A Model of Drying Kinetics of Acacia mangium Wood at Different Temperatures

The objective of this study was to determine the isothermal drying kinetics of Acacia mangium wood blocks by employing a relative humidity-controlled drying chamber. A model was developed based on solution of Fick's second law and evaluated through the coefficient of determination (R ² ), sum of square error (SSE), root mean square error (RMSE), and reduced chi-square (χ ² ). This model was compared to semi-theoretical models which are commonly used to describe the drying behavior of biomass in previous studies. The porosity and shrinkage characteristics of dried specimens were also evaluated. Based on the findings in this work, it was determined that the proposed model resulted in an excellent fit with experimental data for all four drying temperature levels of 30, 35, 40, and 45 ^° C to describe the isothermal drying kinetics of Acacia mangium . It appears that volumetric shrinkage of the samples decreased quadratically with decreasing moisture ratio. The activation energy of the drying process was determined to be 41.07 kJ/mol.     

Potassium hydroxide catalyst supported on palm shell activated carbon for transesterification of palm oil

In this study, potassium hydroxide catalyst supported on palm shell activated carbon was developed for transesterification of palm oil. The Central Composite Design (CCD) of the Response Surface Methodology (RSM) was employed to investigate the effects of reaction temperature, catalyst loading and methanol to oil molar ratio on the production of biodiesel using activated carbon supported catalyst. The highest yield was obtained at 64.1 °C reaction temperature, 30.3 wt.% catalyst loading and 24:1 methanol to oil molar ratio. The physical and chemical properties of the produced biodiesel met the standard specifications. This study proves that activated carbon supported potassium hydroxide is an effective catalyst for transesterification of palm oil.     

Antioxidant Protective Effect of Glibenclamide and Metformin in Combination with Honey in Pancreas of Streptozotocin-Induced Diabetic Rats

Hyperglycemia exerts toxic effects on the pancreatic β-cells. This study investigated the hypothesis that the common antidiabetic drugs glibenclamide and metformin, in combination with tualang honey, offer additional protection for the pancreas of streptozotocin (STZ)-induced diabetic rats against oxidative stress and damage. Diabetes was induced in male Sprague Dawley rats by a single dose of STZ (60 mg/kg; ip). Diabetic rats had significantly elevated levels of lipid peroxidation (TBARS), up-regulated activities of superoxide dismutase (SOD) and glutathione peroxidase (GPx) while catalase (CAT) activity was significantly reduced. Glibenclamide and metformin produced no significant effects on TBARS and antioxidant enzymes except GPx in diabetic rats. In contrast, the combination of glibenclamide, metformin and honey significantly up-regulated CAT activity and down-regulated GPx activity while TBARS levels were significantly reduced. These findings suggest that tualang honey potentiates the effect of glibenclamide and metformin to protect diabetic rat pancreas against oxidative stress and damage.     

Labisia pumila extract protects skin cells from photoaging caused by UVB irradiation

Labisia pumila (Myrsinaceae), known as “Kacip Fatimah,” has been used by many generations of Malay women to induce and facilitate child birth as well as a post partum medicine. However, its topical application on skin has not been reported yet. In this study, we have focused on the anti-photoaging effects of L. pumila. Extract of L. pumila was first analyzed for their antioxidant activities using DPPH (2,2-diphenyl-1-picrylhydrazyl) since UV irradiation is a primary cause of reactive oxygen species (ROS) generation in the skin. The 50% free radical scavenging activity (FSC₅₀) of L. pumila extract was determined to be 0.006%, which was equal to that produced by 156 μM ascorbic acid. TNF-α and cyclooxygenase (COX-2) play a primary role in the inflammation process upon UV irradiation and are known to be stimulated by UVB. Treatment with L. pumila extract markedly inhibited the TNF-α production and the expression of COX-2. Decreased collagen synthesis of human fibroblasts by UVB was restored back to normal level after treatment with L. pumila extract. On the other hand, the enhanced MMP-1 expression upon UVB irradiation was down regulated by L. pumila extract in a dose-dependent manner. Furthermore, treatment of normal keratinocytes with L. pumila extract attenuated UVB-induced MMP-9 expression. These results collectively suggest L. pumila extract has tremendous potential as an anti-photoaging cosmetic ingredient.     

An intelligent method for sizing optimization in grid-connected photovoltaic system

This paper presents an intelligent sizing technique for sizing grid-connected photovoltaic (GCPV) system using evolutionary programming (EP). EP was used to select the optimal set of photovoltaic (PV) module and inverter for the system such that the technical or economic performance of the system could be optimized. The decision variables for the optimization process are the PV module and inverter which had been encoded as specific integers in the respective database. On the other hand, the objective function of the optimization task was set to be either to optimize the technical performance or the economic performance of the system. Before implementing the intelligent-based sizing algorithm, a conventional sizing model had been presented which later led to the development of an iterative-based sizing algorithm, known as ISA. As the ISA tested all available combinations of PV modules and inverters to be considered for the system, the overall sizing process became time consuming and tedious. Therefore, the proposed EP-based sizing algorithm, known as EPSA, was developed to accelerate the sizing process. During the development of EPSA, different EP models had been tested with a non-linear scaling factor being introduced to improve the performance of these models. Results showed that the EPSA had outperformed ISA in terms of producing lower computation time. Besides that, the incorporation of non-linear scaling factor had also improved the performance of all EP models under investigation. In addition, EPSA had also shown the best optimization performance when compared with other intelligent-based sizing algorithms using different types of Computational Intelligence.     

Better Decisions, Better Processes: Using Customer-Integrated Decision Making

As Spectra Precision, Inc., in Dayton, Ohio, embarked on a new Total Quality Management approach, it recognized a need to improve its product development process by getting more customer focused and integrated within all internal functions. By choosing the Customer Integrated Decision Making/ Matrix Analysis (CIDM/MA) process, they brought a rigorous, cross-discipline team-based, transferable decision process into the company. The first project chosen was in one of the traditional market areas that was experiencing significant competitive pressure and internal debate on how to respond. With a team of representatives from engineering, quality, manufacturing, marketing, and sales, the project took three and half months to complete solution planning, in-context interviews, two choice-based market surveys, and internal discussion. Along the way, many key product, market, and competitive decisions were reached and presented to management for acceptance. For the first time management had hard, factual data available for making decisions about corrective actions on subsequent design efforts. Also, using CIDM/MA allowed management to have an integrated decision process that helped in deciding which customer segments were important to focus on, understanding these customers' needs and current perceptions, and focus on the response alternatives. As a result of this first success, a second project was started primarily utilizing an internal facilitator and other divisions of the company were starting to use the process as well.     

Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network

Developing water level forecasting models is essential in water resources management and flood prediction. Accurate water level forecasting helps achieve efficient and optimum use of water resources and minimize flooding damages. The artificial neural network (ANN) is a computing model that has been successfully tested in many forecasting studies, including river flow. Improving the ANN computational approach could help produce accurate forecasting results. Most studies conducted to date have used a sigmoid function in a multi-layer perceptron neural network as the basis of the ANN; however, they have not considered the effect of sigmoid steepness on the forecasting results. In this study, the effectiveness of the steepness coefficient (SC) in the sigmoid function of an ANN model designed to test the accuracy of 1-day water level forecasts was investigated. The performance of data training and data validation were evaluated using the statistical index efficiency coefficient and root mean square error. The weight initialization was fixed at 0.5 in the ANN so that even comparisons could be made between models. Three hundred rounds of data training were conducted using five ANN architectures, six datasets and 10 steepness coefficients. The results showed that the optimal SC improved the forecasting accuracy of the ANN data training and data validation when compared with the standard SC. Importantly, the performance of ANN data training improved significantly with utilization of the optimal SC.     

Fragmented malware through RFID and its defenses

Malware, in essence, is an infiltration to one’s computer system. Malware is created to wreak havoc once it gets in through weakness in a computer’s barricade. Anti-virus companies and operating system companies are working to patch weakness in systems and to detect infiltrators. However, with the advance of fragmentation, detection might even prove to be more difficult. Malware detection relies on signatures to identify malware of certain shapes. With fragmentation, functionality and size can change depending on how many fragments are used and how the fragments are created. In this paper we present a robust malware detection technique, with emphasis on detecting fragmentation malware attacks in RFID systems that can be extended to detect complex obfuscated and mutated malware. After a particular fragmented malware has been first identified, it can be analyzed to extract the signature, which provides a basis for detecting variants and mutants of similar types of malware in the future. Encouraging experimental results on a limited set of recent malware are presented.