**1. Introduction**

Energy self-sufficiency and low-carbon-economy transition are the concepts currently forcing the coal-based energy industry to significantly decrease its produced emissions. The annual consumption of coal (in coal-based industries) in the Czech Republic is over 60 million tons per year, ranking Czech Republic at 17th in worldwide consumption [1]. The Czech energy industry is also dependent on imports of 97% of oil and gas [1]. Several measures were adopted to reduce energy import dependence, including implementation of a higher share of renewable energy sources and more efficient use of

fossil fuels such as brown coal [1]. However, the transition process cannot be sudden and must be gradually implemented by viable investments. One such investment, which seems to be feasible for the current Czech energy industry but also usable for steel industries, is carbon capture technologies (CCTs). These technologies must be carefully assessed and planned for the specific conditions of a given country. There are three parameters that must be considered for the feasibility assessment of CCT—(i) technological feasibility, (ii) economic performance, and (iii) environmental performance. This paper considers each of these three parameters in a new combined analysis.

In the conditions of the Czech operational power units, several CCT options were considered, such as post-combustion technologies of ammonia scrubbing, activated carbon adsorption (PCC-A), and pre-combustion integrated gasification gas cycle with integrated carbonate loop (IGCC-CaL). These technologies are the subject of intensive research and optimization to achieve their implementation into the operational power units. The decision-making process for the choice of suitable technologies may be significantly influenced by environmental performance consideration via comprehensive methodology. Life cycle assessment (LCA) is one of the best certified methods to create environmental models of the considered systems. LCA allows the comparison of the assessed systems among each other [2].

The ammonia scrubbing process of LCA was already performed [3]. The ammonia scrubbing process increased the impact of fossils depletion and mineral resource depletion in comparison with the power unit (250 MWe) without CCT. That is caused by a large amount of additional energy consumption for ammonia solvent preparation. Moreover, energy efficiency of the power unit decreased from 38% to 27%. The environmental problem occurs with the treatment of ammonia salts, currently considered as non-utilized waste. On the other hand, CO2 was captured in a ratio of 90%.

An LCA for the PCC-A system for Czech conditions was recently published by the authors of this paper [4]. The LCA model in the study considers a functional unit nominal power output of 250 MWe. The paper concludes that adding such technology would increase the energy demand (an additional 1133 MJ for hard coal activation) and fossils depletion. The reason for this is the resource consumption of hard coal (additional fresh carbon 23 kg/h) in the production chain of the activated carbon.

IGCC-CaL was not previously assessed for the Czech conditions from the environmental point of view. However, several studies were made for the IGCC systems integration and its environmental assessment. A summary of the following studies can be found in Table 1.

The extensive study by Singh et al. [5] compares the environmental results for 400 MWe power plants with post, pre, and oxy-fuel combustion capture systems. For pre-combustion systems with IGCC based on Selexol absorption, the CO2 capture ratio is 90%, with an energy efficiency of 37.6%. Comparative LCA was made by hybrid LCA approach, using input–output analysis together with the ReCiPe 2008 version 1.02 method. Environmental results of a pre-combustion system show a reduction of 78% in the category of global warming potential (GWP), the highest reduction in comparison with the aforementioned systems. On the other hand, IGCC system contributes to increase of 120% in category of freshwater eutrophicaton (FE), influenced mainly by infrastructure development.

Cormos C. [6] evaluated the techno-economic and environmental performance of IGCC system for power plant concepts of a net power output of about 400–500 MWe. The study states that the introduction of the CCT system decreased net plan energy efficiency by 7.1–9.5%. The environmental part of the study compared the IGCC systems based on the physical solvent Selexol. Environmental impacts refer to the production of 1 kWh of electricity. However, the impacts were categorized in normalized mass and energy flows where the integration of the IGCC systems caused an increase of coal (25%), oxygen (24%), and cooling water (22%) consumption, and the ratio of captured CO2 was modelled at 90%.

*Energies* **2020**, *13*, 4188

**Table 1.** Review of references focused on

global warming potential (GWP),

environmental

acidification

 potential (AP), fossils depletion (FD),

 assessment of

pre-combustion

 integrated gasification

eutrophication

 potential (EP), terrestrial

 gas cycle technology with CO2 capture

acidification

 (TA), and freshwater

(IGCC-CCT),

In another study [7], three following three systems were compared: (i) a conventional supercritical coal power system, (ii) an IGCC-CCS system based on Selexol solvent system, (iii) and an IGCC without CCS. The systems were chosen according to equal coal consumption rather than equal electricity production. The environmental results for IGCC systems per 1 MWh are based on mass–energy balances and show higher water consumption due to the gasification process and the shift reaction in comparison to the power unit without CCT. The IGCC system with CO2 capture was designed for 81% CO2 capture ratio. The IGCC-CCS system reduced net energy efficiency from 32.1% (IGCC without CCS) to 26.1%.

The work of Petrescu et al. [8] compares IGCC power plant with gross electric output of 570.61 MWe and 2 IGCC-CCS systems. Two compared IGCC-CCS systems are based on Ca-based (IGCC-CaL) sorbents and iron-based oxygen carriers (IGCC-FeL). The used method for LCA analysis was CML 2001 using GaBi software. In both scenarios, the highest values refer to GWP, where the majority (85%) comes from coal mining and extraction. The results show that the highest carbon capture rate happens with IGCC-FeL (99.45%), with a net electrical efficiency drop from 45.09% to 38.76%. Energy efficiency dropped for IGCC-CaL from 45.09% to 36.44%, and the capture rate was 91.56%.

Regarding CaO looping, some studies were done for post-combustion CO2 capture. One study [9] considers 600 MWe supercritical pulverized coal power plant as a basis for CCT. According to the study [9], net energy efficiency drops from 39% to 32% due to CaO looping. LCA analysis was done at the endpoint level via SimaPro v8.3 software. The results indicate an increase in resources depletion, ozone depletion, and toxicities. The climate change impact was reduced by 72%.

Clarens et al. [10] compared a sub-critical coal power plant without CCT (500 MWe; net plant efficiency 36.9%), post-combustion capture technologies based on amine absorption (Econamine and Econamine FG+), and CaO looping without capture. This study used the LCA method of ReCiPe v1.04 and Simapro software. The CaO looping results in the best environmental performance among all systems in the categories of ozone depletion, photochemical ozone formation, particulate matter, and water depletion impacts. The CO2 capture ratio was 90% in each CCT systems. However, the net plant efficiency for the Econamine case dropped from 36.9% to 22.3%, for Econamine FG+ to 25.9%, and for CaO loop to 29.6%.

The literature survey clearly shows that very few environmental assessments were done on the subject of pre-combustion IGCC-CaL and none for comparison of IGCC-CaL and PCC-A. Moreover, the data in the studies is, in the majority, based on literature sources and heat-mass models rather than real case studies. Also, the selected environmental assessment methods were based on mass-energy flows analysis or methods such as hybrid LCA or CML.

The first part of the paper is focused on the environmental study that compares both CCT systems integrated into their reference power plants. The environmental study does not evaluate the reference power plants without CCT due to lack of data for a single IGCC system. Moreover, IGCC-CaL was designed as the one whole technology with an already integrated CCT system. Yet, in the case of PCC-A, a recent study [4] published by the same authors compares the reference 250 MWe power unit and the same reference power unit with PCC-A.

The second part of the paper is the economic study of both systems. The economic part compares the investments of both systems (IGCC-CaL, PCC-A) with the case of the energy system without CCT based on the cost and market trend of CO2 allowances.

The third part of the paper combines environmental and economic results to determinate the specifics that can influence the decision-making process for the final technology selection.

The paper has several contributions:

The paper is structured as follows: Section 2.1 defines the LCA methodology, Section 2.2 defines the economical assessment method, Section 3.1 provides a technical description of case study 1, Section 3.2 provides a technical description of case study 2, Section 3.3 defines the systems boundaries, Section 3.4 describes a life cycle inventory, Section 3.5 defines the cost effectiveness parameters, Section 4.1 presents the results of the life cycle impact assessment. Section 4.2 presents the Pareto analysis, Section 4.3 presents the results for cost effectiveness comparison, and Sections 5 and 6 provide the discussion and conclusions.


3.3.5. Effect of Salinity on *S.* × *hainanensis* Seed Germination

As salinity increased, the germination rate, germination potential, radicle length, and intact rate of each group significantly decreased (Figure 5). The germination rate (82.67%) and the germination potential (75.33%) peaked at 0‰ salinity (Figure 5a,b). The difference was not obvious compared to the germination rate (81.33%) and germination potential (72.67%) at 2.5‰ salinity. However, the radicle growth of the latter was more robust and the radicle perfectness ratio was ideal (100%; Figure 5d); radicle length under low salinity (<7.5‰) almost doubled under high salinity (>10‰) (Figure 5c). The appropriate germination salinity of *S.* × *hainanensis* varied from 0‰ to 7‰.

**Figure 5.** Effects of different salinities on indicators. Different letters indicate significant difference (*p* < 0.05).

#### **4. Discussion**


**11. References**

AF progression is reported to be associated with greater PV diameter (Knackstedt et al., 2003; Tsao et al., 2001) as a consequence of PV remodeling (Scharf et al., 2003). Although the relation of AF and PV contraction remains to be fully investigated, augmentation of **Ar** wave (e.g., PVBV) may cause cyclic stretching of highly compliant myocardial sleeves in PV, repetitive ectopic beats and loss of 'sphincter' function, which underlie further PV regurgitation. Therefore, **D** and **Ar** waves augmentation, PV myocardial sleeve stretching and ectopic beats form a vicious cycle leading to PV remodeling. A possible mechanism by

AF is the most common clinical arrhythmia showing progressive features. There has been evidence to suggest PV as a source of abnormal electrical activities initiating and sustaining AF. Currently, PVF recording is feasible in routine Doppler echocardiography, and is essential for evaluation of LA functioning three roles during an entire cardiac cycle such as 'booster pump', 'reservoir' and 'conduit'. This flow pattern recognition is of clinical importance not only in assessing global cardiac performance but also for obtaining considerable information with respect to the pathophysiology and management of AF. There has been a consensus with respect to the PVF pattern during ongoing AF or in patients with permanent AF. However, there has been a controversy concerning PVF profile during sinus rhythm in patients with paroxysmal AF. This indicates the anatomical and pathophysiological complexities of PV-LA junction and AF itself. Time-dependent recovery of LA contractile function (LA stunning) and neuroanatomical modification of PV-LA junction by radiofrequency catheter ablation make this controversy further complicated. In spite of such controversy and complexity, PVF recording has potential benefits to assess

The authors thank the echocardiographic laboratory staff in Kyushu University Hospital,

Alboni, P.; Scarfò, S.; Fucà, G.; Paparella, N. & Yannacopulu, P. (1995). Hemodynamics of

Armour, JA.; Murphy, DA.; Yuan, BX.; Macdonald, S. & Hopkins, DA. (1997). Gross and

Barbier, P.; Alioto, G. & Guazzi, MD. (1994). Left atrial function and ventricular filling in

*College of Cardiology* Vol. 24, No. 1, (July 1994), pp. 165-170, ISSN 0735-1097

idiopathic paroxysmal atrial fibrillation. *Pacing and Clinical Electrophysiology* Vol. 18.

microscopic anatomy of the human intrinsic cardiac nervous system. *The Anatomical Record* Vol. 247, No. 2, (February 1997), pp. 289-298, ISSN 1932-8494 Atwater, BD.; Wallace, TW.; Kim, HW.; Hranitzky, PM.; Bahnson, TD.; Hegland, DD. &

Daubert, JP. (2011). Pulmonary vein contraction before and after radiofrequency ablation for atrial fibrillation. *Journal of Cardiovasuclar Electrophysiology* Vol. 22, No.

hypertensive patients with paroxysmal atrial fibrillation. *Journal of the American*

which PV characteristics contribute to AF progression is demonstrated in **Fig. 10**.

various stages of long-term AF progression and to manage the AF patients.

No. 5 (Pt 1), (May 1995), pp. 980-985, ISSN 0147-8389

2, (February 2011), pp. 169-174, ISSN 1540-8167

**9. Conclusion**

**10. Acknowledgements**

**11. References**

Fukuoka, Japan for contribution to this work.

Pulmonary Venous Flow Pattern and Atrial Fibrillation: Fact and Controversy 95

Scharf, C.; Sneider, M.; Case, I.; Chugh, A.; Lai, SW.; Pelosi, F.; Knight, BP.; Kazerooni, E.;

Sparks, PB.; Jayaprakash, S.; Mond, HG.; Vohra, JK.; Grigg, LE. & Kalman, JM. (1999). Left

Stavrakis, S.; Madden, G.; Pokharel, D.; Po, SS.; Nakagawa, H.; Jackman, WM. & Sivaram,

Steiner, I.; Hájková, P.; Kvasnička, J. & Kholová, I. (2006). Myocardial sleeves of pulmonary

*Virchows Archiv* Vol. 449, No. 1, (July 2006), pp. 88-95, ISSN 0945-6317 Tabata, T.; Thomas, JD. & Klein, AL. (2003). Pulmonary venous flow by Doppler

*Cardiology* Vol. 41, No. 8, (April 2003), pp. 1243-1250, ISSN 0735-1097 Tagawa, M.; Higuchi, K.; Chinushi, M.; Washizuka, T.; Ushiki, T.; Ishihara, N. & Aizawa, Y.

Vol. 28, No. 7, (August 2011), pp. 775-781, ISSN 1540-8175

2003), pp. 150-155, ISSN 1540-8167

0735-1097

Morady, F. & Oral, H. (2003). Anatomy of the pulmonary veins in patients with atrial fibrillation and effects of segmental ostial ablation analyzed by computed tomography. *Journal of Cardiovascular Electrophysiology* Vol. 14, No. 2, (February

atrial mechanical function after brief duration atrial fibrillation. *Journal of the American College of Cardiology* Vol. 33, No. 2, (February 1999), pp. 342-349, ISSN

CA. (2011). Transesophageal echocardiographic assessment of pulmonary veins and left atrium in patients undergoing atrial fibrillation ablation. *Echocardiography*

veins and atrial fibrillation: a postmortem histopathological study of 100 subjects.

echocardiography: revisited 12 years later. *Journal of the American College of*

(2001). Myocardium extending from the left atrium onto the pulmonary veins: a comparison between subjects with and without atrial fibrillation. *Pacing and Clinical Electrophysiology* Vol. 24, No. 10, (October 2001), pp. 1459-1463, ISSN 0147-8389 Takahara, A.; Sugimoto, T.; Kitamura, T.; Takeda, K.; Tsuneoka, Y.; Namekata, I. & Tanaka,

H. (2011). Electrophysiological and pharmacological characteristics of triggered activity elicited in guinea-pig pulmonary vein myocardium. *Journal of Pharmacological Science* Vol. 115, No. 2, (February 2011), pp. 176-181, ISSN 1347-8613

Satomura, K.; Ishihara, M.; Kurita, A. & Ohsuzu, F. (2004). Pulmonary vein dimensions and variation of branching pattern in patients with paroxysmal atrial fibrillation using magnetic resonance angiography. *Japanese Heart Journal* Vol. 45,

Autonomic innervation and segmental muscular disconnections at the human pulmonary vein-atrial junction: implications for catheter ablation of atrialpulmonary vein junction. *Journal of the American College of Cardiology* Vol. 48, No. 1,

AD.; Aras, D.; Kisacik, HL. & Korkmaz, S. (2007). Coagulation, fibrinolytic system activation and endothelial dysfunction in patients with mitral stenosis and sinus rhythm. *Angiology* Vol. 58, No. 1, (February-March 2007), pp. 85-91, ISSN 0003-3197

Chen, SA. (2001). Pulmonary vein dilatation in patients with atrial fibrillation: detection by magnetic resonance imaging. *Journal of Cardiovascular Electrophysiology*

Takase, B.; Nagata, M.; Matsui, T.; Kihara, T.; Kameyama, A.; Hamabe, A.; Noya, K.;

Tan, AY.; Li, H.; Wachsmann-Hogiu, S.; Chen, LS.; Chen, PS. & Fishbein, MC. (2006).

Topaloglu, S.; Boyaci, A.; Ayaz, S.; Yilmaz, S.; Yanik, O.; Ozdemir, O.; Soylu, M.; Demir,

Tsao, HM.; Yu, WC.; Cheng, HC.; Wu, MH.; Tai, CT.; Lin, WS.; Ding, YA.; Chang, MS. &

No. 1, (January 2004), pp. 81-92, ISSN 0021-4868

Vol. 12, No. 7, (July 2001), pp. 809-813, ISSN 1540-8167

(July 2006), pp. 132-143, ISSN 0735-1097

Kosmala, W.; Przewlocka-Kosmala, M. & Mazurek, W. (2006). Abnormalities of pulmonary

Kwaan, HC.; Sakurai, S. & Wang, J. (2003). Rheological abnormalities and thromboembolic

Lindgren, KS.; Pekka Raatikainen, MJ.; Juhani Airaksinen, KE. & Huikuri, HV. (2003).

Maruyama, T.; Kishikawa, T.; Ito, H.; Kaji, Y.; Sasaki, Y. & Ishihara, Y. (2008). Augmentation

Mattioli, AV.; Castelli, A.; Andria, A. & Mattioli, G. (1998). Clinical and echocardiographic

Mori, M.; Kanzaki, H.; Amaki, M.; Ohara, T.; Hasegawa, T.; Takahama, H.; Hashimura, K.;

*Journal of Cardiology* Vol. 57, No. 1, (January 2011), pp. 89-94, ISSN 0914-5087 Nathan, H. & Eliakim, M. (1966). The junction between the left atrium and the pulmonary

Ogawa, K.; Hozumi, T.; Sugioka, K.; Iwata, S.; Otsuka, R.; Takagi, Y.; Yoshitani, H.;

Ren, JF.; Marchlinski, FE.; Callans, DJ. & Zado, ES. (2002). Intracardiac Doppler

Ren, JF.; Marchlinski, FE. & Callans, DJ. (2004). Effect of heart rate and isoproterenol on

8, No. 1, (January-February 1995), pp. 61-69, ISSN 0894-7317

(February 2006), pp.102-106, ISSN 1099-5129

529-534, ISSN 0094-6176

23, ISSN 1099-5129

1371, ISSN 0002-9149

63-69, ISSN 0894-7317

1966), pp. 412-422, ISSN 0009-7322

(November 2002), pp. 1076-1081, ISSN 1540-8167

(June 2004), pp. 265-269, ISSN 1383-875X

venous flow in patients with lone atrial fibrillation. *Europace* Vol. 8, No. 2,

complications in heart disease: spontaneous echo contrast and red cell aggregation (Review). *Seminars in Thrombosis and Hemostasis* Vol. 29, No. 5, (October 2003), pp.

Relationship between the frequency of paroxysmal episodes of atrial fibrillation and pulmonary venous flow pattern. *Europace* Vol. 5, No. 1, (January 2003), pp. 17-

of pulmonary vein backflow velocity during left atrial contraction: a novel phenomenon responsible for progression of atrial fibrillation in hypertensive patients. *Cardiology* Vol. 109, No. 1, (January 2008), pp. 33-40, ISSN 0008-6312 Masuyama, T.; Nagano, R.; Nariyama, K.; Lee, JM.; Yamamoto, K.; Naito, J.; Mano, T.; Kondo,

H.; Hori, M. & Kamada, T. (1995). Transthoracic Doppler echocardiographic measurements of pulmonary venous flow velocity patterns: comparison with transesophageal measurements. *Journal of the American Society of Echocardiography* Vol.

features influencing recovery of atrial function after cardioversion of atrial fibrillation. *Amerian Journal of Cardiology* Vol. 82, No. 11, (December 1998), pp. 1368-

Konno, T.; Hayashi, K.; Yamagishi, M. & Kitakaze, M. (2011). Impact of reduced left atrial functions on diagnosis of paroxysmal atrial fibrillation: results from analysis of time-left atrial volume curve determined by two-dimensional speckle tracking.

veins: an anatomic study of human hearts. *Circulation* Vol. 34, No. 3, (September

Yoshiyama, M. & Yoshikawa, J. (2009). Automated assessment of left atrial function from time-left atrial volume curves using a novel speckle tracking imaging method. *Journal of the American Society of Echocardiography* Vol. 22, No. 1, (January 2009), pp.

echocardiographic quantification of pulmonary vein flow velocity: an effective technique for monitoring pulmonary vein ostia narrowing during focal atrial fibrillation ablation. *Journal of Cardiovascular Electrophysiology* Vol. 13, No. 11,

pulmonary vein flow velocity following radiofrequency ablation: a Doppler color flow imaging study. *Journal of Interventional Cardiac Electrophysiology* Vol. 10, No. 3,

**7**

*USA*

**Phosphodiesterase-5 Inhibitors Improve Left**

Impaired systolic performance and/or diastolic function have long been detrimental consequences of acute myocardial infarction (AMI), which remains a major cause of morbidity and mortality worldwide. Despite considerable therapeutic improvements, left ventricular dysfunction secondary to infarction continues to pose serious health complications, including heart failure (HF), wherein the heart is unable to maintain a cardiac output appropriate for the requirements of the body. Several factors contribute to HF, including adverse ventricular remodeling, progressive hypertrophy and sustained cell death by apoptosis. Therefore, the search for a therapeutic strategy to overcome or mitigate the

Sildenafil citrate (ViagraTM) is the first PDE-5 inhibitor approved for treatment of erectile dysfunction. The discovery of this drug in 1989 was the result of extensive research on chemical agents that hold potential promise in the treatment of coronary heart disease. Initial clinical studies on sildenafil in the early 1990s were not promising with respect to its anti-anginal potential. However, a remarkable side effect was reported by a number of volunteers participating in these investigations; sildenafil seemed to enhance penile erections, which soon thereafter became the main focus of further studies. More than 10 million men worldwide have been treated with sildenafil since its market debut in 1998. Sildenafil is highly specific for PDE-5 inhibition with relatively minor cross-reactivity with PDE-6 (Laties & Fraunfelder, 1999). It has a chemical structure similar to cGMP and inhibits PDE-5 by binding to the cGMP-catalytic sites (Corbin & Francis, 2002) thereby allowing the accumulation of cGMP in the erectile tissue. Two additional agents in this class (vardenafil [LevitraTM] (Porst et al., 2001) and tadalafil [CialisTM]) have also been developed and approved by the FDA for treatment of erectile dysfunction and recently sildenafil and tadalafil were approved for treatment of pulmonary arterial hypertension (PAH) (Corbin & Francis, 2002). PDE-5 inhibitors are structurally similar to cGMP and therefore compete with cGMP for binding to PDE-5 at the catalytic site (reviewed in Kukreja et al., 2005). Interestingly, PDE expression has been reported to change in pathologic conditions. For instance, in patients with cardiovascular disease or diabetes, nitric oxide (NO) levels are

**1. Introduction**

**1.1 PDE-5 Inhibitors**

progression of HF is of paramount importance.

**Ventricular Function in Failing Hearts**

Fadi N. Salloum and Rakesh C. Kukreja

*Virginia Commonwealth University Medical Center Richmond, Virginia*


**1. Introduction**

[96] Yang Y, Sun X. W, Tay B K, You G F, Tan S T,Teo KL. (2008), A p-n homojunctionZ‐ nOnanorod light-emitting diode formedby As ion implantation. Appl.Phys.Lett.

[97] Sun X W, Ling B, Zhao J L,Tan ST, Yang Y, Shen YQ, Dong Z L, Li CX. Ultraviolet emission from a ZnO rod homojunction light-emitting diode. Appl.Phys.Lett.2009;

[98] LimJ H, Kang C K, Kim K K, ParkI K, Hwang D K, Park S J. UV electroluminescence emission from ZnOlight-emittingdiodes grown by high-temperature radio frequency

[99] Fang X, Li JH, Zhao D X, Shen DZ, Li B H, Wang X H. Phosphorus-doped p-type ZnOnanorods and ZnOnanorod p-n homojunction LED fabricated by hydrothermal

[100] Ryu Y R, Lee T-S, Lubguban J A, White H W, Kim BJ, Park YS, Youn C J. (2006), Next generation of oxide photonic devices: ZnO-based ultraviolet lightemitting diodes.

2008;93,253107 (3pages).

58 Optoelectronics - Advanced Materials and Devices

sputtering.Adv.Mater.2006; 18, 2720–2724.

Appl.Phys.Lett. 2006; 88, 241108 (3pages).

method. J. Phys. Chem. C2009; 113, 21208–21212.

95,133124 (3pages).

Photonics puts at stake a wide variety of applications, from applied fields of physics, such as ultrafast all-optical signal processing [1] or pollutant monitoring [2], to more fundamental ones, *e.g.* quantum information [3], and its convergence with electronics at chip-scale level is one of today's great scientific and technological challenges. As a consequence, the full inte‐ gration of optoelectronics devices on existing developed platforms is expected to be the next technological leap, with major breakthroughs in telecommunications, industry and health. While the main building blocks of optical integrated circuitry have been reported in the standard SOI platform [4], coherent light sources still markedly lack to achieve this transi‐ tion of paradigm. To date, the hybrid conjunction of silicon photonics and direct-gap III-V compounds appears to be one of the most promising key technologies towards large-scale photonic integration and scalability [5]. In particular, such photonics platform could capital‐ ize advanced functionalities enabled by guided-wave quadratic nonlinear optics. Thus, the demonstration of the electrically pumped versions of an optical parametric oscillator (OPO) or of a telecom twin-photon source (TTPS) would have a great impact on applications re‐ quiring room-temperature operation and wide tunability.

In this context, the Aluminum Gallium Arsenide (AlGaAs) system is an ideal candidate for the nonlinear photonic design, because of its numerous advantages: high second order susceptibil‐ ity, wide transparency window, good thermal conductivity and monolithic integration.

In order to design efficient frequency converters, the key issue is to keep a constant phase rela‐ tion between the three interacting waves. In general, this is not a trivial task because of the phase-velocity mismatch induced by the material chromatic dispersion [6]. While few phase-

© 2013 Savanier 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, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Savanier et al.; licensee InTech. This is a paper 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.

matching strategies have been investigated, an original modal phase-matching scheme based on Bragg reflector waveguides has been recently addressed, reviving the interest for spontane‐ ous parametric down-conversion (SPDC) in AlGaAs-based waveguides [7,8].

off-the-shelf products, whereas QCLs, thanks to a mature and possibly Sb-free technology, are now finding commercial applications and increasingly replace the outdated lead-salt la‐ ser diodes. Nevertheless, integrated semiconductor sources are still lacking around 3 μm, and apart from few QCL products (*e.g.* λ~3.3 μm by Daylight Solutions), solid state and non‐ linear optics-based sources represent the majority of commercially available sources [17]. Recently, intra-cavity second harmonic generation (SHG) has been reported in QCLs, ex‐ tending their emission range to wavelengths as small as 2.7 μm, at the price of poor conver‐

Technological Challenges for Efficient AlGaAs Nonlinear Sources on Chip

http://dx.doi.org/10.5772/52201

61

Nonlinear optics, by means of difference frequency generation (DFG) and optical parametric oscillation, is a well-known alternative to cover the whole 1-10 μm span. The wide variety of spectral/temporal formats allowed by nonlinear χ(2) processes in transparent materials, en‐ dows parametric sources with a high level of flexibility. Moreover, SPDC is currently the most widely used process to generate quantum photon pairs, which have become one of the building blocks of quantum information. To date, room-temperature SPDC has been report‐ ed in passive AlGaAs waveguides designed to perform 0.775-to-1.55 μm down-conversion [7,8], while entanglement has been demonstrated in light emitting diodes only at cryogenic temperature [19]. Thus, the fabrication of an electrically-pumped version of such light source operating at room temperature in the telecom range also constitutes a high-potential

Fulfilling the phase-matching condition is crucial for efficient three-wave mixing. The classi‐ cal approach to cancel out the phase-velocity mismatch between the interacting waves is to rely on the birefringence of the nonlinear medium. The limited choice of suitable materials led to quasi-phase matching (QPM), well established in ferroelectric crystals, with a great impact on the fabrication of infrared parametric sources. QPM consists in a periodic inver‐ sion of nonlinearity along the propagation direction, minimizing the phase-mismatch to al‐ low the nonlinear interaction to build constructively. In this context, the development of bulk dielectric crystals like periodically-poled LiNbO3 (PPLN) has made them the work‐ horse materials of χ(2) optics. Besides, by implementing a guided-wave configuration in which the three optical modes are confined and can interact over several centimeters, nor‐ malized conversion efficiencies up to ~150 %W-1cm-2 have been demonstrated [20], yielding to the demonstration of compact and efficient photon pairs sources [21] and OPOs [22]. Nonetheless, such setups are composed of discrete optical components with critical align‐ ment and do not lend themselves to optoelectronic integration. That is why direct-gap semi‐ conductor compounds, provided that they have significant second-order nonlinearity, are an attractive platform for the coming years' photonics, thanks to mature nano-fabrication technology. Indeed they promise on-chip integration of both efficient frequency converters and laser pumps. Gallium arsenide (GaAs), or more generally the AlGaAs system, is partic‐ ularly interesting because it exhibits a huge second-order nonlinearity (d14~100 pm/V), a broad transparency window (from 0.9 to 17 μm), and a large variety of design and fabrica‐ tion solutions [23]. Because AlGaAs is neither birefringent nor ferroelectric, phase matching

sion efficiency though [18].

and challenging goal.

**2.2. Integration of nonlinear devices**

In this chapter we will focus on AlGaAs-based nonlinear waveguides in which phase-match‐ ing is achieved through form birefringence, artificially induced in optical heterostructures by selective oxidation of Al-rich layers into Aluminum Oxide (referred to as AlOx thereafter). De‐ spite recent technological improvement and promising performances for frequency conver‐ sion in the near [9] and mid-infrared regions [10], neither the OPO nor the TTPS has been demonstrated yet on chip, because of technological issues, mainly excessive propagation loss‐ es and absence of appropriate built-in cavity. In the second section we present the scientific con‐ text of this work, focusing on AlGaAs integrated nonlinear devices exploiting the so-called form birefringence phase-matching scheme. Section three is devoted to the design procedure and the optimization of the fabrication process of two types of partially oxidized waveguides, while their experimental performances are summarized in section four. A comprehensive study of the different loss mechanisms involved is presented in section five, and the design and fabrication of built-in cavity mirrors is described in the sixth section.


Introductory Chapter: The Fundamentals of Biometrics – A Comprehensive Exploration

*Carlos M. Travieso-González*

## **1. Introduction**

#### **1.1 Unveiling the future for biometrics**

In the age of rapid technological advancement, the quest for secure and convenient identity authentication methods has never been more pressing. Traditional forms of authentication, such as passwords and PINs, are increasingly vulnerable to breaches, leaving individuals and organizations alike vulnerable to identity theft, fraud, and unauthorized access. In response to these challenges, biometrics has emerged as a promising solution, offering a unique fusion of security, convenience, and reliability [1]. From fingerprint scanners and facial recognition systems to iris scanners and voice authentication technologies, biometric systems leverage the distinct physiological and behavioral characteristics of individuals to verify their identities with unparalleled accuracy.

The term "biometrics" derives from the Greek words "bio" (life) and "metrics" (measurement), reflecting its focus on the measurement and analysis of biological traits. Unlike traditional forms of authentication, which rely on knowledge (e.g., passwords) or possession (e.g., ID cards), biometrics authenticates individuals based on who they are, leveraging unique physical or behavioral attributes that are inherently difficult to forge or replicate [2]. This paradigm shift has profound implications for a wide range of applications, from securing access to personal devices and financial accounts to enhancing border security and safeguarding critical infrastructure.

#### **1.2 Biometrics modalities and its applications**

At the heart of biometric technology lies the recognition and analysis of biometric identifiers, which can be broadly classified into two categories: physiological and behavioral [3]. Physiological biometrics are based on anatomical or physiological characteristics of individuals, such as fingerprints, iris patterns, facial features, and DNA profiles. These traits are inherently unique to each individual and remain relatively stable over time, making them ideal for reliable identity authentication. In contrast, behavioral biometrics are based on patterns of behavior or actions exhibited by individuals, such as typing rhythms, gait patterns, facial information [4–6], handwriting [7, 8], and voice characteristics. While behavioral biometrics may exhibit

#### *Biometrics and Cryptography*

greater variability than physiological traits, they offer additional layers of security and can be less intrusive in certain applications.

The roots of biometrics can be traced back to ancient civilizations, where methods such as fingerprints and seals were used to authenticate individuals and documents. However, it was not until the advent of modern computing and digital imaging technologies that biometrics began to realize its full potential. The development of automated fingerprint identification systems (AFIS) in the 1970s marked a significant milestone, paving the way for the widespread adoption of biometric authentication in law enforcement and forensic applications. Since then, rapid advancements in sensor technology, machine learning algorithms, and data processing capabilities have fueled the proliferation of biometric systems across diverse domains.

One of the key strengths of biometrics lies in its ability to provide strong authentication while enhancing user convenience and experience [9]. Unlike passwords or PINs, which can be forgotten, stolen, or shared, biometric traits are inherently tied to the individual and cannot be easily compromised. This inherent convenience has fueled the integration of biometric authentication into a wide range of consumer devices, from smartphones and tablets to laptops and wearables. Today, millions of users around the world rely on biometrics to unlock their devices, authorize transactions, and access digital services with a simple touch, glance, or voice command.

Moreover, biometrics offers a compelling solution to the growing challenge of identity fraud and cybercrime. With traditional authentication methods proving increasingly vulnerable to sophisticated attacks, organizations are turning to biometric technologies to bolster their security posture and protect sensitive data and assets. Biometric authentication not only provides a higher level of assurance than traditional methods but also offers greater scalability and usability, enabling organizations to strike a balance between security and user experience.

However, the widespread adoption of biometrics also raises important questions and concerns regarding privacy, security, and ethical implications. As biometric data becomes increasingly pervasive and interconnected, issues such as data protection, consent, and surveillance have come to the forefront of public discourse. The collection, storage, and processing of biometric data raise unique challenges and risks, requiring careful consideration of legal and regulatory frameworks to safeguard individual rights and mitigate potential abuses.

In this comprehensive exploration of biometrics, we embark on a journey to unravel the intricacies of this transformative technology and its implications for society. Drawing upon insights from diverse disciplines, including computer science, engineering, psychology, law, and ethics, we delve into the evolution, applications, and implications of biometrics in the digital age. Through a synthesis of theoretical perspectives, empirical research, and real-world case studies, we seek to deepen our understanding of the promises and perils of biometric authentication and its role in shaping the future of identity management and security [10].

As we navigate the complex landscape of biometrics, we are reminded of the words of science fiction author Arthur C. Clarke, who famously remarked, "Any sufficiently advanced technology is indistinguishable from magic." Indeed, in the realm of biometrics, the boundaries between science and science fiction blur as we unlock the potential of human biology to transform the way we authenticate, interact, and connect in an increasingly digital world. Join us on this captivating journey into the realm of biometrics, where the future meets the present, and the possibilities are limited only by our imagination.

*Introductory Chapter: The Fundamentals of Biometrics – A Comprehensive Exploration DOI: http://dx.doi.org/10.5772/intechopen.114320*

## **Author details**

Carlos M. Travieso-González

Department of Signals and Communication, Institute for Technological Development and Innovation in Communications, University of Las Palmas, Gran Canaria, Spain

\*Address all correspondence to<EMAIL_ADDRESS>
© 2024 The Author(s). Licensee IntechOpen. 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.

## **References**

[1] Jain AK, Ross A, Nandakumar K. Introduction to Biometrics. Springer; 2016

[2] Wayman JL, Jain AK. Biometric Systems: Technology, Design, and Performance Evaluation. Springer Science & Business Media; 2005

[3] Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of Fingerprint Recognition. Springer Science & Business Media; 2009

[4] Li SZ, Jain AK. Handbook of Face Recognition. Springer Science & Business Media; 2005

[5] Almeida V, Dutta MK, Travieso CM, Singh A, Alonso JB. Automatic age detection based on facial images. In: 2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS). Mathura, India; 2016. pp. 110-114. DOI: 10.1109/ CCIntelS.2016.7878211

[6] Briceño JC, Travieso CM, Alonso JB, Ferrer MA. Robust identification of persons by lips contour using shape transformation. In: 2010 IEEE 14th International Conference on Intelligent Engineering Systems. Las Palmas, Spain; 2010. pp. 203-207. DOI: 10.1109/ INES.2010.5483848

[7] Kutzner T, Travieso CM, Bönninger I, Alonso JB, Vásquez JL. Writer identification on mobile device based on handwritten. In: 2013 47th International Carnahan Conference on Security Technology (ICCST). Medellin, Colombia; 2013. pp. 1-5. DOI: 10.1109/ CCST.2013.6922063

[8] Vargas JF, Travieso CM, Alonso JB, Ferrer MA. Off-line signature verification based on gray level information using wavelet transform and texture features. In: 2010 12th International Conference on Frontiers in Handwriting Recognition. Kolkata, India; 2010. pp. 587-592. DOI: 10.1109/ICFHR.2010.96

[9] Vásquez JL, Ravelo-García AG, Alonso JB, et al. Writer identification approach by holistic graphometric features using off-line handwritten words. Neural Computing and Applications. 2020;**32**:15733-15746. DOI: 10.1007/s00521-018-3461-x

[10] Ratha NK, Connell JH, Bolle RM. Enhancing security and privacy in biometrics-based authentication systems. IBM Systems Journal. 2001;**40**(3):614-634

## **Chapter 6**


**2.1 General block diagram**

A simple block diagram of a standalone distributed (hybrid) power system is shown in Fig. 1. The sources of electric power in this system consist of a DEG, a battery bank, a WTG, and a PV array. The output of the diesel generator is regulated AC voltage, which supplies the load directly through the main distribution transformer. The connection of the battery bank, the WTG, and the PV array are through a DC bus. The control unit regulates the flow of power to and/or from the sources, depending on the load. The load in the hybrid power system can be an AC load, a DC load, a heating load (resistive load bank), or a hybrid load.

electricity in remote Alaskan communities and other remote regions (Denali Commission, 2003), (Drouhillet & Shirazi, 1997), (Dawson & Dewan, 1989), (Wies, et al., 2005a), (Wies, et al., 2005b), (Wies, et al., 2005c), & (Borowy, 1996). Besides reducing fuel consumption, the use of renewable energy sources has been shown to increase system efficiency and reliability, while reducing emissions (Drouhillet & Shirazi, 1997), (Dawson & Dewan, 1989), (Wies, et al., 2005a), (Wies, et al., 2005b), (Wies, et al., 2005c), & (Borowy, 1996). It has been predicted that by the year 2050, despite the increase in the demand for electric power, the global CO2 level which is the major greenhouse gas would be reduced to 75% of its 1985 level due to the increase in the

This remainder of this chapter presents an economic and environmental model for standalone fossil fuel based micro-grid systems employing renewable energy sources based on an existing diesel-electric power generation systems in remote arctic communities. A simulator called the Hybrid Arctic Remote Power Simulator (HARPSim) was developed using MATLAB Simulink to estimate the reduction in fuel consumption of DEGs and the minimization in the cost of producing electricity in remote locations by integrating solar PV and WTGs into the system. HARPSim is used to predict the long-term economic and environmental performance of the system with and without the use of renewable sources in combination with the diesel electric power generation system. A battery bank is also included in the system to serve as a backup and a buffer/storage interface between the

The economic part of the model calculates the fuel consumed, the kilowatt-hours (kWhrs) obtained per liter (gallon) of fuel supplied, and the total cost of fuel. The environmental part of the model calculates the CO2, particulate matter (PM), and the NOx emitted to the atmosphere. The Life Cycle Cost (LCC), net present value (NPV), efficiency, and air emissions results of the Simulink model are compared with those predicted by the Hybrid Optimization Model for Electric Renewables (HOMER) software developed at the National Renewable Energy Laboratory (NREL) (NREL HOMER, 2007). A sensitivity analysis of fuel cost and investment rate on the COE is also performed to illustrate the impact of rising fuel

Distributed generation systems like the one described here are currently used in many parts of the world. While this work focuses on modeling a distributed electric power generation system for the remote arctic community in Alaska, the general model can be applied to any distributed generation system containing these components, but can also be extended to

A simple block diagram of a standalone distributed (hybrid) power system is shown in Fig. 1. The sources of electric power in this system consist of a DEG, a battery bank, a WTG, and a PV array. The output of the diesel generator is regulated AC voltage, which supplies the load directly through the main distribution transformer. The connection of the battery bank, the WTG, and the PV array are through a DC bus. The control unit regulates the flow of power to and/or from the sources, depending on the load. The load in the hybrid power system can be an AC load, a DC load, a heating load (resistive load bank), or a hybrid load.

use of renewable energy sources for energy production (Johansson, et al., 1993).

DEGs and the variable sources of power from solar PV and wind.

costs on the long-term system economics.

**2. Distributed generation system**

include other energy technologies.

**2.1 General block diagram**

Fig. 1. General distributed (hybrid) power generation system.


**7. Presentation**

Because of its origin in the posterior fossa, the presenting symptoms of medulloblastoma are often vague complaints and understandably the diagnosis may be delayed. Presentation depends on various elements of the tumor subject to location, size, duration, compression on the surrounding structures. As these arise from the vicinity of cerebellum and brainstem, often the first feature to appear is instability of gait. Being a midline posterior fossa lesion, trunkal ataxia appears first and appendicular ataxia gradually ensues as the tumor grows bigger to compress the cerebellar hemispheres, and other common cerebellar signs follow with time. When the tumor is big enough to compress the brainstem, long tract signs begin to appear and add more difficulty in movement of the patients. As the tumor grows further, especially downwards, the lower cranial nerves start to get involved and lower cranial nerve palsies manifest. If the tumor grows bigger to occupy and block the Aqueduct of Sylvius, hydrocephalus ensues. Hydrocephalus may also result from blockage of the fourth ventricular outlets, by compression, by the growing tumor, individually or in combination. Hydrocephalus in turn may lead to features of raised Intracranial Pressure (ICP) resulting in headache, nausea, vomiting, irritability, lethargy, behavior alteration, personality change and impaired memory or attention, etc. Raised ICP occasionally gives rise to possibility of having seizure and 6th nerve palsy. Respiratory and cardiac manifestations may be evident, resulting from compromise of the respiratory and cardiac centers in the brainstem. Alteration of level of consciousness, starting from disorientation to deep coma, may result either from raised ICP or from compression on the brainstem. Papilledema or even visual impairment from raised ICP is not very uncommon, especially when presented in late stage.

of tumor removal, detection of residual and recurrent tumor, and tumor deposits in the CSF pathways as well as convenient follow-up studies [4, 15, 42]. Medulloblastomas appear typically hyperdense and sharply demarcated lesion near the fourth ventricle in the plain CT scan with a surrounding hypodense zone of edema which show better delineation with contrast enhancement showing moderate to marked increase in density (**Figure 2**). Cystic components may also be seen. Leptomeningeal tumor deposits appear as areas of increased density in the subarachnoid space. Calcification may be seen in about 10–20% of medulloblastomas [5, 62].

Medulloblastoma

145

http://dx.doi.org/10.5772/intechopen.76783

The relationship between the tumor and the surrounding brain structures can be vividly demonstrated in MRI with and without gadolinium. Screening MRI of the whole spinal axis is capable of displaying and evaluating tumor dissemination, when present and pre-operative

**Figure 2.** Plain axial CT scan (A) showing hyperdense and sharply demarcated lesion near the fourth ventricle with surrounding hypodense area of edema which with contrast enhancement (B) is well demarcated by increased density.

Different sequences of MRI can provide different information to help in diagnosis and treatment planning. Medulloblastomas are hypointense to gray matter on T1-weighted imaging (T1WI) with heterogeneous gadolinium enhancement in 90% of cases, while they are generally iso to hyperintense to gray matter on T2-weighted imaging (T2WI). The heterogeneity in T1WI and T2WI results from cyst formation, calcification or necrosis. Diffusion-weighted imaging (DWI) shows restricted diffusion and in fluid-attenuated inversion recovery (FLAIR) imaging, medulloblastomas are generally hyperintense to surrounding brain (**Figure 3**). MR spectroscopy (MRS) shows elevated choline peaks and decreased creatine and N-acetyl

acetate peaks, with occasional elevation in lactic acid and lipid peaks [62].

**9.2. Magnetic resonance imaging (MRI)**

whole spine MRI is preferred [20, 21].

Medulloblastoma is a very rapidly growing tumor and tends to follow a rapid progression in a very short period of time. The median time between onset of symptom until diagnosis (symptom interval) is 3.3 months (65 days) [65]. Rarely, patients may have symptoms for up to 6 months before diagnosis and younger patients have significantly longer interval to diagnosis while more aggressive subgroups of medulloblastoma have a shorter pre-diagnostic interval [66].


*Article* **Optimization of a Bikeway Network with Selective Nodes**

**C. S. Shui 1,\* and W. L. Chan 2**

Received: 9 October 2019; Accepted: 19 November 2019; Published: 20 November 2019

**Abstract:** Setting up a bikeway network has been recognized as one of the most effective measures to motivate cycling. In fact, a highly connected, exclusive bikeway network that covers all demand sources can be an attractive and time-saving measure, but it requires very high setup costs. The planner often needs to have a trade-off between demand coverage and travel time under a given construction cost. This paper introduces a novel bikeway design problem which determines an optimal bikeway network that covers all potential cycling demand sources with minimal total travel time and under budget constraints. In the context of designing a bike sharing system, the resultant node set of the bikeway network can be interpreted as the locations of the shared bike stations which can cover all cycling demands. A two-stage solution method, by combining the genetic algorithm and a novel elimination heuristic, is proposed to solve the problem by firstly determining the subset of nodes (selected nodes) that can cover all the demand sources and then designing the bikeway network that connects all selected nodes within a given budget. Numerical studies illustrate the advantages of elimination heuristics in solving the proposed problem and the effect of the budget towards the solution fitness with or without a solution. Case studies of two proposed new towns in Hong Kong are provided to illustrate the applicability and effectiveness of the method in bikeway design. This optimization model can be applied to bike-sharing system design problems which aims to cover all demand sources by providing bike stations that are also well connected with exclusive bikeways subject to budget constraints.

**Keywords:** bikeway network design; selective nodes; elimination heuristic; demand coverage

#### **1. Introduction**

Cycling has been receiving attention in the past decade due to its associated benefits, including improving public health, reducing greenhouse gas emissions, alleviating traffic congestion, and increasing the catchment area of public transportation [1]. It is competitive with cars for short trips in urban regions, as it has a lower total travel cost and sometimes a shorter travel time as well [2]. These benefits have motivated a worldwide development of bicycle networks and public bike-sharing systems (PBSs) [3]. At present, there are more than 1,500 PBSs with more than 17 million bicycles in operation [4].

The provision of a connected bikeway network has been proven to be one of the main measures to motivate cycling because it greatly improves cyclists' safety (e.g., references [1,5]). Buehler and Pucher [6] revealed three critical elements that a bikeway network can use to increase cycling levels, including (1) separation with the roadway traffic, (2) high continuity and connectivity, and (3) high bikeway density. These elements can also be internalized in other complex measures such as a bicycle level of service (BLOS), bicycle compatibility index, or level of traffic stress. In other words, a bikeway network that can motivate cycling should be a dense network of exclusive and continuous bikeways, in which the cyclists can ride safely and encounter fewer detours. It is noted that these elements are also implicitly covered in the existing bikeway network design models. The bikeways (and the intersections) are required to meet a predefined level of service, and the trip lengths for the origin-destination pairs should be minimized or below a predefined upper bound ([2,7]). Though the setup of exclusive bikeways offers a safer cycling environment, its adverse impacts on the existing traffic (e.g., reducing the roadway capacity or on-street parking spaces) should be addressed in order to achieve a balance between the performance of the bikeway and the roadway networks. This introduces bikeway network design problems involving a co-existing roadway network (e.g., references [8,9]).

In addition to these elements, the bikeway network design needs to consider the trade-off between the travel time between every origin-destination (O-D) pair and the bikeway construction costs. By increasing the bikeway density, the travel times between the O-D pairs can be reduced, and more cycling activities can thus be motivated ([2,10]). However, higher bikeway density incurs a higher bikeway setup cost. Since the setup costs for a bikeway network are often limited, there is a question on how a bikeway design that minimizes total travel times for all cyclists can be achieved under the budget constraint. However, previous bikeway design studies have not addressed this question and only focus on either cost minimization or travel time minimization (e.g., references [2,7,9]). On the other hand, a bikeway network needs to handle its service coverage. A common approach is to assume an aggregated demand at each potential bike station, while the objective of the bikeway network design is to connect all potential stations with minimal costs in order to cover all demands. However, this approach neglects the fact that the demand points (e.g., transport interchanges, tourist spots, and schools) are not located exactly at but instead close to all potential nodes: as long as there is one station proximate to a demand point, the bikeway network can cover that demand point. In other words, it is sufficient to cover all demands by selecting a set of nodes among all potential nodes (this set of nodes can be named as a selected node set), and the bikeway layout that connects these selected nodes must have a *lower* construction cost than that connects all potential nodes. This concept of demand coverage is modeled in bike station location design (e.g., reference [11]) but never in bikeway network design, which is used to determine the selected node set and connect these nodes by using bikeways. This study, therefore, proposes a new bikeway design problem that minimizes the total system travel time between the selected nodes under both the budget and demand coverage constraints.

As the above novel problem involves two types of discrete decisions (i.e., locations of the selected nodes and built bikeways), this study prefers to develop a two-stage heuristic to solve the proposed problem. The first stage node selection is solved by the classic Genetic Algorithm, which has proved to be powerful in solving many combinatorial problems (e.g., reference [12]). Meanwhile, this stage can also be solved by other meta-heuristics (e.g., a tabu search and large neighborhood search). Nevertheless, no existing heuristic is applicable to solve the second-stage bikeway construction. As a congestion-free transport mode, the travel time of the bikeway is always assumed to be flow-independent and constant (e.g., reference [7]), and therefore the optimal route for all cyclists of each O-D pair is equivalent to the lowest cost route. Nevertheless, this problem is not equivalent to the shortest path problem because of the budget constraint. The overall bikeway construction cost is limited and sometimes needs to be minimized, which is similar to the minimum spanning tree problem or Steiner tree problem. Table 1 compares the common shortest path problems and minimum spanning tree problems with respect to the number of origins and destinations considered in these problems and lists out their design objectives, and the common heuristics used to solve these problems (i.e., references [13–18]). Regarding the problem type, "1", "all", and "some" represent the number of nodes chosen to be the origins/ destinations, in which "some" indicates that a subset of nodes among all nodes are selected to be the origins/ destinations (which cannot be "one" or "all"). It is noted that unselected nodes can be included in the "selective" case if they can reduce the total cost and maintain the connectivity of the selected nodes. The design objectives can be classified into two classes, cost (weight) minimization, and travel time minimization. The former aims to determine a network that connects the nodes with minimal

weight, and the latter aims for a set of minimal cost routes between the origin(s) and the destination(s). In other words, the former guarantees a minimal overall construction cost while the latter provides minimal travel costs between the O-D pairs, but none of them can provide an intermediate solution, which is a network with minimal total travel time under a given budget, especially in the case with a selected set of nodes. In addition, the studied problem should be distinguished from the Generalized Minimum Spanning Tree (G-MST) problem [19] which aims to connect part of the nodes in the network with minimum construction cost. In G-MST, each node has been firstly allocated to a group, and the resultant network requires that all groups should be connected, which implies that at least one node in each group is required to be connected. However, this study does not have any predefined group as not all nodes are selected, so the requirement that at least one element in each group cannot be held. As the studied problem differs from the above-discussed problems significantly, the heuristics used in those problems cannot be applied directly and thus a novel heuristic, namely the elimination heuristic, is constructed to solve the second stage problem. Moreover, as shown in Table 1, both our proposed problem and solution method are novel to the literature while it can be applied in other network design problems.

**Table 1.** Comparisons of existing shortest path and minimum spanning tree problems.

To summarize, the main contributions of this study include:

The outline of this paper is listed below. Section 2 presents the problem descriptions and Section 3 describes the solution method. Section 4 provides the numerical results and Section 5 provides a real case example. A conclusion is given in Section 6.

#### **2. Problem Descriptions**

The design problem is described below. We firstly consider a potential bikeway network *G*(*V*, *E*) with potential bike station location (i.e., node) set *V*, potential bikeway set *E*, and demand point set *D*. The total demand traveling from point *k* to point *l* is denoted as *dkl*, where *k*, *l* ∈ *D*, and each bikeway that directly connects stations *i* and *j*, i.e., *eij* ∈ *E*, has the setup cost *cij* where *i*, *j* ∈ *V*. The travel time *tij* between the nodes can be different with respect to the travel direction to consider the case that the bikeway is built on a slope. In other words, the downhill travel time can be greatly reduced compared to the uphill travel time. Each demand point *k* ∈ *D* is considered as "covered" when there is at least

one proximate bike station for convenient bicycle pick up/ drop off (which should be less than 500 m, as revealed by reference [20]). This defines *Mk* as the set of potential bike stations which are proximate to demand point *k*, where *Mk* ⊂ *V*. It is also assumed that every demand point is covered by at least one potential bike station (i.e., ∄*Mk* = ∅, ∀*k* ∈ *D*). The budget for the total bikeway construction is set to be *B*. For simplicity, all potential bike stations are assumed to have unlimited capacity.

This design problem involves two types of design decisions. The first type is to determine whether a node is picked as a selected node, denoted by a binary decision variable *Xi* which equals 1 if node *i* becomes a selected node and 0 otherwise. A selected node is defined as a node that covers at least one demand source and is connected by at least one bikeway. To separate the selected nodes from the unselected nodes, all selected nodes are put into a new set *S*, where *S* ⊂ *V*. The second type is the binary decision variable for opening a bikeway *Yij*, which equals 1 if the bikeway that connects nodes *i* and *j* is constructed and 0 otherwise. An additional note is that we have assumed that every built bikeway has sufficient capacity to accommodate all bike flows and thus the travel time can be a flow-independent constant. In other words, if the bikeway travel time is flow-dependent, a possible extension is to include the third type of decision variables, flow on each bikeway, in the design problem.

This problem involves determining the bikeway layout under four following conditions: (a) the total travel times of all cyclists is minimized, (b) all demand points are covered, (c) all selected node pairs are connected by bikeways, and (d) the total bikeway setup cost does not exceed the budget.

The design objective based on condition (a) can be expressed as

$$\min \sum\_{i \in \mathcal{S}} \sum\_{j \in \mathcal{S}} d\_{ij} T\_{ij}. \tag{1}$$

where *Tij* denotes the lowest travel time between stations *i* and *j* based on the constructed bikeways, where *i*, *j* ∈ *S*. For the demand between points *k* and *l*, *dkl*, the shortest path is determined among a maximum of |*Mk* |·|*Ml* | possible paths formed by the potential bike stations close to these two points. Among these paths, the shortest path is the one that has the minimum travel time and the origin and destination of the path are selected nodes. When both points share the same selected node, the travel time between the nodes becomes 0. When there are multiple shortest paths with respect to time between a pair of selected nodes, all demands are assigned to the path with the lowest setup cost. As a result, all demands of a pair of selected nodes are only assigned to a single shortest path.

#### **Proposition 1.** *For every feasible solution to the problem, there exists at least one path between every pair of demand sources.*

**Proof.** Condition (b) implies that at least one bike station proximate to a demand source is a selected node to cover all demand points. Thus, both the origin and the destination of every pair of demand sources have been covered by the selected node. From condition (c), every pair of selected nodes must be connected by at least one path. Therefore, there must be at least one path between every pair of demand sources and this completes the proof.

Condition (b) (i.e., demand coverage constraint) is satisfied when all demand points are covered by at least one selected node, which can be expressed as

$$\sum\_{i \in M\_k} X\_i \ge 1, \forall k \in D. \tag{2}$$

Constraint (2) ensures that at least one potential bike station proximate to each demand point is a selected node. The connectivity between the selected nodes (i.e., conditions (c)) is hard to be expressed solely by *Xi* and *Yij* as the paths between the selected nodes can involve links that consist of unselected nodes. Unlike the minimum spanning tree, we can neither exclude cycles nor set exact bounds to the number of links because the final network layout is unlikely to be tree-like. The connectivity condition can, therefore, be described by the following set of necessary but not sufficient conditions: first, each selected node is connected by at least, but not exactly, one bikeway; and second, the two ends of an opened bikeway can be both unselected nodes.

Finally, condition (d) can be formulated as the total bikeway setup cost cannot exceed a given budget *B*, which can be expressed as

$$\sum\_{i \in V} \sum\_{j \in V} c\_{ij} Y\_{i\bar{j}} \le B. \tag{3}$$

As the problem contains the complicated connectivity constraint which cannot be expressed mathematically, metaheuristics should be adopted to solve the problem.

#### **3. The Two-Stage Solution Method**

This section presents a two-stage solution method to solve the proposed problem. A genetic algorithm (GA) is adopted firstly to determine the optimal selected node set, and a proposed elimination heuristic (EH) determines the optimal bikeway layout that minimizes the total travel cost without violating the budgetary constraint based on the selected node set.

#### *3.1. The Genetic Algorithm*

Figure 1 illustrates the general scheme of the GA for determining the optimal node set. At first, a population of solutions is initialized randomly, in which each individual (solution) in GA is represented by a chain of binary digits with length |*V*| (number of potential stations). The number of '1's on each chain can be interpreted as the selected nodes of that solution. After evaluating the fitness of all initial solutions, the truncation selection method is adopted to select the *x*% of the fittest parents in the population and duplicate 1/*x*% times to replace the less fit parents. For example, when the fittest 50% of parents are selected from a population of 100 individuals, the selected parents are duplicated 2 times so that a population of 100 individuals can be maintained. Then the parents would have a certain probability of undergoing crossover and mutation for generating new offspring. In the crossover, a crossover point along the chain is randomly selected, and all digits beyond that point are swapped between the two parents. In mutation, a random position in the solution is picked for flipping the bit at that position (as each position can be either 0 or 1). After the crossover and mutation processes, the solutions are compared to remove replicated solutions. Meanwhile, an elitist selection is carried out in GA by storing the best solution from the current generation to be carried over to the next iteration without alteration. The above process is repeated until the maximum number of iterations is reached. 1 % ⁄

**Figure 1.** General scheme of the genetic algorithm.

Every generated solution needs to undergo a three-stage solution fitness evaluation. The first stage is a demand coverage check that verifies whether the selected nodes can cover all demand sources. The solution is regarded as infeasible if the selected nodes cannot cover all demand points: its fitness is not further evaluated and a large penalty is imposed. In contrast, a selected station set that can cover all demand points is regarded as feasible and its fitness is then evaluated by the elimination heuristic. The second stage is used to evaluate if the cost of the bikeway network falls below the budget. The elimination heuristic (introduced in Section 3.2) aims to determine a feasible bikeway layout for a given selected node set (from stage 1) which connects all selected nodes and does not exceed the budget. If all selected nodes cannot be *completely* connected under the given budget, the selected node set is also classified as infeasible, and a smaller penalty (compared with the penalty for demand coverage) is imposed on that infeasible node set. After screening out the infeasible solutions, the third stage is to determine the objective values (total system travel times) of the remaining feasible solutions, and the solution fitness can be calculated as the reciprocal of the objective value.

#### *3.2. Elimination Heuristic*

The EH is applied to every selected station set which covers all demand points (determined by GA) in order to determine a feasible bikeway layout that does not violate the budget constraint. The steps of the EH are as follows:

Step 1 is to construct a network *GF*(*VF, EF*) which consists of the node set *VF* and path set *EF*, in which *VF* and *EF* correspond to the selected node set *S* and the set of shortest paths between the selected node pairs respectively. To determine *EF* based on the original network *G*(*V*, *E*) and *S*, this study applies the minimum spanning tree algorithm (with respect to travel time) for |*S*| times. The information of all shortest paths between all pairs of selected nodes (e.g., total cost, total travel time, and links included in the path) is then determined and stored. In other words, all selected nodes can reach other selected nodes with the lowest travel times. At this stage, the resultant bikeway network at this step has the lowest total system travel time and the highest total construction cost as most bikeways are constructed.

Step 2 is to determine the order of elimination of the built bikeways given in Step 1 by using the elimination factor *fij* to meet the budget constraint. The link-based factor is composed of two parts, the construction cost *cij*, and the path count *pij*, and expressed as *fij* = *a*·*cij* + *b*/*pij*, where *a* and *b* are the non-negative weights for the cost and reciprocal of the path count, respectively. Path count describes the number of shortest paths using the link which can be determined based on the stored path information in Step 1. In other words, links that have higher costs or fewer shortest paths have a larger elimination factor, which implies higher chances to be eliminated. Using this factor is better than simply removing links with respect to cost because it avoids removing high-cost links used by a large number of shortest paths. For simplicity, the elimination factors of all links remain unchanged throughout the evaluation of each solution. In addition, the bikeways which perform as the only link that connects the selected node with other nodes are automatically removed from the list despite a low path count or a high construction cost.

Step 3 evaluates the connectivity between the selected nodes to determine whether any selected node pairs have been permanently disconnected after each bikeway removal. Instead of reconstructing all paths *EF*, this step focuses on checking those paths that include the removed link. The heuristic determines whether the two ends of the removed link can be connected by a new path using other built bikeways. If they cannot be connected, the removed link is restored; if they can be connected, the travel times of those affected paths are updated.

The calculation of the updated travel time is not equivalent to the simple addition of the difference in travel time between the new path and the removed link, which can be explained by an example shown in Figure 2a,b. In these figures, nodes 1, 3, 4, and 6 are the selected nodes while nodes 2 and 5 are not. The solid lines denote the built bikeways, the black dotted lines with arrow denote the shortest paths between the selected nodes, and the italic numbers denote the link number. Figure 2a,b show that the initial shortest paths between the selected nodes and the case after link 4 is removed, respectively. When link 4 is removed (in Figure 2b), two shortest paths (between nodes 3-6 and nodes 1- 6) need to be re-routed and the updated shortest path between nodes 3 and 6 becomes 2-3-6. Meanwhile, if this updated shortest path sequence between nodes 3 and 6 is directly substituted into the original shortest path between nodes 1 and 6 (i.e., 1-2-4), the shortest path becomes 1-2-2-3-6 that link 2 is visited twice consecutively which shows a redundant visit on link 2. Therefore, the updated shortest path between nodes 1 and 6 should completely remove the duplicated links and thus becomes a path with link sequence 1-3-6.

**Figure 2.** (**a**) Initial shortest routes between selected noes before link removal; (**b**) Updated shortest routes between selected nodes after link removal.

Based on these two cases, the network connectivity evaluation and path updates follow the following steps:

	- (a) If it has no duplicated link, then *T'* is the sum of the original travel time and the travel time difference between *V* and *W*, and the updated link sequence is obtained by simply substituting *V* with *W*.
	- (b) If there are duplicated links, *T'* is calculated by firstly following Step 3.3(a) and then subtracting two times the sum of the duplicated links along the route, and the duplicated links are removed from the link sequence of the updated shortest path formed in Step 3.2.

After removing a link, the path counts of all built bikeways are updated and the built bikeways with zero path count are then immediately removed from the list.

Finally, Step 5 is the termination criteria of the EH in which the heuristic stops only if the total setup cost is lower than the given budget. To distinguish the infeasible solutions with the feasible ones, a large penalty is imposed when the network connectivity is not conserved. This gives preferences to the all-connected networks instead of disjoint networks.

#### **4. Numerical Results**

The numerical studies aim to (1) investigate the effect of the weights for elimination factor, (2) analyze the effect of the budget on the total system travel time and the solution fitness, and (3) examine the effect of the number of demand points on the construction cost. The proposed solution method was coded with Dev-C++ and ran on a computer with 3.40 GHz and 6.00 GB of RAM. The GA has a population size of 30 with a 50% truncation rate, a 40% crossover rate, and a 60% mutation rate.

#### *4.1. E*ff*ect of the Weights in the Elimination Factor*

The solution quality relies strongly on the elimination factor *fij*. The weights for the elimination factor, *a* (for the cost) and *b* (for the path count), should be calibrated to avoid overemphasizing either attribute. In the experiments, the genetic algorithm was run with the Sioux-Falls network (see Figure 3). To fit our test problem, nine demand sources are added to the network. The weight for the cost *a* was kept at 1, while the weight for the path count *b* varied from 0 to 10. Figure 4 shows the effect of the weight of path count on the solution fitness. The solution fitness improves significantly when *b* increases from 0 to 0.25, improves slightly from 0.25 to 3, and remains steady with negligible fluctuations when *b* is greater than 3. The values of *a* and *b* are thus set to 1 and 3, respectively, in the following sections.

**Figure 3.** Sioux-Falls network with demand points.

Figure 5 shows that the solution fitness changes with the value of *b* because the weights for the elimination factor change the order of link elimination and thereby affect the network layout. To study the sole effect of the value of b on the solution fitness, the two networks in Figure 5 use the same selected node set with different values of *b* (i.e., 0 and 3). The results show that the network in Figure 5b has a lower total system travel time than that in Figure 5a, given that both networks do not exceed the budget constraint, mainly because of the differences in the order of link elimination. For example, the high-cost link 4-11 is eliminated immediately when path count is not considered (i.e., *b* = 0). Nevertheless, it is kept when the path count is considered because its high path count lowers its order of link elimination. When this link is kept, the shortest paths which use link 4-11 do not have re-routing and thus a lower total system travel time can be achieved. This shows that the elimination factor can be a better approach than cost-based elimination in lowering the total travel time.

**Figure 5.** Bikeway layout (**a**) when *b* = 0; (**b**) when *b* = 3.

The difference in the value of *b* can result in different selected node set and thus the network layout. Table 2 lists the details of the optimal network layouts with different values of *b*. While all networks have 7 selected nodes, there are two different selected node sets and three different network layouts. For *b* = 0, its selected node set of *b* = 0 is identical to the sets of *b* = 2 and 3 but the resultant network layout has a higher total system travel time. The eliminated links when *b* = 0 are different from those when *b* = 2 and *b* = 3 due to the difference in the elimination factor of the links and thus due to the order of elimination. For *b* = 1, the network layout is different from the layouts when *b* = 2 and 3 due to the difference in the selected node set, so as the eliminated links. When *b* = 2 and 3, the selected node set and the network layout are identical and the total system travel time is the lowest

possible. This shows that the path count can also influence the selected node set and thus the total system travel time.

**Table 2.** Solution fitness under different values of *b.*

#### *4.2. E*ff*ect of the Budget on the Solution Fitness*

This section demonstrates the trade-off between the budget and the solution fitness (as well as the total system travel time) via two cases. The first case adopts a fixed selected node-set consisting of 17 selected nodes from the network illustrated in Figure 3, and it is assumed that the selected node-set covers all the demands. With a defined selected node set, the elimination heuristic was solely used to evaluate the network layout and the total system travel time with different budgets. Figure 6 illustrates that the solution fitness increases when the budget increases, which is consistent with the intuition that a higher budget can construct more bikeways and thus reduces the total system travel time. When the budget is lower than 64, no infeasible solutions can be obtained as at least one pair of selected nodes is unconnected. When the budget equals 64, the bikeway layout is indifferent from the Steiner tree network in which the selected nodes are connected with minimal construction cost. This network has the highest total system travel time because most bikeways that contribute to some of the shortest paths are eliminated to minimize the cost, and therefore those selected node pairs have a longer travel time. On the other hand, the bikeway network has the lowest total system travel time when the budget is at or above 139. At this budget level, all shortest paths between all selected node pairs can be constructed. The minimal total travel time is achieved because all cyclists can travel on the corresponding shortest paths. Furthermore, when the budget increases from 64 to 139, there exist network layouts other than the Steiner tree and all shortest paths' network (as shown in Figure 7). While the budget is in between the upper and lower bounds, these networks have a lower total travel time than the Steiner Tree but a higher travel time than the all-to-all shortest path network. This shows the capability of the elimination heuristic in determining the budget-constrained solutions under the objective of travel time minimization. The existence of intermediate solutions between the solutions of the Steiner tree problem and the Floyd-Warshall algorithm also implies that the design of the bicycle network is sensitive to the budget. As the elimination heuristic can find these intermediate solutions, it allows the planners to use the budget as a parameter to design the bicycle network. The bicycle network obtained by the elimination heuristic can always meet the budget while minimizing the total system travel time.

**Figure 6.** Effect of the budget on solution fitness with fixed selected node set.

**Figure 7.** Bikeway network layout and total system travel time under different budgets.

In the second case, the selected node set is not predefined, but it needs to satisfy the demand coverage constraint. Unlike the first case, the budget level can alter the sets of selected nodes. Figure 8 shows that the solution fitness increases with the budget, which is consistent with the previous results. When the budget is lower than 42, all obtained solutions are infeasible because the built bikeways are insufficient to cover all the demand points. Figure 9 displays the network layout with a budget of 35. Some demand points are uncovered because the budget is not sufficient for building bikeways to connect all of their proximate stations. These results show that this model can determine whether the budget is enough for a feasible design and provide different designs according to the given budget.

**Figure 8.** Effects of the budget on solution fitness without fixed selected node set.

**Figure 9.** Bikeway layout when budget=35.

#### *4.3. E*ff*ect of Number of Demand Points on Construction Cost*

This section investigates the effect of the number of demand points on the construction cost. Different numbers of demand points were inputted into the model, and the minimum cost for covering all demand points was then obtained. The example network is identical to that in Figure 3. Figure 10 shows the relationships between the number of demand points and construction cost.

**Number of demand points Number of demand points Figure 10.** Effects of number of demand points on construction cost.

Figure 10 shows that the construction cost increases with the number of demand points. In general, more demand points require more selected nodes to cover all demands. As a result, more bikeways are constructed to connect these selected nodes, and the construction cost increases. Figure 11 illustrates how the number of demand points affects the number of selected nodes and thereby the construction cost. This figure also indicates that the budget amount affects the maximum number of covered demand points. When the budget is tight, only a few demand points can be covered. In this case, we should carefully investigate the relative importance of the demand points so that we can decide which demand points are worth to cover. In contrast, when the budget is higher, more demand points can be covered so as to increase the coverage of the bikeway network.

**Figure 11.** Effects of number of demand points on the number of selected nodes and construction cost.

#### **5. Application in Hong Kong**

This section aims to show the application of our model in Hong Kong. Two case studies were carried out in Kwu Tung North and Kam Tin (i.e., two proposed new towns in Hong Kong) to illustrate how our model can be applied to develop a new bicycle network and improve the existing network in Hong Kong. Developing a bikeway network coincides with the aim of the Hong Kong government in promoting cycling in new towns and development areas for short-distance commuting [21], in which the government has launched several projects that connect scattered cycle tracks in the New Territories and creates new bicycle networks in tourist spots [22]. Our proposed model can be useful to design the bikeway network in Hong Kong due to three reasons. First, it ensures popular destinations (e.g., public transport interchanges or tourist spots) to be covered by bikeways and interconnected. This is unlike the existing networks where the bikeways connect unpopular locations and thus many of them go unused [23]. Second, this model focuses on a network design with continuous and exclusive bikeways, which is favorable to the vast majority of inexperienced cyclists in Hong Kong who only cycle in a safe cycling environment. Thirdly, this model aims to minimize total travel time under the given budget, which is useful for the government that often has a fixed budget for bikeway construction.

#### *5.1. Design Parameters*

 =ൈ The unit cost of all bikeways is assumed to be the same, which is taken as c = HK\$630 per meter, despite the construction materials, foundation, and technology applied [23]. The cost of building a bikeway between nodes i and j would be *Cij* = *c* × *lij*, where *lij* is the distance between nodes *i* and j. The distance *lij* would also be used for calculating the travel time between the bike stations. It is assumed that the cyclists travel at v = 20 km/h by bike [20]. The travel time between nodes *i* and *j* would be *tij* = *lij*/*v*.

#### *5.2. Case Study in Kwu Tung North*

 = ⁄ Kwu Tung North (KTN) is one of the new development areas in the North East New Territories. It is planned to be 447 ha and to be developed as a mixed development node [24]. As the government aims to provide a quality living environment, a bicycle network will promote cycling within the area. Since there are no existing cycle tracks in KTN, we can demonstrate how our model designs a completely new bicycle network in this area. All the maps were obtained from OpenStreetMap [25], while the potential links and demand points have been identified from the Statutory Planning Portal of the Town Planning Board, Hong Kong [26]. We selected the residential area, the public transport interchange, and the business and technology park to be the demand points, as shown in Figure 12.

**Figure 12.** Demand points, potential nodes, and potential links in Kwu Tung North (KTN).

Figure 13a,b show the bikeway layouts under different budget levels. With a higher budget level (i.e., Figure 13b), the bikeways are denser and the total system travel time is reduced, which is consistent with the numerical results in Section 4. As shown in Figure 13a,b, a higher budget results in a larger number of selected nodes that are located closer to the demand sources and a longer continuous bikeway. From these results, we can conclude that our model can be applied to design a bikeway network in new development areas. The model is particularly useful when the government has a fixed budget, as it can guarantee a below budget design that minimizes the cyclists' total travel time.

**Figure 13.** Bikeway layout in KTN with a budget of (**a**) HK\$1.08M; (**b**) HK\$1.78M.

#### *5.3. Case Study in Kam Tin*

The government has planned a new residential development in Kam Tin (KT) South to increase the local housing supply [27]. The existing bicycle network should be expanded so that the new residential area can be connected to the Kam Sheung Road Station (KSRS). Meanwhile, there are only a small number of cycle tracks and parking sites in KT as shown in Figure 14. To facilitate cycling in KT, more cycle tracks and parking sites should be built. Figure 14 shows the demand points and the

network. It is noted that some roads are not chosen as the potential bikeway locations because they are either too narrow or are owned by private owners.

**Figure 14.** Demand points, potential nodes, and potential links in Kam Tin.

**Time: 1.39 hr**  Figure 15a,b display the final bikeway network layouts under different budget levels. In this case, provided that there are existing bikeways and parking sites, the model was modified to ensure that these existing facilities are not removed in the elimination heuristics. The results of the bikeway designs are consistent with other sections that the total system travel time decreases when more budget is provided. Also, the travel time between the existing bike stations/ parking sites can be reduced because the stations can be connected by a shorter path when more budget is provided. These results show that our model handles the case of system expansion in which the new design can improve the system performance while taking the existing bicycle network and given budget level into consideration.

**Figure 15.** Bikeway layout in KT with a budget (**a**) HK\$0.61M; (**b**) HK\$2.30M.

#### **6. Conclusions and Future Directions**

In spite of the significant role of bikeway in motivating cycling, there are very few existing bikeway network design models, and some design objectives and constraints have not been simultaneously taken into consideration. This study proposes a new bikeway network design problem that minimizes the total system travel time between a set of selected nodes under a budget constraint, in which all potential demands must be covered by the selected nodes and all selected nodes must be interconnected. An efficient two-step solution method is proposed to determine the selected node set and the bikeway layout separately. The genetic algorithm is employed to determine the selected node set that covers all demand points, while a novel elimination heuristic is employed to remove the bikeways between the shortest paths of the selected nodes (constructed by the shortest path algorithm) according to the elimination factor (which is a weighted sum of the setup cost and path count). Numerical studies showed that (1) the weights for elimination factors can significantly change the network layout and thus the solution quality; (2) a higher budget can change the network topology and improve the solution fitness whenever the selected node set is fixed or flexible; and (3) the construction cost increases with the number of demand points. The case studies in two Hong Kong new towns showed that the model can handle design problems with or without existing bikeways under various budget levels. This model can, therefore, be adapted to similar bike-sharing system designs because (1) it ensures that the resultant bike station locations have complete demand coverage, (2) all bike stations can be well-connected by the bikeway network, and (3) the designed network can satisfy the budget constraint. Furthermore, the model is not only applicable to new system designs but can also capture the existence of built bikeways and bike stations for system expansion.

Future research could be done in the following directions. First, other design constraints or measures, such as BLOS, could be included in the model. Second, the model assumes that the demand between every demand point pair is identical, while it can be relaxed in future studies by considering varying demand levels of all demand sources, elastic travel demand with respect to travel time, or uncertainties between the selected nodes. Third, the bikeway setup cost is assumed to be proportional to the length of links only, but it can also be influenced by other factors, such as the type of bikeway (e.g., exclusive bikeway, shared bikeway with vehicle roadway) and pavement type. Future studies can be extended to multi-type bikeway design problems. Fourth, the generalized travel cost of each bikeway can include other attributes in addition to travel time, including the number of turns and intersections, the percentage of greening, and the provision of cycling facilities along with a candidate link. Moreover, the route choice behavior of the cyclists can be remodeled by including other route choice attributes (such as traffic flow of automobile on the edges) or using other mathematical forms such as the bi-objective model [28]. Finally, the Genetic Algorithm used in the two-stage solution method can be replaced by other new meta-heuristics or hybrid meta-heuristics, such as the Artificial Bee Colony algorithm, Tabu Search, or Large Neighborhood Search. Comparative studies can be carried out to determine which of the solution methods has a higher computation efficiency.

**Author Contributions:** Conceptualization, methodology, writing—original draft preparation, writing—review and editing, supervision, project administration, C.S.S.; Validation, formal analysis, investigation, writing—original draft preparation, visualization, W.L.C.

**Funding:** This research received funding from the Ministry of Science and Technology, Taiwan ROC, with funding reference MOST 108 - 2218 - E - 009 - 062.

**Acknowledgments:** We thank the three anonymous reviewers for their constructive comments on the paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

1. Pucher, J.; Dill, J.; Handy, S. Infrastructure, programs, and policies to increase bicycling: An international review. *Prev. Med.* **2010**, *50*, S106–S125. [CrossRef] [PubMed]

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*


*2.3. Spray Drying*

Dehydration of blueberry juice was carried out in a Mini Spray Dryer B290 (Buchi, Switzerland), by feeding mixtures of blueberry juice and maltodextrin (BJ-MX) into the spray dryer at room temperature. Blends of BJ-MX were prepared by adding the necessary amount of maltodextrin in the juice, and by constant mechanical stirring. Hot air was employed as drying vehicle, at a volumetric flow rate of 35 m3/h, and constant pressure of 1.5 bar. The rest of processing conditions were varied according to the testing level of variables described above.

#### *2.4. Content and Retention of Resveratrol*

Quantification of antioxidant content in dry samples was carried out by dissolving 0.5 g of powder in 0.5 mL of phosphoric acid (10% *v*/*v* in water), and 3 mL of methanol used as an extracting solvent. In order to maximize the extraction of antioxidants, the solution was stirred for 5 min, and left resting 24 h in darkness. Solution was filtered in an Acrodisc filter (0.45 μm), and the filtered was diluted with 200 μL of methanol. A constant volume of 10 μL was injected in a high performance liquid chromatography (HPLC) instrument. Injections were carried out by triplicate. Content of resveratrol was quantified by HPLC with a Waters system (Waters Assoc. Milford, MA, USA), equipped with a binary pump, an auto-injector (model 717), and a dual wavelength absorbance detector (model 2487). The analyses were carried out at room temperature, and a pH of 3.0 in the solution. A constant flow rate of 1 mL/min solution of 50% acetonitrile-phosphoric acid was employed as the mobile phase. Detection was set at a wavelength of 306 nm. Chromatographic separation was done with an Agilent Zorbax C-18 column (75 mm × 4.6 mm DI 3.5 μm). All data were analyzed with the Empower Pro software Version 4.0 (Mildford, MA, USA).

Calibration curves were constructed employing a resveratrol HPLC grade standard (>99.0%, Sigma-Aldrich, Toluca, Mexico). A stock solution of 1000 μg/mL, and several aliquots (0.01, 1, 5, 10 and 20 μg/mL) were prepared as the calibration curve. Calibration curves were prepared the same day of injecting in the HPLC. Elution time of resveratrol was 2.5 min, while mobile phase eluted at 0.92 min. The intensity of resveratrol peak linearly increased with concentration. Content of resveratrol in the sample was determined from comparison with the calibration curve.

Resveratrol was evaluated in the original juice, and in dry powders. The content of antioxidants was expressed as micrograms of resveratrol per gram of blueberry juice powder (μg/g). Percent of retention (R) of resveratrol was determined according to equation (1):

$$\text{TR}\left(\%\right) = \frac{\text{Q}\_{\text{P}} \times 100}{\text{Q}\_{\text{l}}},\tag{1}$$

where QP is the content of resveratrol in dry powder (in ppm), and QJ is the content of antioxidants in fresh juice (8.38 ppm).