Objective: To determine, for patients who had identical levels of performance on a monosyllabic word test presented in quiet, whether device differences would affect performance when tested with other materials and in other test conditions.
Design: For Experiment 1, from a test population of 76 patients, three groups (N = 13 in each group) were created. Patients in the first group used the CII Bionic Ear behind-the-ear (BTE) speech processor, patients in the second group used the Esprit3G BTE speech processor, and patients in the third group used the Tempo+ BTE speech processor. The patients in each group were matched on (i) monosyllabic word scores in quiet, (ii) age at testing, (iii) duration of deafness, and (iv) experience with their device. Performance of the three groups was compared on a battery of tests of speech understanding, voice discrimination, and melody recognition. In Experiments 2 (N = 10) and 3 (N = 10) the effects of increasing input dynamic range in the 3G and CII devices, respectively, was assessed with sentence material presented at conversational levels in quiet, conversational levels in noise, and soft levels in quiet.
Results: Experiment 1 revealed that patients fit with the CII processor achieved higher scores than Esprit3G and Tempo+ patients on tests of vowel recognition. CII and Tempo+ patients achieved higher scores than Esprit3G patients on difficult sentence material presented in noise at +10 and +5 dB SNR. CII patients achieved higher scores than Esprit3G patients on difficult sentence material presented at a soft level (54 dB SPL). Experiment 2 revealed that increasing input dynamic range in the Esprit3G device had (i) no effect at conversational levels in quiet, (ii) degraded performance in noise, and (iii) improved performance at soft levels. Experiment 3 revealed that increasing input dynamic range in the CII device improved performance in all conditions.
Conclusions: Differences in implant design can affect patient performance, especially in difficult listening situations. Input dynamic range and the method by which compression is implemented appear to be the major factors that account for our results.
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http://dx.doi.org/10.1097/AUD.0b013e3180312607 | DOI Listing |
Phys Rev Lett
December 2024
University of Strathclyde, Institute of Photonics, SUPA Dept of Physics, Glasgow, United Kingdom.
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In the face of diminishing economic margins, dairy farmers globally are compelled to maintain economic competitiveness. Benchmarking emerges as a strategic tool to establish new, achievable improvement objectives that balance ambition with practicality. This typically requires integrating diverse data sources, such as feed, milk production, diet, and market prices.
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View Article and Find Full Text PDFSci Rep
January 2025
School of ECE, Adama Science and Technology University, Adama, Ethiopia.
This paper details the hardware implementation of a Universal Converter controlled by an Artificial Neural Network (ANN), utilizing key components such as six Insulated Gate Bipolar Transistors (IGBTs), two inductors, and two capacitors for energy storage and voltage smoothing. A Digital Signal Processor (DSP) serves as the core controller, processing real-time input and feedback signals, including voltage and current measurements, to dynamically manage five operational modes: rectifier buck, inverter boost, DC-DC buck, DC-DC boost, and AC voltage control. The pre-trained ANN algorithm generates pulse-width modulation (PWM) signals to control the switching of the IGBTs, optimizing timing and duty cycles for efficient operation.
View Article and Find Full Text PDFInforming and engaging all actors in the land sector, including land-owners and managers, researchers, policy-makers and citizens, on the most effective sustainable land-based solutions and behavioural changes is a key strategy for achieving climate change adaptation and mitigation targets at the global as well as at EU and local level. One requisite to support actors in the land sector is to provide them publicly available, reliable and ready-to-use information related to the implementation of Land-based Adaptation and Mitigation Solutions (LAMS). Here we introduce a LAMS catalogue, a collection of meaningful quantitative and qualitative information on 60 solutions characterised according to a set of specifications (e.
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