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The objective of this paper is to present the performance of Direction of Arrival(DoA) estimation algorithms for underwater sound source localization using an acoustic Vector Sensor Array (VSA) that is developed by the National Institute of Ocean Technology, Chennai. Algorithms such as conventional beam forming, Multiple Signal Classification (MUSIC) with Eigen value decomposition, and MUSIC with Singular Value Decomposition (SVD) are used for estimation of DoA and performance study. An experiment has been conducted with the VSA at the Acoustic Test Facility of NIOT with the source transmission of 1 kHz to 5 kHz for different azimuth angles. The estimation of DoA using the above three algorithms and the comparison of the results on resolution and accuracy have been studied in detail in terms of the number of vector elements. Results reveal that the MUSIC method gives results with higher accuracy and resolution than the conventional method. The maximum deviation from the true angle in the conventional method is 4°; in MUSIC, it is 2°, whereas in MUSIC with SVD, it is 1°. While the standard MUSIC algorithm involves computing the eigenvectors of the covariance matrix, which can be computationally expensive, MUSIC with SVD provides a more efficient way to achieve better results. SVD enables straightforward computation of the signal subspace, making it more practical for real-time applications like coastal surveillance. Further to the laboratory experiment, the vector sensor system has been deployed in an open sea environment near the harbor and a known source experiment is carried out. The DoA estimated using MUSIC with SVD for the field data reveals that the results are in good comparison with the measured azimuth and elevation positions. The deviations in the field results are due to dynamic conditions of the ocean,and more sea trials need to be carried out for further study. [ABSTRACT FROM AUTHOR]

Objective: To assess how staff attitudes before, during, and after implementation of a real-time location system (RTLS) that uses radio-frequency identification tags on staff and patient identification badges and on equipment affected staff’s intention to use and actual use of an RTLS. Participants and Methods: A series of 3 online surveys were sent to staff at an emergency department with plans to implement an RTLS between June 1, 2015, and November 29, 2016. Each survey corresponded with a different phase of implementation: preimplementation, midimplementation, and postimplementation. Multiple logistic regression with backward elimination was used to assess the relationship between demographic variables, attitudes about RTLSs, and intention to use or actual use of an RTLS. Results: Demographic variables were not associated with intention to use or actual use of the RTLS. Before implementation, poor perceptions about the technology’s usefulness and lack of trust in how employers would use tracking data were associated with weaker intentions to use the RTLS. During and after implementation, attitudes about the technology’s use, not issues related to autonomy and privacy, were associated with less use of the technology. Conclusion: Real-time location systems have the potential to assess patterns of health care delivery that could be modified to reduce costs and improve the quality of care. Successful implementation, however, may hinge on how staff weighs attitudes and concerns about their autonomy and personal privacy with organizational goals. With the large investments required for new technology, serious consideration should be given to address staff attitudes about privacy and technology in order to assure successful implementation.

The objective of this study is to synthesize biologically derived CuO, Ag, and Gd2O3 polymetallic nanoparticles (NPs) using Justicia adhatoda bark extract. These nanoparticles were meticulously prepared and characterized through a comprehensive array of spectral analyses, including UV-Visible, FTIR, SEM, EDX, XRD, and thermal studies. UV-Vis spectroscopy revealed distinct absorption peaks in the 200-300 nm range, indicative of the presence of metal oxide nanoparticles. FTIR studies elucidated the interactions between bioorganic compounds and the synthesized NPs. SEM and XRD analyses provided insights into the crystallinity, crystal structure, and morphologies of the nanoparticles. Thermal properties, such as decomposition rates and transition energies, were examined via TGA/DTA. Notably, the PNs exhibited enhanced photocatalytic activity against methylene blue (MB) compared to individual NPs. Additionally, they demonstrated potent antibacterial effects against human pathogens and remarkable antimalarial activity against Aedes aegypti across various concentrations. The novelty of this work lies in the utilization of Justicia adhatoda bark extract for the green synthesis of multifunctional PNs with enhanced properties for diverse applications. [ABSTRACT FROM AUTHOR]

In the title chalcone, C13H11NOS, derived from the condensation of p-tolualdehyde and 1-(1,3-thiazol-2-yl)ethanone, the olefine group has a trans configuration. No classical hydrogen bonding is present in the crystal structure.

Byline: Muthuvel. Kannan, Thallur. Raj Kumar, Srinivasan. Subramanian A 50-year-old woman presented with gross hematuria for 1 month. Clinical examinations, laboratory investigations, ultrasound and contrast computed tomography were normal, except [...]

Byline: Pasupuleti. Kumar, Katari. Venkatesh, Lokanathan. Srikanth, Potukuchi Venkata. Sarma, Akkamgari. Reddy, Srinivasan. Subramanian, Bobbidi. Phaneendra Von Hippel-Lindau (VHL) disease is an autosomal dominant hereditary cancer syndrome that predisposes to [...]

In this article, to overcome the challenges encountered during the discrimination of various failure modes in post impacted/indented glass-fiber-reinforced plastic, techniques like pattern recognition method and advanced signal processing were employed. The significant acoustic emission parameters such as amplitude, rise time, counts, energy, duration, and peak frequency that are acquired during compression after impact test are considered as inputs to cluster validity index and for various clustering techniques such as k-means, fuzzy C-means, and Kohonen's self-organizing map. The acoustic emission count–frequency and amplitude–frequency have no overlapping, whereas other combinations of acoustic emission parameters result in overlapping with four clusters. The clustering techniques are validated by discrete wavelet transform of acoustic emission signals. The discrete wavelet transform was performed on the clustered acoustic emission signals to identify the percentage of energy and frequency content of each level which correlates the different failure modes. The results infer that k-means, fuzzy C-means clustering, and Kohonen's self-organizing map are 94.5%, 97.1%, and 98.6% reliability, respectively, clearly suggesting Kohonen's self-organizing map as the most appropriate technique for the classification of acoustic emission signature. [ABSTRACT FROM AUTHOR]