Technological Innovation Performance Analysis Using Multilayer Networks: Evidence from the Printer Industry

Department of Management EngineeringThe importance of collaboration and technology boundary spanning has been emphasized in other inquiries into technological innovation. Therefore, this research project first tried to investigate the effect of collaboration on technology boundary spanning. Then, we investigated the effect of collaboration and technology boundary spanning on technological innovation within a firm by using a multilayer network to analyze patent data. The aim of this paper is to provide new insight into the process of analyzing patent data using multilayer networks. This empirical study is based on a sample of 408 firms within the printer industry from 1996 to 2005. Starting with a theoretical discussion of R&D collaboration, technology boundary spanning and innovation performance, the importance of a firm???s collaboration and technology boundary spanning in its technology innovation performance was empirically analyzed using patent data. We followed changes in collaboration networks, technology class networks and the connection between them and tried to find the meaning of those changes in firms??? technology innovation performances. We used degree centrality within the collaboration network and the ratio of collaborated patents to the total number of patents in order to measure a firm???s collaboration and formulated technology boundary spanning represented by exploitation and exploration by using edges of the multilayer network. As dependent variables, we used the number of patents and the average number of citations received over three, five, and 10 years to measure the firm???s quantitative and qualitative innovation performance respectively. The results of the analysis can be summarized as follows: a firm???s collaboration has positive effects on both exploitation and exploration. Firms with more collaborations show higher quantitative innovation performances while firms with more collaborations exhibit lower qualitative innovation performance. Exploitation has a positive impact on a firm???s quantitative innovation performance while exploration has negative effects on a firm???s quantitative innovation performance. The relationship between a firm???s exploration activities and a firm???s qualitative innovation performance manifests as an inverted U-shape. On the other hand, a firm???s exploitation activities have a U-shape relationship with the firm???s qualitative innovation performance. The implication of this study is that multilayer networks can be used to analyze patent data. This study used multilayer networks to formulate the exploitation and exploration only. However, in further research it can be utilized to find the hub firms that fuse technologies.clos

Validity of consumer-based physical activity monitors and calibration of smartphone for prediction of physical activity energy expenditure

Accelerometry-based activity monitors have become the standard objective method for assessing physical activity (PA) in field-based research [1]. They are small, non-invasive, easy-to use, and provide an objective indicator of physical activity over extended periods of time. The main advantage from a research perspective is they provide an objective indicator of physical activity behavior, thereby avoiding common sources of error in subjective measurement (e.g., self-report). Because of their storage data capacity, it is possible to monitor behavior over extended periods of time and easy to download the information to a computer for processing. Numerous studies have been published on the reliability and validity of various accelerometry-based physical activity monitors. They have become widely accepted in the field. Over the years, advances in technology have contributed to dramatic improvements in the sophistication of accelerometry-based monitors. Most monitors today now use 3-dimensional accelerometers with higher sampling rates to provide more detailed information. The use of solid-state construction in most devices has also improved reliability and durability of this class of activity monitor. There have also been many advances in methodologies to process accelerometer data, including more standardization in protocols, better handling of missing data, and the application of complex, pattern recognition techniques to distinguish among various types of movement. These advances have collectively helped advance the science (and practice) of physical activity monitoring, but there are numerous challenges that remain to improve the utility\u27s accuracy of accelerometry-based physical activity monitors. One of the most challenging problems has been equating output from different accelerometry-based devices. In theory, accelerometry-based devices all measure the same thing (body acceleration). However, there is considerable variability in sensor properties, filtering, and scaling across different monitoring devices. This has made it impossible to directly compare data from competing instruments. While accelerometers internally measure acceleration in g-forces, most commercially-available devices report data using dimensionless units referred to as counts. Considerable research has been completed to calibrate the various devices against criterion measures, but presently it is not possible to directly equate output from one device to another in a systematic method. In recent years, many new, competing technologies, including built-in accelerometer Smartphones, have been released into the market. These have further compounded the challenge of comparing accelerometry-based devices. In many cases, these devices are released into the marketplace with little or no evidence of their validity. These accelerometry-based physical activity monitors have been used almost exclusively for research, but advances in technology have led to an explosion of new consumer-based activity monitors designed for use by individuals interested in fitness, health, and weight control. Examples include the BodyMedia FIT, Fitbit, Basis B1, Jawbone Up, NikeFuel band, DirectLife, PAM, and Smartphone applications. The development of these consumer-based monitors and applications has been driven, in large part, by the increased availability of low cost accelerometer technology that came about with the incorporation of accelerometers in the Wii. Refinement of other technologies (e.g., Bluetooth) and increased sophistication of websites and personalized social media applications also spurred the movement. These new accelerometry-based monitors provide consumers with the ability to estimate PA and energy expenditure (EE), and track data over time on a personalized web interface. Other technologies have also been adapted to capitalize on consumer interest in health and wellness. Pedometers developed originally to measure steps have been calibrated to estimate EE and to store data over time. Geographical positioning system (GPS) monitors, developed primarily for use in navigation, are now marketed to athletes and recreation enthusiasts to monitor speed and EE from activity. Heart rate monitors, originally marketed to athletes, have also been modified and marketed to appeal to more recreational athletes interested in health and weight control. These devices typically provide an easy-to-use web-interface to enable consumers to monitors PA and EE over time. The increased availability of monitoring technology provides consumers with options for self-monitoring, but these tools may also have applications for applied field-based research or intervention applications designed to promote PA in the population. To date, there is little or no information available to substantiate the validity of these consumer-based activity monitors and accelerometry-based mobile phone applications to assess PA and EE under free-living conditions. It is important to formally evaluate the validity of these various devices so information can be shared with researchers, fitness professionals, and consumers. The series of papers presented in this dissertation will provide a better understanding of the validity of consumer-based physical activity monitors and also evaluate the potential of estimating energy expenditure using built-in technology in Smartphones. The first study (Chapter 3) specifically evaluated the utility of various consumer-based, physical activity, monitoring tools against indirect calorimetry. A unique aspect of this study is that the consumer-based monitors were also directly compared with results from an Actigraph monitor, the most commonly used research-grade monitor used in the field. The second study (Chapter 4) explored the feasibility and utility of using embedded sensors in smart phones for objective activity monitoring. While consumer-monitors are designed to be convenient and easy to use, it is still somewhat burdensome for individuals to have to wear or carry another device. The embedded sensors in current Smartphones (e.g. accelerometers and gyroscope) may have similar (or better) utility than current research or consumer monitors. However, before this can be done, methods need to be developed to compile and use the raw sensor data. Machine learning techniques are widely used in pattern recognition technology and they have been increasingly used in accelerometry-based monitors to detect underlying patterns in the data. In this second study, machine learning techniques are tested to determine the most optimal way to classify physical activity patterns using Smartphone data. Once this is done it will be possible to develop prediction equations that can convert the raw data into estimates of energy expenditure and/or quantify levels of physical activity (see image below). The two studies will advance research on physical activity monitoring techniques and specifically determine the feasibility of utilizing embedded sensors in Smartphones to capture physical activity data under free living conditions. A comprehensive literature review is provided in the next section to summarize the progression of research in this area and to explain the methods for the present study

Validation of the Cosmed Fitmate for predicting maximal oxygen consumption

The primary purpose of this study was to assess the validity of the Cosmed Fitmate (FM) in predicting maximal oxygen consumption (VO2max), compared to the Douglas bag (DB) method. In addition, this study examined whether measuring submaximal VO2, rather than predicting it, can improve upon the prediction of VO2max. Thirty-two males and sixteen females (Mean ± SD: age 31 ± 10 yr, body mass 72.9 ± 13.0 kg, height 1.75 ± 0.09 m, BMI 23.4 ± 3 kg·m-2) volunteered to participate in the study. Each participant completed a submaximal and a maximal treadmill test using the Bruce protocol on two separate occasions. During the submaximal test, VO2max was predicted using the FM, while during the maximal test VO2max was measured using the DB method. The Cosmed Fitmate predicts VO2max by extrapolating the linear regression relating heart rate and measured VO2 to age-predicted maximum heart rate (HR = 220-age). This study also examined the validity of predicting VO2max by using the ACSM metabolic equations to estimate submaximal VO2. VO2max values from the FM, the DB method, and ACSM prediction equations were analyzed using repeated measures ANOVA and linear regression analyses. The level of significance was set at P \u3c 0.05 for all statistical analyses. There was no significant difference between VO2max predicted by the FM (45.6 ml·kg-1·min-1, SD 8.8) and measured by the DB method (46.5 ml·kg-1·min-1, SD 8.8) (p = 0.152). The results of this study showed that a strong positive correlation (r = 0.897) existed between VO2max predicted by the FM and VO2max measured by the DB method, with a standard error of the estimate (SEE) = 3.97 ml·kg-1·min-1. There was a significant difference in VO2max predicted by the ACSM metabolic equations (51.1 ml·kg-1·min-1, SD 7.98) and VO2max measured by the DB method (p = 0.01). The correlation between these variables was r = 0.758 (SEE = 5.26 ml·kg-1·min-1). These findings suggest that the Fitmate is a small, portable, and easy-to-use metabolic system that provides reasonably good estimates of VO2max, and that measuring submaximal VO2, rather than predicting it from the ACSM metabolic equations, improves the prediction of 2max

Highly efficient source for frequency-entangled photon pairs generated in a 3rd order periodically poled MgO-doped stoichiometric LiTaO3 crystal

We present a highly efficient source for discrete frequency-entangled photon pairs based on spontaneous parametric down-conversion using 3rd order type-0 quasi-phase matching in a periodically poled MgO-doped stoichiometric LiTaO3 crystal pumped by a 355.66 nm laser. Correlated two-photon states were generated with automatic conservation of energy and momentum in two given spatial modes. These states have a wide spectral range, even under small variations in crystal temperature, which consequently results in higher discreteness. Frequency entanglement was confirmed by measuring two-photon quantum interference fringes without any spectral filtering.Comment: 4 pages, 4 figures, to be published in Optics Letter

Alleviation of carbon catabolite repression in Enterobacter aerogenes for efficient utilization of sugarcane molasses for 2,3-butanediol production

Table S3. Comparison of fed-batch fermentation with EMY-01, EMY-68, EMY-70S, and EMY-70SP using sugarcane molasses

Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06