Showing papers by "Universiti Teknologi Malaysia published in 2018"

An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges

Abstract: The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of Internet of Things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensor network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies, such as WSN, radio frequency identification, cloud computing, middleware systems, and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business, and marketability.

Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

Abstract: Landslides are a manifestation of slope instability causing different kinds of damage affecting life and property. Therefore, high-performance-based landslide prediction models are useful to government institutions for developing strategies for landslide hazard prevention and mitigation. Development of data mining based algorithms shows that high-performance models can be obtained using ensemble frameworks. The primary objective of this study is to investigate and compare the use of current state-of-the-art ensemble techniques, such as AdaBoost, Bagging, and Rotation Forest, for landslide susceptibility assessment with the base classifier of J48 Decision Tree (JDT). The Guangchang district (Jiangxi province, China) was selected as the case study. Firstly, a landslide inventory map with 237 landslide locations was constructed; the landslide locations were then randomly divided into a ratio of 70/30 for the training and validating models. Secondly, fifteen landslide conditioning factors were prepared, such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, profile curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Relief-F with the 10-fold cross-validation method was applied to quantify the predictive ability of the conditioning factors and for feature selection. Using the JDT and its three ensemble techniques, a total of four landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using area under the receiver operating characteristic (ROC) curve (AUC) and statistical indexes. The result showed that all landslide models have high performance (AUC > 0.8). However, the JDT with the Rotation Forest model presents the highest prediction capability (AUC = 0.855), followed by the JDT with the AdaBoost (0.850), the Bagging (0.839), and the JDT (0.814), respectively. Therefore, the result demonstrates that the JDT with Rotation Forest is the best optimized model in this study and it can be considered as a promising method for landslide susceptibility mapping in similar cases for better accuracy.

A critical review on TiO2 based photocatalytic CO2 reduction system: Strategies to improve efficiency

Abstract: The massive burning of fossil fuels to fulfill the augmenting energy demands of world have triggered the ever-increasing emission of carbon dioxide (CO2); the main cause of global warming. Photocatalytic reduction of CO2 into solar fuels and chemicals using everlasting solar energy seems promising technology to contemporaneously curb the globa1 warming and partially fulfill the energy requirements. This study focused on understanding the main challenges in photocatalytic CO2 reduction systems and strategies to improve the efficiency of solar fuels production. The overview of fundamentals and latest developments in titania (TiO2) based photocatalytic CO2 reduction systems have been discussed. More specifically, thermodynamics, mass transfer, selectivity and reaction mechanism of photocatalytic CO2 reduction are critically deliberated. In the main stream, developments have been categorized as strategies to enhance the different aspects such as visible light response, charge separation, CO2 adsorption and morphology of photo-catalysts for TiO2 based photocatalytic CO2 reduction systems. Different modification techniques to overcome the low efficiency by fabricating advance TiO2 nanocomposites through surface modifications, doping of metals, non-metals and semiconductor are discussed. The challenges lingering on against achieving the higher photocatalytic conversion of CO2 into solar fuels are also investigated. In conclusion, brief perspectives and recommendations on the development of efficient photocatalysts are outlined which would be of vital importance for the improvements of conversion efficiency of CO2 reduction system.

A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques

Abstract: A new method is developed for recommender systemsThe recommender system is developed based on collaborative filteringScalability and sparsity issues in recommender systems are solvedMovieLens and Yahoo! Webscope R4 datasets are used for method evaluationThe method is effective in solving the sparsity and scalability problems in CF Improving the efficiency of methods has been a big challenge in recommender systems It has been also important to consider the trade-off between the accuracy and the computation time in recommending the items by the recommender systems as they need to produce the recommendations accurately and meanwhile in real-time In this regard, this research develops a new hybrid recommendation method based on Collaborative Filtering (CF) approaches Accordingly, in this research we solve two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques Then, we use ontology to improve the accuracy of recommendations in CF part In the CF part, we also use a dimensionality reduction technique, Singular Value Decomposition(SVD), to find the most similar items and users in each cluster of items and users which can significantly improve the scalability of the recommendation method We evaluate the method on two real-world datasets to show its effectiveness and compare the results with the results of methods in the literature The results showed that our method is effective in improving the sparsity and scalability problems in CF

An Enhanced Adaptive P&O MPPT for Fast and Efficient Tracking Under Varying Environmental Conditions

Abstract: This paper proposes an enhanced adaptive perturb and observe (EA-PO second, modified incremental conduction; third, cuckoo search; and fourth, the hybrid ant colony optimization-P&O. The results show that the proposed method tracks the global peak successfully under distinctive patterns of partial shading, when other algorithms fail occasionally. On top of that, it improves the tracking speed by two to three times, while efficiency is maintained over 99%.

Alcohol and ether as alternative fuels in spark ignition engine: A review

Abstract: Energy security and global warming concern are the two main driving forces for the global alcohol development that also have the effort to animate the agro-industry. Generally, alcohol and ether fuels are produced from several sources and can be produced locally. Almost all alcohol fuels have similar combustion and ignition characteristics to existing known mineral fuels. Mainly the ether fuels (MTBE and DME) are used as additives at low blending ratio to enhance the octane number and oxygen content of gasoline. The addition of alcohol and ether fuels to gasoline lead to a complete combustion due to the higher oxygen content, thereby leads to increased combustion efficiency and decreased engine emissions. The objectives of this paper are to systematically review the use of alcohols and ethers including butanol, methanol, ethanol, and fusel oil, MTBE, and DME as fuels in SI engine. Also, the current study has investigated the effects of performance (brake torque, brake power, BSFC, effective efficiency, and EGT), emissions (CO, CO2, NOx and HC) and combustion characteristics of SI engine with alcohol and ether. The increase in engine performance could be attained with an increased compression ratio along with the use of alcohol fuels which have a higher-octane value. Furthermore, alcohol and ether burn very cleanly than regular gasoline and produce lesser carbon monoxide (CO) and nitrogen oxide (NOx). On the other hand, the energy value of alcohol and ether fuels is approximately 30% lower than gasoline; thereby the specific fuel consumption (SFC) will increase simultaneously when using alcohol and ether as a fuel. Finally, this paper also discusses the impacts of alcohol on engine vibration, engine noise, and potential to be used as a gasoline octane enhancer. Alcohol can be used as a pure fuel in spark ignition engine, but it requires some modifications to the engine.

A review of renewable energy development in Africa: A focus in South Africa, Egypt and Nigeria

Abstract: Despite its vast natural resources, African is facing serious challenges in sustainable development in an energy sector, if addressed with dispatch could not only check its indispensable needs, but also mitigate some global phenomenon at stake, such as desertification, environmental degradation and green house emission. This paper reviews the prospects of four major renewable energy sources-hydro, solar, wind and biomass- for each of the three leading countries in Africa namely South Africa, Egypt and Nigeria. Based on literature survey of energy efficiency, all the three countries encourage energy efficiency in varying degrees. In the course of this review, several national energy policy frameworks of these countries were looked into, especially on how African countries could overcome the persistent energy crisis in the continent by utilizing the naturally gifted renewable energy sources. This could only be achievable if proper technology, awareness and skills for harnessing the resources are provided. Also lingering energy challenges such as energy efficiency measures, needs for grid extension, energy storage technology and seasonal variation were carefully highlighted.

Essential Oils: Extraction Techniques, Pharmaceutical And Therapeutic Potential - A Review.

Abstract: Background Essential oils are liquid extracts from aromatic plants, which have numerous applications in multiple industries. There are a variety of methods used for the extraction of essential oils, with each method exhibiting certain advantages and determining the biological and physicochemical properties of the extracted oils. Essential oils from different plant species contain more than 200 constituents which are comprised of volatile and non-volatile components. The application of essential oils as antimicrobial, anticancer, anti-inflammatory and anti-viral agents is due to their effective and efficient properties, inter alia. Method Several advanced (supercritical fluid extraction, subcritical extraction liquid, solvent-free microwave extraction) and conventional (hydrodistillation, steam distillation, hydrodiffusion, solvent extraction) methods have been discussed for the extraction of essential oils. Advanced methods are considered as the most promising extraction techniques due to less extraction time, low energy consumption, low solvent used and less carbon dioxide emission. Conclusion This manuscript reviewed the major research studies in the field and discussed several research findings on the chemical composition of essential oils, methods of oil extraction, and application of these oils in pharmaceutical and therapeutic fields. These essential oils can be used as anticancer, antimicrobial, antiviral, and as skin permeation enhancer agents.

Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues

Abstract: The intelligent transportation system (ITS) concept was introduced to increase road safety, manage traffic efficiently, and preserve our green environment. Nowadays, ITS applications are becoming more data-intensive and their data are described using the “5Vs of Big Data”. Thus, to fully utilize such data, big data analytics need to be applied. The Internet of vehicles (IoV) connects the ITS devices to cloud computing centres, where data processing is performed. However, transferring huge amount of data from geographically distributed devices creates network overhead and bottlenecks, and it consumes the network resources. In addition, following the centralized approach to process the ITS big data results in high latency which cannot be tolerated by the delay-sensitive ITS applications. Fog computing is considered a promising technology for real-time big data analytics. Basically, the fog technology complements the role of cloud computing and distributes the data processing at the edge of the network, which provides faster responses to ITS application queries and saves the network resources. However, implementing fog computing and the lambda architecture for real-time big data processing is challenging in the IoV dynamic environment. In this regard, a novel architecture for real-time ITS big data analytics in the IoV environment is proposed in this paper. The proposed architecture merges three dimensions including intelligent computing (i.e. cloud and fog computing) dimension, real-time big data analytics dimension, and IoV dimension. Moreover, this paper gives a comprehensive description of the IoV environment, the ITS big data characteristics, the lambda architecture for real-time big data analytics, several intelligent computing technologies. More importantly, this paper discusses the opportunities and challenges that face the implementation of fog computing and real-time big data analytics in the IoV environment. Finally, the critical issues and future research directions section discusses some issues that should be considered in order to efficiently implement the proposed architecture.

A review of integrated photocatalyst adsorbents for wastewater treatment

Abstract: Photocatalysis has the best potential to replace the conventional wastewater treatment technology due to its utilization of visible light to photo-degrade organic and inorganic contaminants. However, when applied in slurry form, agglomeration of nanoparticle will lead to serious decrease in photocatalytic performance due to hinderance effect. By combining the photocatalyst and adsorbents, which is designated as integrated photocatalyst adsorbent (IPCA), an adsorbent material which also degrades toxic organic compounds in the presence of UV/visible light irradiation could be produced. The compound does not only preserve all the interesting characteristics of both individual components, but also overcomes serious drawbacks, such as low absorptivity, rapid recombination of photogenerated electrons and hinderance effect of photocatalyst when applied in slurry form. There are several criteria that must be obeyed by the absorbent material used such as high absorption capacity to target compound, reasonable transparency to UV–vis light, high surface area, inhibition of photocatalyst leaching and good stability with dispersing solvent. In this review article, the authors presented an overview on the application of photocatalyst, adsorbents and integrated photocatalyst adsorbents for wastewater treatment. Moreover, the discussions were also focused on the major adsorbent which has been integrated with photocatalyst such as carbon, clays, zeolite matrix materials and others. Additionally, the mechanisms of the adsorption of emerging organic contaminants with adsorbents in IPCA were also discussed to clearly understand the possible interactions between organic contaminants and IPCA. Outlook on IPCA study were also discussed to further broaden the prospective of this technology.

Constructing bio-templated 3D porous microtubular C-doped g-C3N4 with tunable band structure and enhanced charge carrier separation

Abstract: For the first time, the bio-templated porous microtubular C-doped (BTPMC) g-C3N4 with tunable band structure was successfully prepared by simple thermal condensation approach using urea as precursors and kapok fibre which provides a dual function as a bio-templates and in-situ carbon dopant. Prior to the thermal condensation process, the impregnation strategies (i.e. direct wet and hydrothermal impregnation) of urea on the treated kapok fibre (t-KF) were compared to obtained well-constructed bio-templated porous microtubular C-doped g-C3N4. The details on a physicochemical characteristic of the fabricated samples were comprehensively analyze using X-ray diffraction (XRD), Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), Field-emission scanning electron microscopy (FESEM), Transmission electron microscopy (TEM), N2 adsorption-desorption, Thermogravimetric (TGA), and UV–vis spectroscopy. Our finding indicated that the hydrothermal impregnation strategy resulted in well-constructed microtubular structure and more carbon substitution in sp2-hybridized nitrogen atoms of g-C3N4 as compared to the direct wet impregnation. Also, compared to pure g-C3N4, the fabricated BTPMC g-C3N4 exhibited extended photoresponse from the ultraviolet (UV) to visible and near-infrared regions and narrower bandgap. The bandgap easily tuned with the increased t-KF loading in urea precursor which responsible for in-situ carbon doping. Moreover, as compared to pristine g-C3N4, dramatic suppression of charge recombination of the BTPMC g-C3N4 was confirmed through photoluminescence, photocurrent response, and electrochemical impedance spectroscopy. The resultants BTPMC g-C3N4 possesses more stable structure, promoted charge separation, and suitable energy levels of conduction and valence bands for photocatalysis application.

Adsorption kinetics of methylene blue dyes onto magnetic graphene oxide

Abstract: Adsorption is one of the most effective methods for the treatment of wastewater containing dyes owing to its low operating cost, simplicity of process design and smaller amounts of harmful substances. In this work, graphene oxide- magnetic iron oxide nanoparticles (GO-MNP) was synthesized using sonomechanical technique and used as effective adsorbent for synthetic methylene blue (MB) dye removal. Batch adsorption experiments were performed with the variation of initial MB dye concentration, pH solution, adsorbent dosage and contact time. The adsorbent showed significant removal efficiency around 99.6% for MB. It was found that the removal rate of MB dye in the solution was higher when higher pH, larger dosage of adsorbent in solution and longer contact time were used. A regenerative study was carried out and minor reduction in adsorption capacity of the regenerated GO-MNP was observed after 2 cycles. Analysis of adsorption equilibrium revealed that the data is well fitted with Langmuir and Freundlich adsorption isotherm model (R2 > 0.97), indicating multi layer adsorption of dye on the surface of adsorbent. In the case of adsorption kinetics, the GO-MNP adsorbent follows pseudo-second order kinetics model showing R2 > 0.999, whereas for pseudo-first order kinetics model, the value of R2 was significantly lower. The finding of the present work highlights simple fabrication of magnetic GO and its application as efficient and magnetically separable adsorbent for environmental clean-up.