Sustainable Energy and Artificial Intelligence

Abstract This paper presents an approach for automated unsupervised video summarization, that means, nothing more than video is needed to train the model. The goal is to extract a sequence of frames from an input video and assign each frame a score between 0 and 1. By doing so, we can select a subset of the most informative and diverse shots to make a summarized video. We build upon the foundation of SUM-GAN, particularly SUM-GAN-SLA, which utilize Generative Adversarial Networks to compare and distinguish between the original video and its regenerated counterpart. A key contribution of our work lies in the novel biLSTM-based self-attention network that we introduce to handle the crucial scoring layer of our model. We adjusted several aspects of the model, particularly in the loss functions and learning steps, to enhance the training process and achieve superior performance compared to state-of-the-art unsupervised and even supervised methods. To ensure a fair comparison, we evaluate our proposed model using two widely used datasets: SumMe and TVSum. The experimental results highlight the effectiveness of our proposed approach in automated unsupervised video summarization, achieving a 1.2% improvement over the best-performing methods' average F-score on SumMe and TVSum datasets. Additionally, our method ranks second among state-of-the-art unsupervised methods on each dataset. Notably, the top-performing methods exhibited inconsistent results across datasets, underscoring the broader applicability of our approach to diverse types of videos. Furthermore, our method demonstrates competitive performance compared to supervised approaches, with the best supervised method surpassing our results by only 0.75%.

Abstract Automatic categorization of human affective states is one of the main tasks required for implementation of a human-computer interface (HMI). Electroencephalogram (EEG) signals are particularly useful for developing efficient models for the HMI interface. However, the signal patterns associated with the emotional feelings are often subject-dependent, compromising the accuracy of the recognition system. In this paper, a novel sparse framework is proposed to enhance discriminative characteristics of emotional states by utilizing dictionaries as the basis functions. The proposed framework increases the between-class discrimination of the emotional states through the use of class-specified atoms in the dictionaries. In addition to the common representation residual employed by the sparse models, we have utilized separate classifiers to improve the discriminating capability of the proposed model. The proposed model is employed to distinguish between human emotional states based on recorded EEG signals presented in the ‘DEAP’ dataset. Our results are compared with those produced by well-known feature extraction methods and classification approaches indicating the proposed model could identify between-class differences associated with the input patterns. While the representation residual led to limited performance, the advantage of the proposed model was prominent when separate classifiers were applied. Finally, our results were compared with those of relevant investigations reported in the literature indicating that our method identified valence and arousal categories with reasonable accuracy.

Abstract Energy harvesting within Internet of Things (IoT) devices pertains to the methodology of capturing and storing ambient energy to power diminutive, low-energy apparatuses. The phenomenon of surface plasmons, a collective of free electrons associated with graphene, constitutes a compelling subject in the domain of heterogeneous catalysis pertinent to energy harvesting.. As a result, plasmonic catalysts based on graphene are amenable to extensive applications in energy harvesting and energy storage, underpinned by their exceptional characteristics, including a high charge carrier mobility of 20 m²V⁻¹s⁻¹ and a high theoretical surface area of 2630 m²g⁻¹. This research presents both experimental and theoretical analyses employing the Finite-Difference Time-Domain (FDTD) method to investigate the potential of plasmonic-assisted nanocatalysis utilizing graphene oxide for energy harvesting and sustainable chemistry. Ultraviolet (UV) irradiation was employed as a methodical approach to progressively alter the chemical composition and structural characteristics of graphene oxide (GO) flakes, as substantiated by Atomic Force Microscopy (AFM) analysis. The ultrathin coatings and membranes derived from UV-irradiated GO flakes demonstrated the potential for tunable plasmonic energy harvesting. Moreover, the UV-treated superoleophobic GO membranes exhibited remarkable antifouling properties, rendering them highly suitable for advanced Internet of Things (IoT) applications.

Abstract In recent years, due to the energy crisis, the attention of researchers and energy industry experts has been drawn to the use of renewable energy sources. Among renewable energy sources, hydroelectricity is of great importance due to its major advantages, such as its widespread distribution on the earth's surface. Also Studies have shown that hydrokinetic energy can be a suitable alternative to fossil fuels. Savonius turbines are one type of turbine that, when combined with hydrokinetic systems, will produce clean and accessible energy. The Savonius vertical wind turbine must be started non-automatically. Additionally in savonius wind turbine slow speed reduces productivity and increases costs. When this turbine To be used in a flowing water Its characteristics will change and need to be optimized. Accordingly, numerous studies have been conducted on the optimization of these turbines. Accordingly, this article will review and investigate published articles in In this field. It also describes how each of the effective parameters affects the performance of these systems.

Abstract The rapid growth of urban populations and escalating municipal solid waste (MSW) production pose critical waste management challenges globally. Conventional landfilling requires substantial land resources and contributes to long-term environmental pollution, including greenhouse gas emissions and groundwater contamination. Incineration offers a promising alternative with energy recovery potential, but widespread adoption faces two key barriers: high capital costs and the inherently low calorific value of waste due to elevated moisture content (typically 30–50% in MSW). To address these limitations, this study proposes an innovative hybrid system integrating a Rankine cycle with anaerobic digesters, designed to optimize energy recovery from both dry and wet waste streams. The system processes organic waste and leachate in anaerobic digesters to produce methane, while residual waste undergoes incineration. Two operational scenarios are evaluated: direct waste incineration (Scenario 1) and thermal drying-enhanced incineration (Scenario 2). Thermodynamic analysis reveals a 5% improvement in first-law efficiency compared to conventional systems, directly correlated with waste input rates. Specifically, each 5 kg/s increase in raw waste flow boosts efficiency by ~7%, while simultaneously reducing natural gas demand by 10%. These findings demonstrate the system’s dual potential for enhancing energy recovery and advancing sustainable waste management practices.

Abstract Abstract- Various types of solar heaters are designed based on different operational principles. This study focuses on the numerical analysis of the impact of latent heat storage on the performance of a solar water heater over a 24-hour period. A two-dimensional geometry was transiently modeled, and the use of extended surfaces in the air channel and the storage tank containing phase change materials (PCMs) was investigated. Using PCMs in solar latent heat storage systems allows for the storage of energy received from the sun during the day, which can then be utilized for various applications at night. The incorporation of fins enhances the rate of energy storage and release within the system, leading to improved phase change processes. According to the results obtained, increasing the flow velocity enhances the freezing rate, and the use of fins further accelerates this process compared to configurations without fins. By applying extended surfaces placed in the air channel, the heated incoming air is dispersed throughout the channel upon encountering the fins, leading to a more uniform thermal flow. Additionally, after sunset, the heat collected from the absorber is effectively transferred to the air.