Towards synthetic biological approaches to resource utilization on space missions

This paper demonstrates the significant utility of deploying non-traditional biological techniques to harness available volatiles and waste resources on manned missions to explore the Moon and Mars. Compared with anticipated non-biological approaches, it is determined that for 916 day Martian missions: 205 days of high-quality methane and oxygen Mars bioproduction with Methanobacterium thermoautotrophicum can reduce the mass of a Martian fuel-manufacture plant by 56%; 496 days of biomass generation with Arthrospira platensis and Arthrospira maxima on Mars can decrease the shipped wet-food mixed-menu mass for a Mars stay and a one-way voyage by 38%; 202 days of Mars polyhydroxybutyrate synthesis with Cupriavidus necator can lower the shipped mass to three-dimensional print a 120 m³ six-person habitat by 85% and a few days of acetaminophen production with engineered Synechocystis sp. PCC 6803 can completely replenish expired or irradiated stocks of the pharmaceutical, thereby providing independence from unmanned resupply spacecraft that take up to 210 days to arrive. Analogous outcomes are included for lunar missions. Because of the benign assumptions involved, the results provide a glimpse of the intriguing potential of ‘space synthetic biology’, and help focus related efforts for immediate, near-term impact.     

Modelling co-translational dimerization for programmable nonlinearity in synthetic biology

Nonlinearity plays a fundamental role in the performance of both natural and synthetic biological networks. Key functional motifs in living microbial systems, such as the emergence of bistability or oscillations, rely on nonlinear molecular dynamics. Despite its core importance, the rational design of nonlinearity remains an unmet challenge. This is largely due to a lack of mathematical modelling that accounts for the mechanistic basis of nonlinearity. We introduce a model for gene regulatory circuits that explicitly simulates protein dimerization—a well-known source of nonlinear dynamics. Specifically, our approach focuses on modelling co-translational dimerization: the formation of protein dimers during—and not after—translation. This is in contrast to the prevailing assumption that dimer generation is only viable between freely diffusing monomers (i.e. post-translational dimerization). We provide a method for fine-tuning nonlinearity on demand by balancing the impact of co- versus post-translational dimerization. Furthermore, we suggest design rules, such as protein length or physical separation between genes, that may be used to adjust dimerization dynamics in vivo. The design, build and test of genetic circuits with on-demand nonlinear dynamics will greatly improve the programmability of synthetic biological systems.     

A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.     

Improved imperialist competitive algorithms for rebalancing multi-objective two-sided assembly lines with space and resource constraints

In this paper, a mathematical model and an improved imperial competition algorithm (IICA) are proposed to solve the multi-objective two-sided assembly line rebalancing problem with space and resource restrictions (MTALRBP-SR). The aim is to find lines’ rebalance with the trade-off between efficiency, rebalancing cost and smoothing after reconfiguration. IICA utilises a new initialisation heuristic procedure based on classic heuristic rules to generate feasible initial solutions. A novel heuristic assimilation method is developed to vigorously conduct local search. In addition, a group-based decoding heuristic procedure is developed to fulfil the final task reassignment with the additional restrictions. To investigate the performance of the proposed algorithm, it is first tested on MTALRBP of benchmark problems and compared with some existing algorithms such as genetic algorithm, variable neighbourhood search algorithm, discrete artificial bee colony algorithm, and two iterated greedy algorithms. Next, the efficiency of the proposed IICA for solving MTALRBP-SR is revealed by comparison with a non-dominated sorting genetic algorithm (NSGA-II) and two versions of original ICA. Computational results and comparisons show the efficiency and effectiveness of IICA. Furthermore, a real-world case study is conducted to validate the proposed algorithm.     

Possible studies on adhesion in space

This paper reviews possible experiments on adhesion that can be carried out in a space lab environment. After introducing the basic concepts of adhesion physics, the proposed European experiment on surface energy measurement by dynamic methods in space environment is discussed and reviewed. It is concluded that as of now more studies on adhesion in terrestrial environment are desirable than space experiments.     

光纤传感器在生物医学中的应用进展

光纤在医学和生物学中得到了广泛的应用,从光管道和压力传感器到复杂的化学传感器都与光纤有关.相干光纤束可用于内窥镜成像,而单光纤可用于近红外分层成像和光学相干分层成像.采用光纤还能方便地将光辐射传输到组织内,以激活靶标化学治疗药物.利用平面光纤光导将光波传输到测定部位的化学传感技术可以进行光度和荧光分析.光纤化学传感器还具有表面分子识别位点或化学反应部位,可用于特定分子的检测.这些化学传感器基于表面等离子体共振、干涉、光谱测量或荧光测量等原理.酶的生物识别或抗原抗体结合使光纤传感器可以获得高的特异性.近年来,测定的靶标分子的范围已从简单的气体分子和离子发展到了DNA等大分子.     

空心阴极在空间技术中的应用

空心阴极在空间技术中有广泛用途,本文介绍了空心阴极的工作特点,工作原理,并在此基础上综述了空心阴极在离子推力器、霍尔推力器、电动力学系绳、航天器电位控制、空心阴极微推力器的应用,最后就加快发展空心阴极技术提出意见.     

Behavior of bubbles in welding for repairs in space

In order to investigate the difference in the behavior of bubbles between in a microgravity environment and in a terrestrial environment, gas tungsten arc (GTA) welding was performed in both environments. The microgravity environment was produced for 10 seconds with less than 10⁻⁵ G with a drop-shaft type system at the Japan Microgravity Center (JAMIC). The materials used were an aluminum alloy and pure silver. It has become clear that more pores are left in the weld in the microgravity environment than in the terrestrial environment. The bubbles cannot easily be released from the weld pool before solidification due to the lack of buoyancy in the microgravity environment. In the microgravity environment, blowholes are distributed uniformly in the weld and are smaller than those in a terrestrial environment. In the microgravity environment, the weld bead is formed flatly though the weld shape is significantly affected by gravity in the terrestrial environment. This indicates that in the microgravity environment, a larger amount of metal can be welded at once and in any welding position.     

空间站尿液处理技术研究及进展

尿液处理再生技术是空间站环境控制和生命保障系统的重要组成部分之一,为了实现空间站水资源的循环利用和最大可能减少空间站水补给量,尿液再生技术的主要功能是将空间站中航天员排泄的尿液进行收集、处理和净化,从尿液中提取水分用于电解水制氧或航天员生活用水,从而实现空间站用水的回收再利用。文章介绍了目前国内外发展较为成熟的尿液处理技术,如渗透蒸发膜技术和蒸气压缩蒸馏技术,以及正在研究中的新型技术：冷冻浓缩及真空旋转蒸馏技术,并提出了未来发展方向。     

Environmental appraisal of green production systems: Challenges faced by small companies using life cycle assessment

This paper presents a snapshot of key challenges encountered by small and medium-sized enterprises (SMEs) devoted to green production systems acting on the call to substantiate their environmental claims. It highlights the overriding barriers of SMEs to meeting the standards for conducting credible product life cycle assessment (LCA), in terms of accessibility to customised data and epistemological limitations. This is illustrated using a real-world example describing the material and process flows for a recycling company. We demonstrate that the rigour towards compiling large (and in some cases nested) data sets, involving expert know-how and adequate representation of system boundary can pose operational barriers. This is underpinned by complications of conducting green production and process-based LCA, deemed essential in ascertaining product and process sustainability. Finally, the paper discusses issues highlighted by the case study and provides useful directions for production researchers, SMEs and consultants.     

Thermal and fluid mechanical problems in space flight

In this paper the basic research activities being carried out in the Institute of Space and Aeronautical Sciences, University of Tokyo and other establishments have been discussed. Four problems of space flight, namely, thermal design of spacecraft, thermal protection during. planetary entry, surface contamination of spacecraft and microgravity effects in space have been specifically highlighted.     

Competitive and sustainable manufacturing by means of ultra-efficient factories in urban surroundings

The rapid growth of the world population, the finiteness of resources on our planet Earth and the ongoing tendency towards urbanisation result in the need to question our actions and economic activities, also for industrial enterprises. A paradigm shift to decouple growth and resource utilisation is needed. By means of ultra-efficiency, factories are targeted, whose positive impact to their surrounding is optimised instead of only minimising negative influences. In the contribution, the concept of ultra-efficient factories and first tools for their implementation are introduced. Therefore, the term ultra-efficient factory is defined based on the state of the art. On this definition, spheres of activity for ultra-efficient factories under consideration of urban surroundings are determined. For the spheres of activity, criteria to assess the according maturity of companies are identified and aggregated into a comprehensive maturity model. The maturity model enables the determination of potentials for advancements of companies. To support the industrial application of the theoretic work, industrial best practices are determined and a visual tool is developed.     

The G-group heuristic to solve the multi-product,sequencing, lot sizing and scheduling problem in flow shops

This paper presents a new heuristic to solve the problem of making sequencing, lot sizing and scheduling decisions for a number of products manufactured in a flow shop environment, so as to minimize the sum of setup and inventory holding costs while a given demand is fulfilled without backlogging. The proposed solution method first determines sequencing decisions then lot sizing and scheduling decisions are simultaneously determined. This heuristic, called the G-group method, divides the set of products into G groups and requires that products belonging to the same group have the same cycle time. Also, the cycle time of each group is restricted to be an integer multiple of a basic period. For each basic period of the global cycle, the products to be produced during this period and the production sequence to be used are chosen. Then, a non-linear program is solved to determine lot sizes and to construct a feasible production schedule. To evaluate its performance, the G-group method was compared to four other methods. Numerical results show that the proposed heuristic outperforms all these methods.     

Reverse engineering and identification in systems biology: strategies,perspectives and challenges

The interplay of mathematical modelling with experiments is one of the central elements in systems biology. The aim of reverse engineering is to infer, analyse and understand, through this interplay, the functional and regulatory mechanisms of biological systems. Reverse engineering is not exclusive of systems biology and has been studied in different areas, such as inverse problem theory, machine learning, nonlinear physics, (bio)chemical kinetics, control theory and optimization, among others. However, it seems that many of these areas have been relatively closed to outsiders. In this contribution, we aim to compare and highlight the different perspectives and contributions from these fields, with emphasis on two key questions: (i) why are reverse engineering problems so hard to solve, and (ii) what methods are available for the particular problems arising from systems biology?     

钢铁企业实现可持续发展的途径

可持续发展战略是中国钢铁企业的必然选择。实施这一战略可以从环保、节能、资源的综合利用起步，以技术进步、环保投资为支撑，推进循环经济，从而为实现企业与社会长期和谐、协调一致的发展承担起应有的责任。     

Optimising space utilisation in block stacking warehouses

Block stacking storage is an inexpensive storage system widely used in manufacturing systems where pallets of stock keeping units (SKUs) are stored in a warehouse at the finite production rates. However, determining the optimal lane depth that maximises space utilisation under a finite production rate constraint has not been adequately addressed in the literature and is an open problem. In this research, we propose mathematical models to obtain the optimal lane depth for single and multiple SKUs where the pallet production rates are finite. A simulation model is used to evaluate performance of the proposed models under stochastic uncertainty in the major production parameters and the demand.     

向量空间的彩色图像边缘检测

边缘检测是图像处理的重要组成部分之一。拓展小波变换为向量空间形式,并提出了基于向量小波变换的彩色图像边缘检测方法。根据小波变换的特性,检测模值局部极大点作为边缘点。引入大小自适应的卷积模板,小波变换尺度为 2j时,大小为(4×2j-1)×(4×2j-1),使计算量得到有效控制。实验结果证明,该方法继承了小波变换的性能,并充分利用了彩色信息,能完全地检测出彩色图像边缘。     

National aeronautics and space administration needs and trends in cryogenic cooling

An outline of the requirements for cryogenic cooling in space instrumentation is presented. The recent results of several cryogenic cooling programmes are also discussed.     

Aligning business process reengineering in implementing global supply chain systems by the SCOR model

As supply chains continue to replace individual companies as the management arena for value-adding from the beginning of the twenty first century, understanding the supply chain management practices in a globalisation context becomes increasingly important. The Supply Chain Operations Reference (SCOR) Model, which was developed by the experts and practitioners of the Supply Chain Council, is a major framework for supply chain planning that features supply chain management practices and business process reengineering. Despite being an integrative guide with many merits, it only provides a ‘top-down’ approach that requires the comparative analyses of post- and pro-performance indices as a basis of business process modification. This study discusses the limitations of current SCOR analysis and provides a mapping technique—Causes/Effects, the SCOR Standard, and Mutual Solution (CESM)—for gap mapping, problem prioritisation, and business process modification in a supply chain setting. As such, it is one of the early empirical studies combining BPR and SCM disciplines. The research results can facilitate the implementation processes of multinational supply chain projects by identifying the gaps and linking them to the channel entities.     

A hybrid nested partitions algorithm for scheduling flexible resource in flow shop problem

Production scheduling with flexible resources is critical and challenging in many modern manufacturing firms. This paper applies the nested partitions (NP) framework to solve the flexible resource flow shop scheduling (FRFS) problem using an efficient hybrid NP algorithm. By considering the domain knowledge, the ordinal optimisation principle and the NEH heuristics are integrated into the partitioning scheme to search the feasible region. An efficient resource-allocation procedure is built into the sampling scheme for the FRFS problem. A large number of benchmark examples with flexible resources are tested. The test results show that the hybrid NP algorithm is more efficient than either generic NP or heuristics alone. The algorithm developed in this study is capable of selecting the most promising region for a manufacturing system with a high degree of accuracy. The algorithm is efficient and scalable for large-scale problems.