Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers’ water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (), correlation coefficient (), Root Mean Square Error (), Mean Absolute Percentage Error (), and are provided.     

Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm

Microarray technology can be used as an efficient diagnostic system to recognise diseases such as tumours or to discriminate between different types of cancers in normal tissues. This technology has received increasing attention from the bioinformatics community because of its potential in designing powerful decision-making tools for cancer diagnosis. However, the presence of thousands or tens of thousands of genes affects the predictive accuracy of this technology from the perspective of classification. Thus, a key issue in microarray data is identifying or selecting the smallest possible set of genes from the input data that can achieve good predictive accuracy for classification. In this work, we propose a two-stage selection algorithm for gene selection problems in microarray data-sets called the symmetrical uncertainty filter and harmony search algorithm wrapper (SU-HSA). Experimental results show that the SU-HSA is better than HSA in isolation for all data-sets in terms of the accuracy and achieves a lower number of genes on 6 out of 10 instances. Furthermore, the comparison with state-of-the-art methods shows that our proposed approach is able to obtain 5 (out of 10) new best results in terms of the number of selected genes and competitive results in terms of the classification accuracy.     

Quantitative HPLC Analysis of Benzene Derivatives of Melicope Ptelefolia Leaves

A high performance liquid chromatography procedure for the quantitative determination of three marker benzene derivatives, 2,4,6-trihydroxy-3-prenyl acetophenone (tHPA) (1), 2,4,6-trihydroxy-3-geranyl acetophenone (tHGA) (2), and p-O-geranyl coumaric acid (GCA) (3), in the Melicope ptelefolia ethanolic leaf extracts, a medicinal herb obtained from a few locations of the Peninsula Malaysia, was described. The quantitative analysis was performed using high performance liquid chromatography-photodiode array detection on Xterra octadecylsiyl silica (ODS; 3.0?×?150 mm, 3.5 μm) column kept at 32°C, using gradient elution with acetonitrile and water containing 0.1% formic acid at a flow-rate of 1 ml/min with UV detection wavelength at 280 nm. All calibration curves showed good linearity (R² of 0.999 to 1.0000) within the concentrations range of 2.5?×?10^?3 to 0.1 mg/mL. The method was shown to be simple, sensitive, and reliable for qualitative and quantitative analysis of the marker compounds in M. ptelefolia leaf preparations.     

A hybrid approach for learning concept hierarchy from Malay text using artificial immune network

A concept hierarchy is an integral part of an ontology but it is expensive and time consuming to build. Motivated by this, many unsupervised learning methods have been proposed to (semi-) automatically develop a concept hierarchy. A significant work is the Guided Agglomerative Hierarchical Clustering (GAHC) which relies on linguistic patterns (i.e., hypernyms) to guide the clustering process. However, GAHC still relies on contextual features to build the concept hierarchy, thus data sparsity still remains an issue in GAHC. Artificial Immune Systems are known for robustness, noise tolerance and adaptability. Thus, an extension to the GAHC is proposed by hybridizing it with Artificial Immune Network (aiNet) which we call Guided Clustering and aiNet for Learning Concept Hierarchy (GCAINY). In this paper, we have tested GCAINY using two parameter settings. The first parameter setting is obtained from the literature as a baseline parameter setting and second is by automatic parameter tuning using Particle Swarm Optimization (PSO). The effectiveness of the GCAINY is evaluated on three data sets. For further validations, a comparison between GCAINY and GAHC has been conducted and with statistical tests showing that GCAINY increases the quality of the induced concept hierarchy. The results reveal that the parameters value found by using PSO significantly produce better concept hierarchy than the vanilla parameter. Thus it can be concluded that the proposed approach has greater ability to be used in the field of ontology learning.     

Combustion performance evaluation of air staging of palm oil blends

The problems of global warming and the unstable price of petroleum oils have led to a race to develop environmentally friendly biofuels, such as palm oil or ethanol derived from corn and sugar cane. Biofuels are a potential replacement for fossil fuel, since they are renewable and environmentally friendly. This paper evaluates the combustion performance and emission characteristics of Refined, Bleached, and Deodorized Palm Oil (RBDPO)/diesel blends B5, B10, B15, B20, and B25 by volume, using an industrial oil burner with and without secondary air. Wall temperature profiles along the combustion chamber axis were measured using a series of thermocouples fitted axially on the combustion chamber wall, and emissions released were measured using a gas analyzer. The results show that RBDPO blend B25 produced the maximum emission reduction of 56.9% of CO, 74.7% of NOx, 68.5% of SO(2), and 77.5% of UHC compared to petroleum diesel, while air staging (secondary air) in most cases reduces the emissions further. However, increasing concentrations of RBDPO in the blends also reduced the energy released from the combustion. The maximum wall temperature reduction was 62.7% for B25 at the exit of the combustion chamber.     

微波化学反应器中微波辐射器的一体化设计与优化

讨论了一种用于微波化学反应器中的微波辐射器的一体化设计与优化方法。辐射器在工作过程中温度变化剧烈,由此产生的热应力可能造成辐射器破裂,导致反应器不能正常工作,甚至出现爆炸。为避免出现上述现象,基于有限元方法,首先根据电磁场边界条件,对辐射器的传输特性进行分析,初步优化其结构参数,保证辐射器的反射系数尽可能小,以减小微波能量在传输过程中的损耗,然后采用多物理场耦合分析方法,对辐射器进行优化设计,降低了辐射器工作过程中产生的热应力,并避免了在结构突变以及衔接部位产生过大的热应力,改善了辐射器的结构稳定性。     

微波辐射下CaSO4生成反应过程中的热点特性

在微波辐射化学反应过程中，常常会因为存在热点而导致爆炸或反应物烧毁等现象发生。本文以微波辐射下的硫酸钙生成反应为例，利用有限元方法求解Maxwell方程组、流体力学方程以及热传导方程，研究了微波辐射反应过程中热点的变化情况，并用实验验证了计算结果的正确性。研究结果表明：在加热过程中，随反应的进行，热点的位置是在不断变化的，且热点常出现在物质分相界面附近。     

软件无线电中改进CIC滤波器的研究

软件无线电接收机的灵活性之一是在于对调制方式没有限制。为此,不同采样率之间的转换对软件无线电终端来说就显得尤为重要。实现数字的采样率转换在传统方法中就是利用插值,而一般的传统方法不能满足反混叠效应的要求。给出了在采样率转换(SRC)中的一种改进级联积分梳齿(CIC)滤波器,从而更有效地避免了混叠效应。