Experimental evaluation of the impact of ventilation on cooking-generated fine particulate matter in a Chinese apartment kitchen and adjacent room.

Cooking is one of the major contributors to indoor pollution. Fine particulate matter (PM2.5) produced during cooking commonly mix into adjacent rooms and elevates indoor PM2.5 concentrations. The risk of human exposure to cooking-generated PM2.5 is mainly related to the exposure duration and particulate matter (PM) concentration. The PM2.5 concentration is influenced by cooking methods and ventilation patterns. Range hoods and open windows are conventional strategies for lowering the concentration of cooking-generated particles. To decrease PM emissions, kitchen air supply systems have been proposed, providing alternative possibilities for kitchen ventilation patterns. The effects of cooking methods, air supply systems, range hoods, and windows on PM2.5 concentrations must be analyzed and compared. To understand and provide advice on reducing exposure to PM2.5 due to cooking activities, we measured the PM2.5 mass concentration in a kitchen and adjacent room during cooking. The identified factors, including cooking method, range hood use, window status, and air supply system, were varied based on orthogonal design. The delay time between the PM2.5 peak in the kitchen and that in the adjacent room was determined. The degree of exposure risk for cooking-generated PM2.5 was evaluated using the mean exposure dose. The results indicated that the mean PM2.5 mass concentration in the kitchen ranged from 22 to 2296 µg/m3. In descending order, the factors affecting the indoor PM2.5 concentration in the apartment studied were range hood use, cooking methods, window status, and air supply system. The PM2.5 peak in the adjacent room occurred 200-800 s later than that in the kitchen. Other conditions being constant in these experiments, the use of range hoods, air supply systems, and windows reduce exposure doses by 90%, 37%, and 51%, respectively. These research results provide insights for reducing human exposure to cooking-generated PM2.5.

A Comparative Study of Three Systems for Liver Magnetic Resonance Elastography.

BACKGROUND: Different MR elastography (MRE) systems may produce different stiffness measurements, making direct comparison difficult in multi-center investigations. PURPOSE: To assess the repeatability and reproducibility of liver stiffness measured by three typical MRE systems. STUDY TYPE: Prospective. POPULATION/PHANTOMS: Thirty volunteers without liver disease history (20 males, aged 21-28)/5 gel phantoms. FIELD STRENGTH/SEQUENCE: 3.0 T United Imaging Healthcare (UIH), 1.5 T Siemens Healthcare, 3.0 T General Electric Healthcare (GE)/Echo planar imaging-based MRE sequence. ASSESSMENT: Wave images of volunteers and phantoms were acquired by three MRE systems. Tissue stiffness was evaluated by two observers, while phantom stiffness was assessed automatically by code. The reproducibility across three MRE systems was quantified based on the mean stiffness of each volunteer and phantom. STATISTICAL TESTS: Intraclass correlation coefficients (ICC), coefficients of variation (CV), and Bland-Altman analyses were used to assess the interobserver reproducibility, the interscan repeatability, and the intersystem reproducibility. Paired t-tests were performed to assess the interobserver and interscan variation. Friedman tests with Dunn's multiple comparison correction were performed to assess the intersystem variation. P values less than 0.05 indicated significant difference. RESULTS: The reproducibility of stiffness measured by the two observers demonstrated consistency with ICC > 0.92, CV < 4.32%, Mean bias < 2.23%, and P > 0.06. The repeatability of measurements obtained using the electromagnetic system for the liver revealed ICC > 0.96, CV < 3.86%, Mean bias < 0.19%, P > 0.90. When considering the range of reproducibility across the three systems for liver evaluations, results ranged with ICCs from 0.70 to 0.87, CVs from 6.46% to 10.99%, and Mean biases between 1.89% and 6.30%. Phantom studies showed similar results. The values of measured stiffness differed across all three systems significantly. DATA CONCLUSION: Liver stiffness values measured from different MRE systems can be different, but the measurements across the three MRE systems produced consistent results with excellent reproducibility. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Temperature-dependent particle number emission rates and emission characteristics during heating processes of edible oils.

The goal of this research is to investigate the temperature-dependent emission rates of particle numbers and emission characteristics during oil heating. Seven regularly used edible oils were studied in a variety of tests to attain this objective. First, total particle number emission rates ranging from 10 nm to 1 µm were measured, followed by an examination within six size intervals from 0.3 µm to 10 µm. Following that, the impacts of oil volume and oil surface area on the emission rate were investigated, and multiple regression models were developed based on the results. The results showed that corn, sunflower and soybean oils had higher emission rates than other oils above 200 °C, with peak values of 8.22 × 109#/s, 8.19 × 109#/s and 8.17 × 109#/s, respectively. Additionally, peanut and rice oils were observed to emit the most particles larger than 0.3 µm, followed by medium-emission (rapeseed and olive oils) and low-emission oils (corn, sunflower and soybean oils). In most cases, oil temperature (T) has the most significant influence on the emission rate during the smoking stage, but its influence was not as pronounced in the moderate smoking stage. The models obtained are all statistically significant (P < 0.001), with R2 values greater than 0.9, and the classical assumption test concluded that regressions were in accordance with the classical assumptions regarding normality, multicollinearity, and heteroscedasticity. In general, low oil volume and large oil surface area were more recommended for cooking to mitigate UFPs emission.

MR Elastography With Optimization-Based Phase Unwrapping and Traveling Wave Expansion-Based Neural Network (TWENN).

Magnetic Resonance Elastography (MRE) can characterize biomechanical properties of soft tissue for disease diagnosis and treatment planning. However, complicated wavefields acquired from MRE coupled with noise pose challenges for accurate displacement extraction and modulus estimation. Using optimization-based displacement extraction and Traveling Wave Expansion-based Neural Network (TWENN) modulus estimation, we propose a new pipeline for processing MRE images. An objective function with Dual Data Consistency (Dual-DC) has been used to ensure accurate phase unwrapping and displacement extraction. For the estimation of complex wavenumbers, a complex-valued neural network with displacement covariance as an input has been developed. A model of traveling wave expansion is used to generate training datasets for the network with varying levels of noise. The complex shear modulus map is obtained through fusion of multifrequency and multidirectional data. Validation using brain and liver simulation images demonstrates the practical value of the proposed pipeline, which can estimate the biomechanical properties with minimal root-mean-square errors when compared to state-of-the-art methods. Applications of the proposed method for processing MRE images of phantom, brain, and liver reveal clear anatomical features, robustness to noise, and good generalizability of the pipeline.

Temperature-dependent particle mass emission rate during heating of edible oils and their regression models.

Particulate matter emitted by heated cooking oil is hazardous to human health. To develop effective mitigation strategies, it is critical to know the amount of the emitted particles. The purpose of this research is to estimate the temperature-dependent particle mass emission rates of edible oils and to develop models for source strength based on the multiple linear regression method. First, this study examined seven commonly used oils by heating experiments. The emission rates of PM2.5 and PM10 were measured, and the effects of parameters such as oil volume and surface area on the emission rates were also analysed. Following that, the starting smoke points (Ts') and aggravating smoke points (Tss') of tested oils were determined. The results showed that oils with lower smoke points had greater emission rates. Notably, the experiments performed observed that peanut, rice, rapeseed and olive oil generated PM2.5 much faster at 240 °C (2.22, 1.50, 0.82 and 0.80 mg/s, respectively, at the highest emission conditions) than that of sunflower, soybean, and corn oil (0.15, 0.12 and 0.11 mg/s, respectively). The temperature, volume, and surface area of oils all had a significant impact on the particle mass emission rate, with oil temperature being the most influential. The regression models obtained were statistically significant (P < 0.001), with the majority of R2 values greater than 0.85. Using sunflower, soybean and corn oils, which have higher smoke points and lower emission rates, and smaller pans for cooking is therefore recommended based on our research findings.

Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography.

Characterization of biomechanical properties of soft biological tissues is important to understand the tissue mechanics and explore the biomechanics-related mechanisms of disease, injury, and development. The mechanical testing method is the most straightforward way for tissue characterization and is considered as verification for in vivo measurement. Among the many ex vivo mechanical testing techniques, the indentation test provides a reliable way, especially for samples that are small, hard to fix, and viscoelastic such as brain tissue. Magnetic resonance elastography (MRE) is a clinically used method to measure the biomechanical properties of soft tissues. Based on shear wave propagation in soft tissues recorded using MRE, viscoelastic properties of soft tissues can be estimated in vivo based on wave equation. Here, the viscoelastic properties of gelatin phantoms with two different concentrations were measured by MRE and indentation. The protocols of phantom fabrication, testing, and modulus estimation have been presented.

Dust accumulated fungi in air-conditioning system: Findings based on field and laboratory experiments.

This study analyzes the growth and reproduction of dust accumulated fungi (DAF) in an air-conditioning system based on field measurement and molecular biology, laboratory experiment and prediction modelling. The field measurement was conducted to collect dust in filter screen, surface cooler and air supply duct of two air handling units (AHUs). The results indicate that dust volume and fungal number in two AHUs generally met the hygienic specification of public buildings, but the cleansing did not fulfil requirements. High-throughput sequencing was conducted, revealing that the dominant fungal species were Alternaria_betae-kenyensis, Cladosporium_delicatulum, Aspergillus_sydowii, Verticillium_dahliae. Laboratory experiment was conducted to analyze the impact of several factors (e.g. growth time, temperature, relative humidity, duct material) and their combination on the DAF growth. The results indicate that fungal growth increased with time, peaking at 4 days or 5 days. Higher relative humidity or temperature was conducive to fungal growth. The orthogonal experiment revealed that the condition of "antibacterial composite, 22 ± 1 °C and 45%-55% RH" had the strongest inhibiting impact on fungal growth. Logistic model, Gompertz model and square-root model were further developed to predict the fungal growth under different conditions. The results show that the Logistic model had high feasibility and accuracy, the Gompertz model was feasible with lower accuracy and the square-root model was feasible with high accuracy. Overall, this study facilitates the understanding of the DAF growth in air-conditioning ducts, which is important for real-time prediction and timely control of the fungal contamination.

Predicting the concentration of indoor culturable fungi using a kernel-based extreme learning machine (K-ELM).

Indoor fungal is of great significance for human health. The kernel-based extreme learning machine is employed to determine the most important parameters for predicting the concentration of indoor culturable fungi (ICF). For model training and statistical analysis, parameters that contained indoor or outdoor PM10 and PM2.5, RH, Temperature, CO2 and ICF were measured in 85 residential buildings of Baoding, China, from November 2016 to March 2017. The variable selection process contains four different cases to identify the optimal input combination. The results indicate that root mean square error of the optimal input combinations can be improved 5.6% from 1 to 2 input variables, while that could be only improved 1.9% from 2 to 3 input variables. However, considering both precision and simplicity, the combination of indoor PM10 and RH provides a more suitable selection for predicting the ICF.

A preliminary study of the local biomechanical environment of liver tumors in vivo.

PURPOSE: Biomechanical properties can be used as biomarkers to diagnose tumors, monitor tumor development, and evaluate treatment efficacy. The purpose of this preliminary study is to characterize the biomechanical environment of two typical liver tumors, hemangiomas (HEMs) and hepatocellular carcinomas (HCCs), and to investigate the potential of using strain metrics as biomarkers for tumor diagnosis, based on a limited clinical dataset. METHODS: Magnetic resonance (MR) tagging was used to quantify the motion and deformation of the two types of liver tumors. Displacements of the tumors arising from a heartbeat were measured over one cardiac cycle. Local biomechanical conditions of the tumors were characterized by estimating two principal strains (ε1 and ε2 ) and an octahedral shear strain (εsoct ) of the tumor and its peripheral region. Biomechanical conditions of the tumors were compared with those of the arbitrarily selected regions from healthy volunteers. RESULTS: We observed that the HCCs had significantly smaller strain values compared to their peripheral tissues. However, the HEMs did not have significantly different strains from those of the peripheral tissues, and were similar to healthy liver regions. The sensitivity of using ε1 , ε2 , and εsoct to diagnose HCC were all 1, while the sensitivity of using ε1 , ε2 , and εsoct to diagnose HEM were 0.67, 0.17, and 0.67, respectively. CONCLUSIONS: Lagrangian strain metrics provide insight into the biomechanical conditions of certain liver tumors in the human body and may provide another perspective for tumor characterization and diagnosis.

On-site assessments on variations of PM2.5, PM10, CO2 and TVOC concentrations in naturally ventilated underground parking garages with traffic volume.

There have been an increasing number of automobile vehicles in cities, so that newly developed residential areas are mostly designed with underground parking garages (UPGs). For naturally ventilated UPGs, the ventilation performance may be insufficient to discharge totally vehicle-induced pollutants out of the enclosed underground spaces, which consequently results in threats to residents' health. This study, therefore, aims at examining the patterns of pollutant concentrations in naturally ventilation UPGs as well as their sensitivities to traffic volume. In particular, the naturally ventilated UPGs' weekday particulate matters (PM2.5 and PM10), CO2 and TVOC concentration as well as their relationships between traffic volume were quantitively evaluated based on field measurements in eight residential areas in Baoding, China. Results indicated that daily average PM2.5, PM10, CO2 and TVOC concentrations in studied UPGs were 105.81 µg/m3, 464.17 µg/m3, 571 ppm and 24 ppb, respectively. The PM2.5 concentrations in UPGs were slightly higher than that in ambient environments, while the PM10 concentrations in UPGs were significantly higher. Furthermore, both PM10 and TVOC concentrations in UPGs were in significant relationships with traffic volume at the p < 0.01 level, while the concentration of UPG PM2.5 generally exhibited a significant correlation (p < 0.01) with that of the ambient. Nevertheless, a combination of traffic volume, the ambient and accumulative effect was much better to explain the hourly PM10 concentration in UPGs. These findings will be conducive to instruct engineers with fundamental knowledge of UPG ventilation design.

A multi-purpose electromagnetic actuator for magnetic resonance elastography.

An electromagnetic actuator was designed for magnetic resonance elastography (MRE). The actuator is unique in that it is simple, portable, and capable of brain, abdomen, and phantom imagings. A custom-built control unit was used for controlling the vibration frequency and synchronizing the trigger signals. An actuation unit was built and mounted on the specifically designed clamp and holders for different imaging applications. MRE experiments with respect to gel phantoms, brain, and liver showed that the actuator could produce stable and consistent mechanical waves. Estimated shear modulus using local frequency estimate method demonstrated that the measurement results were in line with that from MRE studies using different actuation systems. The relatively easy setup procedure and simple design indicated that the actuator system had the potential to be applied in many different clinical studies.