Human Volunteer Kinematics in Rear-End Sled Collisions

Validation of new crash test dummies for rear-end collision testing requires human response data from pertinent test situations. Eleven human volunteers were exposed to 23 low-speed rear impacts to determine human response in well-defined test seats, and to quantify repeatability, variability and the effect of seat design on human response.

The results showed vertical motion of the volunteers’ H-point caused by ramping up along the seat, and an upward motion of the volunteers’ torso and head. The latter was caused by a combination of ramping up along the seatback and straightening of the thoracic kyphosis. During the first 100 ms, the volunteers flexed their necks. Thereafter, the volunteers extended their necks. These new data have proven to be useful in validation of rear-impact dummies.     

A Comparison of Volunteer,BioRID P3 and Hybrid III Performance in Rear Impacts

The most important tool for testing seat-systems in rear impacts is a crash test dummy. However, investigators have noted limitations of the most commonly used dummy, the Hybrid III. The BioRID I is a step closer to a biofidelic crash test dummy, but it is not user-friendly and the straightening of the thoracic spine kyphosis is smaller than that 220of humans. To reduce these problems, a new BioRID prototype was developed, the P3. It has new neck muscle substitutes, a softer thoracic spine and a softer rubber torso than does the BioRID I.

The BioRID P3 was compared with volunteer test data in a rigid and a standard seal without head restraints. The dummy kinematic performance, pressure distribution between subject and seatback, neck loads and accelerations were compared with those of ten volunteers and a Hybrid III. The BioRID P3 provided repeatable test results and its response was very similar to that of the average volunteer in rear impacts at Δv = 9 km/h.     

Climate Change in Southern and Eastern Africa During the Past Millennium and its Implications for Societal Development

Climatic records from equatorial eastern Africa and subtropical southern Africa have shown that both temperature and the amount of rainfall have varied over the past millennium. Moreover, the rainfall pattern in these regions varied inversely over long periods of time. Droughts started abruptly, were of multi-decadal to multi-centennial length and the changes in the hydrological budget were of large amplitude. Changing water resources in semi-arid regions clearly must have regional influences on both ecological and socio-economic processes. Through a detailed analysis of the historical and paleoclimatic evidence from southern and eastern Africa covering the past millennium it is shown that, depending on the vulnerability of a society, climatic variability can have an immense impact on societies, sometimes positive and sometimes disastrous. Therefore, the interconnected issue of world ecosystem and social resilience is the challenge for decision-makers if sustainable development is to be reached on global and local levels.     

A study on the potential sources of air pollutants observed at Tjörn,Sweden

The Potential Source Contribution Function (PSCF) receptor model combines both chemical and meteorological information. In this study, PSCF was employed to identify the potential source emission regions for aerosol compositions measured at Tjörn, Sweden (58.01 °N, 11.36 °E). PSCF was for the first time applied on a European scale. One hundred and fifty-two four-day air parcel backward trajectories were combined with concentrations of sixteen elements determined in 33 coarse and fine aerosol samples. The observations were made between February 17 and March 26, 1985. The modeling results of the heavy metals V, Pb, Zn, and As are presented and compared with available emission inventory data. A number of known industrialized regions in the former USSR and Europe are found of high potential to be the emission source areas. These areas are in good agreement with the known emission information. The PSCF maps of total sulfur, Non-Seasalt-Sulfur (N.S.S.) and chlorine are also presented. High potential regions in the Arctic area exist in the PSCF map for total sulfur wheres they do not occur in that for N.S.S.     

Biological hydrogen production by Rhodobacter capsulatus in solar tubular photo bioreactor

The purpose of this study was to develop a pilot scale tubular photo bioreactor (80 L) for photo fermentative hydrogen production by photosynthetic purple-non-sulfur bacterium, Rhodobacter capsulatus, operating in outdoor conditions, using acetate as the carbon source. The reactor was operated continuously in fed-batch mode for 30 days throughout December 2008 in Ankara. It was placed in a greenhouse in order to keep the temperature above freezing levels. It was found that R. capsulatus had a rapid growth with a specific growth rate of 0.025 h^?1 in the exponential phase. The growth was defined with modified logistic model for long term duration. The hydrogen production and feeding started in the late exponential phase. Evolved gas contained 99% hydrogen and 1% carbon dioxide by volume. The average molar productivity calculated during daylight hour was 0.31 mol H₂/(m³ h) with regard to the total reactor volume and 0.112 mol H₂/(m²·day) with regard to the total illuminated surface area. It was proven that even at low light intensities and low temperatures, the acetic acid which was fed to the system can be utilized for biosynthesis, growth and hydrogen production. The overall hydrogen yield was 0.6 mole H₂ per mole of acetic acid fed. This study showed that photofermentation in a pilot scale tubular photo bioreactor can produce hydrogen, even in winter conditions.     

Influence of the Anterior and Middle Cranial Fossae on Brain Kinematics During Sagittal Plane Head Rotation

A 2D physical model of the human head was used to investigate how the irregular skull base structure affects brain kinematics during sagittal plane head dynamics. The model consisted of a rigid skull vessel with interchangeable skull base structures. One version of the model used a skull base mimicking the irregular geometry of the human. A second version used a skull base structure approximating the anterior and middle fossae as a flat surface. Silicone gel simulated the brain and was separated from the vessel by a paraffin layer which provided a slip condition at the interface between the gel and vessel. The model was exposed to 7600 rad/s² peak rotational acceleration with 6 ms pulse duration and 5° forced rotation. After 90° free rotation, the model was decelerated during 30 ms. Five repeated tests were conducted with each version. Rigid body displacement, shear strain and principal strains were determined from high-speed video recorded trajectories of grid markers located at different positions in the surrogate brain. The humanlike skull base reduced peak displacements of the inferior surfaces of the temporal and frontal lobes up to 87% and 48%, respectively. Up to 48% and 36% higher peak strains were obtained in the frontal and superior regions of the surrogate brain in the version containing the humanlike skull base. In contrast, the humanlike skull base decreased peak strain up to 28% in the central region of the surrogate brain. The results indicate that the irregular skull base offers natural protection of nerves and vessels passing through fissures and foramina in the cranial floor but also that it affects kinematics in different regions throughout the cerebrum. Implications of these results are discussed with respect to brain injury and modeling of head impact.     

Parameter Evaluation and Model Validation of Ozone Exposure Assessment Using Harvard Southern California Chronic Ozone Exposure Study Data

Abstract

To examine factors influencing long‐term ozone (O₃) exposures by children living in urban communities, the authors analyzed longitudinal data on personal, indoor, and outdoor O₃ concentrations, as well as related housing and other questionnaire information collected in the one‐year‐long Harvard Southern California Chronic Ozone Exposure Study. Of 224 children contained in the original data set, 160 children were found to have longitudinal measurements of O₃ concentrations in at least six months of 12 months of the study period. Data for these children were randomly split into two equal sets: one for model development and the other for model validation. Mixed models with various variance‐covariance structures were developed to evaluate statistically important predictors for chronic personal ozone exposures. Model predictions were then validated against the field measurements using an empirical best‐linear unbiased prediction technique.The results of model fitting showed that the most important predictors for personal ozone exposure include indoor O₃ concentration, central ambient O₃ concentration, outdoor O₃ concentration, season, gender, outdoor time, house fan usage, and the presence of a gas range in the house. Hierarchical models of personal O₃ concentrations indicate the following levels of explanatory power for each of the predictive models: indoor and outdoor O₃ concentrations plus questionnaire variables, central and indoor O₃ concentrations plus questionnaire variables, indoor O₃ concentrations plus questionnaire variables, central O₃ concentrations plus questionnaire variables, and questionnaire data alone on time activity and housing characteristics. These results provide important information on key predictors of chronic human exposures to ambient O₃ for children and offer insights into how to reliably and cost‐effectively predict personal O₃ exposures in the future. Furthermore, the techniques and findings derived from this study also have strong implications for selecting the most reliable and cost‐effective exposure study design and modeling approaches for other ambient pollutants, such as fine particulate matter and selected urban air toxics.     

Bee pollens as biological indicators: An ecological assessment of pollution in Northern Turkey via ICP-MS and XPS analyses

Environmental Science and Pollution Research - In this study, pollens were collected from 25 different locations of Northern Turkey to investigate pollution monitoring. Surface chemistry of pollen...     

Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey

Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960–2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t?+?1 output variable by using t, t???1, t???2, and t???3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE?=?0.9345, OI?=?0.9438, R²?=?0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE?=?0.9528, OI?=?0.9559, R²?=?0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE?=?0.9398, OI?=?0.9483, R²?=?0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.