Sparse episode identification in environmental datasets: The case of air quality assessment

Sparse episode identification in environmental datasets is not only a multi-faceted and computationally challenging problem for machine learning algorithms, but also a difficult task for human-decision makers: the strict regulatory framework, in combination with the public demand for better information services, poses the need for robust, efficient and, more importantly, understandable forecasting models. Additionally, these models need to provide decision-makers with “summarized” and valuable knowledge, that has to be subjected to a thorough evaluation procedure, easily translated to services and/or actions in actual decision making situations, and integratable with existing Environmental Management Systems (EMSs).On this basis, our current study investigates the potential of various machine learning algorithms as tools for air quality (AQ) episode forecasting and assesses them – given the corresponding domain-specific requirements – using an evaluation procedure, tailored to the task at hand. Among the algorithms employed in the experimental phase, our main focus is on ZCS-DM, an evolutionary rule-induction algorithm specifically designed to tackle this class of problems – that is classification problems with skewed class distributions, where cost-sensitive model building is required.Overall, we consider this investigation successful, in terms of its aforementioned goals and constraints: obtained experimental results reveal the potential of rule-based algorithms for urban AQ forecasting, and point towards ZCS-DM as the most suitable algorithm for the target domain, providing the best trade-off between model performance and understandability.     

Iterative multimodal processes scheduling

The paper considers the problem of Automated Guided Vehicles (AGVs) fleet scheduling subject to the right match-up of local cyclic acting AGV schedules to given workpiece machining schedules. The main contribution of this work is the solution to a constraint satisfaction problem aimed at AGVs fleet match-up scheduling while taking into consideration assumed itineraries of concurrently manufactured product types. In other words, assuming a given layout of FMS’s material handling and production routes of simultaneously manufactured work orders as well as cyclic schedules of concurrently manufactured product types, the goal is to provide a declarative model enabling multimodal processes, i.e. employing AGVs, hoists, lifts, etc. demand-responsible scheduling of transportation/handling services. An algebra-like driven approach to cyclic scheduling based on step-by-step composition of multimodal transportation network sub-structures is proposed. Results of computational experiments assessing scalability of the method provided are presented as well.     

Order acceptance and scheduling to maximize total net revenue in permutation flowshops with weighted tardiness

The order acceptance and scheduling (OAS) problem is important in make-to-order production systems in which production capacity is limited and order delivery requirements are applied. This study proposes a multi-initiator simulated annealing (MSA) algorithm to maximize the total net revenue for the permutation flowshop scheduling problem with order acceptance and weighted tardiness. To evaluate the performance of the proposed MSA algorithm, computational experiments are performed and compared for a benchmark problem set of test instances with up to 500 orders. Experimental results reveal that the proposed heuristic outperforms the state-of-the-art algorithm and obtains the best solutions in 140 out of 160 benchmark instances.     

A hybrid differential evolution algorithm for multiple container loading problem with heterogeneous containers

We consider a multiple container loading problem, commonly known as the three-dimensional bin packing problem (3D-BPP), which deals with maximizing container space utilization while the containers available for packing are heterogeneous, i.e., varying in size. The problem has wide applications in cargo transportation, warehouse management, medical packaging, and so on. We develop a differential evolution (DE) algorithm hybridized with a novel packing heuristic strategy, best-match-first (BMF), which generates a compact packing solution based on a given box packing sequence and a container loading sequence. The effectiveness of the proposed algorithm is evaluated on a set of industrial instances and randomly generated instances. The results show that the proposed algorithm outperforms existing solution approaches in terms of solution quality.     

Optimal design of fossil-solar hybrid thermal desalination for saline agricultural drainage water reuse

Ultra-high recovery solar thermal desalination of agricultural drainage water is presented as one solution to the historic extreme drought and long-standing salt accumulation problems facing California's fertile Central Valley region. Building on the results obtained from a recent pilot demonstration of a novel solar thermal desalination system, a techno-economic analysis is presented using an existing agricultural region as a case study. Three strategies are considered: continue retiring farmland as crop productivity wanes in future years, desalinate saline drainage water with a novel distillation process using natural gas as the fuel source, and desalinate using natural gas and solar as a hybrid energy source. The study is cast as a parametric optimization problem taking into account natural gas costs and water purchase contract pricing. The results show that with projections of the long-term effects and cost of salt accumulation in the region, solar thermal desalination is economically favorable over both the alternative of doing nothing (retire farmland) as well as implementing conventional (non-renewable) thermal desalination. Most importantly, the results indicate that solar thermal desalination is an economically-viable solution that can increase the sustainability of farming in the region and create a new, sustainable, scalable source of additional freshwater.     

The comparison analysis of total factor productivity and eco-efficiency in China's cement manufactures

This paper mainly compares total factor productivity and eco-efficiency in China's cement manufactures from 2005 to 2010. First, we evaluate total factor productivity and eco-efficiency of China's cement manufactures through distance function and directional slack-based measure (DSBM) respectively. Furthermore, we also explore the difference of total factor productivity and eco-efficiency. Last, we investigate the determinants of Malmquist, Mamlquist–Luenberger of China's cement manufactures through random-effect Tobit and bootstrap truncated econometric methods. We find that there are some gaps between Malmquist and Mamlquist–Luenberger of China's cement manufactures. Per labor cement industry value has U-shape relationship with both Malmquist and Malmquist–Luenberger. It is necessary to adopt advanced technology to reduce pollutant emissions.     

Optimal allocation of information granularity in system modeling through the maximization of information specificity: A development of granular input space

In this study, we introduce a concept of a granular input space in system modeling, in particular in fuzzy rule-based modeling. The underlying problem can be succinctly formulated in the following way: given is a numeric model, develop an efficient way of forming granular input variables so that the corresponding granular outputs of the model achieve the highest level of specificity. The rationale behind the formulation of the problem is offered along with several illustrative examples. In conjunction with the underlying idea, developed is an algorithmic framework supporting an optimization of the specificity of the model exposed to granular inputs (data). It is dwelled upon one of the principles of Granular Computing, namely an optimal allocation of information granularity. For illustrative purposes, the study is focused on information granules formalized in terms of intervals (however the proposed approach becomes equally relevant for other formalism of information granules). Some comparative analysis with the existing idea of global sensitivity analysis is also carried out by contrasting the essential differences among the two approaches and analyzing the results of computational experiments.     

Exploring elderly users’ MSNS intermittent discontinuance: A dual-mechanism model

Intermittent discontinuance behavior has become one of the significant obstacle factors to hinder the rapid development of elderly user MSNS market. However, most previous studies of intermittent discontinuance ignored the elderly group. Meanwhile, the prior studies rarely explored the inhibitors and disclosed the whole cognitive and emotion forming phase of intermittent discontinuance. To fill these gaps, the present study targets the elderly group and develops a dual mechanism model from both “enabling-inhibiting” and “appraisal-emotional reaction-coping response” perspectives. The empirical study based on the survey of 307 Chinese elderly MSNS users demonstrates that, in enabling mechanism, excluding the social overload, information and system feature overload can increase the elderly users’ fatigue, which will further trigger intermittent discontinuance. In inhibiting mechanism, excepting the autonomy need satisfaction, relatedness and competence need satisfaction from the MSNS have positive association with the elderly users’ emotional attachment towards MSNS, which will further decrease the intermittent discontinuance. The findings imply that, for the coexistence of enabling and inhibiting mechanism, the service providers should not only decrease the overload-fatigue based enablers but also increase the need satisfaction-attachment based inhibitors to control the intermittent discontinuance.     

Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions

In this study, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Therefore, the potential use of such abnormality for autism screening is investigated. The connectivity patterns are estimated from electroencephalogram (EEG) data collected from 8 brain regions under various mental states. The EEG data of 12 healthy controls and 6 autistic children (age matched in 7–10) were collected during eyes-open and eyes-close resting states as well as when subjects were exposed to affective faces (happy, sad and calm). Subsequently, the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques. Performance of the proposed system in each mental state is separately evaluated. The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods.     

PEMFC用Ce-MCM-41/SPEES复合膜的研究

室温下,通过非水热合成法制备了Ce-MCM-41淬灭剂,通过XRF、FT-IR、XRD等表征显示了Ce4+很好地插入到MCM-41骨架中,TEM表征了Ce-MCM-41的多孔结构,利用二甲基亚砜(DMSO)作为捕获剂通过UV-VIS光谱方法表征了Ce-MCM-41淬灭剂捕获自由基的能力.为了降低燃料电池条件下自由基(HO·)对膜的降解制备了Ce-MCM-41/SPEES复合膜,通过离线Fenton实验和在线加速开路降解实验观察了膜的稳定性,结果显示了Ce-MCM-41/SPEES复合膜的稳定性得到了提高.