多元纳米PVC材料的制备及其性能研究

采用原位聚合法将硅烷偶联剂(KH-570)包覆在纳米Ti O2和Mg(OH)2表面,并将其分散于聚氯乙烯(PVC)基体中。通过红外光谱、X射线衍射及扫描电镜对添加纳米颗粒前后的PVC材料进行表征。结果表明,Mg(OH)2的加入能够促使纳米Ti O2与其交联,从而减少纳米颗粒的团聚,提高纳米颗粒的分散性能。同时对PVC材料进行紫外屏蔽性能实验以及氧指数的测定实验,其结果表明,当纳米Ti O2和Mg(OH)2质量比为1∶2时,材料的紫外屏蔽性能与阻燃性能最佳。     

A tree-structured crash surrogate measure for freeways

In this paper, we propose a novel methodology to define and estimate a surrogate measure. By imposing a hypothetical disturbance to the leading vehicle, the following vehicle’s action is represented as a probabilistic causal model. After that, a tree is built to describe the eight possible conflict types under the model. The surrogate measure, named Aggregated Crash Index (ACI), is thus proposed to measure the crash risk. This index reflects the accommodability of freeway traffic state to a traffic disturbance. We further apply this measure to evaluate the crash risks in a freeway section of Pacific Motorway, Australia. The results show that the proposed indicator outperforms the three traditional crash surrogate measures (i.e., Time to Collision, Proportion of Stopping Distance, and Crash Potential Index) in representing rear-end crash risks. The applications of this measure are also discussed.     

Early warning system for rainfall- and snowmelt-induced slope failure in seasonally cold regions

In 2005, the Japanese government launched a new nationwide early warning system for predicting debris flow and slope failure disasters based on rainfall intensity and the Soil Water Index (SWI). However, the Japanese government has not set early warning criteria in many mountain areas. In addition, the existing early warning criteria in some areas are much higher than realistic ones, and snowmelt water is not considered in the calculation of the SWI. These two factors have been the cause of many slope failures in seasonally cold regions, induced by rainfall and/or snowmelt, which were not predicted. Therefore, this study attempts to propose a new determination method for setting early warning criteria for rainfall- and/or snowmelt-induced slope failures in seasonally cold regions. For this purpose, the study firstly proposes a combination model for estimating snow density that incorporates the hourly snowmelt water into the Japanese early warning system more accurately by using meteorological monitoring data and modeled snow density. Next, based on case studies and parametric analyses for slope stability assessment, new early warning criteria are proposed for predicting three different patterns of slope failures under two typical types of precipitation (rainfall and snowmelt) conditions. Finally, a new determination method for setting early warning criteria in seasonally cold regions is proposed by referring to the existing early warning criteria near the target area, in accordance with the precipitation types and the local ground conditions of the slopes. Since the existing early warning criteria near the target area already take the effects of the variations in local geology and geography into account, the new determination method for early warning criteria can be applied to arbitrary areas in seasonally cold regions, without directly considering the local soil properties, in the actual design and maintenance works.     

Dual-PISA: An index for aggregation operations on time series data

Aggregation operations play an essential role in time series database management. As the number of data increases, it is difficult for current solutions, such as summary table and MapReduce-based methods to respond to such queries with low latency. Other approaches, such as segment tree-based methods, have a poor insertion performance when the data size exceeds the available memory. This paper proposes a Persistent Index for Segmented Aggregations (PISA), which has fast insertion performance and low latency for aggregation queries. PISA uses a forest to overcome the performance disadvantage of insertion in traditional segment trees. By defining two kinds of tags, namely code number and serial number, we propose an algorithm to accelerate queries by avoiding unnecessary reading data on disk. Additionally, we extend it to Dual-PISA to tolerate a range of unordered data, which is very important in the real world. Dual-PISA is stored on disk and is hugely memory-efficient — only takes a few hundred bytes of memory for billions of data points. Dual-PISA can be easily implemented on both traditional databases and NoSQL systems. It handles aggregation queries within milliseconds on a commodity server, for a time range that contains tens of billions of data points.     

Inverted index maintenance strategy for flashSSDs: Revitalization of in-place index update strategy

An inverted index is a core data structure of Information Retrieval systems, especially in search engines. Since the search environments have become more dynamic, many on-line index maintenance strategies have been proposed. Previous strategies were designed for HDDs. Consequently, in order to avoid expensive random access cost, Merge-based strategies have been preferred to In-place index update strategies on HDDs. However, flashSSDs have become solid alternatives to HDDs. FlashSSDs currently are adopted in a wide range of areas due to their superior features such as the short access latency, energy efficiency, and high bandwidth. In this article, we first reexamined potentials of In-place index update strategies on flashSSDs. Thanks to the insignificant access latency of flashSSDs, we discovered that In-place index update strategies outperform Merge-based strategies, since In-place index update strategies generate much less amount of I/O than Merge-based strategies despite inducing frequent random accesses. Based on this discovery, we suggest a new inverted index maintenance strategy based on an In-place index update strategy for flashSSDs, called Multipath Flash In-place Strategy (MFIS). To enhance the index maintenance performance, MFIS stores the posting list of each term non-contiguously and exploits the internal parallelism of flashSSDs. Thus, MFIS not only induces the minimum amount of I/O but also utilizes the maximum bandwidth of flashSSDs. Furthermore, MFIS is designed to show high query processing performance by utilizing the internal parallelism of flashSSDs even though the posting list of each term is stored non-contiguously. In our experiments, the index maintenance performance of MFIS was considerably better than other previous maintenance strategies. The index maintenance performance was up to 14.93, 4.04, 5.12, and 2.33 times higher than Merge-based strategies such as Immediate Merge, Geometric Partitioning, Hybrid, and SSD-aware Hybrid, respectively. The query processing performance of MFIS was up to 1.62 times higher than non-contiguous In-place. In addition, MFIS showed almost the best query processing performance as Merge-based strategies did. In conclusion, MFIS is the best on-line inverted index maintenance strategy on flashSSDs in terms of both index maintenance and query processing performance.     

A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand

The fuzzy logical relationships and the midpoints of interval have been used to determine the numerical in-out-samples forecast in the fuzzy time series modeling. However, the absolute percentage error is still yet significantly improved. This can be done where the linguistics time series values should be forecasted in the beginning before the numerical forecasted values obtained. This paper introduces the new approach in determining the linguistic out-sample forecast by using the index numbers of linguistics approach. Moreover, the weights of fuzzy logical relationships are also suggested to compensate the presence of bias in the forecasting. The daily load data from National Electricity Board (TNB) of Malaysia is used as an empirical study and the reliability of the proposed approach is compared with the approach proposed by Yu. The result indicates that the mean absolute percentage error (MAPE) of the proposed approach is smaller than that as proposed by Yu. By using this approach the linguistics time series forecasting and the numerical time series forecasting can be resolved.     

Forecasting conditional climate-change using a hybrid approach

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.     

Optimal design of hybrid power generation system and its integration in the distribution network

The inability of conventional energy sources to fully meet the rapidly increasing energy demands in today’s world has led to the growing importance of hybrid power generation systems that incorporate renewable energy sources. This work proposes an optimally designed multi-source standalone hybrid generation system comprising of photovoltaic panels, wind turbine generators, batteries and diesel generator. This design aims at minimizing emissions and cost, expressed in the form of the Net Present Value (NPV) of the system, while simultaneously maximizing its Energy Index of Reliability (EIR). The designed hybrid power generation system is further integrated into the distribution system as a Distributed Generation (DG); this is to optimally improve the performance of the distribution system by minimizing the total losses and the total voltage deviation of the distribution system. The combined cost and emissions incurred due to the energy purchased from the grid and the energy generated by DG are also reduced. For this purpose an improvised Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed taking care of contradicting objectives. The proposed optimization algorithms are implemented using MATLAB for a standard IEEE 69-bus distribution system, using an hour-wise annual data of Spain. The location and size of DGs and the type and number of each generating source of the hybrid system are considered as decision variables. The effectiveness of the proposed optimal design using the improvised MOPSO algorithm is established in comparison with Improved Hybrid Optimization by Genetic Algorithm (i-HOGA) results.     

Carbon intensity of electricity in ASEAN: Drivers,performance and outlook

The Association of Southeast Asian Nations (ASEAN), with its ten member countries, has a total population exceeding 600 million. Its energy-related CO₂ emissions have been growing and in 2013 amounted to 3.6% of total global emissions. About 40% of ASEAN's energy-related CO₂ emissions are currently attributable to electricity production. In view of this high share, we study the CO₂ emissions of ASEAN's electricity production sector with a focus on the aggregate emission intensity (ACI) given by the level of CO₂ emissions for each unit of electricity produced. Drivers of ACI are analysed for individual countries and spatial analysis is conducted by comparing factors contributing to differences between the ACIs of individual countries and that of the ASEAN average. Arising from these analyses and in light of the current developments, it is concluded that drastic actions need to be taken both at the national and regional levels in order to reduce growth in the region's electricity-related CO₂ emissions. Two key policy issues, namely overcoming national circumstances to improve electricity generation mix and improving power generation efficiency, are further discussed.     

Predicting how consumers perceive the naturalness of snacks: The usefulness of a simple index

For many consumers, it is important that food products be natural. Naturalness is a perceived property of food, but in the present study, we demonstrate that an objectively defined Food Naturalness Index (FNI) predicts perceived naturalness with a high degree of accuracy. A sample of 179 participants ranked 28 snacks, ranging from least natural to the most natural. Correlations on aggregated and individual levels suggest that perceived naturalness is strongly associated with the FNI. The food industry could therefore use the FNI to predict perceived naturalness during the product development phase of snacks, and it might also be a promising tool for regulating the use of naturalness claims in food marketing.