常见人工甜味剂的介绍及其发展建议

人工甜味剂甜度高、无营养、热量低,已在食品工业中得到了广泛的应用,但人工甜味剂的安全性和有效性仍存在争议。本文介绍了糖精、安赛蜜、甜蜜素、阿斯巴甜、纽甜和三氯蔗糖等6种常见人工甜味剂的代谢特征及检测方法,并对甜味剂的发展提出建议,为人工甜味剂的进一步合理开发和安全使用提供参考依据。     

面向作战的侦察情报数据仿真系统设计

针对作战对抗环境下侦察情报数据仿真模拟需求，重点从组件化仿真建模与多维想定编辑入手，提出了一种灵活、可配置的侦察情报数据仿真系统设计及其实现方法。从装备与行为两方面对侦察实体进行抽象，建立组件化仿真模型；并结合实战应用场景从组织、任务、空间、时间与信息交互等多个维度开展侦察行动预案设计，驱动仿真引擎加载相关的规则模型进行仿真推演与解算，输出最终的仿真模拟结果。仿真模拟试验结果证明了该方法的实用性与有效性。     

Social anxiety as a moderator in consumer willingness to accept AI assistants based on utilitarian and hedonic values

This study develops a comprehensive research model to explain user willingness to accept AI assistants, and the acceptance path pertaining to this process. User data was used to test how the advantages of AI assistant (accuracy, responsiveness, compatibility, anthropomorphism, & affinity) influence consumer utilitarian and hedonic value, and explore how their willingness to accept AI assistants is affected by their value perceptions. This research also examines whether social anxiety moderates the relationship between AI assistant advantages and utilitarian/hedonic value. The study reveals that AI assistant advantages are important factors affecting the utilitarian/hedonic value perceived by users, which further influence user willingness to accept AI assistants. The relationships between AI assistant advantages and utilitarian and hedonic value are affected differently by social anxiety. Marketers and managers in the AI context can refer to the study methods to help improve AI assistants and develop more effective marketing strategies for product promotion.     

Employee experience –the missing link for engaging employees: Insights from an MNE's AI-based HR ecosystem

Analyzing multiple data sources from a global information technology (IT) consulting multinational enterprise (MNE), this research unpacks the configuration of a digitalized HR ecosystem of artificial intelligence(AI)-assisted human resource management (HRM) applications and HR platforms. This study develops a novel theoretical framework mapping the nature and purpose of a digitalized AI-assisted HR ecosystem for delivering exceptional employee experience (EX), an antecedent to employee engagement (EE). Employing the theoretical lenses of EX, EE, AI-mediated social exchange, and engagement platforms, this study's overarching aim of this article is to establish how AI-assisted HRM fits into an organization's ecosystem and, second, how it impacts EX and EE. Our findings show that AI-assisted applications for HRM enhance EX and, thus, EE. We also see increases in employee productivity and HR function's effectiveness. Implications for research and practice are also discussed.     

Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks

This paper examines the relevance of sentiment in predicting overall financial system instability using long-run short-term memory networks. Weekly data on the US financial system, consumer sentiment, producer sentiment, and investor sentiment is collected from 21 January 1994 to 27 December 2019, and different models are developed to predict the one-week-ahead levels of financial stress in the US financial system. We find that models using sentiment indices outperform those relying solely on historical financial stress and risk data. This result is robust to comparisons with an alternative deep learning method and out-of-sample predictions. It constitutes an argument in favor of behavioral finance and Minsky’s (Knell, 2015) financial instability hypothesis against the Efficient Market Hypothesis. As it concretely identifies the main indicators for predicting US financial stress one week in advance, the study provides relevant recommendations for policymakers and investors in terms of macroprudential policies and portfolio management.     

基于遗传算法的移动机器人路径规划研究综述

近年来，随着人工智能技术的不断发展，对移动机器人产业的开发和研究受到越来越多的重视。其中，路径规划作为移动机器人设计的关键核心技术之一，更是学术界、产业界关注和研究的热点问题。遗传算法作为智能仿生学算法在移动机器人路径规划中得到了广泛的应用。归纳总结了传统遗传算法、改进遗传算法、混合遗传算法在移动机器人路径规划研究中的应用情况，为相关专业技术人员了解遗传算法在移动机器人路径规划研究中的发展历程及算法应用提供一定的参考。     

基于MA2IDDPG算法的异构多无人机协同突防方法

为了形成智能化异构多无人机协同突防策略，提出了基于多智能体异步模仿深度确定性策略梯度算法的异构多无人机协同突防方法。首先，基于典型DDPG方法，采用异步并行框架进行改进，提高经验的收集效率。其次，分别构建基于专家经验知识的牵引性奖赏函数和基于任务结果的描述性奖赏函数。再次，通过分阶段训练后，使得深度神经网络产生的协同突防策略能够快速达到专家经验知识水平后，进一步提高对抗水平。最后，在仿真实验中，构建了异构多无人机协同突防环境，对改进方法和典型DDPG方法的对抗效果进行了对比。实验结果表明，MA2IDDPG方法能够有效产生多无人机协同突防策略，并且在训练阶段表现更为稳定且对抗效果更优。所设计的创新性MA2IDDPG方法框架可有效应用于无人机群控制领域，特别是对异构无人机群的智能控制方法研究具有重要参考意义。     

E-commerce sellers' ratings: Is user feedback adequate?

The literature on the theory of public procurement points out two well-known informational problems arising out of information asymmetry: (i) adverse selection and (ii) moral hazard. To reduce these issues and foster credibility and trust in the procurement process while maintaining quality and efficiency in public procurement, e-procurement platforms have turned to reputation or rating systems. Therefore, the research and design of such rating systems are crucial. In this study, we discuss the theoretical underpinnings of procurement and employ the information-theoretic, regression analysis, artificial neural network and principal component analysis (PCA) approaches to estimate the weights of the variables entering the rating system. Using real data from Government e-Marketplace, a business-to-business public e-commerce portal, we empirically determine the weights of the rating variables derived from the transaction-level and user feedback data for sellers. The weights obtained from the PCA are the most applicable compared with the other three methods. We compare the old rating system with the newly proposed design using the Wilcoxon signed-rank test. This results in a statistically significant difference between the two ratings. The canonical correlation and Wilks' trial reveal that the ratings derived from transaction-level data and user feedback are uncorrelated to a great extent. Hence, considering only transaction-level data or user feedback is unlikely to divulge sellers' intrinsic worth. E-commerce platforms can use this approach to quickly implement methods to obtain rating scores on a real-time basis for sellers on online platforms.     

Best friend or broken tool? Exploring the co-existence of humans and artificial intelligence in the workplace ecosystem

Artificial intelligence (AI) has become an important topic in business literature and strategy talk. Yet, much of this literature is normative and conceptual in nature. How organizational members perceive AI and the job role changes that come with it is, so far, largely unknown territory for both HR scholars and practitioners. We sought to investigate the relationship between humans and AI and conducted an in-depth exploratory study into the co-existence of humans and two early-stage AI-solutions, one for “low-status” automation and another for “high-status”; augmentation. We suggest that different organizational groups may engage in distinctly different sensemaking processes regarding AI, an important insight for successful HRM strategies when AI is being introduced into the workplace. Moreover, contrary to recent conceptual work, our findings indicate that AI-enabled automation and augmentation solutions may not be detached from nor exist in tension with each other. They are deeply embedded in organizational processes and workflows for which people who co-exist with the technologies must take ownership. Our findings, in part, go against discussions on AI “taking over” jobs or deskilling humans. We describe a more nuanced version of reality fluctuating around the various ways different organizational groups encounter different AI-solutions in their daily work. Finally, our study warns against unconditional technological enthusiasm, managerial ignorance of the nature of work that employees undertake in different organizational groups, and a neglect of the time and effort required to successfully implement AI-solutions that affect not only the home organization but also members of the broader ecosystem.     

Machine learning methods in finance: Recent applications and prospects

We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: (i) the construction of superior and novel measures, (ii) the reduction of prediction error, and (iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest many benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance.