Temburong Bridge,Brunei – Design of two cable stayed bridges

The Temburong Bridge Project is a 30 km long dual two‐lane highway crossing over the Brunei Bay. It will connect the relatively isolated Brunei district of Temburong to the other three Brunei districts. The main objective of the project is to stimulate economic growth in the Temburong region by connecting it to the country's airport and ports. The alignment crosses two navigation channels resulting in the need for two cable stayed bridges – the Brunei Channel Bridge (145 m main span) and the Eastern Channel Bridge (260 m main span). The design of these cable stayed bridges is one of the first applications of the Eurocode to a fully concrete cable stayed bridge. Both cable stayed bridges draw on strong Islamic architectural influences from the region to form a tower shape that is unique and instantly recognisable. Temburong Brücke, Brunei – Entwurf von zwei Schrägkabelbrücken Die Temburong Brücke ist eine 30 km lange Verbindung über die Brunei Bucht mit zwei Richtungsfahrbahnen mit je zwei Fahrstreifen. Sie wird den relativ isolierten Distrikt Temburong mit den anderen drei Distrikten Bruneis verbinden. Hauptanliegen des Projekts ist die Stimulierung von ökonomischem Wachstum in der Temburong Region, indem sie an den Flughafen und Seehafen des Landes angebunden wird. Die Trassierung erfordert die Überquerung zweier Schiffahrtsrouten. Dies wird mit der Anordnung zweier Schrägkabelbrücken ermöglicht, der Brunei Channel Brücke (145 m Hauptspannweite) und der Eastern Channel Brücke (260 m Hauptspannweite). Die Bemessung der beiden Brücken stellt eine der ersten Anwendungen des Eurocode auf Schrägkabelbrücken mit Betonüberbau dar. Optisch sind die beiden Brücken geprägt von der islamischen Architektur der Umgebung, die Pylonform ist ein besonderes Erkennungsmerkmal der Brücken.     

Tocotrienol Rich Fraction Reverses Age-Related Deficits in Spatial Learning and Memory in Aged Rats

Little is known about the effect of vitamin E on brain function. Therefore, in this study we evaluated the effect of tocotrienol rich fraction (TRF) on behavioral impairment and oxidative stress in aged rats. Thirty-six male Wistar rats (young: 3-months-old; aged: 21-months-old) were treated with either the control (olive oil) or TRF (200 mg/kg) for 3 months. Behavioral studies were performed using the open field test and Morris water maze (MWM) task. Blood was taken for assessment of DNA damage, plasma malondialdehyde (MDA) and vitamin E, and erythrocyte antioxidant enzyme activity. Brains were also collected to measure vitamin E levels. Results showed that aged rats exhibited reduced exploratory activity, enhanced anxiety and decreased spatial learning and memory compared with young rats. DNA damage and plasma MDA were increased, and vitamin E levels in plasma and brain were reduced in aged rats. Aged rats supplemented with TRF showed a markedly reduced level of anxiety, improved spatial learning and memory, reduced amount and severity of DNA damage, a reduced level of MDA, and increased levels of antioxidant enzyme activity and plasma/brain vitamin E compared with age-matched controls. In conclusion, TRF supplementation reverses spatial learning and memory decline and decreases oxidative stress in aged rats.     

Computations of shear driven vortex flow in a cylindrical cavity using a modified k-ε turbulence model

In this paper, a new variant of the k-ε turbulence model (Saqr et al., CFD Letters, 1(2) pp. 87–94) is used to compute the shear driven vortex flow in an open cylindrical cavity. The results are compared with published LDA measurements for such flow configuration. The modified turbulence model demonstrated good agreement with experimental results, which further supports its validity in computing vortex dominated flows.     

Hydrogen from solar energy,a clean energy carrier from a sustainable source of energy

Solar energy is going to play a crucial role in the future energy scenario of the world that conducts interests to solar-to-hydrogen as a means of achieving a clean energy carrier. Hydrogen is a sustainable energy carrier, capable of substituting fossil fuels and decreasing carbon dioxide (CO₂) emission to save the world from global warming. Hydrogen production from ubiquitous sustainable solar energy and an abundantly available water is an environmentally friendly solution for globally increasing energy demands and ensures long-term energy security. Among various solar hydrogen production routes, this study concentrates on solar thermolysis, solar thermal hydrogen via electrolysis, thermochemical water splitting, fossil fuels decarbonization, and photovoltaic-based hydrogen production with special focus on the concentrated photovoltaic (CPV) system. Energy management and thermodynamic analysis of CPV-based hydrogen production as the near-term sustainable option are developed. The capability of three electrolysis systems including alkaline water electrolysis (AWE), polymer electrolyte membrane electrolysis, and solid oxide electrolysis for coupling to solar systems for H₂ production is discussed. Since the cost of solar hydrogen has a very large range because of the various employed technologies, the challenges, pros and cons of the different methods, and the commercialization processes are also noticed. Among three electrolysis technologies considered for postulated solar hydrogen economy, AWE is found the most mature to integrate with the CPV system. Although substantial progresses have been made in solar hydrogen production technologies, the review indicates that these systems require further maturation to emulate the produced grid-based hydrogen.     

Sensitivity Analysis on Neural Network Algorithm for Primary Superheater Spray Modeling

ABSTRACT

Nonlinear, large inertia with long dead time is always associated with the main steam temperature parameter in coal fired power plant. Successful control of the main steam temperature within ±2°C of its setpoint is the ultimate target for coal-fired power plant operators. Two of the most common main steam temperature circuit are primary superheater spray and secondary superheater spray. Various methods were used to model the primary superheater spray control valve opening, and the neural network remains one of the most popular choices among researchers. It remains inconclusive which neural network algorithm types, setup, number of layers, and training algorithm will give the best result. As such, the paper shows the best setup for the neural network algorithm based on sensitivity analysis methodology for one hidden layer. The inputs selected for the neural network are generator output, main steam flow, total spray flow, and secondary superheater outlet steam temperature, while the output selected is primary spray flow control valve opening.     

Wiener-based smoother and predictor for massive-MIMO downlink system under pilot contamination

One of the challenges in massive-MIMO system is pilot contamination during the channel estimation process. Pilot contamination can cause error or inaccurate channel estimation process for future fifth generation (5G) downlink transmissions. This paper considers using a Wiener-based filter to smooth and predict the channel estimation to reduce the pilot contamination for more accurate CSI during channel estimation. The simulation results show that the Wiener-based smoothing and predicting technique reduces the effect of pilot contamination and increases the accuracy of CSI during channel estimation process. Wiener smoother (WS) is implemented based on Wiener-based filtering technique. The previous estimated CSI and weight coefficient vector are used to smooth the current estimated CSI by using block data formulation to reduce the effect of pilot contamination. However, WS technique suffers from pilot contamination due to pilot training. This motivates the development of two Wiener predictors (WP), known as WP1 and WP2. The WP1 and WP2 run a prediction technique for CSI and number of pilot training during the prediction period, which is missing from the original WS. Comparison results show that the proposed WS and WP outperforms the conventional minimum mean square error and least square, in terms of channel estimation error and per-cell rate. WP2 perform better than WS and WP1 because of the algorithm complexity that required more information to be updated, stored and processed for prediction. Thus, WP2 requires large computation and matrix operation compared to WS and WP1. The results indicate that the channel estimation error due to pilot contamination can be reduced by using the Wiener-based approaches.     

Mechanochemical durability and self-cleaning performance of zinc oxide-epoxy superhydrophobic coating prepared via a facile one-step approach

Research efforts have intensified on developing superhydrophobic ZnO nanoparticle-based surfaces as they can impart desired hierarchical rough structures and properties. However, the widespread use of these surfaces is impeded by the limitations: complex fabrication procedures, weak adhesion, and limited durability performance. Most of these fabrication processes involved multiple treatment steps included the pre/post-treatment process to modify the textured surfaces. Herein, a convenient and effective one-step strategy is used to synthesis superhydrophobic ZnO-based coating with the introduction of both epoxy resin (EP) as the adhesive promoter and stearic acid (SA) as the low-energy modifier into an aqueous solution containing ZnO nanoparticles and ethanol via solution coating deposition method that binds all the components in a sole system. In the dense network, EP is responsible to render mechanical strength and coating-substrate adhesion, meanwhile, whereas SA functions to reduce the surface energy and preventing the EP to fully envelop the ZnO nanoparticles, thereby achieving a robust hierarchical rough structure, creating a layer of trapped air pockets. The resultant coating exhibits high water repellency, low water adhesion, and excellent self-cleaning ability with water contact and a sliding angle of 160.24° and 3°, respectively. Besides that, the superhydrophobic coating exhibited good mechanical durability after subjected to sandpaper-abrasion for a 20-m distance long and tape-peeling for 200 cycles. Furthermore, the coating still retained its superhydrophobicity after immersed in the corrosive bath with pH concentration ranged from 1 to 13 for 3 h, as well as the direct exposure to the temperature changes from 0 to 150 °C, manifesting its good chemical and thermal stability. This straightforward yet effective approach to develop a superhydrophobic coating with good durability and self-cleaning performance will inspire the scalable fabrication of multifunctional surfaces for practical applications in self-cleaning.     

Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and Support Vector Machine classifiers

Nitrogen (N) is a crucial element in sustaining oil palm production. However, assessing N status of tall perennial crops such as oil palm is complex and not as straightforward as assessing annual crops, due to complex N partitioning, age, and larger amounts of respiratory loads. Hence, the objectives of this study were to evaluate the potential of spectral measurements obtained from leaf scale and machine learning approaches as a rapid tool for quantifying oil palm N status. This study involved assessing the performance of discriminant analysis (DA) and Support Vector Machine (SVM) classifiers for discriminating spectral bands sensitive to N sufficiency levels and comparing the predictive accuracy of DA and SVM for classifying N status of immature and mature oil palms. The experiment was conducted on immature Tenera seedlings (13 months old) and mature Tenera palm stands (9 and 12 years old) that were arranged in Randomized Complete Block Design with treatments varied from 0 to 2 kg N. Generally, the discriminant function of both classifiers was age-dependent. A clear trade-off between the classifiers’ number of spectral bands and their accuracies was observed; the DA with a larger number of optimal spectral bands could discriminate N sufficiency levels of all maturity classes with higher accuracies compared to the SVM, yet the latter could produce reasonable accuracies with a lesser number of spectral bands. N status of all maturity classes could be classified satisfactorily with SVM (71–88%) via the satellite-simulated blue and green bands, signifying the possibility to develop spectral index or an N-sensitive sensor for oil palm.     

Improving spatial ability using a Web-based Virtual Environment (WbVE)

Spatial ability is one of the cognitive means used in problem solving activities particularly when manipulating and processing visuo-spatial information. With the increasing use of network communication, conventional teaching and learning has shifted towards the development of network environments, online learning, real-time interaction and high quality multi-user collaboration. This paper presents our experiment using a Web-based Virtual Environment (WbVE) that has improved a group of pre-service teachers' spatial ability understanding for teaching Engineering Drawing for secondary school subjects. The experiment is carried out in Universiti Pendidikan Sultan Idris (UPSI) comprising of 98 pre-service teachers taking the Computer Aided-Design (CAD) course. Selected subjects were pre-tested at the beginning of the semester with spatial tests focusing on mental rotation and spatial visualization to provide the baseline measurement. A desktop WbVE, which was employed and tested in the CAD laboratories to improve their spatial ability for five weeks of instructional treatment, will be demonstrated. Post-testing of spatial tests revealed significantly the overall spatial ability improvements as measured by the test scores. This paper concludes with a discussion on the impact of WbVE spatial learning that will be used as a precedent in the development of the VE teaching and learning prototypes for secondary school in Malaysia.