In this study, pristine MnO₂ catalyst was synthesized by hydrothermal technique and annealed at 400 and 600°C (MnO₂@400°C and MnO₂@600°C) transforming it into nanorods in a nitrogen atmosphere. The heat treatment process ameliorates the catalytic activity of the MnO₂ catalyst by inducing oxygen vacancies. The catalyst is characterized by scanning electron microscope, and X‐ray diffraction, Fourier transforms infrared spectroscopy (FTIR), Electron paramagnetic resonance (EPR), and X‐ray Photoelectron Spectroscopy, whereas the oxygen reduction reaction (ORR) activity examined through rotating disc electrode and fuel cell test station. To elucidate the effect of sintering temperature on the MnO₂ nanorods, the high angle annular dark‐field (HAADF) imaging was carried out on the samples. MnO₂@400°C showed preeminent robustness to withstand austere alkaline environment during the experiment and possessed high electrocatalytic activity for the ORR with current density and onset potential values of 6.4 mA cm^–2 and 0.80 V VS (RHE), respectively. The single‐cell test experiment of alkaline fuel cell delivered peak power density 81 mW/cm at 50°C. It is believed that this idiosyncratic behavior of MnO₂ nanorods is due to the preferential growth on (211) and (310) indices and coexistence of optimized Mn⁴⁺/Mn³⁺ oxidation states as well as the creation of optimum oxygen vacancies on MnO₂ nanorods. Moreover, the stretching of MnO₂ nanorods occurs with increasing temperature and thus increasing the surface area to volume ratio. Thus thermal treatment shows that MnO₂ is highly sensitive toward temperature variation and optimum ORR results can be obtained at adequate temperatures.

The modern power generation systems are increasing their reliance on high penetrations of distributed energy resources (DERs). However, the optimal dispatching mechanisms mainly rely on central controls which receive the load demand information from the electricity utility providers and allocate the electricity production targets to participating generating units. The lack of transparency and control over the DER fuel inputs makes the physical power purchase agreements (PPAs) a cumbersome task. This research work proposes an innovative fractal moth flame optimization (FMFO) approach to tackle the problem of integrated load dispatch (ILD). The proposed methodology provides a mechanism to integrate the information of the proposed optimizer, i.e., FMFO into the smart contracts enabled by the blockchain technology. This problem entails the allocation of loads to power-generating units in a manner that minimizes the total generation cost in a decentralized manner. To improve the efficiency of dispatch operations in the presence of a substantial integration of wind energy, this study proposes a novel framework based on the principles of fractal heritage, drawing inspiration from the classical MFO method. To assess the effectiveness and adaptability of the algorithm suggested, various non-convex scenarios in the context of optimization for ILD are considered. These scenarios incorporate valve-point loading effects (VPLEs), capacity limitations, power plants with multiple fuel options, and the presence of stochastic wind (SW) power uncertainty, following a Weibull distribution. The findings demonstrate exceptional performance in terms of minimizing fuel generation costs compared to traditional algorithms.

In nonlinear systems, monitoring control behavior, fault occurrence, and latency factor continue to be major obstacles. Traditional control models frequently handle edge–case situations inaccurately and are unable to adjust to dynamic changes. Reinforcement learning control is a novel intelligent framework that incorporates sophisticated modules for nonlinear models in order to overcome these constraints. First, an innovative approach to solving the adaptive tracking algorithm for a type of T–S fuzzy plant is presented in this research. Furthermore, the authors examine time–varying delay with the actuator failure for complex systems that are generally represented by the T–S fuzzy plant. Second, it has been demonstrated that the chosen performance index solves the delayed algebraic Riccati formula, which can be resolved by scheme of repetition methods, when the resultant plant with the tracking signal is constructed under the decoupling delayed T–S fuzzy framework. Third, the coupled delayed algebraic Riccati formulas are then solved using a Reinforcement Learning (RL) technique that makes use of complex model dynamics knowledge. Furthermore, the fundamental RL iteration technique’s convergence is demonstrated. Lastly, the power sector and a F–16 airplane flight examples are provided to demonstrate how the online repetition method outperforms the offline one in terms of tracking accuracy and effectiveness.

High-level synthesis (HLS)-based design methodologies are extremely viable for industries that are sensitive to production costs. In order to have competitive advantage, the ability to have several different implementations of the same algorithm satisfying a diverse range of resolution, cost, and performance constraints is highly desirable. In this paper, we present multiple hardware implementations of the semi-global matching (SGM) algorithm, which is used in stereo vision systems, e.g., for automotive applications. The hardware platform considered in this paper is a Xilinx Zynq system-on-chip. A performance comparison of both HLS-based design and a manual register transfer level (RTL) design in terms of quality of results, flexibility, and design time is also presented. SGM mainly includes a sequence of three processing steps, i.e., the “cost cube calculation” followed by the “path cost computation” and finally the “disparity approximation and minimization”. The path cost processor further performs a pixel-wise processing of the cost cube data along eight distinct path orientations. The baseline algorithmic model usually called the “golden” model utilizes considerably large arrays that are required to be mapped to an external DRAM and brought into the on-chip RAM when required. This necessitates adding both the memory transfer loops as well as insertion of calls to the AXI transactors for accessing the DRAM through the on-chip DDR slave. Furthermore, the initial algorithm (typically single-threaded) must be parallelized to fully exploit the concurrency offered by the target hardware platform. The design space exploration was thus performed by making several considerably different micro-architectural choices. Eventually, we were able to obtain an implementation comparable with the manual RTL design. Both the manual RTL and the HLS designs achieved the target real-time performance of 30 frames/s for the image resolution of 640×480 with a disparity depth of 128 pixels per frame.

Despite exceptional optoelectronic properties and rapidly increasing efficiency of perovskite solar cells (PSCs), the issues of toxicity and device instability have hampered the commercialization of this renewable energy technology. Lead (Pb) being the main culprit creates major environmental risks and therefore must be replaced with a nontoxic material such as tin (Sn), germanium (Ge), etc. Moreover, replacing organic cations in the perovskite's ABX₃ structure with inorganic ones like cesium (Cs) helps aid the stability issues. This study uses six different kesterite-based hole transport layers (HTLs) and three different metal oxide-based electron transport layers (ETLs) to numerically simulate and optimize all-inorganic CsSnGeI₃ PSCs. Metal oxide ETLs are used in this study due to their large band gap, while kesterite HTLs are used due to their excellent conductive properties. All of the simulations are performed under standard testing conditions. A total of 18 novel planar (n-i-p) PSCs are modeled by the combination of various charge transport layers (CTLs), and the device optimization was done to enhance the power conversion efficiencies (PCEs) of the PSCs. Furthermore, the effect of CTLs on the energy band alignment, electric field, quantum efficiency, light absorption, and recombination rate is analyzed. Additionally, a detailed analysis of the impact of defect density (N_t), interface defects (ETL/Perv, Perv/HTL), temperature, and work function on the functionality of 18 different CsSnGeI₃-based PSCs is conducted. The simulation findings demonstrate that SnO₂/CsSnGeI₃/CNTS is the most efficient optimized PSC among all of the simulated structures, with a PCE of 27.33%, J_sc of 28.04 mA/cm², FF of 85%, and V_oc of 1.14 V.

Extension of the main grid to remote areas is economically not feasible. To electrify remote areas, one of the best choices is to install Renewable Energy Sources (RES) as a distributed generation (DG) and thus form a microgrid (MG) in islanded (Stand-alone) mode. In islanded mode, the MG has no support from the national grid. Thus, the overloading of islanded DC MG can collapse DC bus voltage and cause fluctuation in the load. Therefore, the power sharing and the interconnection among the microgrid (MG) cluster are necessary for reliable operation. Many methods for power sharing also aim at minimizing circulating currents which cannot be avoided when every MG feeds their load locally. Therefore, the proper power balancing among generation, loads, and in between MG cluster is challenging in islanded topology. This paper presents an intelligent controller for power sharing among PV-based MG clusters with load management of connected load during power deficiency. The priority is given to the local critical load of each MG. The second priority is given to the remaining load of the respective MG. The least priority is given to the loads connected to the neighboring MGs. The results show that the power continuation to the power-deficient load has been maintained when another MG has surplus power. The circulating current losses between the MG cluster has been fully avoided during no power sharing.