Emergency departments (EDs) have been becoming increasingly congested due to the combined impacts of growing demand, access block and increased clinical capability of the EDs. This congestion has known to have adverse impacts on the performance of the healthcare services. Attempts to overcome with this challenge have focussed largely on the demand management and the application of system wide process targets such as the "four-hour rule" intended to deal with access blocks. In addition, EDs have introduced various strategies such as "fast tracking", "enhanced triage" and new models of care such as introducing nurse practitioners aimed at improving throughput. However, most of these practices require additional resources. Some researchers attempted to optimise the resources using various optimisation models to ensure best utilisation of resources to improve patient flow. However, not all modelling approaches are suitable for all situations and there is no critical review of optimisation models used in hospital EDs. The aim of this article is to review various analytical models utilised to optimise ED resources for improved patient flow and highlight benefits and limitations of these models. A range of modelling techniques including agent-based modelling and simulation, discrete-event simulation, queuing models, simulation optimisation and mathematical modelling have been reviewed. The analysis revealed that every modelling approach and optimisation technique has some advantages and disadvantages and their application is also guided by the objectives. The complexity, interrelationships and variability of ED-related variables make the application of standard modelling techniques difficult. However, these models can be used to identify sources of flow obstruction and to identify areas where investments in additional resources are likely to have most benefit.

Anti-islanding protection is an important requirement which has to be considered prior to the integration of distributed generation into electricity grids. Conventional vector surge (VS) relays are usually used to detect islanding; however, there is a nondetection zone (NDZ) wherein islanding incidents are undetectable by VS relays. This paper proposes a multifeature-based technique for islanding detection in the subcritical region, defined as a subregion of the NDZ. In the proposed method, features are extracted from five network variables. The extracted features are then used as inputs to a support vector machine to classify the event as islanding or nonislanding. A test network was used to generate a large number of islanding and nonislanding events with different load types. The proposed method is tested with the most critical islanding cases associated with NDZ of VS relays. Furthermore, all possible combinations of deficit and excess of active and reactive power imbalance, which may exist during the occurrence of an island, are considered in the testing phase. Experimental results demonstrate that the proposed method can successfully detect islanding events in the subcritical region, where a VS relay is expected to fail.

Citrus macroptera Montr. (C. macroptera) is locally known as Satkara. The fruit of this plant is used as appetite stimulant and in the treatment of fever. This study therefore aimed to evaluate the toxic effects of the fruit extract using some biochemical and hematological parameters in rat model. The effects of methanol extract of Citrus macroptera Montr. fruit administered at 250, 500 and 1000 mg/kg body weight were investigated on hematological and biochemical parameters in Sprague-Dawley female rats. Moreover, histopathological study was performed to observe the presence of pathological lesions in primary body organs. The extract presented no significant effect on body weight, percent water content, relative organ weight and hematological parameters in rat. Significant decrease from control group was observed in the levels of triglyceride, total cholesterol, low density lipoprotein and very low density lipoprotein; thus leading to significant decrease of cardiac risk ratio, castelli's risk index-2, atherogenic coefficient and atherogenic index of plasma at all doses. 500 mg/kg dose significantly decreased alkaline phosphatase (P<0.05), 1000 mg/kg dose significantly increased high density lipoprotein cholesterol (P<0.05) and 250 mg/kg dose significantly decreased the level of glycated hemoglobin (P<0.05) from the control group. There were no significant alterations observed with other serum biochemical parameters. Histopathological study confirmed the absence of inflammatory and necrotic features in the primary body organs. Study results indicate that methanolic fruit extract is unlikely to have significant toxicity. Moreover, these findings justified the cardio-protective, moderate hepato-protective and glucose controlling activities of the fruit extract.

Islanding detection is a critical protection issue, as conventional protection schemes such as vector surge (VS) and rate of change of frequency relays do not guarantee islanding detection for all network conditions. Integration of multiple distributed generation (DG) units of different sizes and technologies into distribution grids makes this issue even more critical. This paper presents a comprehensive analysis of the effectiveness of a new method for islanding detection in DG networks. The proposed method, which is based on multiple features and support vector machine (SVM) classification, has the potential to overcome the limitations of conventional protection schemes. The multifeature-based SVM technique utilizes a set of features generated from numerous set of offline dynamic events simulated under different network contingencies, operating conditions, and power imbalance levels. Parameters (such as voltage, frequency, and rotor angle) showing distinguishable variation during the formation of islanding are selected as features for classification of the events. Features associated with different islanding and nonislanding events are used to train the SVM. The trained SVM is tested on a typical distribution network containing multiple DG units. Simulation results indicate that the proposed method can work effectively with high degree of accuracy under different network contingencies and critical levels of power imbalance that may exist during islanding.

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This paper presents a new method for classification and characterization of voltage dips and swells in electricity networks. The proposed method exploits unique signatures and parameters of three phase voltage signals extracted from the polarization ellipse in three-dimensional (3D) co-ordinates. Five ellipse parameters, which include azimuthal angle, elevation, tilt, semi-minor axis and semi-major axis, are used to classify and characterize voltage dips and swells. Seven types of voltage dips, which include a total of 19 groups of dips incorporating different kinds of balanced (three-phase dips) and unbalanced (single-phase or double-phase) dips, are identified and successfully classified using the 3D polarization ellipse parameters. Two types of voltage swells, which include a total of 6 groups, are also classified using the proposed method. The proposed method is validated using real measurement data, recorded waveforms provided by the IEEE 1159.2 working group, and the data of unbalanced dips associated with phase angle jumps, voltage drops and rotations due to loading effects.

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