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Power hardware in the loop (PHIL) is a modern experimental technique that allows the emulation of a full-scale converter (FSC) with the combination of a scaled-down converter (SDC), a power amplifier, and a real-time simulator, thus enabling the study of real-time interactions of power electronics with large power systems. However, assembling an accurate scaled-down replica of an FSC with off-the-shelf laboratory SDCs is practically impossible due to a mismatch in per unit losses, as well as in the impedance of the $L/LC/LCL$ filter. Consequently, the scaled-up power flow capability of SDCs differs from that of FSCs, restricting emulation to smaller regions of the four quadrants than those corresponding to the FSCs' nominal active and reactive capacity. These PHIL test beds cannot be used to emulate FSCs demanding bidirectional active and reactive power flow. Any scaling method on SDCs, emulating the entire operation of FSCs, demands underutilization of SDCs, reducing the advantages of PHIL tests. This article, therefore, proposes a physics-informed scaling method that exploits power capability curves to emulate FSCs in all four quadrants of operation. This method is independent of SDC topology, filter type, and interfacing methods. A visual identification of semiconductor device constraints bounding the emulation is also presented, utilizing the physics of converter control. A theoretical analysis of the proposed method is presented, followed by validation with MATLAB simulations and experimental tests using a 50-kVA SDC.

Power hardware-in-the-loop (PHIL) is an experimental technique that uses power amplifiers and real-time simulators for studying the dynamics of power electronic converters and electrical grids. Power hardware-in-the-loop (PHIL) tests provide the means for functional validation of advanced control algorithms without the burden of building high-power prototypes during early technology readiness levels. However, replicating the behavior of high-power systems with laboratory scaled-down converters (SDCs) can be complex. Inaccurate scaling of the SDCs coupled with an exclusive focus on instantaneous voltages and currents at the fundamental frequency can lead to PHIL results that are only partially relatable to the high-power systems under study. Test beds that fail to represent switching frequency harmonics cannot be used for studying harmonic penetration or loss characterization of large-scale converters. To tackle this issue, this article proposes a harmonic-invariant scaling method that exploits the volt-ampere rating of preexisting laboratory SDCs for more accurately replicating harmonic phenomena in a PHIL test bench. First, a theoretical analysis of the proposed method is presented and, subsequently, the method is validated with MATLAB simulations and experimental tests.

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems. [ABSTRACT FROM AUTHOR]

Aim: The aim of the study was to estimate mortality rate and trend in the neonate admitted to a surgical neonatal intensive care unit. Methods: This study was a retrospective cohort analysis of all neonatal (from birth to Results: There were a total of 8994 admissions with 425 deaths during the study period, of whom 328 infants met inclusion criteria. In this group 18.9% (n=62) were admitted for a surgical condition, 35.4% (n=116) for cardiac disease and 45.7% (n=150) for other reasons. The median birth weight was 2715g (IQR 1890g-3220g) and the median gestational age was 37 weeks (IQR 33-39 weeks). The inter-quartile range for length of stay was between 2 to 20 days. The overall mortality rate was 3.6% over 16 years. There was a decline in mortality rate from 5.9% in 2000 to 3.5% in 2015 (p=0.06). Female infants accounted for 41% of the deaths. On multivariate analysis only very low birth weight was an independent predictor of mortality for surgical and cardiac deaths compared to deaths by other cause. Conclusions: There has been an overall decline in mortality in the surgical neonatal population from 2000 to 2015.

Toxicological effect of pulp and papermill effuent in the fish, Oreochromis mossambicus was investigated in the present study. Pulp and papermill effluent discharged from Hindustan News Print LTD, Peruva, Kottayam, Kerala were collected and adult fish of size 15±2 g were exposed to a sublethal concentrations (1/5th and 1/10th LC50 value) of the effluent for a period of 10, 20 and 30 days. Pathological conditions such as fatty degeneration (FD), pyknosis (P), haemosiderosis (HS), degenerated renal tubule (DRT) and Vacular hypertrophy of tubular epithelium (VHT). Fatty degeneration (FD) was observed in both concentrations of 10, 20 and 30 days of exposure. Pyknosis (P) was started from 1/5th concentration of 10 days exposed fishes. In experimental fishes haemosiderosis (HS) was found in 1/5th concentration of 20 days and both concentrations of 30 days. Degenerated renal tubule (DRT) was observed in both concentrations of 30 days of papermill effluent exposed fishes. Vacular hypertrophy of tubular epithelium (VHT) and haemosiderosis (HS) was found in 1/5th concentration of 30 days effluent exposed fishes. The present study revealed that the histopathological conditions affect the physiological functions such as metabolic processes and excretion. [ABSTRACT FROM AUTHOR]

Biofilm is one of the basic survival strategies employed by microorganisms in all natural and industrial ecosystems in response to starvation. They are responsible for most of the chronic infections and almost all recalcitrant infections in human beings, as bacteria in biofilm are resistant to both antimicrobials and host defense mechanisms. Biofilms may be associated with each branch in dentistry. However, as an endodontist who are more viable regarding these biofilms during an endodontic procedure should have in-depth knowledge to achieve the treatment goals during the procedure. [ABSTRACT FROM AUTHOR]

Introduction: Periodontal disease is characterised by chronic infection and inflammation in periodontal tissues leading to destruction of alveolar bone with subsequent tooth loss. Periodontal infections are the result of an interaction between tooth associated microbial biofilms and the host defences. Periodontal pathogens can affect local and systemic immune and inflammatory responses., Aim: The aim of the present study was to evaluate serum C-Reactive Protein (CRP), plasma fibrinogen and peripheral blood levels in healthy subjects, chronic and aggressive periodontitis patients., Materials and Methods: A total of 55 subjects, 27 males and 28 females were selected for the study. Blood samples were taken from healthy controls (n=20) and patients with chronic periodontitis (n=20) and aggressive periodontitis (n=15). The periodontal status of each patient was assessed by recording Oral Hygiene Index-Simplified (OHI-S), Bleeding Index (BI), Probing Pocket Depth (PPD) and Clinical Attachment Level (CAL). The levels of serum CRP were measured using high sensitivity Enzyme Linked Immunosorbent Assay (ELISA) and levels of plasma fibrinogen were measured using Quantitative Immunoturbidimetric assay. Data description was done in the form of mean and standard deviation and analysis of data was done using one way ANOVA (Analysis of Variance) and Students t-test to test the statistical significance between groups., Results: The levels of serum CRP and plasma fibrinogen was increased in patients with chronic and aggressive periodontitis when compared to healthy controls (p<0.001). A positive correlation was found to exist between levels of clinical parameters like OHI-S, BI, PPD and CAL when compared with CRP and fibrinogen as well as with the study groups., Conclusion: The finding of the present study suggests the role of serum as a diagnostic marker in inflammatory conditions and indicates that levels of CRP and fibrinogen may serve as important biomarkers for evaluating the association between periodontitis and cardiovascular diseases.