免水型生态卫生设施用降解菌的分离筛选

本文对免水型生态卫生设施中的生物填料及施有机肥农田的土壤进行了人类粪便降解菌的分离。筛选得到的各菌种在实验条件下，降解粪便效果较好，除臭除味，COD下降率在30％左右。其中在土壤中分离出来的9号菌，COD下降率达41％，全部菌种复合培养处理的下降率高达59％。结果表明，实验采用的分离筛选方法是比较有效的，筛选得到了具有较好降解效果的菌种，为降解菌的进一步试验打下了基础。     

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.     

金沙江下段植被NDVI变化趋势及其归因

基于MODIS NDVI数据及标准气象站数据、退耕还林资料,辅以空间统计、叠置分析和趋势分析等方法,研究了金沙江下段植被NDVI时空变化特征及其影响因素,结果表明:从年内来看,金沙江下段植被NDVI变化呈单峰型,3月份为最低值0.55,而9月份为最高值0.75,年际上10年以来植被覆盖总体呈现出增长趋势,且这种增长存在显著的空间异质性;研究区植被覆盖较好,植被NDVI平均值为0.65,海拔3 850m以下植被覆盖随海拔上升而增加,超过3 850m后随海拔升高呈降低趋势;年内植被NDVI受降水量的影响较气温更为明显,对两者均有2个月的滞后期,而年际上植被NDVI则受气温变化的影响较降水量更为突出,且大规模的植被恢复工程对金沙江下段植被覆盖的增加有重要贡献。     

Fundamentals of systems ergonomics/human factors

Ergonomics/human factors is, above anything else, a systems discipline and profession, applying a systems philosophy and systems approaches. Many things are labelled as system in today's world, and this paper specifies just what attributes and notions define ergonomics/human factors in systems terms. These are obviously a systems focus, but also concern for context, acknowledgement of interactions and complexity, a holistic approach, recognition of emergence and embedding of the professional effort involved within organization system. These six notions are illustrated with examples from a large body of work on rail human factors.     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.     

Human tracking system based on adaptive multi-feature mean-shift for robot under the double-layer locating mechanism

Human tracking has been a challenging task for robot in the past decades. In this paper, to realize the human following in a cluttered environment, a human tracking system based on adaptive multi-feature mean-shift (AMF-MS) under the double-layer locating mechanism (DLLM) is proposed to solve the problem of distinguishing target, occlusion, and quick turning. The DLLM, considering the course location processing and fine location processing, is designed to estimate the person’s position using the fusion of heterogeneous data. As an ID tag attached on target can be detected by RF antennas, the course locating method can track the target easily and quickly. The Bayes rule is introduced to calculate the probability where the tag exists due to the instability of RF signals. In the fine locating step, the AMF-MS is proposed because it can reduce computational load and represent target by multi-feature histogram function. Meanwhile, we combine extended Kalman filter and AMF-MS to overcome MS’s inability of occlusion. To control the robot following the target person precisely, an intelligent gear shift strategy based on fuzzy control is implemented by analyzing the robot structure. Experiments demonstrate that the proposed approach is robust to handle complex tracking conditions, and show the system has an optimum performance.     

Model reference adaptive impedance control in Cartesian coordinates for physical human–robot interaction

In this paper, a nonlinear model reference adaptive impedance controller is proposed and tested. The controller provides asymptotic tracking of a reference impedance model for the robot end-effector in Cartesian coordinates applicable to rehabilitation robotics or any other human–robot interactions such as haptic systems. The controller uses the parameters of a desired stable reference model which is the target impedance for the robot’s end-effector. It also considers uncertainties in the model parameters of the robot. The asymptotic tracking is proven using Lyapunov stability theorem. Moreover, the adaptation law is proposed in joint space for reducing the complexity of its calculations; however, the controller and the stability proof are all presented in Cartesian coordinates. Using simulations and experiments on a two DOFs robot, the effectiveness of the proposed controller is investigated.     

重组人干扰素α2b阴道泡腾片抗豚鼠单纯疱疹病毒性阴道炎的疗效

目的观察重组人干扰素α2b阴道泡腾片对单纯疱疹病毒性阴道炎动物模型的疗效。方法采用单纯疱疹病毒感染建立豚鼠实验性阴道炎模型,并分别用重组人干扰素α2b阴道泡腾片600、3 000和15 000 IU/只进行治疗。以阿昔洛韦(无环鸟苷)和干扰素α2b栓剂15 000 IU/只作为对照。对治疗前后豚鼠阴道外观及组织切片进行评分。结果应用重组人干扰素α2b阴道泡腾片对豚鼠实验性单纯疱疹病毒性阴道炎进行治疗后,豚鼠阴道外观病变与组织切片病变评分均显著降低,其中15 000 IU/只效果最好。同样剂量的α2b泡腾片药效优于栓剂。结论重组人干扰素α2b阴道泡腾片对实验性单纯疱疹病毒性阴道炎有明显的疗效。     

重组人干扰素α2b内毒素检查法的研究

目的研究鲎试剂法与家兔法检测重组人干扰素α2b内毒素结果的相关性,并参照家兔热原试验的阈值量,来确定本制品质量标准中内毒素的限量标准。方法分别用不同浓度的内毒素溶液和含不同浓度内毒素的供试品溶液进行家兔热原质试验,确定供试品内毒素限值,并在此限值下进行内毒素检测,方法均参照2000年版《中国药典》和2000年版《中国生物制品规程》进行。结果在含不同浓度内毒素溶液的家兔热原质试验中,致热阈值小于5.0EUml。在含不同浓度内毒素供试品溶液的家兔热原质试验中,致热阈值小于1.25EUml。以此确定本品内毒素限值,检测该制品的内毒素含量,结果均符合规定。结论本品在内毒素含量小于3.5EU500万单位dose的范围内,根据具体的对照实验及制品的用途来规定细菌内毒素限值,可用鲎试剂法代替家兔法检测注射用重组人干扰素α2b冻干制剂的热原质。     

基于似人特性的机器人拟人臂自主抓取动作规划

针对拟型服务机器人在家庭环境中的自主抓取任务,提出了一种强调运动姿态似人特性的机器人手臂动作的运动规划方法.该方法基于人体工程学中的快速上肢评估准则评价机器人运动姿态的似人特性,并在此基础上以机器人传速速率最优为目标规划机器人持物动作的姿态构型.最后以Motoman SDA10D拟人双臂机器人为例,具体介绍了该方法的应用和规划的结果,规划结果证明了该方法的可行性和有效性.