Search

Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels, based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks with no human input and which out-performs human labelling. Segmentation of DRIVE and STARE retina photograph datasets provided near state-of-the-art vessel segmentation, with training on only a small (n = 100) simulated dataset. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care., (© 2024. The Author(s).)

Functional magnetic resonance imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and single-subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single-subject and low-signal fMRI by developing a computationally feasible and methodologically sound model-based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish voxels and regions having different intensities of activation. Our suggested approach is evaluated in realistic two- and three-dimensional simulation experiments as well as on multiple real-world datasets. Finally, the value of our suggested approach in low-signal and single-subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in persistent vegetative state (PVS). Our ability to reliably distinguish activation in this experiment potentially opens the door to the adoption of fMRI as a clinical tool for the improved treatment and therapy of PVS survivors and other patients., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneous devices is challenging. In this paper, we propose a model selection and adaptation system for Federated Learning (FedMSA), which includes a hardware-aware model selection algorithm that trades-off model training efficiency and model performance base on FL developers' expectation. Meanwhile, considering the expected model should be achieved by dynamic model adaptation, FedMSA supports full automation in building and deployment of the FL task to different hardware at scale. Experiments on benchmark and real-world datasets demonstrate the effectiveness of the model selection algorithm of FedMSA in real devices (e.g., Raspberry Pi and Jetson nano).

Background: Antitachycardia pacing (ATP) success rates as low as 50% for fast ventricular tachycardias (VTs) have been reported providing an opportunity for improved ATP to decrease shocks., Objective: The purpose of this study was to determine how a new automated antitachycardia pacing (AATP) therapy would perform compared with traditional burst ATP using computer modeling to conduct a virtual study., Methods: Virtual patient scenarios were constructed from magnetic resonance imaging and electrophysiological (EP) data. Cardiac EP simulation software (CARPEntry) was used to generate reentrant VT. Simulated VT exit sites were physician adjudicated against corresponding clinical 12-lead electrocardiograms. Burst ATP comprised 3 sequences of 8 pulses at 88% of VT cycle length, with each sequence decremented by 10 ms. AATP was limited to 3 sequences, with each sequence learning from the previous sequences., Results: Two hundred fifty-nine unique ATP scenarios were generated from 7 unique scarred hearts. Burst ATP terminated 145 of 259 VTs (56%) and accelerated 2.0%. AATP terminated 189 of 259 VTs (73%) with the same acceleration rate. The 2 dominant ATP failure mechanisms were identified as (1) insufficient prematurity to close the excitable gap; and (2) failure to reach the critical isthmus of the VT. AATP reduced failures in these categories from 101 to 63 (44% reduction) without increasing acceleration., Conclusion: AATP successfully adapted ATP sequences to terminate VT episodes that burst ATP failed to terminate. AATP was successful with complex scar geometries and EP heterogeneity as seen in the real world., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Independent component analysis (ICA) can separate artifactual and neural activity, allowing researchers to remove such artifactual activity and retain a greater percentage of EEG data for analyses. However, manual identification of artifactual components is time-consuming and requires subjective judgment. Automated algorithms, like ADJUST and ICLabel, have been validated on adults, but to our knowledge, no such algorithms have been optimized for pediatric data. Therefore, in an attempt to automate artifact selection for pediatric data collected with geodesic nets, we modified ADJUST's algorithm. Our "adjusted-ADJUST" algorithm was compared to the "original-ADJUST" algorithm and ICLabel in adults, children, and infants on three different performance measures: respective classification agreement with expert coders, the number of trials retained following artifact removal, and the reliability of the EEG signal after preprocessing with each algorithm. Overall, the adjusted-ADJUST algorithm performed better than the original-ADJUST algorithm and no ICA correction with adult and pediatric data. Moreover, in some measures, it performed better than ICLabel for pediatric data. These results indicate that optimizing existing algorithms improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations. Adjusted-ADJUST is freely available under the terms of the GNU General Public License at: https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline/tree/master/adjusted_adjust_scripts., (© 2020 Society for Psychophysiological Research.)

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible., Competing Interests: Conflict of interest statement: The University of Maryland is filing a US patent application that will cover portions of algorithms A2017a and A2017b. R.R., C.D.C., and R.C. are coinventors on this patent., (Copyright © 2018 the Author(s). Published by PNAS.)

Background: Available therapies for Alzheimer's disease (AD) can only alleviate and delay the advance of symptoms, with the greatest impact eventually achieved when provided at an early stage. Thus, early identification of which subjects at high risk, e.g., with MCI, will later develop AD is of key importance. Currently available machine learning algorithms achieve only limited predictive accuracy or they are based on expensive and hard-to-collect information., Objective: The current study aims to develop an algorithm for a 3-year prediction of conversion to AD in MCI and PreMCI subjects based only on non-invasively and effectively collectable predictors., Methods: A dataset of 123 MCI/PreMCI subjects was used to train different machine learning techniques. Baseline information regarding sociodemographic characteristics, clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy was used to extract 36 predictors. Leave-pair-out-cross-validation was employed as validation strategy and a recursive feature elimination procedure was applied to identify a relevant subset of predictors., Results: 16 predictors were selected from all domains excluding sociodemographic information. The best model resulted a support vector machine with radial-basis function kernel (whole sample: AUC = 0.962, best balanced accuracy = 0.913; MCI sub-group alone: AUC = 0.914, best balanced accuracy = 0.874)., Conclusions: Our algorithm shows very high cross-validated performances that outperform the vast majority of the currently available algorithms, and all those which use only non-invasive and effectively assessable predictors. Further testing and optimization in independent samples will warrant its application in both clinical practice and clinical trials.

Information confidentiality is an essential requirement for cyber security in critical infrastructure. Identity-based cryptography, an increasingly popular branch of cryptography, is widely used to protect the information confidentiality in the critical infrastructure sector due to the ability to directly compute the user's public key based on the user's identity. However, computational requirements complicate the practical application of Identity-based cryptography. In order to improve the efficiency of identity-based cryptography, this paper presents an effective method to construct pairing-friendly elliptic curves with low hamming weight 4 under embedding degree 1. Based on the analysis of the Complex Multiplication(CM) method, the soundness of our method to calculate the characteristic of the finite field is proved. And then, three relative algorithms to construct pairing-friendly elliptic curve are put forward. 10 elliptic curves with low hamming weight 4 under 160 bits are presented to demonstrate the utility of our approach. Finally, the evaluation also indicates that it is more efficient to compute Tate pairing with our curves, than that of Bertoni et al., Competing Interests: The authors have declared that no competing interests exist.

Objectives: To evaluate the role of sonography in assessing tongue cancer compared with magnetic resonance imaging (MRI)., Methods: A randomized prospective study was performed on 40 cases of tongue cancer. Magnetic resonance imaging and sonography of the tongue were performed, tumor spreading to particular sites was recorded in all cases. Sonographic and MRI findings were correlated with histopathologic findings in 18 operable cases. In 22 inoperable cases, sonography was compared only with MRI., Results: In operable patients, sonography achieved sensitivity of 61.1%, whereas MRI achieved sensitivity of 94.4%. The difference was statistically significant (P < .05). The results for detection of individual site involvement on sonography and MRI were as follows: intrinsic muscles only, 0 and 6, respectively; tongue base, 5 and 5; genioglossus muscle, 34 and 34; mylohyoid muscle, 9 and 8; sublingual space, 12 and 14; sublingual neurovascular bundle, 12 and 12; submandibular gland, 3 and 3; spread across the lingual septum, 17 and 17; and alveolar involvement, 0 and 1. There was no significant difference (P> .05) between sonography and MRI in detecting involvement of the above-mentioned sites except for cases with only intrinsic muscles and alveolar involvement. Tumors involving intrinsic tongue muscles only were not visualized on sonography. They all underwent surgery, resulting in reduced sensitivity of sonography in operable cases., Conclusions: Even though small tumors were difficult to visualize, sonography can play an important role in assessment of tumor extension in large growths, especially when MRI is unavailable, contraindicated, or unaffordable, and for posttreatment follow-up., (© 2015 by the American Institute of Ultrasound in Medicine.)

Electrocardiogram (ECG)-gated breath-hold cine MRI is considered to be the gold standard test for the assessment of cardiac function. However, it may fail in patients with arrhythmia, impaired breath-hold capacity and poor ECG gating. Although ungated real-time cine MRI may mitigate these problems, commercially available real-time cine MRI pulse sequences using parallel imaging typically yield relatively poor spatiotemporal resolution because of their low image acquisition efficiency. As an extension of our previous work, the purpose of this study was to evaluate the diagnostic quality and accuracy of eight-fold-accelerated real-time cine MRI with compressed sensing (CS) for the quantification of cardiac function in tachycardia, where it is challenging for real-time cine MRI to provide sufficient spatiotemporal resolution. We evaluated the performances of eight-fold-accelerated cine MRI with CS, three-fold-accelerated real-time cine MRI with temporal generalized autocalibrating partially parallel acquisitions (TGRAPPA) and ECG-gated breath-hold cine MRI in 21 large animals with tachycardia (mean heart rate, 104 beats per minute) at 3T. For each cine MRI method, two expert readers evaluated the diagnostic quality in four categories (image quality, temporal fidelity of wall motion, artifacts and apparent noise) using a Likert scale (1-5, worst to best). One reader evaluated the left ventricular functional parameters. The diagnostic quality scores were significantly different between the three cine pulse sequences, except for the artifact level between CS and TGRAPPA real-time cine MRI. Both ECG-gated breath-hold cine MRI and eight-fold accelerated real-time cine MRI yielded all four scores of ≥ 3.0 (acceptable), whereas three-fold-accelerated real-time cine MRI yielded all scores below 3.0, except for artifact (3.0). The left ventricular ejection fraction (LVEF) measurements agreed better between ECG-gated cine MRI and eight-fold-accelerated real-time cine MRI (mean difference, -1.6%) than between ECG-gated cine MRI and three-fold-accelerated real-time cine MRI (mean difference, -5.7%). Eight-fold-accelerated real-time cine MRI with CS yields acceptable diagnostic quality and relatively accurate LVEF measurements in the challenging setting of tachycardia., (Copyright © 2013 John Wiley & Sons, Ltd.)

The objective of this paper is to provide a texture based segmentation algorithm for better delineation of the epithelial layer from histological images in discriminating normal and oral sub-mucous fibrosis (OSF). As per literature and oral clinicians, it is established that the OSF initially originates and propagates in the epithelial layer. So, more accurate segmentation of this layer is extremely important for a clinician to make a diagnostic decision. In doing this, Gabor based texture gradient is computed in gray scale images, followed by preprocessing of the microscopic images of oral histological sections. On the other hand, the color gradients of these images are obtained in the transformed Lab color space. Finally, the watershed segmentation is extended to segment the layer based on the combination of texture and color gradients. The segmented images are compared with the ground truth images provided by the oral experts. The segmentation results depict the superiority of the texture based segmentation in comparison to the Otsu's based segmentation in terms of misclassification error. Results are shown and discussed., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Background: The traditional consensus diagnosis (ConsDx) of normal cognition, mild cognitive impairment (MCI), and dementia relies on the reconciliation of an informant-based report of cognitive and functional impairment by a physician diagnosis (PhyDx), and a neuropsychological diagnosis (NPDx). As this procedure may be labor intensive and influenced by the philosophy and biases of a clinician, the diagnostic algorithm (AlgDx) was developed to identify individuals as cognitively normal, with MCI, or dementia., Methods: The AlgDx combines the PhyDx with the NPDx, using a diagnostic algorithm that provides cognitive diagnoses, as defined by the National Alzheimer Coordinating Center/Uniform Data Set nomenclature. Reliability of the AlgDx was assessed in 532 community-dwelling elderly subjects by its concordance with the ConsDx and association with two biomarkers, medial temporal atrophy (MTA) scores of brain magnetic resonance imaging scans, and Apolipoprotein E (ApoE)-epsilon4 genotype., Results: A high degree of concordance was observed between ConsDx and AlgDx with a weighted Cohen's kappa of 0.84. Concordance of the AlgDx to the same ConsDx categories ranged from 85% to 92%. Excellent discriminative validity was observed using AlgDx, MTA scores, and ApoE-epsilon4 allele frequencies, each of which distinguished subjects with amnestic MCI and dementia from normal subjects., Conclusion: The AlgDx of normal cognition, MCI, and dementia is a valid alternative that reduces time, effort, and biases associated with the ConsDx. The inherent reliability of a fixed algorithm, together with its efficiency and avoidance of individual bias, suggests the AlgDx may be used in longitudinal, multisite clinical trials, and population studies of MCI and dementia.

Estimating the noise parameter in magnitude magnetic resonance (MR) images is important in a wide range of applications. We propose an automatic noise estimation method that does not rely on a substantial proportion of voxels being from the background. Specifically, we model the magnitude of the observed signal as a mixture of Rice distributions with common noise parameter. The expectation-maximization (EM) algorithm is used to estimate all parameters, including the common noise parameter. The algorithm needs initializing values for which we provide some strategies that work well. The number of components in the mixture model also needs to be estimated en route to noise estimation and we provide a novel approach to doing so. Our methodology performs very well on a range of simulation experiments and physical phantom data. Finally, the methodology is demonstrated on four clinical datasets.

Summary: The shorter and vastly more numerous reads produced by second-generation sequencing technologies require new tools that can assemble massive numbers of reads in reasonable time. Existing short-read assembly tools can be classified into two categories: greedy extension-based and graph-based. While the graph-based approaches are generally superior in terms of assembly quality, the computer resources required for building and storing a huge graph are very high. In this article, we present Taipan, an assembly algorithm which can be viewed as a hybrid of these two approaches. Taipan uses greedy extensions for contig construction but at each step realizes enough of the corresponding read graph to make better decisions as to how assembly should continue. We show that this approach can achieve an assembly quality at least as good as the graph-based approaches used in the popular Edena and Velvet assembly tools using a moderate amount of computing resources.

Motivation: Second-generation sequencing technologies produce a massive amount of short reads in a single experiment. However, sequencing errors can cause major problems when using this approach for de novo sequencing applications. Moreover, existing error correction methods have been designed and optimized for shotgun sequencing. Therefore, there is an urgent need for the design of fast and accurate computational methods and tools for error correction of large amounts of short read data., Results: We present SHREC, a new algorithm for correcting errors in short-read data that uses a generalized suffix trie on the read data as the underlying data structure. Our results show that the method can identify erroneous reads with sensitivity and specificity of over 99% and 96% for simulated data with error rates of up to 3% as well as for real data. Furthermore, it achieves an error correction accuracy of over 80% for simulated data and over 88% for real data. These results are clearly superior to previously published approaches. SHREC is available as an efficient open-source Java implementation that allows processing of 10 million of short reads on a standard workstation.

Summary: A new methodology is proposed for clustering datasets in the presence of scattered observations. Scattered observations are defined as unlike any other, so traditional approaches that force them into groups can lead to erroneous conclusions. Our suggested approach is a scheme which, under assumption of homogeneous spherical clusters, iteratively builds cores around their centers and groups points within each core while identifying points outside as scatter. In the absence of scatter, the algorithm reduces to k-means. We also provide methodology to initialize the algorithm and to estimate the number of clusters in the dataset. Results in experimental situations show excellent performance, especially when clusters are elliptically symmetric. The methodology is applied to the analysis of the United States Environmental Protection Agency's Toxic Release Inventory reports on industrial releases of mercury for the year 2000.

Objective: Subjects with mild cognitive impairment (MCI) and mild Alzheimer disease (AD) can be distinguished from elderly subjects with no cognitive impairment (NCI) by the degree of atrophy in the entorhinal cortex (ERC) and the hippocampus (HPC), quantified by volumetric magnetic resonance image (MRI) studies. Because volumetric MRI requires rigorous standards for image acquisition and analysis and is not suitable for routine clinical use, we have used calibrated visual rating to measure atrophy in the ERC, HPC, and perirhinal cortex (PRC) and evaluated its utility in the diagnosis of very early AD., Methods: Thus far, visual rating methods, which have been found to be reliable and sensitive only for measurement of atrophy of the HPC or for the entire medial temporal region, have been found to be relatively insensitive for discriminating mild AD from elderly NCI subjects. We have developed a computer-based visual rating system (VRS) using reference images for calibration of atrophy ratings in several discrete brain regions, including the ERC, HPC, and PRC. The VRS reference images facilitate training of raters and promote standardization of all atrophy ratings. Interrater and intrarater reliability measurements were assessed; subsequently, the ability of VRS to discriminate the diagnoses among 73 elderly subjects was studied (NCI = 27, MCI = 23, and AD = 23)., Results: Kappa values for interrater reliability of the ERC, HPC, and PRC were between 0.75 and 0.94, and for intrarater reliability, they were between 0.84 and 0.93, indicating that VRS enables highly reliable ratings to be obtained. Atrophy ratings in the ERC, HPC, and PRC distinguished AD from NCI subjects but did not distinguish AD from MCI subjects who tended to have intermediate levels of atrophy. Right and left ERC ratings and the right HPC rating distinguished MCI from NCI subjects., Conclusions: The visual rating system is the first semiquantitative method that enables reliable measurements of ERC atrophy, and ERC measurement was found to be the best discriminator between MCI and NCI subjects. Visual rating system is a user-friendly tool that can allow a radiologist or a clinician to use structural MRI scans to be used as a biomarker in the diagnosis of prodromal AD.

Clustering datasets is a challenging problem needed in a wide array of applications. Partition-optimization approaches, such as k-means or expectation-maximization (EM) algorithms, are sub-optimal and find solutions in the vicinity of their initialization. This paper proposes a staged approach to specifying initial values by finding a large number of local modes and then obtaining representatives from the most separated ones. Results on test experiments are excellent. We also provide a detailed comparative assessment of the suggested algorithm with many commonly-used initialization approaches in the literature. Finally, the methodology is applied to two datasets on diurnal microarray gene expressions and industrial releases of mercury.

This paper concentrates on the segmentation of histological images of oral sub-mucous fibrosis (OSF) into its constituent layers. In this regard hybrid segmentation algorithm shows very interesting results. The segmentation results depict the superiority of hybrid segmentation algorithm (HSA) in comparison to region growing algorithm (RGA). In clinical sense, the presented method provides an automatic means for segmenting histological layers (reference class map provided by the expert). The method shows potential in mimicking clinical acumen to act as a support system to oncologist.

As the Fourier transform of time-series data is known as the spectrum, the Fourier transform of the logarithm of the time-series data is called the cepstrum of the data. When cepstral analysis is applied to free induction decay signals of free radicals showing first-order EPR spectra, the identification of nuclear hyperfine coupling constants becomes simple. In a systematic manner, we have examined how the technique of cepstral analysis is affected by the presence of aliasing, noise, uncertainty in the time origin of the free induction decay, the presence of second-order hyperfine couplings, and the applications of various apodization methods. This technique was then applied to analyze the EPR spectrum of anthraquinone anion radical, and anion radicals of porphycene and tetrapropyl-porphycene, and the hyperfine coupling constants thus obtained were compared with published data. A good agreement was always found. We make a case for the usefulness of cepstral analysis in determining the hyperfine coupling constants of complex EPR spectra of organic free radicals.

Motivation: A critical component of in silico analysis of underdetermined metabolic systems is the identification of the appropriate objective function. A common assumption is that the objective of the cell is to maximize growth. This objective function has been shown to be consistent in a few limited experimental cases, but may not be universally appropriate. Here a method is presented to quantitatively determine the most probable objective function., Results: The genome-scale metabolism of Escherichia coli growing on succinate was used as a case-study for analysis. Five different objective functions, including maximization of growth rate, were chosen based on biological plausibility. A combination of flux balance analysis and linear programming was used to simulate cellular metabolism, which was then compared to independent experimental data using a Bayesian objective function discrimination technique. After comparing rates of oxygen uptake and acetate production, minimization of the production rate of redox potential was determined to be the most probable objective function. Given the appropriate reaction network and experimental data, the discrimination technique can be applied to any bacterium to test a variety of different possible objective functions., Supplementary Information: Additional files, code and a program for carrying out model discrimination are available at http://www.engr.uconn.edu/~srivasta/modisc.html.

We use large deviation methods to calculate rates of noise-induced transitions between states in multistable genetic networks. We analyze a synthetic biochemical circuit, the toggle switch, and compare the results to those obtained from a numerical solution of the master equation.

A new method based on a fuzzy mutated genetic algorithm for optimal reconfiguration of radial distribution systems (RDS) is presented. The proposed algorithm overcomes the combinatorial nature of the reconfiguration problem and deals with noncontinuous multi-objective optimization. The attractive features of the algorithm are: preservation of radial property of the network without islanding any load point by an elegant coding scheme and an efficient convergence characteristic attributed to a controlled mutation using fuzzy logic. Index Terms--Fuzzy logic, genetic algorithm, network reconfiguration, radial distribution system.

The paper proposes the hybrid technique to observe the contingency and improve the reliability of radial distribution system (RDS). The hybrid algorithm used is interface of Bat algorithm and particle swarm optimization that is used to examine the contingency and reliability indices in the RDS. The feeder power loss, system node voltage deviation, system average interruption frequency index, system average interruption unavailability index and the energy not supplied at every load position in a sharing scheme are used to resolve the contingencies to minimize the power quality and reliability indices. The proposed techniques are implemented on MATLAB/Simulink platform and experienced on the IEEE 33 bus RDS. To establish the effectiveness of the proposed algorithm, results are compared with the reported Bat and Firefly Algorithm. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to expose students to a simple and efficient load flow algorithm for a radial distribution system (RDS). The basic structure and properties of the RDS are discussed, followed by an illustrative description of the load flow algorithm. The proposed load flow algorithm exploits the tree-type structure of the RDS and uses data structures efficiently. The flowchart and pseudo-codes of the algorithm have been included to help the students in writing their own program. The time and space complexity of the algorithm have also been discussed. The treatment and analysis will be useful for students' understanding of RDS, use of data structures in programming, and approximate complexity analysis of algorithms. The instructors can effectively use the contents of the paper for a basic lecture on RDS (PDF files of the Microsoft PowerPoint slides and C language program code can be downloaded from the website http://www1.mmu.edu.my/~nirod/). [ABSTRACT FROM AUTHOR]

Optimal reactive-power compensation in a radial distribution system requires the determination of the best set of locations for siting capacitors of minimum sizes. The total cost of compensation should be the least and must yield the maximum energy-loss reduction accounting for various load levels. Other controls such as transformer taps, reconfiguration options and existing reactive-power sources must be considered while searching for the optimal solution. Optimal selection of a few best sites from among a large set is a problem of high combinatorial order and difficult to solve using conventional optimisation techniques. Optimal sizing is a problem of a continuous nature. The paper proposes a single dynamic data structure for an evolutionary programming (EP) algorithm that handles the problems of siting and sizing of new shunt capacitors simultaneously while considering transformer taps, existing reactive-power sources and reconfiguration options, accounting for different load levels and time durations. A fuzzy model of the objective function is developed for optimisation in the EP framework. The proposed fuzzy EP method is tested on two cases of a 69-bus radial distribution system. [ABSTRACT FROM AUTHOR]

A simple algorithm is presented to solve three-phase unbalanced radial distribution network (RDN). It solves a simple algebraic recursive expression of voltage magnitude, and all the data are stored in vector form. The algorithm uses basic principles of circuit theory and can be easily understood. Mutual coupling between the phases has been included in the mathematical model. The proposed algorithm has been tested with several distribution networks and the result of an unbalanced RDS is presented in the article. [ABSTRACT FROM AUTHOR]