The Cost of War in Human Dimension: The Case of Lanao del Sur

The conflict in Muslim Mindanao, particularly in Lanao del Sur, is complex, deeply rooted, and multifaceted. Through its long history, the conflict has brought tremendous economic losses but it is in terms of the human dimension that the strongest impact of the conflict is felt. And this is shown clearly in the way that the ARMM has consistently ranked at the bottom among all regions in the Philippines in terms of human development. Read more of the poignant story of Lanao del Sur.armed conflict, human development, forced migration, marginalization, resource exploitation, human security, Lanao del Sur, Philippines

Monolithic GaAs digitizer for space-based laser pulse spreading effect

A 6-bit 1-GHz digitizer was designed to analyze the 1-ns pulse spreading effects in a space based altimeter. The digitizer consisted of four 4-bit flash A/D converters and a 6-bit encoder. Also, the converter utilized four 4-bit converters and a 4-to-6 bit encoder to achieve 6 bit resolution at the 1 GHz sample rate. The design was unique because it utilized only the inverters and NOR gates for the converters and encoder, hence it could be fabricated using the existing state-of-the-art GaAs processing techniques. This GHz digitizer has many commercial applications. It could be applicable to: (1) digital microwave transmission system for the telecommunication industries, (2) pulse monitoring in high kinetic chemical reactions, (3) transient signals in the medical field, and (4) microwave signals in astronomy

Investigation and prediction of slug flow characteristics in highly viscous liquid and gas flows in horizontal pipes

Slug flow characteristics in highly viscous liquid and gas flow are studied experimentally in a horizontal pipe with 0.074 m ID and 17 m length. Results of flow regime map, liquid holdup and pressure gradient are discussed and liquid viscosity effects are investigated. Applicable correlations which are developed to predict liquid holdup in slug body for low viscosity flow are assessed with high viscosity liquids. Furthermore, a mechanistic model is developed for predicting the characteristics of slug flows of highly viscous liquid in horizontal pipes. A control volume is drawn around the slug body and slug film in a slug unit. Momentum equations with a momentum source term representing the significant momentum exchange between film zone and slug body are applied. Liquid viscosity effects are considered in closure relations. The mechanistic model is validated by comparing available pressure gradient and mean slug liquid holdup data produced in the present study and those obtained from literature, showing satisfactory capabilities over a large range of liquid viscosity

Restart time correlation for core annular flow in pipeline lubrication of high-viscous oil

One of the fundamental questions that must be addressed in the effective design and operation of pipeline lubrication of heavy oil is; “how much time will be needed to restart a blocked core annular flow (CAF) line after shutdown due to fouling or pump failures”, if the pipe is to be cleaned using water only. In this work, laboratory results of shutdown and restart experiments of high-viscous oil conducted in a 5.5-m-long PVC horizontal pipe with internal diameter of 26 mm are first presented. A new correlation for the prediction of the restart time of a shutdown core annular flow line is then formulated. The predictive capabilities of the correlation are checked against measured restart time and pressure drop evolution data. Somewhat high but still reasonable predictions are obtained. The restart time correlation, together with the associated correlations formulated as well, can be of practical importance during the engineering design of high-viscous oil pipeline transportation facility for predicting restart process

Leveraging Disease Progression Learning for Medical Image Recognition

Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning. For example, images that belong to different stages of a disease may continuously follow a certain progression pattern. In this paper, we propose a novel method that leverages disease progression learning for medical image recognition. In our method, sequences of images ordered by disease stages are learned by a neural network that consists of a shared vision model for feature extraction and a long short-term memory network for the learning of stage sequences. Auxiliary vision outputs are also included to capture stage features that tend to be discrete along the disease progression. Our proposed method is evaluated on a public diabetic retinopathy dataset, and achieves about 3.3% improvement in disease staging accuracy, compared to the baseline method that does not use disease progression learning