Assessment of Sustainable Yield of Karst Water in Huaibei,China

This paper presents the assessment of sustainable yield in the Huaibei karst water area of Anhui province, China. A review of sustainable yield definition is introduced first in this paper, and sustainable development in karst areas is more difficult due to the complicated hydrogeologic conditions. General hydrogeology of the study area is provided to characterize hydraulic connections between the karst aquifer and an overlying porous aquifer. Groundwater level declines continuously due to over-exploitation of the karst groundwater, and two layers of groundwater dropping funnel were formed in Huaibei. These problems not only threaten the eco-geo-environment, but also compromise the water utilization which depends on the shallow porous water. A “critical water level” is proposed in this study to assess the sustainable yield, and it is determined by the historical exploitation data which represent the relationship between the karst water and the shallow porous water uses. A three layer Artificial Neural Network (ANN) model is used to understand the complex relationship of the karst water level and its influencing factors. Precipitation, exploitation and water level of latest period are chosen as the input nodes, seasonal records of water level are simulated by the ANN model. The sustainable yield is calculated by the trail-and-error adjusting method, and is equal to the pumping rate when the “critical water level” is maintained. The rate of 30.05 MCM/a is the sustainable yield for the Huaibei karst area in 2008, and it is less than the real pumping rate of 35.92 MCM/a. This assessment is meaningful to the management for the Huaibei karst water.     

Variable Structure Control with chattering elimination and guaranteed stability for a generalized T-S model

In this paper, a fuzzy based Variable Structure Control (VSC) with guaranteed stability is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. The main contribution of this work is that, firstly, new functions for chattering reduction and error convergence without sacrificing invariant properties are proposed, which is considered the main drawback of the VSC control. Secondly, the global stability of the controlled system is guaranteed.The well known weighting parameters approach, is used in this paper to optimize local and global approximation and modeling capability of T-S fuzzy model.A one link robot is chosen as a nonlinear unstable system to evaluate the robustness, effectiveness and remarkable performance of optimization approach and the high accuracy obtained in approximating nonlinear systems in comparison with the original T-S model. Simulation results indicate the potential and generality of the algorithm. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved with the proposed FLC-VSC controller. The effectiveness of the proposed controller is proven infront of disturbances and noise effects.     

Global stabilization of a class of delay discrete-time nonlinear systems via state and output feedback

This paper deals with stabilization of a class of delay discrete-time nonlinear systems through state and output feedback. We provide an explicit bounded state feedback law as an extension of the Jurdjevic-Quinn method, from nonlinear theory, to this class of systems. Next, we present a useful and systematic approach to design an observer for the same class of systems. Then, we show how the global stabilization problem via dynamic output feedback can be solved by using the two previous results. Finally, numerical examples are given to illustrate the effectiveness of the proposed design method.     

Technology application in project-based learning

Project-based learning is a student-centered comprehensive instructional approach where students collectively engage themselves in complex learning tasks. Recent advances in educational technologies have made student-centered learning in a technology rich environment both possible and feasible. This paper will report part of a larger study carried out at a school in the United States of America on the use ofconstructivism and technology in project-based learning. Data collection techniques included those typically associated with qualitative field research. In this paper, the technological infrastructure of the school and how technology is used in one of the project-based learning classes will be provided. Based on this study, some suggestions are also provided on how technology can be used in the context of other countries including in Malaysia.     

Effective participation of wind turbines in frequency control of a two-area power system using coot optimization

In this paper, load frequency control is performed for a two-area power system incorporating a high penetration of renewable energy sources. A droop controller for a type 3 wind turbine is used to extract the stored kinetic energy from the rotating masses during sudden load disturbances. An auxiliary storage controller is applied to achieve effective frequency response. The coot optimization algorithm (COA) is applied to allocate the optimum parameters of the fractional-order proportional integral derivative (FOPID), droop and auxiliary storage controllers. The fitness function is represented by the summation of integral square deviations in tie line power, and Areas 1 and 2 frequency errors. The robustness of the COA is proven by comparing the results with benchmarked optimizers including: atomic orbital search, honey badger algorithm, water cycle algorithm and particle swarm optimization. Performance assessment is confirmed in the following four scenarios: (i) optimization while including PID controllers; (ii) optimization while including FOPID controllers; (iii) validation of COA results under various load disturbances; and (iv) validation of the proposed controllers under varying weather conditions.     

Evidence for Glass–glass Interfaces in a Columnar Cu–Zr Nanoglass

Comprehensive analyses of the atomic structure using advanced analytical transmission electron microscopy-based methods combined with atom probe tomography confirm the presence of distinct glass–glass interfaces in a columnar Cu-Zr nanoglass synthesized by magnetron sputtering. These analyses provide first-time in-depth characterization of sputtered film nanoglasses and indicate that glass–glass interfaces indeed present an amorphous phase with reduced mass density as compared to the neighboring amorphous regions. Moreover, dedicated analyses of the diffusion kinetics by time-of-flight secondary ion mass spectroscopy (ToF SIMS) prove significantly enhanced diffusivity, suggesting fast transport along the low density glass–glass interfaces. The present results further indicate that sputter deposition is a feasible technique for reliable production of nanoglasses and that some of the concepts proposed for this new class of glassy materials are applicable.     

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research, a modified fractional order proportional integral derivate (FOPID) control method is proposed for the photovoltaic (PV) and thermoelectric generator (TEG) combined hybrid renewable energy system. The faster tracking and steady-state output are aimed at the suggested maximum power point tracking (MPPT) control technique. The derivative order number (µ) value in the improved FOPID (also known as PI^λD^µ) control structure will be dynamically updated utilizing the value of change in PV array voltage output. During the transient, the value of µ is changeable; it’s one at the start and after reaching the maximum power point (MPP), allowing for strong tracking characteristics. TEG will use the freely available waste thermal energy created surrounding the PV array for additional power generation, increasing the system’s energy conversion efficiency. A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit. The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation (P&O) type MPPT control method. The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source. There is also a better control action and a faster response.     

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS. The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features. Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm. The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance. The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects. The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.     

Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection (FS) technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting DDoS flooding attacks (FA) based on ICMPv6 messages using a Binary Flower Pollination Algorithm (BFPA-FA). The proposed method (BFPA-FA) employs FS technology with a support vector machine (SVM) to identify the most relevant, influential features. Moreover, The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack scenarios. The results show that the proposed method BFPA-FA achieved the best accuracy rate (97.96%) for the ICMPv6 DDoS detection with a reduced number of features (9) to half the total (19) features. The proven proposed method BFPA-FA is effective in the ICMPv6 DDoS attacks via IDS.     

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI

Magnetic Resonance Materials in Physics, Biology and Medicine - Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for...