This research work focuses on the wake flow region of a cylinder with a spoiler on a rough bed under steady flow conditions. The acoustic Doppler velocimetry was used for the measurement of three-dimensional velocity data for two Reynolds numbers in a fully developed turbulent flow around the cylinder with a spoiler. The mean flow velocities, second-order turbulence structures, and conditional statistics were investigated in the wake region of the spoilered cylinder. The flow was separated from the spoiler with the formation of two shear layers between free surface flow and recirculating flow. It is observed that the flow is reattaching to the bed at 11D irrespective of the Reynolds number. Downstream of the cylinder, the mean velocity distributions are asymmetric due to the wall–wake effect, and the point of inflection is observed for each velocity profile at z = 0.40ẟ. The turbulence intensities, Reynolds stresses, and TKE are highly enhanced in the wake region of the cylinder as compared to their respective upstream values for both runs. The turbulence intensities, Reynolds normal stresses, Reynolds shear stresses, and turbulent kinetic energy are attaining peaks at z = 0.4 ẟ for all the streamwise locations, and the peaks are found to be highest at x = 10D. The quadrant analysis results indicate that the sweeps are dominating bursting events in the inner and intermediate layers, while ejections are dominating in the outer layer of the wake region. As the hole size, H increases ejections stress fraction rises as compared to that of the sweeps in the wake region for z = 0.2–0.7 h.

In different engineering processes, the reliability of systems is increasingly evaluated to ensure that the safety-critical process systems will operate within their expected operational boundary for a certain mission time without failure. Different methodologies used for reliability analysis of process systems include Failure Mode and Effect Analysis (FMEA), Fault Tree Analysis (FTA), and Bayesian Networks (BN). Although these approaches have their own procedures for evaluating system reliability, they rely on exact failure data of systems’ components for reliability evaluation. Nevertheless, obtaining exact failure data for complex systems can be difficult due to the complex behaviour of their components, and the unavailability of precise and adequate information about such components. To tackle the data uncertainty issue, this chapter proposes a framework by combining intuitionistic fuzzy set theory and expert elicitation that enables the reliability assessment of process systems using FTA. Moreover, to model the statistical dependencies between events, we use the BN for robust probabilistic inference about system reliability under different uncertainties. The efficiency of the framework is demonstrated through application to a real-world system and comparison of the results of analysis produced by the existing approaches.

Electricity providers aims to deliver uninterrupted electrical services to their customers at minimum cost while providing a satisfactory quality service. Therefore, the power system reliability is essential in power distribution network planning, design, and operation. This paper proposes a novel mathematical model to improve the reliability of reconfigurable distribution networks via investing and operating tie-lines. While the failure is being repaired, tie-lines allow the network operator to transfer loads from failed zones to healthy zones. Constructing new tie-lines could improve the network’s flexibility, aiming to reduce the cost of expected energy not supplied (EENS). The objective function of the proposed method is a trade-off between the investment cost of tie-lines construction in the planning stage, the cost of tie-lines operation (e.g., opening/closing), and the cost of EENS in the operational stage. The model simultaneously evaluates the best combination of investments and network configuration for each contingency while considering network constraints. A multistage mathematical model is developed as mixed-integer linear programming (MILP) to overcome the computational complexity and maintain solver traceability for utility-scale realistic networks. The model can handle the network reconfiguration (NR) considering N-x contingency analysis in the operation stage while deciding the investment in tie-lines in the planning stage. The optimal investment and operation in tie-lines, according to numerical results, can reduce the cost of the Distribution System (DS) while responding with contingencies by 51 to 70%.

The advances in the Internet of Things (IoT) has substantially contributed to the automation of modern societies by making physical things around us more interconnected and remotely controllable over the internet. This technological progress has inevitably created an intelligent society where various mechatronic systems are becoming increasingly efficient, innovative, and convenient. Undoubtedly, the IoT paradigm will continue to impact human life by providing efficient control of the environment with minimum human intervention. However, despite the ubiquity of IoT devices in modern society, the dependability of IoT applications remains a crucial challenge. Accordingly, this paper systematically reviews the current status and challenges of IoT dependability frameworks. Based on the review, existing IoT dependability frameworks are mainly based on informal reliability models. These informal reliability models are unable to effectively evaluate the unified treatment safety faults and cyber-security threats of IoT systems. Additionally, the existing frameworks are also unable to deal with the conflicting interaction between co-located IoT devices and the dynamic features of self-adaptive, reconfigurable, and other autonomous IoT systems. To this end, this paper suggested the design of a novel model-based dependability framework for quantifying safety faults and cyber-security threats as well as interdependencies between safety and cyber-security in IoT ecosystems. Additionally, robust approaches dealing with conflicting interactions between co-located IoT systems and the dynamic behaviours of IoT systems in reconfigurable and other autonomous systems are required.

Aim: This research aims to determine the organizational preventative strategies implemented by dental clinics in Fiji during the COVID-19 pandemic. Methods: This qualitative study was conducted amongst Dental Officers (Dos) and Dental Managers (DMs) who were working at government dental clinics, private dental clinics, and the School of Dentistry and Oral Health clinic (SDOH), in the Central Division, Fiji. A semi-structured open-ended questionnaire was used for data collection through in-depth interviews via zoom. A manual thematic analysis of the data was conducted. Results: Thirty Dos and 17 DMs participated in this study. 16 themes emerged from data analysis: Major Strategies implemented, Staff perception about strategies in place, Triaging and Screening, Hand hygiene, Waiting room changes, Operational Capacity, Universal precautions, Personal Protective Equipment (PPEs), Disinfection and decontamination protocols, Ventilation, Sterilization, Pre-procedural mouth rinse, Waste management, Vaccination status, Bubbles and Adaptation of Protocols. The Dos were generally satisfied with the strategies implemented by the DMs. The DMs along with other Dos had used various guidance documents to devise tailor-made ones suited for dental clinics in Fiji. Conclusion: Various strategies were adopted from several guidelines and tailor-made Standard Operating Procedures (SOPs) for each workplaces were developed by the various DMs. The majority of Dos were in favor of and satisfied with the protocols in place. Future research can be conducted in other divisions and include other health care professionals as well apart from just Dos and DMs.

Fresh water is crucial for life, supporting human civilizations and ecosystems, and its production is one of the global issues. To cope with this issue, we evaluated the performance and cost of a solar-powered direct contact membrane distillation (DCMD) unit for fresh water production in Karachi, Pakistan. The solar water heating system (SWHS) was evaluated with the help of a system advisor model (SAM) tool. The evaluation of the DCMD unit was performed by solving the DCMD mathematical model through a numerical iterative method in MATLAB software®. For the SWHS, the simulation results showed that the highest average temperature of 55.05 ◦C and lowest average temperature of 44.26 ◦C were achieved in May and December, respectively. The capacity factor and solar fraction of the SWHS were found to be 27.9% and 87%, respectively. An exponential increase from 11.4 kg/m2 ·h to 23.23 kg/m2 ·h in permeate flux was observed when increasing the hot water temperatures from 44 ◦C to 56 ◦C. In the proposed system, a maximum of 279.82 L/day fresh water was produced in May and a minimum of 146.83 L/day in January. On average, the solar-powered DCMD system produced 217.66 L/day with a levelized water cost of 23.01 USD/m3

Pesticide contamination is a global issue, affecting nearly 44% of the global farming population, and disproportionately affecting farmers and agricultural workers in developing countries. Despite this, global pesticide usage is on the rise, with the growing demand of global food production with increasing population. Different types of porous materials, such as carbon and zeolites, have been explored for the remediation of pesticides from the environment. However, there are some limitations with these materials, especially due to lack of functional groups and relatively modest surface areas. In this regard, metal-organic frameworks (MOFs) provide us with a better alternative to conventionally used porous materials due to their versatile and highly porous structure. Recently, a number of MOFs have been studied for the extraction of pesticides from the environment as well as for targeted and controlled release of agrochemicals. Different types of pesticides and conditions have been investigated, and MOFs have proved their potential in agricultural applications. In this review, the latest studies on delivery and extraction of pesticides using MOFs are systematically reviewed, along with some recent studies on greener ways of pest control through the slow release of chemical compounds from MOF composites. Finally, we present our insights into the key issues concerning the development and translational applications of using MOFs for targeted delivery and pesticide control.

The flexural performance of geopolymer concrete (GPC) T-beams reinforced longitudinally with GFRP bars under a four-point static bending test was investigated. Six full-scale simply supported T-beams were cast and tested; one control specimen was made with ordinary Portland cement concrete (OPCC), while the other five beams were made of geopolymer concrete. The G-GPC2 was designed to attain the same theoretical moment capacity as the G-OPCC6 control beam. The main parameters investigated were the reinforcement ratio of ρ_f/ρ_b= 0.75, 1.05, 1.12, 1.34 and 1.34 for G-GPC1, G-GPC2, G-GPC3, G-GPC4, and G-GPC5, respectively, and compressive strength of geopolymer concrete. Based on the results of the experiments, the ultimate strain of GPC did not show the same behaviour as that of OPCC, which affects the mode of failure. The beam capacity and deflection were, respectively, overestimated and underestimated using the ACI 440 2R-17 predictive equations.

There is a sharp increase in the number of old people living alone throughout the world. More often than not, such people require continuous and immediate care and attention in their everyday lives, hence the need for round the clock monitoring, albeit in a respectful, dignified and non-intrusive way. For example, continuous care is required when they become frail and less active, and immediate attention is required when they fall or remain in the same position for a long time. To this extent, various monitoring technologies have been developed, yet there are major improvements still to be realised. Current technologies include indoor positioning systems (IPSs) and health monitoring systems. The former relies on defined configurations of various sensors to capture a person's position within a given space in real-time. The functionality of the sensors varies depending on receiving appropriate data using WiFi, radio frequency identification (RFIO), ultrawide band (UWB), dead reckoning (OR), infrared indoor (IR), Bluetooth (BLE), acoustic signal, visible light detection, and sound signal monitoring. The systems use various algorithms to capture proximity, location detection, time of arrival, time difference of arrival angle, and received signal strength data. Health monitoring technologies capture important health data using accelerometers and gyroscope sensors. In some studies, audio fingerprinting has been used to detect indoor environment sound variation and have largely been based on recognising TV sound and songs. This has been achieved using various staging methods, including pre-processing, framing, windowing, time/frequency domain feature extraction, and post-processing. Time/frequency domain feature extraction tools used include Fourier Transforms (FTs}, Modified Discrete Cosine Transform (MDCT}, Principal Component Analysis (PCA), Mel-Frequency Cepstrum Coefficients (MFCCs), Constant Q Transform (CQT}, Local Energy centroid (LEC), and Wavelet transform. Artificial intelligence (Al) and probabilistic algorithms have also been used in IPSs to classify and predict different activities, with interesting applications in healthcare monitoring. Several tools have been applied in IPSs and audio fingerprinting. They include Radial Basis Kernel (RBF), Support Vector Machine (SVM), Decision Trees (DTs), Hidden Markov Models (HMMs), Na'ive Bayes (NB), Gaussian Mixture Modelling (GMM), Clustering algorithms, Artificial Neural Networks (ANNs), and Deep Learning (DL). Despite all these attempts, there is still a major gap for a completely non-intrusive system capable of monitoring what an elderly person living alone is doing, where and for how long, and providing a quick traffic-like risk score prompting, therefore immediate action or otherwise. In this thesis, a cost-effective and completely non-intrusive indoor positioning and activity-monitoring system for elderly people living alone has been developed, tested and validated in a typical residential living space. The proposed system works based on five phases: (1)Set-up phase that defines the typical activities of daily living (TADLs). (2)Configuration phase that optimises the implementation of the required sensors in exemplar flat No.1. (3)Learning phase whereby sounds and position data of the TADLs are collected and stored in a fingerprint reference data set. (4)Listening phase whereby real-time data is collected and compared against the reference data set to provide information as to what a person is doing, when, and for how long. (5)Alert phase whereby a health frailty score varying between O unwell to 10 healthy is generated in real-time. Two typical but different residential flats (referred to here are Flats No.1 and 2) are used in the study. The system is implemented in the bathroom, living room, and bedroom of flat No.1, which includes various floor types (carpet, tiles, laminate) to distinguish between various sounds generated upon walking on such floors. The data captured during the Learning Phase yields the reference data set and includes position and sound fingerprints. The latter is generated from tests of recording a specific TADL, thus providing time and frequency-based extracted features, frequency peak magnitude (FPM), Zero Crossing Rate (ZCR), and Root Mean Square Error (RMSE). The former is generated from distance measurement. The sampling rate of the recorded sound is 44.1kHz. Fast Fourier Transform (FFT) is applied on 0.1 seconds intervals of the recorded sound with minimisation of the spectral leakage using the Hamming window. The frequency peaks are detected from the spectrogram matrices to get the most appropriate FPM between the reference and sample data. The position detection of the monitored person is based on the distance between that captured from the learning and listening phases of the system in real-time. A typical furnished one-bedroom flat (flat No.2) is used to validate the system. The topologies and floorings of flats No.1 and No.2 are different. The validation is applied based on "happy" and "unusual" but typical behaviours. Happy ones include typical TADLs of a healthy elderly person living alone with a risk metric higher than 8. Unusual one's mimic acute or chronic activities (or lack thereof), for example, falling and remaining on the floor, or staying in bed for long periods, i.e., scenarios when an elderly person may be in a compromised situation which is detected by a sudden drop of the risk metric (lower than 4) in real-time. Machine learning classification algorithms are used to identify the location, activity, and time interval in real-time, with a promising early performance of 94% in detecting the right activity and the right room at the right time.

A multipartite system comprised of n subsystems, each of which is described with ‘local variables’ in Z(d) and with a d-dimensional Hilbert space H(d), is considered. Local Fourier transforms in each subsystem are defined and related phase space methods are discussed (displacement operators, Wigner and Weyl functions, etc). A holistic view of the same system might be more appropriate in the case of strong interactions, which uses ‘global variables’ in Z(dn) and a dn-dimensional Hilbert space H(dn). A global Fourier transform is then defined and related phase space methods are discussed. The local formalism is compared and contrasted with the global formalism. Depending on the values of d, n the local Fourier transform is unitarily inequivalent or unitarily equivalent to the global Fourier transform. Time evolution of the system in terms of both local and global variables, is discussed. The formalism can be useful in the general area of Fast Fourier transforms.