Aisha Aldosery

Mosquitos are a fundamental part of testing the novel idea of my PhD, which focuses on developing intervention tools to support developing an early warning system to control the mosquito, thus, combatting mosquito-borne diseases. However, with the COVID-19 pandemic, it was quite hard to fly to Brazil, considered one of the Latin American countries that was hit hard by mosquito-borne disease and has a strong program for mosquito surveillance. Therefore, conducting my fieldwork in a different location was more feasible, such as the Portuguese island of Madeira, located in the northeastern Atlantic Ocean, 900 km from mainland Portugal. A volcanic and subtropical island which seems like a perfect location for mosquitoes, it introduced an efficient program in 2005 focusing on mosquito surveillance. Four field trips have been conducted since November 2021 with Patty Kostkova, my primary supervisor, to achieve my project’s overarching goal. We worked together in designing and presenting several workshops on Madeira mobile app surveillance with the local environmental agents, as well as deploying several devices in the fields for environmental monitoring.

Trip One – Mosquito Ovitrap IOT-based System pilot system.

This trip was the first to Madeira after the COVID-19 pandemic; the trip was in late October 2021 and lasted for about three weeks. The main objectives of my first fieldwork trip (three weeks) were to establish a new collaboration with people from ITI / LARSyS, introduce and discuss my PhD idea with the team, and lastly, build a prototype version of the proposed system. Although the trip was considered short, we achieved a significant project milestone. During this trip, we started by calibrating the water sensors, building the IoT-based unit and deploying the prototype version of the MOISS system to understand how various weather and water parameters influence mosquito breeding and habitat favouring. The first version of the system has been deployed and running since November 2021 at the Natural History Museum of Funchal on Madeira Island. All timely data collected in the field by the sensors, such as the air temperature, humidity, pressure, water temperature, pH, DO, and conductivity, will be used along with the entomological data collected by the environmental agents to design and build a model to provide us with a better understanding of the mosquito’s development and presence.

Deployment of the first version of the MOISS system at the Natural History Museum.

The hardware component of the MOISS system.                                                          

Trip Two – Introducing Madeira Mosquito Surveillance App 

This trip was mainly about the project’s second component, which is about designing a mosquito surveillance app based on the local settings to be adopted by the environmental agents during their routine visits to the mosquito traps. To achieve that, establishing another collaboration with the local health sector is essential. The trip includes a couple of meetings and a workshop:

• Meetings with Dr Bruna Ornelas de Gouveia, Regional Directorate of Health in Madeira Island, to discuss and design the collaboration protocol with the UCL IRDR Centre for Digital Public Health in Emergencies (dPHE). The collaboration entitles us to pilot our app on the island and gives us access to historical mosquito density data.
• Meeting with the technical and GIS team, who showed us the mosquito data, hotspot maps and the effective strategies adopted by the local government to control mosquitoes across the island (https://www.iasaude.pt/Mosquito/ ).
• We ran the first workshop with the environmental agents to introduce the idea of the surveillance app and how it could positively affect their work. During this workshop, we presented some showcases from our Brazilian project (Belmont) and a prototype of the Madeira app. The agents demonstrated different scenarios that could happen on the ground and what actions needed to be considered in each scenario. Finally, we had an interactive session, a very productive session that helped us understand the local settings in different conditions.

Environmental agents, after completing the surveillance app workshop.

Trip Three – Mosquito Ovitrap IOT-based System (MOISS) Large Deployment.

The third fieldwork was the most significant and challenging trip as many milestones needed to be completed, including the IoT-based system units implementation and deployment, along with a lot of logical preparation. Yet, it was one of the most exciting trips to see the theories and paper design coming true. This trip was from July to the beginning of August 2022 (four weeks). The focus of this trip was the MOISS system. During this trip, we calibrated and tested 60 water sensors in a week period, which required specific weather conditions. Then, two engineers from ITI / LARSyS and I assembled 17 system units in a week, including the testing and debugging of each unit. The conducted lab testing was quite challenging, resulting in several issues, including problems with the manufactured IoT shield, slow network connections, power, etc. We ended up with 13 devices deployed across the capital of the Island, Funchal. The decision about how many devices and where to deploy them was collaborative work with environmental agents and the technical team to select suitable study sites based on several criteria, including technical, logistic and mosquito data. The locations include schools, hospitals, one university, the port, and a private building.

Assembly and testing phase of MOISS units at the lab.

MOISS system deployment.

Trip Four – Madeira Mosquito Surveillance App Piloting Workshop

The last trip of this year (September 2022) was a four-day trip for Madeira. The main objective of this trip was to run a three-hour workshop with the environmental agents to show them the first completed developed version of the app, which is designed and implemented based on the requirements collected in the first workshop (second trip). Patty and I gave the agents technical support to install, operate and test the app for about two hours. After that, we had a one-hour interactive session to collect their inputs, which will help us improve the app and develop another sufficient version. The agents were delighted with the mosquito surveillance app and were excited about the next phase, piloting the app for several months.

During this trip, the project gained the attention and interest of local Madeira TV, which was there during the workshop and interviewed Prof Patty Kostkova.

Patty Kostkova interviewed on Telejornal Madeira. Click image to open video (interview at 18:15-20:40).

We are currently looking for funding to develop and deploy the mosquito surveillance mobile app and collect data on a large scale. Finally, although each trip had its challenge, some went differently than we had planned and expected. I have learned much beyond my research scope and gained knowledge on project management and building collaboration. Many thanks to Patty for accompanying me in each project phase and trip to support me in moving the project forward. We had a great time enjoying the weather, and more significantly, we managed to deploy our IoT system and pilot the surveillance app.

Acknowledgements

Trip one was fully funded by the UCL Institution of Risk and Disaster Reduction (IRDR); trip two was fully funded by UCL Mathematical and Physical Science Faculty, PhD Students Travel Grant; trip three was mainly funded by the  UCL IRDR Centre for Digital Public Health in Emergencies (dPHE) and partially by the UCL Institution of Risk and Disaster Reduction (IRDR); trip four was fully funded by my PhD sponsor, King Abdulaziz City for Science and Technology, Saudi Arabia.

A big thanks and appreciation to our IRDR Finance team for their significant support which played a crucial role in helping me while preparing my PhD project. Special thanks to Matthew Lee for his outstanding support in managing equipment quotes and dealing with orders.

Aisha Aldosery is currently a doctoral candidate at the UCL IRDR Centre for Digital Public Health in Emergencies at University College London. She is also a researcher at King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia. She earned her master’s degree in Software System Engineering from UCL. Her broad research areas are software engineering and the Applied Internet of Things. She is particularly interested in designing and developing digital health intervention tools such as surveillance and early warning systems. She is also interested in designing environmental IoT-based sensor devices and analysing sensor data using machine learning methodologies. The focus of Aisha’s PhD research project is investigating mobile apps, the Internet of Things (IoT) and sensing technologies for predicting mosquito populations to combat vector-borne diseases – a pertinent global issue with global research significance.

Earthquake-induced direct and indirect losses tend to be high in highly populated earthquake-prone areas, especially in countries where most of the existing buildings and infrastructure are designed or built according to pre-seismic codes (if any). Therefore, there is a dire need to develop holistic strategies for mitigating and managing seismic risk. On the one hand, this involves risk understanding and quantification (e.g., risk/loss assessment methodologies). On the other hand, there is a crucial need to develop and implement strategies and techniques for repairing and retrofitting existing structures, which should be structurally effective, easy to apply, cost-effective, possibly reversible, and respectful of the architectural, heritage and cultural conservation requirements.

Both in the “diagnosis” and the “prognosis” phases, procedures to assess the structural performance under earthquake loads are paramount. Among many possibilities within the literature, choosing an appropriate assessment procedure depends on a simplicity vs accuracy trade-off governed by technical, economical, and time constraints. Moreover, various stakeholders have different needs on this matter: private owners likely need a detailed assessment focused on individual buildings or small portfolios, while government agencies or (re)insurance companies might look at large portfolios tolerating a lower refinement level and accepting higher uncertainties.

It is fundamental to select a procedure that can highlight the structural weaknesses of the considered structural system, so that it is possible to design retrofit solutions to specifically fix those. One procedure complying with this requirement, while being easy to apply, is SLaMA – Simple Lateral Mechanism Analysis.

Although SLaMA is normally applied using spreadsheets, it allows for defining the nonlinear force-displacement capacity and the sequence of local and global mechanisms of a building. It was introduced for the 1st time in the 2006 version of the New Zealand Society of Earthquake Engineering, NZSEE, Guidelines for the “Assessment and Improvement of the Performance of buildings in earthquakes” (NZSEE 2006), and revamped in the 2017 version (NZSEE 2017), after a substantial amount of research (Gentile 2017, Pampanin 2017; Del Vecchio et al. 2018; Gentile et al. 2019;  Gentile et al. 2019a; 2019b; 2019c; Bianchi et al. 2019). SLaMA is essentially mandatory in New Zealand, since it is required as an essential step before any other seismic numerical analysis is carried out. Its scope, however, is geographically much larger: more than 15 world-class companies (in New Zealand, Italy, Netherlands, UK) are using this method.

“SLaMA Solver Frame” is a free Windows/MacOS app created to enable engineers applying SLaMA using a graphical user interface, and without the need to create ad hoc spreadsheets. This app refers to reinforced concrete frame buildings, which constitute a substantial portion of the building stock in many countries around the world.

As shown in the tutorial video below, SLaMA Solver Frame is completely standalone (i.e., it does not require any other software to be run). It provides a “type and check” environment, in which every time the user inputs a parameter, the app automatically updates specific plots, therefore allowing for continuous cross checks and minimising input error. For each beam and column, SLaMA solver Frame provides their expected failure mode (flexure, bar buckling, shear, lap splice). For each beam column joint sub-assembly within the frame, the app determines its hierarchy of strength, indicating the member-level mechanism that causes its failure. Finally, by composing the results of each sub-assembly, SLaMA solver Frame provides an estimation of the plastic mechanism and the non-linear force-displacement curve.

SLaMA Solver Frame can be downloaded for free (for Windows and MacOS) at https://www.robertogentile.org/en/slamaf/. If you find any bugs, or you have any suggestions/comments, please feel free to report them dropping an email to robstructuralapps@gmail.com.

Disclaimer for SLaMA Solver Frame

SLaMA Solver Frame is provided by Dr Roberto Gentile under the Creative Commons “Attribution-No Derivatives 4.0 International” License. The purpose of SLaMA solver Frame is to cross-check by hand or spreadsheet calculations. This software is supplied “AS IS” without any warranties and support. The Author assumes no responsibility or liability for the use of the software. The Author reserves the right to make changes in the software without notification. The Author also make no representation or warranty that such application will be suitable for the use selected by the user without further calculations and/or checks.

Roberto Gentile is a Lecturer in Crisis and Catastrophe Modelling at IRDR.

References

Bianchi, Ciurlanti, and Pampanin. (2019). A SLaMA-Based Analytical Procedure for the Cost/Performance-Based Evaluation of Buildings. In COMPDYN 2019 – 7th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering. Crete Island, Greece.

Del Vecchio, Gentile, Di Ludovico, Uva, and Pampanin. (2018). Implementation and Validation of the Simple Lateral Mechanism Analysis (SLaMA) for the Seismic Performance Assessment of a Damaged Case Study Building [Open Access]. Journal of Earthquake Engineering 24 (11): 1771–1802. https://doi.org/10.1080/13632469.2018.1483278.

Gentile (2017). Extension, refinement and validation of the Simple Lateral Mechanism Analysis (SLaMA) for the seismic assessment of RC structures. PhD thesis. Polytechnic university of Bari, Italy.

Gentile, Pampanin, Raffaele, and Uva. (2019). Analytical Seismic Assessment of RC Dual Wall/Frame Systems Using SLaMA: Proposal and Validation [Open Access]. Engineering Structures 188: 493–505. https://doi.org/10.1016/j.engstruct.2019.03.029.

Gentile, Pampanin, Raffaele, and Uva. (2019). Non-Linear Analysis of RC Masonry-Infilled Frames Using the SLaMA Method: Part 1—Mechanical Interpretation of the Infill/Frame Interaction and Formulation of the Procedure [Open Access]. Bulletin of Earthquake Engineering 17 (6): 3283–3304. https://doi.org/10.1007/s10518-019-00580-w.

Gentile, Pampanin, Raffaele, and Uva. (2019). Non-Linear Analysis of RC Masonry-Infilled Frames Using the SLaMA Method: Part 2—Parametric Analysis and Validation of the Procedure [Open Access]. Bulletin of Earthquake Engineering 17 (6): 3305–26. https://doi.org/10.1007/s10518-019-00584-6.

Gentile, Del Vecchio, Pampanin, Raffaele, and Uva. (2019). Refinement and Validation of the Simple Lateral Mechanism Analysis (SLaMA) Procedure for RC Frames [Open Access]. Journal of Earthquake Engineering. https://doi.org/10.1080/13632469.2018.1560377.

New Zealand Society for Earthquake Engineering (NZSEE). (2006). Assessment and improvement of the structural performance of buildings in earthquakes. Wellington, New Zealand.

New Zealand Society for Earthquake Engineering (NZSEE). (2017). The Seismic Assessment of Existing Buildings – Technical Guidelines for Engineering Assessments. Wellington, New Zealand.

Pampanin. (2017). Towards the Practical Implementation of Performance-Based Assessment and Retrofit Strategies for RC Buildings: Challenges and Solutions. In SMAR2017- Fourth Conference on Smart Monitoring, Assessment and Rehabilitation of Structures. 13-15 March 2017. Zurich, Switzerland.