The infected food handler can be responsible for the transmission of infectious intestinal diseases (IID) caused by foodborne pathogens. It is believed that personal hygiene practices with varying levels of complexity can help prevent foodborne pathogens from entering the food chain. Overall, it has been difficult to reach a consensus among stakeholders on precisely which intervention measures should be employed in food handling environments to effectively reduce IID rates. Through a study of over 300 reports of outbreaks attributed to ill or asymptomatic food handlers, hazards and contributory factors responsible for foodborne illness outbreaks were identified. With the use of the risk analysis software platforms of Analytica®, @Risk, and GoldSim®, models were created to explore measures of hygiene effectiveness. Through the use of appropriate models, results of various personal hygiene intervention measures were explored for the development of preventive management strategies, designed to improve food‐handling practices at various levels of the food chain. These included exclusion of ill food handlers, vaccination for hepatitis A virus, handwashing combined with drying, wearing of gloves, and use of instant hand sanitizers and fingernail brushes. This was accomplished by modeling pathogen transfer and transmission routes from food handler via foods, beverages and common contact surfaces using GoldSim® and Monte Carlo simulations. A lottery model was also created to understand risk as an interrelated overlapping extremes driven system, comprised of the three components of hygiene frequency, efficacy and cross‐contamination. Data gaps were identified with respect to areas where considerable variability and uncertainty exists in order to establish research priorities.

An increasing number of disease outbreaks have been associated with produce, while pesticide levels continue to be a safety concern. With increased health awareness, fresh produce consumption has increased. As there is a need for microbial and pesticide removal intervention measures of proven efficacy to maintain confidence in food service produce preparation, a series of experiments were undertaken. Produce cleaning methods were tested by measuring removal of gross dirt, wax and environmental contaminants present on produce surface. Tests were performed on apples, cucumbers and lemons using water wash, produce brush, produce cleaner, produce cleaner with paper towel wipe, and water wash and paper towel wipe. Water rinse and paper towel dry was found superior to all other methods tested. Apples contaminated with a cocktail of pesticides were tested in waxed and unwaxed state. Following cleaning by various methods, including produce wash and produce brush, pesticides on skins were extracted and analyzed to determine concentrations of organophosphorous and organochlorine pesticides. In these experiments, it was shown that any treatment that included wiping with paper towels showed increased effectiveness over similar treatments or controls. Microbial efficacy experiments were performed involving 21 different types of laboratory inoculated produce. Two types of inoculum were employed, Tryptone Soya broth (TSB) and ground beef. After inoculation, produce was cleaned by dry wiping with paper towel, using water wash air dry, water wash paper towel dry or dipped in 200 p.p.m. chlorine dip for either 5 s or 1 min and compared to baseline values. One‐minute dip in 200 p.p.m. chlorine solution was more effective than rinsing and drying with a paper towel when TSB inoculum was used (P < 0.05). The effectiveness of the 200 p.p.m. chlorine dip diminished if ground beef was used as a test inoculum, with water rinse and paper towel providing significantly (P < 0.05) improved results. The efficacy shown by paper towels usage in this diverse set of experiments is based on frictional removal of offending soils.

Membrane-type acoustic metamaterials have been proven to exhibit high low-frequency transmission loss despite their small thickness and light weight. To date, analysis has focused primarily on experimental studies in plane-wave tubes and numerical modeling using finite element methods. These methods are inefficient when used for applications that require iterative changes to the structure of the material. In addition, high sound transmission loss with a single layer of such metamaterial only occurs in a narrow frequency range. To facilitate design and optimization of stacked membrane-type acoustic metamaterials, a computationally efficient dynamic model based on the impedance-mobility approach is proposed. Results are verified against a finite element model. Single and double layer transmission loss characteristics are compared. Wide-band high-transmission-loss acoustic metamaterials can be achieved by double layer membranes and using the proposed approach for optimization. The impedance-mobility approach is shown to be very efficient for modeling and optimization of such materials, compared against the conventional finite element approach.

Deep learning is becoming ubiquitous; it is the underlying and driving force behind many heavily embedded technologies in society (e.g., search engines, fraud detection warning systems, and social-media facial recognition algorithms). Over the past few years there has been a steady increase in the number of audio related applications of deep learning. Recently, Nykaza et al. presented a pedagogical approach to understanding how the hidden layers recreate, separate, and classify environmental noise signals. That work presented some feature extraction examples using simple pure tone, chord, and environmental noise datasets. In this paper, we build upon this recent analysis and expand the datasets to include more realistic representations of those datasets with the inclusion of noise and overlapping signals. Additionally, we consider other related architectures (e.g., variant-autoencoders, recurrent neural networks, and fixing hidden nodes/layers), explore their advantages/drawbacks, and provide insights on each technique.

With the advent of reliable and continuously operating noise monitoring systems, we are now faced with an unprecedented amount of noise monitor data. In the context of environmental noise monitoring, there is a need to automatically detect, separate, and classify all environmental noise sources. This is a complex task because sources can overlap, vary by location, and have an unbounded number of noise sources that a monitor device may record. In this study, we synthetically generate datasets that contain Gaussian noise and overlaps for several pre-labeled environmental noise monitoring datasets to examine how well deep learning methods (e.g., autoencoders) can separate environmental noise sources. In addition to examining performance, we also focus on understanding which signal features and separation metrics are useful to this problem.

Environmental noise can cause sleep disturbance, annoyance, complaints, and quite possibly adverse health effects. This is true for continuous noise sources such as steady road traffic noise, impulsive noise sources such as blasts or sonic booms, or sources that fall in-between such as intermittent train and aircraft noise. One way to manage environmental noise is to use noise-monitoring technology to provide both the noise-producers and noise-experiencers feedback on the actual noise environment. Traditional noise-monitoring systems, however, only provide this information at a few locations resulting in an incomplete picture of the noise environment over the entire regions of interest. In this paper, we discuss a framework for providing real-time feedback of the noise environment over a large area (e.g., 100 km2). We show all the steps that are needed to convert the raw noise-monitor data into noise maps and noise impact maps to help manage environmental noise. We discuss the complexity of the problem and present several different ways to visualize the data.

Classification algorithms are an essential component of continuously running environmental noise monitors. Without them, one does not know which noise sources are responsible for the levels recorded by the monitor. This is problematic given that continuously recording monitors may accumulate millions of triggered events and terabytes of data. In this study, we look at the utility of Bayesian classification methods. We compare the performance of these methods to some of the top performing environmental noise classifiers (e.g., support vector machines, random forest, and bagged trees), and discuss the advantages and disadvantages of the Bayesian approach. In particular, we compare the accuracy, number of observations needed to achieve an accurate classification, computation time, and feature importance.

NWU’s McDonald Theatre Auditorium is used for both musical and non-musical performances. The acoustics of the space were analyzed in order to determine whether the space could be modified to better fit its uses. The acoustic characteristics of the room were obtained from impulse responses using the methods established in ISO 3382-1 for measuring the acoustic parameters of a performance space. A total of 22 source/receiver pairs were used. The results indicate a need for increased reverberation in the mid to high frequency ranges of 500–8000 Hz. The experimental results were used to calibrate a virtual model of the space in ODEON acoustics software. Materials in the model were then successfully modified to increase reverberation time and eliminate unwanted flutter echoes to optimize the acoustics to better suit the intended purposes of the space.