Co-gasification of woody biomass and chicken manure: Syngas production, biochar reutilization, and cost-benefit analysis

The management and disposal of livestock manure has become one of the top environmental issues at a global scale in line with the tremendous growth of poultry industry over the past decades. In this work, a potential alternative method for the disposal of chicken manure from Singapore local hen layer farms was studied. Gasification was proposed as the green technology to convert chicken manure into clean energy. Through gasification experiments in a 10 kW fixed bed downdraft gasifier, it was found that chicken manure was indeed a compatible feedstock for gasification in the presence of wood waste. The co-gasification of 30 wt% chicken manure and 70 wt% wood waste produced syngas of comparable quality to that of gasification of pure wood waste, with a syngas lower heating value (LHV) of 5.23 MJ/Nm3 and 4.68 MJ/Nm3, respectively. Furthermore, the capability of the gasification derived biochar in the removal of an emerging contaminant (artificial sweetener such as Acesulfame, Saccharin and Cyclamate) via adsorption was also conducted in the second part of this study. The results showed that the biochar was effective in the removal of the contaminant and the mechanism of adsorption of artificial sweetener by biochar was postulated to be likely via electrostatic interaction as well as specific interaction. Finally, we conducted a cost-benefit analysis for the deployment of a gasification system in a hen layer farm using a Monte Carlo simulation model

Predicting The Helpfulness Of Online Product Reviewers: A Data Mining Approach

The purpose of this study is to propose a data mining approach to predict the helpfulness scores of online product reviewers. Such prediction can facilitate consumers to judge whether to believe or disbelieve reviews written by different reviewers and can help e-stores or third-party product review websites to target and retain quality reviewers. In this study, we identify eight independent variables from the perspectives of reviewers’ review behavior and trust network to predict the helpfulness scores for these reviewers. We adopt M5 and SVM Regression as our underlying learning algorithms. Our empirical evaluation results on the basis of two product categories (i.e., Car and Computer) suggest that our proposed helpfulness prediction technique can predict the helpfulness scores of online product reviewers

Structural Color 3D Printing By Shrinking Photonic Crystals

The rings, spots and stripes found on some butterflies, Pachyrhynchus weevils, and many chameleons are notable examples of natural organisms employing photonic crystals to produce colorful patterns. Despite advances in nanotechnology, we still lack the ability to print arbitrary colors and shapes in all three dimensions at this microscopic length scale. Commercial nanoscale 3D printers based on two-photon polymerization are incapable of patterning photonic crystal structures with the requisite ~300 nm lattice constant to achieve photonic stopbands/ bandgaps in the visible spectrum and generate colors. Here, we introduce a means to produce 3D-printed photonic crystals with a 5x reduction in lattice constants (periodicity as small as 280 nm), achieving sub-100-nm features with a full range of colors. The reliability of this process enables us to engineer the bandstructures of woodpile photonic crystals that match experiments, showing that observed colors can be attributed to either slow light modes or stopbands. With these lattice structures as 3D color volumetric elements (voxels), we printed 3D microscopic scale objects, including the first multi-color microscopic model of the Eiffel Tower measuring only 39-microns tall with a color pixel size of 1.45 microns. The technology to print 3D structures in color at the microscopic scale promises the direct patterning and integration of spectrally selective devices, such as photonic crystal-based color filters, onto free-form optical elements and curved surfaces

Development of a fall detection system based on neural network featuring IoT-Technology

Accidental falls are considered a major cause of accidents that could lead to serious injuries, paralysis, psychological damage, and even deaths, especially for the elderly. Therefore in this project, a neural network-based fall detection system that could automatically detect a fall event is proposed. The system is enhanced with Internet-ofThings (IoT) features that could reduce the response time and efficiently improve the prognosis of fall victims. A 10 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) module is connected to an Intel Edison with Mini Breakout board and mounted on a wearable waist-worn device to continuously record body movements. A backpropagation neural network algorithm has been developed to accurately distinguish falls from different postural transitions during activities of daily living (ADL). A body temperature and heartpulse monitoring device were developed for this system to provide the medical personnel additional information on the body condition of the fall victim. Using the latest IoT-technology, the system can be connected to the internet and provides a continuous and real-time monitoring capability. Once a fall accident happens, the system will be automatically triggered. This will activate an Android App through the Wi-Fi network that will then send an emergency SMS with the actual location and body conditions of the victim to a recipient. A series of falls and ADL simulations were performed by a group of subjects to test and validate the performance of the system. The experiment results showed that the proposed system could obtain a sensitivity of 95.5%, specificity of 96.4%, and accuracy of 96.3%

Predicting The Helpfulness Of Online Product Reviewers: A Data Mining Approach

Abstrac

Free-living marine nematodes community structure in the conservation area (Chaojing Park) and its adjacent area of Keelung, Taiwan

Studies conducted in the same seas or even study sites nearby each other, showed very different community structure, implying the patchiness of free-living marine nematodes which may be related to the sedimentary environment such as sediment type and food availability of the study area. This study was motivated by the concerns about the impacts of high level of anthropogenic activities on Chaojing Park (gazetted as Wanghaixiang’s Chao-Jing Bay Resource Conservation Area (WCJBRA) in 2016). The present study provides baseline knowledge of free-living marine nematode community structure in WCJBRA and identify potential marine nematodes as bioindicators to indicate possible impacts of the anthropogenic activities to the Chaojing Park. A total of 15 stations were selected in the subtidal zones of WCJBRA and its adjacent area. Marine nematode sample collection was carried out on the 13th and 14th of September 2019 using SCUBA diving technique. Results showed positive correlation between nematode density and medium sand (500μm-1.0mm). Presence of certain species such as Daptonema sp., Pomponema sp. and Innocuonema sp. indicates presence of disturbances in S12 and S13. Several species also showed potential to be introduced as indicator for healthy environment subjected to further studies on nematode-pollutants relationship, particularly on autecology as per se. Higher species diversity, H’ index of S1-S8 and S11 was categorised as Good Condition; followed by station with moderate species diversity index (S9, S10, S14 –Moderate Condition) zone; and lastly S12, S13 and S15 (Poor Condition)