All pure bipartite entangled states can be semi-self-tested with only one measurement setting on each party

It has been known that all bipartite pure quantum states can be self-tested, i.e., any such state can be certified completely by initially measuring both subsystems of this state by proper local quantum measurements and subsequently verifying that the correlation between the measurement choices and the outcomes satisfies a specific condition. In such a protocol, a key feature is that the conclusion can still be reliable even if involved quantum measurements are untrusted, where quantum nonlocality is crucial and plays a central role, and this means that each party has to conduct at least two different quantum measurements to produce a desirable correlation. Here, we prove that when the underlying Hilbert space dimension is known beforehand, an arbitrary $d\times d$ bipartite pure state can be certified completely (up to local unitary transformations) by a certain correlation generated by a single measurement setting on each party, where each measurement yields only $3d$ outcomes. Notably, our protocols do not involve any quantum nonlocality. We believe that our result may provide us a remarkable convenience when certifying bipartite pure quantum states in quantum labs.Comment: 9 pages, comments are welcom

More Goals, More Money? An Investigation of Stretch Goals on a Crowdfunding Platform

An increasing body of entrepreneurship research highlights the prevalence of effectuation as an effective strategy in uncertain environments, yet little is known about how investors react to entrepreneurs’ effectucation strategies. In this study, we examine how stretch goals, an effectuation strategy where entrepreneurs adaptively adopt new goals on top of the initial and predetermined goal, affect investors’ decisions. On the one hand, stretch goals may mitigate perceived uncertainty by demonstrating an entrepreneur’s continuous commitment and sharp sense-making of the changing opportunities and whereas, on the other hand, it may raise questions about the entrepreneur’s ability to reach the target, thus increasing uncertainty. An analysis of data from one of the largest crowdfunding platforms in Southeast Asia shows that stretch goal adoption has an instantaneous negative effect on fundraising performance. However, as the fundraising unfolds, entrepreneurs can mitigate, and even reverse the negative effect. Moreover, further exploration demonstrates mechanisms that can worsen or alleviate the initial negative effect. Our study highlights the countervailing effects of stretch goals and has important implications for entrepreneurs that use stretch goals as a strategy to optimize the chances of success in resource acquisition

Stretch Goals and Crowdfunding Success: The Trade-off between Likelihood to Invest and Contribution Amount (ERF)

In order to help campaign creators collect more funds beyond their initial target, crowdfunding platforms provide mechanisms that allow them to adopt more goals, stretch goals, beyond their original target. Our study investigate how stretch goals affect potential backers’ decision-making process. Drawing on the literature on goal setting and decision under uncertainty, we propose that stretch goals are perceived differently during the funding decision process, and thus may lead to opposing effects at each stage of the process. We leverage granular data on funding decisions from a leading crowdfunding platform in Southeast Asia, and our preliminary results show that stretch goals are negatively associated with likelihood to fund while they are positively associated with contribution amount. Our study has the potential to offer a more nuanced understanding of how stretch goals affect crowdfunding performance by unpacking their influence on the funding decision-making process.

Managing emergency situations with lean and advanced manufacturing technologies: an empirical study on the Rumbia typhoon disaster

Purpose – This study aims to examine the impact of lean manufacturing (LM) on the financial performance of companies affected by emergency situations. It additionally explores the role of advanced manufacturing technologies (AMTs) in complementing LM to enhance financial performance in emergency and nonemergency situations.Design/methodology/approach – Both survey and archival data were collected from 219 manufacturing companies in China. With longitudinal data collected before and after an emergency situation (i.e. Typhoon Rumbia), regression analysis was conducted to investigate the effects of LM and AMTs on financial performance in different contexts.Findings – Our results reveal an inverted U-shaped relationship between LM and financial performance in the context of emergency. We also found that AMTs exerted a positive moderation effect on the inverted U-shaped relationship, indicating high levels of AMTs that mitigated the inefficiency of LM in coping with supply chain emergencies.Research limitations/implications – Through simultaneous investigation of LM and AMTs as bundles of practices and their fit with different contexts, this study takes a systems approach to fit that advances the application of contingency theory in the Operations Management literature to more complex patterns of fit. Originality/value – This study illuminates how AMTs support LM practices in facilitating organizational performance in different contexts. Specifically, this study unravels the interaction mechanisms between AMTs and LM in influencing financial performance in emergency and non-emergency situations

Data analytics capability and servitization: the moderated mediation role of bricolage and innovation orientation

Purpose – Despite the potential influence of data analytics capability on servitization, our understanding of the underlying mechanisms of this influence remains unclear. This study explores how data analytics capability affects servitization by examining the mediation effect of bricolage and the conditional role of innovation orientation.Design/methodology/approach – This study employs the moderated mediation method to examine the proposed research model with archival data and multiplerespondent surveys from 1,206 top managers of 402 manufacturing firms in the Yangtze River Delta area in China.Findings – Bricolage partially mediates the positive relationship between data analytics capability and servitization, and innovation orientation positively moderates this effect.Practical implications – Manufacturers can leverage bricolage to materialize data analytics capability for servitization. Manufacturers should also pursue an innovation orientation to fully glean the benefits of bricolage in transforming data analytics capability into servitization.Originality/value – This study opens the black box of how data analytics capability affects servitization by revealing the underlying mechanism of bricolage and the boundary condition role of innovation orientation for this mechanism. It offers valuable insights for practitioners to leverage data analytics to improve servitization through developing bricolage and cultivating a culture of innovation orientation

CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure

Comprehensive analysis of codon bias in 13 Ganoderma mitochondrial genomes

IntroductionCodon usage bias is a prevalent phenomenon observed across various species and genes. However, the specific attributes of codon usage in the mitochondrial genome of Ganoderma species remain unknown.MethodsIn this study, we investigated the codon bias of 12 mitochondrial core protein-coding genes (PCGs) in 9 Ganoderma species, including 13 Ganoderma strains.ResultsThe codons of all Ganoderma strains showed a preference for ending in A/T. Additionally, correlations between codon base composition and the codon adaptation index (CAI), codon bias index (CBI) and frequency of optimal codons (FOP) were identified, demonstrating the impact of base composition on codon bias. Various base bias indicators were found to vary between or within Ganoderma strains, including GC3s, the CAI, the CBI, and the FOP. The results also revealed that the mitochondrial core PCGs of Ganoderma have an average effective number of codons (ENC) lower than 35, indicating strong bias toward certain codons. Evidence from neutrality plot and PR2-bias plot analysis indicates that natural selection is a major factor affecting codon bias in Ganoderma. Additionally, 11 to 22 optimal codons (ΔRSCU>0.08 and RSCU>1) were identified in 13 Ganoderma strains, with GCA, AUC, and UUC being the most widely used optimal codons in Ganoderma. By analyzing the combined mitochondrial sequences and relative synonymous codon usage (RSCU) values, the genetic relationships between or within Ganoderma strains were determined, indicating variations between them. Nevertheless, RSCU-based analysis illustrated the intra- and interspecies relationships of certain Ganoderma species.DiscussionThis study deepens our insight into the synonymous codon usage characteristics, genetics, and evolution of this important fungal group

Predictors of Sales and the Covid-19 Disruption: Evidence from an Online Marketplace

E-commerce platforms have heavily relied on predictive machine learning models to leverage the massive data generated in daily operations. However, by altering online customer purchasing behavior, Covid-19 has distorted sales prediction models used by sellers and e-commerce platforms, which may lead them to inaccurate strategic decisions. Using a dataset comprised of electronics products from Amazon, the preliminary results shows that the importance of several predictors of online sales has changed after the beginning of the pandemic. In particular, the importance of negative related factors was found to have significantly increased after the start of Covid-19. Furthermore, adding aspect-based sentiments was found to significantly improve sales forecasting especially during the period after the beginning of Covid-19. The study contributes to the literature evaluating the effects of Covid-19 on e-commerce by providing an in-depth understanding of these effects from an unexplored perspective of prediction models

Platform Owners’ Selective Entry into Complementary Markets: Balancing Multiple Value Capture Sources

While prior studies have highlighted value capture related incentives behind platform owners’ entry into complementary markets, they have predominantly focused on value capture from a single source but neglected that a platform owner may need to balance the tension between multiple value capture sources when making entry decisions. With an empirical study on Amazon, this research aims to examine platform owners’ selective entry into complementary markets based on a holistic view on the value captured from different sources. We propose that a platform owner is more likely to enter the low- and high-sales markets compared to mediate-sales markets, and the proportion of high-quality complementors in the markets could moderate above relationship. This study contributes to the literature on platform owners’ entry strategies and value management in platform ecosystem by extending the research focus from single source of value capture to the dynamics between multiple value sources