Prediction of primary energy demand in China based on AGAEDE optimal model

In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the model(both linear and quadratic) are optimized by AGA using factors,such as GDP,population,urbanization rate,and RD inputs together with energy consumption structure,that affect demand.Since the spurious regression phenomenon occurs for a wide range of time series analysis in econometrics,we also discuss this problem for the current artificial intelligence model.The simulation results show that the proposed model is more accurate and reliable compared with other existing methods and the China's energy demand will be 5.23 billion TCE in 2020 according to the average results of the AGAEDE optimal model.Further discussion illustrates that there will be great pressure for China to fulfill the planned goal of controlling energy demand set in the National Energy Demand Project(2014—2020).     

Fundamental insights on when social network data are most critical for conservation planning

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state-dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small-scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species-poor sites are subsets of species-rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small-scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un-nested and when social networks are likely to be centralized.     

第四次工业革命对环境治理体系建设的影响与挑战

以人工智能为代表的第四次工业革命发展迅速,特别是能源和新材料技术的突破,极大可能改变人类现有能源格局,并对建立在化石能源思维下的传统环境治理体系带来巨大影响。为此,本文梳理了第四次工业革命代表性的科技进展,分析了其对国家环境治理体系的积极影响:(1)将改变建立在化石能源基础上的工业、农业和城市形态,打破经济发展对资源、能源和土地的高度依赖,彻底告别环境污染和生态破坏;(2)将真正实现面向现在和未来的数据精准决策,显著改善人类应对不确定性环境风险能力。另一方面,由于人工智能会极大提升公民个体对复杂科学技术和社会规则的掌握能力,会改变传统治理结构中政府—市场—社会三元力量对比,给政府在环境治理领域的政策制定和执行带来新的挑战:(1)公众的能力足以使得他们对政府做出的任何不利于自己的政策提出挑战,政府信息公开和透明决策压力空前;(2)社交媒体使得公众对政府及主流媒体信息的关注大大下降,对政府如何利用新型社交媒体进行环境保护宣传教育提出了新的挑战,同时也对社会管理部门如何培育环保社会组织,弥补政府影响力下降所产生的空白提出了新的课题;(3)个体可以同时具有律师、会计、记者等处理复杂社会问题的能力,挑战政府维护社会公平稳定的利益裁量权,对环境司法公正提出更高要求。当前,中国正在进行环境体制改革创新,追求环境治理体系和治理能力现代化,如何关注、分析并积极适应由于技术变革引起的经济基础和上层建筑变化,避免制度资源错配,对于我们这样一个正在进行工业化的后发型国家尤为重要。文章最后建议,要高度关注这个领域的研究前沿进展,在制定发展规划与改革措施过程中,以未来的技术环境为出发点;要加强治理创新,引导技术朝着有利环境保护的方向发展;要加强技术对国家治理体系和治理能力双向影响研究,推进政府改革与创新,为人民提供更好的公共产品。     

A case study on artificial intelligence based cleaner production evaluation system for surface treatment facilities

The metal finishing industry is water intensive. Surveys of South African metal finishing companies indicate that water consumption is as high as 400 L/m² of metal surface treated, whilst best available practice can achieve less than 10 L/m². The industry uses hazardous chemicals such as chrome VI, cadmium, nickel and cyanide. If consumption of these chemicals can be optimized, quantities of heavy metals released into the environment will be reduced. In some cases where cleaner production techniques were applied by local companies, heavy metals have been completely eliminated from effluents discharged to municipal sewers, which represent a significant benefit to the urban environment. This benefit was accompanied by significant reduction in the use of chemicals, with a concomitant cost saving and competitive advantage to the companies concerned.A Danish environmental aid initiative promoted cleaner production in the South African metal finishing industry. Local consultants were trained by Danish experts in this field. The general methodology was to conduct an audit of the chemical, water, human resource and environment aspects of the company and compare it to best available practice. Once the review was completed, a detailed feasibility was performed on systems and equipment required to reduce chemical consumption, water consumption, human resources and environmental impact. Applied to a number of South African companies, these methods have typically achieved reductions of the order of 90% in water use and 50–60% in the use of chemicals.There were difficulties in applying the Danish methodology to South African metal finishing companies, as it makes use of quantitative indices derived from the process operations. The companies are often small and technically unsophisticated, and do not have ready access to the process data that are needed. An alternate system is required to simplify the evaluation and optimization process. This paper proposes a case study on a fuzzy-logic operator based evaluation system that outputs the cleaner production status of the company. The model is compared to an established cleaner production tool.     

Rules of supply and demand regulate recruitment to food in an ant society

The process by which ant scouts move a group of nestmates toward a newly discovered food site is called recruitment. In this paper, I report on the interactions between scouts and nestmates that result in a graded recruitment response to graded food quality in the fire ant, Solenopsis invicta. Twelve experimental groups composed of 100 fire ant workers and 50 fire ant larvae were established (three experimental groups per colony × four stock colonies). Each experimental group was placed in a shallow, artificial nest with a glass cover. After a 48-h period of food deprivation, experimental groups were exposed to one of three concentrations of sugar water. Behavioral interactions between scouts and nestmates in each group were videotaped at 10× magnification for 20 min. Detailed behavioral data on a total of 120 scouts (10 scouts per experimental group) and ~1,000 nestmates (~90 nestmates per experimental group) were transcribed from the videotapes using standard play and frame-by-frame techniques. Throughout the recruitment process, scouts employed six discrete behaviors to inform nestmates of the location and quality of a food site. Scouts laid incoming trails, waggled their heads, increased walking tempo, stroked nestmates with their antennae, advertised with a brief food display, and led groups of nestmates to the food site by laying outgoing trails. In turn, nestmates assessed the food sample with antennae, then responded to or resisted recruitment based on the quality of food advertised, their employment status and their level of hunger. In summary, recruitment was an emergent property based on competent supply and demand decisions made face-to-face inside the nest rather than on the trail or at the food site.Communicated by J. Heinze     

Supervisors' emotional exhaustion and abusive supervision: The moderating roles of perceived subordinate performance and supervisor self‐monitoring

Drawing from conservation of resources theory, this study aims to create new knowledge on the antecedents of abusive supervision. Results across 2 independent field studies within a manufacturing context (Study 1) and a customer service context (Study 2) consistently demonstrated a 3‐way interaction pattern, such that supervisors' experiences of emotional exhaustion, perceived subordinate performance, and self‐monitoring were jointly associated with subordinates' abusive supervision perceptions. A supplementary scenario experiment further corroborated this pattern. Together, the present studies illustrate a contingency model of abusive supervision's origins, highlighting emotional exhaustion as an important risk factor that is particularly likely to trigger abusive behavior among supervisors with lower (rather than higher) self‐monitoring who are faced with a relatively underperforming subordinate. As such, this research advances the abusive supervision literature by offering new insights into the complex resource conservation processes that may give rise to subordinates' abuse perceptions.     

From manager's emotional intelligence to objective store performance: Through store cohesiveness and sales‐directed employee behavior

The relationships among manager's emotional intelligence, store cohesiveness, sales‐directed employee behavior, and objective store performance were investigated. Non‐managerial sales employees of a large retail electronics chain in South Korea (N = 1611) rated the emotional intelligence of their own store managers as well as the group cohesiveness within their stores. Store managers (N = 253) separately rated the sales‐directed behavior of their employees. Objective sales data were collected one month later for each store. No direct relationship between manager emotional intelligence and objective store performance was found. Instead, the results supported the hypothesized four‐variable, three‐path mediation model: store manager's emotional intelligence was related to store cohesiveness, which in turn was related to the sales‐directed behavior of the frontline employees, which ultimately predicted the objective performance of the stores. Manager emotional intelligence and store cohesiveness are seen as intangible organizing resources or socio‐psychological capital for non‐managerial store employees. Implications for future research and more effective management of retail firms are discussed. Copyright © 2015 John Wiley & Sons, Ltd.     

The bright side of emotional labor

Emotional labor (expressing emotions as part of one's job duties, as in “service with a smile”) can be beneficial for employees, organizations, and customers. Meta‐analytical summaries reveal that deep acting (summoning up the appropriate feelings one wants to display) generally has positive outcomes. Unlike surface acting (faking emotions), deep acting does not harm employee well‐being, and deep acting is positively related with job satisfaction, organizational commitment, job performance, and customer satisfaction. Emerging research also suggests that a third form of emotional labor, natural and genuine emotional labor, is a frequently used emotional labor strategy that has positive effects for both employees and customers. We examine how identity processes shape how employees experience emotional labor, and we maintain that when employees identify with their roles, emotional labor augments and affirms their identity. Person‐job fit is an important moderator that influences whether emotional labor enhances or hinders employee well‐being. Emotional labor may also have positive outcomes when organizations grant more autonomy and adopt positive display rules that call for the expression of positive emotions. Recent research also indicates that emotional labor strategies may improve leadership effectiveness. Research opportunities on the bright side of emotional labor are abundant. Copyright © 2015 John Wiley & Sons, Ltd.     

试论高校图书馆开展竞争情报服务与教育

社会对竞争情报人才和竞争情报的需求越来越迫切,各种形式的竞争情报教育与服务发展越来越迅速.目前高校图书馆由于竞争情报意识不强、用户对图书馆的信任程度不够、高校竞争情报工作处在起步阶段等诸多不利因素,因而要增强情报意识和竞争情报宣传与培训,提高竞争情报人员的综合素质.     

基于Multi-agent的矿工情绪稳定性模型构建

为解决由矿工情绪稳定性水平低下引起的不安全行为问题,基于复杂适应系统（CAS）理论和多主体建模与仿真（ABMS）的方法,构建矿工情绪稳定性水平提升模型。通过调节班组协调程度、组织公平程度、安全心理培训水平、受教育程度、个性特征和作业环境舒适度,在NETLOGO仿真平台上分析矿工意志强度、情绪安全倾向度以及管理者领导能力对矿工情绪稳定性水平的影响。研究表明:矿工的情绪稳定性水平提升系统是一种典型的复杂适应系统,矿工情绪稳定性水平是各主体属性相互作用的结果,作用程度存在差异。因此,煤炭企业应考虑各种因素,通过控制和改善各主体的行为以提高矿工情绪稳定性水平。