Industrial arm-type robots have multiple degrees-of-freedom (DoFs) and high dexterity but the use of the roll-pitch-roll wrist configuration yields singularities inside the reachable workspace. Excessive joint velocities will occur when encountering these singularities. Arm-type robots currently don't have enough dexterity to move the end-effector path away from the wrist singularities. Robots with redundant DoFs can be used to provide additional dexterity to avoid the singularities and reduce the excessive joint velocity. An end-effector wrist module is proposed to provide two redundant DoFs when interfaced with an existing 6-DoF robot. The new 8-DoF robot has a compact roll-pitch-yaw wrist that has no singularities inside the reachable workspace. The highly redundant robot can also be used to avoid collisions in various directions. Path tracking simulation examples are provided to show the advantages of the proposed design when compared with existing redundant or nonredundant robots. We expect that this module can serve as a cost-effective solution in applications where singularity-free motion or collision-free motion is required.

It is shown that the excessive inhalation of PM (Particulate Matter) 2.5 will seriously affect the health of human. Many countries have deployed various detectors for air pollution in order to report concentration of PM2.5 to show how much seriousness of air pollution is. But, what more important is how much PM 2.5 has been inhaled by people anytime and anywhere. Therefore, in this paper, we propose a method for monitoring and estimating people's PM2.5 inhalation by using personally physiological data, such as heart rate (HR), and mobile PM2.5 sensor. We can retrieve people's heart rate from company's cloud platform of smart bracelet and then calculate his/her PM2.5 inhalation based on personal HR and minute ventilation (VE). In the method, we first adopt Bruce protocol to measure various functions of body, such as HR and VE, on a treadmill and exploit regression model to build predictive model for VE. Therefore, we can utilize smart bracelet to monitor people heart rate and then calculate PM2.5 inhalation based on personal HR and mobile PM2.5 sensor (MPS). We think that the method can effectively estimate the PM 2.5 inhalation of physical activity.

MOTIVATION: Transcription factor binding sites are known to co-occur in the same gene owing to cooperativity of the transcription factors (TFs) that bind to them. Genome-wide location data can help us understand how an individual TF regulates its target gene. Nevertheless, how TFs cooperate to regulate their target genes still needs further study. In this study, genome-wide location data and expression profiles are integrated to reveal how TFs cooperate to regulate their target genes from the stochastic system perspective. RESULTS: Based on a stochastic dynamic model, a new measurement of TF cooperativity is developed according to the regulatory abilities of cooperative TF pairs and the number of their occurrences. Our method is employed to the yeast cell cycle and reveals successfully many cooperative TF pairs confirmed by previous experiments, e.g. Swi4-Swi6 in G1/S phase and Ndd1-Fkh2 in G2/M phase. Other TF pairs with potential cooperativity mentioned in our results can provide new directions for future experiments. Finally, a cooperative TF network of cell cycle is constructed from significant cooperative TF pairs. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: http://www.ee.nthu.edu.tw/~bschen/cooperativity/