The preparation and characterisation of a series of cationic bimetallic linear triphos-bridged complexes of the type [Mo (CO) (L,L′ or L″–P) (η2-RC2R′)Cp] [BF4] {L,L′ or L″= [WI2 (CO){PhP(CH2CH2PPh2)2–P,P′} (η2-RC2R′)] (L,R=R′=Me; L′,R=R′=Ph; L″,R=Me,R′=Ph}

Baker, Paul K.; Belcher, David E

doi:10.1016/s0277-5387(98)00346-5

Cited by 25 publications

(45 citation statements)

References 20 publications

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“…Reynolds et al (2000), Morss et al (2001) and Morss and Emanuel (2002) explored the concepts of various targeting strategies in the framework of quasi-geostrophic models. Recently, Baker and Daley (2000) developed a new adjoint-based targeting method based on observation sensitivity.…”

Section: Introductionmentioning

confidence: 99%

A comparison of ensemble‐transform Kalman‐filter targeting guidance with ECMWF and NRL total‐energy singular‐vector guidance

Majumdar

Bishop

Buizza

et al. 2002

Quart J Royal Meteoro Soc

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SUMMARYOngoing adaptive observing programmes strive to improve short-and medium-range forecasts of winter weather over populated areas. Aircraft equipped with Global Positioning System (GPS) dropwindsondes are directed over ordinarily data-sparse regions (oceans) to collect observations to enhance the subsequent operational analysis-forecast cycle. Two objective techniques that have been used to identify optimal 'target regions' are the ensemble-transform Kalman lter (ET KF) and the singular-vector technique. The similarities and differences between targeting guidance based on the ET KF and total-energy singular vectors (TESVs) are assessed for ten cases during the North Paci c Experiment (NORPEX). TESVs are computed at the European Centre for MediumRange Weather Forecasts (ECMWF) and the Naval Research Laboratory (NRL) using their respective global models, and the ET KF uses 25 ECMWF ensemble perturbations.Using measures based on (i) rankings of aircraft ight tracks and (ii) spatial similarities between targeting guidance maps, the main nding is that (a) the ET KF guidance is reasonably correlated with TESV guidance for at least seven of the ten NORPEX cases. Other ndings include: (b) the ECMWF and NRL TESV targets sometimes differ signi cantly, (c) the ET KF and TESV guidance maps often display different optimal locations for targeting on smaller scales, but larger-scale aspects are usually similar, (d) the ET KF generally identi es larger regions over which useful observations can be taken compared with the SV technique, and (e) regions that are deemed effective for targeting by both techniques often correspond to baroclinic zones.The ET KF and SV techniques may identify similar regions for targeting if locations of large ensemble-based analysis-error variance coincide with areas where rapid perturbation growth occurs. On the other hand, they may identify different targeting locations for the following two reasons. First, the ET KF implicitly accounts for error correlation length-scales in its predictions of forecast-error variance reduction produced by any set of targeted observations. Hence, it can identify locations for targeted observations that are distant from the regions of high analysis sensitivity that are selected for targeting by the SV technique. Second, ET KF estimates of analysis-error variance are constrained to a subspace of evolved ensemble perturbations and are, therefore, rank de cient.

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Section: Introductionmentioning

confidence: 99%

A comparison of ensemble‐transform Kalman‐filter targeting guidance with ECMWF and NRL total‐energy singular‐vector guidance

Majumdar

Bishop

Buizza

et al. 2002

Quart J Royal Meteoro Soc

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“…The human neuromuscular system has evolved such that activation of motor units occurs asynchronously [1], [20]. It is likely that this occurs because it is a most efficient way of producing muscular force with less susceptibility to fatigue.…”

Section: Discussionmentioning

confidence: 99%

“…This frequency has been used in previous fatigue tests [4] and was chosen for this study because we wanted to explore fatigue of muscle under tetanic conditions. True tetanization occurs in the range of 30-50 Hz [1]. In the range of 21-27 Hz, which we use in some of our applications, the muscles tend to vibrate noticeably at the stimulation frequency.…”

Section: Protocolmentioning

confidence: 98%

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The effect of random modulation of functional electrical stimulation parameters on muscle fatigue

Graham

Thrasher

Popović

2006

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Muscle contractions induced by functional electrical stimulation (FES) tend to result in rapid muscle fatigue, which greatly limits activities such as FES-assisted standing and walking. It was hypothesized that muscle fatigue caused by FES could be reduced by randomly modulating parameters of the electrical stimulus. Seven paraplegic subjects participated in this study. While subjects were seated, FES was applied to quadriceps and tibialis anterior muscles bilaterally using surface electrodes. The isometric force was measured, and the time for the force to drop by 3 dB (fatigue time) and the normalized force-time integral (FTI) were determined. Four different modes of FES were applied in random order: constant stimulation, randomized frequency (mean 40 Hz), randomized current amplitude, and randomized pulsewidth (mean 250 s). In randomized trials, stimulation parameters were stochastically modulated every 100 ms in a range of 15% using a uniform probability distribution. There was no significant difference between the fatigue time measurements for the four modes of stimulation. There was also no significant difference in the FTI measurements. Therefore, our particular method of stochastic modulation of the stimulation parameters, which involved moderate (15%) variations updated every 100 ms and centered around 40 Hz, appeared to have no effect on muscle fatigue. There was a strong correlation between maximum force measurements and stimulation order, which was not apparent in the fatigue time or FTI measurements. It was concluded that a 10-min rest period between stimulation trials was insufficient to allow full recovery of muscle strength.Index Terms-Functional electrical stimulation (FES), isometric contraction, muscle fatigue, spinal cord injury (SCI), stimulation frequency.

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“…In the clinical setting there is minimal set-up time, reduced control over environmental constraints, and little possibility of repeating any one test in the program of treatment undertaken; controllers are required to perform to a minimum standard on a wide number of subjects and conditions. Moreover the underlying musculoskeletal system is highly sensitive to physiological conditions, including skin impedance, temperature, and moisture and electrode placement, in addition to time-varying effects such as spasticity and fatigue [32]. These problems are often exacerbated in the case of stroke because hemiplegic subjects exhibit both voluntary and involuntary responses to applied stimulation.…”

Section: E E E P R O O Fmentioning

confidence: 99%

Iterative Learning Control in Health Care: Electrical Stimulation and Robotic-Assisted Upper-Limb Stroke Rehabilitation

et al. 2012

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A nnually, 15 million people worldwide suffer a stroke, and 5 million are left permanently disabled. A stroke is usually caused when a blood clot blocks a vessel in the brain and acts like a dam, stopping the blood reaching the regions downstream. Alternatively, it may be caused by a hemorrhage, in which a vessel ruptures and leaks blood into surrounding areas. As a result, some of the connecting nerve cells die, and the person commonly suffers partial paralysis on one side of the body, termed hemiplegia. Cells killed in this way cannot regrow, but the brain has some spare capacity and, hence, new connections can be made. The brain is continually and rapidly changing as new skills are learned, new connections are formed, and redundant ones disappear. A person who relearns skills after a stroke goes through the same process as someone learning to play tennis or a baby learning to walk, requiring sensory feedback during the repeated practice of a task. Unfortunately, the problem is that they can hardly move and, therefore, do not receive feedback on their performance.Stroke survivors often have a complex pattern of upperlimb motor impairments, resulting in a loss of functional abilities such as reaching. The relationship between reaching and independence is reflected in measures of functional independence, such as the Barthel index [1] where the ability to reach is required for over half of the activity of daily living tasks. The current prognosis for upper-limb recovery following a stroke is poor, with a review reporting that complete recovery occurs in less than 15% of patients with initial paralysis [2]. Stroke is also an age-related disease [3], placing an increasing burden on long-term health

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The preparation and characterisation of a series of cationic bimetallic linear triphos-bridged complexes of the type [Mo (CO) (L,L′ or L″–P) (η2-RC2R′)Cp] [BF4] {L,L′ or L″= [WI2 (CO){PhP(CH2CH2PPh2)2–P,P′} (η2-RC2R′)] (L,R=R′=Me; L′,R=R′=Ph; L″,R=Me,R′=Ph}

Cited by 25 publications

References 20 publications

A comparison of ensemble‐transform Kalman‐filter targeting guidance with ECMWF and NRL total‐energy singular‐vector guidance

A comparison of ensemble‐transform Kalman‐filter targeting guidance with ECMWF and NRL total‐energy singular‐vector guidance

The effect of random modulation of functional electrical stimulation parameters on muscle fatigue

Iterative Learning Control in Health Care: Electrical Stimulation and Robotic-Assisted Upper-Limb Stroke Rehabilitation

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