A Condition Monitoring System for Electric Vehicle Batteries Based on a Convolutional Neural Network Using Thermal Image

A new monitoring technique has been developed to evaluate the capacity and performance of Lithium-ion batteries batteries by utilizing two convolutional neural networks (CNNs) models, Deep convolutional neural network (DnCNN) and CNN with BFGS quasi-Newton optimization. The system utilizes thermal images of lithium-ion batteries as input for training and testing. DnCNN model is utilised to accurately calculate battery capacity and performance, and the performance is evaluated using mean squared error (MSE) and PSNR. The CNN-based training method employs the BFGS quasi-Newton algorithm to measure battery capacity accurately by evaluating the mean squared error (MSE) and regression results. The proposed condition monitoring system using thermal imaging and CNN models, specifically the CNN- BFGS quasi-Newton algorithm model, can accurately detect battery capacity with an accuracy rate of 98.5%, compared to the DnCNN model with an accuracy rate of 96.7%. The proposed system can address the critical issue of battery capacity and degradation in EVs, providing a more sustainable and efficient alternative for real-time applications

IMPACT OF STRESS ON TYPE 2 DIABETES MELLITUS MANAGEMENT

Background: Type 2 diabetes mellitus (T2DM) is one among the major health and socioeconomic problems worldwide. It is, however, not a somatic illness for which just symptomatic treatment will suffice. Stress is an important factor in not only causing diabetes onset or exacerbation, but also in hampering proper treatment by interfering with the treatment adherence of patients. Hence, it becomes important for physicians to acquaint themselves with the effects of stress on T2DM in order to ensure proper treatment of the latter. Objective: Documentation of effect of stress on the management of T2DM. Subjects and methods: The research was a cross-sectional study on the patients attending Sri Muthukumaran Medical College, Hospital and Research Institute, Mangadu. A total of 400 people, who werepre-established diabetic patients of the hospital of age greater than 30 years, were chosen for the study. The stress levels of the patients were assessed with the Perceived Stress Scale (PSS) and treatment adherence using a questionnaire prepared exclusively for the study. Based on the data, a statistical relationship was framed between the degree of control (treatment adherence) and the stress levels of the patients. Results: - The FBS levels were a direct reflection of the stress levels (P<0.05). - Stress had a major impact on treatment adherence among the diabetic subjects: Increased levels of stress decreased the adherence (P<0.001). - The glycemic index (HbA1C level) was found to be linked to both treatment adherence and stress. Increased adherence kept it at bay (P<0.05) while stress proved abysmal to glycemic control. Conclusion: T2DM is the result of an interplay between various factors; environmental, psychiatric and somatic. Hence, a holistic treatment approach is required, one that involves stress management, education and mental health awareness along with pharmacological treatment, to fully control the disease

Anemia Detection using a Deep Learning Algorithm by Palm Images

Our aim is to detect anemia through a comparative analysis of three convolutional neural network (CNN) models, namely EfficientNet B3, DenseNet121, and CNN AllNet. A collection of 3,000 microscopic palm pictures, including 1,500 anaemic and 1,500 non-anemic samples, was used to train and test the algorithms. The dataset was preprocessed to balance the classes, augment the images, and normalize the pixel values. The models were trained using transfer learning on the ImageNet dataset and fine-tuned on the anemia dataset. The performance of the models was evaluated based on accuracy, precision, recall, and F1-score. The results showed that CNN ALLNET achieved the highest accuracy of 96.8%, followed by DenseNet121 with 94.4%, and EfficientNet B3 with 91.2%. The precision, recall, and F1-score also followed a similar trend. The study concludes that CNN ALLNET is the optimal model for anemia detection due to its high accuracy and overall better performance when compared with the different models. The findings of this research could provide a basis for further studies on anemia detection using CNN models, ultimately improving the accuracy and efficiency of anemia diagnosis and treatment

Doppler effect in fluorine K-Auger line produced in electron-induced core ionization of SF₆

An experimental evidence is reported on the observation of the Doppler effect in fluorine K-Auger line emitted from a core-ionized SF₆ molecule under an impact of 16keVelectrons. The emitting source of the Auger line is found to acquire a kinetic energy of 4.7±0.3keV. We propose that such large energy is released from the Coulomb repulsion taking place between F⁺ and SF₅⁺ fragment ions under influence of an intense focusing field of the incident electrons. In the presence of the Coulomb field of these ions, the Auger line obtains a polarizationP=76%±7%.The authors are thankful to the Department of Science and Technology DST, New Delhi for providing the financial support under a research Project No. SP/S2/L-08/2001

Scale invariant correlations and the distribution of prime numbers

Negative correlations in the distribution of prime numbers are found to display a scale invariance. This occurs in conjunction with a nonstationary behavior. We compare the prime number series to a type of fractional Brownian motion which incorporates both the scale invariance and the nonstationary behavior. Interesting discrepancies remain. The scale invariance also appears to imply the Riemann hypothesis and we study the use of the former as a test of the latter.Comment: 13 pages, 8 figures, version to appear in J. Phys.

Electron Beam Dynamics of SAMEER Linac

In the design of a linac such as the 4 MeV linac fabricated at SAMEER, simulation of electron beam dynamics plays an important role. We study electron beam dynamics to help in design of buncher cavity dimensions, linac length and effect of beam loading on electron energy and spectrum. We have written a program to calculate the electron trajectories for a given power input, with cavity dimensions, rf couplings and electron beam input voltage and current as parameters. By calculating the trajectories of electrons arriving at different rf phases, we get the average electron energy, percent of beam transmitted and electron energy spectrum. By running the program with different input parameters, we can choose the best combination for a required application such as radiography or cancer therapy

High quality InSAR data linked to seasonal change in hydraulic head for an agricultural area in the San Luis Valley, Colorado

In the San Luis Valley (SLV), Colorado legislation passed in 2004 requires that hydraulic head levels in the confined aquifer system stay within the range experienced in the years 1978–2000. While some measurements of hydraulic head exist, greater spatial and temporal sampling would be very valuable in understanding the behavior of the system. Interferometric synthetic aperture radar (InSAR) data provide fine spatial resolution measurements of Earth surface deformation, which can be related to hydraulic head change in the confined aquifer system. However, change in cm-scale crop structure with time leads to signal decorrelation, resulting in low quality data. Here we apply small baseline subset (SBAS) analysis to InSAR data collected from 1992 to 2001. We are able to show high levels of correlation, denoting high quality data, in areas between the center pivot irrigation circles, where the lack of water results in little surface vegetation. At three well locations we see a seasonal variation in the InSAR data that mimics the hydraulic head data. We use measured values of the elastic skeletal storage coefficient to estimate hydraulic head from the InSAR data. In general the magnitude of estimated and measured head agree to within the calculated error. However, the errors are unacceptably large due to both errors in the InSAR data and uncertainty in the measured value of the elastic skeletal storage coefficient. We conclude that InSAR is capturing the seasonal head variation, but that further research is required to obtain accurate hydraulic head estimates from the InSAR deformation measurements

Strengthening the Growth of Indian Defence by Harnessing Nanotechnology - A Prospective

Nano-networking is truly interdisciplinary and emerging field including nanotechnology, biotechnology, and ICT. It is a developing research area which consists of identifying, modeling, analyzing and organizing communication protocols between devices in Nanoscale environments. The main goal is to explore beyond the existing capabilities of Nanodevices by cooperating and sharing information between them. Since conventional communication models are not appropriate to represent Nanonetworks, it is necessary to introduce new communication paradigm in the form of suitable protocols and network architectures. Nanotechnology could greatly improve some of the existing technologies and thus create new operational opportunities or, at least, help the military forces to strengthen themselves in the battlefield. The paper presents a brief overview of nanotechnology applications in defence sector and the challenges towards realization of protocols for Nanocommunication. The research is going forward and one can expect more protection rather than damage in the domain of ‘Nano-age’.Defence Science Journal, 2013, 63(1), pp.46-52, DOI:http://dx.doi.org/10.14429/dsj.63.376