Open-plan office noise: Cognitive performance and restoration

The aim of the present study was to investigate cognitive, emotional, and physiological effects of two open-plan office noise conditions (high noise: 51 LAeq and low noise: 39 LAeq) during work in a simulated open-plan office, followed by four restoration conditions (river movie with sound, only river sound, silence, and office noise) after the work period. Students (N = 47) went through one practice session and two experimental sessions, one each with the low and high noise conditions. In each experimental session they worked for 2 h with tasks involving basic working memory processes. We also took physiological measures of stress (cortisol and catecholamines) and self-reports of mood and fatigue. Analyses indicate that the participants remembered fewer words, rated themselves as more tired, and were less motivated with work in noise compared to low noise. In the restoration phase the participants who saw a nature movie (including river sounds) rated themselves as having more energy after the restoration period in comparison with both the participants who listened to noise and river sounds. Remaining in office noise during the restoration phase also affected motivation more negatively than listening to river sounds or watching the nature movie. The findings bear on the appropriateness of open-plan office designs and the possibilities for restoration available in office settings.     

Besides navigation accuracy: Gender differences in strategy selection and level of spatial confidence

A virtual version of the reorientation task was employed to test new behavioral measures of navigation strategies and spatial confidence within a gender-fair assessment approach. The results demonstrated that, from a behavioral point of view, women had lower level of spatial confidence than men regardless of level of accuracy. Moreover, the way men and women selected spatial strategies depended on the arrangement of spatial cues within the environment. In other terms women relied on landmarks under specific conditions compatible with an adaptive combination/associative model of spatial orientation. Finally, the present study emphasized the importance to assess gender differences taking into account specific affective variables and information selection processing, beyond accuracy.     

Benefits of indoor plants on attention capacity in an office setting

This research studied possible benefits of indoor plants on attention capacity in a controlled laboratory experiment. Participants were 34 students randomly assigned to one of two conditions: an office setting with four indoor plants, both flowering and foliage, or the same setting without plants. Attention capacity was assessed three times, i.e. immediately after entering the laboratory, after performing a demanding cognitive task, and after a five-minute break. Attention capacity was measured using a reading span test, a dual processing task known to tap the central executive function of attention. Participants in the plant condition improved their performance from time one to two, whereas this was not the case in the no-plant condition. Neither group improved performance from time two to three. The results are discussed in the context of Attention Restoration Theory and alternative explanations.     

除草剂三氟羧草醚经口暴露对小鼠学习记忆能力的影响

目前,有关三氟羧草醚(Acifluorfen,AF)的神经毒性未见报道且亟待确定.为探讨AF经口暴露对小鼠学习记忆能力的影响及其可能机制,将30只雄性昆明小鼠随机分成生理盐水对照组、0.13、1.3、13和130 mg·kg~(-1)·d~(-1) AF染毒组共5组,灌胃染毒14 d,进行Morris水迷宫实验,观察脑海马病理切片,并检测脑组织中活性氧(ROS)、丙二醛(MDA)、还原型谷胱甘肽(GSH)、磷酸化cAMP反应元件结合蛋白(pCREB)和脑源性神经营养因子(BDNF)含量.结果显示,与对照组相比,AF染毒剂量分别为13和130 mg·kg~(-1)·d~(-1)时,小鼠行为学上学习记忆能力下降;海马细胞排列松散;出现氧化损伤,其中,ROS含量显著升高(p0.05),GSH含量显著减少(p0.05);神经保护能力减弱,其中,pCREB和BDNF水平显著(p0.05)或极显著(p0.01)下降.结果表明,AF能够导致小鼠学习记忆能力下降,氧化应激和CREB-BDNF通路级联下调可能是AF造成神经毒性的机制之一.     

森林生态系统恢复力评价——以江西省莲花县为例

生态系统恢复力是森林资源可持续发展的中心目标之一。在明确生态系统恢复力定义和尺度的基础上,分析了森林生态系统恢复力的影响因素,从生境条件和生态存储两方面遴选出26个指标,建立了森林生态系统恢复力评价指标体系,并以江西省莲花县为案例区,采用组合赋权法确定了指标权重,通过空间叠加计算了莲花县森林生态系统恢复力。结果表明:森林生态系统恢复力主要受内部存储的影响,其权重达到0.554。莲花县森林生态系统恢复力在0.103到0.464之间,平均值为0.268,恢复力达到或超过平均水平的森林面积为37 907 hm²,占森林总面积的49.2%,整体处于较低水平;分级结果表明仅48.16%的森林达到高或较高恢复力水平;在空间分布上,莲花县森林生态系统恢复力为南高北低,南部和中部的森林大多处于高或较高恢复力水平,北部地区大部分森林处于中等、较低或低恢复力水平。此外,恢复力高的森林沿乡镇边界线分布的特点非常明显。该研究结果可为森林资源管理提供重要科学依据。     

挥发性有机物混合暴露对小鼠脑组织的氧化损伤及学习记忆能力的影响

为探讨挥发性有机物混合急性暴露对小鼠脑组织氧化损伤及学习记忆能力的影响,选用雄性昆明种小鼠50只,随机分为对照组和4个染毒组。1到4号染毒组中甲醛、苯、甲苯和二甲苯浓度依次为:(1.0+1.1+2.0+2.0)、(3.0+3.3+6.0+6.0)、(5.0+5.5+10.0+10.0)、(10.0+11.0+20.0+20.0)mg·m~(-3)。各染毒组混合气体组分的浓度分别是我国室内空气质量标准(GB/T18883—2002)的10、30、50和100倍。结果显示,在Morris水迷宫实验第4天,2、3和4号染毒组小鼠的逃避潜伏期分别为(68.9±10.3)、(72.2±4.0)和(71.5±5.1)s,比对照组(48.5±10.1)s显著延长(P<0.05或P<0.01),但小鼠的脑体比和抓力在染毒期间没有明显变化。同时,随着染毒剂量的增加,小鼠脑组织中GSH含量显著降低,ROS和MDA含量显著升高。研究表明,挥发性有机物混合暴露可导致小鼠学习记忆能力降低,而脑组织氧化损伤可能是引起神经毒性,导致学习记忆能力降低的原因之一。     

Modeling scale up of anthropogenic impacts from individual pollinator behavior to pollination systems

Understanding how anthropogenic disturbances affect plant–pollinator systems has important implications for the conservation of biodiversity and ecosystem functioning. Previous laboratory studies show that pesticides and pathogens, which have been implicated in the rapid global decline of pollinators over recent years, can impair behavioral processes needed for pollinators to adaptively exploit floral resources and effectively transfer pollen among plants. However, the potential for these sublethal stressor effects on pollinator–plant interactions at the individual level to scale up into changes to the dynamics of wild plant and pollinator populations at the system level remains unclear. We developed an empirically parameterized agent-based model of a bumblebee pollination system called SimBee to test for effects of stressor-induced decreases in the memory capacity and information processing speed of individual foragers on bee abundance (scenario 1), plant diversity (scenario 2), and bee–plant system stability (scenario 3) over 20 virtual seasons. Modeling of a simple pollination network of a bumblebee and four co-flowering bee-pollinated plant species indicated that bee decline and plant species extinction events could occur when only 25% of the forager population showed cognitive impairment. Higher percentages of impairment caused 50% bee loss in just five virtual seasons and system-wide extinction events in less than 20 virtual seasons under some conditions. Plant species extinctions occurred regardless of bee population size, indicating that stressor-induced changes to pollinator behavior alone could drive species loss from plant communities. These findings indicate that sublethal stressor effects on pollinator behavioral mechanisms, although seemingly insignificant at the level of individuals, have the cumulative potential in principle to degrade plant–pollinator species interactions at the system level. Our work highlights the importance of an agent-based modeling approach for the identification and mitigation of anthropogenic impacts on plant–pollinator systems.     

英语词汇记忆方法浅谈

运用一些比较适合中国的成人记忆英语词汇的有效方法，试图从英语词汇记忆的规律和成年人学习特点，即机械记忆力逐渐衰退，而对比分析、概括总结和推理猜测的能力却逐渐加强，开拓出成人英语词汇学习的新途径。     

基于GF-LSTM和GAN网络的小样本集人工水体溶解氧浓度预测

采用推流方式改善人工水体溶解氧分布不均衡以防止富营养化时,需要对其分布进行预测来提高推流效率,为此构建了基于生成式对抗网络(GAN,Generative Adversarial Networks)和长短期记忆神经网络(LSTM,Long-Short Term Memory Network)的溶解氧浓度预测模型。以广西大学镜湖35 m2的一片水体区域为研究对象,采用不同电压直流水泵推流,用无人船搭载在线检测仪在一段时间内定时定点采集水体溶解氧浓度数据作为原始数据样本,并采用GAN扩充数据样本。利用遗传算法和改进的一阶滤波算法进行溶解氧的噪声数据处理,结合LSTM网络构建溶解氧浓度预测模型GF-LSTM(Genetic And Filtering Algorithm-Long Short Term Memory Network)。结果表明:相比常用的BP网络,GF-LSTM网络预测的平均误差降低了62%,均方误差降低了75%;相比传统的LSTM网络,GF-LSTM网络预测的平均误差降低了22%,均方误差降低了50%。     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.