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Journal articles on the topic 'Transportation applications'

To see the other types of publications on this topic, follow the link: Transportation applications.

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Barai, Sudhir Kumar. "DATA MINING APPLICATIONS IN TRANSPORTATION ENGINEERING." TRANSPORT 18, no. 5 (October 31, 2003): 216–23. http://dx.doi.org/10.3846/16483840.2003.10414100.

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Data mining is the extraction of implicit, previously unknown and potentially useful information from data. In recent time, data mining studies have been carried out in many engineering disciplines. In this paper the background of data mining and tools is introduced. Further applications of data mining to transportation engineering problems are reviewed. The application of data mining for typical example of ‘Vehicle Crash Study’ is demonstrated using commercially available data mining tool. The paper highlights the potential of data mining tool application in transportation engineering sector.

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2

Carroll, Joseph A. "Tether applications in space transportation." Acta Astronautica 13, no. 4 (April 1986): 165–74. http://dx.doi.org/10.1016/0094-5765(86)90061-5.

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3

Lesuer, Donald R., and Georges J. Kipouros. "Lightweight materials for transportation applications." JOM 47, no. 7 (July 1995): 17. http://dx.doi.org/10.1007/bf03221222.

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4

Latha, Gali Madhavi. "Geocells for Transportation Geotechnical Applications." Indian Geotechnical Journal 51, no. 3 (May 19, 2021): 612–23. http://dx.doi.org/10.1007/s40098-021-00539-2.

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5

Mawarni, Inggar Tri Agustin, and Aji Prasetya Wibawa. "Analisis Persepsi Pengguna terhadap Kualitas Layanan pada Aplikasi Mobile Transportasi Online." Inovbiz: Jurnal Inovasi Bisnis 8, no. 1 (June 11, 2020): 23. http://dx.doi.org/10.35314/inovbiz.v8i1.1286.

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The study was conducted to determine user perceptions of online transportation mobile applications by looking at PlayStore transportation mobile application analysis and user perceptions when using online transportation mobile applications. This research method is a qualitative method. This type of research uses informants, where direct users who use online mobile transportation applications and PlayStore observations as supporters. Data collection techniques carried out in the form of 1) interviews, 2) participant observation, 3) study documentation. The analysis method uses the interactive model of Mile and Hubermen with 4 stages, including collecting data, trying to formulate data or reduce data, after that the data presentation and drawing conclusions. The results showed that online mobile transportation applications have differences in improving service quality and in creating customer satisfaction. However, the observation of user perception shows that the Grab online transportation mobile application is considered to have superior quality and service than the Gojek and OK-Jack online transportation mobile application.

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6

Kopytov, Eugene, and Dmitry Abramov. "Multiple-Criteria Analysis and Choice of Transportation Alternatives in Multimodal Freight Transport System." Transport and Telecommunication Journal 13, no. 2 (January 1, 2012): 148–58. http://dx.doi.org/10.2478/v10244-012-0012-x.

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Multiple-Criteria Analysis and Choice of Transportation Alternatives in Multimodal Freight Transport System In the paper the multimodal freight transportation system with a finite number of known alternatives, defined by the routes and modes, is considered. The objective of research is to suggest the approach for evaluation and choice the alternatives of cargo transportation. The following main tasks are considered: choice of indices characterizing efficiency of multimodal transportations, formation of optimization criteria of the multimodal freight transportation, construction of the model of the multimodal transportation system, calculation the performance criteria of cargo transportation. The study presents the Analytic Hierarchy Process (AHP) as the most suitable approach for comparative evaluation of different routes and modes of cargo transportation.

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7

Nugroho, Thofik, Wing Wahyu Winarno, and Kholid Haryono. "Analisis Pengaruh Dimensi Budaya Terhadap Penggunaan Aplikasi Trasnportasi Online Menggunakan UTAUT2 dan Budaya Hofstede." JURNAL MEDIA INFORMATIKA BUDIDARMA 4, no. 2 (April 25, 2020): 377. http://dx.doi.org/10.30865/mib.v4i2.2062.

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It is important for online transportation application company to know the factors that influence the use of online transportation applications. Culture is an important factor in research into the acceptance of technology use. This study uses the UTAUT2 research model and Hofstede's cultural dimensions. The main objective of this research will be to investigate that culture influences users in using online transportation applications. The measurement method uses PLS SEM. It is proven that culture influences the use of online transportation applications. It is proven that by adding the cultural dimension in the UTAUT2 model, it can better explain the behavior of using online transportation applications

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8

Stenger. "Advances in Information Technology Applications for Supply Chain Management." Transportation Journal 50, no. 1 (2011): 37. http://dx.doi.org/10.5325/transportationj.50.1.0037.

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9

Bugarski, Vladimir, Filip Kulić, Željko Kanović, and Todor Bačkalić. "Fuzzy logic applications in inland waterway transportation." Journal on Processing and Energy in Agriculture 24, no. 3-4 (2020): 115–18. http://dx.doi.org/10.5937/jpea24-30238.

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New applications of a fuzzy expert system (FES) for direct control of traffic lights in the areas of locks and one-way sections on inland waterways are presented. FES implements the knowledge of experts in the field of water transport on a controller. FES-SL (application in ship locks) was created for a single-chamber two-way ship lock. The system decides when the lock process will be performed depending on the current requests and queues. FES-1WS (application in one-way sections) was created for a canal network with sections where one-way vessel traffic is allowed due to the width limitation. The system is applicable for different traffic densities (from 10 to 90 vessels per day) and different lengths of one-way sections (from 2km to 24km).

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10

Et.al, Siti Asma Mohammed. "Bus Tracking App for Universities Transportation." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 1081–84. http://dx.doi.org/10.17762/turcomat.v12i3.845.

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Shuttle buses have become an important means of transportation for students especially for those who rely on it to go to class. However, students often face difficulty knowing the current location of the bus and its estimated arrival time. Some of them are even unaware of the bus schedule provided by higher management. Consequently, they have to wait too long for their respective buses to arrive. Hence, for the convenience of those who want to plan their journey with shuttle buses, two applications are proposed. One application will track the location of the bus and the other application will be used by the students. Both proposed applications will be used along with an Android phone since it is mostly used by students. The main objectives of developing this application are to inform users regarding the current bus location and estimated arrival time. This application also provides users real-time forum so they can start conversations with others with the same application. Besides, the driver's profile is alsoincluded for the user's future reference.

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11

Rossetti, Rosaldo J. F., Joao Emilio Almeida, Zafeiris Kokkinogenis, and Joel Goncalves. "Playing Transportation Seriously: Applications of Serious Games to Artificial Transportation Systems." IEEE Intelligent Systems 28, no. 4 (July 2013): 107–12. http://dx.doi.org/10.1109/mis.2013.113.

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12

Náhlík, Tomáš, and Dana Smetanová. "Applications of Gyroscopic Effect in Transportation." Naše more 65, no. 4 (October 2018): 293–96. http://dx.doi.org/10.17818/nm/2018/4si.24.

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13

Walton, Scott, and Eric Meyer. "Interpreting Cellular Coverage for Transportation Applications." Transportation Research Record: Journal of the Transportation Research Board 1826, no. 1 (January 2003): 32–36. http://dx.doi.org/10.3141/1826-05.

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The process was developed for collecting and analyzing cellular coverage data by applying the results of analysis to analog cellular coverage in the state of Kansas. The type of analysis that is appropriate depends on the purpose for which the information is to be used. Two types of analyses were examined—fixed coverage and mobile coverage. Fixed coverage analysis is needed for such functions as automatic collision notification in which any individual cellular connection can occur from a fixed location. The study showed that the fixed cellular coverage of the Kansas state highway system was good. Only 0.4% of the highway by length had inadequate signal strength for using a 3-W phone (a typical car phone) and 1.7% for using a 0.6-W phone (a typical handheld unit). In contrast, the mobile coverage analysis identified numerous areas where a call from a moving vehicle would be severely limited in duration. This type of analysis is needed for applications such as communications for emergency medical services, for which a vehicle must sustain continuous communications. For example, more than 9% of the state highways by length cannot sustain a call of 30 min with a 0.6-W phone, and in some areas the percentage is considerably higher. For certain applications, this difference may simply translate to inconvenience, but for other purposes it can be very important. The results of the two types of analyses highlight different characteristics of the coverage footprint; one addresses absolute coverage and the other continuity of coverage. The results of the analysis technique relate more directly to the unique characteristics of wireless communications utilization in transportation applications.

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14

Morita, Hiroaki. "Superconductivity Technology Applications for Transportation Systems." Journal of the Japan Welding Society 64, no. 3 (1995): 172–77. http://dx.doi.org/10.2207/qjjws1943.64.172.

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15

Dougherty, Mark S. "Applications of neural networks in transportation." Transportation Research Part C: Emerging Technologies 5, no. 5 (October 1997): 255–57. http://dx.doi.org/10.1016/s0968-090x(97)00013-2.

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16

Descamps, P., F. Bouazza, S. Sadek, and J. Vindevoghel. "Advanced Microwave Sensors for Transportation Applications." IFAC Proceedings Volumes 27, no. 12 (August 1994): 25–30. http://dx.doi.org/10.1016/s1474-6670(17)47439-8.

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17

Florian, Michael, and Michel Gendreau. "Applications of parallel computing in transportation." Parallel Computing 27, no. 12 (November 2001): 1521–22. http://dx.doi.org/10.1016/s0167-8191(01)00102-8.

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18

Siebein, Gary W., Robert M. Lilkendey, Hyun Paek, and Chris Jones. "Soundscape design applications for transportation noise." Journal of the Acoustical Society of America 126, no. 4 (2009): 2307. http://dx.doi.org/10.1121/1.3249509.

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19

Sinha, Kumares C., and Raymond K. Moore. "Editorial: Microcomputer Applications in Transportation Engineering." Journal of Transportation Engineering 113, no. 4 (July 1987): 339–40. http://dx.doi.org/10.1061/(asce)0733-947x(1987)113:4(339).

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20

MARCHALLECK, NICHOLAS, and ABRAHAM KANDEL. "FUZZY LOGIC APPLICATIONS IN TRANSPORTATION SYSTEMS." International Journal on Artificial Intelligence Tools 04, no. 03 (September 1995): 413–32. http://dx.doi.org/10.1142/s0218213095000206.

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The purpose of this paper is to provide a survey of state of the art fuzzy logic applications in the field of transportation, illustrating the usefulness, and the promising future of the fuzzy approach. The majority of the discussion covers the area of fuzzy control. A wide range of Fuzzy Logic Controllers (FLCs) is discussed, ranging from traffic, to aircraft controllers. Although the majority of applications are to surface transportation, surveys of several aerospace applications are also given.

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21

Wagh, Aditya, Yunfei Hou, Chunming Qiao, Longfei Zhang, Xu Li, Adel Sadek, Kevin Hulme, Changxu Wu, Hong-Li Xu, and Liu-Sheng Huang. "Emerging Applications for Cyber Transportation Systems." Journal of Computer Science and Technology 29, no. 4 (July 2014): 562–75. http://dx.doi.org/10.1007/s11390-014-1450-9.

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22

García de Cortazar, M., I. Agote, E. Silveira, P. Egizabal, J. Coleto, and Y. Le Petitcorps. "Titanium composite materials for transportation applications." JOM 60, no. 11 (November 2008): 40–46. http://dx.doi.org/10.1007/s11837-008-0146-4.

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23

Wang, Feng-Yu. "Generalized Transportation-Cost Inequalities and Applications." Potential Analysis 28, no. 4 (March 28, 2008): 321–34. http://dx.doi.org/10.1007/s11118-008-9079-3.

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24

Gomes Correia, A., P. Cortez, J. Tinoco, and R. Marques. "Artificial Intelligence Applications in Transportation Geotechnics." Geotechnical and Geological Engineering 31, no. 3 (November 17, 2012): 861–79. http://dx.doi.org/10.1007/s10706-012-9585-3.

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25

Han, Jie, and Shijin Feng. "Geosynthetics for transportation and environmental applications." Geotextiles and Geomembranes 47, no. 3 (June 2019): 281. http://dx.doi.org/10.1016/j.geotexmem.2019.01.013.

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26

Simkowitz, Howard J. "Transportation applications of geographic information systems." Computers, Environment and Urban Systems 12, no. 4 (January 1988): 253–71. http://dx.doi.org/10.1016/0198-9715(88)90031-2.

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27

Iyer, Lakshmi Shankar. "AI enabled applications towards intelligent transportation." Transportation Engineering 5 (September 2021): 100083. http://dx.doi.org/10.1016/j.treng.2021.100083.

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28

Guerrero-Ibáñez, Antonio, Carlos Flores-Cortés, Pedro Damián-Reyes, and JRG Pulido. "Emerging Technologies in Transportation Systems." International Journal of Wireless Networks and Broadband Technologies 2, no. 4 (October 2012): 12–40. http://dx.doi.org/10.4018/ijwnbt.2012100102.

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Emerging technologies have been used as a complementary tool to solve different problems that modern society faces every day. One area where the emerging technologies have a big opportunity is transportation. As cities become larger, the number of vehicles and the need for transportation is growing quickly. In this sense, the modern society is facing more traffic congestion, higher fuel bills and the increase of CO2 emissions. It is imperative to improve and develop a sustainable transportation system that uses in a better way the existing infrastructure and that infrastructure have to be complemented with the application of emerging technologies. This work gives readers a global vision of traffic and transportation issues and how merging technologies contribute to solve transportation problems. In addition, it analyzes some of the emerging technologies and how each technology might be used to improve transportation systems. Finally, a classification of applications for transportation systems is explained and some real applications that are being developed in both academic and industrial environment are described.

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29

Erdebilli, Babek, and Emine Nur NACAR. "Advances in Big Data Applications for transportation: airline, highway, and railway." Central European Review of Economics and Management 5, no. 2 (May 28, 2021): 121–34. http://dx.doi.org/10.29015/cerem.882.

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Aim: The purpose of this article is to present the latest advances in big data applications in the industries of the transportation sector such as airline, highway, and railway. It is difficult to analyze data in transportation because there is continuous real-time data flow. Since the improvements made are fast with the same logic, it is necessary to catch up with the new developments. Data should be analyzed with the big data concept because data stacks highly contain non-structural data types in transportation data. Although the mentioned industries are complementary to each other, the applications differ depending on the needs of the industry. Thus, solutions to specific problems in different industries using big data applications should be addressed. Design / Research methods: In accordance with the purpose of the study, big data studies that provide added value to the transportation sector were examined. Studies have been filtered through some criteria which are whether the application is adaptable to the industry, the study is available online in full-text, and its references are from respectable sources. Conclusions / findings: All the big data application studies in the academy are not adaptable in real-life problems or suitable for all situations. For this reason, trying all of the applications will lead to moral and material losses for firms. This study is a guideline for companies to follow the developments in the big data concept and to choose the one that suits their problems. Thus, the gap between academia and industry was tried to close. Originality / value of the article: Although studies are referring to big data applications in the transportation sector, this study differs from others in terms of specifically analyzing big data applications in different industries such as airline, highway, and railway in the transportation sector

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30

PAMUŁA, Teresa. "NEURAL NETWORKS IN TRANSPORTATION RESEARCH – RECENT APPLICATIONS." Transport Problems 11, no. 2 (2017): 27–36. http://dx.doi.org/10.20858/tp.2016.11.2.3.

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31

Sathisan Nambisan, Shashi, Srinivas S. Pulugurtha, Jeffery J. Jensen, Robert Puterski, Thomas D. Miller, and Chang Hwan Park. "Prototype Internet Applications for Transportation System Management." Transportation Research Record: Journal of the Transportation Research Board 1660, no. 1 (January 1999): 122–31. http://dx.doi.org/10.3141/1660-16.

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32

Williams, Keith, Michael Olsen, Gene Roe, and Craig Glennie. "Synthesis of Transportation Applications of Mobile LIDAR." Remote Sensing 5, no. 9 (September 18, 2013): 4652–92. http://dx.doi.org/10.3390/rs5094652.

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33

MATSUMOTO, Shoji. "APPLICATIONS OF DISAGGREGATE MODELS TO GOODS TRANSPORTATION." Doboku Gakkai Ronbunshu, no. 353 (1985): 43–51. http://dx.doi.org/10.2208/jscej.1985.43.

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34

Shmueli, Deborah. "Applications of neural networks in transportation planning." Progress in Planning 50, no. 3 (October 1998): 141–204. http://dx.doi.org/10.1016/s0305-9006(98)00015-4.

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35

Szeto, W. Y. "Dynamic Modeling for Intelligent Transportation System Applications." Journal of Intelligent Transportation Systems 18, no. 4 (August 20, 2013): 323–26. http://dx.doi.org/10.1080/15472450.2013.834770.

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36

Descamps, P., J. Vindevoghel, F. Bouazza, and S. Sawsan. "Microwave Doppler sensors for terrestrial transportation applications." IEEE Transactions on Vehicular Technology 46, no. 1 (1997): 220–28. http://dx.doi.org/10.1109/25.554755.

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37

Hendrickson, Chris T. "Applications of Advanced Technologies in Transportation Engineering." Journal of Transportation Engineering 130, no. 3 (May 2004): 272–73. http://dx.doi.org/10.1061/(asce)0733-947x(2004)130:3(272).

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38

Gkoumas, Konstantinos, Oriana De Gaudenzi, and Francesco Petrini. "Energy Harvesting Applications in Transportation Infrastructure Networks." Procedia - Social and Behavioral Sciences 48 (2012): 1097–107. http://dx.doi.org/10.1016/j.sbspro.2012.06.1086.

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39

Causey, Beverley D., Lawrence H. Cox, and Lawrence R. Ernst. "Applications of Transportation Theory to Statistical Problems." Journal of the American Statistical Association 80, no. 392 (December 1985): 903–9. http://dx.doi.org/10.1080/01621459.1985.10478201.

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40

Majewski, A. J., U. Bossel, and R. Steinberger-Wilckens. "Catalytic Reforming System Suitable for Transportation Applications." Fuel Cells 18, no. 4 (May 30, 2018): 535–42. http://dx.doi.org/10.1002/fuce.201700135.

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41

Ayala, Daniel, Ouri Wolfson, Bhaskar Dasgupta, Jie Lin, and Bo Xu. "Spatio-Temporal Matching for Urban Transportation Applications." ACM Transactions on Spatial Algorithms and Systems 3, no. 4 (May 8, 2018): 1–39. http://dx.doi.org/10.1145/3183344.

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42

Tibben‐Lembke, Ronald S., and Dale S. Rogers. "Real options: applications to logistics and transportation." International Journal of Physical Distribution & Logistics Management 36, no. 4 (April 2006): 252–70. http://dx.doi.org/10.1108/09600030610672037.

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43

Lourens, Peter F. "Error analysis and applications in transportation systems." Accident Analysis & Prevention 21, no. 5 (October 1989): 419–26. http://dx.doi.org/10.1016/0001-4575(89)90002-x.

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44

De Philippis, Guido, Alpár Richárd Mészáros, Filippo Santambrogio, and Bozhidar Velichkov. "BV Estimates in Optimal Transportation and Applications." Archive for Rational Mechanics and Analysis 219, no. 2 (September 7, 2015): 829–60. http://dx.doi.org/10.1007/s00205-015-0909-3.

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45

Liu, Guo Xiang. "Applications of Cloud Calculation in the Intelligence Transportation System." Advanced Materials Research 651 (January 2013): 917–21. http://dx.doi.org/10.4028/www.scientific.net/amr.651.917.

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Intelligence transportation system is a new kind of information, intelligence and socialization modern transportation system. Cloud computing is a shared data infrastructure method emerged in recent years, which is able to combine the large procedure pool and the data pool to provide a variety of IT application services. The intelligent transportation systems using cloud calculation technology, on the one hand, can integrate multiple heterogeneous, distributed data sources; on the other hand it is formed by multiple server clusters, providing the basic framework of storage, transmission, processing, integration of massive data. In this paper, by studying the applications of cloud calculation technology in the field of intelligence transportation, the author proposed the ITS platform based on cloud calculation.

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46

Seymour, Edward J., and Raman K. Patel. "Evolving Interoperability with National Transportation Communication for Intelligent Transportation Systems Protocols." Transportation Research Record: Journal of the Transportation Research Board 1651, no. 1 (January 1998): 117–23. http://dx.doi.org/10.3141/1651-17.

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One of the most exciting developments in the intelligent transportation systems (ITS) industry—one that holds so much promise for the future—is the National Transportation Communications for ITS Protocols (NTCIP). NTCIP will help lead the way to achieve a cost-effective deployment and management of ITS applications. It will also influence the design and manufacturing process and should cause changes in the way system integrators do their work. The resulting interoperability of core functionality between transportation devices will be an enabling service for ITS deployment. Developmental efforts, the implications of NTCIP, and the resulting interoperability for transportation applications are reported. The framework of the NTCIP is described and device level interoperability is discussed. NTCIP makes an effort to fulfill the initial needs for interoperable and interchangeable field devices. Current developments of the NTCIP effort are described and interoperability features are identified.

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47

ZHANG, Y. P., and Y. ZHAO. "APPLICATIONS IN THE ADVANCED TRANSPORTATION SYSTEM AND IMPACT ON SUPERCONDUCTIVITY INDUSTRY OF HTSM." International Journal of Modern Physics B 19, no. 01n03 (January 30, 2005): 427–29. http://dx.doi.org/10.1142/s0217979205028724.

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As the information technology grows up and its application penetrates into every area of this world, how to faster and more efficiently transport people and goods is becoming the new social demand, which indicates a new revolution on advanced transportation technology being brewed. High-temperature Superconductivity Maglev (HTSM) is one with the best development potential among most transportation technologies. It could be used in many advanced transportation fields, overcoming the key contradiction and shortcoming of the current transportation patterns such as train, automobile and airplane. On the other hand, HTSM will promote theoretical study and technology exploitation on superconductivity. HTSM's applications in a large scale will bring up profound effect on the forming and development of the superconductivity industry.

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48

Sommerville, F. "Applications of automatic vehicle identification technology." Transport Reviews 11, no. 2 (April 1991): 173–91. http://dx.doi.org/10.1080/01441649108716781.

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49

Selvarajah, K., C. Shooter, L. Liotti, and A. Tully. "Heterogeneous Wireless Sensor Network for Transportation System Applications." International Journal of Vehicular Technology 2011 (May 17, 2011): 1–14. http://dx.doi.org/10.1155/2011/853948.

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The important innovations in wireless and digital electronics will support many applications in the areas of safety, environmental and emissions control, driving assistance, diagnostics, and maintenance in the transport domain. The last few years have seen the emergence of many new technologies that can potentially have major impacts on transportation systems. One of these technologies is Wireless Sensor Networks. A wireless sensor device is typically composed of a processing unit, memory, and a radio chip which allows it to communicate wirelessly with other devices within range. The Embedded Middleware in Mobility Applications (EMMA) project delivers a middleware that aims to facilitate the interaction between sensing technologies in transportation systems. This paper outlines our experience in the EMMA project and provides an illustration of the important role that wireless sensor technology can play in future transportation system. The paper discusses our experience of using heterogeneous sensors to develop transportation system applications in the EMMA project and focuses on how cooperation between vehicle and infrastructure can be addressed. It also presents encouraging results obtained from the experiments in investigating the feasibility of utilising wireless sensor in vehicle and vehicle-to-infrastructure communication in real transportation applications.

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50

Kurniawan, M. Rizki, Oky Dwi Nurhayati, and Kurniawan Teguh Martono. "Sistem Informasi Geografis Pencarian Lokasi Agen Bus dan Travel Terdekat di Kota Semarang Berbasis Mobile dengan Metode Dijkstra." Jurnal Teknologi dan Sistem Komputer 3, no. 2 (April 20, 2015): 302. http://dx.doi.org/10.14710/jtsiskom.3.2.2015.302-310.

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Nowadays, the mobility of people who travel to a place through road transportation is increasing, either private vehicle or public transportation services. By the high intensity of use the road transportation, especially the road transportation service in Semarang, in this cases are bus and travel, it makes the transportation service users often confused in determining transportation agency to be used, especially if user is in hurry. Therefore, transportation user need a mobile application that can provide bus or travel transportation agency locator services which nearby user location and provide destination route which is expected. The Bus and Travel agency – Based Applications Mobile Locator Using Dijkstra Method is the ultimate solution in the search of bus and travel agency in Semarang. The purpose of development this application is developing a search guidance system of bus and travel agency location which nearby user location that can provide an efficient route and save the time. The Bus and Travel agency – Based Applications Mobile Locator is implemented using ionic framework that runs on Android. Sqlite as the media storage of this, allows user to perform agency data management. In Addition, it is supported by the relevance of agency data which is obtained from Department of Transportation, Communication, and Information Central of Java and transportation magazine site. The use of Google Maps API supports search location using Dijkstra Method in search of bus and travel agency, that expected to facilitate the users find travel or bus agency as expected, and its information that’s already available.

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