Environmental and fishing effects on the dynamic of brown tiger prawn (Penaeus esculentus) in Moreton Bay (Australia)

This analysis of the variations of brown tiger prawn (Penaeus esculentus) catch in the Moreton Bay multispecies trawl fishery estimated catchability using a delay difference model. It integrated several factors responsible for variations in catchability: targeting of fishing effort, increasing fishing power and changing availability. An analysis of covariance was used to define fishing events targeted at brown tiger prawns. A general linear model estimated inter-annual variations of fishing power. Temperature induced changes in prawn behaviour played an important role in the dynamic of this fishery. Maximum likelihood estimates of targeted catchability ($3.92 \pm 0.40 \ 10^{-4}$ boat-days$^{-1}$) were twice as large as non-targeted catchability ($1.91 \pm 0.24 \ 10^{-4}$ boat-days$^{-1}$). The causes of recent decline in fishing effort in this fishery were discussed.Comment: revised manuscript following reviewers comments + adding data and code for reader

A kinematic numerical camera model for the SPOT-1 sensor

A novel method for modelling linear push-broom sensors has been developed. A numerical model which incorporates the satellite attitude and position data is used to compute the absolute orientation. This method makes a break with traditional photogrammetric practice, in that instead of using an approach based on collinearity equations, the absolute orientation is computed iteratively using a numerical multi-variable minimisation scheme. All current implementations of the model use the Powell direction-set method, but in principle, any multivariable minimisation scheme could be substituted. The numerical method has significant advantages over the collinearity approach. The number of ground control points needed to form an accurate model is reduced and the numerical approach offers a superior basis for the development of general purpose multi sensor modelling software. In order to test these assertions, a numerical model of the SPOT-1 sensor was coded and tested against a pre-existing collinearity based model. Exhaustive tests showed the numerical model, using 3 or fewer ground control points, consistendy equaled or bettered the performance of the earlier model, using between 6 and 15 ground control points, on the same test data. A general purpose sensor modelling system was developed using the code developed for the initial SPOT-1 model. Currently this system supports many rigid linear sensors systems including SPOT-1, SPOT-2, FTIR, MISR, MEOSS and ASAS. Further extensions to the system to enable it to model non-rigid linear sensors such as AVHRR and ATM are planned. Work to enable the system to perform relative orientations for a variety of sensor types is also ongoing

Improved self-gain in deep submicrometer strained silicon-germanium pMOSFETs with HfSiOx/TiSiN gate stacks

The self-gain of surface channel compressively strained SiGe pMOSFETs with HfSiOx/TiSiN gate stacks is investigated for a range of gate lengths down to 55 nm. There is 125% and 700% enhancement in the self-gain of SiGe pMOSFETs compared with the Si control at 100 nm and 55 nm lithographic gate lengths, respectively. This improvement in the self-gain of the SiGe devices is due to 80% hole mobility enhancement compared with the Si control and improved electrostatic integrity in the SiGe devices due to less boron diffusion into the channel. At 55 nm gate length, the SiGe pMOSFETs show 50% less drain induced barrier lowering compared with the Si control devices. Electrical measurements show that the SiGe devices have larger effective channel lengths. It is shown that the enhancement in the self-gain of the SiGe devices compared with the Si control increases as the gate length is reduced thereby making SiGe pMOSFETs with HfSiOx/TiSiN gate stacks an excellent candidate for analog/mixed-signal applications

Measuring Productivity Change and Efficiency on Irish Farms.

End of Project ReportThis report investigates technical change and levels of technical efficiency on Irish farms using National Farm Survey (N.F.S.) data. It also examines whether levels of technical efficiency are influenced by contact with the extension service. The study utilises a stochastic production frontier approach to measure productivity growth and the technical efficiency of a panel of Irish farms over the period 1984 to 1998. This sample was used to calculate (a) technical change over time as measured by best practice farms and (b) technical efficiency levels of all farms over this period. It, therefore, provides disaggregated estimates of technical change by farming system as well as quantifying the average level of technical efficiency. The project also examines the factors associated with differences in technical efficiency between farms and the impact of extension service contact on farm-level technical efficiency. Mean technical change (i.e. changes in best practice) continued, albeit at a declining rate, throughout the period studied. Significant differences were revealed in the rate of technical change on farms of different types. For example technical change on dairy and crop farms averaged nearly 2 per cent per annum while technical regress occurred on beef and sheep farms. In addition to examining technical change, farm efficiency relative to best practice within each farming system was also measured. Results indicate that farms achieved, on average, approximately 65 per cent of the efficiency level of best practice farms. The average level of farm efficiency has been decreasing by 0.4 per cent per annum indicating that the gap between best practice farms and all farms has been increasing by this amount over time. Thirty one percent of the most efficient farms were dairy farms while 23 per cent were arable farms. Approximately 52 per cent of the least efficient farms were cattle farms while a further 31 per cent were sheep farms. Average efficiency over the period was 34.2 per cent in the least efficient quintile of farms. This compared to almost 90 per cent for the most efficient quintile of farms. A positive relationship between age and efficiency was found up to the age of 49 years after which the relationship between age and efficiency becomes negative. The farm debt to assets ratio was positively related to efficiency while farm size and location in the West of Ireland was negatively related to efficiency. Farms in contact with the extension service were found to be on average 6.5 per cent more efficient than farms without contact. Contact farms with a lower than average dependency on direct payments were a further 6.6 per cent than contact farms with an average dependency on direct payments. Contact farms with a higher than average dependence on direct payments were 1.9 per cent less efficient than the same group of contact farms. However, efficiency on these farms with a high dependence on direct payments was still, on average, higher than on farms with no extension contact.Teagasc Walsh Fellowshi

The impact of self-heating and SiGe strain-relaxed buffer thickness on the analog performance of strained Si nMOSFETs

The impact of the thickness of the silicon–germanium strain-relaxed buffer (SiGe SRB) on the analog performance of strained Si nMOSFETs is investigated. The negative drain conductance caused by self-heating at high power levels leads to negative self-gain which can cause anomalous circuit behavior like non-linear phase shifts. Using AC and DC measurements, it is shown that reducing the SRB thickness improves the analog design space and performance by minimizing self-heating. The range of terminal voltages that leverage positive self-gain in 0.1 μm strained Si MOSFETs fabricated on 425 nm SiGe SRBs is increased by over 100% compared with strained Si devices fabricated on conventional SiGe SRBs 4 μm thick. Strained Si nMOSFETs fabricated on thin SiGe SRBs also show 45% improvement in the self-gain compared with the Si control as well as 25% enhancement in the on-state performance compared with the strained Si nMOSFETs on the 4 μm SiGe SRB. The extracted thermal resistance is 50% lower in the strained Si device on the thin SiGe SRB corresponding to a 30% reduction in the temperature rise compared with the device fabricated on the 4 μm SiGe SRB. Comparisons between the maximum drain voltages for positive self-gain in the strained Si devices and the ITRS projections of supply-voltage scaling show that reducing the thickness of the SiGe SRB would be necessary for future technology nodes

A lexical database for public textual cyberbullying detection

Public textual cyberbullying has become one of the most prevalent issues associated with online safety of young people, particularly on social networks. To address this issue, we argue that the boundaries of what constitutes public textual cyberbullying needs to be first identified and a corresponding linguistically motivated definition needs to be advanced. Thus, we propose a definition of public textual cyberbullying that contains three necessary and sufficient elements: the personal marker, the dysphemistic element and the cyberbullying link between the previous two elements. Subsequently, we argue that one of the cornerstones in the overall process of mitigating the effects of cyberbullying is the design of a cyberbullying lexical database that specifies what linguistic and cyberbullying specific information is relevant to the detection process. In this vein, we propose a novel cyberbullying lexical database based on the definition of public textual cyberbullying. The overall architecture of our cyberbullying lexical database is determined semantically, and, in order to facilitate cyberbullying detection, the lexical entry encapsulates two new semantic dimensions that are derived from our definition: cyberbullying function and cyberbullying referential domain. In addition, the lexical entry encapsulates other semantic and syntactic information, such as sense and syntactic category, information that, not only aids the process of detection, but also allows us to expand the cyberbullying database using WordNet (Miller, 1993)

Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying

[EN] Cyberbullying is a risk associated with the online safety of young people and, in this paper, we address one of its most common implicit forms – negation-based forms. We first describe the role of negation in public textual cyberbullying interaction and identify the cyberbullying constructions that characterise these forms. We then formulate the overall detection mechanism which captures the three necessary and sufficient elements of public textual cyberbullying – the personal marker, the dysphemistic element, and the link between them. Finally, we design rules to detect both overt and covert negation-based forms, and measure their effectiveness using a development dataset, as well as a novel test dataset, across several metrics: accuracy, precision, recall, and the F1-measure. The results indicate that the rules we designed closely resemble the performance of human annotators across all measures.Power, A.; Keane, A.; Nolan, B.; O'neill, B. (2018). Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying. Journal of Computer-Assisted Linguistic Research. 2(1):1-20. doi:10.4995/jclr.2018.891712021Al-garadi, M.A., Varathan, K.D. and Ravana S.D. 2016. "Cybercrime Detection in Online Communications: The Experimental Case of Cyberbullying Detection in the Twitter Network." Computers in Human Behaviour, 63: 433 - 443. https://doi.org/10.1016/j.chb.2016.05.051Allan, K. and Burridge, K. 2006. Forbidden Words: Taboo and Censoring of Language. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511617881Boyd, D. 2007. "Why Youth (Heart) Social Network Sites: The Role of Networked Publics in Teenage Social Life." In MacArthur Foundation Series on Digital Learning, Youth, Identity, and Digital Media, edited by David Buckingham, 1 - 26. Cambridge, MA: MIT Press.Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., and Vakali, A. 2017. "Mean Birds: Detecting Aggression and Bullying on Twitter." Cornell University Library: https://arxiv.org/abs/1702.06877.Chen, Y., Zhou, Y., Zhu, S. and Xu, H. 2012. "Detecting Offensive Language in Social Media to Protect Adolescent Online Safety." Paper presented at the ASE/IEEE International Conference on Social Computing, 71 - 80. Washington, DC, September 3-5. https://doi.org/10.1109/SocialCom-PASSAT.2012.55Dadvar, M., Trieschnigg, D., R. Ordelman, R., and de Jong, F. 2013. "Improving cyberbullying detection with user context." Paper presented at the 35th European conference on Advances in Information Retrieval, 693 - 696. Moscow, March 24-27. https://doi.org/10.1007/978-3-642-36973-5_62de Marneffe, M.C., and Manning, C.D. 2008a. "The Stanford typed dependencies representation." Paper presented at the COLING 2008 Workshop on Cross-framework and Cross-domain Parser Evaluation. Manchester, UK August 23 - 23. https://doi.org/10.3115/1608858.1608859de Marneffe, M.C., and Manning, C. 2008b. "Stanford typed dependencies manual." https://nlp.stanford.edu/software/dependencies_manual.pdf.Dinakar, K., Jones, B., Havasi, C., Lieberman, H., and Picard, R. 2012. "Common sense reasoning for detection, prevention, and mitigation of cyberbullying." ACM Transactions on Interactive Intelligent Systems, 2: 18:1-18:30. https://doi.org/10.1145/2362394.2362400Dooley, J.J., Pyzalski, J., and Cross, D. 2009. "Cyberbullying versus face-to-face bullying - A theoretical and conceptual review." Journal of Psychology, 217: 182-188. https://doi.org/10.1027/0044-3409.217.4.182Goncalves, M. 2011. "Text Classification". In Modern Information Retrieval, the concepts and technology behind search, edited by Ricardo Baeza-Yates and Berthier Ribeiro-Neto, 281 - 336. Pearson Education Limited.Grigg, D.W. 2010. "Cyber-Aggression: Definition and Concept of Cyberbullying." Australian Journal of Guidance and Counselling, 12: 143-156. https://doi.org/10.1375/ajgc.20.2.143Hinduja, S., and Patchin, J.W. 2009. Bullying beyond the schoolyard: preventing and responding to cyber-bullying. Thousand Oaks, CA: Corw2017.Horn, L. R. 1989. A Natural History of Negation. Chicago: University of Chicago Press.Hosseinmardi, H., Han, R., Lv, Q., Mishra, S., and Ghasemianlangroodi, A. 2014a. "Towards Understanding Cyberbullying Behavior in a Semi-Anonymous Social Network." Paper presented at the International Conference on Advances in Social Networks Analysis and Mining. Beijing, August 17-20. https://doi.org/10.1109/ASONAM.2014.6921591Hosseinmardi, H., Rafiq, R. I., Li, S., Yang, Z., Han, R., Lv, Q., and Mishra, S. 2014b. "A Comparison of Common Users across Instagram and Ask.fm to Better Understand Cyberbullying." Paper presented at the 7th International Conference on Social Computing and Networking. Sydney, December 3-5.Huang, Q., Singh, V.K., and Atrey, P.K. 2014. "Cyber Bullying Detection using Social and Textual Analysis." Paper presented at the 3rd International Workshop on Socially-Aware Multimedia, 3 - 6. Orlando, Florida, November 7. https://doi.org/10.1145/2661126.2661133InternetSlang. 2017. "Internet Slang - Internet Dictionary." Accessed October 19. http://www.Internetslang.com/.Kavanagh, P. 2014. "Investigation of Cyberbullying Language & Methods." MSc diss., ITB, Ireland.Kontostathis, A., Reynolds, K., Garron, A. and Edwards, L. 2013. Detecting Cyberbullying: Query Terms and Techniques. Paper presented at the 5th Annual ACM Web Science Conference. Paris, May 2-4. https://doi.org/10.1145/2464464.2464499Langos, C. 2012. "Cyberbullying: The Challenge to Define." Cyberpsychology, Behavior, and Social Networks, 15(6): 285-289. https://doi.org/10.1089/cyber.2011.0588Lawler, J. 2005. "Negation and NPIs." http://www.umich.edu/~jlawler/NPIs.pdfLivingstone, S.,Haddon, L., Görzig, A., and Ólafsson, K. 2011. "EU Kids Online: final report 2011." http://eprints.lse.ac.uk/45490/1/EU%20Kids%20Online%20final%20report%202011%28lsero%29.pdf.Livingstone, S., Mascheroni, G., Ólafsson, K., and Haddon, L. with the networks of EU Kids Online and Net Children Go Mobile. 2014. "Children's online risks and opportunities: Comparative findings from EU Kids Online and Net Children Go Mobile". http://eprints.lse.ac.uk/60513/1/__lse.ac.uk_storage_LIBRARY_Secondary_libfile_shared_repository_Content_EU%20Kids%20Online_EU%20Kids%20Online-Children%27s%20online%20risks_2014.pdf.Nahar, V., Li, X. and Pang, C. 2013. "An Effective Approach for Cyberbullying Detection." Communications in Information Science and Management Engineering, 3:238 - 247.Nandhini, B.S., and Sheeba, J.I. 2015. "Online Social Network Bullying Detection Using Intelligence Techniques." Procedia Computer Science, 45: 485 - 492. https://doi.org/10.1016/j.procs.2015.03.085Navarro, G. and Ziviani, N. 2011. "Documents: Languages & Properties". In Modern Information Retrieval, the concepts and technology behind search, edited by Ricardo Baeza-Yates and Berthier Ribeiro-Neto, 203 - 254. Pearson Education Limited.Nitta, T., Masui, F., Ptaszynski, M., Kimura, Y., Rzepka, R., and Araki, K. 2013. "Detecting Cyberbullying Entries on Informal School Websites Based on Category Relevance Maximization." Paper presented at the 6th International Joint Conference on Natural LanguageProcessing. Nagoya, October 14-19.Norvig, P. 2007. "How to Write a Spelling Corrector." Accessed October 19. http://norvig.com/spell-correct.html.Oracle. 2017. Java™ Platform, Standard Edition 9 API Specification. Accessed October 19. https://docs.oracle.com/javase/9/docs/api/index.html?overview-summary.html.Power, A., Keane, A., Nolan, B., and O'Neill, B. 2017. "A Lexical Database for Public Textual Cyberbullying Detection". Special issue of Revista de lenguas para fines específicos, entitled New Insights into Meaning Construction and Knowledge Representation.Ptaszynski, M., Dybala, P., Matsuba, T., Rzepka, R. and Araki, K. 2010. "Machine Learning and Affect Analysis Against Cyber-Bullying." Paper presented at the 36th AISB Annual Convention. March 29- April 1.Ptaszynski, M., Masui, F., Nitta, T., Hatekeyama, S., Kimura, Y., Rzepka, R., and Araki, K. 2016. "Sustainable Cyberbullying Detection with Category-Maximised Relevance of Harmful Phrases and Double-Filtered Automatic Optimisation." International Journal of Child-Computer Interaction, 8: 15 - 30. https://doi.org/10.1016/j.ijcci.2016.07.002Reynolds, K., Kontostathis, A. and Edwards, L. 2011. "Using Machine Learning to Detect Cyberbullying." Paper presented at the 10th International Conference on Machine Learning and Applications Workshops. Hawaii, December 18-21. https://doi.org/10.1109/ICMLA.2011.152Sourander, A., Brunstein-Klomek, A., Ikonen, M., Lindroos, J., Luntamo, T., Koskelainen, M., Ristkari, T., Hans Helenius, H. 2010. "Psychosocial risk factors associated with cyberbullying among adolescents: A population-based study." Arch Gen Psychiatry, 67: 720-728. https://doi.org/10.1001/archgenpsychiatry.2010.79Unicode. 2017. "Emoticons." Accessed October 19. http://www.unicode.org/.Van Hee, C., Lefever, E.,Verhoeven, B.,Mennes, J.,Desmet, B., DePauw, G., Daelemans, W., and Hoste, V. 2015. "Detection and Fine-GrainedClassificationofCyberbullyingEvents." Paper presented at the annual conference on RANLP. Hissar, September 5-11.Witten, I.H., Frank, E., and Hall, M.A. 2011. Data Mining: Practical Machine Learning Tools and Techniques (3rd edition). Elsevier Inc., USA.Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A., and Edwards, L. 2009. "Detection of harassment on web 2.0." Paper presented at the 1st conference on CAW. Madrid, April 20-24

Digital technologies in bronchiectasis physiotherapy services: A survey of patients and physiotherapists in a UK centre

Introduction We aimed to explore how digital technology is currently used, could be used and how services could be improved in order to optimise bronchiectasis physiotherapy care. Methods Online surveys were designed and distributed amongst people with bronchiectasis and physiotherapists in Northern Ireland. Responses to closed and open question formats were collected and analysed. Results The survey was completed by 48 out of 100 physiotherapists (48%) between January 2020 and January 2021 and by 205 out of 398 people with bronchiectasis (52%) between October 2020 and October 2021. 56% of physiotherapists (27 out of 48) reporting using some type of digital technology to facilitate services, whereas 44% (21 out of 48) reported that they had never used a digital technology in this patient group. When physiotherapists were asked whether they would be likely to use certain remote and/or digital options to deliver follow-up care for airway clearance techniques, most (31–38 out of 48; 65–79%) indicated that they would. Regarding patient responses, most reported that they would use telephone consultation (145 out of 199, 73%) and a smaller proportion were likely to use video consultation (64 out of 199, 32%). The most commonly mentioned theme for improvement amongst patients was follow-ups, while improved access, quality of services and treatments were the most commonly mentioned amongst physiotherapists. Conclusion Despite a large proportion of physiotherapists in this survey reporting no current use of digital technology in bronchiectasis physiotherapy care, there was significant interest and willingness to do so, amongst both physiotherapists and patients. This survey highlighted a range of care areas, specifically follow-up visits, where digital methods could be further explored.</p

Stock Assessment of Ballot's saucer scallop (Ylistrum balloti) in Queensland

This assessment estimates the status of saucer scallops in the main fishing grounds of the Queensland Southern Inshore fishery. The stock assessment data inputs included total harvests, standardised catch rates and fishery independent density estimates.   Analyses suggested that spawning biomass in 2019 fell to around 17 per cent of the unfished level. The report presents recommendations on fishing effort levels to begin rebuilding the stock to levels consistent with 40 per cent of unfished biomass