Evidence that fin whales respond to the geomagnetic field during migration

We challenge the hypothesis that fin whales use a magnetic sense to guide migration by testing for associations between geophysical parameters and the positions where fin whales were observed over the continental shelf off the northeastern United States. Monte Carlo simulations estimated the probability that the distribution of fin whale sighting was random with respect to bottom depth, bottom slope and the intensity and gradient of the geomagnetic field. The simulations demonstrated no overall association of sighting positions with any of these four geophysical parameters. Analysis of the data by season, however, demonstrated statistically reliable associations of sighting positions with areas of low geomagnetic intensity and gradient in winter and fall, respectively, but no association of sighting positions with bathymetric parameters in any season. An attempt to focus on migrating animals by excluding those observed feeding confirmed the associations of sighting positions with low geomagnetic intensity and gradient in winter and fall, respectively, and revealed additional associations with low geomagnetic gradients in winter and spring. These results are consistent with the hypothesis that fin whales, and perhaps other mysticete species, possess a magnetic sense that they use to guide migration

99mTc-Nannocolloid Localization of Lymphorenal Fistula Causing Chyluria

Chyluria is an abnormal condition in which chyle appears in the urine because of a fistulous communication between the lymphatics and the urinary tract. It is not life-threatening and spontaneous regression is reported in 50% of cases. Lymphangiography has been the main imaging modality for localization of the site of fistula, but it is invasive and requires expertise. Lymphoscintigraphy using Tc-99m labelled colloid is a safe, non-invasive, reproducible technique, which bears less radiation exposure. A 67-year-old male presented with 7-month history of chyluria following a spinal surgery. Bilateral lower limb lymphoscintigram revealed sluggish lymph flow in the left lower limb and visualization of tracer in the left kidney consistent with lymphorenal fistula. Subsequent cystography revealed appearance of chylous urine from left ureter. Patient refused surgery

A study to determine significant titre-values of widal test in the diagnosis of enteric fever for a population of north Kerala, India

Objectives: To determine the baseline Widal titres of a study population and to propose titre-values of significance in the diagnosis of enteric fever. Background: Enteric fever is endemic in the Indian subcontinent with high prevalence rates. Etiological diagnosis of the condition is problematic due to several limitations implicit in the region. Singly run Widal test is often the only means available for a workable diagnosis. A knowledge of prevalent baseline titres is essential for its interpretation. Method: We performed the standard Widal tube agglutination test on the serum samples of 250 individuals of the local population selected by using appropriate criteria of inclusion and exclusion criteria. Results: A majority (74.8%) of the subjects were non-reactive. Prevalence of anti-TO antibody was highest (25.2%) followed by that of anti-TH antibody (15.2%), anti-AH antibody (6.8%) and anti-BH antibody (0.8%). Based on the distribution of titre values, the baseline titres determined were 40 for anti-TO and anti-TH antibodies and <20 for anti-AH and anti-BH antibodies. Any value obtained in Widal test over and above these values, i.e., ≥80 for anti-TO and anti-TH antibodies can be considered as significant. Similarly, for anti-AH and anti-BH antibodies, values of ≥20 can be considered as significant for diagnosis in an appropriate clinical setting. Conclusion: The baseline Widal titres of the local population were lower than those found in most other parts of India

Euglycemic diabetic ketoacidosis in a pregnant woman

Abstract Background Euglycemic diabetic ketoacidosis (EDKA) carries serious risks for mortality and morbidity for both the mother and the baby, and it is essential to recognize it early and start immediate treatment. Case Presentation We present a case of EDKA in a 28‐week pregnant woman known to have type 1 diabetes. She was found to have severe acidosis with a blood sugar level of 10.6 mmol/L (190.8 mg/dL) and normal anion gap. She was found to have EDKA, which was confirmed later with a depressed venous pH and bicarbonate level and an increased serum ketone level. The patient's acidosis was not improving significantly with 0.05 units/kg/h of insulin infusion, so a full dose of 0.1 unit/kg/h of insulin infusion was started following a full diabetic ketoacidosis (DKA) protocol regardless of her blood sugar level. The patient showed gradual improvement and was discharged home after 4 days, with follow‐up with endocrinology and obstetrics. Conclusion In conclusion, EDKA is a critical complication of diabetes, especially in pregnant women. Therefore, it is crucial to treat it early and potentially consider following a full DKA protocol using 0.1 unit/kg/h insulin infusion instead of 0.05 unit/kg/h

Relationship between arginase activity and renal function in sera of patients with the two types of diabetes mellitus

Biochemical study was carried out to evaluate the activity of organs in sera of patients with two types of diabetes mellitus-DM ( 1 and 68 of type II ) as compared with healthy individuals as a control group. Biochemical parameters in the sera of groups which include: serum glucose,renal function-urea, creatinine and uric acid, the results indicate that the level of serum glucose,urea and creatinine significantly increased in the sera of patients with type I and II of diabetes militias as compared with a control group with no significant change in sera of patients with the two types of DM for uric acid as compared with the control group.The result showed significant change between control and patients with DM for serum arginase activity, and between the two types of DM, in which the mean±SD were (2.702±0.673)μmol/L/min in sera of type I of DM and (2.384±0.577) μmol/L/min for type II, the results indicate that the level of the enzyme significantly slightly higher in type I than type II. The correlation between the arginase activity and other biochemical parameters in patients with the two types of DM and control were studied, from the results we conclude that the activity of arginase was the effect by the disease (two types of DM), and the effect was slightly higher by type two of DM

Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos

Featured Application This work has applied computer vision and deep learning technology to develop a real-time weapon detector system and tested it on different computing devices for large-scale deployment. Weapon detection in CCTV camera surveillance videos is a challenging task and its importance is increasing because of the availability and easy access of weapons in the market. This becomes a big problem when weapons go into the wrong hands and are often misused. Advances in computer vision and object detection are enabling us to detect weapons in live videos without human intervention and, in turn, intelligent decisions can be made to protect people from dangerous situations. In this article, we have developed and presented an improved real-time weapon detection system that shows a higher mean average precision (mAP) score and better inference time performance compared to the previously proposed approaches in the literature. Using a custom weapons dataset, we implemented a state-of-the-art Scaled-YOLOv4 model that resulted in a 92.1 mAP score and frames per second (FPS) of 85.7 on a high-performance GPU (RTX 2080TI). Furthermore, to achieve the benefits of lower latency, higher throughput, and improved privacy, we optimized our model for implementation on a popular edge-computing device (Jetson Nano GPU) with the TensorRT network optimizer. We have also performed a comparative analysis of the previous weapon detector with our presented model using different CPU and GPU machines that fulfill the purpose of this work, making the selection of model and computing device easier for the users for deployment in a real-time scenario. The analysis shows that our presented models result in improved mAP scores on high-performance GPUs (such as RTX 2080TI), as well as on low-cost edge computing GPUs (such as Jetson Nano) for weapon detection in live CCTV camera surveillance videos.open access</p

A Comparative Analysis of Various Controller Techniques for Optimal Control of Smart Nano-Grid Using GA and PSO Algorithms

A nano-grid is an independent hybrid sustainable framework that utilizes non-renewable and renewable power resources for supplying continuous electrical energy to the load. Considering this scenario, in this research work, photovoltaic (PV) array, wind turbine, and fuel cell are taken as the three generation resources that have been used in the nano-grid. The active and reactive power of the all three generation resources is controlled using various controllers, i.e. integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, fractional order proportional integral derivative (FOPID) and sliding mode controller (SMC). An advanced optimization technique based on a genetic algorithm (GA) and particle swarm optimization (PSO) algorithm has been utilized to optimize all of these controllers. The integral square error is taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes is evaluated to find the best optimal solution. The performance of SMC has surpassed the performance of all other optimized controllers for power stability. In less than 0.267 seconds, the actual power yielded by using SMC is within 1% of the desired power. PSO algorithm has performed better than GA algorithm with all controllers. The worst performance is by FOPID controller with a steady state error of 6071.3W using GA algorithm and have a high magnitude of overshoot and undershoot. Moreover, a smart switching algorithm has been introduced for switching between the generation resources in accordance with the load demand and cost of the system in order to operate the nano-grid more economically. Finally, a case study has been performed in which the smart switching algorithm is utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads

Cascading failures assessment in renewable integrated power grids under multiple faults contingencies

Cascading overload failures occurred in power systems due to higher penetration of renewable energy resources (RERs), which causes uncertainty in a grid. To overcome these cascading overload failures, proper assessment in the form of load flow balancing and transients stability is required in renewable integrated power grids (RIPGs). This problem becomes more critical in the occurrence of multiple intervals faults in multiple interconnected RIPGs, which causes the tripping of several RERs. Due to which outages occurred in various transmission lines, which lead the power system to cascading overload failures. To tackle this problem, hybrid probabilistic modeling is proposed in this paper for balancing load flow and an assessment of transients stability in multiple interconnected RIPGs. For balancing of load flow, a smart node transmission network topology is utilized along with integrating a unified power flow controller (UPFC), while transients instabilities are assessed through a UPFC alone. Contrary to the previously proposed algorithms, which are only suitable to compensate network instabilities in case of only a single interval fault, this work is supported by probabilistic modeling to compensate network instabilities under the occurrence of not only a single interval fault but also in case of more severe multiple intervals faults in multiple interconnected RIPGs that will lead the network to cascading failure outages. Simulation results verify that our proposed probabilistic algorithm achieved near an optimal performance by outperforming the existing proposed methodologies, which are only confined to mitigate the effect of network instabilities only in case of single interval fault and fails to address these network instabilities under the occurrence of severe multiple interval faults, which leads the network to cascading failure outages. These simulation results are also validated through an industrial case study performed on a western Denmark transmission network to show the superiority of our proposed algorithm