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"Tiwari, A"
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AI based UPQC control technique for power quality optimization of railway transportation systems
Metro trains have non-linear load characteristics, which means that the power sent to them gets distorted. Problems are caused by changes in power, swells, harmonics, and other disturbances. In this research, an artificial intelligence-driven control method was used on a unified power quality conditioner (UPQC) to help reduce power quality problems and improve power quality. Three advanced control methods are built and compared using MATLAB Simulink. Some of these methods are the ANN Controller, the NARMA-L2 Controller, and the PI Controller, improved using the Adaptive Lizard Algorithm. The controls' usefulness is judged by how well they lower the total harmonic distortion (THD) in the source current. The results show that all three AI-based controls work much better than the system that was not paid for. The ANN Controller works the best, followed by the NARMA-L2 Controller, and the PI Controller improved with the Adaptive Lizard Algorithm. These AI-driven control methods can enhance power quality and ensure that metro rail systems run smoothly and efficiently, as shown by how well they work. Modern transportation networks need more advanced ways to handle power quality, and this research helps make those solutions come together.
Journal Article
Evaluating the efficacy of Lactobacillus acidophilus derived postbiotics on growth metrics, Health, and Gut Integrity in broiler chickens
Continuous use of antibiotics in poultry feed as growth promoters poses a grave threat to humanity through the emergence of antibiotic resistance, necessitating the exploration of novel and sustainable alternatives. The present study was carried out to evaluate the performance of postbiotics derived from
Lactobacillus acidophilus
in broiler birds. The postbiotics were harvested by culturing probiotic bacteria from the stock cultures at the required temperature and duration under laboratory conditions and supplemented to broilers via feed. For experimentation, 480-day-old CARI-Bro Dhanraja (slow-growing broiler) straight-run chicks were randomly split up into six groups. Treatment groups diets are as follows: T1- Basal diet (BD)+0.2%(v/w) MRS Broth/ uninoculated media; T2 – BD + Antibiotic (CTC); T3- BD + Probiotic; T4, T5 & T6 – BD + postbiotics supplementation of 0.2%, 0.4% and 0.6% (v/w) respectively. The chicks were raised under an intensive, deep litter system with standard protocol for 6 weeks. Results showed that 0.2% of postbiotics (T4) had significantly (
P
< 0.001) higher body weight (1677.52 g) with better FCR (1.75) and immune response. Postbiotic supplementation altered various serum attributes positively, in this study. Significant (
P
< 0.001) reductions in total plate counts (TPC), coliform counts, and maximum
Lactobacillus
counts were recorded in all postbiotic-supplemented groups. The villus height (1379.25 μm), width (216.06 μm) and crept depth (179.25 μm) showed significant (
P
< 0.001) improvement among the treatment groups on the 21st and 42nd day of the experimental trial, with the highest value in the T4 group (0.2% postbiotic supplementation). Jejunal antioxidant values also noted significantly (
P
< 0.001) higher values in T4 group. The study concludes that 0.2% postbiotic supplementation can act as a substitute to antibiotic growth promoters and also combat the disfavour activity of probiotics in broilers.
Journal Article
Large-scale pinball twin support vector machines
Twin support vector machines (TWSVMs) have been shown to be effective classifiers for a range of pattern classification tasks. However, the TWSVM formulation suffers from a range of shortcomings: (i) TWSVM uses hinge loss function which renders it sensitive to dataset outliers (noise sensitivity). (ii) It requires a matrix inversion calculation in the Wolfe-dual formulation which is intractable for datasets with large numbers of features/samples. (iii) TWSVM minimizes the empirical risk instead of the structural risk in its formulation with the consequent risk of overfitting. This paper proposes a novel large scale pinball twin support vector machines (LPTWSVM) to address these shortcomings. The proposed LPTWSVM model firstly utilizes the pinball loss function to achieve a high level of noise insensitivity, especially in relation to data with substantial feature noise. Secondly, and most significantly, the proposed LPTWSVM formulation eliminates the need to calculate inverse matrices in the dual problem (which apart from being very computationally demanding may not be possible due to matrix singularity). Further, LPTWSVM does not employ kernel-generated surfaces for the non-linear case, instead using the kernel trick directly; this ensures that the proposed LPTWSVM is a fully modular kernel approach in contrast to the original TWSVM. Lastly, structural risk is explicitly minimized in LPTWSVM with consequent improvement in classification accuracy (we explicitly analyze the properties of classification accuracy and noise insensitivity of the proposed LPTWSVM). Experiments on benchmark datasets show that the proposed LPTWSVM model may be effectively deployed on large datasets and that it exhibits similar or better performance on most datasets in comparison to relevant baseline methods.
Journal Article
En̲atu vān̲in̲ ñān̲ac cuṭarkaḷ : vāl̲kkaiyin̲ kur̲ikkōḷ kur̲itta uraiyāṭal
Interviews of the President of India, centralised on inspirational life context.
BOOK
AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization
This research paper presents an advanced AI-driven hybrid power quality management system for electrical railways that addresses critical challenges in 25 kV AC traction networks through a novel integration of single-phase PV-UPQC with ANN-Lyapunov control architecture. The system effectively manages voltage unbalance exceeding 2%, high THD, voltage variations of ± 10%, and poor power factor through a dual-approach methodology combining ANN-based reference signal generation with Lyapunov optimization, enabling dynamic parameter tuning and real-time load adaptation. MATLAB/Simulink simulations validate the system’s superior performance, demonstrating significant improvements, including voltage unbalance reduction from 1.5 to 0.8%, THD reduction below 1%, unity power factor correction, 40% faster dynamic response, and DC link voltage regulation within ± 2%, while maintaining 95% overall system efficiency. Integrating ANN-based shunt and series APF control, Lyapunov optimization, and PV integration establishes a robust framework for enhanced energy efficiency and power quality management in modern railway systems.
Journal Article
Pharmacogenetics of antipsychotic-induced weight gain: review and clinical implications
Second-generation antipsychotics (SGAs), such as risperidone, clozapine and olanzapine, are the most common drug treatments for schizophrenia. SGAs presented an advantage over first-generation antipsychotics (FGAs), particularly regarding avoidance of extrapyramidal symptoms. However, most SGAs, and to a lesser degree FGAs, are linked to substantial weight gain. This substantial weight gain is a leading factor in patient non-compliance and poses significant risk of diabetes, lipid abnormalities (that is, metabolic syndrome) and cardiovascular events including sudden death. The purpose of this article is to review the advances made in the field of pharmacogenetics of antipsychotic-induced weight gain (AIWG). We included all published association studies in AIWG from December 2006 to date using the Medline and ISI web of knowledge databases. There has been considerable progress reaffirming previous findings and discovery of novel genetic factors. The
HTR2C
and leptin genes are among the most promising, and new evidence suggests that the
DRD2
,
TNF
,
SNAP-25
and
MC4R
genes are also prominent risk factors. Further promising findings have been reported in novel susceptibility genes, such as
CNR1
,
MDR1
,
ADRA1A
and
INSIG2
. More research is required before genetically informed, personalized medicine can be applied to antipsychotic treatment; nevertheless, inroads have been made towards assessing genetic liability and plausible clinical application.
Journal Article
Growth-and-collapse dynamics of small bubble clusters near a wall
The violent collapse of bubble clusters is thought to damage adjacent material in both engineering and biomedical applications. Yet the complexities of the root mechanisms have restricted theoretical descriptions to significantly simplified configurations. Reduced-physics models based upon either homogenization or arrays of idealized spherical bubbles do reproduce important gross cluster-scale features. However, these models neglect detailed local bubble–bubble interactions, which are expected to mediate damage mechanisms. To describe these bubble-scale interactions, we simulate the expansion and subsequent collapse of a hemispherical cluster of 50 bubbles adjacent to a plane rigid wall, explicitly representing both the asymmetric dynamics of each bubble within the cluster and the compressible-fluid mechanics of bubble–bubble interactions. Results show that the collapse propagates inward, as visualized in experiments, and that geometric focusing generates high impulsive pressures. This gross behaviour is nearly independent of the specific arrangement of bubbles within the cluster and matches predictions from the corresponding particle and homogenized models we consider. The peak pressure in the detailed simulations is associated with the centremost bubble, which causes a corresponding peak pressure on the nearby wall. However, the peak pressures in all cases are a small fraction – over a factor of ten times smaller in many cases – of those predicted in the corresponding reduced models. This is due to the enhanced focusing in the homogeneous model and the spherical constraint on each bubble in the particle models assessed. These would be important factors to consider in any subsequent predictions of wall damage based upon reduced models.
Journal Article