Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation

Although AI-enabled customer relationship management (CRM) systems have gained momentum in healthcare to enhance performance, there is a striking dearth of knowledge on how such capabilities are formed and affect service innovation. The study adopted a mixed-method approach to investigate the underlying phenomena. This research infused resource-based theory, dynamic capability theory, and theory of productivity paradox to investigate how healthcare in India acquires AI-enabled CRM capabilities and enhances service innovation. We identified the facets of AI-enabled CRM capabilities using a case study and developed a framework for AI-enabled CRM capability and service innovation. This study noticed that customer service flexibility (CSF) is a missing link in this relationship. The findings of the quantitative study employing PLS-SEM reveal the linear relationships between AI-enabled CRM capability, CSF, and service innovation. This study explains the formation of AI-enabled CRM capabilities to fill the research gap and direct innovative performance in healthcare, which is an immediate need to sustain in a volatile environment. This study provides theoretical implications to enhance the research stream and practical implications for decision-makers.     

Exploring the factors,affordances and constraints outlining the implementation of Artificial Intelligence in public sector organizations

Artificial Intelligence (AI) is viewed as having great potential for the public sector to improve the management of internal activities and the delivery of public services. However, realizing its potential depends on the proper implementation of the technology, which is characterized by unique factors, that afford or constrain its use. What these factors are and how they affect AI implementation is still poorly understood, and scholars call for studies to add empirical evidence to the existing knowledge. This study relies on a case study methodology and, by adopting an abductive approach, applies a double theoretical perspective: the Technology-Organization-Environment (TOE) framework and the Technology Affordances and Constraints Theory (TACT). Drawing on these combined lenses, we develop a conceptual framework that extends previous studies by showing how AI implementation is the result of a combination of contextual factors that are deeply interrelated and, specifically, how AI-related factors bring new affordances and constraints to the application domain.     

Forecasting movements of stock time series based on hidden state guided deep learning approach

Stock movement forecasting is usually formalized as a sequence prediction task based on time series data. Recently, more and more deep learning models are used to fit the dynamic stock time series with good nonlinear mapping ability, but not much of them attempt to unveil a market system’s internal dynamics. For instance, the driving force (state) behind the stock rise may be the company’s good profitability or concept marketing, and it is helpful to judge the future trend of the stock. To address this issue, we regard the explored pattern as an organic component of the hidden mechanism. Considering the effective hidden state discovery ability of the Hidden Markov Model (HMM), we aim to integrate it into the training process of the deep learning model. Specifically, we propose a deep learning framework called Hidden Markov Model-Attentive LSTM (HMM-ALSTM) to model stock time series data, which guides the hidden state learning of deep learning methods via the market’s pattern (learned by HMM) that generates time series data. What is more, a large number of experiments on 6 real-world data sets and 13 stock prediction baselines for predicting stock movement and return rate are implemented. Our proposed HMM-ALSTM achieves an average 10% improvement on all data sets compared to the best baseline.     

世界森林资源保护及中国林业发展对策分析

根据联合国粮农组织的最新统计结果介绍了世界森林面积、森林拥有量最大的国家的情况和造成森林面积减少的主要原因;论述了世界各国为解决天然林日益减少的问题所采取的发展人工林的共同战略,把大力发展人工林作为21解决世纪对于环境和木材需求的根本措施。林业政策的修订和林业管理机构的改革是许多发展中国家和经济体制转轨国家正在或计划中的重要举措。文章还论述了我国的森林资源现状、林业所面临的问题及发展对策,作者建议采取,建立国家生态林补偿机制和森林生态效益补偿机制、建立保护森林资源、改善生态环境的多元化投资机制、调整林业政策、建立森林资源与环境综合核算体系等措施。     

千烟洲试区湿松人工林中降雨对矿质元素淋溶的影响

对千烟洲试验区湿地松人工降雨后,20种矿质元素淋溶变化变化进行了研究,结果表明：湿地松人工林中的营养元素平均浓度总变化趋势是：林冠穿透水＞树干流>大气降水。在淋溶成分中,林冠穿透水对湿地松人工林的养分物质年贡献较大,占整个淋溶总量70%以上。     

A new design method based on artificial bee colony algorithm for digital IIR filters

Digital filters can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filter having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be based on a global search procedure. Artificial bee colony (ABC) algorithm has been recently introduced for global optimization. The ABC algorithm simulating the intelligent foraging behaviour of honey bee swarm is a simple, robust, and very flexible algorithm. In this work, a new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm.     

基于神经网络的语音识别鲁棒性研究

语音识别技术已经取得令人鼓舞的成就,市场上也出现了许多相对成熟的语音识别产品,但是大部分语音识别系统仍局限于特定的环境,距离真正的实用化还相差很远。本文以提高语音识别系统的鲁棒性为目标,进行了相关的实验和研究。     

Containment control for second-order nonlinear multi-agent systems with aperiodically intermittent position measurements

In some real systems, the intermittent communications and the inaccurate velocity measurements are usually inevitable. To overcome these two communication limitations, this article aims at investigating the containment control problem for a class of second-order multi-agent systems with inherent nonlinear dynamics and aperiodically intermittent position measurements. Under the case that the velocity information is unavailable, a distributed filter is introduced for each second-order follower. Based on the distributed filter, a novel intermittent containment control protocol without velocity measurements is designed. Some sufficient conditions are derived under the common assumption that only relative position measurements between the neighbouring agents are utilized intermittently, and these conditions ensure that the second-order nonlinear multi-agent systems can achieve containment control. Furthermore, some simpler containment conditions are obtained for multi-agent systems with double-integrator dynamics under aperiodically intermittent communications. Finally, numerical simulations are provided to verify the effectiveness of the theoretical results.     

Big data governance of personal health information and challenges to contextual integrity

Pervasive digitization and aggregation of personal health information (PHI), along with artificial intelligence (AI) and other advanced analytical techniques, hold promise of improved health and healthcare services. These advances also pose significant data governance challenges for ensuring value for individual, organizational, and societal stakeholders as well as individual privacy and autonomy. Through a case study of a controversial public-private partnership between Royal Free Trust, a National Health Service hospital system in the United Kingdom, and Alphabet’s AI venture DeepMind Health, we investigate how forms of data governance were adapted, as PHI data flowed into new use contexts, to address concerns of contextual integrity, which is violated when personal information collected in one use context moves to another use context with different norms of appropriateness.     

基于专利计量的人工智能技术前沿识别及趋势分析

以德温特专利数据库(DII)收录的人工智能领域相关专利为数据源,运用专利计量法对专利数据进行时间、地域分布和专利权人分析,展示了人工智能领域研究实力的分布情况;利用信息可视化软件Cite Space绘制人工智能的知识图谱,挖掘出人工智能领域的关键技术、技术热点与前沿领域,并对人工智能的发展趋势进行了分析。