Pentatrichomonas hominis in an immunosuppressed patient with enteric manifestations

Pentatrichomonas hominis is a flagellated protozoa considered to be a commensal that inhabits caecum and large intestine in man. It is regarded to be non-pathogenic, however, it has been postulated that these trichomonads undergo multiplication under favorable conditions for growth and exhibit a form of opportunism eventually causing diarrhea. We report, for the first time in India, a case of diarrhea due to P. hominis in an elderly male with myeloid malignancy that resolved on treatment with metronidazole

A Parametric Test based Analysis of State Estimation Techniques under Data Uncertainties

This work examines the statistical analysis of conventional and evolutionary strategies used to solve state estimation problems. All energy management systems use state estimation to determine the operational condition of the system. Moreover, with the rise of the electrical market and the notion of a smart grid, the assessment of system parameters has received considerable attention. Hence an assessment of the efficiency and robustness of various state estimation techniques used to compute the system parameters is very much required. This paper primarily focuses on the parametric tests used to access and compare the robustness of various state estimators. Case studies are conducted on IEEE 6 bus and 14 bus systems. In addition, this paper also provides a statistical evaluation of the performance of evolutionary algorithms with varying upper and lower optimal solution constraints. Furthermore, the algorithms' robustness under conditions of missing and infringed data is also determined. The findings derived from these estimators are compared with the base values, and the percentage error in estimated values is computed and analysed

Needle Stick Injury among Health Care Workers and Its Aftermath in a Tertiary Care Hospital in East Delhi, India

Needle stick injuries (NSI) present serious occupational threat to healthcare workers (HCW).Due to lack of epidemiological data on NSI in this geographical region, the present study was conducted to estimate incidence rate of NSI, identify factors associated, assess awareness of HCWs and evaluate post-injury sero-reactivity rates. This cross-sectional observational study involved 524 HCWs (151 medical and 373 paramedical staff). A validated questionnaire was filled by investigator using interviewing technique. Blood sample was collected from study subjects who reported NSI within last 28 days, at the time of NSI and subsequently after 1, 3 and 6 months. Screening for HBsAg, anti-HCV and anti-HIV 1/2 antibodies was done using commercially available Enzyme-Linked Immunosorbent Assay Kit. Sixty-three HCWs, comprising mainly of medical staff, gave history of NSI in preceding 28 days. The most frequent procedure leading to NSI included recapping needles and suturing in 28.57%, while commonest root cause was haste in 61.91%. Majority (61.91%, 39/63) suffered from NSI during latter part of their duty hours. None became HBsAg, anti-HCV or HIV seropositive. The proportion of NSI among HCWs who had received training on prevention and management of NSI was significantly lower than those who were untrained. Hence training programs emphasizing on safe techniques must be conducted regularly and HCWs putting in long working hours must be allowed to take breaks. Needle stick injury among health care workers and its aftermath in a tertiary care hospital in East Delhi, India

Power quality event classification under noisy conditions using EMD-based de-noising techniques

The paper deals with the application of two empirical mode decomposition (EMD)-based de-noising techniques in power quality assessment. Distinct threshold parameters at a distinct level of decomposition are employed for noise exclusion. Each of the thresholded intrinsic mode functions (IMFs) are then combined together to yield a de-noised version of the signal. For enhanced performance, various noisy versions from the original signal are created and then de-noised using soft thresholds. The resultant de-noised signals are then averaged to obtain the de-noised version of the signal. In other procedures based on EMD, the signal is chopped into smaller portions each having equal length. Each signal portion is then separately subjected to EMD and then de-noised and later combined to obtain noise-free signal. These de-noised signals are then subjected to Hilbert transform for feature extraction. Fuzzy Product Aggregation Reasoning Rule-based intelligent classifier is used here for classification purpose. A comparative study is also made between S-transform-based and wavelet-transform-based de-noising techniques paper. Real-time implementation of the algorithm on a noisy harmonic signal is also given in this paper

Empirical mode decomposition with Hilbert Transform for power quality assessment

Summary form only given. The aim of this paper is to develop a method based on combination of Empirical Mode Decomposition (EMD) and Hilbert Transform for assessment of power quality events. A distorted waveform can be conceived as superimposition of various oscillating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF). Hilbert transform is applied to first three IMF to obtain instantaneous amplitude and phase which are then used for constructing feature vector. The work evaluates the detection capability of the methodology and a comparison with S-Transform is made to show the superiority of the technique in detecting the PQ disturbance like voltage spike and notch. A Probabilistic Neural Network is used as a mapping function for identifying the various disturbance classes. Results show a better classification accuracy of the methodology

Empirical-mode decomposition with Hilbert transform for power-quality assessment

The aim of this paper is to develop a method based on combination of empirical-mode decomposition (EMD) and Hilbert transform for assessment of power quality events. A distorted waveform can be conceived as superimposition of various oscillating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF). Hilbert transform is applied to first three IMF to obtain instantaneous amplitude and phase which are then used for constructing feature vector. The work evaluates the detection capability of the methodolpogy and a comparison with S-transform is made to show the superiority of the technique in detecting the PQ disturbance like voltage spike and notch. A probabilistic neural network is used as a mapping function for identifying the various disturbance classes. Results show a better classification accuracy of the methodology

Online EMD with Fourier Transform for active shunt filter operation under non sinusoidal supply conditions

The aim of this paper is to compute reference current for the operation of shunt active filter under non-sinusoidal supply condition using local Empirical Mode Decomposition (EMD) and Fourier Transform. The online application of local EMD on distorted voltage waveforms extracts the fundamental oscillating mode which serves two purposes -one in determining the sinusoidal unit template in phase with fundamental component of supply voltage and two in estimating the fundamental frequency of the system using Hilbert Transform. The fundamental frequency information is utilized by the Fourier Transform to extract the magnitude of fundamental and harmonic components present in load current. The fundamental load current magnitude together with unit voltage templates gives an estimate of the reference current. The case study is done under distorted voltage with unknown system frequency in MATLAB/ SIMULINK platform

TS fuzzy based intelligent control of a three phase shunt filter under balanced and unbalanced conditions

This paper deals with an application of Takagi Sugeno (TS) fuzzy logic control in the operation of three phase shunt active power filter to achieve harmonic elimination and real power balance between supply and load. Unlike conventional controllers, fuzzy logic based controllers cover a wider range of operating condition, give allowance to uncertainties and are relatively much simpler to develop. Simulation studies using the TS fuzzy controllers in shunt active filter are already reported in the literature with encouraging results. This paper takes a step ahead and performs real time implementation of the intelligent controller thereby justifying its potentiality

Implementation of empirical mode decomposition for shunt active filter

In this paper, an online EMD (Empirical Mode Decomposition) based algorithm is proposed for the control of a shunt AF (Active Filter). Standard EMD algorithm has worked on the principle of time scale difference between the upper maximum values and lower minimum values of a signal, and for a long time it has been considered good for offline analysis of signals. This paper deals with the real time application of EMD for the control of AF under balanced and unbalanced loads. Linear interpolators are employed for constructions of upper and lower envelopes to avoid computation complexity. Moreover, customized iterative computations for extraction of IMFs (Intrinsic Mode Functions), maintain the efficiency of algorithm. The algorithm extracts the fundamental oscillating component of load current and helps in estimating reference currents. Simulations are performed on a MATLAB/ Simulink platforms. Simulated results are verified with test results of a developed prototype of shunt AF