Energy harvesting from fuel cell bicycles for home DC grids using soft switched DC-DC converter.

Fuel cell vehicles (FCVs) are gaining significance due to their potential to reduce greenhouse gas emissions and dependence on fossil fuels. Their efficient fuel cell cycle makes them ideal for last-mile transportation, offering zero emissions and longer range compared to battery electric vehicles. Additionally, the generation of electricity through fuel cell stacks is becoming increasingly popular, providing a clean energy source for various applications. This paper focuses on utilizing the energy from fuel cycle bicycles when it's not in use and feeding it into the home DC grid. To achieve this, a dual-phase DC to DC converter is proposed to boost stack voltage and integrate with the 24 V DC home grid system. The converter design is simulated using the PSIM platform and tested in a hardware-in-the-loop (HIL) environment with real-time simulation capabilities.

Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network.

The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured k-neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.

Wireless sensing system for the welfare of sewer labourers.

There is a growing demand for the environmental pollution monitoring and control systems. In the view of ever increasing sources of toxic chemicals, these systems should have the facilities to detect and calibrate the source quickly. Toxic gases are the ones that cause health impact but humans are being exposed to it in various situations. These gases have to be monitored such that increase in the normal level of them could be known and proper precaution measures can be undertaken. So, an embedded system is designed using a microcontroller with internet of things, for the purpose of detecting and monitoring the hazardous gas leakage, which aids in the evasion of endangering of human lives. The hazardous gases can be sensed and displayed each and every second, in proximity to one more sensor for tracking heart beats which help to monitor the condition of the sewer labourers. If both the gases along with a pulse detector exceeds the normal level then an alarm is generated immediately and also an alert warning message can be sent to the authorised administrator and as well to the nearest health center to make the sewer labourers feel comfortable with necessary first aid and possibilities with the treatment in the case of emergency. Once the message is received by the health center, they enforce their team with necessary first aid to the current location to save the sewer labourer. Once this system is established for a particular user this will completely become fully automated and does not need any other additional people for monitoring and alerting purpose. It has an advantage over the manual method in offering quick response time and accurate detection of an emergency.

Dosimetric characteristics of a MOSFET dosimeter for clinical electron beams.

The fundamental dosimetric characteristics of commercially available metal oxide semiconductor field effect transistor (MOSFET) detectors were studied for clinical electron beam irradiations. MOSFET showed excellent linearity against doses measured using an ion chamber in the dose range of 20-630cGy. MOSFET reproducibility is better at high doses compared to low doses. The output factors measured with the MOSFET were within +/-3% when compared with those measured with a parallel plate chamber. From 4 to 12MeV, MOSFETs showed a large angular dependence in the tilt directions and less in the axial directions. MOSFETs do not show any dose-rate dependence between 100 and 600MU/min. However, MOSFETs have shown under-response when the dose per pulse of the beam is decreased. No measurable effect in MOSFET response was observed in the temperature range of 23-40 degrees C. The energy dependence of a MOSFET dosimeter was within +/-3.0% for 6-18MeV electron beams and 5.5% for 4MeV ones. This study shows that MOSFET detectors are suitable for dosimetry of electron beams in the energy range of 4-18MeV.

Thermoluminescence dosimetric characteristics of beta irradiated salt.

The thermoluminescence (TL) characteristics of sodium chloride (NaCl), known as common salt, used for cooking purposes (iodised salt), have been studied in the present paper considering its usage as an 'accidental dosemeter' in the case of a nuclear fallout. TL characteristics of common salt have been examined under excitation with a beta dose of 20 Gy from a 90Sr beta source. The salt specimens are used in the form of discs. The average salt grain that sticks to the disc is measured to be approximately 1 mg. The TL of the beta irradiated salt is recorded in the conventional TL apparatus. Initially three peaks were observed at 133, 205 and 238 degrees C. All three peaks are well resolved, having maximum intensity at 238 degrees C. The material under investigation, i.e. 'common salt' possesses many good dosimetric properties and therefore this can be considered as an 'accidental dosemeter'.