To compare the impact attenuating capabilities between ice hockey helmets manufactured with and without XRD impact protection foam, worn with and without a XRD skullcap, at reducing sub-concussive ...head accelerations.
Quasi-experimental laboratory.
Ice hockey helmets were fit onto a Hybrid III 50th Head Form Head and dropped 25 times onto the left temporal side for each condition: XRD foam helmet, XRD foam helmet with XRD skullcap adjunct, non-XRD foam helmet, and non-XRD foam helmet with XRD skullcap adjunct. The helmets were dropped from a height that resulted in sub-concussive linear accelerations (25–80 g's). Using a tri-axial accelerometer, peak linear accelerations (g) were measured, and the average was used to compare impact attenuation properties across the four conditions.
The highest linear accelerations were observed in the XRD foam helmet without skullcap (32.97 ± 0.61 g) and were significantly greater (p < 0.001) than the XRD helmet with skullcap (21.38 ± 0.76 g). The helmet without XRD foam elicited the lowest peak linear accelerations (16.10 ± 0.73 g) which were significantly lower than the XRD foam helmet regardless of whether the skullcap was added (p < 0.001).
Although sub-concussive loads are potentially just as dangerous, much of the research regarding helmet and skullcap efficacy appears to be at high concussive impacts; <70 g's. The findings suggest that helmets with incorporated XRD foam, either within the design or added as an adjunct, are less effective at attenuating linear accelerations at sub-concussive levels than the low-density foam helmet.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
2.
Towards Open Set Deep Networks Bendale, Abhijit; Boult, Terrance E.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
2016-June
Conference Proceeding
Deep networks have produced significant gains for various visual recognition problems, leading to high impact academic and commercial applications. Recent work in deep networks highlighted that it is ...easy to generate images that humans would never classify as a particular object class, yet networks classify such images high confidence as that given class - deep network are easily fooled with images humans do not consider meaningful. The closed set nature of deep networks forces them to choose from one of the known classes leading to such artifacts. Recognition in the real world is open set, i.e. the recognition system should reject unknown/unseen classes at test time. We present a methodology to adapt deep networks for open set recognition, by introducing a new model layer, OpenMax, which estimates the probability of an input being from an unknown class. A key element of estimating the unknown probability is adapting Meta-Recognition concepts to the activation patterns in the penultimate layer of the network. Open-Max allows rejection of "fooling" and unrelated open set images presented to the system, OpenMax greatly reduces the number of obvious errors made by a deep network. We prove that the OpenMax concept provides bounded open space risk, thereby formally providing an open set recognition solution. We evaluate the resulting open set deep networks using pre-trained networks from the Caffe Model-zoo on ImageNet 2012 validation data, and thousands of fooling and open set images. The proposed OpenMax model significantly outperforms open set recognition accuracy of basic deep networks as well as deep networks with thresholding of SoftMax probabilities.
In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the process of detecting ...actions is naturally one of observation and refinement: observing moments in video, and refining hypotheses about when an action is occurring. Based on this insight, we formulate our model as a recurrent neural network-based agent that interacts with a video over time. The agent observes video frames and decides both where to look next and when to emit a prediction. Since backpropagation is not adequate in this non-differentiable setting, we use REINFORCE to learn the agent's decision policy. Our model achieves state-of-the-art results on the THUMOS'14 and ActivityNet datasets while observing only a fraction (2% or less) of the video frames.
This paper develops an operational perspective of supply chain sustainability, by considering it as a risk management process. It explores the nature of sustainability-related supply chain risks, ...distinguishes them from typical supply chain risks and develops an analytical process for their management. An empirical study is conducted to generate insights about how sustainability-related risks should be managed in an integrated way. A mixed method approach is adopted for data collection and analysis. Through an extensive literature review and personal interviews, 30 risks across the three main pillars of sustainability (environmental, social and economic) are identified first. A large survey across different industrial sectors and two exploratory empirical case studies in two textile manufacturing companies are subsequently conducted to assess and analyse several dimensions of sustainability-related risk. The failure mode and effect analysis (FMEA) technique is utilised to assess the relative importance of the selected risks, to identify their potential causes and effects and test potential correlations between the identified risks. Based on the findings of the study, risk treatment strategies are proposed for all the identified sustainability-related supply chain risks. The findings show that endogenous environmental risks are perceived to be the most important across different industries and the interconnectedness between several sustainability-related risks is very high. This points to the need for integrated sustainability risk management approaches to facilitate the development of effective sustainable strategies.
•An operational notion of supply chain sustainability is adopted as a nexus of risks.•Sustainability-related risks are distinguished from typical supply chain risks.•A sustainability risk management framework is developed using the FMEA method.•Risk treatment strategies are proposed for sustainability-related risks.
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Convolutional neural networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are ...often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification. This paper focuses on large-scale scene recognition and makes two major contributions to tackle these issues. First, we propose a multi-resolution CNN architecture that captures visual content and structure at multiple levels. The multi-resolution CNNs are composed of coarse resolution CNNs and fine resolution CNNs, which are complementary to each other. Second, we design two knowledge guided disambiguation techniques to deal with the problem of label ambiguity: 1) we exploit the knowledge from the confusion matrix computed on validation data to merge ambiguous classes into a super category and 2) we utilize the knowledge of extra networks to produce a soft label for each image. Then, the super categories or soft labels are employed to guide CNN training on the Places2. We conduct extensive experiments on three large-scale image datasets (ImageNet, Places, and Places2), demonstrating the effectiveness of our approach. Furthermore, our method takes part in two major scene recognition challenges, and achieves the second place at the Places2 challenge in ILSVRC 2015, and the first place at the LSUN challenge in CVPR 2016. Finally, we directly test the learned representations on other scene benchmarks, and obtain the new state-of-the-art results on the MIT Indoor67 (86.7%) and SUN397 (72.0%). We release the code and models at https://github.com/wanglimin/MRCNN-Scene-Recognition.
A growing number of researchers and practitioners have placed supply chain resilience (SCRE) at the forefront of their research agendas due to an increased susceptibility to disruptive events in ...global supply chains. However, empirical research in this area has been affected by the lack of a validated measurement model. In this context, drawing on dynamic capability theory, this research develops a measurement instrument for SCRE. This research conducts a qualitative field study, followed by a quantitative survey. Content analysis is used to explain various dimensions in the qualitative field study, and partial least squares (PLS)-based structural equation modelling (SEM) is used to analyse the data collected in the quantitative survey. The research is conducted with three rounds of data collection and analyses. The results show that SCRE is a multidimensional and hierarchical construct, which consists of three primary dimensions: proactive capability, reactive capability and supply chain design quality. These three primary dimensions are further operationalized through twelve sub-dimensions. The findings also affirm that the SCRE scale potentially better predicts supply chain operational vulnerability (OV) and supply chain performance (SCP) and conforms to the “technical” and “evolutionary” fitness criteria of dynamic capability theory. The implications of these findings are discussed in the context of theory and practice. Limitations and future avenues of research are also discussed.
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At present, the main raw material for producing graphene is graphite ore. However, researchers actively seek alternative resources due to their high cost and environmental problems. Biomass waste has ...attracted much attention due to its carbon-rich structure and renewability, emerging as a potential raw material for graphene production to be used in sports equipment. However, further progress is required on the quality of graphene produced from waste biomass. This paper, therefore, summarizes the properties, structures, and production processes of graphene and its derivatives, as well as the inherent advantages of biomass waste-derived graphene. Finally, this paper reviews graphene's importance and application prospects in sports since this wonder material has made sports equipment available with high-strength and lightweight quality. Moreover, its outstanding thermal and electrical conductivity is exploited to prepare wearable sensors to collect more accurate sports data, thus helping to improve athletes' training levels and competitive performance. Although the large-scale production of biomass waste-derived graphene has yet to be realized, it is expected that its application will expand to various other fields due to the associated low cost and environmental friendliness of the preparation technique.
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Omnichannel environments where customers shop online and offline at the same retailer are ubiquitous, and are deployed by online-first and traditional retailers alike. We focus on the relatively ...understudied domain of online-first retailers and the engagement of a key omnichannel tactic; specifically, introduction of showrooms (physical locations where customers can view and try products) in combination with online fulfillment that uses centralized inventory management. We ask whether, and if so, how, showrooms benefit the two most basic retail objectives: demand generation and operational efficiency. Using quasi-experimental data on showroom openings by
WarbyParker.com
, the leading and iconic online-first eyewear retailer, we find that showrooms: (1) increase demand overall
and
in the online channel as well; (2) generate
operational spillovers
to the
other
channels by attracting customers who, on average, have a higher cost-to-serve; (3) improve overall operational efficiency by increasing conversion in a sampling channel and by decreasing returns; and (4) amplify these demand and operational benefits in dealing with customers who have the most acute need for the firm’s products. Moreover, the effects we document strengthen with time as showrooms contribute not only to brand awareness but also to what we term
channel awareness
as well. We conclude by elaborating the underlying customer dynamics driving our findings and by offering implications for how online-first retailers might deploy omnichannel tactics.
This paper was accepted by Vishal Gaur, operations management.
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Sports data analysis is becoming increasingly large scale, diversified, and shared, but difficulty persists in rapidly accessing the most crucial information. Previous surveys have focused on the ...methodologies of sports video analysis from the spatiotemporal viewpoint instead of a content-based viewpoint, and few of these studies have considered semantics. This paper develops a deeper interpretation of content-aware sports video analysis by examining the insight offered by research into the structure of content under different scenarios. On the basis of this insight, we provide an overview of the themes particularly relevant to the research on content-aware systems for broadcast sports. Specifically, we focus on the video content analysis techniques applied in sportscasts over the past decade from the perspectives of fundamentals and general review, a content hierarchical model, and trends and challenges. Content-aware analysis methods are discussed with respect to object-, event-, and context-oriented groups. In each group, the gap between sensation and content excitement must be bridged using proper strategies. In this regard, a content-aware approach is required to determine user demands. Finally, this paper summarizes the future trends and challenges for sports video analysis. We believe that our findings can advance the field of research on content-aware video analysis for broadcast sports.
The aim of this work is not only to give an up-to-date review of perishable inventory models, but also of the joint key topics of publications from January 2012 until December 2015 in the research ...area of deteriorating inventory models. The advantage of this review is the ability to quickly find papers according to given key topics. Methodically, this paper is based on the literature review of Bakker et al. (2012). However, we slightly modify the classification of inventory models of perishable goods in our work, and extend the existing key topics.
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