Gap Analysis Report

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A Randomized Controlled Trial of Device Guided, Slow-Paced Respiration in Women with Overactive Bladder Syndrome.

PurposeWe evaluated the effects of device guided, slow-paced respiration on urgency associated urinary symptoms, perceived stress and anxiety, and autonomic function in women with overactive bladder syndrome.Materials and methodsWe performed a randomized, parallel group trial of slow-paced respiration to improve perceived stress and autonomic dysfunction as potential contributors to overactive bladder. Ambulatory women who reported at least 3 voiding or incontinence episodes per day associated with moderate to severe urgency were randomized to use a portable biofeedback device to practice daily, slow, guided breathing exercises or a control device which appeared identical and was reprogrammed to play music without guiding breathing. During 12 weeks we evaluated changes in urinary symptoms by voiding diaries, perceived stress and anxiety by validated questionnaires, and autonomic function by heart rate variability and impedance cardiography.ResultsIn the 161 randomized participants, including 79 randomized to paced respiration and 82 randomized to the control group, the average ± SD baseline frequency of voiding or incontinence associated with moderate to severe urgency was 6.9 ± 3.4 episodes per day. Compared to controls the participants randomized to paced respiration demonstrated greater improvement in perceived stress (average Perceived Stress Scale score decrease 2.8 vs 1.1, p=0.03) but not in autonomic function markers. During 12 weeks the average frequency of voiding or incontinence associated with moderate to severe urgency, which was the study primary outcome, decreased by a mean of 0.9 ± 3.2 episodes per day but no significant between group difference was detected.ConclusionsAmong women with overactive bladder slow-paced respiration was associated with a modest improvement in perceived stress during 12 weeks. However, it was not superior to a music listening control for reducing urinary symptoms or changing autonomic function

Development of a Computational Framework for Big Data-Driven Prediction of Long-Term Bridge Performance and Traffic Flow

Consistent efforts with dense sensor deployment and data gathering processes for bridge big data have accumulated profound information regarding bridge performance, associated environments, and traffic flows. However, direct applications of bridge big data to long-term decision-making processes are hampered by big data-related challenges, including the immense size and volume of datasets, too many variables, heterogeneous data types, and, most importantly, missing data. The objective of this project was to develop a foundational computational framework that can facilitate data collection, data squashing, data merging, data curing, and, ultimately, data prediction. By using the framework, practitioners and researchers can learn from past data, predict various information regarding long-term bridge performance, and conduct data-driven efficient planning for bridge management and improvement. This research project developed and validated several computational tools for the aforementioned objectives. The programs include (1) a data-squashing tool that can shrink years-long bridge strain sensor data to manageable datasets, (2) a data-merging tool that can synchronize bridge strain sensor data and traffic flow sensor data, (3) a data-curing framework that can fill in arbitrarily missing data with statistically reliable values, and (4) a data-prediction tool that can accurately predict bridge and traffic data. In tandem, this project performed a foundational investigation into dense surface sensors, which will serve as a new data source in the near future. The resultant hybrid datasets, detailed manuals, and examples of all programs have been developed and are shared via web folders. The conclusion from this research was that the developed framework will serve practitioners and researchers as a powerful tool for making big data-driven predictions regarding the long-term behavior of bridges and relevant traffic information

Differences in estimates of dental treatment needs and workforce requirements between the standard normative need (WHO model) and sociodental approach to assessing dental need.

Background. Most dental planners use the normative approach for dental workforce planning. An alternative, the sociodental approach of need assessments has been developed to assess dental needs. Studies indicate large differences in needs assessed using the two methods.;Objectives. To assess and compare dental needs and manpower required for dental care of a sample of adult Koreans aged 30 to 64 years using the normative and the sociodental need approaches for three dental treatments restorative, prosthetic and periodontal treatments.;Methods. Assessments of dental needs and time required to treat using two approaches were based on analysis of data obtained from a sub-sample of 1029 30-64 year-old-adults from the 2003 Korean National Oral Health Survey. They were clinically examined for normative needs and interviewed using an Oral Health Related Quality of Life (OHRQoL) measure and their oral health related behaviours to assess propensity. Two needs methods were generated: 1. Normative Need (NN) defined by dental professionals 2. Socio-Dental Approach (SDA) that includes Impact-Related Needs (IRN) using an OHRQoL measure, OIDP, and Propensity-Related Needs (PRN). Amount of dental needs, time to treat, and numbers of dentists needed per 100,000 people were estimated for restorative, prosthetic, and periodontal treatments using NN, IRN and PRN.;Results. Significant differences of about 72% existed between estimates of need for prosthetic treatment using NN and IRN. In workforce estimates, the differences in dentists required to treat 100,000 people were 87.1 dentists would be needed using NN compared to 22.8 dentists for IRN and 18.9 for PRN for prosthetic treatment 22.5 dentists using NN compared to 15.9 or 2.7 using PRN for periodontal treatment and 8.8 dentists using NN compared to 6.6 for PRN for restorative treatment.;Conclusions. The socio-dental approach for assessing dental needs found lower levels of treatment need than the normative approach. The socio-dental approach should be applied to dental workforce planning

Establishment of surface functionalization methods for spore-based biosensors and implementation into sensor technologies for aseptic food processing

Aseptic processing has become a popular technology to increase the shelf-life of packaged products and to provide non-contaminated goods to the consumers. In 2017, the global aseptic market was evaluated to be about 39.5 billion USD. Many liquid food products, like juice or milk, are delivered to customers every day by employing aseptic filling machines. They can operate around 12,000 ready-packaged products per hour (e.g., Pure-Pak® Aseptic Filling Line E-PS120A). However, they need to be routinely validated to guarantee contamination-free goods. The state-of-the-art methods to validate such machines are by means of microbiological analyses, where bacterial spores are used as test organisms because of their high resistance against several sterilants (e.g., gaseous hydrogen peroxide). The main disadvantage of the aforementioned tests is time: it takes at least 36-48 hours to get the results, i.e., the products cannot be delivered to customers without the validation certificate. Just in this example, in 36 hours, 432,000 products would be on hold for dispatchment; if more machines are evaluated, this number would linearly grow and at the end, the costs (only for waiting for the results) would be considerably high. For this reason, it is very valuable to develop new sensor technologies to overcome this issue. Therefore, the main focus of this thesis is on the further development of a spore-based biosensor; this sensor can determine the viability of spores after being sterilized with hydrogen peroxide. However, the immobilization strategy as well as its implementation on sensing elements and a more detailed investigation regarding its operating principle are missing. In this thesis, an immobilization strategy is developed to withstand harsh conditions (high temperatures, oxidizing environment) for spore-based biosensors applied in aseptic processing. A systematic investigation of the surface functionalization’s effect (e.g., hydroxylation) on sensors (e.g., electrolyte-insulator semiconductor (EIS) chips) is presented. Later on, organosilanes are analyzed for the immobilization of bacterial spores on different sensor surfaces. The electrical properties of the immobilization layer are studied as well as its resistance to a sterilization process with gaseous hydrogen peroxide. In addition, a sensor array consisting of a calorimetric gas sensor and a spore-based biosensor to measure hydrogen peroxide concentrations and the spores’ viability at the same time is proposed to evaluate the efficacy of sterilization processes

Particle swarm optimization with sequential niche technique for dynamic finite element model updating

Peer reviewedPostprin