Possible other targets include the gap junction connexins and identifying whether they are regulated differently between males and females. Connexins allow rapid and coordinated electricalexcitationandfacilitateintercellularexchangeofsmallmolecules.Experimentalstudies have shown that normal gap junction expression and phosphorylation in the heart is essential fororganizedmyocellularelectricalactivity[Staufferetal.2011].Interestinglyfemaleheartshave higher levels of cardiac connexin 43 (Cx43) [Tribulova et al. 2005] and lower lethal arrhythmia susceptibility [Knezl et al. 2008]. Further studies are required to confirm whether abnormali‐ ties in cardiac Cx43 expression and phosphorylation are the primary trigger of arrhythmogene‐ sis, since this occurs prior to other structural remodelling changes [Stauffer et al. 2011].

Recent studies have explored the role of microRNAs (miRNAs) in many biological and pathological processes and the role of circulating miRNAs as sensitive biomarkers with aberrant expression of miRNA directly reflecting disease state. Ai and colleagues (2010), recently found upregulated cardiac miR-1 in an animal model of AMI, with similar increases in plasma in patients with AMI. These increased miR-1 levels correlated with abnormal QRS widening in AMI [Ai et al. 2010]. Over-expression of miR-1 has been shown to induce and aggravate arrhythmogenesis. The mechanism proposed is by impairing cardiac conduction by post-transcriptional repression of KCNJ2 that encodes the inward rectifier K+ channel subunit Kir2.1 and GJA1 which encode connexin 43 gap junction channels [Yang et al. 2007].

## **5. Treatment strategies**

disease have the highest rate of SCD, although the contributing factors remain poorly defined. Recent studies have suggested that genetic or environmental factors may predispose to fatal ventricular arrhythmia, particularly in women. However, mutations or rare variants in the cardiac sodium channel SCN5A were found in <2% cases screened and further studies are required [Albert et al. 2008]. Stress cardiomyopathy, also known as takotsubo cardiomyop‐ athy, transient apical ballooning or broken heart syndrome is found predominantly in postme‐ nopausal women [Nef et al. 2010]. Emotional or physical stress trigger symptom onset, which is similar to those in AMI, including sudden onset of chest pain associated with ST-segment elevation, and moderate increases in creatine kinase and troponin levels. Prognosis for stress cardiomyopathy is favorable, although fatal complications may occur, including cardiogenic

Cardiac remodelling following either ischemia or AMI initially develops to compensate for failing cardiac function with evidence of hypertrophy, cardiomyocyte apoptosis, inflammation and fibrosis. Initially these changes are beneficial but ultimately transition to deteriorating cardiac function and lead to heart failure [Abel et al. 2008]. Although left ventricular (LV) dysfunction significantly increases the risk of SCD [Anand et al. 2006; Stecker et al. 2006], other risk factors need to be considered since women are 50% less likely to exhibit severe LV dysfunction, with structurally normal hearts identified at autopsy [Chugh et al. 2003]. Alternative mechanisms of SCD therefore need to be considered, given that women with

The renin-angiotensin-aldosterone system (RAAS) plays a significant role in ischemic heart disease and AMI and blockade of this system has emerged as an important therapeutic intervention. Elevated plasma aldosterone levels are an independent risk factor for mortality during AMI [Beygui et al. 2006; Palmer et al. 2008] and are predictive of cardiovascular events in acute coronary syndrome in the presence or absence of AMI [Tomaschitz et al. 2010]. Aldosterone exerts its actions by interacting with its receptor, the mineralocorticoid receptor (MR) or "aldosterone" receptor. Both aldosterone and the physiological glucocorticoids (cortisol (humans)/corticosterone (rodents), which are at 100-fold higher circulating levels bind to the MR. Selective activation of MR in target tissues is achieved by co-expression of the enzyme 11β-hydroxysteroid dehydrogenase type 2 (11β-HDS2). In contrast with vascular and renal tissues, 11βHSD2 is not expressed in cardiomyocytes [Sheppard & Autelitano 2002], and therefore endogenous glucocorticoids normally do not mimic aldosterone, but act as MR antagonists [Gomez et al 1990; Sato & Funder, 1996; Young & Funder, 1996]. Redox regulation has been shown to modulate glucocorticoid hormone action *in vivo* [Makino et al. 1999] and expression of oestrogen receptors [Tamir et al. 2002]. For every molecule of cortisol converted to cortisone, one molecule of the pyridine nucleotide NAD is reduced to NADH. Since NADH has been shown to activate corepressors for other transcription factors in various systems [Zhang et al. 2002; Fjeld et al. 2003], and changes in redox state may determine cardiomyocyte

Elevated aldosterone levels promote electrical remodelling through activation of MR or "aldosterone" receptors, thus potentially increasing the incidence of sudden cardiac death. In experimental studies, aldosterone has a direct effect on cardiomyocyte calcium [Ouvrard-

shock, malignant arrhythmias and left ventricular free wall rupture.

66 Ischemic Heart Disease

coronary heart disease have significantly lower risk for SCD.

MR activation by glucocorticoids [Mihailidou et al. 2009].

Primary prevention of SCD continues to be a public health challenge since most deaths are among people who were not identified as high risk prior to the event. Implantable cardiovert‐ er–defibrillator (ICD) therapy is the current recommended treatment strategy for high risk patients with severe left ventricular dysfunction (Sudden Cardiac Death in Heart Failure Trial (SCDHeFT), [Bardy et al. 2005]. Subgroup analysis in SCDHeFT as well as in another primary prevention trial, the Defibrillators in Nonischemic Cardiomyopathy Treatment Evaluation (DEFINITE) [Kadish et al. 2004] suggested that women may receive less benefit from ICD therapy than men. Interestingly in both these large trials, only 24%-30% of participants and since women have a lower incidence of VF compared to men [Kim et al. 2001; Wigginton et al. 2002] and therefore may not have been adequately powered to determine the influence of gender on outcome for ICD therapy.

ICDs treat (but do not prevent) the ventricular arrhythmias and therefore increased morbidity remains. ICD therapy is rarely considered where LV function is preserved, except in specific conditions with increased SCD risk such as hypertrophic obstructive cardiomyopathy, long QT and Brugada syndromes, and idiopathic VF [DiMarco 2003]. A community-based study conducted by Stecker and colleagues (2006) showed that only 30% of patients that died from SCD previously had sufficiently decreased LV systolic function to meet the criteria for ICD implantation.Patients with normalLV systolic function were generally younger,predominant‐ ly female and less likely to have an established diagnosis of coronary heart disease. Since ICDs treat the arhythmia but not prevent the underlying cause, there is a need to find new treat‐ ment strategies that target the cellular mechanisms involved. Standard antiarrhythmic medica‐ tion has not reduced (and in some cases, has increased), the incidence of SCD [Lane et al. 2005]. Suitable adjunct treatment in the primary and secondary prevention of SCD includes beta blockers andnon-anti-arrhythmic agents,i.e.,those thatdonotdirectlytargetthe electrophysio‐ logical action in cardiac muscle or specialized conduction system, such as angiotensin convert‐ ing enzyme inhibitors (ACEI), angiotensin receptor–blockers, lipid-lowering agents, aldosterone or MR antagonists, thrombolytic and antithrombotic agents [Lane et al. 2005].

**Author details**

**References**

Anastasia Susie Mihailidou1\*, Rebecca Ritchie2

tal & University of Sydney, Sydney, Australia

death. *Heart*. 86: 624–625.

*Coll Cardiol.* 47(3): SS29., 21.

*Outcomes*. , 3, 135-142.

*J.* 77: 363–370.

Hospital & University of Sydney, Sydney, Australia

\*Address all correspondence to<EMAIL_ADDRESS>
2 Baker IDI Heart & Diabetes Institute, Melbourne, Australia

1 Department of Cardiology & Kolling Medical Research Institute Royal North Shore Hospi‐

3 Perinatal Research Laboratories, Kolling Medical Research Institute Royal North Shore

[1] Zipes, D. P. Wellens HJJ ((1998). Sudden cardiac death. *Circulation. 98*, 2334-2351.

[2] Wong SH, Mulvihill NT, Norton M (2001). Assessing the risk of sudden cardiac

[3] Mazeika P (2001). Aborted sudden cardiac death: a clinical perspective. *Postgrad Med*

[4] Bairey Merz CN, Shaw LJ, Reis SE, et al ((2006). Insights from the NHLBI- Sponsored Women's Ischemia Syndrome Evaluation (WISE) Study. Part II: Gender differences in presentation, diagnosis, and outcome with regard to gender-based pathophysiolo‐ gy of atherosclerosis and macrovascular and microvascular coronary disease. *J Am*

[5] Wake, R, Takeuchi, M, Yoshikawa, J, & Yoshiyama, M. (2007). Effects of gender on prognosis of patients with known or suspected coronary artery disease undergoing contrast-enhanced dobutamine stress echocardiography. *Circ J.* , 71(7), 1060-1066. [6] Xhyheri, B, & Bugiardini, R. (2010). Diagnosis and Treatment of Heart Disease: Are Women Different From Men? *Progress in Cardiovascular Diseases.* , 53, 227-236.

[7] Melloni, C, Berger, J. S, Wang, T. Y, Gunes, F, Stebbins, A, Pieper, K. S, Dolor, R. J, Douglas, P. S, Mark, D. B, & Newby, L. K. (2010). Representation of women in randomized clinical trials of cardiovascular disease prevention. *Circ. Cardiovasc. Qual.*

[8] Bell, D. M, & Nappi, J. (2000). Myocardial infarction in women: a critical appraisal of

[9] Mosca, L, Benjamin, E. J, Berra, K, Bezanson, J. L, Dolor, R. J, Lloyd-jones, D. M, Newby, L. K, Pina, I. L, Roger, V. L, Shaw, L. J, & Zhao, D. (2011). Effectiveness-

gender differences in outcomes. *Pharmacotherapy. 20*, 1034-1044.

and Anthony W. Ashton3

Sex Differences in Sudden Cardiac Death http://dx.doi.org/10.5772/55631 69

It is worth considering the role of aldosterone or MR blockade for both primary prevention of SCD for those at high-risk and as an adjunct therapy based on the important findings from the large randomized clinical studies, the Randomized ALdactone Evaluation Study (RALES) [Pitt et al. 1999] and the Eplerenone Post–Acute Myocardial Infarction Heart Failure Efficacy and Survival Study (EPHESUS) [Pitt et al. 2003], which showed a significant reduction in SCD. The first of these trials, RALES, was a double blind study in patients who had severe heart failure with left ventricular systolic dysfunction and were receiving standard therapy including ACE inhibitor treatment, a loop diuretic, and digoxin. Patients were randomly assigned to receive low dose MR antagonist, spironolactone or placebo added to their standard treatment. The trial was discontinued early due to an interim analysis showing the addition of spironolactone resulted in a 30% reduction in mortality and reducing SCD by 29%. In the next study, EPHESUS [Pitt et al. 2003] was designed to determine whether selective MR blockade with eplerenone could be tolerated by patients with acute myocardial infarction (AMI) complicated by heart failure due to systolic left ventricular dysfunction. Addition of the selective MR antagonist, eplerenone, substantially increased survival (15% reduction in mortality) and decreased hospitalization, and had a 21% reduction in SCD.

#### **6. Conclusion**

Since women do not present with severe left ventricular dysfunction and diagnosed CHD before SCD, they will not receive the current recommended treatment. Prospective studies are required that have the same proportion of women to men with ischemic heart disease to identify the sex-specific risk factors and pathophysiology of ischemic heart disease in women which leads to an adverse cardiovascular outcome [Malenka et al. 2002]. Further investigations are required to examine whether women will show the same benefit to adjunct treatment with angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor–blocking agents, lipidlowering agents, mineralocorticoid receptor antagonists, thrombolytic and antithrombotic agents.

## **Author details**

conditions with increased SCD risk such as hypertrophic obstructive cardiomyopathy, long QT and Brugada syndromes, and idiopathic VF [DiMarco 2003]. A community-based study conducted by Stecker and colleagues (2006) showed that only 30% of patients that died from SCD previously had sufficiently decreased LV systolic function to meet the criteria for ICD implantation.Patients with normalLV systolic function were generally younger,predominant‐ ly female and less likely to have an established diagnosis of coronary heart disease. Since ICDs treat the arhythmia but not prevent the underlying cause, there is a need to find new treat‐ ment strategies that target the cellular mechanisms involved. Standard antiarrhythmic medica‐ tion has not reduced (and in some cases, has increased), the incidence of SCD [Lane et al. 2005]. Suitable adjunct treatment in the primary and secondary prevention of SCD includes beta blockers andnon-anti-arrhythmic agents,i.e.,those thatdonotdirectlytargetthe electrophysio‐ logical action in cardiac muscle or specialized conduction system, such as angiotensin convert‐ ing enzyme inhibitors (ACEI), angiotensin receptor–blockers, lipid-lowering agents, aldosterone or MR antagonists, thrombolytic and antithrombotic agents [Lane et al. 2005].

It is worth considering the role of aldosterone or MR blockade for both primary prevention of SCD for those at high-risk and as an adjunct therapy based on the important findings from the large randomized clinical studies, the Randomized ALdactone Evaluation Study (RALES) [Pitt et al. 1999] and the Eplerenone Post–Acute Myocardial Infarction Heart Failure Efficacy and Survival Study (EPHESUS) [Pitt et al. 2003], which showed a significant reduction in SCD. The first of these trials, RALES, was a double blind study in patients who had severe heart failure with left ventricular systolic dysfunction and were receiving standard therapy including ACE inhibitor treatment, a loop diuretic, and digoxin. Patients were randomly assigned to receive low dose MR antagonist, spironolactone or placebo added to their standard treatment. The trial was discontinued early due to an interim analysis showing the addition of spironolactone resulted in a 30% reduction in mortality and reducing SCD by 29%. In the next study, EPHESUS [Pitt et al. 2003] was designed to determine whether selective MR blockade with eplerenone could be tolerated by patients with acute myocardial infarction (AMI) complicated by heart failure due to systolic left ventricular dysfunction. Addition of the selective MR antagonist, eplerenone, substantially increased survival (15% reduction in mortality) and decreased

Since women do not present with severe left ventricular dysfunction and diagnosed CHD before SCD, they will not receive the current recommended treatment. Prospective studies are required that have the same proportion of women to men with ischemic heart disease to identify the sex-specific risk factors and pathophysiology of ischemic heart disease in women which leads to an adverse cardiovascular outcome [Malenka et al. 2002]. Further investigations are required to examine whether women will show the same benefit to adjunct treatment with angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor–blocking agents, lipidlowering agents, mineralocorticoid receptor antagonists, thrombolytic and antithrombotic

hospitalization, and had a 21% reduction in SCD.

**6. Conclusion**

68 Ischemic Heart Disease

agents.

Anastasia Susie Mihailidou1\*, Rebecca Ritchie2 and Anthony W. Ashton3

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Cardiology & Kolling Medical Research Institute Royal North Shore Hospi‐ tal & University of Sydney, Sydney, Australia

2 Baker IDI Heart & Diabetes Institute, Melbourne, Australia

3 Perinatal Research Laboratories, Kolling Medical Research Institute Royal North Shore Hospital & University of Sydney, Sydney, Australia

## **References**

Based Guidelines for the Prevention of Cardiovascular Disease in Women-2011 Up‐ date. *Circulation*. , 123, 1243-1262.

[20] Lane, R. E, Cowie, M. R, & Chow, A. W. (2005). Prediction and prevention of sudden

Sex Differences in Sudden Cardiac Death http://dx.doi.org/10.5772/55631 71

[21] Singh, S. N, Carson, P. E, & Fisher, S. G. (1997). Non-sustained ventricular tachycar‐

[22] Kannel, W. B, & Schatzkin, A. (1985). Sudden death: lessons from subsets in popula‐

[23] Jouven, X, Desnos, M, Guerot, C, & Ducimetiere, P. (1999). Predicting sudden death in the population: the Paris Prospective Study I. *Circulation. 99*, 1978-1983.

[24] Khot, U. M, Khot, M. B, Bajzer, C. T, Sapp, S. K, Ohman, E. M, Brener, S. J, Ellis, S. G, Lincoff, A. M, & Topol, E. J. (2003). Prevalence of conventional risk factorsin patients

[25] Sandhu, R. K, Jimenez, M. C, Chiuve, S. E, Fitzgerald, K. C, Kenfield, S. A, Tedrow, U. B, & Albert, C. M. (2012). Smoking, smoking cessation, and risk of sudden cardiac

[26] Schatzkln, A, Cupples, L. A, Heeren, T, Morelock, S, & Kannel, W. B. (1984). Sudden death in the Framlngham Heart Study: differences in Incidence and risk factors by

[27] Akiyama, T, Pawitan, Y, Greenberg, H, Kuo, C. S, & Reynolds-haertle, R. A. (1991). Increased risk of death and cardiac arrest from encainide and flecainide in patients after non-Q-wave acute myocardial infarction in the Cardiac Arrhythmia Suppres‐

[28] Marsh, J. D, Lehmann, M. H, Ritchie, R. H, Gwathmey, J. K, Green, G. E, & Schiebing‐ er, R. J. (1999). Androgen receptors mediate hypertrophy in cardiac myocytes. *Circu‐*

[29] Paoletti, R, Cosignani, P. G, Kenemans, P, et al. (1997). Menopause: problems and in‐ terventions in the United States. In: Paoletti R, Cosignani PG, Kenemans P, Samsoe G, Soma M, Jackson AS, editors. Women's Health and Menopause. Norwell, MA:

[30] Poehlman, E. T. (2002). Menopause, energy expenditure, and body composition. *Acta*

[31] Lamon-fava, S. Wilson PWF, Schaefer EJ ((1996). Impact of body mass index on coro‐ nary heart disease risk factors in men and women: the Framingham Offspring study.

[32] Schierbeck, L. L, Rejnmark, L, Tofteng, C. L, Stilgren, L, Eiken, P, Mosekilde, L, Køber, L, & Jensen, J. E. (2012). Effect of hormone replacement therapy on cardiovas‐ cular events in recently postmenopausal women: randomised trial. BMJ. 2012 Oct 9;

death in women" *Circ Arrhythm Electrophysiol*. DOI:CIRCEP.112.975219.

sex and coronary disease status. *Am J Epidemiol. 120*, 888-899.

sion Trial: CAST Investigators. *Am J Cardiol. 68*, 1551-1555.

*lation*. , 98, 256-261.

Kluwer Academic Publishers, , 9-14.

*Arteriosler Thromb Vasc Biol*. 16L:, 1509-1515.

*Obstet Gynecol Scand. 81*, 603-611.

345:e6409. doi:bmj.e6409.

cardiac death in heart failure. *Heart* 2005;, 91, 674-680.

dia in severe heart failure. *Circulation.* , 96, 3794-3795.

tion studies. *J Am Coll Cardiol.* 5: 141B-149B.

with coronary heart disease.*JAMA* , 290, 898-904.

[20] Lane, R. E, Cowie, M. R, & Chow, A. W. (2005). Prediction and prevention of sudden cardiac death in heart failure. *Heart* 2005;, 91, 674-680.

Based Guidelines for the Prevention of Cardiovascular Disease in Women-2011 Up‐

[10] Priori, S. G, Aliot, E, Blomstrom-lundqvist, C, et al. (2001). Task Force on Sudden Cardiac Death of the European Society of Cardiology. *Eur Heart J. 22*, 1374-1450. [11] Zheng, Z-J, Croft, J. B, Giles, W. H, et al. (2001). Sudden cardiac death in the United

[12] Shaw, L. J, Merz, N. B, Pepine, C. J, Reis, S. E, Bittner, V, Kelsey, S. F, Olson, M, John‐ son, B. D, Mankad, S, Sharaf, B. L, Rogers, W. J, Wessel, T. R, Arant, C. B, Pohost, G. M, Lerman, A, Quyyumi, A. A, & Sopko, G. for the WISE Investigators ((2006). In‐ sights From the NHLBI-Sponsored Women's Ischemia Syndrome Evaluation (WISE) Study Part I: Gender Differences in Traditional and Novel Risk Factors, Symptom Evaluation, and Gender-Optimized Diagnostic Strategies. *J Am Coll Cardiol.*

[13] Canto, J. G, Rogers, W. J, Goldberg, R. J, Peterson, E. D, Wenger, N. K, Vaccarino, V, Kiefe, C. I, Frederick, P. D, & Sopko, G. Zheng Z-J for the NRMI Investigators ((2012). Association of age and sex with myocardial infarction symptom presentation and In-

[14] De Vreede-swagemakers, J. J, Gorgels, A. P, Dubois-arbouw, W. I, et al. (1997). Outof-hospital cardiac arrest in the 19900s: a population-based study in the Maastricht area on incidence, characteristics and survival. *J Am Coll Cardiol*. , 30, 1500-1505. [15] Kannel, W. B. Wilson PWF, D'Agostino RB, Cobb J ((1998). Sudden coronary death in

[16] Albert, C. M, Chae, C. U, Grodstein, F, Rose, L. M, Rexrode, K. M, Ruskin, J. N, Stampfer, M. J, & Manson, J. E. (2003). Prospective Study of Sudden Cardiac Death

[17] Dahlberg, S. T. (1990). Gender differences in risk factors for Sudden Cardiac Death.

[18] Kim, C, Fahrenbruch, C. E, Cobb, L. A, et al. (2001). Out-of-hospital cardiac arrest in

[19] Roger, V. L, Go, A. S, Lloyd-jones, D. M, Benjamin, E. J, Berry, J. D, Borden, W. B, Bravata, D. M, Dai, S, Ford, E. S, Fox, C. S, Fullerton, H. J, Gillespie, C, Hailpern, S. M, Heit, J. A, Howard, V. J, Kissela, B. M, Kittner, S. J, Lackland, D. T, Lichtman, J. H, Lisabeth, L. D, Makuc, D. M, Marcus, G. M, Marelli, A, Matchar, D. B, Moy, C. S, Mo‐ zaffarian, D, Mussolino, M. E, Nichol, G, Paynter, N. P, Soliman, E. Z, Sorlie, P. D, Sotoodehnia, N, Turan, T. N, Virani, S. S, Wong, N. D, Woo, D, & Turner, M. B. (2012). Heart Disease and Stroke Statistics-2012 Update A Report From the American

Among Women in the United States. *Circulation* , 107, 2096-2101.

date. *Circulation*. , 123, 1243-1262.

47:4S-20S.

70 Ischemic Heart Disease

States, 1989 to 1998. *Circulation*. , 104, 2158-2163.

Hospital mortality. *JAMA. 307*(8), 813-822.

women. *Am Heart J*. , 136, 205-212.

*Cardiology*. 77 (suppl 2): 31-40.

men and women. *Circulation* , 104, 2699-2703.

Heart Association. *Circulation*. 125: ee230., 12.

[33] Huikuri, H. V, Castellanos, A, & Myerburg, R. J. Sudden death due to cardiac ar‐ rhythmias. *N Engl J Med*. , 345, 1473-1482.

death in patients presenting with acute ST-elevation myocardial infarction. *Circula‐*

Sex Differences in Sudden Cardiac Death http://dx.doi.org/10.5772/55631 73

[47] Palmer, B. R, Pilbrow, A. P, Frampton, C. M, et al. (2008). Plasma aldosterone levels during hospitalization are predictive of survival post-myocardial infarction. *Eur.*

[48] Tomaschitz, A, Pilz, S, Ritz, E, Meinitzer, A, Boehm, B. O, & Marz, W. (2010). Plasma aldosterone levels are associated with increased cardiovascular mortality: the Lud‐ wigshafen Risk and Cardiovascular Health (LURIC) study. *Eur. Heart J. 31*,

[49] Sheppard, K. E, & Autelitano, D. J. (2002). Beta-hydroxysteroid dehydrogenase 1 transforms 11-dehydrocorticosterone into transcriptionally active glucocorticoid in

[50] Gomez-sanchez, E. P, Venkataraman, M. T, Thwaites, D, & Fort, C. (1990). ICV infu‐ sion of corticosterone antagonizes ICV-aldosterone hypertension. *Am. J. Physiol.* 258:

[51] Sato, A, & Funder, J. W. (1996). High glucose stimulates aldosterone-induced hyper‐ trophy via type I mineralocorticoid receptors in neonatal rat cardiomyocytes. *Endo‐*

[52] Young, M. J, & Funder, J. W. (1996). The renin-angiotensin-aldosterone system in ex‐ perimental mineralocorticoid-salt-induced cardiac fibrosis. *Am. J. Physiol.* 271:EE888.,

[53] Makino, Y, Yoshikawa, N, Okamoto, K, Hirota, K, Yodoi, J, Makino, I, & Tanaka, H. (1999). Direct association with thioredoxin allows redox regulation of glucocorticoid

[54] Tamir, S, Izrael, S, & Vaya, J. (2002). The effect of oxidative stress on ERalpha and

[55] Zhang, Q, Piston, D. W, & Goodman, R. H. (2002). Regulation of corepressor function

[56] Fjeld, C. C, Birdsong, W. T, & Goodman, R. H. (2003). Differential binding of NAD+ and NADH allows the transcriptional corepressor carboxyl-terminal binding protein

[57] Mihailidou, A. S. Le TYL, Mardini M, Funder JW ((2009). Glucocorticoids Activate Cardiac Mineralocorticoid Receptors During Experimental Myocardial Infarction.

[58] Ouvrard-pascaud, A, Sainte-marie, Y, Bénitah, J-P, et al. (2005). Conditional minera‐ locorticoid receptor expression in the heart leads to life-threatening arrhythmias. *Cir‐*

to serve as a metabolic sensor. *Proc. Natl. Acad. Sci. USA.* , 100, 9202-9207.

*tion.* , 114, 2604-2610.

*Heart J*. , 29, 2489-2496.

neonatal rat heart. *Endocrinology* , 143, 198-202.

receptor function. *J. Biol. Chem.* , 274, 3182-3188.

by nuclear NADH. *Science* , 295, 1895-1897.

*Hypertension* , 54, 1306-1312.

*culation*. , 11, 3025-3033.

ERbeta expression. *J. Steroid Biochem. Mol. Biol.* , 81, 327-332.

1237-1247.

EE653., 649.

883.

*crinology 137*, 4145-4153.

death in patients presenting with acute ST-elevation myocardial infarction. *Circula‐ tion.* , 114, 2604-2610.

[47] Palmer, B. R, Pilbrow, A. P, Frampton, C. M, et al. (2008). Plasma aldosterone levels during hospitalization are predictive of survival post-myocardial infarction. *Eur. Heart J*. , 29, 2489-2496.

[33] Huikuri, H. V, Castellanos, A, & Myerburg, R. J. Sudden death due to cardiac ar‐

[34] Orencia, A, Bailey, K, Yawn, B. P, & Kottke, T. E. (1993). Effect of gender on longterm outcome of angina pectoris and myocardial infarction/sudden unexpected

[35] Abildstrom, S. Z, Rask-madsen, C, Ottesen, M. M, Andersen, P. K, Rosthoj, S, Torppedersen, C, & Kober, L. (2002). Impact of age and sex on sudden cardiovascular

[36] Adabag, A. S, Therneau, T. M, Gersh, B. J, Weston, S. A, & Roger, V. L. (2008). Sud‐

[37] Verheugt, C. L, Uiterwaal, C. S, Van Der Velde, E. T, Meijboom, F. J, Pieper, P. G, Vliegen, H. W, Van Dijk, A. P, Bouma, B. J, Grobbee, D. E, & Mulder, B. J. (2008). Gender and outcome in adult congenital heart disease. *Circulation*. , 118, 26-32.

[38] Albert, C. M, Mcgovern, B. A, Newell, J. B, & Ruskin, J. N. (1996). Sex differences in

[39] Chiuve, S. E, Fung, T. T, Rexrode, K. M, Spiegelman, D, Manson, J. E, Stampfer, M. J, & Albert, C. M. (2011). Adherence to a low-risk, healthy lifestyle and risk of Sudden

[40] Albert, C. M, Nam, E. G, Rimm, E. B, Jin, H. W, Hajjar, R. J, & Hunter, D. J. MacRae CA, Ellinor PT ((2008). Cardiac Sodium Channel Gene Variants and Sudden Cardiac

[41] Nef, H. M, Möllmann, H, Akashi, Y. J, & Hamm, C. W. (2010). Mechanisms of stress

[42] Abel, E. D, Litwin, S. E, & Sweeney, G. (2008). Cardiac remodeling in obesity. *Physiol*

[43] Anand, K, Mooss, A. N, & Mohiuddin, S. M. (2006). Aldosterone Inhibition Reduces the Risk of Sudden Cardiac Death in Patients with Heart Failure. *JRAAS. 7*, 15-19.

[44] Stecker, E. C, Vickers, C, Waltz, J, Socoteanu, C, John, B. T, Mariani, R, Mcanulty, J. H, Gunson, K, Jui, J, & Chugh, S. S. (2006). Population-based analysis of Sudden Car‐ diac Death with and without left ventricular systolic dysfunction. J Am Coll Cardi‐

[45] Chugh, S. S, Chung, K, Zheng, Z. J, John, B, & Titus, J. L. (2003). Cardiac pathologic findings reveal a high rate of sudden cardiac death of undetermined etiology in

[46] Beygui, F, Collet, J-P, Benoliel, J-J, Vignolles, N, Dumaine, R, Barthélémy, O, & Mon‐ talescot, G. (2006). High plasma aldosterone levels on admission are associated with

death following myocardial infarction. *Heart.* , 88, 573-578.

cardiac arrest survivors. *Circulation*. , 93, 1170-1176.

Cardiac Death among women. *JAMA. 306*(1), 62-69.

(Takotsubo) cardiomyopathy. *Nat. Rev. Cardiol. 7*, 187-193.

Death in Women. *Circulation*. , 117, 16-23.

younger women. *Am Heart J*. , 146, 635-639.

*Rev* , 88, 389-419.

ol. , 47, 1161-1166.

den death after myocardial infarction. *JAMA.* , 300, 2022-2029.

rhythmias. *N Engl J Med*. , 345, 1473-1482.

death. *JAMA 269*, 2392-2397.

72 Ischemic Heart Disease

[59] Mihailidou, AS, Bungaard, H, & Mardini, . (2000). Hyperaldosteronemia in rabbits inhibits the cardiac sarcolemmal Na +-K + pump. *Circ Res*. 86: 37-42.

[70] DiMarco JP ((2003). Implantable cardioverter-defibrillators. *N Engl J Med.* , 349(19),

Sex Differences in Sudden Cardiac Death http://dx.doi.org/10.5772/55631 75

[71] Pitt, B, Zannad, F, Remme, W. J, et al. (1999). The effect of spironolactone on morbidi‐ ty and mortality in patients with severe heart failure: Randomized Aldactone Evalua‐

[72] Pitt, B, Remme, W, Zannad, F, et al. (2003). Eplerenone Post-Acute Myocardial Infarc‐ tion Heart Failure Efficacy and Survival Study Investigators. Eplerenone, a selective aldosterone blocker, in patients with left ventricular dysfunction after myocardial in‐

[73] Malenka, D. J, Wennberg, D. E, Quinton, H. A, et al. (2002). Gender-related changes in the practice and outcomes of percutaneous coronary interventions in northern

tion Study Investigators. *N Engl J Med*. , 341, 709-717.

New England 1994 to 1999. *J Am Coll Cardiol*. , 40, 2092-2101.

farction. *N Engl J Med.* , 348, 1309-1321.

1836-1847.

[70] DiMarco JP ((2003). Implantable cardioverter-defibrillators. *N Engl J Med.* , 349(19), 1836-1847.

[59] Mihailidou, AS, Bungaard, H, & Mardini, . (2000). Hyperaldosteronemia in rabbits

[60] Mihailidou, A. S, Mardini, M, Funder, J. W, & Raison, M. (2002). Mineralocorticoid and Angiotensin Receptor Antagonism During Hyperaldosteronemia. *Hypertension*. ,

[61] Tsuji, H. Venditti FJ Jr., Evans JC, Larson MG, Levy D ((1994). The associations of lev‐ els of serum potassium and magnesium with ventricular premature complexes (the

[62] Stauffer, B. L, Sobus, R, & Sucharov, C. C. (2011). Sex differences in cardiomyocyte

[63] Tribulova, N, Dupont, E, Soukup, T, Okruhlicova, L, & Severs, N. J. (2005). Sex differ‐ ences in connexin-43 expression in left ventricles of aging rats. *Physiol Res.* , 54,

[64] Knezl, V, Bacova, B, Kolenova, L, Mitasikova, M, Weismann, P, Drimal, J, & Tribulo‐ va, N. (2008). Distinct lethal arrhythmias susceptibility is associated with sex-related difference in myocardial connexin-43 expression. *Neuro Endocrinol Lett.* , 29, 798-801.

[65] Ai, J. Zhang R Li Y, Pu J, Lu Y, Jiao J, Li K, Yue B, Li Z, Wangg R, Wang L, Li Q, Wanga N, Shan N, Li Z, Yang B ((2010). Circulating microRNA-1 as a potential novel biomarker for acute myocardial infarction. *Biochem. Biophys. Res. Comm.* , 391, 73-77.

[66] Yang, B, Lin, H, Xiao, J, Lu, Y, Luo, X, Li, B, et al. (2007). The muscle-specific micro‐ RNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and

[67] Bardy, G. H, Lee, K. L, Mark, D. B, Poole, J. E, Packer, D. L, Boineau, R, Domanski, M, Troutman, C, Anderson, J, Johnson, G, Mcnulty, S. E, Clapp-channing, N, David‐ son-ray, L. D, Fraulo, E. S, Fishbein, D. P, Luceri, R. M, & Ip, J. H. for the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT) Investigators ((2005). Amiodarone or an implantable cardioverter-defibrillator for congestive heart failure. N Engl J

[68] Kadish, A, Dyer, A, Daubert, J. P, & Quigg, R. Estes NAM, Anderson KP, Calkins H, Hoch D, Goldberger J, Shalaby A, Sanders WE, Schaechter A, Levine JH, for the De‐ fibrillators in Non-Ischemic Cardiomyopathy Treatment Evaluation (DEFINITE) In‐ vestigators ((2004). Prophylactic defibrillator implantation in patients with

[69] Wigginton, J. G, Pepe, P. E, Bedolla, J. P, Detamble, L. A, & Atkins, J. M. (2002). Sex related differences in the presentation and outcome of out-of-hospital cardiopulmo‐ nary arrest: A multiyear, prospective, population-based study. Crit Care Med.

nonischemic dilated cardiomyopathy. *N Engl J Med.* , 350, 2151-2158.

connexin43 expression. J Cardiovasc Pharmacol. 2011 July ; , 58(1), 32-39.

inhibits the cardiac sarcolemmal Na +-K + pump. *Circ Res*. 86: 37-42.

Framingham Heart Study). *Am J Cardiol. 74*, 232-235.

40, 124-129.

74 Ischemic Heart Disease

705-708.

KCNJ2. *Nat. Med*. , 13, 486-491.

Med 2005;, 352, 225-37.

30(Suppl 4):SS136., 131.

**Chapter 5**

**Costs of Hospitalizations with a Primary Diagnosis of**

Acute Myocardial Infarction (AMI) is both a common and deadly type of cardiac event in the United States. Although the age-adjusted hospitalization rate for AMI and its in-hospital case fatality rates have both declined since the mid-1990s, there were still 634,000 inpatient admissions in 2009 for which AMI was listed as the primary diagnosis [1, 2]. Moreover, Americans suffered an estimated 610,000 first-time AMIs and 325,000 recurrent attacks, and 133,958 deaths in 2008 [2]. Because the declines in hospitalization and in-hospital mortality rates have been associated with more aggressive therapeutic interventions [1], it is impor‐

To evaluate specifically the cost-effectiveness of various interventions against AMI, direct cost estimates of AMI are required [3-5]. Surprisingly, however, these cost estimates have not been comprehensively examined in the U.S. Many studies have investigated the economic burden of AMI, but all had some limitations [6-17]. Furthermore, in part because of limitations in avail‐ able studies, the costs of coronary heart disease (CHD) were used in one study to represent the costs for AMI [6], albeit this is inappropriate. For example, a previous study of insured adults aged 18-64 years found that only about 30% of CHD cases represented AMI [9]. Moreover, the American Heart Association recently estimated that the total prevalence of CHD among per‐ sons aged ≥20 years was 7% but the AMI prevalence of AMI in this group was 3.1% [2]. In addi‐ tion, in 2005, hospitalization costs for AMI admissions among adults aged 18-64 years were about \$5000 more than those for CHD admissions of non-AMI [9]. Clearly, information on costs that does not clearly distinguish between AMI and non-AMI admissions is of little use in

> © 2013 Wang et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

**Acute Myocardial Infarction Among Patients Aged**

**18-64 Years in the United States**

Guijing Wang, Zefeng Zhang, Carma Ayala,

Additional information is available at the end of the chapter

tant to evaluate the cost-effectiveness of these interventions.

evaluating the cost-effectiveness of interventions to treat AMI [18].

Diane Dunet and Jing Fang

http://dx.doi.org/10.5772/53499

**1. Introduction**


2. Energy management methodology

construction materials, climate of the region, fenestration, user's energy management, thermal insulation, lighting or internal heat loads, among the most important ones. However, social-

One of the main problems encountered in HVAC systems is oversizing. This leads to an unnecessary increase of electrical demand on the power supply system and its consequent environmental impact [2]. For example, in the northwest region of Mexico which is characterized by a dry hot climate in summer, maximum outdoor temperatures of 45–47C and even higher are usually encountered in July and August. For these conditions, retailers and HVAC technicians normally size the capacity of air conditioning using a rule of thumb that has been deeply rooted for years: "1 ton capacity for 20 square meters of cooled area." This criterion is

This heuristic approach does not take into account if the building is well insulated or if there are elements for energy saving. As a consequence, the air conditioner is generally oversized by a factor which varies from 1.5 to 2. On the other hand, since 1990, in Mexicali, Baja California, Mexico, a city with more than one million inhabitants, the federal government initiated a sponsored program through Comisión Federal de Electricidad (CFE), the state electric power company, intended to support energy saving strategies and specific actions oriented to the residential sector. These actions started by financing insulation of ceilings and the replacement of incandescent lamps with fluorescent ones. Subsequently, in 1997 began the replacement of old low-efficiency air conditioning units with high-efficiency equipment. In this program, the package, split, mini-split and window type units were considered and by 2003 the actions to replace old low-efficiency refrigerators were launched. This program called Program for Integral Systematic Saving [(ASI) by its acronym in Spanish] still continues to operate and has even

Energy efficiency of residential housing and buildings is of concern not only in Mexico but around the world because of its impact on future energy requirements, due to the accelerated growth of our modern cities during the next decades. Thus, several studies oriented to analyze and improve efficiency of this sector using regional passive elements (attics, ventilation, gardening, wall materials, roof ponds, and insulation) or feasible strategies of energy saving have

On the other hand, recent studies are being developed to investigate the utilization of nanoparticles as energy carriers or thermal storage materials for applications in heat transfer systems. The effect of these materials on the effectiveness of heat transfer in heat exchangers of different geometries has been simulated by taking advantage of latent heat of meltingsolidification change of phase. The use of nanoparticles added to reflective paints as an additional energy saving option has also been proposed. However, even more research is still needed in this field to consider these materials as a truly effective, technical, safe and economic

Nevertheless, there is still no standardized criterion for the correct sizing of cooling or heating units applicable in Mexico, so oversizing becomes a serious problem for the energy supplier

economic and cultural issues also play a significant role [1].

generally applied without any valid technical or scientific basis.

expanded to other cities with hot climate in the country [3].

been carried out [4–7].

14 HVAC System

option [8–12].

### 2.1. Climatology: degree-days and degree-hours

For understanding the energy behavior of a building or a household, it is imperative to know very well the climate of the region where it is located since its climate determines whether heating, cooling or both are required for any period of the year. This is also essential for calculation of heat gain or heat losses. It does not matter if the building is in the design stage or it has already been built; any energy analysis is intimately related to local climate. As previously mentioned, degree-days have been the main variable used to correlate energy and climate. Before having the advantages provided today by the availability of current online weather databases, the cooling or heating degree-days were computed just considering a simple arithmetic addition or subtraction between the outdoor temperature and an estimated base temperature.

As an example, at any given day, the maximum temperature reached 35�C and the minimum temperature was 22�C, the average temperature is Tav ¼ ð Þ 35 þ 22 =2 ¼ 28:5�C. The most used base temperature in the USA is 18.3�C (65�F). Since Tav is greater than 18.3�C, the degree-days are then (28.5�18.3) = 10.2 cooling degree-days. The same base temperature is also utilized for estimating heating degree-days [13].

However, there are other criteria for calculating degree-days as indicated by Bromley [14] who takes into account not only a variable base temperature but also the duration of the temperature above or below such reference temperature. This information can be obtained online when a weather station in the city or location of interest is available, but what happens if information is not reliable or does not exist at all? In such a case, data from the nearest station or from a region with a similar climate would be useful. Once the degree-day values are obtained for a particular location, then they could be correlated seeking for some relationship between, for example, power consumption or demand to establish patterns of energy behavior in buildings or even to estimate the impact of global warming on the climatology of a particular location [15–17].

In developed countries, a large amount of climatological information is available; however, in nondeveloped countries such as Mexico or in Latin America, there is a lack of data for many locations excepting maybe for the most important cities. Therefore, we have been working on collecting and processing information provided by the National Meteorological Service of 40 Mexican cities with a population of at least 100,000 inhabitants. Instead of working with the degree-day approach, hourly averages were obtained and analyzed on this basis. These are called degree-hours (DH). DH take into account not only the degrees of the outdoor temperature above or below the reference temperature but also the duration of these variations over a day. This procedure can be done for every single day of the year. In Figure 1, the concept of degree-hours is shown graphically. This figure also shows the mathematical definition once a function for hourly outdoor temperature is provided.

month, the daily average temperature is calculated as Tdaily ¼ ð Þ Tmax þ Tmin =2 and the monthly average temperature is obtained by averaging the corresponding daily average temperatures. On the other hand, the electricity tariffs in Mexico are based on the average monthly summer temperature. However, even if the monthly average temperature of two or more places is quite similar, this does not mean that the degree-hours are to be the same too. This can be clearly observed in Figure 2 where the totalized degree-hours during summertime of these two Mexican cities located at the Northwest of the country are compared. The period analyzed was from May to October, and the data were recorded from 1993 to 2001. The statistical difference between DH for both locations is 15%. Thus, as indicated by several authors, the difference, (in percentage) of DH should be similar to the difference (in percentage) of the consumed power by two buildings with the same characteristics situated one in each city, when their energy consumption is compared to each other. In fact, a recent work performed by a graduate student at the Institute of Engineering analyzed the energy consumption of a sample of just over one hundred homes of residential users in these two cities. For the results to be valid, the factors that could introduce variability such as cooled area, constructive system, orientation, occupancy, fenestration or air conditioners efficiency were carefully revised and controlled when analyzed statistically. The results effectively point out that Mexicali users spent, on average, 15% more energy in the summer than Hermosillo's [19]. This figure con-

Methodology of Energy Management in Housing and Buildings of Regions with Hot and Dry Climates

http://dx.doi.org/10.5772/intechopen.78341

17

The averaged monthly DH for a period of 9 years (1993–2001) for the same two cities are shown in Figure 3. As it is seen, there are appreciable variations between one region and another. Mexicali's DH are greater for the period of May to September except in June where both cities behave in a very similar way. October is still a summer month in Hermosillo but not in Mexicali. Otherwise, for the nonsummer months, Mexicali's DH have large negative values while Hermosillo's DH are practically zero. This indicates that Mexicali is significantly colder than the capital of Sonora, an example of an extreme climate. Nevertheless, it should be noted

firms that electricity consumption and DH are proportional.

Figure 2. Average summertime cooling degree-hours for two Mexican cities with similar climate.

Two temperatures were set as baselines for summer and winter: 29.5C (85F) for cooling DH and 15.5C (60F) for heating DH. These values were selected based upon the weather conditions of the northwest of Mexico where the most severe climate in the country is said to be, especially in summertime. As an example, the maximum recorded temperature in Mexicali occurred on July 28, 1995—51.8C (124F). In addition, the inhabitants of this region have developed a special acclimatization for these severe conditions not existing in other cities with less aggressive climates. These conditions lead us to consider that the comfort zone is slightly different for the population living in our region from that commonly reported by The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) [18].

As an example of the application of the DH in the climatological analysis, the results for two cities of northwestern Mexico, Mexicali and Hermosillo, during the summer season are shown in Figure 2. Mexicali, the capital of Baja California, is located just on the border with the State of California, USA, while Hermosillo, the capital of Sonora, is 700 km apart to the south. The average monthly temperature in July and August for both locations is very similar, 33C and 32.2C, respectively. Statistically there is no difference between them. For each day of a given

Figure 1. Degree-hours definition.

month, the daily average temperature is calculated as Tdaily ¼ ð Þ Tmax þ Tmin =2 and the monthly average temperature is obtained by averaging the corresponding daily average temperatures. On the other hand, the electricity tariffs in Mexico are based on the average monthly summer temperature. However, even if the monthly average temperature of two or more places is quite similar, this does not mean that the degree-hours are to be the same too. This can be clearly observed in Figure 2 where the totalized degree-hours during summertime of these two Mexican cities located at the Northwest of the country are compared. The period analyzed was from May to October, and the data were recorded from 1993 to 2001. The statistical difference between DH for both locations is 15%. Thus, as indicated by several authors, the difference, (in percentage) of DH should be similar to the difference (in percentage) of the consumed power by two buildings with the same characteristics situated one in each city, when their energy consumption is compared to each other. In fact, a recent work performed by a graduate student at the Institute of Engineering analyzed the energy consumption of a sample of just over one hundred homes of residential users in these two cities. For the results to be valid, the factors that could introduce variability such as cooled area, constructive system, orientation, occupancy, fenestration or air conditioners efficiency were carefully revised and controlled when analyzed statistically. The results effectively point out that Mexicali users spent, on average, 15% more energy in the summer than Hermosillo's [19]. This figure confirms that electricity consumption and DH are proportional.

Mexican cities with a population of at least 100,000 inhabitants. Instead of working with the degree-day approach, hourly averages were obtained and analyzed on this basis. These are called degree-hours (DH). DH take into account not only the degrees of the outdoor temperature above or below the reference temperature but also the duration of these variations over a day. This procedure can be done for every single day of the year. In Figure 1, the concept of degree-hours is shown graphically. This figure also shows the mathematical definition once a

Two temperatures were set as baselines for summer and winter: 29.5C (85F) for cooling DH and 15.5C (60F) for heating DH. These values were selected based upon the weather conditions of the northwest of Mexico where the most severe climate in the country is said to be, especially in summertime. As an example, the maximum recorded temperature in Mexicali occurred on July 28, 1995—51.8C (124F). In addition, the inhabitants of this region have developed a special acclimatization for these severe conditions not existing in other cities with less aggressive climates. These conditions lead us to consider that the comfort zone is slightly different for the population living in our region from that commonly reported by The Ameri-

can Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) [18].

As an example of the application of the DH in the climatological analysis, the results for two cities of northwestern Mexico, Mexicali and Hermosillo, during the summer season are shown in Figure 2. Mexicali, the capital of Baja California, is located just on the border with the State of California, USA, while Hermosillo, the capital of Sonora, is 700 km apart to the south. The average monthly temperature in July and August for both locations is very similar, 33C and 32.2C, respectively. Statistically there is no difference between them. For each day of a given

function for hourly outdoor temperature is provided.

16 HVAC System

Figure 1. Degree-hours definition.

The averaged monthly DH for a period of 9 years (1993–2001) for the same two cities are shown in Figure 3. As it is seen, there are appreciable variations between one region and another. Mexicali's DH are greater for the period of May to September except in June where both cities behave in a very similar way. October is still a summer month in Hermosillo but not in Mexicali. Otherwise, for the nonsummer months, Mexicali's DH have large negative values while Hermosillo's DH are practically zero. This indicates that Mexicali is significantly colder than the capital of Sonora, an example of an extreme climate. Nevertheless, it should be noted

Figure 2. Average summertime cooling degree-hours for two Mexican cities with similar climate.

 Outdoor temperature using the Fourier-type model. Mexicali, August 21.

μ

the day August 21.

Tmax � T tð Þ Tmax � Tmin

Methodology of Energy Management in Housing and Buildings of Regions with Hot and Dry Climates

t¼1

μð Þ� t μ � � � � cos

The model was tested using hourly data for various locations in Mexico and Cuba as well as for different years. The statistical deviation from experimental and calculated values did not exceed 5%. For purposes of thermal analysis calculation and buildings simulation, this figure is more than acceptable. Furthermore, the model was integrated as a linked module into the simulator which was developed by the authors to compute the hourly outdoor temperature for a selected location provided the maximum and minimum temperature of any given day. On the other hand, the simulator is based on the ASHRAE's Transfer Function method [21] which, instead of using a large climate database, it used the Fourier model which was incorporated to generating daily hourly temperatures by using only Tmax and Tmin. The main reason for doing this is that for certain locations only maximum and minimum temperatures are recorded or available. In Figure 4, the experimental and the calculated values using Eq. (1) are shown for

μð Þ� t μ � � � � sin 2πt

(2)

19

μð Þt (3)

http://dx.doi.org/10.5772/intechopen.78341

24 � � (5)

24 � � (6)

Θnð Þt , n ¼ 1, 2,…Ndays (4)

2πt

Figure 3. Average monthly DH for two Mexican cities (1993–2001). Tref = 29.5�C (summer), 15.5�C (winter).

that carrying out this type of analysis requires having reliable climatological information. On this depends greatly the success of our work.

#### 2.2. Outdoor temperature model

In order to facilitate the calculation of either DH analysis or thermal simulation of a building the authors developed a simple Fourier-type model for estimating hourly outdoor temperature from data recorded by meteorological stations. When working with climate data for the first time, there is a huge amount of information to process and organize and it is a common practice to work with extensive databases. The aim of using a mathematical model is to reduce the complexity of calculation when performing thermal simulation in buildings. This led us to simplify the energy analysis and assessment of a particular system. Besides, the use of a model let us working with only a few set of correlation coefficients instead of an enormous quantity of data, thus releasing disk and memory space. When analyzing the outdoor temperature of a particular region, no significant changes have been observed in its seasonal patterns along a period of several years. This means that the monthly average temperature remains almost the same year after year over a period of time and the natural variation encountered from a year to another is compensated when more years are taken into account. Although this premise is valid for monthly temperature, Mexicali's DH have increased consistently since 2007 [20].

Since the outdoor temperature is given on an hourly arrangement, the model was intended to be on the same basis. So, the model for estimating the hourly temperature is stated as

$$\Theta(t) = \langle \mu \rangle + A \cos \left(\frac{2\pi t}{24}\right) + B \sin \left(\frac{2\pi t}{24}\right) \tag{1}$$

where Θð Þt represents the dimensionless hourly outdoor temperature for a particular day defined in Eq. (2), Tmax and Tmin are the recorded maximum and minimum temperature and T tð Þ the calculated value of temperature for time t.

Methodology of Energy Management in Housing and Buildings of Regions with Hot and Dry Climates http://dx.doi.org/10.5772/intechopen.78341 19

$$
\Theta(t) = \frac{T\_{\text{max}} - T(t)}{T\_{\text{max}} - T\_{\text{min}}} \tag{2}
$$

μ � �, A and B are the mean value and the two coefficients of correlation given by:

that carrying out this type of analysis requires having reliable climatological information. On

Figure 3. Average monthly DH for two Mexican cities (1993–2001). Tref = 29.5�C (summer), 15.5�C (winter).

In order to facilitate the calculation of either DH analysis or thermal simulation of a building the authors developed a simple Fourier-type model for estimating hourly outdoor temperature from data recorded by meteorological stations. When working with climate data for the first time, there is a huge amount of information to process and organize and it is a common practice to work with extensive databases. The aim of using a mathematical model is to reduce the complexity of calculation when performing thermal simulation in buildings. This led us to simplify the energy analysis and assessment of a particular system. Besides, the use of a model let us working with only a few set of correlation coefficients instead of an enormous quantity of data, thus releasing disk and memory space. When analyzing the outdoor temperature of a particular region, no significant changes have been observed in its seasonal patterns along a period of several years. This means that the monthly average temperature remains almost the same year after year over a period of time and the natural variation encountered from a year to another is compensated when more years are taken into account. Although this premise is valid for monthly temperature, Mexicali's DH have increased consistently since 2007 [20].

Since the outdoor temperature is given on an hourly arrangement, the model was intended to

where Θð Þt represents the dimensionless hourly outdoor temperature for a particular day defined in Eq. (2), Tmax and Tmin are the recorded maximum and minimum temperature and

2πt 24

þ B sin 2πt

24

(1)

be on the same basis. So, the model for estimating the hourly temperature is stated as

þ A cos

ΘðÞ¼ t μ

T tð Þ the calculated value of temperature for time t.

this depends greatly the success of our work.

2.2. Outdoor temperature model

18 HVAC System

$$
\langle \mu \rangle = \frac{1}{24} \sum\_{t=1}^{24} \mu(t) \tag{3}
$$

$$\mu(t) = \frac{1}{N} \sum\_{n=1}^{N} \Theta\_n(t), n = 1, 2, \dots \\ \text{Ndays} \tag{4}$$

$$A = \frac{2}{24} \sum\_{t=1}^{24} \left[ \mu(t) - \langle \mu \rangle \right] \cos \left( \frac{2\pi t}{24} \right) \tag{5}$$

$$B = \frac{2}{24} \sum\_{t=1}^{24} \left[ \mu(t) - \langle \mu \rangle \right] \sin \left( \frac{2\pi t}{24} \right) \tag{6}$$

The model was tested using hourly data for various locations in Mexico and Cuba as well as for different years. The statistical deviation from experimental and calculated values did not exceed 5%. For purposes of thermal analysis calculation and buildings simulation, this figure is more than acceptable. Furthermore, the model was integrated as a linked module into the simulator which was developed by the authors to compute the hourly outdoor temperature for a selected location provided the maximum and minimum temperature of any given day. On the other hand, the simulator is based on the ASHRAE's Transfer Function method [21] which, instead of using a large climate database, it used the Fourier model which was incorporated to generating daily hourly temperatures by using only Tmax and Tmin. The main reason for doing this is that for certain locations only maximum and minimum temperatures are recorded or available. In Figure 4, the experimental and the calculated values using Eq. (1) are shown for the day August 21.

Figure 4. Outdoor temperature using the Fourier-type model. Mexicali, August 21.

Another advantage of using the Fourier-type model is that DH calculation becomes quite simple, so it is not necessary to integrate numerically a large quantity of data every time it is needed, but only performing the integration once by using the model already obtained and substituting the appropriate values of μ � �, A and B for the location of interest. In this manner, the cooling DH are defined as

$$DH\_{\text{cooling}} = \int\_{T\_0}^{T} \int\_0^{24} T(t)dt dT \tag{7}$$

conduction. TFM converts radiation heat gains on walls as a function of the named sol-air

Methodology of Energy Management in Housing and Buildings of Regions with Hot and Dry Climates

http://dx.doi.org/10.5772/intechopen.78341

21

αIt h0

� εδR (9)

A ¼ αIt þ h0ð Þ� t0 � ts εδR (10)

te ¼ t0 þ

The heat flow can be calculated from a heat balance as

q

temperature defined as

Figure 5. General structure of the thermal simulator.

te = Sol-air temperature

t0 = Outdoor temperature

ts = External surface temperature

where

After substitution of Eq. (1) and Eq. (2) onto Eq. (7) and integrating, we obtain

$$DH\_{cooling} = 24\left[ (T\_{\text{max}} - T\_0) - \left< \mu \right> (T\_{\text{max}} - T\_{\text{min}}) \right] \tag{8}$$

where T0 is the reference temperature. The same procedure applies for heating DH.

#### 2.3. Thermal simulation

The computational program has been one of the most useful tools for energy analysis. The software is built in modular form, and each module performs a specific type of calculation; moreover, because of its versatility, it is programmed on Excel spreadsheets. The general structure of the program is shown in Figure 5. In the module of data entry, all the required information about location, building dimensions, orientation, materials, occupancy, fenestration, internal loads, lighting, actual or estimated HVAC efficiency, and so on, is entered. The module is linked to an internal database where other information such as latitude, longitude, maximum and minimum temperatures, solar parameters, ASHRAE coefficients and thermal resistances is stored.

The next stage is calculating the hourly outdoor temperature, the sol-air temperature and the amount of direct and diffuse radiation over the surfaces of the building. The ASHRAE Transfer Function method is then used for estimating hourly heat gains, cooling load and heat extraction rate. As mentioned earlier, instead of using a large climatological database for each location, the program was configured to work with only a representative sample of data. We have observed that correlation between degree-hours (DH) and the rate of heat gain, cooling load and heat extraction is excellent for regions of dry, hot climates. It is common to encounter a correlation coefficient over 97% with these three factors. However, the DH correlate very well not only with cooling load or heat gain but also with monthly electricity consumption or demand as well as with air conditioning consumption.

#### 2.3.1. ASHRAE transfer functions method

The simulator used in this work is based on the Transfer Functions method (TFM), as reported by the ASHRAE [22]. The authors developed an earlier version of the simulator in 1996 adapting this methodology from the version published in 1992. However, the program has been updated and improved constantly ever since. TFM consists basically in providing a model to consider the dynamic response of a building to heat transfer. As it is known, heat is introduced into a space by mean of three basic mechanisms, namely, radiation, convection and Methodology of Energy Management in Housing and Buildings of Regions with Hot and Dry Climates http://dx.doi.org/10.5772/intechopen.78341 21

Figure 5. General structure of the thermal simulator.

conduction. TFM converts radiation heat gains on walls as a function of the named sol-air temperature defined as

$$t\_e = t\_0 + \frac{\alpha I\_t}{h\_0} - \varepsilon \delta R \tag{9}$$

The heat flow can be calculated from a heat balance as

$$\frac{q}{A} = \alpha I\_t + h\_0(t\_0 - t\_s) - \varepsilon \delta R \tag{10}$$

where

Another advantage of using the Fourier-type model is that DH calculation becomes quite simple, so it is not necessary to integrate numerically a large quantity of data every time it is needed, but only performing the integration once by using the model already obtained and

> ðT T0

The computational program has been one of the most useful tools for energy analysis. The software is built in modular form, and each module performs a specific type of calculation; moreover, because of its versatility, it is programmed on Excel spreadsheets. The general structure of the program is shown in Figure 5. In the module of data entry, all the required information about location, building dimensions, orientation, materials, occupancy, fenestration, internal loads, lighting, actual or estimated HVAC efficiency, and so on, is entered. The module is linked to an internal database where other information such as latitude, longitude, maximum and minimum temperatures, solar parameters, ASHRAE coefficients and thermal

The next stage is calculating the hourly outdoor temperature, the sol-air temperature and the amount of direct and diffuse radiation over the surfaces of the building. The ASHRAE Transfer Function method is then used for estimating hourly heat gains, cooling load and heat extraction rate. As mentioned earlier, instead of using a large climatological database for each location, the program was configured to work with only a representative sample of data. We have observed that correlation between degree-hours (DH) and the rate of heat gain, cooling load and heat extraction is excellent for regions of dry, hot climates. It is common to encounter a correlation coefficient over 97% with these three factors. However, the DH correlate very well not only with cooling load or heat gain but also with monthly electricity consumption or

The simulator used in this work is based on the Transfer Functions method (TFM), as reported by the ASHRAE [22]. The authors developed an earlier version of the simulator in 1996 adapting this methodology from the version published in 1992. However, the program has been updated and improved constantly ever since. TFM consists basically in providing a model to consider the dynamic response of a building to heat transfer. As it is known, heat is introduced into a space by mean of three basic mechanisms, namely, radiation, convection and

ð24 0

DHcooling ¼

After substitution of Eq. (1) and Eq. (2) onto Eq. (7) and integrating, we obtain

DHcooling ¼ 24 ð Þ� Tmax � T0 μ

where T0 is the reference temperature. The same procedure applies for heating DH.

� �, A and B for the location of interest. In this manner,

� �ð Þ Tmax � Tmin

� � (8)

T tð ÞdtdT (7)

substituting the appropriate values of μ

the cooling DH are defined as

20 HVAC System

2.3. Thermal simulation

resistances is stored.

demand as well as with air conditioning consumption.

2.3.1. ASHRAE transfer functions method

te = Sol-air temperature

t0 = Outdoor temperature

ts = External surface temperature

It= Total solar incident radiation on the surface

h0 = Convection heat transfer on the external surface

δR = Difference between long-wave radiation from surroundings and the radiation emitted by a black body at the same temperature as outdoor temperature

ε = Surface emittance

Also, TFM requires generating a series of Transfer Function coefficients which are used to estimate U-values for each wall or roof into the equation of energy balance. The first step is calculating hourly heat gains by means of the equation

$$q\_{\varepsilon,\theta} = A \left[ \sum\_{n=0} b\_n (t\_{\varepsilon,\theta - n\delta}) - \sum\_{n=1} d\_n \left\{ \frac{\left(q\_{\varepsilon,\theta - n\delta}\right)}{A} \right\} - t\_{\text{rc}} \sum\_{n=0} c\_n \right] \tag{11}$$

provided. ASHRAE publications enlist a large amount of data for materials mostly used in nonresidential and residential buildings. Nevertheless, some materials and construction designs commonly encountered in Latin America do not match as the ones reported by ASHRAE exactly. Therefore, for these cases, the corresponding data and coefficients had to be estimated and incorporated to the simulator. For a more detailed discussion of TFM, see

Methodology of Energy Management in Housing and Buildings of Regions with Hot and Dry Climates

http://dx.doi.org/10.5772/intechopen.78341

23

When performing a thermal simulation of a building using the Transfer Functions method, calculated heat gains, cooling loads and heat extraction rates are delivered by the program on an hourly basis for a given day. This characteristic allows sizing the capacity of HVAC system and determines when the highest peak demand will occur or what the most important elements responsible for gaining/releasing heat or consuming/demanding power are. Then the results are totalized for that particular day. This procedure must be repeated every single day

In order to improve the calculation performance of the simulator without decreasing accuracy and reliability of the results, the authors decided to carry out a simpler procedure by correlating the degree-hours with daily heat gain, cooling load and heat extraction rates for a few representative days of a year. The reason for doing this is the hypothesis that if the degreehours are a realistic way of correlating the climate, then they would be capable of explaining or, at least, correlating the energy behavior of a building with reasonable feasibility. According to this premise, the DH for several arrangements were tested and finally it was concluded that 20 summer days are quite enough to reach the same quality and accuracy of results than if a

In Figures 6 and 7, the plot of DH versus daily heat gain, cooling load and extraction rates is shown for Mexicali, México and Cienfuegos and Cuba, respectively. It can be observed that the

Figure 6. DH correlation with cooling load and heat extraction rates. Mexicali, México.

[18, 23–25].

2.3.2. Simulation improvement by using DH correlations

over a year or for a certain period of time as needed.

365-day calculation was made.

where

qe,θ = Wall or roof heat gain for time θ

qe,θ�nδ = Wall or roof heat gain for the time θ � nδ

A = Surface area

θ = time

```
δ = Time interval
```
te,θ�nδ = Sol-air temperature at time θ � nδ

trc = Constant interior temperature

bn, cn, dn = Conduction transfer coefficients

Once hourly heat gains have been computed, they can be transformed into hourly cooling loads with the expression

$$Q\_{\theta} = \nu \mathfrak{o} \mathfrak{q}\_{\theta} + \nu \mathfrak{u} \mathfrak{q}\_{\theta-\delta} + \nu \mathfrak{z} \mathfrak{q}\_{\theta-2\delta} - \mathfrak{w} \mathfrak{q} Q\_{\theta-\delta} - \mathfrak{w} \mathfrak{Q}\_{\theta-2\delta} \tag{12}$$

where

Qθ = Cooling load for time θ

qθ = Conduction heat gain for time θ

υ0, υ1, υ2, w1, w2 = Space weight coefficients

Eq. (12) takes into account the dynamic response of the building from a semi-steady state approach once the other variables such as fenestration, occupancy or lighting coefficients are provided. ASHRAE publications enlist a large amount of data for materials mostly used in nonresidential and residential buildings. Nevertheless, some materials and construction designs commonly encountered in Latin America do not match as the ones reported by ASHRAE exactly. Therefore, for these cases, the corresponding data and coefficients had to be estimated and incorporated to the simulator. For a more detailed discussion of TFM, see [18, 23–25].

#### 2.3.2. Simulation improvement by using DH correlations

q

22 HVAC System

A ¼ Heat flow per unit area

It= Total solar incident radiation on the surface

h0 = Convection heat transfer on the external surface

calculating hourly heat gains by means of the equation

2 4

n¼0

bnð Þ� te,θ�nδ

qe,θ ¼ A X

qe,θ�nδ = Wall or roof heat gain for the time θ � nδ

qe,θ = Wall or roof heat gain for time θ

te,θ�nδ = Sol-air temperature at time θ � nδ

bn, cn, dn = Conduction transfer coefficients

trc = Constant interior temperature

loads with the expression

Qθ = Cooling load for time θ

qθ = Conduction heat gain for time θ

υ0, υ1, υ2, w1, w2 = Space weight coefficients

a black body at the same temperature as outdoor temperature

δR = Difference between long-wave radiation from surroundings and the radiation emitted by

Also, TFM requires generating a series of Transfer Function coefficients which are used to estimate U-values for each wall or roof into the equation of energy balance. The first step is

> X n¼1 dn

Once hourly heat gains have been computed, they can be transformed into hourly cooling

Eq. (12) takes into account the dynamic response of the building from a semi-steady state approach once the other variables such as fenestration, occupancy or lighting coefficients are

Qθ ¼ υ0qθ þ υ1qθ�δ þ υ2qθ�2δ � w1Qθ�δ � w2Qθ�2δ (12)

8 < :

qe,θ�nδ � �

9 = ; � trc

X n¼0 cn 3

5 (11)

A

α = Surface absorptivity

ε = Surface emittance

where

θ = time

where

A = Surface area

δ = Time interval

When performing a thermal simulation of a building using the Transfer Functions method, calculated heat gains, cooling loads and heat extraction rates are delivered by the program on an hourly basis for a given day. This characteristic allows sizing the capacity of HVAC system and determines when the highest peak demand will occur or what the most important elements responsible for gaining/releasing heat or consuming/demanding power are. Then the results are totalized for that particular day. This procedure must be repeated every single day over a year or for a certain period of time as needed.

In order to improve the calculation performance of the simulator without decreasing accuracy and reliability of the results, the authors decided to carry out a simpler procedure by correlating the degree-hours with daily heat gain, cooling load and heat extraction rates for a few representative days of a year. The reason for doing this is the hypothesis that if the degreehours are a realistic way of correlating the climate, then they would be capable of explaining or, at least, correlating the energy behavior of a building with reasonable feasibility. According to this premise, the DH for several arrangements were tested and finally it was concluded that 20 summer days are quite enough to reach the same quality and accuracy of results than if a 365-day calculation was made.

In Figures 6 and 7, the plot of DH versus daily heat gain, cooling load and extraction rates is shown for Mexicali, México and Cienfuegos and Cuba, respectively. It can be observed that the

Figure 6. DH correlation with cooling load and heat extraction rates. Mexicali, México.

between calculated and measured values reported by manufacturers [26]. For older or used equipment, a less than unity correction factor must be used. This factor depends on several issues such as type of unit, age, model or dirt on heat transfer surfaces and should be found experimentally. Of course, such an investigative task is not feasible for most situations. In regions where humidity is small and if the HVAC unit receives appropriate maintenance, the factor value is 1 for all practical purposes. Besides, according to manufacturers, if any significant reduction of EER occurs during the first 5 years of use, it will be attributable to different

Methodology of Energy Management in Housing and Buildings of Regions with Hot and Dry Climates

http://dx.doi.org/10.5772/intechopen.78341

25

An example of the EER variation for a given day is presented in Figure 8. For that day, the minimum and maximum temperatures are 43.3 and 27.5�C, respectively. EER is plotted as a function of time and its corresponding value can be read on the left axis while the outdoor temperature is allocated on the right axis. From this graph, it can be seen that as the tempera-

Once the calculations described in previous sections have been completed, the program generates a set of daily and monthly values of DH, cooling load and power consumption. Likewise, the values and percentage of the most important variables contributing to cooling/heating loads are internally stored and displayed for a better understanding. The most important results are presented in a separate spreadsheet where power consumption, maximum peak demand and billing are displayed on a monthly basis. Also, the contribution to cooling load of roof, walls, fenestration, occupancy, lighting, internal loads and infiltration/ventilation is displayed in a graph to show the user in an easy way how the heat loads are distributed all over the building

Finally, beside the monthly consumption table of results, the billing is added. In this manner, not only the energy balance is in sight but also the economic aspects can be visualized at the

causes such as refrigerant leakage or major damage to the equipment, among others.

ture rises, the efficiency decreases proportionally.

and which of them are the most relevant over the total load.

Figure 8. EER variation as a function of outdoor temperature (split units).

same time which as a whole allow the user to make better decisions.

2.3.4. Integrating results

Figure 7. DH correlation with cooling load and head extraction rates. Cienfuegos, Cuba.

correlation coefficients R2 are excellent for both cities. It must be noted that meteorological data for Cienfuegos are available only on a 3-h basis. In spite of this poor quantity of data, the results are still quite satisfactory. Correlation equations are readily obtained from DH and the three variables mentioned earlier, although DH versus cooling load rate (CLR) and DH versus heat extraction rate (HER) are most useful for our purposes.

Thermostat temperature is the reference baseline to calculate DH for the rest of the days of the year. Once the equation is available for CLR, its daily values are generated for the entire year and linked to the next module where daily heat extraction rates are estimated in the same manner as done for cooling load. When the TFM calculation is complete, the results of daily heat extraction rates are then correlated with DH for estimating the power consumption of the building while also including the variation of the air conditioning efficiency. The model that describes how the energy efficiency ratio (EER) varies as the outdoor temperature does is given below. At this stage, the EER values can now be applied together with HER correlation equation for computing the power consumed by HVAC system over the analyzed period of time.

#### 2.3.3. Efficiency of HVAC systems of air-cooled condensing units

It is well known that the energy efficiency of air-conditioning systems varies strongly with the outdoor temperature, and in turn, temperature varies throughout the day. This is especially true for HVAC systems whose condensing units are cooled by outdoor air. Air is not a good conductor of heat and special care must be taken to maintain the proper operating conditions of the equipment. For this reason, a model considering this hourly variation has been incorporated into the simulator for calculating the rate of heat extraction. This allows better accuracy of the results. The model is quite simple and takes the form [26].

$$EER = \varepsilon^{(a-b\mathcal{T})} \tag{13}$$

where EER is the Energy Efficiency Ratio (W/W, kBtu/kWh), a and b are correlation coefficients depending on the unit model and system (package or split) and T is the outdoor temperature ( �F). It should be noted that Eq. (13) fits EER for new units within less than a � 1% difference between calculated and measured values reported by manufacturers [26]. For older or used equipment, a less than unity correction factor must be used. This factor depends on several issues such as type of unit, age, model or dirt on heat transfer surfaces and should be found experimentally. Of course, such an investigative task is not feasible for most situations. In regions where humidity is small and if the HVAC unit receives appropriate maintenance, the factor value is 1 for all practical purposes. Besides, according to manufacturers, if any significant reduction of EER occurs during the first 5 years of use, it will be attributable to different causes such as refrigerant leakage or major damage to the equipment, among others.

An example of the EER variation for a given day is presented in Figure 8. For that day, the minimum and maximum temperatures are 43.3 and 27.5�C, respectively. EER is plotted as a function of time and its corresponding value can be read on the left axis while the outdoor temperature is allocated on the right axis. From this graph, it can be seen that as the temperature rises, the efficiency decreases proportionally.

#### 2.3.4. Integrating results

correlation coefficients R2 are excellent for both cities. It must be noted that meteorological data for Cienfuegos are available only on a 3-h basis. In spite of this poor quantity of data, the results are still quite satisfactory. Correlation equations are readily obtained from DH and the three variables mentioned earlier, although DH versus cooling load rate (CLR) and DH versus

Thermostat temperature is the reference baseline to calculate DH for the rest of the days of the year. Once the equation is available for CLR, its daily values are generated for the entire year and linked to the next module where daily heat extraction rates are estimated in the same manner as done for cooling load. When the TFM calculation is complete, the results of daily heat extraction rates are then correlated with DH for estimating the power consumption of the building while also including the variation of the air conditioning efficiency. The model that describes how the energy efficiency ratio (EER) varies as the outdoor temperature does is given below. At this stage, the EER values can now be applied together with HER correlation equation for computing the

It is well known that the energy efficiency of air-conditioning systems varies strongly with the outdoor temperature, and in turn, temperature varies throughout the day. This is especially true for HVAC systems whose condensing units are cooled by outdoor air. Air is not a good conductor of heat and special care must be taken to maintain the proper operating conditions of the equipment. For this reason, a model considering this hourly variation has been incorporated into the simulator for calculating the rate of heat extraction. This allows better accuracy

EER ¼ e

where EER is the Energy Efficiency Ratio (W/W, kBtu/kWh), a and b are correlation coefficients depending on the unit model and system (package or split) and T is the outdoor temperature

�F). It should be noted that Eq. (13) fits EER for new units within less than a � 1% difference

ð Þ a�bT (13)

heat extraction rate (HER) are most useful for our purposes.

Figure 7. DH correlation with cooling load and head extraction rates. Cienfuegos, Cuba.

power consumed by HVAC system over the analyzed period of time.

2.3.3. Efficiency of HVAC systems of air-cooled condensing units

of the results. The model is quite simple and takes the form [26].

(

24 HVAC System

Once the calculations described in previous sections have been completed, the program generates a set of daily and monthly values of DH, cooling load and power consumption. Likewise, the values and percentage of the most important variables contributing to cooling/heating loads are internally stored and displayed for a better understanding. The most important results are presented in a separate spreadsheet where power consumption, maximum peak demand and billing are displayed on a monthly basis. Also, the contribution to cooling load of roof, walls, fenestration, occupancy, lighting, internal loads and infiltration/ventilation is displayed in a graph to show the user in an easy way how the heat loads are distributed all over the building and which of them are the most relevant over the total load.

Finally, beside the monthly consumption table of results, the billing is added. In this manner, not only the energy balance is in sight but also the economic aspects can be visualized at the same time which as a whole allow the user to make better decisions.

Figure 8. EER variation as a function of outdoor temperature (split units).

#### 2.4. Successful application of the management approach

In 1990, the Mexican federal government started a local program intended to save energy in residential households in the city of Mexicali. The first stage of the program consisted of roof insulation and by 1997, the replacement of old low-efficiency air conditioning systems with new high-efficiency units was initiated. The replacement of refrigerators with more than 10 years of use began in 2003. This program for energy saving is called Program for Integral Systematic Saving ((ASI) by its acronym in Spanish) and remains in progress currently.

Author details

References

Marianela Soledad Reinhardt

enbuild.2010.10.011

enbuild.2014.12.034

leng.2018.03.011

Carlos Pérez-Tello\*, Héctor Campbell-Ramírez, José Alejandro Suástegui-Macías and

Methodology of Energy Management in Housing and Buildings of Regions with Hot and Dry Climates

http://dx.doi.org/10.5772/intechopen.78341

27

Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, México

[1] Yang L, Yan H, Lam J. Thermal comfort and building energy consumption implications— A review. Applied Energy. 2014;115:164-173. DOI: 10.1016/j.apenergy.2013.10.062

[2] Djunaedy E, van den Wymelenberg K, Acker B, Thimmana H. Oversizing of HVAC system: Signatures and penalties. Energy and Buildings. 2011;43:468-475. DOI: 10.1016/j.

[3] Program for Integral Systematic Saving. 2016. Available from: http://programaasibc.com.

[4] Dabaieha M, Wanas O, Amer Hegazyc M, Johanssona E. Reducing cooling demands in a hot dry climate: A simulation study for non-insulated passive cool roof thermal performance in residential buildings. Energy and Buildings. 2015;89:142-152. DOI: 10.1016/j.

[5] Goudarzia H, Mostafaeipour A. Energy saving evaluation of passive systems for residential buildings in hot and dry regions. Renewable and Sustainable Energy Reviews. 2017;

[6] Chwieduk D. Impact of solar energy on the energy balance of attic rooms in high latitude countries. Applied Thermal Engineering. 2018;136:548-559. DOI: 10.1016/j.appltherma-

[7] Raut A, Gomez C. Assessment of thermal and energy performance of thermally efficient sustainable wall system for Malaysian low cost housing. Applied Thermal Engineering.

[8] Kaiser J, Diener L, Wick P. Nanoparticles in paints: A new strategy to protect façades and surfaces. Journal of Physics. 2013;429. conference 1. DOI: 10.1088/1742-6596/429/1/012036

[9] Aranzabe E, Villasante P, March R, Arriortua M, Vadillo J, Larranaga A, Aranzabe A. Preparation and characterization of high NIR reflective pigments based in ultramarine

blue. Energy and Buildings. 2016;126:170-176. DOI: 10.1016/j.enbuild.2016.05.011

2018;136:309-318. DOI: 10.1016/j.applthermaleng.2018.03.017

\*Address all correspondence to<EMAIL_ADDRESS>
mx/nosotros.php. [Accessed: May 03, 2018]

68:432-446. DOI: 10.1016/j.rser.2016.10.002

The application of the methodology outlined earlier allowed to sustain the energy saving strategies and actions of the ASI program, first in the city of Mexicali then in the rest of the country. Thermal simulation and DH methodology have proven to be a powerful tool for over more than 20 years in which a large number of projects have been developed with the residential, public, governmental and industrial sectors. One of them provided the technical support for the creation of the ASI program. [27]. An evaluation of the impact on consumption habits of inhabitants due to the actions and strategies carried out by the ASI program was made recently [28].

#### 3. Conclusions

The methodology described earlier has proven to be a useful tool for the analysis and evaluation of projects related to saving and efficient use of energy. The use of DH as a primary variable has shown to be an excellent option for the characterization of the climate of a region, especially in hot-dry environments. However, its application is not restricted only to these climates, but it can also be extended to any climate without reducing reliability. Likewise, the utilization of DH greatly simplifies and speeds up the calculation when performing thermal simulation due to its excellent degree of correlation with heat gain, cooling load and heat extraction rates. At the same time, applying the DH criterion also allows to estimate the electrical consumption and demand of HVAC systems in a reliable way. A representative example of the usefulness of this methodology has been its application to the development and assessment of those projects sustaining the energy saving programs of the Mexican federal government, which are currently grouped as the ASI program.

The ASI program started its application as a local energy-saving program in 1990 in the city of Mexicali, with a current population of over 1 million inhabitants, and because of the success and positive impacts achieved in reducing peak demand and energy saving, it has been extended to warm-climate regions all over the country.

#### Acknowledgements

The authors wish to thank to PFCE program 2018 for funding publishing fees to Universidad Autónoma de Baja California, Instituto de Ingeniería.


**2. Types of skin cancer**

**a.** Basal Cell Carcinoma (BCC): This is the most common form of skin cancer and accounts for more than 90% of all skin cancers in the United States. BCC causes damage by growing and invading the surrounding tissue and usually does not metastasize to other parts of the body. Intermittent sun exposure (especially early in life), age and light colored skin are important factors in the development of BCC. Approximately a fifth of BCCs, develop in regions that are not sun-exposed such as chest, arms, neck, back and scalp [2]. Weak‐ ening of the immune system on account of the disease or immune-suppressive drugs is known to promote the risk of developing BCCs. Usually BCC begins as a small, domeshaped bump and is covered by small superficial blood vessels called telangiectases and its texture is often shiny and translucent. Hereditary predisposition to BCC [3,4] occurs

© 2013 Tarapore; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

among individuals with albinism and Xeroderma Pigmentosum. These disorders can be linked to either instability of the skin or diminished pigmentation.

is on account of the larger, more melanized melanosomes located in the epidermis of dark skin individuals that absorb and scatter more light energy than the smaller, less melanized melanosomes of white skin. The incidence rate of skin cancer (both melanoma and nonmelanoma) has increased significantly in the last decade [15]; particularly among young women. For most individuals, exposure to UVR from the sun is the main source of skin cancer. Nonetheless, some individuals are exposed to high UV doses through artificial sources – sunbeds and sunlamps used for tanning purposes. Indoor tanning is widespread in most developed countries in Northern Europe, Australia and the United States [16]. Intense early sunburns and blistering sunburns are closely associated with melanoma development [17,18, 19]. Statistics indicate that one severe childhood sunburn is associated with a two-fold increase in melanoma risk [20]. Chronic UV exposure results in increased skin aging, wrinkles, uneven skin pigmentation, loss of elasticity and a distribution in the skin barrier function [21]. Chronic UVR exposure is an important risk factor in the development of actinic keratosis (precursor of

An Overview of Important Genetic Aspects in Melanoma

http://dx.doi.org/10.5772/55257

45

**Ultraviolet (UV radiation)** UVA UVB

**Genetic syndromes** Xeroderma pigmentosum Oculocutaneous albinism Basal cell nevus syndrome

> **Ionizing radiation** X-rays

**Other risk factors** Artificial UV radiation (tanning) Smoking Color of skin (having fair skin, especially with blue or hazel eyes) History of precursor lesions Chronically injured or non-healing wounds Working outdoors Increasing age

SCCs).

**Table 1.** Risk factors for skin cancer [1-8]


*5.2. Spectral Distributions over Propagation Channels*

In this section, we evaluate the changes suffered in the frequency and time domain when the recorded ANEs were propagated through the three different multi-path channels (models A, B, and C, as explained in Section 4.2). Figures 16–18 show the outcomes related to different ANEs (i.e., the noise of an aeroplane, a brake, and a truck). For the sake of brevity we only show these three examples, as they are representative of the phenomena we want to outline. For each of them, we show the spectrogram of the emitted signal in the upper plot and then we divide the remaining plot into three rows, one for each model. In the first column, from row two to four, we show the spectrogram of the received signal through models A, B, and C, respectively.

**Figure 16.** Accumulated energy along time and frequency, through channels A, B, and C, of an audio signal of an aeroplane at sensor hb137.

In the second column, from row two to four, we show the accumulated energy throughout the whole bandwidth for each time point, for each propagating model. Finally, in the third column, from row two to four, we show the accumulated energy throughout the whole time duration of the ANE for each frequency, for each propagating model. The spectrogram divides the signal into 40 ms-length segments, which were windowed with a Hanning window to reduce leakage and transformed into the frequency domain by means of a 2048-points Fast Fourier Transform (FFT), diplayed in logarithmic scale. Consecutive segments were overlapped by a factor of 87.5% to maximise the probability of detection. The plots of accumulated energy, in time and frequency, were normalised to the maximum energy and displayed in logarithmic scale. In these plots, we show the accumulated energy of the ANEs prior to being transmitted in black and the accumulated energy after being transmitted through models A, B, and C in blue, red, and green, respectively.

In all three figures, we can observe that the accumulated energy throughout the whole frequency band (middle column) at the emitter was more similar to the accumulated energy at the receiver when

propagating through a low delay spread channel (i.e., models A and C), rather than through a higher delay spread channel (i.e., model B). The important factor that enables the spreading of energy over time is not the number of paths but the time distance between them, as well as the weight of each of them.

In all three figures, we can also underline the fact that the accumulated energy throughout the whole time span of the recorded ANEs (right column) approached the original level (i.e., prior to being propagated through the channel) when propagating through a high delay spread channel. In other words, it appears that channels with narrow coherence bandwidths managed to confine the energy into a similar range of values as the original signal. In a different manner, when facing wider coherence bandwidth channels (e.g., models A and C) the accumulated energy in the time domain tended to differ from the original one.

**Figure 17.** Accumulated energy along time and frequency, through channels A, B, and C, of an audio signal of an brake at sensor hb137.

The following step of this analysis will be to systematically analyse all ANEs from both sensors when transmitted through different types of channels (e.g., narrow streets, streets with tall buildings, wide streets with high background noise, and so on). The goal will be to find a systematic repetition of received signal characteristics related to environment factors. Another interesting point will be to study time-variant channels to check whether the time variation factor affects the received signal characteristics.

**Figure 18.** Accumulated energy along time and frequency, through channels A, B, and C, of an audio signal of an truck at sensor hb145.


**4.2 Clinical presentation**

Due to the variance in patient age, young children cannot report the location of pain, however, in those that are conversant most complain of back or neck pain, with radicular pain being less common [18]. Similarly, the neurological dysfunction, particularly sensory disturbance, is harder to define than in an adult population. However, most children are affected by neurological dysfunction (97%), most commonly spinal cord dysfunction, although isolated nerve root dysfunction is also reported [18].

Nearly one third of all reported cases are initially suspected of having an alternative diagnosis, most commonly Guillain-Barre syndrome, meningitis, cerebral palsy, Grisel's syndrome or an acute abdomen [18].