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Aeromonas spp are the most common devastating fish bacterial pathogens associated with a wide range of cultured freshwater fish resulting in mass mortality. The present study plots the Aeromonas research output in fish during 1998–2020 by means of the Web of Science (WoS) database. A total of 1944 publications were used for this investigation to evaluate the scientific productivity in this selected subject area. Global publication share, rank, and its development in terms of research and bibliometric indices such as overall citations, h-index, and average citation per paper were also analysed. China ranked first in terms of total publications among the most productive countries and accounted for 26.18% of the total publications. The network visualisation map revealed that the lead collaborating country was China; however, the collaboration between countries was found to be very less. Lokta's law was noticed not to be valid for Aeromonas research. The study showed that the funding granted to the research investigations and their research productivity in terms of total publications were correlated positively. The results of the current study displayed the trend of "Aeromonas infection in fish" related research investigations published during 1998–2020. This study also highlighted the focused research areas on this topic on which the future areas of research can be determined by fellow researchers in this specific field. This study also suggests enhancing the research efforts on this bacterial pathogen which has the potential to implicate huge economic loss to the fish farming population in the world. [ABSTRACT FROM AUTHOR]

This paper points out the error in the HJB equation in a previous published paper and presents the correct HJB equation. We show that the Hamilton-Jacobi-Bellman (HJB) equation for the salesperson's problem is not correct because of the effect of a positive discount factor for the infinite-horizon risk-sensitive control. As a result, their analyses and insights are incorrect. We present the correct HJB equation for the salesperson's problem, which is a partial differential equation. Thus, further investigation on the correct solutions is needed to better understand the problem. History: K. Sudhir served as the senior editor for this article. Funding: X. Kong acknowledges financial support from the Natural Science Foundation of China [Grants 72101243 and 72091212/72091210] and the Fundamental Research Funds for the Central Universities [Grant WK2040000033]. The research of Y. Yu is partially supported by the Hong Kong Research Grants General Proposal Fund [Grant 9043432], CityU [Grants 7005793 and 7005591], and the Laboratory for AI-Powered Financial Technologies. [ABSTRACT FROM AUTHOR]

In this paper, we consider optimization problems over closed embedded submanifolds of Rn , which are defined by the constraints c(x) = 0. We propose a class of constraint-dissolving approaches for these Riemannian optimization problems. In these proposed approaches, solving a Riemannian optimization problem is transferred into the unconstrained minimization of a constraint-dissolving function (CDF). Different from existing exact penalty functions, the exact gradient and Hessian of CDF are easy to compute. We study the theoretical properties of CDF and prove that the original problem and CDF have the same first-order and second-order stationary points, local minimizers, and Łojasiewicz exponents in a neighborhood of the feasible region. Remarkably, the convergence properties of our proposed constraint-dissolving approaches can be directly inherited from the existing rich results in unconstrained optimization. Therefore, the proposed constraint-dissolving approaches build up short cuts from unconstrained optimization to Riemannian optimization. Several illustrative examples further demonstrate the potential of our proposed constraint-dissolving approaches. Funding: The research of N. Xiao and K.-C. Toh is supported by the Ministry of Education of Singapore Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010]. The research of X. Liu is supported in part by the National Natural Science Foundation of China [Grants 12125108, 11971466, 12288201, 12021001, and 11991021]; the National Key R&D Program of China [Grants 2020YFA0711900 and 2020YFA0711904]; and the Key Research Program of Frontier Sciences, Chinese Academy of Sciences [Grant ZDBS-LY-7022]. [ABSTRACT FROM AUTHOR]

The paper develops a new dynamic choice modeling framework and applies it to examine owner decisions in P2P sharing markets. Peer-to-peer (P2P) sharing marketplaces enable sharing of idle resources. When a renter requests an owner's resource, the owner needs to decide whether to accept the request: accepting it helps the owner fill up the idle periods of the resource and generate a payoff but reduces the flexibility to serve a future request for a longer duration. This paper develops a framework to uncover the tradeoffs faced by owners on these platforms when making acceptance decisions, which can be used by owners to optimize their decisions and by platforms to improve their operations. The model explicitly accommodates two types of owners: some are attentive to the availability states of their cars and forward-looking, whereas others myopically make the acceptance decisions. Applying the model to unique data from a leading peer-to-peer car sharing platform in China, we obtain similar sizes of both types of owners and find that female, experienced, and younger owners are more likely to be strategic. The results also reveal the differentiated preferences of the two types of owners toward their renters. Building on model estimates, we calibrate the option value of each day in the future (i.e., the value of having the day available) for strategic owners and find it to first increase, then decrease. Two counterfactual analyses are conducted. The first analysis shows that if the platform imposes a minimum rental duration, strategic owners may become more reluctant to accept requests, even if the current availability state entails a higher expected payoff. The second analysis shows that with better understanding of its owners, the platform can greatly improve the matching efficiency by optimal (re)allocation of rental requests, a move that benefits almost all participants in the business. History: K. Sudhir served as the senior editor for this article. Funding: This work was supported by the Hong Kong Polytechnic University [Grant A0038957] and the National University of Singapore [Research Grant R-316-000-104-133]. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mksc.2022.1369. [ABSTRACT FROM AUTHOR]

We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (respectively, allows limited) constraint violation for distributions residing (respectively, not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type 1 Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness, and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, ϕ-divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on an adaptive network lot-sizing problem demonstrate the modeling flexibility of our proposal and its emphases on globalized robustness to constraint violation. History: Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: Z. Chen was supported by [General Research Fund Grant 9043424, NSFC/RGC Joint Research Scheme N_CityU105/21] from the Hong Kong Research Grants Council. S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"] and the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0274) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0274). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We study adjustable distributionally robust optimization problems, where their ambiguity sets can potentially encompass an infinite number of expectation constraints. Although such ambiguity sets have great modeling flexibility in characterizing uncertain probability distributions, the corresponding adjustable problems remain computationally intractable and challenging. To overcome this issue, we propose a greedy improvement procedure that consists of solving, via the (extended) linear decision rule approximation, a sequence of tractable subproblems—each of which considers a relaxed and finitely constrained ambiguity set that can be iteratively tightened to the infinitely constrained one. Through three numerical studies of adjustable distributionally robust optimization models, we show that our approach can yield improved solutions in a systematic way for both two-stage and multistage problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Financial support by the Early Career Scheme from the Hong Kong Research Grants Council [Project No. CityU 21502820], the CityU Start-Up Grant [Project No. 9610481], the CityU Strategic Research Grant [Project No. 7005688], the National Natural Science Foundation of China [Project No. 72032005], and Chow Sang Sang Group Research Fund sponsored by Chow Sang Sang Holdings International Limited [Project No. 9229076] is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0181), as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0181). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Under the current universal trend towards larger grant sizes in research funding systems, we focus on how large of a grant size is appropriate. We study the directional returns to scale (RTS) to assess whether current grant sizes are the most productive. We take the General Program of the National Natural Science Foundation of China (NSFC) as an example and select three samples of physics, geography and management for an empirical study. We find that the optimal input direction and the most productive grant size scale is different for the three disciplines; based on the current grant size, physics should not expand the grant size and team size input, geography should further increase the grant size to improve performance and management should further expand the team size rather than the grant size. In this paper, we demonstrate a new method to calculate the optimal direction, which is the lowest rate of congestion, according to the characteristics of the General Program. Based on these results, we also calculate the most productive scale size. This method has certain value for project management. [ABSTRACT FROM AUTHOR]

Corporate bonds with large increases in implied volatility over the past month underperform those with large decreases in implied volatility by 0.6% per month. In contrast to existing studies that show implied volatility changes carry information about fundamental news, our evidence suggests that implied volatility changes contain information about uncertainty shocks to the firm. Our results are consistent with the notion that informed traders with new information about firm risk prefer to trade in the option market and the corporate bond market underreacts to this information. This paper was accepted by Haoxiang Zhu, finance. Funding: We thank the Canadian Derivatives Institute for financial support. J. Cao and X. Zhan acknowledge generous financial support of the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants GRF 14501720, 14500919]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4379. [ABSTRACT FROM AUTHOR]

This study identifies information accessibility as a determinant of corporate innovation. Using the sudden termination of Google's search services in China, we find a persistently large negative effect on the intensity and quality of innovation among firms relying on foreign knowledge. The results are stronger for firms in industries dominated by foreign technology, in regions with local web filters, and for firms with fewer alternative sources of foreign information. We also find that the affected firms cite fewer foreign patents and their innovation efficiency declines after Google's exit. Overall, the results are consistent with the view that access to information is an important driver of technological progress. This paper was accepted by Kay Giesecke, finance. Funding: D. Kong acknowledges financial support from the National Natural Science Foundation of China [Grants 71802160, 71991473, and 71772178] and the Major Project of National Social Science Foundation of China [Grant 21ZDA010]. C. Lin acknowledges financial support from the National Natural Science Foundation of China [Grant 71790601]. L. Wei acknowledges financial support from the Research Grant Council of the Hong Kong Special Administrative Region, China [Project T35/710/20R]. J. Zhang acknowledges financial support fromthe PRC Ministry of Education Youth Project for Humanities and Social Science Research [Grant 18XJC630008]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2021.4224. [ABSTRACT FROM AUTHOR]

The sustainable development of higher education in China has been a key priority for the national, social, economic and political development. Responding to the severe competition in various university ranking systems, most universities in China have set aims to enhance their sustainability in research and publication. There has been a prominent conflict that young scholars are expected to be productive, with publications in academic journals, competitive in receiving national and municipal research grants, and prestigious in the national and international arenas, or they will be terminated by the ‘six-year-up-or-out’ policy. Recent reform in higher education that calls for a sustainable development for young researchers is a strategy to revert the side effects from global university ranking systems by nurturing young researchers in their early academic lives, enhancing their productivity in research and publication internationally, and enhancing their global competitiveness without harming sustainability in academic development. This research explored (i) the difficulties that most young scholars face in sustainable academic research development, (ii) the factors that enhance or inhibit research productivity of young researchers, and (iii) the work lives in their early-career development in China. A qualitative study was conducted with data obtained from semistructured, in-depth interviews of 24 young university researchers from three provinces and a municipality in China. Findings show that factors that relate to sustainable research productivity are individual attributes, discipline attributes, institutional attributes and policy attributes. Lastly, suggestions for policy making in higher education and for improving sustainable research development of young researchers in China are provided and implications for future research are discussed. [ABSTRACT FROM AUTHOR]