Optimization and feasibility analysis of a microscale pin-fins heat sink of an ultrahigh concentrating photovoltaic system

Nowadays, the most recent optical configuration based on Cassegrain and Fresnel lens designs of concentrator photovoltaic(CPV) has shown a race to achieve the ultrahigh concentration ratio. Still, none of those has experimentally shown an optical concentration ratio (GC) beyond 2000 suns. This is because their energy concentration ratios are challenged by the excessive temperature raised throughout the optical stages, which diminishes the efficiency of the solar cell. In this context, this research work aims to numerically investigate a microscale pin-fins heat sink configuration to enhance the thermal performance and the cost-competitivity of ultrahigh CPV thermal receiver. The impacts of the solar cell area, cell efficiency, and heat sink's material have been analyzed and discussed. The results showed that a circular pin-fins heat sink could accomplish a drop of 23.28% in the maximum operating cell temperature at 10 000 suns for cell area of 1 × 1 mm² relatively compared to the conventional flat-plate heat sink. Furthermore, for a circular pin-fins heat sink with a cell area of 2 × 2 mm², the cell temperature started exceeding the safe operating range of temperature (80°C) at 8000 suns with an average temperature of 96.1°C and reaching a maximum of 113.91°C at 10 000 suns. A gradient temperature on the planar direction of the aluminum circular pin-fins heat sink was about 1.187°C at 10 000 suns whereas 0.703°C was recorded in the case of a copper circular pin-fins heat sink. The circular pin-fins heat sink showed the highest thermal performance resulting in maintaining the solar cell temperature within its safe operating range even beyond 10 000 suns. From an economic point of view, aluminum circular pin-fins heat sink has been found to be less costly than the copper one. Finally, it was found that at 8000 suns, the flat-plate heat sink cost is more expensive than the traditional pin-fins heat sink by 14.7%, where the flat-plate heat sink becomes the worst economic configuration at 10 000 suns. At that concentration ratio, the cost has increased by 43.38%, 5.75%, and 10.61% compared to the traditional pin-fins heat sink, cylindrical pin-fins heat sink, and circular pin-fins heat sink, respectively.     

Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

The emergence of Beyond 5G (B5G) and 6G networks translated personal and industrial operations highly effective, reliable, and gainful by speeding up the growth of next generation Internet of Things (IoT). Industrial equipment in 6G encompasses a huge number of wireless sensors, responsible for collecting massive quantities of data. At the same time, 6G network can take real-world intelligent decisions and implement automated equipment operations. But the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be taken. This has become mandatory for optimal resource allocation in 6G-enabled industrial applications. In this scenario, the current research paper introduces a new metaheuristic resource allocation strategy for cluster-based 6G industrial applications, named MRAS-CBIA technique. MRAS-CBIA technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial applications. The proposed MRAS-CBIR technique involves three major processes. Firstly, Weighted Clustering Technique (WCT) is employed to elect the optimal Cluster Heads (CHs) or coordinating agents with the help of three parameters namely, residual energy, distance, and node degree. Secondly, Decision Tree-based Location Prediction (DTLP) mechanism is applied to determine the exact location of Management Agent (MA). Finally, Fuzzy C-means with Tunicate Swarm Algorithm (FCM-TSA) is used for optimal resource allocation in 6G industrial applications. The performance of the proposed MRAS-CBIA technique was validated and the results were examined under different dimensions. The resultant experimental values highlighted the superior performance of MRAS-CBIR technique over existing state-of-the-art methods.     

Influence of Filler on the Microstructure,Mechanical Properties and Residual Stresses in TIG Weldments of Dissimilar Titanium Alloys

The influence of titanium alloy (Ti-5Al-2.5Sn) and commercially pure titanium (cpTi) as fillers on dissimilar pulsed tungsten inert gas weldments of Ti-5Al-2.5Sn/cpTi was investigated in terms of microstructure,mechanical/nano-mechanical proper-ties,and residual stresses.A partial martensitic transformation was observed in the weldments for all the welding conditions due to high heat input.The microstructure evolved in the FZ/cpTi interfacial region was observed to be the most sensitive to the proportion of α stabilizer in the filler alloy.Furthermore,the addition of filler alloy improved the tensile properties and nano-mechanical response of the weld joint owing to the increased volume of metal in the weld joint.As compared to the Ti-5Al-2.5Sn wire,the use of cpTi filler wire proved to be better in terms of energy absorbed during tensile and impact tests,tensile strength and ductility of the dissimilar welds.An asymmetrical residual stresses profile was observed close to the weld centerline,with high compressive stresses on the Ti-5Al-2.5Sn side for both the weldments obtained with and without filler wires.This was attributed to mainly the low thermal conductivity of Ti-5Al-2.5Sn.The presence of residual stresses also influenced the nano-hardness profile across the weldments.     

Development and evaluation of pluronic- and methylcellulose-based thermoreversible drug delivery system for insulin

The objective of the current work was to develop and evaluate thermoreversible subcutaneous drug delivery system for Insulin. Thermoreversible in-situ gel system was developed and evaluated both in-vitro and in-vivo comprising of pluronic F-127 alone or in combination with methylcellulose in different ratios. The drug release kinetics and mechanism was predicted by applying various mathematical models to the in-vitro dissolution data. Rabbits were used as animal model following subcutaneous injection to predict various pharmacokinetic parameters by applying Pk-Summit® software. The in-vitro and in-vivo data revealed that the formulation IPM 15/3 consisting of the pluronic F-127 (15% w/v) and methylcellulose (3% w/v) was the most robust and capable formulation for extending the drug release and maintaining basal plasma insulin level between 10 and 40?µU/ml for 240?h (10?d).     

iFrag: interference-aware frame fragmentation scheme for wireless sensor networks

Reliable data transmission is a fundamental challenge in resource-constrained wireless sensor networks. In current implementations, a single bit error requires retransmitting the entire frame. This incurs extra processing overhead and power consumption, especially for large frames. Frame fragmentation into small blocks with individual error detection codes can reduce the unnecessary retransmission of the correctly received blocks. The optimal block size, however, varies based on the wireless channel conditions. In this paper, we propose an interference-aware frame fragmentation scheme called iFrag. iFrag dynamically adjusts the number of blocks inside a frame based on current channel conditions, and effectively addresses all challenges associated with such dynamic partitioning. Through analytical and experimental results, we show that iFrag achieves up to \(3\times\) improvement in goodput when the channel is noisy, while reducing the delay by 12 % compared to other static fragmentation approaches. On average, it shows a 13 % gain in goodput across all channel conditions used in our experiments. Our testbed results also show that iFrag lowers the energy consumed per useful bit by 60 %, as improved data transmission reliability reduces the number of frame retransmissions which increases the motes energy efficiency.     

Temperature and Humidity Controlled Test Bench for Temperature Sensor Characterization

A temperature and humidity-controlled test bench for a wirelessly-powered ultra-low-power temperature sensor IC is presented. It consists of a closed metallic structure of 0.02 m³, forming a faraday-cage around the design under test (DUT), thermally insulated using Polyethylene foam, to provide electromagnetic interference (EMI) clean and thermally stable test environment with an operating temperature range of -10 °C to 100 °C. The temperature control with a settling accuracy of?±?0.6 °C is achieved with air-cooled 100 W Peltier modules, having fast dynamics to reach 95% of the required temperature within 15 min. The humidity is controlled by air circulation through a desiccant pocket, managed at around 15% to avoid water droplets during defrosting. A controllable vacuum of?~?1.3 kPa is achieved through a vacuum pump when?<?15% of de-humification is needed. The system operates at a lower power consumption of 30 W during the temperature retention phase, with acoustic noise of 58 dB-SPL achieved.

     

Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection from the cloud and IoT environments. The proposed MFO-RELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT platforms. To accomplish this, the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful format. Besides, the proposed models will receive the pre-processing data and carry out the classifier method. For boosting the efficiency of the proposed models, the MFO technique was utilized to it. The experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset, and the outcomes are examined under distinct aspects.