In Silico Study on Tumor-Size-Dependent Thermal Profiles inside an Anthropomorphic Female Breast Phantom Subjected to Multi-Dipole Antenna Array

Electromagnetic hyperthermia as a potent adjuvant for conventional cancer therapies can be considered valuable in modern oncology, as its task is to thermally destroy cancer cells exposed to high-frequency electromagnetic fields. Hyperthermia treatment planning based on computer in silico simulations has the potential to improve the localized heating of breast tissues through the use of the phased-array dipole applicators. Herein, we intended to improve our understanding of temperature estimation in an anatomically accurate female breast phantom embedded with a tumor, particularly when it is exposed to an eight-element dipole antenna matrix surrounding the breast tissues. The Maxwell equations coupled with the modified Pennes’ bioheat equation was solved in the modelled breast tissues using the finite-difference time-domain (FDTD) engine. The microwave (MW) applicators around the object were modelled with shortened half-wavelength dipole antennas operating at the same 1 GHz frequency, but with different input power and phases for the dipole sources. The total input power of an eight-dipole antenna matrix was set at 8 W so that the temperature in the breast tumor did not exceed 42 °C. Finding the optimal setting for each dipole antenna from the matrix was our primary objective. Such a procedure should form the basis of any successful hyperthermia treatment planning. We applied the algorithm of multi for multi-objective optimization for the power and phases for the dipole sources in terms of maximizing the specific absorption rate (SAR) parameter inside the breast tumor while minimizing this parameter in the healthy tissues. Electro-thermal simulations were performed for tumors of different radii to confirm the reliable operation of the given optimization procedure. In the next step, thermal profiles for tumors of various sizes were calculated for the optimal parameters of dipole sources. The computed results showed that larger tumors heated better than smaller tumors; however, the procedure worked well regardless of the tumor size. This verifies the effectiveness of the applied optimization method, regardless of the various stages of breast tumor development.     

Rolling Kanban: a new visual tool to schedule family batch manufacturing processes with kanban

Rolling Kanban is the name of a kanban management methodology for batch processes manufacturing environment. Proposed in the early 2000s by FESTO Consulting, for two decades we have lost knowledge both from operative and scientific point of view. Basically, Rolling Kanban means a visual planning methodology based on the production of product-families and variants where: (i) set-up times are reduced between the products of the same family, and (ii) relevant times (dozens of minutes if not even hours) must be considered for changeover between products of different families. In addition, the cyclic production sequence between different product families cannot be maintained. Considering that very few technical information and documents are available about this approach, the main objective of this paper is to retrieve and present, for the first time to the scientific community, the Rolling Kanban methodology. Besides, a real industrial implementation concerning a manufacturer of domestic fittings is discussed as a case study. More specifically, two novel versions of the original Rolling Kanban technique are fully presented to effectively overcome certain limits and criticalities found during its operative use, such as the difficulty to realise a pull production, considering increased set-up time for changeover between products of different families.     

Disassembly sequence planning using a Flatworm algorithm

Disassembly Sequence Planning (DSP) refers to a disassembly sequence based on the disassembly properties and restrictions of the product parts that meets the benefit goal. This study aims to reduce the number of changes in disassembly direction and disassembly tools so as to reduce the disassembly time. This study proposes a novel Flatworm algorithm that evolves through the regenerative properties of the flatworm. It is similar to the evolutionary concept of genetic algorithms, with evolution as the main idea, but without crossover, mutation or replication mechanisms in the evolutionary processes. Instead, it is based upon the characteristics of the growth, fracture and regeneration mechanisms of the flatworm. The Flatworm algorithm features a variety of disassembly combinations and excellent mechanisms to avoid the local optimal solution. In particular, it has the advantage of keeping a good disassembly combination from being destroyed. In this study, it is compared with two genetic algorithms and two ant colony algorithms and tested in three examples of different complexity: a ceiling fan, a printer, and 150 simulated parts. The solution searching ability and execution time are compared upon the same evaluation standard. The test results demonstrate that the novel Flatworm algorithm proposed in this study is superior to the two genetic algorithms and ant colony algorithms in solution quality.     

透视中国综合防灾减灾的主要进展及其挑战和战略对策

系统总结中国自然灾害风险主要特点和综合防灾减灾工作的现状特征,全面梳理综合防灾减灾工作的发展趋势,分析综合防灾减灾面临的三大挑战,提出有针对性和可实施性的五大战略对策。一方面,未来100年中国的气候变暖趋势将进一步加剧,极端天气事件发生的频率将增大,荒漠化、海平面上升、冰川退缩趋势将进一步加重,全球气候变化所引发的各类巨灾和由此触发的各类自然灾害的突发性、并发性、群发性、异常性日益凸显。另一方面,中国能源结构以煤为主,同时是世界上进口石油资源的大国,随着中国经济的发展,未来能源消费和二氧化碳排放量还将持续增长,经济社会发展对能源安全、粮食安全、生态安全提出了更高要求。为适应气候变化背景下的各类灾害带来的不利影响,中国应全面提高监测预警水平,建立综合防灾减灾体系,全面提高应对突发灾害和巨灾的应急能力;推动建立区域综合防灾减灾范式,系统实施绿色经济战略,大力推进节能减排,转变经济发展方式。     

Multi-stage power source and grid coordination planning method considering grid uniformity

Given the increasing uncertainties in power supply and load, this paper proposes the concept of power source and grid coordination uniformity planning. In this approach, the standard deviation of the transmission line load rate is considered as the uniformity evaluation index for power source and grid planning. A multi-stage and multi-objective optimization model of the power source and grid expansion planning is established to minimize the comprehensive cost of the entire planning cycle. In this study, the improved particle swarm optimization algorithm and genetic algorithm are combined to solve the model, thus improving the efficiency and accuracy of the solution. The analysis of a simple IEEE Garver’s 6-node system shows that the model and solution method are effective and feasible. Moreover, they are suitable for the coordinated planning of the power source and grid under a diversified nature of power supply and load.     

Ten complementary measures to assist with environmental watering programs in the Murray–Darling river system,Australia

Restoration programmes for degraded aquatic ecosystems frequently focus on flow restoration or reinstatement, including recovery targets for volumes of water to be used for environmental benefit. Australia's Murray–Darling Basin is an example of a major system undergoing substantial water reform to balance the needs of competing users, including the environment, within the constraints of an arid climate. This reform revolves around accounting for finite volumes of water that have been shared amongst water users. We argue that while recovering water will provide good outcomes, as a sole intervention, it is not enough to deliver the desired environmental benefits of the reform given the significantly altered state of the catchment. Here, we present 10 measures that could be used to complement planned water recovery actions. These “complementary measures” integrate recovery actions, which when strategically combined with water delivery would significantly enhance water reform efforts to generate environmental outcomes in a highly modified system.     

Estonian urbanism 1935–1955: the Soviet-era implementation of pre-war ambitions

Estonian 1940s–1950s town planning practices show that Stalinist principles were in line with those of Estonian architects during the 1930s pre-war, independence period. However, between 1944 and 1955, within the context of the Soviet regime’s occupation, urban planning was faced with rigid ideological constraints. After the Second World War, Estonian architects were forced to abandon projects in historical city centres, which focused on maintaining local natural conditions and cultural heritage, as well as using local materials. Some existing town centres, such as in Tallinn, Narva, and Pärnu, were reconstructed after suffering damage in the war, as well as for ideological reasons. Yet, during this time period, most efforts were directed towards building new industrial towns in East Estonia that exemplified a Stalinist utopia; this also presented the Soviet regime with an opportunity to exploit local mineral resources.     

Motion Planning by T‐RRT with Potential Function for Vertical Articulated Robots

RRT (rapidly exploring random tree) with random sampling is an effective method for path planning, and is often used for robot manipulators. The RRT has many modified methods for applying various problems and conditions. Particularly, T‐RRT (Transition‐based RRT) one of those has advantage that it is able to adopt arbitrary evaluation function. In this paper, a novel path planning method based on the T‐RRT is proposed for ensuring “quality” of a generated path. Then, its effectiveness is evaluated via comparison with other sampling‐based methods using simulation of the industrial robot having seven DOFs.     

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.     

基于协同多目标算法的多机器人路径规划

多机器人路径规划是群体机器人协同工作的前提,其特点是在防碰撞与避障的前提下追求多方面资源的最小消耗.针对这一特点,提出协同非支配排序遗传算法,解决具有多个优化目标的多机器人路径规划问题;运用改进的多目标优化算法,克服多目标优化取权值的不足,同时考虑机器人能源与时间两大资源,以多机器人的路径总长度、总平滑度、总耗时为规划目标.同时引入合作型协同算法框架,将难以求解的多变量问题分组求解.每个机器人的路径视为子种群,子种群通过带精英策略的非支配排序遗传算法,进化并筛选出子种群的部分进入协同进化,每次迭代更新外部的精英解集,最终生成一组非支配路径解.仿真结果表明,在栅格地图环境下,本文算法可有效实现多移动机器人的多优化目标路径规划.