A Review of the Role of Natural Clay Minerals as Effective Adsorbents and an Alternative Source of Minerals

The minerals with unique properties such as natural clay minerals (NCMs) have promising approach in environmental and industrial sphere. In fact, under some specific conditions the NCMs could be used either as effective adsorbent material or alternative source of minerals. This chapter presents an outline of a general review of factors that affect the application ability of NCMs and a descriptive analysis of NH4+ and REE adsorption behavior and extraction of rare earth elements (REE) by an ion-exchange with NH4+ ions onto NCMs. Clays and NCMs both effectively remove various contaminants from aqueous solution and serve as alternative sources of minerals, as extensively discussed in this chapter. This review compiles thorough literature of current research and highlights the key findings of adsorption (NH4+ and REE) that use different NCMs as adsorbents or alternative sources of minerals (i.e., REE). The review confirmed that NCMs excellently remove different cations pollutants and have significant potential as alternative source of REE. However, modification and further development of NCMs applications for getting the best adsorption and the best extraction of REE onto NCMs, which would enhance pollution control and leaching system is still needed

Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model

Background Scarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease. Methods Four statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error. Results The four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year. Conclusions The SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever

Anomaly detection based on zone partition for security protection of industrial cyber-physical systems

A developing trend of traditional industrial systems is the integration of the cyber and physical domain to improve flexibility and the efficiency of supervision, management and control. But, the deep integration of these Industrial Cyber-Physical Systems (ICPSs), increases the potential for security threats. Attack detection, which forms initial protective barrier, plays an important role in overall security protection. However, most traditional methods focused on cyber information and ignored any limitations that might arise from the characteristics of the physical domain. In this paper, an anomaly detection approach based on zone partition is designed for ICPSs. In detail, initially an automated zone partition method ensuring crucial system states can be observed in more than one zone is designed. Then, methods of building zone function model which do not require any prior knowledge of the physical system are presented before analyzing the anomaly based on zone information. Finally, an experimental rig is constructed to verify the effectiveness of the proposed approach. The results demonstrate that the approach presents a high accuracy solution which also performs effectively in realtime

Anomaly Detection Based on Zone Partition for Security Protection of Industrial Cyber-Physical Systems

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A developing trend of traditional industrial systems is the integration of the cyber and physical domain to improve flexibility and the efficiency of supervision, management and control. But, the deep integration of these Industrial Cyber-Physical Systems (ICPSs), increases the potential for security threats. Attack detection, which forms initial protective barrier, plays an important role in overall security protection. However, most traditional methods focused on cyber information and ignored any limitations that might arise from the characteristics of the physical domain. In this paper, an anomaly detection approach based on zone partition is designed for ICPSs. In detail, initially an automated zone partition method ensuring crucial system states can be observed in more than one zone is designed. Then, methods of building zone function model which do not require any prior knowledge of the physical system are presented before analyzing the anomaly based on zone information. Finally, an experimental rig is constructed to verify the effectiveness of the proposed approach. The results demonstrate that the approach presents a high accuracy solution which also performs effectively in realtime

Effect of Different Arbuscular Mycorrhizal Fungi on Growth and Physiology of Maize at Ambient and Low Temperature Regimes

The effect of four different arbuscular mycorrhizal fungi (AMF) on the growth and lipid peroxidation, soluble sugar, proline contents, and antioxidant enzymes activities of Zea mays L. was studied in pot culture subjected to two temperature regimes. Maize plants were grown in pots filled with a mixture of sandy and black soil for 5 weeks, and then half of the plants were exposed to low temperature for 1 week while the rest of the plants were grown under ambient temperature and severed as control. Different AMF resulted in different root colonization and low temperature significantly decreased AM colonization. Low temperature remarkably decreased plant height and total dry weight but increased root dry weight and root-shoot ratio. The AM plants had higher proline content compared with the non-AM plants. The maize plants inoculated with Glomus etunicatum and G. intraradices had higher malondialdehyde and soluble sugar contents under low temperature condition. The activities of catalase (CAT) and peroxidase of AM inoculated maize were higher than those of non-AM ones. Low temperature noticeably decreased the activities of CAT. The results suggest that low temperature adversely affects maize physiology and AM symbiosis can improve maize seedlings tolerance to low temperature stress