Speech understanding in quiet and in noise with the bone-anchored hearing aids Baha Compact and Baha Divino

CONCLUSIONS: Speech understanding is better with the Baha Divino than with the Baha Compact in competing noise from the rear. No difference was found for speech understanding in quiet. Subjectively, overall sound quality and speech understanding were rated better for the Baha Divino. OBJECTIVES: To compare speech understanding in quiet and in noise and subjective ratings for two different bone-anchored hearing aids: the recently developed Baha Divino and the Baha Compact. PATIENTS AND METHODS: Seven adults with bilateral conductive or mixed hearing losses who were users of a bone-anchored hearing aid were tested with the Baha Compact in quiet and in noise. Tests were repeated after 3 months of use with the Baha Divino. RESULTS: There was no significant difference between the two types of Baha for speech understanding in quiet when tested with German numbers and monosyllabic words at presentation levels between 50 and 80 dB. For speech understanding in noise, an advantage of 2.3 dB for the Baha Divino vs the Baha Compact was found, if noise was emitted from a loudspeaker to the rear of the listener and the directional microphone noise reduction system was activated. Subjectively, the Baha Divino was rated statistically significantly better in terms of overall sound quality.

Comparison of musical activities of cochlear implant users with different speech-coding strategies

Speech coding might have an impact on music perception of cochlear implant users. This questionnaire study compares the musical activities and perception of postlingually deafened cochlear implant users with three different coding strategies (CIS, ACE, SPEAK) using the Munich Music Questionnaire. Overall, the self-reported perception of music of CIS, SPEAK, and ACE users did not differ by very much.

Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.

Formal speech stylistics and type A behavior in 38 subjects during nonstress interviews

Open-ended interviews of 90 min length of 38 patients were analyzed with respect to speech stylistics, shown by Schucker and Jacobs to differentiate individuals with type A personality features from those with type B. In our patients, Type A/B had been assessed by the Bortner Personality Inventory. The stylistics studied were: repeated words swallowed words, interruptions, simultaneous speech, silence latency (between question and answer) (SL), speed of speech, uneven speed of speech (USS), explosive words (PW), uneven speech volume (USV), and speech volume. Correlations between both raters for all speech categories were high. Positive correlations between extent of type A and SL (r = 0.33; p = 0.022), USS (r = 0.51; p = 0.002), PW (r = 0.46; p = 0.003) and USV (r = 0.39; p = 0.012) were found. Our results indicate that the speech in nonstress open-ended interviews of type A individuals tends to show a higher emotional tension (positive correlations for USS PW and USV) and is more controlled in conversation (positive correlation for SL).

Research Partnerships for Addressing Global Challenges: Opening Speech

Humankind today is challenged by numerous threats brought about by global change. Climate has been and is being modified by human activities, which calls for mitigation and adaptation measures at an unprecedented scale. Natural resources have been degraded by human development by means of land cover and land use changes, for which protective and restoration measures have to be taken by land users and governments in most countries of the North and South. Low levels of economic development and insufficient policies in most developing countries have led to widespread poverty, which affects nearly half of the world’s population and directly threatens almost one billion people. Finally, uncontrolled economic growth has increased disparities between and within populations and has led to widespread environmental problems in many nations. Generating and sharing knowledge is a key to addressing such global challenges. Knowledge can be used to develop the best solutions and to avoid or repair threats. Research partnerships have proven to be suitable means to bridge the divides and disparities between knowledge societies and developing countries, thereby reducing gaps. Research partnerships are tools for further capacity development and thereby lead to societal empowerment. Institutional settings allowing for research partnerships are needed both in the North and the South, so that the different networks can work together in a long-term enabling environment.

Flow-Centric, Back-In-Time Debugging

Conventional debugging tools present developers with means to explore the run-time context in which an error has occurred. In many cases this is enough to help the developer discover the faulty source code and correct it. However, rather often errors occur due to code that has executed in the past, leaving certain objects in an inconsistent state. The actual run-time error only occurs when these inconsistent objects are used later in the program. So-called back-in-time debuggers help developers step back through earlier states of the program and explore execution contexts not available to conventional debuggers. Nevertheless, even back-in-time debuggers do not help answer the question, ``Where did this object come from?'' The Object-Flow Virtual Machine, which we have proposed in previous work, tracks the flow of objects to answer precisely such questions, but this VM does not provide dedicated debugging support to explore faulty programs. In this paper we present a novel debugger, called Compass, to navigate between conventional run-time stack-oriented control flow views and object flows. Compass enables a developer to effectively navigate from an object contributing to an error back-in-time through all the code that has touched the object. We present the design and implementation of Compass, and we demonstrate how flow-centric, back-in-time debugging can be used to effectively locate the source of hard-to-find bugs.