Value chain analysis of bio-coal business in Finland: Perspectives from multiple value chain members

The purpose of this study is to provide a comprehensive outline of the Value Chain (VC)¹ of bio-coal in Finland, define the proper business models of bio-coal business, and investigate the interrelationships between VC activities. The major findings are: 1) the study found significant interrelationships between support and primary activities in the bio-coal VC which suggest that we should not emphasis only the core value/primary activities of the bio-coal business, but pay more attentions to the “side values”/support activities; 2) The study identified political and environmental factors as the effective drivers, while technology and cost-profit uncertainty as major restrictions for the Finnish bio-coal business. The authors would like to suggest that instead of purely “environmental concerns”, the term “sustainability credentials” should be emphasized, which means value creating and adding items should not limit to monetary values, but consider social and environmental value propositions. 3) The study identified “low competition” in the current bio-coal business in Finland, which implies opportunities of market entrance for Finnish and international companies; 4) The study identified that the current Finnish bio-coal model is the decentralized model at the local level. However, the future Finnish bio-coal model will be a large scaled model, with a focus on export sales.     

Nonlinear dynamic behaviors and control based on simulation of high-purity heat integrated air separation column

In this paper, the dynamic behaviors on the basis of simulation for high-purity heat integrated air separation column (HIASC) are studied. A nonlinear generic model control (GMC) scheme is proposed based on the nonlinear behavior analyses of a HIASC process, and an adaptive generic model control (AGMC) scheme is further presented to correct the model parameters online. Related internal model control (IMC) scheme and multi-loop PID (M-PID) scheme are also developed as the comparative base. The comparative researches are carried out among these linear and nonlinear control schemes in detail. The simulation research results show that the proposed AGMC schemes present advantages in both servo control and regulatory control for the high-purity HIASC.     

高温氢工质热物理性质计算分析

氢工质在新能源与动力、航天推进、化工材料等领域有着广泛应用。通过开展高温氢工质热力学与输运性质研究，建立了原子态氢、分子态氢、热解平衡态氢的热物理性质计算模型，开发了热物性计算程序Prop_H_H2，适用范围为温度100~3 500 K、压力104~5×107 Pa 。验证表明，Prop_H_H2在适用范围内计算氢工质的物性参数合理可靠，在温度200~3 000 K、压力104~107 Pa范围内，程序预测值更加准确，相对偏差在±5%左右。本研究可为氢工质相关的航天推进、应用物理学、能源动力等行业的科研和应用提供支持借鉴。     

Sustainability of ride-hailing services in China’s mobility market: A simulation model of socio-technical system transition

As a symbol of sharing economy, ride-hailing services have spread to Asia, where various forms of services have been established according to the existing socio-technical regimes, including legal, policy, and environmental concerns. China, in particular, has accepted ride-hailing services by revising existing institutions. This study analyzes China’s acceptance of ride-hailing services in the socio-technical system context set up for legacy services. Simulation modeling, combined with transition theory and an agent-based model, is used to analyze the data. This study calculates consumer disutility based on mobility market share, reflecting consumer preference, and predicts the sustainability of ride-hailing services. The simulation results conclude that legalization and socio-technical context are of importance for sustainable mobility.     

基于空气质量模拟的江苏省大气污染物排放清单比较研究

大气污染物排放清单是空气质量模拟和空气污染治理的重要依据.本研究比较分析了两套覆盖江苏省的2017年大气污染物排放清单，即分别由上海市环境科学研究院、江苏省环境科学研究院编制的"长三角清单"和"江苏省清单"，并结合区域空气质量模型CMAQ评估不同清单对长三角地区2017年1、4、7、10月的空气质量模拟的影响.清单比较结果表明，除二氧化硫（SO₂）以外，江苏省清单估算的各污染物排放量较长三角清单低.通过与观测数据比较，发现两套清单对SO₂、氮氧化物（NO_x）、臭氧（O₃）和细颗粒物（PM_2.5）的模型模拟性能均较好.江苏省清单与长三角清单两者的模拟结果空间分布接近，其中江苏省清单模拟的PM_2.5和O₃在长三角多数地区略低于长三角清单的模拟结果（1月O₃除外）.江苏省清单与长三角清单均能够用于空气质量模式模拟，可为江苏地区的细颗粒物和光化学烟雾污染的控制策略制定提供参考.     

Control of the particle microstructure during the synthesis of bulk-polymerized transparent methacrylate-acrylonitrile-butadiene-styrene resin

A series of methacrylate-acrylonitrile-butadiene-styrene (MABS) resins was prepared using bulk polymerization. The polarity of the continuous phase and the compatibility of two phases were changed by adjusting the methyl methacrylate (MMA) content, choosing values that were close to styrene-butadiene rubber solubility value. The possibility of controlling the microstructure of the MABS resin by changing the polarity of the components and the compatibility of two phases was assessed. The dynamic mechanical analysis shows that the compatibility of two phases varies with the MMA content. The morphological analysis shows that increasing MMA contents results in a gradual decrease in the sub-inclusion structure with a network skeleton of rubber particles, and that all the particles become solid rubber when the MMA content reaches 75%. The sub-inclusion structure reappears but does not have a network skeleton when the MMA content is 90%. The impact strength and morphological analysis indicate that the solid rubber particles and the sub-inclusion structure with a network skeleton provide excellent toughness, while the sub-inclusion structure without a network skeleton does not. In contrast, the transmittance of the ABS resin first increased and then decreased with increasing MMA content.     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

An analytical model for optimizing the performance of graphene based silicon Schottky barrier solar cells

In this paper, a model taking into account the effects of carrier loss mechanisms has been developed. The model simulates the photovoltaic properties of the graphene/n-type silicon Schottky barrier solar cells (G/n-Si_SBSC), and it can reproduce the experimentally determined parameters of the G/n-Si_SBSC. To overcome the low efficiencies of G/n-Si_SBSC, their performances have been optimized by modifying the work function of graphene and Si properties, accounted for variation of its thickness and doping level. The obtained results show that the work function of graphene has the major impact on the device performance. Also, the temperature dependence of the G/n-Si_SBSC performance is investigated.