scholarly journals Characterizing Steam Penetration through Thermal Protective Fabric Materials

Textiles ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 16-28
Author(s):  
Sumit Mandal ◽  
Guowen Song
Keyword(s):  
High Performance ◽  
Health And Safety ◽  
Industrial Workers ◽  
Key Factors ◽  
Textile Materials ◽  
Protective Fabrics ◽  
Time Required ◽  
Fabric Materials ◽  
Protective Performance ◽  
The Relationship

This study performs an analysis of steam penetration through thermal protective fabric materials. Different, multilayered thermal protective fabrics were selected and tested in a laboratory-simulated steam exposure, and their steam protective performance (SPP) was measured in terms of the time required to generate second-degree burns on the bodies of wearers. Additionally, the total transmitted thermal energy (TTTE) through the fabrics during testing was measured. Through statistical analysis, it was established that fabric properties, namely air permeability and thickness, are the key factors that affect the SPP and TTTE; the relationship among the fabric properties, SPP, and TTTE is also summarized. Theoretically, it has been found that heat and mass (steam) transfer occur through fabrics in the course of steam exposure, which mainly affect the SPP and TTTE. This study could help textile/materials engineers to develop high performance thermal protective fabrics for the increased occupational health and safety of firefighters and industrial workers.

scholarly journals Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure

2021 ◽  
pp. 152808372098497
Author(s):  
Sumit Mandal ◽  
Guowen Song ◽  
Rene M Rossi ◽  
Indu B Grover
Keyword(s):  
Thermal Energy ◽  
Health And Safety ◽  
Empirical Modeling ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Artificial Neural Network Ann ◽  
Protective Fabrics ◽  
Time Required ◽  
Protective Performance ◽  
Best Fit

This study aims to characterize and model the thermal protective fabrics usually used in workwear under Molotov cocktail exposure. Physical properties of the fabrics were measured; and, thermal protective performances of the fabrics were evaluated under a fire exposure generated from the laboratory-simulated Molotov cocktail. The performance was calculated in terms of the amount of thermal energy transmitted through the fabrics; additionally, the time required to generate a second-degree burn on wearers’ bodies was predicted from the calculated transmitted thermal energy. For the characterization, the parameters that affected the protective performance were identified and discussed with regards to the theory of heat and mass transfer. The relationships between the properties of the fabric systems and the protective performances were statistically analyzed. The significant fabric properties affecting the performance were further employed in the empirical modeling techniques − Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for predicting the protective performance. The Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of the developed MLR and ANN models were also compared to identify the best-fit model for predicting the protective performance. This study found that thermal resistance and evaporative resistance are two significant properties (P-Values < 0.05) that negatively affect the transmitted thermal energy through the fabric systems. Also, R2 and RMSE values of ANN model were much higher (R2 = 0.94) and lower (RMSE = 37.42), respectively, than MLR model (R2 = 0.73; RMSE = 191.38); therefore, ANN is the best-fit model to predict the protective performance. In summary, this study could build an in-depth understanding of the parameters that can affect the protective performance of fabrics used in the workwear of high-risk sectors employees and would provide them better occupational health and safety.

scholarly journals A categorization tool for fabric systems used in firefighters' clothing based on their thermal protective and thermo-physiological comfort performances

2018 ◽  
Vol 89 (16) ◽  
pp. 3244-3259 ◽  
Author(s):  
Sumit Mandal ◽  
Simon Annaheim ◽  
Andre Capt ◽  
Jemma Greve ◽  
Martin Camenzind ◽  
...  
Keyword(s):  
Performance Index ◽  
Protective Clothing ◽  
High Performance ◽  
Clustering Algorithm ◽  
Health And Safety ◽  
Thermal Protective Performance ◽  
Thermal Protective Clothing ◽  
Low Performance ◽  
Fabric System ◽  
Protective Performance

Fabric systems used in firefighters' thermal protective clothing should offer optimal thermal protective and thermo-physiological comfort performances. However, fabric systems that have very high thermal protective performance have very low thermo-physiological comfort performance. As these performances are inversely related, a categorization tool based on these two performances can help to find the best balance between them. Thus, this study is aimed at developing a tool for categorizing fabric systems used in protective clothing. For this, a set of commercially available fabric systems were evaluated and categorized. The thermal protective and thermo-physiological comfort performances were measured by standard tests and indexed into a normalized scale between 0 (low performance) and 1 (high performance). The indices dataset was first divided into three clusters by using the k-means algorithm. Here, each cluster had a centroid representing a typical Thermal Protective Performance Index (TPPI) value and a typical Thermo-physiological Comfort Performance Index (TCPI) value. By using the ISO 11612:2015 and EN 469:2014 guidelines related to the TPPI requirements, the clustered fabric systems were divided into two groups: Group 1 (high thermal protective performance-based fabric systems) and Group 2 (low thermal protective performance-based fabric systems). The fabric systems in each of these TPPI groups were further categorized based on the typical TCPI values obtained from the k-means clustering algorithm. In this study, these categorized fabric systems showed either high or low thermal protective performance with low, medium, or high thermo-physiological comfort performance. Finally, a tool for using these categorized fabric systems was prepared and presented graphically. The allocations of the fabric systems within the categorization tool have been verified based on their properties (e.g., thermal resistance, weight, evaporative resistance) and construction parameters (e.g., woven, nonwoven, layers), which significantly affect the performance. In this way, we identified key characteristics among the categorized fabric systems which can be used to upgrade or develop high-performance fabric systems. Overall, the categorization tool developed in this study could help clothing manufacturers or textile engineers select and/or develop appropriate fabric systems with maximum thermal protective performance and thermo-physiological comfort performance. Thermal protective clothing manufactured using this type of newly developed fabric system could provide better occupational health and safety for firefighters.

Fabric Materials and Sportswear Anti-Drag Performance Relationship

2013 ◽  
Vol 821-822 ◽  
pp. 305-308
Author(s):  
Yu Xiu Yan ◽  
Li Juan Lou ◽  
Jian Wei Tao ◽  
Zi Min Jin
Keyword(s):  
Wind Tunnel ◽  
Wind Tunnel Experiments ◽  
Wind Resistance ◽  
Textile Materials ◽  
Plain Style ◽  
Wind Resistance Performance ◽  
Fabric Materials ◽  
Resistance Performance ◽  
The Relationship ◽  
Curve Equation

In this study four kinds of materials on the market most commonly used for sportswear are selected to knit one sports vest in plain style, woven by seamless circular knitting machine. We use wind tunnel tests to do the experiments and gather datum of experiments and analyze them to research the relationship between textile materials and wind resistance performance of sportswear. The results showed that: in the wind tunnel experiments, when wind speed is between 3.3 m/s and 9.35m/s, the wind resistance performance of the four experiment clothes is almost the same; When between 9.35 m/s and 11.00m/s, their wind resistance performance demonstrates a dispersion level; When it is between 11.00m/s and 18.70m/s, their differences of wind resistance performance between the four clothes are significant. The wind resistance performance could be sequenced from the good to the poor like this: 15.6tex combed cotton + coolmax modified polyester, 7.7tex/48f nylon, 15.6tex combed cotton, 9.7tex modal +15.6tex TC, and the fitting curve equation is obtained from the measured datum analysis through the SPSS software.

The Awareness Level of the Safety and Health of the Operational Level

2017 ◽  
Vol 3 (1) ◽  
pp. 42
Author(s):  
Roshanira Che Mohd Noor ◽  
Nur Atiqah Rochin Demong
Keyword(s):  
Health And Safety ◽  
Health Conditions ◽  
Effective Strategies ◽  
Operational Level ◽  
Safety And Health ◽  
Health Factors ◽  
The Cost ◽  
The Relationship ◽  
Awareness Level

Providing a safe and healthy workplace is one of the most effective strategies in for holding down the cost of doing construction business. It was a part of the overall management system to facilitate themanagement of the occupational health and safety risk that are associated with the business of the organization. Factors affected the awareness level inclusive of safety and health conditions, dangerous working area, long wait care and services and lack of emergency communication werethe contributed factors to the awareness level for the operational level. Total of 122 incidents happened at Telekom Malaysia Berhad as compared to year 2015 only 86 cases. Thus, the main objective of this study was to determine the relationship between safety and health factors and the awareness level among operational workers.The determination of this research was to increase the awareness level among the operational level workerswho committing to safety and health environment.

Examining Organizational, Cultural, and Individual-Level Factors Related to Workplace Safety and Health Awareness and Risks: A Systematic Review and Metric Analysis (Preprint)

2018 ◽  
Author(s):  
Edmund W. J. Lee ◽  
Han Zheng ◽  
Htet Htet Aung ◽  
Megha Rani Aroor ◽  
Chen Li ◽  
...  
Keyword(s):  
Systematic Review ◽  
Citation Count ◽  
Workplace Safety ◽  
Key Factors ◽  
Health Awareness ◽  
Individual Level ◽  
Metric Analysis ◽  
Safety And Health ◽  
The Impact ◽  
The Relationship

BACKGROUND Promoting safety and health awareness and mitigating risks are of paramount importance to companies in high-risk industries. Yet, there are very few studies that have synthesized findings from existing online workplace safety and health literature to identify what are the key factors that are related to (a) safety awareness, (b) safety risks, (c) health awareness, and (d) health risks. OBJECTIVE As one of the first systematic reviews in the area of workplace health and safety, this study aims to identify the factors related to safety and health awareness as well as risks, and systematically map these factors within three levels: organizational, cultural, and individual level. Also, this review aims to assess the impact of these workplace safety and health publications in both academic (e.g., academic databases, Mendeley, and PlumX) and non-academic settings (e.g., social media platform). METHODS The systematic review was conducted in line with procedures recommended by Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). First, Proquest, ScienceDirect and Scopus were identified as suitable databases for the systematic review. Second, after inputting search queries related to safety and health awareness and risks, the articles were evaluated based on a set of inclusion and exclusion criteria. Third, the factors identified in the included articles were coded systematically. Fourth, the research team assessed the impact of the articles through a combination of traditional and new metric analysis methods: citation count, Altmetric Attention Score, Mendeley readers count, usage count, and capture count. RESULTS Out of a total of 4,831 articles retrieved from the three databases, 51 articles were included in the final sample and were systematically coded. The results revealed six categories of organizational (management commitment, management support, organizational safety communication, safety management systems, physical work environment, and organizational environment), two cultural (interpersonal support and organizational culture), and four individual (perception, motivation, attitude and behavior) level factors that relate to safety and health awareness and risk. In terms of impact, the relationship between citation count and the various metrics measuring academic activity (e.g., Mendeley readers, usage count, and capture count) were mostly significant while the relationship between citation count and Altmetric Attention Score was non-significant. CONCLUSIONS This study provides a macro view of the current state of workplace safety and health research and gives scholars an indication on some of the key factors of safety and health awareness and risks. Researchers should also be cognizant that while their work may receive attention from the scholarly community, it is important to tailor their communication messages for the respective industries they are studying to maximize the receptivity and impact of their findings. CLINICALTRIAL N.A.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

scholarly journals Comparison of the sensitivity of Western blotting between PVDF and NC membranes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yufang Xiang ◽  
Yuanyuan Zheng ◽  
Shaobo Liu ◽  
Gang Liu ◽  
Zhi Li ◽  
...  
Keyword(s):  
Western Blotting ◽  
Polyvinylidene Fluoride ◽  
Staining Intensity ◽  
Detection Sensitivity ◽  
Key Factors ◽  
Post Translational Modifications ◽  
Factors Affecting ◽  
Molecular Weights ◽  
Direct Blue ◽  
The Relationship

AbstractWestern blotting (WB) is one of the most widely used techniques to identify proteins as well as post translational modifications of proteins. The selection of electroblotted membrane is one of the key factors affecting the detection sensitivity of the protein which is transferred from gel to membrane in WB. The most common used membranes are polyvinylidene fluoride (PVDF) and nitrocellulose (NC) membranes. Which membrane of these two is more suitable for WB has not been reported so far. Here, by incubating proteins which were transferred to PVDF or NC membranes with a series of antibodies and different types of lectins, we investigated the relationship between the binding ability of these two membranes to proteins or glycoproteins and the molecular weight of the target protein. The antibody re-probed ability of the two membranes was also explored. Moreover, we verified the above results by directly incubating proteins having different molecular weights onto PVDF or NC membranes. Bound proteins were stained with direct blue-71, and the staining intensity was quantitated by scanning and densitometry.

scholarly journals Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3315
Author(s):  
Aida-Ștefania Manole ◽  
Radu-Ioan Ciobanu ◽  
Ciprian Dobre ◽  
Raluca Purnichescu-Purtan
Keyword(s):  
High Performance ◽  
Geographical Area ◽  
Mobility Model ◽  
Routing Algorithms ◽  
Message Delivery ◽  
Opportunistic Network ◽  
Key Factors ◽  
Network Algorithms ◽  
Music Festival ◽  
Social Metrics

Constant Internet connectivity has become a necessity in our lives. Hence, music festival organizers allocate part of their budget for temporary Wi-Fi equipment in order to sustain the high network traffic generated in such a small geographical area, but this naturally leads to high costs that need to be decreased. Thus, in this paper, we propose a solution that can help offload some of that traffic to an opportunistic network created with the attendees’ smartphones, therefore minimizing the costs of the temporary network infrastructure. Using a music festival-based mobility model that we propose and analyze, we introduce two routing algorithms which can enable end-to-end message delivery between participants. The key factors for high performance are social metrics and limiting the number of message copies at any given time. We show that the proposed solutions are able to offer high delivery rates and low delivery delays for various scenarios at a music festival.

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