Incomplete analytic hierarchy process with minimum weighted ordinal violations

ABSTRACT

Incomplete pairwise comparison matrices offer a natural way of expressing preferences in decision-making processes. Although ordinal information is crucial, there is a bias in the literature: cardinal models dominate. Ordinal models usually yield nonunique solutions; therefore, an approach blending ordinal and cardinal information is needed. In this work, we consider two cascading problems: first, we compute ordinal preferences, maximizing an index that combines ordinal and cardinal information; then, we obtain a cardinal ranking by enforcing ordinal constraints. Notably, we provide a sufficient condition (that is likely to be satisfied in practical cases) for the first problem to admit a unique solution and we develop a provably polynomial-time algorithm to compute it. The effectiveness of the proposed method is analyzed and compared with respect to other approaches and criteria at the state of the art.     

Soybean Trypsin Inhibitor Assay: Further Improvement of the Standard Method Approved and Reapproved by American Oil Chemists’ Society and American Association of Cereal Chemists International

For measuring trypsin inhibitor (TI) activities in soybean products, the current standard method, approved and reapproved by American Oil Chemists Society (Method Ba 12-75) and American Association of Cereal Chemists International (Method 22-40.01), features mixing trypsin with a series of inhibitor levels and then adding a substrate to start the colorimetric reaction. Yet, previous studies have shown flaws with the method, particularly with using several inhibitor levels and the sequence of adding the substrate last. The present study showed that with varying levels of dilution and volumes of a dilute sample extract, the pH of the premix (the mixture of a dilute sample extract and trypsin solution) ranged 3.30–3.60 for raw soy flour, and 3.20–6.70 for toasted soy. Within these premix pH ranges, the standard method of adding substrate last would give TI values equal to or less than those measured by the same method except for adding the enzyme last. The standard method was subsequently improved by using a single sample extract level and the enzyme-last sequence. Other modifications included making stock solutions for reagents, adding Ca²⁺ to the trypsin solution, diluting sample extracts to a level that causes 30–70% of inhibition, and running both reference and sample blanks for better controls. Alternatively, the full volume assay (10 mL total, as in the standard method) was further modified by using half the volume of each reagent with the same concentration. Compared to the standard method, the improved methods gave more consistent results when assaying 11 selected soy products. The half volume (5 mL) and full volume methods gave the same results, but the former could increase assay sensitivity and reduce amounts of reagents used.     

基于一种改进逐点比较插补法的激光打标机控制加工算法

在激光打标系统中,逐点比较插补法是一种普遍使用的算法。这种算法在控制激光运动轨迹和对图像轨迹的拟合过程中,起到极其重要的作用。由于逐点比较插补法具有运算直观,脉冲输出均匀,调节方便等特点,所以是一种较为适合打标机系统应用的插补算法。但是传统的逐点比较插补算法存在精度低和速度慢等问题。为了改善该算法的性能,对传统逐点比较插补算法进行了改进,并进行了计算和模拟仿真。结果表明改进比较插补算法在精度和速度上都有明显提高。     

碎软煤层条带瓦斯地面高效抽采技术研究

为了提高碎软煤层条带瓦斯抽采效率和效果,基于目前地面瓦斯抽采主要采用垂直井或从式井的方式抽采效果差、效率低的现状,通过理论和实验分析论证了穿岩层压裂改造煤储层的可行性,提出了在目标煤层顶板岩层中钻水平井,并通过垂直向下射孔以及采用泵送桥塞分段进行压裂的方式进行地面瓦斯抽采。试验结果表明:顶板分段压裂水平井单井产量高、高稳产期更长、产量衰减更慢;有效水平井段控制区域内瓦斯下降均匀,更有利于进行条带瓦斯抽采;相同投资条件下,采用水平井的方式瓦斯抽采效率和投入产出比更高。     

中美典型高压页岩气藏钻井提速技术对比与启示

美国Haynesville高压页岩气气藏与中国长宁—威远国家级页岩气示范区下志留统龙马溪组高压页岩气藏的地质、钻井条件最为类似,前者的钻井提速技术思路对于我国页岩气快速钻井也具有重要的参考借鉴意义。为了实现我国页岩气藏的钻井提速,通过对比中国和美国典型高压页岩气藏钻井提速技术,分析探讨了上述两个气藏在钻井提速技术方面的异同点。研究结果表明:①目前美国Haynesville等页岩气藏已将水平段井眼尺寸从215.9 mm缩小到171.5 mm或161.1 mm,单井钻井投资降低超过25%,节能减排优势明显;②美国同行采用的"高效钻头+配套井下提速工具+钻井参数优化软件/装备"的硬研磨性难钻地层系统化钻井提速技术,值得国内重视、借鉴、攻关、应用;③"控压钻井+降密度"钻井技术能够在高压页岩层段钻井提速中起到至关重要的提速应用效果。结论认为,应积极在国内国家级页岩气示范区开展缩小井身结构尺寸及其相关的钻井配套装备、工具、软件的应用探索试验;在目的层段值得持续探索、优化、应用控压钻井技术。     

Qualification of an ultrasonic flow meter as a transfer standard for measurements at Reynolds numbers up to 4×106 between NMIJ and PTB

The quality of any laboratory intercomparison depends to a large extent on the performance of the used flow meter. To find a flow meter that is capable of reaching a reproducibility better than 0.05% requires bounding all involved influence quantities down to the required level. The present paper describes the efforts performed while qualifying a time-of-flight ultrasonic flow meter as a transfer standard. It was determined that the most relevant influence quantity besides the flow profile within the bulk flow is the effect caused by the transducer pockets in the meter body. By taking advantage of a specially designed window chamber, it was possible to determine the magnitude of the errors introduced by the transducer pockets and to define, based on the findings, a procedure to perform a bilateral comparison between the hot water calibration facilities of the Physikalisch-Technische Bundesanstalt and the National Institute of Advanced Industrial Science and Technology. The results of the bilateral comparison are presented.     

CBK searching (chemistry-biology-keyword): Performing cross-discipline collaborative searches

A debate over whether a search is a chemistry or a biology search is becoming increasingly more frequent within search teams. The line between non-text chemistry and biology searching has become blurred in recent years. The appearance of “non-natural” amino acids and modified peptides, for example, has given rise to the questions: Is it a structure? Is it a sequence? Could it be both? It may not be a question of one or the other, but perhaps working with a colleague to bring the best possible answer to the client. Several examples clarify the line and present guidance when beginning a search. Consider the size of the molecule, how many amino acids are present and what the client needs in order to best cover all bases and create a more comprehensive search report.     

Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

Model predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems. To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. The novelty of our methodology lies in the application of POD's highly efficient linear decomposition for the consequent conversion of any distributed multi-dimensional space-state model to a reduced 1-dimensional model, dependent only on time, which can be handled effectively as a black-box through ANNs. Hence we construct a paradigm, which allows the application of NMPC to complex nonlinear high-dimensional systems, even input/output systems, handled by black-box solvers, with significant computational efficiency. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The stabilization/destabilization of a tubular reactor with recycle is used as an illustrative example to demonstrate the efficiency of our methodology. Case studies with inequality constraints are also presented.     

Wavelet-based hybrid natural image modeling using generalized Gaussian and α-stable distributions

Natural image is characterized by its highly kurtotic and heavy-tailed distribution in wavelet domain. These typical non-Gaussian statistics are commonly described by generalized Gaussian density (GGD) or α-stable distribution. However, each of the two models has its own deficiency to capture the variety and complexity of real world scenes. Considering the statistical properties of GGD and α-stable distributions respectively, in this paper we propose a hybrid statistical model of natural image’s wavelet coefficients which is better in describing the leptokurtosis and heavy tails simultaneously. Based on a clever fusion of GGD and α-stable functions, we establish the optimal parametric hybrid model, and a close-formed Kullback–Leibler divergence of the hybrid model is derived for evaluating model accuracy. Experiment results and comparative studies demonstrate that the proposed hybrid model is closer to the true distribution of natural image’s wavelet coefficients than the single modeling using GGD or α-stable, while is beneficial for applications such as image comparison.