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In the past few decades, concrete has been the most widely used material for structural applications in the world and uses steel reinforcement as aide to meet the flexural, tensile and ductility demands required of concrete structures. Manufacturing of concrete and steel reinforced concrete structures is associated with millions of tons of carbon dioxide emissions and mineral waste. This activity is also responsible for the depletion of a large number of non-renewable resources. Reinforcing steel is also a high cost material, consumes a lot of energy in its production. Consequently, the use of natural fibres as an alternative for steel reinforcement is widely investigated, to promote the use of sustainable concrete structures. This study aims to investigate the effect on durability, flexural, compressive, tensile properties and workability of concrete by incorporating coir fibre at varying fibre content to find the fibre content which gives optimum results. The fibre contents used were 0%, 0.5%, 1.0%, 1.5% and 2.0% by weight of cement. Furthermore, the effect of modifying the surface of the coir fibres by alkali treatment (i.e. 5 wt.% NaOH solution) and coating the fibres with epoxy paint and polyurethane varnish on coir fibre reinforced concrete (CFRC) were also investigated. Tests conducted on the CFRC specimens included slump test and flexural, compressive and tensile strength tests. Water absorption and sorptivity tests were also conducted to investigate the durability. Slump (workability) and unit weight reduced with an increase in fibre content. The surface modification methods used, had resulted in an increased workability and a reduced unit-weight. A coconut fibre content of 1% produced the best combination of flexural, tensile and compressive properties. Water absorption and sorption rate per unit time, increased with an increase of coir fibre content. It is also found that epoxy paint and alkali treatment of the fibres has a positive effect on the mechanical strength properties and also the durability and workability of the CFRC specimens. However, polyurethane varnish coating had a detrimental effect on the mechanical strength properties of the CFRC specimens.

People with type 1 diabetes mellitus (T1DM) must continuously monitor their blood glucose levels and regulate them by insulin dosing to stay in a safe range. A reliable glucose prediction technique could pre-alert the risk of abnormal glycaemia in the near future. In this paper, we apply an attention-based deep network for glucose prediction, which models both the temporal dependencies and the physiological relations among glucose and glucose-related life events, including insulin and carbohydrate intake. We also propose to make use of the knowledge learned from non-target subjects with the same disease to improve the personalised prediction by applying parameters transfer. Our approach was evaluated on the standard benchmark OhioT1DM dataset, where the experiments achieved average root mean square errors over the 12 subjects of 17.82 mg/dL for 30 minutes and 28.54 mg/dL for 60 minutes. Additionally, our ablation experiments indicated that the use of transfer learning constantly improved the prediction. On this basis, we conclude that our approach achieves state-of-the-art performance with statistical significance, and data from other people with T1DM could help on improving personalised predictions. We release our codes at https://github.com/r-cui/GluPred under the MIT license.

BACKGROUND Type 1 diabetes (T1D) is a chronic autoimmune disease in which a deficiency in insulin production impairs the glucose homeostasis of the body. Continuous subcutaneous infusion of insulin is a commonly used treatment method. Artificial pancreas systems (APS) use continuous glucose level monitoring and continuous subcutaneous infusion of insulin in a closed-loop mode incorporating a controller (or control algorithm). However, the operation of APS is challenging because of complexities arising during meals, exercise, stress, sleep, illnesses, glucose sensing and insulin action delays, and the cognitive burden. To overcome these challenges, options to augment APS through integration of additional inputs, creating multi-input APS (MAPS), are being investigated. OBJECTIVE The aim of this survey is to identify and analyze input data, control architectures, and validation methods of MAPS to better understand the complexities and current state of such systems. This is expected to be valuable in developing improved systems to enhance the quality of life of people with T1D. METHODS A literature survey was conducted using the Scopus, PubMed, and IEEE Xplore databases for the period January 1, 2005, to February 10, 2020. On the basis of the search criteria, 1092 articles were initially shortlisted, of which 11 (1.01%) were selected for an in-depth narrative analysis. In addition, 6 clinical studies associated with the selected studies were also analyzed. RESULTS Signals such as heart rate, accelerometer readings, energy expenditure, and galvanic skin response captured by wearable devices were the most frequently used additional inputs. The use of invasive (blood or other body fluid analytes) inputs such as lactate and adrenaline were also simulated. These inputs were incorporated to switch the mode of the controller through activity detection, directly incorporated for decision-making and for the development of intermediate modules for the controller. The validation of the MAPS was carried out through the use of simulators based on different physiological models and clinical trials. CONCLUSIONS The integration of additional physiological signals with continuous glucose level monitoring has the potential to optimize glucose control in people with T1D through addressing the identified limitations of APS. Most of the identified additional inputs are related to wearable devices. The rapid growth in wearable technologies can be seen as a key motivator regarding MAPS. However, it is important to further evaluate the practical complexities and psychosocial aspects associated with such systems in real life.

There is limited understanding on factors that contribute to throwing related injuries that frequently occur in sports such as Baseball, Cricket and Javelin throwing.This preliminary study focuses on the development of a real time wearable system focusing on extracting key parameters related to potential upper arm injuries associated with the throwing action in the game of Cricket. A wearable system is developed to analyze Electromyography (EMG) signals for detecting muscle activity and Inertial Measurement Unit (IMU) data for monitoring the arm motion.The extracted parameters are then used for analysis, focusing on detecting established indicators of potential injuries. Additionally, an unsupervised learning algorithm is developed towards identifying novel relationships indicating potential injuries.

Photoplethysmography (PPG) can be identified as a non invasive technique which measures the blood volume changes in the blood vessels, through which key health indicators could be identified and used for abnormality detection. PPG has been integrated in numerous modern applications. This research is conducted in order to evaluate the possibility of applying PPG to the human sole and identifying the optimum regions of measurement. The research calculates the normalized cross correlation between the PPG extracted from the finger and different regions of the sole, and develops an algorithm for Pulse Rate estimation.

Neoseiulus baraki Athias-Henriot (Acari: Phytoseiidae) is an important predatory mite of the coconut mite, Aceria guerreronis Keifer (Acari: Eriophyidae) and attempts are now being made to evaluate the effectiveness of augmenting N. baraki to control coconut mite in the field. These studies require a steady supply of N. baraki in large numbers. One major constraint of the existing method to mass rear N. baraki on Tyrophagus putrescentiae Shrank (Acari: Acaridae) in a closed arena without a water barrier was frequent contamination of cultures by other mites. This paper describes the development of an efficient method to mass rear N. baraki in the laboratory with less monitoring and relatively minimal contaminants. Three box-type arenas and a tray-type arena were tested to mass rear T. putrescentiae . Mites were successfully developed on two box-type arenas and the tray-type arena, but the production (2197 mites per tray in 4 weeks) of T. putrescentiae was significantly higher on the tray-type arena. A 110-fold increase of T. putrescentiae was achieved in tray-type arenas in 4 weeks. None of the box-type arenas could be maintained more than 5 weeks without acarine and fungal contaminations. N. baraki could be successfully reared on the tray-type arena at least for 6 weeks. More than 4800 mites per tray, a 240-fold increase was achieved in 5 weeks when T. putrescentiae was added at 3-week intervals. Advantages and disadvantages of the new method are described.