Technology-mining model concerning operation characteristics of technology and service providers

Owing to the high variety of industry development, technology and service providers (TSPs) are dedicated to the development of applied technologies. The operation purposes of these service providers are to provide customized services and technologies that meet the various client requirements and improve the capability of their clients via various projects. To outreach services to related enterprises successfully, the TSPs need an effective approach to explore the technology demands of enterprises. Based on the client demands, TSPs can establish effective operation strategies and technology development direction. Previously, the technology-development strategies are determined by domain experts or sales through trial and error. With regard to the operation requirements of the TSPs, this paper presents a technology-mining model based on their contract/project history. The aims of the proposed model focus on exploring technologies with higher client demands and significant research and development returns so that the TSPs can effectively expand and continue their business. Furthermore, the promotion sequence of different technologies can also be determined via the proposed-mining model. By application of the technology-mining model, effective suggestions can be provided to the TSPs for business extension.     

An examination of compaction meter value for asphalt pavement compaction evaluation

Abstract

The potential of using compaction meter value (CMV) for evaluating the compaction of asphalt pavements has been hindered by the fact that the value of CMV can be affected by many factors, which include not only roller operation parameters, but also the temperature of asphalt layer and the underlying support. However, the conventional data processing of CMV usually ignores these factors. The study proposed a new approach to thoroughly examine the relationships between CMV and other factors. Using a field project in Tennessee, USA, the intelligent compaction data were examined to establish the relationships between CMV and other operation parameters such as roller frequency and amplitude first, then the effects of asphalt temperature and underlying support were analysed further utilising the original Witczak model and Abaqus software. After eliminating the influence of other factors, the proposed method could improve the correlation between the asphalt mixture density and CMV.     

纤维增强聚合物复合材料-钢复合圆管约束混凝土轴压性能预测模型

纤维增强聚合物复合材料（FRP）-钢复合圆管约束混凝土由于具有很好的综合性能,近年来得到了广泛的研究,尚缺乏完整的应力-应变关系全曲线计算模型。本文在综合作者现有研究成果的基础上,广泛收集了96个FRP-钢复合圆管约束混凝土柱的轴心受压试验结果,系统分析了试件参数的影响规律,提出了FRP-钢复合圆管约束混凝土的应力-应变关系曲线的完整计算模型,包括峰值应力、峰值应变、极限应力和极限应变的计算方法,并建议了应力-应变关系全曲线预测模型,模型较好地预测了FRP-钢复合圆管约束混凝土的应力-应变关系曲线的特征,预测结果与试验结果吻合较好。建议模型具有较好的通用性和准确性。      

支持服务关联的全局最优服务选择算法

针对面向服务的军事综合电子信息系统候选服务的服务质量往往依赖于其他候选服务的问题,提出了一种支持服务关联的全局最优服务选择方法.该方法将基于QoS的军事信息服务选择问题建模为带QoS约束的多目标组合优化问题,采用基于独立成分分析(ICA)的多目标分布估计算法同时优化多个目标函数,最终产生一组满足约束条件的Pareto最优解服务组合集.对比实验结果表明,基于ICA的多目标分布估计算法通过概率图模型可以描述服务间的相互关系,可以更好地解决存在服务关联的组合服务选择问题,具有良好的有效性和可行性.     

The manufacture and investigation of multi-walled carbon nanotube/polypyrrole/EVA nano-polymeric composites for electromagnetic interference shielding

In this study, the multi-walled carbon nanotube (MWNT)-polypyrrole (PPy) was synthesized by chemical oxidative polymerization using cetyltrimethylammonium bromide (CTAB) as the cationic surfactant. The high electric conductivity of the MWNT-PPy(Cl⁻) was affirmed by the measurement of 4-point-probe method. Then, the MWNT-PPy(Cl⁻) was used as conductive fillers and was compounded with ethylene-vinyl acetate (EVA) by blending to form polymeric composite (MWNT-PPy(Cl⁻)/EVA) that possesses excellent electromagnetic interference shielding (EMI SE). Because of the high conductivity of the MWNT-PPy(Cl⁻) (29/71 wt.%), the MWNT-PPy(Cl⁻) would interdict the conductive network in the composites. This polymeric composite material could shield the electromagnetic wave. As the cationic surfactant CTAB was added in the MWNT-PPy(Cl⁻) chemical oxidative polymerization process, the effect of the surfactant was dispersed, absorbed and attenuated on the MWNT in the chemical oxidative polymerization. For this reason, the EMI SE of the MWNT-PPy(Cl⁻)/EVA composites had an obvious improvement from 10-15 dB to 45-55 dB. In this investigation, the optimum conductive filler was 75.0 phr PPy(Cl⁻) coated on 18.0 phr MWNT. The morphology of the composite also presented a uniform conducting network when a lot of MWNT-PPy(Cl⁻) was added.     

A comparison of different methods to solve inverse biharmonic boundary value problems

The Boundary Element Method (BEM) is applied to solve numerically some inverse boundary value problems associated to the biharmonic equation which involve over‐ and under‐specified boundary portions of the solution domain. The resulting ill‐conditioned system of linear equations is solved using the regularization and the minimal energy methods, followed by a further application of the Singular Value Decomposition Method (SVD). The regularization method incorporates a smoothing effect into the least squares functional, whilst the minimal energy method is based on minimizing the energy functional for the Laplace equation subject to the linear constraints generated by the BEM discretization of the biharmonic equation. The numerical results are compared with known analytical solutions and the stability of the numerical solution is investigated by introducing noise into the input data. Copyright © 1999 John Wiley & Sons, Ltd.     

JackPot: A novel model to study the influence of current non-uniformity and cabling patterns in cable-in-conduit conductors

JackPot is a new model that is used to analyse how and to what extend current non-uniformity among strands in a cable-in-conduit conductor (CICC) affects its performance. The joints at the extremities of the CICCs in coils and short samples introduce a non-uniform current distribution among the strands. A detailed and quantitative study down to strand level is required to explain the involved phenomena, to understand their implications on short sample and coil tests and to provide adequate solutions for improvements. The model can be used to evaluate the influence of the joint design and to define its baseline requirements for short-sample qualification testing, and for optimum magnet performance of for example the ITER coils.JackPot is an electrical network model that simulates the interaction between the superconducting strands in the cable (following their precise trajectories), the interstrand contact resistances, the conduit, and the cable’s connection to the joints. The backbone of JackPot is its cable geometry model, from which all relevant properties are derived. All parameters are derived from well defined experimental measurements on conductor sections and joints, except the axial strain for Nb₃Sn strands, which is the only free parameter in the model.The simulations demonstrate that the current non-uniformity is the source for a number of observed phenomena. Another conclusion is that completely filling the bottom joints and upper terminations of a short sample with solder, opposed to only (partly) soldering the cable surface, improves short-sample testing significantly for qualifying the ITER type CICCs. This paper describes the model and gives a few examples of applications for its validation.     

Development and analyses of data-driven models for predicting the bed depth profile of solids flowing in a rotary kiln

Soft computing data-driven modeling (DDM) techniques have attracted the attention of many researchers across the globe as they do not require deep knowledge of the complex physical process. In the present research, data-driven based models have been developed using support vector regression (SVR), multilayer perceptron neural network (MLP), radial basis function neural network (RBFNN) and general regression neural networks (GRNN) techniques for predicting the bed depth profile of solids flowing in a rotary kiln. The performances of the developed models were compared and evaluated against the experimental results in terms of statistical measures such as coefficient of determination (R²), and average absolute relative error (AARE). The obtained results and findings from this research have revealed that data-driven models can predict the bed depth profile of solids flowing in a rotary kiln quite accurately. The SVR-based model exhibited the lowest AARE value of 1.72% and highest R² value of 0.9981 while GRNN, RBFNN, and MLP models gave corresponding values of AARE as 3.69%, 55.13%, 98.15% and those of R² as 0.9898, 0.0052 and 0.0081, respectively. Moreover, the developed DDM-based models i.e. GRNN, RBFNN, and MLP models overcame the limitations of the existing solutions which involved iterative numerical procedure entailing high degree of computational complexity.     

A case study: Reliability‐based product development and improvement model

A field data‐driven reliability and improvement program was introduced to ForHealth Technologies, Inc. (FHT) in December 2004 for its IntelliFill i.v. (IFiv) System. Since then, FHT has developed and executed a unique reliability‐based product development and improvement model. As a result of implementing this model, from January 2005 to February 2007, the reliability of IFiv System improved its Mean Syringes Between Failures by 1703%, Operator Interventions Per Thousand Syringes by 45%, and Mean Time Between Failures improved by 1725% with system uptime of over 99%. Furthermore, system‐related product costs were reduced by over 40%. IFiv has now delivered over 16 million doses without any medication errors and helped hospital pharmacies realize their drug cost savings along with higher operational efficiencies. The purpose of this case study is to share FHT's success story of conducting reliability‐based product development and improvement program, while keeping the process simple with fast results. This model is applicable at system level for all industries with positive bottom line impact. Copyright © 2007 John Wiley & Sons, Ltd.     

Applied patent mining with topic models and meta-data: A comprehensive case study

The number of patent applications has grown to a size that makes it necessary to process a large amount of data as automated as possible. For professionals analyzing technical developments, there is much need for quickly gaining an overview of a technical domain. We have conducted a large case study that covers over 3 years of work at the Austrian Patent Office, where we developed a toolchain of text mining methods for use by patent experts. Thus, acceptance by the domain expert community has been the major factor in our study. Apart from practical aspects, we experimented with a method to enrich the usual pure text-based topic modeling approaches with available meta-data in the form of IPC symbols, in an attempt to guide the topics towards readily recognizable labels. In this work, we describe this set of tools and the best practices and lessons learned during this extensive period, covering 7 case studies, out of which we describe two in detail.     

A head-to-head comparative study of the conditional performance of control charts based on estimated parameters

Implementation of the Shewhart, CUSUM, and EWMA charts requires estimates of the in-control process parameters. Many researchers have shown that estimation error strongly influences the performance of these charts. However, a given amount of estimation error may differ in effect across charts. Therefore, we perform a pairwise comparison of the effect of estimation error across these charts. We conclude that the Shewhart chart is more strongly affected by estimation error than the CUSUM and EWMA charts. Furthermore, we show that the general belief that the CUSUM and EWMA charts have similar performance no longer holds under estimated parameters.     

Acoustic chemometric prediction of total solids in bioslurry: A full-scale feasibility study for on-line biogas process monitoring

Dry matter is an important process control parameter in the bioconversion application field. Acoustic chemometrics, as a Process Analytical Technology (PAT) modality for quantitative characterisation of dry matter in complex bioslurry systems (biogas fermentation), has not been successful despite several earlier dedicated attempts. A full-scale feasibility study based on standard addition experiments involving natural plant biomass was conducted using multivariate calibration (Partial Least Squares Regression, PLS-R) of acoustic signatures against dry matter content (total solids, TS). Prediction performance of the optimised process implementation was evaluated using independent test set validation, with estimates of accuracy (slope of predicted vs. reference values) and precision (squared correlation coefficient, r²) of 0.94 and 0.97 respectively, with RMSEP of 0.32% w/w (RMSEP_rel = 3.86%) in the range of 5.8-10.8% w/w dry matter. Based on these excellent prediction performance measures, it is concluded that acoustic chemometrics has come of age as a full grown PAT approach for on-line monitoring of dry matter (TS) in complex bioslurry, with a promising application potential in other biomass processing industries as well.     

改性PU/PMMA半IPNs与BaTiO3纳米复合体系相容性研究

从分子结构设计出发,以丙烯酸改性聚氨酯与聚甲基丙烯酸甲酯为原料,通过同步互穿工艺,制备了室温固化互穿聚合物网络,进而与BaTiO3纳米粉复合,获得纳米复合体系,分别采用IR、TMA,TEM等技术检测了互穿聚合过程和网络的相容性。结果表明：通过确定的同步互穿工艺可有效地控制两相间的分离程度;IPN中大量的互穿缠结结构使体系的玻璃化转变温区有效衔接,且体系中化学交联结构的引入,可大大改善组分间的相容性,使聚合物两相间及复合体系的相畴尺寸控制在纳米级范围内。     

The SDGs and the sustainability assessment of private-sector projects: theoretical conceptualisation and comparison with current practice using the case study of the Asian Development Bank

The Sustainable Development Goals (SDGs) are receiving increasing attention in the private sector; however ex post assessment of operational sustainability performance remains well in advance of ex ante sustainability assessment of capital projects. It is also not yet commonplace to structure emergent forms of sustainability assessment to support the SDGs explicitly. This article reviews theoretical frameworks that aim to conceptualise sustainability assessment, and then explores the potential to develop SDG-focussed forms for use at the project-level. The extent of enhancement needed to current practice is illustrated using the case study of the Asian Development Bank. This analysis reveals that sustainability is being presented as a goal of project-level assessment and there has been progress towards delivering on this by, in particular, extending the thematic coverage. Amongst the key challenges remaining are how to address trade-offs (typically unavoidable for projects) and how to connect the SDGs, set at a global level, to the local-level where projects are considered. These findings, which reveal the gap between theory and practice, can inform efforts to develop SDG-focussed sustainability assessment.     

Biosorption of cadmium (II) and lead (II) from aqueous solutions using mushrooms: A comparative study

Sorption capacity of oyster mushroom (Pleurotus platypus), button mushroom (Agaricus bisporus) and milky mushroom (Calocybe indica) were evaluated on biosorption of heavy metals, viz. cadmium (II) and lead (II) from aqueous solutions. The optimum sorption conditions were studied for each metal separately. The desired pH of the aqueous solution was found to be 6.0 for the removal of cadmium (II) and 5.0 for removal of lead (II) for all the mushrooms. The percent removal of both the metals was found to increase with the increase in biosorbent dosage and contact time. The fitness of the biosorption data for Langmuir and Freundlich adsorption models was investigated. It was found that biosorption of cadmium (II) and lead (II) ions onto the biomass of the three mushrooms were better suitable to Langmuir than Freundlich adsorption model. P. platypus showed the highest metal uptake potential for cadmium (q_max 34.96 mg/g) whereas A. bisporus exhibited maximum potential for lead (q_max 33.78 mg/g). Milky mushroom showed the lowest metal uptake capacity for both the metals. The present data confirms that mushrooms may be used as efficient biosorbent for the removal of cadmium (II) and lead (II) ions from aqueous solution.     

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Abstract

Automated vehicle acceptance (AVA) is a necessary condition for the realisation of higher-level objectives such as improvements in road safety, reductions in traffic congestion and environmental pollution. On the basis of a systematic literature review of 124 empirical studies, the present study proposes MAVA, a multi-level model to predict AVA. It incorporates a process-oriented view on AVA, considering acceptance as the result of a four-stage decision-making process that ranges from the exposure of the individual to automated vehicles (AVs) in Stage 1, the formation of favourable or unfavourable attitudes towards AVs in Stage 2, making the decision to adopt or reject AVs in Stage 3, to the implementation of AVs into practice in Stage 4. MAVA incorporates 28 acceptance factors that represent seven main acceptance classes. The acceptance factors are located at two levels, i.e., micro and meso. Factors at the micro-level constitute individual difference factors (i.e., socio-demographics, personality and travel behaviour). The meso-level captures the exposure of individuals to AVs, instrumental domain-specific, symbolic-affective and moral-normative factors of AVA. The literature review revealed that 6% of the studies investigated the exposure of individuals to AVs (i.e., knowledge and experience). 22% of the studies investigated domain-specific factors (i.e., performance and effort ­expectancy, safety, facilitating conditions, and service and vehicle ­characteristics), 4% symbolic-affective factors (i.e., hedonic motivation and social influence), and 12% moral-normative factors (i.e., perceived benefits and risks). Factors related to a person’s socio-demographic profile, travel behaviour and personality were investigated by 28%, 15% and 14% of the studies, respectively. We recommend that future studies empirically verify MAVA using longitudinal or experimental studies.     

Recursive modeling of loss of control in human and organizational processes: A systemic model for accident analysis

A recursive model of accident investigation is proposed by exploiting earlier work in systems thinking. Safety analysts can understand better the underlying causes of decision or action flaws by probing into the patterns of breakdown in the organization of safety. For this deeper analysis, a cybernetic model of organizational factors and a control model of human processes have been integrated in this article (i.e., the viable system model and the extended control model). The joint VSM–ECOM framework has been applied to a case study to help safety practitioners with the analysis of patterns of breakdown with regard to how operators and organizations manage goal conflicts, monitor work progress, recognize weak signals, align goals across teams, and adapt plans on the fly. The recursive accident representation brings together several organizational issues (e.g., the dilemma of autonomy versus compliance, or the interaction between structure and strategy) and addresses how operators adapt to challenges in their environment by adjusting their modes of functioning and recovery. Finally, it facilitates the transfer of knowledge from diverse incidents and near misses within similar domains of practice.     

A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regression tree (CART) method, but with multiple linear regression models as its leaves. The problem with M5P for accident duration prediction, however, is that whereas linear regression assumes that the conditional distribution of accident durations is normally distributed, the distribution for a “time-to-an-event” is almost certainly nonsymmetrical. A hazard-based duration model (HBDM) is a better choice for this kind of a “time-to-event” modeling scenario, and given this, HBDMs have been previously applied to analyze and predict traffic accidents duration. Previous research, however, has not yet applied HBDMs for accident duration prediction, in association with clustering or classification of the dataset to minimize data heterogeneity. The current paper proposes a novel approach for accident duration prediction, which improves on the original M5P tree algorithm through the construction of a M5P-HBDM model, in which the leaves of the M5P tree model are HBDMs instead of linear regression models. Such a model offers the advantage of minimizing data heterogeneity through dataset classification, and avoids the need for the incorrect assumption of normality for traffic accident durations. The proposed model was then tested on two freeway accident datasets. For each dataset, the first 500 records were used to train the following three models: (1) an M5P tree; (2) a HBDM; and (3) the proposed M5P-HBDM, and the remainder of data were used for testing. The results show that the proposed M5P-HBDM managed to identify more significant and meaningful variables than either M5P or HBDMs. Moreover, the M5P-HBDM had the lowest overall mean absolute percentage error (MAPE).     

A wintertime study of polycyclic aromatic hydrocarbons in PM(2.5) and PM(2.5-10) in Beijing: assessment of energy structure conversion

Sixteen priority polycyclic aromatic (PAHs) in PM(2.5) and PM(2.5-10) samples collected from 20 sites in Beijing, China in December 2005 and January 2006 were analyzed to determine the composition, spatial distribution and sources. Total PAHs of PM(2.5) and PM(2.5-10) ranged from 5.2 to 1062.2 ng m(-3) and 7.6 to 759.7 ng m(-3), respectively, categorized as heavier pollution. Among five kind of functional zones involved, industrial center, commercial area and village were heavily polluted. The mean concentration of PAHs in PM(2.5) of 407 ng m(-3) was 1.67-fold of that in PM(2.5-10), which was relatively high compared to the previous studies (winter in 2001 and 2002). The most evident change was the increase of Flu, BbkF and InP, which are believed to be less harmful and related to the increasing use of clean energy. However, pollution distribution was spatially heterogeneous inside the city. The most polluted sites located in the southeast of the city. Unlike previous studies, fluoranthene was the most abundant component quantified, which could be associated with increasing use of natural gas as clean energy. Compositional analysis and principal component analysis (PCA) suggested that different kinds of combustion were the main source of the PAHs in PM, though contribution of coal was still evident.