Operator's manual : grinding machine, machine tool attachment (U.S. Electrical Tool Co. model HLGE-12) (3460-243-9607).

"November 1965."

The Electrical review.

Description based on: Vol. 198, no. 21 (28 May/4 June 1976); title from caption.

Electric circuits and machines,

The 1941 edition has title: Electrical circuits and machines.

Proceedings of the Institution of Electrical Engineers.

Vols.1-87,1872-1940 also called no.1-258.

The role of morphine-6-glucuronide (M6G) in pain control

Morphine-6beta-D-glucuronide (M6G) is an analgesically active metabolite of morphine, accounting for approximate to10% of the morphine dose when administered by systemic routes to humans. Although M6G is more hydrophilic than morphine, it crosses the blood-brain barrier, albeit relatively slowly. For this reason, it is generally thought that, after chronic dosing, M6G contributes significantly to the analgesic effects of systemically administered morphine. Owing to its polar nature, M6G is cleared from the systemic circulation primarily via renal elimination. As M6G accumulates in patients with renal impairment, there is an increased risk of M6G-induced respiratory depression in renal failure patients who are being dosed chronically with systemic morphine. Consistent with its analgesic and respiratory depressant properties, M6G binds to the p-opioid receptor in a naloxone-reversible manner. Although the affinity of M6G for the mu-opioid receptor is similar to or slightly less than that of morphine, preclinical studies in rodents show that M6G is one to two orders of magnitude more potent than morphine when administered by central routes. This major discrepancy between the markedly higher intrinsic antinociceptive potency of M6G relative to morphine, despite their similar p-opioid receptor binding affinities, is difficult to reconcile. It has been proposed that M6G mediates its pain-relieving effects through a novel 'M6G opioid receptor', while others have argued that M6G may have higher efficacy than morphine for transduction of intracellular events. When administered by parenteral routes to rodents, M6G's antinociceptive potency is no more than twofold higher than morphine. In humans, the analgesic efficacy and respiratory depressant potency of M6G relative to morphine have been assessed in a number of short-term studies involving the intrathecal or intravenous routes of administration. For example, in hip replacement patients, intrathecal M6G provided excellent postoperative analgesia but the occurrence of late respiratory depression in 10% of these patients raised serious concern about safety. In postoperative patients, intravenous M6G administered by means of patient-controlled analgesia (PCA), or bolus plus PCA, produced no analgesia in one study and limited analgesia in another. Similarly, there was a lack of significant analgesia in healthy volunteers who received intravenous M6G for the alleviation of experimental pain (carbon dioxide applied to the nasal mucosa). In contrast, satisfactory analgesia was produced by bolus doses of intravenous M6G administered to patients with cancer pain, and to healthy volunteers with experimentally-induced ischaemic, electrical or thermal (ice water) pain. Studies to date in healthy volunteers suggest that intravenous M6G may be a less potent respiratory depressant and have a lower propensity for producing nausea and vomiting than morphine. However, it is unclear whether equi-analgesic doses of M6G and morphine were compared. Clearly, more extensive short-term trials, together with studies involving chronic M6G administration, are necessary before the potential clinical utility of M6G as an analgesic drug in its own right can be determined.

Improving learning in undergraduate control engineering courses using context-based learning models

Control Engineering is an essential part of university electrical engineering education. Normally, a control course requires considerable mathematical as well as engineering knowledge and is consequently regarded as a difficult course by many undergraduate students. From the academic point of view, how to help the students to improve their learning of the control engineering knowledge is therefore an important task which requires careful planning and innovative teaching methods. Traditionally, the didactic teaching approach has been used to teach the students the concepts needed to solve control problems. This approach is commonly adopted in many mathematics intensive courses; however it generally lacks reflection from the students to improve their learning. This paper addresses the practice of action learning and context-based learning models in teaching university control courses. This context-based approach has been practised in teaching several control engineering courses in a university with promising results, particularly in view of student learning performances.

A bang-bang PLL employing dynamic gain control for low jitter and fast lock times

Bang-bang phase detector based PLLs are simple to design, suffer no systematic phase error, and can run at the highest speed a process can make a working flip-flop. For these reasons designers are employing them in the design of very high speed Clock Data Recovery (CDR) architectures. The major drawback of this class of PLL is the inherent jitter due to quantized phase and frequency corrections. Reducing loop gain can proportionally improve jitter performance, but also reduces locking time and pull-in range. This paper presents a novel PLL design that dynamically scales its gain in order to achieve fast lock times while improving fitter performance in lock. Under certain circumstances the design also demonstrates improved capture range. This paper also analyses the behaviour of a bang-bang type PLL when far from lock, and demonstrates that the pull-in range is proportional to the square root of the PLL loop gain.

Biologically inspired joint control for a humanoid robot

The GuRm is a 1.2m tall, 23 degree of freedom humanoid consuucted at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRw projcct is the development of appropriate learning strategies for control and coodinadon of the robot’s many joints. The development of learning strategies is Seen as a way to sidestep the inherent intricacy of modeling a multi-DOP biped robot. This paper outlines the approach taken to generate an appmpria*e control scheme for the joinis of the GuRoo. The paper demonsrrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-fonward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on thc CMAC architecture. Results from tats on the GuRoo platform show that both systems provide improvements in stability and tracking of joint control.

Learning to play Pac-Man: An evolutionary, rule-based approach

Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. We describe an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset. with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.