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In recent years, frequent outbreaks of natural disasters cause large losses to the development of society and economics and threaten the safety of people's lives and property. When the emergencies occur in a region with a high population density, people in the affected areas need both evacuation and a large amount of emergency materials. In this case, it is extremely necessary to respond quickly and arrange rescue work. This study constructed nonlinear integer programming models for personnel evacuation and materials distribution under natural disasters, aiming to determine the optimal solutions of evacuation and delivery. Finally, it tests the feasibility of the model and investigates the sensitivity of the optimal objectives on rescue conditions based on a case study. This research will provide some support for decision makers to design reasonable and effective rescue plans about personnel refuge arrangement and emergent materials distribution.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cie.2009.01.015 Byline: Huajun Tang, T.C.E. Cheng, C.T. Ng Keywords: Finite dominating sets; Multi-facility ordered median problem; Pseudo-equilibria; Algorithms Abstract: We identify a finite dominating set (FDS) for a special case of the multi-facility ordered median problem in networks, in which the I'-weights can take at least two different values. This FDS result not only includes the FDS research for the p-center problem, but also extends the case of a Author Affiliation: Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong SAR, China Article History: Received 18 September 2008; Revised 24 December 2008; Accepted 20 January 2009

Optical access of a mouse brain using microscopes is the key to study brain structures and functions in vivo. However, the opaque skull of a mouse has to be either opened or thinned in an invasive way to attain an adequate imaging depth in the brain. Mild skull optical clearing is highly desired, but its chemical mechanism is far from being understood. Here, we unraveled the molecular process underlying optical clearing of the mouse skull by label-free hyperspectral stimulated Raman scattering (SRS) microscopy, thereby discovering the optimal clearing strategy to turn a turbid skull into a transparent skull window. Furthermore, we demonstrated in vivo three-photon imaging of vascular structures as deep as 850 μm in the cortex of the mouse brain. Coherent Raman based microspectroscopy holds great promise to advance skull and tissue clearing methods in the future.

Lysosome-selective Raman probe, Lyso-BADY, was created with improved Raman signal intensity, stability as well as specificity under the visualization of hyperspectral stimulated Raman scattering (SRS) microscopy. Prolonged exposure to irradiation further proved its photostability and potentiality for continuous imaging.

ABSTRACTAccurate cropland maps are important input for various proposes, such as ecosystem service and land cover change monitoring, and the representativeness of sample training samples influence the cropland mapping accuracy significantly. This study aims to propose a new sampling workflow based on unsupervised cluster and multi-temporal interpretation (UCMT) for cropland mapping. The monthly composited image time series were unsupervised clustered using the Iterative Self organizing Data Analysis (ISODATA) and optimal temporal phases were selected using the Gini importance score calculated from Ramdom Forest (RF). Training samples for each cluster were generated and visually interpreted for the optimal temporal phases. The cropland of each temporal phase was identified using the corresponding training samples, and the cropland maps were generated by merging multi-temporal cropland results. Results in two study regions showed that training samples generated using UCMT had good potential to identify cropland with overall accuracies higher than 94% in both study regions. In addition, comparing with randomly generated training samples, UCMT samples were less affected by training sample size as Producer’s accuracies and User’s accuracies were higher than 80% when 100 training samples used.

Methamphetamine use disorder (MUD) can cause impulsive behavior, anxiety, and depression. Stimulation of the left dorsolateral prefrontal cortex in MUD patients by intermittent theta burst repetitive transcranial magnetic stimulation (iTBS-rTMS) is effective in reducing cravings, impulsive behavior, anxiety, and depression. The purpose of this study was to explore whether these psychological factors helped to predict MUD patients' responses to iTBS-rTMS treatment.

Accurate information of cropland area and spatial location is critical for studies of national food security, global environmental change, terrestrial ecosystem geophysics and the geochemical cycle. In this paper, we compared five global cropland datasets in circa 2010 of China from in terms of cropland area and spatial location, including GlobalLand30, FROM-GLC, GlobCover, MODIS Collection 5, and MODIS Cropland. The results showed that the accuracies of cropland area and spatial location of GlobeLand30 were higher than the other four products. The cropland areas of the five products varied in most of the provinces. Compared with the statistical data, the best goodness of fit was obtained using GlobeLand30, followed by MODIS Collection 5 and FROM-GLC, with MODIS Cropland and GlobCover having the poorer accuracies. Regarding the spatial location of cropland, GlobeLand30 achieved the best accuracy, followed by FROM-GLC and MODIS Collection 5, with GlobCover and MODIS Cropland having the poorer accuracies. In addition, the spatial agreement of the five datasets was reduced from agricultural production area to pastoral area and significantly affected by elevation and slope factors. Although the spatial resolution of MODIS Collection 5 was the lowest, accuracies of the cropland area and spatial location were better than those of GlobCover and MODIS Cropland. Therefore, high spatial resolution remote sensing images can help to improve the accuracy of the dataset during land cover mapping, while it is also important to select a suitable classification method. Furthermore, in northwestern and southeastern China, spectral mixing pixels are universal because of the complicated landscape and fragmentized topography and result in uncertainty and poor consistency when using the five products. Therefore, these regions require additional attention in future cropland mapping studies.

Water plays a vital role in biological metabolism and it would be essential to trace the water content non-invasively, such as leveraging the vibrational absorption peak of the O–H bond. However, due to the lack of an efficient laser source, it was challenging to image the water content in the deep tissue with micron-level spatial resolution. To address this problem, we develop a high-power hybrid optical parametrically-oscillating emitter (HOPE) at 1930 nm, at which the vibrational absorption peak of the O–H bond locates. The maximum pulse energy is over 1.74 μJ with a pulse repetition rate of 50 kHz and a pulse width of 15 ns. We employ this laser source in the optical-resolution photoacoustic microscopy (OR-PAM) system to image the water content in the phantom and the biological tissue in vitro. Our 1930-nm OR-PAM could map the water content in the complex tissue environment at high spatial resolution, deep penetration depth, improved sensitivity, and suppressed artifact signal of the lipid.

Lack of accurate maps on the extent of global cropland, and particularly the spatial distribution of major crop types, hampers policy and strategic investment and could potentially impede efforts to improve food security in an environment characterized by continued market volatility and a changing climate. Here we discuss the pressing need for the provision of spatially explicit cropland datasets at a global scale and review the strengths and weaknesses of the various approaches used to develop such data.

Understanding crop patterns and their changes on regional scale is a critical requirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model (SPAM) has been developed for presenting spatiotemporal dynamics of maize cropping system in Northeast China during 1980–2010. The simulated results indicated that (1) maize sown area expanded northwards to 48°N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation (less than 100 m) as well as higher elevation (mainly distributed between 200 m and 350 m); (2) maize yield has been greatly promoted for most planted area of Northeast China, especially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; (3) maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.

This paper aims to reduce the population and CO 2 emission of the free shuttle bus (FSB) in Macau. Firstly, we investigate the operational conditions of the FSBs among the 15 casinos in Macau, including the transit time of FSBs and the number of visitors. Then we build a series of integer programming models to optimise the population of FSBs with a reduction of 49.4%. Finally, based on the reduction result of the vehicle population, we conclude that the CO 2 emission of FSBs could be reduced by 60.4% with the present service level.

Rice’s spatial-temporal distributions, which are critical for agricultural, environmental and food security research, are affected by natural conditions as well as socio-economic developments. Based on multi-source data, an effective model named the Spatial Production Allocation Model (SPAM) which integrates arable land distribution, administrative unit statistics of crop data, agricultural irrigation data and crop suitability data, was used to get a series of spatial distributions of rice area and production with 10-km pixels at a national scale — it was applied from the early 1980s onwards and used to analyze the pattern of spatial and temporal changes. The results show that significant changes occurred in rice in China during 1980–2010. Overall, more than 50% of the rice area decreased, while nearly 70% of rice production increased in the change region during 1980–2010. Spatially, most of the increased area and production were in Northeast China, especially, in Jilin and Heilongjiang; most of the decreased area and production were located in Southeast China, especially, in regions of rapidly urbanization in Guangdong, Fujian and Zhejiang. Thus, the centroid of rice area was moved northeast approximately 230 km since 1980, and rice production about 320 km, which means rice production moved northeastward faster than rice area because of the significant rice yield increase in Northeast China. The results also show that rice area change had a decisive impact on rice production change. About 54.5% of the increase in rice production is due to the expansion of sown area, while around 83.2% of the decrease in rice production is due to contraction of rice area. This implies that rice production increase may be due to area expansion and other non-area factors, but reduced rice production could largely be attributed to rice area decrease.

The nestedness property has become an increasingly important means for devising efficient algorithms for network location problems. There have been attempts to explore the nestedness property of network location problems with some special cases of the convex ordered median objectives. However, there is little research on the nestedness property for those problems with the concave ordered median objectives. This paper constructs a tree network Tand shows that the nestedness property cannot hold for the concave ordered median problem, which fills a gap in the research on the nestedness property. Finally, the authors pose an open problem on identifying the nestedness property for the continuous strategic ordered median problem.

A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined (DSC) approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average (SARIMA) models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term (∼10-year) environmental planning and decision making.

the University of Maryland Dataset (UMD), the International Geosphere-Biosphere Programme Data and Information System Cover (IGBP-DISCover), the Moderate Resolution Imaging Spectrometer (MODIS) dataset, and the Global Land Cover 2000 (GLC2000). Although produced primarily for global applications, these global land cover datasets are often used for national or regional applications. The objective of this study was to examine the similarities and differences between these datasets in mapping Chinese cropland and to highlight their strengths and weaknesses when used at the national and regional scales. First, the total areas (in ha) of the cropland mapped by the four datasets were compared at the Chinese national, regional, and provincial levels. Second, we performed a pixel-by-pixel comparison of the spatial locations of cropland mapped by the four datasets. These datasets were then separately compared with the Chinese 1996 National Land Survey Dataset (NLSD-1996) to assess their accuracy in cropland mapping. The results show that, despite some agreement, there are discrepancies between the datasets, and the type and extent varies between regions. There are many reasons for the discrepancies, the main ones being the diverse land cover classification schemes, coarse spatial resolution, per-pixel classification approach, and landscape heterogeneity.

Assessments of agriculture at the national scale require the best possible information on the distribution of cropland and the management of that cropland. Official cropland statistics for China contain much information on the distribution of crop types, but are known to underestimate total cropland area and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but usually supply little information on crop type or management. We combined 1990 county-scale agricultural census statistics on total cropland area and sown area of 17 major crops with a fine-resolution land-cover map derived from 1995-96 optical remote sensing (Landsat Thematic Mapper) data to generate 0.5° resolution maps of the distribution of 47 different single- and multi-crop rotations in mainland China. We estimated that, of 1.3-million km2 of cropland, approximately 60% was single-cropped, 30% was double-cropped, and 10% was triple-cropped. Total sown or planted area was 2.0-million km2.

No abstract.

Synthesis of the LD-3 electro-optic polymer has resulted in a highly reliable nonlinear organic material. Such a success has been impeded from further progress because the LD-3 films produced thus far are too thin (<0.5 µm) to form a waveguide. Further details of material synthesis have to be studied to solve this problem. We report the formation of thick LD-3 films ranging from 1.2 to 2.4 µm by introducing cyclopentanone as the new solvent in polymer synthesis. The formation of multimode planar waveguides on silicon with a waveguide loss of approximately 1.3 dB/cm at 1.3 µm is demonstrated. Preliminary studies aimed at forming true-time-delay lines are conducted by use of various waveguide lengths in conjunction with an optical heterodyne technique. Waveguide settings equivalent to different true-time-delay lines with a delay time of up to 0.27 ns and a base bandwidth of 25 GHz are demonstrated with a signal-to-noise ratio of 15 dB.

Soil organic carbon (SOC) is the most critical component of global carbon cycle in grassland ecosystems. There has been growing interest in understanding SOC dynamics and driving forces of grassland biomes at various temporal and spatial scales. Up to now, estimates of long‐term and large‐scale changes in SOC of grassland biomes have been mostly based on modeling approaches and manipulative experiments, rather than direct measurements. During 2007–2011, we repeated 141 soil profiles of the sampling in 1963–1964 (up to 1‐m depth) to quantify the long‐term changes of SOC storage in the major grassland types of Inner Mongolia in order to tease apart the relative contributions of climate change and grazing. We found that SOC decreased in all soil types, except in the eolian sandy soils, from 1963 to 2007, with an average reduction rate of 1.8 kg C m−2(~22.9% or 0.52% year−1) in the grassland biome of Inner Mongolia. We quantitatively clustered the soils into four groups using principal component analysis (PCA) and detected clear spatial dependency of the changes on climate and grazing. The climate change was responsible for 15.3–34.9% of the total SOC variations, whereas grazing intensity accounted for <9.5% of the changes. Our findings indicated that climate change, rather than grazing, was the primary forcing for the changes in SOC of Inner Mongolia grasslands. We presume that other driving forces, such as changes in nongrazing‐resultant wind erosion and atmospheric nitrogen deposition, might have played a role albeit their effects need to be further examined. SOC decreased at an average rate of 0.52% year−1in the grassland biome of Inner MongoliaClimate change rather than grazing was the major contributor to long‐term soil carbon losses