A Non-intrusive Heuristic for Energy Messaging Intervention Modelled using a Novel Agent-based Approach

In response to the increased energy consumption in residential buildings, various efforts have been devoted to increase occupant awareness using energy feedback systems. However, it was shown that feedback provided by these systems is not enough to inform occupant actions to reduce energy consumption. Another approach is to control energy consumption using automated energy management systems. The automatic control of appliances takes-out the occupant sense of control, which is proved to be uncomfortable in many cases. This paper proposes an energy messaging intervention that keeps the control for occupants whilst supporting them with actionable messages. The messages inform occupants about energy waste incidents happening in their house in real-time, which enables occupants to take actions to reduce their consumption. Besides, a heuristic is defined to make the intervention non-intrusive by controlling the rate and time of the messages sent to occupants. The proposed intervention is evaluated in a novel layered agentbased model. The first layer of the model generates detailed energy consumption and realistic occupant activities. The second layer is designed to simulate the peer pressure effect on the energy consumption behaviour of the individuals. The third layer is a customisable layer that simulates energy interventions. The implemented intervention in this paper is the proposed non-intrusive messaging intervention. A number of scenarios are presented in the experiments to show how the model can be used to evaluate the proposed intervention and achieve energy efficiency targets

Activity Recognition with Evolving Data Streams: A Review

Activity recognition aims to provide accurate and opportune information on people’s activities by leveraging sensory data available in today’s sensory rich environments. Nowadays, activity recognition has become an emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or ambient sensors. This paper surveys the two overlapped areas of research of activity recognition and data stream mining. The perspective of this paper is to review the adaptation capabilities of activity recognition techniques in streaming environment. Categories of techniques are identified based on different features in both data streams and activity recognition. The pros and cons of the algorithms in each category are analysed and the possible directions of future research are indicated

A Fine-Grained Random Forests using Class Decomposition

Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, especially ensembles. It can be a computationally efficient way to provide a linearly separable dataset without the need for feature engineering required by techniques like Support Ve]ctor Machines (SVM) and Deep Learning. For ensembles, the decomposition is a natural way to increase diversity; a key factor for the success of ensemble classifiers. In this paper, we propose to adopt class decomposition to the state-of-the-art ensemble learning Random Forests. Medical data for patient diagnosis may greatly benefit from this technique, as the same disease can have a diverse of symptoms. We have experimentally validated our proposed method on a number of datasets in that are mainly related to the medical domain. Results reported in this paper shows clearly that our method has significantly improved the accuracy of Random Forests

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Image-based rendering and modelling for surgical simulation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Co-eye: A multi-resolution ensemble classifier for Symbolically Approximated Time Series

Time series classification (TSC) is a challenging task that attracted many researchers in the last few years. One main challenge in TSC is the diversity of domains where time series data come from. Thus, there is no “one model that fits all” in TSC. Some algorithms are very accurate in classifying a specific type of time series when the whole series is considered, while some only target the existence/nonexistence of specific patterns/shapelets. Yet other techniques focus on the frequency of occurrences of discriminating patterns/features. This paper presents a new classification technique that addresses the inherent diversity problem in TSC using a nature-inspired method. The technique is stimulated by how flies look at the world through “compound eyes” that are made up of thousands of lenses, called ommatidia. Each ommatidium is an eye with its own lens, and thousands of them together create a broad field of vision. The developed technique similarly uses different lenses and representations to look at the time series, and then combines them for broader visibility. These lenses have been created through hyper-parameterisation of symbolic representations (Piecewise Aggregate and Fourier approximations). The algorithm builds a random forest for each lens, then performs soft dynamic voting for classifying new instances using the most confident eyes, i.e, forests. We evaluate the new technique, coined Co-eye, using the recently released extended version of UCR archive, containing more than 100 datasets across a wide range of domains. The results show the benefits of bringing together different perspectives reflecting on the accuracy and robustness of Co-eye in comparison to other state-of-the-art techniques

Inhalable nano-embedded microspheres as an emerging way for local treatment of lung carcinoma: Benefits, Methods of preparation & characterizaton

Lung cancer is the leading cause of cancer deaths worldwide, and this makes it an attractive disease to review and possibly improve therapeutic treatment options. he extreme lethality of lung cancer is ascribed to the lack of early diagnostic strategies as in almost 50 % of the cases the disease is confirmed in stage IV, leaving low chance of survival. The inaccessibility to the deeper portions of the lung for conventional therapy further adds up to the complication in the treatment process. Surgery, radiation, chemotherapy, targeted treatments, and immunotherapy separate or in combination are commonly used to treat lung cancer. However, these treatment types may cause different side effects, and chemotherapy-based regimens appear to have reached a therapeutic plateau. Hence, effective, better- tolerated treatments are needed to address and hopefully overcome this conundrum. Nanocarriers through inhalational route offer many advantages like; 1) they achieve uniform distribution of drug among the alveoli, 2) better solubilization of the drug, 3) sustained drug release which subsequently decreases dosing frequency, 4) better patient compliance, 5) lesser side effects, and 6) improved drug internalization to the cells .Therefore, targeted inhalable NP delivery to the lungs is a potential area of research in cancer nanotechnology that catches the attention of many formulation scientists, oncologists, and biomedical researchers. Based on this literature review, we will discuss the development, characterization, and benefits of inhalable nanocarriers for local treatment of lung carcinoma.</p