World-to-digital microfluidics for transformation and enzymatic assays

Digital microfluidics (DMF) is a technique for the manipulation of discrete droplets on an array of electrodes, which allows the controlled movement of fluids and represents an alternative from the conventional microfluidic paradigm of transporting fluids in enclosed channels. One of the major benefits of DMF is that fluid motion and control is achieved without external pumps and fabricated valves – it only requires the use of electric fields. The automation component of DMF has pushed the barriers of this ‘lab-on-chip’ technology; however, integration with external components (i.e. world-to-chip) interfaces has been a challenge. For example, the delivering of the biological fluids to the chip and integrating temperature control on a single platform are considered as two world-to-chip challenges in DMF. To address these two challenges, my thesis describes two world-to-chip components that are integrated with the DMF device: reagent delivery and temperature control. This new platform enables us to perform a variety of biological or chemical experiments on a chip with reduced manual intervention. Specifically, the new platform enabled an increase in reservoir volume on the chip by 40-fold from ~10 µL to 400 µL which allowed more reproducible dispensing and eliminated the need to refill the reservoirs during the biological assay. In addition, we integrated a closed-loop temperature control system that enabled fast and rapid changes in temperature on the chip. To show the utility of the world-to-chip interfaces, we validated the system by automating bacterial transformation and enzymatic assay procedures, which show that both procedures require world-to-chip interfaces for accurate and precise implementation. Overall, we propose that this system has the potential to be integrated for other types of biological assays and experiments which require fluidic control, automation, and temperature control

Review of fault location methods for distribution power system

For the past fifty years, electric power systems have rapidly grown. This has resulted in a large increase of the number of lines in op eration an d their total length. These lines experience faults which are caused by storms, lightning, snow, freezing rain, insulation breakdown and short circuits caused by birds and other external objects. In most cases, electrical faults manifest in mechanical damage, which must be repaired before returning the line to service. The restoration can be expedited if the location of the fault is either known or can be estimated with reasonable accuracy. Speedy an d precise fault location plays an important role in accelerating system restoration, reducing outage time and significantly improving system reliability. This paper provides a comprehensive review of the conceptual aspects as well as recent algorithmic developments for fault location on distribution system. Several fundamentally different approaches are discussed in the paper together with the factors affecting the assumptions of the underlying concepts and the various criteria used in the different approaches are reviewed

A combined analytical method for optimal location and sizing of distributed generation considering voltage stability and power loss in a power distribution system

In this paper, a multi-objective analytical method to evaluate the impacts of optimal location and sizing of distributed generation is presented. This method is based on an analysis of the exact loss formula and continuous power flow in a radial distribution system. Based on two methods of analysis, power loss and weakest voltage buses and lines are calculated and then the optimal size of distributed generation is determined. After that, by considering the minimum power losses and the maximisation of voltage stability, the proposed index determines and ranks positions to decide the optimal distributed generation location in the system. This method allows us to find the best places and size to connect a number of distributed generation units by optimising the objective functions. The simulation results were obtained using a 33-bus radial distribution system to determine the location and size of the distributed generation units. The results show the effectiveness of voltage profile improvement, loading factor improvement and power loss reduction. Further, the problems of a single objective function and the placement of the distributed generation unit using analytical methods are solved by the proposed approach

Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems

This article presents a classification methodology based on probabilistic neural networks. To automatically select the training data and obtain the performance evaluation results, the “K-fold” cross-validation method is used. Then, the probabilistic neural network is compared with the feed-forward neural network and the radial basis function network. The goal is to propose a classifier that is capable of recognizing 11 classes of three-phase distribution system faults to solve the complex fault (three-phase short-circuit) classification problem for reducing the multiple-estimation problem to estimate the fault location in radial distribution systems. The data for the fault classifier is produced by DigSilent Power Factory, Integrated Power System Analysis Software on an IEEE 13-node test feeder. A selection of features or descriptors obtained from voltages and currents measured in the substation are analyzed and used as input of the probabilistic neural network classifier. It is shown that the probabilistic neural network approach can provide a fast and precise operation for various faults. The simulation results also show that the proposed model can successfully be used as an effective tool for solving complicated classification problems

A proposed genetic algorithm to optimize service restoration in electrical networks with respect to the probability of transformers failure

Power system reliability, stability and efficiency are the most important issues to insure continuously feeding of customers. However in process of time, system will be age and the probability of failures will increase and faults inevitably will occur. When a fault occurs, the first reaction is isolation of the faulty area, then with aid of software and/or skillful person quick restoration is essentially needed. To minimize the out-of-service area and activity time of restoration many methods are suggested depend on objectives and constraints of restoration strategy. In many researches a Genetic Algorithm is employed as a powerful tool to solve this multi-objective, multi-constraint optimization problem. Out-of-service area minimization, reduce the number of switching operation and minimizing the minimum electrical power loss in restored system are the prior objectives of restoration plan. In this paper, as transformers are the most expensive and more effective equipments in the electrical network, failure probability increasing is introduced as a new constraint in genetic algorithm by authors. Expected results of this new algorithm should lead to a new plan of restoration in permissible ranges of transformer loading in respect of their age, previous experienced faults and condition monitoring

Static voltage stability analysis using generalized regression neural network

The ability of power system to maintain steady voltage at all the buses after happening a disturbance from a given initial operation condition is defined the voltage stability in the system. The focus of this paper is on voltage stability monitoring using the generalized regression neural network by improving algorithm. In this paper, to identify load buses and certain operation condition, the static voltage stability method in power systems is presented. Based on load buses, the index of voltage stability is obtained from the voltage equation derived from a two bus network. The proposed methods are tested on the IEEE-14 bus test system

Optimal penalty method in distribution service restoration using genetic algorithm

As an efficient scheme for service restoration in distribution systems has a vital role in improving reliability of the system and also satisfactory of customers, significant efforts assigned in solving the problem. The major challenge is to reduce computation burden while covering all the possible answers in reasonable time and effort. Furthermore, restoration is a multi-objective, multi-constraint optimization problem. The considered restoration objective functions in this study include the minimization of outage area, minimizing of power loss and minimizing of number of switching whilst considering the technical constraints. This study presents a new approach of supply restoration service using the Genetic Algorithm (GA). A new hybrid Genetic Algorithm is proposed for reducing the search space. The proposed technique is implemented to improve the penalty strategy to enhance the performance of algorithm and reduce the convergence iteration. The effectiveness of the proposed method is demonstrated by testing on a 33-bus test system. Comparisons show the improvements in reducing of number of iteration after restoration. Findings through comparisons are shown that the proposed method will be able to do full restoration and energize all loads

Multi-objective service restoration in distribution networks using genetic algorithm

Electricity is the backbone of each industrialised society and economy. Modern countries are not used to having even short power blackouts. As an effective postfault supply restoration strategy for distribution networks plays a key part in improving service reliability and enhancing customer satisfaction, where there has been considerable research effort focused on this problem. The main challenge has been in reducing the search space so as to achieve an optimal solution within an acceptable computing burden. Furthermore, restoration is a multi-objective problem that used for solving the minimization of out of service area, minimization of switching operation and minimization of power loss whilst considering the technical constraints. This thesis presents a new approach of supply restoration service using the Genetic Algorithm. The GA is robust in searching a global optimal solution for the large-scale combinatorial optimization problems. A new hybrid Genetic Algorithm is proposed for reducing the search space and execution burden in solving the supply restoration problems. A proposed algorithm is investigated for radiality checking that is found very efficient in distribution restoration problems. Another proposed technique is implemented to improve the penalty strategy to enhance the performance of algorithm and reduce the convergence iteration. The effectiveness of the proposed method is demonstrated by testing on two case studies, a 33-bus test system and a 16 bus test system. Then the results are compared with the previous works all using GA in restoration. Comparisons show the improvements in reducing of number of iteration and fulfilling the radiality of the system after restoration. Findings through comparisons are shown that the proposed method will be able to do full restoration and energize all loads. Also, full reenergizing of all loads as the most important objective function is satisfied with less number of switching and better voltage profile. According to the comparison of the result of thesis with other previous work,it can be observed that reducing the number of iteration is significantly reduced. Results shows very low iteration number and low computation burden compare to other previous works

Investigating Delay Causes in Patients\' Discharge in Educational Hospitals of Ahvaz Jundishapur University of Medical Sciences from Nurses and Physicians\' Perspective, 2016

Background: Delay in patient's timely discharge can affect the efficiency of beds, outcomes of treatment and hospital indicators. The aim of this study was to determine delay causes in the discharge of patients in educational hospitals of Ahvaz Jundishapur University of medical sciences from nurses and physicians' perspective, 2016. Methods: The population of this analytic and cross-sectional study consisted of physicians and nurses in 3 educational hospitals affiliated with Ahvaz Jundishapur University of Medical Sciences. In this study, 90 physicians and 275 nurses were randomly selected from these hospitals. Data were collected using self-made questionnaire. The data were analyzed by SPSS 20 software using descriptive statistics (mean, standard deviation) and analytic statistic tests (independent t-test and ANOVA). Results: According to the results of the study, in hospitals A and B, &ldquo;Financial unaffordability with the mean scores of 3.29 &plusmn; 0.95 and 3.36 &plusmn; 1.70 was the most important cause of delayed discharge, respectively. However, in hospital C, &ldquo;Delay recording and sending information by healthcare department&rdquo; with the mean score of 3.87 &plusmn; 0.71 was the most important cause of delay in discharge (p < 0.05) from the nurses and physicians' perspectives. In nurses' point of view, &ldquo;Financial unaffordability&rdquo; (3.24 &plusmn; 1.41) and in physicians', point of view &ldquo;delay in sending settlements by pharmacy and laboratory&rdquo; (3.15 &plusmn; 0.70) were the most important reasons for delay in patients&rsquo; discharge. Conclusions: Based on the findings of this study, providing supportive mechanisms for patients, developing insurance and monitoring the optimal performance of hospital units can reduce the delay in timely discharge of patients; since delay in discharge can increase patients' dissatisfaction, reduce hospital beds efficiency and defect hospital indicators