The European,Japanese and US protective helmet,gloves and boots for firefighters: thermoregulatory and psychological evaluations

The purpose of this study was to investigate the physiological and subjective responses of the European, Japanese (JPN) and US firefighters' helmet, gloves and boots for international standardisation. Three experimental conditions were evaluated (clothing mass: 9.4, 8.2 and 10.1 kg for the three conditions, respectively) at the air temperature of 32°C and 60% relative humidity. The results showed that there was no significant difference among the three conditions in oxygen consumption, heart rate, total sweat rate, rectal temperature and mean skin temperature, whereas peripheral temperatures and subjective perceptions were lower in the JPN condition than in the other conditions (P < 0.05). These results indicate that a 0.5-kg reduction in helmet mass and a 1.1-kg reduction in boot mass during exercise resulted in a significant decrease in head and leg temperatures and subjective perceptions, while a 1.9-kg reduction in total clothing mass had insignificant influences on the metabolic burden and overall body temperature.     

Evaluation of landscape and instream modeling to predict watershed nutrient yields

The project goal was to loosely couple the SWAT model and the QUAL2E model and compare their combined ability to predict total phosphorus (TP) and NO₃-N plus NO₂-N yields to the ability of the SWAT model with its completely coupled water quality components to predict TP and NO₃-N plus NO₂-N yields from War Eagle Creek watershed in Northwest Arkansas. Model predictions were compared using a statistical approach to identify significant differences between the two modeling methods. Results from two variations of the Pearson product-moment correlation (p < 0.05) indicated that correlation coefficients and regression slopes for the two data sets were not significantly different. This implies that neither modeling method was significantly better in predicting monthly TP and NO₃-N plus NO₂-N yields from the watershed. Additionally, no significant differences were present between predicted outputs of the SWAT model with instream components active compared with when instream components were inactive, indicating a need for further testing and refinement of the SWAT algorithms simulating instream processes. We can further infer that the instream processes available in SWAT may not be enhancing its predictive abilities as far as simulating instream components.     

Hybrid modeling to predict the economic feasibility of mining undiscovered porphyry copper deposits

Few studies attempt to model the economic feasibility of mining undiscovered mineral resources given the sparseness of data; and the coupled, nonlinear, spatial, and temporal relationships among variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 203 porphyry copper deposit sites across the world. The sparse data set includes one dependent variable indicating the economic feasibility, and seventy two independent variables from categories describing characteristics of mining method, metallurgy, dimensions, economics, and amount. Analysis of component planes reveals relations and strengths in the underlying SOM multivariate density function which are used to impute missing values. Application of the Davies–Bouldin criteria to k-means clusters of SOM neurons identified 14 regional economic resource units (conceptual models). A best subsets approach applied to median values from these models identified 20 statistically significant combinations of variables. During model fitting by the multiple linear regression technique, only four of the empirical models had variables that were all significant at the 95% confidence level. The best model explained 98% of the variability in economic feasibility and incorporated variables describing distance to natural gas, road, and water; and the total amount of resources. This model was independently validated by comparing predictions of economic feasibility at 68 mine sites not included in the training data. Eighty-four percent of the reported economic feasibility is correctly predicted with 8 false positives and 2 false negative. We demonstrate the application of this model to a permissive copper porphyry tract that crosses a portion of British Columbia and Yukon territories of Canada. The proposed hybrid approach provides an alternative modeling paradigm for translating estimates of contained metal into meaningful societal measures.     

A survey on geometrical reconstruction as a core technology to sketch-based modeling

In this work, the background and evolution of three-dimensional reconstruction of line drawings during the last 30 years is discussed. A new general taxonomy is proposed to make apparent and discuss the historical evolution of geometrical reconstruction and their challenges. The evolution of geometrical reconstruction from recovering know-how stored in engineering drawings to sketch-based modeling for helping in the first steps of conceptual design purposes, and the current challenges of geometrical reconstruction are discussed too.     

Application of soft computing to predict blast-induced ground vibration

In this study, an attempt has been made to evaluate and predict the blast-induced ground vibration by incorporating explosive charge per delay and distance from the blast face to the monitoring point using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 2-5-1 architecture was trained and tested using 130 experimental and monitored blast records from the surface coal mines of Singareni Collieries Company Limited, Kothagudem, Andhra Pradesh, India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) by ANN and conventional vibration predictors. Results were compared based on coefficient of determination and mean absolute error between monitored and predicted values of PPV.     

Response to thermal and physical strain during flashover training in Croatian firefighters

Flashover training (FOT) for firefighters is a simulation of the flashover phenomenon under controlled conditions. This study assessed arterial blood pressure (BP) and its response to thermal and physical strain during FOT in 48 professional and 18 volunteer firefighters.     

Application of thermodynamic modeling to slag-metal equilibria in steelmaking

The knowledge of the non-metallic phases in equilibrium with liquid steel is very important to the design and control of the results of steelmaking operations, in particular deoxidation, desulfurization and inclusion shape control. Thus, through the control of steel and slag composition it is possible, for instance, to tailor non-metallic inclusions to achieve maximum deformability, to avoid nozzle clogging in continuous casting and to optimize the use of deoxidants. However, the solution of this type of problems requires adequate thermodynamic models and corresponding databases both for the metal phase and for the slag.

Close interaction with the steelmaking shop makes it possible a) to evaluate the calculation needs for solving these problems, b) apply the selected models and databases to these questions and c) have a feedback with respect to how adequate the solution is to the actual problem.

In this work the current models used for these applications are briefly reviewed, their limitations highlighted and the results of their applications both to laboratory and to plant data are presented and compared.     

An investigation of the suitability of Artificial Neural Networks for the prediction of core and local skin temperatures when trained with a large and gender-balanced database

Neural networks have been proven to successfully predict the results of complex non-linear problems in a variety of research fields, including medical research. Yet there is paucity of models utilising intelligent systems in the field of thermoregulation. They are under-utilized for predicting seemingly random physiological responses and in particular never used to predict local skin temperatures; or core temperature with a large dataset. In fact, most predictive models in this field (non-artificial intelligence based) focused on predicting body temperature and average skin temperature using relatively small gender-unbalanced databases or data from thermal dummies due to a lack of larger datasets.This paper aimed to address these limitations by applying Artificial Intelligence to create predictive models of core body temperature and local skin temperature (specifically at forehead, chest, upper arms, abdomen, knees and calves) while using a large and gender-balanced experimental database collected in office-type situations.A range of Neural Networks were developed for each local temperature, with topologies of 1–2 hidden layers and up to 20 neurons per layer, using Bayesian and the Levemberg-Marquardt back-propagation algorithms, and using various sets of input parameters (2520 NNs for each of the local skin temperatures and 1760 for the core temperature, i.e. a total of 19400 NNs). All topologies and configurations were assessed and the most suited recommended. The recommended Neural Networks trained well, with no sign of over-fitting, and with good performance when predicting unseen data. The recommended Neural Network for each case was compared with previously reported multi-linear models. Core temperature was avoided as a parameter for local skin temperatures as it is impractical for non-contact monitoring systems and does not significantly improve the precision despite it is the most stable parameter. The recommended NNs substantially improve the predictions in comparison to previous approaches. NN for core temperature has an R-value of 0.87 (81% increase), and a precision of ±0.46 °C for an 80% CI which is acceptable for non-clinical applications. NNs for local skin temperatures had R-values of 0.85-0.93 for forehead, chest, abdomen, calves, knees and hands, last two being the strongest (increase of 72% for abdomen, 63% for chest, and 32% for calves and forehead). The precision was best for forehead, chest and calves, with about ±1.2 °C, which is similar to the precision of existent average skin temperature models even though the average value is more stable.     

Rule-based Mamdani-type fuzzy modeling of skin permeability

Two Mamdani type fuzzy models (three inputs–one output and two inputs–one output) were developed to predict the permeability of compounds through human skin. The models were derived from multiple data sources including laboratory data, published data bases, published statistical models, and expert opinion. The inputs to the model include information about the compound (molecular weight and octonal–H₂O partition coefficient) and the application temperature. One model included all three parameters as inputs and the other model only included information about the compound. The values for mole molecular weight ranged from 30 to 600 Da. The values for the log of the octonal–H₂O partition coefficient ranged from −3.1 to 4.34. The values for the application temperature ranged from 22 to 39 °C. The predicted values of the log of permeability coefficient ranged from −5.5 to −0.08.Each model was a collection of rules that express the relationship of each input to the permeability of the compound through human skin. The quality of the model was determined by comparing predicted and actual fuzzy classification and defuzzification of the predicted outputs to get crisp values for correlating estimates with published values. A modified form of the Hamming distance measure is proposed to compare predicted and actual fuzzy classification. An entropy measure is used to describe the ambiguity associated with the predicted fuzzy outputs.The three input model predicted over 70% of the test data within one-half of a fuzzy class of the published data. The two input model predicted over 40% of the test data within one-half of a fuzzy class of the published data. Comparison of the models show that the three input model exhibited less entropy than the two input model.     

Application of a parallel DSMC technique to predict flowcharacteristics in microfluidic filters

Using a parallel implementation of the direct simulation Monte Carlo (DSMC) method, periodic MEMS microfilters are studied in detail. The dependence of the flow characteristics on geometry, Knudsen number, pressure difference, spacing between the filter elements, and accommodation coefficients are investigated. By comparing DSMC results with the widely used analytical formulas, the validity range of the analytical approaches is evaluated. The simulation results show that velocity slip exists both on the filter channel walls and on the filter membrane and results in an increased flow rate. Velocity slip increases strongly with decreasing accommodation coefficients. For long channels, this results in a strong increase in flow rate; whereas for short channels, the increase in flow rate is limited. For the filter separations considered in this paper, we observe that separation between filter channels does not influence the flow rate within each channel     

价格着色Petri网在工作流建模中的应用

在着色网的基础上扩展了价格信息,提出了价格着色网,用来对业务流程进行价格建模并对其作相应的成本预算分析.其中讨论了价格着色网的变迁步规则,提出了价格着色网中的标识--经济标识的定义,并有针对性地提出了最小成本构造算法.最后以一个业务流程实例演示了该模型的应用,得出结论为价格着色网能够较好地解决业务流程建模中的成本预算分析问题而且该模型中的最小成本构造算法是有效的.     

Application of an expert system to predict thermal conductivity of rocks

In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using support vector machine (SVM). Training of the SVM network was carried out using 102 experimental data sets of various rocks, whereas 25 new data sets were used for the testing of the TC by SVM model. Multivariate regression analysis (MVRA) has also been carried out with same data sets that were used for the training of SVM model. SVM and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by SVM and MVRA was 0.994 and 0.918, respectively, whereas MAE was 0.0453 and 0.2085 for SVM and MVRA, respectively.     

Physical and thermal strain of firefighters according to the firefighting tactics used to suppress wildfires

The aim of this study was to analyse the physiological strain of firefighters, using heart rate (HR) and core temperature, during real wildfire suppression according to the type of attack performed (direct, indirect or mixed). Three intensity zones were established according to the HR corresponding to the ventilatory threshold (VT) and respiratory compensation threshold (RCT): zone 1, RCT. The exercise workload (training impulse (TRIMP)), the physiological strain index (PSI) and the cumulative heat strain index(CHSI) were calculated using the time spent in each zone, and the HR and core temperature, respectively. Significantly higher mean HR, time spent in Z2 and Z3 and TRIMP h(-1) were found in direct and mixed versus indirect attacks. The highest PSI and CHSI were observed in the direct attack. In conclusion, exercise strain and combined thermal strain, but not core temperature during wildfire suppression, are related to the type of attack performed. STATEMENT OF RELEVANCE: Our findings demonstrated that wildfire firefighting is associated with high physiological demands, which vary significantly depending on the tactics chosen for performing the task. These results should be kept in mind when planning programmes to improve wildland firefighters' physical fitness, which will allow improvement in their performance.     

Physical and thermal strain of firefighters according to the firefighting tactics used to suppress wildfires

The aim of this study was to analyse the physiological strain of firefighters, using heart rate (HR) and core temperature, during real wildfire suppression according to the type of attack performed (direct, indirect or mixed). Threeintensity zones were established according to the HR corresponding to the ventilatory threshold (VT) and respiratory compensation threshold (RCT): zone 1, <VT; zone 2 (Z2), between VT and RCT; zone 3 (Z3), >RCT. The exercise workload (training impulse (TRIMP)), the physiological strain index (PSI) and the cumulative heat strain index(CHSI) were calculated using the time spent in each zone, and the HR and core temperature, respectively. Significantly higher mean HR, time spent in Z2 and Z3 and TRIMP h^?1 were found in direct and mixed versus indirect attacks. The highest PSI and CHSI were observed in the direct attack. In conclusion, exercise strain and combined thermal strain, but not core temperature during wildfire suppression, are related to the type of attack performed.

Statement of relevance: Our findings demonstrated that wildfire firefighting is associated with high physiological demands, which vary significantly depending on the tactics chosen for performing the task. These results should be kept in mind when planning programmes to improve wildland firefighters' physical fitness, which will allow improvement in their performance.     

Application of machine learning to predict the recurrence-proneness for cervical cancer

This study applied advanced machine learning techniques, widely considered as the most successful method to produce objective to an inferential problem of recurrent cervical cancer. Traditionally, clinical diagnosis of recurrent cervical cancer was based on physician’s clinical experience with various risk factors. Since the risk factors are broad categories, years of clinical study and experience have tried to identify key risk factors for recurrence. In this study, three machine learning approaches including support vector machine, C5.0 and extreme learning machine were considered to find important risk factors to predict the recurrence-proneness for cervical cancer. The medical records and pathology were accessible by the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrate that C5.0 model is the most useful approach to the discovery of recurrence-proneness factors. Our findings suggest that four most important recurrence-proneness factors were Pathologic Stage, Pathologic T, Cell Type and RT Target Summary. In particular, Pathologic Stage and Pathologic T were important and independent prognostic factor. To study the benefit of adjuvant therapy, clinical trials should randomize patients stratified by these prognostic factors, and to improve surveillance after treatment might lead to earlier detection of relapse, and precise assessment of recurrent status could improve outcome.     

Immune and inflammatory responses of Australian firefighters after repeated exposures to the heat

When firefighters work in hot conditions, altered immune and inflammatory responses may increase the risk of a cardiac event. The present study aimed to establish the time course of such responses. Forty-two urban firefighters completed a repeat work protocol in a heat chamber (100 ± 5°C). Changes to leukocytes, platelets, TNFα, IL-6, IL-10, LPS and CRP were evaluated immediately post-work and also after 1 and 24 h of rest. Increases in core temperatures were associated with significant increases in leukocytes, platelets and TNFα directly following work. Further, platelets continued to increase at 1 h (+31.2 ± 31.3 × 10⁹ l, p < 0.01) and remained elevated at 24 h (+15.9 ± 19.6 × 10⁹ l, p < 0.01). Sustained increases in leukocytes and platelets may increase the risk of cardiac events in firefighters when performing repeat work tasks in the heat. This is particularly relevant during multi-day deployments following natural disasters.

Practitioner Summary: Firefighters regularly re-enter fire affected buildings or are redeployed to further operational tasks. Should work in the heat lead to sustained immune and inflammatory changes following extended rest periods, incident controllers should plan appropriate work/rest cycles to minimise these changes and any subsequent risks of cardiac events.     

Satellite-based and mesoscale regression modeling of monthly air and soil temperatures over complex terrain in Turkey

Simple regression algorithms were developed to quantify spatio-temporal dynamics of minimum and maximum air temperatures (T_min and T_max, respectively) and soil temperature for a depth of 0-5 cm (T_soil-5cm) across complex terrain in Turkey using Moderate Resolution Imaging Spectroradiometer (MODIS) data at a 500-m resolution. A total of 762 16-day MODIS composites (127 images × 6 bands) between 2000 and 2005 were averaged over a monthly basis to temporally match monthly T_min, T_max, and T_soil-5cm from 83 meteorological stations. A total of 60 (28 temporally averaged plus 32 time series-based) linear regression models of T_min, T_max, and T_soil-5cm were developed using best subsets procedure as a function of a combination of 12 explanatory variables: six MODIS bands of blue, red, near infrared (NIR), middle infrared (MIR), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI); four geographical variables of latitude, longitude, altitude, and distance to sea (DtS); and two temporal variables of month, and year. The best multiple linear regression models elucidated 65% (RMSE = 5.9 °C), 65% (RMSE = 5.1 °C), and 57% (RMSE = 6.9 °C) of variations in T_min, T_max, and T_soil-5cm, respectively, under a wide range of T_min (−34 to 25 °C), T_max (0.2-47 °C) and T_soil-5cm (−9 to 40 °C) observed at the 83 stations.     

Characterisation of regional skin temperatures in recreational surfers wearing a 2-mm wetsuit

The purpose of this study was to investigate skin temperatures across surfers’ bodies while wearing a wetsuit during recreational surfing. Forty-six male recreational surfers participated in this study. Participants were instrumented with eight wireless iButton thermal sensors for the measurement of skin temperature, a Polar RCX5 heart rate monitor and a 2-mm full wetsuit. Following instrumentation, participants were instructed to engage in recreational surfing activities as normal. Significant differences (p < 0.001) in skin temperature (T_sk) were found across the body while wearing a wetsuit during recreational surfing. In addition, regional skin temperature changed across the session for several regions of the body (p < 0.001), and the magnitude of these changes varied significantly between regions. We show for the first time that significant differences exist in skin temperature across the body while wearing a wetsuit during a typical recreational surfing session. These findings may have implications for future wetsuit design.

Practitioner Summary: This study investigated the impact of wearing a wetsuit during recreational surfing on regional skin temperatures. Results from this study suggest that skin temperatures differ significantly across the body while wearing a 2-mm wetsuit during recreational surfing. These findings may have implications for future wetsuit design.