Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets.

The adoption of the Internet of Things (IoT) in smart household energy systems offers new opportunities for efficiency and automation, while also posing substantial security challenges. These systems utilize diverse standards and protocols to autonomously access, collect, and share energy-related data over distributed networks. However, this interconnectivity increases their vulnerability to cyber threats, making the system vulnerable to cyber threats. The literature reveals numerous cases of cyberattacks on IoT-based energy infrastructures, primarily involving unauthorized access, data breaches, and device exploitation. Therefore, designing a robust ecosystem with secure and efficient access control (AC), while safeguarding user functionality and privacy, is essential. This paper proposes a dynamic attribute-based access control (ABAC) model that leverages a hybrid blockchain architecture to enhance security and trust in smart household energy systems. The proposed architecture integrates Hyperledger Fabric for managing user, resource, and device attributes using smart contracts, while Hyperledger Besu enforces decentralized access policies. Additionally, a trust recalibration mechanism dynamically adjusts access permissions based on behavioral analysis, mitigating unauthorized access risks and improving energy system adaptability. Experimental results demonstrate the model’s effectiveness in securing IoT smart home energy, while ensuring seamless device onboarding and efficient access control.

To elucidate the response of different solvents such as isopropyl-alcohol (IPA) and acetone for polyaniline-emeraldine-base (PANI), the charge transport mechanism is investigated as a function of temperature in the presence of different solvents. From SEM and XRD characterization, it is noted that each solvent improves the surface smoothness and negligible solvent traces were observed in the final thin-film devices. It is further observed that all devices follow space-charge-limited-current (SCLC) model to define their electrical responses. Conductivity was measured directly through four-probe method, while mobility was estimated from SCLC model and then both conductivity and mobility of PANI are compared with the given solvent at different temperatures. Similarly, it is also realized that the IPA solvent improves conductivity, mobility and degradation of PANI thin-film due to complex behaviour of solvent induced self-organization of molecular chains and reduction of residual traps as a function of temperature.

A novel voltage mode first order active only tuneable all pass filter (AOTAPF) circuit configuration is presented. The AOTAPF has been designed using ±0.7 V, 16 nm carbon nanotube field effect transistor (CNFET) Technology. The circuit uses CNFET based varactor and unity gain inverting amplifier (UGIA). The presented AOTAPF is realized with three N-type CNFETs and without any external passive components. It is to be noted that the realized circuit uses only two CNFETs between its supply-rails and thus, suitable for low-voltage operation. The electronic tunability is achieved by varying the voltage controlled capacitance of the employed CNFET varactor. By altering the varactor tuning voltage, a wide tunable range of pole frequency between 34.2 GHz to 56.9 GHz is achieved. The proposed circuit does not need any matching constraint and is suitable for multi-GHz frequency applications. The presented AOTAPF performance is substantiated with HSPICE simulation program for 16 nm technology-node, using the well-known Stanford CNFET model. AOTAPF simulation results verify the theory for a wide frequency-range.

Energy consumption has drastically increased to meet the growing demand of domestic and industrial usage needs. This has led to a significant rise in the contribution of renewable energy sources, owing to their eco-friendly nature. Solar photovoltaic (PV)-based power generation plays an important role and is growing rapidly. However, it faces challenges due to its inherently low output voltage and non-linear characteristics, which limit its efficiency and performance. These limitations necessitate the use of DC–DC converters to optimize voltage levels and ensure efficient energy transfer, making them a crucial component in PV systems. Among them, non-isolated converters were preferred due to their compact size and their ability to effectively control the output of solar PV. This article critically reviews various non-isolated DC–DC converters, such as conventional, hybrid, and high-gain converters, and analyzes their performance for optimal selection. A thorough study, including mathematical modeling and performance validation through simulation, is presented in detail. The critical discussion and comparison of the various converters will significantly help design engineers and researchers in selecting the appropriate converter for solar PV applications.

Deepfake technology is causing unprecedented threats to the authenticity of digital media, and demand is high for reliable digital media detection systems. This systematic review focuses on an analysis of deepfake detection methods using deep learning approaches, machine learning methods, and the classical methods of image processing from 2018 to 2025 with a specific focus on the trade-off between accuracy, computing efficiency, and cross-dataset generalization. Through lavish analysis of a robust peer-reviewed studies using three benchmark data sets (FaceForensics++, DFDC, Celeb-DF) we expose important truths to bring some of the field’s prevailing assumptions into question. Our analysis produces three important results that radically change the understanding of detection abilities and limitations. Transformer-based architectures have significantly better cross-dataset generalization (11.33% performance decline) than CNN-based (more than 15% decline), at the expense of computation (3–5× more). To the contrary, there is no strong reason to assume the superiority of deep learning, and the performance of traditional machine learning methods (in our case, Random Forest) is quite comparable (accuracy of 99.64% on the DFDC) with dramatically lower computing needs, which opens up the prospects for their application in resource-constrained deployment scenarios. Most critically, we demonstrate deterioration of performance (10–15% on average) systematically across all methodological classes and we provide empirical support for the fact that current detection systems are, to a high degree, learning dataset specific compression artifacts, rather than deepfake characteristics that are generalizable. These results highlight the importance of moving from an accuracy-focused evaluation approach toward more comprehensive evaluation approaches that balance either generalization capability, computational feasibility, or practical deployment constraints, and therefore further direct future research efforts towards designing systems for detection that could be deployed in practical applications.