Achieving sustainability in manufacturing via organisational and operational learning

The contribution contained in this paper focuses on providing a possible framework or approach in respect of how manufacturing companies can economically introduce environmentally friendly practices to their production operations, whilst at the same time encouraging organisational and operational learning with the aim of eventual evolution of the firm into an eco-efficient concern. A key premise associated with the approach advocated is economical organisational and operational learning over time, thus providing a sequential movement of the company through various stages of cultural change and technological capability to eventually achieve eco-efficiency in its production activities. At each stage in the process, it is suggested that a balance must be maintained between on the one hand, a reduction in operational economics consistent with on the other hand, an improvement in the environmental sustainability of company activities. To that end, some previously suggested monitoring metrics are examined in the text for their efficacy and economic rapport via hypothetical examples of how they might be applied in practice to monitor movement towards industrial–environmental sustainability.     

Employees' self‐efficacy and perception of individual learning in teams: The cross‐level moderating role of team‐learning behavior

Despite the importance of employee learning for organizational effectiveness, scholars have yet to identify the factors that influence employees' perception of individual learning. This paper identified employees' self‐efficacy as a potential antecedent to their perception of individual learning in the context of teamwork. We also hypothesized that team‐learning behavior had a moderating effect on the relationship between employees' self‐efficacy and their perception of individual learning. We conducted a study of 236 teams working in a retail firm, comprising 236 team supervisors and 1397 employees, and analyzed the data using hierarchical linear modeling. This study revealed that employees' individual‐level self‐efficacy was positively associated with their perception of individual learning in teams. Additionally, team‐learning behaviors moderated the positive relationship between employees' self‐efficacy and the perception of individual learning. This study has theoretical and practical implications for a more nuanced understanding of the perception of individual learning in the context of teamwork. Copyright © 2016 John Wiley & Sons, Ltd.     

Can Desalination and Clean Energy Combined Help to Alleviate Global Water Scarcity?

The major present hindrance in using desalination to help alleviate global water scarcity is the cost of this technology, which, in turn is due to energy cost involved. This study examines historical trends in desalination and breaks up the cost of desalination into energy based and nonenergy based. It then develops the learning curves (relationship between cumulative production and market price) for desalination. Assuming that the photovoltaic (PV) technology will be the dominant form of energy used in the desalination process, the existing PV learning curve and desalination learning curve are combined to explore the viability of large‐scale adoption of desalination in the future. The world has been divided into seven regions and it is assumed that water demand from desalinated water will be met only within the 100‐km coastal belt. It is shown that, in most of the regions, other than sub‐Saharan Africa, Central America, and South Asia (where water tariffs are low), the desalination (without considering energy) becomes viable by 2040. For PV technology, less than 1 million MW per annum growth is required till 2050 to make it affordable. Globally, desalination with renewable energy can become a viable option to replace domestic and industrial water demand in the 100‐km coastal belt by 2050.     

基于深度学习方法的PM2.5精细化时空估算模型

提出一种基于深度学习方法的地面PM_2.5浓度时空估算模型（PM_2.5-DNN）,该模型基于葵花-8卫星反演的AOD数据,结合PM_2.5监测站和气象站点观测数据对北京市地面PM_2.5浓度进行了逐时的高精度模拟,同时将PM_2.5-DNN模型的模拟性能与当前的主流方法进行了对比研究.结果表明,使用PM_2.5-DNN模型估算的北京地区1km分辨率每小时地面PM_2.5浓度与地表监测站观测数据对比的一致性较好,模型估算精度可达到R²=0.88,性能优于当前的主流方法.本文所提出的方法适用于区域尺度PM_2.5浓度时空分布细粒度建模与估算,采用端到端的训练方式构建模型,为精细的PM_2.5浓度估算提供了一个简便而有效的方法模型.     

Computer-based training for safety: comparing methods with older and younger workers

INTRODUCTION: Computer-based safety training is becoming more common and is being delivered to an increasingly aging workforce. Aging results in a number of changes that make it more difficult to learn from certain types of computer-based training. Instructional designs derived from cognitive learning theories may overcome some of these difficulties. METHODS: Three versions of computer-based respiratory safety training were shown to older and younger workers who then took a high and a low level learning test. RESULTS: Younger workers did better overall. Both older and younger workers did best with the version containing text with pictures and audio narration. DISCUSSION: Computer-based training with pictures and audio narration may be beneficial for workers over 45 years of age. IMPACT ON INDUSTRY: Computer-based safety training has advantages but workers of different ages may benefit differently. Computer-based safety programs should be designed and selected based on their ability to effectively train older as well as younger learners.     

VOCs、细菌及颗粒物混合暴露致小鼠学习记忆障碍机制研究

室内空气污染的现状是多种污染源共存,挥发性有机物(volatile organic compounds, VOCs)、细菌及颗粒物等是典型室内空气污染物。神经行为学毒性是室内空气污染引起的敏感毒性作用之一。为了探讨VOCs、细菌及颗粒物混合暴露对小鼠学习记忆能力的影响及机制,选用雄性昆明小鼠70只,随机分为对照(G1)和2～7(G2～G7)号染毒组。采用水迷宫和抓力仪测定小鼠的学习记忆潜伏期和抓力,染毒结束后测定全脑中活性氧(ROS)、丙二醛(MDA),神经递质谷氨酸(Glu)、乙酰胆碱(Ach)含量以及胆碱能系统的乙酰胆碱转移酶(ChAT)和乙酰胆碱酯酶(TChE)活力,同时分析脑源性神经营养因子(BDNF)、胶质细胞神经营养因子(GDNF)及神经生长因子(NGF)的水平。结果显示,第6天,G5、G6及G7小鼠的抓力、逃避潜伏期及在原平台所在象限的探索时间较对照组存在显著差异,并且伴有ROS、MDA含量的显著升高,Glu含量的显著升高,Ach含量、ChAT及TChE活力的显著降低,以及神经营养因子的显著下调(P<0.05或P<0.01)。研究结果表明,VOCs、颗粒物及细菌混合暴露能够导致小鼠学习记忆障碍,混合暴露引起的氧化损伤诱导的神经兴奋或抑制性毒性作用,以及神经营养因子沿轴突逆向传递降低或中断两方面作用导致神经递质产生和释放异常,进而引起学习记忆能力降低。     

中国高新技术产业能源消耗与碳排放分析

基于2000-2007年中国高新技术产业经济发展与能源消耗(简称能耗)数据,统计分析了中国高新技术产业能耗强度变化趋势,并采用“学习曲线”方法论证了高新技术产业能耗的特征;在此基础上,采用政府间气候变化专门委员会(IPCC)温室气体指导方针的方法核算了中国高新技术产业的碳排放量及排放强度.结果表明:(1)中国高新技术产...     

基于深度学习技术的藻类智能监测系统开发

浮游藻类对水环境的变化非常敏感,是评价水环境质量的重要指示生物.传统的浮游藻类监测依靠人工采样分析,需要专业检测人员使用显微镜对藻细胞逐一鉴定并计数,耗时耗力且严重依赖检测人员的专业知识与鉴定经验,限制了浮游藻类监测工作的标准化推广和普及应用.利用神经网络模型建立了一套浮游藻类智能监测系统,该系统能够实现浮游藻类检测的...     

运用机器学习方法预测空气中臭氧浓度

臭氧(O_3)浓度变化与天然源、移动源和点源的排放量存在某些隐含的关联。根据臭氧浓度变化的特性,基于污染源在线排放数据、气象监测数据以及空气质量监测数据构造特征,运用机器学习方法进行逐小时臭氧浓度预测。该方法不仅充分利用了臭氧浓度变化时序数据,而且考虑了气象条件变化对污染物浓度变化的影响,最重要的是将点源排放氮氧化物这一臭氧生成的重要前体物纳入模型考虑。在金砖国家领导人厦门会晤前后(2017年8月31日至9月9日),运用该方法对厦门市溪东、洪文、鼓浪屿和湖里中学四个大气自动监测站的臭氧小时浓度平均值进行滚动预报,比较准确地模拟出臭氧浓度的日周期性变化,同时对峰值和低谷能够进行较为有效的捕捉和刻画。按照《环境空气质量标准》(GB3095—2012)臭氧日最大八小时浓度平均值进行评价,四个站点均取得了90%的预报等级准确率。