Development of a project level performance measurement model for improving collaborative design team work

This research explored a new direction of improving collaborative design by performance measurement. More specifically, a novel 3-dimensional performance measurement model is developed and the purpose of this model is to help project managers improve team collaboration by indicating strengths and weaknesses of team members during the project development process. Considering the complexity of collaborative design work, a multiple criteria model is proposed to reflect the design dynamics, which highlights five performance indicators: efficiency, effectiveness, collaboration, management skills and innovation. These five indicators are mostly influenced by role-based performance measurement criteria (the second dimension). Design and development process (time) is also considered (the third dimension). This 3D model allows all involved design participants to measure work performance at any time during the product development process. In order to develop this model, the role-based task analysis and industrial survey methods were utilized. Three groups of role-based product design and development performance measurement criteria were identified for measuring design performance of the top managers, middle managers and individual designers in a project team. A 3-dimensional performance measurement method was proposed to calculate final performance scores from a performance measurement matrix. The proposed model was evaluated as a tool which can support project managers to reduce potential design and collaboration risks and increase confidence in decision-making process. The model has been discussed on implementing in a web-based application for measuring design performance throughout the product design and development proces

Modeling Sketching Primitives to Support Freehand Drawing Based on Context Awareness

Freehand drawing is an easy and intuitive method for thinking input and output. In sketch based interface, there lack support for natural sketching with drawing cues, like overlapping, overlooping, hatching, etc. which happen frequently in physical pen and paper. In this paper, we analyze some characters of drawing cues in sketch based interface and describe the different types of sketching primitives. An improved sketch information model is given and the idea is to present and record design thinking during freehand drawing process with individuality and diversification. The interaction model based on context is developed which can guide and help new sketch-based interface development. New applications with different context contents can be easily derived from it and developed further. Our approach can support the tasks that are common across applications, requiring the designer to only provide support for the application-specific tasks. It is capable of and applicable for modeling various sketching interfaces and applications. Finally, we illustrate the general operations of the system by examples in different applications

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS

Monocular depth estimation for glass walls with context: a new dataset and method

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We annotate the depth levels of transparent glass walls by propagating the context depth values within neighboring flat areas, and the glass segmentation mask and instance level line segments of glass edges are also provided. On the other hand, a tailored monocular depth estimation method is proposed to fully activate the glass wall contextual understanding. First, we propose to exploit the glass structure context by incorporating the structural prior knowledge embedded in glass boundary line segment detections. Furthermore, to make our method adaptive to scenes without structure context where the glass boundary is either absent in the image or too narrow to be recognized, we propose to derive a reflection context by utilizing the depth reliable points sampled according to the variance between two depth estimations from different resolutions. High-resolution depth is thus estimated by the weighted summation of depths by those reliable points. Extensive experiments are conducted to evaluate the effectiveness of the proposed dual context design. Superior performances of our method is also demonstrated by comparing with state-of-the-art methods. We present the first feasible solution for monocular depth estimation in the presence of glass walls, which can be widely adopted in autonomous navigation

Analgesic Effect of Zanthoxylum nitidum Extract in Inflammatory Pain Models Through Targeting of ERK and NF-κB Signaling

BackgroundZanthoxylum nitidum (Roxb.) DC., also named Liang Mianzhen (LMZ), one kind of Chinese herb characterized with anti-inflammatory and relieving pain potency, which is widely used to treat injuries, rheumatism, arthralgia, stomach pain and so on in China. But its mechanism related to the anti-hyperalgesic has not been reported. The aim of this study was to investigate the analgesic activity of Liang Mianzhen on mice with Complete Freund adjuvant (CFA)-induced chronic inflammatory pain. Meanwhile, the peripheral and central mechanisms of analgesic effect of Liang Mianzhen were further examined via observing the effects of Liang Mianzhen on the signal pathway associated with inflammatory induced hyperalgesia.MethodsThe inflammatory pain model was established by intraplantar injection of CFA in C57BL/6J mice. After 1 day of CFA injection, the mice were treated with LMZ (100 mg/kg) for seven consecutive days, and the behavioral tests were measured after the daily intragastric administration of LMZ. The morphological changes on inflamed paw sections were determined by hematoxylin eosin (HE) staining. Changes in the mRNA expression levels of tumor necrosis factor (TNF-α), interleukin-6 (IL-6), interleukin-1β (IL-1β) and nuclear factor κB p65 (NF-κBp65) were measured on day seven after CFA injection by using real-time quantitative PCR analysis and enzyme linked immunosorbent assay (ELISA) method, respectively. Moreover, immunohistochemistry and western blotting were used to detect extracellular regulated protein kinases 1/2 (ERK1/2) and NF-κB signal pathway activation.ResultsThe extract of LMZ (100 mg/kg) showed a significant anti-inflammatory and analgesic effect in the mice model. The paw edema volume was significantly reduced after the administration of LMZ compared to CFA group, as well as the paw tissues inflammatory damage was relived and the numbers of neutrophils in mice was reduced significantly. The CFA-induced mechanical threshold and thermal hyperalgesia value were significant improved with LMZ treatment at day three to day seven. We also found the mRNA levels of TNF-α, IL-1β, IL-6 and NF-κBp65 were down-regulate after 7 days from the LMZ treatment compared to CFA group. Meanwhile, LMZ significantly suppressed over-expression of the phosphorylation of ERK1/2 and NF-κBp65 in peripheral and central.ConclusionThe present study suggests that the extract of LMZ attenuates CFA-induced inflammatory pain by suppressing the ERK1/2 and NF-κB signaling pathway at both peripheral and central level

Soil chemistry, metabarcoding, and metabolome analyses reveal that a sugarcane—Dictyophora indusiata intercropping system can enhance soil health by reducing soil nitrogen loss

IntroductionGreater amounts of fertilizer are applied every year to meet the growing demand for food. Sugarcane is one of the important food sources for human beings.MethodsHere, we evaluated the effects of a sugarcane—Dictyophora indusiata (DI) intercropping system on soil health by conducting an experiment with three different treatments: (1) bagasse application (BAS process), (2) bagasse + DI (DIS process), and (3) the control (CK). We then analyzed soil chemistry, the diversity of soil bacteria and fungi, and the composition of metabolites to clarify the mechanism underlying the effects of this intercropping system on soil properties.Results and discussionSoil chemistry analyses revealed that the content of several soil nutrients such as nitrogen (N) and phosphorus (P) was higher in the BAS process than in the CK. In the DIS process, a large amount of soil P was consumed by DI. At the same time, the urease activity was inhibited, thus slowing down the loss of soil in the DI process, while the activity of other enzymes such as β-glucosidase and laccase was increased. It was also noticed that the content of lanthanum and calcium was higher in the BAS process than in the other treatments, and DI did not significantly alter the concentrations of these soil metal ions. Bacterial diversity was higher in the BAS process than in the other treatments, and fungal diversity was lower in the DIS process than in the other treatments. The soil metabolome analysis revealed that the abundance of carbohydrate metabolites was significantly lower in the BAS process than in the CK and the DIS process. The abundance of D(+)-talose was correlated with the content of soil nutrients. Path analysis revealed that the content of soil nutrients in the DIS process was mainly affected by fungi, bacteria, the soil metabolome, and soil enzyme activity. Our findings indicate that the sugarcane–DIS intercropping system can enhance soil health

Exploring Barriers and Opportunities in Adopting Crowdsourcing Based New Product Development in Manufacturing SMEs

Crowdsourcing is an innovative business practice of obtaining needed services, ideas, or content or even funds by soliciting contributions from a large group of people (the ‘Crowd’). The potential benefits of utilizing crowdsourcing in product design are well-documented, but little research exists on what are the barriers and opportunities in adopting crowdsourcing in new product development (NPD) of manufacturing SMEs. In order to answer the above questions, a Proof of Market study is carried out on crowdsourcing-based product design under an Innovate UK funded Smart project, which aims at identifying the needs, challenges and future development opportunities associated with adopting crowdsourcing strategies for NPD. The research findings from this study are reported here and can be used to guide future development of crowdsourcing-based collaborative design methods and tools and provide some practical references for industry to adopt this new and emerging collaborative design method in their business

Using design performance measurement as a strategy to improve collaborative design performance

This research investigates how to improve collaborative design performance by the implementation of performance measurement. A Design Performance Measurement (DPM) framework is developed to measure collaborative design performance and identify strengths and weaknesses of a design team during a design process. Additionally, it has been found that decision making efficiency is the most important DPM criteria for measuring design team member’s collaborative design efficiency; delivering to the design brief for effectiveness; ability to clear team goal/objectives for collaborative; decision making skill for management; and ability to deliver design competitive advantage for innovation. These results can be used to conduct a precise and accurate DPM in a design project team during a design process

Development of a design performance measurement matrix for improving collaborative design during a design process

Purpose of this paper: the purpose of this paper is to investigate how to measure collaborative design performance and, in turn, improve the final design output during a design process, with a clear objective to develop a Design Performance Measurement (DPM) matrix to measure design project team members¹ design collaboration performances.Design/methodology/approach: the methodology adopted in this research uses critical literature reviews, in-depth focus groups interviews and a questionnaire survey.Findings: the main finding of this study is a DPM matrix that addresses five DPM indicators: efficiency, effectiveness, collaboration, management skill, and innovation, and 25 detailed DPM criteria. It was found that decision-making efficiency is the most important DPM criteria for collaborative design efficiency; plus delivering to the brief for effectiveness; clear team goal/objectives for collaboration; decision-making ability for management skill; and competitive advantage for innovation.Research limitations/implications: as the present study was focused on exploring DPM during a design process, some key DPM criteria, which are not measurable during a design development process, were not included in this study. The proposed multi-feedback approach for DPM matrix implementation needs to be validated in future research.Practical implications: the DPM matrix can be applied to support a design manager in measuring and improving collaborative design performance during a design process, by reviewing and modifying collaborative design development, identifying the design team strengths and weaknesses, improving team communication, and suggesting suitable responsive actions.Original/values: the major contribution of this study is the investigation and development of a DPM matrix to measure collaborative design performance during a design process<br/