Effect of Polyethylene Glycol and Activated Carbon Macroparticles on Thermal Conductivity of Paraffin Wax for Thermal Storage Applications.

Low thermal conductivity is the major obstacle for the wide range utilization of phase change materials (PCMs), especially organic PCMs, for most practical applications in thermal engineering. This study investigates the potential of enhancing the charging and discharging rates of organic PCM (RT44HC) by introducing polyethylene glycol (PEG) and activated carbon macroparticles (ACMPs). Different concentrations of PEG and ACMPs ranging from 0.3 wt% to 2 wt% were tested separately. The optimized concentrations found were used as dual reinforcements to attain the highest possible thermal conductivity. The specimens were tested for a complete charging-discharging cycle using an improvised thermal apparatus. Use of ACMP alone resulted in a minimal reduction in complete charging-discharging time due to the settlement of ACMPs at the bottom after 2-3 heating-cooling cycles. However, the addition of PEG with ACMPs exhibited a reduction in charging-discharging time due to the formation of a stable dispersion. PEG served as a stabilizing agent for ACMPs. The lowest charging-discharging time of 180 min was exhibited by specimens containing 1 wt% PEG and 0.5 wt% ACMPs which is 25% lower compared to bare PCM.

IoT malware detection architecture using a novel channel boosted and squeezed CNN.

Interaction between devices, people, and the Internet has given birth to a new digital communication model, the internet of things (IoT). The integration of smart devices to constitute a network introduces many security challenges. These connected devices have created a security blind spot, where cybercriminals can easily launch attacks to compromise the devices using malware proliferation techniques. Therefore, malware detection is a lifeline for securing IoT devices against cyberattacks. This study addresses the challenge of malware detection in IoT devices by proposing a new CNN-based IoT malware detection architecture (iMDA). The proposed iMDA is modular in design that incorporates multiple feature learning schemes in blocks including (1) edge exploration and smoothing, (2) multi-path dilated convolutional operations, and (3) channel squeezing and boosting in CNN to learn a diverse set of features. The local structural variations within malware classes are learned by Edge and smoothing operations implemented in the split-transform-merge (STM) block. The multi-path dilated convolutional operation is used to recognize the global structure of malware patterns. At the same time, channel squeezing and merging helped to regulate complexity and get diverse feature maps. The performance of the proposed iMDA is evaluated on a benchmark IoT dataset and compared with several state-of-the CNN architectures. The proposed iMDA shows promising malware detection capacity by achieving accuracy: 97.93%, F1-Score: 0.9394, precision: 0.9864, MCC: 0. 8796, recall: 0.8873, AUC-PR: 0.9689 and AUC-ROC: 0.9938. The strong discrimination capacity suggests that iMDA may be extended for the android-based malware detection and IoT Elf files compositely in the future.

Unmanned Aerial Vehicle for Laser Based Biomedical Sensor Development and Examination of Device Trajectory.

Controller design and signal processing for the control of air-vehicles have gained extreme importance while interacting with humans to form a brain-computer interface. This is because fewer commands need to be mapped into multiple controls. For our anticipated biomedical sensor for breath analysis, it is mandatory to provide medication to the patients on an urgent basis. To address this increasingly tense situation in terms of emergencies, we plan to design an unmanned vehicle that can aid spontaneously to monitor the person's health, and help the physician spontaneously during the rescue mission. Simultaneously, that must be done in such a computationally efficient algorithm that the minimum amount of energy resources are consumed. For this purpose, we resort to an unmanned logistic air-vehicle which flies from the medical centre to the affected person. After obtaining restricted permission from the regional administration, numerous challenges are identified for this design. The device is able to lift a weight of 2 kg successfully which is required for most emergency medications, while choosing the smallest distance to the destination with the GPS. By recording the movement of the vehicle in numerous directions, the results deviate to a maximum of 2% from theoretical investigations. In this way, our biomedical sensor provides critical information to the physician, who is able to provide medication to the patient urgently. On account of reasonable supply of medicines to the destination in terms of weight and time, this experimentation has been rendered satisfactory by the relevant physicians in the vicinity.