\r\n\tTopics covered include but are not limited to: Hydrologic Cycle (Precipitation, Runoff, Infiltration and their Measurement, Land surface interaction); Hydrologic Analysis (Hydrograph, Wave routing, Hydrologic statistics, Frequency Analysis); Applied Hydrology (Applications in Engineering, Sciences and Agriculture, Design storms, Risk analysis, Case studies); Computational Hydrology (Numerical modeling, Hydrologic modeling and forecasting, Flow visualization, Model validation, Parameter estimation); Interdisciplinary Hydrology (Hydrometeorology, Impact of Climate Change, Precipitation data analysis, Mathematical concepts, Natural hazards); Radar Hydrology (Precipitation estimation techniques, Promise and Challenges in Radar technology, Uncertainty in radar precipitation estimates).
\r\n
\r\n\tThe contents covered in this book will serve as a valuable reference guide to students, researchers, government agencies and practicing engineers who work in hydrology and related areas. We hope that this book will open new directions in basic and applied research in hydrological science.
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1. Introduction
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Mitigating hot and cold injuries is a complex problem and has been shown to have significant links to a number of individualized factors, to include race, gender, job specialty, and geographic origin [1, 2]. There are many other individualized elements (e.g., fitness, body composition, and genetics) that are intuitively linked to these health outcomes; however, there is a lack of adequate data to scale that sufficiently addresses these issues.
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The history of characterizing heat exchange and thermoregulatory functions in humans can be traced back to the late 1770s; where British military physiologist, Sir Charles Blagden conducted descriptive studies of man, dog, and beef steak responses in a hot room [3]. Mathematically describing heat exchange theory has roots in physics and with the development of the laws of thermodynamics and heat exchange, specifically as described in Fourier’s law [4] a mathematical expression of the dynamics of heat balance in solids, simplified as:
where \n\n\nρ\n\n is density (g/m3), c is specific heat [(kcal/°K. kg)], k is heat conductance [kcal/(hr cm °K)], T is temperature (°K), t is time (hours), and H is the net flow rate of heat other than by diffusion.
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Key work by Pennes in 1948 [5], reported measured temperatures of tissue and blood at the forearm and enabled the creation of the bioheat transfer equation. This equation has proven to be a key underlying basis of future models, seen as:
where k (w/m °C) is the tissue thermal conductivity, T is tissue temperature in °C, \n\n\nq\np\n\n\n (w/m3) is energy deposition rate, \n\n\nq\nm\n\n\n (w/m3) is metabolism, W (kg/m3/s) is local tissue blood perfusion rate, \n\n\nC\nb\n\n\n (J/kg/°C) is specific blood heat, \n\n\nT\na\n\n\n (°C) is arterial temperature, ρ (kg/m3) is the tissue density, and \n\n\nc\np\n\n\n (J/kg/°C) is the specific tissue heat.
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Conceptually, heat exchange between the human and the environment was first described by Lefevre in 1911; where he characterized the human as a sphere with an internal core that exchanged heat through the shell into the environment [6]. In 1934, Burton applied Fourier’s law, presenting this exchange mathematically and describing the human as one uniform cylinder in what is considered by many as the first visual conceptualization of human thermoregulatory modeling [7].
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Representation of the human in a thermoregulatory model is most often done by sectioning the human into nodes, segments, and elements; typically using one of four different designs, (1) one-node, (2) two-node, (3) multi-node, or (4) multi-element [8]. An example of the difference between these designs is shown in Figure 1; while the multi-element approach is more realistic human shape (e.g., finite analysis distribution). Typically each node represents an independent layer with unique thermal properties, each segment represents a section or grouped section of an area of the body, and each element represents multiple thermal components that make up the whole body (often more geometrically accurate to the shape of the human).
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Figure 1.
Example of model designs.
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One node models are essentially empirically derived and do not include elements within the thermoregulatory response system. There are several one node thermoregulatory models that have been used extensively over time to predict core body temperature and thermal discomfort within a given environment [9, 10, 11, 12].
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Simple two-node models describe specific thermodynamic responses of a single segment, typically separated into concentric core and shell nodes. They have often been used examine thermal discomfort and physiological responses, to include the work by Gagge and Nishi [13, 14, 15], and several others [16, 17, 18, 19]. Two node model approaches have been used where the two node design was applied to multi-segments [20, 21, 22, 23]. Multi-node models are essentially expanded versions of the two-node methods with additional shells or layers within them where the heat balance is calculated for each layer. Multi-node models, with both single- and multi-segment designs have become the more prevalent approach. The first multi-node model was developed by Crosbie et al. [24] and has been followed by many since [25, 26, 27, 28, 29]. Notable is the work of Solwijk and Hardy [30, 31, 32, 33], where they first introduced the concepts of temperature set points and negative feedback in a controlled theory design. Their work has been built upon by many researchers over time [34, 35, 36, 37, 38, 39, 40, 41, 42]. The first multi-element model was originally published in 1961 by Wissler, and later improved upon [43, 44, 45]. Additional multi-element models include work by Smith [46], with the first three-dimensional (3D) transient multi-element model. As computation methods improved, a series of improvements has led to more realistic and complex models [8, 47, 48, 49, 50].
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While the majority of these models were developed with the intent of characterizing thermoregulation in various environments; several have been designed specifically to address cold environments or thermoregulatory events that specifically address cold issues (e.g., finger, hand, foot temperatures). With the intricacies of human response to cold, studies have focused on extremities, the specific areas most subject to cold injuries. One of the first attempts was by Molnar in 1957, used a heat balance approach to study hand temperature responses to cold [51]. This work was followed by work focused on finger freezing points [52, 53, 54, 55, 56, 57] and whole hand modeling [58, 59]. Specific models have also been developed of the foot [60], toes [61], and facial tissues [62, 63]. Cold survival models have been developed over time to make predictions in both open air and submerged environments [64, 65, 66, 67, 68].
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2. Clinical definitions of cold injuries
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Characterizing cold related injuries is fairly complex, as the responses to cold have higher individual variability when compared to heat related injuries. From a clinical perspective, cold related injuries can be broadly divided into three categories: frostbite, nonfreezing cold injuries, and hypothermia. In addition, each of these has varying levels of severity and subcategories associated to them.
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Frostbite is below the point at which skin tissue begins to freeze. While 0°C (32°F) is traditionally considered the freezing point of water, the freezing point of skin is understood to be marginally lower due to electrolytes [69]. Observed freezing points range from as low as −4.8°C to as high as −0.6°C [69, 70].
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Nonfreezing cold injuries include an array of injury events where tissue freezing has not occurred but damage occurs. The level of severity of nonfreezing injuries is determined by the temperature, duration, and wetness of the exposure to the tissue. Four of the more common specific types of nonfreezing injuries include immersion (trench) foot, chilblain, cold urticaria, and cold-induced bronchoconstriction [71].
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Immersion foot is a nonfreezing injury. The foot presents swollen, the skin is red initially but as severity increases the skin becomes lower in oxygen saturation and becomes cyanotic (purple, bluish discoloration) [69, 71]. Immersion foot is most often reported after tissue have been exposed for extended periods of time to non-freezing temperatures, between 0 and 15°C (32–60°F) [71]. The term ‘immersion’ itself refers to when the foot is actually immersed in water when the foot is wet within boots for sustained periods of time [69, 71].
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Chilblain is a fairly common nonfreezing injury to the skin. It can occur during 1–5 hours of temperatures below 16°C (60°F) [69]. Cold urticaria is expressed as a quick onset of redness, swelling and itchiness of the skin in response to short-term exposure (i.e., minutes) to cold environments [71]. Cold-induced bronchoconstriction is a physiological response where an individual’s airways are narrowed during exercise in cold environments [69, 71, 72, 73].
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Hypothermia is a broad category of cold injury and is clinically described to be the point at which core body temperature has dropped below 35°C (95°F) [74]. However, hypothermia is more specifically defined with four levels of severity; where normothermia (normal temperature level) is approximately 37°C (98.6°F), mild hypothermia is between 91.4–95°C (33–35°F), moderate hypothermia being 85.2–89.6°C (29–32°F), and severe hypothermia being 56.7–82.4°C (13.7–28°F) [69, 71]. Figure 2 outlines specific core temperature reference points associated with physiological responses using work by Castellani et al. [69] and Pozos and Danzl [74] and described in Army Guidance [75].
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Figure 2.
The range of human core temperatures and associated physiological responses [76].
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3. Basics of thermophysiology
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The human body is capable of maintaining thermal balance while operating within a wide range of temperatures. The human system generally maintains an internal core temperature (Tc) of approximately 37°C. Due to natural circadian rhythm, Tc fluctuates ~0.5°C daily. However, Tc can fluctuate based on physical activity or environmental conditions, and may range from 36.0–40.0°C. The microenvironment created between human skin and clothing typically must remain within 28–30°C to maintain thermal homeostasis at rest [45]. This microenvironment changes significantly with physical activity due to metabolic heat production and air movement.
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Humans have an internal control system, primarily the preoptic area of the anterior hypothalamus, responsible for maintaining healthy body temperature. The hypothalamus uses feedback from two main sources, the skin and the blood. When temperature changes (hot or cold) are identified by either of these two sources, impulses are sent to the hypothalamus which in turn directs physiological changes to compensate for these temperatures. To protect from cold or heat injury, the human body attempts to either generate or dissipate heat to stay warm or cool off. Heat production is a natural process for humans and is a function of metabolism, oxidation of foods, and muscular activity. Heat transfer between the human and environment occurs via four pathways: conduction, convection, radiation, and evaporation. This heat exchange process is typically referred to as heat or thermal energy balance, and can be described in the heat balance equation:
where S is heat storage; M is metabolic rate; W is work rate; R is radiation; C is convection; K is conduction; and E is evaporation. Radiation is heat that is transferred via electromagnetic waves (e.g., solar radiation). Conduction is heat transfer due to the body’s direct contact with a solid object (e.g., touching a cold surface). Convection is heat transfer between the body and a fluid such as air or water. Evaporation is heat loss to the environment due to the phase change from liquid to vapor, typically associated with evaporation of sweat and respiratory water.
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Hyperthermia is when heat gain exceeds heat loss; while hypothermia occurs when body temperature drops below normal levels as heat production is inadequate to compensate for the rate of heat loss to the environment [77].
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Vasoconstriction and vasodilation are the two key physiological responses of how heat transfer is regulated from the body to the periphery [78, 79]. Vasoconstriction is the constriction of blood vessels and occurs in response to cold environments to reduce the amount of blood flow to the skin. Vasoconstriction protects the internal organs from cold exposure but increases cold injury risk in the extremities due to lower blood flow and lower skin temperatures. Vasoconstriction in effect creates a two-layer distribution of body temperature; a cold outer shell surrounding a warmer core. The colder outer shell reduces heat loss to the environment by reducing the temperature gradient between the skin surface and the environment, and a colder surface radiates less heat.
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Vasodilation is essentially the opposite of vasoconstriction; where blood vessels open to allow increased blood flow across the body and out to the extremities to enable increased heat dissipation [78, 79]. During these responses, there are other associated physiological responses that help compensate for the increased skin blood flow (e.g., increased heart rate and cardiac output).
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The extremities are more affected by cold exposure than other parts of the body. When the human body cools, blood flow is reduced to the extremities (i.e., the hands and feet) decreasing the amount of warm blood flowing to these areas. It is a challenge to protect the hands and feet as they have lower metabolic heat production of the hands and feet due to their inherently small muscle mass and large surface area to mass ratio.
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From a functional perspective, the balance of control within the human system depends on the response to cold exposure and interaction between peripheral (skin) and core body temperatures with the central nervous system (CNS) and the various physiological responses (Figure 3); [74].
\n
Figure 3.
Peripheral (skin) and core temperature influence on central nervous system (CNS) and physiological outcomes.
\n
\n
\n
4. Importance of clothing
\n
Clothing has long been used to provide protection from environmental elements (heat, cold, etc.) or physical or biological hazards (e.g., rocks, thorns). Clothing properties and requirements vary widely among users and use cases. A single clothing ensemble cannot protect an individual from the extremes of the temperature spectrum of earth, being approximately −89°C at its coldest and 58°C at its warmest. However, clothing is a toll to protect each end of this spectrum of environmental extremes [80]. However, protections must be based on use cases to achieve the desired thermal comfort. For example, protective equipment for American football players (i.e., pads and helmet) is vastly different than protective equipment worn by soldiers (i.e., body armor, ballistic helmet). It should be noted that added protection may increase the thermal burden to wearers, and thus increases risk of heat injuries [81, 82, 83].
\n
It is critical to understand the clothing option tradespace in order to predict and prepare for the impact clothing has on protecting or impairing human health. That is to say, the selection of the proper clothing, requires an understanding of how the human (physiology, anthropology, etc.), the anticipated activities (i.e., work rate, length of exposure and metabolic heat production), the work environments (temperature, humidity, etc.), and the biophysical properties of clothing worn (heat transfer performance) will interact in each workplace scenario.
\n
\n
4.1 Clothing biophysics
\n
Clothing protects the wearer from environmental threats, but may impose a level of thermal burden. Both the biophysical resistances (thermal and evaporative) and spectrophotometric (reflectance, absorptivity, and transmittance) properties of clothing can have a significant influence on the impact of the environment on the wearer. Measurements of the biophysical properties of clothing can be used to model the impacts on thermal sensation (e.g., thermal comfort) and thermoregulatory responses (e.g., heat strain, cold protection). The thermal and evaporative resistances, wind effects, and spectrophotometric properties of the clothing are critical measurements for this purpose.
\n
\n
4.1.1 Thermal and evaporative resistance
\n
Sweating thermal manikins have long been used to provide biophysical measures of clothing and equipment worn by the human [84]. While direct biophysical comparisons can be helpful, i.e., comparing one ensemble’s value to another [85], a more informative approach is to combine these measured values with thermoregulatory modeling. Models enable the prediction of thermoregulatory responses based on different individuals, as well as varied environments, clothing, or activity levels.
\n
The current standard for thermal manikin testing calls for two fundamental measures: thermal resistance (Rt\n) [86] and evaporative resistance (Ret\n) [87]. These two measures represent the dry heat exchange (Rt\n: convection, conduction, and radiation) and wet heat exchange (Ret\n: evaporation). After converting both Rt\n and Ret\n into units of clo and im [88, 89], a ratio can be used to describe an ensemble’s evaporative potential (im/clo) [90].
\n
Each ensemble should be tested using chamber conditions from the American Society for Testing and Materials (ASTM) standards for assessing Rt\n (ASTM F1291-16) and Ret\n (ASTM F2370-16) [86, 87] (Table 1).
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Variable (unit)
\n
Skin/surface temperature (Ts\n, °C)
\n
Ambient temperature (Ta\n, °C)
\n
Relative humidity (RH, %)
\n
Wind velocity (V, ms−1)
\n
Saturation (%)
\n
\n\n\n
\n
\nRt\n (m2 K/W)
\n
35
\n
20
\n
50
\n
0.4
\n
0
\n
\n
\n
\nRet\n (m2 Pa/W)
\n
35
\n
35
\n
40
\n
0.4
\n
100
\n
\n\n
Table 1.
American Society for Testing and Materials standard chamber and manikin conditions for testing thermal (Rt\n) and evaporative (Ret\n) resistance.
\n
Thermal resistance (Rt\n) is the dry heat transfer from the surface of the manikin through the clothing and into the environment, mainly from convection, and described as:
where Ts is surface temperature and Ta is the air temperature, both in °C or °K. Q is power input (W) to maintain the surface (skin) temperature (Ts) of the manikin at a given set point; A is the surface area of the measurement in m2. These measures of Rt\n can then be converted to units of clo:
\n
\n\n1\n\nclo\n=\n6.45\n\n\nI\nT\n\n\n\nE5
\n
where IT is the total insulation including boundary air layers. Evaporative resistance (Ret) is heat loss from the body in isothermal conditions (Ts ≈ Ta), described as:
where Psat is vapor pressure in Pascal at the surface of the manikin (assumed to be fully saturated), and Pa is ambient vapor pressure, in Pascal, of the chamber environment. Measures of Ret can then be converted to a vapor permeability index (im), a non-dimensional measure of water vapor resistance of materials defined as:
4.1.2 Wind effects on thermal and evaporative resistance
\n
In order to use the biophysical measures, i.e., measures of Rt\n (clo) and Ret\n (im) for thermoregulatory modeling there is a need to first estimate the effects of wind velocity on the biophysical characteristics of the ensemble (i.e., to determine how wind affects clo and im values). These effects are typically referred to as wind velocity coefficients or gamma values (g) [91]. Historically, these coefficients were determined by collecting measurements of both Rt\n and Ret\n at multiple wind velocities above the ASTM standard of 0.4 m/s. However, recent work suggests these coefficient values can be accurately estimated from single wind velocity tests [91, 92].
\n
Clothing properties and wind coefficients are critical inputs to a number of predictive mathematical models [10, 11, 93, 94], as they use these values to describe wind-related effects, such as intrinsic insulation (Icl\n) and intrinsic permeability index (icl\n) for either the whole body or segments of the body, as seen with:
where Ia\n is insulation measured on a nude thermal manikin, It\n is total insulation, and (fcl\n) is clothing area factor, calculated by:
\n
\n\n\nf\ncl\n\n=\n\nA\n\nA\ncl\n\n\n\nE9
\n
where A (m2) is surface area of the nude manikin, and Acl\n (m2) is surface area the clothed manikin.
\n
True measures of Acl\n require a three-dimensional scan. However, methods for estimating Acl\n have been derived by McCullough et al. [95]. Simplified or estimated Acl\n and fcl\n is often used where a value of 1 is assumed for warm-weather or indoor clothing. For cold-weather clothing a value would be calculated from:
While these estimation methods have been studied and produce acceptable variance between estimated and direct measured results [96], there are questions whether estimates remain acceptable for clothing insulation outside typical cold weather clothing insulation ranges, e.g., 0.2–1.7 clo [97].
\n
Most clothing-based thermal models, by design, predict human thermoregulatory responses to various environmental conditions and therefore require quantitative insights into the change in clothing properties with changes in wind velocity. Furthermore, elements of wind can significantly influence physiological responses and injury outcomes in cold environments due to wind chill effects [69, 98, 99]. There has been work to develop that relates exposure time to predicted injury (e.g., frostbite) likely to occur due to temperature and levels of wind speed exposure [98].
\n
\n
\n
\n
\n
5. Modeling risk and predicting heat and cold related injuries
\n
Mathematical models can predict the human thermal response (e.g., metabolic heat production, core body temperature (Tc\n), endurance time) resulting from activity, environment, and clothing. These mathematical models are typically binned into one of three categories, either as rational, empirical, or hybrid. Rational (mechanistic) models mathematically represent phenomena based on an understanding of physics and physiology (biology, chemistry, physics). Empirical models mathematically reflect the observed relationship among experimental data. While both methods, rational and empirical, are scientifically valid approaches, perhaps the most effective approach is the hybrid or mixed model method that uses a combination of the two.
\n
\n
5.1 Rational models
\n
Rational modeling incorporates equations that describe heat balance and thermoregulatory processes [100]. Two fundamental equations are used to describe internal heat balance and for heat exchange between skin and environment. One equation outlines the temperature gradient change from core to skin and can be seen as:
where \n\nρ\n\n is tissue mass (kg m−3), c is the specific heat of the tissue (kJ kg−1 °C−1), T is the tissue temperature (°C), \n\nt\n\n is time (sec), qm is metabolic heat production rate (W m−3), λ is the tissue heat conductivity (W m−1 °C−1), \n\n\n∇\n2\n\n\n is a Laplace transform for heat conduction based on the tissue temperature gradient, \n\n\nω\nbl\n\n\n is blood flow rate (m3 s−1 m−3 tissue), \n\n\nρ\nbl\n\n\n is blood flow mass (kg m−3), \n\n\nc\nbl\n\n\n is the blood specific heat (kJ kg−1 °C−1), and \n\n\nT\nbl\n\n\n is the blood temperature (°C).
\n
The second equation describes heat exchange from the skin surface to the environment as:
where λ is the tissue heat conductivity (W m−1 °C−1), T is tissue temperature (°C), \n\nn\n\n is the tissue coordinate normal to the skin surface; while the balance is the array of avenues of heat exchange (W m−2): R is radiative, C is convective, K is conductive, and E is evaporative.
\n
Rational models of thermoregulatory processes usually include equations for the controlling signals of the thermoregulation system and equations for thermoregulatory actions such as sweating, vasodilation, vasoconstriction, and shivering.
\n
Understanding the interplay between each of the different layers of the human (grossly consisting of core, muscle, fat, and skin) along with clothing and air layers within clothing is only the first step to modeling the human’s response in a given environment. Figure 4 shows the rational basis behind the SCENARIO model where the human is mathematically represented as one multi-layer cylinder, based on the relationship of the layers of the human, their respective physiological responses, and clothing [93, 94].
\n
Figure 4.
Fundamental rational basis (SCENARIO model) [93], reused with permission. Note: BFcr\n is core blood flow, BFmu\n is muscle blood flow, BFfat\n is muscle blood flow, BFsk\n is skin blood flow.
\n
\n
\n
5.2 Empirical models
\n
Empirical models are mathematical representations of data, often using statistical methods such as regression or correlational analysis. An example model is the Heat Strain Decision Aid (HSDA), empirically derived by the U.S. Army from an extensive database of human studies that incorporates the biophysics of heat exchange [10, 11, 101] and predicts core temperature, maximum work times, sustainable work-rest cycles, water requirements, and the estimated likelihood of heat casualties. This model has been used to derive guidance and doctrine for military [102] and fluid intake guidance for the public [103]. The basis of HSDA includes both principles of heat exchange along with empirical predictions of physiological responses. Collectively 16 inputs from four elements (individual characteristics, physical activity, clothing biophysics, and environmental conditions) are used to mathematically predict the rise in core body temperature during physical activity [10].
\n
\n
\n
5.3 Simple models
\n
Originally developed by Holmér [104], a simple calculation was adopted by the International Organization Standardization (ISO) technical report (ISO 11079) [105], as an evaluation metric of the insulation required (IREQ) for given environments and activities to compare ensemble performance. The IREQ method functionally describes the concept for balancing the heat exchange between the human and the environment, and simplified as:
where M is metabolic heat produced, W is effective mechanical work and collectively M-W represents the heat produced within the human; while the opposite side of this balance, Eres\n and Cres\n represent the respiratory heat exchange (evaporative and convective), and E, K, R, and C represent the conventional heat exchange methods (evaporative, conductive, radiative, and convective) and S is heat storage.
\n
The IREQ equation illustrates the rational balance between thermal insulation and heat transfer, seen as:
where tsk\n is mean skin temperature, tcl\n clothing surface temperature, and \n\nM\n−\nW\n−\n\nE\nres\n\n−\n\nC\nres\n\n−\nE\n=\nR\n+\nC\n.\n\n\n
\n
This method also determines the minimum and neutral IREQ (IREQmin and IREQneutral), and describes amounts of insulation needed to maintain thermal balance (minimum) and to maintain an equilibrium balance (neutral). The ISO 11079 also outlines general scenarios for the minimum required insulation (IREQmin) for multiple work intensities and environments. Collectively this method provides a simple method for evaluating the effectiveness of specific cold weather clothing at protecting from cold injuries [106].
\n
\n
\n
5.4 Key elements for model development
\n
When developing a cold-based thermal model there are a number of physiological, environmental, and biophysical parameters that can and should be considered. Particular attention should be paid to the extremity temperatures blood flow and metabolic heat production.
\n
\n
5.4.1 Blood flow
\n
As blood flow is a major component to the overall movement of heat, it is important to be able to predict blood flow to the muscle, skin, and distribution of blood flow to these regions within the body. Table 2 outlines some historical methods used in models for predicting each of these elements.
Methods for predicting skin blood flow in thermoregulatory models.
Note: qs\n and qs,r\n are skin blood flow and rate; AVD is active vasodilation; CVC is cutaneous vascular conductance—addition of M (mediated), L (locally), and E (effect of exercise); \n\nβ\n\n\n\ndil\n and \n\nβ\n\n\n\nstr\n are control coefficients for vasodilation and vasoconstriction; warms and colds refer to calculated net warm and cold receptors; \n\nb\n\nf\nforearm\n\n\n is blood flow at the forearm; \n\n\n\nγ\ndil\n\n\n and \n\n\nγ\nstr\n\n\n are distribution coefficients for vasodilation and vasoconstriction; cm\n is a proportionality coefficient; and MW\n is metabolic heat produced from exercise.
\n
\n
\n
5.4.2. Shivering
\n
Shivering is where, in response to cold exposure, muscles involuntarily contract rhythmically off and on in an attempt to increase body temperature [74]. During cold exposure the shivering response is a critical element to model, as the production of heat protects the body core temperature despite skin to the ambient heat loss. Table 3 outlines some of the modeling approaches that have been used to predict the shivering response as they relate to the total metabolic rate (M) and the heat production from shivering (Mshiv\n).
Methods for predicting shivering related model calculations.
Note: T is temperature; \nh\n is head; set is set point of temperatures; Wa,m\n is a weighting coefficient; qs\n is heat flux \ns\n is skin; BMI is body mass index; \nty\n is Tympanic membrane; \nre\n is rectal; and \nes\n is esophageal; BF% is body fat percentage.
\n
\n
\n
5.4.3 Metabolic heat production
\n
An individual’s metabolic heat production can be estimated at rest and during activity using the assumed basal rate of 58.2 W/m2 [121] and the estimated metabolic equivalents (METS) of activity; where 1 MET is resting. Ainsworth et al. [122] outlines a wide range of activities and their associated MET level for reference. However, there are metabolic rate estimation methods available based on energy costs of standing or walking (Table 4). Recently work has also been published that makes corrections to some of these prediction methods specific to traveling over snow terrain [123].
Methods for predicting metabolic rates during walking or standing.
Note: G is grade (° for Ref. [125], % for others); Ht, height (inches for Ref. [129]); L, external load (kg); M, mass (kg); η, terrain factor; S, speed (mph for Ref. [129], m s−1 for others); VO2-rest, resting oxygen consumption (ml kg−1 min−1); Wt, weight (lbs).
\n
\n
\n
\n
\n
6. Summary and discussion
\n
Mathematical models and decision aids are tools for inspiring advancements within the field of thermophysiology, and for providing solutions to help mitigate injury risk.
\n
Scientifically based models have been used in the development of public [97, 98, 103, 104, 130, 131, 132] and military guidance [75, 131, 133], for forensic assessments [134, 135, 136, 137, 138, 139, 140], as well in the creation of operational tools for survival [141, 142]. Notably, the use of Xu and Werner’s six cylinder model [41] was used to develop the Probability of Survival Decision Aid (PSDA), a computer model used to predict hypothermia and dehydration impact on functional time (i.e., duration of ability for useful work), and survival time while exposed to marine environments [67, 143, 144]. The PSDA model is underpinned by the rational principles described herein and the outputs are provided in a customized graphical user interface. This tool has been transitioned for use by Search and Rescue (SaR) personnel and continues to be refined and verified based on real-world feedback and data collected [144].
\n
There is a need for continued advancement in the development of individualized modeling methods such as finite element models as well as providing models and decision aids that can be used in dynamic settings and for complex scenarios with prolonged durations. Additionally, inclusion of probabilistic and statistically based risk factors should be used as elements that help improve individualized predictions. The accessibility of the information from these tools continues to be a challenge for the scientific community. While providing usable information to the public, military, and other user communities should be the ultimate goal of these work efforts; feedback from these communities should be translated back to the scientists to ensure relevant improvements are made from real-world information.
\n
\n
Acknowledgments
\n
This work is dedicated to the memory of Dr. Eugene H. Wissler (1927–2018). His pioneering efforts modeling heat transfer in the human body provided critical ground work that continues to be emulated by researchers.
\n
The authors would also like to thank Dr. Scott Montain for oversight and review of this chapter.
\n
Conflict of interest
The authors have no conflicts of interest to declare. Funding for this work has been provided by U.S. Army Medical Research and Materiel Command (USAMRMC), Military Operational Medicine Research Program (MOMRP).
\n
Disclaimer
\n
The views expressed in this paper are those of the authors and do not reflect the official policy of the Department of Army, Department of Defense, or the US Government.
\n
\n',keywords:"hypothermia, cold injury, clothing, military, biophysics, survival",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/63852.pdf",chapterXML:"https://mts.intechopen.com/source/xml/63852.xml",downloadPdfUrl:"/chapter/pdf-download/63852",previewPdfUrl:"/chapter/pdf-preview/63852",totalDownloads:493,totalViews:0,totalCrossrefCites:0,dateSubmitted:"June 11th 2018",dateReviewed:"August 31st 2018",datePrePublished:"November 5th 2018",datePublished:"May 20th 2020",dateFinished:null,readingETA:"0",abstract:"The ability to model and simulate the rise and fall of core body temperature is of significant interest to a broad spectrum of organizations. These organizations include the military, as well as both public and private health and medical groups. To effectively use cold models, it is useful to understand the first principles of heat transfer within a given environment as well as have an understanding of the underlying physiology, including the thermoregulatory responses to various conditions and activities. The combination of both rational or first principles and empirical approaches to modeling allow for the development of practical models that can predict and simulate core body temperature changes for a given individual and ultimately provide protection from injury or death. The ability to predict these maximal potentials within complex and extreme environments is difficult. The present work outlines biomedical modeling techniques to simulate and predict cold-related injuries, and discusses current and legacy models and methods.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/63852",risUrl:"/chapter/ris/63852",signatures:"Adam W. Potter, David P. Looney, Xiaojiang Xu, William R. Santee and Shankar Srinivasan",book:{id:"8091",title:"Autonomic Nervous System Monitoring",subtitle:"Heart Rate Variability",fullTitle:"Autonomic Nervous System Monitoring - Heart Rate Variability",slug:"autonomic-nervous-system-monitoring-heart-rate-variability",publishedDate:"May 20th 2020",bookSignature:"Theodoros Aslanidis",coverURL:"https://cdn.intechopen.com/books/images_new/8091.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"200252",title:"Dr.",name:"Theodoros",middleName:null,surname:"Aslanidis",slug:"theodoros-aslanidis",fullName:"Theodoros Aslanidis"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"262568",title:"Mr.",name:"Adam",middleName:null,surname:"Potter",fullName:"Adam Potter",slug:"adam-potter",email:"adam.w.potter.civ@mail.mil",position:null,institution:null},{id:"271096",title:"Dr.",name:"David",middleName:null,surname:"Looney",fullName:"David Looney",slug:"david-looney",email:"david.p.looney4.civ@mail.mil",position:null,institution:null},{id:"271097",title:"Dr.",name:"Xiaojiang",middleName:null,surname:"Xu",fullName:"Xiaojiang Xu",slug:"xiaojiang-xu",email:"xiaojiang.xu.civ@mail.mil",position:null,institution:null},{id:"271098",title:"Dr.",name:"William",middleName:null,surname:"Santee",fullName:"William Santee",slug:"william-santee",email:"william.r.santee.ctr@mail.mil",position:null,institution:null},{id:"271099",title:"Prof.",name:"Shankar",middleName:null,surname:"Srinivasan",fullName:"Shankar Srinivasan",slug:"shankar-srinivasan",email:"srinivsh@shp.rutgers.edu",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Clinical definitions of cold injuries",level:"1"},{id:"sec_3",title:"3. Basics of thermophysiology",level:"1"},{id:"sec_4",title:"4. Importance of clothing",level:"1"},{id:"sec_4_2",title:"4.1 Clothing biophysics",level:"2"},{id:"sec_4_3",title:"Table 1.",level:"3"},{id:"sec_5_3",title:"4.1.2 Wind effects on thermal and evaporative resistance",level:"3"},{id:"sec_8",title:"5. Modeling risk and predicting heat and cold related injuries",level:"1"},{id:"sec_8_2",title:"5.1 Rational models",level:"2"},{id:"sec_9_2",title:"5.2 Empirical models",level:"2"},{id:"sec_10_2",title:"5.3 Simple models",level:"2"},{id:"sec_11_2",title:"5.4 Key elements for model development",level:"2"},{id:"sec_11_3",title:"Table 2.",level:"3"},{id:"sec_12_3",title:"Table 3.",level:"3"},{id:"sec_13_3",title:"Table 4.",level:"3"},{id:"sec_16",title:"6. Summary and discussion",level:"1"},{id:"sec_17",title:"Acknowledgments",level:"1"},{id:"sec_20",title:"Conflict of interest",level:"1"},{id:"sec_17",title:"Disclaimer",level:"1"}],chapterReferences:[{id:"B1",body:'\nCarter IIIR, Cheuvront SN, Williams JO, Kolka MA, Stephenson LA, Sawka MN, et al. Epidemiology of hospitalizations and deaths from heat illness in soldiers. Medicine & Science in Sports & Exercise. 2005;37(8):1338-1344\n'},{id:"B2",body:'\nDeGroot DW, Castellani JW, Williams JO, Amoroso PJ. Epidemiology of US Army cold weather injuries, 1980-1999. Aviation, Space, and Environmental Medicine. 2003;74(5):564-570\n'},{id:"B3",body:'\nBlagden C. Experiments and observations in an heated room. Philosophical Transactions. 1775;65:111-123\n'},{id:"B4",body:'\nFourier J. Theorie analytique de la chaleur, par M. Fourier. Paris, France: Chez Firmin Didot, père et fils; p. 1822\n'},{id:"B5",body:'\nPennes HH. 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Journal of Applied Physiology. 1999;87(5):1957-1972\n'},{id:"B26",body:'\nFiala D, Lomas KJ, Stohrer M. Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions. International Journal of Biometeorology. 2001;45(3):143-159\n'},{id:"B27",body:'\nKingma BR. Human thermoregulation; a synergy between physiology and mathematical modeling [doctoral dissertation]. Maastricht University\n'},{id:"B28",body:'\nKingma BR, Frijns AJ, Saris WH, van Steenhoven AA, van Marken Lichtenbelt WD. Mathematical modeling of human thermoregulation: A neurophysiological approach to vasoconstriction. In: Computational Intelligence. Berlin, Heidelberg: Springer; 2012. pp. 307-316\n'},{id:"B29",body:'\nDavoodi F, Hasanzadeh H, Zolfaghari SA, Maerefat M. Developing a new individualized 3-node model for evaluating the effects of personal factors on thermal sensation. Journal of Thermal Biology. 2017;69:1-2\n'},{id:"B30",body:'\nStolwijk JAJ, Hardy JD. Temperature regulation in man—A theoretical study. Pflügers Archiv für die Gesamte Physiologie des Menschen und der Tiere. 1966;291:129-162\n'},{id:"B31",body:'\nStolwijk JAJ. A Mathematical Model of Physiological Temperature Regulation in Man. NASA-CR-1855. Washington, DC: National Aeronaucis and Space Administration; 1971\n'},{id:"B32",body:'\nStolwijk JAJ. Mathematical models of thermal regulation. Annals of the New York Academy of Sciences. 1980;335(1):98-106\n'},{id:"B33",body:'\nStolwijk JAJ, Hardy JD. “Control of Body Temperature”. Comprehensive Physiology 2011, Supplement 26: Handbook of Physiology, Reactions to Environmental Agents. First Published in Print 1977. John Wiley & Sons, Inc.; 2010. pp. 45-68. DOI: 10.1002/cphy.cp090104\n'},{id:"B34",body:'\nMontgomery LD. A model of heat transfer in immersed man. Annals of Biomedical Engineering. 1974;2(1):19-46\n'},{id:"B35",body:'\nMontgomery LD. Biothermal simulation of scuba divers. Aviation, Space, and Environmental Medicine. 1975;46(6):814-818\n'},{id:"B36",body:'\nKuznetz LH. Control of thermal balance by a liquid circulating garment based on a mathematical representation of the human thermoregulatory system [Ph.D. thesis]. Berkeley: California Univ.; 1976\n'},{id:"B37",body:'\nGordon RG, Roemer RB, Horvath SM. A mathematical model of the human temperature regulatory system-transient cold exposure response. IEEE Transactions on Biomedical Engineering. 1976;6:434-444\n'},{id:"B38",body:'\nTikuisis PE, Gonzalez RR, Pandolf KB. Thermoregulatory model for immersion of humans in cold water. Journal of Applied Physiology. 1988;64(2):719-727\n'},{id:"B39",body:'\nTikuisis PE, Gonzalez RR, Pandolf KB. Prediction of human thermoregulatory responses and endurance time in water at 20 and 24 degrees C. Aviation, Space, and Environmental Medicine. 1988;59(8):742-748\n'},{id:"B40",body:'\nWerner J, Webb P. A six-cylinder model of human thermoregulation for general use on personal computers. The Annals of Physiological Anthropology. 1993;12:123-134\n'},{id:"B41",body:'\nXu X, Werner J. A dynamic model of the human/clothing/environment-system. Applied Human Science: Journal of Physiological Anthropology. 1997;16(2):61-75\n'},{id:"B42",body:'\nMunir A, Takada S, Matsushita T. Re-evaluation of Stolwijk\'s 25-node human thermal model under thermal-transient conditions: Prediction of skin temperature in low-activity conditions. Building and Environment. 2009;44(9):1777-1787\n'},{id:"B43",body:'\nWissler EH. Steady-state temperature distribution in man. Journal of Applied Physiology. 1961;16:734-740\n'},{id:"B44",body:'\nWissler EH. A mathematical model of the human thermal system. The Bulletin of Mathematical Biophysics. 1964;26:147-166\n'},{id:"B45",body:'\nWissler EH. In: Shitzer A, Eberhart R, editors. Mathematical Simulation of Human Thermal Behavior Using Whole Body Models. New York: Plenum Press, Heat Transfer in Medicine and Biology; 1985. pp. 325-373\n'},{id:"B46",body:'\nSmith CE. A transient 3-D model of the human thermal system [doctoral dissertation, PhD Thesis]. Kansas State University; 1991\n'},{id:"B47",body:'\nFerreira MS, Yanagihara JI. A transient three-dimensional heat transfer model of the human body. International Communications in Heat and Mass Transfer. 2009;36(7):718-724\n'},{id:"B48",body:'\nSchwarz M, Krueger MW, Busch HJ, Benk C, Heilmann C. Model-based assessment of tissue perfusion and temperature in deep hypothermic patients. IEEE Transactions on Biomedical Engineering. 2010;57(7):1577-1586\n'},{id:"B49",body:'\nSun X, Eckels S, Zheng ZC. An improved thermal model of the human body. HVAC&R Research. 2012;18(3):323-338\n'},{id:"B50",body:'\nTang Y, He Y, Shao H, Ji C. Assessment of comfortable clothing thermal resistance using a multi-scale human thermoregulatory model. International Journal of Heat and Mass Transfer. 2016;98:568-583\n'},{id:"B51",body:'\nMolnar GW. Heat transfer through the hand. In: Fisher FR, editor. Protection and Functioning of the Hands in Cold Climates. Washington, DC: National Academy of Sciences; 1957:15-45\n'},{id:"B52",body:'\nShitzer AV, Stroschein LA, Santee WR, Gonzalez RR, Pandolf KB. Quantification of conservative endurance times in thermally insulated cold-stressed digits. Journal of Applied Physiology. 1991;71(6):2528-2535\n'},{id:"B53",body:'\nShitzer AV, Stroschein LA, Gonzalez RR, Pandolf KB. Lumped-parameter tissue temperature-blood perfusion model of a cold-stressed fingertip. Journal of Applied Physiology. 1996;80(5):1829-1834\n'},{id:"B54",body:'\nShitzer A, Stroschein LA, Vital P, Gonzalez RR, Pandolf KB. Numerical analysis of an extremity in a cold environment including countercurrent arterio-venous heat exchange. Journal of Biomechanical Engineering. 1997;119(2):179-186\n'},{id:"B55",body:'\nTikuisis P, Ducharme MB. Finite-element solution of thermal conductivity of muscle during cold water immersion. Journal of Applied Physiology. 1991;70(6):2673-2681\n'},{id:"B56",body:'\nDucharme MB, Tikuisis P. Forearm temperature profile during the transient phase of thermal stress. European Journal of Applied Physiology and Occupational Physiology. 1992;64(5):395-401\n'},{id:"B57",body:'\nTikuisis P. Finger cooling during cold air exposure. Bulletin of the American Meteorological Society. 2004;85(5):717-724\n'},{id:"B58",body:'\nMontgomery LD, Williams BA. Effect of ambient temperature on the thermal profile of the human forearm, hand, and fingers. Annals of Biomedical Engineering. 1976;4(3):209-219\n'},{id:"B59",body:'\nLotens WA. Simulation of hand cooling due to touching cold materials. 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Prediction of survival time at sea based on observed body cooling rates. Aviation, Space, and Environmental Medicine. 1997;68(5):441-448\n'},{id:"B66",body:'\nXu X, Tikuisis P, Gonzalez R, Giesbrecht G. Thermoregulatory model for prediction of long-term cold exposure. Computers in Biology and Medicine. 2005;35(4):287-298\n'},{id:"B67",body:'\nXu X, Turner CA, Santee WR. Survival time prediction in marine environments. Journal of Thermal Biology. 2011;36(6):340-345\n'},{id:"B68",body:'\nXu X, Tikuisis P, Giesbrecht G. A mathematical model for human brain cooling during cold-water near-drowning. Journal of Applied Physiology. 1999;86(1):265-272\n'},{id:"B69",body:'\nCastellani JW, Young AJ, Ducharme MB, Giesbrecht GG, Glickman E, Sallis RE. Prevention of cold injuries during exercise. Medicine & Science in Sports & Exercise. 2006;38:2012-2029\n'},{id:"B70",body:'\nKeatinge WR, Cannon P. Freezing-point of human skin. Lancet. 1960;I:11-14\n'},{id:"B71",body:'\nHamlet MP. Nonfreezing cold injuries. In: Auebach PS, editor. Textbook of Wilderness Medicine. St. Louis, MO: Mosby; 2001. pp. 129-134\n'},{id:"B72",body:'\nEvans TM, Rundell KW, Beck KC, Levine AM, Baumann JM. Cold air inhalation does not affect the severity of EIB after exercise or eucapnic voluntary hyperventilation. Medicine & Science in Sports & Exercise. 2005;37(4):544-549\n'},{id:"B73",body:'\nWilber RL, Rundell KW, Szmedra L, Jenkinson DM, Im J, Drake SD. Incidence of exercise-induced bronchospasm in Olympic winter sport athletes. Medicine & Science in Sports & Exercise. 2000;32(4):732-737\n'},{id:"B74",body:'\nPozos RS, Danzl DF. Human physiological responses to cold stress and hypothermia. In: Pandolf KB, Burr RE, editors. Textbooks of Military Medicine: Medical Aspects of Harsh Environments. Vol. 1. Falls Church, YA: Office of the Surgeon General, U. S. Army; 2002. pp. 351-382\n'},{id:"B75",body:'\nDepartment of the Army. Prevention and Management of Cold-Weather Injuries. Washington, DC: 2005. Report No.: TB MED 508\n'},{id:"B76",body:'\nSawka MN, Latzka WA, Montain SJ, Cadarette BS, Kolka MA, Kraning KK, et al. Physiologic tolerance to uncompensable heat: Intermittent exercise, field vs laboratory. Medicine and Science in Sports and Exercise. 2001;33(3):422-430\n'},{id:"B77",body:'\nSawka MN, Young AJ. Physiological systems and their responses to conditions of heat and cold. In: American College of Sports Medicine. ACSM\'s Advanced Exercise Physiology. Philadelphia, PA: Lippincott Williams & Wilkins; 2006. pp. 535-563\n'},{id:"B78",body:'\nJohnson JM, Niederberger MA, Rowell LB, Eisman MM, Brengelmann GL. Competition between cutaneous vasodilator and vasoconstrictor reflexes in man. Journal of Applied Physiology. 1973;35:798-803\n'},{id:"B79",body:'\nCharkoudian N. Skin blood flow in adult human thermoregulation: How it works, when it does not, and why. Mayo Clinic Proceedings. 2003;78(5):603-612\n'},{id:"B80",body:'\nAstrand A, Rodahl I. Textbook of Work Physiology. New York, NY: McGraw Hill; 1986. pp. 104-112\n'},{id:"B81",body:'\nPotter AW, Gonzalez JA, Karis AJ, Xu X. Biophysical assessment and predicted thermophysiologic effects of body armor. PLoS One. 2015;10(7):e0132698\n'},{id:"B82",body:'\nPotter AW, Karis AJ, Gonzalez JA. Biophysical characterization and predicted human thermal responses to US army body armor protection levels (BAPL). In: Technical Report, T13-5, ADA#585406. Natick, MA 01760 USA: U.S. Army Research Institute of Environmental Medicine ; 2013. Available from: www.dtic.mil/dtic/tr/fulltext/u2/a585406.pdf\n\n'},{id:"B83",body:'\nLarsen B, Netto K, Aisbett B. The effect of body armor on performance, thermal stress, and exertion: A critical review. Military Medicine. 2011;176(11):1265-1273\n'},{id:"B84",body:'\nXu X, Gonzalez JA, Karis AJ, Rioux TP, Potter AW. Use of thermal mannequins for evaluation of heat stress imposed by personal protective equipment. In: Shiels B, Lehtonen K, editors. Performance of Protective Clothing and Equipment: 10th Volume, Risk Reduction through Research and Testing, ASTM STP1593. West Conshohocken, PA: ASTM International; 2016. pp. 286-296\n'},{id:"B85",body:'\nPotter AW, Gonzalez JA, Karis AJ, Santee WR, Rioux TP, Blanchard LA. Biophysical characteristics and measured wind effects of chemical protective ensembles with and without body armor. In: Technical Report, T15-8; ADA#621169. Natick, MA, 01760, USA: US Army Research Institute of Environmental Medicine; 2015. Available from: www.dtic.mil/dtic/tr/fulltext/u2/a621169.pdf\n\n'},{id:"B86",body:'\nAmerican Society of Testing and Materials International (ASTM). Standard Test Method for Measuring the Thermal Insulation of Clothing Using a Heated Manikin (ASTM F1291-16) [Standard]. Philadelphia, Pa.: ASTM; 2016\n'},{id:"B87",body:'\nAmerican Society of Testing and Materials International (ASTM). Standard Test Method for Measuring the Evaporative Resistance of Clothing Using a Sweating Manikin (ASTM F2370-16) [Standard]. Philadelphia, Pa.: ASTM; 2016\n'},{id:"B88",body:'\nGagge AP, Burton AC, Bazett HC. A practical system of units for the description of the heat exchange of man with his environment. Science. 1941;94:428-430\n'},{id:"B89",body:'\nWoodcock AH. Moisture transfer in textile systems, Part I. Textile Research Journal. 1962;32(8):628-633\n'},{id:"B90",body:'\nWoodcock AH. Moisture permeability index—A new index for describing evaporative heat transfer through fabric systems. In: Technical Report (TR-EP-149). Natick, MA 01702, USA: Quartermaster Research and Engineering Command; 1961\n'},{id:"B91",body:'\nPotter AW, Gonzalez JA, Karis AJ, Rioux TP, Blanchard LA, Xu X. Impact of estimating thermal manikin derived wind velocity coefficients on physiological modeling. In: Technical Report, ADA#607972. Natick, MA, 01760, USA: US Army Research Institute of Environmental Medicine; 2014. Available from: www.dtic.mil/dtic/tr/fulltext/u2/a607972.pdf\n\n'},{id:"B92",body:'\nPotter AW. Method for estimating evaporative potential (im/clo) from ASTM standard single wind velocity measures. In: Technical Report, T16-14, ADA#637325. Natick, MA, 01760, USA: US Army Research Institute of Environmental Medicine; 2016. Available from: www.dtic.mil/dtic/tr/fulltext/u2/a637325.pdf\n\n'},{id:"B93",body:'\nKraning KK II, Gonzalez RR. A mechanistic computer simulation of human work in heat that accounts for physical and physiological effects of clothing, aerobic fitness, and progressive dehydration. Journal of Thermal Biology. 1997;22(4):331-342\n'},{id:"B94",body:'\nWelles AP, Tharion WJ, Potter AW, Buller MJ. Novel method of estimating metabolic rates of soldiers engaged in chemical biological defense training. In: Technical Report, T17-02, ADA#1022691. Natick, MA, 01760, USA: US Army Research Institute of Environmental Medicine; 2017\n'},{id:"B95",body:'\nMcCullough EA, BW J, Huck J. A data base for estimating clothing insulation. ASHRAE Transactions. 1985;91:29-47\n'},{id:"B96",body:'\nAl-ajmi FF, Loveday DL, Bedwell KH, Havenith G. Thermal insulation and clothing area factors of typical Arabian Gulf clothing ensembles for males and females: Measurements using thermal manikins. Applied Ergonomics. 2008;39:407-414\n'},{id:"B97",body:'\nISO 9920:2007. Ergonomics of the thermal environment. Estimation of thermal and insulation and evaporative resistance of a clothing ensemble. Geneva: International Organisation for Standardisation; 2007\n'},{id:"B98",body:'\nOsczevski R, Bluestein M. The new wind chill equivalent temperature chart. Bulletin of the American Meteorological Society. 2005;86(10):1453-1458\n'},{id:"B99",body:'\nNational Weather Service. Windchill Temperature Index. Office of Climate, Water, and Weather Services. Washington, D.C.: National Oceanic and Atmospheric Administration; 2001\n'},{id:"B100",body:'\nXu X, Tikuisis P. Thermoregulatory modeling for cold stress. Comprehensive Physiology. 2014;4:1-25\n'},{id:"B101",body:'\nGonzalez RR, McLellan TM, Withey WR, Chang SK, KB P. Heat strain models applicable for protective clothing systems: Comparison of core temperature response. Journal of Applied Physiology. 1997;83(3):1017-1032\n'},{id:"B102",body:'\nDepartment of the Army and Air Force. In: Heat Stress Control and Heat Casualty Management. TB-MED 507. Washington, DC: Government Printing Office; 2003\n'},{id:"B103",body:'\nInstitute of Medicine. Dietary Reference Intakes for Water, Potassium, Sodium, Chloride, and Sulfate. Washington, DC: The National Academies Press; 2005\n'},{id:"B104",body:'\nHolmér I. Required clothing insulation (IREQ) as an analytical index of cold stress. ASHRAE Transactions. 1984;90(6):1116-1128\n'},{id:"B105",body:'\nISO 11079. Ergonomics of the thermal environment—determination and interpretation of cold stress when using required clothing insulation (IREQ) and local cooling effects. Geneva: International Organisation for Standardisation; 2007\n'},{id:"B106",body:'\nPotter AW, Gonzalez JA, Carter AJ, Looney DP, Rioux TP, Srinivasan S, et al. Comparison of Cold Weather Clothing Biophysical Properties: US Army, Canadian Department of National Defence, and Norwegian Military. United States: US Army Research Institute of Environmental Medicine Natick; 2018\n'},{id:"B107",body:'\nCharkoudian N, Johnson JM. Reflex control of cutaneous vasoconstrictor system is reset by exogenous female reproductive hormones. Journal of Applied Physiology. 1999;87:381-385\n'},{id:"B108",body:'\nCharkoudian N, Stephens DP, Pirkle KC, Kosiba WA, Johnson JM. Influence of female reproductive hormones on local thermal control of skin blood flow. Journal of Applied Physiology. 1999;87:1719-1723\n'},{id:"B109",body:'\nHodges GJ, Kosiba WA, Zhao K, Alvarez GE, Johnson JM. The role of baseline in the cutaneous vasoconstrictor responses during combined local and whole body cooling in humans. American Journal of Physiology Heart & Circulatory Physiology. 2007;293:H3187-H3192\n'},{id:"B110",body:'\nJohnson JM, Yen TC, Zhao K, Kosiba WA. Sympathetic, sensory, and nonneuronal contributions to the cutaneous vasoconstrictor response to local cooling. American Journal of Physiology Heart & Circulatory Physiology. 2005;288:H1573-H1579\n'},{id:"B111",body:'\nStephens DP, Aoki K, Kosiba WA, Johnson JM. Nonnoradrenergic mechanism of reflex cutaneous vasoconstriction in men. American Journal of Physiology Heart & Circulatory Physiology. 2001;280:H1496-H1504\n'},{id:"B112",body:'\nThompson CS, Holowatz LA, Kenney WL. Attenuated noradrenergic sensitivity during local cooling in aged human skin. Journal of Physiology. 2005;564:313-319\n'},{id:"B113",body:'\nThompson CS, Kenney WL. Altered neurotransmitter control of reflex vasoconstriction in aged human skin. Journal of Physiology. 2004;558:697-704\n'},{id:"B114",body:'\nThompson-Torgerson CS, Holowatz LA, Flavahan NA, Kenney WL. Rho kinase-mediated local cold-induced cutaneous vasoconstriction is augmented in aged human skin. American Journal of Physiology Heart & Circulatory Physiology. 2007;293:H30-H36\n'},{id:"B115",body:'\nWissler EH. A quantitative assessment of skin blood flow in humans. European Journal of Applied Physiology. 2008;104:145-157\n'},{id:"B116",body:'\nCooper KE, Edholm OG, Mottram RF. The blood flow in skin and muscle of the human forearm. The Journal of Physiology. 1955;128(2):258-267\n'},{id:"B117",body:'\nNadel ER, Horvath SM, Dawson CA, Tucker A. Sensitivity to central and peripheral thermal stimulation in man. Journal of Applied Physiology. 1970;29:603-609\n'},{id:"B118",body:'\nTimbal J, Boutelier C, Loncle M, Bougues L. Comparison of shivering in man exposed to cold in water and in air. Pflügers Archiv. 1976;365:243-248\n'},{id:"B119",body:'\nHayward JS, Eckerson JD, Collis ML. Thermoregulatory heat production in man: Prediction equation based on skin and core temperatures. Journal of Applied Physiology. 1977;42:377-384\n'},{id:"B120",body:'\nTikuisis P, Giesbrecht GG. Prediction of shivering heat production from core and mean skin temperatures. European Journal of Applied Physiology and Occupational Physiology. 1999;79:221-229\n'},{id:"B121",body:'\nParsons R. ASHRAE Handbook—Fundamentals. Atlanta, GA: American Society of Heating, Refrigerating and Air-conditioning Engineers; 1997\n'},{id:"B122",body:'\nAinsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: An update of activity codes and MET intensities. Medicine & Science in Sports & Exercise. 2000;32(suppl. 1):S498-S504\n'},{id:"B123",body:'\nRichmond PW, Potter AW, Looney DP, Santee WR. Terrain coefficients for predicting energy costs of walking over snow. Applied Ergonomics. 2019;74:48-54\n'},{id:"B124",body:'\nLooney DP, Potter AW, Pryor JL, Bremmer PE, Chalmers CR, McCLung HL, et al. Metabolic costs of standing and walking in healthy adults: A meta-egression. Medicine & Science in Sports & Exercise. 2019;51(2)\n'},{id:"B125",body:'\nAmerican College of Sports Medicine. ACSM\'s Guidelines for Exercise Testing and Prescription. Philadelphia, PA: Lippincott Williams & Wilkins; 2013\n'},{id:"B126",body:'\nBalogun JA, Martin DA, Clendenin MA. Human energy expenditure during level walking on a treadmill at speeds of 54-130 m min-1. International Disability Studies. 1989;11(2):71-74\n'},{id:"B127",body:'\nBrowning RC, Reynolds MM, Board WJ, Walters KA, Reiser RF. Obesity does not impair walking economy across a range of speeds and grades. Journal of Applied Physiology. 2013;114(9):1125-1131\n'},{id:"B128",body:'\nPandolf KB, Givoni B, Goldman RF. Predicting energy expenditure with loads while standing or walking very slowly. Journal of Applied Physiology. 1977;43(4):577-581\n'},{id:"B129",body:'\nWorkman JM, Armstrong BW. Metabolic cost of walking: Equation and model. Journal of Applied Physiology. 1986;61(4):1369-1374\n'},{id:"B130",body:'\nSantee WR. Windchill index and military applications. Aviation, Space, and Environmental Medicine. 2002;73:699-702\n'},{id:"B131",body:'\nSantee WR, Reardon MJ, Pandolf KB. Modeling the physiological and medical effects of exposure to environmental extremes. In: Friedl KE, Santee WR, editors. Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine. Fort Detrick, MD: Office of The Surgeon General United States Army; 2012. pp. 39-72\n'},{id:"B132",body:'\nCastellani JW, O’Brien C, Baker-Fulco C, Sawka MN, Young AJ. Sustaining Health & Performance in Cold Weather Operations. USARIEM TN/02-2, ADA 395745. Natick, MA: US Army Research Institute of Environmental Medicine; 2001\n'},{id:"B133",body:'\nFriedl KE. Predicting human limits—The special relationship between physiology research and the Army mission. In: Friedl KE, Santee WR, editors. Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine: Problems and Concepts in Military Operational Medicine. Fort Detrick, MD: Office of The Surgeon General United States Army; 2012. pp. 1-38\n'},{id:"B134",body:'\nden Hartog EA, Lotens WA. Postmortem time estimation using body temperature and a finite-element computer model. European Journal of Applied Physiology. 2004;92:734-737\n'},{id:"B135",body:'\nMall G, Eisenmenger W. Estimation of time since death by heat-flow finite-element model. Part I: Method, model, calibration and validation. Legal Medicine (Tokyo, Japan). 2005;7:1-14\n'},{id:"B136",body:'\nMall G, Eisenmenger W. Estimation of time since death by heat-flow finite-element model part II: Application to non-standard cooling conditions and preliminary results in practical casework. Legal Medicine (Tokyo, Japan). 2005;7:69-80\n'},{id:"B137",body:'\nKanawaku Y, Kanetake J, Komiya A, Maruyama S, Funayama M. Computer simulation for postmortem cooling processes in the outer ear. Legal Medicine. 2007;9:55-62\n'},{id:"B138",body:'\nSmart JL. Estimation of time of death with a fourier series unsteady state heat transfer model. Journal of Forensic Science. 2010;55:1481-1487\n'},{id:"B139",body:'\nSmart JL, Kaliszan M. The post mortem temperature plateau and its role in the estimation of time of death. A review. Legal Medicine. 2012;14:55-62\n'},{id:"B140",body:'\nSmart JL, Kaliszan M. Use of a finite element model of heat transport in the human eye to predict time of death. Journal of Forensic Science. 2013;58:S69-S77\n'},{id:"B141",body:'\nBesnard Y, Launay JC, Guinet-Lebreton A, Savourey G. PREDICTOL: A computer program to determine the thermophysiological duration limited exposures in various climatic conditions. Computer Methods and Programs in Biomedicine. 2004;76:221-228\n'},{id:"B142",body:'\nXu X, Amin M, Santee WR. Probability of Survival Decision Aid (PSDA). USARIEM T08/05, ADA478415. Natick, MA: US Army Research Institute of Environmental Medicine; 2008\n'},{id:"B143",body:'\nKeefe AA, Tikuisis P. A Guide to Making Stochastic and Single Point Predictions Using the Cold Exposure Survival Model (CESM). DRDC-TORONTO-TM-2008-061. Defence Research and Development Toronto (Canada); 2008\n'},{id:"B144",body:'\nXu X, Allen A, Rioux T, Patel T, Sinha P, et al. Refinement of probability of survival decision aid (PSDA). In: Technical Note, TN14-02, ADA#599590. Natick, MA 01760 USA: U.S. Army Research Institute of Environmental Medicine; 2014\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Adam W. Potter",address:"adam.w.potter.civ@mail.mil",affiliation:'
Biophysics and Biomedical Modeling Division, United States Army Research Institute of Environmental Medicine, United States of America
Rutgers University, School of Biomedical and Health Sciences, United States of America
'},{corresp:null,contributorFullName:"David P. Looney",address:null,affiliation:'
Biophysics and Biomedical Modeling Division, United States Army Research Institute of Environmental Medicine, United States of America
Rutgers University, School of Biomedical and Health Sciences, United States of America
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His research\ninterests cover climate diagnostics and prediction, as well as severe\nweather systems. He is particularly interested in connecting the\nweather and climate processes. Dr. Wang obtained Ph.D. in Meteorology\nfrom Iowa State University, USA. He has worked as research associate\nin the Utah Climate Center with a research focus on regional climate\nvariability.",institutionString:null,institution:{name:"Utah State University",institutionURL:null,country:{name:"United States of America"}}},{id:"101250",title:"Dr.",name:"Sandro",surname:"Carniel",slug:"sandro-carniel",fullName:"Sandro Carniel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/101250/images/2237_n.jpg",biography:"Oceanographer at the National Research Council, Inst. of Marine Sciences (Venice).",institutionString:null,institution:{name:"National Research Council",institutionURL:null,country:{name:"Italy"}}},{id:"102903",title:"Prof.",name:"Yuriy",surname:"Kuleshov",slug:"yuriy-kuleshov",fullName:"Yuriy Kuleshov",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"106232",title:"Dr.",name:"Robert",surname:"Gillies",slug:"robert-gillies",fullName:"Robert Gillies",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Dr. Robert R. Gillies is the director of the Utah Climate Center and an associate professor\nin the Department of Plants, Soils and Climate, Utah State University, USA. His research\narea includes land surface process, remote sensing, and climate change. Dr. Gillies obtained\nPh.D. in Meteorology and Remote Sensing from the University of Newcastle, U.K. He has\nworked as research associate in the Department of Meteorology, Pennsylvania State\nUniversity, and assistant professor in the Department of Plants, Soils and Biometeorology,\nUtah State University.",institutionString:null,institution:{name:"Utah State University",institutionURL:null,country:{name:"United States of America"}}},{id:"107519",title:"Dr.",name:"Aniello",surname:"Russo",slug:"aniello-russo",fullName:"Aniello Russo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Marche Polytechnic University",institutionURL:null,country:{name:"Italy"}}},{id:"107520",title:"Dr.",name:"Maja",surname:"Krzelj",slug:"maja-krzelj",fullName:"Maja Krzelj",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Split",institutionURL:null,country:{name:"Croatia"}}},{id:"114771",title:"Dr.",name:"Jin-Ho",surname:"Yoon",slug:"jin-ho-yoon",fullName:"Jin-Ho Yoon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Pacific Northwest National Laboratory",institutionURL:null,country:{name:"United States of America"}}},{id:"134347",title:"Dr.",name:"Vernon",surname:"Kousky",slug:"vernon-kousky",fullName:"Vernon Kousky",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"138580",title:"Dr.",name:"Wan-Ru (Judy)",surname:"Huang",slug:"wan-ru-(judy)-huang",fullName:"Wan-Ru (Judy) Huang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
\n"}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},profiles:[{id:"6700",title:"Dr.",name:"Abbass A.",middleName:null,surname:"Hashim",slug:"abbass-a.-hashim",fullName:"Abbass A. Hashim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/6700/images/1864_n.jpg",biography:"Currently I am carrying out research in several areas of interest, mainly covering work on chemical and bio-sensors, semiconductor thin film device fabrication and characterisation.\nAt the moment I have very strong interest in radiation environmental pollution and bacteriology treatment. The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. 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I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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