advancement in Nano-Sheet Field Effect Transistor (NSFET) technology. The device’s analog/Radio Frequency (RF) performance are evaluated with respect to the main geometric parameters of GNSFET, such as the gate-length, the channel-width, and the channel- thickness. Key analog/RF performance metrics, including the following: ION/IOFF ratio, subthreshold-swing SS, transconductance gm, transconductance efficiency gm/Id, output conductance gds, gate capacitance Cgg, cut-off frequency FT, and intrinsic gain Av. Output results reveal that scaling-down Tch to 6 nm, Wch to 14 nm, and Lg to 16 nm enhances the ION/IOFF ratio to 5.2×1011 and reduces SS to 60.05 mV/dec, which creates better switching efficiency. Reducing Tch from 8 nm to 6 nm, and Wch from 18 nm to 12 nm boosts the ION/IOFF ratio by 34.48% and 33.19%, respectively, highlighting the main role of device’s small geometry in improving electrostatic control. Transconductance gm increases by 22.51% when Lg is scaled from 16 nm to 12 nm, while Cgg decreases with reductions in Lg, Wch, and Tch, aligning with graphene’s inherent high carrier mobility. Notably, FT improves by 11.71% under Tch scaling, underscoring graphene’s potential for high-frequency RF applications. Although, intrinsic-gain (Av) benefits from Wch and Tch scaling, it exhibits a significant augmentation of 38.88% when Lg is increased from 12 nm to 16 nm, reflecting a trade-off between gain optimization and speed.
This study evaluates the Technical Efficiency (TE) and Total Factor Productivity (TFP) of 54 Indian Electricity Distribution Utilities (IEDUs) from 2020 to 2023 using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). The Charnes-Cooper-Rhodes (CCR) model revealed that only 35.18% of DMUs (19 out of 54) were technically efficient, with average efficiency improving marginally from 0.84 to 0.88. Slack-based inefficiencies resulted in an estimated input overutilization of Rs. 63,270.12 crore, highlighting significant operational inefficiencies. Further examined the Total Productivity (TFP) of IEDUs, revealing a 2.5% improvement in operational efficiency (efficiency change = 1.025), driven by gains in pure efficiency (1.036). Although a 7.6% decline in Technological Change (TECHCH = 0.924), due to outdated infrastructure and regulatory delays, offset these gains. Persistent Aggregate Technical & Commercial (AT&C) losses averaged 17.71%, leading to a 5.3% decrease in TFP. Efficient Decision-Making Units (DMUs) incurred lower costs per million units sold (Rs. 0.776 crores) than inefficient units (Rs. 0.924 crores), underscoring the importance of operational optimization. This study employs a DEA–Malmquist framework on 54 IEDUs during 2020–2023 a reform-critical period encompassing post-UDAY, RDSS, and COVID-19 to quantify inefficiency costs, benchmark peer utilities, and provide strategic direction for enhancing operational performance, financial sustainability, and sector-wide reforms.
The integration of Terrestrial Networks (TNs) and Non-Terrestrial Networks (NTNs) is a foundational requirement for Sixth Generation (6G), enabling ubiquitous, resilient, and globally inclusive connectivity. However, existing surveys typically analyze this integration by concentrating on individual dimensions—such as architectural design, control and virtualization mechanisms, or Artificial Intelligence (AI)—while giving limited attention to sustainability considerations. This paper addresses this gap by introducing a unified architecture–AI–sustainability triadic framework, which forms the core contribution of the review. First, the paper provides a structured architectural synthesis that clarifies how different integration models influence the design and operational behavior of TN–NTN systems. Second, it consolidates the role of AI in enabling intelligent, adaptive, and context-aware network operation across integrated space–air–ground environments. Third, it advances sustainability as a primary design principle by synthesizing emerging strategies aimed at improving energy and carbon efficiency in future 6G infrastructures. By examining these three dimensions collectively, the review offers a coherent and comprehensive perspective on TN–NTN convergence, identifies persistent challenges including interoperability limitations and standardization gaps, and outlines future research directions needed to develop resilient, intelligent, and environmentally responsible 6G ecosystems aligned with United Nation Sustainable Development Goals (UN SDGs).
Environmental sustainability policy relies on public attitudes to gauge acceptance and effectiveness, but traditional survey methods fall short in capturing emerging sentiment trends. This study applies machine learning and deep learning techniques, such as Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM), to analyze social media discussions about environmental policies. It turns unstructured data into structured sentiment values and evaluates them using an Extended Policy Analytical Framework. This framework includes region-specific analysis, event-related sentiment trends, emotion profiling, and explainable AI (SHAP-Shapley Additive Explanations) for clarity. BERT outperformed other models, achieving 0.97 accuracy, followed by random forest at 0.94. The results reveal notable sentiment changes around key global policy events, such as COP26 in India (+0.14) and U.S. carbon tax proposals (−0.15), along with emotional trends related to specific issues. A comparative regional analysis showed a performance drop of 5% to 7%, indicating regional differences. Overall, the research demonstrates that explainable AI-driven sentiment analysis can provide useful information to improve policy design and communication.
As generative Artificial Intelligence (AI) models become increasingly integrated into software development workflows, understanding their efficiency and code quality is critical. This study offers a comprehensive comparison of three leading AI models—ChatGPT GPT-4-turbo, Claude Sonnet, and DeepSeek-V3—for automated code generation, focusing specifically on sorting algorithms. The models are evaluated across multiple metrics including execution time, memory usage, peak memory consumption, logical and physical file sizes, and code readability. Python implementations of Insertion Sort, Merge Sort, Quick Sort, and Heap Sort are generated by each model and benchmarked in a consistent Linux Docker environment. Results reveal that ChatGPT leads in overall efficiency, with the fastest average execution time, the lowest peak memory usage, and the highest readability scores. DeepSeek demonstrated competitive performance, especially in producing readable code, while Claude showed higher memory consumption and lower readability. This analysis provides practical insight into the trade-offs between code quality and system performance in AI-generated programming, offering valuable guidance for researchers and developers alike.
In Low-Power and Lossy Networks (LLNs), significant challenges arise due to the severe constraints on energy, memory, and processing capacities. These limitations hinder the efficiency of routing protocols like Routing Protocol for Low-Power and Lossy Networks (RPL), particularly in dynamic network topologies where frequent changes occur. We propose an innovative architecture integrating Software Defined Networking (SDN) and Network Function Virtualization (NFV) with RPL protocol to address this issue. SDN and NFV technologies introduce greater flexibility, programmability, and centralized control, enabling the network to adapt more effectively to dynamic changes. Our proposed Software Defined architecture for Low-Power and Lossy Network (SD-LLN) leverages these technologies to enhance RPL’s adaptability and resilience in varying network conditions. We improve network performance by integrating SDN/NFV with RPL, particularly in routing efficiency, stability, and resource optimization. Compared to traditional RPL implementations, our approach demonstrates significant advantages in providing an efficient and dynamic solution for managing dynamic LLNs. The results highlight the potential of this method in managing LLNs, offering a robust framework to effectively overcome dynamic, resource-constrained environments.
This paper presents a novel hybrid control strategy for DC-DC boost converters in Photovoltaic (PV) systems, combining Model Predictive Control (MPC) with Convolutional Neural Networks (CNN) to optimise the duty cycle in real time. The MPC–CNN framework leverages MPC’s predictive accuracy and constraint-handling with CNN’s fast inference and adaptability, ensuring robust voltage regulation under varying irradiance and load conditions. The hybrid scheme dynamically selects between CNN and MPC outputs based on real-time performance metrics, balancing response speed and control precision. Simulations with a 100-kW dynamic Photovoltaic (PV) profile demonstrate that MPC achieves near-zero steadystate error, while CNN provides faster transient responses. The hybrid controller surpasses both in maintaining voltage stability and energy efficiency. Additionally, a performance comparison of semiconductor technologies—Silicon (Si), Silicon Carbide (SiC), and Gallium Nitride (GaN)—shows that GaN-based converters achieve the best results, with 97.6% efficiency, 2.8 V ripple, 6.4 W switching loss, and the fastest transient response of 0.17 ms. These findings confirm the effectiveness of the CNN-enhanced MPC approach and establish GaN’s superiority for compact, high-performance PV applications. Future work will explore real-time embedded implementation, adaptive CNN retraining, and integration into smart grid and energy management systems.
The development of automation in the fish industry, a vital sector of the food industry, is a highly relevant and essential topic. This development is essential for boosting output and mitigating the risk of future food shortages brought on by the world’s population expansion. Automatic fish classification using computer vision has been widely developed in fish industry automation, and a lot of research on that topic has been published. However, while some research has produced promising results using complex methods, others have applied simpler approaches with less satisfactory outcomes. This study suggests a straightforward but efficient technique for differentiating between fish species by concentrating on their main characteristics, such as body form and scale patterns. To effectively support these image capturing properties, the Lanczos re-sampling technique is used in this study. Additionally, our basic deep learning model can correctly learn and identify fish species thanks to a fish picture categorization engine created using Google Teachable Machine. Utilizing the Fish-Pak dataset, a popular fish picture dataset frequently used in studies on fish species classification, the suggested approach successfully overcomes the difficulty and attains a high accuracy rate of 97.16%.
The rising prevalence of Myocardial Infarction (MI) and limited clinical resources highlight the need for accurate, automated diagnostic tools. This study presents a Machine Learning (ML) framework for early MI prediction using both structured health records and Electrocardiogram (ECG) data. Multiple ML algorithms—including ridge classifier, radius neighbor classifier, linear SVC, and extra trees classifier—are evaluated on two publicly available datasets and two clinical datasets collected from hospitals. The additional trees classifier achieves the highest training accuracy of 1.00, with consistent performance across datasets. For ECG-based diagnosis, a deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is developed using the ECG Heartbeat Categorization Dataset. It classifies five heartbeat types: normal, Fusion of Paced and Normal (FPAN), Fusion of Ventricular and Normal (FVAN), Atrial Premature Contractions (APC), and Premature Ventricular Contractions (PVC). The model achieves a testing accuracy of 0.98, supported by strong precision and recall across classes. The novelty of this study lies in its integration of public and real-world datasets, noise-augmented training to improve ECG robustness, and a multi-class CNN–RNN framework that enhances generalizability beyond conventional binary classifiers. The proposed approach contributes to more reliable and interpretable cardiovascular diagnostics, with strong potential for clinical deployment and improved patient outcomes.
Real-time analysis of streaming data is crucial in agricultural environmental monitoring to address quickly changing conditions like seasonal weather changes. Concept drift, where the statistical characteristics of input data evolve, poses a significant problem for static machine learning models. This research presents a drift-aware framework based on a hybrid adaptive windowing method combined with an Online Sequential Extreme Learning Machine (OS-ELM). The strategy involves a multidimensional extension of Adaptive Windowing (ADWIN) that is supplemented by the Kolmogorov–Smirnov statistical test and Hoeffding’s bound to identify and respond to realtime drift. An experimental Internet of Things (IoT) platform was constructed to gather environmental parameters such as temperature, humidity, soil moisture, light, pH, and rainfall. Empirical tests on real and synthetic datasets show that the new framework greatly enhances predictive performance, from 85.86 percent to 97.29 percent when drift handling is activated. The findings emphasize the significance of combining adaptive learning with drift detection for accurate and dependable prediction in precision agriculture.
This thesis demonstrates an intelligent system for the early detection of plant diseases using deep learningbased image processing. The primary objective is to support food security and enhance agricultural productivity. The proposed system utilizes Contrast-Limited Adaptive Histogram Equalization (CLAHE) and a U-shaped convolutional Neural Network (U-Net) for image segmentation, aiming to enhance image quality and accurately localize diseased regions. The feature extraction includes integration of deep features of Convolutional Neural Networks (CNN) and the Residual Network-18 architecture-based model GramNet together with the texture feature extracted from the graylevel co-occurrence matrix. All features are concatenated to create a comprehensive feature representation. A stacking ensemble approach is adopted for classification, utilizing support vector machines, K-nearest neighbors, and extreme gradient boosting as base classifiers. In contrast, the light gradient boosting machine is the final meta-classifier. An experimental evaluation was conducted on a publicly available rice disease dataset containing 11,790 samples, which demonstrates that the model achieves 99.99% accuracy on the training data and 96.90% on the testing data. This work demonstrates the effectiveness of integrating preprocessing, hybrid feature extraction, and ensemble learning in the early detection of plant diseases.
Low-Density-Parity-Check (LDPC) codes are a cornerstone for achieving robust error correction capabilities in 5G New Radio applications, significantly improving the reliability of data transmission across noisy and unpredictable wireless channels. Since an evaluation and discussion of the performance with channel coding is significantly absent in two-dimensional Index Modulation (IM)-Differential Chaos Shift Keying (DCSK) schemes. Therefore, in this study, the 5G new radio LDPC codes based generalized joint subcarrier-time index modulation DCSK system (5G NR-LDPC-GJSTIM-DCSK) is proposed, where 5G NR-LDPC codes are used as channel coding. The aim is to improve the system’s performance specifically across AWGN (additive white gaussian noise) and multipath Rayleigh fading channels, when contrasted with the uncoded GJSTIM-DCSK. In the suggested system, the detected index bits at the receiver are transformed into soft bits and combined with the soft demodulated bits before the decoding process. The results show that the highest coding improvements are 11.6 dB for AWGN channels and 22.2 dB for Rayleigh fading channels, using a code rate of 1/5, a codeword length of 7648, and a spreading factor of 256. Additionally, the performance of the 5G NR-LDPCGJSTIM- DCSK system surpasses the 5G NR-LDPC-GSIMDCSK- II’s performance and is nearly equivalent to the 5G NR-LDPC-GFTIM-DCSK-II’s performance at high SNR (Signal to Noise Ratio).
