Industrial developments in the oil and gas, petrochemical, pharmaceutical and food sector have contributed to the large production of oily wastewater worldwide. Oily wastewater pollution affects drinking water and groundwater resources, endangers aquatic life and human health, causes atmospheric pollution, and affects crop production. Several traditional and conventional methods were widely reported, and the advantages and limitations were discussed. However, with the technology innovation, new trends of coupling between techniques, use of new materials, optimization of the cleaning process, and multiphysical approach present new paths for improvement. Despite these trends of improvement and the encouraging laboratory results of modern and green methods, many challenges remain to be raised, particularly the commercialization and the global aspect of these solutions and the reliability to reduce the system’s maintenance and operational cost. In this review, the well-known oily wastewater cleaning methods and approaches are being highlighted, and the obstacles faced in the practical use of these technologies are discussed. A critical review on the technologies and future direction as the road to commercialization is also presented to persevere water resources for the benefit of mankind and all living things.

The Industrial Internet of Things (IIoT) can transform an existing isolated industrial system to a connected network. The IIoT and the related wireless connectivity requirements for industrial sensors are very significant. The deployed sensors in IIoT monitors the conditions of the industrial devices and machines. Therefore, reliability and security become the most important concerns in IIoT. This introduces many familiar and ever-increasing risks associated with the industrial system. The IIoT devices can be vulnerable to vast array of viruses, threats, and attacks. Therefore, an efficient protection strategy is required to ensure that the millions of IIoT devices are safe from these risks. However, resource constraint IIoT devices have not been designed to have effective security features. Due to this, in recent years, cloud, fog, and edge-based IIoT has received great attention in the research community. The computationally intensive tasks such as security, data analytics, decision making, and reporting are performed at the cloud or fog using a powerful computing infrastructure. The data security of the IIoT device has been provided by employing improved Rivest-Shamir-Adelman (RSA) and hash signatures. The proposed RSA algorithm has a four-prime number of 512- bits. The device authentication is performed by employing a hash signature. For long network life, an efficient clustering technique for the sensor devices which is based on node degree(N), distance from the cluster(D), residual energy(R), and fitness (NDRF) has been proposed. The fitness of the sensor nodes is computed using the Salp swarm algorithm (SSA). In order to reduce the latency and communication overhead for IIoT devices a resource scheduling using SoftMax deep neural network (DNN) is proposed. All the requests coming from the cluster head are classified using SoftMax-DNN for best resource scheduling on the basis of storage, computing, and bandwidth requirements. The proposed framework produces superior results, especially in terms of energy consumption, latency, and strength of security.

In this paper, a nonlinear robust optimal control (NROC) scheme for uncertain two-axis motion control system via adaptive dynamic programming (ADP) and neural networks (NNs) is proposed. The two-axis motion control system is an X-Y table actuated by permanent-magnet linear synchronous motor (PMLSM) servo drives. First, the motions of the tracking contour in X-axis and Y-axis of the X-Y table are stabilized through feedback linearization control (FLC) laws. However, the control performance may be destroyed due to parameter uncertainties and compounded disturbances. Therefore, to improve the robustness of the control system, a NROC is designed to achieve this purpose. The tracking control problem of the X-Y table with uncertainties is transformed to a regulation problem. Then, it is solved by an infinite horizon optimal control using a critic NN. Consequently, the NN is developed via ADP learning algorithm to facilitate the online solution of the Hamilton-Jacobi-Bellman (HJB) equation corresponding to the nominal system for approximating the optimal control law. The uniform ultimate boundedness of the closed-loop system is proved utilizing the Lyapunov approach and the tracking error asymptotically converges to a residual set. The validation of the proposed control schemes are carried out through experimental analysis. The control algorithms have been implemented using a DSP control board. A comparison of control performances using FLC, adaptive FLC and FLC-based NROC are investigated. From the experimental results, the dynamic behaviors of the two-axis control system using the proposed FLC-based NROC can achieve robust optimal control performance against parameter uncertainties and compounded disturbances.

Drought is a severe environmental disaster that results in significant social and economic damage. As such, efficient mitigation plans must rely on precise modeling and forecasting of the phenomenon. This study was designed to enhance drought forecasting through developing and evaluating the applicability of three hybrid models—the hidden Markov model–genetic algorithm (HMM–GA), the auto-regressive integrated moving average–genetic algorithm (ARIMA–GA), and a novel auto-regressive integrated moving average–genetic algorithm–ANN (ARIMA–GA–ANN)—to forecast the standard precipitation index (SPI) in the Bisha Valley, Saudi Arabia. The accuracy of the models was investigated and compared with that of classical HMM and ARIMA based on a performance evaluation and visual inspection. Furthermore, the multi-class Receiver Operating Characteristic-based Area under the Curve (ROC–AUC) was applied to evaluate the ability of the hybrid model to forecast drought events. We used data from 1968 to 2008 to train the models and data from 2009 to 2019 for validation. The performance evaluation results confirmed that the hybrid models provided superior results in forecasting the SPI one month in advance. Furthermore, the results demonstrated that the GA-induced improvement in the HMM forecasts was matched by an approximate 16.40% and 23.46% decrease in the RMSE in the training and testing results, respectively, compared to the classical HMM model. Consequently, the RMSE values of the ARIMA–GA model were reduced by an average of 10.06% and 9.36% for the training and testing processes, respectively. Finally, the ARIMA–GA–ANN, which combined the strengths of the linear stochastic model ARIMA and a non-linear ANN, achieved a greater reduction values in RMSE by an average of 32.82% and 27.47% in comparison with ARIMA in the training and testing phases, respectively. The ROC–AUC results confirmed the capability of the developed models to distinguish between events and non-events with reasonable accuracy, implying the appropriateness of these models as a tool for drought mitigation and warning systems.

This paper presents a robust adaptive backstepping control (RABC) for high-speed permanent-magnet synchronous motor (HSPMSM) drive system. The proposed RABC achieves high performance operation by incorporating an ideal backstepping controller (IBC), a recurrent radial basis function neural network (RRBFNN) uncertainty observer, and a robust controller. The Lyapunov stability theorem is utilized to design the IBC as a position controller of the HSPMSM servo drive system. To enhance the disturbance rejection capability during parameter changes, certain information is needed within the backstepping control law so that the system performance would not sorely be affected. To mitigate the need for the lumped parameter uncertainties within the backstepping controller, an online adaptive observer based on RRBFNN is designed to estimate the nonlinear parameter uncertainties. Moreover, the robust controller is intended to retrieve the remaining of the RRBFNN approximation errors. To assure the stability of the proposed RABC, the Lyapunov stability analysis is used to derive the online adaptive control laws. The performance of the proposed RABC is verified by simulation and experimental analysis under different operating conditions and parameter uncertainties. The test results validate the effectiveness of the proposed RABC scheme to achieve preferable tracking performance regardless of external disturbances and parameter uncertainties.

Sterilization methods for individuals and facilities are extremely important to enable human beings to continue the basic tasks of life and to enable safe and continuous interaction of citizens in society when outbreaks of viral pandemics such as the coronavirus. Sterilization methods, their availability in gatherings, and the efficiency of their work are among the important means to contain the spread of viruses and epidemics and enable societies to practice their activities almost naturally. Despite the effective solutions given by traditional methods of surface disinfection, modern nanotechnology has proven to be an emergent innovation to protect against viruses. On this note, recent scientific breakthroughs have highlighted the ability of nanospray technology to attach to air atoms in terms of size and time-period of existence as a sterilizer for renewed air in large areas for human gatherings. Despite the ability of this method to control the outbreak of infections, the mutation of bactericidal mechanisms presents a great issue for scientists. In recent years, science has explored a more performant approach and techniques based on a surface-resistance concept. The most emergent is the self-defensive antimicrobial known as the self-disinfection surface. It consists of the creation of a bacteria cell wall to resist the adhesion of bacteria or to kill bacteria by chemical or physical changes. Besides, plasma-mediated virus inactivation was shown as a clean, effective, and human healthy solution for surface disinfection. The purpose of this article is to deepen the discussion on the threat of traditional methods of surface disinfection and to assess the state of the art and potential solutions using emergent nanotechnology.