塔里木盆地塔西南坳陷柯东构造带甫沙4井原油来源及充注过程

甫沙4井位于塔里木盆地塔西南坳陷昆仑山前冲断带的柯东构造带上，北部和东部分别发育有柯克亚和柯东1井油气田。为研究甫沙4井原油来源与充注过程，对原油样品和连续抽提后的含油砂样各组分（游离态、束缚态、包裹体）进行GC、GC?MS和 GC?IRMS分析，与柯克亚凝析油气田油样进行油—油对比。结果表明：甫沙4井晚期充注原油组分具有C_29?32重排藿烷、重排甾烷和Ts相对含量高，C_27?29甾烷ααα 20R分布呈反“L”型，以及正构烷烃单体碳同位素值较低等特征，与柯克亚凝析油气田来源于二叠系普司格组（P_2?3p）烃源岩的主体原油（I类）地球化学特征一致。而早期充注的原油组分具有重排藿烷、重排甾烷和Ts相对含量较低，C_27?29甾烷ααα 20R分布呈“V”型，以及正构烷烃单体碳同位素值较高等特征，与柯克亚凝析油气田来源于中—下侏罗统湖相泥岩的II类原油地球化学特征一致。甫沙4井经历3个阶段成藏过程：①在上新世，二叠系烃源岩于生油晚期阶段生成的I类原油运移至柯克亚构造带或柯东构造带深部形成油藏；②在更新世早期，侏罗系烃源岩于生油早—中期生成的II类原油运移至甫沙4井白垩系储层；③在第四纪，强烈的构造作用使深部I类原油沿断裂调整进入甫沙4井白垩系储层。最终造成甫沙4井白垩系储层II类原油先充注，I类原油后充注的特殊现象。     

Contract duration: Barrier or bridge to successful public-private partnerships?

Public-private partnerships (PPPs) are predominantly executed through the use of formal contracts which define the relationship between a public agency and private supplier. As such, a PPP is not a generic contract between buyer and supplier but rather a specific contractual arrangement between a public buyer and private supplier. The contract captures the responsibilities of each party in achieving a specific set of performance objectives. Given budgetary pressures faced by public agencies, there is an increasing need for suppliers to make investments which could reduce future-year costs of meeting contract performance objectives. This research addresses two overarching questions specific to the issue of private, supplier-side investment. First, “how does public agency and private supplier perception of risk influence contract duration?” and second, “how does contract duration influence private investment?” To answer these questions, structured interviews were conducted with those in public agencies and private, supplier firms actively engaged in PPPs. Factors suspected to contribute to the risk position of public and private actors were evaluated. Additionally, linkages between public buyer-private supplier risk position, contract duration and private, supplier-side investment were addressed. Outcomes suggest properly structured long-term contracts may: 1) provide the risk mitigation mechanisms needed for both public and private actors, and 2) facilitate private, supplier-side investment.     

The application of decision support tools and the influence of local data in prioritizing barrier removal in lower Michigan,USA

Web-based decision support tools (DSTs) can be useful to facilitate decision-making processes for managing complex natural resource systems. However, the alignment of DSTs with the objectives in governmental policies or management plans and the influence of limited local data on the outputs of these tools may reduce the use of DSTs by decision makers. In this study, we examined the outcomes of web-based DSTs when different types of local data were incorporated and demonstrated a way to incorporate outputs from multiple DSTs or local inventories to benefit barrier removal decisions. Restoring habitat connectivity in rivers in northwest lower Michigan, USA, was used as a case study due to the abundance of local inventory data and web-based DSTs. We found that, when compared to prioritizations made using local data, some DSTs could produce similar outcomes (in barriers selected, cost, and the benefit for migratory fish) with limited data, but the trade-offs among users' objectives might influence the cost and effectiveness of DSTs' outputs. Improving the ability of DSTs to incorporate objectives consistent with policy and stakeholders' values (e.g., restore certain species or sedimentation control) across management scales can help close the gap between tool recommendations and management decisions while making the barrier removal prioritization process transparent and efficient.     

A decision fusion based methodology for fault Prognostic and Health Management of complex systems

Prognostic and Health Management (PHM) represents an active field of research and a major scientific challenge in many domains. It usually focuses on the failure time or the Remaining Useful Life (RUL) prediction of a system.This paper presents a generic framework, based on a discrete Bayesian Network (BN), particularly tailored for decision fusion of heterogeneous prognostic methods. The BN parameters are computed according to the fixed prognostic objectives.The effectiveness of the proposed decision fusion based methodology for the prognostic is demonstrated through the RULs estimation of turbofan engines. The application highlights the ability of the approach to estimate RULs which overpasses the performance of most other published results in the literature.     

Discovery of path-attribute dependency in manufacturing environments: A process mining approach

The more knowledge industrial practitioners detain of their production processes, the more they are capable of performing process improvements. Nonetheless, there may exist process characteristics and dependencies that are not easily extractable from business models, such as routing dependent attributes. This paper introduces an algorithm-driven framework to establish whether process path decisions influence the attributes in non-direct sequences, e.g., deploying machine A instead of machine B affects the % of rejected parts on the process, 4 stages down the line. This problem is shown to bears similarities with sequential pattern mining problems. The basis of the solution framework relies on process mining and data mining techniques. The approach proposed is applied on a real industrial log, unveiling deficiencies in the system and providing further improvement recommendations.     

A wavelet-based multi-dimensional temporal recurrent neural network for stencil printing performance prediction

The solder paste printing (SPP) is a critical procedure in a surface mount technology (SMT) based assembly line, which is one of the major attributes to the defect of the printed circuit boards (PCBs). The quality of SPP is influenced by multiple factors, such as the squeegee speed, pressure, the stencil separation speed, cleaning frequency, and cleaning profile. During printing, the printer environment is dynamically varying due to the physical change of solder paste, which can result in a dynamic variation of the relationships between the printing results and the influential factors. To reduce the printing defects, it is critical to understand such dynamic relationships. This research focuses on determining the printing performance during printing by implementing a wavelet filtering-based temporal recurrent neural network. To reduce the noise factor in the solder paste inspection (SPI) data, this research applies a three-dimensional dual-tree complex wavelet transformation for low-pass noise filtering and signal reconstruction. A recurrent neural network is utilized to model the performance prediction with low noise interference. Both printing sequence and process setting information are considered in the proposed recurrent network model. The proposed approach is validated using practical dataset and compared with other commonly used data mining approaches. The results show that the proposed wavelet-based multi-dimensional temporal recurrent neural network can effectively predict the printing process performance and can be a high potential approach in reducing the defects and controlling cleaning frequency. The proposed model is expected to advance the current research in the application of smart manufacturing in surface mount technology.     

A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective

Visual data analysis can be envisioned as a collaboration of the user and the computational system with the aim of completing a given task. Pursuing an effective system‐user integration, in which the system actively helps the user to reach his/her analysis goal has been focus of visualization research for quite some time. However, this problem is still largely unsolved. As a result, users might be overwhelmed by powerful but complex visual analysis systems which also limits their ability to produce insightful results. In this context, guidance is a promising step towards enabling an effective mixed‐initiative collaboration to promote the visual analysis. However, the way how guidance should be put into practice is still to be unravelled. Thus, we conducted a comprehensive literature research and provide an overview of how guidance is tackled by different approaches in visual analysis systems. We distinguish between guidance that is provided by the system to support the user, and guidance that is provided by the user to support the system. By identifying open problems, we highlight promising research directions and point to missing factors that are needed to enable the envisioned human‐computer collaboration, and thus, promote a more effective visual data analysis.     

A novel multi-focus image fusion method based on distributed compressed sensing

Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations.