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Researchers working in the field of unmanned aerial vehicles are observing rapid developments in the field, such as the use of quadrotors to grasp, manipulate, and transport objects. These advancements are made possible through sophisticated control systems, new types of actuation and sensing technologies, and a greater understanding of the aerodynamic effects and gyroscopic moment generated by the rotating blades of the quadrotor involved. Therefore, this research work proposes four novel optimized control laws, i.e., conditioned adaptive barrier-based double integral super twisting sliding mode controller (CABDIST-SMC), BF double integral SMC (BF-DISMC), BF integral SMC, and barrier function-based SMC control laws, for attitude, heading, position, and altitude trajectory tracking of the quadcopter. The non-linear model of the quadcopter is developed using the Lagrange formalism in MATLAB ODE-45 environment, including the gyroscopic moments and aerodynamic effects, and a Lyapunov stability analysis is performed to ensure the asymptotic stability of the system. A 3D-helical complex trajectory is employed to analyze the performance and consistency of the proposed optimized controllers. For performance comparison, the proposed four variants of the SMC are compared with three traditional control strategies, i.e., backstepping SMC, optimized adaptive SMC via particle swarm optimization algorithm, and improved adaptive SMC. These traditional control strategies have also been developed and tested in MATLAB-Simulink. All the controllers are optimized using the improved grey wolf optimization algorithm, and to determine the best control law, six performance indexes, i.e., mean absolute percentage error (MAPE), root mean square error (RMSE), integral square error (ISE), integral absolute error (IAE), integral time absolute error (ITAE), and integral time square error (ITSE) are used. A hardware-in-loop (HIL) test is also performed to validate the performance of the designed novel control law. The C2000 Delfino MCU F28379D launchpad is used for performing the HIL validation test. The simulated results showed that the performance of optimized CABDIST-SMC surpassed all other control laws for altitude, heading, position, and attitude trajectory tracking. It has achieved MAPE of 0.02%, RMSE of 3.9912e−5, ISE of 3.1859e−8, IAE of 7.9823e−4, ITAE of 0.0080, and ITSE of 3.1849e−7 in case of yaw angle control. The proposed control laws have successfully passed the HIL validation test, and real-time implementation of the proposed control laws indicates no significant variations compared to the simulated results. [ABSTRACT FROM AUTHOR]

This study is based on gray-box modeling for the prediction of optimum mass flow rates of inlet streams of a Plate and Fin Heat Exchanger under uncertainty. A first principle model of the Plate and Fin Heat Exchanger was developed in Aspen Exchanger Design and Rating. Genetic algorithm was integrated with the first principle model to achieve the highest possible exit temperature of the cold process stream under uncertainty. A dataset of uncertain process conditions and their corresponding optimum inlet flow rates derived through the first principle-genetic algorithm integration was used to develop an artificial neural networks model. The artificial neural networks model was then integrated with the first principle model by replacing the genetic algorithm to form a novel gray-box framework. The proposed gray box model, i.e., artificial neural networks and first principle integration, achieved a higher effectiveness and higher outlet temperature than those derived through the straight run first principle model. The performance of the gray box framework was also comparable to the first principle integrated genetic algorithm approach and significantly minimized the computation time needed for estimating the optimum process conditions. First principle integrated genetic algorithm approach enhanced the effectiveness of the straight run model by 3.05%. Performance of the proposed gray-box model was comparable to the integrated framework of the genetic algorithm with the first principle model but was significantly faster. The developed artificial neural networks model was employed as a surrogate in Sobol and FAST sensitivity analysis framework to identify the impact of input variables on output variables. The proposed gray box based method enhanced the capability of the plate and fin heat exchanger to recover energy from the process stream and its robustness to cope with uncertainty. The proposed approach is suitable for real-time application and would contribute to laying a foundation for petroleum refinery 4.0. • A Gray-box (GB) modeling framework was developed for the optimization of process conditions of a plate and fin heat exchanger of a tile industry under uncertainty. • The GB framework was based on the integration of first principle (FP) model and Artificial Neural Networks (ANN) model. • The proposed GB approach attained the optimum conditions in a significantly less period of time than the conventional FP-GA framework. [ABSTRACT FROM AUTHOR]

The current study explores a new direction of research in which the magnetohydrodynamic (MHD) effects on two-dimensional nanofluid boundary layer flow under the influence of radiation are investigated in a porous medium using inverse multiquadric (IMQ) radial basis neural networks (RBNNs) i.e. IMQ-RBNNs. A quite new computational approach is used here in which artificial neural networks (ANNs) acquired IMQ radial basis function as an activation function in an amalgam of globally searching solver named genetic algorithms (GAs) together with an efficient local searching sequential quadratic programming (SQP) solver. The basic equations that represent the fluid flow are first obtained in the form of partial differential equations (PDEs) and then converted into dimensionless ordinary differential equations (ODEs) by applying similarity transformations. The dynamical properties of the nanofluid including velocity, temperature along mass concentration are analyzed by varying physical parameters including radiation parameter, magnetic parameter, constant moving parameter, heat source parameter, Brownian motion parameter and Eckert number. Moreover, the escalation in the value of the constant moving parameter diminishes the fluid velocity but this effect is reversed in the case of magnetic parameter. The increasing values of heat source parameter, Brownian motion parameter and Eckert number inflate the nanofluid temperature but a reversal effect is observed in the case of radiation parameter. It is also observed that larger values of heat Brownian motion parameter and radiation parameter amplify the nanofluid concentration but an opposite effect is obtained in the case of heat source parameter. The numerical results of the proposed model obtained through the IMQ-RBNNs based solver optimized with GASQP algorithm. The efficacy and tendency of rapid convergence of the proposed solver are endorsed through graphical illustrations and tables using well-known statistical operators with various fitness analyses on MATLAB are also part of this research. [ABSTRACT FROM AUTHOR]

In this study, an artificial intelligence-based framework was developed to monitor the efficiency of the carbon capture unit, i.e., the Sour Compression Unit (SCU) and a cryogenic unit, in a cement manufacturing plant. Initially, an Aspen Plus-based model of the SCU and the cryogenic unit was developed. The process model was then transformed into dynamic mode through the interfacing of MATLAB and Excel with Aspen Plus to capture real-time process behavior. Five hundred fifty data samples were generated by varying the process conditions, i.e., inlet water flow rate, temperature, pressure, etc. The dataset was then used to develop ensemble models. Output compositions of the models were CO 2 , SO 2 , and NO. The ensemble models were used as surrogates in Sobol and Fourier Amplitude Sensitivity Test (FAST) frameworks to find the most sensitive process conditions that affect the efficiency of the process. The correlation coefficients based on the ensemble models for estimation of the composition of CO 2 , SO 2 , and NO, were 0.9888, 0.9663, and 0.9970, respectively. Based on the sensitivity analysis, the inlet flue gas temperature and pressure were the dominant variables that affect the mole fraction of SO 2 and NO in the flue gas. Besides, the CO 2 recovery was found heavily dependent on temperature of the flash tanks. The proposed framework was highly accurate and will provide a base for real-time implementation of the concept of smart factory in cement manufacturing plants. [ABSTRACT FROM AUTHOR]

In this research-based study, the electro-magneto impacts are numerically observed during Darcy-Forchheimer (DF) viscous fluid flow having nonlinear form of thermal radiation. This two-dimensional fluid flow over a stretchable sheet is exposed to joule heating, thermal convection and viscous dissipation. The entire computational work is successfully performed using reliable neuro computing technique (NCT) through a viable combination of genetic algorithms (GAs) a well-known global solver and a famous local solver named sequential quadratic programming (SQP) i-e NCT-GA-SQP. Partial differential equations (PDEs) that express the DF viscous fluid flow are successfully converted into a third order nonlinear system of ordinary differential equations (ODEs) through manipulation of similarity variables. Numerical illustrations with strong accuracy and effective graphical representations are adopted to visualize the velocity profile, thermal distribution and concentration of fluid flow due to the variational effects of various physical parameters namely the electric field parameter (E), magnetic parameter (M), the porosity parameter (λ), the radiation parameter (Rd), the Prandtl number (Pr), the chemical reaction parameter (γ) and the Schmidt number (Sc). The reference solutions attained through well-known Adams method are used to validate the numerical results gathered through complexity analysis and different statistical operators and are closely matched which guarantees the reliability of the proposed solver. [ABSTRACT FROM AUTHOR]

Conventional fuels for power systems are now plaguing the environment more than ever before. Efforts to curb the environmental impacts of fossil fuels are now driving modern microgrids (MGs) to utilize green energy sources. In this regard, DC microgrids are well known for their versatility. Considering the rising significance of DC-MGs in future, this paper presents an aggregated DC microgrid model comprising of wind resource, photovoltaics, fuel cell, battery, and a supercapacitor. The model also includes DC–DC power converters and a two-level control structure. Firstly, the high-level of the proposed control structure constitutes an artificial neural network for maximum power point tracking and an intelligent energy management powered by a fuzzy logic controller. At this control level, the unified operation of the fuel cell and storage components under various dynamic conditions is ensured. Furthermore, the low-level control structure is based on an adaptive terminal sliding mode non-linear controller that derives the control signal for the converters. Finally, results showed that the energy management system along with the adaptive terminal sliding mode control successfully regulated the DC bus voltage by keeping the sources within their operational constraints. Moreover, the proposed non-linear controller demonstrated model's robustness, successfully converged the sliding surface to zero in finite time, ensured global asymptotic stability and effectively dealt with the model uncertainties. All the simulation leading to these results were performed in MATLAB/Simulink and further evaluation was carried out by hardware in loop experiments using C2000 Delfino microcontroller F28397D launchpad. • An optimum design of hybrid DC microgrid integrated with two renewable energy resources, an auxiliary source and hybrid energy storage system. • Multi-level control based on ANN, fuzzy Logic and adaptive terminal sliding mode non-linear controller for power balance and control. • HIL verification of controller performance using Delfino Microcontroller F28397D launchpad. [ABSTRACT FROM AUTHOR]

[Display omitted] • A comprehensive calculation and analysis of defect properties in the corundum, monoclinic, and orthorhombic Ga 2 O 3 phases are presented. • The Ga-rich growth environment in all Ga 2 O 3 phases favors the spontaneous formation of oxygen vacancies with concentrations that can reach as high as 1021 cm−3. • The antisite defects contribution in intrinsic defect density is not negligible in an O-rich environment, and can reach a concentration of 1014 cm−3. • The charge transition levels in all phases are at deep levels within the bandgap. • Raman spectroscopy in an as-grown β-Ga 2 O 3 and a high temperature annealed β-Ga 2 O 3 reveals an increase in point defect concentration, correlating the defect formation energies to experimental observation. Ultrawide bandgap gallium oxide (Ga 2 O 3) is a promising material for power semiconductor devices and deep ultraviolet (UV) solar-blind photodetectors. Understanding the properties of point defects in Ga 2 O 3 is necessary to realize better-performing devices. A comprehensive study based on density functional theory (DFT), using the generalized gradient approximation (GGA): Perdew-Burke-Ernzerhof (PBE) exchange–correlation functional, of point defects in corundum (α), monoclinic (β), and orthorhombic (ε) phases of Ga 2 O 3 is presented. The point defects include vacancies, interstitials, antisites, and extrinsic impurities in various phases of Ga 2 O 3. Defect formation energies, charge transition energy levels, and defect concentrations variation with temperature are listed and presented under both gallium-rich (Ga-rich) and oxygen-rich (O-rich) growth conditions. The formation energy diagrams predict that the Ga 2 O 3 phases favor the formation of Ga and O vacancies and the incorporation of extrinsic impurities. The calculations also show that the charge transition levels are deep inside the bandgap regardless of the Ga 2 O 3 phase and growth environment. The impacts of temperature on the intrinsic point defects are analyzed by probing the vibrational modes of β-Ga 2 O 3 thin films grown in a metal–organic chemical vapor deposition (MOCVD) system at 700 °C temperature and annealed at 1100 °C in an O-rich environment. [ABSTRACT FROM AUTHOR]

Acid mine drainage (AMD) is a highly acidic solution rich in heavy metals and produced by mining activities. It can severely inhibit the growth of plants, and microbial communities and disturb the surrounding ecosystem. In recent years, the use of different bioremediation technologies to treat AMD pollution has received widespread attention due to its environment-friendly and low-cost nature. Various active and passive remediation technologies have been developed for the treatment of AMD. The active treatment involves the use of different chemical compounds while passive treatments utilize natural and biological processes like constructed wetlands, anaerobic sulfate-reducing bioreactors, anoxic limestone drains, vertical flow wetlands, limestone leach beds, open limestone channels, and various organic materials. Moreover, different nanomaterials have also been successfully employed in AMD treatment. There are also reports on certain plant growth-promoting rhizobacteria (PGPR) which have the potential to enhance the growth and productivity of plants under AMD-contaminated soil conditions. PGPR applied to plants with phytoremediation potential called PGPR-assisted phytoremediation has emerged as an economical and environment-friendly approach. Nevertheless, various approaches have been tested and employed, all the approaches have certain limitations in terms of efficiency, secondary pollution of chemicals used for the remediation of AMD, and disposal of materials used as sorbents or as phytoextractants as in the case of PGPR-assisted phytoremediation. In the future, more research work is needed to enhance the efficiency of various approaches employed with special attention to alleviating secondary pollutants production and safe disposal of materials used or biomass produced during PGPR-assisted phytoremediation. [Display omitted] • Acid mine drainage (AMD) severely inhibits plant growth and microbial communities. • Active treatment technologies involve the use of different chemical compounds. • Passive treatment technologies utilize natural and biological processes. • Plant growth promoting rhizobacteria (PGPR) enhance plant biomass and phytoextraction. • PGPR-assisted phytoremediation is an economical and eco-friendly approach. [ABSTRACT FROM AUTHOR]

Reducing cadmium (Cd) bioavailability and rhizobacterial Cd mobilization functions in the rhizosphere via the inoculation of screened microbial inoculum is an environmental-friendly strategy to improve safety of crop grains. In this study, Bacillus Cereus , a model Cd resistant strain, was selected to explore its effects on Cd bioavailability and uptake, bacterial metabolic functions related to Cd mobilization. Results indicated that inoculation of Bacillus Cereus in maize roots of sand pot with water-soluble Cd (0.06–0.15 mg/kg) and soil pot with high Cd-contaminated soil (total Cd: 2.33 mg/kg; Cd extracted by NH 4 NO 3 : 38.6 μg/kg) could decrease water-soluble Cd ion concentration by 7.7–30.1% and Cd extracted with NH 4 NO 3 solution by 7.8–22.5%, inducing Cd concentrations in maize grains reduced by 10.6–39.9% and 17.4–38.6%, respectively. Even for a single inoculation in soil, Cd concentration in maize grains still satisfy food safety requirements (Cd content: 0.1 mg/kg dry weight) due to its successful colonization on root surface of maize. Bacillus Cereus could enrich more plant growth promotion bacteria (PGPB) and down-regulate the expression of genes related to bacterial motility, membrane transports, carbon and nitrogen metabolism in the rhizosphere soil, decreasing Cd bioavailability in soil. Approximately 80% Cd2+ in media was transferred into intracellular, meanwhile Cd salts (sulfide and/or phosphate) were produced in Bacillus Cereus through biomineralization process. Overall, this study could provide a feasible method for improving safety of maize grains via the inoculation of Bacillus Cereus under Cd pollution. [Display omitted] • Bacillus Cereus decreases water soluble Cd concentration by 8–30% in sand pot. • Single inoculation can ensure grains safety of maize in high Cd-contaminated soil. • Bacillus Cereus inhibits expression of rhizobacterial genes related to Cd mobilization. • Cadmium salts (Cd-S, Cd-P etc.) were formed with via biomineralization in medium. [ABSTRACT FROM AUTHOR]

This article investigates the existence and viability of some traversable wormholes involving electric charge effects in f (R , T) gravity. We use the negative energy density obtained from the Casimir source in this regard, and the consequences of the electromagnetic field produced by an electric charge are taken into account. In order to obtain wormhole shape function, we apply some noteworthy Casimir energy density systems like two parallel plates, two parallel cylinders, two spheres that are placed far apart, and the generalized uncertainty principle (GUP) correction. We analyze the fundamental wormhole conditions as well as the validity of null energy condition by taking variations of coupling parameter λ of f (R , T) gravity and the electric charge Q. Next, we explore the dynamics of gravitational active mass of the formulated wormhole solutions depending on Q. Furthermore, we study the volume integral quantifier of these charged Casimir wormhole solutions and also, determine the corresponding values of complexity factor. It is shown that the obtained wormhole solutions exhibit valid behavior and the respective values of complexity factor always fall within a certain range. [ABSTRACT FROM AUTHOR]

Magnesium ion batteries (MIBs) are one of the vital strategies for the next-generation energy needs and computational simulations can accelerate this search many folds. In this paper, we suggest a novel cathode material, quinone functionalized hexahydroxytriphenylene (HHTP) and diphenylbutadiynebis (boronic acid) (DPB) based porous polymer (QHHTP-DPB), for Mg-ion batteries. The material is stable with highly porous structure and strong affinities for Mg ions to attach to C=O sites of the quinone rings. The single unit of the polymer layer can be fully loaded with Mg ions at various active sites exhibiting a specific theoretical-capacity of 720.48 mAhg−1 and positive low open-circuit voltage of 0.39 V. No structural variation is observed in QHHTP-DPB and a larger decrease in the HOMO-LUMO gap occurs after Mg ions adsorption onto the polymer, reflecting the better cycling performance and high capacity for Mg ion batteries. These results show that our designed quinone functionalized HHTP-DPB porous polymer could be a futuristic cathode material for MIBs and needs further theoretical and experimental investigations. [Display omitted] • QHHTP-DPB based porous polymer is studied for Mg-ion batteries. • The QHHTP-DPB polymer significantly stored a maximum of 10 Mg ions. • The QHHTP-DPB has high specific capacity of 720.48 mAh/g. [ABSTRACT FROM AUTHOR]

[Display omitted] • Benzoquinone-based microporous polymer is designed as cathode for magnesium-ion batteries that exhibits high stability. • Strong anchoring capability of magnesium ions with polymer have larger binding energies. • The polymer has high storage capacity i.e. twelve Mg ions without any structure distortion/volume change. • The material has high specific capacity of 467.038 mAh/g versus previously reported cathode materials for magnesium-ion batteries. Rechargeable magnesium-ion batteries (MIBs) can fulfill large-scale energy storage needs due to the abundance of Mg but technical challenges due to complexity of its chemistry results uncertainties about their commercialization. We for the first report benzoquinone-based microporous conjugated polymer (BQMCP) as cathode material for MIBs using density functional theory. It is dynamically and thermodynamically stable with a strong affinity for Mg-ions. The Mg ions strongly attaches to the C O group (redox-active sites) than π-bonds of BQMCP. The binding energy demonstrates that the interaction of the 2 Mg-ion system (BE = −47.32 kcal.mol−1) is stronger than the mono-Mg ion system (BE = −39.57 kcal.mol−1). The attachment of first Mg2+ to the BQMCP facilitate the attachment of another Mg ion. The BQMCP shows higher positive redox potential, which decreases with the increase of Mg atoms with the high storage capacity of twelve Mg ions/ BQMCP unit at redox-active sites without any structure distortion. Mg ions successfully entraps in bulk porous stacked BQMCP layers with higher negative binding energy values. The results demonstrates that the BQMCP is a promising cathode material for magnesium ion batteries in future. [ABSTRACT FROM AUTHOR]

In this work, spin-polarized density functional theory calculations are conducted to evaluate the possible applicability of a single Si atom doped boron nitride graphyne-like nansoheet (Si@BN-yne) for reduction of nitrous oxide (N 2 O). The calculations show that Si-doping in BN graphene is energetically favorable, and the resulting Si@BN-yne is both dynamically and thermodynamically stable. According to our findings, N 2 O spontaneously dissociates when it interacts with the Si@BN-yne from its O site without the need for an energy barrier, releasing 2.89 eV of energy. The adsorption energy of CO molecule on the Si@BN-yne is less negative than that of N 2 O, implying that N 2 O will predominately occupy the catalyst surface. The CO + O ad reaction is used to remove the remaining oxygen atom (O ad) from the Si@BN-yne surface. The calculations show that the reaction proceeds through a low energy barrier of 0.05 eV, which is much lower than the previously reported catalysts. This demonstrates the high catalytic activity of Si@BN-yne nanosheet. Furthermore, the adsorption of H 2 O and O 2 species on the Si@BN-yne nanosheet is investigated. The results show that the presence of these species has no effect on the catalytic activity of the Si@BN-yne for N 2 O reduction. These results show that the proposed novel Si@BN-yne catalyst can be regarded as an efficient material in the development of promising active catalysts for N 2 O elimination from the environment. [Display omitted] • Si-doping modifies the electronic structure of BN-yne. • N 2 O interacts weakly with the pristine BN-yne. • N 2 O dissociates spontaneously over Si@BN-yne. • The activation barrier for removing O ad from Si@BN-yne is 0.05 eV. [ABSTRACT FROM AUTHOR]

• Hybrid ant genetic algorithm for efficient task scheduling in cloud datacenter is proposed. • Reduces scheduling solution space. • Minimizes workflow execution time by load balancing. • Less response time than genetic algorithm. Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting loaded virtual machines. Due to the minimum solution space of proposed algorithm, convergence and response time is significantly decreased. It finds a feasible scheduling solution to minimize the running time of workflows and tasks. The proposed algorithm achieved 64% decrease in execution time and 11% decrease in overall data center costs. [Display omitted] [ABSTRACT FROM AUTHOR]