Maternal Malnutrition Affects Hepatic Metabolism through Decreased Hepatic Taurine Levels and Changes in HNF4A Methylation

Fetal programming implies that the maternal diet during pregnancy affects the long-term health of offspring. Although maternal diet influences metabolic disorders and non-alcoholic fatty liver disease in offspring, the hepatic mechanisms related to metabolites are still unknown. Here, we investigated the maternal diet-related alterations in metabolites and the biological pathway in male offspring at three months of age. Pregnant rats were exposed to 50% food restriction during the prenatal period or a 45% high-fat diet during the prenatal and postnatal periods. The male offspring exposed to food restriction and high-fat diets had lower birth weights than controls, but had a catch-up growth spurt at three months of age. Hepatic taurine levels decreased in both groups compared to controls. The decreased hepatic taurine levels in offspring affected excessive lipid accumulation through changes in hepatocyte nuclear factor 4 A methylation. Moreover, the alteration of gluconeogenesis in offspring exposed to food restriction was observed to a similar extent as that of offspring exposed to a high fat diet. These results indicate that maternal diet affects the dysregulation in hepatic metabolism through changes in taurine levels and HNF4A methylation, and predisposes the offspring to Type 2 diabetes and non-alcoholic fatty liver disease in later life.     

Design of energy‐efficient train speed profiles considering fixed‐block signaling system

In dense traffic railway networks, trains may often slow down or stop between stations owing to previous train delays. If preceding train trajectory can be predicted, energy‐efficient driving can be achieved by suppressing unnecessary speed changes. In this paper, we propose an algorithm to find energy‐efficient driving considering fixed‐block signaling (FBS) system by using dynamic programming (DP). DP is suitable for use because it can optimize the control inputs with discrete and state constraints. In this paper, we discuss energy‐efficient driving by considering a FBS system using some case studies of simulation. In the simulation, we examine a technique to drive an express train in an energy‐efficient way when the preceding local train is running toward the station with passing loops. The results show that the proposed method can derive complex speed profiles for energy‐efficient driving and the train can be operated with a maximum reduced energy consumption of 8.3%.     

基于凸闭包集的不规则三角网算法研究与实现

不规则三角网是影响土石方量计算的关键问题之一，虽然目前已有大量研究人员对Delaunay三角网进行优化，但仍然缺少对三角网生成速率和计算精度的综合研究。针对此问题，基于VB.NET平台的点、边以及三角形的结构化数据结构设计快速生成凸闭包集形成Delaunay三角网，通过四边形空圆特性以及对角线最短原则对构网结果进行检验和优化。试验计算表明，该算法的计算结果与经典DTM法、方格网法的土方量计算相对误差均在0.30%以内。该方法不仅具有较高的组网效率，并能保持较好的计算精度。     

The constrained two-dimensional guillotine cutting problem with defects: an ILP formulation,a Benders decomposition and a CP-based algorithm

This paper addresses a variant of two-dimensional cutting problems in which rectangular small pieces are obtained by cutting a rectangular object through guillotine cuts. The characteristics of this variant are (i) the object contains some defects, and the items cut must be defective-free; (ii) there is an upper bound on the number of times an item type may appear in the cutting pattern; (iii) the number of guillotine stages is not restricted. This problem commonly arises in industrial settings that deal with defective materials, e.g. either by intrinsic characteristics of the object as in the cutting of wooden boards with knotholes in the wood industry, or by the manufacturing process as in the production of flat glass in the glass industry. We propose a compact integer linear programming (ILP) model for this problem based on the discretisation of the defective object. As solution methods for the problem, we develop a Benders decomposition algorithm and a constraint-programming (CP) based algorithm. We evaluate these approaches through computational experiments, using benchmark instances from the literature. The results show that the methods are effective on different types of instances and can find optimal solutions even for instances with dimensions close to real-size.     

A new DEA common-weight multi-criteria decision-making approach for technology selection

This paper addresses an advanced manufacturing technology selection problem by proposing a new common-weight multi-criteria decision-making (MCDM) approach in the evaluation framework of data envelopment analysis (DEA). We improve existing technology selection models by giving a new mathematical formulation to simplify the calculation process and to ensure its use in more general situations with multiple inputs and multiple outputs. Further, an algorithm is provided to solve the proposed model based on mixed-integer linear programming and dichotomy. Compared with previous approaches for technology selection, our approach brings new contributions. First, it guarantees that only one decision-making unit (DMU) (referring to a technology) can be evaluated as efficient and selected as the best performer while maximising the minimum efficiency among all the DMUs. Second, the number of mixed-integer linear programs to solve is independent of the number of candidates. In addition, it guarantees the uniqueness of the final optimal set of common weights. Two benchmark instances are used to compare the proposed approach with existing ones. A computational experiment with randomly generated instances is further proceeded to show that the proposed approach is more suitable for situations with large datasets.     

Multi-product continuous plant scheduling: combination of decomposition,genetic algorithm,and constructive heuristic

We propose a polylithic method for medium-term scheduling of a large-scale industrial plant operating in a continuous mode. The method combines a decomposition approach, a genetic algorithm (GA) and a constructive MILP-based heuristic. In the decomposition, decisions are made at two levels, using the rolling horizon approach. At the upper level, a reduced set of products and the time period is chosen to be considered in the lower level. At the lower level, a short-term scheduling MILP-model with event-based representation is used. A heuristic solution to the lower level problem is found using a constructive Moving Window heuristic guided by a genetic algorithm. The GA is applied for finding efficient utilisation of critical units in the lower level problem. For solving the one unit scheduling problem, a parallel dynamic programming algorithm is proposed. Implementation of the dynamic programming algorithm for a graphics processing unit (GPU) is incorporated in the GA for improving its performance. The experimental study of the proposed method on a real case of a large-scale plant shows a significant improvement of the solution quality and the solving time comparing to the pure decomposition algorithm proposed in the earlier study, and confirmed suitability of the proposed approach for the real-life production scheduling. In particular, the reduction of the number of changeovers and their duration in the obtained solution as well as the CPU time of solving the problem was about 60% using the new approach.     

珲春地区高瓦斯矿井煤自然发火标志气体研究

为了探究珲春地区高瓦斯矿井煤自然发火情况，选取该地区板石煤矿22、23a和八连城煤矿18#、26#共4个煤层进行程序升温特性实验，分析了CO及烃类气体产生量随温度的变化规律，优选自然发火标志气体，测算煤自燃临界温度。结果表明，板石22、23a和八连城18#、26#四个煤层的自燃临界温度分别为101.0℃、97.6℃、121.0℃、169.1℃。CO和C2H4的初现温度大约在30℃与80～120℃，且产生量随温度单调递增，可作为煤自燃预测预报的主要参考指标|而同时，为了保证检测的全面与准确，还可以将规律性良好的其他烃类气体、烯烷比和链烷比进行辅助参考。     

Development of strategic hydrogen refueling station deployment plan for Korea

The Republic of Korea government has set yearly targets of hydrogen cars and buses and plans to install hydrogen refueling stations nationwide. This paper proposes a methodology for developing a strategic deployment plan with three mathematical models. For a given target, future refueling demand locations and amount from general road and expressway are systematically estimated. First, the required number of refueling stations to satisfy the target covering ratio of the total demand set by the government is determined by the Station number determination model. Next, the locations of the capacitated stations and the allocation of demand to the stations are determined by the second Max cover and the third p-median models. Since the max covering is more important than minimizing the travel time, the two models are used sequentially. The nationwide hydrogen station deployment plan for the years 2022–2040 obtained by the proposed methodology is reported.     

Selecting supplier combination based on fuzzy multicriteria analysis

Existing multicriteria analysis (MCA) methods are probably ineffective in selecting a supplier combination. Thus, an MCA-based fuzzy 0-1 programming method is introduced. The programming relates to a simple MCA matrix that is used to select a single supplier. By solving the programming, the most feasible combination of suppliers is selected. Importantly, this result differs from selecting suppliers one by one according to a single-selection order, which is used to rank sole suppliers in existing MCA methods. An example highlights such difference and illustrates the proposed method.     

Semantics for consistent activation in context-oriented systems

ContextContext-oriented programming languages provide dedicated programming abstractions to define behavioral adaptations and means to combine those adaptations dynamically according to sensed context changes. Some of these languages feature programming abstractions to explicitly define interaction dependencies among contexts. However, the semantics of context activation and the meaning of dependency relations have been described only informally, which in some cases has led to incorrect specifications, faulty implementations and inconsistent system behavior.ObjectiveWith the aim of avoiding faulty implementations and inconsistencies during system execution, this paper proposes both a formal and run-time model of contexts, context activation and context interaction.MethodAs a formal and computational basis, we introduce context Petri nets, a model based on Petri nets, which we found to match closely the structure of contexts in context-oriented systems. The operational semantics of Petri nets permits the modeling of run-time context activations. Existing Petri net analyses allow us to reason about system properties. As validation, we carried out small and medium-sized case studies.ResultsIn the cases explored, context Petri nets served effectively as underlying run-time model to ensure that declared context interaction constraints remain consistent during context manipulation. Moreover, context Petri nets enabled us to analyze certain properties regarding the activation state of particular contexts.ConclusionContext Petri nets thus proved to be appropriate to encode and manage the semantics of context activation, both formally and computationally, so as to preserve the consistency of context-oriented systems.