27,498 research outputs found

    Does international patent collaboration have an effect on entrepreneurship?

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    .Entrepreneurship is one of the main pillars of growth in any economy. Achieving a high rate of entrepreneurship in a region has become the priority objective of governments and firms. However, in many cases, new firm creation is conditioned by relations or collaboration in innovation with agents from other countries. Previous literature has analyzed the mechanisms that foster entrepreneurship. This paper attempts to shed light on the influence of international patent collaboration (IPC) on entrepreneurial activity at country level taking into account the timing of this relationship. An empirical study is proposed to verify whether IPC leads to greater entrepreneurship and to analyze the gestation period between international patenting actions and firm creation. Using the Generalized Method of Moments, the two hypotheses proposed were tested in a data panel of 30 countries for the period 2005–2017. Results show the influence of IPC in promoting entrepreneurship in the same year, but especially in the following year. The study offers implications for entrepreneurs and public agents. IPC affects the integration and interaction of international agents in a country, favors the production of new knowledge, and increases positive externalities in a territory. All this facilitates the creation of new companies with a high innovative component.S

    Sponsorship image and value creation in E-sports

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    .E-sports games can drive the sports industry forward and sponsorship is the best way to engage consumers of this new sport. The purpose of this study is to examine the effect of sponsorship image and consumer participation in co-creation consumption activities on fans’ sponsorship response (represented by the variables interest, purchase intention and word of mouth) in e-sports. Four antecedent variables build sponsorship image (i.e., ubiquity of sport, sincerity of sponsor, attitude to sponsor and team identification). A quantitative approach is used for the purposes of this study. Some 445 questionnaires were filled in by fans who watch e-sports in Spain; these are analyzed using partial least squares structural equation modeling (PLS-SEM). The outcomes show that sponsor antecedents are crucial factors if a sponsor wants to change their sponsorship image and influence sponsorship response, and that it is also possible to use participation to improve responsesS

    Exploring environmental concerns on digital platforms through big data: the effect of online consumers’ environmental discourse on online review ratings

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    By deploying big data analytical techniques to retrieve and analyze a large volume of more than 2.7 million reviews, this work sheds light on how environmental concerns expressed by tourists on digital platforms, in the guise of online reviews, influence their satisfaction with tourism and hospitality services. More specifically, we conduct a multi-platform study of Tripadvisor.com and Booking.com online reviews (ORs) pertaining to hotel services across eight leading tourism destination cities in America and Europe over the period 2017–2018. By adopting multivariate regression analyses, we show that OR ratings are positively influenced by both the presence and depth of environmental discourse on these platforms. Theoretical and managerial contributions, and implications for digital platforms, big data analytics (BDA), electronic word-of-mouth (eWOM) and environmental research within the tourism and hospitality domain are examined, with a view to capturing, empirically, the effect of environmental discourse presence and depth on customer satisfaction proxied through online ratings

    Analysis of reliable deployment of TDOA local positioning architectures

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    .Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applicationS

    Formal Innovations to Clinical Cognitive Science and Assessment

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    Mathematical modeling is increasingly driving progress in clinical cognitive science and assessment. Mathematical modeling is essential for detecting certain effects of psychopathology – mental disturbance--through comprehensive understanding of tell-tale cognitive variables such as workload capacity and efficiency in using capacity, and their contrast under quantitative measurement. The research paradigm guiding this formal clinical science is outlined. An example using a distinctive cognitive abnormality in schizophrenia – taking longer to cognitively represent encountered stimulation – provides an illustration of a quantitative framework for studying intricate mental health-impairing phenomena. Added benefits of formal developments, among others, include symptom description and prediction, new methods of cognitive- and statistical-science grounded clinical assessment over time, both for individuals and treatment regimens, and refinement of the cognitive-function side of clinical functional neuroimaging

    Examination of a Brief, Self-Paced Online Self-Compassion Intervention Targeting Intuitive Eating and Body Image Outcomes among Men and Women

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    Ideals for appearance and body image are pervasive in Western culture in which men and women are portrayed with unrealistic and often unattainable standards (Ferguson, 2013; Martin, 2010). Exposure and reinforcement have created a culture of social acceptance and internalization of these ideals, contributing to pervasive body image disturbance (i.e., body dissatisfaction; Fallon et al., 2014; Stice, 2001; Thompson & Stice, 2001; Thompson et al., 1999). Research has suggested that body dissatisfaction is expressed differently across sexes (Grossbard et al., 2008), with attention to thin ideals among women and muscular ideals among men. Body dissatisfaction has been linked to numerous poor outcomes, including dieting, unhealthy weight control behaviors, disordered eating, and increased psychopathology. Although dieting is one of the primary mechanisms employed to reduce body dissatisfaction (Thompson & Stice, 2001), research has shown that such efforts are contraindicated as dieting predicts weight gain over time (Pietiläinen et al., 2012) as well as preoccupation with food, disordered eating, eating disorders, emotional distress, and higher body dissatisfaction (Grabe et al., 2007; Johnson & Wardle, 2005; Neumark- Sztianer et al., 2006; Paxton et al., 2006; Tiggemann, 2005). Restrictive dietary behaviors suppress physiological cues to eat (e.g., hunger) that presents a vulnerability to eating in response to alternative cues, both internal (e.g., emotions) and external (e.g., availability of food). Intuitive eating is a non-restrictive approach to eating that encourages adherence to internal physiological cues to indicate when, what, and how much to eat (Tylka, 2006) and has demonstrated an inverse relationship with disordered eating, restrained eating, food preoccupation, dieting, body dissatisfaction, and negative affect (Bruce & Ricciardelli, 2016). Self-compassion, relating to oneself in a caring and supportive manner (Neff, 2003a), has been proposed as a pathway to increase intuitive eating and reduce body dissatisfaction (Neff & Knox, 2017; Schoenefeld & Webb, 2013; Webb & Hardin, 2016). Research has highlighted the efficacy of self-compassion interventions in addressing weight-related concerns (Rahimi-Ardabili et al., 2018) as well as brief experiential exercises for reducing body dissatisfaction (Moffitt et al., 2018). Additionally, there is a growing body of evidence supporting the efficacy of internet-based self-compassion interventions (Mak et al., 2018; Kelman et al., 2018; Nadeau et al., 2020). The purpose of the current study was to examine the effectiveness of a brief, self-paced online self-compassion intervention targeting body image and adaptive eating behaviors and potential mechanisms of change (e.g., self-compassion and psychological flexibility) among undergraduate men and women. This study also examined outcomes among men and women in the area of self-compassion, body dissatisfaction, and intuitive eating as research has highlighted the need to determine who benefits more from self-compassion interventions (Rahimi-Ardabili et al., 2018). The study compared a one-hour, self-guided online self-compassion intervention to an active control condition. The intervention was comprised of psychoeducation, experiential exercises, and mindfulness practice designed to increase self-compassion surrounding body image and eating behaviors. In contrast, the active control condition consisted of self-care recommendations and self-assessments for nutrition, exercise, and sleep. The study was administered over three parts (e.g., baseline, intervention, and follow-up) in which variables of interest were assessed at each time point. Outcome variables included self-compassion, intuitive eating, disordered eating, body appreciation, muscle dysmorphia, internalized weight bias, fear of self-compassion, and psychological inflexibility. Participants were randomized on a 2:1 intervention to control ratio at the second time point in order to make comparisons between groups while simultaneously having sufficient power for examining mediation and moderation within the treatment condition. Overall, 1023 individuals (64% women, Mage = 18.9, 67.4% white) signed informed consent and participated in at least one part of the study whereas 101 participants (71% women, Mage = 19.3, 71% white) completed all three study portions. As predicted, self-compassion was correlated with all variables of interest, and all study variables were correlated with each other (p < .01). In contrast to hypothesized outcomes, the self-compassion condition failed to demonstrate improvements across time or between conditions on all study outcomes. These results persisted when participants were screened for levels of intuitive eating as well. Contrary to prediction, internalized weight bias, muscle dysmorphia, and fear of self-compassion demonstrated increased levels within the intervention condition and decreases in the control condition. There were significant gender differences on multiple outcome variables, with men demonstrating higher levels of self-compassion and body appreciation whereas women endorsed higher levels of disordered eating, internalized weight bias, muscle dysmorphia, and psychological inflexibility. Additionally, there were significant gender interactions for internalized weight bias, body appreciation, and muscle dysmorphia. The interactions existed such that men demonstrated increased internalized weight bias and muscle dysmorphia across time whereas women displayed decreased weight bias and muscle dysmorphia. The opposite pattern was found within body appreciation; women demonstrated increased body appreciation across time while men reported decreased levels of body appreciation. Despite this study’s intent to examine underlying mechanisms of change, the condition in which participants were randomly selected did not have any relationship, positive or negative, with the outcome variables of interest. As such, mediation within the current study was not conducted as it would violate statistical assumptions required to examine this hypothesis. Finally, upon examining the moderating relationship of fear of self-compassion between self-compassion and outcome variables, there were main effects for self-compassion on intuitive eating, emotional eating, internalized weight bias, body appreciation, and psychological inflexibility as well as main effects of fear of self-compassion on psychological inflexibility. There were significant interactions for intuitive eating and emotional eating, such that as fear of self-compassion increased, the effect of self-compassion on intuitive eating decreased, and the effect of self-compassion on reducing emotional eating behaviors decreased. Overall, the brief, self-paced online intervention delivered in the current study did not prove to be an effective means for improving self-compassion, intuitive eating, body appreciation, disordered eating, muscle dysmorphia, and psychological inflexibility. Nevertheless, the relationships between self-compassion and outcome variables of interest throughout the study mirror that of the existing literature. Findings from this study, in general, were also consistent with differences between men and women despite a gap in the research for intervention outcomes. Although fear of self-compassion demonstrated a moderating effect on the relationship between self-compassion and intuitive eating as well as emotional eating, this does not account for the lack of significant findings. The context surrounding this study, such as the COVID-19 pandemic, provided a considerable challenge to examining the efficacy of the current intervention. However, the findings of this study suggest future research will likely need to identify ways to enhance the delivery of experiential exercises that encourage engagement, provide a safe and warm environment for participants, and create flexibility and willingness surrounding painful and difficult experiences in order to undermine internalized and socially accepted beliefs about body image and eating behaviors

    Comparative study of classification algorithms for quality assessment of resistance spot welding joints from preand post-welding inputs

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    Resistance spot welding (RSW) is a widespread manufacturing process in the automotive industry. There are different approaches for assessing the quality level of RSW joints. Multi-input-single-output methods, which take as inputs either the intrinsic parameters of the welding process or ultrasonic nondestructive testing variables, are commonly used. This work demonstrates that the combined use of both types of inputs can significantly improve the already competitive approach based exclusively on ultrasonic analyses. The use of stacking of tree ensemble models as classifiers dominates the classification results in terms of accuracy, F-measure and area under the receiver operating characteristic curve metrics. Through variable importance analyses, the results show that although the welding process parameters are less relevant than the ultrasonic testing variables, some of the former provide marginal information not fully captured by the latter

    Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process

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    In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov- ernment through the ELKARTEK program (OILTWIN project, ref. KK- 2020/00052)

    The influence of blockchains and internet of things on global value chain

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    Despite the increasing proliferation of deploying the Internet of Things (IoT) in global value chain (GVC), several challenges might lead to a lack of trust among value chain partners, e.g., technical challenges (i.e., confidentiality, authenticity, and privacy); and security challenges (i.e., counterfeiting, physical tempering, and data theft). In this study, we argue that Blockchain technology, when combined with the IoT ecosystem, will strengthen GVC and enhance value creation and capture among value chain partners. Thus, we examine the impact of Blockchain technology when combined with the IoT ecosystem and how it can be utilized to enhance value creation and capture among value chain partners. We collected data through an online survey, and 265 UK Agri-food retailers completed the survey. Our data were analyzed using structural equation modelling (SEM). Our finding reveals that Blockchain technology enhances GVC by improving IoT scalability, security, and traceability when combined with the IoT ecosystem. Which, in turn, strengthens GVC and creates more value for value chain partners – which serves as a competitive advantage. Finally, our research outlines the theoretical and practical contribution of combining Blockchain technology and the IoT ecosystem
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