126 research outputs found

    Novel Method of Identifying Fingerprint Using Minutiae Matching in Biometric Security System

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    Fingerprint is one of the best apparatus to identify human because of its uniqueness, details information, hard to change and long-term indicators of human identity where there are several biometric feature that can be recycled to endorse the individuality. Identification of fingerprint is very important in forensic science, trace any part of human, collection of crime part and proof from a crime. This paper presents a new method of identifying fingerprint in biometrics security system. Fingerprint is one of the best example in biometric security because it can identify personal information and it is much secure than any other biometric identification system. The experimental result exhibits the performance of the proposed method

    Vasopressin for the management of catecholamine-resistant anaphylactic shock

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    Severe anaesthetic anaphylaxis is relatively uncommon. Oxygen, fluids and epinephrine are considered to be the mainstay for treatment of cardiovascular collapse and current guidelines for the management of anaphylaxis list only epinephrine as a vasopressor to use in the event of a cardiovascular collapse. Recently, evidence has emerged in the support of the use of vasopressin in cardiopulmonary resuscitation, it is also recommended for the treatment of ventricular fibrillation, septic shock and post-cardiopulmonary bypass distribution shock. Currently, there is no algorithm or guideline for the management of anaphylaxis that include the use of vasopressin. We report a 24-year-old woman who developed severe anaphylactic shock at induction of anaesthesia while undergoing laparoscopic cholecystectomy. Circulation shock was refractory to epinephrine and high doses of pure alpha-agonist phenylephrine and norepinephrine. Single intravenous dose of two units of vasopressin re-established normal circulation and blood pressure

    Thar drought: A complete public health failure

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    EFFECT OF FABRIC STRUCTURE ON RIB FABRIC PROPERTIES

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    In this work,1×1Rib,1×1Skeleton rib, 2×2English rib, 2×2Swiss rib, 6×3Derby rib were produced with 20/2 Ne and 32/2 Ne combed ring yarn and V-bed knitting machine of 14 Gauge. In addition, Wales per 3cm, Course per 3cm, Stitch density, Stitch length, Tightness factor, GSM, Dimensional Stability of fabric were tested. According to test result, Wales per 3cm, Stitch density, Cover factor, GSM of 1×1Rib were higher than the 1×1Skeleton rib;Wales per 3cm, Course per 3cm, Stitch density, Stitch length, Cover factor, Shrinkage%, extension% of 2×2English rib were higher than the 1×1Rib; Wales per 3cm, Stitch density, GSM, Shrinkage%, extension% of 2×2Swiss rib were higher than the 1×1Rib; in 6×3Derby Rib values of the properties were higher than other structure; shrinkage and extension percentage increase with the increase of needle drop in knitting

    Correlation of prothrombin time and activated partial thromboplastin time with serum immunoglobulin and M-band in newly diagnosed multiple myeloma patients

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    Background: Multiple myeloma is the second most frequent malignancy which constitute 13% of hematologic cancers. Thrombotic and hemorrhagic complications have been frequently observed in multiple myeloma patients. Methods: The study was conducted in the department of pathology, Government medical college Srinagar. A total of fifty (50) patients were recruited for the study. The patients were advised coagulation profile and complete myeloma profile. Results: Our findings indicate that prolonged PT is associated with high serum IgG levels. A mild to moderate correlation was seen with kappa-free light chains and an inverse correlation was seen between PT and lmbda-free light chains. Conclusions: Screening of multiple myeloma for hemostatic abnormalities at the diagnosis should improve prognosis in such cases

    Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

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    Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity , using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset ( H-Activity ), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research.publishedVersio

    Enhancements in 3D dosimetry measurement using polymer gel and MRI

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    The effects of varying the concentrations of cross-linker N, N-methyelene-bis-acrylamide (BIS) from 2% to 4%, and 2-hydroxyethylacrylate(HEA) monomer from 2% to 4% at 5% gelatin on the dose response of BIS–HEA–gelatin (BHEAG) aqueous polymer gel dosimeters have been studied using magnetic resonance imaging (MRI) for relaxation rate (R2) of water proton. The dosimeters were irradiated with 60Co teletherapy -ray source at a constant dose rate, receiving doses up to 30 Gy. The radiation polymerization occurs and increases with increasing initial dose. R2 is found to decrease mono-exponentially with depth inside the polymer gel and depend strongly upon the initial concentrations of co-monomers (HEA and BIS). Dose–depth map for BHEAG gel was determined for different concentrations of co-monomer (HEA and BIS). The percentage dose depth was also evaluated which leads to a good agreement with the ionization chamber measurements

    Exposure to mild blast forces induces neuropathological effects, neurophysiological deficits and biochemical changes

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    Direct or indirect exposure to an explosion can induce traumatic brain injury (TBI) of various severity levels. Primary TBI from blast exposure is commonly characterized by internal injuries, such as vascular damage, neuronal injury, and contusion, without external injuries. Current animal models of blast-induced TBI (bTBI) have helped to understand the deleterious effects of moderate to severe blast forces. However, the neurological effects of mild blast forces remain poorly characterized. Here, we investigated the effects caused by mild blast forces combining neuropathological, histological, biochemical and neurophysiological analysis. For this purpose, we employed a rodent blast TBI model with blast forces below the level that causes macroscopic neuropathological changes. We found that mild blast forces induced neuroinflammation in cerebral cortex, striatum and hippocampus. Moreover, mild blast triggered microvascular damage and axonal injury. Furthermore, mild blast caused deficits in hippocampal short-term plasticity and synaptic excitability, but no impairments in long-term potentiation. Finally, mild blast exposure induced proteolytic cleavage of spectrin and the cyclin-dependent kinase 5 activator, p35 in hippocampus. Together, these findings show that mild blast forces can cause aberrant neurological changes that critically impact neuronal functions. These results are consistent with the idea that mild blast forces may induce subclinical pathophysiological changes that may contribute to neurological and psychiatric disorders

    Renewable energy re-distribution via multiscale IoT for 6G-oriented green highway management

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    This is an accepted manuscript of an article published by IEEE on 16/09/2022, available online: https://ieeexplore.ieee.org/document/9894364 The accepted version of the publication may differ from the final published version.While recent works on investigating renewable energy sources for powering the highway offer promising solutions for sustainable environments, they are often impeded by unequal distribution of sources across the region due to variations in solar exposure and road intensity that electromagnetically and mechanically generate the energy. By exploiting viable gathering of massive renewable energy data using the Internet of Things (IoT), this paper proposes a framework for improved highway-energy management based on the unmanned aerial vehicle-assisted wireless energy re-distribution of the harvested renewable energy. Combining both massive low-rate sensing with high-speed 6G-envisioned transmission for data aggregation, the IoT architecture is of multi-scale, consisting of: i) global data exchange and analytics for energy mapping, re-distribution planning and forecasting, and ii) local data sensing and processing at individual highway lampposts for micro-energy management. The feasibility of the networked energy system is analyzed via analytical cost-reliability analyses. The cost analysis demonstrates the cost-effectiveness through the lowest Requirement of Energy and Cost of Energy for the setup and maintenance. The reliability analysis reveals the energy plus (E + ) feature of the system in certain conditions with enhanced reliability in adverse weathers that impact energy generation. With multi-scale data connectivity to intelligently manage standalone renewable energy, this work puts forward a viable idea of 6G use cases with massively networked energy sensors with a vision of achieving super-connected and intelligence-equipped highways.Published versio
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