PURPOSE: The Nasality Severity Index 2.0 (NSI 2.0) forms a new, multiparametric approach in the assessment of hypernasality. To enable clinical implementation of this index, the short- and long-term test-retest reliability of this index was explored. METHODS: In 40 normal-speaking adults (mean age 32y, SD 11, 18-56y) and 29 normal-speaking children (mean age 8y, SD 2, 4-12y), the acoustic parameters included in the NSI 2.0 (i.e. nasalance of the vowel /u/ and an oral text, and the voice low tone to high tone ratio (VLHR) of the vowel /i/) were obtained twice at the same test moment and during a second assessment two weeks later. After determination of the NSI 2.0, a comprehensive set of statistical measures was applied to determine its reliability. RESULTS: Long-term variability of the NSI 2.0 and its parameters was slightly higher compared to the short-term variability, both in adults and in children. Overall, a difference of 2.82 for adults and 2.68 for children between the results of two consecutive measurements can be interpreted as a genuine change. With an ICC of 0.84 in adults and 0.77 in children, the NSI 2.0 additionally shows an excellent relative consistency. No statistically significant difference was withheld in the reliability of test-retest measurements between adults and children. CONCLUSION: Reliable test-retest measurements of the NSI 2.0 can be performed. Consequently, the NSI 2.0 can be applied in clinical practice, in which successive NSI 2.0 scores can be reliably compared and interpreted. LEARNING OUTCOMES: The reader will be able to describe and discuss both the short-term and long-term test-retest reliability of the Nasality Severity Index 2.0, a new multiparametric approach to hypernasality, and its parameters. Based on this information, the NSI 2.0 can be applied in clinical practice, in which successive NSI 2.0 scores, e.g. before and after surgery or speech therapy, can be compared and interpreted.
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Objectives: The purpose of the current study was to assess the short-term effectiveness of short and intensive speech therapy provided to patients with cleft (lip and) palate (C(L)P) in terms of articulation and resonance. Methods: Five Ugandan patients (age: 7.3-19.6 years) with non-syndromic C(L)P received six hours of individualized speech therapy in three to four days. Speech therapy focused on correct phonetic placement and contrasts between oral and nasal airflow and resonance. Speech evaluations performed before and immediately after speech therapy, including perceptual and instrumental assessment techniques, were compared. Results: Post-therapy, improvement of speech was noted for most of the patients, although to varying degrees. Clinically relevant progress of objective nasalance values and/or articulation was obtained in four patients. Overall, two patients showed normal speech intelligibility, while three patients required additional speech therapy. Conclusion: These preliminary short-term results demonstrate that short and intensive speech therapy can be effective for patients with C(L)P in countries with limited access to speech-language therapy. However, further research is needed on the long-term effectiveness and the advantages of applying this treatment protocol in countries with good access to speech therapy. Learning outcomes: The reader will be able to (1) list the challenges in resource poorcountries to achieve access to speech-language therapy services, (2) describe when the application of speech therapy is appropriate in patients with C(L)P, (3) describe the speech therapy that can be applied to reduce compensatory articulation and resonance disorders in patients with C(L)P, and (4) list the (possible) advantages of short, intensive speech therapy for both resource-poor and developed countries.
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This paper presents a case study where a model predictive control (MPC) logic is developed for energy flexible operation of a space heating system in an educational building. A Long Short-Term Memory Neural Network (LSTM) surrogate model is trained on the output of an EnergyPlus building simulation model. This LSTM model is used within an MPC framework where a genetic algorithm is used to optimize setpoint sequences. The EnergyPlus model is used to validate the performance of the control logic. The MPC approach leads to a substantial reduction in energy consumption (7%) and energy costs (13%) with improved comfort performance. Additional energy costs savings are possible (7–16%) if a sacrifice in indoor thermal comfort is accepted. The presented method is useful for developing MPC systems in the design stages where measured data is typically not available. Additionally, this study illustrates that LSTM models are promising for MPC for buildings.
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Background: Globalisation trends such as increased migration to andwithin European countries have led to even greater cultural diversityin European societies. Cultural diversity increases the demand ofcultural competency amongst professionals entering their workfield. In particular, healthcare professionals need knowledge and skillsto equip them to work with clients from different cultural backgrounds.Within higher education (HE), the professional developmentof cultural competency should ideally feature in undergraduate educationand is often promoted as a by-product of a study abroadperiod. However, recognising that logistical and financial barriersoften exist for extended study abroad, one alternative approachcould be participation, at home or abroad, in a short-term internationalprogramme set within students’ own HE institutions.Purpose: The aim of this study was to explore HE students’ experiencesof participating in international ‘short-term mobility week’programmes at three European universities.Methods: Each university involved in the research offered short termprogrammes for healthcare professions students at their owninstitution, where both local students and students from abroadcould participate. Participants were healthcare students in theprogramme at one of the three universities. Data were collectedthrough focus group interviews (4–8 students per group; n = 25).The data were transcribed and then analysed qualitatively, usinga content comparison method.Results: The analysis identified six categories, which reflectedstudents’ journeys within the short-term international experiences.Conclusions: The analysis suggested that, for these students,engagement in a short-term mobility week programme providedvaluable opportunities for encounters with others, which contributedto personal and professional development, greater confidencein the students’ own professional identities, as well as anincreasing sense of cultural awareness.
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It is argued that sustainability can 't be achieved on an individual level. It has to be achieved on a collective level, not in the short term but in the long term. However, sustainability has disappeared more than ever, precisely because of the scaling-up that technology has brought about. To stay with the stadium metaphor: we all stand and scream for new technology to regain both vision and visibility.
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Charging infrastructure in neighborhoods is essential for inhabitants who use electric vehicles. The development of public charging infrastructure can be complex because of its dependency on local grid conditions, the responsibility to prepare for anticipated fleet growth policies, and the implicit biases that may occur with the allocation of charging resources. How can accessible EV charging be ensured in the future, regardless of energy infrastructure and socio-economic status of the neighborhood? This study aims to represent the decision-making in the allocation of public charging infrastructure and ensure that various key issues are accounted for in the short-term and long-term decision making. The paper first identifies these issues, then describes the decision-making process, and all of these are summarized in a visual overview describing the short-term and long-term decision loop considering various key indicators. A case study area is identified by comparing locally available data sources in the City of Amsterdam for future simulation.
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In elite basketball, players are exposed to intensified competition periods when participating in both national and international competitions. How coaches manage training between matches and in reference to match scheduling for a full season is not yet known. Purpose: First, to compare load during short-term match congestion (ie, ≥2-match weeks) with regular competition (ie, 1-match weeks) in elite male professional basketball players. Second, to determine changes in well-being, recovery, neuromuscular performance, and injuries and illnesses between short-term match congestion and regular competition. Methods: Sixteen basketball players (age 24.8 [2.0] y, height 195.8 [7.5] cm, weight 94.8 [14.0] kg, body fat 11.9% [5.0%], VO2max 51.9 [5.3] mL·kg−1·min−1) were monitored during a full season. Session rating of perceived exertion (s-RPE) was obtained, and load was calculated (s-RPE × duration) for each training session or match. Perceived well-being (fatigue, sleep quality, general muscle soreness, stress levels, and mood) and total quality of recovery were assessed each training day. Countermovement jump height was measured, and a list of injuries and illnesses was collected weekly using the adapted Oslo Sports Trauma Research Center Questionnaire on Health Problems. Results: Total load (training sessions and matches; P
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
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Several studies found that classrooms' indoor environmental quality (IEQ) can positively influence in-class activities. Understanding and quantifying the combined effect of four indoor environmental parameters, namely indoor air quality and thermal, acoustic, and lighting conditions on people is essential to create an optimal IEQ. Accordingly, a systematic approach was developed to study the effect of multiple IEQ parameters simultaneously. Methods for measuring the IEQ and students' perceived IEQ, internal responses, and academic performance were derived from literature. Next, this systematic approach was tested in a pilot study during a regular academic course. The perceptions, internal responses, and short-term academic performance of participating students (n = 163) were measured. During the pilot study, the IEQ of the classrooms varied slightly. Significant associations (p < 0.05) were observed between these natural variations and students' perceptions of the thermal environment and indoor air quality. These perceptions were significantly associated with their physiological and cognitive responses (p < 0.05). Furthermore, students' perceived cognitive responses were associated with their short-term academic performance (p < 0.01). The observed associations confirm the construct validity of the systematic approach. However, its validity for investigating the influence of lighting remains to be determined.
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BACKGROUND: The population of elderly patients with burn injuries is growing. Insight into long-term mortality rates of elderly after burn injury and predictors affecting outcome is limited. This study aimed to provide this information.METHODS: A multicentre observational retrospective cohort study was conducted in all three Dutch burn centres. Patients aged ≥65 years, admitted with burn injuries between 2009 and 2018, were included. Data were retrieved from electronic patient records and the Dutch Burn Repository R3. Mortality rates and standardized mortality ratios (SMRs) were calculated. Multivariable logistic regression was used to assess predictors for in-hospital mortality and mortality after discharge at 1 year and five-year. Survival analysis was used to assess predictors of five-year mortality.RESULTS: In total, 682/771 admitted patients were discharged. One-year and five-year mortality rates were 8.1 and 23.4%. The SMRs were 1.9(95%CI 1.5-2.5) and 1.4(95%CI 1.2-1.6), respectively. The SMRs were highest in patients aged 75-80 years at 1 year (SMRs 2.7, 95%CI 1.82-3.87) and five-year in patients aged 65-74 years (SMRs 10.1, 95%CI 7.7-13.0). Independent predictors for mortality at 1 year after discharge were higher age (OR 1.1, 95%CI 1.0-1.1), severe comorbidity, (ASA-score ≥ 3) (OR 4.8, 95%CI 2.3-9.7), and a non-home discharge location (OR 2.0, 95%CI 1.1-3.8). The relative risk of dying up to five-year was increased by age (HR 1.1, 95%CI 1.0-1.1), severe comorbidity (HR 2.3, 95%CI 1.6-3.5), and non-home discharge location (HR 2.1, 95%CI 1.4-3.2).CONCLUSION: Long-term mortality until five-year after burn injury was higher than the age and sex-matched general Dutch population, and predicted by higher age, severe comorbidity, and a non-home discharge destination. Next to pre-injury characteristics, potential long-lasting systemic consequences on biological mechanisms following burn injuries probably play a role in increased mortality. Decreased health status makes patients more prone to burn injuries, leading to early death.
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