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.

The intermittent nature of wind energy raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. One solution to overcome this problem is wind energy forecasting. A precise forecast can help to develop appropriate incentives and well-functioning electric markets. The paper presents a comprehensive review of existing research and current developments in deterministic wind speed and power forecasting. Firstly, we categorize wind forecasting methods into four broader classifications: input data, time-scales, power output, and forecasting method. Secondly, the performance of wind speed and power forecasting models is evaluated based on 634 accuracy tests reported in twenty-eight published articles covering fifty locations of ten countries. From the analysis, the most significant errors were witnessed for the physical models, whereas the hybrid models showed the best performance. Although, the physical models have a large normalized root mean square error values but have small volatility. The hybrid models perform best for every time horizon. However, the errors almost doubled at the medium-term forecast from its initial value. The statistical models showed better performance than artificial intelligence models only in the very short term forecast. Overall, we observed the increase in the performance of forecasting models during the last ten years such that the normalized mean absolute error and normalized root mean square error values reduced to about half the initial values.

This paper proposes a modular gripping mechanism for the manipulation of multiple objects. The proposed micro gripper combines traditional machining techniques with MEMS technologies to produce a modular mechanism consisting of a sturdy, compliant aluminium base and replaceable end-effectors. This creates an easily-customisable solution for micro manipulation with an array of different micro tips for different applications. We have provided the kinematic analysis for the gripper to predict the output and have also optimised design parameters based on FEA (finite element analysis) simulation and the effects of altering flexure beam lengths. The gripper is operated by a piezo actuator capable of 18 $\mathsf{\mu }$ m displacement at 150 V of applied DC voltage. This is then amplified by a factor of 8.1 to a maximum tip displacement of 154 $\mathsf{\mu }$ m. This is achieved by incorporating bridge and lever amplifying techniques into the design. An initial experimental analysis of the micro gripper is provided to investigate the performance of the micro gripper and to gauge the accuracy of the theory and simulation through comparison with experimental results.

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.