Editorial Challenge: From a Quarterly to a Bimonthly Journal

Starting with issue 4 of volume 7(2012) International Journal of Computers Communications & Control (INT J COMPUT COMMUN, IJCCC) [4] is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE) [2].Beginning with issue 1 of volume 8(2013) IJCCC will be published as a bimonthly journal (6 issues/year) [5]

Foreword

Volume 8, Issue 2 of the Journal of South Carolina Water Resources (JSCWR) includes four articles. Three articles focus on the crucial factors of public perceptions and communications across various stakeholder groups. The fourth article examines the hydrologic modeling of a coastal forest watershed. Additionally, an informative guest commentary about the US Environmental Protection Agency (EPA) Water Quality Exchange (WQX) and Water Quality Portal (WQP) was contributed by authors from the EPA and South Carolina Department of Health and Environmental Control (SCDHEC)

In-situ and remote monitoring of environmental water quality

Environmental water pollution affects human health and reduces the quality of our natural water ecosystems and resources. As a result, there is great interest in monitoring water quality and ensuring that all areas are compliant with legislation. Ubiquitous water quality monitoring places considerable demands upon existing sensing technology. The combined challenges of system longevity, autonomous operation, robustness, large-scale sensor networks, operationally difficult deployments and unpredictable and lossy environments collectively represents a technological barrier that has yet to be overcome[1]. Ubiquitous sensing envisages many aspects of our environment being routinely sensed. This will result in data streams from a large variety of heterogeneous sources, which will often vary in their volume and accuracy. The challenge is to develop a networked sensing infrastructure that can support the effective capture, filtering, aggregation and analysis of such data. This will ultimately enable us to dynamically monitor and track the quality of our environment at multiple locations. Microfluidic technology provides a route to the development of miniaturised analytical instruments that could be deployed remotely, and operate autonomously over relatively long periods of time (months–years). An example of such a system is the autonomous phosphate sensor[2] which has been developed at the CLARITY Centre, in Dublin City University. This technology, in combination with the availability of low power, reliable wireless communications platforms that can link sensors and analytical devices to online databases and servers, form the basis for extensive networks of autonomous analytical ‘stations’ or ‘nodes’ that will provide high quality information about key chemical parameters that determine the quality of our aquatic environment. The system must also have sufficient intelligence to enable adaptive sampling regimes as well as accurate and efficient decision-making responses. A particularly exciting area of development is the combination of remote satellite/aircraft based monitoring with the in-situ ground-based monitoring described above. Remote observations from satellites and aircraft can provide significant amounts of information on the state of the aquatic environment over large areas. As in-situ deployments of sensor networks become more widespread and reliable, and satellite data becomes more widely available, information from each of these sources can complement and validate the other, leading to an increased ability to rapidly detect potentially harmful events, and to assess the impact of environmental pressures on scales ranging from small river catchments to the open ocean. In this paper, we will assess the current status of these approaches, and the challenges that must be met in order to realise the vision of true internet- or global-scale monitoring of our environment. References: [1] Integration of analytical measurements and wireless communications – Current issues and future strategies. King Tong Lau, Sarah Brady, John Cleary and Dermot Diamond, Talanta 75 (2008) 606–612. [2] An autonomous microfluidic sensor for phosphate: on-site analysis of treated wastewater. John Cleary, Conor Slater, Christina McGraw and Dermot Diamond, IEEE Sensors Journal, 8 (2008) 508-515

Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88. doi:10.1016/j.envsoft.2018.02.013Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1-27. doi:10.1080/0361092740882710

Experimental Assessment of Time Reversal for In-Body to In-Body UWB Communications

[EN] The standard of in-body communications is limited to the use of narrowband systems. These systems are far from the high data rate connections achieved by other wireless telecommunication services today in force. The UWB frequency band has been proposed as a possible candidate for future in-body networks. However, the attenuation of body tissues at gigahertz frequencies could be a serious drawback. Experimental measurements for channel modeling are not easy to carry out, while the use of humans is practically forbidden. Sophisticated simulation tools could provide inaccurate results since they are not able to reproduce all the in-body channel conditions. Chemical solutions known as phantoms could provide a fair approximation of body tissues¿ behavior. In this work, the Time Reversal technique is assessed to increase the channel performance of in-body communications. For this task, a large volume of experimental measurements is performed at the low part of UWB spectrum (3.1-5.1 GHz) by using a highly accurate phantom-based measurement setup. This experimental setup emulates an in-body to in-body scenario, where all the nodes are implanted inside the body. Moreover, the in-body channel characteristics such as the path loss, the correlation in transmission and reception, and the reciprocity of the channel are assessed and discussed.This work was supported by the Programa de Ayudas de Investigacion y Desarrollo (PAID-01-16) from Universitat Politecnica de Valencia and by the Ministerio de Economia y Competitividad, Spain (TEC2014-60258-C2-1-R), by the European FEDER funds.Andreu-Estellés, C.; Garcia-Pardo, C.; Castelló-Palacios, S.; Cardona Marcet, N. (2018). Experimental Assessment of Time Reversal for In-Body to In-Body UWB Communications. Wireless Communications and Mobile Computing (Online). (8927107):1-12. https://doi.org/10.1155/2018/8927107S1128927107Fireman, Z. (2003). Diagnosing small bowel Crohn’s disease with wireless capsule endoscopy. Gut, 52(3), 390-392. doi:10.1136/gut.52.3.390Burri, H., & Senouf, D. (2009). Remote monitoring and follow-up of pacemakers and implantable cardioverter defibrillators. Europace, 11(6), 701-709. doi:10.1093/europace/eup110Scanlon, W. G., Burns, B., & Evans, N. E. (2000). Radiowave propagation from a tissue-implanted source at 418 MHz and 916.5 MHz. IEEE Transactions on Biomedical Engineering, 47(4), 527-534. doi:10.1109/10.828152Chavez-Santiago, R., Garcia-Pardo, C., Fornes-Leal, A., Valles-Lluch, A., Vermeeren, G., Joseph, W., … Cardona, N. (2015). Experimental Path Loss Models for In-Body Communications within 2.36-2.5 GHz. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2015.2418757Khaleghi, A., Chávez-Santiago, R., & Balasingham, I. (2010). Ultra-wideband pulse-based data communications for medical implants. IET Communications, 4(15), 1889. doi:10.1049/iet-com.2009.0692Khaleghi, A., Chávez-Santiago, R., & Balasingham, I. (2011). Ultra-wideband statistical propagation channel model for implant sensors in the human chest. IET Microwaves, Antennas & Propagation, 5(15), 1805. doi:10.1049/iet-map.2010.0537Kurup, D., Scarpello, M., Vermeeren, G., Joseph, W., Dhaenens, K., Axisa, F., … Vanfleteren, J. (2011). In-body path loss models for implants in heterogeneous human tissues using implantable slot dipole conformal flexible antennas. EURASIP Journal on Wireless Communications and Networking, 2011(1). doi:10.1186/1687-1499-2011-51Floor, P. A., Chavez-Santiago, R., Brovoll, S., Aardal, O., Bergsland, J., Grymyr, O.-J. H. N., … Balasingham, I. (2015). In-Body to On-Body Ultrawideband Propagation Model Derived From Measurements in Living Animals. IEEE Journal of Biomedical and Health Informatics, 19(3), 938-948. doi:10.1109/jbhi.2015.2417805Shimizu, Y., Anzai, D., Chavez-Santiago, R., Floor, P. A., Balasingham, I., & Wang, J. (2017). Performance Evaluation of an Ultra-Wideband Transmit Diversity in a Living Animal Experiment. IEEE Transactions on Microwave Theory and Techniques, 65(7), 2596-2606. doi:10.1109/tmtt.2017.2669039Anzai, D., Katsu, K., Chavez-Santiago, R., Wang, Q., Plettemeier, D., Wang, J., & Balasingham, I. (2014). Experimental Evaluation of Implant UWB-IR Transmission With Living Animal for Body Area Networks. IEEE Transactions on Microwave Theory and Techniques, 62(1), 183-192. doi:10.1109/tmtt.2013.2291542Chou, C.-K., Chen, G.-W., Guy, A. W., & Luk, K. H. (1984). Formulas for preparing phantom muscle tissue at various radiofrequencies. Bioelectromagnetics, 5(4), 435-441. doi:10.1002/bem.2250050408Cheung, A. Y., & Koopman, D. W. (1976). Experimental Development of Simulated Biomaterials for Dosimetry Studies of Hazardous Microwave Radiation (Short Papers). IEEE Transactions on Microwave Theory and Techniques, 24(10), 669-673. doi:10.1109/tmtt.1976.1128936YAMAMOTO, H., ZHOU, J., & KOBAYASHI, T. (2008). Ultra Wideband Electromagnetic Phantoms for Antennas and Propagation Studies. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E91-A(11), 3173-3182. doi:10.1093/ietfec/e91-a.11.3173Lazebnik, M., Madsen, E. L., Frank, G. R., & Hagness, S. C. (2005). Tissue-mimicking phantom materials for narrowband and ultrawideband microwave applications. Physics in Medicine and Biology, 50(18), 4245-4258. doi:10.1088/0031-9155/50/18/001Yilmaz, T., Foster, R., & Hao, Y. (2014). Broadband Tissue Mimicking Phantoms and a Patch Resonator for Evaluating Noninvasive Monitoring of Blood Glucose Levels. IEEE Transactions on Antennas and Propagation, 62(6), 3064-3075. doi:10.1109/tap.2014.2313139Gezici, S., Zhi Tian, Giannakis, G. B., Kobayashi, H., Molisch, A. F., Poor, H. V., & Sahinoglu, Z. (2005). Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. IEEE Signal Processing Magazine, 22(4), 70-84. doi:10.1109/msp.2005.1458289Marinova, M., Thielens, A., Tanghe, E., Vallozzi, L., Vermeeren, G., Joseph, W., … Martens, L. (2015). Diversity Performance of Off-Body MB-OFDM UWB-MIMO. IEEE Transactions on Antennas and Propagation, 63(7), 3187-3197. doi:10.1109/tap.2015.2422353SHI, J., ANZAI, D., & WANG, J. (2012). Channel Modeling and Performance Analysis of Diversity Reception for Implant UWB Wireless Link. IEICE Transactions on Communications, E95.B(10), 3197-3205. doi:10.1587/transcom.e95.b.3197Pajusco, P., & Pagani, P. (2009). On the Use of Uniform Circular Arrays for Characterizing UWB Time Reversal. IEEE Transactions on Antennas and Propagation, 57(1), 102-109. doi:10.1109/tap.2008.2009715Chavez-Santiago, R., Sayrafian-Pour, K., Khaleghi, A., Takizawa, K., Wang, J., Balasingham, I., & Li, H.-B. (2013). Propagation models for IEEE 802.15.6 standardization of implant communication in body area networks. IEEE Communications Magazine, 51(8), 80-87. doi:10.1109/mcom.2013.6576343Andreu, C., Castello-Palacios, S., Garcia-Pardo, C., Fornes-Leal, A., Valles-Lluch, A., & Cardona, N. (2016). Spatial In-Body Channel Characterization Using an Accurate UWB Phantom. IEEE Transactions on Microwave Theory and Techniques, 64(11), 3995-4002. doi:10.1109/tmtt.2016.2609409Pahlavan, K., & Levesque, A. H. (2005). Wireless Information Networks. doi:10.1002/0471738646Qiu, R. C., Zhou, C., Guo, N., & Zhang, J. Q. (2006). Time Reversal With MISO for Ultrawideband Communications: Experimental Results. IEEE Antennas and Wireless Propagation Letters, 5, 269-273. doi:10.1109/lawp.2006.875888Ando, H., Takizawa, K., Yoshida, T., Matsushita, K., Hirata, M., & Suzuki, T. (2016). Wireless Multichannel Neural Recording With a 128-Mbps UWB Transmitter for an Implantable Brain-Machine Interfaces. IEEE Transactions on Biomedical Circuits and Systems, 10(6), 1068-1078. doi:10.1109/tbcas.2016.251452

Research Agenda for Studying Open Source II: View Through the Lens of Referent Discipline Theories

In a companion paper [Niederman et al., 2006] we presented a multi-level research agenda for studying information systems using open source software. This paper examines open source in terms of MIS and referent discipline theories that are the base needed for rigorous study of the research agenda

Perceptions of gender balance of IS journal editorial positions

An analysis of 18,854 editorial positions on IS journals was undertaken to examine the perceived gender balance of those positions as an indication of their contribution towards a positive role model for females considering an IS academic career. The nature and extent of perceived gender balance is examined in terms of overall composition of editorial positions, journal prestige and the specific area within IS covered by a journal. The results indicate that perceived gender balance of editorial positions reflects that of ICT academia generally, and that female representation appears to be concentrated in journals covering areas that are traditionally seen as female occupations, e.g., health, education, librarianship. As such, little or no encouragement is given to females considering an IS academic career.<br /