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This paper presents a novel hybrid structural control solution designed for distillation systems that utilize a solar source alongside an electrical grid. The power conversion architecture incorporates a reversible bridge rectifier and a quadratic boost converter. The hybrid photovoltaic grid configuration offers several benefits, including source complementarity, enhanced dependability, and energy availability aligned with power requirements. Leveraging a photovoltaic source operating at maximum power facilitates energy conservation. On the control front, an adaptive technique based on a reference model is proposed. Fuzzy logic governs the quadratic boost converter, simplifying the management of its complex nonlinear nature. The control strategy aims to maximize solar power utilization, minimize harmonic components in the grid current, synthesize an adaptive controller, and achieve a near-unit power factor on the grid. The simulation results for a steady distillation system demonstrate promising findings. Despite variations in irradiation, load power, and grid drops, the system maintains a minimal bus voltage ripple, remaining close to the intended value. Optimization of the panel-generated power leads to improved PV source utilization and enhanced system efficiency. Furthermore, the combination with an electrical grid achieves a low rate of grid current distortion and a unitary power factor. [ABSTRACT FROM AUTHOR]

This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact. [ABSTRACT FROM AUTHOR]

In this paper, a novel robust finite-time control scheme is specifically designed for a class of under-actuated nonlinear systems. The proposed scheme integrates a reaching phase-free integral backstepping method with an integral terminal fractional-order sliding mode to ensure finite-time stability at the desired equilibria. The core of the algorithm is built around proportional-integral-based nonlinear virtual control laws that are systematically designed in a backstepping manner. A fractional-order integral terminal sliding mode is introduced in the final step of the design, enhancing the robustness of the overall system. The robust nonlinear control algorithm developed in this study guarantees zero steady-state errors at each step while also providing robustness against matched uncertain disturbances. The stability of the control scheme at each step is rigorously proven using the Lyapunov candidate function to ensure theoretical soundness. To demonstrate the practicality and benefits of the proposed control strategy, simulation results are provided for two systems: a cart–pendulum system and quadcopter UAV. These simulations illustrate the effectiveness of the proposed control scheme in real-world scenarios. Additionally, the results are compared with those from the standard literature to highlight the superior performance and appealing nature of the proposed approach for underactuated nonlinear systems. This comparison underscores the advantages of the proposed method in terms of achieving robust and stable control in complex systems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nonlinear speed control method for permanent magnet synchronous motors that enhances their robustness and tracking performance. This technique integrates a sliding-mode disturbance observer and variable-gain fractional-order super-twisting sliding-mode control within a vector-control framework. The proposed control scheme employs a sliding-mode control method to mitigate chattering and improve dynamics by implementing fractional-order theory with a variable-gain super-twisting sliding manifold design while regulating the speed of the considered motor system. The aforementioned observer is suggested to enhance the control accuracy by estimating and compensating for the lumped disturbances. The proposed methodology demonstrates its superiority over other control schemes such as traditional sliding-mode control, super-twisting sliding-mode control, and the proposed technique. MATLAB/Simulink simulations and real-time implementation validate its performance, showing its potential as a reliable and efficient control approach for the system under study in practical applications. [ABSTRACT FROM AUTHOR]

This work presented power usage scheduling by engaging consumers in demand response program (DRP) with and without using renewable energy generation (REG). This power usage scheduling problem was modeled as an optimization problem, which was solved using an energy scheduler (ES) based on the crossover mutated enhanced wind-driven optimization (CMEWDO) algorithm. The CMEWDO was an enhanced wind-driven optimization (WDO) algorithm, where the optimal solution returned from WDO was fed to crossover and mutation operations to further achieve the global optimal solution. The developed CMEWDO algorithm was verified by comparing it with other algorithms like the whale optimization algorithm (WOA), enhanced differential evolution algorithm (EDE), and the WDO algorithm in aspects of the electricity bill and peak to average demand ratio (PADR) minimization without compromising consumers' comfort. Also, the developed CMEWDO algorithm has a lower computational time (measured in seconds) and a faster convergence rate (measured in number of iterations) than the standard WDO algorithm and other comparative algorithms. [ABSTRACT FROM AUTHOR]

Integrating Green Renewable Energy Sources (GRES) as substitutes for fossil fuel-based energy sources is essential for reducing harmful emissions. The GRES are intermittent and their integration into the conventional IEEE 30 bus configuration increases the complexity and nonlinearity of the system. The Grey Wolf optimizer (GWO) has excellent exploration capability but needs exploitation capability to enhance its convergence speed. Adding particle swarm optimization (PSO) with excellent convergence capability to GWO leads to the development of a novel algorithm, namely a Grey Wolf particle swarm optimization (GWPSO) algorithm with excellent exploration and exploitation capabilities. This study utilizes the advantages of the GWPSO algorithm to solve the optimal power flow (OPF) problem for adaptive IEEE 30 bus systems, including thermal, solar photovoltaic (SP), wind turbine (WT), and small hydropower (SHP) sources. Weibull, Lognormal, and Gumbel probability density functions (PDFs) are employed to forecast the output power of WT, SP, and SHP power sources after evaluating 8000 Monte Carlo possibilities, respectively. The multi-objective green economic optimal solution consisted of 11 control variables to reduce the cost, power losses, and harmful emissions. The proposed method to address the OPF problem is validated using an adaptive IEEE bus system. The proposed GWPSO algorithm is evaluated by comparing it with PSO and GWO optimization algorithms in terms of achieving an optimal green economic solution for the adaptive IEEE 30 bus system. This evaluation is conducted within the confines of the same test system using identical system constraints and control variables. The integration of a small SHP with WT and SP sources, along with the proposed GWPSO algorithm, led to a yearly cost reduction ranging from $ 19,368 to $ 30,081. Simulation findings endorsed that the proposed GWPSO algorithm executes fruitfully compared to alternative algorithms regarding a consistent convergence curve and robustness, proving its potential as a viable choice for achieving cost-effective solutions in power systems incorporating GRES. [ABSTRACT FROM AUTHOR]

Embryonic genome activation (EGA) is a critical step during embryonic development. Several transcription factors have been identified that play major roles in initiating EGA; however, this gradual and complex mechanism still needs to be explored. In this study, we investigated the role of nuclear transcription factor Y subunit A (NFYA) in bovine EGA and bovine embryonic development and its relationship with the platelet-derived growth factor receptor-β (PDGFRβ) by using a potent selective activator (PDGF-BB) and inhibitor (CP-673451) of PDGF receptors. Activation and inhibition of PDGFRβ using PDGF-BB and CP-673451 revealed that NFYA expression is significantly (p < 0.05) affected by the PDGFRβ. In addition, PDGFRβ mRNA expression was significantly increased (p < 0.05) in the activator group and significantly decreased (p < 0.05) in the inhibitor group when compared with PDGFRα. Downregulation of NFYA following PDGFRβ inhibition was associated with the expression of critical EGA-related genes, bovine embryo development rate, and implantation potential. Moreover, ROS and mitochondrial apoptosis levels and expression of pluripotency-related markers necessary for inner cell mass development were also significantly (p < 0.05) affected by the downregulation of NFYA while interrupting trophoblast cell (CDX2) differentiation. In conclusion, the PDGFRβ-NFYA axis is critical for bovine embryonic genome activation and embryonic development. [ABSTRACT FROM AUTHOR]

Accurate load forecasting plays a crucial role in the effective energy management of smart cities. However, the smart cities' residents' load profile is nonlinear, having high volatility, uncertainty, and randomness. Forecasting such nonlinear profiles requires accurate and stable prediction models. On this note, a prediction model has been developed by combining feature preprocessing, a multilayer perceptron, and a genetic wind-driven optimization algorithm, namely FPP-MLP-GWDO. The developed hybrid model has three parts: (i) feature preprocessing (FPP), (ii) a multilayer perceptron (MLP), and (iii) a genetic wind-driven optimization (GWDO) algorithm. The MLP is the key part of the developed model, which uses a multivariate autoregressive algorithm and rectified linear unit (ReLU) for network training. The developed hybrid model known as FPP-MLP-GWDO is evaluated using Dayton Ohio grid load data regarding aspects of accuracy (the mean absolute percentage error (MAPE), Theil's inequality coefficient (TIC), and the correlation coefficient (CC)) and convergence speed (computational time (CT) and convergence rate (CR)). The findings endorsed the validity and applicability of the developed model compared to other literature models such as the feature selection–support vector machine–modified enhanced differential evolution (FS-SVM-mEDE) model, the feature selection–artificial neural network (FS-ANN) model, the support vector machine–differential evolution algorithm (SVM-DEA) model, and the autoregressive (AR) model regarding aspects of accuracy and convergence speed. The findings confirm that the developed FPP-MLP-GWDO model achieved an accuracy of 98.9%, thus surpassing benchmark models such as the FS-ANN (96.5%), FS-SVM-mEDE (97.9%), SVM-DEA (97.5%), and AR (95.7%). Furthermore, the FPP-MLP-GWDO significantly reduced the CT (299s) compared to the FS-SVM-mEDE (350s), SVM-DEA (240s), FS-ANN (159s), and AR (132s) models. [ABSTRACT FROM AUTHOR]

This paper presents a fixed-time neuro-adaptive control design for a class of uncertain under-actuated nonlinear systems (UNS) using a non-singular fast terminal sliding mode control (TSMC) with a radial basis function (RBF)-based estimator to achieve the convergence and robustness against the uncertainties. The mathematical model of the considered class is reduced into an equivalent regular form. A fast TSMC is designed for the transformed form to improve the control performance and annihilate the associated singularity problem of the conventional TSMC. Lyapunov stability theory ensures the steering of the sliding manifold and system states in fixed time. RBF neural networks are adaptively estimate the nonlinear drift functions. The theoretical design, analysis, and simulations of cart-pendulum and quadcopter demonstrate the feasibility and benefits of the regular form transformation and the designed control design. Comparing the proposed synthesis with the standard literature presents the attractive nature of proposed method for such a class. [ABSTRACT FROM AUTHOR]

The present study recorded the vascular Flora of Landi Kotal Valley, Khyber Agency. The whole area was surveyed during the year 2012-2013 and a total of 94 plant species distributed in 85 genera and 48 families were recorded from the research area. Out of 94 species, 5 species belonging to 5 genera and 4 families were Monocots while the rest of the 89 species belonging to 80 genera and 44 families were Dicots. Maximum genera and species were in case of Asteraceae i.e. 10 genera being (11.76 %) and 12 species being (12.77 %) of the total genera and species. Asteraceae is followed by Lamiaceae with 7 genera being (8.24 %) and 7 species being (7.45 %) and Papilionaceae having 5 genera (5.88 %) and 6 species (6.38 %), which is then followed by Solanaceae having 4 genera (4.71 %) and 6 species (6.38 %). Solanaceae is followed by Mimosaceae with 3 genera (3.53 %) and 4 species (4.25 %) followed by Boraginaceae and Zygophyllaceae represented by 3 genera and 3 species each. The habitof plant species showed that 53.19 % were herbs, 23.40 % shrubs, 2.12 % subshrubs, 13.82 % trees, 1.06 % corms, 1.06 % bulbs, 2.12 % succulents, 1.06 % grass and 2.12 % climbers. According to life form observed, the perennials were 47.87 %, deciduous 10.63 %, evergreen 8.51 %, annuals 31. 91 % and biennials were 1.06 %. [ABSTRACT FROM AUTHOR]

In this instant research, some 31 taxa of weeds belonging to 30 genera and 18 families were evaluated for their local and medicinal uses. Asteraceae was represented by eight species (26.66%) followed by Poaceae and Polygonaceae with three (10%) species each. Caryophyllaceae and Euphorbiaceae were presented by two (6.6%) species each while the remaining families i.e. Apiaceae, Cannabaceae, Chenopodiaceae, Convolvulaceae, Cyperaceae, Fumariaceae, Malvaceae, Oxalidaceae, Papilionaceae, Plantaginaceae, Primulaceae, Ranunculaceae and Zygophyllaceae by one (3.3%) species each. Forty (40) informants belonging to different age groups and tribes were interviewed randomly for documentation of diverse information regarding the weed species. These species are used for various purposes including vegetable five (16.6%), pot herb two (6.6%), skin diseases three (10.0%), fodder three (10%), ornamental two (6.6%), increase lactation two (6.6%), fever four (13.3%), diarrhea two (6.6%), neurological disorders one (3.3%), urinary tract one (3.3%), dysentery two (6.6%), malaria two (6.6%), diuretic eight (26.6%), blood purifier one (3.3%), toothache one (3.3%), jaundice three (10.0%), constipation two (6.6%), narcotic one (3.3%), healing wounds two (6.6%), ulcer one (3.3%), boils one (3.3%), abdominal pain two (6.6%), laxative four (13.3%), stomach problem three (10%), stimulant two (6.6%) and one species was used in hypertension (3.3%). It is concluded from the present research that further studies are required for the proper management and utilization of weed species and documentation of indigenous and ecological knowledge regarding the weed flora of the area. [ABSTRACT FROM AUTHOR]