A collaboration framework to support decision making in new product development with the supply chain

Management use the supply chain features more frequently, as the increasing rate of product introduc-tions demands more efforts from a business to deliver new products effectively and efficiently. To produce products at the targeted cost, time, and quality, the supply chain must be aligned with product development processes. This will allow manufacturing firms to overcome problems such as (partially) failed product launches due to the lack of timely provision of parts and systems caused by insufficient capacities in the supply chain. With integrated New Product Development (NPD) and Supply Chain Management (SCM), enterprises have the benefit of increased supply chain capability, thus increasing the effectiveness of new product introduction and improving their overall performance. In this re-search, the authors have tried to link NPD of an automotive manufacturer to its global network of suppliers. The integration points in the integrated NPD and SCM framework will provide guidelines to identifying where critical decision are made in collaboration with the supply chain

Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

This research investigates the application of CT pulmonary images to the detection and characterisation of TB at five levels of severity, in order to monitor the efficacy of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images in which abnormalities occupy only limited regions, a 3D block-based residual deep learning network (ResNet) coupled with injection of depth information (depth-Resnet) at each layer was implemented. Progress in evaluation has been accomplished in two ways. One is to assess the proposed depth-Resnet in prediction of severity scores and another is to analyse the probability of high severity of TB. For the former, delivered results are of 92.70 ± 5.97% and 67.15 ± 1.69% for proposed depth-Resnet and ResNet-50 respectively. For the latter, two additional measures are put forward, which are calculated using (1) the overall severity (1 to 5) probability, and (2) separate probabilities of both high severity (scores of 1 to 3) and low severity (scores of 4 and 5) respectively, when scores of 1 to 5 are mapped into initial probabilities of (0.9, 0.7, 0.5, 0.3, 0.2) respectively. As a result, these measures achieve the averaged accuracies of 75.88% and 85.29% for both methods respectively

Analysing TB severity levels with an enhanced deep residual learning– depth-resnet

This work responds to the Competition of Tuberculosis Task organised by imageCLEF 2018. While Task #3 appears to be challenging, the experience was very enjoyable. If time had been permitted, it was certain that more accurate results could have been achieved. The authors submitted 2 runs. Based on the given training datasets with severity levels of 1 to 5, an enhanced deep residual learning architecture, depthResNet, is developed and applied to train the datasets to classify 5 categories. The datasets are pre-processed with each volume being segmented into twenty- 128×128×depth blocks with ~64 pixel overlaps. While each block has been predicted with a severity level, assembling all constituent block scores together to give an overall label for the concerned volume tends to be more challenging. Since the probability of high severity is not provided from the training datasets, which bears little resemblance to the classification probability, the submission of probability for the first run was manually assigned as 0.9, 0.7, 0.5, 0.3, and 0.1 to severity levels of 1 to 5 respectively. After the deadline was extended, the model was re-trained with frame numbers increased from 1 to 8, which takes much longer to train. In addition, a new measure was introduced to calculate the overall probability of high severity based on the block scores. As a result, with regard to classification accuracy, the 2nd submitted run achieved place 14 over a total of 36 submissions, a significant improvement from position of 35 from the first run

A life cycle model for Product-Service Systems design

Western manufacturing companies are developing innovative ways of delivering value that competes with the low cost paradigm. One such strategy is to deliver not only products, but systems that are closely aligned with the customer value proposition. These systems are comprised of integrated products and services, and are referred to as Product-Service Systems (PSS). A key challenge in PSS is supporting the design activity. In one sense, PSS design is a further extension of concurrent engineering that requires front-end input from the additional downstream sources of product service and maintenance. However, simply developing products and service packages is not sufficient: the new design challenge is the integrated system. This paper describes the development of a PSS data structure that can support this integrated design activity. The data structure is implemented in a knowledge base using the Protégé knowledge base editor

Integrating product knowledge with modular product structures in PLM

The changes in world economy are changing very fast and the company knowledge assets and processes are becoming primary source of organization which is intellectual property that need securely stored and maintained. Challenges that companies are facing today such as need to reduce time-to- market, the development and manufacture costs, or to manage complex products with advancing technology. Due to recent global financial crisis price competition in the market has led companies to fight with competitors for limited orders. The external pressure on delivery time has increased, which again has put internal pressure on bringing down development time, which leads for collaborative work environments. Modularisation of product structures will facilitate in collaborating design activities between a diversity of disciplines in global companies, which again involves supporting computer based tools for enhancing interaction, communication and design management. Product Lifecycle Management (PLM) serves as particularly useful tool for product data and knowledge management. The deployment of a PLM tool has been seen as an important facilitator for achieving success with the modular design strategy. One of the biggest challenges in implementing new techniques is how to handle existing knowledge and / or information. This paper describes how modular product structure can be implemented in PLM and connects relevant product knowledge at different levels when the product is generated in the process of new product development. This will enable to trace the information across products to compare existing information and reuse for future products

A Framework for distributed Workflows, Peer-to-Peer and PLM/PDM collaborations to support OEMs and SMEs

The recent development of communication technology and hardware devices has made it possible for messages to reach anybody, anywhere at anytime. One such technology is Peer-to-Peer (P2P) networking. The use of this technology however, is limited to mobile phones and swapping music in the internet for home users. To deploy this development into industry, there is a requirement to improve to sharing information in a collaborative and distributed product developing environment. The aim of this paper is therefore to discuss the development of a framework to enhance the integrity of data sharing and efficiency of network communication for the collaboration of Small and Medium Enterprises (SMEs) and Original Equipment Manufacturers (OEMs). The main technologies used in the framework are the P2P decentralized network together with workflow technology and Product Life Management System (PLM). In addition, the paper is also highlighted the security issues arise to implement the P2P applications within the framework

Influence of uncertainty in dielectric properties on the design performance of a tunable composite right/left handed leaky wave antenna

Uncertainties of the order of 8 % in the accuracy of lithography used to define co-planar waveguides on ferroelectric thin films lead to a similar uncertainty in the value of relative permittivity of the film extracted from measurements. When such films are used as the tunable elements in a tunable composite right/left handed leaky wave antenna, such variations of the capacitance of the varactors can lead to a reduction in radiation and total efficiency around of the order of 1 dB in 5 dB due to the appearance of a bandgap in the frequency response

Investigation of HNF-1B as a diagnostic biomarker for pancreatic ductal adenocarcinoma

Background: Diagnosing pancreatic ductal adenocarcinoma (PDAC) in the setting of metastasis with an unknown primary remains very challenging due to the lack of specific biomarkers. HNF-1B has been characterized as an important transcription factor for pancreatic development and was reported as a biomarker for clear cell subtype of PDAC. Methods: To investigate the diagnostic role of HNF-1B for PDAC, we used tissue microarray (TMA) and immunohistochemistry (IHC) to characterize HNF-1B expression in a large cohort of carcinomas, including 127 primary PDACs, 47 biliary adenocarcinomas, 17 metastatic PDACs, and 231 non-pancreaticobiliary carcinomas. Results: HNF-1B was expressed in 107 of 127 (84.3%) of PDACs, 13 of 15 (86.7%) of cholangiocarcinomas, 13 of 18 (72%) of ampullary carcinomas, and 13 of 14 (92.9%) of gallbladder adenocarcinomas. Notably, HNF-1B was expressed in 16 of 17 (94.1%) of metastatic PDACs. Among the non-pancreaticobiliary cancers, HNF-1B was expressed in ~ 77% clear cell carcinomas of the kidney and ovarian clear cell carcinomas. Gastroesophageal, lung, and prostate adenocarcinomas occasionally expressed HNF-1B in up to 37% cases. HNF-1B was completely negative in hepatocellular, colorectal, breast, and lung squamous cell carcinomas. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of HNF-1B for primary pancreaticobiliary carcinoma is 84, 68, 66, 85, and 75%, respectively. HNF-1B expression was not significantly associated with overall survival in patients with PDAC, but tumor size \u3e /=2 cm and high tumor grade were significantly associated with worse overall survival in multivariate analyses. Conclusions: HNF-1B may be used in surgical pathology to aid the diagnosis of metastatic pancreatic and biliary carcinoma with a panel of other markers to exclude lung, kidney, prostate, and Mullerian origins

Star Formation Rate Indicators in Wide-Field Infrared Survey Preliminary Release

With the goal of investigating the degree to which theMIR luminosity in theWidefield Infrared Survey Explorer (WISE) traces the SFR, we analyze 3.4, 4.6, 12 and 22 {\mu}m data in a sample of {\guillemotright} 140,000 star-forming galaxies or star-forming regions covering a wide range in metallicity 7.66 < 12 + log(O/H) < 9.46, with redshift z < 0.4. These star-forming galaxies or star-forming regions are selected by matching the WISE Preliminary Release Catalog with the star-forming galaxy Catalog in SDSS DR8 provided by JHU/MPA 1.We study the relationship between the luminosity at 3.4, 4.6, 12 and 22 {\mu}m from WISE and H\alpha luminosity in SDSS DR8. From these comparisons, we derive reference SFR indicators for use in our analysis. Linear correlations between SFR and the 3.4, 4.6, 12 and 22 {\mu}m luminosity are found, and calibrations of SFRs based on L(3.4), L(4.6), L(12) and L(22) are proposed. The calibrations hold for galaxies with verified spectral observations. The dispersion in the relation between 3.4, 4.6, 12 and 22 {\mu}m luminosity and SFR relates to the galaxy's properties, such as 4000 {\deg}A break and galaxy color.Comment: 10 pages, 3 figure

Electrochemically Generated Acid and Its Containment to 100 Micron Reaction Areas for the Production of DNA Microarrays

An addressable electrode array was used for the production of acid at sufficient concentration to allow deprotection of the dimethoxytrityl (DMT) protecting group from an overlaying substrate bound to a porous reaction layer. Containment of the generated acid to an active electrode of 100 micron diameter was achieved by the presence of an organic base. This procedure was then used for the production of a DNA array, in which synthesis was directed by the electrochemical removal of the DMT group during synthesis. The product array was found to have a detection sensitivity to as low as 0.5 pM DNA in a complex background sample