Browsing by Author "Al-Bahadly I"

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    Development of a Robotic Capsule for in vivo Sampling of Gut Microbiota
    (Institute of Electrical and Electronics Engineers, 2022-10-01) Rehan M; Al-Bahadly I; Thomas DG; Avci E
    Human gut microbiota can provide comprehensive information about the health of a host but the tools to collect microbiome samples are not currently available. A standalone wireless robotic capsule that has been developed in this study, collects the microbiota both from lumen (capsule surrounding) and intestinal wall (mucosa layer) for the first time. First, a two-way shape memory alloy (SMA) spring actuation system was developed by tackling the high-drain current requirement of SMAs. The actuator can produce up to 800 mN force that was sufficient to collect samples. Second, successful encapsulation of the collected sample to avoid contamination was realised by testing 3 main sealing materials. Third, the robotic capsule was tested in a gut simulator that mimics in-vivo environment to ensure successful and safe travel of the capsule along the gastrointestinal tract. Finally an in vitro experimental setup that keeps an intestine alive for 6 hours was used to optimise the sample collection. The capsule collected 128 μL and 107 μL samples (which are sufficient quantities for microbiome analysis) from duodenual and ileal tissues of a sheep. The proposed robotic capsule has a potential to become a vital apparatus for clinicians to sample human and animal gut in the future.
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    Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model
    (IEEE, 2021-06-08) Yousuf MU; Al-Bahadly I; Avci E; Do TD
    Markov chains (MC) are statistical models used to predict very short to short-term wind speed accurately. Such models are generally trained with a single moving window. However, wind speed time series do not possess an equal length of behavior for all horizons. Therefore, a single moving window can provide reasonable estimates but is not an optimal choice. In this study, a forecasting model is proposed that integrates MCs with an adjusting dynamic moving window. The model selects the optimal size of the window based on a similar approach to the leave-one-out method. The traditional model is further optimized by introducing a self-adaptive state categorization algorithm. Instead of synthetically generating time series, the modified model directly predicts one-step ahead wind speed. Initial results indicate that adjusting the moving window MC prediction model improved the forecasting performance of a single moving window approach by 50%. Based on preliminary findings, a novel hybrid model is proposed integrating maximal overlap discrete wavelet transform (MODWT) with auto-regressive integrated moving average (ARIMA) and adjusting moving window MC. It is evident from the literature that MC models are suitable for predicting residual sequences. However, MCs were not considered as a primary forecasting model for the decomposition-based hybrid approach in any wind forecasting studies. The improvement of the novel model is, on average, 55% for single deep learning models and 30% for decomposition-based hybrid models.
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    Smart capsules for sensing and sampling the gut: status, challenges and prospects
    (BMJ Publishing Group Ltd on behalf of the British Society of Gastroenterology, 2024-01) Rehan M; Al-Bahadly I; Thomas DG; Young W; Cheng LK; Avci E
    Smart capsules are developing at a tremendous pace with a promise to become effective clinical tools for the diagnosis and monitoring of gut health. This field emerged in the early 2000s with a successful translation of an endoscopic capsule from laboratory prototype to a commercially viable clinical device. Recently, this field has accelerated and expanded into various domains beyond imaging, including the measurement of gut physiological parameters such as temperature, pH, pressure and gas sensing, and the development of sampling devices for better insight into gut health. In this review, the status of smart capsules for sensing gut parameters is presented to provide a broad picture of these state-of-the-art devices while focusing on the technical and clinical challenges the devices need to overcome to realise their value in clinical settings. Smart capsules are developed to perform sensing operations throughout the length of the gut to better understand the body's response under various conditions. Furthermore, the prospects of such sensing devices are discussed that might help readers, especially health practitioners, to adapt to this inevitable transformation in healthcare. As a compliment to gut sensing smart capsules, significant amount of effort has been put into the development of robotic capsules to collect tissue biopsy and gut microbiota samples to perform in-depth analysis after capsule retrieval which will be a game changer for gut health diagnosis, and this advancement is also covered in this review. The expansion of smart capsules to robotic capsules for gut microbiota collection has opened new avenues for research with a great promise to revolutionise human health diagnosis, monitoring and intervention.
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    Towards Gut Microbiota Sampling Using an Untethered Sampling Device
    (IEEE, 2021-09-09) Rehan M; Al-Bahadly I; Thomas DG; Avci E
    Recent studies suggest that human gut microbiota can act as a bio-marker for human health. Also, it can function as a potential tool to understand stress and anxiety. However, the conventional tools have limitations acquiring samples of gut microbiota without contamination. In this work, an untethered robotic capsule prototype is developed that can actively collect the microbiota from the mucosa layer of the small intestine for the first time with the potential to avoid the upstream and downstream contamination. An analytical model for quantifying the peristaltic forces and developing two-way shape memory alloy spring actuator is presented. For the first time, a novel two-way shape memory alloy spring actuator (5 mm x \phi ~4 mm) is used to perform the sampling inside the gut. The spring actuator can apply 675 mN force, which is sufficient to perform in vivo sampling. A specialised experimental setup that can keep the freshly dissected intestine alive for 6 hours is utilised to test the robotic capsule. The robotic capsule prototype has collected an average of 200~\mu L and 112~\mu L sample from living pig duodenal and ileal tissues respectively i.e. in the presence of peristaltic forces. The robotic capsule was also tested on intestine of other species including cow and sheep and collected an average of 160~\mu L and 185~\mu L of content respectively from the living post-mortem tissues. The collected sample size for all the species is feasible to analyse the microbiota through next generation sequencing techniques. The experimental setup is a reliable proxy to in-vivo behaviour and the robotic capsule experimental result is promising in terms of in situ collection of microbiota.
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    Wind speed prediction for small sample dataset using hybrid first-order accumulated generating operation-based double exponential smoothing model
    (John Wiley & Sons, Inc, 2022-03-09) Yousuf MU; Al-Bahadly I; Avci E
    Wind power generation has recently emerged in many countries. Therefore, the availability of long-term historical wind speed data at various potential wind farm sites is limited. In these situations, such forecasting models are needed that comprehensively address the uncertainty of raw data based on small sample size. In this study, a hybrid first-order accumulated generating operation-based double exponential smoothing (AGO-HDES) model is proposed for very short-term wind speed forecasts. Firstly, the problems of traditional Holt's double exponential smoothing model are highlighted considering the wind speed data of Palmerston North, New Zealand. Next, three improvements are suggested for the traditional model with a rolling window of six data points. A mixed initialization method is introduced to improve the model performance. Finally, the superiority of the novel model is discussed by comparing the accuracy of the AGO-HDES model with other forecasting models. Results show that the AGO-HDES model increased the performance of the traditional model by 10%. Also, the modified model performed 7% better than other considered models with three times faster computational time.