A collaboration platform for enabling industrial symbiosis : towards creating a self-learning waste-to-resource database for recommending industrial symbiosis transactions using text analytics

Industrial Symbiosis (IS) adopts a collaborative approach, which aims to re-channel resources – traditionally considered spent and non-productive – towards alternative value-adding pathways. Empirically, the concept of IS has been rapidly implemented in practice through a facilitated approach, whereby businesses are engaged and “match-made” via a facilitating body. While recommending alternative pathways for companies to establish IS-based transactions is a long-standing practice, recent technological advancement has shifted the nature of this task from one that is based purely on human intellect and reasoning, towards one which leverages intelligent recommendation algorithms to provide relevant suggestions. Traditionally, these recommendation engines rely on manually populated knowledge bases that are not only labor-intensive to build but also costly to maintain. This work presents the creation of a self-learning waste-to-resource database supporting an IS recommendation system by utilizing text analytics techniques. We further demonstrate its practical application to support IS facilitating bodies in their core activity

Radome Design with Improved Aerodynamics and Radiation for Smart Antennas in Automotive Applications

More applications are using wireless communication, where the radomes become more important as they are essential in the performance of antennas as well as protect antennas from environmental conditions. The focus in this study is to design a radome for a customized smart antenna such that the attenuation of antenna signal and the increase in drag coefficient of vehicle are minimized. This paper presents a novel method of radome design and the simulation results demonstrate that the drag coefficient of the vehicle is slightly increased by less than 2% while the loss to the signal strength is less than 0.5 dB

Accumulation of metals in GOLD4 COPD lungs is associated with decreased CFTR levels

Abstract Background The Cystic Fibrosis Transmembrane conductance Regulator (CFTR) is a chloride channel that primarily resides in airway epithelial cells. Decreased CFTR expression and/or function lead to impaired airway surface liquid (ASL) volume homeostasis, resulting in accumulation of mucus, reduced clearance of bacteria, and chronic infection and inflammation. Methods Expression of CFTR and the cigarette smoke metal content were assessed in lung samples of controls and COPD patients with established GOLD stage 4. CFTR protein and mRNA were quantified by immunohistochemistry and quantitative RT-PCR, respectively. Metals present in lung samples were quantified by ICP-AES. The effect of cigarette smoke on down-regulation of CFTR expression and function was assessed using primary human airway epithelial cells. The role of leading metal(s) found in lung samples of GOLD 4 COPD patients involved in the alteration of CFTR was confirmed by exposing human bronchial epithelial cells 16HBE14o- to metal-depleted cigarette smoke extracts. Results We found that CFTR expression is reduced in the lungs of GOLD 4 COPD patients, especially in bronchial epithelial cells. Assessment of metals present in lung samples revealed that cadmium and manganese were significantly higher in GOLD 4 COPD patients when compared to control smokers (GOLD 0). Primary human airway epithelial cells exposed to cigarette smoke resulted in decreased expression of CFTR protein and reduced airway surface liquid height. 16HBE14o-cells exposed to cigarette smoke also exhibited reduced levels of CFTR protein and mRNA. Removal and/or addition of metals to cigarette smoke extracts before exposure established their role in decrease of CFTR in airway epithelial cells. Conclusions CFTR expression is reduced in the lungs of patients with severe COPD. This effect is associated with the accumulation of cadmium and manganese suggesting a role for these metals in the pathogenesis of COPD

Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

Background: Emergency abdominal surgery is associated with poor patient outcomes. We studied the effectiveness of a national quality improvement (QI) programme to implement a care pathway to improve survival for these patients. Methods: We did a stepped-wedge cluster-randomised trial of patients aged 40 years or older undergoing emergency open major abdominal surgery. Eligible UK National Health Service (NHS) hospitals (those that had an emergency general surgical service, a substantial volume of emergency abdominal surgery cases, and contributed data to the National Emergency Laparotomy Audit) were organised into 15 geographical clusters and commenced the QI programme in a random order, based on a computer-generated random sequence, over an 85-week period with one geographical cluster commencing the intervention every 5 weeks from the second to the 16th time period. Patients were masked to the study group, but it was not possible to mask hospital staff or investigators. The primary outcome measure was mortality within 90 days of surgery. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN80682973. Findings: Treatment took place between March 3, 2014, and Oct 19, 2015. 22 754 patients were assessed for elegibility. Of 15 873 eligible patients from 93 NHS hospitals, primary outcome data were analysed for 8482 patients in the usual care group and 7374 in the QI group. Eight patients in the usual care group and nine patients in the QI group were not included in the analysis because of missing primary outcome data. The primary outcome of 90-day mortality occurred in 1210 (16%) patients in the QI group compared with 1393 (16%) patients in the usual care group (HR 1·11, 0·96–1·28). Interpretation: No survival benefit was observed from this QI programme to implement a care pathway for patients undergoing emergency abdominal surgery. Future QI programmes should ensure that teams have both the time and resources needed to improve patient care. Funding: National Institute for Health Research Health Services and Delivery Research Programme

Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

BACKGROUND: Emergency abdominal surgery is associated with poor patient outcomes. We studied the effectiveness of a national quality improvement (QI) programme to implement a care pathway to improve survival for these patients. METHODS: We did a stepped-wedge cluster-randomised trial of patients aged 40 years or older undergoing emergency open major abdominal surgery. Eligible UK National Health Service (NHS) hospitals (those that had an emergency general surgical service, a substantial volume of emergency abdominal surgery cases, and contributed data to the National Emergency Laparotomy Audit) were organised into 15 geographical clusters and commenced the QI programme in a random order, based on a computer-generated random sequence, over an 85-week period with one geographical cluster commencing the intervention every 5 weeks from the second to the 16th time period. Patients were masked to the study group, but it was not possible to mask hospital staff or investigators. The primary outcome measure was mortality within 90 days of surgery. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN80682973. FINDINGS: Treatment took place between March 3, 2014, and Oct 19, 2015. 22 754 patients were assessed for elegibility. Of 15 873 eligible patients from 93 NHS hospitals, primary outcome data were analysed for 8482 patients in the usual care group and 7374 in the QI group. Eight patients in the usual care group and nine patients in the QI group were not included in the analysis because of missing primary outcome data. The primary outcome of 90-day mortality occurred in 1210 (16%) patients in the QI group compared with 1393 (16%) patients in the usual care group (HR 1·11, 0·96-1·28). INTERPRETATION: No survival benefit was observed from this QI programme to implement a care pathway for patients undergoing emergency abdominal surgery. Future QI programmes should ensure that teams have both the time and resources needed to improve patient care. FUNDING: National Institute for Health Research Health Services and Delivery Research Programme

A comparison between cannulated and non-cannulated cancellous screws used in the fixation of simulated femoral neck fracture

There are an increasing number of femoral neck fractures worldwide which may rise to 6.3 million cases by the year 2050. The associated healthcare costs are also expected to increase significantly to $8.7 billion. Screw fixation was considered as ideal treatment of undisplaced or minimally displaced neck fractures in terms of cost and strength. A comparison of the strength of fixation using cannulated versus non-cannulated screws was done via experiments. A jig prototype was designed and manufactured to ensure the accuracy of the screws position and the repeatability of all experimental conditions. The repeatability was confirmed by a coefficient of variations data which range from 5% to 13%. Experiments were conducted to test for differences in the maximum load to failure, maximum displacement at failure and stiffness for both types of screw fixation. A mean maximum load to failure at 2452.25 N, maximum displacement at failure at 22.39 mm and stiffness of 110.06 N/mm was observed for the non-cannulated screw fixation. A mean maximum load to failure of 2013.21 N, maximum displacement at failure at 21.29 mm and stiffness of 94.77 N/mm was observed for the cannulated screw fixation. The mean maximum load to failure as well as stiffness was higher for the non-cannulated screw fixation with statistical significance.Bachelor of Engineering (Mechanical Engineering

NNFacet: splitting neural network for concurrent smart sensors

Various deep neural networks (DNNs) including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have shown appealing performance in various classification tasks. However, due to their large sizes, a single DNN often cannot fit into the memory of resource-constrained smart IoT sensors. This paper presents a DNN splitting framework called NNFacet that aims to run a DNN-based classification task on a total of $N$ concurrent battery-based sensors observing the same physical process. We begin with determining the importance of all CNN filters or RNN units in learning each class. Then, an optimization problem divides the class set into $N$ subsets and assigns them to the sensors, where the important CNN filters or RNN units associated with a class subset form a small model that is deployed to a sensor. Lastly, a multilayer perceptron is trained and deployed to a cloud or edge server, which yields the final classification result based on the low-dimensional features extracted by the sensors using their small models for the same observation. We apply NNFacet to three case studies of voice sensing, vibration sensing, and visual sensing. Extensive evaluation shows that NNFacet outperforms four baseline approaches in terms of system lifetime, latency, and classification accuracy.Nanyang Technological UniversitySubmitted/Accepted versionThis study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner, HP Inc., through the HP-NTU Digital Manufacturing Corporate Lab

The Structure, Expression, and Function Prediction of DAZAP2, A Down-Regulated Gene in Multiple Myeloma

In our previous studies, DAZAP2 gene expression was down-regulated in untreated patients of multiple myeloma (MM). For better studying the structure and function of DAZAP2, a full-length cDNA was isolated from mononuclear cells of a normal human bone marrow, sequenced and deposited to Genbank (AY430097). This sequence has an identical ORF (open reading frame) as the NM_014764 from human testis and the D31767 from human cell line KG-1. Phylogenetic analysis and structure prediction reveal that DAZAP2 homologues are highly conserved throughout evolution and share a polyproline region and several potential SH2/SH3 binding sites. DAZAP2 occurs as a single-copy gene with a four-exon organization. We further noticed that the functional DAZAP2 gene is located on Chromosome 12 and its pseudogene gene is on Chromosome 2 with electronic location of human chromosome in Genbank, though no genetic abnormalities of MM have been reported on Chromosome 12. The ORF of human DAZAP2 encodes a 17-kDa protein, which is highly similar to mouse Prtb. The DAZAP2 protein is mainly localized in cytoplasm with a discrete pattern of punctuated distribution. DAZAP2 may associate with carcinogenesis of MM and participate in yet-to-be identified signaling pathways to regulate proliferation and differentiation of plasma cells

Configuration-adaptive wireless visual sensing system with deep reinforcement learning

Visual sensing has been increasingly employed in various industrial applications including manufacturing process monitoring and worker safety monitoring. This paper presents the design and implementation of a wireless camera system, namely, EFCam, which uses low-power wireless communications and edge-fog computing to achieve cordless and energy-efficient visual sensing. The camera performs image pre-processing and offloads the data to a resourceful fog node for advanced processing using deep models. EFCam admits dynamic configurations of several parameters that form a configuration space. It aims to adapt the configuration to maintain desired visual sensing performance of the deep model at the fog node with minimum energy consumption of the camera in image capture, pre-processing, and data communications, under dynamic variations of the monitored process, the application requirement, and wireless channel conditions. However, the adaptation is challenging due to the complex relationships among the involved factors. To address the complexity, we apply deep reinforcement learning to learn the optimal adaptation policy when a fog node supports one or more wireless cameras. Extensive evaluation based on trace-driven simulations and experiments show that EFCam complies with the accuracy and latency requirements with lower energy consumption for a real industrial product object tracking application, compared with five baseline approaches incorporating hysteresis-based and event-triggered adaptation.This work was supported in part by Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, under Grant RIE2020 and in part by cash and in-kind contribution from the industry partner, HP Inc., through the HP-NTU Digital Manufacturing Corporate Lab