Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners’ preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors – by helping them to monitor, refine, and improve e-learning modules and courses according to the learners’ behaviour.     

A UNIFIED FRAMEWORK OF TARGETED MARKETING USING CUSTOMER PREFERENCES

One of the fundamental tasks of targeted marketing is to elicit associations between customers and products. Based on the results from information retrieval and utility theory, this article proposes a unified framework of targeted marketing. The customer judgments of products are formally described by preference relations and the connections of customers and products are quantitatively measured by market value functions. Two marketing strategies, known as the customer‐oriented and product‐oriented marketing strategies, are investigated. Four marketing models are introduced and examined. They represent, respectively, the relationships between a group of customers and a group of products, between a group of customers and a single product, between a single customer and a group of products, and between a single customer and a single product. Linear and bilinear market value functions are suggested and studied. The required parameters of a market value function can be estimated by exploring three types of information, namely, customer profiles, product profiles, and transaction data. Experiments on a real‐world data set are performed to demonstrate the effectiveness of the proposed framework.     

Occurrence patterns of alien freshwater turtles in a large urban pond ‘Archipelago’ (Rome,Italy): Suggesting hypotheses on root causes

The patterns of the occurrence and distribution of alien freshwater turtle species in an urban pond archipelago (Rome, Italy) were analysed, with the aim of exploring the role of a set of factors (type of ponds, landscape context, size area, distance from the nearest road) with a generalized linear model approach. A total of 311 ponds subdivided in three types (fountains, small basins, lakes) embedded in different landscape contexts (public parks, private parks, urban areas) at differing distances from the nearest road were sampled. Six non‐native freshwater turtle species in 31 sites were recorded (9.97%). Lakes exhibited the highest occurrence rates of alien freshwater turtles, compared to small basins and fountains. Freshwater turtle species in urbanized areas were only observed in parks (both public and private). In both the public and private parks, the lakes exhibited the highest percentage of occupied sites, with fountains being the lowest. A direct and significant relationship was observed between pond size and species richness. The distance from the nearest road did not appear to affect species richness. A first interpretation of the data from this study facilitated the postulation of two a posteriori hypotheses that should be tested, as follows: (i) the causal process of turtle release is random, and the rate of extinction (and recapture) is higher in smaller ponds, thus producing the observed pattern; and (ii) the turtle release is not random, and people actively select the ponds they consider more suitable for their pet animals. In this study, it appears the lakes were perceived by those who abandon their pets as the most ecologically suitable habitats among other pond types to accommodate the different species of turtles. Knowledge of people's attitudes in regard to releasing pet animals also might assist managers of public green spaces to develop strategies aimed to preserve local biodiversity, and to educate the public about the conservation issue represented by the alien species.     

生物陶瓷人工骨制备的新方法

主要介绍了近年来发展起来的两种新技术 :快速原型制造技术和放电等离子烧结技术。概括了两种技术的原理、特点以及在人工骨制备中的应用 ,提出了用这两种技术制备人工骨的设想。     

Development of large-scale stacked-type electrostatic actuators for use as artificial muscles

Electrostatic actuators have the advantages of light weight, flexibility, and high energy efficiency, which make them suitable for use as artificial muscles. However, a traditional electrostatic actuator cannot generate long strokes and a high force density at the same time because such actuator would excessively widen the gap between the electrodes because of its structure. This paper presents a newly developed large-scale stacked-type electrostatic actuator (LSEA) intended for use as an artificial muscle for robots. LSEA is a multi-stacked electrostatic actuator that can be linearly contracted by the application of a voltage. It has a unique structure that prevents overextension of the gap between the electrodes. It can therefore generate a large force. The spring characteristics and the relationship between the contractive force and the stroke were experimentally determined. The findings showed that LSEA prevents the overextension of the gap between the electrodes and has a high contraction ratio that is equivalent to that of a mammalian skeletal muscle.     

A Dantzig–Wolfe decomposition algorithm for linear economic model predictive control of dynamically decoupled subsystems

This paper presents a warm-started Dantzig–Wolfe decomposition algorithm tailored to economic model predictive control of dynamically decoupled subsystems. We formulate the constrained optimal control problem solved at each sampling instant as a linear program with state space constraints, input limits, input rate limits, and soft output limits. The objective function of the linear program is related directly to the cost of operating the subsystems, and the cost of violating the soft output constraints. Simulations for large-scale economic power dispatch problems show that the proposed algorithm is significantly faster than both state-of-the-art linear programming solvers, and a structure exploiting implementation of the alternating direction method of multipliers. It is also demonstrated that the control strategy presented in this paper can be tuned using a weighted ℓ₁-regularization term. In the presence of process and measurement noise, such a regularization term is critical for achieving a well-behaved closed-loop performance.     

人工鱼群—粒子群混合算法优化进港航班排序

针对空中交通管理中的进港航班排序问题, 提出了人工鱼群—粒子群混合算法(AFPSO)这一航班排序算法来优化进港航班排序, 使时段内进港航班队列总延误时间最少。算法结合了基本人工鱼群算法(AFSA)和基本粒子群算法(PSO)各自的优点, 先以AFSA在全局寻找满意的解域, 再以PSO算法在这些解域中进行快速的局部搜索获得精确解, 最终使算法提高收敛速度和搜索精度。仿真结果表明, 在单跑道和双跑道情况下, AFPSO算法使得航班队列总延误时间比FCFS调度方法减少了20. 9%和34. 4%, 比基本AFSA减少了3. 2%和3. 5%。算法得到的满意解能够为自动化空中交通管理提供实时支持。     

高温变换催化剂制备条件的神经网络优化

将影响低汽气比条件下LB型节能高温变换催化剂活性的主要因素作为人工神经网络的特征输入向量,将全部实验数据分为训练集和预测集,运用Matlab神经网络工具箱,按改进的Bayes自动归一化算法建立反向传播神经网络模型,不仅可防止网络陷入局部最小,而且提高了网络训练精度和泛化能力。适当拓宽正交实验各因素的水平范围,经过不同因素、不同水平间的组合模拟,预测出LB型节能高温变换催化剂的最佳制备条件为氧化铈质量分数0.76%、氧化铜质量分数5.8%、氧化铬质量分数8.6%、氧化镧质量分数1.0%、铁液浓度92 g/L、中和过程最终pH值9.5。在最佳条件下试制催化剂在低汽气比下的平均活性达77.6%。