Very Massive Stars and the Eddington Limit

We use contemporary evolutionary models for Very Massive Stars (VMS) to assess whether the Eddington limit constrains the upper stellar mass limit. We also consider the interplay between mass and age for the wind properties and spectral morphology of VMS, with reference to the recently modified classification scheme for O2-3.5If*/WN stars. Finally, the death of VMS in the local universe is considered in the context of pair instability supernovae.Comment: 6 pages, 4 figures, from "Four Decades of Massive Star Research" (Quebec, Jul 2011), ASP Conf Ser, in press (L. Drissen, C. Robert, N. St-Louis, A.F.J. Moffat, eds.

Holographic Screening Length in a Hot Plasma of Two Sphere

We study the screening length of a quark-antiquark pair moving in a hot plasma living in two sphere $S^2$ manifold using AdS/CFT correspondence where the background metric is four dimensional Schwarzschild-AdS black hole. The geodesic solution of the string ends at the boundary is given by a stationary motion in the equatorial plane as such the separation length $L$ of quark-antiquark pair is parallel to the angular velocity $\omega$. The screening length and the bound energy are computed numerically using Mathematica. We find that the plots are bounded from below by some functions related to the momentum transfer $P_c$ of the drag force configuration. We compare the result by computing the screening length in the quark-antiquark reference frame where the gravity dual are "Boost-AdS" and Kerr-AdS black holes. Finding relations of the parameters of both black holes, we argue that the relation between mass parameters $M_{Sch}$ of the Schwarzschild-AdS black hole and $M_{Kerr}$ of the Kerr-AdS black hole in high temperature is given by $M_{Kerr}=M_{Sch}(1-a^2l^2)^{3/2}$, where $a$ is the angular momentum parameter.Comment: Major revision: title changed, adding authors, 13 pages, 8 figures, etc. Accepted for publication in European Physical Journal

Role of membrane surface charge and complexation-ultrafiltration for heavy metals removal: a mini review

Membrane technology is a promising water purification unit operation from household use to industrial application owing to its simplicity of operation, efficient recovery and minimum need for chemical and space allocation. Due to that reason, study on the membrane applications have becoming more popular among scientific community nowadays and one of the applications is removal of heavy metal using ultrafiltration (UF). However, a stand-alone UF will be not able to carry out the removal of heavy metals effectively. Certain modification is required in order to enhance its rejection via unique facilitated mechanism. Thus in this review, role of surface charge interaction as well as the method of complexation-ultrafiltration were discussed

Improvising Intrusion Detection for Malware Activities on Dual-Stack Network Environment

Malware is software which was invented and meant for doing harms on computers. Malware is becoming a significant threat in computer network nowadays. Malware attack is not just only involving financial lost but it can also cause fatal errors which may cost lives in some cases. As new Internet Protocol version 6 (IPv6)emerged, many people believe this protocol could solve most malware propagation issues due to its broader addressing scheme. As IPv6 is still new compares to native IPv4, some transition mechanisms have been introduced to promote smoother migration. Unfortunately, these transition mechanisms allow some malwares to propagate its attack from IPv4 to IPv6 network environment. In this paper, a proof of concept shall be presented in order to show that some existing IPv4 malware detection technique need to be improvised in order to detect malware attack in dual-stack network more efficiently. A testbed of dual-stack network environment has been deployed and some genuine malware have been released to observe their behaviors. The results between these different scenarios will be analyzed and discussed further in term of their behaviors and propagation methods. The results show that malware behave differently on IPv6 from the IPv4 network protocol on the dual-stack network environment. A new detection technique is called for in order to cater this problem in the near future

NPK detection spectroscopy on non-agriculture soil

Soil is a medium for plant roots to grow, absorb water and necessary solutes for growth. Soil macronutrient testing is helpful for determining the nutrients content in soil before applying fertilizer for quality and process controls of agricultural produce and soil fertility. Spectroscopy is an emerging technology which is rapid and simple has been widely used in agricultural and food analysis processes. The capability of spectroscopy to characterize material from the transmission or absorbance has been used in this paper to measure nitrogen (N), phosphorus (P) and potassium (K) content in non-agriculture soil. The paper details preliminary characterization of soil spectroscopy with a Deuterium-Halogen light source and Ocean Optic spectrometer to measure the absorbance level of the macronutrients. The extracted nutrients were mixed with the colour reagent and specific colored solution was developed. Two soil samples have been employed for the experimental characterization, which are mud flood and kaolin. The result shows that high absorbance level of N at 450 nm in wavelength, P at 750 nm for both samples. The absorbance level of K was measured high at 500nm for mud flood and 450nm for kaolin. In addition, the tested macronutrients give similar wavelength of peak absorbance level at 970 nm for both samples. For future works, the optical measurements will be implemented using visible and near infrared LED and the photodetector in order to replace the spectrometer usage for soil spectroscopy. This would lead to achieve the primary objective of this research in developing a simple and low cost spectroscopy uses light-emitting diode (LED)

Spatio-temporal wave pattern using multi-dimensional clustering method for exploring ocean energy potential

Wave is formed from the movement of air caused by pressure variations that make airflow move from high pressure toward places of low pressure. Understanding the wave patterns is challenging since it is highly changeable in space as they travel in variety of directions and heights. Wave are also changing over time especially during the monsoon seasons. Hence, to extract significant information from this highly changeable behaviour of wave this study has utilized a multi-dimensional clustering technique called co-clustering. This technique is able to cluster spatio-temporal data with similar behavior into spatial and temporal components simultaneously. To reveal the spatial and temporal patterns, an algorithm called Bregman Block Average co-clustering with I-divergence (BBAC_I) has been implemented for extracting wave patterns. In order to discover the wave behaviour, the extracted wave patterns were visualized in the form of heatmap that contain information of co-clusters; spatial clusters and temporal clusters dimensions. Then, both spatial and temporal clusters from the heatmap were transformed into geographical maps to depict the variation of wave patterns based on their individual dimension. From these maps, we could observe the distribution of 8 different group of clusters that representing the spatial wave patterns. Furthermore, 5 individual maps have been produced to depict the temporal wave patterns across the study area. Finally, the obtained maps were interpreted in the form of wave height which were found to be within 0.4 to 1.4 m heights. The wave height information can be used for identifying their potential for ocean energy harvesting along the coastal area. In generally, the generated spatio-temporal wave patterns from this study could aid Malaysian marine agencies to provide strategic planning for proposing future ocean energy in Malaysian coastal area

Halalnet: A Deep Neural Network That Classifies the Halalness of Slaughtered Chicken from Their Images

Halal requirement in food is important for millions of Muslims worldwide especially for meat and chicken products, insuring that slaughter houses adhere to this requirement is a challenging task to do manually. In this paper a method is proposed that uses a camera that takes images of slaughtered chicken on the conveyor in a slaughter house, the images are then analyzed by a deep neural network to classify if the image is of a halal slaughtered chicken or not. However, traditional deep learning models require large amounts of data to train on, which in this case these amounts of data were challenging to collect especially the images of non-halal slaughtered chicken, hence this paper shows how the use of one shot learning (Lake, Brenden, Salakhutdinov, Ruslan, Gross & Jas, 2011) and transfer learning (Yosinski, Clune, Bengio & Lipson, 2014) can reach high accuracy on the few amounts of data that were available. The architecture used is based on the Siamese neural networks architecture which ranks the similarity between two inputs (Koch, Zemel & Salakhutdinov, 2015) while using the Xception network (Chollet, 2017) as the twin networks. We call it HalalNet. This work was done as part of SYCUT (syriah compliant slaughtering system) which is a monitoring system that monitors the halalness of the slaughtered chicken in a slaughter house. The data used to train and validate HalalNet was collected from the Azain slaughtering site (Semenyih, Selangor, Malaysia) containing images of both halal and non-halal slaughtered chicken