This paper introduces quantum entangled reservoir computing (QERC), a unified glass box control paradigm for battery energy storage systems (BESS) that replaces opaque black box learning with a transparent, physics inspired framework. Unlike randomly initialized neural networks or conventional Echo state networks, QERC employs a fixed complex valued reservoir constructed using unitary entanglement matrices, ensuring inherent Lyapunov stability and energy conserving dynamic behaviour analogous to quantum chaotic systems. Implemented in MATLAB/Simulink on a heavily modified IEEE 14-bus renewable microgrid, a rigorous benchmark selected to validate topological robustness under severe solar, wind intermittency and non-linear dynamic load changes, the proposed architecture simultaneously addresses five coupled objectives, voltage regulation, frequency support, state of charge (SOC) estimation, harmonic mitigation, and battery loss minimization, within a single coherent computational substrate, thereby avoiding fragmented cascaded control loops. By leveraging high bandwidth internal dynamics and deterministic 10 mu s computational latency, QERC operates as an embedded virtual active power filter (V-APF), reducing grid total harmonic distortion (THD) from above 5% to below 3.5% under dynamic loading conditions. A regularized single shot ridge regression training strategy penalizes high frequency control jitter, suppressing battery micro-cycling and yielding approximately 35.9% reduction in battery operational losses. Performance evaluation demonstrates voltage regulation within +/- 0.028p.u, frequency containment within +/- 0.14 Hz, and highly accurate SOC estimation with 0.21% RMSE, by consistently outperforming a comprehensive suite of state of art baselines, spanning predictive (MPC), heuristic (FA-PSO), classical (VSG), and deep learning (RL) paradigms. These results validate the broad applicability of QERC. These results establish QERC as interpretable and computationally efficient control framework that bridges high performance AI methods with the stringent stability and real-time requirements of modern power microgrids.

Because of their adaptability, three-link robotic manipulator (TLRM) devices are frequently used in industrial automation; yet, their operational complexity makes precise control difficult. In order to improve trajectory tracking along the X and Y directions, an adaptive fractional-order fuzzy PID (AFOFPID) controller is suggested. The optimisation criterion is a weighted mixture of the integral of absolute error (IAE) and the integral of absolute changes in controller output (IACCO), and the controller parameters are adjusted using the Cuckoo Search Algorithm (CSA). To manage changes in system dynamics, the AFOFPID combines a fuzzy logic structure with an adaptive mechanism, and the fractional-order component enhances stability and robustness even further.

Secure data sharing and trust management remain a significant challenge in the wireless sensor network because the heterogeneous nature of these networks creates challenges associated with network lifetime, energy efficiency, and security. However, while the incorporation of blockchain technology for enabling secure data sharing offers robust performance, it is still prone to certain limitations, such as performance degradation, limited scalability, and key size dependencies. Thus, to address these challenges, this research proposed an Adaptive Rivest Shamir Advanced Encryption Standard-based multi-parameter Ideal queuing algorithm (AdRS-MPIQ) for secure routing in WSNs. The Multi-parameter Ideative Queuing algorithm effectively selects cluster heads and optimizes energy-efficient paths. By doing so, it enhances network security and extends its lifetime by avoiding local optima and increasing convergence speed. Additionally, the adaptive nature of the AdRS-MPIQ algorithm dynamically chooses the key size according to the size of the data, thus ensuring that the encryption remains secure over time. The comparative evaluation demonstrates the efficiency of the AdRS-MPIQ algorithm, achieving a minimal decryption time of 2.018 seconds(s) and a reduced gas consumption of 344.87 KB when tested with 150 nodes.

Accurate state of charge (SOC) estimation is critical for reliable battery energy storage system (BESS) operation in renewable grids. However, conventional integer-order and deterministic models often fail to capture long-memory electrochemical diffusion or quantify uncertainty under dynamic conditions. To address this, this paper proposes the probabilistic fractional-order Mamba Kolmogorov-Arnold network (PFO-Mam-KAN) framework. This architecture uniquely integrates a linear-complexity temporal Mamba encoder to efficiently model long sequences, a probabilistic KAN decoder for estimating time-varying parameter distributions, and a differentiable fractional-order physics layer using constant phase elements, to capture non-Markovian dynamics. These components are unified via an adaptive Kalman-gated correction mechanism that dynamically modulates reliance on measurements based on propagated uncertainty. Validated on a hybrid AC/DC microgrid using the Sandia database, the framework achieves a superior SOC estimation RMSE of 0.31% and MAE of 0.18%, outperforming contemporary advanced observers. The model demonstrates improved SOC estimation under 3% measurement noise and temperature variations (10 degrees C-40 degrees C), while exhibiting less computation burden.

Wireless Sensor Networks (WSN) are significant for various applications, however ensuring data security and energy consumption remains a critical challenge. The conventional methods lacked sufficient security, exhibited communication overhead, and energy inefficiencies. Therefore, this research proposes the Distributed Fractional Hawk Optimization (DtFHO) algorithm to address the limitations in cluster head selection for secure WSN routing. The integration of fractional theory improves the convergence speed and exploitation balance in cluster head selection. To secure the data routing, a blockchain network is employed, which maintains a transparent record of routing paths while preventing malicious node entries. Furthermore, the modified End-to-End Homomorphic encryption enables secure data sharing without decrypting sensitive information at intermediate nodes. Through considering the multimetric factors, the DtFHO algorithm offers a secure routing path, making it highly effective for large-scale and sensitive network scenarios. The DtFHO showcases a robust performance by achieving a minimum transaction time of 2.013 seconds, memory usage of 347.95 Kilobytes, Gas usage of 345.84 Kilobytes, encryption time of 2.012 seconds, and a maximum throughput ratio of 0.748, normalized energy of 0.766 Joules, with 153 alive nodes compared to the conventional methods.

This paper proposes a quantum-inspired neuromorphic active inference (Qi-NAI) framework for fractional-order state-of-charge (SOC) estimation and intelligent energy management of hybrid AC/DC microgrids integrated with battery energy storage systems (BESS). Conventional BESS controllers suffer from SOC estimation drift, delayed reactive control, fragmented optimization, and poor handling of stochastic renewable intermittency due to their inability to capture electrochemical memory dynamics and uncertainty-aware predictive behaviour. To overcome these limitations, a unified three-layer intelligent architecture is developed comprising: (i) a Weibull-indexed quantum-inspired fractional-order perception layer for simultaneous SOC/SOH estimation, (ii) a neuromorphic active inference decision layer employing liquid state machine dynamics and variational free-energy minimization for anticipatory voltage-frequency regulation, and (iii) a multi-objective special relativity search (MOSRS) optimization layer enhanced with Rayleigh probabilistic exploration for economic dispatch and predictive inverter control. In addition, a quantum-probabilistic predictive switching (QPPS) mechanism integrated with finite control set model predictive control enables uncertainty-aware real-time inverter switching while minimizing harmonic distortion and switching losses. The proposed framework is validated on a modified IEEE 123-bus hybrid AC/DC microgrid under weak-grid conditions, renewable intermittency, severe load disturbances, and islanding events. Simulation results demonstrate superior performance compared with PI, MPC, EKF, and AI-based controllers. The proposed Qi-NAI framework achieves SOC estimation RMSE and MAE values of 0.045% and 0.038%, respectively, limits frequency deviation to 0.03 Hz, restores voltage stability within 12 s, maintains total harmonic distortion below 1.75%, reduces operational losses by up to 17%, extends battery lifespan to 13.32 years, and achieves a levelized cost of storage of $0.0631/kWh.

Wireless Sensor Network (WSN) data routing is the most crucial operation as it involves significant delivery of the data packets to the base stations. Although many security mechanisms and routing protocols are designed to enhance WSN data security, the prevalence of increased computational overheads, limited available resources, and link or node failures lowers the quality of service routing. The research thus proposes an effective Incentive learning-based Convolutional Neural Network with Gradient Boosting Machine(ICNBM) for packet validation, and Distributed Optimization-enabled Modified Homomorphic Encryption(DMHE) for secure WSN data routing. The sleep scheduling mechanism effectively balances the network longevity and the data security, through addressing the resource constraints by entering the low-power sleep mode when not involved in active data transmission. The encrypted data aggregation characteristics of the DMHE validate the data integrity through minimizing the computational requirements. Eventually, the Distributed Optimization Algorithm(DiOA) developed for optimal Cluster Head selection improves the packet delivery ratios through reducing the energy constraints and security threats. Furthermore, the incentive learning and the blockchain integration enable the research to overcome security vulnerabilities and reduce redundant data transmission. The research findings validate the superiority of the ICNBM for 90% training in terms of 97.98% accuracy, 98.06% sensitivity, and 97.79% specificity.

The transition toward inverter-dominated renewable microgrids is frequently hindered by grid instability and prohibitive battery energy storage system replacement costs. Traditional control strategies often prioritize immediate grid regulation while neglecting electrochemical constraints, which accelerates battery health degradation. This paper proposes a neuromorphic active inference with liquid dynamics (NAI-LD) methodology, a bio-inspired architecture that interprets the microgrid as a dynamical system seeking operational homeostasis. Utilizing continuous-time liquid time-constant neural network, the controller adaptively modulates processing speed, contracting its temporal horizon during disturbances and expanding it during steady state to suppress noise. Evaluated in a simulation environment under severe stochastic transients, the NAI-LD controller restricted frequency excursions to ± 0.025 Hz and total harmonic distortion to a range of 2.3 to 2.7%. By incorporating expected free energy minimization, the framework establishes an intrinsic self-preservation mechanism to reduce daily micro-cycling. Based on the simulated trajectories, techno-economic analysis projects a potential 31.99% reduction in annualized operational expenditure and a theoretical 2.9-year extension of battery hardware lifespan. However, these findings are inherently limited by the simulation environment, the degradation projections are strictly bounded to the semi-empirical characteristics of lithium iron phosphate chemistry and do not currently account for physical communication latencies, sensor noise, or analog-to-digital conversion jitter. While the average algorithm execution time of 0.62 milliseconds indicates computational feasibility for standard digital signal processors, subsequent hardware-in-the-loop experimental validation remains necessary to bridge these modeling limitations and confirm the projected system performance in physical field deployments.

Perovskites offer many advantages like high efficiency, cost-effective, and easy fabrication of malleable solar cells. Nonetheless, enduring stability and eco-friendly lead (Pb)-free applications pose obstacles for commercialization. Mostly, the most efficient perovskite solar cells (PSC’s) reported use toxic lead (Pb) and volatile polymers as absorption and hole/electron transport layers. So, in this research, we introduced and simulated the photovoltaic (PV) response of Pb-free methylammonium germanium iodide (MAGeI3) with a wide bandgap (1.9 eV) as an absorption layer. Different hole transport layers (HTLs), such as copper oxide (Cu₂O), copper iodide (CuI), and copper thiocyanate (CuSCN), utilized to investigate the impact of HTL improved charge extraction and transfer properties, increasing the efficiency (η) of CuI-based perovskite solar cells to 23.9

This paper presents a Nonlinear Fuzzy PID Controller optimized using the Grey Wolf Algorithm (GW-FPID) for a three-link manipulator system, with a comparative evaluation against PID and Fuzzy PID controllers. The PID controller struggles with nonlinearities and disturbances, while the Fuzzy PID controller improves adaptability but is limited by predefined rules. The proposed GW-NFPID controller, optimized using the Grey Wolf Algorithm, dynamically adjusts control parameters, achieving superior trajectory tracking, reduced overshoot, and enhanced disturbance rejection. The results demonstrate that GW-NFPID outperforms all other controllers, making it ideal for industrial robotics, medical robots, autonomous manipulators, and service robots requiring precision and adaptability. By integrating intelligent control and metaheuristic optimization, the GW-FPID controller proves to be a robust solution for nonlinear robotic systems. Future work may focus on real-time implementation and hybrid optimization techniques for further enhancements.

Abstract A smart micro grid technology application facilitates the integration of renewable energy and increase its penetration. A smart grid is an electrical network which is built on advanced technology that uses dual-way digital communication to transmit electricity to buyers. The smart grid was created with the aim of using smart meters to overcome the problems faced by traditional grids. Microgrids allow for the integration of multiple renewable energy sources at different levels, improving the power system’s reliability, sustainability and efficiency. Remote places, spacecraft and maritime applications all use DC microgrids. Solar photovoltaic (PV) systems, wind energy, fuel cells, battery management systems, supercapacitors, and loads make up a DC microgrid. In this paper, some of the interesting approaches for optimal energy sharing in the hybrid microgrid are discussed.

In the over-nominal wind speed region, the power output of a wind turbine is controlled by adjusting the blade pitch angle. A wind turbine exhibits nonlinear relations with varying wind speeds; therefore, designing a suitable pitch angle controller for the wind turbines is a significant engineering challenge. The current article primarily focuses on developing a Takagi–Sugeno fuzzy logic (TSFL) tuned PID pitch controller for a wind turbine connected to an electric generator through a 2-mass drive train. Further, the second stage presents a comparative analysis between optimized and Unoptimised power outputs from the permanent magnet synchronous generator. The Genetic Algorithm (GA) modifies the mutation rate and crossover point number. MATLAB/Simulink software validated the GA approach and produced superior results. Thus, the proposed GA-optimized controller better adjusts the wind turbine’s blade pitch angle at higher wind speeds than the unoptimized pitch controller.

Fully unsecured network - the mobile operator transformation However, many of them can take advantage from the network and modify SPED architecture if O SEPD will be able to provide access to some applications as proposed in what is recommended by the Open Source Policy and Evolved over Darwin Alliance - O SPED. This work overcomes the main research challenges of the O SPED technique regarding how to apply during array construction. We examine O SPED innovation, how the communications between O SPED condominiums are arranged and provide an insight into node splitting functionality separation across radio and supply units via the OSi interface with 3GPP criteria. Supporting Technologies: O SPED Antenna Methods Problems and Solutions Now we examine some of the troubles associated with ITS antenna tactics, lastly relocating on to a conversation of viable alternatives in terms of equally technical as perfectly economical requirements. In addition, we recommend utilizing the zero forcing equaliser as a precoding vector for channel information based antenna strategy. This is one way to reduce radio unit interferences and allow for adaptability in a heavily trafficked connection environment by baking the precoding over wireless units.

Information networks now have greater transmission speed and dependability because to developments in informatization. The creation and maintenance of both new and current standards are part of this strategy. Numerous firms are continuously developing and implementing next-generation communication networks. This article provides a quick overview of software applications that are presently on the market that let you examine and assess data network activity. A tool with enough built-in capability and the option to add your own implementation was thought to be especially appropriate for mobile phone systems. We've determined that the appropriate network simulator is NS3. Reviews of this tool were combined with the development of solutions. From the time a network node travels until it is disconnected from the base station, you may gauge the dependability of data transmission.

Three-link robotic manipulator systems (TLRMS) often used in automation industries offer many capabilities, but become very complex in terms of their control and operations. In order to enhance trajectory tracking in the X and Y axes, this study investigates the application of a fractional-order nonlinear proportional, integral, and derivative (FONPID) controller for a three-link robotic manipulator system (TLRMS). Using a cost function that combines the integral of square error (ISE) and the integral of absolute change in controller output (IACCO), the cuckoo search algorithm (CSA) maximises the performance of the controller. The fractional-order term enhances the robustness and the nonlinear term supports the adaptiveness of the FONPID controller. The fractional-order proportional, integral, and derivative (FOPID) and classic PID controllers are contrasted with the FONPID controller's efficacy. The findings show that the CSA-tuned FONPID performs better than the other controllers, providing more robust and accurate tracking. By demonstrating fractional-order control's promise for intricate robotic systems, this study advances the discipline.

Fifth-generation mobile networks will provide answers to this high demand for mobile traffic, since different types of services require distinct solutions. Even millimeter wave spectrum can provide very High data rates. Fiber has several advantages over other media for analog radio, including low cost, economic power consumption and greater spectral efficiency. As a result, mmWave ARoF promises to provide a common radio interface for the public in 5G fronthaul architectures.The 5G standard employs orthogonal frequency division multiplexing and thus the 5G mmWave ARoF systems should also use this waveform. However, among all of the detrimental parameters in mmWave wideband ARoF systems, phase noise is one. Consequently, in this study we experimentally investigate phase noise using our experimental setup. The aforementioned setup architecture significantly facilitates the iterative bumps of final phase noise which can be accomplished, stage by ionic level by use of several additional Faraday- cages. In addition, it offers an original method for minimizing OFDM receiver phase distortion. To validate the effectiveness of this method, we empirically evaluate it by adapting the system to other noise phases and subcarrier spacings. The results exemplarymmWave OFDM ARoF for 5G and beyond, and illustrates the workability of proposed technique on scenarios like this.

The tactile web is the evolution of this global product network — machine-to-machine communications and human-to-machine interactions. Focused on interactive real-time applications, TI is available in technology, business and society. The fifth generation of technology does this by awful fast able ultra-reliable low latency... technologies. TI applications required high latency and reliability. Under 3rd generation consortium's URLLC (Ultra Reliable Low Latency Communications), it is expected to target as low latency of less than one millisecond and above the dependability should be greater/ equal 99.9949% for a single packet delivery transfer of size up to maximum=32 bytes[htmlspecialchars] 3GPPa s new radio access method and recommend 5g new air interfaces, band split diagonal multiplexed in adjustable frequency mode. However, the introduction of a RAT based around distinct physical layer technology results in some game-changing future technologies; largely surrounding network intelligence. It is expected that machine learning techniques will be necessary to design complex algorithms for QoS-aware radio resource management exploiting network resources and meeting 5G NR URLLC requirements in these scenarios. Therefore in this work we provide the adaptation of federated reinforcement learning machine learning approach with respect to 5G NR URLLC and also brief-out the relevant achievements for that. In this NR we deliver a detailed analysis of the TI-supporting Top layer signal access schemes for URL LC. Moreover, we define seven important potential future uses cases for Falkland Islands NR URL LC accelerators in 5G applications..

Four various fractional-order nonlinear proportional-integral-derivative (FONPID) controllers are compared in this paper. They were all tuned using the Cuckoo Search Algorithm (CSA) based on various performance metrics, including Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Square Error (ITSE) and Integral Time Absolute Error (ITAE). In particular, a three-link robotic manipulator is used in the research to assess how these performance indices affect the overall control accuracy and stability of a nonlinear multi-input multi-output (MIMO) system. To assess control accuracy, trajectory tracking performance, extensive simulations were run. The outcomes show that the system responsiveness, and computational efficiency all differ significantly depending on the performance parameter selected for the FONPID controller optimization. The study emphasizes the benefits of applying CSA for FONPID controller optimization and offers insightful information on tuning techniques for intricate MIMO systems.

Reducing or limiting the turbine’s performance at high wind speeds can be effectively achieved by altering the pitch angle of the blades. Big wind turbines with variable pitch control usually use PID control to maintain a constant output power when wind speeds surpass the rated limit. However, the traditional PID controller finds it difficult to achieve the desired performance due to the daily and hourly fluctuations in wind velocity, which are unpredictable. This is mainly because pitch angle and wind speed exhibit a non-linear association at high wind speeds. This study uses a PID controller tuned by an adaptive fuzzy logic structure (F-PID) to study how power generation is controlled in variable-speed wind turbines (VSWT). To test the effectiveness of the control approach and the system itself, the pitch angle control system was simulated using the MATLAB/Simulink simulation platform. An artificial wind profile was used to test the controller’s effectiveness, and the findings showed that the recommended controllers were indeed successful at controlling power. The suggested controller is compared with the standard PID controller to highlight the improvements of the approach.

Zinc oxide (ZnO), a material with excellent electron mobility and a low-temperature requirement for production, is a promising option for use as an electron transport layer in perovskite solar cells (PSCs). However, it does have the drawback of having a low open-circuit voltage ([Formula: see text]). Herein, to increase the [Formula: see text] parameter of ZnO-based PSCs, graphene quantum dots (GQDs) are incorporated into the ZnO precursor and used as desirable ETL for PSCs. The presence of GQDs in ZnO ETL facilitated photo-electrons at the ETL/perovskite interface by reducing charge transfer resistance in this interface. Compared to the net ZnO-based PSCs, solar cells using GQD-doped ZnO as ETL have better stability, comparable [Formula: see text], higher [Formula: see text], and FF. The best GQD-doped-ZnO ETL-based PSCs recorded the highest power conversion efficiency of 20.23% with [Formula: see text] of 1.130[Formula: see text]V. Meanwhile, the boosted PCE of FAPbI3-based PSCs is achieved due to the improved perovskite crystal quality, the effective defect passivation effect of GQDs at ZnO/FAPbI3 interface, and the increased electrical conductivity of ZnO ETL. In addition, the GQD-doped ETL devices showed higher ambient air stability than the devices with net ZnO ETLs.