Dress for success: the effect of exhibitors’ dress conformity and self-construal on attendees’ approach behavior

ABSTRACT

The purpose of the current study is to investigate how attendees’ approach behavior is influenced by exhibitors’ dress conformity. A 2 × 2 between-subjects quasi-experimental design was utilized for this study. Our results showed that conforming dress for exhibitors can lead to a higher level of approach behavior of trade show attendees as compared with nonconforming dress, and the effect of exhibitors’ dress styles on approach behavior is moderated by attendees’ self-construal. Findings of this study provide significant insights for trade show exhibitors and attendees.     

剖线强度梯度变化的作物软硬变化区精确划分

[目的]软硬变化区的划分是进行遥感变化检测技术识别农作物的基础。为解决人工判读方法中存在人为主观因素影响以及自动判别和交互式判别方法对不同研究区的适用性问题,该文提出基于剖线梯度变化(Profile based Gradient Change Magnitude,PGCM)进行软硬变化区划分的方法。[方法]该研究选择破碎农业景观种植区域为研究区,计算拔节期Quick Bird影像和播种期模拟影像两个时相的变化强度,从作物地块内部向外绘制剖线,利用剖线强度的梯度变化确定硬变化区(Hard change region,HCR)、软变化区(Soft change region,SCR)和未变化区(Non change region,NCR)3者间的划分阈值。[结果]从识别结果来看,PGCM能够有效在地块边界处探测到软变化像元,进而确定HCR、SCR。像元分辨率在5~60 m不同尺度下,识别HCR区混入比例为11%~16%,混入比例随着分辨率下降而降低;NCR区混入比例为3%~4%,受分辨率尺度影响不大;SCR区识别比例为74%~86%,识别精度较高,识别结果与冬小麦空间分布结果保持一致。[结论]PGCM方法能够自动、便捷地确定阈值,摆脱人工判定的主观性,有效地划分出HCR、SCR和NCR 3个区域,为进一步HCR、SCR区内的作物识别提供基础。     

高分1号冬小麦解译面积核算方法研究

[目的]以高分1号(GF-1)融合2m卫星遥感影像为基础数据源,结合土地利用现状数据、高分多源遥感影像和地面样方等数据,对冬小麦分类提取中存在的面积误差问题进行研究和分析。[方法]文章以河南省永城市为研究区,在冬小麦提取结果聚类处理基础上,基于线性地物缓冲区数据,采用GIS空间运算实现线性地物面积扣除,接着分析了样方数据和土地利用现状数据再扣除零星地物面积比例上的差异,并采用样方零星地物平均扣除系数对全市各乡镇耕地与非耕地中冬小麦提取面积进行了相关统计和误差分析。[结果]永城市冬小麦最终解译面积11. 29万hm~2,其中线性地物和零星地物扣除面积分别为6 613. 08hm~2和3 875. 22hm~2,占研究区冬小麦解译面积的5. 86%和3. 32%,与统计上报数据相比,其处理前后误差由14. 12%降低至4. 41%,有效地提高冬小麦提取面积精度。[结论]误差来源分析与修正对冬小麦解译面积核算精度具有重要影响,该研究为县级区域尺度下冬小麦面积提取核算提供了思路和借鉴。     

An evaluation framework for developing green infrastructure by using a new hybrid multiple attribute decision-making model for promoting environmental sustainability

With the expansion of urbanization caused by the growth of population and industrial activities, the urban/city and suburban areas are facing a variety of environmental threats. Although more research and urban policy has advocated and practiced the development of green infrastructure (GI) to support urban sustainable environment, the evaluation framework for the development of GI for promoting environmental sustainability is still insufficient. Moreover, the Analytic Hierarchy Process (AHP) commonly applied in published literature, makes an unrealistic assumption of independent relationships among dimensions/criteria in decision making for satisfying the real-world problem. Therefore, the purpose of this study is to construct the evaluation framework, including four dimensions and related ten criteria, using a new hybrid-modified multiple attribute decision-making (MADM) model for developing and improving the GI for promoting environmental sustainability. This MADM model is combined with three different methodologies of MADM, including the Decision-Making Trial and Evaluation Laboratory (DEMATEL) for constructing the influential network relation map (INRM) to explore the complex influential inter-relationships and DEMATEL based on Analytic Network Process (DANP) for determining the influential weights with the VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for evaluating and presenting improvement strategies for six different GIs. The empirical study indicates that DEMATEL and DANP Results suggest that decision-makers should pay more attention to the improvement of Design (D₄) and Materials (D₂) in terms of dimensions when utilizing the GI to promote environmental sustainability. Because these dimensions are enhanced, Species (D₁) and Energy (D₃) will be improved in synchronization. From the perspective of criteria, five are key core criteria and need to be focused on first: increasing the green coverage rate (B₉), utilizing sustainable materials (B₄), using ecological engineering (B₈), shaping species biodiversity (B₁), and reducing energy consumption (B₅). Modified VIKOR reveals that “grass swales” are a comparatively better choice among six GIs for promoting environmental sustainability toward achieving the aspiration level. Therefore, this MADM model is beneficial to provide a more convincing assessment framework and improvement strategies for the development of GI for promoting environmental sustainability. As a result, these modified MADM models can be shown more conveniently and reasonably than traditional methods such as traditional AHP or ANP method.     

Quantifying and understanding land cover changes by large and small oil palm expansion regimes in the Peruvian Amazon

With rapid increases in global food demand and production, oil palm expansion constitutes a major emerging challenge for forest conservation in Amazonia and other tropical forest regions. This threat is evident in the Peruvian Amazon, where local and national incentives for oil palm cultivation along with growing large-scale investments translate into accelerated oil palm expansion. Environmental sustainability of oil palm cultivation in the Peruvian Amazon is contingent on policy incentives for expansion onto already-cleared lands instead of biodiverse, high carbon primary rainforests. Previous research indicates that while industrial plantations use less land area than local smallholders, companies have a higher tendency to expand into primary rainforests. However, the motivations behind these differing expansion scenarios remain unclear. In this study we combine data from optical and radar satellite sensors with training information, field discussions, and review of public documents to examine the policy incentives and spatial patterns associated with oil palm expansion by smallholders and industries in one of Peru’s most rapidly changing Amazonian landscapes: the Ucayali region of the city of Pucallpa. Based on our satellite-based land cover change analysis, we found that between 2010 and 2016, smallholders utilized 21,070 ha more land area for oil palm than industries but industrial expansion occurred predominantly in old growth forests (70%) in contrast to degraded lands for smallholders (56%). Our analysis of national policies related to oil palm expansion reveal policy loopholes associated with Peru’s “best land use” classification system that allow for standing forests to undergo large-scale agricultural development with little government oversight. We conclude that both sectors will need careful, real-time monitoring and government engagement to reduce old-growth forest loss and develop successful strategies for mitigating future environmental impacts of oil palm expansion.     

Implementing sustainable purchasing and supply management (SPSM): A Delphi study on competences needed by purchasing and supply management (PSM) professionals

This research is about a Delphi study that was conducted in the context of the development of a competence model for sustainable purchasing and supply management (SPSM). Based on the findings of a systematic literature review (SLR), it addresses the research gap in this area with the aim to foster the integration of sustainability in purchasing and supply management (PSM) in higher education and in professional training approaches for PSM professionals. The qualitative study was conducted with 16 Delphi experts in the field of PSM and sustainability, applying the critical incident technique (CIT). Together with the findings of the SLR, it led to a specific set of SPSM competences. The theoretical background is based on PSM research, educational sciences and organizational psychology.     

无人机遥感系统的研究进展与应用前景

对于无人机遥感来说,融合了无人机技术与遥感技术。在无人机遥感技术应用的过程中,涵盖了诸多不同类型的先进技术,比如,无人机驾驶飞行技术、通讯技术、遥感传感器技术等,存在着显著的智能化、自动化等不同方面的特征,能够迅速得到相关资源,并且系统掌握国土、环境等不同方面的信息,然后加以实时化处理,通过建模与科学分析,能够制定出无人机遥感技术运用的具体策略,凸显出其良好的功效。本文针对无人机遥感系统的研究进展进行了详细地分析,同时说明了无人机遥感系统的应用前景,进而对有关研究人员形成一定的启发。     

基于信息辅助块稀疏贝叶斯学习的直扩通信窄带干扰抑制

现有基于Nyquist-Shannon采样定理的窄带干扰(Narrowband Interference,NBI)抑制方法存在应用受限于采样率较高的问题。应用压缩感知(Compressive Sensing,CS)理论解决上述问题，利用NBI在频域表现出的块稀疏特性以及直接序列扩频(Direct Sequence Spread Spectrum,DSSS)信号的类噪声特性，提出了基于块稀疏贝叶斯学习(Block Sparse Bayesian Learning,BSBL)框架的DSSS通信NBI抑制模型。实现干扰抑制后，利用传统的CS重构算法实现DSSS信号的压缩域解调。为进一步提高算法性能，将NBI稀疏分块的块内自相关矩阵建模为单位矩阵，提出了信息辅助BSBL(Aid BSBL,ABSBL)算法，设计了基于ABSBL的DSSS通信NBI抑制算法。该算法在保持较好NBI抑制性能的条件下，提高了运算效率并且不依赖NBI的稀疏结构。仿真验证和对比分析结果表明，所提方法能够有效抑制DSSS通信中的NBI，在干扰强度相同的条件下，NBI带宽越小、压缩率越大，算法对NBI的抑制性能越好。     

基于图形小波变换的遥感图像表示与去噪

离散小波变换（Discrete Wavelet Transform，DWT）通常用于图像的表示。然而，对于具有不规则形状边缘的图像，尤其是对于纹理和细节信息较多的遥感图像，DWT却很难有效表示，进而影响后续去噪效果。针对该问题，提出了一种基于图形小波变换（Graphic Wavelet Transform，GWT）的图像去噪方法。首先，将图像表示为图形信号，并通过该图形信号的谱表示构造相应的变换矩阵；然后，设计了一种改进自适应阈值的图像去噪方法，在GWT变换域内对图像去噪。实验结果表明，与常用的图像去噪方法相比，所提算法能够提供更好的图像主观质量。采用均方根误差（Root Mean Square Error，RMSE）和峰值信噪比（Peak Signal-to-Noise Ratio，PSNR）作为客观指标，结果表明，采用所提方法得到的重建图像客观质量更优。