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.     

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.     

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.     

Modes of making smart cities: Or,practices of variegated smart urbanism

The ‘actually existing’ smart city is not a monolith. It is not directed by a universal logic, nor does it develop in a standardised way. As recent research has argued, the spatial, material, and political contexts of cities have major influence over what smart urbanism looks like in practice. This paper adds analytical depth to, and broadens the geographical scope of, research on the variegated modes of making smart cities. Based on empirical research in multiple Australian cities we use three case studies to explore three different modes of smart urbanism, each one centred on the interests of a different key actor: corporate-centric, citizen-centric, and planner-centric. These different modes can, and do, co-exist in the same city. At times, they are competing logics that fight to pull the city in different directions. Yet, they can also work together to shape smart city initiatives. In describing these different modes, we pay particular attention to the ways that these projects and strategies must contend with the already existing spatial, cultural, and political contexts of each place.     

重庆主城区城市空间结构演变与优化——基于公共服务功能组织视角

城市空间结构与城市公共服务功能组织密切相关。重庆主城区作为典型的"多中心、组团式"城市,在城市公共服务功能的就近组织、公共服务需求的均衡化方面具有先天的优势。本文梳理重庆主城区空间结构的演进历程以及与公共服务需求变化的互动关系,辨识随着城市功能由生产向消费转型,空间面临的结构问题及其内在动因,借鉴马斯洛理论,提出顺应当前及未来公共服务需求层次的分异,城市空间结构应从"多中心、组团式"进一步向"分层多中心、组团式"优化,引导形成相对集中、功能综合的基层"小、微中心"体系,以有序组织公共服务功能,最大限度地减少非必要交通出行,降低居民出行的时耗,提升城市效率,并借鉴恩格尔系数构建衡量城市效率的评价框架,为破解山地城市空间结构问题,推动城市空间转型提供新的思考视角。